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The 2012 season brought a STAR WARS Day that was memorable to say the least! Darth Vader and Storm Troopers filled the seats at...
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{
"pile_set_name": "OpenWebText2"
}
|
Does a 'reverse' atopic march exist?
The classical description of the atopic march usually refers to the progression from atopic dermatitis towards asthma, but this pathway has been questioned. We assessed in a prospective observation the possible onset of atopic dermatitis in children with asthma alone at baseline, and evaluated retrospectively their characteristics. Seven hundred and forty-five children (360 male, 6-9 years of age) with asthma alone, without food allergy or atopic dermatitis, were followed-up with regular visits for 9 years. 692 children completed the 9-year observation, and 20% of them were found to have developed atopic dermatitis at 9 years. Comparing retrospectively the children who developed AD with the remaining, no significant difference existed at baseline concerning the demographic characteristics and family history. There was a significantly higher proportion ( chi2 = 0.01) of subjects with single sensitization to mites and a significantly lower proportion of polysensitized subjects ( chi2 = 0.01) within the children who developed AD. Sensitization to foods appeared in 9% of children who developed AD and in 3.8% in the other children (NS). According to these observations, the development of a particular allergic disease does not necessarily follow the classical paradigm of the atopic march.
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{
"pile_set_name": "PubMed Abstracts"
}
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Friends Who Are Going
Friends Attending
Friends Attending
Friends Attending
Sales Have Ended
Online pre-registration has ended, but we still have a few seats available. Please contact us directly to check on the status and register: info@hismanhattan.org or 785-537-3988.
Event description
Description
"Go into all the World / The World at Our Doorstep"
God has called us to go into all the world, making disciples of every nation. We are blessed in Manhattan, Kansas, because the world has come to us! International students representing 105 nations are right on our doorstep!
Come hear personal stories from around the world about how God is touching hearts and changing lives.
This year's HIS Partner Dinner will be held in a NEW location - Houston Street Ballroom, 427 Houston Street in Manhattan, Kansas. There's no cost to attend. After the meal and formal program, there will be an opportuntiy to make a free-will financial gift to support the ongoing ministry of Helping International Students.
You won't want to miss this inspiring, heart-warming event!
* Traveling to Manhattan for the K-State vs. Oklahoma State Footabll game on Saturday? You're in luck! This year's Partners Dinner will be on a Friday evening. Be sure to join us! *
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{
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/*
MIT License (MIT)
Copyright (c) 2015 Clement CN Tsang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#import "CTAssetsPickerDefines.h"
#import "CTAssetsPickerController.h"
#import "CTAssetsPickerController+Internal.h"
#import "CTAssetCollectionViewController.h"
#import "CTAssetCollectionViewCell.h"
#import "CTAssetsGridViewController.h"
#import "PHAssetCollection+CTAssetsPickerController.h"
#import "PHAsset+CTAssetsPickerController.h"
#import "PHImageManager+CTAssetsPickerController.h"
#import "NSBundle+CTAssetsPickerController.h"
@interface CTAssetCollectionViewController()
<PHPhotoLibraryChangeObserver, CTAssetsGridViewControllerDelegate>
@property (nonatomic, weak) CTAssetsPickerController *picker;
@property (nonatomic, strong) UIBarButtonItem *cancelButton;
@property (nonatomic, strong) UIBarButtonItem *doneButton;
@property (nonatomic, copy) NSArray *fetchResults;
@property (nonatomic, copy) NSArray *assetCollections;
@property (nonatomic, strong) PHCachingImageManager *imageManager;
@property (nonatomic, strong) PHAssetCollection *defaultAssetCollection;
@property (nonatomic, assign) BOOL didShowDefaultAssetCollection;
@property (nonatomic, assign) BOOL didSelectDefaultAssetCollection;
@end
@implementation CTAssetCollectionViewController
- (instancetype)init
{
if (self = [super initWithStyle:UITableViewStylePlain])
{
_imageManager = [PHCachingImageManager new];
[self addNotificationObserver];
}
return self;
}
- (void)viewDidLoad
{
[super viewDidLoad];
[self setupViews];
[self localize];
[self setupDefaultAssetCollection];
[self setupFetchResults];
[self registerChangeObserver];
}
- (void)viewWillAppear:(BOOL)animated
{
[super viewWillAppear:animated];
[self setupButtons];
[self updateTitle:self.picker.selectedAssets];
[self updateButton:self.picker.selectedAssets];
[self selectDefaultAssetCollection];
}
- (void)dealloc
{
[self unregisterChangeObserver];
[self removeNotificationObserver];
}
#pragma mark - Reload user interface
- (void)reloadUserInterface
{
[self setupViews];
[self setupButtons];
[self localize];
[self setupDefaultAssetCollection];
[self setupFetchResults];
}
#pragma mark - Accessors
- (CTAssetsPickerController *)picker
{
return (CTAssetsPickerController *)self.splitViewController.parentViewController;
}
- (NSIndexPath *)indexPathForAssetCollection:(PHAssetCollection *)assetCollection
{
NSInteger row = [self.assetCollections indexOfObject:assetCollection];
if (row != NSNotFound)
return [NSIndexPath indexPathForRow:row inSection:0];
else
return nil;
}
#pragma mark - Setup
- (void)setupViews
{
self.tableView.rowHeight = UITableViewAutomaticDimension;
self.tableView.estimatedRowHeight =
self.picker.assetCollectionThumbnailSize.height +
self.tableView.layoutMargins.top +
self.tableView.layoutMargins.bottom;
self.tableView.separatorStyle = UITableViewCellSeparatorStyleNone;
}
- (void)setupButtons
{
if (self.doneButton == nil)
{
NSString *title = (self.picker.doneButtonTitle) ?
self.picker.doneButtonTitle : CTAssetsPickerLocalizedString(@"Done", nil);
self.doneButton =
[[UIBarButtonItem alloc] initWithTitle:title
style:UIBarButtonItemStyleDone
target:self.picker
action:@selector(finishPickingAssets:)];
}
if (self.cancelButton == nil)
{
self.cancelButton =
[[UIBarButtonItem alloc] initWithTitle:CTAssetsPickerLocalizedString(@"Cancel", nil)
style:UIBarButtonItemStylePlain
target:self.picker
action:@selector(dismiss:)];
}
}
- (void)localize
{
[self resetTitle];
}
- (void)setupFetchResults
{
NSMutableArray *fetchResults = [NSMutableArray new];
for (NSNumber *subtypeNumber in self.picker.assetCollectionSubtypes)
{
PHAssetCollectionType type = [PHAssetCollection ctassetPickerAssetCollectionTypeOfSubtype:subtypeNumber.integerValue];
PHAssetCollectionSubtype subtype = subtypeNumber.integerValue;
PHFetchResult *fetchResult =
[PHAssetCollection fetchAssetCollectionsWithType:type
subtype:subtype
options:self.picker.assetCollectionFetchOptions];
[fetchResults addObject:fetchResult];
}
self.fetchResults = [NSMutableArray arrayWithArray:fetchResults];
[self updateAssetCollections];
[self reloadData];
[self showDefaultAssetCollection];
}
- (void)updateAssetCollections
{
NSMutableArray *assetCollections = [NSMutableArray new];
for (PHFetchResult *fetchResult in self.fetchResults)
{
for (PHAssetCollection *assetCollection in fetchResult)
{
BOOL showsAssetCollection = YES;
if (!self.picker.showsEmptyAlbums)
{
PHFetchOptions *options = [PHFetchOptions new];
options.predicate = self.picker.assetsFetchOptions.predicate;
if ([options respondsToSelector:@selector(setFetchLimit:)])
options.fetchLimit = 1;
NSInteger count = [assetCollection ctassetPikcerCountOfAssetsFetchedWithOptions:options];
showsAssetCollection = (count > 0);
}
if (showsAssetCollection)
[assetCollections addObject:assetCollection];
}
}
self.assetCollections = [NSMutableArray arrayWithArray:assetCollections];
}
- (void)setupDefaultAssetCollection
{
if (!self.picker || self.picker.defaultAssetCollection == PHAssetCollectionSubtypeAny) {
self.defaultAssetCollection = nil;
return;
}
PHAssetCollectionType type = [PHAssetCollection ctassetPickerAssetCollectionTypeOfSubtype:self.picker.defaultAssetCollection];
PHFetchResult *fetchResult = [PHAssetCollection fetchAssetCollectionsWithType:type subtype:self.picker.defaultAssetCollection options:self.picker.assetCollectionFetchOptions];
self.defaultAssetCollection = fetchResult.firstObject;
}
#pragma mark - Rotation
- (void)viewWillTransitionToSize:(CGSize)size withTransitionCoordinator:(id<UIViewControllerTransitionCoordinator>)coordinator
{
[super viewWillTransitionToSize:size withTransitionCoordinator:coordinator];
[coordinator animateAlongsideTransition:^(id<UIViewControllerTransitionCoordinatorContext> context) {
[self updateTitle:self.picker.selectedAssets];
[self updateButton:self.picker.selectedAssets];
} completion:nil];
}
#pragma mark - Notifications
- (void)addNotificationObserver
{
NSNotificationCenter *center = [NSNotificationCenter defaultCenter];
[center addObserver:self
selector:@selector(selectedAssetsChanged:)
name:CTAssetsPickerSelectedAssetsDidChangeNotification
object:nil];
[center addObserver:self
selector:@selector(contentSizeCategoryChanged:)
name:UIContentSizeCategoryDidChangeNotification
object:nil];
}
- (void)removeNotificationObserver
{
[[NSNotificationCenter defaultCenter] removeObserver:self name:CTAssetsPickerSelectedAssetsDidChangeNotification object:nil];
[[NSNotificationCenter defaultCenter] removeObserver:self name:UIContentSizeCategoryDidChangeNotification object:nil];
}
#pragma mark - Photo library change observer
- (void)registerChangeObserver
{
[[PHPhotoLibrary sharedPhotoLibrary] registerChangeObserver:self];
}
- (void)unregisterChangeObserver
{
[[PHPhotoLibrary sharedPhotoLibrary] unregisterChangeObserver:self];
}
#pragma mark - Photo library changed
- (void)photoLibraryDidChange:(PHChange *)changeInstance
{
// Call might come on any background queue. Re-dispatch to the main queue to handle it.
dispatch_async(dispatch_get_main_queue(), ^{
NSMutableArray *updatedFetchResults = nil;
for (PHFetchResult *fetchResult in self.fetchResults)
{
PHFetchResultChangeDetails *changeDetails = [changeInstance changeDetailsForFetchResult:fetchResult];
if (changeDetails)
{
if (!updatedFetchResults)
updatedFetchResults = [self.fetchResults mutableCopy];
updatedFetchResults[[self.fetchResults indexOfObject:fetchResult]] = changeDetails.fetchResultAfterChanges;
}
}
if (updatedFetchResults)
{
self.fetchResults = updatedFetchResults;
[self updateAssetCollections];
[self reloadData];
}
});
}
#pragma mark - Selected assets changed
- (void)selectedAssetsChanged:(NSNotification *)notification
{
NSArray *selectedAssets = (NSArray *)notification.object;
[self updateTitle:selectedAssets];
[self updateButton:selectedAssets];
}
- (void)updateTitle:(NSArray *)selectedAssets
{
if ([self isTopViewController] && selectedAssets.count > 0)
self.title = self.picker.selectedAssetsString;
else
[self resetTitle];
}
- (void)updateButton:(NSArray *)selectedAssets
{
self.navigationItem.leftBarButtonItem = (self.picker.showsCancelButton) ? self.cancelButton : nil;
self.navigationItem.rightBarButtonItem = [self isTopViewController] ? self.doneButton : nil;
if (self.picker.alwaysEnableDoneButton)
self.navigationItem.rightBarButtonItem.enabled = YES;
else
self.navigationItem.rightBarButtonItem.enabled = (self.picker.selectedAssets.count > 0);
}
- (BOOL)isTopViewController
{
UIViewController *vc = self.splitViewController.viewControllers.lastObject;
if ([vc isMemberOfClass:[UINavigationController class]])
return (self == ((UINavigationController *)vc).topViewController);
else
return NO;
}
- (void)resetTitle
{
if (!self.picker.title)
self.title = CTAssetsPickerLocalizedString(@"Photos", nil);
else
self.title = self.picker.title;
}
#pragma mark - Content size category changed
- (void)contentSizeCategoryChanged:(NSNotification *)notification
{
[self reloadData];
}
#pragma mark - Reload data
- (void)reloadData
{
if (self.assetCollections.count > 0)
[self.tableView reloadData];
else
[self.picker showNoAssets];
}
#pragma mark - Table view data source
- (NSInteger)numberOfSectionsInTableView:(UITableView *)tableView
{
return 1;
}
- (NSInteger)tableView:(UITableView *)tableView numberOfRowsInSection:(NSInteger)section
{
return self.assetCollections.count;
}
- (UITableViewCell *)tableView:(UITableView *)tableView cellForRowAtIndexPath:(NSIndexPath *)indexPath
{
PHAssetCollection *collection = self.assetCollections[indexPath.row];
NSUInteger count;
if (self.picker.showsNumberOfAssets)
count = [collection ctassetPikcerCountOfAssetsFetchedWithOptions:self.picker.assetsFetchOptions];
else
count = NSNotFound;
static NSString *cellIdentifier = @"CellIdentifier";
CTAssetCollectionViewCell *cell = [tableView dequeueReusableCellWithIdentifier:cellIdentifier];
if (cell == nil)
cell = [[CTAssetCollectionViewCell alloc] initWithThumbnailSize:self.picker.assetCollectionThumbnailSize
reuseIdentifier:cellIdentifier];
[cell bind:collection count:count];
[self requestThumbnailsForCell:cell assetCollection:collection];
return cell;
}
- (void)requestThumbnailsForCell:(CTAssetCollectionViewCell *)cell assetCollection:(PHAssetCollection *)collection
{
NSUInteger count = cell.thumbnailStacks.thumbnailViews.count;
NSArray *assets = [self posterAssetsFromAssetCollection:collection count:count];
CGSize targetSize = [self.picker imageSizeForContainerSize:self.picker.assetCollectionThumbnailSize];
for (NSUInteger index = 0; index < count; index++)
{
CTAssetThumbnailView *thumbnailView = [cell.thumbnailStacks thumbnailAtIndex:index];
thumbnailView.hidden = (assets.count > 0) ? YES : NO;
if (index < assets.count)
{
PHAsset *asset = assets[index];
[self.imageManager ctassetsPickerRequestImageForAsset:asset
targetSize:targetSize
contentMode:PHImageContentModeAspectFill
options:self.picker.thumbnailRequestOptions
resultHandler:^(UIImage *image, NSDictionary *info){
[thumbnailView setHidden:NO];
[thumbnailView bind:image assetCollection:collection];
}];
}
}
}
- (NSArray *)posterAssetsFromAssetCollection:(PHAssetCollection *)collection count:(NSUInteger)count;
{
PHFetchOptions *options = [PHFetchOptions new];
options.predicate = self.picker.assetsFetchOptions.predicate; // aligned specified predicate
options.sortDescriptors = @[[NSSortDescriptor sortDescriptorWithKey:@"creationDate" ascending:YES]];
PHFetchResult *result = [PHAsset fetchKeyAssetsInAssetCollection:collection options:options];
NSUInteger location = 0;
NSUInteger length = (result.count < count) ? result.count : count;
NSArray *assets = [self itemsFromFetchResult:result range:NSMakeRange(location, length)];
return assets;
}
- (NSArray *)itemsFromFetchResult:(PHFetchResult *)result range:(NSRange)range
{
if (result.count == 0)
return nil;
NSIndexSet *indexSet = [NSIndexSet indexSetWithIndexesInRange:range];
NSArray *array = [result objectsAtIndexes:indexSet];
return array;
}
#pragma mark - Table view delegate
- (void)tableView:(UITableView *)tableView didSelectRowAtIndexPath:(NSIndexPath *)indexPath
{
PHAssetCollection *collection = self.assetCollections[indexPath.row];
CTAssetsGridViewController *vc = [CTAssetsGridViewController new];
vc.title = self.picker.selectedAssetsString ? : collection.localizedTitle;
vc.assetCollection = collection;
vc.delegate = self;
UINavigationController *nav = [[UINavigationController alloc] initWithRootViewController:vc];
nav.delegate = (id<UINavigationControllerDelegate>)self.picker;
[self.picker setShouldCollapseDetailViewController:NO];
[self.splitViewController showDetailViewController:nav sender:nil];
}
#pragma mark - Show / select default asset collection
- (void)showDefaultAssetCollection
{
if (self.defaultAssetCollection && !self.didShowDefaultAssetCollection)
{
CTAssetsGridViewController *vc = [CTAssetsGridViewController new];
vc.title = self.picker.selectedAssetsString ? : self.defaultAssetCollection.localizedTitle;
vc.assetCollection = self.defaultAssetCollection;
vc.delegate = self;
UINavigationController *nav = [[UINavigationController alloc] initWithRootViewController:vc];
nav.delegate = (id<UINavigationControllerDelegate>)self.picker;
[self.picker setShouldCollapseDetailViewController:(self.picker.modalPresentationStyle == UIModalPresentationFormSheet)];
[self.splitViewController showDetailViewController:nav sender:nil];
NSIndexPath *indexPath = [self indexPathForAssetCollection:self.defaultAssetCollection];
[self.tableView selectRowAtIndexPath:indexPath animated:YES scrollPosition:UITableViewScrollPositionTop];
self.didShowDefaultAssetCollection = YES;
}
}
- (void)selectDefaultAssetCollection
{
if (self.defaultAssetCollection && !self.didSelectDefaultAssetCollection)
{
NSIndexPath *indexPath = [self indexPathForAssetCollection:self.defaultAssetCollection];
if (indexPath)
{
[UIView animateWithDuration:0.0f
animations:^{
[self.tableView selectRowAtIndexPath:indexPath
animated:(!self.splitViewController.collapsed)
scrollPosition:UITableViewScrollPositionTop];
}
completion:^(BOOL finished){
// mimic clearsSelectionOnViewWillAppear
if (finished && self.splitViewController.collapsed)
[self.tableView deselectRowAtIndexPath:indexPath animated:YES];
}];
}
self.didSelectDefaultAssetCollection = YES;
}
}
#pragma mark - Grid view controller delegate
- (void)assetsGridViewController:(CTAssetsGridViewController *)picker photoLibraryDidChangeForAssetCollection:(PHAssetCollection *)assetCollection
{
NSIndexPath *indexPath = [self indexPathForAssetCollection:assetCollection];
if (indexPath)
{
[self.tableView reloadRowsAtIndexPaths:@[indexPath] withRowAnimation:UITableViewRowAnimationNone];
[self.tableView selectRowAtIndexPath:indexPath animated:NO scrollPosition:UITableViewScrollPositionNone];
}
}
@end
|
{
"pile_set_name": "Github"
}
|
Why Greens? Why Good?
“A glass half-full” describes my approach to life, especially for all things bad. Being “for” a thing rather than “against” its opposite may seem trite, but it works for me, focusing energy and activity in a positive fashion to achieve an otherwise difficult outcome. Whatever the venue, and however slowly the process may be, calm persistence yields better, more solid results than angry resistance.
I’m a veggie-eater. Until now, I’d rarely refer to myself as vegetarian or vegan, but let’s call a spade a spade. Eating food directly from the earth – rather than through a middle man on the food chain – is inexpensive and plentiful, immediate rewards being excellent health, boundless energy, and we’ve gained an advantage in the last century of knowing more about how nutrition – good and bad – affects us. Eliminating factory-raised meat was a choice made months ago, based on my own compassion for living things, and the wish to show by example (not by words and wishes) that change in an otherwise entrenched system can happen with positive, small and deliberate steps.
The things I do can be done by anyone. I am not a genius; I don’t have a college degree. I’ve never written a book. But I have learned a lot about many things, and knowledge is power. Being one person doing what works again and again, tweaking here and there, has made me adept at many things, and I exist in a perpetual state of learning and improvement.
Occasionally I discover something that just plain “feels wrong,” like the pain and fear inflicted upon billions of living beings just for sake of satisfying our taste buds – and not even necessarily for nourishment. Emotion brings me to action, though work always starts with myself first. One small step in the desired direction, leads to another, and yet another. The rest is easy, flowing I’d say.
Here are just some of the things that makes Greens for Good an easy platform for me.
* * *
The Sun in Edible Form.
The leaves of edible plants (I call them Greens) contain all the stuff that is easily absorbed for cellular development and health of the living organism that digests it. Humans are no different. Macro-nutrients – fats, proteins, and carbohydrates – as well as the full spectrum of minerals, vitamins, and essential amino acids can be found in a variety of plant parts (leaf, stem, fruit, root, stamen). They grow without our help, abundantly on healthy soil, ripe for the picking and the eating. Born from the sun and soil, greens rely only on the water, atmosphere, and microorganisms under the surface for their success. They sustain all life above the soil, yet they don’t even contain a central nervous system – they do not feel fear or pain. Greens are responsible for the oxygen we breathe and for removing the CO2 that would otherwise kill us.
Versatility.
Eating plants is easy. Eating plants is tasty. Eating plants satisfies hunger and energy requirements. Eating plants (usually) does not require energy put in before consumption (heat or cooking). At the cellular level, eating plants helps build muscle tissue, brain tissue, repairs cells, prevents disease, activates our immune system, and generally keeps us healthy. Most plants hold their full nutritional value days or even weeks after they’ve been “picked.” Some plants require no refrigeration for long-term (or even short-term) storage.
Cost Effective.
Pound-for-pound, the cost of store-bought vegetables is far less than the cost of meats – even the “cheap” ones we currently enjoy. Plants require fewer inputs of water and nutrients – the sun and carbon dioxide are their main foods – and plant waste is readily and easily re-absorbed by the atmosphere and the soil, promoting the process to continue over and over. It’s a perfect, closed-loop system, developed over hundreds of millions of years. The system willcontinue to sustain trillions if not ka-trillions of earth beings on the planet – it already does, every day – until the sun dies. The energy saved in not having to cook this food saves even more. As with any “middle man” situation, growing your own is the ultimate in cost savings.
.Seeds — Earth’s Food Packaging.
Plants require three things to grow: sunshine, water, and healthy soil. They are grown in many ways: 1) “wild” for foraging, 2) in a planned, well-kept garden, 3) at a large-scale farming operation, or 4) in containers inside at a sunny window or outside on a balcony. There is a plant that be grown successfully at any time of the year. Anyone – brown and green thumbs alike – anywhere, any place can grow plants. (Okay, maybe not in Antarctica, but who wants to live there?)
The Simplest Form of Protest.
The simple act of eating a more plant-based diet goes a long way toward improving the environment and the lives of factory-farmed animals. The industry has developed only recently in human history at our continual request for fast, cheap food that tastes good, but doesn’t necessarily carry any health benefits. The scale of land and water wastes from this industry alone are astounding. The last few decades have improved on the process of satisfying the human appetite for flesh, though any consideration for the well-being of these animals (they do feel terror and pain) seems almost an after-thought – if considered at all. All factory-farm animal abuse or neglect is entirely unnecessary. In the words of Jenny Brown:
If we can live happy, healthy lives without causing harm to others, why wouldn’t we?
A Green Diet Sustains.
Humans are incredibly versatile beings. In our history, we have enjoyed a host of locally-dictated diets ranging from vegetarian to carnivorous, insects to fish, eggs and milk to grains. All diets (even a vegan one) have their upsides and their downsides but were mostly practiced out of necessity – where and how the human lived. A predominately plant-based diet is generally agreed as acceptable, if not optimum, for human health, growth, and longevity. Many plants are notorious for preventing disease and degeneration in humans.
Reconnect With Home.
Knowing first hand what it’s like to grow, pick, and eat a fruit from a plant connects me with the soil in ways that simply digging in it cannot. Civilized society has systematically disconnected us from the animals we know simply as “food,” from the environment that sustains us (air, water, soil), and from our split-second of time in the earth’s history. The time has come to reconnect with our world. Animals are here with us, not for us.
Big changes happen when small changes are made by many, thoughtful individuals, many times over.
~ Shannon @ Greens For Good
August 7, 2012
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9 thoughts on “Why Greens? Why Good?”
This blog comes at an interesting time for me….I’ve been heavily contemplating this, myself, recently. I totally agree with everything you are saying….I will be very curious to follow along. Good luck with it!! 🙂
Excited to read more, Shannon! I applaud your efforts and can’t wait to share your blog with my on the verge of going vegan friends. I’m also excited to see what I learn, because as you know, I’m not vegetarian or vegan, but I think I might be swayed. 🙂
|
{
"pile_set_name": "Pile-CC"
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|
Distribution of 3-hydroxy-3-methylglutaryl-CoA reductase in isolated villus and crypt cells of chick duodenum, jejunum and ileum.
3-Hydroxy-3-methylglutaryl-CoA reductase (EC 1.1.1.34), the major rate-limiting enzyme of cholesterogenesis, was studied in epithelial cells isolated in a villus to crypt gradient from chick duodenum, jejunum and ileum, in order to resolve the apparent controversy that exists on the anatomical localization of sterol synthesis in the intestine. Consistent separation was demonstrated by using the marker enzymes alkaline phosphatase, specific to the villus cells, and thymidine kinase, specific to the crypt cells. No relative difference in stability was observed, as shown by the equal distribution of acid phosphatase. Cells were 90-95 per cent viable. The highest specific activity of reductase was located in the microsomal fraction (41 per cent of the total). The mitochondria had lower specific activity (8 per cent of the total). The distribution of reductase activity in epithelial cells of the villus-crypt axis was also studied. The specific activity in each cell fraction from chick duodenum was clearly lower than that in jejunum and ileum. The jejunal and ileal crypt regions showed lower specific activity than the villus cells. About 70 per cent of total reductase activity was found in cells from the upper and the mid villus fraction in each intestinal segment.
|
{
"pile_set_name": "PubMed Abstracts"
}
|
The Tor Project confirmed today that one of its prominent developers, Jacob Appelbaum, stepped down in response to what it called “public allegations of sexual mistreatment.” The Tor Project, which develops the Tor browser and network, had previously only acknowledged Appelbaum’s departure in a one-sentence statement Thursday afternoon, but went into further detail about his resignation after rumors of assault emerged online.
Tor is free software that channels internet traffic through a series of relays to anonymize its users. In addition to his security research at the Tor Project, Appelbaum is a journalist who worked on WikiLeaks and the Edward Snowden disclosures. Rolling Stone called him the “public face of the Tor Project” in a 2010 profile that detailed his involvement with Tor and WikiLeaks. Before joining Tor, Appelbaum worked on security for Greenpeace and the Rainforest Action Network.
Tor Project executive director Shari Steele said in today’s statement that allegations of sexual assault had followed Appelbaum for quite some time. “These types of allegations were not entirely new to everybody at Tor; they were consistent with rumors some of us had been hearing for some time. That said, the most recent allegations are much more serious and concrete than anything we had heard previously,” Steele wrote.
Steele added that The Tor Project had heard allegations from several victims about Appelbaum’s behavior towards them. The Tor Project has hired a legal firm to investigate the statements, but Steele said she did not expect that the results of the investigation would be made public.
Steele initially announced Appelbaum’s resignation in a simple statement on Thursday: “Long time digital advocate, security researcher, and developer Jacob Appelbaum stepped down from his position at The Tor Project on May 25, 2016,” she wrote.
Despite the terse announcement, the backstory of Appelbaum’s resignation quickly emerged online.
Andrea Shepard, a Tor developer, tweeted the decoded version of a message she’d originally posted on May 24, one day before Appelbaum stepped down. “It seems one rapist is one rapist too many,” she wrote. (SHA-256 references the hash used to encode the original message.)
Precommitment revealed: sha256("It seems one rapist is one rapist too manyn") (https://t.co/gUpiPKI0st) — Andreⓐ (@puellavulnerata) June 3, 2016
Alison Macrina, the founder of The Library Freedom Project, also referenced the allegations on Twitter, saying she had spoken to several victims. The Library Freedom Project is an organization that educates librarians about privacy and collaborates with the Tor Project to establish Tor exit nodes in libraries. “no more open secrets, no more missing stairs. you’re not alone. you were never alone. and I’m pretty sure things are just getting started,” Macrina tweeted.
Several anonymous accounts of assaults allegedly committed by Appelbaum were also posted on a website bearing his name, but TechCrunch has not yet been able to verify them.
Steele said the Tor Project would work to foster a safer environment. “Going forward, we want the Tor community to be a place where all participants can feel safe and supported in their work. We are committed to doing better in the future. To that end, we will be working earnestly going forward to develop policies designed to set up best practices and to strengthen the health of the Tor community,” Steele wrote.
Update 6/6: Appelbaum posted a response to the allegations against him on Twitter, saying they are part of a “calculated and targeted attack” intended to undermine his advocacy work.
“I want to be clear: the accusations of criminal sexual misconduct against me are entirely false,” Appelbaum wrote. “Though the damage to my reputation caused by these allegations alone is impossible to undo, I nonetheless take the concerns of the Tor community seriously.”
Appelbaum suggested that he would sue his accusers if necessary to clear his name, calling the allegations libelous. His full statement is here.
|
{
"pile_set_name": "OpenWebText2"
}
|
Q:
unable to write to CSV after replace
I have an input file, in which I am making an string replace operation.
I read the file cell by cell, replace the string and then write it back to a new CSV file.
input_file = open('/Users/tcssig/Desktop/unstacked2.csv', 'r', encoding='utf-8')
output_file = open('/Users/tcssig/Desktop/unstacked3.csv', 'w', encoding='utf-8')
writer = csv.writer(output_file , delimiter=' ')
reader = csv.reader(input_file)
for row in reader:
for string in row:
data = [string.replace('read','write')]
print(data)
writer.writerow(data)
Above code runs well, but I get an empty output file.
Example of data :
reading reading reading reading
interval 0 1 2 3
who axis
Mikael X 0 10 20 30
Mikael Y 50 40 30 20
Mikael Z 100 90 80 70
Mike X 0 0.1 0.2 0.3
Mike Y 0.5 0.4 0.3 0.2
Mike Z 1 0.9 0.8 0.7
What am i missing?
A:
Content of input file:
"Roll No" English read Science
"Roll No" English Write Science
Problem with your code:
As mentioned by @Scott, files are not closed.
Your are reading cell by for string in row: and replacing string there. But after replacement you are writing that cell as row in your file. For example, output file with your code looks file :
Roll No
English
read
Science
This is due to above mentioned reason i.e. you are writing each cell.
How to make it working?
Comments inline with code
import csv
input_file = open('mark.csv', 'r', encoding='utf-8')
output_file = open('result.csv', 'w')
writer = csv.writer(output_file , delimiter=' ', encoding='utf-8')
reader = csv.reader(input_file)
for row in reader:
#Initailize empty list for each row
data = []
for string in row:
#Replace and add to data list
data.append(string.replace('read','write'))
#Now write complete
writer.writerow(data)
input_file.close()
output_file.close()
Output:
"Roll No" English write Science
"Roll No" English Write Science
You can achieve same thing without csv module.
with open("mark.csv") as input_file:
with open("result.csv",'w') as output_file:
for line in input_file:
new_line = (line.replace("read","write")).replace(","," ")
output_file.write(new_line)
|
{
"pile_set_name": "StackExchange"
}
|
A chef de cozinha e apresentadora do GNT, Bela Gil declarou neste 8 de março uma receita infalível para se presentear as mulheres no dia internacional delas: “você pode substituir rosas por respeito, por exemplo”
Na internet, alguns homens estão oferecendo pedido de desculpas no lugar do tradicional parabéns pelo dia internacional da mulher. “Desculpas em nome do nosso gênero, alguns de nós estamos trabalhando todos os dias para melhorar”, declarou um homem em seu Facebook.
|
{
"pile_set_name": "OpenWebText2"
}
|
N-3 fatty acids and pregnancy outcomes.
To discuss new data from the literature on the relationship between the supply of n-3 polyunsaturated fatty acids during pregnancy and pregnancy outcomes, evaluated as the fatty acid composition of blood and breast milk, fetal and infantile development and maternal health. Supplementation of alpha-linolenic acid in high doses or docosahexaenoic acid in low doses did not result in a significant enhancement of the blood docosahexaenoic acid status of the offspring. In contrast, supplementation of docosahexaenoic acid in relatively high doses led to significant increases in infantile docosahexaenoic acid values and to a significant enhancement of breast milk docosahexaenoic acid content. Electroretinogram data obtained during the first week of life and pattern-reversal visual evoked potentials investigated at 50 and 66 weeks postconception were significantly associated with the docosahexaenoic acid status of the infant at birth. Children whose mothers received docosahexaenoic acid supplementation during pregnancy and lactation scored better in mental processing tests carried out at 4 years than children whose mothers received placebo. Beneficial health outcomes are more likely to result from supplementation with docosahexaenoic acid itself, rather than its precursor alpha-linolenic acid. Trials have shown that a higher maternal docosahexaenoic acid intake during pregnancy may be favourable for the visual and cognitive development of the offspring. The significant positive association between maternal docosahexaenoic acid intake during pregnancy and the children's mental processing scores at 4 years suggest that optimization of the docosahexaenoic acid status of expectant women may offer long-term developmental benefits to their children.
|
{
"pile_set_name": "PubMed Abstracts"
}
|
Susan Fuller’s family has launched a fight to get the killer driver that took her life locked up for longer.
The mum-of-three died after Sean Herman mowed her down in his car in front of her beloved sons.
The killer was jailed for seven years after he admitted manslaughter at Newcastle Crown Court this week, a sentence that has left Susan’s family devastated.
The Fullers have vowed not to give up their fight for justice.
Susan’s wife, David, has told Chronicle Live that he has met with police since Monday’s court hearing and is now planning to appeal against the sentence.
Collect picture of Susan Fuller and her husband David (Image: Handout)
And the family has now started a petition calling for Herman’s jail term to be extended.
David, 65, said: “We are going to appeal against the sentence, but it’s just another road to go down and we seem to run into a dead end each time. I’m not holding my breath, but it’s not over yet.”
Susan, 63, did not stand a chance when Herman ploughed into her in an Audi outside her Wallsend home, in October last year.
The slight grandmother was pinned against a wall and her sons, Barrie, 37, Dale 30 and 28-year-old Scott, watched on helplessly as the car drove over her leaving her with catastrophic injuries from which she could never recover.
Flowers at the scene on Coldstream Gardens where Susan Fuller died (Image: Newcastle Chronicle)
Herman was arrested soon after and charged with murder, and the Fuller brothers began to prepare to take to the witness stand and relive the moment they watched their mum die.
However, on Monday the 24-year-old pleaded guilty to manslaughter. The Crown Prosecution Service accepted the plea and the murder case was dropped.
Herman was sentenced to seven years by the Judge, Mr Justice Goss.
Sean Herman, who pleaded guilty to manslaughter after the death of Susan Fuller (Image: Northumbria Police)
More than 250 people have signed the ‘Justice for Susan’ petition since it was launched.
It’s description says: “She was a loving wife, a mother of three sons, a sister, an aunty and a loyal friend to anyone that was lucky enough to know her. Sean Herman was sentenced to seven years for taking the life of Susan Fuller. He will serve only half of that sentence behind bars, an insult to the beautiful woman who lost her life.
“Please sign this petition to voice your disgust at the sentence given to Sean Herman. Please acknowledge that Susan Fuller’s life was worth so much more than this.”
Scott told Chronicle Live how he is still haunted by the moment he watched his mum get hit by the car.
“I see flashbacks every day,” he said. “I knew as soon as he drove off that she was dead. She was just a lovely woman. She would do anything for you.
“I just don’t know how he’s got seven years. He’s taken a life. If he’s big enough to do the crime he should be big enough to do the time. She has done nothing to anyone.”
Susan Fuller (Image: Handout)
Susan was a well-known and popular member of the community in Howdon. She had worked at the Ministry in Longbenton and the Wills factory in Wallsend.
Paying tribute to his wife, David said: “We went to the same school and we moved in the same circle of friends. I shared a flat with her and her sister when I was working abroad, then when I came back we got together and we got married when I was 28.
“I have known her since she was 14 years old. Susan has never done any harm to anyone in her life. She was a kind, generous person. She loved animals.”
“She totally doted on the lads. Everybody that she knew thought the world of her. When we retired we were going to sell the house and move to Northumberland. Somewhere with a bit of land. All that has gone now.”
Read More
Herman, of Tillmouth Avenue, Seaton Delaval, Northumberland, who has one previous conviction for careless driving, was told he must serve half of the seven-year sentence behind bars, minus the time he has done on remand.
He will be banned from driving for three years after his release. But the sentence has left the Fullers furious.
Dale said: “We have got no closure. We have just been left with anger. People are messaging me on Facebook asking how he only got seven years, but I’ve got no answer. It just seems like you are allowed to kill people these days. He’s going to think he’s untouchable when he gets out of jail.”
|
{
"pile_set_name": "Pile-CC"
}
|
class AddBudgetTranslations < ActiveRecord::Migration[4.2]
def change
create_table :budget_translations do |t|
t.integer :budget_id, null: false
t.string :locale, null: false
t.timestamps null: false
t.string :name
t.index :budget_id
t.index :locale
end
end
end
|
{
"pile_set_name": "Github"
}
|
Q:
Track an instance in C#?
Is there a way to track a single instace in C#/.NET in Visual Studio while debugging? I find it would be really useful sometimes.
Another way to look at it would be breakpoints on instances rather than code. Therefore, every time my instance is accessed and/or modified the execution stops and I am presented with the line of code which accesses/modifies my instance.
In C++ the equivalence would be monitoring the piece of memory where the instance is located, or simply a pointer to the instance. This approach doesn't work with managed code as the objects in .NET are moved around, therefore I need an equivalence for pointers in C++.
I am aware of WeakReferences in C# but I am not sure if they are of any use while debugging?
Edit1:
This question is different from "When debugging, is there a way to tell if an object is a different instance? " as I am not interested in comparing two references, but I want to access a single object.
A:
There's nothing that I'm aware of out of the box, but VS does support conditional breakpoints. One option would be to:
Place breakpoints on all of the methods on your class that you're interested in
Debug your code through until the first of these is hit
Find the HashCode of the instance
Make all of the breakpoints coditional on GetHashCode() == the hash code you previously retrieved
Let the application run on until the breakpoint is hit again
Look in the Call Stack window to see which line of code is calling your method
A little clunky, but will work...
|
{
"pile_set_name": "StackExchange"
}
|
Q:
Why is this meta content redirect not working
The default document for the IIS .Net project is a Default.htm file. The following code reflects the contents of the Default.htm file.
When it runs, it does not redirect to the Project1 folder, but looks for the Login.aspx in the current directory instead (example: www.website.com/Login.aspx when it should be www.website.com/Project1/Login.aspx). I assumed my url tag was incorrect, however it is without flaw.
<html>
<head>
<meta HTTP-EQUIV="REFRESH" content="0; url=Project1/Login.aspx">
<title>Welcome</title>
</head>
<body>
<p>Loading ...</p>
<p>Please click <a href="Project1/Login.aspx">here</a> to login</p>
</body>
</html>
Why does it not look in the Project1 folder for the Login.aspx?
A:
You need to change to this (add slash at the beginning of url):
<meta HTTP-EQUIV="REFRESH" content="0; url=/Project1/Login.aspx">
|
{
"pile_set_name": "StackExchange"
}
|
Article content continued
Thursday’s report, which looked at cannabis spending in Canada from 1961 through 2017, aimed to “present a before and after picture of the cannabis sector,” said Tony Peluso, Statistics Canada’s director of international accounts and trade.
He said that more than 90 per cent of cannabis use last year came from non-legal providers and the rest from licensed producers for medical purposes.
Statistic Canada says the price of pot peaked in 1989 and has been declining since 1990. A gram of pot costs about $7.50 according to today’s report.
The government is using web-based crowd sourcing to get current costs of pot by consumers in Canada.
Consumers can anonymously fill out a form on a Statistic Canada website and answer questions about the cannabis purchase price, quantity, quality and the city along with the purpose (medicinal or recreational).
Since 2000, an average of 18 per cent of pot purchases were made by 15-17 year-olds, and 33 per cent of pot purchases were by 18-24 year olds. Those aged 25-44, meanwhile, made up 40 per cent of pot purchasers.
But the demographics are changing.
Boomers and those aged 45-64 have become larger consumers of the drug, making up 23 per cent of the overall share of consumers in 2017. That’s up from 4 per cent in 1975.
Baby Boomers are “bringing cannabis use with them,” Peluso noted.
Provinces and territories will set the price of cannabis, while the federal and provincial governments are working to determine taxes on cannabis products.
|
{
"pile_set_name": "OpenWebText2"
}
|
It’s been a few weeks since WCS Austin. Even though the finals were awesome, especially with Neeb winning in America, there are still a lot of problems I would like to address. This is not specific to the event, but more to WCS in general.
The WCS 2017 announcement was received quite well. The commitment from Blizzard for the next 2 years was great to hear. Despite downsizing in number of events, there would still be 4 throughout the year. I expected them to be similar to majors in Dota & CSGO. This meant longer events, lots of content & promotion. Basically, less events would make it possible for each of them to really mean something.
After IEM Katowice I was expecting an event on, or close to, the same level. IEM Katowice was a really refreshing event with high production value, minimal downtime due to jumping into games played offstream and a lot of broadcast days. Not everything was cramped up into 3 days. Sadly, WCS Austin felt very lackluster after Katowice.
Promotion ahead of event
WCS Challenger is perfect to build storylines and should be heavily promoted. The promotion of the event seemed to be limited to twitter tweets. There were no official write-ups or interviews. The fight for the ladder qualifier is amazing to watch, seeing the ladder points go up and down and wondering who will eventually qualify, but no effort was put into making something to follow it in the final hours.
During the main event, it would have been fun to see how each player qualified. Maybe review a bit of their journey. Some graphics on who the player had to beat in the qualifiers, or how many games a ladder player had to play to qualify. There are lots of community sites that already offer very detailed statistics, working with them would be excellent.
The main event had no real promotion other than twitter either. The event just started. No trailer, no player spotlights. Nothing. The promotion was basically: Player announcement, Talent announcement, survival guide. This was disappointing considering the amazing WCS Signature Series leading up to BlizzCon. I was expecting this level of promotion prior to WCS 2017, but sadly it does not appear to be the case. The Signature series is probably far out of budget, but flying the players in a few days earlier to shoot some promotional material would have already helped. A good example from nation wars: https://gfycat.com/DimpledDeliciousAmericancrocodile
Of course not all promotion can be forced, part of it is up to the community. But Blizzard should do their part properly. Maybe even work with community members to get the word out there.
Mainstream content & the format
There were 3 days of offline content. This is 25% of all the offline foreign WCS content we will see this year. A lot of the content was covered by online streams. I do not think this should be done, or only limited amounts. Why not cover group stage 1 when you finally have all the players in the same spot? It might as well have been played online.
For the players, exposure is important. When you go to an offline event and your game might have been casted on an online stream, it’s not much different from playing in an online cup. This is no disrespect to any of the online casters, but if you qualify for the event, fly out and play in the group stage while having none of your games casted/just an online stream this can’t be right. You get next to no exposure.
The format also lend no help in building any storylines. The event went from 96 players to 16 in one day. One of the main stories the first day was GAMETIME taking out uThermal and going to group stage 2. He also went through to group stage 3, but this wasn’t even enough to make it to a second day.
The coverage on the mainstream was lackluster. Out of the 160 bo3s played on the first day, the main stream covered 6. The total live broadcast time, including all downtime/half an hour countdown lasted 7 hours on the first day.
The second day was much more of a marathon, with 12 bo5s planned on the main stream. There wasn’t even enough time, and one of the bo5s had to be moved to a different stream. The total broadcast time was 13 hours.
Then the third day there were only 4 players left, so 2 bo5s and 1 bo7. A total broadcast time of 6 hours. The days were incredibly uneven.
Dreamhack has lost its identity
Dreamhack SC2 events used to be the ones to watch. Marathon casts with incredible amount of games, downtime filled with funny songs and lots of fun filler content. Players doing the Macarena, iNcontroL trolling in interviews. It would cumulate into a top 8 show on the final day with the hyped up intro video, crowd pans and player introductions.
This has changed so much in the last 2 years where they went to a very streamlined show. All the events would blend together and most resources were going into other games. I was hoping to see some of it back this year. Sadly it was not the case. I doubt we will see any improvements this year, but with a bit of luck we will see something next year.
Unfortunately all this might come down to tightening of resources by Blizzard. However, even with fewer resources, there are problems that can be solved with minimal investment.
There are still many passionate fans out there, and not all hope is lost. But the path WCS is going it will keep on declining.
|
{
"pile_set_name": "OpenWebText2"
}
|
Mount Buggery
Mount Buggery may mean:
Mount Buggery (Alpine Shire, Victoria)
Mount Buggery (Wangaratta, Victoria)
|
{
"pile_set_name": "Wikipedia (en)"
}
|
Hakkemose Brickworks
Hakkemose Brickworks (Danish: Hakkemose Teglværk) was a Danish brickyard and ceramics factory located at Taastrup, Denmark. The central lake in Hakkemosen is its former clay pit.
History
Inventor and mechanic Johannes Peter Langgaard purchased the farm Hakkemose at Taastrup in 1847 and founded the brickyard at the site on 10 November that same year.
Langgaard had studied the latest technological trends in Germany and constructed a machine for the production of bricks. In 1868 , he also began the construction of a Hoffmann kiln with 15 chambers. Each chamber had room for 22,500 bricks.
Hakkemose Brickworks had by the 1870s developed into the largest brickyard in the country. It produced 7 million bricks in 1872. In 1883, it also started a production of terracotta objects and faience cocklestoves. The brickyard was represented and won awards on the 1872 Nordic Exhibition in Copenhagen and again on the Nordic Exhibition of 1888.
Langgaard passed away in 1890 and his heirs sold the brickyard to a British consortium in 1895. The clay deposits had been depleted in 1908 and the Hoffmann kiln was demolished in 1909. The factory was from then on used for production of ceramic objects and tiles. Morten Korch was managing director of the factory from October 1909 to September 1911. It closed in 1915.
Legacy
All the brickyard buildings have been demolished. Hakkemosegård's main wing was demolished in the 1990s. Charlottegård, which was built for Langgaard in 1858 and named after his wife, has survived. The central lake in Hakkemosen is its former clay pit.
Bricks from Hakkemosegaard are stamped with J. P. Langgard's name and trademark. The trademark featured a bee hive. Buildings constructed with bricks from Hakkemose include Vridsløselille State Prison, Sankt Hans Hospital and Axelhus at Vesterbrogade 2B, Axelhus was built for Langgaard in 1874. The sculptures on the facade and Countess Danner's coat of arms at Jægerspris Castle was also produced at Hakkemose Brickworks.
Further reading
Hegner Christiansen, Jørgen: Historien om et teglværk: Hakkemose Teglværk 1847-1895, Byhistorisk Samling og Arkiv i Høje-Taastrup Kommune, 1995.
Germann, Marianne: Optøjer mod fremmedarbejderne på Hakkemose Teglværk 1864, Årsskrift for Høje-Taastrup Kommunes Lokalhistoriske Arkiv 1988 (1988), s. 68-77.
Korch,Morten A.: Der går ingen lige vej -, Branner og Korch 1980.
Meier, F.J.: Noget mere om Keramik - dansk, norsk, svensk paa Udstillingen, In: Tidsskrift for Kunstindustri, 1888, 4. årgang, s. 129-136.
See also
Urban Jürgensen
References
External links
Image of brick from Hakkemose Brickworks
Category:Brickworks in Denmark
Category:Ceramics manufacturers of Denmark
Category:Danish companies established in 1847
Category:Companies based in Høje-Taastrup Municipality
|
{
"pile_set_name": "Wikipedia (en)"
}
|
Q:
Will using UUID to as list keys cause unnecessary re-renders in React?
I have a list of items that doesn't contain enough data to generate a unique key. If I use the uuid library to generate an ID, will a single item change also causes the other items to re-render since their key will change each time?
const people = [
{
gender: 'male',
firstName: 'david',
},
{
gender: 'male',
firstName: 'david',
},
{
gender: 'male',
firstName: 'joe',
},
]
const renderPeople = () => {
return people.map(person => {
return (
<div key={uuid.v4() /* a new value each time? */ }>
<p>{person.gender}</p>
<p>{person.firstName}</p>
</div>
)
})
}
some time later... one of the davids changed
const people = [
{
gender: 'male',
firstName: 'david',
},
{
gender: 'male',
firstName: 'davidzz',
},
{
gender: 'male',
firstName: 'joe',
},
]
A:
<div key={uuid.v4()}> assigns new key for each <div> every time, so it is useless.
If the array stays same, UUID should be generated on array creation.
If the array changes, e.g. received from HTTP request, UUIDs for elements with same content will be generated again.
In order to avoid that, key should be specific to person entity. It's always preferable to use internal identifiers (database IDs) where available for unambiguity.
If identifiers are not available, key may depend on element contents:
return (
<div key={JSON.stringify(person)}>
<p>{person.gender}</p>
<p>{person.firstName}</p>
</div>
)
It's more efficient to hash elements once at the time when an array is created, e.g. with uuid:
import uuidv3 from 'uuid/v3';
...
for (const person of people) {
person.key = uuidv3(JSON.stringify(person), uuidv3.URL);
}
Or use dedicated hashing function like object-hash.
Notice that hashing may result in key collisions if there are elements with same contents.
|
{
"pile_set_name": "StackExchange"
}
|
Q:
Record Count functoid returns aggregate count for non-flattend target message
I tried to use the Record Count functoid to map the number of sub-records of an record that itself occurs 0 to unbounded to a message with each record containing a field holding the number of sub-records:
root+ +root
| |
+foo+ +foo+
| |
+bar+ -RecordCount- barcount
|
+xyz
However my current map aggregates the count of all bar records and returns it in every foo\barcount.
Sample source message
<root>
<foo>
<Id>1</Id>
<bar>
<xyz />
</bar>
<bar>
<xyz />
</bar>
</foo>
<foo>
<Id>2</Id>
<bar>
<xyz />
</bar>
<bar>
<xyz />
</bar>
</foo>
</root>
... and the result is
<root>
<foo>
<Id>1</Id>
<barcount>4</barcount>
</foo>
<foo>
<Id>2</Id>
<barcount>4</barcount>
</foo>
</root>
... whereas I expected
<root>
<foo>
<Id>1</Id>
<barcount>2</barcount>
</foo>
<foo>
<Id>2</Id>
<barcount>2</barcount>
</foo>
</root>
A:
I solved this issue by replacing the Record Count functoid with a Call XSLT Template Scripting functoid.
The XSLT template looks like this:
<xsl:template name="CountMyBar">
<xsl:param name="fooId" />
<xsl:element name="barcount">
<xsl:value-of select="count(//foo[Id=$fooId]/bar)" />
</xsl:element>
</xsl:template>
and the input to the scripting functoid is the Id field from foo.
|
{
"pile_set_name": "StackExchange"
}
|
Q:
Detect faulty drive in RAID 10 array
I've been told that I can only verify my HW RAID array is working perfectly with KVM. However, I want to be automatically notified when there is a problem by my server.
Is there a way via SSH (that will be called via system() in php) that can detect that a drive is having problems? I don't need to identify which drive.
I have thought of one theory but I don't know if it will work in practice. If I were to run a PHP script to fopen('/dev/[filesystem]', 'r') and seeked every xGB for 1 byte and it seeks a position of the filesystem that's having problems, it should return an error. Am I correct in thinking this idea?
I use XFS filesystem, I have heard of xfs_check but that says it needs to be ran in read-only mode which is inconvenient.
I use 3ware RAID controller.
A:
Install the 3Ware tools (tw_cli) on your machine.
After you have installed them, get the id # of the controller (I've never understood the system behind it, for all I know it might be random):
$ tw_cli show
Ctl Model (V)Ports Drives Units NotOpt RRate VRate BBU
------------------------------------------------------------------------
c0 9550SXU-4LP 4 2 1 0 1 1 -
You can then query the array status with
$ tw_cli /c0 show
Unit UnitType Status %RCmpl %V/I/M Stripe Size(GB) Cache AVrfy
------------------------------------------------------------------------------
u0 RAID-1 OK - - - 74.4951 ON OFF
Port Status Unit Size Blocks Serial
---------------------------------------------------------------
p0 NOT-PRESENT - - - -
p1 NOT-PRESENT - - - -
p2 OK u0 74.53 GB 156301488 9QZ07NP2
p3 OK u0 74.53 GB 156301488 9QZ08DS2
Obviously, this will look different on your machine. These example where lifted from here.
To actively verify (scrub) your drives, use
$ tw_cli /c0/u0 start verify
For automatic notifications, you should setup a monitoring system, e.g. Nagios or Icinga and use a plugin that checks the health of the array with the help of tw_cli. These plugins work nicely without Nagios/Icinga as well and could be easily used in a minimal monitoring system in form of a cron job that sends a mail of the plugin doesn't return 0.
|
{
"pile_set_name": "StackExchange"
}
|
Q:
MasterPage objects returning as null
I've got an ASP.net application that is used to display information queried from our ERP system onto various web pages. The main object is called an EpicorUser, and it basically encapsulates all current information about an employee.
This object is used to fill in a bunch of various fields on the master page such as Full Name, current activity, clock in/out times, etc.
I am trying to pass this object from the MasterPage into the content pages to avoid needlessly querying the WebService that serves this information. The problem is, when I access the object from a ContentPage, it is always null. I know it has been populated because my MasterPage content is all filled in correctly.
I am trying to access the MasterPage 'CurrentUser' object from my ContentPage like this:
**MasterPage Codebehind:**
public EpicorUser CurrentUser; //This object is populated once user has authenticated
///This is in my ContentPage ASPX file so I can reference the MasterPage from codebehind
<%@ MasterType VirtualPath="~/Pages/MasterPage/ShopConnect.Master" %>
**ContentPage CodeBehind:**
string FullName = Master.CurrentUser.UserFileData.FullName; //CurrentUser is null(but it shouldn't be)
Strange thing is, I had another content page where this system worked fine. It has also stopped working, and I don't think I have changed anything on the masterpage that could cause this. I had set the CurrentUser as a public property so I could access
I went as far a creating a method to re-populate the object from the master page, and calling it from the code-behind on the contentpage:
**ContentPage code-behind:**
EpicorUser CurrentUser = Master.GetCurrentUserObject();
**MasterPage Method being invoked:**
public EpicorUser GetCurrentUserObject()
{
using (PrincipalContext context = new PrincipalContext(ContextType.Domain, "OFFICE"))
{
UserPrincipal principal = UserPrincipal.FindByIdentity(context, HttpContext.Current.User.Identity.Name);
EpicorUser CurrentEmployee = RetrieveUserInfoByWindowsID(principal.SamAccountName);
return CurrentUser; //Object is NOT null before the return
}
}
**ContentPage code-behind return:**
EpicorUser CurrentUser = Master.GetCurrentUserObject(); //But object is now null once we return
Stepping through the code shows me that the CurrentUser object is populated correctly in the MasterPage code behind, but once it is returned to the ContentPage code behind, it is now null!
Anyone know where the disconnect is?
A:
Content Page is loaded first and then Master page will be loaded. So, your property could be blank when it is accessed in the content page. You can try creating a public method(to return UserObject) on the master page and then call the method from content page.
Another option is
creating a base page class(inherit all content pages) and create a property to return the user object. So, all pages can access the value
EDIT:
public class BasePageClass : System.Web.UI.Page
{
public List<string> LookupValues
{
get
{
if (ViewState["LookupValues"] == null)
{
/*
* create default instance here or retrieve values from Database for one time purpose
*/
ViewState["LookupValues"] = new List<string>();
}
return ViewState["LookupValues"] as List<string>;
}
}
}
public partial class WebForm6 : BasePageClass
{
protected void Page_Load(object sender, EventArgs e)
{
}
protected void MyButton_Click(object sender, EventArgs e)
{
//access lookup properties
List<string> myValues = LookupValues;
}
}
|
{
"pile_set_name": "StackExchange"
}
|
Q:
How to detect if sprite is being touched?
I'm trying to figure out how to know if the user is touching an sprite.
I simplified my code to the bare bones, on init I create a single sprite called "button" and then I try to know when the user is touching it/stops touching.
This is what I'm trying now:
-(id)initWithSize:(CGSize)size {
if (self = [super initWithSize:size]) {
self.backgroundColor = [SKColor colorWithRed:0 green:0 blue:0 alpha:1.0];
// Add button
SKSpriteNode *sprite = [SKSpriteNode spriteNodeWithImageNamed:@"button"];
sprite.name = @"button";
sprite.position = CGPointMake(CGRectGetMidX(self.frame), CGRectGetMidY(self.frame));
[self addChild:sprite];
}
return self;
}
-(void)touchesBegan:(NSSet *)touches withEvent:(UIEvent *)event {
UITouch *touch = [touches anyObject];
CGPoint location = [touch locationInNode:self];
SKNode *node = [self nodeAtPoint:location];
if ([node.name isEqualToString:@"button"]) {
NSLog(@"Started touch on sprite");
}
}
-(void)update:(CFTimeInterval)currentTime {
/* Called before each frame is rendered */
}
However though I can tell if a touch starts on the button, I cannot tell if the user ends the touch or moves out of the sprite (or vicebersa). How can I do this?
A:
Add to properties: (allows you to access them in various methods)
@property (nonatomic) BOOL touchInSprite;
@property (nonatomic) SKSpriteNode * sprite;
@end
Add methods:
- (void) touchesBegan:(NSSet *)touches withEvent:(UIEvent *)event
{
UITouch * touch = [touches anyObject];
CGPoint location = [touch locationInNode:self];
if ([self.sprite containsPoint: location])
self.touchInSprite = true;
else
self.touchInSprite = false;
}
- (void) touchesMoved:(NSSet *)touches withEvent:(UIEvent *)event
{
UITouch * touch = [touches anyObject];
CGPoint location = [touch locationInNode:self];
if ([self.sprite containsPoint: location])
{
self.touchInSprite = true;
}
else
{
self.touchInSprite = false;
//user stop touches it
}
}
- (void) touchesEnded:(NSSet *)touches withEvent:(UIEvent *)event
{
UITouch * touch = [touches anyObject];
CGPoint location = [touch locationInNode:self];
if (self.startTouchValid == true)
{
//Perform action
}
}
|
{
"pile_set_name": "StackExchange"
}
|
1995–96 Tunisian Ligue Professionnelle 1
The 1995–96 Tunisian Ligue Professionnelle 1 season was the 70th season of top-tier football in Tunisia.
Results
League table
Result table
References
1995–96 Ligue 1 on RSSSF.com
Category:Tunisian Ligue Professionnelle 1 seasons
Tun
|
{
"pile_set_name": "Wikipedia (en)"
}
|
Skilled & experienced car body Paint Sprayer required for full time vacancy. You will be familiar with water based paints & have extensive hands on experience in a car painting environment.
Key Tasks for a Vehicle Sprayer:Prepare vehicles to be sprayed- Mix paint to ensure a perfect match- Spray painting vehicles- Quality check on completion ensuring there are no defects
Experience and Requirements of a Paint Sprayer:- The Ideal applicant will be able to produce a high quality of paint spraying from start to finish and you must have experience in water based paints- You will be an experienced Vehicle Paint Sprayer / Paint Technician with a stable history and must hold a - City Guilds qualification or equivalent- You will work to a high standard and demonstrate a sound up-to-date knowledge of vehicle repair techniques
|
{
"pile_set_name": "Pile-CC"
}
|
Q:
Python socket error resilience / workaround
I have a script running that is testing a series of urls for availability.
This is one of the functions.
def checkUrl(url): # Only downloads headers, returns status code.
p = urlparse(url)
conn = httplib.HTTPConnection(p.netloc)
conn.request('HEAD', p.path)
resp = conn.getresponse()
return resp.status
Occasionally, the VPS will lose connectivity, the entire script crashes when that occurs.
File "/usr/lib/python2.6/httplib.py", line 914, in request
self._send_request(method, url, body, headers)
File "/usr/lib/python2.6/httplib.py", line 951, in _send_request
self.endheaders()
File "/usr/lib/python2.6/httplib.py", line 908, in endheaders
self._send_output()
File "/usr/lib/python2.6/httplib.py", line 780, in _send_output
self.send(msg)
File "/usr/lib/python2.6/httplib.py", line 739, in send
self.connect()
File "/usr/lib/python2.6/httplib.py", line 720, in connect
self.timeout)
File "/usr/lib/python2.6/socket.py", line 561, in create_connection
raise error, msg
socket.error: [Errno 101] Network is unreachable
I'm not at all familiar with handling errors like this in python.
What is the appropriate way to keep the script from crashing when network connectivity is temporarily lost?
Edit:
I ended up with this - feedback?
def checkUrl(url): # Only downloads headers, returns status code.
try:
p = urlparse(url)
conn = httplib.HTTPConnection(p.netloc)
conn.request('HEAD', p.path)
resp = conn.getresponse()
return resp.status
except IOError, e:
if e.errno == 101:
print "Network Error"
time.sleep(1)
checkUrl(url)
else:
raise
I'm not sure I fully understand what raise does though..
A:
Problem with your solution as it stands is you're going to run out of stack space if there are too many errors on a single URL (> 1000 by default) due to the recursion. Also, the extra stack frames could make tracebacks hard to read (500 calls to checkURL). I'd rewrite it to be iterative, like so:
def checkUrl(url): # Only downloads headers, returns status code.
while True:
try:
p = urlparse(url)
conn = httplib.HTTPConnection(p.netloc)
conn.request('HEAD', p.path)
resp = conn.getresponse()
return resp.status
except IOError as e:
if e.errno == 101:
print "Network Error"
time.sleep(1)
except:
raise
Also, you want the last clause in your try to be a bare except not an else. Your else only gets executed if control falls through the try suite, which can never happen, since the last statement of the try suite is return.
This is very easy to change to allow a limited number of retries. Just change the while True: line to for _ in xrange(5) or however many retries you wish to accept. The function will then return None if it can't connect to the site after 5 attempts. You can have it return something else or raise an exception by adding return or raise SomeException at the very end of the function (indented the same as the for or while line).
A:
If you just want to handle this Network is unreachable 101, and let other exceptions throw an error, you can do following for example.
from errno import ENETUNREACH
try:
# tricky code goes here
except IOError as e:
# an IOError exception occurred (socket.error is a subclass)
if e.errno == ENETUNREACH:
# now we had the error code 101, network unreachable
do_some_recovery
else:
# other exceptions we reraise again
raise
|
{
"pile_set_name": "StackExchange"
}
|
Other companies may hold their new features for the next year’s major launch, but not Lavasoft – we’re going to give them to you as we develop them so you benefit now! Here’s what you’re seeing in your Ad-Aware program – and why it’s important to your privacy and security.
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|
{
"pile_set_name": "Pile-CC"
}
|
"This new updated and expanded 10th anniversary edition of The Biology of Belief will forever change how you think about your own thinking. Stunning new scientific discoveries about the biochemical effects of the brain's functioning show that all the cells of your body are affected by your thoughts. Bruce H. Lipton, Ph.D., a renowned cell biologist, describes the precise molecular pathways through which this occurs. Using simple language, illustrations, humor, and everyday examples, he demonstra
"In the late nineteenth century, the Harvard College Observatory began employing women as calculators, or "human computers," to interpret the observations their male counterparts made via telescope each night. At the outset this group consisted of the wives, sisters, and daughters of the resident astronomers, but soon the female corps included graduates of the new women's colleges--Vassar, Wellesley, Radcliffe, and Smith. As photography transformed the practice of astronomy, the ladies turned fr
How do species evolve? Richard Dawkins, one of the world's most eminent zoologists, likens the process to scaling a huge, Himalaya-size peak, the Mount Improbable of his title. An alpinist does not leap from sea level to the summit; neither does a species utterly change forms overnight, but instead follows a course of "slow, cumulative, one-step-at-a-time, non-random survival of random variants"--A course that Charles Darwin, Dawkins's great hero, called natural selection. Illustrating his argum
No telescope? No problem! Learn how to spot the International Space Station, follow the moon through its phases, find planets, and watch meteor showers. The author also introduces readers to the best apps and websites to help skywatchers enjoy the wonders of the night sky without the need for a telescope or other expensive equipment.
Presents an accessible, in-depth look at the history of numbers and their applications in life and science, from math's surreal presence in the virtual world to the debates about the role of math in science.
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|
{
"pile_set_name": "Pile-CC"
}
|
Introduction {#s1}
============
Extensive characterization of cancer genomes has begun to change the classification of neoplasms and the choice of therapies ([@bib20]). The genetic profiles of most cancers are notoriously heterogeneous, often including thousands of mutations affecting genes with a wide range of credentials\-\--from those well-known to drive oncogenic behavior to those not known to have a role in pathogenesis. Moreover, cancers continue to accumulate mutations during carcinogenesis, producing tumor subclones with selectable features such as drug resistance or enhanced growth potential ([@bib39]).
Despite this heterogeneity, consistent patterns have been observed, such as the high frequency of gain-of-function or loss-of-function mutations affecting specific proto-oncogenes or tumor suppressor genes in cancers that arise in certain cell lineages. Conversely, coincident mutations in certain genes are rare, even when those genes are frequently mutated individually in specific types of cancer ([@bib28]). Examples of these 'mutually exclusive' pairs of mutations have been reported in a variety of cancers ([@bib72]; [@bib65]; [@bib52]; [@bib57]; [@bib67]); the mutual exclusivity has usually been attributed either to a loss of a selective advantage of a mutation in one gene after a change in the other has occurred ('functional redundancy') or to the toxicity (including 'synthetic lethality') conferred by the coexistence of both mutations in the same cells.
We recently reported that the mutual exclusivity of gain-of-function mutations of *EGFR* and *KRAS*, two proto-oncogenes often individually mutated in lung adenocarcinomas (LUADs), can be explained by such synthetic toxicity, despite the fact that products of these two genes operate in overlapping signaling pathways and might have been mutually exclusive because of functional redundancies ([@bib65]). Support for the idea that the mutual exclusivity of *KRAS* and *EGFR* mutations is synthetically toxic in LUAD cells was based largely on experiments in which we used doxycycline (dox) to induce expression of mutant *EGFR* or *KRAS* alleles controlled by a tetracycline (tet)-responsive regulatory apparatus in LUAD cell lines containing endogenous mutations in the other gene ([@bib65]). When we forced mutual expression of the pair of mutant proteins, the cells exhibited signs of RAS-induced toxicity, such as macropinocytosis and cell death. In addition, we observed increased phosphorylation of several proteins known to operate in the extensive signaling network downstream of RAS, implying that excessive signaling, driven by the conjunction of hyperactive EGFR and KRAS proteins, might be responsible for the observed toxicity.
Recognizing that such synthetic toxicities might be exploited for therapeutic purposes, we have extended our studies of signaling via the EGFR-RAS axis, with the goal of better understanding the biochemical events that are responsible for the previously observed toxicity in LUAD cell lines. In the work reported here, we have used a variety of genetic and pharmacological approaches to seek evidence that identifies critical mediators of the previously observed toxicities. Based on several concordant findings, we argue that activation of extracellular signal-regulated kinases (ERK1 and ERK2), serine/threonine kinases in the EGFR-RAS-RAF-MEK-ERK pathway, is a critical event in the generation of toxicity, and we show that at least one feedback inhibitor of the pathway, the dual specificity phosphatase, DUSP6, is a potential target for therapeutic inhibitors that could mimic the synthetic toxicity that we previously reported.
Results {#s2}
=======
Synthetic lethality induced by co-expression of mutant KRAS and EGFR is mediated through increased ERK signaling {#s2-1}
----------------------------------------------------------------------------------------------------------------
In previous work, we established that mutant EGFR and mutant KRAS are not tolerated in the same cell (synthetic lethality), by placing one of these two oncogenes under the control of an inducible promoter in cell lines carrying a mutant allele of the other oncogene. These experiments provided a likely explanation for the pattern of mutual exclusivity in LUAD ([@bib65]). While we documented several changes in cellular signaling upon induction of the second oncogene to produce toxicity, we did not establish if there is a node (or nodes) in the signaling network sensed by the cell as intolerable when both oncoproteins are produced. If such a node exists, we might be able to prevent toxicity by down-modulating the levels of activity; conversely, we might be able to exploit identification of that node to compromise or kill cancer cells.
To seek critical nodes in the RAS signaling pathway, we extended our previous study using the LUAD cell line we previously characterized (PC9, bearing the EGFR mutation, E746_A750del) and two additional LUAD lines, H358 and H1975. H358 cells express mutant KRAS (G12C), and H1975 cells express mutant EGFR (L858R/T790M). As in our earlier work, we introduced tet-regulated, mutant *KRAS* (G12V) into these lines to regulate mutant KRAS in an inducible manner and used the same vector encoding GFP rather than KRAS as a control. This single-vector system includes rtTA constitutively expressed from a ubiquitin promoter, allowing us to induce KRAS with the addition of dox ([@bib40]).
KRAS or GFP were appropriately induced after adding dox to the growth medium used for these cell lines ([Figure 1A](#fig1){ref-type="fig"}). To establish whether induction of a mutant *KRAS* transgene is detrimental to H358 cells producing endogenous mutant KRAS or H1975 cells producing mutant EGFR proteins, we cultured cell lines in dox for 7 days and measured the relative numbers of viable cells with Alamar blue. As we previously showed, the number of viable PC9 cells is reduced by inducing mutant KRAS ([Figure 1A](#fig1){ref-type="fig"}). Similarly, when mutant KRAS was induced in either H358 or H1975 cells for seven days, we observed fewer viable cells compared to cells grown without dox or to cells in which GFP was induced ([Figure 1A](#fig1){ref-type="fig"}). These results indicate that increased activity of the RAS pathway, either in LUAD cells with an endogenous *KRAS* mutation (H358 cells) or with an endogenous *EGFR* mutation (PC9 and H1975 cells) is toxic to these cell lines.
{#fig1}
We previously documented increases in phosphorylated forms of the stress kinases, phospho-JNK (P-JNK) and phospho-p38 (P-p38), as well as in phospho-ERK (P-ERK or P-p44/42), in one of these cell lines (PC9) 72 hr after treatment with dox ([@bib65]; [@bib67]). We used a phospho-protein array to assess the status of protein activation more broadly after KRAS induction, using PC9-tetO-KRAS cells after 1 and 5 days of dox treatment ([Figure 1B](#fig1){ref-type="fig"}, [Figure 1---figure supplement 1A](#fig1s1){ref-type="fig"}). After 5 days, we again observed increases in P-JNK, P-p38, and P-ERK ([Figure 1---figure supplement 1A](#fig1s1){ref-type="fig"}), suggesting that three major branches of the MAPK pathway are activated after extended induction of mutant KRAS. In addition, several other proteins show enhanced phosphorylation at this time. At 24 hr after addition of dox, however, only P-ERK and P-AKT show a pronounced increase ([Figure 1B](#fig1){ref-type="fig"}). Specifically, the stress kinases, JNK and p38, were not detected as phosphorylated proteins with the protein array. A possible interpretation of these findings is that ERK may be phosphorylated relatively soon after induction of mutant KRAS, with subsequent phosphorylation (and activation) of stress kinases and several other proteins. We also observed increased phosphorylation of ERK 24 hr after induction of mutant KRAS by western blot in all three LUAD cell lines ([Figure 1C](#fig1){ref-type="fig"}). In H358 and in H1975-based cell systems we observed persistently increased levels of P-ERK and, ultimately, the presence of cleaved PARP ([Figure 1---figure supplement 1B](#fig1s1){ref-type="fig"}). We previously reported multiple mechanisms of RAS-induced toxicity in PC9-tetO-KRAS cells ([@bib65]). Based on the cleavage of PARP in the studies shown here, apoptosis appears to be at least one of the mechanisms of reduced viability in H358 and H1975 cell lines.
The results shown in [Figure 1](#fig1){ref-type="fig"} suggest that ERK itself could be the signaling node that causes a loss of viable cells when inappropriately activated. As one test of this hypothesis, we used trametinib ([@bib21]), an inhibitor of MEK, the kinase that phosphorylates ERK, to ask whether reduced levels of P-ERK would protect cells from the toxicity caused by induction of mutant KRAS. In all three LUAD cell lines, trametinib completely or partially rescued the loss of viable cells caused by induction of mutant KRAS by dox ([Figure 1D](#fig1){ref-type="fig"}, [Figure 1---figure supplement 1C](#fig1s1){ref-type="fig"}). We confirmed that doses of trametinib that protected cells from the toxic effects of seven days of treatment with dox were associated with reduced levels of P-ERK after 24 hr of induction of mutant KRAS ([Figure 1D](#fig1){ref-type="fig"}). A PI3K inhibitor, buparlisib, did not rescue mutant KRAS-induced lethality in H358-tetO-KRAS cells ([Figure 1---figure supplement 1D](#fig1s1){ref-type="fig"}), implying that the toxic effects of KRAS are not mediated by enhanced signaling via PI3K.
To extend these findings and further challenge the hypothesis that P-ERK is an important node in the cell signaling network downstream of KRAS that confers cell toxicity, we transduced LUAD cell lines with retroviral vectors encoding shRNAs that 'knock down' expression of ERK1 or ERK2. Using two different shRNAs for each gene, as well as a non-targeted shRNA vector as control, we stably reduced the levels of ERK1 or ERK2 in the three LUAD cell lines ([Figure 1E](#fig1){ref-type="fig"}). When PC9 and H358 lines were treated with dox to assess the effects of ERK1 or ERK2 knockdowns on the loss of viable cells, we found that depletion of ERK2, but not ERK1, rescued cells from KRAS toxicity after 7 days in dox ([Figure 1E](#fig1){ref-type="fig"}). In H1975 cells, however, neither knockdown of ERK1 nor of ERK2 prevented KRAS-induced cell toxicity. Since trametinib rescues the number of viable cells after induction of KRAS in H1975 cells ([Figure 1D](#fig1){ref-type="fig"}), it seemed possible that either ERK1 or ERK2 might be sufficient to mediate RAS-induced toxicity in this line. In that case, it would be necessary to reduce the levels or the activity of both ERK proteins to rescue H1975 cells from toxicity. We tested this idea by treating dox-induced H1975-tetO-KRAS cells with SCH772984 ([@bib43]), a drug that inhibits the kinase activity of both ERK1 and ERK2 ([Figure 1---figure supplement 1E](#fig1s1){ref-type="fig"}). As we observed with the MEK inhibitor, trametinib, in other lines ([Figure 1D](#fig1){ref-type="fig"}, far right), the ERK inhibitor reduces KRAS-associated toxicity in H1975 cells with concomitant reductions of P-ERK1 and P-ERK2 ([Figure 1---figure supplement 1E](#fig1s1){ref-type="fig"}).
To examine this issue in a different way, we performed a genome-wide CRISPR-Cas9 screen to evaluate mechanisms of mutant KRAS-induced toxicity in an unbiased manner. After growing H358-tetO-KRAS cells for 7 days following introduction of the appropriate vectors carrying Cas9 and a library of DNA encoding gene-targeted RNAs (see Materials and methods), guide RNA (sgRNA) targeting ERK2 (MAPK1) was highly enriched in cells grown in the presence of doxycycline ([Figure 1---figure supplement 1F](#fig1s1){ref-type="fig"}, [Supplementary file 1](#supp1){ref-type="supplementary-material"}). Guide RNA targeting RAF1 (CRAF) was also significantly enriched. Data from this CRISPR-Cas9 genome-wide screen strongly suggests that depletion of critical proteins in the RTK-RAS pathway can mitigate the toxicity induced by excess RAS activation. Collectively, our data suggest that LUAD cell lines are sensitive to inappropriate hyperactivation of the ERK signaling node and that toxicity mediated by activation of the RAS pathway is ERK-dependent.
DUSP6 is a major regulator of negative feedback, expressed in LUAD cells, and associated with KRAS and EGFR mutations and with high P-ERK levels {#s2-2}
------------------------------------------------------------------------------------------------------------------------------------------------
The evidence that hyperactive ERK signaling has toxic effects on LUAD cells raises the possibility that cancers driven by mutations in the RAS pathway may have a mechanism to 'buffer' P-ERK levels and thereby avoid reaching a lethal signaling threshold. Genes encoding negative feedback regulators are typically activated at the transcriptional level by the EGFR-KRAS-ERK pathway to place a restraint on signaling ([@bib4]). Such feedback regulators previously implicated in the control of EGFR-KRAS-ERK signaling include the six dual specificity phosphatases (DUSP1-6), the four sprouty proteins (SPRY1-4) and the three sprouty-related, EVH1 domain-containing proteins (SPRED1-3) ([@bib4]; [@bib34]). To begin a search for possible negative regulators of RAS-mediated signaling in LUAD cells driven by mutations in either *KRAS* or *EGFR*, we asked whether mutations in either proto-oncogene would up-regulate one or multiple members of these families of regulators, based on the assumption that such proteins might constrain P-ERK levels, leading to optimal growth without cytotoxic effects.
To search for potential negative regulators specifically involved in LUAD, we compared amounts of RNAs from *DUSP, SPRY* and *SPRED* gene families in tumors with and without mutations in either *KRAS* or *EGFR*, using RNA-seq data from The Cancer Genome Atlas (TCGA) ([@bib8]) ([Figure 2A,B](#fig2){ref-type="fig"} and [Figure 2---figure supplement 1A,B](#fig2s1){ref-type="fig"}). *DUSP6* was the only negative-feedback regulatory gene with significantly different levels of expression when we compared tumors with mutations in either *KRAS* or *EGFR* with tumors without such mutations (Bonferoni corrected p \< 0.01, two-tailed t-test with Welch's correction). Further, *DUSP6* mRNA was significantly up-regulated in LUAD tumors with mutations in common RTK-RAS pathway components compared to those without, consistent with a role of DUSP6 in regulating EGFR-KRAS-ERK signaling ([Figure 2---figure supplement 1C](#fig2s1){ref-type="fig"}) ([@bib4]; [@bib44]; [@bib45]; [@bib22]; [@bib30]; [@bib73]). *DUSP6* RNA was also present at higher levels in LUADs with *EGFR* or *KRAS* mutations than in tumors without such mutations in an independent collection of 83 tumors collected at the British Columbia Cancer Agency (BCCA, p = 0.004), confirming the findings derived from the TCGA dataset ([Figure 2C](#fig2){ref-type="fig"} and [Figure 2---figure supplement 1D](#fig2s1){ref-type="fig"}). Furthermore, *DUSP6* RNA was more abundant in EGFR/KRAS mutant LUADs than in normal lung tissue (p\<0.0001) whereas no significant differences in *DUSP6* levels were observed between normal lung tissue and tumors without mutations in either of these two genes (p = 0.64) ([Figure 2C](#fig2){ref-type="fig"} and [Figure 2---figure supplement 1D](#fig2s1){ref-type="fig"}).
{#fig2}
To ascertain whether *DUSP6* is up-regulated specifically in tumors driven by mutant KRAS or mutant EGFR signaling rather than in tumors associated with activation of other oncogenic pathways, we measured *DUSP6* RNA in experimental systems driven by the activation of various oncogenes. In transgenic mouse models of lung cancer, *Dusp6* RNA was present at significantly higher levels in the lungs of mice bearing tumors driven by mutant *EGFR* or *KRAS* transgenes than in normal mouse lung epithelium ([Figure 2D](#fig2){ref-type="fig"}) ([@bib17]; [@bib18]; [@bib54]). In contrast, *Dusp6* RNA levels were not significantly different in lungs from mice with tumors driven by MYC and in normal mouse lung tissue ([Figure 2D](#fig2){ref-type="fig"}). Similarly, increased levels of *DUSP6* RNA were observed in primary human epithelial cells only when the cells were also transduced with mutant *RAS* genes, but not with a variety of other oncogenes or with plasmids encoding GFP (p \< 0.0001) ([Figure 2E](#fig2){ref-type="fig"}) ([@bib6]). Lastly, our LUAD cell lines engineered to produce KRAS^G12V^ in response to dox showed an increase in *DUSP6* RNA that correlated with augmented phosphorylation of ERK and cell toxicity ([Figure 2F](#fig2){ref-type="fig"}). It is unclear why increased levels of *DUSP6* RNA are not sufficient to decrease P-ERK in these inducible systems; this may reflect the localization of P-ERK, which we have not explored here. Together, these findings suggest that DUSP6 is a critical negative feedback regulator activated in response to oncogenic signaling by mutant RAS or EGFR proteins in LUAD.
In our previous study ([@bib65]) (see also [Figure 1---figure supplement 1A](#fig1s1){ref-type="fig"}), we found that co-induction of oncogenic KRAS and EGFR activated not only ERK, but also JNK and p38 MAPK pathways, albeit at later times. To investigate whether *DUSP6* is up-regulated solely in response to phosphorylation of ERK or also in response to phosphorylation of JNK and p38, we assessed the relationship of amounts of *DUSP6* RNA in tumors with levels of P-ERK, P-JNK and P-p38 proteins as determined for TCGA ([@bib8]), using the Reverse Phase Protein Array (RPPA). LUADs with a *KRAS* or an *EGFR* mutation contained significantly higher levels of P-ERK -- but not of P-JNK or P-p38 -- than did tumors without those mutations, consistent with a role for these oncogenes in ERK activation ([Figure 2G](#fig2){ref-type="fig"}). Furthermore, tumors with high *DUSP6* RNA have relatively high amounts of P-ERK but not of P-JNK or P-p38 ([Figure 2H](#fig2){ref-type="fig"}). Lastly, there is a positive correlation between P-ERK levels and *DUSP6* RNA in LUAD ([Figure 2I](#fig2){ref-type="fig"}), whereas no such association was observed between *DUSP6* RNA and P-JNK or P-p38 ([Figure 2---figure supplement 1E,F](#fig2s1){ref-type="fig"}). Together, these observations support the proposal that *DUSP6* is expressed in response to activation of ERK and that it serves as a major negative feedback regulator of ERK signaling in LUAD, buffering the potentially toxic effects of ERK hyperactivation.
Knockdown of DUSP6 elevates P-ERK and reduces viability of LUAD cells with either KRAS or EGFR oncogenic mutations {#s2-3}
------------------------------------------------------------------------------------------------------------------
If DUSP6 is a negative feedback regulator of RAS signaling through ERK, then inhibiting the function of DUSP6 in LUAD cell lines driven by oncogenic KRAS or EGFR should cause hyperphosphorylation and hyperactivity of ERK, possibly producing a signaling intensity that causes cell toxicity, as observed when we co-express mutant KRAS and EGFR. Consistent with this prediction, introduction of *DUSP6*-specific siRNA pools into PC9 cells decreased DUSP6 levels and reduced the number of viable cells to levels similar to those observed when mutant *EGFR*, the driver oncogene, was itself knocked down ([Figure 3A](#fig3){ref-type="fig"}). siRNA pools for either *DUSP6* or *EGFR* decreased DUSP6 protein levels. A decrease in DUSP6 protein levels with siRNA against *EGFR* RNA can be explained by a reduction in EGFR protein levels causing a decrease in ERK activation ([Figure 3A](#fig3){ref-type="fig"}) and subsequently diminishing expression of *DUSP6*, a direct negative feedback regulator of ERK activity. Importantly, almost complete knockdown of DUSP6 was required to elicit toxic effects in PC9 cells.
{#fig3}
The pool of Dharmacon-synthesized siRNAs we used is composed of 4 individual siRNAs (labeled DUSP6-6,7,8 and 9, [Figure 3](#fig3){ref-type="fig"} and [Figure 3---figure supplement 1A,B](#fig3s1){ref-type="fig"}). We tested the individual siRNAs to confirm knockdown of DUSP6 protein and assess cell viability after siRNA treatment ([Figure 3---figure supplement 1A,B](#fig3s1){ref-type="fig"}). Treatment of PC9 cells with any one of three particular siRNAs resulted in a significant decrease in DUSP6 levels (particularly DUSP6-6 and DUSP6-7), however, the number of viable cells on day 5 was greater than in cells treated with the non-targeting control siRNA ([Figure 3---figure supplement 1A,B](#fig3s1){ref-type="fig"}). This observation was in contrast to the loss of cell viability we documented with the siRNA pool against DUSP6 ([Figure 3](#fig3){ref-type="fig"}). However, treatment with one other siRNA in the pool, DUSP6-8, resulted in the greatest depletion in DUSP6 protein and also a striking loss of cell viability ([Figure 3---figure supplement 1A,B](#fig3s1){ref-type="fig"}), consistent with the results from the siRNA pool. This suggests that DUSP6 protein levels need to be substantially depleted to exert an effect in PC9 cells.
Because only one siRNA in the pool (DUSP6-8) had a deleterious effect on PC9 cells, we confirmed the effects of this siRNA by utilizing another siRNA that targets a different region of DUSP6 mRNA (A 5' coding sequence is targeted by DUSP6-Qiagen, whereas a 3' coding sequence is targeted by DUSP6-8). DUSP6-Qiagen suppresses DUSP6 protein to a level similar to what we observed with the siRNA pool ([Figure 3B,C](#fig3){ref-type="fig"}). We also observed a loss of cell viability in PC9s cells treated with DUSP6-Qiagen siRNA comparable to that of the siRNA pool, suggesting these effects are not off-target ([Figure 3B,C](#fig3){ref-type="fig"}).
While it was anticipated that knockdown of mutant EGFR would diminish the numbers of viable cells by reducing levels of P-ERK and its growth-promoting signal, cells in which DUSP6 was knocked down with siRNAs also displayed reduced P-ERK levels five days after transfection, not the expected increase in phosphorylation of ERK ([Figure 3A](#fig3){ref-type="fig"}). One way to reconcile this apparent discrepancy is to examine the kinetics of phosphorylation and dephosphorylation of ERK after manipulation of the abundance of DUSP6 and its resulting effects on RAS signaling. To determine whether an initial, transient increase in P-ERK occurred after nearly complete knockdown of DUSP6, preceding the observed reduction in viable cells, we measured P-ERK in two cell lines with mutations in *EGFR* (PC9 and H1975 cells), one cell line with a mutation in *KRAS* (A549 cells) and a lung squamous cell carcinoma with wildtype *EGFR* and *KRAS* (HCC95 cells) 24 hr after addition of DUSP6 siRNA. In the three cell lines assessed with mutant *EGFR* or *KRAS*, there was a small but consistent increase (\~1.5 fold) in P-ERK 24 hr after receiving DUSP6 siRNA, compared to non-targeting siRNA controls ([Figure 3D](#fig3){ref-type="fig"}). Within 5 days, knockdown of DUSP6 reduced the numbers of viable cells in the LUAD lines with activating *KRAS* or *EGFR* mutations (PC9, H1975 and A549 cells), but not in a cell line with no known activating mutations affecting the EGFR-KRAS-ERK pathway (HCC95 cells) ([Figure 3E](#fig3){ref-type="fig"}).
Mirroring the decrease in viability, cleaved PARP was also induced five days after DUSP6 knockdown in EGFR/KRAS mutant, but not EGFR/KRAS wildtype cells ([Figure 3---figure supplement 1C](#fig3s1){ref-type="fig"}). While there was no correlation between sensitivity to DUSP6 knockdown and basal DUSP6 protein levels, KRAS or EGFR mutant cell lines demonstrate higher P-ERK levels and/or a high P-ERK to DUSP6 protein ratio that could contribute to P-ERK hyperactivity and the subsequent decrease in cell viability after inhibition of DUSP6 ([Figure 3](#fig3){ref-type="fig"}-figure supplement D,E,F). Lastly, as described above, reduction of ERK1 or ERK2 levels with shRNAs in EGFR-mutant PC9 cells partially rescued the decreased cell viability caused by DUSP6 knockdown, suggesting that ERK -- at least in part - mediates the toxic effects of DUSP6 inhibition ([Figure 3---figure supplement 1G,H,I](#fig3s1){ref-type="fig"}). These data suggest that knockdown of DUSP6 or potentially other negative feedback regulators that can increase P-ERK would reduce cell viability in cells containing an oncogenic *KRAS* or *EGFR* mutation.
Pharmacological inhibition of DUSP6 reduces the number of viable LUAD cells bearing mutations that activate the ERK pathway {#s2-4}
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The results presented thus far suggest that LUAD cells with mutations in *KRAS* or *EGFR* depend on negative regulators like DUSP6 to attenuate P-ERK for survival, offering a potentially exploitable vulnerability that could be useful therapeutically. However, blocking synthesis of DUSP6 efficiently with siRNA is difficult, in part because reduced levels of DUSP6 lead to increased levels of phosphorylated ERK, stimulating a subsequent increase in *DUSP6* mRNA. As *DUSP6* mRNA rises, more siRNA may be required to sustain the reduction of DUSP6. Based on this negative feedback cycle, we reasoned that pharmacological inhibition of the enzymatic activity of DUSP6 would be more effective. A small molecule inhibitor of DUSP6, (E)−2-benzylidene-3-(cyclohexylamino)−2,3-dihydro-1H-inden-1-one (BCI), was identified through an in vivo chemical screen for activators of fibroblast growth factor signaling in zebrafish ([@bib41]; [@bib33]). BCI inhibits DUSP6 allosterically, binding near the active site of the phosphatase, inhibiting activation of the catalytic site after binding to its substrate, ERK ([@bib41]). BCI also selectively inhibits DUSP1, which, like DUSP6, has catalytic activity dependent on substrate binding. However, as demonstrated in [Figure 2A](#fig2){ref-type="fig"}, *DUSP1* is not significantly up-regulated in LUADs with *EGFR* or *KRAS* mutations. Furthermore, siRNA-mediated knockdown of DUSP1, as opposed to knockdown of DUSP6, has no effect on viability of EGFR-mutant H1975 cells, suggesting that DUSP6 should be the main target of BCI ([Figure 4---figure supplement 1A,B](#fig4s1){ref-type="fig"}).
We tested 11 lung cancer cell lines - 8 with a *KRAS* or *EGFR* mutation and 3 with no known activating mutations in these genes -- with a dosing strategy covering the previously determined active range of the drug ([@bib61]). We predicted that cancer lines with mutations in *KRAS* or *EGFR* would be more sensitive to the potential effects of BCI treatment on numbers of viable cells, since DUSP6 would be required to restrain the toxic effects of P-ERK in these cells. Our findings are consistent with this prediction ([Figure 4A,B](#fig4){ref-type="fig"}). The cell lines fell into three categories of sensitivity: 1) the most sensitive lines, with IC50s between 1--3 uM and with \> 90% loss of viable cells at 3.2 uM, all harbored *KRAS* or *EGFR* mutations; 2) the one line with intermediate sensitivity, H1437 (IC50 \> 4 uM), contains an activating mutation in *MEK* (Q56P); and 3) the relatively insensitive lines (IC50s ≥ 5 uM) lack known mutations affecting the EGFR-KRAS-ERK signaling pathway. The insensitive cell lines did not demonstrate the marked (\> 90%) reduction in numbers of viable cells observed with the sensitive cell lines and only sensitive cell lines showed induction of cleaved PARP after BCI treatment ([Figure 4---figure supplement 1C](#fig4s1){ref-type="fig"}). Together, these data suggest that pharmacological inhibition of DUSP6 specifically kills cells with *EGFR* or *KRAS*-mutations.
![Treatment with the DUSP6 inhibitor BCI selectively kills LUAD cell lines with KRAS or EGFR mutation, implying a dependence on ERK-mediated signaling.\
(**A--B**) BCI induces toxicity specifically in lung cancer cell lines with mutations in genes encoding components in the EGFR-KRAS-ERK pathway. (**A**) Eleven lung cancer cell lines were treated with increasing doses of BCI for 72 hr based on the reported effective activity of the drug ([@bib61]). Cell lines could be assigned to three distinct groups: sensitive (red), intermediate (green) and insensitive (black). All sensitive cell lines contained either *EGFR* or *KRAS* mutations; the intermediate and insensitive cell lines were wild-type for genes encoding components of the EGFR-KRAS-ERK signaling pathway (as determined by the Sanger Cell Line Project and the Cancer Cell Line Encyclopedia \[[@bib5]\]). Experiments were done in biological duplicate with the average values presented ±SEM. (**B**) Crystal Violet stain of cells plated in the indicated doses of BCI or control (0 = 0.1% DMSO) for 72 hr. Sensitive cells with a *KRAS* mutation (H358 cells; denoted with red underlining) show a more pronounced decrease in cell number than do cells without oncogenic mutations in genes encoding components of the EGFR-KRAS-ERK pathway (H1648 cells; black underlining). Experiments were done in biological duplicate with a representative image shown. (**C**) BCI increases P-ERK levels specifically in BCI-sensitive cell lines. Sensitive lines (H358, PC9, H1975 and A549; red underlining) and insensitive lines (HCC95 and H1648; black underlining) were treated with the indicated doses of BCI or vehicle control (0.1% DMSO) for 30 min, and the levels of ERK (p44/p42) and P-ERK (P-p44/42 T202/Y204) assessed by Western blot. P-ERK appeared in the sensitive cells at low doses of BCI, but P-ERK levels did not increase in the insensitive cells at the tested doses of BCI. (**D**) Dosimetry plots from the experiment shown in panel. (**C**) (**E--F**) Cell lines sensitive to BCI are also dependent on P-ERK for survival. BCI-sensitive cells with oncogenic mutations in *EGFR* or *KRAS* (PC9 and H358, respectively; red underlining) and BCI-insensitive cells (H1648 and HCC95; black underlining) were treated with the indicated doses of the MEK inhibitor trametinib for 72 hr; viable cells were measured with Alamar blue and compared to cells receiving the vehicle control (0 = 0.1% DMSO). (**E**) Treatment with trametinib decreased P-ERK levels as determined by western blot. (**F**) The reduction in P-ERK corresponded to a greater decrease in viable cells in BCI-sensitive lines (red coloring), compared to BCI-insensitive cell lines (black coloring).](elife-33718-fig4){#fig4}
P-ERK levels increase in LUAD cells after inhibition of DUSP6 by BCI, and P-ERK is required for BCI- mediated toxicity {#s2-5}
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Based on findings in the preceding section, we predicted that BCI-mediated inhibition of DUSP6 would increase P-ERK to toxic levels, similar to the effects of co-expressing mutant *KRAS* and *EGFR*. To test this proposal, we measured total ERK and P-ERK after BCI treatment in sensitive and insensitive cell lines. A subset of the most sensitive cell lines, H358 (KRAS mutant) and PC9 and H1975 (EGFR mutants), demonstrated a large, dose-dependent increase in P-ERK in response to BCI treatment, with appreciable increases observed even at the lowest doses tested (1 uM) ([Figure 4C,D](#fig4){ref-type="fig"}). This induction of P-ERK precedes the appearance of cleaved PARP and cell death, as indicated by a time course of observations after BCI treatment in KRAS-mutant H358 cells ([Figure 4---figure supplement 1D](#fig4s1){ref-type="fig"}). Likewise, another sensitive cell line, A549 (KRAS mutant), demonstrated an increase in P-ERK, albeit at higher BCI concentrations, consistent with a less acute BCI sensitivity ([Figures 3C](#fig3){ref-type="fig"} and [4C,D](#fig4){ref-type="fig"}). Conversely, BCI did not induce increases in P-ERK in the insensitive cell lines HCC95 and H1648, even at the highest levels of BCI (10 uM) ([Figure 4C,D](#fig4){ref-type="fig"}). Importantly, cell lines sensitive to BCI were also dependent on sustained P-ERK signaling for survival, as the MEK inhibitor trametinib, while effectively reducing P-ERK in all cell lines, reduced cell viability to a greater degree in BCI- sensitive lines (H358 and PC9) compared to BCI-insensitive lines (H1648 and HCC95; [Figure 4E,F](#fig4){ref-type="fig"}). Thus, the oncogenic mutation profile and dependency on activation of the EGFR-RAS-ERK pathway correlates with dependence on DUSP6 activity. These correlations are likely to reflect the central significance of P-ERK as a determinant of cell growth and viability.
To confirm whether P-ERK is involved in regulation of BCI-mediated cell death, we treated KRAS mutant H358 cells with a combination of BCI and the ERK1/2 inhibitor VX-11E, predicting that simultaneous inhibition of DUSP6 and ERK would mitigate the toxic effects of BCI treatment. Unlike other ERK inhibitors such as SCH772984, VX-11E does not block ERK phosphorylation, but instead limits ERK activity following phosphorylation ([@bib10]). Consistent with this, while no difference in P-ERK induction was observed, VX-11E treatment limited BCI- induced phosphorylation of the downstream ERK target RSK ([Figure 4---figure supplement 1F](#fig4s1){ref-type="fig"}). In addition, treatment with VX-11E lead to a relative increase in the number of viable cells after BCI treatment in a dose-dependent manner, with higher VX-11E concentrations demonstrating less decline in viability in response to BCI compared to lower doses ([Figure 4---figure supplement 1E](#fig4s1){ref-type="fig"}). Together, these data suggest that ERK activation plays a vital role in mediating the inhibitory effects of BCI treatment in KRAS or EGFR mutant lung cancer cells.
To further understand BCI-mediated toxicity, we searched for potential resistance mechanisms through an unbiased, genome-wide CRISPR screen of the type described earlier ([Figure 1---figure supplement 1F](#fig1s1){ref-type="fig"}). If loss of genes targeted by guide RNA confers resistance, that can reveal the nature of the pathway being targeted, since inhibited expression of the gene mitigates the effects of the drug. We performed this screen in H460 cells that are mutant (Q61H) for KRAS and sensitive to BCI ([Figure 4A](#fig4){ref-type="fig"}). In the screen, we found that sgRNAs targeting KRAS were significantly enriched in *KRAS*-mutated H460 cells upon treatment with BCI compared to untreated controls ([Figure 4---figure supplement 1G](#fig4s1){ref-type="fig"}, [Supplementary file 1](#supp1){ref-type="supplementary-material"}). Guide RNA targeting *KRAS* were depleted in the absence of drug suggesting a dependence on mutant KRAS in this cell line. These results suggest that KRAS pathway activity is a major determinant of sensitivity to BCI ([Figure 4---figure supplement 1G](#fig4s1){ref-type="fig"}). To validate these results, we cloned two individual sgRNAs targeting *KRAS* and transduced H460 cells. After 7 days of puromycin selection, the polyclonal population was evaluated for KRAS depletion ([Figure 4---figure supplement 1H](#fig4s1){ref-type="fig"}). The KRAS-targeted and control H460 cells were treated at this time point with a dose response of BCI for 72 hr. Cells that contained sgRNAs against *KRAS* were less sensitive to BCI than cells containing control sgRNA and un-manipulated cells ([Figure 4---figure supplement 1I](#fig4s1){ref-type="fig"}).
We also generated two clones of DUSP6-deficient H358 cells using CRISPR-Cas9 and independent guide RNAs ([Figure 4---figure supplement 1J](#fig4s1){ref-type="fig"}). Unexpectedly, both clones remained responsive to BCI's cell killing activity ([Figure 4---figure supplement 1K](#fig4s1){ref-type="fig"}). These results may be explained by the presence of DUSP1 ([Figure 4---figure supplement 1J](#fig4s1){ref-type="fig"}) and the reported activity of BCI against DUSP1 in addition to DUSP6. Further studies will be required to ascertain if these cells are still dependent on P-ERK for BCI-mediated sensitivity through DUSP1 or through another mechanism. While BCI sensitivity may not be solely due to DUSP6, our genome-wide screen for resistance to BCI suggests activation of the RAS pathway is at least partly required.
To further test RAS pathway dependency and its relation to BCI sensitivity, we predicted that stimulating the EGFR-RAS-ERK pathway in a BCI-insensitive cell line would make the cells more dependent on DUSP6 activity and more sensitive to BCI. Using HCC95 lung squamous carcinoma cells, which express relatively high levels of wild-type EGFR ([Figure 5A](#fig5){ref-type="fig"}), we showed that EGF increased the levels of both P-EGFR and P-ERK, confirming activation of the relevant signaling pathway ([Figure 5A,B](#fig5){ref-type="fig"}, [Figure 5---figure supplement 1](#fig5s1){ref-type="fig"}). In addition, BCI further enhanced the levels of P-ERK, especially in the EGF-treated cells, with dose-dependent increases; these findings are similar to those observed in cell lines with *EGFR* or *KRAS* mutations ([Figure 4C,D](#fig4){ref-type="fig"}). After pretreatment with EGF (100 ng/mL) for ten days and treating the cells with increasing doses of BCI to inhibit DUSP6, 3 uM BCI reduced the number of viable HCC95 cells by approximately 40% compared to the control culture that did not receive EGF ([Figure 5C](#fig5){ref-type="fig"}). This outcome implies that prolonged EGF treatment and subsequent activation of P-ERK signaling makes HCC95 cells dependent on DUSP6 activity, as also observed in cell lines with *EGFR* or *KRAS* mutations ([Figure 4A](#fig4){ref-type="fig"}). Taken together, these findings suggest that LUAD cells with *KRAS* or *EGFR* mutations are sensitive to BCI because the drug acutely increases P-ERK beyond a tolerable threshold in a manner analogous to the synthetic lethality we previously described in LUAD lines after co-expression of mutant KRAS and EGFR ([@bib65]).
{#fig5}
Discussion {#s3}
==========
The pattern of mutual exclusivity observed with mutant *EGFR* and mutant *KRAS* genes in LUAD is a consequence of synthetic lethality, not pathway redundancy; co-expression of these oncogenes is toxic, resulting in loss of viable cells ([@bib65]; [@bib67]). There are reports of exceptions to this mutual exclusivity but these arise in conditions that include inhibition of EGFR ([@bib7]; [@bib55]). This is to be expected, as cells treated with kinase inhibitors are not experiencing the effects of both oncogenes (i.e. mutant EGFR and mutant KRAS). A cancer cell that has not been exposed to inhibitors (e.g. against mutant EGFR) could arise, particularly at an advanced stage of disease, with activating mutations in both EGFR and KRAS; but we would anticipate that other events---like decreased RAS-GTP levels\-\--might prevent P-ERK from reaching toxic levels.
Despite the possible exceptions, it remains critical to understand why, based on the pattern of mutual exclusion, cells are generally unable to tolerate the combination of these two oncogenes more readily. And what are the biochemical mechanisms by which the toxicity is mediated, might be modulated to avoid lethality, or could be exploited therapeutically? To address these questions, we began by regulating the expression of mutant *KRAS* in LUAD cell lines carrying mutant *RAS* or *EGFR* alleles. The levels of RAS activation in these cells are not expected to mirror what is found in tumors; these levels presumably will exceed what tumors can tolerate. We suggest that tumor cells could experience this state during progression, particularly when co-mutations in the RAS pathway have occurred. Understanding how the toxicity arises provides insight into mutual exclusivity and how limits for RAS activation may be set and exploited in cancer cells.
Our efforts to answer these questions have led to the conclusions that the toxicity is mediated through the hyperactivity of phosphorylated ERK1/2 and that inhibition of DUSP6 may re-create the toxicity through the role of this phosphatase as a negative regulator of ERK1/2. Several results reported here support these conclusions: (i) the previously reported toxicity that results from co-expression of mutant *EGFR* and mutant *KRAS* is accompanied by an early increase in the phosphorylation of ERK1/2, and the effects can be attenuated by inhibiting MEK (which phosphorylates and activates ERK1/2) or by reducing ERK levels with inhibitory RNAs; (ii) DUSP6, a phosphatase known to be a feedback inhibitor of ERK activity, is present at relatively high levels in LUADs with *EGFR* and *KRAS* mutations; and (iii) inhibition of DUSP6, either by introduction of siRNAs or by treatment with the drug BCI, reduces the number of viable LUAD cells with *EGFR* or *KRAS* mutations or of BCI-resistant cells exposed to EGF.
Taken in concert, these findings support a general hypothesis about cell signaling. Activation of a biochemical signal from a critical node, such as ERK, in a signaling pathway must rise to a certain level to drive neoplastic changes in cell behavior; if signal intensity falls below that level, the cells may revert to a normal phenotype or initiate cell death as a manifestation of what is often called 'oncogene addiction" ([@bib47]; [@bib69]; [@bib15]; [@bib66]; [@bib59]). Conversely, if the intensity of signaling rises to exceed a higher threshold, the cells may display a variety of toxic effects, including senescence, vacuolization, or apoptosis ([@bib65]; [@bib11]; [@bib58]; [@bib27]; [@bib48]; [@bib75]). In this model, two approaches to cancer therapy can be envisioned: (i) blocks to signaling that reverse the oncogenic phenotype or induce the apoptosis associated with oncogene addiction, or (ii) enhancements of signaling that cause selective toxicity in cells with pre-existing oncogenic mutations, a form of synthetic lethality that depends on changes that produce a gain rather than a loss of function. The former is exemplified by using inhibitors of EGFR kinase activity to induce remissions in LUAD with EGFR mutations ([@bib37]; [@bib49]; [@bib50]). Based on the findings presented here, the latter strategy might be pursued by using inhibitors of DUSP6 or other negative feedback regulators to block its usual attenuation of signals emanating from activated ERK1/2.
Several factors are likely to determine the threshold for producing the cell toxicity driven by hyperactive signaling nodes, such as ERKs, in cancer cells. These factors are likely to include allele-specific attributes of oncogenic mutations in genes such as *KRAS* ([@bib26]) and *BRAF* ([@bib26]; [@bib71]; [@bib46]); the cell lineage in which the cancer has arisen ([@bib61]; [@bib71]; [@bib74]); the levels of expression of mutant cancer genes ([@bib75]; [@bib46]; [@bib12]; [@bib2]); the co-existence of certain additional mutations ([@bib5]); and the multiple proteins that negatively regulate oncogenic proteins through feedback loops, such as MIG6 on EGFR ([@bib2]; [@bib38]; [@bib3]), GAPs on RAS proteins ([@bib13]; [@bib68]), or SPROUTYs and DUSPs on kinases downstream of RAS ([@bib30]; [@bib61]; [@bib74]). All such factors would need to be considered in the design of therapeutic strategies to generate signal intensities that are intolerable specifically in cancer cells. DUSP6 is a well-established negative regulator of ERK activation in a normal cellular context (reviewed in [@bib29], and [@bib64]), so it is perhaps not surprising that this protein appears to have a critical role in persistently limiting ERK activation, even in a pathological context such as cancer.
The findings presented here, as well as recent results from others ([@bib61]; [@bib35]; [@bib70]), support several underlying features of a therapeutic strategy based on inordinate signaling activity involving RAS proteins: that the activity of ERK needs to be actively controlled in cancer cells of diverse tissue origins; that hyperactivation of ERK can be deleterious to cells; and that inhibition of negative regulators like DUSP6 can create a toxic cellular state. This leads to the hypothesis that cancer cells *dependent* on ERK signaling have an active RTK-RAS-RAF-MEK pathway that produces levels of activated (phosphorylated) ERK1/2 that *require* attenuation. In other words, ERK-dependent tumor cells, including cancers driven by mutant RTK, RAS, BRAF, or MEK proteins, will have a vulnerability to hyperactivated ERK and that vulnerability can potentially be exploited by inhibition of feedback regulators like DUSP6.
Relevant to this concept are recent studies that address 'drug addiction' whereby cells lose viability when the inhibitor (e.g. vemurafenib) is removed ([@bib25]; [@bib32]; [@bib14]; [@bib42]; [@bib63]). These scenarios, in which an additional mutation can arise in the RTK-RAS-RAF-MEK pathway, create conditions similar to those we have modeled, once the inhibitor is removed. Additionally, Hata et al. have shown that mutations can arise while cells are exposed to a drug; as mentioned above, such mutations might appear to violate patterns of mutual exclusivity but the pattern only arose because of pathway down-modulation ([@bib24]) Recently, Leung et al. have found a similar dependency on ERK activation limits in mutant BRAF-driven melanoma ([@bib35]).
The mechanisms of cell toxicity that arise from hyper-activation of ERK are likely to be diverse. We previously documented autophagy, apoptosis and macropinocytosis in cells expressing mutant EGFR and mutant KRAS, and others have described parthanatos and pseudosenescence as mechanisms for cell death from hyper-activation of ERK ([@bib25]). ERK-dependent processes may differ from cell type to cell type based on mutation profiles and cellular state at the time of ERK activation. This same dependence on ERK (ERK2 specifically) has been documented for senescence when mutant RAS is introduced into normal cells ([@bib60]).
The hypothesis that DUSP6 regulates ERK activity in the presence of signaling through the RAS pathway is particularly attractive in view of the frequency of *RAS* gene mutations in human cancers and the difficulties of targeting mutant RAS proteins ([@bib62]; [@bib51]; [@bib16]). Because DUSP6 directly controls the activities of ERK1 and ERK2, rather than proteins further upstream in the signaling pathway, it appears to be well-situated for controlling both the signal delivered to ERK through the activation of RAS and the signal emitted by phosphorylated ERK. Recently, Wittig-Blaich et al. have also found that inhibition of DUSP6 by siRNA was toxic in melanoma cells carrying mutant BRAF ([@bib70]). Inhibition of other DUSPs, like DUSP5, that regulate ERK1 and ERK2 may create similar vulnerabilities and should be explored ([@bib30]; [@bib31]). These ideas should provoke searches for inhibitors of DUSPs and other feedback inhibitors of this signaling pathway, as well as experiments that better define the downstream mediators and the consequences of non-attenuated ERK signaling.
Materials and methods {#s4}
=====================
Cell lines and culture conditions {#s4-1}
---------------------------------
PC9 (PC-9), H358 (NCI-H358), H1975 (NCI-H1975), H1648 (NCI-H1648), A549, H460 (NCI-H460), H23 (NCI-H23), H2122 (NCI-H2122), H1650 (NCI-H1650), H2009 (NCI-H2009), H2030 (NCI-H2030), H1437 (NCI-H1437) and HCC95 cells were obtained from American Type Tissue Culture (ATCC) or were a kind gift from Dr. Adi Gazdar (UTSW) or Dr. Romel Somwar (MSKCC). Cell lines were periodically checked for mycoplasm contamination and found to be negative. Cells have been validated by STR profiling. For experiments involving doxycycline inducible constructs, cells were maintained in RPMI-1640 medium (Lonza) supplemented with 10% Tetracycline-free FBS (Clontech) or FBS that was tested to be Tet-free (VWR Life Science Seradigm), 10 mM HEPES (Gibco) and 1 mM Sodium pyruvate (Gibco). For other experiments, cells were grown in RPMI-1640 medium (Thermo Fisher) supplemented with 10% FBS (Sigma), 1% Glutamax (Thermo Fisher) and Pen/Strep (Thermo Fisher). Cells were cultured at 37°; air; 95%; CO2, 5%. Where indicated, doxycycline hyclate (Sigma-Aldrich) was added at the time of cell seeding at 100 ng/ml. Trametinib (Selleckchem), Buparlisib (Selleckchem), SCH772984 (Selleckchem), Dual Specificity protein phosphatase 1/6 inhibitor (BCI) (Calbiochem), and EGF recombinant human protein solution (Thermo Fisher) were added at the time of cell seeding at the indicated doses.
Plasmids and generation of stable cell lines {#s4-2}
--------------------------------------------
Plasmids used were identical to those described in a prior publication ([@bib65]). In brief, DNAs encoding mutant KRAS or GFP were cloned into pInducer20, a vector that carries a tetracycline response element for dox-dependent gene control and encodes rtTA, driven from the UbC promoter ([@bib40]). Lentivirus was generated using 293 T cells (ATCC), psPAX2 \#12260 (Addgene, Cambridge, MA) and pMD2.G (Addgene plasmid\#12259). Polyclonal cell lines (H358-tetO-GFP, H358-tetO-KRAS^G12V^, PC9-tetO-GFP, H1975-tetO-GFP) and single cell-derived clonal cell lines (PC9-tetO-KRAS^G12V^, H1975-tetO-KRAS^G12V^) were used. pLKO.1-based lentiviral vectors were used to establish cells stably expressing shRNAs for the indicated genes. Knockdown was achieved using two independent shRNAs targeting *ERK1* (noted in text as A4 or ERK1-4 and A5 or ERK1-5) or *ERK2* (noted in text as G6 or ERK2-6 and G7 or ERK2-7) RNAs. shRNA-GFP: GCAAGCTGACCCTGAAGTTCAT shRNA-ERK1 (A4): CGACCTTAAGATTTGTGATTT shRNA-ERK1 (A5): CTATACCAAGTCCATCGACAT shRNA-ERK2 (G6): TATTACGACCCGAGTGACGAG shRNA-ERK2 (G7): TGGAATTGGATGACTTGCCTA shRNAs targeting GFP or a scramble sequence were used as controls. shRNA constructs were kindly provided by J. Blenis, Weill Cornell Medicine. Lentivirus was generated using 293 T cells as above. After transduction, polyclonal cells were selected with puromycin and maintained as a stable cell line.
Measurements of protein levels {#s4-3}
------------------------------
Cells were lysed in RIPA buffer (Boston Bioproducts) containing Halt protease and phosphatase inhibitor cocktail (Thermo Fisher). For experiments involving dox-inducible constructs, lysates were cleared by centrifugation, and protein concentration determined by Pierce BCA protein assay kit (Thermo Fisher). Samples were denatured by boiling in loading buffer (Cell Signaling). 20 μg of lysates were loaded on 10% MiniProtean TGX gels (Bio-Rad), transferred to Immun-Blot PVDF membranes (Bio-Rad), blocked in TBST (0.1% Tween-20) and 5% milk. For all other experiments, samples were denatured by boiling in loading buffer (BioRad) and 25 μg of lysates were loaded on 4--12% Bis-Tris gradient gels (Thermo Fisher), run using MOPS buffer, transferred to Immobilon-P PVDF membranes (Millipore) and blocked in TBST (0.1% Tween-20)/5% BSA (Sigma).
Primary incubation with antibodies was performed overnight at 4° in 5% BSA, followed by appropriate HRP-conjugated secondary antisera (Santa Cruz Biotechnology) and detected using ECL (Thermo Fisher). Antibodies were obtained from Cell Signaling and raised against the following proteins: phospho p-38 (4511), p38 (8690), p-p44/p42 (ERK1/2) (9101), p44/p42 (ERK1/2) (4695), p-SAPK/JNK (4668), SAPK/JNK (9252), P-EGFR (3777, 2234), EGFR (2232), KRAS (8955), PARP (9542), cleaved-PARP (5625), α-Tubulin (3873) and β-Actin (3700, 4970). Additionally, we used an antibody against GFP (A-21311, Thermo Fisher), DUSP1 (ab1351, abcam) and DUSP6 (ab76310, abcam and SC-377070, SC-137426, Santa Cruz)..
For 24 hr time course experiments, 100,000 cells (PC9, H1975) or 500,000 cells (H358) per well were seeded in a 6-well plate and stimulated with dox or dox and drug. For 5 day experiments, 25,000 cells were seeded in 6-well format. For 7 day time course experiments, 300,000 cells (H358) or 30,000 cells (H1975) were seeded into 10 cM plates and media was changed every day.
For proteome profiler array, 200 ug of total lysate was incubated on membranes in the A/B set (ARY003B, R and D Systems) and processed according to protocol (R and D Systems). Film exposures were scanned and spot density quantified using Image Studio Lite (Licor). Data were plotted in Microsoft Excel.
For western blots with BCI and Trametinib, cells were seeded to achieve 80% confluency 18 hr post seeding. Medium was aspirated and replaced with antibiotic-free medium containing drug at indicated concentrations and incubated for 30 min. Cells were lysed and protein levels assessed as stated above. Quantification of western blot images was performed using ImageJ software. Scanned files were saved in TIFF format, and background was subtracted from all images. Rectangle tool was used to fully encompass each separate band. Rectangles and bands were assigned lanes and histogram plots were generated based on each lane. Each histogram was enclosed using a straight line across the bottom and the 'magic wand' tool generated a value for area of histogram. These values were exported to and assessed using Excel and Graphpad Prism software.
Measurements of viable cells {#s4-4}
----------------------------
For experiments with dox-inducible constructs, cells were seeded into media containing doxycycline (100 ng/ml) and/or drug (Trametinib, SCH772984). Media (with or without doxycycline or drug) were replenished every 3 days during the 7 days. At indicated time points, medium was aspirated and replaced with medium containing Alamar Blue (Thermo Fisher). Fluorescence intensities from each well were read in duplicate on a FLUOstar Omega instrument (BMG Labtech), and data plotted in Microsoft Excel. Cells were seeded in triplicate in 24-well format at 1,000 cells/well (PC9 or H1975 derivatives) or 5,000 cells/well (H358 derivatives). For other experiments, cells were grown in 6-well plates, Alamar Blue added, and intensities measured for each well in quadruplicate using a Cytation 3 Multi Modal Reader with Gen5 software (BioTek).
For crystal violet assays, cells were seeded to achieve 80--90% confluency at the end point in the absence of drug treatment. 18 hr later, medium was aspirated and replaced with medium containing drug. Cells were incubated for 72 hr, washed with PBS and Crystal Violet solution (Sigma) was added and incubated for 2 min before washing again with PBS and imaging.
Genomic datasets and analyses {#s4-5}
-----------------------------
RNA-Seq (RSEM) data for EGFR-KRAS-ERK pathway phosphatases (DUSP1-6, SPRED1-3, SPRY1-4) along with corresponding mutational data for *EGFR*, *KRAS, MET, ERBB2, BRAF, NF1, NRAS* and *HRAS* for 230 lung adenocarcinoma tumors from The Cancer Genome Atlas ([@bib8]) were downloaded from cBioPortal (<http://www.cbioportal.org/>) ([@bib9]; [@bib19]). Expression of each gene was compared between tumors with *KRAS* or *EGFR* mutations and those without, using an unpaired T-Test. Resulting p-values were adjusted for multiple comparisons using a Bonferroni correction and the --Log~2~ value plotted as an indication of significance. Normalized expression values (sample gene value -- median gene expression across all samples/row median absolute deviation) for each gene were also plotted using MORPHEUS software (<https://software.broadinstitute.org/morpheus>, Broad Institute) as a heat map. Expression of DUSP6 was also individually compared for tumors with EGFR mutation only, KRAS mutation only, or any RTK-RAS-ERK pathway mutation (EGFR, KRAS, MET, BRAF, ERBB2, NRAS, HRAS or NF1) vs those wild-type for the in each instance using a two-tailed Mann-Whitney U-Test in Prism 7 (Graphpad).
Reverse phase protein array (RPPA) data (replicate-base normalized \[[@bib1]\]) for 182/230 tumors were downloaded from the UCSC Cancer Genomics Browser. Levels of MAPKPT202Y204, P38PT180Y18 and JNKPT183Y185 were compared between samples with a *KRAS* or *EGFR* mutation and those without, using the Mann-Whitney U-Test.. Likewise, samples were separated into groups with high and low DUSP6 expression levels, based on the highest and lowest *DUSP6* expression quartiles; MAPKPT202Y204, P38PT180Y18 and JNKPT183Y185 levels were compared between the groups as above. Lastly, MAPKPT202Y204 levels from RPPA (RBN values) were correlated with *DUSP6* expression (Log~2~ RSEM values), and the Pearson correlation coefficient and p-value determined. As phospho-protein levels were predicted to be higher in samples with KRAS or EGFR mutation or high DUSP6, one-tailed p-values were calculated.
*DUSP6* expression was also compared between tumors with and without *EGFR* or *KRAS* mutations in 83 tumors and matched normal lung tissues from the BC Cancer Agency (BCCA) and deposited in the Gene Expression Omnibus (GSE75037) as described above. Similarly, *DUSP6* expression was compared between human epithelial cells expressing various oncogenes or GFP control (GSE3151) ([@bib6]). Lastly, Affymetrix Mouse Genome 430 2.0 Arrays were used to profile the lung from genetically engineered mouse models of lung cancer with and without the expression of different driver oncogenes (EGFR-DEL, EGFR-L858R, KRAS-G12D and MYC) ([@bib18]; [@bib54]; [@bib53]) and levels of DUSP6 compared using a two-tailed Mann-Whitney U-Test in Prism seven software (Graphpad).
siRNA transfections {#s4-6}
-------------------
For the time course experiments, 50,000 cells (PC9) per well were seeded in a 6-well plate. For the endpoint experiments, 50,000 cells (PC9, PC9-shERK1-5, PC9-shERK2-7, PC9-shScramble) or 75,000 cells (1975, A549, HCC95) per well were seeded. Cells were then transfected with ON-TARGETplus siRNA pools (Dharmacon) against the following targets as previously described ([@bib36])\-\-- EGFR (L-003114-00-0010), KIF11 (L-003317-00-0010), KRAS (L-005069-00-0010), DUSP6 (L-003964-00-0010)---as well as a non-targeting control (D-001810-10-20). In addition, to test specificity for DUSP6, siRNAs comprising the pool (J-003964-06-0005, J-003964-07-0005, J-003964-08-0005 and J-003964-09-0005) were also tested individually. An additional siRNA (Hs_DUSP6_6 FlexiTube siRNA SI03106404, Qiagen) targeting a different region of DUSP6 coding sequence than J-003964-08-0005 was tested to establish that the decreased viability was not due to off target effects.
DUSP6-8 (Dharmacon) Target Sequence: GGCATTAGCCGCTCAGTCA
DUSP6-Qiagen (Qiagen) Target Sequence: GTCGGAAATGGCGATCAGCAA
For consistent transfection efficiency across experiments, 10 uL of 20 uM siRNA pool was added in 190 uL of OptiMEM (Life Technologies) and 5 uL of Dharmafect was added in 195 uL of OptiMEM (Life Technologies) at room temperature. The siRNA and Dharmafect suspensions were mixed and incubated for 20 min prior to transfection. Media was changed 24 hr after transfection. For sustained knockdown of targets, transfections were conducted on Day 0 and again on Day 3. Viable cells were measured using Alamar Blue as described above. For the time course experiment, cell viability was determined on Day 1, Day 3 (prior to second transfection) and Day 5 or only on Day 5. Results were compared between each siRNA and non-targeting control using a one-sample t-test as previously described ([@bib36]).
BCI dose-response treatments {#s4-7}
----------------------------
Dose-response curves for BCI were established using a modified version of the protocol previously described ([@bib36]). Briefly, cells were seeded in quadruplicate at optimal densities into 96-well plates containing media with and without BCI at indicated doses in 0.1% DMSO. Viable cells were measured 72 hr later with Alamar Blue as described above. All experiments were performed in at least biological duplicate and plotted ±SEM. For HCC95 sensitization assays, cells were cultured with or without 100 ng/mL of EGF Recombinant Human Protein Solution (Life Technologies) for 10 days prior to seeding in 96-well plates for BCI dose response assays with or without EGF. The cells were allowed to adhere for 24 hr before treatment with 17 different concentrations of BCI, ranging from 0 to 8 uM, with 0.5 uM increment doses at 0.1% DMSO concentration. Additionally, 100 uM of Etoposide (0.1% DMSO) was added as a positive control for cell death. Cell viability was determined after 72 hr of drug exposure using Alamar Blue. Graphpad Prism software was used to create dose response curves.
For BCI rescue experiments, 75,000 H358 cells were seeded in 6-well plates and adhered for 24 hr. After attachment, the cells were treated with varying combinations of VX-11e and BCI with the final DMSO concentration at 0.1% in each well. Cells were treated for 72 hr and then the media was switched with fresh media containing Alamar blue for viability assessment. Resulting values for each BCI + VX-11e containing well were normalized to well containing corresponding concentration of VX-11e only. Experiments were performed in biological triplicate and the average ±SEM plotted.
Quantitative RT-PCR {#s4-8}
-------------------
Cells were homogenized and RNA extracted using the RNeasy Mini kit (Qiagen) according to the manufacturer's instructions. cDNA was prepared using the High-Capacity cDNA Reverse Transcription kit (Thermo Fisher). RT--PCR reactions were carried out using the TaqMan Gene Expression Master Mix (Thermo Fisher) and TaqMan Gene Expression Assays (Thermo Fischer) for *DUSP6* (Hs00169257_m1) and *GAPDH* (Hs99999905_m1). Reactions were run on a QuantStudio6 Real Time PCR system (Thermo Fisher). The ΔΔCt method was used for relative expression quantification using the average cycle thresholds.
Genome-wide CRISPR screens {#s4-9}
--------------------------
Genome-wide screens were performed with the Toronto Knockout version 3 (TKOv3) library ([@bib23]). Lentivirus was generated from the TKOv3 library in low passage (\<10) 293FT cells (Thermo Fisher) using Lipofectamine 3000 (Thermo Fisher). Approximately 120 million target cells were then infected with the TKOv3 library virus at an MOI of 0.3, in order to achieve an average 500-fold representation of the sgRNAs after selection. Cells were selected on puromycin for 7 days and then 35 million cells were seeded in culture. For the depletion screens, cells were passaged every 3 days, and after 14 population doublings, 35 million cells were harvested for genomic DNA extraction. For the enrichment screens, media (containing BCI or doxycycline) was changed every 3 days until cell death was no longer observed, at which point the remaining cells were harvested for genomic DNA extraction. sgRNA inserts were amplified with NEBNext High-Fidelity 2X PCR Master Mix (New England BioLabs). Samples were then purified and sequenced on a NextSeq 500 kit (Illumina).
For validation of the screen, two separate guides targeting KRAS were cloned into lentiCRISPR v2^75^, lentivirus generated and H460 cells were transduced. Seven days after puromycin selection cells were harvested for protein analysis and seeded in the presence of BCI. A guide against LacZ was used as a control.
sgRNA_Lacz: GAGCGAACGCGTAACGCGAA sgRNA_KRAS-1: GGACCAGTACATGAGGACTG sgRNA_KRAS-2: GTAGTTGGAGCTGGTGGCGT
For targeting of *DUSP6*, two separate guides were cloned into lentiCRISPR v2, lentivirus generated, and H358 cells were transduced. A clonal population of cells were expanded and screened by western blotting and by DNA sequencing of the *DUSP6* locus.
sgRNA_DUSP6-1: GTGCGCGCGCTCTTCACGCG sgRNA_DUSP6-2: ACTCGTATAGCTCCTGCGGC
Analysis of CRISPR screen {#s4-10}
-------------------------
Sequencing reads were aligned to the reference library to determine the abundance of each sgRNA. sgRNAs with less than 30 raw read counts were excluded from further analysis. The read counts were then normalized to the total number of reads obtained from the respective sample. The log2 fold-change of each sgRNA was calculated by adding a pseudocount of 1 and comparing the abundance of the sgRNAs in the final cell population to their respective abundance in the TKOv3 plasmid library. Finally, genes were ranked according to the second-most enriched or second-most depleted sgRNA.
Funding Information
===================
This paper was supported by the following grants:
- http://dx.doi.org/10.13039/501100000024Canadian Institutes of Health Research PJT-148725 to William W Lockwood.
- http://dx.doi.org/10.13039/501100004376Terry Fox Research Institute to William W Lockwood.
- http://dx.doi.org/10.13039/501100000245Michael Smith Foundation for Health Research Scholar Award to William W Lockwood.
- http://dx.doi.org/10.13039/100000002National Institutes of Health to Harold Varmus.
- Meyer Cancer Center at Weill Cornell Medicine to Harold Varmus.
- BC Cancer Foundation to William W Lockwood.
We would like to thank Katerina Politi (Yale University) for providing gene expression data from her transgenic mice. We would like to thank members of the Varmus lab for useful discussions and Oksana Mashadova, in particular, for experimental help.
Additional information {#s5}
======================
No competing interests declared.
Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing---original draft, Project administration, Writing---review and editing.
Data curation, Formal analysis, Investigation, Methodology.
Data curation, Formal analysis, Investigation, Methodology.
Data curation, Formal analysis.
Resources, Data curation, Formal analysis, Investigation.
Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing---original draft, Project administration, Writing---review and editing.
Conceptualization, Supervision, Writing---original draft, Project administration, Writing---review and editing.
Additional files {#s6}
================
10.7554/eLife.33718.012
###### Table containing the log2 fold change values for all sgRNAs from CRISPR-Cas9 screens.
10.7554/eLife.33718.013
Data availability {#s7}
-----------------
All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2 and Figure 2-supplemental figure 1 in the Methods section and/or in the text.
The following previously published datasets were used:
CancerGenome Atlas Research Network2014TCGA LUADcBioPortalluad_tcga_pub
GazdarAGirardLStephenLWanLZhangW2017Expression profiling of 83 matched pairs of lung adenocarcinomas and non-malignant adjacent tissueNCBI Gene Expression OmnibusGSE75037
NevinsJR2005Oncogene Signature DatasetNCBI Gene Expression OmnibusGSE3151
10.7554/eLife.33718.021
Decision letter
Cooper
Jonathan A
Reviewing Editor
Fred Hutchinson Cancer Research Center
United States
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
\[Editors' note: formal revisions were requested, following approval of the authors' plan of action.\]
Thank you for submitting your article \"Hyperactivation of ERK by mutation-driven RAS signaling or by inhibition of DUSP6 is toxic to lung adenocarcinoma cells\" for consideration by *eLife*. Your article has been reviewed by three peer reviewers, one of whom (Thomas Look) is a member of our Board of Reviewing Editors, and Jonathan Cooper as the Senior Editor.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this letter to express the many issues that we feel must be addressed if this work is to advance to publication.
Summary:
In this manuscript Unni et al., describe their work on understanding the mechanism leading to cytotoxicity following forced overexpression of KRAS in LUAD cell lines harboring EGFR or KRAS mutations. They conclude that such toxicity owes to hyperactivation of pERK, which occurs through DUSP6. They show that MEK/ERK inhibitor treatment rescues the effect of forced overexpression while reducing the levels of pERK and that DUSP6 mRNA levels are increased in LUAD harboring EGFR or KRAS mutations relative to those with a wild type status of these proteins. Genetic or pharmacologic targeting of DUSP was found to induce pERK (transiently) and lead to diminished cell proliferation in EGFR/KRAS mutant cell lines but not in those with a wild type status. While overall the findings are intriguing, there are gaps in the experimental evidence provided which bias their conclusions. If the authors can address the issues noted below however this could be a very interesting article.
We have the following suggestions to improve the manuscript. For clarity, we address below the essential revisions related to the results presented in each figure.
1\) Figure 1 a) Figure 1A: The expression level of tet-driven ectopic KRAS^G12V^ is very high in each of the three lung adenocarcinoma lines. The authors should demonstrate by western blotting whether these levels are comparable to endogenous KRAS levels found in lung adenocarcinoma lines. Studies from Tyler Jacks on mutant KRAS in knockin mice over 10 years ago emphasized the importance of expression levels of mutant KRAS to mediate transformation in lung adenocarcinoma. Massive overexpression of KRAS^G12V^ can induce senescence rather than transformation in most cell types.
b\) Figure 1D: The trametinib/SCH984 experiments in Figure 1D require additional appropriate controls; the authors should show the effect of treatment in the absence of dox stimulation. Also, why are different concentrations of trametinib used?
c\) In addition to the biochemistry and cell count data shown in each panel of Figure 1, straightforward studies need to be done to establish the cellular mechanism underlying the cell growth phenotype. Before and after induction of KRAS^G12V^ using dox, studies of cell cycle progression by DNA flow cytometry, for β-GAL expression assays should be done to assess senescence, and cleaved caspase 3, TUNEL and PARP cleavage assays should be done to assess apoptosis. The reduction of cell number 7-days after dox induction of KRAS^G12V^ of \~ 20-40 percent of control could be due to growth arrest, senescence or apoptosis. This will be important to establish with regard to the author\'s interpretation that over activation of ERK leads to synthetic lethality. The need for studies of cellular phenotype in the context of the experiments shown in Figure 1 apply broadly to experiments throughout the paper.
d\) Does overexpression of KRASG12C in EGFR or KRAS WT cells also induce toxicity which can be mitigated by co-treatment with trametinib in cells with no activating mutations in EGFR-RAS-ERK pathway? The levels of overexpressed KRASG12C are so high that such levels could induce toxicity by exceeding the upper threshold of RAS-mediated signaling even in cells without hyperactive EGFR-KRAS-ERK signaling.
e\) Since the main claim is that hyperactivation of ERK leads to toxicity, the authors ought to replicate their dox-inducible experiments with active ERK to show that these also lead to cytotoxicity. Also, just because inhibiting MEK/ERK reverses the some of the phenotype in plastic, this doesn\'t exclude that other RAS effectors are also involved (see induction of pAKT in addition to pERK in Figure 1B). The authors should thus carry out similar siRNA or inhibitor studies to demonstrate that the cytotoxic effects of KRAS overexpression are not due to pAKT.
2\) Figure 2 a) Figure 2A: There should be symbols under the heat map denoting which tumors are KRAS mutant and which are EFGR mutant. Are there any tumors with ALK/ROS1/RET activation by translocation? Are there tumors with NF1, MET and BRAF mutations? These should be addressed since most of lung adenocarcinoma have activation of RTK/RAS/RAF pathway (TCGA, 2014) and increased dusp6 is among the five gene signatures to predict poor outcome (Chen et al., 2007).
b\) After describing the emergence of DUSP6 as a key gene responding to activated ERK based on their own studies, the authors should put their findings of DUSP6 elevated expression in the context of previous work. It has been known for over 10 years that DUSP6 is transcriptionally regulated by ERK-responsive ETS transcription factors downstream of MAPK activation and that DUSP6 serves as a major negative feedback regulator of ERK signaling (Reffas et al., 2000; Kawakami et al., 2003; Eblaghie et al., 2003; Li et al., 2007; Ekerot et al., 2008; Furukawa et al., 2008; Jurek et al., 2009).
c\) Figure 2B and C: For these panels, please show separate data categories for KRAS mutant and EGFR mutant tumors. The box plots shown do not really look statistically significantly different. The medians are close, and the quartiles are largely overlapping. Which statistical test was used to show significant differences? Has a biostatistician been consulted about whether parametric or non-parametric tests would be better for these comparisons? A detailed statistical section is needed in the Materials and methods section, outlining the statistical tests used for the data shown in each figure.
d\) In Figure 2 A-C, the authors must also include the correlation between DUSP6 and P-p38 and p-JNK to show the readers that no correlation exists between them.
e\) Figure 2D: Please provide information or citations about the mouse models used in this figure.
f\) Figure 2E: The Material/Methods description about this experiment seems to be missing. Expression levels need to be shown of the transduced onco-proteins by Western blot to verify overexpression.
g\) Figure 2G and H: Same comments as 2B and C.
3\) Figure 3 a) Figure 3A: Only one siRNA was used for DUSP6 or EGFR and there is no rescue experiment to prove that the effect is due to knockdown of the intended target. DUSP6 de-phosphorylates ERK so ERK phosphorylation is expected to be increased with DUSP6 knockdown. Please explain why Figure 3A shows the opposite with reduced p-ERK after siDUSP6 knockdown.
b\) Figure 3A: Can the toxicity mediated by DUSP6 knockdown in PC9 cells be reduced by co-treatment with trametinib (Figure 3A)? This would help confirm that toxicity caused by depletion of DUSP6 in these cells is due to increased pERK levels.
c\) Figure 3B: PC9 cells in this panel show increased p-ERK after DUSP6 kd. Please explain why this is the opposite result compared to Panel A.
d\) Figure 3B and C: Did the authors determine if the levels of DUSP6 in their EGFR and KRAS wild-type cell lines, HCC95 and H1648, are not as high as in the cell lines with EGFR or KRAS activating mutations? Lower levels of DUSP6 would indicate that these cells do not need to have similar buffering ability as the EGFR or KRAS mutant cell lines and would be consistent with their findings in Figure 2. In Figure 3B, it seems that DUSP6 is also buffering the pERK levels in HCC95 because knockdown of DUSP6 increases the levels of pERK in these cells by over 1.5 fold, similar to the cells with EGFR or KRAS activating mutations; but this increase in pERK has no impact on the cell survival (Figure 3C). This needs to be reconciled with the authors\' main claim.
e\) There seems to be a disconnect between the first part of the manuscript, where the authors describe the mechanism of toxicity caused by forced overexpression of KRAS and the second part, where they describe the effect of DUSP inhibition in cell lines with endogenous KRAS/EGFR mutations. To close this gap, the authors need to determine the levels of DUSP6 protein in their dox-inducible KRAS models and how they correlate with pERK. They should determine if the induction of pERK is transient, as that observed with DUSP6 siRNA, or sustained. Finally, they should use DUSP6 siRNA or BCI to determine if this reverses the effect of forced KRAS expression on pERK or proliferation. Another question that has not been addressed is why doesn\'t forced overexpression of KRAS induce sufficient DUSP6 to override the induction of pERK, if DUSP expression is under the control of EGFR/KRAS? Could there be other factors involved? If the authors can experimentally address these it would be a significant improvement.
f\) More evidence is needed to show that the increase in DUSP mRNA is associated with an increased in DUSP6 protein levels or increased DUSP activity. The data in Figure 2I, where the authors compare the relationship between pERK (measured by RPPA) and DUSP6 mRNA is the only such evidence provided. This is underwhelming because the correlation is not great (r=0.1) and because the pERK does not seem to have been controlled for total ERK.
g\) Finding that DUSP6 siRNA caused only a transient elevation in pERK (followed by inhibition of pERK at longer intervals), while mimicking the antiproliferative effect observed with forced overexpression of KRAS needs to be clarified with additional experiments. As it stands, it is difficult to agree with the authors conclusion that DUSP6 is the main mediator of the proliferative effect or that the effect of DUSP6 is through pERK. One consideration may be to use constitutively active ERK (i.e. DD phosphomimetic mutants) and attempt to reverse the effect of DUSP6. This is another point that, if addressed experimentally, could add value to the manuscript.
h\) If indeed it is true that a transient induction in pERK leads to cytotoxicity several days later, then does EGF stimulation (which also causes a transient induction in pERK) have the same effect in EGFR WT/KRAS MT cells?
i\) All three reviewers noted that HCC95 (RRID:CVCL_5137) is a squamous cell carcinoma line. This not comparable to lung adenocarcinoma because this tumor type has different pathway dependencies than LUAD (TCGA, Nature 2012). A wild type LUAD line should be tested instead.
j\) Figure 3C: Same comments as 3A.
k\) The investigators might consider inactivating DUSP6 with CRISPR-Cas9 in these cell lines to show genetic dependence.
4\) Figure 4 a) Figure 4A: 11 lung cancer cell lines were treated with BCI and viable cells were measured after 72-hour treatment. Most of these cell lines are fast growing cells with doubling time around 20\~30 hours. Reduction of viable cells by 72-hours does not necessarily mean cell killing. TUNEL or caspase-3/PARP western blot needs to be performed to detect the levels of apoptosis. Senescence and cell cycle arrest should be examined too.
b\) Figure 4C and D: p-ERK levels at a single time point was measured in multiple cell lines (30 min). In order to explain the loss of viable cells at 72hours in Figure 4A, p-ERK should be measured at serial time points such as 1, 6, 12, 24, 48, 72 hours.
c\) The data on BCI are very interesting but it\'s not clear how have the authors established that BCI is selective for DUSP? Sensitive and insensitive cell lines all have IC50s in the 1-5μM range (12/13 cells lines tested). What is the IC50 of the inhibitor in DUSP6 KD or KO cells (or in DUSP1/6 double KD/KO cells)? How do the authors know that the antiproliferative effect of BCI is due to its ability to induce pERK? These questions need to be addressed experimentally. The authors should also attempt to show that the inhibitor has an effect in vivo, given their claim that such an approach could be therapeutically beneficial in patients.
d\) Figure 4E and F: Four cell lines were treated with 125nM trametinib for 72 hours. 125nM is a very high dose for trametinib-sensitive cell lines such as H358 (reported IC50 \~50nM). Apoptosis levels should be measured to document any cell death.
5\) Figure 5.
See comments for Figure 3B. HCC95 is a SQCC cell line that is quite different from the LUAD cells used in the rest of this manuscript.
a\) In figures where the authors make a comparison of protein levels under different conditions the uncut blot with all the lanes on the same blot must be shown. It is not ideal to make a comparison between levels of proteins on two different strips of blots. For example, in Figure 5B, authors claim that EGF increase levels of pEGFR and pERK in HCC95 cells when the pEGFR and pERK levels with and without EGF are on two different strips of blots. This is important to confirm that BCI treatment increases pERK levels in EGFR treated cells. Quantifications should be shown in addition.
Overall comment:
The authors should soften earlier claims that EGFR-mutations and KRAS-mutations are synthetically lethal in the adenocarcinoma subtype of NSCLC (Unni et al., 2015). Recent genetic studies of EGFR-mutant lung cancer (Blakely et al., 2017) have shown that 2.5%\~4.7% EGFR-mutant lung cancer also harbor KRAS copy number gain or activating mutations. Tumor genomic analyses have indicated that bona fide driver mutations causing lung adenocarcinoma are not as mutually exclusive as previously thought (e.g. PMIDs: 25301630, 28498782, 28445112, 29106415, and other recent publications), particularly in metastatic lung adenocarcinoma instead of early-stage disease and/or during the evolution of treatment resistance in metastatic disease. Further, preclinical studies have shown acquired KRAS gain/activation in response to EGFRi in EGFR-mutant NSCLC, indicating that this can be a mechanism of drug resistance (Politi et al., 2000; Eberlein et al., 2015). The authors should acknowledge these recent works and temper their claim of absolute mutually exclusivity in this disease, at least under these more advanced-stage disease contexts and in the light of the emerging literature.
\[Editors\' note: further revisions were requested prior to acceptance, as described below.\]
Thank you for resubmitting your work entitled \"Hyperactivation of ERK by mutation-driven RAS signaling or by inhibition of DUSP6 is toxic to lung adenocarcinoma cells\" for further consideration at *eLife*. First, let me apologize profusely for the very long time this has taken. The problem is basically that the reviewers are at an impasse and could not decide on a course of action.
Briefly, this is a follow up to a previous paper, that reported anti-proliferative effects of activation of Ras and EGFR in the same cancer cells. This new paper has provides evidence that the anti-proliferative effect of over-expressed, active Ras is due to ERK hyperactivation, and that DUSP6 is critical to prevent adverse effects of active ERK in lung cancer cells. In the revision, the authors have added results using ERK shRNA, ERKi, and a CRISPR screen that together provide convincing evidence that ERK mediates the antiproliferative effect of oncogene overexpression. The finding that sgKRAS are enriched in the BCI screen also indirectly supports that conclusion. The authors have also ruled out that the PI3K pathway is not involved by adding PI3K inhibitor experiments. These additions greatly strengthen the conclusion that hyperactivation of ERK is detrimental. However, the evidence that DUSP6 plays a key role is not convincing.
The DUSP6 knockdown was done with a single siRNA with no rescue experiment. (Other siRNAs did not effectively inhibit DUSP6 expression). Attempts to recapitulate the result with a DUSP6 CRISPR knockout were unsuccessful. Therefore, the conclusion that DUSP6 is necessary relies in large part on the specificity of the chemical BCI. The DUSP6 KO cells have the same IC50 to BCI, while DUSP1 siRNA did not affect proliferation in their system. Based on these results, the authors cannot claim that the BCI effect is DUSP6 specific nor that the BCI effect in DUSP6KO cells is driven by DUSP1 inhibition. It probably isn\'t, and by inference, the observed phenotype is not dependent on DUSP6 alone but on other ERK specific DUSPs as well.
The reviewers disagreed whether these weaknesses undermined the impact to the point where the paper was unsuitable for publication, or whether lengthy additional experiments would be needed. The *eLife* approach is not to ask for multiple rounds of revision. In this spirit, I suggest two possibilities:
Either
\- Provide convincing evidence that validates DUSP6 as the key enzyme that downregulates ERK in lung cancer cells (e.g. try more siRNAs to find another that gives strong knockdown, and/or rescue DUSP6 siRNA with DUSP6 from mouse, or with silent mutations in the siRNA target sequence).
Or,
\- Modify the title and abstract to allow for the possibility that other DUSPs are involved and be more open about the shortcomings of the results.
10.7554/eLife.33718.022
Author response
\[Editors' notes: the authors' response after being formally invited to submit a revised submission follows.\]
> Summary:
>
> In this manuscript Unni et al., describe their work on understanding the mechanism leading to cytotoxicity following forced overexpression of KRAS in LUAD cell lines harboring EGFR or KRAS mutations. They conclude that such toxicity owes to hyperactivation of pERK, which occurs through DUSP6. They show that MEK/ERK inhibitor treatment rescues the effect of forced overexpression while reducing the levels of pERK and that DUSP6 mRNA levels are increased in LUAD harboring EGFR or KRAS mutations relative to those with a wild type status of these proteins. Genetic or pharmacologic targeting of DUSP was found to induce pERK (transiently) and lead to diminished cell proliferation in EGFR/KRAS mutant cell lines but not in those with a wild type status. While overall the findings are intriguing, there are gaps in the experimental evidence provided which bias their conclusions. If the authors can address the issues noted below however this could be a very interesting article.
The main message of our paper is that p-ERK hyperactivation is intolerable in cancer cells and that this property---the toxic consequences of exceeding a certain level of activated (phosphorylated) ERK---creates a therapeutic target in RAS pathway-mutated cancers: DUSP6, an ERK phosphatase that plays a major role in modulating the activity of ERK in lung cancer cells.
We arrived at these findings by studying the mutually exclusive pattern of EGFR and KRAS mutations in lung adenocarcinoma. As your review points out, there may be exceptions to this mutual exclusivity, but we do not believe they undermine our arguments. Nevertheless, we will make changes to the Discussion section to include the observations that the reviewers cite and also note their shortcomings (such as the difficulty of knowing whether normally excluded combinations of mutations have occurred in the same cell or separately in tumor subclones). Of particular relevance, p-ERK intolerance has also been documented in 'drug addicted' cells when inhibitors are removed (Kong et al., 2017; Hong et al., 2018, Das Thakur et al., 2013, Moriceau et al., 2015, Sun et al., 2014). Cells in these conditions appear to violate the mutual exclusivity pattern, however, we would argue that these mutations could have arisen while cells were on drug (Hata et al., 2016).
> We have the following suggestions to improve the manuscript. For clarity, we address below the essential revisions related to the results presented in each figure.
>
> 1\) Figure 1 a) Figure 1A: The expression level of tet-driven ectopic KRAS^G12V^ is very high in each of the three lung adenocarcinoma lines. The authors should demonstrate by western blotting whether these levels are comparable to endogenous KRAS levels found in lung adenocarcinoma lines. Studies from Tyler Jacks on mutant KRAS in knockin mice over 10 years ago emphasized the importance of expression levels of mutant KRAS to mediate transformation in lung adenocarcinoma. Massive overexpression of KRAS^G12V^ can induce senescence rather than transformation in most cell types.
Our intention was to force excess RAS pathway activation (beyond what is present in tumor cells) and determine if this is tolerated. The purpose of doing this was to model what might be happening when co-mutations arise in the RAS pathway. We recognize that these levels of RAS may not be commonly experienced by tumor cells and will state this explicitly in the text. Even though our system, like many others, is artificial, it provides an experimental platform for understanding why hyper activation of ERK occurs and allowed us to define a vulnerability (DUSP6 inhibition) that we could exploit.
We have now stated this in the text (Results section).
b\) Figure 1D: The trametinib/SCH984 experiments in Figure 1D require additional appropriate controls; the authors should show the effect of treatment in the absence of dox stimulation. Also, why are different concentrations of trametinib used?
Different concentrations were required to rescue the phenotype in different cells. We will now provide a plot to show dose response (plus/minus dox and plus/minus drug). The degree to which p-ERK is induced in each cell line appeared to require different concentrations of a MEK inhibitor to reset p-ERK back to an acceptable level.
A dose response curve for doxycycline plus trametinib is now plotted and shown in Figure 1---figure supplement 1C.
> c\) In addition to the biochemistry and cell count data shown in each panel of Figure 1, straightforward studies need to be done to establish the cellular mechanism underlying the cell growth phenotype. Before and after induction of KRAS^G12V^ using dox, studies of cell cycle progression by DNA flow cytometry, for β-GAL expression assays should be done to assess senescence, and cleaved caspase 3, TUNEL and PARP cleavage assays should be done to assess apoptosis. The reduction of cell number 7-days after dox induction of KRAS^G12V^ of \~ 20-40 percent of control could be due to growth arrest, senescence or apoptosis. This will be important to establish with regard to the author\'s interpretation that over activation of ERK leads to synthetic lethality. The need for studies of cellular phenotype in the context of the experiments shown in Figure 1 apply broadly to experiments throughout the paper.
We have previously published some of the effects of co-induction of mutant EGFR and mutant KRAS. In that study, we documented apoptosis, autophagy, vacuolization and macropinocytosis in cell lines similar to those we now use (Unni et al., 2015). A recent study (Hong et al., 2018) found that cancer cells that were 'drug addicted' die by apoptosis, parthanatos and pseudosenescence when inhibitors were removed (similar p-ERK overload principle). For these reasons there are likely to be several distinct mechanisms that result in a loss of cell viability. Our goal here has been to focus on the factors that generate the cytotoxic signal, rather than on a cell's response to the signal. Nevertheless, in response to the reasonable concerns raised by this comment, we will assess the extent of apoptosis by measuring cleaved PARP, CASP3 activity, or Annexin V levels to help clarify our statements about cell toxicity.
We have measured cleaved PARP in the H358 and H1975 experimental systems described in this manuscript and some assays are shown in Figure 1---figure supplement 1. We characterized mechanisms of cell toxicity in PC9 cells that express both mutant EGFR and mutant KRAS in our earlier paper in *eLife*.
d\) Does overexpression of KRASG12C in EGFR or KRAS WT cells also induce toxicity which can be mitigated by co-treatment with trametinib in cells with no activating mutations in EGFR-RAS-ERK pathway? The levels of overexpressed KRASG12C are so high that such levels could induce toxicity by exceeding the upper threshold of RAS-mediated signaling even in cells without hyperactive EGFR-KRAS-ERK signaling.
Overexpression of KRAS G12V is likely to result in cell death or senescence in a variety of cell lines, as others have shown in the past. However, the motivation for experiments in Figure 1 was to test the limits of *cancer* cells to activation of the RAS pathway in a reproducible way that could allow us to study the mechanism by which the toxic signals arise.
We have added a comment on RAS-mediated senescence in the text (Discussion section).
> e\) Since the main claim is that hyperactivation of ERK leads to toxicity, the authors ought to replicate their dox-inducible experiments with active ERK to show that these also lead to cytotoxicity. Also, just because inhibiting MEK/ERK reverses the some of the phenotype in plastic, this doesn\'t exclude that other RAS effectors are also involved (see induction of pAKT in addition to pERK in Figure 1B). The authors should thus carry out similar siRNA or inhibitor studies to demonstrate that the cytotoxic effects of KRAS overexpression are not due to pAKT.
Including data that an inducible active ERK2 allele is toxic would be valuable. However, creating and characterizing these lines will take a significant amount of time. We have prioritized other experiments that the reviewers advise that will strengthen our paper. We did show increases in p-AKT at an early time point and it is possible that effectors of RAS other than RAF proteins can also be toxic to cells. To address this point, tetO KRAS G12V cells will be treated with a PI3K inhibitor (to inhibit AKT phosphorylation) and placed on dox. We will document the effects on cell viability, and on p-AKT and p-ERK signaling.
We have included data with a PI3K inhibitor (buparilisib) in H358-tetO-KRAS cells (Figure---figure supplement 1D). Using the same cell line, a genome wide CRISPR-Cas9 screen did not reveal an enrichment of guide RNA targeting PIK3CA (Figure 1---figure supplement 1F and Supplementary file 1).
> 2\) Figure 2a) Figure 2A: There should be symbols under the heat map denoting which tumors are KRAS mutant and which are EFGR mutant. Are there any tumors with ALK/ROS1/RET activation by translocation? Are there tumors with NF1, MET and BRAF mutations? These should be addressed since most of lung adenocarcinoma have activation of RTK/RAS/RAF pathway (TCGA, Nature 2014) and increased dusp6 is among the five gene signatures to predict poor outcome (Chen et al., 2007).
Symbols will be added for mutant KRAS, EGFR, BRAF, NF1, MET, ERBB2 etc. Translocations were not assessed in this data set. The Chen et al., five gene signatures is interesting (*DUSP6, MMD, STAT1, ERBB3* and *LCK*). Perhaps this signature is most common in KRAS mutant tumors, something Chen et al., do not address. We will comment on these observations in the revised manuscript.
We have now indicated which tumors are KRAS mutants and which are EGFR mutants in Figure 2A. In addition, we have added another heat map with NF1, BRAF, MET, ERBB2, NRAS and HRAS status indicated as Figure 2---figure supplement 1A. Further, we have compared levels of DUSP6 mRNA among all tumors with RTK-RAS-RAF pathway mutations vs those with only wild type components in this pathway; see Figure---figure supplement 1C.
> b\) After describing the emergence of DUSP6 as a key gene responding to activated ERK based on their own studies, the authors should put their findings of DUSP6 elevated expression in the context of previous work. It has been known for over 10 years that DUSP6 is transcriptionally regulated by ERK-responsive ETS transcription factors downstream of MAPK activation and that DUSP6 serves as a major negative feedback regulator of ERK signaling (Reffas et al., 2000; Kawakami et al., 2003; Eblaghie et al., 2003; Li et al., 2007; Ekerot et al., 2008; Furukawa et al., 2008; Jurek et al., 2009).
The reviewers correctly point to many papers that highlight the significance of DUSP6 in controlling ERK activity. Our main point in this analysis was to discover which of the prominent negative regulators (DUSPs, SPRYs and SPREDs) have been significantly modulated in lung adenocarcinoma. This revealed that lung adenocarcinoma with mutations in KRAS or EGFR appear to rely on DUSP6 to actively restrain the RTK-RAS-RAF-MEK-ERK pathway. Our work also emphasizes that tumor cells have a level of ERK activation that is *still* subject to negative feedback regulation and that this reliance is a vulnerability based on the data we show in Figure 1. We will mention several of the papers the reviewers cite to properly document the previous work with DUSP6.
The papers suggested by the reviewers are cited through two reviews in the text (Discussion section).
> c\) Figure 2B and C: For these panels, please show separate data categories for KRAS mutant and EGFR mutant tumors. The box plots shown do not really look statistically significantly different. The medians are close, and the quartiles are largely overlapping. Which statistical test was used to show significant differences? Has a biostatistician been consulted about whether parametric or non-parametric tests would be better for these comparisons? A detailed statistical section is needed in the Materials and methods section, outlining the statistical tests used for the data shown in each figure.
We will include a detailed analysis of the statistical tests used and the rationale. We will also separate KRAS and EGFR mutant cases. However, it should be noted that assessing KRAS and EGFR mutant tumors in separate groups will limit sample numbers. As a result, statistical power will be reduced, especially in RPPA assessment where the number of samples available is already limiting. It was for this reason that samples with KRAS and EGFR mutations were pooled. Of note, the box plots in Figure 2B and C are on a log scale, suggesting that the differences seen between the medians are quite large.
We have now included more details about the statistical tests used in the methods section. We have also separated EGFR and KRAS mutant tumors and compared each to KRAS/EGFR WT groups and included these data in Figure 2---figure supplement 1B,D).
d\) In Figure 2 A-C, the authors must also include the correlation between DUSP6 and P-p38 and p-JNK to show the readers that no correlation exists between them.
These data are part of 2H. No correlations were observed. We will also provide plots similar to 2I for p-JNK and p-p38.
Correlation plots for P-p38 and P-JNK have been included as Figure 2---figure supplement 1E,F.
> e\) Figure 2D: Please provide information or citations about the mouse models used in this figure.
We will provide the proper citations of the mouse models used (Politi et al., 2006; Fisher et al., 2001; Felsher and Bishop, 1999).
The appropriate citations for the mouse models are now included (subsection "DUSP6 is a major regulator of negative feedback, expressed in LUAD cells, and associated with KRAS and EGFR mutations and with high P-ERK levels").
> f\) Figure 2E: The Material/Methods description about this experiment seems to be missing. Expression levels need to be shown of the transduced onco-proteins by Western blot to verify overexpression.
These data were retrieved from a previous publication and not adequately cited (GSE3151, Bild et al., 2006; Kim et al., 2010). We will correct this in the text to clearly state that it is from publicly available data.
This citation has been added, and the origin of the data is now clearly stated in the text (subsection "DUSP6 is a major regulator of negative feedback, expressed in LUAD cells, and associated with KRAS and EGFR mutations and with high P-ERK levels").
> g\) Figure 2G and H: Same comments as 2B and C
EGFR and KRAS mutant tumors will be assessed separately. However, as mentioned above, combining these genotypes provides greater statistical power.
Addressed above and in Figure 2---figure supplement 1.
> 3\) Figure 3 a) Figure 3A: Only one siRNA was used for DUSP6 or EGFR and there is no rescue experiment to prove that the effect is due to knockdown of the intended target. DUSP6 de-phosphorylates ERK so ERK phosphorylation is expected to be increased with DUSP6 knockdown. Please explain why Figure 3A shows the opposite with reduced p-ERK after siDUSP6 knockdown.
We have used pooled siRNA in the knockdown experiments. We now have PC9 cells expressing wildtype or catalytically inactive DUSP6. These cells will be used to verify that our DUSP6 siRNA is on-target (using siRNA against the 3'UTR of the endogenous *DUSP6* which is not represented in the transgenes).
Unfortunately, despite repeated efforts, no siRNAs targeting the 3'UTR proved to be effective at knocking down DUSP6. In addition to using the original siRNA pool, we have now transfected PC9 cells with each individual siRNA comprising the pool, four in total, all of which target the coding region. This revealed a dose-dependent effect of knockdown on growth inhibition: suppression but incomplete knockdown stimulated cell growth, whereas more complete inhibition was toxic to cells (Figure 3---figure supplement 1A,B). This conforms with our hypothesis and suggests the siRNAs for DUSP6 are on target.
The challenging aspect of this study was that knockdown of DUSP6 will result in increased p-ERK leading to increased DUSP6 mRNA, which is being targeted by the siRNA ('technical' feedback loop). p-ERK is reduced in this figure probably because the measurement was made on day 5 (see 3B) when the cells are dying. We will include a measure of apoptosis (cleaved PARP) at this time point. We will also provide a longer exposure of the western blot showing the efficiency of DUSP6 knockdown as there could be remaining DUSP6 protein. This may contribute to the decreased p-ERK on day 5.
We have assessed cleaved PARP on day 5 after DUSP6 knockdown and shown that it is indeed induced in EGFR mutant H1975 cells but not EGFR/KRAS wild-type HCC95 cells (Figure 3---figure supplement 1C). We conclude that P-ERK levels are low at day 5 after DUSP6 knockdown because cells with increased P-ERK have already become non-viable by this time. This point was further investigated in BCI experiments below.
> b\) Figure 3A: Can the toxicity mediated by DUSP6 knockdown in PC9 cells be reduced by co-treatment with trametinib (Figure 3A)? This would help confirm that toxicity caused by depletion of DUSP6 in these cells is due to increased pERK levels.
This experiment was tried several times, but we could not find a dose of Trametinib that rescues lethality. MEK and ERK inhibitors are lethal in this line (PC9) and this presents a technical problem: both ERK inhibition and ERK hyperactivation are not tolerated. However, H1975 cells are much more tolerant to ERK inhibition using SCH772984. Experiments are underway to treat H1975 cells that have received siRNA against DUSP6 with an ERK inhibitor like SCH772984 to try and rescue the loss of cell viability.
Addition of drug (SCH772984) to cells transfected with siRNA against DUSP6 led to indiscriminate toxicity; cells could not withstand the stress of transfection coupled with an ERK inhibitor. As an alternative strategy, we knocked down DUSP6 with siRNA in PC9 cells co-transduced with shRNAs targeting either ERK1 or ERK2 as used in Figure 1. Stable, viable cells were established with reduced ERK levels. These cells displayed increased relative viability after DUSP6 knockdown compared to shScramble control cells, further suggesting that ERK plays a role in mediating the toxic effects of DUSP6 inhibition (now shown in Figure 3---figure supplement 1G,H,I).
> c\) Figure 3B: PC9 cells in this panel show increased p-ERK after DUSP6 kd. Please explain why this is the opposite result compared to Panel A.
These data are from 24hr samples, not 5 day samples (Figure 3A). We expect that acute loss of DUSP6 should increase levels of p-ERK that will then be part of a feedback loop of *DUSP6* activation, followed by de-phosphorylation of ERK.
As mentioned above, 24 hours after DUSP6 knockdown, cells are still viable and P-ERK is induced whereas, at day 5, cells have induced cleaved PARP and demonstrate substantially decreased viability. We postulate that cells transfected with siRNA for DUSP6 develop high levels of P-ERK and subsequent cell death, leaving only cells with lower P-ERK at day 5.
> d\) Figure 3B and C: Did the authors determine if the levels of DUSP6 in their EGFR and KRAS wild-type cell lines, HCC95 and H1648, are not as high as in the cell lines with EGFR or KRAS activating mutations? Lower levels of DUSP6 would indicate that these cells do not need to have similar buffering ability as the EGFR or KRAS mutant cell lines and would be consistent with their findings in Figure 2. In Figure 3B, it seems that DUSP6 is also buffering the pERK levels in HCC95 because knockdown of DUSP6 increases the levels of pERK in these cells by over 1.5 fold, similar to the cells with EGFR or KRAS activating mutations; but this increase in pERK has no impact on the cell survival (Figure 3C). This needs to be reconciled with the authors\' main claim.
We will provide the basal levels of DUSP6 across the lines in one figure. Additionally, we will include the quantitation of p-ERK changes from our independent experiments to help establish the fold changes.
We have included immunoblots indicating the basal levels of DUSP6 and P-ERK across the panel of cell lines (Figure 3---figure supplement 1D). Due to the variability associated with transfection-based experiments, we have now compiled dosimetry for three independent western blots in Figure 3B and plotted the results. This revealed that EGFR or KRAS mutant, but not wild type, cells consistently demonstrate increased P-ERK upon DUSP6 knockdown. All these results and their potential implications are now described in the text (subsection "Knockdown of DUSP6 elevates P-ERK and reduces viability of LUAD cells with either KRAS or EGFR oncogenic mutations").
e\) There seems to be a disconnect between the first part of the manuscript, where the authors describe the mechanism of toxicity caused by forced overexpression of KRAS and the second part, where they describe the effect of DUSP inhibition in cell lines with endogenous KRAS/EGFR mutations. To close this gap, the authors need to determine the levels of DUSP6 protein in their dox-inducible KRAS models and how they correlate with pERK. They should determine if the induction of pERK is transient, as that observed with DUSP6 siRNA, or sustained. Finally, they should use DUSP6 siRNA or BCI to determine if this reverses the effect of forced KRAS expression on pERK or proliferation. Another question that has not been addressed is why doesn\'t forced overexpression of KRAS induce sufficient DUSP6 to override the induction of pERK, if DUSP expression is under the control of EGFR/KRAS? Could there be other factors involved? If the authors can experimentally address these it would be a significant improvement.
We don't understand why the reviewers believe there is a "disconnect" between the early and late phases of the manuscript. In fact, we believe that there is a logical flow from identification of p-ERK as the locus that transmits a toxic signal to the implication of DUSP6 as a critical regulator of the activity of ERK. So the notion of an informational gap is not clear to us. Nevertheless, we agree that the situation is complicated by the kinetics of activation and de-activation of the components of the signaling system, and we will follow the request to obtain more kinetic data. We will perform time course experiments in tetO-RAS lines, measuring p-ERK induction and DUSP6 protein levels at 1,3,5 and 7 days. Contrary to the reviewers' speculation, we would not anticipate that DUSP6 siRNA or BCI would reverse the effects of forced KRAS expression; they should potentiate the effects of KRAS. On the other hand, it is unclear why DUSP6 cannot 'override' the induction of p-ERK. It is possible that p-ERK has fully localized to the nucleus and is no longer accessible to DUSP6. We will consider these possibilities in the revised manuscript.
The kinetics of p-ERK induction have been provided for H358 (days 1, 3, 5 and 7) and H1975 (day 7) cells in Figure 1---figure supplement 1B). The time course of induction of p-ERK upon treatment of H358 cells with BCI (at 1, 6, 12, 24, 48, 72 hours) is provided in Figure 4---figure supplement 1D. Experiments with BCI provide initial assessment of the kinetics of induction of p-ERK upon DUSP6 inactivation. The kinetics suggest that p-ERK induction for at least 24 hours is required before markers of apoptosis (PARP cleavage) are detected. Similar kinetics of p-ERK induction (at least 24-48 hours) before PARP cleavage detection were observed for H358-tetO-KRAS cells (subsection "Synthetic lethality induced by co-expression of mutant KRAS and EGFR is mediated through increased ERK Signalling", subsection "P-ERK levels increase in LUAD cells after inhibition of DUSP6 by BCI, and P-ERK is required for BCI-mediated
toxicity.").
> f\) More evidence is needed to show that the increase in DUSP mRNA is associated with an increased in DUSP6 protein levels or increased DUSP activity. The data in Figure 2I, where the authors compare the relationship between pERK (measured by RPPA) and DUSP6 mRNA is the only such evidence provided. This is underwhelming because the correlation is not great (r=0.1) and because the pERK does not seem to have been controlled for total ERK.
The RPPA assays (from TCGA) are controlled for total protein. The time course studies in tetO lines and BCI-treated cell lines may help address the issue of 'sufficient' levels of DUSP6.
We have performed and included in the manuscript time course experiments for both dox treatment in TetO cell lines and BCI treated cells as described above.
> g\) Finding that DUSP6 siRNA caused only a transient elevation in pERK (followed by inhibition of pERK at longer intervals), while mimicking the antiproliferative effect observed with forced overexpression of KRAS needs to be clarified with additional experiments. As it stands, it is difficult to agree with the authors conclusion that DUSP6 is the main mediator of the proliferative effect or that the effect of DUSP6 is through pERK. One consideration may be to use constitutively active ERK (i.e. DD phosphomimetic mutants) and attempt to reverse the effect of DUSP6. This is another point that, if addressed experimentally, could add value to the manuscript.
The 'transient elevation' in p-ERK with siRNA against DUSP6 may be a technical limitation of this assay as previously described. The time course studies in tetO lines and with BCI will help establish this. We will try and rescue the effects of siRNA against DUSP6 and BCI in H1975 cells---a cell line that is tolerant to ERK inhibition and provides an experimental system to 'dial' back appropriate p-ERK levels. A constitutively active ERK mutant is likely to be lethal in the presence of tet-induced mutant KRAS; for instance, ERK mutants have been documented to have a lethal effect in melanoma cells (Goetz et al., 2014). We will focus our attention on H1975 cells to rescue the effects of DUSP6 siRNA and BCI w/ MEK or ERK inhibition.
As mentioned above, we have used shRNA to inhibit ERK1 or ERK2 in PC9 cells and inhibition of ERK1 or ERK2 limited the toxic effects of knocking down DUSP6 (Figure 3---figure supplement 1G,H,I). In addition, we observed similar reductions in the toxic effects of BCI when H358 cells were co-treated with an ERK inhibitor (Figure 4---figure supplement 1E). These experiments further reinforce the conclusion that inhibition of DUSP6 (genetically or pharmacologically) decreases viability of EGFR or KRAS mutant cells -- at least partially -- through ERK induction.
> h). If indeed it is true that a transient induction in pERK leads to cytotoxicity several days later, then does EGF stimulation (which also causes a transient induction in pERK) have the same effect in EGFR WT/KRAS MT cells?
As previously mentioned, the effects of transient vs. prolonged p-ERK will be addressed by studying the time course of response to BCI in cells and to mutant KRAS induced by dox. Transient treatment of cells with EGF is not expected to cause cytotoxicity based on our earlier experiments. In fact, transient administration of EGF to HCC95 cells failed to shift the IC50 for BCI; only prolonged exposure to EGF did that (Figure 5A). We will consider inclusion of this information during revision of the manuscript.
We have provided time course experiments of dox-mediated induction in TetO-KRAS cells (Figure 1---figure supplement 1B) and of BCI treatment in H358 cells (Figure 4---figure supplement 1D). Both approaches caused an initial increase in P-ERK levels coupled with later induction of cleaved-PARP and subsequent decrease in P-ERK (Figure 4---figure supplement 1D).
> i\) All three reviewers noted that HCC95 (RRID:CVCL_5137) is a squamous cell carcinoma line. This not comparable to lung adenocarcinoma because this tumor type has different pathway dependencies than LUAD (TCGA, 2012). A wild type LUAD line should be tested instead.
HCC95 cells were used because they are a cancer cell line (lung) that does not have mutations in EGFR or other examined components of the RAS pathway. We will state in the text that this is a squamous lung cancer cell line. Based on our data, cells with a mutation in the RAS pathway are likely be vulnerable to DUSP6 inhibition, regardless of cell lineage. Cells without these mutations (like HCC95) illustrate cancers (of any origin) that we would predict to be un-responsive to DUSP6 inhibition.
We have noted the nature of HCC95 cells in the text (subsection "Knockdown of DUSP6 elevates P-ERK and reduces viability of LUAD cells with either KRAS or EGFR oncogenic mutation").
> j\) Figure 3C: Same comments as 3A.
Addressed above.
> k\) The investigators might consider inactivating DUSP6 with CRISPR-Cas9 in these cell lines to show genetic dependence.
We have now created PC9 and H358 cells in which *DUSP6* has been deleted or damaged using CRISPR-Cas9. We anticipate that the cells selected for this loss may have developed other mechanisms to maintain p-ERK or may have other pathways activated to bypass the need for p-ERK to mediate survival. Thus, they may or may not be uniquely vulnerable to inhibition of MEK or ERK. We will analyze the recently identified mutant cells for p-ERK levels, growth rates, and sensitivities to BCI and to inhibition of MEK and ERK.
We created H358 cells deficient in DUSP6 (Figure 4---figure supplement 1J,K). These cells were equally sensitive to BCI as cells that were targeted with a control (lacZ) sgRNA. We suspect that DUSP1 may be controlling p-ERK levels in the absence of DUSP6, and BCI has known specificity towards DUSP1 in addition to DUSP6. Additionally, these cell lines were derived from clones, so it is possible that new mutations or pathway re-wirings have taken place and that they continue to control p-ERK.
> 4\) Figure 4 a) Figure 4A: 11 lung cancer cell lines were treated with BCI and viable cells were measured after 72-hour treatment. Most of these cell lines are fast growing cells with doubling time around 20\~30 hours. Reduction of viable cells by 72-hours does not necessarily mean cell killing. TUNEL or caspase-3/PARP western blot needs to be performed to detect the levels of apoptosis. Senescence and cell cycle arrest should be examined too.
We agree that the reduction in numbers of viable cells does not reveal the mechanism by which the numbers were reduced, and we have been careful to avoid any suggestion that it does (e.g. by labeling our charts "number of viable cells"). As explained earlier, our focus has been on the generation of the toxic signal, not the response to it. Nevertheless, in response to the reviewers' concerns on this point, we will examine cells for apoptosis by measuring PARP cleavage.
We have assessed cleaved-PARP after BCI treatment in a panel of seven sensitive and insensitive cell lines and in a time course experiment in H358s (Figure 4---figure supplement 1). Only sensitive cell lines induced cleaved PARP after treatment.
> b\) Figure 4C and D: p-ERK levels at a single time point was measured in multiple cell lines (30 min). In order to explain the loss of viable cells at 72hours in Figure 4A, p-ERK should be measured at serial time points such as 1, 6, 12, 24, 48, 72 hours.
We will provide a time course of our measurements of p-ERK and DUSP6 in H1975 and H358 cells during treatment with BCI.
We provide a time course for H358 cells treated with BCI at the time points suggested by the reviewer in Figure 4---figure supplement 1.
> c\) The data on BCI are very interesting but it\'s not clear how have the authors established that BCI is selective for DUSP? Sensitive and insensitive cell lines all have IC50s in the 1-5μM range (12/13 cells lines tested). What is the IC50 of the inhibitor in DUSP6 KD or KO cells (or in DUSP1/6 double KD/KO cells)? How do the authors know that the antiproliferative effect of BCI is due to its ability to induce pERK? These questions need to be addressed experimentally. The authors should also attempt to show that the inhibitor has an effect in vivo, given their claim that such an approach could be therapeutically beneficial in patients.
A potential target of BCI is DUSP1, but we have data showing that siRNA against DUSP1 is not lethal in H1975 cells while siRNA to DUSP6 is. We will now also try to rescue the effects of BCI in H1975 cells by inhibiting ERK, using this cell line for the reasons described above (3b,g). In addition to PC9 CRISPR-Cas9 DUSP6 knockout cells described above (Figure 3, comment k), we have generated PC9 cells expressing wild type and catalytically inactive DUSP6. We will use these cell models to determine if there is a shift in the BCI IC50 with manipulation of DUSP6 to further evaluate its role as the biological target of BCI.
Importantly, we have now completed a genome-wide CRISPR screen in H460 cells (an KRAS mutant cell line sensitive to BCI), looking for loss of function mutations that confer resistance to BCI. The most highly enriched guide RNA in this screen was specific for KRAS, suggesting that BCI is 'on-target' with respect to its proposed role in causing excessive activation of the RAS pathway. We will confirm these findings by measuring BCI sensitivity in H460 cells treated with siRNA against KRAS and expect to include a version of the results in the revised paper to further support our interpretation of our findings with BCI.
We have included data showing that DUSP1 knockdown, as opposed to DUSP6 knockdown, is not lethal in H1975 cells (Figure 4---figure supplement 1A,B). Further, we have demonstrated that co-treatment with an ERK inhibitor decreases the toxic effects of BCI in H358 cells (Figure 4---figure supplement 1E,F). Lastly, we added the genome-wide CRISPR screen data in H460 cells showing that sgRNAs for KRAS are enriched upon BCI treatment, which we have subsequently confirmed using individual sgRNAs and BCI dose response experiments. Together, these results confirm that BCI works mainly through DUSP6 in the context described and mediates its toxic effects through ERK.
> d\) Figure 4E and F: Four cell lines were treated with 125nM trametinib for 72 hours. 125nM is a very high dose for trametinib-sensitive cell lines such as H358 (reported IC50 \~50nM). Apoptosis levels should be measured to document any cell death.
The purpose of these experiments was to address whether there was a correlation between the sensitivity of cells to ERK inhibition *and* their sensitivity to ERK hyperactivation. This correlation appears to hold (PC9 and H358 vs. HCC95 and H1648).
> 5\) Figure 5.
>
> See comments for Figure 3B. HCC95 is a SQCC cell line that is quite different from the LUAD cells used in the rest of this manuscript.
>
> a\) In figures where the authors make a comparison of protein levels under different conditions the uncut blot with all the lanes on the same blot must be shown. It is not ideal to make a comparison between levels of proteins on two different strips of blots. For example, in Figure 5B, authors claim that EGF increase levels of pEGFR and pERK in HCC95 cells when the pEGFR and pERK levels with and without EGF are on two different strips of blots. This is important to confirm that BCI treatment increases pERK levels in EGFR treated cells. Quantifications should be shown in addition.
Data in the western blots are normalized to total ERK and actin to make the values comparable. This will be explicitly stated in methods. Additionally, samples grown with and without EGF will be run in the same gel for the highest dose of BCI to provide a visual comparison.
We have included a western blot using extracts of cells treated and not treated with EGF, run on the same gel in Figure 5---figure supplement 1.
> Overall comment:
>
> The authors should soften earlier claims that EGFR-mutations and KRAS-mutations are synthetically lethal in the adenocarcinoma subtype of NSCLC (Unni et al., 2015). Recent genetic studies of EGFR-mutant lung cancer (Blakely et al., Nature Genetics 2017) have shown that 2.5%\~4.7% EGFR-mutant lung cancer also harbor KRAS copy number gain or activating mutations. Tumor genomic analyses have indicated that bona fide driver mutations causing lung adenocarcinoma are not as mutually exclusive as previously thought (e.g. PMIDs: 25301630, 28498782, 28445112, 29106415, and other recent publications), particularly in metastatic lung adenocarcinoma instead of early-stage disease and/or during the evolution of treatment resistance in metastatic disease. Further, preclinical studies have shown acquired KRAS gain/activation in response to EGFRi in EGFR-mutant NSCLC, indicating that this can be a mechanism of drug resistance (Politi et al., 2000; Eberlein et al., 2015). The authors should acknowledge these recent works and temper their claim of absolute mutually exclusivity in this disease, at least under these more advanced-stage disease contexts and in the light of the emerging literature.
As noted earlier, we will provide a more thorough discussion of these points in the manuscript.
The additional discussion of these issues has been added to the text (Discussion section). We hope that with these changes and additions to the manuscript, the revised version will suitable for re-submission and consideration for publication at *eLife*.
\[Editors\' note: further revisions were requested prior to acceptance, as described below.\]
> Thank you for resubmitting your work entitled \"Hyperactivation of ERK by mutation-driven RAS signaling or by inhibition of DUSP6 is toxic to lung adenocarcinoma cells\" for further consideration at eLife. First, let me apologize profusely for the very long time this has taken. The problem is basically that the reviewers are at an impasse and could not decide on a course of action.
>
> Briefly, this is a follow up to a previous paper, that reported anti-proliferative effects of activation of Ras and EGFR in the same cancer cells. This new paper has provides evidence that the anti-proliferative effect of over-expressed, active Ras is due to ERK hyperactivation, and that DUSP6 is critical to prevent adverse effects of active ERK in lung cancer cells. In the revision, the authors have added results using ERK shRNA, ERKi, and a CRISPR screen that together provide convincing evidence that ERK mediates the antiproliferative effect of oncogene overexpression. The finding that sgKRAS are enriched in the BCI screen also indirectly supports that conclusion. The authors have also ruled out that the PI3K pathway is not involved by adding PI3K inhibitor experiments. These additions greatly strengthen the conclusion that hyperactivation of ERK is detrimental. However, the evidence that DUSP6 plays a key role is not convincing.
>
> The DUSP6 knockdown was done with a single siRNA with no rescue experiment. (Other siRNAs did not effectively inhibit DUSP6 expression). Attempts to recapitulate the result with a DUSP6 CRISPR knockout were unsuccessful. Therefore, the conclusion that DUSP6 is necessary relies in large part on the specificity of the chemical BCI. The DUSP6 KO cells have the same IC50 to BCI, while DUSP1 siRNA did not affect proliferation in their system. Based on these results, the authors cannot claim that the BCI effect is DUSP6 specific nor that the BCI effect in DUSP6KO cells is driven by DUSP1 inhibition. It probably isn\'t, and by inference, the observed phenotype is not dependent on DUSP6 alone but on other ERK specific DUSPs as well.
>
> The reviewers disagreed whether these weaknesses undermined the impact to the point where the paper was unsuitable for publication, or whether lengthy additional experiments would be needed. The eLife approach is not to ask for multiple rounds of revision. In this spirit, I suggest two possibilities:
>
> Either
>
> \- Provide convincing evidence that validates DUSP6 as the key enzyme that downregulates ERK in lung cancer cells (e.g. try more siRNAs to find another that gives strong knockdown, and/or rescue DUSP6 siRNA with DUSP6 from mouse, or with silent mutations in the siRNA target sequence).
>
> Or,
>
> \- Modify the title and abstract to allow for the possibility that other DUSPs are involved and be more open about the shortcomings of the results.
1\) To test additional DUSP6 siRNAs for their effects on protein abundance and cell fitness, we obtained a DUSP6-specific siRNA from Qiagen (the previous siRNAs were prepared by Dharmacon). In contrast to the DUSP6-8 siRNA that targeted a sequence in the 3' domain of DUSP6 mRNA, the new species (called DUSP6-Qiagen in Figure 3B,C) targeted a sequence in the 5' coding region of DUSP6 mRNA and reduced levels of DUSP6 protein in PC9 cells to levels similar to those achieved with one of the previously tested Dharmacon siRNAs (DUSP6-8 in Figure 3B and Figure 3---figure supplement 1A) and with the pool of four Dharmacon siRNAs (DUSP6-pool in Figure 3B and Figure 3---figure supplement 1A). Furthermore, DUSP6-Qiagen reduced the number of viable PC9 cells to a level similar to that observed with the pooled Dharmacon siRNAs (Figure 3C). We have described the effects of this second effective inhibitory RNA in the text and conclude that it strengthens the case for a central role of DUSP6 in regulation of ERK activity in RTK-RAS-driven LUAD. We also point out that this conclusion is supported by the correlation between the effects of one Dharmacon siRNA (DUSP6-8) on both DUSP6 protein levels and cell fitness (Figure 3---figure supplement 1A,B).
2\) We also attempted, unsuccessfully, to rescue the effects of DUSP6 siRNA by generating a plasmid encoding *DUSP6* mRNA with several synonymous mutations in the coding sequence to render the mRNA target sequence resistant to the siRNA without changing the protein sequence. For a variety of technical reasons related to transfection procedures, we have not been able to perform these experiments in a reproducible manner. We are convinced that the work required to carry out a satisfying rescue experiment would take an unreasonable amount of time and inappropriately delay publication, when we have provided the requested data with a second effective siRNA.
3\) Despite our positive findings with the Qiagen siRNA, we recognize that our conclusions about the role of DUSP6 in regulation of the activity of ERK kinases should be cautious. (DUSP6 may not be the only important regulator and we cannot fully exclude some off-target effects of our siRNAs.) We have therefore removed specific mention of DUSP6 in the title of the manuscript, and we have modulated the description of the results in the abstract, along the lines suggested in your letter.
One other relevant item: two recent papers confirm the significance of the level of ERK kinase activity in another cancer type, melanoma, and address the possible role of DUSP6. Leung et al., over-express *ERK2* in melanoma cell lines and show that high levels of ERK2 protein are toxic specifically in lines that carry BRAF V600E. Wittig-Blaich et al., use a complex screening method to identify genes that produce a synthetic lethality when disrupted in melanoma cell lines carrying the BRAF V600E mutation; one of the five implicated genes is *DUSP6*, allowing the authors to draw conclusions similar to our own. We mention and cite these papers (Leung et al., 2018 and Wittig-Blaich et al., 2017) in the Discussion section.
In addition to the changes that address your main concerns, we have found a few places in the text that lacked clarity upon careful re-reading of our previously submitted revision.
[^1]: These authors contributed equally to this work.
[^2]: These authors also contributed equally to this work.
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Jonathan Weinert
Jonathan Weinert
Jonathan Weinert won the 2006 Nightboat Poetry Prize for his debut, In the Mode of Disappearance. Recent work appears in The Kenyon Review, Blackbird, Bellingham Review, Third Coast, 32 Poems, and elsewhere. He is a poetry editor of the online journal Perihelion.
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BAY VILLAGE, Ohio -- David Kaman of Bay Village may be one of Cedar Point's biggest enthusiasts, and he has a Cedar Point memorabilia-stuffed room in his home to prove it.
Kaman has a collection of more than 1,000 Cedar Point post cards dating back to the early 1900s. The post cards, neatly tucked into individual plastic slips and bound in photo albums, are scrawled with messages that recall blissful, contented days at the amusement park through the park's history.
On the towering bookcase in Kaman's Cedar Point room - so tall that it has an attached rolling ladder to reach the top shelves - is a gold-colored sand pail with matching shovel that dates back to the early days of the Hotel Breakers at Cedar Point.
"This pail is from the early 1900s," says Kaman, pointing to a pail engraved with 'The finest bathing beach in the world' and matching shovel he found at an estate sale in Findlay about 25 years ago.
Kaman is a season passholder who has been among the first to ride the park's most wicked roller coasters, including the Raptor, the Mantis, and this year's 223-foot, 75-mile-an-hour Valravn coaster.
His home, which feels like a cross between museum and a gift shop, includes a top-shelf assortment of wood carvings that include animals, a totem pole, a boat and other figures.
He says every year a wood carver at the Frontier Trail at Cedar Point makes about a dozen of one item. "It takes him all summer," says Kaman. "Every year the first one he makes he sells to me. It's just a friendship that we developed.
Tucked on the highest shelves of the bookcase in Kaman's home are five wood miniature replicas of riverboats that once floated in the Cedar Point lagoon.
In an amazingly orderly arrangement, the 15-foot-high or so bookcase is further brimming with newer and historic Cedar Point-themed plates, drinking glasses, mugs, bottles, buttons, photos, coupons for rides dating back to the 1960s, Halloween memorabilia and much more.
Kaman has a sign for the Blue Streak roller coast at Cedar Point. Large Cedar Point banners and smaller pennants decorate the walls, along with colorful park maps dating back to 1960.
What ignited Kaman's fascination with Cedar Point?
He grew up in Sandusky and got a job at the theme park in 1973, when he was 18.
"I was hired to change light bulbs. I worked from eleven thirty at night until eight in the morning changing bulbs. I got to see a completely different side of the park because I saw it at night," he says. "And I felt like I was the first person in Ohio to see the sun come up. At the crack of dawn it was so still."
His family spent many delightful days at Cedar Point, says Kaman, an attorney with three grown sons. Of all the memories in his collection, his favorite is a picture of his mother on a carousel horse taken in 1931.
"Since I was 7 years old we went there every year," he says.
Want to nominate a Cool Space?
Kaman's father collected Cedar Point postcards, but Kaman has taken it way beyond his dad's level of enthusiasm. Over the years, the younger Kaman has combed estate and postcard sales with the purpose of snaring Cedar Point souvenirs.
"These postcards and other things represent hundreds and hundreds of hours riding around with my three sons to garage sales and postcards shows," he says. "We'd be on like a scavenger hunt for Cedar Point things. Sometimes I'm just at the right place at the right time."
But he also had an inside advantage for acquiring Cedar Point goodies.
"When I worked at Cedar Point a lot of my co-workers ended up going into management positions," he says. "They would call me up and say, 'Hey David we're getting rid of some old signs. Do you want one?' This past year they took down lights posts from the 1900s. I'm hoping to get one. They're very ornate."
For the most part, Kaman's collection is neatly contained to his Cedar Point Room, but 400 mugs are stored in his garage.
Lots of people have asked why he doesn't hunt for Cedar Point items on eBay, but what's the fun in that?
"To me that takes the fun out of it," he says. "I like to interact with people when I'm looking for things."
Kaman says even today, visiting Cedar Point brings the same thrill that he felt as a child.
"I just fell in love with the park, and the people," he says, adding no matter what problems you have, "you don't think about them when you're on a roller coaster."
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Q:
Get all users, with all their images
I have 2 mysql tables :
users table
table that contains gallery images for each user
My users table looks like :
id | name
---------
1 Ryan
2 James
3 Dave
My user_gallery_images tables looks like :
id | user_id | image
--------------------
1 1 image.jpg
2 1 image2.jpg
3 2 image3.jpg
4 2 image4.jpg
I was wondering if there was a query that would retrieve all users, and get all the images for that user.
The expected result should look like :
id | name | images
-------------------
1 Ryan image.jpg,image2.jpg
2 James image3.jpg,image4.jpg
3 Dave
Thank you
A:
You will have to use a LEFT JOIN rather than an INNER JOIN because you want to retrieve David who doesn't have images. And you will need to use GROUP_CONCAT
SELECT u.id, u.name, GROUP_CONCAT(image) from users u
LEFT JOIN user_gallery_images g
ON u.id = g.user_id GROUP by u.id
Note this query will work on mysql 5.7 only if you have a PRIMARY KEY on users.id. Will work on mysql < 5.7 regardless of the primary key
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Parkinson's disease with and without REM sleep behaviour disorder: are there any clinical differences?
Rapid eye movement sleep behaviour disorder (RBD) may serve as a useful indicator to approach Parkinson's disease (PD); however, PD patients do not always exhibit RBD. We wondered whether the presence of RBD would be reflected in the expansion of PD lesions and represent the same PD entity. We examined the clinical differences between PD with and without RBD and studied the frequency of RBD-like symptoms (RBD-s) and clinical differences in 150 PD patients, including 81 patients (54.0%) who satisfied the International Classification of Sleep Disorders, Revised, minimum clinical criteria for RBD. RBD-s preceding the appearance of parkinsonism were found in 44.4% of patients. Statistically, the presence of RBD-s was associated with ages above 65 years, male gender, constipation, dopa-induced dyskinesia and 'sleep attack', with odds ratios of 3.709, 2.469, 2.184, 5.046 and 6.562, respectively. No differences were found between the 2 groups with regard to symptoms at PD onset, disease duration, Hoehn-Yahr stage, hallucination, dementia, wearing-off, orthostatic hypotension, cerebral blood flow and antiparkinsonism drugs. In the early stage, RBD and autonomic system dysfunction are important factors in the progression of PD.
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MANLY, NSW/Australia (Friday, 17 February, 2012) – The Australian Open of Surfing presented by Hurley and Billabong has seen surfing’s past, present and future stars doing battle in front of thousands of fans at Manly Beach. Today the surfers were forced to put their aerial skills on display in small 2 foot (1 meter) wind swell conditions.
Matt Banting (AUS) is on fire this week at the Australian Open Of Surfing after advancing to the Final of the Pro Junior division on the weekend and today defeating ASP World Title Series surfers – Jordy Smith (ZAF) and Matt Wilkinson (AUS).
“I’m rapt! I’m really happy with my surfing right now,” Banting said. “I’ve made the final of the Pro Junior, and I’ve been surfing every day since then so I feel like I know the waves pretty well here. It’s great to have a heat with those guys, let alone win, they’ve been my heroes since I was a little kid.”
Day 7 standout Joel Parkinson. Photo: ASP/Owen
Joel Parkinson (AUS) advanced to the round of 16 with a solid heat win this afternoon alongside Kolohe Andino (USA) who finished 2nd. Parkinson was looking sharp both in the air and on the open wave face and finished the heat with a big air-reverse.
“It’s crazy sitting in the water and looking back at a packed out beach,” Parkinson said. “The surf is small but we’re all dealing with the same conditions, I actually caught some fun ones. I’m a bit of a momentum builder, I don’t want to surf my best heat in the first or second round, I want to try and peak towards the end.”
Mitch Crews (AUS) continued to impress at the Australian Open Of Surfing, racking up another heat win to advance to the round of 16, the first man-on-man round of the contest. Crews managed to find some long right-handers and belted out some big turns.
“It’s fun, it’s tiny, but you can still generate speed in the pocket,” Crews said. “I saw that Ace (Buchan) went into the lead at the end there and it fired me up, then I got a good wave and tried to go big to take the lead back off him. I’m frothing.”Ke
Tomas Hermes (BRA) looked right at home in today’s challenging conditions, finding a few nice waves and going big with a couple of clean airs.
“The waves are like where I’m from in Brazil,” Hermes said. “It’s feels great to win the heat because this contest is so hard and full of really good surfers. This event is huge, just like the US Open, it’s great to be here in Manly.”
Keanu Asing (HAW) advanced to the next round after finishing 2nd to an in-form Jesse Mendes (BRA). Asing’s powerful carves and huge inverted airs have been impressing the judges and the masses of onlookers on the sand.
“To make this heat feels so good, especially after losing first round in Brazil last week,” Asing said. “Our heat got lucky we probably had the best waves of the afternoon, so it was great see some nice waves and good surfing. Every heat at this contest is a tough one, I’m just going to keep doing what I’ve been doing and hopefully go a lot further.”
The last heat of the day saw an upset with the number 2 seed, Taj Burrow (AUS) eliminated by Granger Larson (HAW) and Peterson Crisanto (BRA).
Tomorrow the Australian Open Of Surfing will see Men’s ASP 6-Star action and will also feature skating competitions and demos, music concerts, athlete signing sessions and much more. The event is free for the public to view in person and live on the internet via australianopenofsurfing.com
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Semantic Segmentation
Introduction
Semantic Segmentation of an image is to assign each pixel in the input image a semantic class in order to get a pixel-wise dense classification. While semantic segmentation / scene parsing has been a part of the computer vision community since 2007, but much like other areas in computer vision, major breakthrough came when fully convolutional neural networks were first used by 2014 Long et. al. to perform end-to-end segmentation of natural images.
Figure : Example of semantic segmentation (Left) generated by FCN-8s ( trained using pytorch-semseg repository) overlayed on the input image (Right)
The FCN-8s architecture put forth achieved a 20% relative improvement to 62.2% mean IU on Pascal VOC 2012 dataset. This architecture was in my opinion a baseline for semantic segmentation on top of which several newer and better architectures were developed.
Fully Convolutional Networks (FCNs) are being used for semantic segmentation of natural images, for multi-modal medical image analysis and multispectral satellite image segmentation. Very similar to deep classification networks like AlexNet, VGG, ResNet etc. there is also a large variety of deep architectures that perform semantic segmentation.
I summarize networks like FCN, SegNet, U-Net, FC-Densenet E-Net & Link-Net, RefineNet, PSPNet, Mask-RCNN, and some semi-supervised approaches like DecoupledNet and GAN-SS here and provide reference PyTorch and Keras (in progress) implementations for a number of them. In the last part of the post I summarize some popular datasets and visualize a few results with the trained networks.
Network Architectures
A general semantic segmentation architecture can be broadly thought of as an encoder network followed by a decoder network. The encoder is usually is a pre-trained classification network like VGG/ResNet followed by a decoder network. The decoder network/mechanism is mostly where these architectures differ. The task of the decoder is to semantically project the discriminative features (lower resolution) learnt by the encoder onto the pixel space (higher resolution) to get a dense classification.
Unlike classification where the end result of the very deep network ( i.e. the class presence probability) is the only important thing, semantic segmentation not only requires discrimination at pixel level but also a mechanism to project the discriminative features learnt at different stages of the encoder onto the pixel space. Different architectures employ different mechanisms (skip connections, pyramid pooling etc) as a part of the decoding mechanism.
A number of above architectures and loaders for datasets is available in PyTorch at:
A more formal summarization of semantic segmentation ( including recurrent style networks ) can also be found here
Fully Convolution Networks (FCNs)
CVPR 2015 Fully Convolutional Networks for Semantic Segmentation Arxiv
We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.
Figure : The FCN end-to-end dense prediction pipeline.
A few key features of networks of this type are:
The features are merged from different stages in the encoder which vary in coarseness of semantic information .
. The upsampling of learned low resolution semantic feature maps is done using deconvolutions which are initialized with billinear interpolation filters .
. Excellent example for knowledge transfer from modern classifier networks like VGG16, Alexnet to perform semantic segmentation
Figure : Transforming fully connected layers into convolutions enables a classification network to output a class heatmap.
The fully connected layers ( fc6 , fc7 ) of classification networks like VGG16 were converted to fully convolutional layers and as shown in the figure above, it produces a class presence heatmap in low resolution, which then is upsampled using billinearly initialized deconvolutions and at each stage of upsampling further refined by fusing (simple addition) features from coarser but higher resolution feature maps from lower layers in the VGG 16 ( conv4 and conv3 ) . A more detailed netscope-style visualization of the network can be found in at here
In conventional classification CNNs, pooling is used to increase the field of view and at the same time reduce the feature map resolution. While this works best for classification as the end goal is to just find the presence of a particular class, while the spatial location of the object is not of relevance. Thus pooling is introduced after each convolution block, to enable the succeeding block to extract more abstract, class-sailent features from the pooled features.
Figure : The FCN-32s Architecture
On the other hand any sort of operation - pooling or strided convolutions is deterimental to for semantic segmentation as spatial information is lost. Most of the architectures listed below mainly differ in the mechanism employed by them in the decoder to recover the information lost while reducing the resolution in the encoder. As seen above, FCN-8s fused features from different coarseness ( conv3 , conv4 and fc7 ) to refine the segmentation using spatial information from different resolutions at different stages from the encoder.
Figure : Gradients at conv layers when training FCNs Source
The first conv layers captures low level geometric information and since this entrirely dataset dependent you notice the gradients adjusting the first layer weights to accustom the model to the dataset. Deeper conv layers from VGG have very small gradients flowing as the higher level semantic concepts captured here are good enough for segmentation. This is what amazes me about how well transfer learning works.
Left : Deconvolution (Transposed Convolution) and Right : Dilated (Atrous) Convolution Source
Other important aspect for a semantic segmentation architecture is the mechanism used for feature upsampling the low-resolution segmentation maps to input image resolution using learned deconvolutions or partially avoid the reduction of resolution altogether in the encoder using dilated convolutions at the cost of computation. Dilated convolutions are very expensive, even on modern GPUs. This post on distill.pub explains in a much more detail about deconvolutions.
SegNet
2015 SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Arxiv
The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN and also with the well known DeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance.
Figure : The SegNet Architecture
A few key features of networks of this type are:
SegNet uses unpooling to upsample feature maps in decoder to use and keep high frequency details intact in the segmentation.
to upsample feature maps in decoder to use and keep high frequency details intact in the segmentation. This encoder doesn’t use the fully connected layers (by convolutionizing them as FCN) and hence is lightweight network lesser parameters.
Figure : Max Unpooling
As shown in the above image, the indices at each max-pooling layer in encoder are stored and later used to upsample the correspoing feature map in the decoder by unpooling it using those stored indices. While this helps keep the high-frequency information intact, it also misses neighbouring information when unpooling from low-resolution feature maps.
U-Net
MICCAI 2015 U-Net: Convolutional Networks for Biomedical Image Segmentation Arxiv
The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU
Figure : The U-Net Architecture
U-Net simply concatenates the encoder feature maps to upsampled feature maps from the decoder at every stage to form a ladder like structure. The network quite resembles Ladder Networks type architecture.
feature maps to upsampled feature maps from the at every stage to form a ladder like structure. The network quite resembles Ladder Networks type architecture. The architecture by its skip concatenation connections allows the decoder at each stage to learn back relevant features that are lost when pooled in the encoder.
U-Net achieved state-of-art results on EM Stacks dataset which contained only 30 densely annoted medical images and other medical image datasets and was later extended to a 3D version 3D-U-Net. While U-Net was initally published for bio-medical segmentation, the utility of the network and its capacity to learn from very little data, it has found use in several other fields satellite image segmentation and also has been part of winning solutions of many kaggle contests on medical image segmentation.
Fully Convolutional DenseNet
2016 The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation Arxiv
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.
Figure : The Fully Convolutional DenseNet Architecture
Fully Convolutional DenseNet uses a DenseNet as it’s base encoder and also in a fashion similar to U-Net concatenates features from encoder and decoder at each rung.
E-Net and Link-Net
2016 ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Arxiv 2017 LinkNet: Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation Blog
In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18× faster, requires 75× less FLOPs, has 79× less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network
LinkNet can process an input image of resolution 1280x720 on TX1 and Titan X at a rate of 2 fps and 19 fps respectively
Left : The LinkNet Architecture Right : The encoder and decoder blocks used in LinkNet
The LinkNet Architecture resembles a ladder network architecture where feature maps from the encoder (laterals) are summed with the upsampled feature maps from the decoder (verticals). Also note that the decoder block consists of considerable less parameters due to it’s channel reduction scheme. A feature map with shape [H, W, n_channels] is first convolved with a 1*1 kernel to get a feature map with shape [H, W, n_channels / 4 ] and then a deconvolution takes it to [2*H, 2*W, n_channels / 4 ] a final 1*1 kernel convolution to take it to [2*H, 2*W, n_channels / 2 ] . Thus the decoder block fewer parameters due to this channel reduction scheme. These networks, while being considerably close to state-the-art accuracy, can perform segmentation in real-time on embedded GPUs.
Mask R-CNN
2017 Mask R-CNN Arxiv
The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
Top : The Mask R-CNN Segmentation Pipeline
Bottom : The auxillary segmentation branch in addition to original Faster-RCNN architecture
The Mask R-CNN architecture is fairly simple, it an extension of popular Faster R-CNN architecture with requisite changes made to perform semantic segmentation.
Some Key features of this architecture are:
Faster R-CNN with an auxillary branch to perform semantic segmentation.
The RoIPool operation used for attending to each instance, has been modified to RoIAlign which avoids spatial quantization for feature extraction since keeping spatial-features intact in the highest resolution possible is important for semantic segmentation.
operation used for attending to each instance, has been modified to which avoids spatial quantization for feature extraction since keeping spatial-features intact in the highest resolution possible is important for semantic segmentation. Mask R-CNN was combined with Feature Pyramid Networks (which performs pyramid pooling of features in a style similar to PSPNet ) achieves state-of-the-art results on MS COCO dataset.
There is no working implementation of Mask R-CNN available online as of 01-06-2017 and it has not been benchmarked on Pascal VOC, but the segmentation masks as shown the paper look very close to ground truth.
PSPNet
CVPR 2017 PSPNet: Pyramid Scene Parsing Network Arxiv
In this paper, we exploit the capability of global context information by different-regionbased context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixellevel prediction. The proposed approach achieves state-ofthe-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark.
Top : The PSPNet Architecture
Bottom : The Spatial Pyramid Pooling in visualized in detail using netscope
Some Key features of this architecture are:
PSPNet modifies the base ResNet architecture by incorporating dilated convolutions and the features, after the inital pooling, is processed at the same resolution ( 1/4th of the original image input) throughout the encoder network until it reaches the spatial pooling module.
and the features, after the inital pooling, is processed at the same resolution ( of the original image input) throughout the encoder network until it reaches the spatial pooling module. Introcution of auxillary loss at intermediate layers of the ResNet to optimize learning overall learning.
at intermediate layers of the ResNet to optimize learning overall learning. Spatial Pyramid Pooling at the top of the modified ResNet encoder to aggregate global context.
Figure : An illustration to showcase the importance of global spatial context for semantic segmentation. It shows the relationship between receptive field and size across layers. In this case, the larger and more discriminative receptive (blue) maybe of importance in refining the representation carried by an earlier layer (orange) to resolve ambiguity.
The PSPNet architecture is currently the state-of-the-art in CityScapes, ADE20K and Pascal VOC 2012 (without MS COCO training data unlike most other methods). A full visualisation of the network in netscope can be found here.
RefineNet
CVPR 2017 RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation Arxiv
Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training.
Top : The RefineNet Architecture
Bottom : Building Blocks of RefineNet - Residual Conv Units, Multiresolution Fusion and Chained Residual Pooling.
RefineNet approaches the problem of spatial resolution reduction in conventional convnets in a manner very different to PSPNet (which uses computationally expensive dilated convolutions). The proposed achitecture iteratively pools features increasing resolutions using special RefineNet blocks for several ranges of resolutions and finally produces a high resolution segmentation map.
Some features of this architecture are:
Uses inputs at multiple resolutions , fuses the extracted features and passes them to the next stage.
, fuses the extracted features and passes them to the next stage. Introduces Chained Residual Pooling which is able to capture background context from a large image region. It does so by efficiently pooling features with multiple window sizes and fusing them together with residual connections and learnable weights
which is able to capture background context from a large image region. It does so by efficiently pooling features with multiple window sizes and fusing them together with residual connections and learnable weights All feature fusion is done using sum (ResNet style) to allow end-to-end training.
(ResNet style) to allow end-to-end training. Uses vanilla ResNet style residual layers without expensive dilated convolutions
G-FRNet
CVPR 2017 G-FRNet: Gated Feedback Refinement Network for Dense Image Labeling Arxiv
In this paper we propose Gated Feedback Refinement Network (G-FRNet), an end-to-end deep learning framework for dense labeling tasks that addresses this limitation of existing methods. Initially, GFRNet makes a coarse prediction and then it progressively refines the details by efficiently integrating local and global contextual information during the refinement stages. We introduce gate units that control the information passed forward in order to filter out ambiguity.
Top : The G-FRNet Architecture
Bottom : The Gated Refinement Unit
Most architectures above rely on simple feature passing from encoder to decoder using concatenation , unpooling or simple sum . However, The information that flows from higher resolution ( less discrimnative ) layers in the encoder to the corresponding upsampled feature maps in the decoder may or may not be of utility for segmentation. Gating the information flow from the encoder to the decoder at each stage using Gated Refinement Feedback Units can assist the decoder in resolving ambiguities and forming more relevant gated spatial context.
On a side note - The experiments in this paper reveal that ResNet is a far superior encoder base than VGG16 for semantic segmentation tasks. Something which I wasn’t able to find in any of the previous papers.
Semi-Supervised Semantic Segmentation
DecoupledNet
NIPS 2015 Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Arxiv
Contrary to existing approaches posing semantic segmentation as a single task of region-based classifi- cation, our algorithm decouples classification and segmentation, and learns a separate network for each task. In this architecture, labels associated with an image are identified by classification network, and binary segmentation is subsequently performed for each identified label in segmentation network. It facilitates to reduce search space for segmentation effectively by exploiting class-specific activation maps obtained from bridging layers.
Figure : The DecoupledNet Architecture
This was perhaps the first semi-supervised approach for semantic segmentation using fully convolutional networks.
Some sailent features of this approach are:
Decouples the classification and the segmentation tasks , thus enabling pre-trained classification networks to be plugged and played.
, thus enabling pre-trained classification networks to be plugged and played. Bridge Layers between the classification and segmentation networks produces class-sailent feature map (for class k ) which are then used by the segmentation network to produce a binary segmentation map (for class k )
between the classification and segmentation networks produces class-sailent feature map (for class ) which are then used by the segmentation network to produce a binary segmentation map (for class ) This method however needs k passes to segment k classes in an image.
GAN Based Approaches
2017 Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network Arxiv
In particular, we propose a semi-supervised framework ,based on Generative Adversarial Networks (GANs), which consists of a generator network to provide extra training examples to a multi-class classifier, acting as discriminator in the GAN framework, that assigns sample a label y from the K possible classes or marks it as a fake sample (extra class). To ensure higher quality of generated images for GANs with consequent improved pixel classification, we extend the above framework by adding weakly annotated data, i.e., we provide class level information to the generator.
Figure : Weekly Supervised (Class level labels) GAN
Figure : Semi-Supervised GAN
Datasets
Dataset Training Testing #Classes CamVid 468 233 11 PascalVOC 2012 9963 1447 20 NYUDv2 795 645 40 Cityscapes 2975 500 19 Sun-RGBD 10355 2860 37 MS COCO ‘15 80000 40000 80 ADE20K 20210 2000 150
Results
Figure : Sample semantic segmentation maps generated by FCN-8s ( trained using pytorch-semseg repository) overlayed on the input images from Pascal VOC validation set
Figure: The boat and myself segmented, Alongside Neva River
Debugging
In case this doesn’t work for you, or if there is a mistake/typo, open up an issue in the repo or feel free to shoot a mail at meetshah1995@ee.iitb.ac.in
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Two Redmond residents were dragged out of their vehicle, beaten, stabbed, and robbed Sunday evening in northeast Bend, leading to the arrest of two women and three men from Bend — two for attempted murder, authorities reported.
According to Sgt. Kurt Koester with Bend Police, Redmond residents Felix Serriteno Ruiz, 28, and Fabiola Hurtado Hernandez, 19 were sitting in their vehicle near northeast Carl Street in Bend about 6:15 p.m. March 18 when they were confronted by five suspects.
Officers responding to a reported assault in progress involving multiple suspects found Serriteno Ruiz beaten and stabbed multiple times, and Hernandez beaten unconscious.Both victims were transported by ambulance to St. Charles Medical Center in Bend, with Hernandez quickly regaining consciousness.
Investigation determined that an undisclosed amount of money was stolen from Hernandez’ purse, the sergeant said, adding that a police k-9 dog assisted in locating evidentiary items related to the case.
Three suspects were apprehended while fleeing the scene in a vehicle, Koester reported.Emanuel Garcia-Hernandez, 19, Monica Aria Magallanes, 27, and Adelina Arias Madrigal, 23, were taken into custody and lodged in the Deschutes County Jail.
Two other suspects, Ricardo BravoRosas, 26, and Delfino Bravo Rosas, 25, were arrested after they were found hiding in a neighboring yard, Sgt. Koester said.
Ricardo Bravo Rosas and Delfino Bravo Rosas were both charged with attempted murder, two counts of first-degree assault, conspiracy, and tampering with evidence.Delfino Bravo Rosas was also charged with identity theft and possession of a forged instrument.
Emmanuel Garcia-Hernandez was charged with first-degree conspiracy to commit assault, identity theft, and possession of a forged instrument.
Monica Aria Magallanes and Adelina Arias Madrigal were both charged with first-degree robbery and third-degree assault.
no one knows what really happened or how the whole thing came about. no one cared to listen but ricardo bravos life and three others got changed for the worst because of one mistake of defending themselves. only god can judge not people. if it was left to the people our world would be doomed. it is already with racism and hatred. ricardo,adelina,delfino,monica,emanuel are all good people and deserve fairness.
(Posted on August 28, 2007, 5:20 pm blushes)
The whole thing now is history. Ricardo convicted for defending himself and his brother. Welcome to the US legal system, or rather the BEND-OREGON legal system, where its residents sick and tired of the many things the illegals do (drugs, fights, etc... of course their white and other citizens don't do things like that) convicted a young man that was a good worker, brother, friend. Felix, the allegde victim was waiting to be nominated by the pope to sainthood. The poor fellow was beat up by a man smaller in size, and just because. He even cried in court. When he arrived to the hospital he changed his name "because he was affraid Ricardo would chase all the way to the hospital and finish him". Never mind he is an illegal and that is what illegals do, use fake or stolen identities. The DAs, so naive the poor couple, took the bait with hook and sinker all the way to their gall bladders. And the Jury, with the exception of one decent woman, who was upset at the turn out, got read of a rotten, nasty, marihuana smoker little Mexican punk (as if the remainder 85% of the Bend citizens don't smoke the stuff). Never mind the testimony his boss gave in favor of Ricardo and how he described the real Felix in action, lazy, bad worker, trouble maker, and with typical signs of drug addiction to heavy drugs such as crystal. I was every day observing this trial, and I am ashamed of my countrymen. Good for the only Juror who had guts to overcome her disgust about these foreigners and was able to provide justice, at least in a small portion, to Ricardo Bravo Rosas. Felix deserved every bit of it, for being a trouble maker, fight seeker drug addict.
(Posted on August 3, 2007, 1:06 am Sam)
I think it was a very idiotic thing to do to a human being period.
(Posted on March 26, 2007, 12:26 pm anonymous)
I'm sure this was drug related. Why else would they beat and stab someone?
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Acute and Recurrent Pericarditis.
Acute and recurrent pericarditis is the most common pericardial syndrome encountered in clinical practice either as an isolated process or as part of a systemic disease. The diagnosis is based on clinical evaluation, electrocardiogram, and echocardiography. The empiric therapy is based on nonsteroidal anti-inflammatory drugs plus colchicine as first choice, resorting to corticosteroids for specific indications (eg, systemic inflammatory disease on corticosteroids, pregnancy, renal failure, concomitant oral anticoagulants), for contraindications or failure of the first-line therapy. The most common complication is recurrence, occurring in up to 30% of cases after a first episode of pericarditis.
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Yoni, a local resident recently returned from reserve duty,
gave a ride to a “Yerushalmi” Jew from the hassidic enclave of Ramat Beit
Shemesh Bet and was shocked to hear that his passenger did not even not know
that his country was at war.
“Vos?” he asked Yoni in Yiddish.
“We are at war?” There are those among the Israeli-born
haredim who do not listen to the radio, read newspapers or own a television.
Young
Americans who served in the IDF but live in the States are arriving in Israel
to join their combat units.
Twenty-four-year
old Shmulik Lazaroff, one of 11 children in his family, grew up in Houston,
Texas, where his parents serve as Chabad emissaries. Five years ago, he
immigrated to Israel and began rabbinical school studies. Soon, he was drafted
into an infantry unit, and got to know Aud and others at the Michael Levine
Center.
“The Lubavitch
rebbe said that 'whoever serves in the IDF gets his place reserved in the world
to come.'” And he definitely wants to serve. “God willing. I will return to my
unit,” he says.
Shalom Lakein, 21,
from Brooklyn, was having the same thoughts. Lakein is also a Chabadnik, and
one of six brothers, three of whom have served in the Israeli army. Lakein, who
served in Golani, was released from the army four months ago. Now he wants back
in.
Amid all the
Facebook posts about the heart-rending violence taking place at this moment in
Israel and Gaza,this
photoof a bomb shelter door in Ashdod leapt
out. It says that the bomb shelter is only for men and boys.
New
immigrants from Ethiopia get a severe reality check mere days after their
arrival in Israel as bombs fall around their absorption center. Despite the
shock, they're adapting quickly and looking to pitch in.
Chief Rabbi Amar: “Just like the People of Israel did not
travel while the pillar of cloud was in the encampment and rested above the
Tabernacle, so too today the People of Israel feel safe because of your
presence here, and will not wander or escape while you are guard the people
dwelling in Zion.”
The
writer is director of the Rabbinical Court of the Israel Council of Progressive
Rabbis
My
own experience on the municipal front in Israel is that there is nothing like
concerted pressure from our friends in North American when it comes to forcing
city officials to respect the rights and needs of all religious streams.
In an entirely different context, the European Central Bank has
forced the Greek government to take unpalatable steps to bring about economic
reforms as the price for a bailout. Is it too much to hope that North American
Jewry will employ similar tactics when it comes to coercing Israel to live up
to the ideals upon which it was founded?
The writer is the rabbi of Har Adar and a senior
research fellow at the Van Leer Jerusalem Institute
In an era characterized by an endless
variety of modern and haredi Orthodox communities, do the wars against Reform
Jews meananything? Is it
possible that inciting statements are made in order to serve the battle between
Orthodox groups for internal needs of de-legitimization?
In this era of empowerment, is it not time for Orthodoxy to
forsake the expressions of weakness which it was characterized by up to 40 or
50 years ago, and stop responding out of unjustified fear?
The Orthodox activists' struggle against Reform Jews' right to be
recognized by the State is wrong or hypocritical, or both. It also contradicts
Israel's essence as the Jewish nation state and a democratic state, which must
make room for all factions, communities and streams.
"I've
learned that there are 50 shades of black," Anat Hoffman said, speaking of
her attempts to assert her religious rights with Jerusalem's ultra-Orthodox
religious establishment. "Most ultra-Orthodox can tolerate a group of
women praying once a month at the Kotel. If you can't, don't come between 7 and
8 in the morning 11 times a year."
Like
Hoffman, Rabbi Uri Ayalon, CEO of the pro-pluralism Hatnua Yerushalmit, said
the growing ultra-Orthodox population in Jerusalem is not forcing a liberal
retreat from the city. His organization bought space for 140 outdoor ads
depicting female activists, to prove there would not be a backlash from
ultra-Orthodox Jews for displaying pictures of women.
"Only
four were damaged," he said. "What's happening in Jerusalem is not
being done by the ultra-Orthodox, but by what we think they will say and do."
Hoffman,
who also leads the Reform Movement's Israel Religious Action Center, said she
wished Israel reflected the diversity of her GA audience.
"This
is how Israel should be - a supermarket. All forms of Judaism legal. All state
funded, or all not state funded. May the best rabbi win."
[Reform President Rabbi Richard] Jacobs is
presumably referring to the fact that only Orthodox marriages, divorces,
burials and conversions are officially recognized by the central government,
and outside a small handful of municipalities and local councils, only the
Orthodox streams receive public funding.
This is discrimination, but not
against non-Orthodox Jews. Rather, it's in favor of a bloated and corrupt
Orthodox establishment. Some may see this as semantic hairsplitting, but I
think there's a fundamental difference.
I don’t believe
that there can be a serious and responsible grappling with the challenge of
Jewish Peoplehood without confronting the reality that Israel stops this
plurality at its borders, let alone celebrating it.
From the fact
that no non-Orthodox rabbi can officiate at a legal wedding in Israel to the
arrest of Anat Hoffman for wearing a talit and reciting the Sh’ma at the Kotel
– how can we speak of affirming Jewish Peoplehood without strongly confronting Israel's partial function as an antidote to this goal.
Instead of
focusing on the centrality of Israel and the impulse of cheering everything
that Israel does, Jewish Peoplehood celebrates the plurality and dynamism of
Jewish life around the world with Israel as a major hub of Jewish cultural
creativity, a hub that is in constant interaction with Jews around the world.
Instead of a
model that displays actors on the stage vs. spectators, Jewish Peoplehood talks
about partnership, engagement and dialogue as organizing principles of
contemporary Jewish life.
The state does not
accept the Egged bus cooperative's decision to bar all advertisements
with pictures of people from its buses in Jerusalem, the government told the
High Court of Justice last week.
"The companies thought that by not publishing ads with men,
either, they solved the problem,"[Yerushalmim attorney] said. "The
state is telling them they haven't solved the problem. But now I want to
understand what the state is doing about it. Does it intend to revoke the
license, or impose sanctions to enforce its decision?"
"People
realize that my kitchen is Glatt kosher, only I don't have a certificate from
the rabbinate. Yet the fears that I would be harmed by the lack of a
certificate proved unfounded. I have kippa-wearing diners who tell me they come
because I display a kashrut certificate from conscience, and not that of the
rabbinate."
Rabbi David Stav, chair
of Tzohar: "Without a chief rabbinate, the rabbi believes the
Jewish People would “have been split into two to three nations,” because of the
issue of Jewish identity. “Nobody would have recognized the [other group’s]
Jewish identity.”
The upcoming end of current Chief
Rabbi Yona Metzger’s term provides “a real window of opportunity that is open
now, and will be closed for 10 years if we don’t take it today, and if it will
be closed for 10 years, I guess it will be closed forever.”
The prenuptial “Agreement for Mutual Respect” [which]
obligates, under Halacha and general Israeli law, a recalcitrant spouse to pay
additional support payments once the other spouse has initiated the divorce
process and efforts toward marital reconciliation (if so desired) have failed.
What needs to be prevented is yet another
“creative formula” that will leave the inequity undented. Gradually increasing
the numbers of conscripted ultra-orthodox youths is likely the best available
option, which could also be imposed at random. A net could be cast
unpredictably and whoever is caught in it must serve or face personal
consequences.
The deterrent value of possible punishment cannot be
underestimatedand might facilitate the conscription of greater
numbers of eligible haredim.
A consumer
boycott of the group's Shefa Shuk outlets by their target market, the
ultra-Orthodox community, since the start of 2008 led to more trouble for the
company.
Through no
fault of Vurembrand, the boycott was called against David Wiessman in reaction
to Dor Alon's acquisition of the 24/7 chain AM:PM which, as the name indicates,
operates on Shabbat.
The target
chosen from among Wiessman's operations was Shefa Shuk, where sales and profits
began plummeting. Only 10 of its original 40 outlets remained viable when, in
September 2011, they were renamed Zol B'Shefa. They currently include 17
stores.
And finally, the GPT suffers from JFNA’s own lack of clarity
about its purpose.
Is JFNA a trade
association that offers services to constituent federations? A Jewish
“government” or representative that lobbies in Washington and Jerusalem? A
professional advisory (or even decision making) body where federation dollars
are divvied up and shipped to projects and organizations?
This
program is designed for Israeli educators seeking to implement pluralistic
Jewish education in Israeli educational institutions and is organized in
conjunction with a M.A. degree in Jewish Education from the Melton Centre for
Jewish Education at the Hebrew University in Jerusalem.
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Connect to a Lync Meeting by phone with Lync Web App
When you join a Lync Meeting from Lync Web App, install the plug-in to hear the meeting audio from your computer’s speakers or from a headset attached to your computer. Use the mic on your computer or headset to talk to other participants.
If a participant is sharing their video, you can see it in the meeting window even if your computer doesn’t have a camera. If it does, and you want to show yourself to the other participants, point to the video icon and click Start My Video.
To be able to use your computer’s mic and speakers or a headset, you have to install the plug-in. If you choose not to, or if your system administrator has disabled using computer audio and video in meetings, you can connect to the meeting audio using your phone. The Join Meeting Audio dialog box displays after you join the meeting. Depending on the settings configured by your system administrator, any combination of the following options will display. Choose the option that works best for you.
Use this option
to do this
Using my computer
Connect to the meeting audio using your computer’s mic and speakers.
Have the meeting call me¹
Have the conference call you at the phone number you specify.
I will dial in to the meeting
Dial into the meeting from your phone using one of the numbers displayed.
¹ This feature is available only if the meeting organizer’s Lync account is enabled for Enterprise Voice. To learn more, contact your technical support personnel.
After you connect to the meeting audio and video from your computer, your credentials to access the Internet might need to be authenticated if your organization requires it. In the dialog box that displays, type your credentials and click OK.
Get meeting audio after joining a meeting
If you install the plug-in and join the meeting using your computer, but it doesn’t have an attached audio device, you won’t hear the meeting audio. However, you can view participant videos and all shared content. To get the meeting audio, do one of the following:
Connect either a headset or an external mic and speakers to your computer. Then, point to the phone icon and click Lync Call.
Point to the phone icon and click New Number. In the Call New Phone Number dialog box, type a phone number, and then click Call Me. The conference will call you at this number¹.
Dial the conference number listed in the meeting invitation from your phone and enter the conference ID when prompted.
¹ This feature is available only if the meeting organizer’s Lync account is enabled for Enterprise Voice. To learn more, contact your technical support personnel.
Note: The options available to you depend on what your technical support team has enabled for your account.
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As the agency’s chief prosecutor, Griffin can only work with the complaints that cross his desk. It was either luck or fate, then, that a series of new cases would give him the opportunity to help fundamentally reshape the rules that govern companies that increasingly rely on subcontractors, temporary workers, franchise employees and the like.
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Many of those complaints arose from McDonalds workers who say franchisees retaliated against them for protesting over wages. Griffin, in bringing their case, asserts that McDonalds headquarters has enough control over its franchisees’ operations to be equally responsible for their missteps.
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A key test of that theory begins Thursday, when McDonald’s lawyers will face off against Griffin’s in a New York City courtroom. From the workers’ perspective, their complaint targets a problem that’s gotten much worse in recent years: The company that calls the shots is not actually the one who signs their paycheck.
That case, and another decided last year called Browning Ferris Industries, has alarmed a host of industries that have come to rely on franchising and other arms-length relationships to shed responsibility for the people who do their work. Trade associations say they saw such attacks coming, and that is why they resisted Griffin’s appointment from the get-go; they said he would tilt the agency in favor of labor.
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“Have I fulfilled their expectations?” Griffin chuckles, wryly, upon being told of such predictions.
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From the angry language of industry leaders and conservative groups, the attempts to get the NLRB’s actions rolled back legislatively, and pronouncements by politicians — including Jeb Bush, who had a whole anti-NLRB plank in his now-defunct presidential campaign platform — of the need to rein in the “unaccountable” agency, it seems the answer is yes.
“If you’re a management person, you’re going to say Mr. Griffin’s term is one of the most dramatic activist terms of any general counsel in history,” said Michael Lotito, co-chair of the Workplace Policy Institute at the management-side law firm Littler Mendelson.
A dynamic duo
Griffin is actually one of two Obama appointees who, in their separate legal domains, have taken on the project of ensuring that bargaining rights and wage protections are upheld by the companies that ultimately govern the terms and conditions of their employment.
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The other is David Weil, a rumpled professor who had spent a career studying the enforcement of labor laws in an outsourced world when Obama plucked him from Boston University three years ago to serve as wage and hour administrator at the Department of Labor.
Weil’s confirmation hearing was nearly as rough as Griffin’s. The position had been empty for nearly a decade, with two nominations already having been withdrawn in the face of GOP objections. And Weil had just published the most powerful book of his career: “The Fissured Workplace,” a tour through the ways in which he argues industries remade themselves for maximum efficiency and minimum responsibility for workers.
Ultimately, in early 2014, he was voted through. With no time to waste, Weil set about remaking the Labor Department’s enforcement strategy to reflect his understanding of how businesses had changed.
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All too often, Weil says, they have misclassified employees as independent contractors to avoid paying benefits like minimum wage, overtime, unemployment insurance and workers compensation, or brought in temporary staffing agencies that can be swapped out as soon as they get too expensive. So with a beefed up inspection staff, the department has done extensive market research to target investigations in industries like construction and light manufacturing, where abuses are most common.
“We’re trying to understand the way the world is structured in order to maximize our impact,” explained Weil, in a January interview.
Weil’s diagnosis of the problem has had far-reaching influence within the administration — including upon Griffin. The general counsel cited Weil’s research in a brief in the Browning-Ferris case that laid out how the labor relations board should expand its definition of an employer to include not just a company that exerted direct control over workers, but also those who simply reserve the right to do so — reflecting the “economic reality” of their business practices. In its decision, the board adopted Griffin’s recommendation nearly in full.
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Their harmonious approaches have raised suspicions on Capitol Hill that Griffin and Weil are mounting a coordinated assault on businesses that depend on all forms of subcontracting, a push that has now surfaced at the Occupational Safety and Health Administration and Equal Employment Opportunity Commission. Republicans on the House Education and Workforce Committee demanded to see any correspondence between the two, which they said would be “inappropriate.” And indeed, some evidence of communication between the two agencies was produced, although the Department of Labor says that's entirely above board.
"Federal agencies can foster a more efficient and effective government by working together to learn best practices and to broaden understanding of topical developments in relevant legal issues," said a spokesman for the department.
Nevertheless, Griffin and Weil say they didn’t know each other well before going into public service, and have since only seen each other at the occasional event. And Griffin has been around in the labor movement long enough to understand the changing economic realities facing workers himself.
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Mike Fanning, who served as general counsel of the union before Griffin took over and calls him "one of the brightest, hardest working, and sweetest guys you’ll ever meet,” recalls a case before the NLRB that had been appealed all the way to the Supreme Court. In order to write a brief, they needed evidence from their members in large hospitals. Griffin, who grew up in Buffalo as the child of Catholic civil rights activists, was assigned to collect it.
"He was the kind of guy who would walk into any boiler room in America and sit down with the guys and say ‘I’m sorry I’m interrupting, I know it’s your lunchtime, but this is what I’ve got to do.’" — Former IUOE General Counsel Mike Fanning
“He basically got on an airplane for six weeks, and was in the basements of hospitals, interviewing engineers, putting together the history,” Fanning recalls. “He was the kind of guy who would walk into any boiler room in America and sit down with the guys and say ‘I’m sorry I’m interrupting, I know it’s your lunchtime, but this is what I’ve got to do.’ He found guys to testify, but they wouldn’t trust me, they would only trust Dick.”
Although never working directly for a union, Weil hasn’t lived his life in an ivory tower either. According to a 2014 Boston Globe profile, he dropped out of high school and spent a year doing manual labor in California before going to college. That kind of experience — along with extensive academic research — helped him understand something that’s been evident to the labor movement for a long time.
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“The reason it’s important and edifying for advocates who work with low-wage workers is that these people in the administration are starting to call attention to the problem,” says Cathy Ruckelshaus, general counsel for the liberal National Employment Law Project. "That makes a huge difference, because they know it in their bones."
Franchise fury
Weil and Griffin’s actions have prompted yelps of protest from a broad range of industries that rely on “fissuring,” as a broad range of contracted work has come to be known. But none has resisted as loudly as the franchise industry, through its trade group the International Franchise Association, which sees the action around joint employment as simply an indication that the administration is following organized labor’s agenda.
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“When you have an administration that is pro-union, and appoints people who are pro-union, and you have unions spending a tremendous amount of money, it provides a fertile environment for unions and employment-related causes to take on high visibility,” says Stuart Hershman, a longtime franchise lawyer who advises the IFA.
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The problem, Hershman says, is that franchisors don’t know what kinds of assistance they can provide to franchisees without becoming a joint employer. Franchisors are already spending more on lawyers to try to adapt to the new rules, he says, but they fear it won’t be enough. According to Ruckelshaus, of NELP, the number of cases being filed against companies as joint employers is rising as well.
In response, the association has built a grassroots lobbying network to try to push Congress to stop the Department of Labor and NLRB from pursuing franchisors as joint employers of their franchisees’ workers.The trade group has also recently advocated for laws adopted in a handful of states that formally state a franchisor can’t be held responsible for the actions of its employees — that doesn’t protect them from federal law enforcement, but it’s something.
As a result of the uproar, Griffin and Weil have engaged in an unusual amount of dialogue with trade groups, appearing at conferences and taking private meetings to explain their approach. Just a few weeks ago, Griffin flew down to San Antonio on a Saturday to answer questions at the IFA’s board meeting, in hopes of providing more clarity. People in attendance say they appreciated the gesture, but they were not put at ease.
"He said, 'I don’t understand why you guys are so upset about this,’” recalls IFA President Robert Cresanti, of Griffin’s presentation. “I think when I walked away from this thing, in my head the phrase that kept ringing was, 'this guy is really well intentioned, but we can’t afford to live in a world where intentions matter more than results.' And the result here is the destruction of the franchise industry. And it is slow, and it is not seismic, it’s just piece by piece by piece."
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Resistance, a key concept in learning our lessons of forgiveness, is rarely used in the Course. Yet only it can satisfactorily explain the common
paradox of sincerely attempting to learn and live the Course's principles, while, frustratingly, not doing just that. This paradox is explored: the
many forms of resistance and their basis in the fear of love; Freud's valuable insights; and the miracle–looking with Jesus without judgment at our
investment in maintaining the ego.
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'I jumped on his head myself!': Pregnant woman is jailed for organising gang beating of innocent man who was wrongly named as a paedophile on vigilante website
Rachel Ashworth, 28, led the vicious attack on vulnerable epileptic victim
Antonio Cressoti, 39, was punched, kicked and beaten in his own home
He had been falsely branded a paedophile on website 'Predator Watch'
Fraudulent website put details on and demanded money to remove them
Ashworth, of Colne, Lancashire, bragged about the assault on Facebook
Pregnant Ashworth admitted assault and was jailed for 16 months
Jailed: Mother-to-be Rachel Ashworth, 28, pictured, was sentenced to 16 months in prison
A mother-to-be led a savage assault on a man wrongly identified as a paedophile.
Rachel Ashworth and two accomplices burst into the home of Antonio Cressoti and beat him so badly he was left unconscious.
They kicked and punched him to the floor before smashing an ornament over his head.
Ashworth, 28, later boasted of the attack on Facebook, saying: ‘I even jumped on his head myself. Any paedophile deserves hanging.’
But Mr Cressoti, 39, had never committed a sex offence, Burnley Crown Court heard yesterday.
Ashworth, who is studying beauty therapy, had seen his name on Predators Watch, a Facebook site identifying suspected paedophiles.
She recruited two other people and went to Mr Cressoti’s home early on the morning of Sunday, June 16, and started banging on the door.
When Mr Cressoti opened it Ashworth, who he had known for around a month, shouted: ‘You are a paedophile b*****d’ before the assault began.
Kimberley Obrusik, prosecuting, told the court: ‘The victim must have become unconscious as the next thing he was aware of was hearing a car speeding away. He called the police and ambulance and was kept in hospital for 24 hours.’
Mr Cressoti suffered a broken rib and severe cuts and bruising. ‘The complainant has no antecedents for any sexual offences in any way,’ Miss Obrusik added.
‘He suffers from epilepsy and was vulnerable. Since the assault, there have been significant complications.’
After her arrest, Ashworth, from Colne in Lancashire, told detectives that Mr Cressoti was a paedophile and it was ‘all over the internet’.
On Facebook she had also written that she had ‘defo found the right one’ and bragged that her victim’s face would be a mess.
The court heard that Predators Watch had identified innocent people and then demanded money for these names to be removed. The site has since been closed down.
Ashworth, a first time offender who is 15 weeks pregnant with her first child, admitted causing actual bodily harm but refused to name her two accomplices.
Kristian Cavanagh, defending, said the defendant knew it was a serious offence. ‘She recognises it was a stupid thing to do, including the entry on Facebook,’ she added. ‘She took it off within an hour and has since left Facebook.’
Sentencing Ashworth to 16 months in prison, Judge Beverley Lunt said: ‘This was vigilantism, wrongly.
Sentencing at Burnley Crown Court, pictured, Judge Beverley Lunt told Ashworth: 'You identified this entirely innocent man and he's now seeking help with mental issues, suffers panic attacks'
'You identified this entirely innocent man and he’s now seeking help with mental issues, suffers panic attacks, his epilepsy has been made worse and he’s moved out of the area because he was afraid to stay where he was.’ He said the Facebook entries were ‘disgraceful’.
‘You are 15 weeks pregnant, but that cannot be used as a shield to stop an appropriate sentence being imposed.’
In May, Gary Cleary hanged himself in Leicestershire after he was confronted by members of the now defunct vigilante group Letzgohunting.
The group claimed to have posed as a 14-year-old girl in an online chatroom to make contact with the 29-year-old engineer, who had a girlfriend.
He was found dead in his garage four days later.
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Tech
Google Glass Not for Sale Until 2014
Google Glass won't be available for purchase until 2014, according to a new report.
Although Google began seeding early versions of Glass to developers in April, it kept the release date of the real product vague. Initial reports claimed Glass could be ready for the public as early as the end of 2013.
Now, it looks like that date has either been pushed back — or perhaps it was never real to begin with. Citing correspondence with a Google spokesperson, Computerworld states the "new" ship date for Glass will come in 2014.
For Glass fans, this means there will be no connected headgear under any Christmas trees this holiday season, as others claimed. It also means competitors like the Recon Jet and Telepathy will have chances to hit the market first — not to mention current players such as Vuzix.
However, it also means Google will have a few extra months to perfect the Glass user experience. We've been using Glass here at Mashablesince May, and although it shows much promise, there is still room for improvement. For example, it doesn't have a lock screen, and the wireless connection can be very unreliable with certain phones.
One thing that likely will not change substantially is the form factor. Sources at Google told Mashable at the company's I/O developer conference in May that the current design for the head-mounted display/computer is most likely final, but that alternative frames — some perhaps designed by Warby Parker — may be an option.
Do you think a 2014 release date will make much difference in Glass' success? Share your thoughts in the comments.
Google Glass Web Browser
The Web, Through Glass
With the XE7 software update, Google Glass gets its own web browser. It's surprisingly functional, giving you the option to view the website of any search result you get through Google.
Mashable Site
Mashable uses responsive design to adapt to a user's browser and device. It looks and works fine in Glass, although the gestures to navigate between columns (Hot, Rising and New) won't work. There's workaround, though.
Navigation
You can scroll through a site by swiping the touchpad on Glass. You can also navigate by tapping with two fingers and holding, then moving your head. It's actually very natural.
Zoom
You can zoom by swiping with two fingers -- useful for sites that aren't optimized for mobile.
Links
When content is clickable, the browser asks if you want to select the link.
YouTube Video
Videos on YouTube play just fine in the Glass browser. Some other sites (Vimeo and DailyMotion) do not.
Loading Screen
When you load a site, you can see the URL onscreen.
DailyMotion
Videos on some sites won't play on Glass.
No Input
You can click on form fields, but there's no way to fill them out (not even with voice).
Easy Access to Google
When you tap, the menu of options is split into individual commands. Google is first, providing easy access to the web browser.
Mashable
is a global, multi-platform media and entertainment company. Powered by its own proprietary technology, Mashable is the go-to source for tech, digital culture and entertainment content for its dedicated and influential audience around the globe.
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Q:
R - How to fill a matrix by columns
I am trying to fill the columns of a matrix with the subsets of an built-in data frame. The resulting matrix should have dimensions 16 by 11 and each subset is 16 integers long.
I have written the following for loop:
A <- unique(DNase$Run)
z <- matrix(data =NA, ncol=11, nrow =16)
for (i in A) {
z[,i] <- subset(DNase$density, DNase$Run==i)
}
and I obtain the following error:
Error in [<-(*tmp*, , i, value = c(0.017, 0.018, 0.121, 0.124, 0.206, :
no 'dimnames' attribute for array
Could anyone kindly explain where the confusion comes from?
Many thanks in advance!
A:
Cheap Solution
Since the DNase data.frame is already ordered by the Run factor, we can actually form the desired output matrix with a simple call to matrix():
matrix(DNase$density,16);
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] 0.017 0.045 0.070 0.011 0.035 0.086 0.094 0.054 0.032 0.052 0.047
## [2,] 0.018 0.050 0.068 0.016 0.035 0.103 0.092 0.054 0.043 0.094 0.057
## [3,] 0.121 0.137 0.173 0.118 0.132 0.191 0.182 0.152 0.142 0.164 0.159
## [4,] 0.124 0.123 0.165 0.108 0.135 0.189 0.182 0.148 0.155 0.166 0.155
## [5,] 0.206 0.225 0.277 0.200 0.224 0.272 0.282 0.226 0.239 0.259 0.246
## [6,] 0.215 0.207 0.248 0.206 0.220 0.277 0.273 0.222 0.242 0.256 0.252
## [7,] 0.377 0.401 0.434 0.364 0.385 0.440 0.444 0.392 0.420 0.439 0.427
## [8,] 0.374 0.383 0.426 0.360 0.390 0.426 0.439 0.383 0.395 0.439 0.411
## [9,] 0.614 0.672 0.703 0.620 0.658 0.686 0.686 0.658 0.624 0.690 0.704
## [10,] 0.609 0.681 0.689 0.640 0.647 0.676 0.668 0.644 0.705 0.701 0.684
## [11,] 1.019 1.116 1.067 0.979 1.060 1.062 1.052 1.043 1.046 1.042 0.994
## [12,] 1.001 1.078 1.077 0.973 1.031 1.072 1.035 1.002 1.026 1.075 0.980
## [13,] 1.334 1.554 1.629 1.424 1.425 1.424 1.409 1.466 1.398 1.340 1.421
## [14,] 1.364 1.526 1.479 1.399 1.409 1.459 1.392 1.381 1.405 1.406 1.385
## [15,] 1.730 1.932 2.003 1.740 1.750 1.768 1.759 1.743 1.693 1.699 1.715
## [16,] 1.710 1.914 1.884 1.732 1.738 1.806 1.739 1.724 1.729 1.708 1.721
This of course depends on the aforementioned ordering, which can be verified with a call to rle():
do.call(data.frame,rle(levels(DNase$Run)[DNase$Run]));
## lengths values
## 1 16 1
## 2 16 2
## 3 16 3
## 4 16 4
## 5 16 5
## 6 16 6
## 7 16 7
## 8 16 8
## 9 16 9
## 10 16 10
## 11 16 11
Robust Solution
If we don't want to depend on that ordering, we can use reshape() as follows, and we get a nice bonus of column names, if you want that:
reshape(cbind(DNase[c('Run','density')],id=ave(c(DNase$Run),DNase$Run,FUN=seq_along)),dir='w',timevar='Run')[-1];
## density.1 density.2 density.3 density.4 density.5 density.6 density.7 density.8 density.9 density.10 density.11
## 1 0.017 0.045 0.070 0.011 0.035 0.086 0.094 0.054 0.032 0.052 0.047
## 2 0.018 0.050 0.068 0.016 0.035 0.103 0.092 0.054 0.043 0.094 0.057
## 3 0.121 0.137 0.173 0.118 0.132 0.191 0.182 0.152 0.142 0.164 0.159
## 4 0.124 0.123 0.165 0.108 0.135 0.189 0.182 0.148 0.155 0.166 0.155
## 5 0.206 0.225 0.277 0.200 0.224 0.272 0.282 0.226 0.239 0.259 0.246
## 6 0.215 0.207 0.248 0.206 0.220 0.277 0.273 0.222 0.242 0.256 0.252
## 7 0.377 0.401 0.434 0.364 0.385 0.440 0.444 0.392 0.420 0.439 0.427
## 8 0.374 0.383 0.426 0.360 0.390 0.426 0.439 0.383 0.395 0.439 0.411
## 9 0.614 0.672 0.703 0.620 0.658 0.686 0.686 0.658 0.624 0.690 0.704
## 10 0.609 0.681 0.689 0.640 0.647 0.676 0.668 0.644 0.705 0.701 0.684
## 11 1.019 1.116 1.067 0.979 1.060 1.062 1.052 1.043 1.046 1.042 0.994
## 12 1.001 1.078 1.077 0.973 1.031 1.072 1.035 1.002 1.026 1.075 0.980
## 13 1.334 1.554 1.629 1.424 1.425 1.424 1.409 1.466 1.398 1.340 1.421
## 14 1.364 1.526 1.479 1.399 1.409 1.459 1.392 1.381 1.405 1.406 1.385
## 15 1.730 1.932 2.003 1.740 1.750 1.768 1.759 1.743 1.693 1.699 1.715
## 16 1.710 1.914 1.884 1.732 1.738 1.806 1.739 1.724 1.729 1.708 1.721
Note that technically the above object is a data.frame, but you can easily coerce to matrix with as.matrix().
Explanation of Your Error
The reason why your code is failing is as follows. First, notice that the DNase$Run vector is actually an ordered factor:
class(DNase$Run);
## [1] "ordered" "factor"
Your A variable will therefore also be an ordered factor, just with the unique values from DNase$Run.
Now, when you use a for-loop to iterate over a factor (ordered or otherwise), it uses the levels (character strings) as the iteration value (as opposed to the integer enumeration values that are stored internally). Demo:
for (i in factor(letters[1:5])) print(i);
## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"
Thus, your i loop variable is being assigned to the levels character strings of DNase$Run. And, since your z matrix has no dimnames, trying to index its columns with a character string is failing with the error message "no 'dimnames' attribute for array".
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Tsukuba FC
is a football (soccer) club based in Tsukuba, which is located in Ibaraki Prefecture in Japan. They play in the Kantō Soccer League, which is part of Japanese Regional Leagues.
History
Tied to University of Tsukuba, the club was initially founded in 1993 as a women's football club. Shortly after that, a men's version of the club was launched. In 2000s, even U-12 and U-15 squad were created and Tsukuba FC have the goal of reaching professional football in 2020.
In 2014, the club reached Kanto Soccer League, winning immediately 2nd Division and now trying to reach Japan Football League. Their plans revealed the will to build a stadium for 30,000 people in the near future.
Current squad
Updated to 15 October 2017.
League record
References
External links
Official Site
Official Facebook Page
Official Twitter Account
Category:Football clubs in Japan
Category:Sport in Ibaraki Prefecture
Category:Tsukuba, Ibaraki
Category:Association football clubs established in 1993
Category:1993 establishments in Japan
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Report: Romney’s campaign better than Obama’s at preventing foreign donations, but not perfect
A report from the conservative Government Accountability Institute (GAI) which found Barack Obama’s re-election campaign likely broke federal law by soliciting campaign contributions from overseas also took some issue with GOP candidate Mitt Romney’s campaign finance apparatus.
Unlike Obama’s campaign, Romney requires online credit card donors to input Card Verification Value (CVV) data consisting of “a three or four digit number generally imprinted on the back of the card” in order “to verify that the person executing the purchase physically possesses the card.”
“The donation page on Mitt Romney’s campaign website requires contributors to enter the CVV,” according to the GAI report. “Were the Romney campaign to turn off the CVV (current laws do not require it), the campaign would become more vulnerable.”
GAI reported that about 11.9 percent of Romney’s website traffic comes from “foreign sources,” something that may raise suspicions. The Romney campaign website does, however, require donors to specifically identify themselves as proper donors and it’s unclear if foreign traffic would have anything to do with actual donations.
GAI said the “full extent” of Romney’s campaign donation bundlers “is not known” because of his campaign’s lack of transparency with regard to that information. GAI noted bipartisan calls for Romney to release that information.
“During the 2012 campaign, the Romney team has received some criticism for its campaign fundraising as it relates to foreign connections,” GAI reported. “An email chain circling within the banking giant Credit Suisse soliciting donations for Mitt Romney began with U.S. citizens but was ultimately sent to foreign staffers, including those in the firm’s London office.”
“Some bankers claimed that they felt the need to make the contributions because the executive who sent the email was the one who determined their bonuses. Also, Romney has held private fundraising events overseas asking for funds from Americans living overseas. One such event was a dinner in London hosted by the British Bank Barclay’s and Chief Executive Bob Diamond, a U.S. citizen. Guests were told to bring a passport to prove their citizenship.”
“The Romney campaign has also been criticized for using bundlers, men and women who collect donations and ‘bundle’ them together for the campaign, who are registered foreign agents,” GAI added.
“Ignacio E. Sanchez, one of Romney’s bundlers, is a registered foreign agent for the United Arab Emirates and a presidential candidate for the Dominican Republic. Another registered foreign agent bundling for Romney is Tom Loeffler of Akin Gump, a former congressman turned lobbyist who has represented the government of Saudi Arabia and Hong Kong.”
In those bundling cases, it appears the Romney campaign followed all specific U.S. passport requirements and no evidence of any wrongdoing has been presented.
GAI also cautioned that a “Twitter account that appears to be from the Romney campaign tweets in Arabic, presumably to a foreign audience,” and that “Romney campaign’s Facebook page is available on Arab Facebook.” The Romney campaign says that the Twitter account GAI cites isn’t a campaign account.
Such arrangements, the group concluded, could encourage foreign nationals to engage with the campaign as fundraisers. Unlike the Obama campaign’s apparatus, though, no evidence of specific wrongdoing or potential illegal activity has been presented with regard to the Romney campaign.
Romney spokeswoman Andrea Saul told The Daily Caller that the campaign is “very familiar with the laws on this important topic and our campaign goes to great lengths to abide by them. It is ludicrous to claim otherwise, especially based on such thin, baseless allegations.”
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At each tasting guests will be invited to sample 8 varieties of wine from around the world, all while overlooking the ice. Constellation Wines will also be on hand to provide their expertise on the wines. When the tasting ends guests can take their seats and enjoy a great night of Cyclones hockey.
Tickets for each event are $32 each and include a Cyclones game ticket, wine tasting, a commemorative glass and Hors D’oeuvres (which look pretty fantastic). If you already have a ticket to the game you can upgrade to include the tasting event for $22.
The Tasting begins at 6:00 PM with the Cyclones game starting at 7:30. For more information you can visit the event web page or call 513.421.PUCK.
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Elizabeth Whitaker
Elizabeth Whitaker may refer to:
Elizabeth Whitaker (Wyandot)
Elizabeth Whitaker (author) and inventor
Elizabeth Whittaker, character in Poirot's Hallowe'en Party
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#: bootstrap_simple.php:65 bootstrap_simple.php:67
msgid "上一页"
msgstr "上一頁"
#: bootstrap_simple.php:78 bootstrap_simple.php:80
msgid "下一页"
msgstr "下一頁"
#: defaults.php:112
#, php-format
msgid "共 %d 条"
msgstr "共 %d 條"
#: defaults.php:201
msgid "前往"
msgstr "前往"
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msgid "页"
msgstr "頁"
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President Obama’s recent talk of an “all-of-the-above” approach to energy is no doubt partially motivated by election year politics. That said, there is an important kernel of truth underlying it: no one approach can solve our nation’s future energy needs. While some renewable energy advocates want the industry to be the White Knight, the reality is that meeting our energy needs will require fossil fuels and renewables not merely to co-exist, but to co-develop.
This co-existence is particularly critical for two sectors of the industry: solar and natural gas.
Why these two? Solar photovoltaic (PV) technology has fundamentally shifted energy production and consumption in the past ten years. For example, Germany recently set a record by producing 50 percent of the nation’s midday electricity needs from solar. Solar is also cheaper than ever before thanks to massive cost reductions. Solar has earned a place at the mainstream energy table.
It is also clear that the development of hydraulic fracturing (“fracking”) technology shares the spotlight with solar PV as the greatest revolution in energy in the last 50 years. In just a few short years, our domestic outlook has changed from one of resource shortage to one of abundance. Many unanswered questions remain around fracking’s impact on health and the environment, but the fact remains that just like solar, it has radically increased our energy options.
Many current debates seem to pose the question as to which technology will win. The reality is that it’s a false dilemma -- both must (and can) win if we are to be successful in our transition to a responsible clean energy future in which cheap solar, wind, and natural gas displace coal as our primary fossil fuel for electricity production.
Neither of the extreme scenarios makes sense. It won’t be a natural gas 'too cheap to meter' scenario that some appear ready to embrace, nor will it be the 'renewable-only future' that others are advocating. The truth is far more nuanced. Those who understand the industry are far better positioned to see past the short-term chaos of solar and the euphoria of natural gas to the long-term balance that will shape the energy industry’s future. And both technologies fit into a compelling vision of a future grid in which renewables generate a large portion of our energy supply, while natural gas provides grid and price stability.
The current politically charged discussion that pits natural gas against solar and vice versa benefits no one, and obscures a tremendous opportunity for collaboration. As someone with 13 years in the energy business (both natural gas and solar), I think it is time for these two sectors of the energy industry to set aside differences, and work to achieve what’s best for our economy, our people and our environment.
Here are three suggestions for getting to a constructive dialog:
Stop beating on each other. Personally, I remain an environmentalist and hope to see fracking carefully regulated as with all industrial impacts. Professionally, it is time for the renewable energy industry, including solar, to stop seeing their success in fracking’s demise. We have to begin to articulate a common vision for energy policy with a big enough tent to sell it politically. It is time that solar trade associations and lobbyists joined more closely with those of other players in the power industry.
Push regulators, ratepayers, politicians and others to be 'long-term greedy.' It is tempting to make investment decisions based on today’s short-sighted views on gas prices and solar power. As I set out below, those short-sighted views have some serious flaws, and due to the capital intensiveness of the power industry, it is hard to adjust policy quickly without disrupting markets. We must keep decision makers away from the temptation of a less balanced energy plan based on short-run cost advantages.
Create a comprehensive U.S. National Energy Policy. This list would be incomplete without the mythical holy grail of next steps: a unifying, environmentally responsible and economically plausible national energy policy. If we can make progress on the first two items, maybe we will have the beginnings of a consensus that not only has a reasonable plan for a national energy policy, but a political base broad enough to make it happen.
As mentioned, there is a lot of speculation about how the huge decline in estimates for long-run natural gas prices will spell the demise of solar and other renewables. The reality is that this is a world ideally suited for solar, for the following reasons:
Most importantly, solar can fill in for natural gas' greatest weakness: price volatility. Solar’s cost is known upfront, whereas a gas-fired generator is stuck with long-run natural gas prices as they go up and down throughout the 20- to 40-year life of the power plant. With solar, you know what the price is for 20-plus years into the future.
The installed price of solar has plummeted and continues to fall. Assuming a long-run natural gas price in the $5 to $7/Million Metric British Thermal Units (MMBTU) range, solar can be competitive with gas-fired wholesale electricity in the next two to five years.
Solar generation tends to peak when electricity demand peaks, given the correlation between hot sunny days and air conditioning use. In this way, solar is a good hedge to short-term spikes in the need for power that might otherwise cause volatility in the natural gas market if that power were supplied by gas-fired generators.
It’s true that the forecasted $2.00 drop in long-run natural gas prices could eat up almost all of the benefit of declining solar costs. But familiar flaws in our industry’s ability to estimate long-run gas prices and the uniquely complementary relationship of solar and gas are likely to yield a more balanced ending to this story.
Natural gas prices have always fluctuated wildly (in a range of $2.50 to $14 per MMBTU). Expectations that long-run prices will do anything different in the future are difficult to believe. But you don’t have to take my word for it. Long-term gas contracts (more than 3 years) are priced around $5/MMBTU, so even industry participants anticipate the likelihood of doubling today’s spot prices.
Just as liquefied natural gas-importing terminals were meant to provide an equilibrium long-run price of gas around $6 to $7/MMBTU in the late 1990s and early 2000s (something that did not occur), today's projections look similar. The dirt-cheap price of producing highly subsidized, massively overbuilt shale gas is simply escalated at inflation and offered up as a projection for the long-run price of natural gas. Having heard this story before, I am incredulous, and here’s why:
We know that gas supply is plentiful right now and that there's a lot more down there than we thought. But we don't really know how much of it there is at each price point of extraction or what this new resource looks like in terms of long-term production.
Short-run demand for natural gas can be volatile, while supply can be hard to adjust. A hot summer day with some pipeline constraints or greater-than-expected seasonal demand and we could be looking at a nice, frothy gas market again.
Demand for natural gas tracks the economic cycle. We are still in a period of low capacity in U.S. manufacturing, power generation and almost all other uses of North American natural gas. The hype around cheap, plentiful gas and the push to kill coal are likely to result in significant substitution, changes in the fundamentals of natural gas demand and a much stronger recovery in demand than is currently priced into today's forward curve. This could result in much higher long-run prices than most are predicting.
None of this is to question the fundamental shift in our energy options that fracking has introduced, but a radical increase in the total long-run supply of gas may not alter the problematic elements of short-run gas supply and gas demand. Moreover, structural changes in response to this increase in the long-run supply of gas may fundamentally change demand for gas, leaving the long-run impact of “fracking” on gas prices very uncertain.
So the likely outcome of the above analysis is a world with long-run gas prices of $5 to $7/MMBTU and the equivalent price for solar. I can find nothing to complain about in that scenario -- with solar, wind, and natural gas at all-time-low rates, it means we can deliver a stable, robust, and cleaner generating fleet with almost no long-term cost impact over the current regime. It’s time to embrace that future and work together to make it a reality.
***
Sheldon Kimber is the Chief Operating Officer at Recurrent Energy and leads all North American project development, expansion, and origination activities for the firm. In this role, Sheldon drove the expansion of the company’s development strategy from a small-scale rooftop developer with a less than 100-megawatt pipeline to a leading utility-scale PV developer with hundreds of megawatts of contracted projects and a pipeline of more than two gigawatts. Formerly Recurrent Energy’s vice president of finance, Sheldon was instrumental in developing and negotiating the company’s first projects, fundraising efforts, and joint venture agreements.
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11th Air Defense Artillery Brigade (United States)
The 11th Air Defense Artillery Brigade is an air defense artillery brigade of the United States Army stationed at Fort Bliss.
Organization
11th Air Defense Artillery Brigade (11th ADAB):
Headquarters and Headquarters Battery (HHB)
1st Battalion, 43rd Air Defense Artillery Regiment (1-43rd ADAR) (Patriot)
2nd Battalion, 43rd Air Defense Artillery Regiment (2-43rd ADAR) (Patriot)
3rd Battalion, 43rd Air Defense Artillery Regiment (3-43rd ADAR) (Patriot)
5th Battalion, 52nd Air Defense Artillery Regiment (5-52nd ADAR) (Patriot)
A Battery, 2nd Air Defense Artillery Regiment (A-2nd ADAR) Terminal High Altitude Area Defense (THAAD)
B Battery, 2nd Air Defense Artillery Regiment (B-2nd ADAR) Terminal High Altitude Area Defense (THAAD)
E Battery, 3rd Air Defense Artillery Regiment (E-3rd ADAR) Terminal High Altitude Area Defense (THAAD)
A Battery, 4th Air Defense Artillery Regiment (A-4th ADAR) Terminal High Altitude Area Defense (THAAD)
A Battery, 5th Air Defense Artillery Regiment (A-5th ADAR) Terminal High Altitude Area Defense (THAAD, forward deployed to Guam)
Lineage
The unit was initially constituted 25 January 1907 in the Regular Army as the 133rd Company, Coast Artillery Corps. Organized 1 August 1907 at Fort Terry, New York. Redesignated 3 July 1916 as the 3d Company, Fort Terry (New York). Redesignated 31 August 1917 as the 13th Company, Coast Defenses of Long Island Sound. Redesignated in December 1917 as Battery A, 56th Artillery (Coast Artillery Corps). Demobilized 31 July 1921 at Camp Jackson, South Carolina.
Reconstituted 1 June 1922 in the Regular Army; concurrently consolidated with the 4th Company, Coast Defenses of Long Island Sound (organized in June 1917 as the 7th Company, Fort H.G. Wright (New York); redesignated 31 August 1917 as the unit was redesignated as the 133rd Company, Coast Artillery Corps.
Redesignated 1 July 1924 as Headquarters Battery, 11th Coast Artillery (Headquarters, 11th Coast Artillery, concurrently constituted and activated at Fort H.G. Wright, New York). Inactivated 7 April 1944 at Fort Leonard Wood, Missouri. Disbanded 14 June 1944.
Headquarters and Headquarters Battery, 11th Coast Artillery, reconstituted 28 June 1950 in the Regular Army; concurrently consolidated with Headquarters and Headquarters Battery, 11th Antiaircraft Artillery Group (active), and Antiaircraft Artillery Group. Inactivated 27 April 1953 at Fort Tilden, New York. Activated 15 January 1955 at Camp Stewart, Georgia. Redesignated 20 March 1958 as Headquarters and Headquarters Battery, 11th Artillery Group. Inactivated 26 August 1960 at Rehoboth Defense Area, Massachusetts.
Activated 1 May 1967 at Fort Carson, Colorado. Headquarters and Headquarters Battery 11th Air Defense Artillery was inactivated 26 May 1967 at Fort Carson, Colorado. Activated 1 September 1971 at Fort Bliss, Texas. Redesignated 15 March 1972 as Headquarters and Headquarters Battery, 11th Air Defense Artillery Group. Reorganized and redesignated 16 December 1980 as Headquarters and Headquarters Battery, 11th Air Defense Artillery Brigade.
Headquarters and Headquarters Battery, 11th Antiaircraft Artillery Group was constituted 19 December 1942 in the Army of the United States as Headquarters and Headquarters Battery, 11th Antiaircraft Automatic Weapons Group. Activated 20 January 1943 at Camp Davis, North Carolina. Redesignated 26 May 1943 as Headquarters and Headquarters Battery, 11th Antiaircraft Artillery Group. Inactivated 6 October 1945 in Germany. Allotted 9 December 1948 to the Regular Army. Activated 15 January 1949 at Fort Bliss, Texas.
Recent history
The brigade served in the Persian Gulf War, during which the brigade recorded the first intercept of a ballistic missile in combat. Prior to its deployment it consisted of:
1st Battalion, 2nd ADA (Chaparral)
2nd Battalion, 7th ADA (Patriot)
3rd Battalion, 43rd ADA (Patriot)
2nd Battalion, 1st ADA Task Force with 2-1 ADA (Hawk) and 2-43 ADA (Patriot)
The 1st Battalion, 2nd ADA was left behind at Fort Stewart when the brigade deployed. Battery D, 1st Battalion, 7th ADA (Patriot) was attached from 94th ADA Brigade, 32nd AADCOM in Europe, and 2nd Battalion, 43rd ADA was attached from 10th ADA Brigade, 32nd AADCOM.
Writer Thomas D. Dinackus notes that every battalion that was part of the brigade received the Valorous Unit Award, despite three of the battalions (those not equipped with Patriot) not having fired a single shot in anger.
References
External links
The Institute of Heraldry: 11th Air Defense Artillery Brigade
011
Category:1907 establishments in New York (state)
Category:Military units and formations established in 1907
|
{
"pile_set_name": "Wikipedia (en)"
}
|
Q:
Under my constructor for an array, I have a toString method that prints out the contents. But it's telling me it isn't resolved to a variable
So my constructor creates an array, and I want my toString method to display the contents. However, I'm getting an error telling me that table[i] can not be resolved to a variable, even though it was created in the constructor. Please help!
public int size = 38;
public int first = 0;
public int last = 2;
public int count = 1;
public Table()
{
int[] table = new int[size];
table[0] = first;
table [size-1] = last;
for(int i = 1; i < size-1; i++){
if(count == first | count == last)
count++;
table[i] = count;
count++;
}
}
public String toString(){
String string = "Wheel: 0";
for(int i = 1; i < size; i++)
string = string + "-" + table[i] ; //table[i] CAN NOT BE RESOLVED TO A VARIABLE
return string;
}
A:
Your table is defined locally in your constructor.
int[] table = new int[size];
You have to declare it outside the constructor:
int[] table;
public Table()
{
table = new int[size];
...
|
{
"pile_set_name": "StackExchange"
}
|
<html>
<head>
<meta charset="utf-8" />
<title>Test #1 for bug #1043537</title>
<style>
div {
width: 200px;
height: 200px;
background: yellow;
}
div:before {
content: '';
background: hotpink;
display: block;
width: 40px;
height: 40px;
overflow: hidden;
resize: both;
}
</style>
</head>
<body>
<div></div>
</body>
</html>
|
{
"pile_set_name": "Github"
}
|
[On the problem of industrial accidents under drug influence (author's transl)].
Industrial accidents resulting from technical defects have decreased in the last eights because of improved safety precautions. In contrast, accidents resulting from "human error" are increasing steadily. Toxicological urine analysis for drugs--directed mainly at soporifics, sedatives, tranquilizers, and pain-relievers--on 84 patients involved in industrial accidents yielded the following results. 1. Drugs were identified in 44 patients (= 52%). 2. In 13.4 patients, more than one drug was identified (= 16%). 3. Only five of the 44 patients admitted on being questioned that they had taken drugs (= 10%). On the other hand, in a control group of 47 persons who and not suffered any accident, drugs were detected in 19 cases (= 40%). The results show that the physician will have to take into account that healthy and efficient persons, too, are very often likely to practise drug abuse. It must be considered probable that this helps to promote accidents. Medical prescription, especially of neuroleptics and psychotropics, as well as of sedatives, should be practiced more.
|
{
"pile_set_name": "PubMed Abstracts"
}
|
Neighbor wants me to sleep with his wife
Get over 50 fonts, text formatting, optional watermarks and NO adverts! Get your free account now!
Watching a movie with my husband and our neighbors - during the movie my neighbor keeps showing me nudes of his wife
Check out all our blank memes
|
{
"pile_set_name": "OpenWebText2"
}
|
Life Is Fragile: Make Time for What Matters and Let Go of What Doesn’t
“Life is precious as it is. All the elements for your happiness are already here. There is no need to run, strive, search, or struggle. Just be.” ~Thich Nhat Hanh
I lost one of my very best friends when we were both just twenty-nine years old. In the time since, I’ve thought about him on most days.
Thinking about him sometimes makes me well up and feel sad. Sometimes it makes me think about the hole him not being here has left. Selfishly, perhaps, I think about how much I miss him.
Sometimes I laugh aloud at the thought of a funny moment we shared, or a personal trait he had. I often draw inspiration from the zest for life he had, his drive to succeed.
Although he died at a relatively early age we had been firm friends since we were twelve when, realizing he lived on my street, I walked up to him in class and introduced myself and we started to walk to and from school together.
That was the beginning of a wonderful friendship. As kids, we spent the evenings hitting tennis balls until it got dark or listening to music and talking about girls. As we grew, we shared lots of firsts together—first holidays away without parents, first serious girlfriends, first homes, first relationship heartbreaks. In his case, him becoming a father.
We celebrated, we laughed, we cried, we got into mischief, we supported each other. We did all the things really close friends do for each other over the course of many years.
Brad had a zest for life. Always the first up on the dance floor at a party. Always ready with a funny anecdote or story. He had a genuineness that most people warmed to. I was, and am, lucky to call him my friend.
Sometimes I think how unfair it is that was cut short so early, even though I am aware that cancer is no respecter of age or what type of person you are.
Most often, though, thinking about him now brings a clarity and peace to my thoughts. Problems I had been focused on melt away. I gain a fresh perspective because I become acutely aware of how precious this life is.
The Fragility of Life
We all lose people we’re close to if we stick around long enough ourselves. This is an inconvenient truth of life. There is a fragility to it.
There are no guarantees. No order or set amount of time our loved ones will be there for us. No promise that how we feel, and what we can do today, will be how we feel and what we can do tomorrow. No promise that the health and relative wealth we enjoy today will be with us in the morning.
Facing up to the fragility of life can be scary. It can also be empowering. It can help us hold onto a perspective that supports us living a life rich with positive experiences. It can leave us with a conviction to make the most of our days.
Applying Focus to Our Days
One of the great ironies of our lives is that so many of us choose to stay busy, but then we complain that we don’t have time for our passion projects and goals. We put things off until tomorrow, as if we have unlimited time to make our dreams happen.
The book we promised to write.
The new skill we put off another year to learn.
The dream trip we promised ourselves and our family for the last five years.
We all do it, too much of the time.
When we view life through the lens of having a finite amount of time, we are more likely to make better use of that time.
Gratitude for the Way Things Are
While striving for new goals is to be admired, we also need to learn to enjoy the present moment. To make time to enjoy our successes, small and big, and celebrate the way things are.
Traveling has become a passion for me, mostly because I married someone that has the travel bug who has opened up the world to me, literally. I get to travel more than most—it’s a priority in our lives. Dream trips have become a reality for me. However, I don’t take this for granted.
Every time I travel and visit somewhere new for the very first time, I’ll take a moment to pause and reflect on how lucky I am to experience this new adventure. I pause to think about the friend I lost, and others that are not so lucky. I try to embrace this feeling of gratitude fully. It helps me experience this new place on a deeper level.
I try to hold onto this feeling and let it spill over into other areas of my life. When I gain some perspective, I realize that many of my problems are fairly minor.
My train is running late, and when it turns up it’s packed.
The coffee machine has broken, and I can’t get my regular latte from my favorite café on the commute into work.
What do all of the above ‘problems’ have in common? They are, of course, first world problems. There are so many people in the world worse off than I am—people that endure unimaginable hardships on a daily basis, just trying to live their lives.
I try to remember this so I don’t overlook the precious gifts I already have in my life, and so I don’t complain about “how tough I have it,” when really, I’m only dealing with minor annoyances and inconveniences.
I’m not always successful of course. I still get in my own way more often than I should, as we all do from time to time. I’m a work in progress, but practicing gratitude has helped me keep perspective.
Learning to Let Things Go
Anger, hate, regret, envy, disappointment. All can become toxic emotions that eat us up.
None of these emotions are really useful, or get us very far, yet we hold onto them, as if they are fuel.
In my own case, I can, and do, take inspiration from others, but I am aware that if I start to compare too much, envy can follow. I have to watch this.
If I even come close to feeling envy for someone else that I perceive to have more success than me, or be somewhere I want to be, I try to remind myself that I don’t know how these people actually feel.
I don’t know what their story is or how much they have had to sacrifice. I don’t know if they are truly happy, or they’re just masking deep insecurity or self-doubt with lies and a smile. This helps me let go of the desire to compare and simply commit to my own journey.
The same is true of regret. It’s an emotion I have done my best to distance myself from. I’m human and I make mistakes, mistakes I don’t want to repeat. I’ve hurt people close to me that I never want to hurt again with foolish acts or careless words at times. But beating myself up again and again for those mistakes is futile. It’s a waste of the precious life I am lucky to live.
I’m someone that believes in living life. I have lessons to learn, and can use those to fuel me trying to be a better version of me.
Maybe this outlook and approach to life is all part of the aging and maturing process. Or maybe it’s because I’ve gained a rounder perspective of who I am and how lucky I am, and learned to let go of these emotions. Seeing them for what they are, a waste of my focus.
And to be totally transparent, I am very much still a work in progress. I’m far from Zen-like calm all of the time. I still get frustrated at things I shouldn’t. I can still overreact to situations at times. I can still carry a grudge more than I would like to. I still feel the bitter feeling of disappointment in others at times, even though I know this is more about my own expectations than them. I’m getting better at letting things go but I still have a way to go.
When we truly embrace the fact that our lives are precious, we can choose to leave the negativity behind. We can choose to let go of the things that don’t matter so much, on closer inspection.
Making Time for Those That Matter Most
A finite amount of time in this world means we have to prioritize. We have to say no to some things so we can say yes to those things that matter most to us.
This means ensuring there is quality space in our days for our families, our friends, and ourselves. I’m not talking about five minutes snatched here and there while staring at a screen; I’m talking about quality time where we are fully present with those around us and our surroundings.
In the case of time for ourselves, quality time checking in with ourselves can involve a long walk, some mediation, any other act of self-care.
Fleeting Moments in Time
Facing up to the fact that we all have a temporary place in this world should be reason enough to apply a degree of clarity and purpose to our days.
We need to make time for the people that matter most to us.
We need to make time for ourselves.
We need to make time to dream out loud.
It’s wonderful and admirable to work hard, but we need to ensure we’re making ample time to celebrate our successes and enjoy our journeys.
These are fleeting and precious moments in time. Let’s make the most of them.
Note: This article is dedicated to Brad, always the first one up on the dance floor, consistently the greatest ally you could wish to have. I miss you, my friend.
|
{
"pile_set_name": "Pile-CC"
}
|
Introduction
============
Detection and adequate response to nonself is essential for survival and development in all multicellular organisms. An important part of the innate immune detection in plants and animal lineages is ensured by a class of signal transducing proteins known as NB-LRR proteins in plants and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) in animals ([@evu251-B61]).
Plant NB-LRR proteins sense the presence of fungal, oomycete, nematode, bacterial, or viral pathogens and trigger an immune response in the form of a localized cell death reaction termed the hypersensitive response ([@evu251-B45]; [@evu251-B42]). NB-LRR proteins represent the resistance proteins involved in effector-triggered immunity as they sense strain-specific pathogen effectors or the modification of self, induced by these effectors. Plant genomes encode large repertoires of NB-LRR proteins with up to several hundred members. NB-LRR genes are typically highly polymorphic between individuals and subject to positive diversifying selection resulting from the host-pathogen arms race.
Animal NLRs, in turn, are activated by relatively invariant MAMPs (microbe-associated molecular patterns) and at least in mammals, the number of NLRs is more limited than in plant genomes ([@evu251-B49]; [@evu251-B82]). Animal NLRs and plant NB-LRR receptors are collectively designated NLRs and are members of the family of STAND proteins (signal-transducing ATPase with numerous domains), ([@evu251-B58]; [@evu251-B23]). These proteins typically comprise a central nucleotide binding and oligomerization domain (NOD) linked to an N-terminal effector domain and a C-terminal domain composed of superstructure-forming repeats such as LRR, WD, HEAT, ANK, or TPR motifs. One can distinguish two main classes of NOD domains: The NACHT (named after the NAIP, CIITA, HET-E and TP-1 proteins) and the NB-ARC domain. In general, plant NB-LRR proteins display an NB-ARC NOD domain whereas animal NLRs display a NACHT domain, although many instances of NB-ARC STAND proteins are described also in animal lineages. In most cases, the C-terminal domain of plant and animal NLRs corresponds to a LRR domain, but other types of repeat domains have been reported for instance in fish and marine invertebrates such as *Hydra* and the coral *Acropora digitifera* ([@evu251-B95]; [@evu251-B56]; [@evu251-B37]). The N-terminal effector domains are variable and either correspond to coiled-coil or Toll/interleukin-1 receptor (TIR) domains in plants ([@evu251-B42]), whereas CARD, BIR, PYD, death domain (DD), and DED are found in animals ([@evu251-B64]). In addition to these domains, a variety of other N-terminal domains, sometimes restricted to a given phylum, has been reported ([@evu251-B37]; [@evu251-B107]). In spite of the remarkable overall resemblance between these immune receptors in plant and animal lineages, it is unclear if this similarity is the result of evolutionary conservation ([@evu251-B5]; [@evu251-B61]; [@evu251-B106]). It has been proposed that build-up of NLRs is the result of convergent evolution by association of a limited set of preexisting domains such as NOD and LRR domains. Remarkably, NLRs appear not only to be involved in the immune response to pathogenic nonself, but an emerging trend reveals that these receptors may also control other forms of biotic interactions, for instance between animal hosts and their symbiotic microbiome ([@evu251-B21]).
With an estimated 5.1 million species, the fungal kingdom represents a major eukaryotic lineage and a sister group of the holozoa ([@evu251-B8]; [@evu251-B38]). Because of their overall organization, most cells in fungal organisms are in direct contact with their biotic environment. In addition to a variety of pathogenic and symbiotic interactions, fungi are also exposed to diverse adverse biotic interactions as hosts of a variety of pathogens and parasites such as mycoviruses, mycophagic bacteria, mycoparasitic fungi, and grazing nematodes ([@evu251-B59]; [@evu251-B74]; [@evu251-B9]; [@evu251-B26]; [@evu251-B81]). In the recent years, the awareness for the existence and importance of fungal nonself recognition and defense systems is gradually increasing. Based on the common central role for STAND proteins as intracellular innate immune receptors in plant and animals, it is not unreasonable to suppose that STAND proteins may play similar roles in fungi. And indeed, there is evidence for the involvement of STAND proteins in the detection of nonself and the control of programmed cell death in fungi, thus stressing the analogy between animal and plant NLRs. The HET-E protein of *Podospora anserina*, one of the founding members defining the NACHT domain, is involved in a fungal nonself recognition and programmed cell death process termed heterokaryon incompatibility ([@evu251-B83]; [@evu251-B53]). Incompatibility is triggered when genetically distinct individuals belonging to the same fungal species undergo cell fusion and corresponds to a pleiotropic cellular response culminating in the programmed cell death of the fusion cell ([@evu251-B76]; [@evu251-B7]). HET-E has a tripartite domain organization typical of STAND proteins, with a central NACHT domain, a C-terminal WD40 repeat domain and an N-terminal HET domain. The HET domain is found in different proteins involved in fungal incompatibility and corresponds to a death effector domain ([@evu251-B91]; [@evu251-B69]). HET-E is part of a larger gene family comprising ten members, termed NWD genes. Five of these proteins also comprise an N-terminal HET domain and two of those correspond to genetically identified incompatibility genes (HET-D and HET-R) ([@evu251-B68], [@evu251-B71]; [@evu251-B17]). The five other members display different N-terminal domains. The WD repeat regions of the members of the gene family are hypervariable. The repeats show a high level of internal repeat conservation, and are undergoing concerted evolution, meaning that repeat shuffling and exchanges occur both within and between members of the gene family ([@evu251-B71]; [@evu251-B18]). In addition, the repeat region is subjected to positive diversifying selection operating specifically on four amino acid positions of each individual repeat, which map to the protein--protein interaction surface of the WD-repeat β-propeller structure. Another member of the gene family, termed NWD2, shows an N-terminal domain homologous to the prion-forming domain of the HET-s prion protein of *P.anserina*. It is proposed that NWD2 acts as an activator of the HET-S pore forming toxin by triggering transconformation of its prion-forming domain and subsequent activity of the HeLo toxicity domain ([@evu251-B34]; [@evu251-B24]; [@evu251-B84]; [@evu251-B88]). This mode of signal transduction between a STAND protein and an trans-acting effector domain was proposed to be widespread in fungi and in addition to the \[Het-s\] prion-forming motif, two additional prion-like motifs (termed σ and PP) have been described ([@evu251-B24]). These motifs were found as N-terminal domains of STAND proteins of various types such as NACHT-WD, NACHT-ANK, or NB-ARC-TPR proteins. It was recently shown that the \[Het-s\] prion domain, and the N-terminal prion motif of NWD2 can functionally replace the PYD region in NLRP3-mediated CARD activation ([@evu251-B14]). Involvement of STAND proteins in incompatibility is not restricted to *P. anserina*, as the *vic 2* and *vic 4* loci of the chestnut blight fungus *Cryphonectria parasitica* were found to encode STAND proteins ([@evu251-B20]). Although fungal STANDs have been initially identified in the context of heterokaryon incompatibility (conspecific nonself recognition), it appears that the role of fungal STAND proteins is not limited to heterokaryon incompatibility, as the number of STAND-encoding genes greatly exceed the number of incompatibility genes. There are several reports indicating that STAND proteins are polymorphic and rapidly evolving and subject to extensive expansion in paralogous gene families in a variety of fungal species ([@evu251-B30]; [@evu251-B62]; [@evu251-B12]; [@evu251-B54]; [@evu251-B108]; [@evu251-B41]; [@evu251-B101]). In *Tuber melanosporum*, an expanded *nank* (NACHT ANK) family is, in addition, characterized by a remarkable diversification mechanism based on alternative splicing of multiple codon-sized microexons ([@evu251-B41]).
Based on the similarity between fungal STAND proteins and plant and animal NLRs and their involvement in nonself recognition and programmed cell death, we have proposed that STAND protein may also correspond to general nonself receptors in fungi ([@evu251-B70]). This proposed function could account for their high level of polymorphism and rapid diversification, and their expansion in certain species critically depends on interorganismal interactions. Although the genomics of NLRs in plant and animal species and lineages has been the subject of many studies, the overall distribution and organization of NLR-related genes in the fungal phylum has not been investigated systematically to date. The fungal phylum offers the advantage of an extensive genomic coverage with several hundred completed genomes currently available ([@evu251-B35]). Herein, we have analyzed 198 complete fungal genomes (corresponding to 164 different species) for the presence of NLR related proteins. We report on the NLR domain architecture, variability and repertoire size in these 164 fungal species. We find evidence of extensive variation of NLR copy numbers both within and between species. Several NLR domain architectures appear presently restricted to the fungal phylum, whereas others also exist in animal or plant lineages. NLRs appear restricted to filamentous species and are missing from yeast genomes, suggesting that presence of NLRs is associated with multicellularity. Our data suggest an extensive modularity of domain associations, with recurring inventions of domain architectures. Finally, a proportion of the C-terminal domains of NLRs show strong internal conservation, as described for the rapidly evolving HNWD family of *P.anserina*. We find evidence for positive diversifying selection acting on C-terminal domains of the TPR and ANK type, as previously reported for the WD repeats. This overall picture of NLR protein repertoire in fungal genomes now highlights similarities and differences between nonself recognition strategies in different eukaryotic lineages and sheds new light on the evolutionary history of this type of receptors.
Materials and Methods
=====================
Identification
--------------
IR and functionally validated (FV) queries were obtained by extraction of NACHT and NB-ARC domains from the full-length sequences according to PfamA PF05729.7 and PF00931.17 profile matches ([@evu251-B31]). PSI-BLAST searches ([@evu251-B2]) with three iterations and an *E* value cut-off of 10^−5^ were carried independently for each query sequence on the NCBI "nr" database (June 27, 2013), and then combined. The candidate set was pruned from sequences with multiple disjoint matches to the queries and from very short sequences (below 100 amino acids). Then it was limited to sequences from complete or draft whole-genome sequencing and resequencing projects, according to Genome OnLine Database (\[[@evu251-B66]\], as of September 18, 2013), for which at least 2,000 sequences were available in the nr database. Intrastrain identical copies of sequences were removed, whereas interstrain identical sequences were kept. Boundaries of the NB domain were determined as the longest stretch of matches from all NACHT and NB-ARC queries in the PSI-BLAST search. Proportional Venn diagrams were generated using BioVenn ([@evu251-B40]).
Noncanonical P-loop variants were detected by inspecting single-residue changes at the four conserved positions of the motif in multiple sequence alignments of NB domains generated by Clustal Omega 1.1.0 ([@evu251-B89]), with two iterations separately for nonredundant sets of 4596 NACHT and 1174 NB-ARC STANDs found in the entire nr database (not limited to whole-genome projects).
Annotation
----------
In-house signatures were generated using HMMER 3.0 ([@evu251-B28]) for the HET-s, PP, and σ prion-forming domains, and the NAD1, Goodbye, HeLo-like, sesA, and sesB domains. Representative sequences of prion-forming domains were aligned using several tools: ClustalW 2.1 ([@evu251-B57]), ClustalOmega 1.2.0 ([@evu251-B89]), Mafft 7.029b ([@evu251-B51]), and Muscle 3.8.31 ([@evu251-B29]). The best alignments in terms of the normalized Median Distance (norMD, \[[@evu251-B99]\]) were used for the HMM training with default parameters ([@evu251-B27]). A single representative sequence for each nonprionic domain was submitted to the HHsenser web tool (PSI-BLAST parameters: *E* value cut-off of 10^−3^, coverage of hits at least 50% \[[@evu251-B94]\]) to build a data set including at least 500 sequences in the "permissive" alignment. "Strict" alignments were retrieved and used in iterative HMM training. After each round of the training, sequences with the score below 25.0 or the score/bias ratio below ten were excluded from the alignments; the procedure was repeated until convergence. Finally, the in-house HMMs were included in a PfamA-style repository with their sequences and domain thresholds set to 25.0.
STAND sequences were scanned using PfamA and in-house signatures. Particular annotation was attributed to a given domain if the HMM profile match was entirely contained within domain boundaries extended by a 20 residue-wide envelope. In the case of overlapping annotations from the same PfamA clan, the hit with the lower *E* value was chosen, except for the P-loop NTP-ase clan (CL0023), where NACHT or NB-ARC annotations were always preferred (if above the PfamA threshold). Annotations from repeat-containing clans: Ankyrin (CL0465), Beta propeller (CL0186), and TPR (CL0020, includes HEAT repeats) were merged to three main categories: ANK, WD40, and TPR, respectively. Highly overlapping N-terminal annotations, as well as three prion-forming domain annotations were merged (see Results). Conflicting annotations from PfamA HeLo and in-house HeLo-like signatures were resolved in the favor of the former. Numerical suffixes of signature names were truncated and sequential occurrences of identical annotations were squeezed. Domain associations were visualized using the graphviz package ([@evu251-B32]).
Distribution of domain architectures was quantified by means of paralog and ortholog hits. Ortholog index counted number of species (distinguished by binomial name) in which a given architecture was found. Paralog index summed the number of sequences with a given architecture in all species (the average number was added if several strains were sequenced for the particular species).
Phylogeny
---------
All phylogenetic trees were calculated through the maximum likelihood estimation based on alignments of NB or N-terminal domains extracted from nonidentical sequences. In each case, the best alignment was selected according to the norMD score out of alignments generated by the same MSA tools as above. Then, the alignment was pruned from columns with more than 50% of gaps (using trimAl \[[@evu251-B15]\]) and submitted to PhyML 3.0 ([@evu251-B36]) with default options (model LG, tree topology search NNI). Interstrain identical sequences were added to the trees after estimation. Phylogenetic trees were drawn using the R project with the "ape" (version 3.0-8, \[[@evu251-B72]\]) and "phangorn" (version 1.7-4, \[[@evu251-B86]\]) packages, and the TreeDyn editor ([@evu251-B19]; [@evu251-B25]).
Genes with no clear ortholog in all ("orphans") or in some other strains ("semiorphans") from the same species were identified according to co-phenetic distances between leaves in multistrain phylogenetic trees (using the R package ape). To detect highly homologous pairs of NB domain sequences associated with different N-terminal domains, BLASTP scores ([@evu251-B2]) were calculated in the all-against-all manner for the entire data set of NB domains. A target was counted as highly similar to the query if the match scored at least 99% of the maximum score obtained by the query. To avoid false positives, only matches with at least 80% identity over 100 or more amino acids were counted; sequences with unknown N-terminal annotation were also excluded.
Repeat Domain Analysis
----------------------
Highly internally conserved repeats were detected using T-reks ([@evu251-B46]) with customized parameters (PSIM = 0.85, kmeans = 10, overlapfilter on, external MSA: ClustalW 2.1, and Muscle 3.6); repeat regions shorter than 100 amino acids were filtered out. Sequences from dikarya, metazoan, and viridiplantae belonging to ANK, WD40, and TPR clans were extracted according to designation in the Pfam repository (27.0) and availability in the nr database (June 27, 2013). The content of highly internally conserved repeats was calculated as above. "Skipredundancy" from the EMBOSS package ([@evu251-B80]) was used to obtain the nonredundant count of the highly conserved repeats.
For analysis of *P. anserina* ANK and TPR motifs, genes-encoding STAND proteins with individual repeats displaying over 85% internal conservation were analyzed, excluding *hnwd* family members, resulting in a set of ten genes. They code for TPR, ankyrin, or HEAT repeats. For each gene, the repeat-encoding DNA was polymerase chain reaction (PCR)-amplified from five wild isolates from the Wageningen collection (Wa94, Wa 96, Wa97, Wa99, and Wa100) ([@evu251-B100]), gel-purified, cloned in the XL-PCR-TOPO plasmid (Invitrogen, Life Technologies) and sequenced. Sequences were manually assembled before further analysis. In addition, sequences from the S strain were extracted from the *P. anserina* genome sequence and were added to the data set.
Protein repeats were identified using RADAR ([@evu251-B39]). Individual repeats were then aligned using ClustalW and a neighbour-joining tree constructed using MEGA5 ([@evu251-B97]). Sequences clustering together with a high bootstrap support were analysed further, and the other were discarded from the data set. For each data set, sequences in duplicates were then discarded, so that a single copy of each repeat sequence was maintained. To detect signs of positive selection, five analyses (SLAC, FEL, REL, MEME, FUBAR) were conducted for each data set using the HYPHY suite ([@evu251-B77]; [@evu251-B78]). The cut-off was set at 95% confidence interval for SLAC, FEL, MEME, and FUBAR analyses, and over 100 for REL analysis. We considered codons as being submitted to positive selection when they were detected as such by at least three of these approaches. As recombination can lead to false-positive identification by these methods, we also ran PARRIS, which account for the possibility of recombination and is proven to be more robust in these conditions ([@evu251-B85]). Also, only positions where three or more codons were identified were considered to be under positive diversifying selection. Homology modeling of TPR, ANK, and HEAT repeats was performed using HHPred ([@evu251-B94]), and protein structure graphics were obtained using Polyview ([@evu251-B79]).
Results
=======
Identification of the Fungal STAND NLR Repertoires
--------------------------------------------------
To identify NLR-like proteins in the different complete fungal genomes, we have used NACHT and NB-ARC NOD domains from previously identified STAND proteins as queries. We have defined three different query sets. The first set comprised a list of fungal STAND proteins previously identified in the context of the study of fungal incompatibility. This query set we termed IR (incompatibility-related) includes the *P.anserina* HET-E, HET-D, and HET-R incompatibility genes and the fungal STAND proteins comprising a putative prion-forming domain ([@evu251-B71]; [@evu251-B24]). A second set, termed FV, was constituted of plant and animal proteins that have been validated as bona fide NLRs in functional studies and include, for instance, human NOD1 and NOD2, the NALP receptors and *Arabidopsis* RPP1, 8 and 13 and RPS 2, 4, and 5. A third set, termed PD (phylogenetically diverse), comprised an ensemble of STAND proteins with NACHT and NB-ARC Pfam-A annotations with a large phylogenetic distribution, ranging from bacteria to plants and animals and included sequences from different major lineages ([supplementary file S1](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The NB-ARC and NACHT sequences were extracted from the different query set and used in PSI-BLAST searches with three iterations and an *E* value cut-off of 10^−5^ on the complete annotated genome sequences of 198 strains of 164 fungal species (corresponding to the complete fungal genomes deposited at NCBI at the time of the study, [supplementary file S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The IR, FV, and PD query sets recovered 5,571, 1,053, and 4,657 hits, respectively ([supplementary fig. S1](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The IR recovered the most hits, whereas the FV set led to the lowest number of hits, but FV hits were almost entirely included in the IR and PD sets. The FV query did recover only a very limited number of NACHT domain STAND proteins, but was more efficient in the identification of NB-ARC STAND proteins ([supplementary fig. S1](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). We included all hits in our candidate set, which thus adds up to 5,616 sequences ([supplementary file S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online) corresponding to 4,476 (79.7%) and 1,144 (20.4%) NACHT and NB-ARC hits, respectively (four sequences were hit by both NACHT and NB-ARC queries). Hits were found in 122(101) of the 198(164) strains (species). In these 122 strains, there is mean number of STANDs per genome of 46, with a median of 37.
Fungal NLR Domain Annotation
----------------------------
Next, we have annotated the hit sequences using Pfam and in-house annotation tools. Among the NOD domains, NACHT were more frequent than NB-ARC domains, but both categories were abundant ([fig. 1](#evu251-F1){ref-type="fig"} and [supplementary fig. S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The NACHT to NB-ARC ratio is 5:1. This contrasts with the situation observed in *viridiplantae*, where NB-ARC largely predominates (NACHT to NB-ARC ratio based on Pfam annotations is 1:180). In bacterial STAND proteins, both types are common; however, NB-ARC domains are also more abundant than NACHT domains (approximately in a 2:1 ratio). The higher occurrence of NACHT versus NB-ARC makes the fungal NLR candidate set more animal-like, because in metazoans NACHT domains are more frequent than NB-ARC (in a 17:1 ratio). A remaining 28% of the NOD regions picked up in the BLAST searches were neither annotated as NACHT nor NB-ARC by Pfam. Among the candidates were also a number of sequences showing noncanonical P-loop motifs with recurrent variations around the canonical GXXXXGKT/S motif ([supplementary table S1](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online), a situation also described in plant NLRs ([@evu251-B10]). F[ig]{.smallcaps}. 1.---Domain annotation in the fungal NLR set. Pie charts show the distribution of domain annotation in the N-terminal, NOD, and C-terminal domains, respectively. In each pie chart, the light gray corresponds to the fraction of domains with no annotation.
NLRs have a typical tripartite domain organization, with a central NOD flanked N-terminally by an effector domain and C-terminally by an autoinhibitory/ligand-binding domain, often composed of superstructure-forming repeats ([@evu251-B58]). For annotation of the N-terminal domains in addition to the Pfam annotation, we have generated HMM signatures for a series of additional domains that have been found previously as N-terminal domains of fungal STAND proteins ([@evu251-B71]; [@evu251-B24]). Signatures were generated for the HET-s, PP, and σ prion-forming domains, the NAD1, Goodbye, HeLo-like, sesA, and sesB domains ([@evu251-B24]). HMM signatures were generated starting from a relevant individual sequence or a sequence alignment in the case of the short prion-forming motifs (see Materials and Methods). After annotation of the hit sequences, a strong overlap between the sesA and HeLo-like annotated set as well as the NAD1 and Goodbye annotated set was noticed, indicating that these domains are in fact related. For the sake of simplicity, we chose to merge these annotations using the Goodbye and HeLo-like designation for the NAD1/Goodbye group and sesA/HeLo-like group, respectively. It was noted previously that the sesB domain is related to lipases with α/β hydrolase fold ([@evu251-B33]; [@evu251-B24]), and not surprisingly, there was also some level of overlap between the sesB annotation and Pfam annotation related to α/β hydrolases. In this case also, we chose to merge the sesB and the α/β hydrolase Pfam annotations into a single category. We also merged the three PFD signature (HET-s, PP, and σ) into a single category. These motifs are unrelated in primary structure but have similar presumed functions. Among the Pfam annotations, we retained for these analyses only annotations that occur at least ten times in the set. A variety of other N-terminal and C-terminal annotations occur in a very limited number of NLR candidates ([supplementary file S3](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Materia](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1)l online) and were not analyzed further. We end up this way with 12 annotation categories for the N-terminal domains ([fig. 1](#evu251-F1){ref-type="fig"} and [table 1](#evu251-T1){ref-type="table"}). Among the annotated domains, the most frequent domains encountered as N-terminal effector domains are the Goodbye-like, HeLo-like, sesB-like, and PNP_UDP domains (each in the range of 20%). Then, the HET, Patatin, HeLo, and PFD domains are still relatively common (in the 4--1% range), while the other domains represent less than 1% of the annotations ([fig. 1](#evu251-F1){ref-type="fig"} and [supplementary fig. S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The PNP_UDP domain has been previously identified as an N-terminal effector domain in NLR proteins from the coral *A.digitifera* ([@evu251-B37]), and a sesB-related α/β hydrolase fold was found in a putative NLR in a bryophyte ([@evu251-B105]). Globally, roughly half of the sequences show no annotation in the region N-terminal to the NOD domain. In particular, in the basidiomycota, our annotation of the N-terminal domain is very limited with about only 15% of the sequences receiving an annotation ([supplementary fig. S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). Table 1List of the 12 Annotations Classes Retained for the N-Terminal Domains of Fungal NLRsDesignationPutative FunctionReference and/or PFAM ID.C2Membrane targetingPF00168Goodbye-likeUnknown[@evu251-B24], this studyHeLoPore formation[@evu251-B88]/PF14479HeLo-likeUnknown[@evu251-B33], this studyHETUnknown[@evu251-B91]/PF06985PatatinPhospholipasePF01734Peptidase S8Serine proteasePF00082PFDSignal transduction[@evu251-B24]/PF11558PKinaseProtein kinase domainPF00069PNP_UDPPhosphorylasePF01048RelA_SpoTppGpp synthesisPF04607sesB-likeLipase, esterase[@evu251-B33], this study
In the domain C-terminal of the NOD domains, again only 52% of the sequences matched a Pfam A annotation. Ankyrin, WD-40 and TPR motifs corresponded to, respectively, 42, 29, and 25 % of the annotated sequences ([fig. 1](#evu251-F1){ref-type="fig"} and [supplementary fig. S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). In ascomycota, ANK repeats were more abundant whereas WD40 repeats prevailed in basidiomycota. No LRR motifs were found in agreement with a previous study ([@evu251-B93]).
We conclude that fungal genomes encode a variety of NLR-like proteins with a great diversity of N-terminal and C-terminal repeat domains. Whereas the NACHT and NB-ARC, and ANK, WD, and TPR domains have been previously found in plant and animal STANDs, only a fraction of the N-terminal domains (like the PNP_UDP) have also been found in NLRs from other phyla. A large fraction (roughly 50%) of the N-terminal and C-terminal domains do not respond to known annotations.
Diversity and Plasticity in Domain Architectures
------------------------------------------------
Next, we analyzed the domain architectures of the fungal NLR candidate set. Globally, there is a great diversity of domain architectures. To illustrate this aspect, we focused our analysis on the 1,228 sequences for which all three domains (N-, NOD, C-) have an annotation. The 12 annotated effector domains and NACHT and NB-ARC NOD domains can in principle lead to 24 (12 × 2) domain associations, and of those, 21 occur in our candidate set. Similarly, all six combinations of NACHT and NB-ARC with WD, TPR, and ANK motifs are found in the set. Globally, of the 72 possible tripartite domain architectures (12 effector domains × 2 NOD domains × 3 repeat domain), 32 are actually found in the set ([fig. 2](#evu251-F2){ref-type="fig"}). In general, for a given N-terminal domain, a type of architecture for the NOD and C-terminal domain predominates. Some domains show a strong bias in association, for instance HeLo-like and Patatin are almost invariably associated with NACHT and NB-ARC, respectively. Others like HET have a more equilibrated association with either NACHT or NB-ARC. This preferential combinatorial domain association is presented for the 12 N-terminal effector domain types ([fig. 3](#evu251-F3){ref-type="fig"}). There is also a preferential association between NOD types and C-terminal repeat type; NACHT is preferentially followed by ANK or WD whereas NB-ARC preferentially by TPR ([supplementary fig. S3](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). These preferential association trends always suffer exceptions, as a small fraction of the NB-ARC domains are associated with ANK or WD, and a small fraction of the NACHTs is followed by TPRs. The fact that in our sequence set some domain architectures are encountered only once suggests that some of the missing architectures might be identified by analyzing additional species. F[ig]{.smallcaps}. 2.---Domain architectures of fungal NLRs. The figures list the domain architectures found in 1,228 NLR candidates with tripartite annotation. For each of the architectures, the total count and percentages are given. F[ig]{.smallcaps}. 3.---Diagram of preferential domain associations in fungal NLRs. For each of the 12 annotation classes for the N-terminal domains of the fungal NLRs, the type of NOD, and C-terminal domain that are found associated with it are shown. The size of the disk is proportional to the abundance of a given architecture. For the NOD domains, "UNK" denotes unknown (nonannotated) domains. For the C-terminal domains, "REST" denotes unknown (nonannotated) domains and other annotations (distinct from WD, TPR, ANK).
When inspecting the distribution of annotated N-terminal domains in phylogenetic trees based on the NOD domains, it appears that phylogeny of the N-terminal domains is frequently distinct from that of the NODs. This is apparent in two ways. First, in the global candidate set, the phylogenic trees based on the N-terminal domains are not congruent with the phylogenies of the NODs ([supplementary fig. S4](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). Then, when generating phylogenetic trees from the NLR complement from a given species based on the NOD sequences, domain architectures based on N-terminal domains do no form monophyletic groups but rather are to some extent scattered in different branches of the tree. For instance, in the phylogenetic tree based on the NOD domain of the NLR complement of the species *Bipolaris maydis*, the HET domain is found in different branches of the tree. The same is true for PNP_UDP, Goodbye, and HeLo-like domains ([supplementary fig. S5](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). This distribution and the observed combinatorial domain association suggest that de novo generation of specific domain architectures can occur by domain fusion events between N-terminal domains and a different lineage of NODs. In order to explore this aspect, we analyzed our NLR candidate set for situations in which a given NOD is highly similar to a NOD embedded in a distinct domain architecture. [Table 2](#evu251-T2){ref-type="table"} lists such situations in which highly similar NODs (between 80% and 99% identity) are associated with totally distinct N-terminal domains. Such situations can be explained by envisioning relatively recent domain fusion events, in which an N-terminal domain was swapped for another. Table 2Pairs of NLRs with Highly Homologous NOD Domains and Distinct N-Terminal DomainsGi Ident 1Tax Name 1N-Term 1NOD 1C-Term 1Gi Ident 2Tax Name 2N-Term 2NOD 2C-Term 2ScoreIdentity \[%\]156035777*S. sclerotiorum* 1980 UF-70**HELO-LIKE**NACHTWD40156060563*S.sclerotiorum* 1980 UF-70**SESB-LIKE**NACHTWD4024097.5156044028*S. sclerotiorum* 1980 UF-70**HELO-LIKE**NACHTWD40156060563*S.sclerotiorum* 1980 UF-70**SESB-LIKE**NACHTWD4026799.2156050803*S. sclerotiorum* 1980 UF-70**HELO-LIKE**NACHTUNK156060563*S. sclerotiorum* 1980 UF-70**SESB-LIKE**NACHTWD4023892.4451851214*B. sorokiniana* ND90Pr**HET**NACHTWD40189211806*P. tritici-repentis* Pt-1C-BFP**HELO-LIKE**NACHTWD4035488.3189209021*P. tritici-repentis* Pt-1C-BFP**HET**NACHTWD40189211806*P. tritici-repentis* Pt-1C-BFP**HELO-LIKE**NACHTWD4035086.8189209021*P. tritici-repentis* Pt-1C-BFP**HET**NACHTWD40482814165*S. turcica* Et28A**HELO-LIKE**NACHTWD4034984.8225559733*A. capsulatus* G186AR**PNP_UDP**NACHTWD40159124379*Aspergillus fumigatus* A1163**HELO-LIKE**NACHTWD4030682.3242760112*Talaromyces stipitatus* ATCC 10500**HELO-LIKE**UNKUNK212547165*T. marneffei* ATCC 18224**PNP_UDP**NACHTTPR21989.3242760112*T. stipitatus* ATCC 10500**HELO-LIKE**UNKUNK212547167*T. marneffei* ATCC 18224**PNP_UDP**NACHTTPR21989.3322704939*M. anisopliae* ARSEF 23**SESB-LIKE**NACHTANK342868671*Fusarium oxysporum* Fo5176**PNP_UDP**NACHTANK31780.8322704939*M. anisopliae* ARSEF 23**SESB-LIKE**NACHTANK475672654*F. oxysporum* f. sp. cub. race 4**PNP_UDP**UNKANK31980.8347826932*B. fuckeliana* T4**HET**NB-ARCTPR472238659*B. fuckeliana* BcDW1**SESB-LIKE**NB-ARCTPR56390.0353243899*Piriformospora indica* DSM 11827**HELO-LIKE**NACHTUNK353245097*Pi. indica* DSM 11827**SESB-LIKE**NACHTWD4030980.8402073505*G. graminis* var. *tritici* R3**HELO-LIKE**NACHTUNK402073554*G. graminis* var. tritici R3**RELA_SPOT**NACHTUNK33983.6402073505*G. graminis* var. *tritici* R3**HELO-LIKE**NACHTUNK402073555*G. graminis* var. tritici R3**RELA_SPOT**NACHTWD4033983.6402073505*G. graminis* var. *tritici* R3**HELO-LIKE**NACHTUNK402085097*G. graminis* var. tritici R3**RELA_SPOT**NACHTWD4033883.6429861644*C. gloeosporioides* Nara gc5**GOODBYE-LIKE**NACHTUNK429850945*C. gloeosporioides* Nara gc5**PNP_UDP**NACHTUNK22482.1429861644*C. gloeosporioides* Nara gc5**GOODBYE-LIKE**NACHTUNK429853607*C. gloeosporioides* Nara gc5**PNP_UDP**NACHTWD4022280.646130696*F. graminearum* PH-1**HELO-LIKE**NACHTWD4046138235*F. graminearum* PH-1**PNP_UDP**NACHTUNK33885.1[^2]
Together, these observations suggest the existence of a combinatorial assortment of the N-terminal, NOD, and C-terminal repeat domains in fungal STAND proteins that resulted in a large diversity of domain architectures. The fact that domain architecture types do not represent a monophyletic group and the existence of highly similar NODs associated with distinct N-terminal domains, suggest that domain architecture invention events are not limited to a ancestral founding events but may reoccur frequently.
Highly Conserved WD, ANK, and TPR Domains Are Enriched in Fungal NLRs
---------------------------------------------------------------------
The analysis of STAND protein evolution in *Podospora* has revealed the existence of a NACHT-WD gene family (*nwd*), characterized by WD-repeats showing a high level of internal repeat conservation, meaning that the individual WD-repeats of a given gene are highly similar to each other (with about 85% identity at the amino acid level) ([@evu251-B83]; [@evu251-B71]; [@evu251-B18]). This internal repeat conservation is associated with a concerted evolution of the repeats, caused by constant reshuffling and exchanges of repeats both within a given gene or between different members of the gene family, which allows for rapid diversification ([@evu251-B71]; [@evu251-B18]). To determine if the presence of highly conserved repeats is a more general occurrence in fungal NLR proteins, we analyzed the NLR set for the presence of internally conserved repeats. Globally, 16% of the annotated repeats were found to show high internal conservation (over 85% identity over a minimum total length of 100 amino acids); respectively, 10%, 21.2%, and 21.6 % of ANK, TPR, and WD-repeats showed high internal conservation (the proportions varied somewhat between ascomycetes and basidiomycetes), ([fig. 4](#evu251-F4){ref-type="fig"}*A*). These observations indicate that the internal repeat conservation noted for WD repeats in *P.anserina* is a common property of a significant proportion of the NLR-like proteins and that this phenomenon is also encountered with ANK and TPR motifs both in ascomycetes and basidiomycetes. We have analyzed the occurrence of such highly conserved repeats in ANK, TPR, and WD-type repeats in plants, metazoan, and fungi ([supplementary table S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). We found that the fraction of repeats with high internal conservation is globally very low (0.4%, 0.8%, and 1.2% in viridiplantae, metazoan, and dikarya, respectively). There is thus a specific enrichment for highly conserved repeats in fungal NLR proteins. In dikarya, occurrence of highly conserved ANK, TPR, and WD repeats occurs mainly in NLR-like proteins, which globally account for 60-70% of the occurrence of highly conserved repeats. We conclude that highly conserved ANK, TPR, and WD repeats are highly enriched in fungal NLRs, as compared with their global occurrence. F[ig]{.smallcaps}. 4.---Superstructure-forming repeat domains of fungal NLRs. (*A*) Pie chart of repeat type found in ascomycete (top) and basiodiomycete (bottom) NLR candidates. For each repeat type, the fraction of repeats showing high internal conservation (HiC, 85% identity over at least 100 amino acids) is shown. (*B*) Distribution of the number of repeats in fungal NLR candidates for ANK, TPR, and WD repeats (Pfam signatures PF00023, PF13374, and PF00400, respectively). (*C*) Repeat length distribution in fungal NLR candidates for highly conserved ANK, TPR, and WD repeats.
The distribution of the number of repeats per gene was different in ANK and TPR, compared with WD repeats. There was a gradual decrease in the class size with increasing number of repeats per protein in the case of ANK and TPR, while in the case of WD, class sizes were relatively constant from 1 to 14 repeats but then dropped sharply above 14 repeats (enough for the formation of two β-propellers; [fig. 4](#evu251-F4){ref-type="fig"}*B*). This difference might be related to the fact that ANK and TPR motifs form open-ended superstructures ([@evu251-B43]) rather than closed circular structures (β-propellers) in the case of WD-repeats ([@evu251-B96]). In the case of the TPR motifs, there is also apparently a preference for an even number of repeats. The maximum number of WD repeats was 21, which corresponds to the highest number of WD-repeats identified so far in a WD β-propeller domain and could allow for formation of a triple β-propeller. The occurrence of a low number (\<6--7) WD repeats, which a priori do not allow for formation of a closed β-propeller, might be due to the presence of cryptic repeats too degenerate to match Pfam signatures. The size distribution of the repeats corresponded to a very narrow range, typically 33-34 and 42-43 for ANK and WD repeats, respectively. Most TPR motifs were 42 amino acids in length, with only a minor fraction corresponding to in the canonical 34 amino acid length ([fig. 4](#evu251-F4){ref-type="fig"}*C*).
Next, we analyzed whether or not highly conserved repeats are randomly associated with the different N-terminal effector domains. All frequent N-terminal domains can be found associated with highly conserved repeats, but it appears that certain N-terminal domains are preferentially associated with highly conserved repeats, as for instance the HET domain but also the prion-forming domains, whereas others like the Goodbye domains are very seldom associated with this type of repeats ([supplementary table S3](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online).
Phylogenetic Distribution
-------------------------
Next, we analyzed the phylogenetic distribution of NLRs in fungi ([fig. 5](#evu251-F5){ref-type="fig"} and [supplementary file S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). NLRs were absent from certain lineages; in particular, no hits were found in any of the 38 analyzed Saccharomycotina genomes, or in the Schizosaccharomycetes. Similarly, we found no hits in early branching lineages of the microsporidia, chytrids, and mucorales. In contrast, hits were abundant in major basidiomycetes (agaricomycetes, 1,589 hits in 22 species, 72 hits per species) and ascomycetes lineages (pezizomycotina, 3,955 hits in 98 species, 40 hits per species). When comparing the annotation of the ascomycota and basidiomycota ensembles, three main trends are apparent. The ratio of NACHT to NB-ARC is slightly different in both lineages, with NB-ARC being rarer in basidiomycota (with a 1:8 ratio of NB-ARC to NACHT, compared with 1:4 in ascomycota). The abundance of the different types of repeat motifs also differs in both lineages: WD, ANK, and TPR account for 27, 9 and 8% of the C-terminal domain annotations in basidiomycota, compared with 10, 27, and 14% in ascomycota. The higher abundance of NB-ARC and TPR motifs in ascomycotina is expected, considering the preferential association of NB-ARC with TPR motifs ([supplementary fig. S3](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). The level of annotation of the N-terminal domains is very different in both lineages, with only 13% of the sequences receiving an annotation in the basidiomycota, compared with 63% for the ascomycota. This difference is probably related to the fact that our in-house annotations derive from ascomycete sequences. F[ig]{.smallcaps}. 5.---Phylogenic distribution of fungal NLRs. The list of species and strains in which NLR candidates were identified is shown together with their phylogenetic position. For each strain/species, the total count of NLR candidates and of the different N-terminal domains, NOD domains, and C-terminal repeat domains is given, as well as the count and the fraction of the repeat domains that show high internal conservation (HiC).
Variation in the number of NLRs per genome is extreme, ranging from 1 (or 0) to 274 in the endophytic basidiomycetes species *Piriformospora indica.* In that species, NLR-like proteins correspond to 2.3% of the total proteins. Fifteen species show more than 100 NLR genes. There can be strong variations in the number of hits even between related species. For instance, within the *Aspergillus* genus, NLR numbers range from 12 to 99. The same is true even between strains belonging to the same species, as discussed below. Yet, in certain lineages of the pezizomycotina, there appears to be some group-specific increase or decrease in the number of hits. In particular, the hypocreales containing several Trichoderma species have significantly higher numbers of NLRs than the rest of the pezizomycotina (78 genes per species as opposed to 40, *P* = 0.006). The onygenales group containing several dermatophytes shows less hits than the rest of the pezizomycotina (16 genes per species, *P* = 0.018).
We compared the occurrence of the 12 different N-terminal domains in the different species and there again the diversity between species is considerable. None of the 12 annotated domains has a universal distribution in all species displaying NLRs but some are found in a large fraction of species like the Goodbye-like, HeLo-like, and sesB-like domains found in NLRs of 88, 73, and 75 species, respectively. Other domains are found in a narrow species range, like the C2 domain found only in a few basidiomycota. As already noted for the total NLRs numbers, there is a high variability in the number of domain occurrences, even for closely related species, with for instance the number of PNP_UDP NLRs ranging from 2 to 23 in different species of the *Aspergillus* genus. Some domains show a strong tendency for marked expansions, while other are usually found as a single occurrence. We calculated a paralog-to-ortholog index, corresponding to the ratio of number of occurrences of the domain to the number of species in which the domain is encountered. The domains showing the highest number of occurrences per species were PNP_UDP and Goodbye, with a mean occurrence of 7.5 and 7.4 per species, respectively, while in contrast HeLo and Patatin domains showed the lowest occurrence (1,4 and 1,7) ([supplementary table S4](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). These two domains are most generally found as one or two occurrences per species, but some rare exceptions of marked expansion occur as for instance for the HeLo domain in the *fusaria*.
When considering the C-terminal repeat domains, the fraction of repeats with high internal conservation varies dramatically between species from 0 to up to 58% in *Laccaria bicolor*. 72 strains, among the 122 displaying NLRs proteins, have at least one gene with internally conserved WD, TPR, or ANK repeats ([fig. 5](#evu251-F5){ref-type="fig"} and [supplementary file S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). Species in which such NLR-like proteins with high conserved repeats are particularly abundant are *L.bicolor,B.maydis*, and *Talaromyces stipitatus.*
HSP90 and its co-chaperones SGT1 and RAR1 play important roles in NLR function both in plants and animals ([@evu251-B48]). We analyzed the complete fungal genomes for presence of putative SGT1 and RAR1 homologs and found SGT1 matches in all analyzed complete genomes and RAR1 matches in 111 out of 122 strains displaying NLR matches.
Intraspecific Variation Reveals Extensive Polymorphism of the Fungal NLR Repertoire
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Previous reports suggest that fungal STAND proteins show high level of intraspecific variation ([@evu251-B71]; [@evu251-B30]; [@evu251-B12]; [@evu251-B41]). In addition, the extensive variation in STAND copy numbers in different species and the specific expansion of certain domain architectures in certain lineages suggest a death-and-birth evolution of these genes in fungi ([fig. 5](#evu251-F5){ref-type="fig"}), ([@evu251-B62]; [@evu251-B54]; [@evu251-B108]; [@evu251-B101]). In order to document this aspect, we chose to assess intraspecific variability in NLR proteins in our candidate set. We have thus specifically compared the NLR complement in 15 species for which the sequences of several strains are available. To compare the gene complement in each strain, phylogenetic trees were constructed based on the NOD domains only, and the trees were inspected for conservation of orthologous pairs (or triplets) between strains ([supplementary fig. S6](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). In all 15 analyzed species, some level of polymorphism in the NLR complement is observed. A variable fraction of the NLR sequences lack a clear ortholog in the other analyzed strain(s). [Table 3](#evu251-T3){ref-type="table"} presents, for each of the 15 species, the number of NLR proteins that are polymorphic, including the number of NLRs that are orphans (defined as a gene that does not show a clear ortholog in other strains from the same species) or semiorphans (when a pair of orthologous genes are found in two strains but not in a third). We find that NLR proteins are polymorphic between strains of the same species, and the fraction of polymorphic NLRs is systematically higher than for the total proteome (of note however is the fact that the level of polymorphism in the total proteome varies dramatically in different species, as the fraction of polymorphic proteins varies from 9.6% in *Penicillium digitatum* to 100% in *Rhizoctonia solani*). In addition, in many species, a significant proportion of the NLR candidates do not have a conserved ortholog in the other strain(s) (i.e., orphans or semiorphans). For instance, in *Aspergillus niger* CBS 513.88, ten sequence show no ortholog in the other strain (ATCC 1015) with a cut-off distance value of 1, which corresponds to about 50% identity. Inspection of the phylogenetic trees reveals the existence of numerous such orphans as well as strain-specific expansion in certain branches of the tree, arguing for a birth-and-death evolution of these sequences ([supplementary fig. S6](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). This strain-specific expansion is for instance evident *Fusarium oxysporum* Fo5176. Table 3Polymorphism of NLR-Like Proteins in Different Strains from the Same Species[^3]
Relation of HET Domain to TIR Domains
-------------------------------------
The HET domain acquired this designation because it was found in different proteins involved in nonself recognition in the form of heterokaryon incompatibility in fungi ([@evu251-B91]). In particular, this domain constitutes the N-terminal effector domain of the HNWD family members, which includes the *het-e*, *het-d*, and *het-r* incompatibility genes. Functional studies have identified this domain as being a cell death and incompatibility effector domain in *P. anserina* ([@evu251-B69]). We now find that the HET domain is relatively frequent as N-terminal domain of fungal NLR-like proteins and that it is often found associated with highly conserved repeats, potentially capable of rapid diversification. Because this study shows that many species display HET domain NLR-like proteins, we analyzed this domain further.
We first conducted PSI-BLAST searches in the nr ("nonredundant") database with the HET-e1 HET domain by excluding fungal sequences and found that homologs of this domain are also found outside of the fungal kingdom in Stramenopiles, Haptophyceae, Choanoflagellates, green algae, and bryophytes. Next, we used Hidden Markov model searches to identify remote homologs of the HET-e1 HET domain. Both algorithms that we used (HHpred and JackHHmer) identified similarity to TIR domains. In particular, the two best hits in HHpred were to structure-based profiles constructed from the TIR domain of PdTIR from *Paracoccus denitrificans* ([@evu251-B16]) and of the TcpB protein from *Brucella melitensis* ([@evu251-B50]; [@evu251-B1]; [@evu251-B92]). [Figure 6](#evu251-F6){ref-type="fig"} presents an alignment of fungal HET domain proteins with bacterial and human TIR domains of known structure and related HET domains from phylogenetically diverse origins. The region of similarity of roughly 100 amino acids encompasses three main conserved blocks. These blocks of similarity map to the elements of secondary structure of the TIR domain α/β fold; the alignment, however, does not include the entire TIR domain, as similarity drops off after the region corresponding to helix αC. TIR domains function as adaptor domains in cell death and immune defense signaling cascades and function by interacting with partner TIR domains ([@evu251-B65]). This potential homology between HET and TIR domains suggests that HET domains may function by recruiting HET domain proteins and signaling downstream. F[ig]{.smallcaps}. 6.---Alignment of fungal HET domains with TIR domain proteins. The TIR domains of two bacterial proteins of known structure and of the human TLR1 TIR domain (boxed in red) are aligned with the HET domains of *P. anserina* HET-e1 and *Neurospora crassa* TOL (boxed in blue) together with related sequence of diverse phylogenetic origin annotated as HET domains in Pfam. On top of the alignment, the elements of secondary structure of *Brucella* TcpB are shown. Sequence designations are as follows: Paracoccus, *Paracoccus denitrificans,* gi\|500070302; Brucella, *Brucella melitensis*, gi\|516360271; Human Tlr1, *Homo sapiens*, gi\|194068387; Candidatus, *Candidatus Accumulibacter*, gi\|589611804; Emiliania, *Emiliania huxleyi*, gi\|551574256; Ectocarpus, *Ectocarpus siliculosus,* gi\|298709304; Thalassiosira, *Thalassiosira pseudonana*, gi\|224000455; Salpingoeca, *Salpingoeca rosetta*, gi\|514691135, Physcomitrella, *Physcomitrella patens,* gi\|168042266; *Podospora*, *P. anserina*, gi\|3023956 (HET-e1); Neurospora*, Neurospora crassa*, gi\|553134703 (TOL).
ANK and TPR Motifs of NLR Proteins of P. *anserina* Show Repeat Length Polymorphism and Positive Diversifying Selection
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Superstructure-forming repeats with high internal conservation are enriched in fungal NLRs. These repeats belong to three types of superstructure-forming repeats, WD, ANK, and TPR motifs. We have previously shown that WD repeats of NLR-like proteins show extensive repeat size polymorphism in *Podospora* and are subject to concerted evolution and positive diversifying selection ([@evu251-B71]; [@evu251-B18]). We extended this analysis to ANK and TPR motif NLR proteins of *Podospora,* in order to determine whether repeat size polymorphism and diversifying selection was a common property of such repeat domains. We selected eight *P. anserina* NLR-encoding genes showing highly conserved ANK and TPR motifs, and PCR-amplified the repeat region from genomic DNA from five different wild isolates. For each locus, sequence analysis revealed repeat number polymorphism (RNP) ([table 4](#evu251-T4){ref-type="table"}). ANK repeat numbers ranged from 7 to above 16, whereas TPR motif numbers ranged from 2 to above 14. The RNPs observed suggest frequent recombination between repeats within a locus, and possibly between loci encoding the same type of repeats, as previously reported for WD-repeats ([@evu251-B71]; [@evu251-B18]). Table 4Repeat number polymorphism in ANK and TPR Repeat Domains of NLR Proteins from *Podospora anserina*Pa_2\_8180 PNP-UDP/ NACHT/ ANKPa_3\_8560 PNP-UDP/ NACHT/ ANKPa_2\_10340 sesB-like/ NB-ARC/ TPRPa_3\_9910 PFD/ NB6ARC/ TPRPa_5\_8060 PFD/ NB-ARC/ TPRPa_6\_7270 sesB-Like/ NACHT/ TPR(HEAT)Pa_6\_7950 sesB-like/ NACHT/ TPRPa_7\_3550 UNK/ NB-ARC/ TPRS107114410211Wa94ND8\>1597313NDWa96812\>1498611\>13Wa9713\>14ND119129\>13Wa99\>16112107NDND\>13Wa10010107NDND1010\>13Total\>57\>62\>494335414551Unique2236391921192929[^4]
Next, we selected one ANK repeat locus and one TPR motif locus for which we had sequenced the highest number of repeats (Pa_3\_8560 and Pa_2\_10340, respectively) and analysed the variability of the repeats from individual loci. For each locus, individual repeat sequences were aligned and analysed for position under positive selection (see Materials and Methods) ([fig. 7](#evu251-F7){ref-type="fig"}). Five positions showed signs of positive selection in the ANK repeats and three in the TPR motifs. To locate the positive selection and polymorphic sites on the repeat domain structure, the repeats were homology-modeled to ANK and TPR domains of known structure. The TPR motif domain of Pa_2\_10340 was modeled using the human kinesin light chain 2 structure (Protein Data Bank \[PDB\] ID 3EDT) as template. In the TPR motifs, all positive selection sites as well as the other polymorphic position mapped to the concave side of the TPR structure in the α-helical regions. The ANK repeat domain of Pa_3\_8560 was modeled using the structure of artificial ANK repeat domain of engineered protein OR264 (PDB ID 4GPM) as template. In the ANK repeats, with one exception, the positive selection and polymorphic site also mapped on the concave surface of the ANK repeat domain in the inner helices and the β-hairpin/loop region, which correspond to the binding interface of ANK repeats based on cocrystal structures ([@evu251-B43]). F[ig]{.smallcaps}. 7.---Hypervariable sites in *P. anserina* TPR and ANK repeats of NLRs. (*A*) Alignment of individual TPR motif sequences found in different alleles of *Pa_2\_10340* (sesB-like/NB-ARC/TPR) is shown. Positions under positive selection are marked with a red dot; other highly variable positions are marked with a yellow dot. The TPR domain of Pa_2\_10340 was modeled using the human kinesin light chain 2 structure as (PDB ID 3EDT) as the template. Color coding of the positive selection and variable sites is as above. (*B*) Alignment of individual ANK repeat sequences found in different alleles of *Pa_3\_8560* (PNP_UDP/NACHT/ANK) is shown. Positions under positive selection are marked with a red dot, other highly variable positions are marked with a yellow dot. The ANK repeat domain of *Pa_3\_8560* was modeled using the structure of the artificial ANK repeat domain of the engineered protein OR264 (PDB ID 4GPM) as the template. Color coding of the positive selection and variable sites is as above.
We also analysed two putative proteins from different species to determine whether this localization of the polymorphisms might be common to other ANK and TPR motifs. We chose the ANK and TPR proteins with the highest number of highly conserved ANK and TPR motifs, gi116208038 from *Chaetomium globosum* (PNP_UDP/NACHT/ANK) and gi255934897 from *Penicillium chrysogenum* (UNK/AAA/TPR), with, respectively, 21 ANK repeats and 21 TPR motifs. By comparing the repeats and mapping the variable positions onto a homology model (PDB ID 4GPM for ANK and 3EDT for TPR), we found that polymorphisms map to the same positions in the α-helices of the concave surface of the TPR domain and to the inner helices and β-hairpin/loop region of the concave interface of the ANK domain ([supplementary fig. S7](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). Based on the localization of these polymorphic sites, it can be inferred that if repeat contraction/expansion/shuffling occurs in these genes, these events will lead to ANK and TPR arrays with modified binding interfaces.
Collectively, these analyses suggest that the evolution of ANK and TPR motifs of *Podospora* NLR candidates is analogous to the evolution of highly conserved WD repeats of NLR-like proteins, which has been described previously ([@evu251-B71]; [@evu251-B18]).
Discussion
==========
In plants and animals, NLRs are essential components of innate immunity. Work on fungal incompatibility revealed the existence of NLR homologs in fungi with functions in the detection and response to nonself. Herein, we have analysed close to 200 fungal genomes for the presence of NLR candidates and describe the identified sequences. We find that multicellular pezizomycetes and agaricomycete generally encode large and diverse repertoires of NLR-like genes.
Diversity of N-Terminal Effector Domains
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Many of the N-terminal effector domains of fungal NLRs remain completely uncharacterized, in particular in basidiomycotina. We have nevertheless defined 12 main annotation classes for these N-terminal domains that roughly accounts for 50% of the sequence set. For some of these domain classes, functional information is available, although it is in most cases fragmentary. One of the domains, that was previously identified as an effector domain in animal NLRs is the PNP_UDP domain. Indeed, this domain was found as N-terminal domain of NLRs in the coral *A.digitifera* ([@evu251-B37]), and also as an effector domain associated with a DD in sponge ([@evu251-B107])*.* In addition, we reveal a remote similarity between the HET domain and the TIR domain, originally identified in Toll-like receptors in mammals and also found as the N-terminal domains of a large fraction of plant NLRs. Considering this similarity, it might be hypothesized that HET domain fungal NLRs are functionally analogous to plant TIR-NB-LRR proteins. TIR domains regulate immune responses by homo and heterodimerization; HET-domain containing NLRs like the *P.anserina* HET-e, HET-d, and HET-r proteins may therefore mediate the incompatibility response by interaction with downstream HET domain proteins acting as adaptor domains.
A large fraction of the N-terminal domains is related to the HeLo domain identified in the HET-s prion protein of *P. anserina* ([@evu251-B34]; [@evu251-B88]). This domain is a cell death execution domain that can be activated following prion transconformation of the PFD region of HET-S. The HeLo domain is then translocated to the cell membrane, where it functions as a pore-forming toxin ([@evu251-B63]; [@evu251-B88]). The HeLo domain is found as the N-terminal domain of NLRs in many different species, but even more frequent is a variant form of this domain that we term HeLo-like, which could potentially play a similar role in cell death execution. Another abundant class is the sesB-like domain, which corresponds to a predicted lipase domain ([@evu251-B33]; [@evu251-B24]). This lipase domain is found in the human SERAC1 protein, which was found to be involved in a metabolic disease ([@evu251-B103]). Human SERAC1 displays phospholipid esterase activity and is able to modify lipid composition of the plasma membrane. It might be that sesB-like domains induce specific plasma membrane modification in response to nonself. Our annotation list contains another lipase domain, namely the Patatin domain. Interestingly, the Patatin lipase domain was involved in the control of PCD and defense in plants ([@evu251-B13]; [@evu251-B55]; [@evu251-B52]). Based on the fact that one of the incompatibility genes of the fungus *C.parasitica* encodes an NLR with a Patatin domain, it can be reasonably inferred that Patatin-like domains might also function in the control of cell death in fungi. Considering that the C2 domain, found as N-terminal effector domain in basidiomycete NLR candidates, is a lipid-binding domain ([@evu251-B22]), it appears that a significant fraction of the identified N-terminal domain of fungal NLRs target membranes or lipids.
The RelA_SpoT domain was so far only described in bacterial and plant chloroplast proteins ([@evu251-B4]); we now identify it as the N-terminal effector domain of fungal NLRs. This enzymatic activity-carrying domain is responsible for the synthesis of the ppGpp bacterial alarmone, which mediates the stringent response in bacteria ([@evu251-B11]). One possible explanation of the presence of this domain as an N-terminal domain of fungal NLRs would be that fungi exploit the prokaryotic signaling ppGpp cascade to manipulate bacterial pathogens, competitors, or symbionts. The same might be true for the PNP_UDP class. Based on the analysis of the PNP_UDP domain in NLRs from *P.anserina*, these domains are predicted to be methylthioadenosine/*S*-adenosylhomocysteine nucleosidases, which are involved in the synthesis of quorum-sensing molecules like Al-2 ([@evu251-B73]). Maybe these effector domains manipulate prokaryotic signaling in the context of adverse or beneficial interactions.
Globally, when one considers domains found N-terminal to the NOD domain in this NLR collection, two main categories emerge. Class 1 domains correspond to domains that have a proposed enzymatic function or a potential direct role in cell death induction. In this first class, one finds the proposed PNP_UDP, RelA_SpoT, sesB-like, and Patatin lipase domains and also the HeLo and HeLo-like proposed pore-forming toxin domains. In this class, the N-terminal domain is believed to represent a direct effector of the NLR activation. Class 2 domains correspond to domains that more likely have an adaptor function, a situation more typical of plant and animal NLRs, where domains such as CARD, PYD, and TIR recruit effectors by homotypic domain interactions to signal downstream, rather than representing the terminal effector of the immune cascade. CARD and PYD mediate NLR signaling by a prion-like mechanism, involving formation of higher-order complexes ([@evu251-B104]; [@evu251-B14]; [@evu251-B60]). The three prion-forming domains (HET-s, PP and σ) associated with fungal NLRs correspond to this second class ([@evu251-B24]). The HET domain, possibly homologous to the TIR domain, also likely corresponds to this second class. Many of the fungal NLR-related proteins fall into class 1, while apparently in plant and animal lineages this situation is less frequent, although as mentioned previously PNP_UDP NLRs have been described in corals ([@evu251-B37]). In complex (multicellular) bacterial STAND proteins, the presence of N-terminal domains with predicted enzymatic activity such as a metacaspase domain is common, in particular in cyanobacteria ([@evu251-B58]; [@evu251-B3]). It might be that at the base of STAND protein evolution the all-in-one architecture is ancestral and that incorporation of adaptor domains between the NLR receptor and the effector represents a further sophistication of the signaling process.
Superstructure-Forming Repeat Domains in Fungal NLR-Related Proteins
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We failed to identify NLR candidates with LRR motifs, a situation already reported in a study specifically tailored to the identification of LRR pattern-recognition receptors in fungal genomes ([@evu251-B93]). Instead, STAND proteins displayed ANK, TPR, and WD motifs. ANK, TPR, and WD motifs were found associated with NLRs in the coral *A.digitifera* ([@evu251-B37])*.* Similarly, in their analysis of the repertoire of NLRs in the sponge *Amphimedon queenslandica,* [@evu251-B107] also reported that in several holozoan and nonholozoan genomes NACHT domain proteins are associated with ANK, TPR, and WD repeats, but no LRR motifs were found ([@evu251-B107]). Based on the unified NLR nomenclature proposed in 2008, these authors stated that such non-LRR STAND proteins should not be designated NLR and that this designation should be restricted to proteins encompassing LRR motifs. In that sense, all candidates identified in the fungal genomes would not represent bona fide NLRs. We do however consider that given the combinatorial association of different repeat domains with NACHT or NB-ARC domains in fungi and other lineages, it is reasonable to assume that regardless of the type of superstructure-forming repeats they harbor, these NLR-related genes display related functions. Although a restrictive nomenclature offers the advantage of simplicity, because it is based on domain architecture, it may not be optimal for a global understanding of the role and evolution of NLR-related genes across phyla. As another illustration of this principle, the DEATH-NACHT domain proteins are found in the cnidarian *Hydra magnipalpillata* that lack LRR motifs but cluster with vertebrate NLRs ([@evu251-B107]). Similarly, [@evu251-B37] also favor the notion that different NOD/WD, ANK, TPR, and LRR associations are ancestral and that in certain lineages, NOD/LRR architectures have flourished whereas other architectures were lost ([@evu251-B37]). Following this plausible model, it might be proposed that the NOD/LRR architecture was specifically lost in the fungal lineage while NOD/TPR, ANK, and WD architecture were expanded. NLR loss in certain lineages is not uncommon; nematodes and arthropods are apparently devoid of NLRs ([@evu251-B61]; [@evu251-B37]) and TIR-NB-LRRs have been reduced or lost in monocotyledon plants ([@evu251-B47]).
A significant fraction of the superstructure-forming repeat domains in fungal NLRs show strong internal conservation, a situation we have previously described for the WD-repeat domains of the *nwd* gene family of *Podospora* ([@evu251-B83]; [@evu251-B71]; [@evu251-B18]). We have found that this internal conservation corresponded to the concerted evolution of the repeats both within and between members of the gene family, and was typically associated with repeat number polymorphism. In addition, these WD-repeats show positive diversifying selection at specific codon positions, corresponding to amino acid positions defining the ligand-binding interface of the WD β-propeller structure ([@evu251-B71]). Due to the high conservation of the repeats, these sequences are prone to RIP (repeat induced point mutation), a genomic defense mechanism that mutates and methylates repeated sequences premeiotically in fungi ([@evu251-B87]). At least in *Podospora*, the effect of RIP on these repeat regions might represent a mechanism of hypermutation, allowing a rapid diversification of these sequences. We have proposed that the combination of these evolutionary mechanisms constitutes a process for generating extensive polymorphism at loci that require rapid diversification. This study now suggests that this regimen of concerted evolution and positive diversifying selection might be of general relevance to the evolution of a fraction of fungal NLRs. We find that many superstructure-forming repeat domains in fungal NLR show strong internal repeat conservation and that in *Podospora,* ANK and TPR motifs also show RNP and signs of positive selection at positions predicted to be located in the interaction surfaces in the ANK and TPR structures. In the context of nonself recognition, rapid diversification of the receptors might be particularly critical; it appears that the modularity and plasticity properties of superstructure-forming repeats might have been exploited in many fungal species, to allow diversification of their NLR repertoires.
Among the three superstructure-forming repeat types, ANK repeats were the most common in fungal NLRs candidates. The involvement of ANK repeats in host-symbiont or host--parasite interaction was highlighted by previous studies, showing that ANK repeat proteins are enriched in symbiotic and obligate intracellular bacterial species, as compared with free-living species ([@evu251-B44]). Similarly, a rapidly evolving family of ANK repeat proteins was found to control host--parasite interaction in *Wolbachia* ([@evu251-B90]). ANK repeat domain proteins were also found to be specifically enriched during expression changes associated with nonself recognition in *Podospora* ([@evu251-B7]). Thus, across phyla, ANK repeat domains appear often to be involved in the regulation of inter-organismal interactions.
Architectural Diversity of Building Blocks in NLRs
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One of the marked characteristics of the fungal NLRs is the extensive domain architecture diversity. Studies of the NLR repertoires in lower animals already hinted at this diversity in domain architecture ([@evu251-B56]; [@evu251-B37]; [@evu251-B107]). The description of the fungal NLRs further illustrates this diversity. Even with the very partial annotation, we establish a great variety of architectures, revealing a combinatorial association of different N-terminal, NOD and repeat domains. This diversity is evident both in the phylum and within a given species, which can display tens of different NLR domain architectures. Importantly, in many cases a given domain architecture does not have a monophyletic ancestry. Rather, it appears that reoccurring domain fusion events lead to multiple independent inventions of the same architectures. These domain associations appear not to be limited to ancestral events, as suggested by the fact that NOD with 99% identity can be found associated with totally distinct N-terminal domains. These observations, as well as the species or strain-specific expansions of paralogs, are compatible with the notion that fungal NLRs evolve by a birth-and-death regimen. Others have previously documented the role of birth-and-death evolution in fungal *het* gene homologs in the basidiomycetes ([@evu251-B101]). This apparent plasticity of the NLR repertoire, based on the combinatorial association of a variety of effector, NOD and C-terminal receptor domains, might represent a mechanism that allows a rapid adaptation of the NLR repertoire in the arms-race with the variable biotic environment. The combinatorial build-up of an immune repertoire from a limited set of elementary domain is also a general characteristic of the immune-related proteins in plants and animals ([@evu251-B67]).
Phylogenetic Distribution of NLRs and Possible Functions in Immunity and Beyond
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Our analysis of the phylogenetic distribution of NLR homologs in fungi indicates that their presence is apparently restricted to filamentous multicellular fungi. We found no NLR homologs in yeast species. The simplest interpretation of this lack of NLR homologs in yeast species is that this gene family was lost in unicellular fungi, because the constraints on the management of biotic interactions are fundamentally different for multi and unicellular organisms. *Soma* and *germen* are essentially one and the same thing in the latter organisms, therefore the maintenance of a machinery aimed at protection of the *soma* against parasitism may not be required in yeasts, in particular when considering that one common outcome of NLR-controlled defense in animals, plants, and fungi is programmed cell death. We also failed to identify NLR-related genes in early branching non-dikarya fungal lineages of the chytrids, microsporidia, and mucorales and also in some dikarya basidiomycete lineages such as the tremellomycetes and the pucciniomycotina, in agreement with previous studies ([@evu251-B101]). This could indicate that NLR-like genes were lost in these lineages or that the level of divergence of the NACHT and NB-ARC domains used in our search prevented their detection.
Within the filamentous agaricomycotina and pezizomycotina, the number of NLR homologs varies dramatically between species. One may attend to establish a relationship between the species ecology and the constitution of the NLR homolog repertoire ([supplementary file S2](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [Supplementary Material](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) online). This can only be made with extreme caution, because in many cases, the information available on the species ecology is at best fragmentary and many species have multiple habitats and life-styles. In some groups, there is a significant enrichment or scarcity of NLRs. For instance, animal dermatophytes of the onygenales have in general few NLR genes. But it is difficult to determine whether this is related to the phylogenetic position or to ecology. If the function of NLR homologs in fungi is related to innate immunity, the prediction might be that fungi potentially in relation to diverse pathogens or competitors or hosts should be particularly enriched in terms of NLR repertoire, and reciprocally, that in fungi living in less populated niches, smaller repertoires could be sufficient. This prediction might be verified in some instances as, for example, in the case of the highly versatile pathogens like *Fusarium* species or mycoparasitic Trichoderma species, in which the repertoire is large. In the thermophile *Chaetomium thermophilum,* the citrus fruit pathogen *Pe. digitatum* or the "whisky fungus" *Baudonia compniacensis* have small repertoires and inhabit restrictive niches. Similarly, specialized pathogens, such as *Claviceps purpurea*, might be protected against microbial competitors by their host immune system, which could explain the low number of NLRs.
The current view of the role of the NLRs in the animal lineage is expanding. Initially viewed as immune receptors whose role is to detect and respond to pathogenic nonself, it is becoming apparent that these receptors are also critical for the management of other nonpathogenic biotic interactions, notably with the symbiotic microbiome ([@evu251-B21]). For instance, the human NOD2 NLR is required for the establishment of a commensal microbiome in the intestine ([@evu251-B75]). Similarly, it has been proposed that the expanded NLR repertoires in the coral *A.digitifera* could be devoted to the interaction with an obligate dinoflagellate endosymbiont ([@evu251-B37]). In the fungal kingdom, it has been emphasized that pathogenesis and symbiotic interaction are based on similar mechanisms ([@evu251-B102]). It might thus be proposed that part of the NLR repertoires found in fungi might function in the control of a variety of biotic interactions and not be strictly devoted to an immune function per se (understood as the response to pathogenic nonself). These proteins might be involved in the control of nonself recognition in the context of fungal pathogenicity, or symbiosis in the form of ECM formation, endophytic growth, lychen formation, or interaction with symbiotic endobacteria. As already mentioned, NACHT domain proteins are specifically expressed during mycorhizal symbiosis in *L.bicolor,* and in *T.melanosporum,* an expanded family of NACHT-ANK proteins is characterized by a remarkable mechanism of diversification based on alternative splicing of codon-sized mini exons ([@evu251-B62]; [@evu251-B41]). In this study, the species showing the highest number of NLRs is *Pi. indica,* which is an endophytic fungus ([@evu251-B108]).
Conclusion
==========
Fungal NLR homologs have been shown, in two species, to be involved in nonself recognition and in the control of PCD ([@evu251-B83]; [@evu251-B20]). We now report that filamentous fungi possess variable repertoires of NLR homologs, which show similarities and differences with NLRs in plant and animal lineages. This glimpse of fungal NLR diversity represents a further opportunity in comparative immunology for a more complete understanding of the build-up and evolution of immunity in eukaryotes. Although viridiplantae NLR repertoires are characterized by their considerable size (NLR repertoires with several hundreds of NB-LRR genes are not uncommon), mammalian NLR repertoires are fixed and reduced, most likely due of the presence of an adaptive immune system ([@evu251-B61]). In lower animals, NLR repertoires appear more extended, with again up to several hundred NLR genes in certain species ([@evu251-B56]; [@evu251-B37]; [@evu251-B107]). The fungal NLR repertoires similarly appear highly variable, but only exceptionally reach the complexity found in lower animals and land plants. The common occurrence of rapidly evolving ANK, TPR, and WD nonetheless may entail these repertoires with the plasticity required to cope with a complex and changing biotic environments. Animal and plant NLRs employ mechanistically distinct strategies for defense, in the form of intracellular PAMP detection in animals and ETI (effector-triggered immunity) in plants ([@evu251-B61]). It will be of interest to determine which strategies have been adopted in the fungal lineage. The involvement of NLR-like proteins in incompatibility, in which cell death is triggered by the recognition of an allelic variant of an endogenous protein by an NLR is compatible with a model of effector-triggered immunity ([@evu251-B70]; [@evu251-B20]; [@evu251-B6]). Fungi possess extremely diverse lifestyles involving a variety of obligate or facultative biotic interactions; further functional studies are now required to understand which role these fungal NLR homologs play in the management of these diverse interorganismal interactions and which mechanistic strategies underlie NLR function in fungi.
Supplementary Material
======================
[Supplementary files S1--S3](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), [figures S1--S7](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1), and [tables S1--S4](http://gbe.oxfordjournals.org/lookup/suppl/doi:10.1093/gbe/evu251/-/DC1) are available at *Genome Biology and Evolution* online (<http://www.gbe.oxfordjournals.org/>).
###### Supplementary Data
This work was funded by a grant from the Agence National de la Recherche (ANR Blanc "Mykimun"), by the Australian Research Council and by the National Health and Medical Research Council (NHMRC). B.K. is an NHMRC Research Fellow. W.D. is on leave from Faculty of Fundamental Problems of Technology, Wroclaw University of Technology, Poland.
[^1]: **Associate editor**: Kenneth Wolfe
[^2]: N[ote]{.smallcaps}.---Gi Ident, GenBank identification.
[^3]: N[ote]{.smallcaps}.---Orph, orphan; Ident, identification; Semiorph, Semiorphan.
[^4]: N[ote]{.smallcaps}.---ND, not determined.
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Current assemblies including a silicon-based part are generally secured by bonding. This type of operation requires extremely delicate application which makes it expensive.
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{
"pile_set_name": "USPTO Backgrounds"
}
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Q:
Optimizing array transposing function
I'm working on a homework assignment, and I've been stuck for hours on my solution. The problem we've been given is to optimize the following code, so that it runs faster, regardless of how messy it becomes. We're supposed to use stuff like exploiting cache blocks and loop unrolling.
Problem:
//transpose a dim x dim matrix into dist by swapping all i,j with j,i
void transpose(int *dst, int *src, int dim) {
int i, j;
for(i = 0; i < dim; i++) {
for(j = 0; j < dim; j++) {
dst[j*dim + i] = src[i*dim + j];
}
}
}
What I have so far:
//attempt 1
void transpose(int *dst, int *src, int dim) {
int i, j, id, jd;
id = 0;
for(i = 0; i < dim; i++, id+=dim) {
jd = 0;
for(j = 0; j < dim; j++, jd+=dim) {
dst[jd + i] = src[id + j];
}
}
}
//attempt 2
void transpose(int *dst, int *src, int dim) {
int i, j, id;
int *pd, *ps;
id = 0;
for(i = 0; i < dim; i++, id+=dim) {
pd = dst + i;
ps = src + id;
for(j = 0; j < dim; j++) {
*pd = *ps++;
pd += dim;
}
}
}
Some ideas, please correct me if I'm wrong:
I have thought about loop unrolling but I dont think that would help, because we don't know if the NxN matrix has prime dimensions or not. If I checked for that, it would include excess calculations which would just slow down the function.
Cache blocks wouldn't be very useful, because no matter what, we will be accessing one array linearly (1,2,3,4) while the other we will be accessing in jumps of N. While we can get the function to abuse the cache and access the src block faster, it will still take a long time to place those into the dst matrix.
I have also tried using pointers instead of array accessors, but I don't think that actually speeds up the program in any way.
Any help would be greatly appreciated.
Thanks
A:
Cache blocking can be useful. For an example, lets say we have a cache line size of 64 bytes (which is what x86 uses these days). So for a large enough matrix such that it's larger than the cache size, then if we transpose a 16x16 block (since sizeof(int) == 4, thus 16 ints fit in a cache line, assuming the matrix is aligned on a cacheline bounday) we need to load 32 (16 from the source matrix, 16 from the destination matrix before we can dirty them) cache lines from memory and store another 16 lines (even though the stores are not sequential). In contrast, without cache blocking transposing the equivalent 16*16 elements requires us to load 16 cache lines from the source matrix, but 16*16=256 cache lines to be loaded and then stored for the destination matrix.
A:
Unrolling is useful for large matrixes.
You'll need some code to deal with excess elements if the matrix size isn't a multiple of the times you unroll. But this will be outside the most critical loop, so for a large matrix it's worth it.
Regarding the direction of accesses - it may be better to read linearly and write in jumps of N, rather than vice versa. This is because read operations block the CPU, while write operations don't (up to a limit).
Other suggestions:
1. Can you use parallelization? OpenMP can help (though if you're expected to deliver single CPU performance, it's no good).
2. Disassemble the function and read it, focusing on the innermost loop. You may find things you wouldn't notice in C code.
3. Using decreasing counters (stopping at 0) might be slightly more efficient that increasing counters.
4. The compiler must assume that src and dst may alias (point to the same or overlapping memory), which limits its optimization options. If you could somehow tell the compiler that they can't overlap, it may be great help. However, I'm not sure how to do that (maybe use the restrict qualifier).
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The Cross River State Police Command has arrested one notorious street light vandal and 12 gang armed robbery suspects, who were alleged to have killed a Police inspector, Ebri Ogban in Calabar, the state capital. Disclosing this to DAILY POST in Calabar, the Commissioner of Police, CP Mohammed Inuwa Hafiz, said the suspect, Edet Ekpenyong [ ]
Police arrest 12 suspected notorious armed robbers, one other in Calabar
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{
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Tigran Gharabaghtsyan
Tigran Gharabaghtsyan (, born 6 June 1984 in Yerevan, Soviet Union) is a retired Armenian football striker. He was a member of the Armenia national team, for which he has twice appeared since his debut in a friendly match against Panama on 14 January 2007.
Achievements
Armenian Premier League with Pyunik Yerevan: 2006, 2007, 2008
Armenian Supercup with Pyunik Yerevan: 2006
Bulgarian Cup finalist with Cherno More Varna: 2008
External links
Profile at FFA website
Category:Living people
Category:1984 births
Category:Armenian footballers
Category:Armenia international footballers
Category:Armenian expatriate footballers
Category:FC Urartu players
Category:FC Pyunik players
Category:PFC Cherno More Varna players
Category:FC Atyrau players
Category:Armenian Premier League players
Category:First Professional Football League (Bulgaria) players
Category:Expatriate footballers in Bulgaria
Category:Armenian expatriate sportspeople in Bulgaria
Category:Armenian expatriate sportspeople in Kazakhstan
Category:Sportspeople from Yerevan
Category:Association football forwards
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{
"pile_set_name": "Wikipedia (en)"
}
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---
abstract: 'Information transfer which reveals the state variation of variables usually plays a vital role in big data analytics and processing. In fact, the measures for information transfer could reflect the system change by use of the variable distributions, similar to KL divergence and Renyi divergence. Furthermore, in terms of the information transfer in big data, small probability events usually dominate the importance of the total message to some degree. Therefore, it is significant to design an information transfer measure based on the message importance which emphasizes the small probability events. In this paper, we propose a message importance transfer measure (MITM) and investigate its characteristics and applications on three aspects. First, the message importance transfer capacity based on MITM is presented to offer an upper bound for the information transfer process with disturbance. Then, we extend the MITM to the continuous case and discuss the robustness by using it to measuring information distance. Finally, we utilize the MITM to guide the queue length selection in the caching operation of mobile edge computing.'
author:
-
-
-
title: |
State Variation Mining: On Information Divergence with Message Importance in Big Data\
[^1]
---
information transfer measure, message importance measure, big data analysis, mobile edge computing (MEC), queue theory
Introduction
============
Recently, the amount of data is exploding rapidly and the computing complexity for data processing is also increasing. To some degree, this phenomenon is resulted from more and more mobile devices as well as the growing service of clouds. In the literature, it is favored to process the collected data to dig out the hidden important information. On one hand, it is necessary to improve the computation platforms for big data processing, such as cloud computing, fog computing and mobile edge computing (MEC). On the other hand, a series of algorithmic technologies for big data analysis and mining are required, such as neural networks and machine learning, as well as distributed parallel computing, etc.
In many scenarios of big data, the small probability events attract more attention than the large probability ones. That is, the rarity of small probability events has higher value in use. For instance, on anti-terrorist activities, there are only a few illegal people and hazardous agent that should be supervised especially [@Counterterrorism-systems]. Moreover, in terms of the synthetic ID detection, it just focuses on a small number of artificial identities for financial frauds [@A-comprehensive-survey]. Actually, how to mine and characterize small probability events becomes more challenging and more significant in modern life.
From the perspective of information theory, small probability events detection can be regarded as a kind of clustering problem. In particular, a graph-based rare category detection was presented based on the global similarity matrix [@Graph-based-rare]. Furthermore, a time-flexible rare category detection was also designed by resorting to the time-evolving of graphs [@Rare-category-detection]. In spite of these efficient algorithms for some special applications, it is worth noting that they were designed by traditional information measures and theory, which originate from the viewpoint of typical events, namely the large probability events.
Review of Message Importance Measure
------------------------------------
As two fundamental measures in information theory, Shannon entropy and Renyi entropy play a crucial role in many applications including communication engineering, estimation theory, hypothesis testing and pattern recognition. However, they are not suitable enough for small probability events mining in the big data scenarios. To do this, the message importance measure (MIM), a new information measure, is proposed to reflect the significance of small probability events. Thus, let us review the definition of MIM briefly first [@message-importance-measure-and-its-application-to-minority-subset-detection-in-big-data].
In a finite alphabet, for a given probability distribution $P=\{ p(x_1), p(x_2),..., p(x_n)\}$, the MIM with importance coefficient $\varpi \geq 0$ is defined as $$L(P,\varpi) = \log\big\{ \sum\limits_{x_i} p(x_i)e^{\varpi\left(1-p(x_i)\right)} \big\},$$ which measures the information importance of the distribution. Then, by setting the parameter $\varpi=1$ and simplifying the form of MIM, it is easy to obtain its fundamental definition as follows.
\[defn:MIM\] For the discrete probability $P$=$\{p(x_1)$, $p(x_2)$, ...,$ p(x_n)\}$, the MIM can be given by $$\label{MIM_discrete1}
\begin{aligned}
L(P)
& = \sum\limits_{x_i} p(x_i) e^{-p(x_i)}.\\
\end{aligned}$$
Comparing with Shannon entropy and Renyi entropy, the MIM replaces the corresponding logarithm operator or polynomial operator with the exponential form so that the weight factors of small probability elements can be amplified much more. This can help to reflect the significance of small probability events from the viewpoint of information measure.
In addition, as a kind of evaluation criteria, Fadeev’s postulates are commonly used to describe the information measures including Shannon entropy and Renyi entropy [@On-measures-of-entropy-and-information]. In this case, for two independent random distributions $P$ and $Q$, Renyi entropy has a weaker postulate than Shannon entropy, that is $$H(PQ) = H(P) + H(Q),$$ where the function $H(\cdot)$ denotes the corresponding information measure. Similarly, the MIM has a much weaker postulate than Renyi entropy, as follows $$H(PQ) \le H(P) + H(Q).$$ Therefore, in the sense of generalized Fadeev’s postulates, the MIM can be reasonably viewed as a kind of information measure similar to Shannon entropy and Renyi entropy.
Message Importance Transfer Measure
-----------------------------------
For an information transfer process, we consider such a model that all the $P$ and $Q$ satisfies the Lipschitz condition as follows, $$\label{eq.Lipschitz}
|H(P)- H(Q)| \le \lambda\|P-Q\|_{1},$$ where $P$ and $Q$ denote the original probability distribution and the final one respectively in the information transfer process; $\lambda>0$ is the Lipschitz constant; $H(\cdot)$ denotes a kind of information measure function; $\| \cdot \|_{1}$ denotes the $l_1$-norm measure.
Here, we shall investigate and measure information transfer process by use of the message importance. Actually, how to characterize the message importance variation in the processing of big data is a critical and interesting problem. On account of Definition \[defn:MIM\], it is available to regard the MIM as an element to measure the message importance variation for a dynamic system. Then, a new information transfer measure based on the MIM is defined as follows.
\[defn:MID\] For two discrete probability $Q=\{q(x_1), q(x_2),$ $... , q(x_n)\}$ and $P=\{p(x_1), p(x_2), ... , p(x_n)\}$ satisfying the constraint in Eq. (\[eq.Lipschitz\]), the message importance transfer measure (MITM) is defined as $$\label{MID_discrete}
\begin{aligned}
& D_{I}(Q||P)
= \sum\limits_{x_i} \{ q(x_i) e^{-q(x_i)} -p(x_i) e^{-p(x_i)} \}.\\
\end{aligned}$$
Note that the Definition \[defn:MID\] characterizes the information transfer from the statistics. That is, we can make use of MITM to measure the change of message importance focusing on small probability events in an information transfer process. Actually, there exist a variety of different information measures handling the problem of information transfer process. Shannon entropy and Renyi entropy are applicable to intrinsic dimension estimation [@On-local-intrinsic-dimension-estimation]. As well, the NMIM can be used in anomaly detection [@Non-parametric-Message-Important-Measure]. Moreover, the directed information and Schreiber’s transfer entropy [@Measuring-information-transfer] are commonly applied to inferring the causality structure and characterizing the information transfer process. In addition, referring to the idea from dynamical system theory, new information transfer measures are proposed to explore and exploit the causality between states in the system control [@Causality-preserving-information-transfer-measure].
However, in spite of numerous kinds of information measures, few works focus on how to characterize the information transfer from the perspective of message importance in big data. To this end, the MITM different from the above information measures is introduced.
Organization
------------
We organize the rest of this paper as follows. In Section II, we introduce the message importance transfer capacity measured by the MITM to describe the information transfer with disturbance. In Section III, we extend the MITM to the continuous case to investigate the variation of message importance in the information transfer process. In Section IV, the MITM is used to discuss the queue length selection for the data caching in MEC from the viewpoint of queue theory. Moreover, some simulations are presented to validate our theoretical results. Finally, we conclude it in Section VI.
Message Importance Transfer Capacity Based on Message Importance Transfer Measure
=================================================================================
In this section, we will introduce the MITM to characterize the information transfer process shown in Fig. \[fig\_transfer\_system\]. To do so, we define the message importance transfer capacity measured by the MITM as follows.
![Information transfer system model.[]{data-label="fig_transfer_system"}](transfer_system.eps){width="3.6in"}
\[defn:C\_D\] Assume that there exists an information transfer process (from the variable $X$ to $Y$) as, $$\label{eq.relation_1}
\begin{aligned}
\big\{ X, p(y-\delta_0|x), Y-\delta_0 \big| \delta_0 \in \{ \delta | \delta \sim p(\delta)\} \big\},
\end{aligned}$$ where $\delta$ denotes a disturbance following distribution $p(\delta)$ and $\delta_0$ is a certain element from the support set of $\delta$. In brief, Eq. (\[eq.relation\_1\]) can also be written as $$\label{eq.relation}
\begin{aligned}
\big\{ X, p(\tilde y|x), \tilde Y \big\},
\end{aligned}$$ where $\tilde y=y-\delta_0$ and $\tilde Y=Y-\delta_0$. Furthermore, $p(\tilde y|x)$ denotes a probability distribution matrix describing the information transfer from the variable $X$ following the distribution $p(x)$ to $\tilde Y$ following the distribution $p(\tilde y)$. We define the message importance transfer capacity as $$\begin{aligned}\label{eq.D_channel_average}
& C = \sum\limits_{\delta_0 \in \{\delta | \delta \sim p(\delta) \}} p(\delta_0) \tilde C(\delta_0), \\
\end{aligned}$$ in which $$\begin{aligned}\label{eq.D_channel}
& \tilde C(\delta_0)= \max\limits_{p(x)} \{ L(\tilde Y) - L(\tilde Y|X)\}, \\
\end{aligned}$$ where $p(\tilde y_j) = \sum\limits_{x_i} p(x_i)p(\tilde y_j|x_i)$, $L(\tilde Y)=\sum\limits_{\tilde y_j} p(\tilde y_j)e^{-p(\tilde y_j)}$, $L(\tilde Y|X) = \sum_{\tilde y_j}\sum_{x_i} p(x_i,\tilde y_j)e^{-p(\tilde y_j|x_i)}$ with the constraint $|L(\tilde Y)- L(\tilde Y|X)| \le \lambda\|p(\tilde y)-p(\tilde y|x)\|_{1}$.
In order to have an insight into the applications of message importance transfer capacity, some specific information transfer scenarios are discussed as follows.
Binary symmetric information transfer
-------------------------------------
\[prop.symmetric\] Assume that there exists an information transfer process as same as that mentioned in Eq. (\[eq.relation\_1\]) and Eq. (\[eq.relation\]), where the disturbance $\delta$ follows a binary uniform distribution (namely $p$($\delta$)= (1/2, 1/2)), and the information transfer matrix is $$\begin{aligned}
p(\tilde y|x) = \left [
\begin{matrix}
1-\beta & \beta \\
\beta & 1-\beta
\end{matrix}
\right ],
\end{aligned}$$ which indicates that variables $X$ and $\tilde Y$ both obey the binary distributions. In this case, the message importance transfer capacity is $$\begin{aligned}
C(\beta) = e^{ -\frac{1}{2} } - L(\beta),
\end{aligned}$$ where $L(\beta)= \beta e^{-\beta} + (1-\beta)e^{-(1-\beta)} $ ($0<\beta<1 $) and $|C(\beta)| \le \lambda\|p(\tilde y)-p(\tilde y|x)\|_{1}$ ($\lambda \ge \frac{e^{ -\frac{1}{2} } - \beta e^{-\beta} + (1-\beta)e^{-(1-\beta)}}{|1-2\beta|} $).
Considering a variable $X$ following the binary distribution $(p, 1-p)$, it is not difficult to see that $$\begin{aligned}
L(\tilde Y|X)
& = \beta e^{-\beta} + (1-\beta)e^{-(1-\beta)}.
\end{aligned}$$ Moreover, according to Eq. (\[eq.D\_channel\_average\]) and Eq. (\[eq.D\_channel\]), we have message importance transfer capacity as $$\begin{aligned}
& C(p, \beta) = \max\limits_{p} \Big\{ [p+ \beta(1-2p)]e^{-[p+ \beta(1-2p)]} \\
& + [(1-p)+ \beta(2p-1)]e^{-[(1-p)+ \beta(2p-1)]} \Big\} - L(\beta).\\
\end{aligned}$$ Then, it is readily seen that $$\begin{aligned}
& \frac{\partial C(p, \beta)}{\partial p} = (1-2\beta)\Big\{ [1-p-\beta(1-2p)]e^{-[p+ \beta(1-2p)]} \\
& - [1-(1-p)-\varepsilon(2p-1)]e^{-[(1-p)+ \beta(2p-1)]} \Big\}. \\
\end{aligned}$$
In the light of the monotonically decreasing of $\frac{\partial C(p, \beta)}{\partial p}$ for $p \in [0,1]$, it is apparent that $p=1/2$ is the only solution for $\frac{\partial C(p, \beta)}{\partial p} =0 $. Therefore, the proposition can be testified.
According to Proposition \[prop.symmetric\], on one hand, when $\beta=1/2$, that is, the information transfer process is just random, we will gain the lower bound of $C(\beta)$, namely $C(\beta) =0$. On the other hand, when $\beta=0$, we will have the maximum message importance transfer capacity.
Strongly symmetric information transfer
---------------------------------------
Assume that the information transfer process described by Eq. (\[eq.relation\_1\]) and Eq. (\[eq.relation\]), has a strongly symmetric information transfer matrix $$\begin{aligned}
p(\tilde y|x) = \left [
\begin{matrix}
1-\beta & \frac{\beta}{K-1} &...& \frac{\beta}{K-1} \\
\frac{\beta}{K-1} & 1-\beta & ...& \frac{\beta}{K-1} \\
...& ... & ... & ...\\
\frac{\beta}{K-1} &...& \frac{\beta}{K-1} & 1-\beta
\end{matrix}
\right ],
\end{aligned}$$ and its disturbance $\delta$ follows an uniform distribution (namely $p$($\delta$)= (1/K,... 1/K)), which indicates that variables $X$ and $\tilde Y$ both follow $K$-ary distributions. Then, we have the message importance transfer capacity as $$\begin{aligned}
C(\beta) = e^{-\frac{1}{K}}- \{ (1-\beta)e^{- (1-\beta) } + \beta e^{-\frac{\beta}{K-1} } \},
\end{aligned}$$ where the parameter $\beta \in (0,1)$ and $|C(\beta)| \le \lambda\|p(\tilde y)-p(\tilde y|x)\|_{1}$ ($\lambda \ge \frac{ e^{-{1}/{K}}- (1-\beta)e^{- (1-\beta) } - \beta e^{-{\beta}/{K-1} }}{2|1-\beta-1/K|} $).
This Corollary is an extension of Proposition \[prop.symmetric\]. First, on account of the information transfer matrix and the Eq. (\[MIM\_discrete1\]), we have $$\begin{aligned}
L(\tilde Y|X)
& = \beta e^{-\frac{\beta}{K-1}} + (1-\beta)e^{-(1-\beta)}.
\end{aligned}$$
Then, similar to the proof of Proposition \[prop.symmetric\], we can also use Lagrange multiplier method to obtain the message information transfer capacity. In this case, the distribution of $\tilde Y$ should satisfy $p(\tilde y_1)=p(\tilde y_2)=...=p(\tilde y_K)=1/K$.
In addition, consider that the probability distribution of variable $X$ is $\{p(x_1),p(x_2),...,p(x_K) \}$. In the strongly symmetric transfer matrix, if the variable $X$ follows uniform distribution, namely $p(x_1)=p(x_2)=...=p(x_K)=1/K$, we will have $$\begin{aligned}
p(\tilde y_j)
& = \sum\limits_{i=1}^{K} p(x_i, \tilde y_j)= \sum\limits_{i=1}^{K}p(x_i) p( \tilde y_j|x_i)\\
& = \frac{1}{K} \sum\limits_{i=1}^{K} p(\tilde y_j|x_i)= \frac{1}{K},
\end{aligned}$$ which indicates that $\tilde Y$ also follows the uniform distribution.
Therefore, it is testified that when the variable $X$ follows an uniform distribution which leads to the uniform distribution for variable $\tilde Y$, we will obtain the message importance transfer capacity $C(\beta)$.
Message Importance Transfer Measure in Continuous Cases
=======================================================
Similar to the definition \[defn:MIM\] and \[MIM\_discrete1\], we can extend the two definition to the case with continuous distributions as follows $$\label{MIM_continue1}
\begin{aligned}
L(f(x))
& = \int_{S_x} f(x) e^{-f(x)}dx, \quad \quad x \in S_x,\\
\end{aligned}$$ $$\label{MID_continue}
\begin{aligned}
D_{I}(g(x)||f(x))
& = L(g(x))-L(f(x)) \\
& = \int_{S_x} { g(x) e^{-g(x)}- f(x) e^{-f(x)} }dx , x \in S_x,\\
\end{aligned}$$ where $g(x)$ and $f(x)$ are two probability distributions with respect to the variable $X$ in a given interval $S_x$. Moreover, $L(f(x))$ and $D_{I}(g(x)||f(x))$ can be regarded as the continuous MIM and MITM. Then, we investigate the variation of message importance by using the continuous MITM, which can also reflect the robustness of continuous MITM. Consider the observation model, $\mathcal{P}_{g_0|f_0}$: $f_0(x) \to g_0(x)$, that denotes an information transfer map for the variable $X$ from the probability distribution $f_0(x)$ to $g_0(x)$. By using the similar way in [@An-information-theoretic-approach], the relationship between $f_0(x)$ and $g_0(x)$ can be described as $$\label{gx0_fx0}
g_0(x)= f_0(x) + \epsilon f_0^{\alpha}(x)u(x),$$ and the constraint condition satisfies $$\label{condition.gx0_fx0}
\int_{S_x}\epsilon f_0^{\alpha}(x)u(x) dx=0,$$ where $\epsilon$ and $\alpha$ are adjustable coefficients. $u(x)$ is a perturbation function of the variable $X$ in the interval $S_x$.
Then, by using the above model, the end-to-end information distance measured by continuous MITM is given as follows.
\[prop.disturbance\] For two probability distributions $g_0(x)$ and $f_0(x)$ whose relationship satisfies the conditions Eq. (\[gx0\_fx0\]) and Eq. (\[condition.gx0\_fx0\]), the information distance measured by continuous MITM is given by $$\begin{aligned}
& D_{I}(g_0(x)||f_0(x))\\
& = \int_{S_x} \left\{ g_0(x) e^{-g_0(x)}- f_0(x) e^{-f_0(x)} \right\} dx \\
& = \epsilon \sum\limits_{i=1}^{\infty}\frac{(-1)^i(i+1)}{i!} \int_{S_x} f_0^{i+\alpha}(x)u(x) dx\\
& + \frac{\epsilon^2}{2} \sum\limits_{i=1}^{\infty}\frac{(-1)^i(i+1)}{(i-1)!} \int_{S_x} f_0^{i-1+2\alpha}(x)u^2(x) dx + o(\epsilon^2),
\end{aligned}$$ where $\epsilon$ and $\alpha$ denote parameters, $u(x)$ is a function of the variable $X$ in the interval $S_x$, $|D_{I}(g_0(x)||f_0(x))| \le \int_{S_x}|\epsilon f_0^{\alpha}(x)u(x)| dx$ which satisfies the constraint Eq. (\[eq.Lipschitz\]).
In fact, Proposition \[prop.disturbance\] describes the perturbation between $f_0(x)$ and $g_0(x)$. Furthermore, we can obtain the continuous MITM between two distributions $g_1^{(u)}$ and $g_2^{(u)}$ based on the same reference distribution $f_0(x)$, which is given by $$\begin{aligned}
& D_{I}(g_1^{(u)}(x)||g_2^{(u)}(x))\\
& = [L(g_1^{(u)}(x))-L(f_0(x))]-[L(g_2^{(u)}(x))-L(f_0(x))]\\
& = \epsilon \sum\limits_{i=1}^{\infty}\frac{(-1)^i(i+1)}{i!} \int_{S_x} f_0^{i+\alpha}(x)[u_1(x) - u_2(x)]dx\\
& + \frac{\epsilon^2}{2} \sum\limits_{i=1}^{\infty}\frac{(-1)^i(i+1)}{(i-1)!} \int_{S_x} f_0^{i-1+2\alpha}(x) [u_1^2(x)-u_2^2(x)] dx\\
& + o(\epsilon^2),
\end{aligned}$$ where the $\epsilon$ and $\alpha$ are parameters, $u_1(x)$ and $u_2(x)$ are functions of the variable $X$, and $$g_1^{(u)}(x)= f_0(x) + \epsilon f_0^{\alpha}(x)u_1(x), \quad \forall x\in S_x,$$ $$g_2^{(u)}(x)= f_0(x) + \epsilon f_0^{\alpha}(x)u_2(x), \quad \forall x\in S_x,$$ with the constraint $|D_{I}(g_1(x)||g_2(x))| \le \int_{S_x}|\epsilon f_0^{\alpha}(x)\{u_{1}(x)-u_{2}(x)\}| dx$.
It is apparent that when the parameter $\epsilon$ is small enough, the continuous MITM is convergent with the order of $O(\epsilon)$. Actually, this provides a way to apply the continuous MITM to measure the variantion of message importance, if the system does not have relatively large change.
Application in Mobile Edge Computing with the M/M/s/k queue
============================================================
Consider the MEC system that consists of numerous mobile users, an edge server, and a central cloud. The queue model on the edge server can be considered as the M/M/s/k queue, where the first and the second $M$ denote the request interarrival time of mobile users and service request time in the edge server respectively, and both of them follow exponential distribution; $s$ is the parallel processing core number; $k$ denotes the queuing buffer size [@multi-objective-optimization-for-computation-offloading].
In order to save resources of system, we now consider a more complicated M/M/s/k model which has the request lose depending on the queue length, namely the real arrival rate satisfies $\tilde \lambda_j=\tilde \lambda \cdot h_j$ ($\tilde \lambda$ is the original arrival rate and the parameter $h_j= \frac{1}{1+j}$ depends on the queue length $j$) [@An-explicit-solution; @On-multiserver-feedback]. In fact, the state probability of this queue model is derived from the stationary process, namely a dynamic equilibrium based on birth and death process. In this case, we can obtain the steady queue state probability $p_{k,j}$ ($j=0,..., s+k$) as follows $$\label{eq.p_0}
p_{k,0}= \Big[ \sum\limits_{j=0}^{s-1} \frac{a^j}{j!j!} + \frac{a^s}{s!} \cdot \sum\limits_{j=s}^{s+k} \frac{\rho^{j-s} }{j!} \Big]^{-1}, \ $$ $$\label{eq.p_k_j}
p_{k,j} = \frac{a^j}{j!j!}p_{k,0}, \quad (0<j<s),\qquad \quad \ $$ $$\label{eq.p_k_s}
p_{k,j} = \frac{a^s}{s!j!} \rho^{j-s} p_{k,0}, \quad ( s \le j \le s+k),$$ where $s$ is the number of servers, $k$ is the buffer or caching size, the traffic intensity $\rho= a /s$ as well as $a= \tilde \lambda / \tilde \mu$ ($\tilde \lambda$ and $\tilde \mu$ are the original arrival rate and service rate respectively).
As for the MITM, it can be used to distinguish the state probability distributions in the above M/M/s model. By use of Taylor series expansion, the approximate MIM is given by $$\label{eq.MIM_MMsk}
\begin{aligned}
& \sum\limits_{j=0}^{s+k} p_{k,j} e^{-p_{k,j}}
= \sum\limits_{j=0}^{s+k} p_{k,j}[1-p_{k,j} + O(p_{k,j}^2)]\\
& \doteq 1 - p_{k,0}^2 \bigg\{ \sum\limits_{j=0}^{s-1} {(\frac{a^j}{j!j!})^2}+ (\frac{a^s}{s!})^2 \sum\limits_{j=s}^{s+k} (\frac{\rho^{j-s}}{j!})^2 \bigg\}. \\
\end{aligned}$$ Then, referring to Eq. (\[eq.MIM\_MMsk\]), we can use MITM to characterize the message importance gap for the M/M/s model as follows.
\[pro.MID\_queue\] As for the M/M/s model mentioned in Eq. (\[eq.p\_0\])-(\[eq.p\_k\_s\]), the information difference between two queue state probability distributions $P_{k}= \{{p}_{k,0}, {p}_{k,1}, ..., {p}_{k,s+k}, 0, 0,..., 0\}$ and $P_{k+1} = \{{p}_{k+1,0},$ ${p}_{k+1,1}, ..., {p}_{k+1,s+k+1}, 0,..., 0\}$ with buffer size $k$ and $k+1$ respectively, can be measured by MITM as $$\label{MID_queueing}
\begin{aligned}
& D_I(P_{k+1}||P_{k})\\
& = \sum\limits_{j=0}^{s+k+1} {p}_{k+1,j} e^{-{p}_{k+1,j}} - \sum\limits_{j=0}^{s+k}{p}_{k,j} e^{-{p}_{k,j}} \\
& \doteq \Big\{ \frac{1}{(\varphi_1 + \varphi_2 \sum\limits_{j=s}^{s+k}\frac{\rho^{j-s}}{j!})^2}
- \frac{1}{(\varphi_1 + \varphi_2 \sum\limits_{j=s}^{s+k+1}\frac{\rho^{j-s}}{j!})^2} \Big\} \\
& \quad \cdot \Big\{ \sum\limits_{j=0}^{s-1} {(\frac{a^j}{j!j!})^2}
+ \varphi_2^2 \sum\limits_{j=s}^{s+k} (\frac{\rho^{j-s}}{j!})^2 \Big\} \\
& \quad - \frac{ \varphi_2^2 \rho^{2k+2} }{ [(s+k+1)!]^2
\big(\varphi_1^2+ \varphi_2^2\sum\limits_{j=s}^{s+k} \frac{\rho^{j-s}}{j!}\big) },
\end{aligned}$$ where $p_{k,j}$ and $ p_{k+1,j}$ are queue state probability in the M/M/s/[k]{} and M/M/s/[k+1]{} models with the constraint $|D_I(P_{k+1}||P_{k})| \le \lambda\|P_{k+1}-P_{k}\|_{1}$, as well as the parameter $\varphi_1$ and $\varphi_2$ are given by $ \varphi_1= \sum_{j=0}^{s-1} {a^j}/{(j!j!)}$ and $\varphi_2= {a^s}/{s!}$.
Similarly, it is not difficult to derive the MITM between the queue state probability distributions $P_{\infty} = \{ {p}_{\infty,0}, {p}_{\infty,1}, ...,$ ${p}_{\infty,\infty} \}$ and $P_{k}= \{{p}_{k,0}, {p}_{k,1}, ..., {p}_{k,s+k}, 0, 0,..., 0\}$ with buffer size $\infty$ and $k$, which is given by $$\label{MID_queueing_infty}
\begin{aligned}
& D_I(P_{\infty}|| P_{k}) \\
& \doteq \Big\{ \frac{1}{(\varphi_1 + \varphi_2 \sum\limits_{j=s}^{s+k}\frac{\rho^{j-s}}{j!})^2}
- \frac{1}{\big[ \varphi_1 + \varphi_2 (\frac{e^{\rho}}{\rho^s}
- \sum\limits_{j=0}^{s-1}\frac{\rho^{j-s}}{j!}) \big]^2} \Big\}\\
& \quad \cdot \Big\{ \sum\limits_{j=0}^{s-1} {(\frac{a^j}{j!j!})^2}
+ \varphi_2^2 \sum\limits_{j=s}^{s+k} (\frac{\rho^{j-s}}{j!})^2 \Big\}\\
& \quad - \frac{ \varphi_2^2 \Big(\frac{e^{\rho}}{\rho^s}
- \sum\limits_{j=0}^{s+k}\frac{\rho^{j-s}}{j!}\Big) }{ \Big[ \varphi_1 + \varphi_2 (\frac{e^{\rho}}{\rho^s}
- \sum\limits_{j=0}^{s-1}\frac{\rho^{j-s}}{j!}) \Big]^2}.
\end{aligned}$$
Moreover, for the queue length selection, it is required that the distinction between two distribution $P_{\infty}$ and $P_{k}$ should be small enough, namely, $|D_{I}(P_{\infty}|| P_{k})| \le \epsilon $ ($\epsilon$ is a small parameter). Since that the lower bound of buffer size is complicated, we have a looser lower bound as follows $$\begin{aligned}
& k \ge
\frac{ \ln \Big\{ 1-\frac{1-\rho}{\varphi_2} \big[ ({\varphi}/{\sum\limits_{j=0}^{s-1} (\frac{a^j}{j!j!})^2})^{-\frac{1}{2}} -\varphi_1 \big] \Big\} }{\ln \rho} -1,
\end{aligned}$$ where the parameter $\varphi$ is given by $$\begin{aligned}
\varphi= \epsilon +{\sum\limits_{j=0}^{s-1}(\frac{a^j}{j!j!})^2+\frac{\varphi_2^2 e^{\rho}}{\rho^s} }{\Big[ \varphi_1 + \varphi_2 (\frac{e^{\rho}}{\rho^s}
- \sum\limits_{j=0}^{s-1}\frac{\rho^{j-s}}{j!}) \Big]^{-2} }.
\end{aligned}$$ It is easy to see that $\epsilon$ plays a key role in the caching size selection when using finite size caching to imitate the infinite caching working mode.
Similar to MITM, the KL divergence between the queue state probability distributions with buffer size $k+1$ and $k$ is given by $$\begin{aligned}
& D(P_{k} || P_{k+1})\\
& = \sum\limits_{j} p_{k,j} \log \frac{1}{p_{k+1,j}} - \sum\limits_{j} p_{k,j} \log \frac{1}{p_{k,j}}\\
& = \log \Big\{ 1+ \frac{ \rho^{k+1}}{(s+k+1)!\big(\varphi_1+\varphi_2 \sum\limits_{j=s}^{s+k} \frac{\rho^{j-s}}{j!}\big)} \Big\},
\end{aligned}$$ where the parameters $p_{k,j}$, $p_{k+1,j}$, $\varphi_1$ and $\varphi_2$ are the same as them in Proposition \[pro.MID\_queue\].
Likewise, we can derive the KL divergence between the queue state distributions with buffer size $\infty$ and $k$ as $$\begin{aligned}
D(P_{k}||P_{\infty})
& = \log \frac{ \varphi_1 + \varphi_2 (\frac{e^{\rho}}{\rho^s}- \sum_{j=0}^{s-1} \frac{\rho^{j-s}}{j!})}{ \varphi_1+ \varphi_2 \sum_{j=s}^{s+k}\frac{\rho^{j-s}}{j!} }.
\end{aligned}$$
For the queue length selection with KL divergence, we have a looser lower bound of buffer size as follows $$\begin{aligned}
k \ge \frac{\ln \Big\{ 1- \frac{(1-\rho)}{ 2^{\epsilon} \varphi_2}\big[ \varphi_1(1-2^{\epsilon})
+ \varphi_2(\frac{e^{\rho}}{\rho^s} - \sum\limits_{j=0}^{s-1} \frac{\rho^{j-s}}{j!})
\big] \Big\}}{\ln \rho} -1.
\end{aligned}$$ To validate our derived results in theory, some simulations are presented. The events arrivals are listed in Table \[table\_arrival\]. It is readily seen that they have the same average interarrival time as $1/\tilde \lambda_{j,0}$. Besides, the traffic intensity is selected as $\rho = 0.9$ in all discussed cases.
[|c|c|c|c|]{}
--------------
Type of
Distribution
--------------
: The Interarrival Time Distributions of Events’ Arrivals[]{data-label="table_arrival"}
&
--------------
Exponential
Distribution
--------------
: The Interarrival Time Distributions of Events’ Arrivals[]{data-label="table_arrival"}
&
--------------
Uniform
Distribution
--------------
: The Interarrival Time Distributions of Events’ Arrivals[]{data-label="table_arrival"}
&
--------------
Normal
Distribution
--------------
: The Interarrival Time Distributions of Events’ Arrivals[]{data-label="table_arrival"}
\
$P(X)$ & $X \sim E({\tilde \lambda_{j,0}})$ & $X \sim U(0,2/{\tilde \lambda_{j,0}})$ & $X \sim N(\frac{1}{\tilde \lambda_{j,0}}, \frac{1}{{\tilde \lambda_{j,0}}^2} )$\
In Fig. \[fig\_kk\_performance\] and \[fig\_inftyk\_performance\], the legends $D_I$-$Sim$, $D_I$-$Ana$ and $D$-$Sim$, $D$-$Ana$ denote the simulation results and the analytical results for MITM and KL divergence, respectively. It is illustrated that the convergence of MITM is faster than that of KL divergence, which indicates that MITM may provide a reasonable lower bound to select the caching size for MEC. In addition, we can see that the Poisson distribution corresponds the worst case for the arrival process among the three discussed cases with respect to the convergence of both MITM and KL divergence.
Conclusion
==========
In this paper, we investigated the information transfer problem in big data and proposed an information measure, i.e., MITM. Furthermore, this information measure has its own dramatic characteristics on paying more attention to the message importance hidden in big data. This makes the information measure as a promising tool for information transfer measure in big data. We presented the message importance transfer capacity measured by the MITM which can give an upper bound for the information transfer with disturbance. Furthermore, the MITM was extended to the continuous case to investigate the variation of message importance in the information transfer process. In addition, we employed the MITM to discuss the caching size selection in the MEC.
[00]{} A. Zieba, “Counterterrorism systems of spain and poland: comparative studies,” *Przeglad Politologiczny*, no. 3, pp. 6578, Mar. 2015.
C. Phua, V. Lee, K. Smith, and R. Gayler, “A comprehensive survey of data mining-based fraud detection research,” in *Proc. Intelligent Computation Tech. and Automation (ICICTA)*, pp. 50–53, 2010.
J. He, Y. Liu, and R. Lawrence, “Graph-based rare category detection,” in *Proc. 8th IEEE Int. Conf. Data Mining*, Houston, TX, 2008, USA, pp. 418–425.
D. Zhou, K. Wang, N. Cao, and J He, “Rare category detection on time-evolving graphs,” in *Proc. 15th IEEE Int. Conf. Data Mining*, Atlantic City, NJ, USA, 2015, pp. 1550–4786.
P. Fan, Y. Dong, J. Lu, and S. Liu, “Message importance measure and its application to minority subset detection in big data,” in *Proc. IEEE Globecom Workshops (GC Wkshps)*, Washington D.C., USA, Dec. 2016, pp 1–5.
A. Renyi, “On measures of entropy and information,” in *Proc. 4th Berkeley Symp. Math. Statist. and Probability*, vol. 1. 1961, pp. 547–561.
K. M. Carter, R. Raich, and A. O. Hero, “On local intrinsic dimension estimation and its applications,” *IEEE Trans. Signal Process.*, vol. 58, no. 2, pp. 650–663, Feb. 2010.
S. Liu, R. She, P. Fan, K. B. Letaief, “Non-parametric Message Importance Measure: Storage Code Design and Transmission Planning for Big Data,” *IEEE Trans. Commun.*, pp. 1–1, Jun. 2018.\[DOI: 10.1109TCOMM.2018.2847666\] T. Schreiber, “Measuring information transfer,” *Physical Review Letters*, vol. 85, no. 2, pp. 461–464, July, 2000.
S. Sinha and U. Vaidya, “Causality preserving information transfer measure for control dynamical system,’’ in *Proc. IEEE 55th Conference on Decision and Control (CDC)* , Las Vegas, USA, Dec. 2016, pp. 7329–7334.
S. Huang, A. Makur, L. Zheng, and G. W. Wornell, “An information-theoretic approach to universal feature selection in high-dimensional inference,” in *Proc. 2017 IEEE International Symposium on Information Theory (ISIT)*, Aachen, Germany, June. 2017, pp. 1336–1340.
L. Liu, Z. Chang, X. Guo, and T. Ristaniemi, “Multi-objective optimization for computation offloading in mobile-edge computing,’’ In *Proc. IEEE Symposium on Computers and Communications (ISCC)*, Heraklion, Greece, July. 2017, pp 832–837.
G. Koole, P. Nain, “An explicit solution for the value function of a priority queue,” *Queueing Systems*, vol. 47, no. 3, pp. 251–282, July, 2004.
B.K. Kumar, J. Raja, “On multiserver feedback retrial queues with balking and control retrial rate,” *Ann. Oper. Res*, vol. 141, no. 1, pp. 211–232, Jan., 2006.
[^1]: We indeed appreciate the support of the National Natural Science Foundation of China (NSFC) No. 61771283.
|
{
"pile_set_name": "ArXiv"
}
|
Diane Mizota
Diane Kiyomi Mizota (born September 9, 1973) is an American dancer, actress, and TV personality.
Early life
Diane Mizota, a Japanese American, was born in Los Angeles, California and raised in Danville, California. She studied dance in high school and in UCLA and graduated summa cum laude with a degree in Communication Studies.
Filmography
Film
Television
External links
Diane Mizota's website
Category:1973 births
Category:Actresses from Los Angeles
Category:American actresses of Japanese descent
Category:American female dancers
Category:American film actresses
Category:American television actresses
Category:American television personalities
Category:Dancers from California
Category:Living people
Category:Television personalities from California
Category:20th-century American actresses
Category:20th-century American dancers
Category:21st-century American actresses
Category:21st-century American dancers
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{
"pile_set_name": "Wikipedia (en)"
}
|
The Horror of the Heights
"The Horror of the Heights" is a short story by Arthur Conan Doyle. It was first published in Strand Magazine in 1913.
Synopsis
The story is told through a blood-stained notebook discovered on the edge of a farm in Withyham. The notebook is written by a Mr. Joyce-Armstrong, and the first two and last pages are missing; the notebook is thus dubbed the "Joyce-Armstrong Fragment".
Joyce-Armstrong, a brave aviator, had been curious over the deaths of certain pilots who tried to break the current height record of 30,000 feet. Recent casualties involve some strange deaths – one, Hay Connor, died after landing while he was still in his plane, while another, Myrtle, was discovered with his head missing. Joyce-Armstrong speculates that the answer to these deaths may be the result of what he calls "air-jungles":
There are jungles of the upper air […] One of them lies over the Pau-Biarritz district of France. Another is just over my head as I write here in my house in Wiltshire. I rather think there is a third in the Homburg-Wiesbaden district.
Joyce-Armstrong takes his monoplane to a height of 40,000 feet and is nearly hit by three meteors. It is then that he learns that his speculations are right: entire ecosystems (air-jungles) exist high in the atmosphere, and are inhabited by huge, gelatinous, semi-solid creatures. After going through a flock of animals superficially resembling jellyfish and snakes, Joyce-Armstrong is attacked by a more solid-looking but amorphous creature with a beak and tentacles, from which he narrowly escapes. He then returns to the ground.
The aviator writes he will be going up again to the air-jungle to bring back proof of his discoveries, but here the fragment ends, save for one last sentence which reads:
"Forty-three thousand feet. I shall never see earth again. They are beneath me, three of them. God help me; it is a dreadful death to die!"
The narrative outside the notebook then explains that Joyce-Armstrong has been missing and that his monoplane was discovered in a wreck on the border of Kent and Sussex.
Collections
The story has appeared in a number of collections, the earliest being Danger! and Other Stories (1918), as well as in more general collections like Volume 5 of The Road to Science Fiction.
Adaptations
The story formed a part of Forgotten Futures III.
See also
Crawfordsville monster
Notes
References
"The Horror of the Heights" at Locus Magazine's Index to Science Fiction
"The Horror of the Heights" at the Index to Science Fiction Anthologies and Collections, Combined Edition
"The Horror of the Heights" at the FictionMags Index
External links
Tales of Terror and Mystery at Project Gutenberg
Horror of the Heights at the Literature Page
Horror of the Heights with illustrations from its original publication at Forgotten Futures
Horror of the Heights Scan of the original magazine pages at Archive.org
Category:1913 short stories
Category:Horror short stories
Category:Short stories by Arthur Conan Doyle
Category:Works originally published in The Strand Magazine
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{
"pile_set_name": "Wikipedia (en)"
}
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Dark-Web Drug Dealer Arrested After He Travelled US for World Beard Championships
United States authorities arrested suspected dark web drug kingpin late last month while he was travelling from his base in France to the United States of America for this year’s annual World Beard and Mustache Championships.
Gal Vallerius, a 38-year-old French national, was travelling to Austin, Texas, for the competition but was caught by U.S. authorities on August 31 upon landing at Atlanta International Airport on a distribution complaint filed in Miami federal court, The Miami Herald reported Tuesday.
Authorities confirmed Vallerius’ identity to the online moniker “OxyMonster,” which was previously used to sell drugs on an illegal underground dark web marketplace called Dream Market by searching his laptop that the brown-beard contestant carried with him.
Alleged Moderator/Admin Of Dark-Web Dream Market
According to Drug Enforcement Administration (DEA) affidavit filed in September, Vallerius was an administrator, senior moderator and vendor on Dream Market, an eBay-type marketplace for illegal narcotics and drug paraphernalia.
Vallerius was suspected of openly advertising and selling drugs including cocaine, LSD, methamphetamine, fentanyl and oxycodone, on Dream Market between May 2015 and August 2017.
Vallerius’ laptop also contained the Tor browser, which lets users hide their true internet protocol (IP) addresses, allowing them to operate anonymously on the network.
According to the Miami Herald, the suspect is now expected to be transferred from Atlanta to Miami, where he will be facing a fresh conspiracy indictment that carries up to life in federal prison.
U.S. authorities have been cracking down on dark web marketplaces. A few months back, Europol along with FBI, DEA (Drug Enforcement Agency) and Dutch National Police disrupted two major underground markets, AlphaBay and Hansa.
Benefitted from the shutdown of its rivals, Dream Market had a total of 94,236 listings as of 29 August 2017. Now, if Vallerius is found admin of the platform, his arrest could bring an end to Dream Market as well.
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Several of the hedge fund industry's top managers trained under legendary investor, Julian Robertson at Tiger Management. When these managers started their own firms, they were collectively referred to as the "Tiger Cubs", and several of them formed the nucleus of the Greenwich hedge fund community. The act of creative destruction (phenomenon) is playing out once again, as highly trained former managers of Tudor Investment Corp., have set out on their own to create the next generation of hedge fund firms … the "Tudor Cubs"...
Please join us for a fascinating behind the scenes look at the evolution of the hedge fund industry as seen through the eyes of PM's from this elite investment company. Learn what life was like at one of the world’s largest and most successful hedge fund firms, and what it is like today to be an emerging manager in an increasingly competitive and expensive field.
Lindsay Politi - Head of Inflation Strategies, One River Asset Management
Lindsay has nearly 20-years of investment experience as a fixed income portfolio manager and global macro hedge fund manager at Wellington Management and Tudor Investment Corporation.
At Wellington, Lindsay was one of the top 5 global TIPS managers by assets under management. She managed over $10 Billion in inflation-linked assets with top quintile track record in peer group returns and information ratios.
Bill holds a PhD degree in Economics from Yale University and a BA from Fudan University (China). He also taught finance at University of Michigan Ross School of Business.
Mark Heffernan - CEO, Alwyne Management
Mr. Heffernan started his career at Goldman Sachs as a proprietary trader in currencies and interest rates. Mr. Heffernan worked at Goldman Sachs from 1985 to 1991. From 1992 to 2001, Mr. Heffernan was a proprietary trader with Tudor Investment Corporation, where he developed and managed their London office. Mr. Heffernan ran Sotoha Trading, LP from 2003 to 2004.
From 2004 to 2008, Mr. Heffernan participated in various ventures, including founding insidesoccer.com. In March 2008, Mr. Heffernan became a fund manager at Tudor Investment Corporation managing a macro portfolio. In January 2016 Mr. Heffernan became a volunteer assistant coach to the University of Virginia Men’s Soccer Team.
Mr. Heffernan holds a BA in Economics from Cambridge University, earned in 1985.
Spencer Lampert - Chairman, CIO at Exelauno Capital Management
Spencer Lampert is a portfolio manager focusing on discretionary macro trading. Spencer joined Tudor in 1987 and created the firm’s global equity trading department. Spencer then served as the Director of Research for Tudor's Global Macro Trading. In 2016, Spencer retired from Tudor to launch Exelauno Capital, a global macro hedge fund in Greenwich.
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{
"pile_set_name": "Pile-CC"
}
|
Assemble computers for your home or office use.Please give your specifications to me .After that we can assemble it with reasonable price with warranty.We are also under take computer repairs we can visit your home or office to solve your problem.
Assemble computers for your home or office use.Please give your specifications to me .After that we can assemble it with reasonable price with warranty.We are also under take computer repairs we can visit your home or office to solve your problem.
Assemble computers for your home or office use.Please give your specifications to me .After that we can assemble it with reasonable price with warranty.We are also under take computer repairs we can visit your home or office to solve your problem.
|
{
"pile_set_name": "Pile-CC"
}
|
/*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
*
* This is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of
* the License, or (at your option) any later version.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this software; if not, write to the Free
* Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
* 02110-1301 USA, or see the FSF site: http://www.fsf.org.
*/
package org.xwiki.vfs;
import java.net.URI;
import java.nio.file.Path;
import org.xwiki.component.annotation.Role;
/**
* Helper to create a {@link Path} instance from an XWiki VFS URI
* (e.g. {@code attach:Sandbox.WebHome@my.zip/some/path}).
*
* @version $Id: 3d7d7f2f6e2dae579cfca2d26e4203b21178c243 $
* @since 8.4RC1
*/
@Role
public interface VfsPathFactory
{
/**
* @param uri the XWiki VFS URI (e.g. {@code attach:Sandbox.WebHome@my.zip/some/path})
* @return the corresponding NIO2 {@link Path} instance
* @throws VfsException if the URI is not valid or the user doesn't have permissions to access it
*/
Path create(URI uri) throws VfsException;
}
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{
"pile_set_name": "Github"
}
|
Q:
the method addHeader (String, String) is undefined for the type HttpGet
I have this program:
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.impl.client.HttpClientBuilder;
public class ApplicationRESTFul {
public static void main(String[] args) {
String url = "http://www.google.com/search?q=httpClient";
HttpClient client = HttpClientBuilder.create().build();
HttpGet request = new HttpGet(url);
request.addHeader("Accept", "application/json");
}
}
But I got this message from eclipse
the method addHeader (String, String) is undefined for the type HttpGet
I am using this library and as I see in the documentation , the method should exist (org.apache.httpcomponents.httpclient_4.5)
http://hc.apache.org/httpcomponents-client-ga/httpclient/apidocs/org/apache/http/client/methods/HttpGet.html
A:
I solved it by adding httpcore JAR to class path. Adding the dependency from maven adds the httpcore JAR too and not just the httpclient JAR, and that's why it works too.
A:
importing the depency from maven instead to add the lib in the classpath solved the problem
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>4.3.6</version>
</dependency>
|
{
"pile_set_name": "StackExchange"
}
|
Italian ship UIOM
UIOM is a planned ocean-going Hydrographic survey vessel of the Marina Militare to be replace Italian ship Ammiraglio Magnaghi (A5303) from 2020
Characteristics
UIOM will be a multipurpose vessel with various operational capabilities, including:
hydrographic and oceanographic surveying;
humanitarian intervention (evacuation) and medical support operations;
maritime search and rescue including diving activities;
command and control platform;
mine countermeasures (MCM) operations management;
helicopter and boat operations.
Driving design parameters are the efficiency in the whole speed range, extended range, remarkable seaworthiness performances. Due to the optimization of spaces, the ship is highly flexible in terms of configuration, embarked equipment and capabilities.
UIOM will be able to embark a few standard ISO1C containers, .
See also
Research vessel
References
Category:Ships built by Fincantieri
Category:2010s ships
Category:Proposed ships
Category:Auxiliary ships of the Italian Navy
Category:Ships built in Italy
Category:Survey ships
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{
"pile_set_name": "Wikipedia (en)"
}
|
The Tecfidera vs placebo had a better result than with the other oral meds, but as the main ingredient is associated with a risk of PML, your doctor should have done preliminary testing for you and also he should have a monitoring plan for you.
I read patient feedback (not a clinical trial result) and the complaints most often heard were nausea, hair loss, and depression. Known side effects from the use of Tecfidera can include flushing, abdominal pain, diarrhea and nausea.
Tammy, the clinical trials for Tecfidera show a high rate of GI upset mostly in the first two months, followed by a high rate of lowered WBC (white blood count) - this is concerning to me, because it means you have less white blood cells to fight off infection. This, doesnt get better with time, whereas the GI upset does.
Hair loss, nausea, diarrhea and all the others are consistently reported.
The drug is brand new. There is no long term data on it. Its hard to say what they will find a year from now. In Europe they linked it to PML the brain infection linked to Tysabri. In the US, you have to be tested for JC VIrus, the virus also linked to PML. IF you have JC Virus, you can still take Tecfidera but its recommended that you have blood work every 6 weeks and MRIs twice a year.
Thats about all anyone knows at this point- again, its too new to know what the long term affects are. If you start it, please let us know how you are doing!
Nikki
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{
"pile_set_name": "Pile-CC"
}
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The Today Show type TV Show
After revealing himself to be HIV positive in November 2015, Charlie Sheen returned to Today Tuesday morning, sharing an update on his life with the disease, reflecting on past regrets, and speaking out against a controversial treatment he sought in Mexico earlier this year.
During an interview with Today‘s Matt Lauer, Sheen revealed his biggest regrets concerning his life, career, and behavior. “I regret not using a condom one or two times when this whole thing happened,” he said. “I regret ruining Two and a Half Men. I regret not being more involved in my children’s lives growing up… But, we can only move forward from today, and they wouldn’t call it the past if it wasn’t.”
He further elaborated on the alternative treatment method he sought south of the border, one that made headlines for its unusual methodology. “That didn’t go so well. That man is a criminal; he’s a charlatan,” the 50-year old said, revealing that, while under Dr. Sam Chachoua’s care, the virus count in his bloodstream jumped from 0 to 7,000. “He’s hurting a lot of good and decent people.”
In January, Sheen visited The Dr. Oz Show to give an update on his health, revealing he was off his HIV medications ahead of traveling to Mexico for the alternative treatment which, according to the doctor, involved spending several months injecting himself with Sheen’s blood and using goat’s milk to help cure the actor’s HIV. Sheen previously noted their time together amounted to little more than a day, and denounced the doctor’s approach to curing him on Twitter.
Currently undergoing treatment as part of an FDA trial, which allowed the actor to declare himself “undetectable,” Sheen described his current healthcare routine as “one shot per week, as opposed to pills everyday,” noting the change is “not just physical, but it’s psychological… This is the future of treatment what I’m doing now.”
On his previous Today appearance, Sheen said he was diagnosed with HIV more than four years prior. “It’s a hard three letters to absorb. It’s a turning point in one’s life,” the actor said, admitting he’d spent more than $10 million to keep the condition secret as he endured various “shakedowns” over the years regarding his health. “What people forget is that that’s money they’re taking from my children… I trusted them and they were deep in my inner circle, and I thought they could be helpful. My trust turned to their treason.”
Sheen rose to prominence in the late 1980s, after roles in films like Platoon (1986), Wall Street (1987), and Major League (1989). He later made waves in 2011 after a public meltdown that resulted in his firing from the hit CBS series Two and a Half Men. The 50-year old later returned to TV on the FX sitcom Anger Management, which ran for 100 episodes between 2012 and 2014. Though it notched the most-watched premiere for a series in the network’s history, the show made headlines for the alleged on-set conflict between Sheen and costar Selma Blair, who left the series into its second season after reportedly clashing with the actor during production.
The actor will next appear alongside Whoopi Goldberg in the indie drama Nine Eleven, which will revolve around five people trapped in an elevator in the World Trade Center on Sept 11., 2001.
Watch Sheen’s full interview on Today in the video below.
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12 Ways to Eat More Vegetables and Fruit
Join Cooking Light in our effort to change the way we approach fruits and vegetables. With our easy tips, you’ll be on your way to five a day. By Sidney Fry, MS, RD
Join Cooking Light in our effort to change the way we approach fruits and vegetables. With our easy tips, you’ll be on your way to five a day. By Sidney Fry, MS, RD
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December 09, 2010
1 of 24
Eat More Fruits and Veggies
You’ve heard it before… Americans just aren’t getting enough fruits and vegetables. And as the foundation of a healthy diet, consider fruits and vegetables your fountain of youth. Rich in vitamins, nutrients, antioxidants, fiber, and water, it’s hard to understand how so many can resist that gorgeous nutritional profile. But the hard facts tell us that less than 30% of us – that’s seven out of every ten Americans – are failing to meet the recommended 5 A Day.
Enter in The 12 Healthy Habits. We’re not asking for a revolution. Just a few small and very simple changes to make you eat better, feel better, and create an overall new sense of well-being. Step one: Eat more Fruits & Veggies. Yes, we are actually asking you to eat more of something. How often do you get to hear that? Here are 12 simple ways to get you eating veggies and fruits today.
Tip 1: Boost Your Breakfast
The importance of eating breakfast is immeasurable. Not only does it break the fast and jumpstart your metabolism, but it also boosts your performance at work or school, helps with weight maintenance, and for the purposes of Healthy Habit #1, is the perfect time get in an extra fruit or vegetable serving for the day.
Stir berries (fresh or frozen), dried fruit, or banana slices into yogurt, cereal, or oatmeal. Our Overnight Maple-Raisin Oatmeal (shown here) boasts a full serving of fruit plus the benefits of oats. Every 1/2 cup of fruit you add is a serving.
• Make a smoothie. Combine some low fat milk or yogurt, 1/2 cup frozen berries and a banana for a super easy blended breakfast – and 2 entire fruit servings!
3 of 24Photo: Brian Kennedy
Boost Your Breakfast
• Add peppers, tomatoes, mushrooms or onions to your eggs for a delicious omelet, or pile the whole scramble on your favorite bread, tortilla, or bagel for a booster breakfast sandwich.
• Have a glass of juice. Make sure it’s made from 100% fruit juice, and limit yourself to eight ounces per day to avoid too many added sugars.
• Don’t have time for breakfast? Whole fruits are quick, prep-free, on-the-go solution. Grab an apple, peach, banana, or orange and enjoy it on your way to work.
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4 of 24Photo: Greg Dupree
Tip 2. Double the Veggies
In soups, salads, pastas, sandwiches, pizzas, and casseroles, most recipes call for a certain amount of vegetables. Our advice? Double the amount called for in the original recipe. You are already doing the prep work; so a little extra chopping can go a long way for your vegetable intake.
• Stir extra veggies into soups. Don’t be afraid to steer off the beaten recipe path just a bit. When it comes to something like soups, an overdose of chopped vegetables will not ruin the recipe. It will enhance the flavor, nutritional value, and your daily vegetable tally. A half cup of chopped vegetables and a whole cup of dark leafy greens is another serving. In Veggie-Quinoa Soup(shown), you can double the amount of carrots, celery, red bell peppers, zucchini…the possibilities are endless.
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5 of 24Photo: Greg Dupree
Double the Veggies
• Pile them on the pizza. Don’t hold back on the veggies! Add extra veggies to a frozen pizza, order double veggies from delivery, or create your own where the sky is the limit.
• Cram them into casseroles. Cooking up a Mexican casserole? Add some extra peppers, mushrooms, and squash. Don’t be shy with topping with tomato- and veggie-heavy salsa, either. Eggplant Parmesan? Double the eggplant. Chicken Pot Pie? Double those peas and carrots. You’ve got the idea.
• Stuff them into sandwiches. A sandwich is another blank canvas just waiting to get stuffed with color. Take your routine turkey sandwich and jazz it up with sliced apples, cucumber, zucchini, sprouts, and spinach. A 1/2 cup of this colorful combination just scored you another serving.
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6 of 24Photo: Jennifer Causey
Tip 3. Be a Sneaky Chef
Sometimes, it’s okay to be sneaky in the kitchen. Try these tips to sneak in one or two extra servings into your day. An added bonus? You’ll be adding a new twist to an old favorite recipe.
• Grate your way to goodness. Shred or grate fruits and vegetables down, or puree them up and see how creative you can get with your favorite recipes. Grated zucchini and carrots do wonders for turkey burgers, meatloaf (like our Veggie-Packed Meat Loaf shown here), and meatballs, adding both moisture and nutrients to the dish.
• Puree cooked cauliflower, winter squash, or red peppers and stir them into sauces, mashed potatoes, pot pies, or even mac and cheese.
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7 of 24Photo: Jennifer Causey
Be a Sneaky Chef
• The secret is in the sauce. Make a mean marinara that’s loaded with vegetables. In addition to your traditional tomato sauce base, use any combination of chopped mushrooms, eggplant, onions, peppers, squash, and carrots. This versatile sauce can then be used in a variety of creative ways to add both flavor, as well as a serving of vegetables to your day. Spoon it over noodles, mix it into lasagna, start it as a soup base, spread it over pizza crust, or use it as a dipping sauce.
• Bribe yourself with baked goods. Both vegetables and fruits are healthy, delicious, and fabulous additions to breads, cakes, biscuits, and pies. Both savory and sweet, what better way to add a vegetable serving to your day?
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8 of 24Photo: Jennifer Causey
Tip 4. Make-Ahead Meatless Mondays
The campaign for “Meatless Monday” is gaining popularity. The concept is simple: One day a week, cut out the meat. (And Monday seems to be a good day to try.) It’s a great way to eat more fruit and vegetables. By eliminating meat once a week, you may reduce your risk of cancer and heart disease, support sustainability, and even come out saving a buck or two. To make your goal even more attainable, use your Meatless Monday as a make-ahead day to prepare extra fruits and vegetables for the week.
• Choose a day convenient to you to leave meat out of your diet. Use this as a “day of preparation” for the entire week to assist your goal to increase your fruits and vegetables by three servings a day.
Make-Ahead Meatless Mondays
• As your main meatless Monday dish, make a couscous, wild rice, or other grain salad like this Quinoa Bibimbop Bowlspacked with seasonal vegetables. Enjoy throughout the week in wraps, over a bed of spinach, or heated into omelets.
• Sauté or grill extra vegetables on your meatless Monday, and continue to use the leftovers later in the week in pasta dishes, soups, sandwiches, and salads.
• Make a large batch of fruit salad to have on hand for meals and snacks.
• Become a food processing pro – Use the shredding blade to grate squash, carrots, zucchini, turnips, onions, sweet potatoes, etc. Bag them up and keep them easily accessible in the refrigerator. Add them to sauces, soups, stir-fry, casseroles, pizzas.
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10 of 24Photo: Jennifer Causey
Tip 5: Feature a Fresh New Vegetable Each Week
Try to experiment with a new seasonal vegetable (or fruit) each week. Don’t try a tomato in December. You are far more likely to fall in love with its lush, juicy, tangy taste in the height of summer.
• If there is a local farmer’s market nearby, support your community and pay them a visit. Get the whole family involved. Allow either yourself, or a family member to choose a new item from the produce section and add it to your meal.
• Cooking for one? Invite a friend or two over to try the new dish with you. Two heads are often better than one, and you can both learn together.
• Once spring is here, U-pick farms are a fun way to get up close and personal with your produce.
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11 of 24Photo: Trinette Reed / Getty
Feature a Fresh New Vegetable Each Week
• As your peruse your monthly food magazines, cookbooks, and food blogs, print off, photo copy, or tear out any new recipes that feature an unfamiliar fruit or vegetable you’d like to try. Keep them in a folder for easy access and pull one out on your “Make-Ahead” day to try.
• On a budget? Check the weekly specials at your local grocery store and choose one of the items on special that week. The specials often reflect the abundance of certain seasonal produce.
• Check out our “What’s in Season” guide to find out what produce is in season right now, recipe suggestions, and prep tips.
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12 of 24Photo: Colin Price
Tip 6: Salute the Snack
Snacks have gotten a bad rap. A healthy snack can help you curb hunger throughout the day and provide energy and important nutrients. Make all of your snacks revolve around fruits and vegetables. Stock countertops, pantries, refrigerators (at home and work), desk, car, and purse with some form of fruit or veggie.
• Keep a bowl of fresh fruit on the counter at home or on your desk for a healthy (and eye-appealing) quick fix.
• Keep dried fruit in your car or purse for busy days when a breather is just not an option.
• Pack pre-cut fruit and veggies into snack-size bags for perfectly-portioned munchies. Keep them eye level in the fridge for easy access.
• Swap up your afternoon soda for 1/2 cup of 100% juice to squeeze in an extra serving.
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13 of 24Photo: Katherine Flynn
Tip 7: Don't Skip Dessert
Desserts tend to be regarded as a sweet treat for special occasions only. But a fruit-based dessert has the ability to offer a light, refreshing, naturally-sweet ending to a satisfying meal, with the added bonus of an extra fruit serving.
• Take those plain old bananas and grapes to a whole new level with a freezing frenzy. Freeze grapes and bananas for a super satisfying, pop-able delight. For an added yum-factor, dip half a banana in a small amount of antioxidant-rich dark chocolate.
Don't Skip Dessert
• Eating ice cream or frozen yogurt? Pile on 1/2 cup of fresh peaches, mangos or berries for a serving of fruit.
• Cut out the crust. Our favorite fruit pie recipes get placed on the “special occasions” list for one reason only: the buttery, fat-laden crust. The solution? Get rid of it. Place the filling of your favorite fruit (or pumpkin, as shown) pie recipe in individual ramekins. Bake until set and enjoy a serving of warm, satisfying fruit pie without the rich crust.
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15 of 24Photo: Jennifer Causey
Tip 8: Say Yes to Salads
Salads have the potential to be a Healthy Habit gold mine, rich in fruits, vegetables, and nutritional value. But we’re not talking about salads with a leaf of iceberg, and loads of bacon, cheese, and ranch. We’re talking dark green leafy beds with colorful, crunchy toppings.
• Start one meal a day with a small salad. Get creative. One cup of leafy greens + 1/2 cup of fruit or veggie toppings = 2 servings.
• Alternate your greens from the normal Romaine or iceberg… for general rule of thumb, the darker the greens the more nutrient rich they are.
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16 of 24Photo: Monica Buck
Say Yes to Salads
• Supersize your salad. Just think of the possibilities of an entrée-sized salad. One cup of leafy greens is a serving; pile on healthy toppings, and every 1/2 cup of chopped fruits and vegetables is another serving. You can easily get half your daily fruits and vegetables packed into one glorious salad.
• Don’t cheat yourself on the dressing. Be moderate, but be tasteful. A lot of the fat-free and low-fat dressings out there are full of sugar and sodium and are completely deprived on flavor. A few splashes of a good, heart-healthy canola- or olive-oil based dressings can do wonders to that bed of greens.
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17 of 24Photo: Hector Manuel Sanchez
Tip 9: Smoothie Break
The great thing about a smoothie is the open invitation to creativity. You are your own mixologist. Try something new, like mango, papaya, or even cucumber. You can knock out all three of your added fruits and vegetables with one push of the pulse button. The key here is not to confuse a smoothie with a milkshake. When you make your own, you are the artist in control of the color palette of fresh fruits.
Make sure that fruit is the base of your creation—too much fruit juice can rapidly add calories without providing any of the heart-healthy and digestive-friendly fiber that you get from the fruit itself. Enjoy for breakfast, as part of a balanced lunch, snack, or even dessert.
• Go savory. We get it. Not everyone gets excited when they look at a plate of raw vegetables. But pair them with a nutty hummus, zesty ranch, creamy avocado, and fiery salsa and now we’re talking. Crunchy crudités take on a whole new life with just a smidge of extra punch from a flavor-packed dip, like our creamy Zesty Green Goddess Dip (shown).
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20 of 24Photo: Sabra Dips
Dig the Dip
• Go store-bought. You’ve taken the time to cut and pre-portion your dip-able delights, but we don’t all have time to make everything from scratch. There are some great lightened-up store-bought dips that pair perfectly with our crunchy crudités. Check the produce and deli selections of your local grocery store for available selections.
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21 of 24Photo: Jennifer Causey
Tip 11. Recreate the Chip
As America’s all-time favorite snack—the potato chip (deep fried in oil, over salted, and overly enjoyed by many)—has become the lunch time side dish and snack time staple. There is something about that salty, crunchy satisfaction that is difficult to deny. So don’t deny yourself; instead, continue with the chip concept, but make them yourself. The trick: Oven-bake them, and be open to giving the potato a rest. You can make your own vegetable crisps that end up cheaper, healthier, and quite possibly the most fun way to eat your fruits and vegetables. Bag them for your own on-the-go snack, use them as dippers, or munch on them with your next meal.
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Recreate the Chip
• Glorify the greens. Send those potatoes home green with envy. Not only are greens an excellent source of vitamin A, vitamin K, vitamin C, and calcium, but they make fabulous chips. Kale, mustard greens, collard greens… crunch and munch away on our delightful twist on greens.
• Go bananas. For a sweet treat, slice up moderately ripe bananas or plantains and either slow roast them or lightly sauté them for a crispy exterior and fruity flavor.
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23 of 24Photo: Justin Walker
Tip 12: Bag the Bread
We’re not playing nutrition police on the bread group. Carbohydrates are an essential energy-boosting part of a healthy diet. Let’s just say most of us do not struggle to get enough of our daily bread. Try replacing one bread serving a day with a fruit or vegetable, and you’ll be a step ahead.
• Love the lettuce wrap. Instead of bread or tortillas, make your next sandwich or wrap inside a leafy green. Stack 2 or 3 large, leafy greens such as Bibb lettuce, romaine, red lettuce, cabbage, or radicchio and pile on the fixings. Enjoy the added crunch factor.
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24 of 24Photo: Colin Price
Bag the Bread
• Flip the chip and dip. Swap those chips for fresh crunchy crudités such as broccoli, carrots, cucumbers, snow peas or endive lettuce.
• Nix the Noodles. Try spaghetti squash. The name says it all with this veggie varietal. Once baked, spaghetti squash can be flaked with a fork to reveal spaghetti like strands to offer the perfect bed for your favorite pasta sauce.
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I keenly dislike the phrase ‘strong character’. It’s a cliché and it is often unclear what is meant – either an individual who is well-defined, and/or easily identifiable, or actually physically and/or emotionally tough. And it is different to powerful. Power is about agency. You can be well-defined or even tough, without power. For example, Jayne from Firefly is tough, but lacks power (especially when caught trying to betray his crew). Inara Serra has power through her social status in the Alliance and financial independence but she is not gonna beat Jayne at arm wrestling.
I am particularly sick to the back teeth of female characters always being described thus, in addition to the word feisty. Can everyone (mainly journalists) just stop with the inane shorthand about books and films and the roles women have? If you have to explain what ‘strong’ means, in any conversation about character, it fails the common sense test.
If she is really a well-rounded character she will not just be ‘feisty’, but reactive and interactive as the plot and situations call for, as close as one can get to a human being IRL.
Speaks for itself, yeah?
Think of you or me. We’re not just feisty or upset or ecstatic or strong, or morose, we’re complex beings of changing emotions and clear and hidden motivations usually trying to justify our existences and fending mostly for ourselves out in the wilds of modern urban civilisation. Why should the worlds and characters we attempt to create be any less complex? Why reduce women to one or two adjectives? This also goes for super intelligent nylon, fourth dimensional angels, sentient rocks, demonic robots, psychic cats and time travelling trees.
Few beings are seldom all good, or all feisty or always strong, because that equals boring and means they do not undergo an arc. Most people and characters also believe they are just and every character needs something to play off. Every character believes they are the hero in their own story. And always, what they should be, is made to feel real, or as real as possible. If that means your super-intelligent nylon is a bit moody when hungry, why not demonstrate it? If it means your demonic robot cares about the annihilation of earth and also rescues puppies, again, you can make it work. Make your physically strong character have mental or emotional weaknesses, or undergo situations where strength of that kind doesn’t matter.
For some obvious instances: Thor wouldn’t have changed his attitude without undergoing physical weakness in a completely foreign world. And as intelligent as he is, The Doctor wouldn’t need companions if he always knew what to do in every situation, and was always infallible. He needs characters to do what he can’t or shouldn’t do.
If creators continue to subvert expectations while taking readers and viewers along, it all helps change the culture we are in. And it helps in the story to bring out the light and shade in any situation, move the plot along and the audience finds out more about the character, without hideous introductions something along the lines of: ‘Hey there, I’m the Nylonic Wonder, feed me or else’.
This is a long and is good, so go read this. As for comics, there is the Hawkeye Initiative. Once you see it, it speaks for itself.
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Relaxation length
Relaxation length is a property of pneumatic tires that describes the delay between when a slip angle is introduced and when the cornering force reaches its steady-state value.
It is also described as the distance that a tire rolls before the lateral force builds up to 63% of its steady-state value.
It can be calculated as the ratio of cornering stiffness over the lateral stiffness, where
cornering stiffness is the ratio of cornering force over slip angle, and
lateral stiffness is the ratio of lateral force over lateral displacement.
Values
Pacejka gives a rule of thumb that "at nominal vertical load the relaxation length is of the order of magnitude of the wheel radius". Relaxations lengths have been found to be between 0.12 and 0.45 meters, with higher values corresponding to higher velocities and heavier loads. Tests on motorcycle tires have found that the ratio of cornering stiffness over lateral stiffness produces values 20-25% higher than those calculated as 63% of the steady state-value. The relaxation length associated with camber thrust has been found to be nearly zero.
Importance
A tire's relaxation length controls how much the tire will contribute to speed wobble.
See also
Bicycle and motorcycle dynamics
Pneumatic trail
Vehicle dynamics
References
Category:Tires
Category:Automotive steering technologies
Category:Motorcycle dynamics
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Main menu
Tag Archives: dialogue
“He was small.” D couldn’t remember exactly where she was and had to review the room, her hands, the view from the window to get herself back. “And he talked a lot. Incessant, that’s what my mother called him.”
“This guy just left me seven texts.” E selected ‘all’ and punched delete. “He couldn’t drive, didn’t have his driver’s license. We were driving up to Lake George, and I left him in my car, in front of a liquor store. I was gone for less than two minutes. When I came out, there’s a cop writing a ticket, and this guy is just sitting in the car, pretending he doesn’t see anything.”
“Seven messages. Who does that?”
“He could have said something to the cop. Right?”
“Just leave one message. One.”
The sun was low across the water, making the world look like it had drowned. “I asked him to talk to someone for me, to introduce me to a client. He wouldn’t do it.”
“What you lookin’ at? Who said you could look at me like that, sir?” He was young, maybe 25, with a stylish felt hat and two bright gold studs. “Who do you think you are? You know what would happen if you did that in the hood? I’ll tell you what would happen. First I’d get up in your face…”
Like everyone on the subway, Micaela and I hoped the stylish young man would stop yelling at the 60-year-old on the bench opposite.
“And then I’d fuck your daughter, man–”
That was too much. “Okay, that’s enough.”
He flashed his eyes at me, trying to mock. “Let me make my point, man! I’m making my point!”
“You’re yelling profanities on the subway.”
He smirked, pulling one of his earplugs half out. “If we was in the hood, me and my goons would fuck you up.”
“Just listen to your music and leave everyone alone.”
“In the fuckin’ hood–”
“Enough of that.” Another man stepped in, and the stylish young man quieted down, only chuckling to himself.
An uneasy silence fell over the car. I told Micaela about being spied on at the conference and tried to make it funny.
“I’m trying to make a point, man!” The stylish young man suddenly stood and glared at me with crazy eyes. “Let me tell you about the fucking hood, man.”
“People just want to go home after working.” It seemed I was stuck with him now. “They don’t want to be yelled at.”
“I don’t want to be paid by you, man! I don’t want your money.”
“You’re yelling profanities on the subway.”
“You don’t pay me, man! I don’t want your money!”
First one voice and then another spoke out. “Stop it! Nobody wants to hear you!”
“In the hood, I’d get my goons–”
“Nobody cares!” A distant voice snapped.
“I’m trying to make a point. I don’t need you people ganging up on me. I don’t need that. In the hood–”
The subway doors open behind me, and the stylish young man came past. He didn’t even look at me, at anybody, and instead to yelling on the platform. “I’m trying to make a point, man. You can’t fuckin’ look at me like that, man!”
“And he was like, you’re such a dummy.” “Dummy? He said that?” “Yeah, you’re such a dummy. Get it?” “I can’t believe he said that.” “He was like, ‘Dummy’!” “Dummy?” “Exactly. Dummy.” “Dummy? You’re serious?” “Yeah, dummy! Get it?” “I would have kneed him in the balls.” “Dummy!” “It isn’t funny.” “Yes, it is.”
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Email hub
The term Mail Hub is used to denote an MTA (message transfer agent) or system of MTAs used to route email but not act as a mail server (having no end-user email store) since there is no MUA (mail user agent) access. Examples could include dedicated anti-SPAM appliances, anti-virus engines running on dedicated hardware, email gateways and so forth.
DNS Based Mail Hub
A first example for a Mail Hub consisting of a network of MTAs would be that of a typical small-to-medium size Internet service provider (ISP), or for a FOSS corporate mail system. This solution is very good for developing nation ISPs and NGOs. As well as any other low-budget but high availability mail system needs. This is mostly due to not using expensive Network level switches and hardware.
Simple DNS MX record based Mail Hub cluster with parallelism and front-end failover and load balancing is illustrated in the following diagram:
The servers would be all Linux x86 servers with low cost SATA or PATA hard disk storage. The front-end servers would most likely run Postfix with Spamassassin and ClamAV. This RAIS server Cluster would then overcome the problem with Perl based Spamassassin being too CPU and memory hungry for low cost servers. The solution presented here is based on all GPL FOSS free software, but of course there are alternative configurations using other free or non-free software.
References
Mail Clustering, , ISOC, 2005.
Category:Email
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Basilica of St. Lawrence
The Basilica of St. Lawrence may refer to:
Basilica of St. Lawrence, Asheville, located in Asheville, North Carolina, United States of America
Basilica of St. Lawrence, Florence, located in Florence, Italy
Papal Basilica of Saint Lawrence outside the Walls, located in Rome, Italy
See also
Basilica di San Lorenzo (disambiguation)
Saint Lawrence (disambiguation)
St. Laurence's Church (disambiguation)
Cathedral of Saint Lawrence (disambiguation)
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[Histochemical study of the subcommissural organ in chickens during development].
Some histochemical and particularly histoenzymological tests are performed on the subcommissural organ of chick embryos. A secretory activity appears about the 7th day. In 10 days old embryos and new hatched chicken the enzyme activities are of rather low intensity. Compared with the 10 days embryos, the newborn show some increase, but compared with the adult birds the activities remain weak. However the acid phosphatase activity is higher in the subcommissural organ than in the ependyma even in 10 days embryos.
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Q:
pdb is not working in django doctests
So I created the following file (testlib.py) to automatically load all doctests (throughout my nested project directories) into the __tests__ dictionary of tests.py:
# ./testlib.py
import os, imp, re, inspect
from django.contrib.admin import site
def get_module_list(start):
all_files = os.walk(start)
file_list = [(i[0], (i[1], i[2])) for i in all_files]
file_dict = dict(file_list)
curr = start
modules = []
pathlist = []
pathstack = [[start]]
while pathstack is not None:
current_level = pathstack[len(pathstack)-1]
if len(current_level) == 0:
pathstack.pop()
if len(pathlist) == 0:
break
pathlist.pop()
continue
pathlist.append(current_level.pop())
curr = os.sep.join(pathlist)
local_files = []
for f in file_dict[curr][1]:
if f.endswith(".py") and os.path.basename(f) not in ('tests.py', 'models.py'):
local_file = re.sub('\.py$', '', f)
local_files.append(local_file)
for f in local_files:
# This is necessary because some of the imports are repopulating the registry, causing errors to be raised
site._registry.clear()
module = imp.load_module(f, *imp.find_module(f, [curr]))
modules.append(module)
pathstack.append([sub_dir for sub_dir in file_dict[curr][0] if sub_dir[0] != '.'])
return modules
def get_doc_objs(module):
ret_val = []
for obj_name in dir(module):
obj = getattr(module, obj_name)
if callable(obj):
ret_val.append(obj_name)
if inspect.isclass(obj):
ret_val.append(obj_name)
return ret_val
def has_doctest(docstring):
return ">>>" in docstring
def get_test_dict(package, locals):
test_dict = {}
for module in get_module_list(os.path.dirname(package.__file__)):
for method in get_doc_objs(module):
docstring = str(getattr(module, method).__doc__)
if has_doctest(docstring):
print "Found doctests(s) " + module.__name__ + '.' + method
# import the method itself, so doctest can find it
_temp = __import__(module.__name__, globals(), locals, [method])
locals[method] = getattr(_temp, method)
# Django looks in __test__ for doctests to run. Some extra information is
# added to the dictionary key, because otherwise the info would be hidden.
test_dict[method + "@" + module.__file__] = getattr(module, method)
return test_dict
To give credit where credit is due, much of this came from here
In my tests.py file, I have the following code:
# ./project/tests.py
import testlib, project
__test__ = testlib.get_test_dict(project, locals())
All of this works quite well to load my doctests from all of my files and subdirectories. The problem is that when I import and invoke pdb.set_trace() anywhere, this is all I see:
(Pdb) l
(Pdb) args
(Pdb) n
(Pdb) n
(Pdb) l
(Pdb) cont
doctest is apparently capturing and mediating the output itself, and is using the output in assessing the tests. So, when the test run completes, I see everything that should have printed out when I was in the pdb shell within doctest's failure report. This happens regardless of whether I invoke pdb.set_trace() inside a doctest line or inside the function or method being tested.
Obviously, this is a big drag. Doctests are great, but without an interactive pdb, I cannot debug any of the failures that they are detecting in order to fix them.
My thought process is to possibly redirect pdb's output stream to something that circumvents doctest's capture of the output, but I need some help figuring out the low-level io stuff that would be required to do that. Also, I don't even know if it would be possible, and am too unfamiliar with doctest's internals to know where to start. Anyone out there have any suggestions, or better, some code that could get this done?
A:
I was able to get pdb by tweaking it. I just put the following code at the bottom of my testlib.py file:
import sys, pdb
class TestPdb(pdb.Pdb):
def __init__(self, *args, **kwargs):
self.__stdout_old = sys.stdout
sys.stdout = sys.__stdout__
pdb.Pdb.__init__(self, *args, **kwargs)
def cmdloop(self, *args, **kwargs):
sys.stdout = sys.__stdout__
retval = pdb.Pdb.cmdloop(self, *args, **kwargs)
sys.stdout = self.__stdout_old
def pdb_trace():
debugger = TestPdb()
debugger.set_trace(sys._getframe().f_back)
In order to use the debugger I just import testlib and call testlib.pdb_trace() and am dropped into a fully functional debugger.
|
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Rescued baby elephant reunited with wild mother
The rescued calf drinks from her mother's breast after a tense reunion on Thursday afternoon. The young animal was rescued after falling down a mountainside on the night of Feb 24 in Thung Tako district, Chumphon. (Photo by Amnart Thongdee)
CHUMPHON: A baby elephant rescued after falling down a mountainside in Thung Tako district has been reunited with her mother and accepted back into the herd, after three weeks apart.
The dramatic yet heartwarming reunion was witnessed by a team of delighted forestry officials, vets and volunteers.
The baby elephant, given the name Phang Boonmak, was seen happily drinking milk from its mother’s breast, surrounded by members of the herd after initial fears she may be rejected after so long apart. Tranquilizer darts were ready in case they attacked.
The calf was believed to be less than 10 days old when rescued, but still weighed a respectable 200 kilogrammes.
Phang Boonmak fell down a mountainside in Thung Tako late at night on Feb 24.
Ngao Waterfall National Park rangers and volunteers came to the rescue in the morning after hearing the herd making loud noises in the area overnight. They found and took the calf to an animal care unit for treatment.
On Thursday, the once-again healthy youngster was taken to a forested area in tambon Khao Khai of Sawi district, Chumphon. A team of forest rangers hazed the calf's mother and herd down from a mountain and into the area.
Phang Boonmak was waiting, alone inside a loosely made rope fence. Around 4.30pm seven wild elephants walked towards her. For the next hour there was a lot of trumpeting and rumbling, and the team members became anxious the reunion might fail.
The herd gradually calmed down and the baby was reunited with her mother. She drank milk from her mother’s breast for the first time since they were parted, and the team members sighed with relief and joy.
The herd drifted back into the forest. It normally roams in Lang Suan, Thung Tako and Sawi districts.
Phang Boonmak reunites with her wild mother and the herd in Chumphon on Thursday. (Photo by Amnart Thongdee)
The team was armed with tranquiliser darts, just in case. (Photo by Amnart Thongdee)
The calf after her rescue from a mountainside in Chumphon on Feb 25. (Photo by Amnart Thongdee)
Separated from her mother, a youngster still has to feed. (Photo by Amnart Thongdee)
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Synopsis of the genus Pseudosparna Mermudes & Monné (Coleoptera, Cerambycidae, Lamiinae), with description of two new species .
Two new species of Pseudosparna Mermudes & Monné, 2009 are described: P. tucurui sp. nov. from Brazil (Pará) and P. pichincha sp. nov. from Ecuador (Pichincha). A key to the species of Pseudosparna is also included.
|
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A transgender woman took her own life by setting herself on fire in Portland last week, and according to reports, she might have done it to protest mental health challenges and online bullying.
Chloe Sagal, a 31-year-old game developer, self-immolated last Thursday, and though nearby witnesses managed to put out the fire, she later died at a hospital, according to the Oregonian. Her friends said the cyberbullying she had been experiencing for the past several years had led to her mental decline and instability.
In 2013, Sagal reportedly became the target for Kiwi Farms, a message board that describes itself as a “community dedicated to discussing eccentric people who voluntarily make fools of themselves” but which has also been described as a haven for cyberstalkers who bully and harass.
As New York magazine wrote in 2016, Kiwi Farms, which got its start on 4chan, “specializes in harassing people they perceive as being mentally ill or sexually deviant in some way.”
Sagal came to Kiwi Farms members’ attention after she was discovered to have attempted to crowdfund her gender surgery despite her originally saying on Indiegogo that she needed money for a life-saving operation to remove metal shrapnel from her body. After Eurogamer reported about the $30,000 that had been raised on Indiegogo for Sagal’s campaign, Kiwi Farms members reportedly began to harass her on multiple online platforms.
Her friends told the Oregonian that she constantly thought about suicide, and one friend told the newspaper, “One factor that made it much harder for her to get help was that whenever she talked about suicide, [Kiwi Farms members] would report her Facebook page and get it locked down. This had happened multiple times in the month prior to her death.”
On the Kiwi Farms thread that linked to news of her death, Sagal is misgendered over and over again and she’s mocked for her death and for the way she committed suicide.
After one commenter wrote that Kiwi Farms shouldn’t be blamed for her death because an old post about Sagal hadn’t been active for six months, another wrote, “I mean, that isn’t really an indication that we didn’t do it—it’s not like something in (sic) she read in the thread a year or two ago couldn’t have lead (sic) to this. Not that it matters, or anything. Someone who really wants to kill themselves is going to do it eventually, nothing you can really do about it.”
Another commenter wrote on a different thread, “I do hope they blame us for literally murdering another troon. Dibs on credit.”
One trans activist, though, has accused Sagal of harassment. In 2016, Zinnia Jones said Sagal, who created the indie game Homesick, threatened to kill her.
I feel like when I get a death threat it's probably a good idea to save it for the record. Here's one from just now. pic.twitter.com/ft289KZS5i — Zinnia, adult demon female (@ZJemptv) August 22, 2016
I do. Chloe Sagal literally threatened to find me and kill me. She's a known harasser of trans people and everyone's aware of her. Sad that you have to dig this deep to maintain your own relevance. https://t.co/WsWnWZ6JK4 — Zinnia, adult demon female (@ZJemptv) June 22, 2018
A few days before her death, police were informed that Sagal wanted to hurt herself, and after finding her with a machete, they took her into custody and sent her to a mental health crisis center. She was released, and a few days later, she was dead.
In a note she reportedly wrote to friends the week of her death and that was obtained by the newspaper, she said, “My death cannot be silent. It has to be loud and political. My entire life, my experience, my education has led up to this moment. I can only expect trauma and death from my existence.”
For more information about suicide prevention or to speak with someone confidentially, contact the National Suicide Prevention Lifeline (U.S.) or Samaritans (U.K.). If you need to speak to counselors with experience dealing with transgender issues, contact Trans Lifeline at (877) 565-8860 (U.S.) or (877) 330-6366 (Canada).
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Muse Boards
Calvin Klein S/S 2018
Calvin Klein
Spring/ Summer 2018
Since taking the creative reins of Calvin Klein just over one year ago, Raf Simons has established a concise group of core design narratives that the Belgium designer has been expanding upon each season. These visual representations of classic Americana culture have solidified the brand as the quintessentially modern American fashion house, all while establishing a feverishly loyal fan base of committed consumers.
Posted June 18th, 2018By
Colby Mugrabi
For spring/summer 2018, Simons’ second runway collection for the house, the designer expanded on key silhouettes and hallmark accessories introduced in his premier collection – tailored pants, denim, contemporary western button-downs, cowboy boots and quilts – further modernizing these creative narratives through novel fabrication techniques and a newly-established collaboration with the Warhol Foundation; an apt partnership symbolizing the genesis of two Western icons.
There was an underlying duality of happy and horror this season, most appropriately expressed in the pop-centric collection’s visual motifs and methods of fabrication – from fitted, monochromatic rubber separates to giant colorful pom-pom dresses; Warhol, himself, drew sizable inspiration from the concept of creative contradiction, often employing bright colors and the art of repetition to numb the eye to dark, at times tragic subject matter. Case in point, Warhol’s ‘Disaster Series’ from the 1960s, of which multiple images from this iconic body of work appeared on garments throughout the show. At closer consideration, the collection’s silkscreened motifs are the most obvious but perhaps not the most significant connection between Raf’s Calvin Klein and the Pop artist; both Simons and Warhol share in like methods of creation, grounded in their establishment of a trademark visual language from which they continue to build, all while maintaining genuine to their initial creative objective.
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Public
Imogene Benavidez's home timeline
Notices
This is the timeline for imogenebe and friends but no one has posted anything yet. Why not register an account and then nudge imogenebe or post a notice to them.
Business flourish here
ScoopHot is a premium lead generation platform. We help local and global businesses with Brand Marketing, Social Media Marketing and as Business Advertising Platforms. Blog, Post events, Bookmark, Use polls for market research, Ask questions, and more!
News & Media
Feeds
ScoopHot is a premium lead generation platform. We help businesses with Brand Marketing, Social Media Marketing and as Business Advertising Platforms. Blog, Post events, Bookmark, Use polls for market research, Ask questions, or Other kinds of business related data. Sync with Facebook, Twitter, Email all at one place. Lets buzz what's hot about your business...
|
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}
|
Q:
What are the most popular galaxies for which we have images?
The only galaxies I can think of (not being an astronomer) are Andromeda and Milky Way. There are 51 near galaxies, but they all pretty much say "satellite of Milky way" or "satellite of Andromeda". There are 100k+ galaxies in the local supercluster, and that page seems to have a better list:
Corvus
Coma Berenices
Ursa Major
Virgo
Sculptor
etc.
If you had to rank them in order of prominence in the scientific community or in popular science, wondering what the top 10 or 20 galaxies would be (for which we have photos).
I am trying to come up with a list of images for educational purposes that are potentially somewhat familiar to laymen audiences, or which would be useful to introduce to laymen audiences.
A:
Any such list is going to be terribly subjective. Since I'm an astronomer who studies galaxies, I'll go ahead and throw out a subjective list of the more famous, photogenic, and/or scientifically well-studied galaxies. The first six are in the Local Group (LMC and SMC are satellites of the Milky Way, M32 is a satellite of Andromeda).
Milky Way
Andromeda (M31)
Large Magellanic Cloud
Small Magellanic Cloud
Triangulum (M33)
M32
Sombrero (M104)
Pinwheel (M101)
Whirlpool (M51a)
M64 (Black Eye)
M74 (NGC 628)
M81
M82 (Cigar)
M87
M100
NGC 891
NGC 1068 (M77)
NGC 1300
NGC 1365
Centaurus A
Cygnus A
|
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|
Q:
Only one button in a panel with multiple togglebuttons changes color - wxPython
I want to set the color of a toggle button of my choice in the panel that I have created. The problem is that in the numerous toggle buttons that I have displayed on my panel when I want to change the color of each one only the color of the last button changes. Here's my code:
import wx
class Frame(wx.Frame):
def __init__(self):
wx.Frame.__init__(self,None)
self.panel = wx.Panel(self,wx.ID_ANY)
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.flags_panel = wx.Panel(self, wx.ID_ANY, style = wx.SUNKEN_BORDER)
self.sizer.Add(self.flags_panel)
self.SetSizer(self.sizer,wx.EXPAND | wx.ALL)
self.flags = Flags(self.flags_panel, [8,12])
self.flags.Show()
class Flags (wx.Panel):
def __init__(self,panel, num_flags = []):#,rows = 0,columns = 0,radius = 0, hspace = 0, vspace = 0,x_start = 0, y_start = 0
wx.Panel.__init__(self,panel,-1, size = (350,700))
num_rows = num_flags[0]
num_columns = num_flags[1]
x_pos_start = 10
y_pos_start = 10
i = x_pos_start
j = y_pos_start
buttons = []
for i in range (num_columns):
buttons.append('toggle button')
self.ButtonValue = False
for button in buttons:
index = 0
while index != 15:
self.Button = wx.ToggleButton(self,-1,size = (10,10), pos = (i,j))
self.Bind(wx.EVT_TOGGLEBUTTON,self.OnFlagCreation, self.Button)
self.Button.Show()
i += 15
index += 1
j += 15
i = 10
self.Show()
def OnFlagCreation(self,event):
if not self.ButtonValue:
self.Button.SetBackgroundColour('#fe1919')
self.ButtonValue = True
else:
self.Button.SetBackgroundColour('#14e807')
self.ButtonValue = False
if __name__ == '__main__':
app = wx.App(False)
frame = Frame()
frame.Show()
app.MainLoop()
A:
Your problem is quite simple. The last button is always changed because it's the last button defined:
self.Button = wx.ToggleButton(self,-1,size = (10,10), pos = (i,j))
Each time through the for loop, you reassign the self.Button attribute to a different button. What you want to do is extract the button from your event object and change its background color. So change your function to look like this:
def OnFlagCreation(self,event):
btn = event.GetEventObject()
if not self.ButtonValue:
btn.SetBackgroundColour('#fe1919')
self.ButtonValue = True
else:
btn.SetBackgroundColour('#14e807')
self.ButtonValue = False
See also:
http://www.blog.pythonlibrary.org/2011/09/20/wxpython-binding-multiple-widgets-to-the-same-handler/
|
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/******/ (function(modules) { // webpackBootstrap
/******/ // The module cache
/******/ var installedModules = {};
/******/
/******/ // The require function
/******/ function __webpack_require__(moduleId) {
/******/
/******/ // Check if module is in cache
/******/ if(installedModules[moduleId]) {
/******/ return installedModules[moduleId].exports;
/******/ }
/******/ // Create a new module (and put it into the cache)
/******/ var module = installedModules[moduleId] = {
/******/ i: moduleId,
/******/ l: false,
/******/ exports: {}
/******/ };
/******/
/******/ // Execute the module function
/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
/******/
/******/ // Flag the module as loaded
/******/ module.l = true;
/******/
/******/ // Return the exports of the module
/******/ return module.exports;
/******/ }
/******/
/******/
/******/ // expose the modules object (__webpack_modules__)
/******/ __webpack_require__.m = modules;
/******/
/******/ // expose the module cache
/******/ __webpack_require__.c = installedModules;
/******/
/******/ // define getter function for harmony exports
/******/ __webpack_require__.d = function(exports, name, getter) {
/******/ if(!__webpack_require__.o(exports, name)) {
/******/ Object.defineProperty(exports, name, { enumerable: true, get: getter });
/******/ }
/******/ };
/******/
/******/ // define __esModule on exports
/******/ __webpack_require__.r = function(exports) {
/******/ if(typeof Symbol !== 'undefined' && Symbol.toStringTag) {
/******/ Object.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });
/******/ }
/******/ Object.defineProperty(exports, '__esModule', { value: true });
/******/ };
/******/
/******/ // create a fake namespace object
/******/ // mode & 1: value is a module id, require it
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/******/ // mode & 4: return value when already ns object
/******/ // mode & 8|1: behave like require
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/******/ if(mode & 1) value = __webpack_require__(value);
/******/ if(mode & 8) return value;
/******/ if((mode & 4) && typeof value === 'object' && value && value.__esModule) return value;
/******/ var ns = Object.create(null);
/******/ __webpack_require__.r(ns);
/******/ Object.defineProperty(ns, 'default', { enumerable: true, value: value });
/******/ if(mode & 2 && typeof value != 'string') for(var key in value) __webpack_require__.d(ns, key, function(key) { return value[key]; }.bind(null, key));
/******/ return ns;
/******/ };
/******/
/******/ // getDefaultExport function for compatibility with non-harmony modules
/******/ __webpack_require__.n = function(module) {
/******/ var getter = module && module.__esModule ?
/******/ function getDefault() { return module['default']; } :
/******/ function getModuleExports() { return module; };
/******/ __webpack_require__.d(getter, 'a', getter);
/******/ return getter;
/******/ };
/******/
/******/ // Object.prototype.hasOwnProperty.call
/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
/******/
/******/ // __webpack_public_path__
/******/ __webpack_require__.p = "";
/******/
/******/
/******/ // Load entry module and return exports
/******/ return __webpack_require__(__webpack_require__.s = "./app.ts");
/******/ })
/************************************************************************/
/******/ ({
/***/ "./app.ts":
/*!****************!*\
!*** ./app.ts ***!
\****************/
/*! no static exports found */
/***/ (function(module, exports, __webpack_require__) {
"use strict";
eval("\nexports.__esModule = true;\nvar foo;\nfoo.bar = 'foobar';\n\n\n//# sourceURL=webpack:///./app.ts?");
/***/ })
/******/ });
|
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"pile_set_name": "Github"
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|
[SOCIAL SERVICES ORGANIZATION FOR ELDERLY CITIZENS AND DISABLED PERSONS IN SOUTH FEDERAL DISTRICT OF RUSSIA].
The article presents a comparative analysis of the effectiveness of the individual rehabilitation programs among elderly citizens and disabled persons of the Astrakhan region, the part of the South Federal District of Russia. We analyzed the data of the statistical survey of the social services provided rehabilitation facilities for the elderly and disabled people in the Astrakhan region. Analytical results thus obtained shown that the network of agencies and centers of social rehabilitation in the Astrakhan region did not correspond to the needs of elderly people and disabled persons. The negative dynamics in the number of social care centers as well as in the number of people who were provided with their services revealed the need for optimization of the institutional structure and its management. These specific characteristics of the social rehabilitation services in the Astrakhan region thus identified should be taken into consideration in order to improve the rehabilitation programs among elderly citizens and disabled persons in the South Region of the Russian Federation.
|
{
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|
Bulgaria in the Junior Eurovision Song Contest 2014
Bulgaria selected their Junior Eurovision Song Contest 2014 entry through an internal selection. On 26 July 2014 the Bulgarian broadcaster BNT stated that they will return to the contest with Krisia Todorova. This was later confirmed by the EBU on 1 August 2014. Krisia Todorova, Hasan and Ibrahim Ignatov represented Bulgaria with the song Planet of the Children. Despite being one of the favourites to win the contest, it finished second with 147 points.
Internal selection
On 25 July 2014 the Novini.bg stated that Bulgaria would return to the contest with Krisia Todorova singing and Hasan and Ibrahim playing the piano. Despite originally being considered speculation, the next day the Bulgarian broadcaster revealed that Todorova would actually represent Bulgaria in the 2014 contest. The EBU confirmed this news a week later. On 9 October, Todorova presented her Junior Eurovision entry Planet of the Children live on Slavi's Show on bTV.
At Junior Eurovision
At the running order draw which took place on 9 November 2014, Bulgaria were drawn to perform second on 15 November 2014, following and preceding .
Final
Krisia Todorova stood in the centre of the stage, where she performed her song. She was wearing a beautiful full length black and white dress, with a red bow on the back. Ibrahim was in a white suit, and played the white piano, while Hasan was wearing a black suit, and played the black piano. The backdrop was blue, with clouds, winter trees, and bright green flowers. During the chorus, the backdrop transformed into lovely mountains, with a white snowy road that leads to a huge castle covered in snow, just like in a fairytale.
Voting
The voting during the final consisted of 50 percent public televoting and 50 percent from a jury deliberation. The jury consisted of five music industry professionals who were citizens of the country they represent, with their names published before the contest to ensure transparency. This jury was asked to judge each contestant based on: vocal capacity; the stage performance; the song's composition and originality; and the overall impression by the act. In addition, no member of a national jury could be related in any way to any of the competing acts in such a way that they cannot vote impartially and independently. The individual rankings of each jury member were released one month after the final.
Following the release of the full split voting by the EBU after the conclusion of the competition, it was revealed that Bulgaria had placed first with the public televote and fourth with the jury vote. In the public vote, Bulgaria scored 143 points, while with the jury vote, Bulgaria scored 86 points.
Below is a breakdown of points awarded to Bulgaria and awarded by Bulgaria in the final and the breakdown of the jury voting and televoting conducted during the final.
Points awarded to Bulgaria
Points awarded by Bulgaria
Split voting results
See also
Junior Eurovision Song Contest
Junior Eurovision Song Contest 2014
Bulgaria in the Junior Eurovision Song Contest
References
Category:2014 in Bulgaria
Category:Countries in the Junior Eurovision Song Contest 2014
2014
|
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|
INTRODUCTION {#s1}
============
Oral cancer is the tenth most prevalent cancer accounting for almost 300,000 new cases annually worldwide, with two thirds occurring in developing countries \[[@R1], [@R2]\]. Although tobacco smoking and alcohol drinking are major risk factors for oral cancer, there are still 15-20% non-smokers and non-drinkers developing this disease \[[@R3]\], indicating that genetic factors, alone or interaction with environmental factors may also be important in the development of this cancer \[[@R4], [@R5]\].
Many epidemiologic studies demonstrated that some single nucleotide polymorphisms (SNPs) were correlated with oral cancer susceptibility \[[@R6], [@R7]\]. Recently, a genome-wide association study (GWAS) identified a new susceptibility loci at fatty acid desaturase 1 (*FADS1*) gene (rs174549) associated with laryngeal squamous cell carcinoma (LSCC) in Chinese population \[[@R8]\]. *FADS1* encodes delta-5 desaturases and is involved in the metabolism of polyunsaturated fatty acids (PUFAs). Several SNPs at the *FADS1* gene influence the concentration of long-chain PUFAs in plasma \[[@R9]\]. Previous experimental studies found that PUFAs and its metabolites could inhibit tumor proliferation and invasion in head and neck cancer cell \[[@R10], [@R11]\]. However, so far, there is limited epidemiologic research on the role of *FADS1* gene polymorphism in oral cancer risk.
Fish, the major dietary sources of long chain PUFAs, is a favorite food for residents of Fujian (a province located on the southeast coast of China). The protective effect of fish intake was showed in several cancers (such as esophagus, gastric, liver, etc.)\[[@R12]--[@R14]\]. Edefonti et al.\[[@R15]\] found unsaturated fats dietary pattern could reduce the risk of oral and pharyngeal cancer. However, to date, relatively few studies have reported the fish intake related to oral cancer. Moreover, it is still unclear whether *FADS1* gene polymorphism and its interaction with fish intake could contribute to the prevention of oral cancer risk.
Therefore, the present case-control study was to assess the independent and combined effects of the new susceptibility loci (rs174549) and fish consumption on oral cancer in southeast China.
RESULTS {#s2}
=======
The distribution of all subjects on demographic variables and potential confounding factors are described in Table [1](#T1){ref-type="table"}. There were no significant differences between cases and controls in demographic characteristics (except that greater proportions of cases were lower educated and rural settings). As expected, smoking, drinking, vegetables and fruits intake were associated with oral cancer risk (*P*\<0.05). The main histology types were squamous cell carcinoma (85.43%) and adenocarcinoma (8.94 %).
###### Distribution of selected characteristics among case and control subjects
Variables Case (%) n = 302 Control (%) n = 574 *P* value
-------------------------- ------------------ --------------------- -----------
Age (years) 0.149
≤44 46(15.23) 117(20.38)
45-59 143(47.35) 279(48.61)
60-74 87(28.81) 137(23.87)
≥75 26(8.61) 41(7.14)
Gender 0.104
Male 200(66.23) 348(60.63)
Female 102(33.77) 226(39.37)
Education Level \<0.001
Illiteracy 40(13.25) 71(12.37)
Primary-Middle school 189(62.58) 256(44.60)
High school or above 73(24.17) 247(43.03)
Marital status 0.113
Married 268(88.74) 528(91.99)
Others 34(11.26) 46(8.01)
Residence \<0.001
Rural 154(50.99) 161(28.05)
Urban 148(49.01) 413(71.95)
Family history of cancer 0.660
No 235(77.81) 454(79.09)
Yes 67(22.19) 120(20.91)
Tobacco smoking \<0.001
No 143(47.35) 408(71.08)
Yes 159(52.65) 166(28.92)
Alcohol drinking \<0.001
No 179(59.27) 459(79.97)
Yes 123(40.73) 115(20.03)
Vegetables \<0.001
≤1 time/day 139(46.03) 168(29.27)
\>1times/day 163(53.97) 406(70.73)
Fruits \<0.001
≤3 times/week 233(77.15) 276(48.08)
\>3 times/week 69(22.85) 298(51.92)
Table [2](#T2){ref-type="table"} shows the effects of rs174549 polymorphism and fish consumption on oral cancer. Genotype distribution of *FADS1* (rs174549) among controls was in agreement with Hardy-Weinberg equilibrium (*P*\>0.05). After adjustment for potential confounders, *FADS1* A variant allele was associated with a significantly decreased risk of oral cancer: the ORs were 0.65 (95% CI: 0.42-0.99) for codominant model and 0.67 (95% CI: 0.46-0.98) for recessive model. Moreover, when stratified by demographic characteristics, the statistically significant reverse associations were only emerged in men and those age ≤ 60 years. When stratified by main lifestyle factors, the protective effect of AA genotype was especially evident in smokers and alcohol drinkers (Figure [1](#F1){ref-type="fig"}).
###### Effects of *FADS1* rs174549 polymorphism and fish intake on oral cancer
Variable Case (%) (n = 302) Control (%) (n = 574) Unadjusted odds ratios (95% CI) Adjusted odds ratios^a^ (95% CI)
------------------------- -------------------- ----------------------- --------------------------------- ----------------------------------
rs1745496 (P~HWE~=0.32)
Codominant model
GG 106(35.10) 169(29.44) 1.00 1.00
AG 147(48.67) 274(47.74) 0.86(0.62-1.17) 0.86(0.62-1.20)
AA 49(16.23) 131(22.82) 0.60(0.40-0.90) 0.65(0.42-0.99)
Dominant model
GG 106(35.10) 169(29.44) 1.00 1.00
AG+AA 196(64.90) 405(70.56) 1.30(0.96-1.74) 1.26(0.92-1.72)
Recessive model
GG+AG 253(83.77) 443(77.18) 1.00 1.00
AA 49(16.23) 131(22.82) 0.65(0.46-0.94) 0.67(0.46-0.98)
Fish intake
0-2 times/week 138(45.69) 147(25.61) 1.00 1.00
3-6 times/week 112(37.09) 178(31.01) 0.67(0.48-0.93) 0.85(0.58-1.25)
≥7 times/week 52(17.22) 249(43.38) 0.22(0.15-0.32) 0.27(0.18-0.42)
P for trend \<0.001 \<0.001
^a^ Adjusted for age, gender, education, marital status, residence, family cancer history, smoking, drinking, vegetables and fruits.
{#F1}
Additionally, with regard to fish consumption, the frequency of fish intake was categorized into three groups according to the tertiles of controls (0-2 times/week, 3-6 times/week, ≥7 times/week). Fish intake ≥7 times/week showed a 73% reduction in risk for oral cancer compared to those who ate fish less than 2 times/week (OR: 0.27, 95% CI: 0.18-0.42). Moreover, there was a tendency of decreased risk with the increasing frequency of fish consumption (all P for trend \<0.001).
We further evaluated the joint effects of rs174549 polymorphism in recessive model and fish consumption on the risk of oral cancer (Table [3](#T3){ref-type="table"}). A significantly lower OR was observed in individuals who carrying AA genotype and consumed fish ≥7times/week compared with GG+AG carriers who ate fish less than 2 times/week (OR: 0.30, 95% CI: 0.14-0.63). Moreover, a positive multiplicative interaction between *FADS1* gene and fish intake for oral cancer was found (OR~multiplicative~ = 0.70, 95% CI: 0.51-0.96, *P*=0.028; data not shown).
###### Interactions between *FADS1* rs174549 polymorphism and fish intake in oral cancer
Variables Cases (%)N =302 Controls (%)N = 574 OR (95% CI) OR^a^ (95% CI)
----------- ---------------- ----------------- --------------------- ----------------- -----------------
FADS1 Fish intake
GG+AG 0-2 times/week 120(39.74) 120(20.91) 1.00 1.00
GG+AG 3-6 times/week 94(31.13) 124(21.60) 0.76(0.52-1.10) 0.93(0.60-1.42)
GG+AG ≥7times/week 39(12.91) 199(34.67) 0.20(0.13-0.30) 0.25(0.15-0.40)
AA 0-2 times/week 18(5.96) 27(4.70) 0.67(0.35-1.27) 0.74(0.36-1.52)
AA 3-6 times/week 18(5.96) 54(9.41) 0.33(0.18-0.60) 0.50(0.25-0.97)
AA ≥7times/week 13(4.30) 50(8.71) 0.26(0.13-0.50) 0.30(0.14-0.63)
^a^ Adjusted for age, gender, education, marital status, residence, family cancer history, smoking, drinking, vegetables and fruits.
DISCUSSION {#s3}
==========
To our knowledge, this case-control study is the first to report independent and joined effects of the new susceptibility loci in *FADS1* (rs174549) and fish consumption on oral cancer in southeast China. We found AA genotype was associated with a decreased risk of oral cancer compared to the GG genotype. Moreover, fish intake ≥7times/week also reduced the risk of oral cancer. Furthermore, there was a positive multiplicative interaction between *FADS1* gene and fish intake for oral cancer.
To date, only a GWAS study revealed that *FADS1* polymorphism (rs174549) showed a protective effect on LSCC \[[@R8]\]. This finding is consistent with the present results. Although the mechanism of rs174549 polymorphism on oral cancer is not clear, previous study found there were 49 SNPs in high linkage disequilibrium with rs174549 in the same chromosome, and these SNPs might influence the expression of *FADS1* through their effects on host genes \[[@R8]\]. Moreover, *FADS1* variation could suppress inflammatory response through influencing the metabolism of PUFAs. The main metabolites of PUFAs include arachidonic acid (AA; a pro-inflammatory factor), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) (EPA and DHA are anti-inflammatory factors). Horiguchi et al.\[[@R16]\] found *FADS1* polymorphism (rs174547) was correlated with lower AA, but unchanged for EPA or DHA. Yao et al.\[[@R17]\] showed *FADS1-FADS2* gene cluster variation could inhibit the conversion of α-linolenic acid (ALA) to AA. Tanaka et al.\[[@R18]\] demonstrated rs174537 polymorphism in *FADS1* could increase the expression of EPA. Therefore, we speculated that imbalance between AA and EPA and DHA might be a mechanism of *FADS1* rs174549 polymorphism on oral cancer.
Our study revealed that fish intake might be a benefit factor for oral cancer risk, which is consistent with previous studies \[[@R19]\]. Higher intakes of fish which contain key anti-inflammatory nutrients (LC-PUFA, EPA, DHA, etc.)\[[@R20]\], have been reported to suppress inflammation, oxidative stress and cancer risk in animal study \[[@R21]\] and observational study \[[@R22]\]. Additionally, Actis et al.\[[@R23]\] found dietary lipids (especially for n-3 fatty acids) also reduced cell proliferation and differentiation of murine oral squamous epithelium. Therefore, these have been reasonably hypothesized that fish consumption may lower the risk of oral cancer.
Interestingly, our results demonstrated a significant gene-diet interaction between *FADS1* gene and fish intake for oral cancer risk. Yeates et al.\[[@R24]\] showed that *FADS1* gene variant was associated with decreased level of AA in serum and increased ALA to DHA in high fish intakes population. An explanation for our finding might be that the genetic variation in *FADS1* could interact with dietary fish oil to increase delta-5 activity and change LC-PUFA proportions \[[@R25]\].
There are some limitations in this study. First, the present study only evaluated the effect of total fish consumption on oral cancer, and did not further to analyze the effects of different fish species and the preparation methods. Hence, these factors should be taken into account in future studies. Second, only one SNP was chosen based on a recent GWAS which may not represent a comprehensive view of *FADS1* gene variation. Further studies on variation in susceptible regions of *FADS1* gene are needed. Third, since this is a very preliminary study, further animal or cell experiments with more rigorous design are also required to explore the mechanisms.
In conclusion, our results suggest that *FADS1* rs174549 polymorphism showed a protective role in etiology of oral cancer. Moreover, fish intake may be an interacting factor that decreases oral cancer risk in individuals with the mutant genotype of rs174549. Further research on gene-diet interaction in oral cancer is warranted to obtain more conclusive outcomes.
MATERIALS AND METHODS {#s4}
=====================
Study design and population {#s4_1}
---------------------------
This hospital-based case-control study was conducted from September 2005 to September 2010 in Fujian, China. 305 oral cancer patients were recruited from the First Affiliated Hospital of Fujian Medical University. As reported previously \[[@R26]\], inclusion criteria of cases were as follows: (1) all cases were newly diagnosed and histologically confirmed primary oral cancer; (2) all cases are Chinese Han population and live in Fujian Province; (3) all cases aged 20 to 80 years. Patients with second primary, recurrent oral cancer, previous radiotherapy or chemotherapy, were excluded from this study.
579 cancer-free control subjects were selected from the physical examination population in the same hospital and frequency-matched to the cases group by gender and age (±3 years). The cancer-free status was ascertained according to the results of physical examination. Those who were direct relatives to the cases or had a previous history of cancer were excluded. The recruiting rate for oral cancer patients was 98.3% and the rate for control subjects was 96.9%. The present study was approved by the Institutional Review Board (IRB) of Fujian Medical University (Fuzhou, China). All participants agreed to this study and signed a consent form.
Data and sample collections {#s4_2}
---------------------------
All epidemiological data were collected by face-to-face interview using a standardized questionnaire, including information on demographic characteristics, smoking, drinking, diet factors, residential history, and family history of cancer. The subjects were considered smokers if they had smoked at least 100 cigarettes during their lifetime. Alcohol consumers were defined as those who had consumed at least 1 drink/week continuously for at least 6 months. A 5-10 ml blood sample was collected from each subjects with an EDTA-coated vacuum tube and stored at −80°C.
Selection of SNPs and genotyping {#s4_3}
--------------------------------
Genomic DNA was extracted from whole-blood samples using the Qiagen Blood Kit (Qiagen, Chatsworth, CA). All samples were genotyped by the 50-nuclease TaqMan assay, using the ABI PRISM 7900HT Sequence Detection System (ABI, Foster City, CA). TaqMan primers and FAM- or VIC-labeled probes were designed using the Primer Express Oligo Design software v2.0 (ABI PRISM). The PCR primers were as follows: forward 5'-CCAGCCTGTCTACTTTCCCA-3' and reverse 5'-TCTACGTCCGCT TCTTCCTCAC-3'. Amplification conditions were as follows: 95°C for 10 min, then followed by 40 cycles of 95°C for 5 sec, and 60°C for 30 sec. Allelic Discrimination Sequence Detector Software was used to read the completed PCR plates with an ABI 7900HT Sequence Detector in the end point mode. For the assessment of genotyping results, laboratory personnel were blinded to the case-control status. All assays were carried out in 384-well arrays with 8 no-template controls and 8 duplicated samples in each plate for quality control. Approximately 5% of the samples were randomly repeated for quality control purposes. Genotyping call rates were over 99.0% and the concordance rate reached 100%. Due to genotyping failure of some DNA samples, only 302 cases and 574 control subjects with complete genotyping data can be used for further analysis.
Statistical analysis {#s4_4}
--------------------
Statistical software R (version 3.1.1) was used for our data analyses. The χ^2^ test was performed for the socio-demographic covariates of the case and control subjects. Hardy--Weinberg equilibrium was conducted using a goodness-of-fit χ^2^ test with linkage disequilibrium analyzer (LDA) software v.1.0 for SNP among the controls. Unconditional logistic regression models were used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs). Interaction between the SNP and fish intake was evaluated using unconditional logistic regression model. Statistical significance was considered at the *P*\<0.05 level.
This work was supported by Scientific Research Program of Education Department of Fujian Province (No.JAT160207), Joint Funds for the Innovation of Science and Technology of Fujian province (No.2016Y9033) and Natural Science Foundation of Fujian Province (No.2015J01304). We are great thank to Prof. Dongxin Lin and all staff of State Key Laboratory of Molecular Oncology in Chinese Academy of Medical Sciences for great help with genotyping.
**CONFLICTS OF INTEREST**
The authors declare no conflicts of interest.
|
{
"pile_set_name": "PubMed Central"
}
|
Preparation of human chromosomes for high resolution scanning electron microscopy.
The addition of ethidium bromide during the last 2.5-3 h of lymphocyte culturing restricted chromosome contraction and preserved the banding structure in scanning electron microscopy. Treatment of the chromosomes with trypsin and use of impregnation with osmium tetroxide and thiocarbohydrazide resulted in a structural preservation of high resolution quality.
|
{
"pile_set_name": "PubMed Abstracts"
}
|
Q:
Internet Explorer 11 issue
I am working on selenium automation through IE web browser. Sometimes while invoking browser the actions are done very slowly.
For example, If I comment a user id(abcd), IE typing like a(taking a minute)b(taking minute),c(taking a minute)..... I checked the internet speed and clear the cache cookies and all. Sometimes it's happening.
Please suggest any solutions.
A:
Most likely it is due to 64 bit IEWebDriver. Switch to 32 bit IEWebDriver and check if it fixes your issue.
|
{
"pile_set_name": "StackExchange"
}
|
44459*o/6 - 20372/3 = 0.
-3704, -11, -1, 1
Solve -g**2/7 - 4832*g/7 + 4833/7 = 0.
-4833, 1
Factor 2*u**4/9 + 68*u**3 - 410*u**2/3 + 616*u/9.
2*u*(u - 1)**2*(u + 308)/9
Factor z**3 + 2312*z**2 + 1334021*z - 2677298.
(z - 2)*(z + 1157)**2
Factor 2*f**2 + 11818*f - 94672.
2*(f - 8)*(f + 5917)
Factor -6*c**3 - 206*c**2 - 604*c + 496.
-2*(c + 4)*(c + 31)*(3*c - 2)
Suppose 4*v**2 + 1443896*v - 1443900 = 0. What is v?
-360975, 1
Solve -2*n**5/5 + 16*n**4/5 + 10*n**3 - 328*n**2/5 + 72*n = 0.
-5, 0, 2, 9
Factor 5*s**3 - 630*s**2 - 2580*s - 2600.
5*(s - 130)*(s + 2)**2
Factor -4*i**3 + 2520524*i**2.
-4*i**2*(i - 630131)
Determine p, given that -4*p**4 - 3172*p**3 - 12644*p**2 - 15788*p - 6312 = 0.
-789, -2, -1
Factor 2*r**3/9 + 58*r**2/3 + 2464*r/9 + 1960/3.
2*(r + 3)*(r + 14)*(r + 70)/9
Suppose 2*q**5/5 - 508*q**4/5 + 7262*q**3/5 - 21796*q**2/5 - 46144*q/5 + 61184/5 = 0. What is q?
-2, 1, 8, 239
Determine p, given that -2*p**4 + 1820*p**3 - 374066*p**2 - 18192720*p - 199840032 = 0.
-21, 476
Let -17*o**2 + 19871*o + 2338 = 0. What is o?
-2/17, 1169
Factor j**4/3 + 16663*j**3/9 + 16660*j**2/9.
j**2*(j + 1)*(3*j + 16660)/9
Determine d, given that -50*d**5 + 96160*d**4 + 752202*d**3 + 1714192*d**2 + 1034920*d + 185376 = 0.
-4, -3, -2/5, 1931
Suppose -2*g**4 - 26*g**3 + 136*g**2 + 2520*g + 7200 = 0. What is g?
-12, -6, -5, 10
Determine h so that -20*h**5 - 328*h**4 - 1536*h**3 - 1496*h**2 + 404*h + 672 = 0.
-8, -7, -1, 3/5
What is o in -27*o**4/4 - 435*o**3/4 - 423*o**2/4 + 435*o/4 + 225/2 = 0?
-15, -10/9, -1, 1
What is q in -2*q**2/5 + 487046*q/5 - 487044/5 = 0?
1, 243522
Solve 12065*u**3 - 132685*u**2 + 289230*u + 720 = 0 for u.
-6/2413, 3, 8
Find z, given that -8*z**4/11 - 974*z**3/11 - 16792*z**2/11 - 4736*z = 0.
-407/4, -16, -4, 0
Solve -12*x**2/5 + 2097*x - 58305 = 0.
115/4, 845
Suppose -m**5/3 + 15*m**4 + 193*m**3/3 + 49*m**2 = 0. What is m?
-3, -1, 0, 49
Factor x**4/7 + 942*x**3/7 + 270948*x**2/7 + 14763970*x/7 - 2640819957/7.
(x - 69)*(x + 337)**3/7
Let 7*k**5 - 26*k**4 + 4*k**3 + 42*k**2 - 27*k = 0. Calculate k.
-9/7, 0, 1, 3
Factor -m**4 + 99*m**3 + 9315*m**2 + 147609*m - 529254.
-(m - 162)*(m - 3)*(m + 33)**2
Determine g so that 2*g**4/11 + 10880*g**3/11 + 21752*g**2/11 = 0.
-5438, -2, 0
Find d, given that d**3 - 25246*d**2 + 159314880*d + 318730752 = 0.
-2, 12624
Solve -y**5/6 + 31*y**4/3 - 290*y**3/3 - 1721*y**2/3 + 29381*y/6 + 59290/3 = 0 for y.
-5, -4, 11, 49
Let 4*o**2/3 - 825404*o/3 - 275136 = 0. What is o?
-1, 206352
Factor 2*r**2/13 - 29306*r/13 - 87936/13.
2*(r - 14656)*(r + 3)/13
Find t such that 2*t**5/15 + 3004*t**4/5 + 10170026*t**3/15 + 13524008*t**2/5 + 40536008*t/15 = 0.
-2251, -2, 0
Suppose 147*p**5/5 - 69909*p**4/5 - 528096*p**3/5 + 318876*p**2/5 - 46368*p/5 = 0. What is p?
-8, 0, 2/7, 483
Factor 4*d**3/5 - 448*d**2/5 - 254476*d/5 - 17263688/5.
4*(d - 338)*(d + 113)**2/5
Solve 3*l**2/4 - 1125*l/2 - 6777/4 = 0 for l.
-3, 753
Factor 2*q**4/13 - 119154*q**3/13 + 238300*q**2/13.
2*q**2*(q - 59575)*(q - 2)/13
Factor -5*u**2 - 530*u - 12920.
-5*(u + 38)*(u + 68)
Factor -h**3 - 2280*h**2 - 9107*h - 6828.
-(h + 1)*(h + 3)*(h + 2276)
Find g, given that 2*g**4/15 + 2612*g**3/15 + 847576*g**2/15 - 684868*g/3 + 171610 = 0.
-655, 1, 3
Factor -m**3/2 - 405*m**2/2 - 54567*m/2 - 2445363/2.
-(m + 123)*(m + 141)**2/2
Factor c**3/4 + 41*c**2 - 167*c/4 - 165/2.
(c - 2)*(c + 1)*(c + 165)/4
Factor 3*z**2 + 17547*z + 35082.
3*(z + 2)*(z + 5847)
Suppose -5*j**4 + 140*j**3 + 10860*j**2 - 160160*j - 6852160 = 0. Calculate j.
-28, 38, 46
Factor 25*c**3 - 685*c**2 + 380*c + 28000.
5*(c - 25)*(c - 8)*(5*c + 28)
Suppose -b**4 - 196*b**3 + 812*b**2 + 2400*b = 0. Calculate b.
-200, -2, 0, 6
Factor 2*q**2 + 166960*q + 667808.
2*(q + 4)*(q + 83476)
Factor 11848712*k**2 + 29208*k + 18.
2*(2434*k + 3)**2
Factor -4*w**4/5 - 124*w**3/5 + 2324*w**2/5 - 1764*w.
-4*w*(w - 7)**2*(w + 45)/5
Let 5*j**5/2 + 10*j**4 - 1820*j**3 - 35*j**2 + 10835*j/2 + 3625 = 0. Calculate j.
-29, -1, 2, 25
Find f, given that 57*f**3 + 4554*f**2 + 89295*f - 9450 = 0.
-45, -35, 2/19
Factor 576*n**4 - 70368*n**3 + 1618756*n**2 + 34201492*n.
4*n*(n + 13)*(12*n - 811)**2
Let k**4/5 - 389*k**3/5 - 783*k**2/5 + 389*k/5 + 782/5 = 0. Calculate k.
-2, -1, 1, 391
Factor 2*l**5/5 + 52*l**4/5 + 386*l**3/5 + 624*l**2/5 + 288*l/5.
2*l*(l + 1)**2*(l + 12)**2/5
Solve 2*w**4/15 + 1118*w**3/15 - 2*w**2/15 - 1118*w/15 = 0 for w.
-559, -1, 0, 1
Solve -b**4/4 + 25*b**3/2 - 539*b**2/4 + 245*b/2 = 0.
0, 1, 14, 35
Let 45*i**3 + 2010*i**2 + 1325*i + 220 = 0. Calculate i.
-44, -1/3
Let 15*o**2 - 805*o + 9880 = 0. Calculate o.
19, 104/3
Find p such that 5*p**5 - 67*p**4 - 5299*p**3 - 59425*p**2 - 161278*p + 74360 = 0.
-13, -5, 2/5, 44
Factor -3*k**5/8 + 1299*k**4/8 + 2607*k**3/8 + 1305*k**2/8.
-3*k**2*(k - 435)*(k + 1)**2/8
Let -n**3/3 + 10555*n**2/3 - 9285761*n + 9282243 = 0. What is n?
1, 5277
Determine l, given that l**5/4 - 21*l**4/2 + 137*l**3/2 - 9*l**2 - 1067*l/4 - 357/2 = 0.
-1, 3, 7, 34
What is j in 2*j**4/7 - 20*j**3 - 78*j**2 + 7660*j/7 + 2336 = 0?
-8, -2, 7, 73
Let -4*x**3 + 2848*x**2 - 11340*x + 8496 = 0. Calculate x.
1, 3, 708
Let -5*x**5 + 1255*x**4 - 103365*x**3 + 2764125*x**2 + 2868750*x = 0. Calculate x.
-1, 0, 75, 102
What is u in 4*u**3 - 328*u**2 - 6884*u + 15048 = 0?
-19, 2, 99
Factor -5*o**5 + 8760*o**4 - 34990*o**3 + 52460*o**2 - 34965*o + 8740.
-5*(o - 1748)*(o - 1)**4
Factor q**2/4 - 1490905*q/4.
q*(q - 1490905)/4
Factor -7771*k**2/4 - 38851*k/4 + 5.
-(k + 5)*(7771*k - 4)/4
Find s such that -18*s**5/5 + 258*s**4/5 - 786*s**3/5 - 18*s**2/5 + 804*s/5 - 48 = 0.
-1, 1/3, 1, 4, 10
Suppose -2*v**4/23 + 18*v**3/23 - 6*v**2/23 - 74*v/23 - 48/23 = 0. Calculate v.
-1, 3, 8
Factor -2*z**4 - 14*z**3 + 20*z**2 + 32*z.
-2*z*(z - 2)*(z + 1)*(z + 8)
Suppose -v**4/8 + 261*v**3/8 - 17609*v**2/8 + 80139*v/8 + 49005/4 = 0. What is v?
-1, 6, 121, 135
Find h, given that 38*h**4 + 288*h**3 + 360*h**2 = 0.
-6, -30/19, 0
Factor -5*g**4 - g**3 + 122*g**2 + 340*g + 264.
-(g - 6)*(g + 2)**2*(5*g + 11)
Solve c**4 - 12646*c**3 + 39866353*c**2 + 720670248*c + 3247632144 = 0.
-9, 6332
Factor -3*s**2 + 333*s + 29406.
-3*(s - 169)*(s + 58)
Suppose -3*n**4/7 - 86445*n**3/7 - 118453914*n**2 - 376670907396*n + 14871067330392 = 0. What is n?
-9618, 39
Let -2*d**5/9 + 1718*d**4/9 - 168568*d**3/3 + 54812704*d**2/9 - 1143803200*d/9 + 2148495200/3 = 0. What is d?
10, 15, 278
Find l, given that -2*l**3/11 + 23072*l**2/11 - 6061624*l + 796815456/11 = 0.
12, 5762
Let -2*h**3 + 49552*h**2 - 306776378*h - 1842442572 = 0. Calculate h.
-6, 12391
Suppose 4*a**3/7 + 92*a**2/7 - 200132*a/7 - 200220/7 = 0. What is a?
-235, -1, 213
Find v, given that -2*v**2 + 27912*v = 0.
0, 13956
Solve 3*k**2 - 1815*k - 18450 = 0 for k.
-10, 615
What is m in 4*m**5/3 + 346*m**4/3 + 3014*m**3 + 67264*m**2/3 + 125344*m/3 - 26880 = 0?
-40, -36, -7, -4, 1/2
Factor 2*o**2/9 + 48280*o/9 + 10728.
2*(o + 2)*(o + 24138)/9
Suppose 6*s**2/17 - 12862*s/17 - 4288/17 = 0. Calculate s.
-1/3, 2144
Let -25*k**4 - 2340*k**3 - 16435*k**2 - 8100*k + 6020 = 0. Calculate k.
-86, -7, -1, 2/5
Factor -3*l**3/8 + 6279*l**2/8 - 3135*l + 6267/2.
-3*(l - 2089)*(l - 2)**2/8
Let -o**2/4 + 269*o/2 + 2715/4 = 0. What is o?
-5, 543
Factor 2*a**2/3 - 1195924*a/3 + 178779276722/3.
2*(a - 298981)**2/3
Factor 5*a**3 + 34305*a**2 + 274200*a + 548240.
5*(a + 4)**2*(a + 6853)
Factor m**2/3 - 209513*m/3 + 139674.
(m - 209511)*(m - 2)/3
Let 2*h**3 + 452*h**2 + 3078*h + 2628 = 0. What is h?
-219, -6, -1
What is i in -4*i**2 + 1856*i + 1860 = 0?
-1, 465
Solve 2*m**2/5 - 6132*m/5 + 1226 = 0 for m.
1, 3065
Factor -54*y**3 - 13548*y**2 - 2316992*y/3 - 38299904/3.
-2*(y + 178)*(9*y + 328)**2/3
Factor -3*d**2 + 132*d - 1020.
-3*(d - 34)*(d - 10)
Factor -9*l**3 + 883*l**2 - 971*l + 97.
-(l - 97)*(l - 1)*(9*l - 1)
Factor y**4 - 476*y**3 + 2825*y**2 - 2350*y.
y*(y - 470)*(y - 5)*(y - 1)
Find z such that -5*z**4 - 5340*z**3 - 1365225*z**2 + 32672750*z = 0.
-545, 0, 22
Factor 2*b**3/3 - 1544*b**2/3 + 308506*b/3 - 4021948/3.
2*(b - 379)**2*(b - 14)/3
Let -4*t**3/7 - 18908*t**2/7 - 245072*t/7 + 37712 = 0. Calculate t.
-4714, -14, 1
Determine x, given that -461650*x**2 + 461655*x - 5 = 0.
1/92330, 1
Factor 2*q**5 - 6796*q**4 + 5766402*q**3 + 11553200*q**2 + 5780000*q.
2*q*(q - 1700)**2*(q + 1)**2
Determine h so
|
{
"pile_set_name": "DM Mathematics"
}
|
Q:
Perl - undefined subroutine
I have the following Perl code:
use Email::Sender::Simple;
use IO::Socket::SSL;
IO::Socket::SSL::set_defaults(SSL_verify_mode => SSL_VERIFY_NONE);
Email::Sender::Simple::sendmail($email, { transport => $transport });
When I run it I get this error:
Undefined subroutine &Email::Sender::Simple::sendmail called at script.pl line 73.
If I change the code to have the following, then it works:
use Email::Sender::Simple qw(sendmail);
sendmail($email, { transport => $transport });
Can someone explain why I had to change the code for sendmail, while I did NOT have to change the code for set_defaults to look like:
use IO::Socket::SSL qw(set_defaults);
set_defaults(SSL_verify_mode => SSL_VERIFY_NONE);
A:
Take a look at the code Email/Sendmail/Simple.pm. There is no sendmail subroutine in that program. Instead, if you look at the header, you'll see:
use Sub::Exporter -setup => {
exports => {
sendmail => Sub::Exporter::Util::curry_class('send'),
try_to_sendmail => Sub::Exporter::Util::curry_class('try_to_send'),
},
};
I'm not familiar with Sub::Exporter, but I did notice this description.
The biggest benefit of Sub::Exporter over existing exporters (including the ubiquitous Exporter.pm) is its ability to build new coderefs for export, rather than to simply export code identical to that found in the exporting package.
Oh...
So, the purpose of using Sub::Exporter is to export subroutine names that aren't subroutines in your package.
If you're interested, you can read the tutorial of Sub::Exporter, but it appears it has the ability to export subroutines under different names.
Thus, Email::Sender::Simple::sendmail isn't a subroutine, but that sendmail can still be exported.
|
{
"pile_set_name": "StackExchange"
}
|
1. Field of the Invention
The invention relates to a parking brake and shift mechanism, and more particularly to a parking brake and shift mechanism for vehicles.
2. Description of the Related Art
The two design parking break and shift mechanism designs for four wheeled vehicles comprise transmission-mounted and frame-mounted. Both serve as a parking brake and shift mechanism by preventing movement of the transmission shaft. Regardless of design all parking brake mechanisms are placed outside of the engine. The design and fabrication of outside the engine parking brake and shift mechanisms is not only more complex but occupies a larger volume.
Motorcycle engines are popularly employed in a great variety of four wheeled vehicles, such as all terrain vehicles (ATVs) due to their low weight and small size. Various ATV parking brake and shift mechanisms employ a latching mechanism (such as a rocker arm or cam) to latch the parking shift gear wheel to the transmission shaft. This type of mechanism, however, is rather complex. Moreover, if the existing design of the engine and related equipment is modified by the addition of a parking brake and shift mechanism, the level of complexity persists, resulting in difficulty in layout, excessive design modification, and increased development costs.
|
{
"pile_set_name": "USPTO Backgrounds"
}
|
---
abstract: 'Modern approaches for multi-person pose estimation in video require large amounts of dense annotations. However, labeling every frame in a video is costly and labor intensive. To reduce the need for dense annotations, we propose a PoseWarper network that leverages training videos with sparse annotations (every $k$ frames) to learn to perform dense temporal pose propagation and estimation. Given a pair of video frames—a labeled Frame A and an unlabeled Frame B—we train our model to predict human pose in Frame A using the features from Frame B by means of deformable convolutions to implicitly learn the pose warping between A and B. We demonstrate that we can leverage our trained PoseWarper for several applications. First, at inference time we can reverse the application direction of our network in order to propagate pose information from manually annotated frames to unlabeled frames. This makes it possible to generate pose annotations for the entire video given only a few manually-labeled frames. Compared to modern label propagation methods based on optical flow, our warping mechanism is much more compact ($6$M vs $39$M parameters), and also more accurate ($88.7\%$ mAP vs $83.8\%$ mAP). We also show that we can improve the accuracy of a pose estimator by training it on an augmented dataset obtained by adding our propagated poses to the original manual labels. Lastly, we can use our PoseWarper to aggregate temporal pose information from neighboring frames during inference. This allows us to obtain state-of-the-art pose detection results on PoseTrack2017 and PoseTrack2018 datasets. Code has been made available at: <https://github.com/facebookresearch/PoseWarper>.'
author:
- |
Gedas Bertasius$^{1,2}$, Christoph Feichtenhofer$^{1}$, Du Tran$^{1}$, Jianbo Shi$^{2}$, Lorenzo Torresani$^{1}$\
$^{1}$Facebook AI, $^{2}$University of Pennsylvania
bibliography:
- 'gb\_bibliography.bib'
title: |
Learning Temporal Pose Estimation\
from Sparsely-Labeled Videos
---
Introduction
============
In recent years, visual understanding methods [@gberta_2015_CVPR; @xie2016groups; @DBLP:journals/corr/ToshevS13; @gberta_2017_CVPR; @SPP; @he2017maskrcnn; @lin2017focal; @ren2015faster; @girshick15fastrcnn; @girshick2014rcnn; @guptaECCV14; @44872; @dai16rfcn; @DBLP:journals/corr/RedmonDGF15; @DBLP:journals/corr/RedmonF16] have made tremendous progress, partly because of advances in deep learning [@NIPS2012_4824; @43022; @Simonyan14c; @He2016DeepRL], and partly due to the introduction of large-scale annotated datasets [@imagenet_cvpr09; @coco_paper]. In this paper we consider the problem of pose estimation, which has greatly benefitted from the recent creation of large-scale datasets [@Iqbal_CVPR2017; @xiao2018simple]. Most of the recent advances in this area, though, have concentrated on the task of pose estimation in still-images [@DBLP:journals/corr/ToshevS13; @xiao2018simple; @cao2017realtime; @DBLP:conf/eccv/NewellYD16; @DBLP:conf/cvpr/WeiRKS16; @sun2019deep]. However, directly applying these image-level models to video is challenging due to nuisance factors such as motion blur, video defocus, and frequent pose occlusions. Additionally, the process of collecting annotated pose data in multi-person videos is costly and time consuming. A video typically contains hundreds of frames that need to be densely-labeled by human annotators. As a result, datasets for video pose estimation [@Iqbal_CVPR2017] are typically smaller and less diverse compared to their image counterparts [@coco_paper]. This is problematic because modern deep models require large amounts of labeled data to achieve good performance. At the same time, videos have high informational redundancy as the content changes little from frame to frame. This raises the question of whether every single frame in a training video needs to be labeled in order to achieve good pose estimation accuracy.
To reduce the reliance on densely annotated video pose data, in this work, we introduce the PoseWarper network, which operates on sparsely annotated videos, i.e., videos where pose annotations are given only every $k$ frames. Given a pair of frames from the same video—a labeled Frame A and an unlabeled Frame B—we train our model to detect pose in Frame A, using the features from Frame B. To achieve this goal, our model leverages deformable convolutions [@8237351] across space and time. Through this mechanism, our model learns to sample features from an unlabeled Frame B to maximize pose detection accuracy in a labeled Frame A.
Our trained PoseWarper can then be used for several applications. First, we can leverage PoseWarper to propagate pose information from a few manually-labeled frames across the entire video. Compared to modern optical flow propagation methods such as FlowNet2 [@DBLP:journals/corr/IlgMSKDB16], our PoseWarper produces more accurate pose annotations ($88.7\%$ mAP vs $83.8\%$ mAP), while also employing a much more compact warping mechanism ($6$M vs $39$M parameters). Furthermore, we show that our propagated poses can serve as effective pseudo labels for training a more accurate pose detector. Finally, our PoseWarper can be used to aggregate temporal pose information from neighboring frames during inference. This naturally renders the approach more robust to occlusion or motion blur in individual frames, and leads to state-of-the-art pose detection results on the PoseTrack2017 and PoseTrack2018 datasets [@Iqbal_CVPR2017].
Related Work
============
**Multi-Person Pose Detection in Images.** The traditional approaches for pose estimation leverage pictorial structures model [@conf/cvpr/AndrilukaRS09; @5540156; @BMVC.24.12:abbreviated; @DBLP:conf/iccv/PishchulinAGS13; @YiYang:2011:APE:2191740.2192012], which represents human body as a tree-structured graph with pairwise potentials between the connected body parts. These approaches have been highly successful in the past, but they tend to fail if some of body parts are occluded. These issues have been partially addressed by the models that assume a non-tree graph structure [@DGLG13; @1541292; @Sigal:CVPR:2006; @10.1007/978-3-540-88690-7_53]. However, most modern approaches for single image pose estimation are based on convolutional neural networks [@DBLP:journals/corr/ToshevS13; @he2017maskrcnn; @xiao2018simple; @cao2017realtime; @DBLP:conf/eccv/NewellYD16; @DBLP:conf/cvpr/WeiRKS16; @sun2019deep; @7780881; @NIPS2014_5291; @6909696; @insafutdinov2016deepercut; @pishchulin16cvpr; @NIPS2014_5573; @DBLP:conf/cvpr/PapandreouZKTTB17]. The method in [@DBLP:journals/corr/ToshevS13] regresses $(x,y)$ joint coordinates directly from the images. More recent work [@DBLP:conf/eccv/NewellYD16] instead predicts pose heatmaps, which leads to an easier optimization problem. Several approaches [@cao2017realtime; @DBLP:conf/cvpr/WeiRKS16; @7780881; @Belagiannis17] propose an iterative pose estimation pipeline where the predictions are refined at different stages inside a CNN or via a recurrent network. The methods in [@he2017maskrcnn; @xiao2018simple; @DBLP:conf/cvpr/PapandreouZKTTB17] tackle pose estimation problem in a top-down fashion, first detecting bounding boxes of people, and then predicting the pose heatmaps from the cropped images. The work in [@cao2017realtime] proposes part affinity fields module that captures pairwise relationships between different body parts. The approaches in [@insafutdinov2016deepercut; @pishchulin16cvpr] leverage a bottom-up pipeline first predicting the keypoints, and then assembling them into instances. Lastly, a recent work in [@sun2019deep], proposes an architecture that preserves high resolution feature maps, which is shown to be highly beneficial for the multi-person pose estimation task.
**Multi-Person Pose Detection in Video.** Due to a limited number of large scale benchmarks for video pose detection, there has been significantly fewer methods in the video domain. Several prior methods [@Iqbal_CVPR2017; @insafutdinov2017; @girdhar2018detecttrack] tackle a video pose estimation task as a two-stage problem, first detecting the keypoints in individual frames, and then applying temporal smoothing techniques. The method in [@SongWVH17] proposes a spatiotemporal CRF, which is jointly optimized for the pose prediction in video. The work in [@Charles16] proposes a personalized video pose estimation framework, which is accomplished by finetuning the model on a few frames with high confidence keypoints in each video. The approaches in [@Pfister15a; @Zhang_2018_CVPR] leverage flow based representations for aligning features temporally across multiple frames, and then using such aligned features for pose detection in individual frames.
In contrast to these prior methods, our primary objective is to learn an effective video pose detector from sparsely labeled videos. Our approach has similarities to the methods in [@Pfister15a; @Zhang_2018_CVPR], which use flow representations for feature alignment. However, unlike our model, the methods in [@Pfister15a; @Zhang_2018_CVPR] do not optimize their flow representations end-to-end with respect to the pose detection task. As we will show in our experiments, this is important for strong performance.
![A high level overview of our approach for using sparsely labeled videos for pose detection. Faces in the figure are artificially masked for privacy reasons. In each training video, pose annotations are available only every $k$ frames. During training, our system considers a pair of frames–a labeled Frame A, and an unlabeled Frame B, and aims to detect pose in Frame A, using the features from Frame B. Our training procedure is designed to achieve two goals: 1) our model must be able to extract motion offsets relating these two frames. 2) Using these motion offsets our model must then be able to rewarp the detected pose heatmap extracted from an unlabeled Frame B in order to optimize the accuracy of pose detection in a labeled Frame A. After training, we can apply our model in reverse order to propagate pose information across the entire video from ground truth poses given for only a few frames.[]{data-label="main_fig"}](./paper_figures/main/main_approach6_noface.pdf){width="0.82\linewidth"}
The PoseWarper Network
======================
**Overview.** Our goal is to design a model that learns to detect pose from sparsely labeled videos. Specifically, we assume that pose annotations in training videos are available every $k$ frames. Inspired by a recent self-supervised approach for learning facial attribute embeddings [@Wiles18a], we formulate the following task. Given two video frames—a labeled Frame A and an unlabeled Frame B—our model is allowed to compare Frame A to Frame B but it must predict Pose A (i.e., the pose in Frame A) using the features from Frame B, as illustrated in Figure \[main\_fig\] (top).
At first glance, this task may look overly challenging: how can we predict Pose A by merely using features from Frame B? However, suppose that we had body joint correspondences between Frame A and Frame B. In such a scenario, this task would become trivial, as we would simply need to spatially “warp” the feature maps computed from frame B according to the set of correspondences relating frame B to frame A. Based on this intuition, we design a learning scheme that achieves two goals: 1) By comparing Frame A and Frame B, our model must be able to extract motion offsets relating these two frames. 2) Using these motion offsets our model must be able to rewarp the pose extracted from an unlabeled Frame B in order to optimize pose detection accuracy in a labeled Frame A. To achieve these goals, we first feed both frames through a backbone CNN that predicts pose heatmaps for each of the frames. Then, the resulting heatmaps from both frames are used to determine which points from Frame B should be sampled for detection in Frame A. Finally, the resampled pose heatmap from Frame B is used to maximize accuracy of Pose A.
![An illustration of our PoseWarper architecture. Given a labeled Frame A and an unlabeled Frame B, which are separated by $\delta$ steps in time, our goal is to detect pose in a labeled Frame A using the features from an unlabeled Frame B. First, we predict pose heatmaps for both frames. Then, we compute the difference between pose heatmaps in Frame A and Frame B and feed it through a stack of $3 \times 3$ residual blocks. Afterwards, we attach five $3 \times 3$ convolutional layers with dilation rates $d \in \{3, 6, 12, 18, 24\}$ and predict five sets of offsets $o^{(d)}(p_n)$ for each pixel location $p_n$. The predicted offsets are used to rewarp pose heatmap B. All five rewarped heatmaps are then summed and the resulting tensor is used to predict pose in Frame A.[]{data-label="arch_fig"}](./paper_figures/arch/arch5_noface.pdf){width="1\linewidth"}
**Backbone Network.** Due to its high efficiency and accuracy, we use the state-of-the-art High Resolution Network (HRNet-W48) [@sun2019deep] as our backbone CNN. However, we note that our system can easily integrate other architectures as well. Thus, we envision that future improvements in still-image pose estimation will further improve the effectiveness of our approach.
**Deformable Warping.** Initially, we feed Frame A and Frame B through our backbone CNN, which outputs pose heatmaps and . Then, we compute the difference . The resulting feature tensor is provided as input to a stack of $3 \times 3$ simple residual blocks (as in standard ResNet-18 or ResNet-34 models), which output a feature tensor . The feature tensor is then fed into five $3 \times 3$ convolutional layers each using a different dilation rate $d \in\{3, 6, 12, 18, 24\}$ to predict five sets of offsets $o^{(d)} (p_n)$ at all pixel locations $p_n$. The motivation for using different dilation rates at the offset prediction stage comes from the need to consider motion cues at different spatial scales. When the body motion is small, a smaller dilation rate may be more useful as it captures subtle motion cues. Conversely, if the body motion is large, using large dilation rate allows us to incorporate relevant information further away. Next, the predicted offsets are used to spatially rewarp the pose heatmap $f_{B}$. We do this for each of the five sets of offsets $o^{(d)}$, and then sum up all five rewarped pose heatmaps to obtain a final output , which is used to predict pose in Frame A.
We implement the warping mechanism via a deformable convolution [@8237351], which takes 1) the offsets $o^{(d)}(p_n)$, and 2) the pose heatmap $f_{B}$ as its inputs, and then outputs a newly sampled pose heatmap . The subscript $(A,B)$ is used to indicate that even though was resampled from tensor , the offsets for rewarping were computed using , which contains information from both frames. An illustration of our architecture is presented in Figure \[arch\_fig\].
**Loss Function.** As in [@sun2019deep], we use a standard pose estimation loss function which computes a mean squared error between the predicted, and the ground-truth heatmaps. The ground-truth heatmap is generated by applying a 2D Gaussian around the location of each joint.
**Pose Annotation Propagation.** During training, we force our model to warp pose heatmap from an unlabeled frame B such that it would match the ground-truth pose heatmap in a labeled Frame A. Afterwards, we can reverse the application direction of our network. This then, allows us to propagate pose information from manually annotated frames to unlabeled frames (i.e. from a labeled Frame A to an unlabeled Frame B). Specifically, given a pose annotation in Frame A, we can generate its respective ground-truth heatmap by applying a 2D Gaussian around the location of each joint (identically to how it was done in [@xiao2018simple; @sun2019deep]. Then, we can predict the offsets for warping ground-truth heatmap to an unlabeled Frame B, from the feature difference . Lastly, we use our deformable warping scheme to warp the ground-truth pose heatmap to Frame B, thus, propagating pose annotations to unlabeled frames in the same video. See Figure \[main\_fig\] (bottom) for a high-level illustration of this scheme.
**Temporal Pose Aggregation at Inference Time.** Instead of using our model to propagate pose annotations on training videos, we can also use our deformable warping mechanism to aggregate pose information from nearby frames during inference in order to improve the accuracy of pose detection. For every frame at time $t$, we want to aggregate information from all frames at times $t+\delta$ where $\delta \in \{-3,-2,-1,0,1,2,3\}$. Such a pose aggregation procedure renders pose estimation more robust to occlusions, motion blur, and video defocus.
Consider a pair of frames, $I_t$ and $I_{t+\delta}$. In this case, we want to use pose information from frame $I_{t+\delta}$ to improve pose detection in frame $I_t$. To do this, we first feed both frames through our trained PoseWarper model, and obtain a spatially rewarped (resampled) pose heatmap , which is aligned with respect to frame $I_t$ using the features from frame $I_{t+\delta}$. We can repeat this procedure for every $\delta$ value, and then aggregate pose information from multiple frames via a summation as $\sum_{\delta}g_{t,t+\delta}$. **Implementation Details.** Following the framework in [@sun2019deep], for training, we crop a $384 \times 288$ bounding box around each person and use it as input to our model. During training, we use ground truth person bounding boxes. We also employ random rotations, scaling, and horizontal flipping to augment the data. To learn the network, we use the Adam optimizer [@DBLP:journals/corr/KingmaB14] with a base learning rate of $10^{-4}$, which is reduced to $10^{-5}$ and $10^{-6}$ after $10$, and $15$ epochs, respectively. The training is performed using $4$ Tesla M40 GPUs, and is terminated after $20$ epochs. We initialize our model with a HRNet-W48 [@sun2019deep] pretrained for a COCO keypoint estimation task. To train the deformable warping module, we select Frame B, with a random time-gap $\delta \in [-3,3]$ relative to Frame A. To compute features relating the two frames, we use twenty $3 \times 3$ residual blocks each with $128$ channels. Even though this seems like many convolutional layers, due to a small number of channels in each layer, this amounts to only $5.8$M parameters (compared to $39$M required to compute optical flow in [@DBLP:journals/corr/IlgMSKDB16]). To compute the offsets , we use five $3 \times 3$ convolutional layers, each using a different dilation rate ($d=3,6,12,18,24$). To resample the pose heatmap , we employ five $3 \times 3$ deformable convolutional layers, each applied to one of the five predicted offset maps . The five deformable convolution layers too employ different dilation rates of $3,6,12,18,24$. During testing, we follow the same two-stage framework used in [@sun2019deep; @xiao2018simple]: first, we detect the bounding boxes for each person in the image using the detector in [@girdhar2018detecttrack], and then feed the cropped images to our pose estimation model.
![The results of a video pose propagation task by our PoseWarper and FlowNet2 [@DBLP:journals/corr/IlgMSKDB16]. The first frame in each $3$-frame sequence illustrates a [*labeled*]{} reference frame at time t. For simplicity, we show only the “right ankle” body joint for one person, denoted by a **[ pink]{}** circle in each of the frames (please zoom in for a clearer view). The second frame depicts our propagated “right ankle” detection from the labeled frame in time t to the unlabeled frame in time t+1. The third frame shows the propagated detection in frame t+1 produced by the FlowNet2 baseline. In contrast to our method, FlowNet2 fails to propagate poses when there is large motion, blurriness or occlusions.[]{data-label="flownet2_comparison_fig"}](./paper_figures/flownet2_comparison/flownet2_comparison5_v2_noface.pdf){width="0.92\linewidth"}
Experiments
===========
In this section, we present our results on the PoseTrack [@Iqbal_CVPR2017] dataset. We demonstrate the effectiveness of our approach on three applications: 1) video pose propagation, 2) training a network on annotations augmented with propagated pose pseudo-labels, 3) temporal pose aggregation during inference.
Video Pose Propagation
----------------------
**Quantitative Results.** To verify that our model learns to capture pose correspondences, we apply it to the task of video pose propagation, i.e., propagating poses across time from a few labeled frames. Initially, we train our PoseWarper in a sparsely labeled video setting according to the procedure described above. In this setting, every $7^{th}$ frame of a training video is labeled, i.e. there are $6$ unlabeled frames between each pair of manually labeled frames. Since each video contains on average 30 frames, we have approximately $5$ annotated frames uniformly spaced out in each video. Our goal then, is to use our learned PoseWarper to propagate pose annotations from manually-labeled frames to all unlabeled frames in the same video. Specifically, for each labeled frame in a video, we propagate its pose information to the three preceding and three subsequent frames. We train our PoseWarper on sparsely labeled videos from the training set of PoseTrack2017 [@Iqbal_CVPR2017] and then perform our evaluations on the validation set.
To evaluate the effectiveness of our approach, we compare our model to several relevant baselines. As our weakest baseline, we use our trained HRNet [@sun2019deep] model that simply predicts pose for every single frame in a video. Furthermore, we also include a few propagation baselines based on warping annotations using optical flow. The first of these uses a standard Farneback optical flow [@Farneback:2003:TME:1763974.1764031] to warp the manually-labeled pose in each labeled frame to its three preceding and three subsequent frames. We also include a more advanced optical flow propagation baseline that uses FlowNet2 optical flow [@DBLP:journals/corr/IlgMSKDB16]. Finally, we evaluate our PoseWarper model.
In Table \[pose\_propagation\_table\], we present our quantitative results for video pose propagation. The evaluation is done using an mAP metric as in [@insafutdinov2016deepercut]. Our best model achieves a $88.7\%$ mAP, while the optical flow propagation baseline using FlowNet2 [@DBLP:journals/corr/IlgMSKDB16] yields an accuracy of $83.8\%$ mAP. We also note that compared to the FlowNet2 [@DBLP:journals/corr/IlgMSKDB16] propagation baseline, our PoseWarper warping mechanism is not only more accurate, but also significantly more compact ($6$M vs $39$M parameters).
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Method Head Shoulder Elbow Wrist Hip Knee Ankle Mean
---------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------- ---------- ------- ------- ------ ------ ------- ------
Pseudo-labeling w/ HRNet [@sun2019deep] 79.1 86.5 81.4 74.7 81.4 79.4 72.3 79.3
Optical Flow Propagation (Farneback [@Farneback:2003:TME:1763974.1764031]) 76.5 82.3 74.3 69.2 80.8 74.8 70.1 75.5
Optical Flow Propagation (FlowNet2 [@DBLP:journals/corr/IlgMSKDB16]) 82.7 91.0 83.8 78.4 89.7 83.6 78.1 83.8
PoseWarper (no dilated convs) 86.1 91.7 88.0 83.5 90.2 87.3 84.6 87.2
PoseWarper (1 dilated conv) 85.0 91.6 88.0 83.7 89.6 87.3 84.7 87.0
PoseWarper (2 dilated convs) 85.8 92.4 88.8 84.9 91.0 88.4 86.0 88.0
PoseWarper (3 dilated convs) 86.1 92.6 89.2 85.5 91.3 88.8 86.3 88.4
PoseWarper (4 dilated convs) **86.3 & 92.6 & **89.5 & 85.9 & **91.9 & 88.8 & 86.4 & 88.6\
PoseWarper (5 dilated convs) & 86.0 & **92.7 & **89.5 & **86.0 & 91.5 & **89.1 & **86.6 & **88.7\
******************
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
: The results of video pose propagation on the PoseTrack2017 [@Iqbal_CVPR2017] validation set (measured in mAP). We propagate pose information across the entire video from the manual annotations provided in few frames. To study the effect of different levels of dilated convolutions in our PoseWarper architecture, we also include several ablation baselines (see the bottom half of the table).[]{data-label="pose_propagation_table"}
**Ablation Studies on Dilated Convolution.** In Table \[pose\_propagation\_table\], we also present the results investigating the effect of different levels of dilated convolutions in our PoseWarper architecture. We evaluate all these variants on the task of video pose propagation. First, we report that removing dilated convolution blocks from the original architecture reduces the accuracy from $88.7$ mAP to $87.2$ mAP. We also note that a network with a single dilated convolution (using a dilation rate of $3$) yields $87.0$ mAP. Adding a second dilated convolution level (using dilation rates of $3,6$) improves the accuracy to $88.0$. Three dilation levels (with dilation rates of $3,6,12$) yield a mAP of $88.4$ and four levels (dilation rates of $3,6,12,18$) give a mAP of $88.6$. A network with $5$ dilated convolution levels yields $88.7$ mAP. Adding more dilated convolutions does not improve the performance further. Additionally, we also experimented with two networks that use dilation rates of $1,2,3,4,5$, and $4,8,16,24,32$, and report that such models yield mAPs of $88.6$ and $88.5$, respectively, which are slightly lower.
**Qualitative Comparison to FlowNet2.** In Figure \[flownet2\_comparison\_fig\], we include an illustration of the motion encoded by PoseWarper, and compare it to the optical flow computed by FlowNet2 for the video pose propagation task. The first frame in each $3$-frame sequence illustrates a [*labeled*]{} reference frame at time t. For a cleaner visualization, we show only the “right ankle” body joint for one person, which is marked with a **[ pink]{}** circle in each of the frames. The second frame depicts our propagated “right ankle” detection from the labeled frame in time t to the unlabeled frame in time t+1. The third frame shows the propagated detection in frame t+1 produced by the FlowNet2 baseline. These results suggest that FlowNet2 struggles to accurately warp poses if 1) there is large motion, 2) occlusions, or 3) blurriness. In contrast, our PoseWarper handles these cases robustly, which is also indicated by our results in Table \[pose\_propagation\_table\] (i.e., $88.7$ vs $83.8$ mAP w.r.t. FlowNet2).
Data Augmentation with PoseWarper
---------------------------------
Here we consider the task of propagating poses on sparsely labeled training videos using PoseWarper, and then using them as pseudo-ground truth labels (in addition to the original manual labels) to train a standard HRNet-W48 [@sun2019deep]. For this experiment, we study the pose detection accuracy as a function of two variables: 1) the total number of sparsely-labeled videos, and 2) the number of manually-annotated frames per video. We aim to study how much we can reduce manual labeling through our mechanism of pose propagation, while maintaining strong pose accuracy. Note, that we first train our PoseWarper on sparsely labeled videos from the training set of PoseTrack2017 [@Iqbal_CVPR2017]. Then, we propagate pose annotations on the same set of training videos. Afterwards, we retrain the model on the joint training set comprised of sparse manual pose annotations and our propagated poses. Lastly, we evaluate this trained model on the validation set.
![A figure illustrating the value of a) training a standard HRNet [@sun2019deep] using our propagated pose pseudo labels (left), and b) our temporal pose aggregation scheme during inference. In both settings, we study pose detection performance as a function of 1) number of sparsely-labeled training videos (with $1$ manually-labeled frame per video), and 2) number of labeled frames per video (with $50$ sparsely-labeled videos in total). All baselines are based on retraining the standard HRNet [@sun2019deep] model on the different training sets. The “GT (1x)” baseline is trained in a standard way on sparsely labeled video data. The “GT (7x)” baseline uses $7$x more manually annotated data relative to the “GT (1x)” baseline. Our approach on the left subfigure (“GT (1x) + pGT (6x)”), augments the original sparsely labeled video data with our propagated pose pseudo labels ($6$ nearby frames for every manually-labeled frame). Lastly, in b) “GT (1x) + T-Agg” denotes the use of PoseWarper to fuse pose information from multiple neighboring frames during inference (training is done as in “GT (1x)” baseline). From the results, we observe that both application modalities of PoseWarper provide an effective way to achieve strong pose accuracy while reducing the number of manual annotations.[]{data-label="results_fig"}](./paper_figures/results/results6.pdf){width="1\linewidth"}
All results are based on a standard HRNet [@sun2019deep] model trained on different forms of training data. “GT (1x)” refers to a model trained on sparsely labeled videos using ground-truth annotations only. “GT (7x)” baseline employs $7$x more manually-annotated poses relative to “GT (1x)” (the annotations are part of the PoseTrack2017 training set). In comparison, our approach (“GT (1x) + pGT (6x)”), is trained on a joint training set consisting of sparse manual pose annotations (same as “GT (1x)” baseline) and our propagated poses (on the training set of PoseTrack2017), which we use as pseudo ground truth data (pGT). As before, for every labeled frame we propagate the ground truth pose to the $3$ previous and the $3$ subsequent frames, which allows us to expand the training set by $7$ times.
Based on the results in the left subfigure of Figure \[results\_fig\], we can draw several conclusions. First, we note that when there are very few labeled videos (i.e., $5$), all three baselines perform poorly (leftmost figure). This indicates that in this setting there is not enough data to learn an effective pose detection model. Second, we observe that when the number of labeled videos is somewhat reasonable (e.g., $50-100$), our approach significantly outperforms the “GT (1x)” baseline, and is only slightly worse relative to the “GT (7x)” baseline. As we increase the number of labeled videos, the gaps among the three methods shrink, suggesting that the model becomes saturated.
As we vary the number of labeled frames per video (second leftmost figure), we notice several interesting patterns. First, we note that for a small number of labeled frames per video (i.e., $1-2$) our approach outperforms the “GT (1x)” baseline by a large margin. Second, we note that the performance of our approach and the “GT (7x)” becomes very similar as we add $2$ or more labeled frames per video. These findings further strengthen our previous observation that PoseWarper allows us to reduce the annotation cost without a significant loss in performance.
Improved Pose Estimation via Temporal Pose Aggregation
------------------------------------------------------
In this subsection we assess the ability of PoseWarper to improve the accuracy of pose estimation at test time by using our deformable warping mechanism to aggregate pose information from nearby frames. We visualize our results in Figure \[results\_fig\] b), where we evaluate the effectiveness of our temporal pose aggregation during inference for models trained a) with a different number of labeled videos (second rightmost figure), and b) with a different number of manually-labeled frames per video (rightmost figure). We compare our approach (“GT (1x) + T-Agg.”) to the same “GT (7x)” and “GT (1x)” baselines defined in the previous subsection. Note that our method in this case is trained exactly as “GT (1x)” baseline, the only difference comes from the inference procedure.
When the number of training videos and/or manually labeled frames is small, our approach provides a significant accuracy boost with respect to the “GT (1x)” baseline. However, once, we increase the number of labeled videos/frames, the gap between all three baselines shrinks, and the model becomes more saturated. Thus, our temporal pose aggregation scheme during inference is another effective way to maintain strong performance in a sparsely-labeled video setting.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Dataset Method Head Shoulder Elbow Wrist Hip Knee Ankle Mean
--------- ------------------------------------------------------------------------------------------------------- ------ ---------- ------- ------- ------ ------ ------- ------
Girdhar et al. [@girdhar2018detecttrack] 72.8 75.6 65.3 54.3 63.5 60.9 51.8 64.1
Xiu et al. [@xiu2018poseflow] 66.7 73.3 68.3 61.1 67.5 67.0 61.3 66.5
Bin et al [@xiao2018simple] 81.7 83.4 80.0 72.4 75.3 74.8 67.1 76.7
HRNet [@sun2019deep] 82.1 83.6 80.4 73.3 75.5 75.3 68.5 77.3
MDPN [@DBLP:conf/eccv/GuoTLCLW18] 85.2 88.5 83.9 77.5 79.0 77.0 71.4 80.7
**PoseWarper & 81.4 & 88.3 & 83.9 & 78.0 & 82.4 & 80.5 & 73.6 & **81.2\
& Girdhar et al. [@girdhar2018detecttrack] & - & - & - & - & - & - & - & 59.6\
& Xiu et al. [@xiu2018poseflow] & 64.9 & 67.5 & 65.0 & 59.0 & 62.5 & 62.8 & 57.9 & 63.0\
& Bin et al [@xiao2018simple] & 80.1 & 80.2 & 76.9 & 71.5 & 72.5 & 72.4 & 65.7 & 74.6\
& HRNet [@sun2019deep] & 80.1 & 80.2 & 76.9 & 72.0 & 73.4 & 72.5 & 67.0 & 74.9\
& **PoseWarper & 79.5 & 84.3 & 80.1 & 75.8 & 77.6 & 76.8 & 70.8 & **77.9\
& AlphaPose [@Fang_2017_ICCV] & 63.9 & 78.7 & 77.4 & 71.0 & 73.7 & 73.0 & 69.7 & 71.9\
& MDPN [@DBLP:conf/eccv/GuoTLCLW18] & 75.4 & 81.2 & 79.0 & 74.1 & 72.4 & 73.0 & 69.9 & 75.0\
& **PoseWarper & 79.9 & 86.3 & 82.4 & 77.5 & 79.8 & 78.8 & 73.2 & **79.7\
& AlphaPose++ [@DBLP:conf/eccv/GuoTLCLW18; @Fang_2017_ICCV] & - & - & - & 66.2 & - & - & 65.0 & 67.6\
& MDPN [@DBLP:conf/eccv/GuoTLCLW18] & - & - & - & 74.5 & - & - & 69.0 & 76.4\
& **PoseWarper & 78.9 & 84.4 & 80.9 & 76.8 & 75.6 & 77.5 & 71.8 & **78.0\
****************
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
: Multi-person pose estimation results on the validation and test sets of PoseTrack2017 and PoseTrack2018 datasets. Even though our model is designed to improve pose detection in scenarios involving sparsely-labeled videos, here we show that our temporal pose aggregation scheme during inference is also useful for models trained on densely labeled videos. We improve upon the state-of-the-art single-frame baselines [@xiao2018simple; @sun2019deep; @DBLP:conf/eccv/GuoTLCLW18].[]{data-label="sota_table"}
Comparison to State-of-the-Art
------------------------------
We also test the effectiveness of our temporal pose aggregation scheme, when the model is trained on the full PoseTrack [@Iqbal_CVPR2017] dataset. Table \[sota\_table\] compares our method to the most recent approaches in this area [@girdhar2018detecttrack; @xiu2018poseflow; @xiao2018simple; @sun2019deep]. These results suggest that although we designed our method to improve pose estimation when training videos are sparsely-labeled, our temporal pose aggregation scheme applied at inference is also useful for models trained on densely-labeled videos. Our PoseWarper obtains $81.2$ mAP and $77.9$ mAP on PoseTrack2017 validation and test sets respectively, and $79.7$ mAP and $78.0$ mAP on PoseTrack2018 validation and test sets respectively, thus outperforming prior single frame baselines [@girdhar2018detecttrack; @xiu2018poseflow; @xiao2018simple; @sun2019deep].
Interpreting Learned Offsets
----------------------------
Understanding what information is encoded in our learned offsets is nearly as difficult as analyzing any other CNN features [@journals/corr/ZeilerF13; @journals/corr/YosinskiCNFL15]. The main challenge comes from the high dimensionality of offsets: we are predicting $c \times k_h \times k_w$ $(x,y)$ displacements for every pixel for each of the five dilation rates $d$, where $c$ is the number of channels, and $k_h, k_w$ are the convolutional kernel height and width respectively.
In columns $3,4$ of Figure \[offsets\_fig\], we visualize two randomly-selected offset channels as a motion field. Based on this figure, it appears that different offset maps encode different motions rather than all predicting the same solution (say, the optical flow between the two frames). This makes sense, as the network may decide to ignore motions of uninformative regions, and instead capture the motion of different human body parts in different offset maps (say, a hand as opposed to the head). We also note that the magnitudes of our learned offsets encode salient human motion (see Column 5 of Figure \[offsets\_fig\]).
Lastly, to verify that our learned offsets encode human motion, for each point $p_n$ denoting a body joint, we extract our predicted offsets and train a [*linear*]{} classifier to regress the ground truth $(x,y)$ motion displacement of this body joint. In Column 7 of Figure \[offsets\_fig\], we visualize our predicted motion outputs for every pixel. We show Farneback’s optical flow in Column 6. Note that in regions containing people, our predicted human motion matches Farneback optical flow. Furthermore, we point out that compared to the standard Farneback optical flow, our motion fields look less noisy.
Conclusions
===========
In this work, we introduced PoseWarper, a novel architecture for pose detection in sparsely labeled videos. Our PoseWarper can be effectively used for multiple applications, including video pose propagation, and temporal pose aggregation. In these settings, we demonstrated that our approach reduces the need for densely labeled video data, while producing strong pose detection performance. Furthermore, our state-of-the-art results on PoseTrack2017 and PoseTrack2018 datasets demonstrate that our PoseWarper is useful even when the training videos are densely-labeled. Our future work involves improving our model ability to propagate labels and aggregate temporal information when the input frames are far away from each other. We are also interested in exploring self-supervised learning objectives for our task, which may further reduce the need of pose annotations in video. We will release our source code and our trained models upon publication of the paper.
![In the first two columns, we show a pair of video frames used as input for our model. The $3^{rd}$ and $4^{th}$ columns depict $2$ randomly selected offset channels visualized as a motion field. Different channels appear to capture the motion of different body parts. In the $5^{th}$ column, we display the offset magnitudes, which highlight salient human motion. Finally, the last two columns illustrate the standard Farneback flow, and the human motion predicted from our learned offsets. To predict human motion we train a **linear** classifier to regress the ground-truth $(x,y)$ displacement of each joint from the offset maps. The color wheel, at the bottom right corner encodes motion direction.[]{data-label="offsets_fig"}](./paper_figures/interpreting_offsets/interpreting_offsets2_noface.pdf){width="1\linewidth"}
|
{
"pile_set_name": "ArXiv"
}
|
Q:
Change password page
I'm having trouble with this PHP code, it seems logical to me but as I'm new to PHP and MySQL, I am obviously wrong.
I'm trying to set up a change password page for an assignment, and I can't see where I have gone wrong, the code is as follows:
session_start();
if(isset($_SESSION['uname'])){
echo "Welcome " . $_SESSION['uname'];
}
require_once 'PHP/Constants.php';
$conn = new MySQLi(DB_SERVER, DB_USER, DB_PASSWORD, DB_NAME) or die ('There was a problem connecting to the database');
$query = "SELECT * FROM user";
$result = mysqli_query($conn, $query);
while ($pwdReq = mysqli_fetch_array($result)){
if ($pwdReq['Password'] == $_POST['oldPwd']) {
if ($_POST['confPwd'] == $_POST['newPwd']){
$change = "INSERT INTO user(Password) VALUES ('newPwd')";
$pwdChange = mysqli_query($conn, $change);
} else return "The new passwords do not match!";
} else return "Please enter a correct password!";
}
The body Of my page is as follows:
<form method="post" action="">
<h2>Change Password</h2>
<p>
<label for="oldPwd">Old Password:</label>
<input type="password" name="oldPwd" />
</p>
<p>
<label for="newPwd">New Password:</label>
<input type="password" name="newPwd" />
</p>
<p>
<label for="confPwd">Confirm Password:</label>
<input type="password" name="confPwd" />
</p>
<p>
<input type="submit" id="submit" value="Submit" name="submit" />
</p>
</form>
When the page runs all I get is as follows:
Notice: Undefined index: oldPwd in C:\Program Files
(x86)\xampp\htdocs\Assignment\change_password.php on line 11
Thank you in advance for any help I receive - Nick
A:
Always check your POST values before you do anything
You should select the single record that matches uname
prepare your queries to avoid SQL injection
Here's an improved version of your script:
<?php
session_start();
if(isset($_SESSION['uname'])){
echo "Welcome " . $_SESSION['uname'];
}
if(isset($_POST['oldPwd']) &&
isset($_POST['newPwd']) &&
isset($_POST['confPwd']){
//Values are set
require_once 'PHP/Constants.php';
$conn = new MySQLi(DB_SERVER, DB_USER, DB_PASSWORD, DB_NAME)
or die ('There was a problem connecting to the database');
//Select user from DB where username matches Session
$query = "SELECT * FROM user WHERE uname = ?";
//prepare query
$stmt = mysqli_prepare($conn, $query);
mysqli_stmt_bind_param($stmt, "s", $_SESSION['uname']);
$result = mysqli_stmt_execute($stmt);
$row = mysqli_fetch_assoc($result);
//get the password from DB
$pwdReq = $row['Password'];
if ($pwdReq == $_POST['oldPwd']){
if($_POST['confPwd'] == $_POST['newPwd'])) {
$change = "INSERT INTO user(Password) VALUES (?)";
$stmt = mysqli_prepare($conn, $change);
mysqli_stmt_bind_param($stmt, "s", $_POST['newPwd']);
mysqli_stmt_execute($stmt);
echo "Password has been changed";
} else{
echo "The new password does not match confirmation";
}
}else{
echo "Old password not matching the database";
}
}else{
echo "oldPwd, confPwd, or confPwd is not set";
}
?>
if oldPwd, confPwd, or confPwd is not set you will need to figure why. This is not PHP fault anymore. You will need to look in your html and make sure the script is receiving these values
|
{
"pile_set_name": "StackExchange"
}
|
The BBC has defended new CBBC transgender drama Just a Girl against criticisms that it is unsuitable for young children.
Cat Lewis, chief executive of Nine Lives Production Company which made a documentary called I am Leo, about a boy born as a girl called Lily, says the drama promotes awareness and understanding of transgender issues.
She discusses the programme with Laura Perrins, co-editor of The Conservative Woman website, who argues: "It's not the job of the BBC to encourage children to change their gender."
(Image: Just a Girl, credit: BBC)
|
{
"pile_set_name": "OpenWebText2"
}
|
#-*- coding: ISO-8859-1 -*-
# pysqlite2/test/factory.py: tests for the various factories in pysqlite
#
# Copyright (C) 2005-2007 Gerhard Häring <gh@ghaering.de>
#
# This file is part of pysqlite.
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# arising from the use of this software.
#
# Permission is granted to anyone to use this software for any purpose,
# including commercial applications, and to alter it and redistribute it
# freely, subject to the following restrictions:
#
# 1. The origin of this software must not be misrepresented; you must not
# claim that you wrote the original software. If you use this software
# in a product, an acknowledgment in the product documentation would be
# appreciated but is not required.
# 2. Altered source versions must be plainly marked as such, and must not be
# misrepresented as being the original software.
# 3. This notice may not be removed or altered from any source distribution.
import unittest
import sqlite3 as sqlite
class MyConnection(sqlite.Connection):
def __init__(self, *args, **kwargs):
sqlite.Connection.__init__(self, *args, **kwargs)
def dict_factory(cursor, row):
d = {}
for idx, col in enumerate(cursor.description):
d[col[0]] = row[idx]
return d
class MyCursor(sqlite.Cursor):
def __init__(self, *args, **kwargs):
sqlite.Cursor.__init__(self, *args, **kwargs)
self.row_factory = dict_factory
class ConnectionFactoryTests(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:", factory=MyConnection)
def tearDown(self):
self.con.close()
def CheckIsInstance(self):
self.assertTrue(isinstance(self.con,
MyConnection),
"connection is not instance of MyConnection")
class CursorFactoryTests(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:")
def tearDown(self):
self.con.close()
def CheckIsInstance(self):
cur = self.con.cursor(factory=MyCursor)
self.assertTrue(isinstance(cur,
MyCursor),
"cursor is not instance of MyCursor")
class RowFactoryTestsBackwardsCompat(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:")
def CheckIsProducedByFactory(self):
cur = self.con.cursor(factory=MyCursor)
cur.execute("select 4+5 as foo")
row = cur.fetchone()
self.assertTrue(isinstance(row,
dict),
"row is not instance of dict")
cur.close()
def tearDown(self):
self.con.close()
class RowFactoryTests(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:")
def CheckCustomFactory(self):
self.con.row_factory = lambda cur, row: list(row)
row = self.con.execute("select 1, 2").fetchone()
self.assertTrue(isinstance(row,
list),
"row is not instance of list")
def CheckSqliteRowIndex(self):
self.con.row_factory = sqlite.Row
row = self.con.execute("select 1 as a, 2 as b").fetchone()
self.assertTrue(isinstance(row,
sqlite.Row),
"row is not instance of sqlite.Row")
col1, col2 = row["a"], row["b"]
self.assertTrue(col1 == 1, "by name: wrong result for column 'a'")
self.assertTrue(col2 == 2, "by name: wrong result for column 'a'")
col1, col2 = row["A"], row["B"]
self.assertTrue(col1 == 1, "by name: wrong result for column 'A'")
self.assertTrue(col2 == 2, "by name: wrong result for column 'B'")
col1, col2 = row[0], row[1]
self.assertTrue(col1 == 1, "by index: wrong result for column 0")
self.assertTrue(col2 == 2, "by index: wrong result for column 1")
def CheckSqliteRowIter(self):
"""Checks if the row object is iterable"""
self.con.row_factory = sqlite.Row
row = self.con.execute("select 1 as a, 2 as b").fetchone()
for col in row:
pass
def CheckSqliteRowAsTuple(self):
"""Checks if the row object can be converted to a tuple"""
self.con.row_factory = sqlite.Row
row = self.con.execute("select 1 as a, 2 as b").fetchone()
t = tuple(row)
def CheckSqliteRowAsDict(self):
"""Checks if the row object can be correctly converted to a dictionary"""
self.con.row_factory = sqlite.Row
row = self.con.execute("select 1 as a, 2 as b").fetchone()
d = dict(row)
self.assertEqual(d["a"], row["a"])
self.assertEqual(d["b"], row["b"])
def CheckSqliteRowHashCmp(self):
"""Checks if the row object compares and hashes correctly"""
self.con.row_factory = sqlite.Row
row_1 = self.con.execute("select 1 as a, 2 as b").fetchone()
row_2 = self.con.execute("select 1 as a, 2 as b").fetchone()
row_3 = self.con.execute("select 1 as a, 3 as b").fetchone()
self.assertTrue(row_1 == row_1)
self.assertTrue(row_1 == row_2)
self.assertTrue(row_2 != row_3)
self.assertFalse(row_1 != row_1)
self.assertFalse(row_1 != row_2)
self.assertFalse(row_2 == row_3)
self.assertEqual(row_1, row_2)
self.assertEqual(hash(row_1), hash(row_2))
self.assertNotEqual(row_1, row_3)
self.assertNotEqual(hash(row_1), hash(row_3))
def tearDown(self):
self.con.close()
class TextFactoryTests(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:")
def CheckUnicode(self):
austria = unicode("Österreich", "latin1")
row = self.con.execute("select ?", (austria,)).fetchone()
self.assertTrue(type(row[0]) == unicode, "type of row[0] must be unicode")
def CheckString(self):
self.con.text_factory = str
austria = unicode("Österreich", "latin1")
row = self.con.execute("select ?", (austria,)).fetchone()
self.assertTrue(type(row[0]) == str, "type of row[0] must be str")
self.assertTrue(row[0] == austria.encode("utf-8"), "column must equal original data in UTF-8")
def CheckCustom(self):
self.con.text_factory = lambda x: unicode(x, "utf-8", "ignore")
austria = unicode("Österreich", "latin1")
row = self.con.execute("select ?", (austria.encode("latin1"),)).fetchone()
self.assertTrue(type(row[0]) == unicode, "type of row[0] must be unicode")
self.assertTrue(row[0].endswith(u"reich"), "column must contain original data")
def CheckOptimizedUnicode(self):
self.con.text_factory = sqlite.OptimizedUnicode
austria = unicode("Österreich", "latin1")
germany = unicode("Deutchland")
a_row = self.con.execute("select ?", (austria,)).fetchone()
d_row = self.con.execute("select ?", (germany,)).fetchone()
self.assertTrue(type(a_row[0]) == unicode, "type of non-ASCII row must be unicode")
self.assertTrue(type(d_row[0]) == str, "type of ASCII-only row must be str")
def tearDown(self):
self.con.close()
class TextFactoryTestsWithEmbeddedZeroBytes(unittest.TestCase):
def setUp(self):
self.con = sqlite.connect(":memory:")
self.con.execute("create table test (value text)")
self.con.execute("insert into test (value) values (?)", ("a\x00b",))
def CheckString(self):
# text_factory defaults to unicode
row = self.con.execute("select value from test").fetchone()
self.assertIs(type(row[0]), unicode)
self.assertEqual(row[0], "a\x00b")
def CheckCustom(self):
# A custom factory should receive an str argument
self.con.text_factory = lambda x: x
row = self.con.execute("select value from test").fetchone()
self.assertIs(type(row[0]), str)
self.assertEqual(row[0], "a\x00b")
def CheckOptimizedUnicodeAsString(self):
# ASCII -> str argument
self.con.text_factory = sqlite.OptimizedUnicode
row = self.con.execute("select value from test").fetchone()
self.assertIs(type(row[0]), str)
self.assertEqual(row[0], "a\x00b")
def CheckOptimizedUnicodeAsUnicode(self):
# Non-ASCII -> unicode argument
self.con.text_factory = sqlite.OptimizedUnicode
self.con.execute("delete from test")
self.con.execute("insert into test (value) values (?)", (u'ä\0ö',))
row = self.con.execute("select value from test").fetchone()
self.assertIs(type(row[0]), unicode)
self.assertEqual(row[0], u"ä\x00ö")
def tearDown(self):
self.con.close()
def suite():
connection_suite = unittest.makeSuite(ConnectionFactoryTests, "Check")
cursor_suite = unittest.makeSuite(CursorFactoryTests, "Check")
row_suite_compat = unittest.makeSuite(RowFactoryTestsBackwardsCompat, "Check")
row_suite = unittest.makeSuite(RowFactoryTests, "Check")
text_suite = unittest.makeSuite(TextFactoryTests, "Check")
text_zero_bytes_suite = unittest.makeSuite(TextFactoryTestsWithEmbeddedZeroBytes, "Check")
return unittest.TestSuite((connection_suite, cursor_suite, row_suite_compat, row_suite, text_suite, text_zero_bytes_suite))
def test():
runner = unittest.TextTestRunner()
runner.run(suite())
if __name__ == "__main__":
test()
|
{
"pile_set_name": "Github"
}
|
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