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"title": "MovementFinder: Visual analytics of origin-destination patterns from geo-tagged social media",
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"abstract": "Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.",
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"content": "Geo-tagged social media data can be viewed as sampling of people's trajectories in daily life. It consists of people's movements and embeds the semantics of movements. However, it is challenging to reveal patterns from the sparse and irregular sampling data. We proposed an interactive multi-filter visualization approach to analyze the spatial-temporal movement pattern in people's daily life. People's trajectories are visualized on the map with multiple functional layers. With our visual analytics tools, users are able to drill down to details, with the awareness of the origin-destination flow patterns of spatial, temporal, and semantic meaning.",
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"title": "Origin-Destination Trajectory Diversity Analysis: Efficient Top-k Diversified Search",
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"title": "Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data",
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"title": "2017 IEEE International Conference on Data Mining (ICDM)",
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"volume": "0",
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"year": "2017",
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"title": "A Probabilistic Geographical Aspect-Opinion Model for Geo-Tagged Microblogs",
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"abstract": "Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods.",
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"content": "Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods.",
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"fullName": "Aman Ahuja",
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"fullName": "Wei Wei",
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"fullName": "Wei Lu",
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"proceeding": {
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"title": "2012 IEEE 31st International Symposium on Reliable Distributed Systems (SRDS 2012)",
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"doi": "10.1109/SRDS.2012.19",
"title": "Detecting Anomalous User Behaviors in Workflow-Driven Web Applications",
"normalizedTitle": "Detecting Anomalous User Behaviors in Workflow-Driven Web Applications",
"abstract": "Web applications are increasingly used as portals to interact with back-end database systems and support business processes. This type of data-centric workflow-driven web application is vulnerable to two types of security threats. The first is an request integrity attack, which stems from the vulnerabilities in the implementation of business logic within web applications. The second is guideline violation, which stems from privilege misuse in scenarios where business logic and policies are too complex to be accurately defined and enforced. Both threats can lead to sequences of web requests that deviate from typical user behaviors. The objective of this paper is to detect anomalous user behaviors based on the sequence of their requests within a web session. We first decompose web sessions into workflows based on their data objects. In doing so, the detection of anomalous sessions is reduced to detection of anomalous workflows. Next, we apply a hidden Markov model (HMM) to characterize workflows on a per-object basis. In this model, the implicit business logic involved in this object defines the unobserved states of the Markov process, where the web requests are observations. To derive more robust HMMs, we extend the object-specific approach to an object-cluster approach, where objects with similar workflows are clustered and HMM models are derived on a per-cluster basis. We evaluate our models using two real systems, including an open source web application and a large web-based electronic medical record system. The results show that our approach can detect anomalous web sessions and lend evidence to suggest that the clustering approach can achieve relatively low false positive rates while maintaining its detection accuracy.",
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"content": "Web applications are increasingly used as portals to interact with back-end database systems and support business processes. This type of data-centric workflow-driven web application is vulnerable to two types of security threats. The first is an request integrity attack, which stems from the vulnerabilities in the implementation of business logic within web applications. The second is guideline violation, which stems from privilege misuse in scenarios where business logic and policies are too complex to be accurately defined and enforced. Both threats can lead to sequences of web requests that deviate from typical user behaviors. The objective of this paper is to detect anomalous user behaviors based on the sequence of their requests within a web session. We first decompose web sessions into workflows based on their data objects. In doing so, the detection of anomalous sessions is reduced to detection of anomalous workflows. Next, we apply a hidden Markov model (HMM) to characterize workflows on a per-object basis. In this model, the implicit business logic involved in this object defines the unobserved states of the Markov process, where the web requests are observations. To derive more robust HMMs, we extend the object-specific approach to an object-cluster approach, where objects with similar workflows are clustered and HMM models are derived on a per-cluster basis. We evaluate our models using two real systems, including an open source web application and a large web-based electronic medical record system. The results show that our approach can detect anomalous web sessions and lend evidence to suggest that the clustering approach can achieve relatively low false positive rates while maintaining its detection accuracy.",
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"abstract": "The analysis of incidents is important for preventing collisions between moving objects. However, it requires that many cases of incidents be collected as target data. The purpose of this study was to develop a method that supports the collection of many cases of anomalous (near-miss) approach from massive movement data. To achieve this, a relative superposition method is proposed. Relative superposition is a type of visualization method, in which the trajectories of pairs of moving objects are transformed, focusing on their relative positional relationships. The method is expected to facilitate the search for anomalous approach cases. This paper describes five types of relative superposition, together with a visual tool in which they are implemented. Moreover, it presents a case study in which anomalous approach cases of ships were searched by applying the tool to actual the automatic identification system (AIS) data.",
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"title": "Beyond Replication: Augmenting Social Behaviors in Multi-User Virtual Realities",
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"abstract": "This paper presents a novel approach for the augmentation of social behaviors in virtual reality (VR). We designed three visual transformations for behavioral phenomena crucial to everyday social interactions: eye contact, joint attention, and grouping. To evaluate the approach, we let users interact socially in a virtual museum using a large-scale multi-user tracking environment. Using a between-subject design (N = 125) we formed groups of five participants. Participants were represented as simplified avatars and experienced the virtual museum simultaneously, either with or without the augmentations. Our results indicate that our approach can significantly increase social presence in multi-user environments and that the augmented experience appears more thought-provoking. Furthermore, the augmentations seem also to affect the actual behavior of participants with regard to more eye contact and more focus on avatars/objects in the scene. We interpret these findings as first indicators for the potential of social augmentations to impact social perception and behavior in VR.",
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"affiliation": "University of Würzburg, HCI Group",
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"fullName": "Constantin Klelnbeck",
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"affiliation": "Friedrich-Alexander University Erlangen-NOrnberg (FAU), Programming Systems Group",
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"abstract": "Advanced Persistent Threat (APT) is considered a serious problem faced by governments and enterprises. The detection method of APT should be different from general cyber-attacks because of its characteristics of persistence, camouflage and indirection, and there's no strategy that is effective enough at present. In this paper, we introduce business scenarios of email into APT detection to make our approach more targeted to anomalous behaviors which are caused by APT than other general cyber-attacks. We combine the key information of email protocols and factors of behaviors in business scenarios to extract more targeted features and learn deep features using deep learning algorithms. Then we summarize Tactics, Techniques, and Procedures (TTPs) based on email business scenarios in order to detect APT emails more effectively. Our experiments are conducted on a real dataset that up to 10,850,796 emails from October 2016 till January 2021, and we discovered 3 cases of real APT campaigns and also a new type of APT behaviors that is not detected by previous works.",
"abstracts": [
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"content": "Advanced Persistent Threat (APT) is considered a serious problem faced by governments and enterprises. The detection method of APT should be different from general cyber-attacks because of its characteristics of persistence, camouflage and indirection, and there's no strategy that is effective enough at present. In this paper, we introduce business scenarios of email into APT detection to make our approach more targeted to anomalous behaviors which are caused by APT than other general cyber-attacks. We combine the key information of email protocols and factors of behaviors in business scenarios to extract more targeted features and learn deep features using deep learning algorithms. Then we summarize Tactics, Techniques, and Procedures (TTPs) based on email business scenarios in order to detect APT emails more effectively. Our experiments are conducted on a real dataset that up to 10,850,796 emails from October 2016 till January 2021, and we discovered 3 cases of real APT campaigns and also a new type of APT behaviors that is not detected by previous works.",
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"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences,Beijing,China",
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"abstract": "Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email’s subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present Holmes, an efficient and lightweight semantic based engine for anomalous email detection. Holmes can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of Holmes in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, Holmes shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that Holmes can discover more concealed malicious emails that are immune from several commercial detection tools.",
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"content": "Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email’s subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present Holmes, an efficient and lightweight semantic based engine for anomalous email detection. Holmes can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of Holmes in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, Holmes shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that Holmes can discover more concealed malicious emails that are immune from several commercial detection tools.",
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"abstract": "Developers authoring HTML documents define elements following patterns which establish and reflect the visual structure of a document, such as making all images in a footer the same height by applying a class to each. To surface these patterns to developers and support developers in authoring consistent with these patterns, we propose a mixed human-AI technique for creating code patterns. Patterns are first learned from individual HTML documents through a decision tree, generating a representation which developers may view and edit. Code patterns are used to offer developers autocomplete suggestions, list examples, and flag violations. To evaluate our technique, we conducted a user study in which 24 participants wrote, edited, and corrected HTML documents. We found that our technique enabled developers to edit and correct documents more quickly and create, edit, and correct documents more successfully.",
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"abstract": "Probabilistic topic models are powerful techniques which are widely used for discovering topics or semantic content from a large collection of documents. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g persons, location, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as ontologies and Linked Open Data (LOD), which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel entity-based topic model, called EntLDA, to effectively integrate an ontology with an entity topic model to improve the topic modeling process. Furthermore, to increase the coherence of the identified topics, we introduce a novel ontology-based regularization framework, which is then integrated with the EntLDA model. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of the topics.",
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"content": "Objects in the periphery of fisheye images can become extremely distorted. This distortion can cause false positives and missed detections for automated object detection systems. This is problematic, not only for systems which have been trained on perspective images, but also for those that have been explicitly trained on fisheye data. In this paper we propose a new cost function for training object detectors on fisheye images. We model fisheye image distortion as an imbalanced domain problem and develop a domain association loss function to approach it with deep learning. We define separate domains based on the level of distortion within the image plane and propose a new objective function, inspired by the recently introduced focal loss for object detection, which we call a spatial focal loss. Our proposed loss incorporates a domain-modulating term which re-weights samples from different domains to encourage the learning of domain-invariant features. We implement spatial focal loss function in the YOLOv2 architecture and evaluate it on the task of pedestrian detection in a fisheye dataset captured by a 360 camera system mounted on a moving vehicle and labeled with over 11,000 pedestrian instances. Our experiments demonstrate that spatial focal loss can improve model performance in the highly distorted image periphery versus existing loss functions, including focal loss, without sacrificing performance in the less distorted image center, with no adaptations to network architectures required. By analyzing the locations of missed detections, we show further evidence that our loss function can improve the learning of domain-invariant features.",
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"abstract": "The fisheye image is the image taken with a fisheye camera, in which the object suffer from distortion. The street view building images are always taken by fisheye cameras due to the boarder view of the very cameras. So, the distortion of the images must be corrected before further applications. This paper compared three state of the art's distortion correction algorithms, including algorithm based on Polynomial Model, algorithm based on Division Model, and algorithm in [1]. The comparison results on both synthetic and real data show that algorithm in [1] performs the best.",
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"content": "Test flight meteorological guarantee is different from military and civil aviation meteorological guarantee, mainly in terms of aircraft type, flight subjects, and guarantee mode, etc. Based on the above reasons test flights have more complex and diversified requirements for meteorological conditions, and at the same time, flying under dangerous weather conditions has high challenges for meteorological guarantee personnel in terms of psychological quality, technical level, communication ability and guarantee plan. By studying the strong convective weather affecting the test flight mission process in the past five years (2016-2020), we summarize the strong convective weather forecast and warning guarantee method, and use the fine grid 3-hour forecast from the European Centre for Medium-Range Weather Forecasting (ECMWF), Doppler radar of Xi'an station and satellite cloud maps to focus on the analysis of the squall line weather forecast and the whole process of test flight guarantee on June 13, 2020, to demonstrate the established The short-range forecasting and warning method and guarantee model were established to improve the strong convective weather forecasting and warning guarantee model and method, to realize the transformation from theoretical model to practical application, to lay a solid foundation for the strong convective weather process forecasting and guarantee capability, and to provide reference for future flight rehearsal guarantee under complex weather conditions.",
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"title": "NanoAsPipe: A transcriptome analysis and alternative splicing detection pipeline for MinION long-read RNA-seq",
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"abstract": "After the next-generation sequencing technology, long-read sequencing technologies were developed. Both the advantages and the drawbacks of long-read sequencing technologies are obvious. Here, we take advantages of long-read sequencing technology, MinION, to develop a comprehensive and user-friendly pipeline, NanoAsPipe, to identify the transcriptomic profiles, isoform profiles, and alternative splicing (AS) events of long-read RNA-seq data on the Open Science Grid (OSG). NanoAsPipe includes RNA-seq data preprocessing, mapping and alternative splicing analysis to simultaneously account for AS events. NanoAsPipe was applied to two long-read datasets and its performance was evaluated by comparing discovered AS events with gene annotation and results from short-read RNA-seq data. Majority of predicted AS events by NanoAsPipe was confirmed by the gene annotation and there is a high correlation between results from long-read data and short-read data. NanoAsPipe was implemented as a software package, which is available for academic research at http://sysbio.unl.edu/NanoAsPipe. The user may run the workflow based on the OSG environment or switch it to other computing setup environments.",
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"affiliation": "School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA",
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"affiliation": "School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA",
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"abstract": "In the simplest model of protein production, a gene gives rise to a single protein; DNA is transcribed to form pre-mRNA, which is converted to mRNA by splicing or removing introns. The result is a chain of exons that is translated to form a protein. Alternative splicing of exons may result in the formation of multiple proteins from the same gene sequence. However, not all of these proteins may be functional. Thus, we ask whether we can predict and rank (in order of frequency of occurrence and functional importance) the set of possible proteins for a gene. Herein we describe a tool that predicts the relative frequencies of isoforms that can be produced from a given gene.",
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"title": "Detection and Prediction of Alternative Splicing within Acceptor/Donor Sites in pre-mRNA of Arabidopsis thaliana",
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"abstract": "Alternative splicing is an important process in gene expression. Presently, most studies aimed at detecting alternatively spliced genes use ESTs (expressed sequence tags). However, reliance on ESTs might have some weaknesses in predicting alternative splicing. It is difficult to predict whether or not alternative splicing exists for those genes where ESTs are not available. In addition, since the EST libraries are often not clearly organized and annotated, we can pick erroneous ESTs. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose a method that primarily uses pre-mRNAs. It is achieved by a decision tree algorithm using codons, three nucleotides, as attributes for each chromosome in Arabidopsis thaliana. Each decision tree shows that alternative and normal splicing have different splicing patterns according to triplet nucleotides. Based on the patterns, alternative splicing of unlabeled genes can also be predicted.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Alternative splicing is an important process in gene expression. Presently, most studies aimed at detecting alternatively spliced genes use ESTs (expressed sequence tags). However, reliance on ESTs might have some weaknesses in predicting alternative splicing. It is difficult to predict whether or not alternative splicing exists for those genes where ESTs are not available. In addition, since the EST libraries are often not clearly organized and annotated, we can pick erroneous ESTs. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose a method that primarily uses pre-mRNAs. It is achieved by a decision tree algorithm using codons, three nucleotides, as attributes for each chromosome in Arabidopsis thaliana. Each decision tree shows that alternative and normal splicing have different splicing patterns according to triplet nucleotides. Based on the patterns, alternative splicing of unlabeled genes can also be predicted.",
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"normalizedAbstract": "Alternative splicing is an important process in gene expression. Presently, most studies aimed at detecting alternatively spliced genes use ESTs (expressed sequence tags). However, reliance on ESTs might have some weaknesses in predicting alternative splicing. It is difficult to predict whether or not alternative splicing exists for those genes where ESTs are not available. In addition, since the EST libraries are often not clearly organized and annotated, we can pick erroneous ESTs. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose a method that primarily uses pre-mRNAs. It is achieved by a decision tree algorithm using codons, three nucleotides, as attributes for each chromosome in Arabidopsis thaliana. Each decision tree shows that alternative and normal splicing have different splicing patterns according to triplet nucleotides. Based on the patterns, alternative splicing of unlabeled genes can also be predicted.",
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"affiliation": "Department of Computer Science, University of Massachusetts, Lowell, MA 01854. Email: mpark@cs.uml.edu",
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"affiliation": "Department of Biological Sciences, University of Maine, Orono, ME 04469. Email: kil-young.yun@umit.maine.edu",
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"affiliation": "Department of Computer Science, University of Massachusetts, Lowell, MA 01854. Email: kdaniels@cs.uml.edu",
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"title": "Vials: Visualizing Alternative Splicing of Genes",
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"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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"article": {
"id": "1A9WbJsJ128",
"doi": "10.1109/BIBM52615.2021.9669396",
"title": "GraphIsoFun: a graph neural network based approach for splice isoform function prediction",
"normalizedTitle": "GraphIsoFun: a graph neural network based approach for splice isoform function prediction",
"abstract": "Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Alternative splicing has a significant impact in the process of gene regulation and it greatly raises the complexity of protein species and functions. At present, the gene function has been extensively studied, and there are multiple public databases of gene function annotations. Due to the high cost and the large number of isoforms, it is currently impossible to conduct extensive experimental analysis on isoform functions. Functions at the level of splicing isoforms remain largely unknown. Computational prediction provides an alternative to functional annotation of splice isoforms. Here we present GraphIsoFun, a novel computational approach to predict isoform function by integrating both gene and isoform-level expression information with graph neural network. It first constructs a heterogeneous co-expression network with both genes and isoforms being nodes. This network integrates the rich association between isoforms and genes, and provides a bridge for connecting isoform and gene functions. Then GraphIsoFun uses the graph neural network to mine the information of the co-expression network and obtains the final prediction score of isoforms for each function. The experiments on multiple datasets show that GraphIsoFun surpasses existing methods based on different evaluation indicators.",
"fno": "09669396",
"keywords": [
"Bioinformatics",
"Biological Techniques",
"Genetics",
"Genomics",
"Graph Theory",
"Molecular Biophysics",
"Neural Nets",
"Proteins",
"Multiple Public Databases",
"Gene Function Annotations",
"Isoform Level Expression Information",
"Graph Neural Network Based Approach",
"Splice Isoform Function Prediction",
"Gene Regulation",
"Graph Iso Fun Approach",
"Heterogeneous Coexpression Network",
"Gene Expression",
"Protein Species",
"Protein Functions",
"Proteins",
"Protein Engineering",
"Costs",
"Annotations",
"Databases",
"Splicing",
"Conferences",
"Alternative Splicing",
"Isoform Function",
"Gene Function",
"Graph Neural Network"
],
"authors": [
{
"affiliation": "Central South University,School of Computer Science and Engineering,Changsha,China,Hunan,410083",
"fullName": "Shuo Zhang",
"givenName": "Shuo",
"surname": "Zhang",
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},
{
"affiliation": "Central South University,School of Computer Science and Engineering,Changsha,China,Hunan,410083",
"fullName": "Changhuo Yang",
"givenName": "Changhuo",
"surname": "Yang",
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{
"affiliation": "Central South University,School of Computer Science and Engineering,Changsha,China,Hunan,410083",
"fullName": "Hong-Dong Li",
"givenName": "Hong-Dong",
"surname": "Li",
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{
"affiliation": "Central South University,School of Computer Science and Engineering,Changsha,China,Hunan,410083",
"fullName": "Jianxin Wang",
"givenName": "Jianxin",
"surname": "Wang",
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{
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"doi": null,
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"id": "proceedings/bibm/2012/2559/0/06392734",
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"id": "proceedings/bibm/2012/2559/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine",
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{
"id": "trans/tg/2016/01/07192691",
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"doi": null,
"abstractUrl": "/journal/tg/2016/01/07192691/13rRUxBJhFz",
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"title": "IEEE Transactions on Visualization & Computer Graphics",
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{
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"doi": null,
"abstractUrl": "/journal/tb/2023/01/09697358/1AC4hUTzFw4",
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"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
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"id": "proceedings/bibm/2019/1867/0/08982956",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2019/08982956/1hgugmxX4XK",
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{
"id": "trans/tb/2022/04/09353272",
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"doi": null,
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{
"id": "trans/tb/2022/05/09468392",
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"doi": null,
"abstractUrl": "/journal/tb/2022/05/09468392/1uPuLzmdBS0",
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{
"proceeding": {
"id": "12OmNxE2mVy",
"title": "Computing, Measurement, Control and Sensor Network, International Conference on",
"acronym": "cmcsn",
"groupId": "1801748",
"volume": "0",
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"year": "2012",
"__typename": "ProceedingType"
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"article": {
"id": "12OmNCd2rIa",
"doi": "10.1109/CMCSN.2012.105",
"title": "Increasing Visual Perception Brain Stroke Detection System",
"normalizedTitle": "Increasing Visual Perception Brain Stroke Detection System",
"abstract": "Stroke has been the third among the top ten leading causes of death in Taiwan. The danger is not only high mortality, but also gave the family a heavy economic. Recently, inspects the ischemic stroke the method to be divided into two categories: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The former CT is the most regularly adoption because done once the time of the latter MRI is longer than the time of the former CT, and the latter MRI is the most expensive than the former CT. Due to academic studies of ischemic stroke detection is very finite, it is low successful rate to correctly detect stroke initial period. In tradition, we have to depend on radiologist¡¦s professional knowledge to diagnose ischemic stroke image, it will make radiologists increase fatigue so that made an error diagnostic. Therefore, we propose a Increasing visual perception brain stroke detection system. We use mathematic morphology to extract brain area. Then using median filter to remove noise, and uses canny edge detection to find out the edge of the brain tissue, and setting peak value in edge histogram as seed to perform region growing. Finally, we can clearly recognize the area of stroke. In the experimental result the recognition successful rate can reach 85%. Our successful rate cannot reach 100% because each patient¡¦s brain CT image is not certainly upright and foursquare. Future research direction, we will use other methods to correct gradient and CT image for each patient to improve successful rate of detection.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Stroke has been the third among the top ten leading causes of death in Taiwan. The danger is not only high mortality, but also gave the family a heavy economic. Recently, inspects the ischemic stroke the method to be divided into two categories: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The former CT is the most regularly adoption because done once the time of the latter MRI is longer than the time of the former CT, and the latter MRI is the most expensive than the former CT. Due to academic studies of ischemic stroke detection is very finite, it is low successful rate to correctly detect stroke initial period. In tradition, we have to depend on radiologist¡¦s professional knowledge to diagnose ischemic stroke image, it will make radiologists increase fatigue so that made an error diagnostic. Therefore, we propose a Increasing visual perception brain stroke detection system. We use mathematic morphology to extract brain area. Then using median filter to remove noise, and uses canny edge detection to find out the edge of the brain tissue, and setting peak value in edge histogram as seed to perform region growing. Finally, we can clearly recognize the area of stroke. In the experimental result the recognition successful rate can reach 85%. Our successful rate cannot reach 100% because each patient¡¦s brain CT image is not certainly upright and foursquare. Future research direction, we will use other methods to correct gradient and CT image for each patient to improve successful rate of detection.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Stroke has been the third among the top ten leading causes of death in Taiwan. The danger is not only high mortality, but also gave the family a heavy economic. Recently, inspects the ischemic stroke the method to be divided into two categories: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The former CT is the most regularly adoption because done once the time of the latter MRI is longer than the time of the former CT, and the latter MRI is the most expensive than the former CT. Due to academic studies of ischemic stroke detection is very finite, it is low successful rate to correctly detect stroke initial period. In tradition, we have to depend on radiologist¡¦s professional knowledge to diagnose ischemic stroke image, it will make radiologists increase fatigue so that made an error diagnostic. Therefore, we propose a Increasing visual perception brain stroke detection system. We use mathematic morphology to extract brain area. Then using median filter to remove noise, and uses canny edge detection to find out the edge of the brain tissue, and setting peak value in edge histogram as seed to perform region growing. Finally, we can clearly recognize the area of stroke. In the experimental result the recognition successful rate can reach 85%. Our successful rate cannot reach 100% because each patient¡¦s brain CT image is not certainly upright and foursquare. Future research direction, we will use other methods to correct gradient and CT image for each patient to improve successful rate of detection.",
"fno": "4738a429",
"keywords": [
"Ichemic Stroke",
"Computed Tomography",
"Magnetic Resonance Imaging",
"Mathematic Morphology",
"Region Growing"
],
"authors": [
{
"affiliation": null,
"fullName": "Ming Sian Lee",
"givenName": "Ming Sian",
"surname": "Lee",
"__typename": "ArticleAuthorType"
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{
"affiliation": null,
"fullName": "Chiun Li Chin",
"givenName": "Chiun Li",
"surname": "Chin",
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{
"affiliation": null,
"fullName": "Ya Wen Lee",
"givenName": "Ya Wen",
"surname": "Lee",
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{
"affiliation": null,
"fullName": "Chian Yun Lee",
"givenName": "Chian Yun",
"surname": "Lee",
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{
"affiliation": null,
"fullName": "Yan Ru Chen",
"givenName": "Yan Ru",
"surname": "Chen",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2012-07-01T00:00:00",
"pubType": "proceedings",
"pages": "429-432",
"year": "2012",
"issn": null,
"isbn": "978-0-7695-4738-1",
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{
"id": "proceedings/bibm/2021/0126/0/09669719",
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"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669719/1A9VVWEHKeY",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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{
"id": "proceedings/compsac/2019/2607/1/260701a720",
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"doi": null,
"abstractUrl": "/proceedings-article/compsac/2019/260701a720/1cYizfffQMU",
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"id": "proceedings/compsac/2019/2607/1",
"title": "2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)",
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{
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"doi": null,
"abstractUrl": "/proceedings-article/bigdataservice/2019/005900a284/1dDLV3zkdfW",
"parentPublication": {
"id": "proceedings/bigdataservice/2019/0059/0",
"title": "2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)",
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{
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"doi": null,
"abstractUrl": "/proceedings-article/ichi/2019/08904574/1f8N71cs18c",
"parentPublication": {
"id": "proceedings/ichi/2019/9138/0",
"title": "2019 IEEE International Conference on Healthcare Informatics (ICHI)",
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{
"proceeding": {
"id": "12OmNzcxZq8",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"acronym": "bibm",
"groupId": "1001586",
"volume": "0",
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"year": "2014",
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"article": {
"id": "12OmNqBtiFK",
"doi": "10.1109/BIBM.2014.6999336",
"title": "Regional homogeneity change in female depressive patients after abdominal acupuncture treatment",
"normalizedTitle": "Regional homogeneity change in female depressive patients after abdominal acupuncture treatment",
"abstract": "Background: Clinical studies showed that abdominal acupuncture was an effective and safe treatment for depression. However, the underlying neural mechanism was still largely unknown. The present study investigated the brain spontaneous activity changes following with the abdominal acupuncture treatment in female depressive patients with different levels, to explore the neural basis underpinning abdominal acupuncture treatment efficacy. Methods: Fifteen female depression patients (nine mild and six moderate-serious participants) were recruited for the present study, these patients treated by abdominal acupuncture for 4 weeks. The resting-state functional magnetic resonance imaging (fMRI) data was collected at before and after treatment stages. The changes of resting state regional homogeneity (ReHo) were examined between these two stages, to suggest the acupuncture treatment effect on spontaneous neural activity. Further analysis was performed to explore the correlation between ReHo changes and clinical measurements (Self-rating depression scale SDS and Montgomery-Asberg Depression Rating Scale MADRS scores). Results: The scores of SDS and MADRS had gradually declined after-treatment stage in mild and moderate-serious depression, compared with those of before-treatment stage. There were statistically differences of SDS and MADRS scores after treatment at 1st, 2nd, 3rd and 4th week's (P<0.05). Compared with the before-treatment state in mild depression, brain regions with increased ReHo were found in Frontal_Sup_L, Frontal_Inf_Tri_L, Frontal_Inf_Tri_R, and Parietal_Inf_R. Meanwhile, the brain regions including Temporal_Pole_Sup_R, Occipital_Inf_R, Occipital_Inf_R, Temporal_Sup_R, Cuneus_R and Temporal_Mid_L were observed with decreased ReHo. In moderate-serious depression, brain regions with increased ReHo were found in Frontal_Med_Orb_Rand, and Cingulum_Mid_R; meanwhile, the brain regions including Lingual_L and Postcentral_R were observed with decreased ReHo. There were a large of quantity regional homogeneity changes which correlated with scores of SDS and MADRS. Conclusion: Abdominal acupuncture treatment could induce spontaneous brain activity change in depression patients. However, there were different regional homogeneity changes in different depression levels. ReHo changes in mild depression were mainly distributed in prefrontal and temporal lobe, but the ReHo change in prefrontal, Parietal, Occipital lobe and limbic system in moderate-serious depression. Prefrontal region took important roles in the acupuncture treatment effect on depression patients. Together, the findings of the present study provided new evidence for that abdominal acupuncture was an effective treatment for depression, and the ReHo was efficacy evaluation objective indicators of acupuncture treatment for depression.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Background: Clinical studies showed that abdominal acupuncture was an effective and safe treatment for depression. However, the underlying neural mechanism was still largely unknown. The present study investigated the brain spontaneous activity changes following with the abdominal acupuncture treatment in female depressive patients with different levels, to explore the neural basis underpinning abdominal acupuncture treatment efficacy. Methods: Fifteen female depression patients (nine mild and six moderate-serious participants) were recruited for the present study, these patients treated by abdominal acupuncture for 4 weeks. The resting-state functional magnetic resonance imaging (fMRI) data was collected at before and after treatment stages. The changes of resting state regional homogeneity (ReHo) were examined between these two stages, to suggest the acupuncture treatment effect on spontaneous neural activity. Further analysis was performed to explore the correlation between ReHo changes and clinical measurements (Self-rating depression scale SDS and Montgomery-Asberg Depression Rating Scale MADRS scores). Results: The scores of SDS and MADRS had gradually declined after-treatment stage in mild and moderate-serious depression, compared with those of before-treatment stage. There were statistically differences of SDS and MADRS scores after treatment at 1st, 2nd, 3rd and 4th week's (P<0.05). Compared with the before-treatment state in mild depression, brain regions with increased ReHo were found in Frontal_Sup_L, Frontal_Inf_Tri_L, Frontal_Inf_Tri_R, and Parietal_Inf_R. Meanwhile, the brain regions including Temporal_Pole_Sup_R, Occipital_Inf_R, Occipital_Inf_R, Temporal_Sup_R, Cuneus_R and Temporal_Mid_L were observed with decreased ReHo. In moderate-serious depression, brain regions with increased ReHo were found in Frontal_Med_Orb_Rand, and Cingulum_Mid_R; meanwhile, the brain regions including Lingual_L and Postcentral_R were observed with decreased ReHo. There were a large of quantity regional homogeneity changes which correlated with scores of SDS and MADRS. Conclusion: Abdominal acupuncture treatment could induce spontaneous brain activity change in depression patients. However, there were different regional homogeneity changes in different depression levels. ReHo changes in mild depression were mainly distributed in prefrontal and temporal lobe, but the ReHo change in prefrontal, Parietal, Occipital lobe and limbic system in moderate-serious depression. Prefrontal region took important roles in the acupuncture treatment effect on depression patients. Together, the findings of the present study provided new evidence for that abdominal acupuncture was an effective treatment for depression, and the ReHo was efficacy evaluation objective indicators of acupuncture treatment for depression.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Background: Clinical studies showed that abdominal acupuncture was an effective and safe treatment for depression. However, the underlying neural mechanism was still largely unknown. The present study investigated the brain spontaneous activity changes following with the abdominal acupuncture treatment in female depressive patients with different levels, to explore the neural basis underpinning abdominal acupuncture treatment efficacy. Methods: Fifteen female depression patients (nine mild and six moderate-serious participants) were recruited for the present study, these patients treated by abdominal acupuncture for 4 weeks. The resting-state functional magnetic resonance imaging (fMRI) data was collected at before and after treatment stages. The changes of resting state regional homogeneity (ReHo) were examined between these two stages, to suggest the acupuncture treatment effect on spontaneous neural activity. Further analysis was performed to explore the correlation between ReHo changes and clinical measurements (Self-rating depression scale SDS and Montgomery-Asberg Depression Rating Scale MADRS scores). Results: The scores of SDS and MADRS had gradually declined after-treatment stage in mild and moderate-serious depression, compared with those of before-treatment stage. There were statistically differences of SDS and MADRS scores after treatment at 1st, 2nd, 3rd and 4th week's (P<0.05). Compared with the before-treatment state in mild depression, brain regions with increased ReHo were found in Frontal_Sup_L, Frontal_Inf_Tri_L, Frontal_Inf_Tri_R, and Parietal_Inf_R. Meanwhile, the brain regions including Temporal_Pole_Sup_R, Occipital_Inf_R, Occipital_Inf_R, Temporal_Sup_R, Cuneus_R and Temporal_Mid_L were observed with decreased ReHo. In moderate-serious depression, brain regions with increased ReHo were found in Frontal_Med_Orb_Rand, and Cingulum_Mid_R; meanwhile, the brain regions including Lingual_L and Postcentral_R were observed with decreased ReHo. There were a large of quantity regional homogeneity changes which correlated with scores of SDS and MADRS. Conclusion: Abdominal acupuncture treatment could induce spontaneous brain activity change in depression patients. However, there were different regional homogeneity changes in different depression levels. ReHo changes in mild depression were mainly distributed in prefrontal and temporal lobe, but the ReHo change in prefrontal, Parietal, Occipital lobe and limbic system in moderate-serious depression. Prefrontal region took important roles in the acupuncture treatment effect on depression patients. Together, the findings of the present study provided new evidence for that abdominal acupuncture was an effective treatment for depression, and the ReHo was efficacy evaluation objective indicators of acupuncture treatment for depression.",
"fno": "06999336",
"keywords": [
"Correlation",
"Biomedical Imaging",
"Educational Institutions",
"Needles",
"Hospitals",
"Roads",
"Montgomery Asberg Depression Rating Scale",
"Female Depression",
"Abdominal Acupuncture",
"Resting State Functional Magnetic Resonance Imaging",
"Regional Homogeneity",
"Self Rating Depression Scale"
],
"authors": [
{
"affiliation": "The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 111 Dade Road, Guangzhou 510120, China",
"fullName": "Jian Liu",
"givenName": "Jian",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 111 Dade Road, Guangzhou 510120, China",
"fullName": "Bo Liu",
"givenName": "Bo",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 111 Dade Road, Guangzhou 510120, China",
"fullName": "Guangning Nie",
"givenName": "Guangning",
"surname": "Nie",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Yongchuan Hospital of Chinese Medicine, 2 Guest Road, Chongqing 402160, China",
"fullName": "Bowei Wang",
"givenName": "Bowei",
"surname": "Wang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Guangzhou University of Chinese Medicine, 12 Airport Road, Guangzhou 510405, China",
"fullName": "Xuan Feng",
"givenName": "Xuan",
"surname": "Feng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 111 Dade Road, Guangzhou 510120, China",
"fullName": "Xiaoyun Wang",
"givenName": "Xiaoyun",
"surname": "Wang",
"__typename": "ArticleAuthorType"
}
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"pubDate": "2014-11-01T00:00:00",
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"year": "2014",
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{
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"title": "2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
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"year": "2013",
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"article": {
"id": "12OmNvqEvNP",
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"title": "A novel prognostic model to predict the recovery of ischemie stroke patients",
"normalizedTitle": "A novel prognostic model to predict the recovery of ischemie stroke patients",
"abstract": "Evaluating the prognosis of ischemie stroke can provide useful information for clinical treatment. A novel prognostic model to predict the recovery of ischemie stroke was constructed in this study. We built a prognostic model mainly based on these items including the National Institutes of Health Stroke Scale (NIHSS) score and Traditional Chinese Medicine (TCM)symptoms & signs and their changes during the first 3 days of ischemie stroke patients in three TCM hospitals. Selecting the optimal cut-offvalue for evaluating the prognosis with the Receiver Operating Characteristic (ROC) curve analysis. The result of validation on the data of west medicine hospital showed that the model had a good sensitivity and specificity for judging the prognosis of ischemie stroke.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Evaluating the prognosis of ischemie stroke can provide useful information for clinical treatment. A novel prognostic model to predict the recovery of ischemie stroke was constructed in this study. We built a prognostic model mainly based on these items including the National Institutes of Health Stroke Scale (NIHSS) score and Traditional Chinese Medicine (TCM)symptoms & signs and their changes during the first 3 days of ischemie stroke patients in three TCM hospitals. Selecting the optimal cut-offvalue for evaluating the prognosis with the Receiver Operating Characteristic (ROC) curve analysis. The result of validation on the data of west medicine hospital showed that the model had a good sensitivity and specificity for judging the prognosis of ischemie stroke.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Evaluating the prognosis of ischemie stroke can provide useful information for clinical treatment. A novel prognostic model to predict the recovery of ischemie stroke was constructed in this study. We built a prognostic model mainly based on these items including the National Institutes of Health Stroke Scale (NIHSS) score and Traditional Chinese Medicine (TCM)symptoms & signs and their changes during the first 3 days of ischemie stroke patients in three TCM hospitals. Selecting the optimal cut-offvalue for evaluating the prognosis with the Receiver Operating Characteristic (ROC) curve analysis. The result of validation on the data of west medicine hospital showed that the model had a good sensitivity and specificity for judging the prognosis of ischemie stroke.",
"fno": "06732623",
"keywords": [
"Hospitals",
"Predictive Models",
"Bismuth",
"History",
"Prognostics And Health Management",
"Educational Institutions",
"Standards",
"Ischemie Stroke",
"Prognostic Model",
"Recovery"
],
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{
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{
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"givenName": "Caihong",
"surname": "Fu",
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{
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"givenName": "Huanqin",
"surname": "Li",
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{
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"acronym": "itme",
"groupId": "1002567",
"volume": "0",
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"year": "2016",
"__typename": "ProceedingType"
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"id": "12OmNxG1yC0",
"doi": "10.1109/ITME.2016.0075",
"title": "Technology and Methods of Improving the Success Rate of MSCT-Guided Puncture",
"normalizedTitle": "Technology and Methods of Improving the Success Rate of MSCT-Guided Puncture",
"abstract": "Objective: To explore the technology and methods of improving the success rate of MSCT-guided puncture. Methods: Under the guidance of the 64-Slice CT, 58 patients had percutaneous biopsies. After completing the puncture, the occurrence of complications and treatment were observed by CT. The pathologic examination results were compared with the original clinical diagnosis, the reason which produced a variety of differences and complications was analyzed. Results: In this group, 58 patients were successfully punctured, postoperative scan showed pneumothorax in 1 case. 2 patients had a small amount of bleeding, after 5 minutes observation, their bleeding remitted, and no further bleeding after hemostatic drug treatment. Other important complications were not found. Five hours after the puncture, the 58 patients had no complications. For 58 patients in this study, the diseases of the 57 patients were diagnosedby pathological detections, detection success rate reached to 98.3%. Compared with diagnosis before puncture, diagnosis accurate rate was increased by 19.2% and 11.5%. One patient's disease was not diagnosed because his pathological detection result didn't return. Conclusions: Multi-slice CT-guided percutaneous biopsy can greatly improve the success rate. It's a new, safe and accurate technology which can help diagnosis and treatment.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Objective: To explore the technology and methods of improving the success rate of MSCT-guided puncture. Methods: Under the guidance of the 64-Slice CT, 58 patients had percutaneous biopsies. After completing the puncture, the occurrence of complications and treatment were observed by CT. The pathologic examination results were compared with the original clinical diagnosis, the reason which produced a variety of differences and complications was analyzed. Results: In this group, 58 patients were successfully punctured, postoperative scan showed pneumothorax in 1 case. 2 patients had a small amount of bleeding, after 5 minutes observation, their bleeding remitted, and no further bleeding after hemostatic drug treatment. Other important complications were not found. Five hours after the puncture, the 58 patients had no complications. For 58 patients in this study, the diseases of the 57 patients were diagnosedby pathological detections, detection success rate reached to 98.3%. Compared with diagnosis before puncture, diagnosis accurate rate was increased by 19.2% and 11.5%. One patient's disease was not diagnosed because his pathological detection result didn't return. Conclusions: Multi-slice CT-guided percutaneous biopsy can greatly improve the success rate. It's a new, safe and accurate technology which can help diagnosis and treatment.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Objective: To explore the technology and methods of improving the success rate of MSCT-guided puncture. Methods: Under the guidance of the 64-Slice CT, 58 patients had percutaneous biopsies. After completing the puncture, the occurrence of complications and treatment were observed by CT. The pathologic examination results were compared with the original clinical diagnosis, the reason which produced a variety of differences and complications was analyzed. Results: In this group, 58 patients were successfully punctured, postoperative scan showed pneumothorax in 1 case. 2 patients had a small amount of bleeding, after 5 minutes observation, their bleeding remitted, and no further bleeding after hemostatic drug treatment. Other important complications were not found. Five hours after the puncture, the 58 patients had no complications. For 58 patients in this study, the diseases of the 57 patients were diagnosedby pathological detections, detection success rate reached to 98.3%. Compared with diagnosis before puncture, diagnosis accurate rate was increased by 19.2% and 11.5%. One patient's disease was not diagnosed because his pathological detection result didn't return. Conclusions: Multi-slice CT-guided percutaneous biopsy can greatly improve the success rate. It's a new, safe and accurate technology which can help diagnosis and treatment.",
"fno": "3906a306",
"keywords": [
"Lesions",
"Lungs",
"Needles",
"Cancer",
"Pathology",
"Computed Tomography",
"Puncture",
"Biopsy",
"Computed Tomography",
"Pathological Detection"
],
"authors": [
{
"affiliation": null,
"fullName": "Zhiwei Huang",
"givenName": "Zhiwei",
"surname": "Huang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Bo Xiao",
"givenName": "Bo",
"surname": "Xiao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Qian Liu",
"givenName": "Qian",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Tian Tian",
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"abstract": "Stroke disease incurs long periods of hospital and community care, with associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies and care options for therapy and care. Previously we have developed a modeling framework which classifies patients with respect to their length of stay (LOS); phase-type models can then be used to describe patient flows for each class. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be included. We here add costs to this model of the total stroke care system and determine moments for total cost of a cohort of patients and also the separate costs in different phases and stages of care. Based on stroke patients' data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the costs of thrombolysis, a clot-busting therapy under different regimes.",
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"proceeding": {
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"title": "2018 9th International Conference on Information Technology in Medicine and Education (ITME)",
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"doi": "10.1109/ITME.2018.00019",
"title": "Acupuncture Combined with Musical Therapy in the Treatment of Post-Stroke Depression: A Case Report",
"normalizedTitle": "Acupuncture Combined with Musical Therapy in the Treatment of Post-Stroke Depression: A Case Report",
"abstract": "Rationale: The clinical treatment of mental symptoms of post-stroke depression (PSD) mainly focuses on western medicines which lead to great pain to patients due to unavoidable side effects. In this study, simultaneous treatment of both body and mind consisting of traditional Chinese medicine (TCM) acupuncture combined with musical therapy was conducted to a patient with PSD, with satisfactory clinical efficacy obtained. Patient concerns: The patient was a 53-year old male who suddenly developed weakness of limbs on right side 1 month ago and was diagnosed with cerebral infarction by cranial CT. After treatment in local hospital, he still had inarticulateness and weakness of limbs on right side, complicated with depression symptoms marked by low spirit and pessimism, etc. Diagnosis: The patient in this study was diagnosed by comprehensive assessment methods consisting of biological indicator detection, behavioral indicator detection and imageological detection. Interventions: Simultaneous treatment of both body and mind consisting of TCM acupuncture combined with musical therapy was conducted. Outcomes: After treatment, patient’s symptoms, nerve function and activities of daily living (ADL) were improved significantly, Hamilton (HAMD) Depression Scale (17 items) score was reduced, and levels of biological indicators interleukin (IL)-6, tumor necrosis factor-α (TNF-α), high-sensitivity C-reactive protein (Hs-CRP) and Homocysteine (Hcy) were reduced and improved significantly. Lessons: Acupuncture combined with musical therapy had significant clinical efficacy in the treatment of PSD and deserved to be further studied. Abbreviations: PSD: post-stroke depression; TCM: traditional Chinese medicine; ADL: activities of daily living; HAMD: Hamilton; IL-6: interleukin-6; TNF-α: tumor necrosis factor-α; Hs-CRP: high-sensitivity C-reactive protein; Hcy: Homocysteine.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Rationale: The clinical treatment of mental symptoms of post-stroke depression (PSD) mainly focuses on western medicines which lead to great pain to patients due to unavoidable side effects. In this study, simultaneous treatment of both body and mind consisting of traditional Chinese medicine (TCM) acupuncture combined with musical therapy was conducted to a patient with PSD, with satisfactory clinical efficacy obtained. Patient concerns: The patient was a 53-year old male who suddenly developed weakness of limbs on right side 1 month ago and was diagnosed with cerebral infarction by cranial CT. After treatment in local hospital, he still had inarticulateness and weakness of limbs on right side, complicated with depression symptoms marked by low spirit and pessimism, etc. Diagnosis: The patient in this study was diagnosed by comprehensive assessment methods consisting of biological indicator detection, behavioral indicator detection and imageological detection. Interventions: Simultaneous treatment of both body and mind consisting of TCM acupuncture combined with musical therapy was conducted. Outcomes: After treatment, patient’s symptoms, nerve function and activities of daily living (ADL) were improved significantly, Hamilton (HAMD) Depression Scale (17 items) score was reduced, and levels of biological indicators interleukin (IL)-6, tumor necrosis factor-α (TNF-α), high-sensitivity C-reactive protein (Hs-CRP) and Homocysteine (Hcy) were reduced and improved significantly. Lessons: Acupuncture combined with musical therapy had significant clinical efficacy in the treatment of PSD and deserved to be further studied. Abbreviations: PSD: post-stroke depression; TCM: traditional Chinese medicine; ADL: activities of daily living; HAMD: Hamilton; IL-6: interleukin-6; TNF-α: tumor necrosis factor-α; Hs-CRP: high-sensitivity C-reactive protein; Hcy: Homocysteine.",
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"normalizedAbstract": "Rationale: The clinical treatment of mental symptoms of post-stroke depression (PSD) mainly focuses on western medicines which lead to great pain to patients due to unavoidable side effects. In this study, simultaneous treatment of both body and mind consisting of traditional Chinese medicine (TCM) acupuncture combined with musical therapy was conducted to a patient with PSD, with satisfactory clinical efficacy obtained. Patient concerns: The patient was a 53-year old male who suddenly developed weakness of limbs on right side 1 month ago and was diagnosed with cerebral infarction by cranial CT. After treatment in local hospital, he still had inarticulateness and weakness of limbs on right side, complicated with depression symptoms marked by low spirit and pessimism, etc. Diagnosis: The patient in this study was diagnosed by comprehensive assessment methods consisting of biological indicator detection, behavioral indicator detection and imageological detection. Interventions: Simultaneous treatment of both body and mind consisting of TCM acupuncture combined with musical therapy was conducted. Outcomes: After treatment, patient’s symptoms, nerve function and activities of daily living (ADL) were improved significantly, Hamilton (HAMD) Depression Scale (17 items) score was reduced, and levels of biological indicators interleukin (IL)-6, tumor necrosis factor-α (TNF-α), high-sensitivity C-reactive protein (Hs-CRP) and Homocysteine (Hcy) were reduced and improved significantly. Lessons: Acupuncture combined with musical therapy had significant clinical efficacy in the treatment of PSD and deserved to be further studied. Abbreviations: PSD: post-stroke depression; TCM: traditional Chinese medicine; ADL: activities of daily living; HAMD: Hamilton; IL-6: interleukin-6; TNF-α: tumor necrosis factor-α; Hs-CRP: high-sensitivity C-reactive protein; Hcy: Homocysteine.",
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"fullName": "Facai Lin",
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"fullName": "Yuhang Qin",
"givenName": "Yuhang",
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"fullName": "Jianzhu Wang",
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"fullName": "Feng Hao",
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"fullName": "Yunchuan Wu",
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"abstract": "The Dodge Poetry Festival is a biennial four-day event at which American and international poets read their own poetry out loud to relatively large, attentive audiences. In this paper, we report on a prototype graphical interface designed to let users access high quality video recordings of these poetry readings. The goal is to create an interface that draws high school students in particular into the experience of listening to poetry. This prototype is the first step in a plan to build a working system for a small portion of the Archive collection that can then be realistically evaluated. From a historical perspective, the archive provides a high-quality record of poets of the late 20<sup>th</sup> and early 21<sup>st</sup> centuries reading their own poems and and discussing their work with festival attendees.",
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"abstract": "The special grammar and abstruse rhetoric of Tang and Song poetry doomed the diversity of poetry research. In order to help scholars explore the information space of poetry, we took into account the breadth and depth of learning, combined with heterogeneous data sources such as poetry, videos, textbooks, knowledge graph and other media sources, and carried out visual modeling. We provide a visual and informative platform to demonstrate the potential power of visualization. It supports comprehensive search capabilities for multiple types of data, online generation of poets' knowledge graphs. Relationship diagram, word cloud diagram, pie chart and other visual charts are displayed on the platform, so that users can complete exploratory visual analysis by dragging, clicking and typing.",
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"abstract": "Research integrity is crucial to ensuring the trustworthiness of scientific discoveries. This work is aimed at detecting misbehaviors targeting scientific workflows, which are computing paradigms widely used to facilitate scientific collaborations across multiple geographically distributed research sites. We develop a new system called RAMP(Real-Time Aggregated Matrix Profile) for real-time anomaly detection in scientific workflow systems. RAMP builds upon an existing time series data analysis technique called Matrix Profile to detect anomalous distances among subsequences of event streams collected from scientific workflows in an online manner. Using an adaptive uncertainty function, the anomaly detection model is dynamically adjusted to prevent high false alarm rates. RAMP can incorporate user feedback on reported anomalies and modify model parameters to improve anomaly detection accuracy. Our experimental results from applying RAMP to the logs generated by DATAVIEW, a scientific workflow platform, show that RAMP is able to identify a varied range of anomalies with high accuracy for both interleaved and non-interleaved workflow executions in real time.",
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"abstract": "Originally developed for military flight simulators in the 1990s, the Simulator Sickness Questionnaire (SSQ) has been widely adopted to quantify sickness elicited by modern virtual reality systems. We illustrate the inherent challenges of applying the SSQ in virtual reality research and highlight large differences that can be found in related work. Based on our observations, we conclude by providing suggestions on how to improve the expressiveness of SSQ results in future studies and encourage researchers to consider simpler measurement methods if their research questions allow.",
"abstracts": [
{
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"content": "Originally developed for military flight simulators in the 1990s, the Simulator Sickness Questionnaire (SSQ) has been widely adopted to quantify sickness elicited by modern virtual reality systems. We illustrate the inherent challenges of applying the SSQ in virtual reality research and highlight large differences that can be found in related work. Based on our observations, we conclude by providing suggestions on how to improve the expressiveness of SSQ results in future studies and encourage researchers to consider simpler measurement methods if their research questions allow.",
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"normalizedAbstract": "Originally developed for military flight simulators in the 1990s, the Simulator Sickness Questionnaire (SSQ) has been widely adopted to quantify sickness elicited by modern virtual reality systems. We illustrate the inherent challenges of applying the SSQ in virtual reality research and highlight large differences that can be found in related work. Based on our observations, we conclude by providing suggestions on how to improve the expressiveness of SSQ results in future studies and encourage researchers to consider simpler measurement methods if their research questions allow.",
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