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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2304.07094 | Roy de Kleijn | Rutger Goekoop, Roy de Kleijn | Hierarchical network structure as the source of hierarchical dynamics
(power law frequency spectra) in living and non-living systems: how
state-trait continua (body plans, personalities) emerge from first principles
in biophysics | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Living systems are hierarchical control systems that display a small world
network structure, in which many smaller clusters are nested within fewer
larger ones, producing a fractal-like structure with a power-law cluster size
distribution (a mereology). Apart from their structure, the dynamics of living
systems also shows fractal-like qualities: the timeseries of inner message
passing and overt behavior contain high frequencies or states (treble) that are
nested within lower frequencies or traits (bass), producing a power-law
frequency spectrum that is known as a state-trait continuum in the behavioral
sciences. Here, we argue that the power-law dynamics of living systems results
from their power-law network structure: organisms vertically encode the deep
spatiotemporal structure of their (anticipated) environments, to the effect
that many small clusters near the base of the hierarchy produce high frequency
signal changes and fewer larger clusters at its top produce ultra-low
frequencies. Such ultra-low frequencies produce physical as well as behavioral
traits (i.e. body plans and personalities). Nested-modular structure then
causes higher frequencies to be embedded within lower frequencies, producing a
power law state-trait continuum. At the heart of such dynamics lies the need
for efficient energy dissipation through networks of coupled oscillators, which
also governs the dynamics of non-living systems (e.g. earthquakes, stock market
fluctuations). Since hierarchical structure produces hierarchical dynamics, the
development and collapse of hierarchical structure (e.g. during maturation and
disease) should leave specific traces in the dynamics of nested modular systems
that may serve as early warning signs to system failure. The applications of
this idea range from (bio)physics and phylogenesis to ontogenesis and clinical
medicine.
| [
{
"created": "Fri, 14 Apr 2023 12:30:35 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Apr 2023 10:49:04 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Jun 2023 09:31:14 GMT",
"version": "v3"
}
] | 2023-06-22 | [
[
"Goekoop",
"Rutger",
""
],
[
"de Kleijn",
"Roy",
""
]
] | Living systems are hierarchical control systems that display a small world network structure, in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution (a mereology). Apart from their structure, the dynamics of living systems also shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or states (treble) that are nested within lower frequencies or traits (bass), producing a power-law frequency spectrum that is known as a state-trait continuum in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms vertically encode the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies produce physical as well as behavioral traits (i.e. body plans and personalities). Nested-modular structure then causes higher frequencies to be embedded within lower frequencies, producing a power law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.g. earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g. during maturation and disease) should leave specific traces in the dynamics of nested modular systems that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine. |
2305.08159 | Yongcheng Yao | Yongcheng Yao | Altered Topological Properties of Functional Brain Network Associated
with Alzheimer's Disease | 32 pages,17 figures, 5 tables, | null | null | null | q-bio.NC cs.CV | http://creativecommons.org/licenses/by/4.0/ | Functional Magnetic Resonance Imaging (fMRI) is commonly utilized to study
human brain activity, including abnormal functional properties related to
neurodegenerative diseases. This study aims to investigate the differences in
the topological properties of functional brain networks between individuals
with Alzheimer's Disease (AD) and normal controls. A total of 590 subjects,
consisting of 175 with AD dementia and 415 age-, gender-, and
handedness-matched controls, were included. The topological properties of the
brain network were quantified using graph-theory-based analyses. The results
indicate abnormal network integration and segregation in the AD group. These
findings enhance our understanding of AD pathophysiology from a functional
brain network structure perspective and may aid in identifying AD biomarkers.
Supplementary data to aid in the validation of this research are available at
https://github.com/YongchengYAO/AD-FunctionalBrainNetwork.
| [
{
"created": "Sun, 14 May 2023 13:39:12 GMT",
"version": "v1"
},
{
"created": "Tue, 16 May 2023 03:34:03 GMT",
"version": "v2"
}
] | 2023-05-17 | [
[
"Yao",
"Yongcheng",
""
]
] | Functional Magnetic Resonance Imaging (fMRI) is commonly utilized to study human brain activity, including abnormal functional properties related to neurodegenerative diseases. This study aims to investigate the differences in the topological properties of functional brain networks between individuals with Alzheimer's Disease (AD) and normal controls. A total of 590 subjects, consisting of 175 with AD dementia and 415 age-, gender-, and handedness-matched controls, were included. The topological properties of the brain network were quantified using graph-theory-based analyses. The results indicate abnormal network integration and segregation in the AD group. These findings enhance our understanding of AD pathophysiology from a functional brain network structure perspective and may aid in identifying AD biomarkers. Supplementary data to aid in the validation of this research are available at https://github.com/YongchengYAO/AD-FunctionalBrainNetwork. |
2006.06311 | Ece Kocagoncu | Ece Kocagoncu, Anastasia Klimovich-Gray, Laura E Hughes, James B Rowe | Evidence and implications of abnormal predictive coding in dementia | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The diversity of cognitive deficits and neuropathological processes
associated with dementias has encouraged divergence in pathophysiological
explanations of disease. Here, we review an alternative framework that
emphasises convergent critical features of pathophysiology, rather than the
loss of memory centres or language centres, or singular neurotransmitter
systems. Cognitive deficits are interpreted in the light of advances in
normative accounts of brain function, based on predictive coding in
hierarchical neural networks. The predicting coding rests on Bayesian
integration of beliefs and sensory evidence, with hierarchical predictions and
prediction errors, for memory, perception, speech and behaviour. We describe
how analogous impairments in predictive coding in parallel neurocognitive
systems can generate diverse clinical phenomena, in neurodegenerative
dementias. The review presents evidence from behavioural and neurophysiological
studies of perception, language, memory and decision-making. The re-formulation
of cognitive deficits in dementia in terms of predictive coding has several
advantages. It brings diverse clinical phenomena into a common framework, such
as linking cognitive and movement disorders; and it makes specific predictions
on cognitive physiology that support translational and experimental medicine
studies. The insights into complex human cognitive disorders from the
predictive coding model may therefore also inform future therapeutic
strategies.
| [
{
"created": "Thu, 11 Jun 2020 10:23:08 GMT",
"version": "v1"
}
] | 2020-06-12 | [
[
"Kocagoncu",
"Ece",
""
],
[
"Klimovich-Gray",
"Anastasia",
""
],
[
"Hughes",
"Laura E",
""
],
[
"Rowe",
"James B",
""
]
] | The diversity of cognitive deficits and neuropathological processes associated with dementias has encouraged divergence in pathophysiological explanations of disease. Here, we review an alternative framework that emphasises convergent critical features of pathophysiology, rather than the loss of memory centres or language centres, or singular neurotransmitter systems. Cognitive deficits are interpreted in the light of advances in normative accounts of brain function, based on predictive coding in hierarchical neural networks. The predicting coding rests on Bayesian integration of beliefs and sensory evidence, with hierarchical predictions and prediction errors, for memory, perception, speech and behaviour. We describe how analogous impairments in predictive coding in parallel neurocognitive systems can generate diverse clinical phenomena, in neurodegenerative dementias. The review presents evidence from behavioural and neurophysiological studies of perception, language, memory and decision-making. The re-formulation of cognitive deficits in dementia in terms of predictive coding has several advantages. It brings diverse clinical phenomena into a common framework, such as linking cognitive and movement disorders; and it makes specific predictions on cognitive physiology that support translational and experimental medicine studies. The insights into complex human cognitive disorders from the predictive coding model may therefore also inform future therapeutic strategies. |
2006.01283 | Avi Flamholz | Yinon M. Bar-On, Ron Sender, Avi I. Flamholz, Rob Phillips, Ron Milo | A quantitative compendium of COVID-19 epidemiology | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Accurate numbers are needed to understand and predict viral dynamics.
Curation of high-quality literature values for the infectious period duration
or household secondary attack rate, for example, is especially pressing
currently because these numbers inform decisions about how and when to lockdown
or reopen societies. We aim to provide a curated source for the key numbers
that help us understand the virus driving our current global crisis. This
compendium focuses solely on COVID-19 epidemiology. The numbers reported in
summary format are substantiated by annotated references. For each property, we
provide a concise definition, description of measurement and inference methods,
and associated caveats. We hope this compendium will make essential numbers
more accessible and avoid common sources of confusion for the many newcomers to
the field such as using the incubation period to denote and quantify the latent
period or using the hospitalization duration for the infectiousness period
duration. This document will be repeatedly updated and the community is invited
to participate in improving it.
| [
{
"created": "Mon, 1 Jun 2020 21:45:03 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jun 2020 20:04:09 GMT",
"version": "v2"
},
{
"created": "Thu, 9 Jul 2020 17:16:06 GMT",
"version": "v3"
}
] | 2020-07-10 | [
[
"Bar-On",
"Yinon M.",
""
],
[
"Sender",
"Ron",
""
],
[
"Flamholz",
"Avi I.",
""
],
[
"Phillips",
"Rob",
""
],
[
"Milo",
"Ron",
""
]
] | Accurate numbers are needed to understand and predict viral dynamics. Curation of high-quality literature values for the infectious period duration or household secondary attack rate, for example, is especially pressing currently because these numbers inform decisions about how and when to lockdown or reopen societies. We aim to provide a curated source for the key numbers that help us understand the virus driving our current global crisis. This compendium focuses solely on COVID-19 epidemiology. The numbers reported in summary format are substantiated by annotated references. For each property, we provide a concise definition, description of measurement and inference methods, and associated caveats. We hope this compendium will make essential numbers more accessible and avoid common sources of confusion for the many newcomers to the field such as using the incubation period to denote and quantify the latent period or using the hospitalization duration for the infectiousness period duration. This document will be repeatedly updated and the community is invited to participate in improving it. |
1208.3438 | Joel Miller | Joel C. Miller | Epidemics on networks with large initial conditions or changing
structure | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Recently developed techniques to study the spread of infectious
diseases through networks make assumptions that the initial proportion infected
is infinitesimal and the population behavior is static throughout the epidemic.
The models do not apply if the initial proportion is large (and fail whenever
R_0<1), and cannot measure the impact of an intervention.
Methods: In this paper we adapt "edge-based compartmental models" to
situations having finite-sized initial conditions.
Results: The resulting models remain simple and accurately capture the effect
of the initial conditions. It is possible to generalize the model to networks
whose partnerships change in time.
Conclusions: The resulting models can be applied to a range of important
contexts. The models can be used to choose between different interventions that
affect the disease or the population structure.
| [
{
"created": "Thu, 16 Aug 2012 18:51:29 GMT",
"version": "v1"
}
] | 2012-08-17 | [
[
"Miller",
"Joel C.",
""
]
] | Background: Recently developed techniques to study the spread of infectious diseases through networks make assumptions that the initial proportion infected is infinitesimal and the population behavior is static throughout the epidemic. The models do not apply if the initial proportion is large (and fail whenever R_0<1), and cannot measure the impact of an intervention. Methods: In this paper we adapt "edge-based compartmental models" to situations having finite-sized initial conditions. Results: The resulting models remain simple and accurately capture the effect of the initial conditions. It is possible to generalize the model to networks whose partnerships change in time. Conclusions: The resulting models can be applied to a range of important contexts. The models can be used to choose between different interventions that affect the disease or the population structure. |
2009.12368 | Abhinav Sagar | Abhinav Sagar | Generate Novel Molecules With Target Properties Using Conditional
Generative Models | The assumptions used while carrying out experiments was found to be
incorrect | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drug discovery using deep learning has attracted a lot of attention of late
as it has obvious advantages like higher efficiency, less manual guessing and
faster process time. In this paper, we present a novel neural network for
generating small molecules similar to the ones in the training set. Our network
consists of an encoder made up of bi-GRU layers for converting the input
samples to a latent space, predictor for enhancing the capability of encoder
made up of 1D-CNN layers and a decoder comprised of uni-GRU layers for
reconstructing the samples from the latent space representation. Condition
vector in latent space is used for generating molecules with the desired
properties. We present the loss functions used for training our network,
experimental details and property prediction metrics. Our network outperforms
previous methods using Molecular weight, LogP and Quantitative Estimation of
Drug-likeness as the evaluation metrics.
| [
{
"created": "Tue, 15 Sep 2020 18:59:26 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Oct 2021 19:33:02 GMT",
"version": "v2"
}
] | 2021-10-08 | [
[
"Sagar",
"Abhinav",
""
]
] | Drug discovery using deep learning has attracted a lot of attention of late as it has obvious advantages like higher efficiency, less manual guessing and faster process time. In this paper, we present a novel neural network for generating small molecules similar to the ones in the training set. Our network consists of an encoder made up of bi-GRU layers for converting the input samples to a latent space, predictor for enhancing the capability of encoder made up of 1D-CNN layers and a decoder comprised of uni-GRU layers for reconstructing the samples from the latent space representation. Condition vector in latent space is used for generating molecules with the desired properties. We present the loss functions used for training our network, experimental details and property prediction metrics. Our network outperforms previous methods using Molecular weight, LogP and Quantitative Estimation of Drug-likeness as the evaluation metrics. |
1704.08062 | Gianna Vivaldo | Gianna Vivaldo and Elisa Masi and Cosimo Taiti and Guido Caldarelli
and Stefano Mancuso | Beyond the network of plants volatile organic compounds | null | null | null | null | q-bio.QM physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plants emission of volatile organic compounds (VOCs) is involved in a wide
class of ecological functions, as VOCs play a crucial role in plants
interactions with biotic and abiotic factors. Accordingly, they vary widely
across species and underpin differences in ecological strategy. In this paper,
VOCs spontaneously emitted by 109 plant species (belonging to 56 different
families) have been qualitatively and quantitatively analysed in order to
classify plants species. By using bipartite networks methodology, based on
recent advancements in Complex Network Theory, and through the application of
complementary classical and advanced community detection algorithms, the
possibility to classify species according to chemical classes such as terpenes
and sulfur compounds is suggested. This indicates complex network analysis as
an advantageous methodology to uncover plants relationships also related to the
way they react to the environment where they evolve and adapt.
| [
{
"created": "Wed, 26 Apr 2017 11:25:30 GMT",
"version": "v1"
}
] | 2017-04-27 | [
[
"Vivaldo",
"Gianna",
""
],
[
"Masi",
"Elisa",
""
],
[
"Taiti",
"Cosimo",
""
],
[
"Caldarelli",
"Guido",
""
],
[
"Mancuso",
"Stefano",
""
]
] | Plants emission of volatile organic compounds (VOCs) is involved in a wide class of ecological functions, as VOCs play a crucial role in plants interactions with biotic and abiotic factors. Accordingly, they vary widely across species and underpin differences in ecological strategy. In this paper, VOCs spontaneously emitted by 109 plant species (belonging to 56 different families) have been qualitatively and quantitatively analysed in order to classify plants species. By using bipartite networks methodology, based on recent advancements in Complex Network Theory, and through the application of complementary classical and advanced community detection algorithms, the possibility to classify species according to chemical classes such as terpenes and sulfur compounds is suggested. This indicates complex network analysis as an advantageous methodology to uncover plants relationships also related to the way they react to the environment where they evolve and adapt. |
2009.00744 | David Winkler | Sakshi Piplani, Puneet Singh, Nikolai Petrovsky, David A. Winkler | Computational screening of repurposed drugs and natural products against
SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies | 32 pages, 8 figures, 1 table plus supplementary information | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | There remains an urgent need to identify existing drugs that might be
suitable for treating patients suffering from COVID-19 infection. Drugs rarely
act at a single molecular target, with off target effects often being
responsible for undesirable side effects and sometimes, beneficial synergy
between targets for a specific illness. Off target activities have also led to
blockbuster drugs in some cases, e.g. Viagra for erectile dysfunction and
Minoxidil for male pattern hair loss. Drugs already in use or in clinical
trials plus approved natural products constitute a rich resource for discovery
of therapeutic agents that can be repurposed for existing and new conditions,
based on the rationale that they have already been assessed for safety in man.
A key question then is how to rapidly and efficiently screen such compounds for
activity against new pandemic pathogens such as COVID-19. Here we show how a
fast and robust computational process can be used to screen large libraries of
drugs and natural compounds to identify those that may inhibit the main
protease of SARS-Cov-2 (3CL pro, Mpro). We show how the resulting shortlist of
candidates with strongest binding affinities is highly enriched in compounds
that have been independently identified as potential antivirals against
COVID-19. The top candidates also include a substantial number of drugs and
natural products not previously identified as having potential COVID-19
activity, thereby providing additional targets for experimental validation.
This in silico screening pipeline may also be useful for repurposing of
existing drugs and discovery of new drug candidates against other medically
important pathogens and for use in future pandemics.
| [
{
"created": "Tue, 1 Sep 2020 23:22:25 GMT",
"version": "v1"
}
] | 2020-09-03 | [
[
"Piplani",
"Sakshi",
""
],
[
"Singh",
"Puneet",
""
],
[
"Petrovsky",
"Nikolai",
""
],
[
"Winkler",
"David A.",
""
]
] | There remains an urgent need to identify existing drugs that might be suitable for treating patients suffering from COVID-19 infection. Drugs rarely act at a single molecular target, with off target effects often being responsible for undesirable side effects and sometimes, beneficial synergy between targets for a specific illness. Off target activities have also led to blockbuster drugs in some cases, e.g. Viagra for erectile dysfunction and Minoxidil for male pattern hair loss. Drugs already in use or in clinical trials plus approved natural products constitute a rich resource for discovery of therapeutic agents that can be repurposed for existing and new conditions, based on the rationale that they have already been assessed for safety in man. A key question then is how to rapidly and efficiently screen such compounds for activity against new pandemic pathogens such as COVID-19. Here we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-Cov-2 (3CL pro, Mpro). We show how the resulting shortlist of candidates with strongest binding affinities is highly enriched in compounds that have been independently identified as potential antivirals against COVID-19. The top candidates also include a substantial number of drugs and natural products not previously identified as having potential COVID-19 activity, thereby providing additional targets for experimental validation. This in silico screening pipeline may also be useful for repurposing of existing drugs and discovery of new drug candidates against other medically important pathogens and for use in future pandemics. |
1507.03311 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Effect of Inter-Modular Connection on Fast Sparse Synchronization in
Clustered Small-World Neural Networks | null | Phys. Rev. E 92, 052716 (2015) | 10.1103/PhysRevE.92.052716 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a clustered network with small-world sub-networks of inhibitory
fast spiking interneurons, and investigate the effect of inter-modular
connection on emergence of fast sparsely synchronized rhythms by varying both
the inter-modular coupling strength $J_{inter}$ and the average number of
inter-modular links per interneuron $M_{syn}^{(inter)}$. In contrast to the
case of non-clustered networks, two kinds of sparsely synchronized states such
as modular and global synchronization are found. For the case of modular sparse
synchronization, the population behavior reveals the modular structure, because
the intra-modular dynamics of sub-networks make some mismatching. On the other
hand, in the case of global sparse synchronization, the population behavior is
globally identical, independently of the cluster structure, because the
intra-modular dynamics of sub-networks make perfect matching. We introduce a
realistic cross-correlation modularity measure, representing the
matching-degree between the instantaneous sub-population spike rates of the
sub-networks, and examine whether the sparse synchronization is global or
modular. Furthermore, we characterize the modular and global sparse
synchronization by employing the realistic sub- and whole-population order
parameters and statistical-mechanical measures. The roles of $J_{inter}$ and
$M_{syn}^{(inter)}$ are thus found as follows. For large $J_{inter}$, due to
strong inhibition it plays a destructive role to "spoil" the pacing between
spikes, while for small $J_{inter}$ it plays a constructive role to "favor" the
pacing between spikes. In contrast, $M_{syn}^{(inter)}$ seems to play a role
just to favor the pacing between spikes. With increasing $M_{syn}^{(inter)}$,
the pacing degree between spikes increases monotonically thanks to the increase
in the degree of effectiveness of global communication between spikes.
| [
{
"created": "Mon, 13 Jul 2015 02:42:16 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Sep 2015 06:48:08 GMT",
"version": "v2"
}
] | 2015-12-02 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We consider a clustered network with small-world sub-networks of inhibitory fast spiking interneurons, and investigate the effect of inter-modular connection on emergence of fast sparsely synchronized rhythms by varying both the inter-modular coupling strength $J_{inter}$ and the average number of inter-modular links per interneuron $M_{syn}^{(inter)}$. In contrast to the case of non-clustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intra-modular dynamics of sub-networks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intra-modular dynamics of sub-networks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching-degree between the instantaneous sub-population spike rates of the sub-networks, and examine whether the sparse synchronization is global or modular. Furthermore, we characterize the modular and global sparse synchronization by employing the realistic sub- and whole-population order parameters and statistical-mechanical measures. The roles of $J_{inter}$ and $M_{syn}^{(inter)}$ are thus found as follows. For large $J_{inter}$, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small $J_{inter}$ it plays a constructive role to "favor" the pacing between spikes. In contrast, $M_{syn}^{(inter)}$ seems to play a role just to favor the pacing between spikes. With increasing $M_{syn}^{(inter)}$, the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. |
1906.03542 | Ranjan Anantharaman | Ranjan Anantharaman, Kimberly Hall, Viral Shah, Alan Edelman | Circuitscape in Julia: High Performance Connectivity Modelling to
Support Conservation Decisions | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connectivity across landscapes influences a wide range of
conservation-relevant ecological processes, including species movements, gene
flow, and the spread of wildfire, pests, and diseases. Recent improvements in
remote sensing data suggest great potential to advance connectivity models, but
computational constraints hinder these advances. To address this challenge, we
upgraded the widely-used Circuitscape connectivity package to the high
performance Julia programming language. Circuitscape.jl allows users to solve
problems faster via improved parallel processing and solvers, and supports
applications to larger problems (e.g., datasets with hundreds of millions of
cells). We document speed improvements of up to 1800\%. We also demonstrate
scaling of problem sizes up to 437 million grid cells. These improvements allow
modelers to work with higher resolution data, larger landscapes and perform
sensitivity analysis effortlessly. These improvements accelerate the pace of
innovation, helping modelers address pressing challenges like species range
shifts under climate change. Our collaboration between ecologists and computer
scientists has led to the use of connectivity models to inform conservation
decisions. Further, these next generation connectivity models will produce
results faster, facilitating stronger engagement with decision-makers.
| [
{
"created": "Sat, 8 Jun 2019 23:46:47 GMT",
"version": "v1"
}
] | 2019-06-11 | [
[
"Anantharaman",
"Ranjan",
""
],
[
"Hall",
"Kimberly",
""
],
[
"Shah",
"Viral",
""
],
[
"Edelman",
"Alan",
""
]
] | Connectivity across landscapes influences a wide range of conservation-relevant ecological processes, including species movements, gene flow, and the spread of wildfire, pests, and diseases. Recent improvements in remote sensing data suggest great potential to advance connectivity models, but computational constraints hinder these advances. To address this challenge, we upgraded the widely-used Circuitscape connectivity package to the high performance Julia programming language. Circuitscape.jl allows users to solve problems faster via improved parallel processing and solvers, and supports applications to larger problems (e.g., datasets with hundreds of millions of cells). We document speed improvements of up to 1800\%. We also demonstrate scaling of problem sizes up to 437 million grid cells. These improvements allow modelers to work with higher resolution data, larger landscapes and perform sensitivity analysis effortlessly. These improvements accelerate the pace of innovation, helping modelers address pressing challenges like species range shifts under climate change. Our collaboration between ecologists and computer scientists has led to the use of connectivity models to inform conservation decisions. Further, these next generation connectivity models will produce results faster, facilitating stronger engagement with decision-makers. |
2006.02431 | Ben Blaiszik | Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin
Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind
Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens,
Hubertus van Dam, Rick Wagner | Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First
Data Release | 11 pages, 5 figures | null | null | null | q-bio.BM cs.LG q-bio.QM stat.ML | http://creativecommons.org/publicdomain/zero/1.0/ | Researchers across the globe are seeking to rapidly repurpose existing drugs
or discover new drugs to counter the the novel coronavirus disease (COVID-19)
caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One
promising approach is to train machine learning (ML) and artificial
intelligence (AI) tools to screen large numbers of small molecules. As a
contribution to that effort, we are aggregating numerous small molecules from a
variety of sources, using high-performance computing (HPC) to computer diverse
properties of those molecules, using the computed properties to train ML/AI
models, and then using the resulting models for screening. In this first data
release, we make available 23 datasets collected from community sources
representing over 4.2 B molecules enriched with pre-computed: 1) molecular
fingerprints to aid similarity searches, 2) 2D images of molecules to enable
exploration and application of image-based deep learning methods, and 3) 2D and
3D molecular descriptors to speed development of machine learning models. This
data release encompasses structural information on the 4.2 B molecules and 60
TB of pre-computed data. Future releases will expand the data to include more
detailed molecular simulations, computed models, and other products.
| [
{
"created": "Thu, 28 May 2020 01:33:07 GMT",
"version": "v1"
}
] | 2020-06-04 | [
[
"Babuji",
"Yadu",
""
],
[
"Blaiszik",
"Ben",
""
],
[
"Brettin",
"Tom",
""
],
[
"Chard",
"Kyle",
""
],
[
"Chard",
"Ryan",
""
],
[
"Clyde",
"Austin",
""
],
[
"Foster",
"Ian",
""
],
[
"Hong",
"Zhi",
""
],
[
"Jha",
"Shantenu",
""
],
[
"Li",
"Zhuozhao",
""
],
[
"Liu",
"Xuefeng",
""
],
[
"Ramanathan",
"Arvind",
""
],
[
"Ren",
"Yi",
""
],
[
"Saint",
"Nicholaus",
""
],
[
"Schwarting",
"Marcus",
""
],
[
"Stevens",
"Rick",
""
],
[
"van Dam",
"Hubertus",
""
],
[
"Wagner",
"Rick",
""
]
] | Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products. |
2103.15773 | Hamdan Awan | Hamdan Awan, Raviraj S. Adve, Nigel Wallbridge, Carrol Plummer and
Andrew W. Eckford | Modelling the Role of Inter-cellular Communication in Modulating
Photosynthesis in Plants | 6 pages, 6 figures- Submitted for Publication in IEEE Transactions on
Molecular, Biological and Multi-scale Sciences | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we show how inter-cellular molecular communication may change
the overall levels of photosynthesis in plants. Individual plant cells respond
to external stimuli, such as illumination levels, to regulate their
photosynthetic output. Here, we present a mathematical model which shows that
by sharing information internally using molecular communication, plants may
increase overall photosynthate production. Numerical results show that higher
mutual information between cells corresponds to an increase in overall
photosynthesis by as much as 25 per cent. This suggests that molecular
communication plays a vital role in maximising the photosynthesis in plants and
therefore suggests new routes to influence plant development in agriculture and
elsewhere.
| [
{
"created": "Mon, 29 Mar 2021 17:05:29 GMT",
"version": "v1"
}
] | 2021-03-30 | [
[
"Awan",
"Hamdan",
""
],
[
"Adve",
"Raviraj S.",
""
],
[
"Wallbridge",
"Nigel",
""
],
[
"Plummer",
"Carrol",
""
],
[
"Eckford",
"Andrew W.",
""
]
] | In this paper we show how inter-cellular molecular communication may change the overall levels of photosynthesis in plants. Individual plant cells respond to external stimuli, such as illumination levels, to regulate their photosynthetic output. Here, we present a mathematical model which shows that by sharing information internally using molecular communication, plants may increase overall photosynthate production. Numerical results show that higher mutual information between cells corresponds to an increase in overall photosynthesis by as much as 25 per cent. This suggests that molecular communication plays a vital role in maximising the photosynthesis in plants and therefore suggests new routes to influence plant development in agriculture and elsewhere. |
2112.00344 | Wei-Cheng Tseng | Wei-Cheng Tseng, Po-Han Chi, Jia-Hua Wu, Min Sun | Leveraging Sequence Embedding and Convolutional Neural Network for
Protein Function Prediction | Published in NeurIPS 2018 Machine Learning for Molecules and
Materials Workshop | null | null | null | q-bio.QM cs.AI cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The capability of accurate prediction of protein functions and properties is
essential in the biotechnology industry, e.g. drug development and artificial
protein synthesis, etc. The main challenges of protein function prediction are
the large label space and the lack of labeled training data. Our method
leverages unsupervised sequence embedding and the success of deep convolutional
neural network to overcome these challenges. In contrast, most of the existing
methods delete the rare protein functions to reduce the label space.
Furthermore, some existing methods require additional bio-information (e.g.,
the 3-dimensional structure of the proteins) which is difficult to be
determined in biochemical experiments. Our proposed method significantly
outperforms the other methods on the publicly available benchmark using only
protein sequences as input. This allows the process of identifying protein
functions to be sped up.
| [
{
"created": "Wed, 1 Dec 2021 08:31:01 GMT",
"version": "v1"
}
] | 2021-12-02 | [
[
"Tseng",
"Wei-Cheng",
""
],
[
"Chi",
"Po-Han",
""
],
[
"Wu",
"Jia-Hua",
""
],
[
"Sun",
"Min",
""
]
] | The capability of accurate prediction of protein functions and properties is essential in the biotechnology industry, e.g. drug development and artificial protein synthesis, etc. The main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges. In contrast, most of the existing methods delete the rare protein functions to reduce the label space. Furthermore, some existing methods require additional bio-information (e.g., the 3-dimensional structure of the proteins) which is difficult to be determined in biochemical experiments. Our proposed method significantly outperforms the other methods on the publicly available benchmark using only protein sequences as input. This allows the process of identifying protein functions to be sped up. |
2010.00940 | Ivan Slapnicar | Eric Goles, Ivan Slapni\v{c}ar and Marco A. Lardies | Universal evolutionary model for periodical species | 22 pages, 9 figures | Complexity, vol. 2021, Article ID 2976351, 15 pages, 2021 | 10.1155/2021/2976351 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-world examples of periods of periodical organisms range from cicadas
whose life-cycles are larger prime numbers, like 13 or 17, to bamboos whose
periods are large multiples of small primes, like 40 or even 120. The
periodicity is caused by interaction of species, be it a predator-prey
relationship, symbiosis, commensialism, or competition exclusion principle. We
propose a simple mathematical model which explains and models all those
principles, including listed extremel cases. This, rather universal,
qualitative model is based on the concept of a local fitness function, where a
randomly chosen new period is selected if the value of the global fitness
function of the species increases. Arithmetically speaking, the different
observed interactions are related to only four principles: given a couple of
integer periods either (1) their greatest common divisor is one, (2) one of the
periods is prime, (3) both periods are equal, or (4) one period is an integer
multiple of the other.
| [
{
"created": "Fri, 25 Sep 2020 19:13:36 GMT",
"version": "v1"
},
{
"created": "Wed, 19 May 2021 19:42:05 GMT",
"version": "v2"
}
] | 2021-10-13 | [
[
"Goles",
"Eric",
""
],
[
"Slapničar",
"Ivan",
""
],
[
"Lardies",
"Marco A.",
""
]
] | Real-world examples of periods of periodical organisms range from cicadas whose life-cycles are larger prime numbers, like 13 or 17, to bamboos whose periods are large multiples of small primes, like 40 or even 120. The periodicity is caused by interaction of species, be it a predator-prey relationship, symbiosis, commensialism, or competition exclusion principle. We propose a simple mathematical model which explains and models all those principles, including listed extremel cases. This, rather universal, qualitative model is based on the concept of a local fitness function, where a randomly chosen new period is selected if the value of the global fitness function of the species increases. Arithmetically speaking, the different observed interactions are related to only four principles: given a couple of integer periods either (1) their greatest common divisor is one, (2) one of the periods is prime, (3) both periods are equal, or (4) one period is an integer multiple of the other. |
2212.03344 | Jorge Yago Malo | Jorge Yago Malo, Guido Marco Cicchini, Maria Concetta Morrone, Maria
Luisa Chiofalo | Quantum spin models for numerosity perception | 19 pages, 9 figures | null | 10.1371/journal.pone.0284610 | null | q-bio.NC quant-ph | http://creativecommons.org/licenses/by/4.0/ | Humans share with animals, both vertebrates and invertebrates, the capacity
to sense the number of items in their environment already at birth. The
pervasiveness of this skill across the animal kingdom suggests that it should
emerge in very simple populations of neurons. Current modelling literature,
however, has struggled to suggest a simple architecture carrying out this task,
with most proposals suggesting the emergence of number sense in multi-layered
complex neural networks, and typically requiring supervised learning. We
present a simple quantum spin model with all-to-all connectivity, where
numerosity is encoded in the spectrum after stimulation with a number of
transient signals occurring in a random or orderly temporal sequence. We use a
paradigmatic simulational approach borrowed from the theory and methods of open
quantum systems out of equilibrium, as a possible way to describe information
processing in neural systems. Our method is able to capture many of the
perceptual characteristics of numerosity in such systems. The frequency
components of the magnetization spectra at harmonics of the system's tunneling
frequency increase with the number of stimuli presented. The amplitude decoding
of each spectrum, performed with an ideal-observer model, reveals that the
system follows Weber's law, one of the hallmarks of numerosity perception
across the animal kingdom. This contrasts with the well-known failure to
reproduce Weber's law with linear system or accumulators models.
| [
{
"created": "Tue, 6 Dec 2022 21:49:08 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Sep 2023 12:53:05 GMT",
"version": "v2"
}
] | 2023-10-02 | [
[
"Malo",
"Jorge Yago",
""
],
[
"Cicchini",
"Guido Marco",
""
],
[
"Morrone",
"Maria Concetta",
""
],
[
"Chiofalo",
"Maria Luisa",
""
]
] | Humans share with animals, both vertebrates and invertebrates, the capacity to sense the number of items in their environment already at birth. The pervasiveness of this skill across the animal kingdom suggests that it should emerge in very simple populations of neurons. Current modelling literature, however, has struggled to suggest a simple architecture carrying out this task, with most proposals suggesting the emergence of number sense in multi-layered complex neural networks, and typically requiring supervised learning. We present a simple quantum spin model with all-to-all connectivity, where numerosity is encoded in the spectrum after stimulation with a number of transient signals occurring in a random or orderly temporal sequence. We use a paradigmatic simulational approach borrowed from the theory and methods of open quantum systems out of equilibrium, as a possible way to describe information processing in neural systems. Our method is able to capture many of the perceptual characteristics of numerosity in such systems. The frequency components of the magnetization spectra at harmonics of the system's tunneling frequency increase with the number of stimuli presented. The amplitude decoding of each spectrum, performed with an ideal-observer model, reveals that the system follows Weber's law, one of the hallmarks of numerosity perception across the animal kingdom. This contrasts with the well-known failure to reproduce Weber's law with linear system or accumulators models. |
1711.08686 | Pamela Harris | Hector Ba\~nos, Nathaniel Bushek, Ruth Davidson, Elizabeth Gross,
Pamela E. Harris, Robert Krone, Colby Long, Allen Stewart and Robert Walker | Dimensions of Group-based Phylogenetic Mixtures | 24 pages, 4 figures | null | 10.1007/s11538-018-0489-0 | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study group-based Markov models of evolution and their
mixtures. In the algebreo-geometric setting, group-based phylogenetic tree
models correspond to toric varieties, while their mixtures correspond to secant
and join varieties. Determining properties of these secant and join varieties
can aid both in model selection and establishing parameter identifiability.
Here we explore the first natural geometric property of these varieties: their
dimension. The expected projective dimension of the join variety of a set of
varieties is one more than the sum of their dimensions. A join variety that
realizes the expected dimension is nondefective. Nondefectiveness is not only
interesting from a geometric point-of-view, but has been used to establish
combinatorial identifiability for several classes of phylogenetic mixture
models. In this paper, we focus on group-based models where the equivalence
classes of identified parameters are orbits of a subgroup of the automorphism
group of the group defining the model. In particular, we show that, for these
group-based models, the variety corresponding to the mixture of $r$ trees with
$n$ leaves is nondefective when $n \geq 2r+5$. We also give improved bounds for
claw trees and give computational evidence that 2-tree and 3-tree mixtures are
nondefective for small~$n$.
| [
{
"created": "Thu, 23 Nov 2017 13:31:36 GMT",
"version": "v1"
}
] | 2018-11-26 | [
[
"Baños",
"Hector",
""
],
[
"Bushek",
"Nathaniel",
""
],
[
"Davidson",
"Ruth",
""
],
[
"Gross",
"Elizabeth",
""
],
[
"Harris",
"Pamela E.",
""
],
[
"Krone",
"Robert",
""
],
[
"Long",
"Colby",
""
],
[
"Stewart",
"Allen",
""
],
[
"Walker",
"Robert",
""
]
] | In this paper we study group-based Markov models of evolution and their mixtures. In the algebreo-geometric setting, group-based phylogenetic tree models correspond to toric varieties, while their mixtures correspond to secant and join varieties. Determining properties of these secant and join varieties can aid both in model selection and establishing parameter identifiability. Here we explore the first natural geometric property of these varieties: their dimension. The expected projective dimension of the join variety of a set of varieties is one more than the sum of their dimensions. A join variety that realizes the expected dimension is nondefective. Nondefectiveness is not only interesting from a geometric point-of-view, but has been used to establish combinatorial identifiability for several classes of phylogenetic mixture models. In this paper, we focus on group-based models where the equivalence classes of identified parameters are orbits of a subgroup of the automorphism group of the group defining the model. In particular, we show that, for these group-based models, the variety corresponding to the mixture of $r$ trees with $n$ leaves is nondefective when $n \geq 2r+5$. We also give improved bounds for claw trees and give computational evidence that 2-tree and 3-tree mixtures are nondefective for small~$n$. |
2405.07835 | Moo K. Chung | Moo K. Chung, Ji Bi Che, Veena A. Nair, Camille Garcia Ramos, Jedidiah
Ray Mathis, Vivek Prabhakaran, Elizabeth Meyerand, Bruce P. Hermann, Jeffrey
R. Binder, Aaron F. Struck | Topological Embedding of Human Brain Networks with Applications to
Dynamics of Temporal Lobe Epilepsy | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We introduce a novel, data-driven topological data analysis (TDA) approach
for embedding brain networks into a lower-dimensional space in quantifying the
dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional
magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal
projection of 0D and 1D topological features, allowing for the visualization
and modeling of the dynamics of functional human brain networks in a resting
state. We then quantify the topological disparities between networks to
determine the coordinates for embedding. This framework enables us to conduct a
coherent statistical inference within the embedded space. Our results indicate
that brain network topology in TLE patients exhibits increased rigidity in 0D
topology but more rapid flections compared to that of normal controls in 1D
topology.
| [
{
"created": "Mon, 13 May 2024 15:21:30 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Chung",
"Moo K.",
""
],
[
"Che",
"Ji Bi",
""
],
[
"Nair",
"Veena A.",
""
],
[
"Ramos",
"Camille Garcia",
""
],
[
"Mathis",
"Jedidiah Ray",
""
],
[
"Prabhakaran",
"Vivek",
""
],
[
"Meyerand",
"Elizabeth",
""
],
[
"Hermann",
"Bruce P.",
""
],
[
"Binder",
"Jeffrey R.",
""
],
[
"Struck",
"Aaron F.",
""
]
] | We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology. |
1708.09312 | Edgar Herrera-Delgado | Edgar Herrera-Delgado (1 and 2), Ruben Perez-Carrasco (3), James
Briscoe (1) and Peter Sollich (2) ((1) The Francis Crick Institute, London,
UK, (2) King's College London, London, UK, (3) University College London,
London, UK) | Memory functions reveal structural properties of gene regulatory
networks | 28 pages, 8 figures | PLoS Comput Biol. 2018 Feb 22;14(2):e1006003 | 10.1371/journal.pcbi.1006003 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory networks (GRNs) control cellular function and decision making
during tissue development and homeostasis. Mathematical tools based on
dynamical systems theory are often used to model these networks, but the size
and complexity of these models mean that their behaviour is not always
intuitive and the underlying mechanisms can be difficult to decipher. For this
reason, methods that simplify and aid exploration of complex networks are
necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori
projection. By first converting a thermodynamic state ensemble model of gene
regulation into mass action reactions we derive a general method that produces
a set of time evolution equations for a subset of components of a network. The
influence of the rest of the network, the bulk, is captured by memory functions
that describe how the subnetwork reacts to its own past state via components in
the bulk. These memory functions provide probes of near-steady state dynamics,
revealing information not easily accessible otherwise. We illustrate the method
on a simple cross-repressive transcriptional motif to show that memory
functions not only simplify the analysis of the subnetwork but also have a
natural interpretation. We then apply the approach to a GRN from the vertebrate
neural tube, a well characterised developmental transcriptional network
composed of four interacting transcription factors. The memory functions reveal
the function of specific links within the neural tube network and identify
features of the regulatory structure that specifically increase the robustness
of the network to initial conditions. Taken together, the study provides
evidence that Zwanzig-Mori projections offer powerful and effective tools for
simplifying and exploring the behaviour of GRNs.
| [
{
"created": "Wed, 30 Aug 2017 15:03:13 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Mar 2018 10:45:45 GMT",
"version": "v2"
}
] | 2018-03-16 | [
[
"Herrera-Delgado",
"Edgar",
"",
"1 and 2"
],
[
"Perez-Carrasco",
"Ruben",
""
],
[
"Briscoe",
"James",
""
],
[
"Sollich",
"Peter",
""
]
] | Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. |
1509.02697 | Daniele Marinazzo | Sebastiano Stramaglia, Mario Pellicoro, Leonardo Angelini, Enrico
Amico, Hannelore Aerts, Jesus Cort\'es, Steven Laureys, Daniele Marinazzo | Conserved Ising Model on the Human Connectome | null | Chaos 27, 047407 (2017) | 10.1063/1.4978999 | null | q-bio.NC cond-mat.dis-nn physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamical models implemented on the large scale architecture of the human
brain may shed light on how function arises from the underlying structure. This
is the case notably for simple abstract models, such as the Ising model. We
compare the spin correlations of the Ising model and the empirical functional
brain correlations, both at the single link level and at the modular level, and
show that their match increases at the modular level in anesthesia, in line
with recent results and theories. Moreover, we show that at the peak of the
specific heat (the \it{critical state}) the spin correlations are minimally
shaped by the underlying structural network, explaining how the best match
between structure and function is obtained at the onset of criticality, as
previously observed. These findings confirm that brain dynamics under
anesthesia shows a departure from criticality and could open the way to novel
perspectives when the conserved magnetization is interpreted in terms of an
homeostatic principle imposed to neural activity.
| [
{
"created": "Wed, 9 Sep 2015 09:51:53 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Apr 2017 15:12:44 GMT",
"version": "v2"
}
] | 2017-04-07 | [
[
"Stramaglia",
"Sebastiano",
""
],
[
"Pellicoro",
"Mario",
""
],
[
"Angelini",
"Leonardo",
""
],
[
"Amico",
"Enrico",
""
],
[
"Aerts",
"Hannelore",
""
],
[
"Cortés",
"Jesus",
""
],
[
"Laureys",
"Steven",
""
],
[
"Marinazzo",
"Daniele",
""
]
] | Dynamical models implemented on the large scale architecture of the human brain may shed light on how function arises from the underlying structure. This is the case notably for simple abstract models, such as the Ising model. We compare the spin correlations of the Ising model and the empirical functional brain correlations, both at the single link level and at the modular level, and show that their match increases at the modular level in anesthesia, in line with recent results and theories. Moreover, we show that at the peak of the specific heat (the \it{critical state}) the spin correlations are minimally shaped by the underlying structural network, explaining how the best match between structure and function is obtained at the onset of criticality, as previously observed. These findings confirm that brain dynamics under anesthesia shows a departure from criticality and could open the way to novel perspectives when the conserved magnetization is interpreted in terms of an homeostatic principle imposed to neural activity. |
q-bio/0610037 | Simone Bianco | Simone Bianco, Massimiliano Ignaccolo, Mark S. Rider, Mary J. Ross,
Phil Winsor, and Paolo Grigolini | Brain, Music and non-Poisson Renewal Processes | 14 pages, 4 figures. Updated content. Updated figures | null | 10.1103/PhysRevE.75.061911 | null | q-bio.NC | null | In this paper we show that both music composition and brain function, as
revealed by the Electroencephalogram (EEG) analysis, are renewal non-Poisson
processes living in the non-ergodic dominion. To reach this important
conclusion we process the data with the minimum spanning tree method, so as to
detect significant events, thereby building a sequence of times, which is the
time series to analyze. Then we show that in both cases, EEG and music
composition, these significant events are the signature of a non-Poisson
renewal process. This conclusion is reached using a techniques of statistical
analysis recently developed by our group, the Aging Experiment (AE). First, we
find that in both cases the distances between two consecutive events are
described by non-exponential histograms, thereby proving the non-Poisson nature
of these processes. The corresponding survival probabilities $\Psi(t)$ are well
fitted by stretched exponentials ($\Psi(t) \propto exp(-(\gamma t)^\alpha$),
with $0.5 <\alpha <1$.) The second step rests on the adoption of the AE, which
shows that these are renewal processes. We show that the renewal stretched
exponential is the emerging tip of an iceberg, whose underwater part has slow
tails with an inverse power law structure with power index $\mu = 1 + \alpha$.
We find that both EEG and music composition yield $\mu < 2$. On the basis of
the recently discovered complexity matching effect, according to which a
complex system $S$ with $\mu_S < 2$ responds only to a complex driving signal
$P$ with $\mu_P = \mu_S$, we conclude that the results of our analysis may
explain the influence of music on human brain.
| [
{
"created": "Thu, 19 Oct 2006 17:22:41 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jan 2007 17:11:33 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Mar 2007 17:12:40 GMT",
"version": "v3"
}
] | 2009-11-13 | [
[
"Bianco",
"Simone",
""
],
[
"Ignaccolo",
"Massimiliano",
""
],
[
"Rider",
"Mark S.",
""
],
[
"Ross",
"Mary J.",
""
],
[
"Winsor",
"Phil",
""
],
[
"Grigolini",
"Paolo",
""
]
] | In this paper we show that both music composition and brain function, as revealed by the Electroencephalogram (EEG) analysis, are renewal non-Poisson processes living in the non-ergodic dominion. To reach this important conclusion we process the data with the minimum spanning tree method, so as to detect significant events, thereby building a sequence of times, which is the time series to analyze. Then we show that in both cases, EEG and music composition, these significant events are the signature of a non-Poisson renewal process. This conclusion is reached using a techniques of statistical analysis recently developed by our group, the Aging Experiment (AE). First, we find that in both cases the distances between two consecutive events are described by non-exponential histograms, thereby proving the non-Poisson nature of these processes. The corresponding survival probabilities $\Psi(t)$ are well fitted by stretched exponentials ($\Psi(t) \propto exp(-(\gamma t)^\alpha$), with $0.5 <\alpha <1$.) The second step rests on the adoption of the AE, which shows that these are renewal processes. We show that the renewal stretched exponential is the emerging tip of an iceberg, whose underwater part has slow tails with an inverse power law structure with power index $\mu = 1 + \alpha$. We find that both EEG and music composition yield $\mu < 2$. On the basis of the recently discovered complexity matching effect, according to which a complex system $S$ with $\mu_S < 2$ responds only to a complex driving signal $P$ with $\mu_P = \mu_S$, we conclude that the results of our analysis may explain the influence of music on human brain. |
0802.0914 | Sebastian Roch | Elchanan Mossel and Sebastien Roch and Mike Steel | Shrinkage Effect in Ancestral Maximum Likelihood | null | null | null | null | q-bio.PE cs.CE math.PR math.ST stat.TH | null | Ancestral maximum likelihood (AML) is a method that simultaneously
reconstructs a phylogenetic tree and ancestral sequences from extant data
(sequences at the leaves). The tree and ancestral sequences maximize the
probability of observing the given data under a Markov model of sequence
evolution, in which branch lengths are also optimized but constrained to take
the same value on any edge across all sequence sites. AML differs from the more
usual form of maximum likelihood (ML) in phylogenetics because ML averages over
all possible ancestral sequences. ML has long been known to be statistically
consistent -- that is, it converges on the correct tree with probability
approaching 1 as the sequence length grows. However, the statistical
consistency of AML has not been formally determined, despite informal remarks
in a literature that dates back 20 years. In this short note we prove a general
result that implies that AML is statistically inconsistent. In particular we
show that AML can `shrink' short edges in a tree, resulting in a tree that has
no internal resolution as the sequence length grows. Our results apply to any
number of taxa.
| [
{
"created": "Thu, 7 Feb 2008 06:52:44 GMT",
"version": "v1"
}
] | 2017-07-24 | [
[
"Mossel",
"Elchanan",
""
],
[
"Roch",
"Sebastien",
""
],
[
"Steel",
"Mike",
""
]
] | Ancestral maximum likelihood (AML) is a method that simultaneously reconstructs a phylogenetic tree and ancestral sequences from extant data (sequences at the leaves). The tree and ancestral sequences maximize the probability of observing the given data under a Markov model of sequence evolution, in which branch lengths are also optimized but constrained to take the same value on any edge across all sequence sites. AML differs from the more usual form of maximum likelihood (ML) in phylogenetics because ML averages over all possible ancestral sequences. ML has long been known to be statistically consistent -- that is, it converges on the correct tree with probability approaching 1 as the sequence length grows. However, the statistical consistency of AML has not been formally determined, despite informal remarks in a literature that dates back 20 years. In this short note we prove a general result that implies that AML is statistically inconsistent. In particular we show that AML can `shrink' short edges in a tree, resulting in a tree that has no internal resolution as the sequence length grows. Our results apply to any number of taxa. |
1611.03119 | Thierry Mora | Zachary Sethna, Yuval Elhanati, Crissy S. Dudgeon, Curtis G. Callan
Jr., Arnold Levine, Thierry Mora, Aleksandra M. Walczak | Mouse T cell repertoires as statistical ensembles: overall
characterization and age dependence | null | Proc. Nat. Acad. Sci. USA 114(9) pp. 2253-2258 (2017) | 10.1073/pnas.1700241114 | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability of the adaptive immune system to respond to arbitrary pathogens
stems from the broad diversity of immune cell surface receptors (TCRs). This
diversity originates in a stochastic DNA editing process (VDJ recombination)
that acts each time a new immune cell is created from a stem cell. By analyzing
T cell sequence repertoires taken from the blood and thymus of mice of
different ages, we quantify the significant changes in this process that occur
in development from embryo to young adult. We find a rapid increase with age in
the number of random insertions in the VDJ recombination process, leading to a
dramatic increase in diversity. Since the blood accumulates thymic output over
time, blood repertoires are mixtures of different statistical recombination
processes and, by unraveling the mixture statistics, we can obtain a clear
picture of the time evolution of the early immune system. Sequence repertoire
analysis also allows us to detect the effect of selection on the output of the
VDJ recombination process. The effects we find are nearly identical between
thymus and blood, suggesting that they mainly reflect selection for proper
folding of the TCR receptor protein.
| [
{
"created": "Wed, 9 Nov 2016 22:35:52 GMT",
"version": "v1"
}
] | 2017-06-02 | [
[
"Sethna",
"Zachary",
""
],
[
"Elhanati",
"Yuval",
""
],
[
"Dudgeon",
"Crissy S.",
""
],
[
"Callan",
"Curtis G.",
"Jr."
],
[
"Levine",
"Arnold",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] | The ability of the adaptive immune system to respond to arbitrary pathogens stems from the broad diversity of immune cell surface receptors (TCRs). This diversity originates in a stochastic DNA editing process (VDJ recombination) that acts each time a new immune cell is created from a stem cell. By analyzing T cell sequence repertoires taken from the blood and thymus of mice of different ages, we quantify the significant changes in this process that occur in development from embryo to young adult. We find a rapid increase with age in the number of random insertions in the VDJ recombination process, leading to a dramatic increase in diversity. Since the blood accumulates thymic output over time, blood repertoires are mixtures of different statistical recombination processes and, by unraveling the mixture statistics, we can obtain a clear picture of the time evolution of the early immune system. Sequence repertoire analysis also allows us to detect the effect of selection on the output of the VDJ recombination process. The effects we find are nearly identical between thymus and blood, suggesting that they mainly reflect selection for proper folding of the TCR receptor protein. |
1711.08078 | Mauricio Carrillo-Tripp Dr. | Mauricio Carrillo-Tripp, Leonardo Alvarez-Rivera, Omar Israel
Lara-Ram\'irez, Francisco Javier Becerra-Toledo, Adan Vega-Ram\'irez,
Emmanuel Quijas-Valades, Eduardo Gonz\'alez-Zavala, Julio Cesar
Gonz\'alez-V\'azquez, Javier Garc\'ia-Vieyra, Nelly Beatriz Santoyo-Rivera,
Amilcar Meneses-Viveros, Sergio Victor Chapa-Vergara | HTMoL: full-stack solution for remote access, visualization, and
analysis of Molecular Dynamics trajectory data | null | null | 10.1007/s10822-018-0141-y | null | q-bio.BM cs.ET | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The field of structural bioinformatics has seen significant advances with the
use of Molecular Dynamics (MD) simulations of biological systems. The MD
methodology has allowed to explain and discover molecular mechanisms in a wide
range of natural processes. There is an impending need to readily share the
ever-increasing amount of MD data, which has been hindered by the lack of
specialized tools in the past. To solve this problem, we present HTMoL, a
state-of-the-art plug-in-free hardware-accelerated web application specially
designed to efficiently transfer and visualize raw MD trajectory files on a web
browser. Now, individual research labs can publish MD data on the Internet, or
use HTMoL to profoundly improve scientific reports by including supplemental MD
data in a journal publication. HTMoL can also be used as a visualization
interface to access MD trajectories generated on a high-performance computer
center directly.
Availability: HTMoL is available free of charge for academic use. All major
browsers are supported. A complete online documentation including instructions
for download, installation, configuration, and examples is available at the
HTMoL website http://htmol.tripplab.com. Supplementary data are available
online. Corresponding author: mauricio.carrillo@cinvestav.mx
| [
{
"created": "Tue, 21 Nov 2017 23:00:19 GMT",
"version": "v1"
}
] | 2018-08-29 | [
[
"Carrillo-Tripp",
"Mauricio",
""
],
[
"Alvarez-Rivera",
"Leonardo",
""
],
[
"Lara-Ramírez",
"Omar Israel",
""
],
[
"Becerra-Toledo",
"Francisco Javier",
""
],
[
"Vega-Ramírez",
"Adan",
""
],
[
"Quijas-Valades",
"Emmanuel",
""
],
[
"González-Zavala",
"Eduardo",
""
],
[
"González-Vázquez",
"Julio Cesar",
""
],
[
"García-Vieyra",
"Javier",
""
],
[
"Santoyo-Rivera",
"Nelly Beatriz",
""
],
[
"Meneses-Viveros",
"Amilcar",
""
],
[
"Chapa-Vergara",
"Sergio Victor",
""
]
] | The field of structural bioinformatics has seen significant advances with the use of Molecular Dynamics (MD) simulations of biological systems. The MD methodology has allowed to explain and discover molecular mechanisms in a wide range of natural processes. There is an impending need to readily share the ever-increasing amount of MD data, which has been hindered by the lack of specialized tools in the past. To solve this problem, we present HTMoL, a state-of-the-art plug-in-free hardware-accelerated web application specially designed to efficiently transfer and visualize raw MD trajectory files on a web browser. Now, individual research labs can publish MD data on the Internet, or use HTMoL to profoundly improve scientific reports by including supplemental MD data in a journal publication. HTMoL can also be used as a visualization interface to access MD trajectories generated on a high-performance computer center directly. Availability: HTMoL is available free of charge for academic use. All major browsers are supported. A complete online documentation including instructions for download, installation, configuration, and examples is available at the HTMoL website http://htmol.tripplab.com. Supplementary data are available online. Corresponding author: mauricio.carrillo@cinvestav.mx |
1905.05827 | Yejin Kim | Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, Licong Cui | Discriminative Sleep Patterns of Alzheimer's Disease via Tensor
Factorization | null | null | null | PMC7153114 | q-bio.NC cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sleep change is commonly reported in Alzheimer's disease (AD) patients and
their brain wave studies show decrease in dreaming and non-dreaming stages.
Although sleep disturbance is generally considered as a consequence of AD, it
might also be a risk factor of AD as new biological evidence shows. Leveraging
National Sleep Research Resource (NSRR), we built a unique cohort of 83 cases
and 331 controls with clinical variables and EEG signals. Supervised tensor
factorization method was applied for this temporal dataset to extract
discriminative sleep patterns. Among the 30 patterns extracted, we identified 5
significant patterns (4 patterns for AD likely and 1 pattern for normal ones)
and their visual patterns provide interesting linkage to sleep with repeated
wakefulness, insomnia, epileptic seizure, and etc. This study is preliminary
but findings are interesting, which is a first step to provide quantifiable
evidences to measure sleep as a risk factor of AD.
| [
{
"created": "Tue, 14 May 2019 20:21:58 GMT",
"version": "v1"
}
] | 2020-06-26 | [
[
"Kim",
"Yejin",
""
],
[
"Jiang",
"Xiaoqian",
""
],
[
"Chen",
"Luyao",
""
],
[
"Li",
"Xiaojin",
""
],
[
"Cui",
"Licong",
""
]
] | Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages. Although sleep disturbance is generally considered as a consequence of AD, it might also be a risk factor of AD as new biological evidence shows. Leveraging National Sleep Research Resource (NSRR), we built a unique cohort of 83 cases and 331 controls with clinical variables and EEG signals. Supervised tensor factorization method was applied for this temporal dataset to extract discriminative sleep patterns. Among the 30 patterns extracted, we identified 5 significant patterns (4 patterns for AD likely and 1 pattern for normal ones) and their visual patterns provide interesting linkage to sleep with repeated wakefulness, insomnia, epileptic seizure, and etc. This study is preliminary but findings are interesting, which is a first step to provide quantifiable evidences to measure sleep as a risk factor of AD. |
2111.06693 | Matthias Griebel | Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft,
Philip Tovote, Robert Blum, Christoph M. Flath | Deep-learning in the bioimaging wild: Handling ambiguous data with
deepflash2 | null | null | null | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present deepflash2, a deep learning solution that facilitates the
objective and reliable segmentation of ambiguous bioimages through multi-expert
annotations and integrated quality assurance. Thereby, deepflash2 addresses
typical challenges that arise during training, evaluation, and application of
deep learning models in bioimaging. The tool is embedded in an easy-to-use
graphical user interface and offers best-in-class predictive performance for
semantic and instance segmentation under economical usage of computational
resources.
| [
{
"created": "Fri, 12 Nov 2021 12:35:26 GMT",
"version": "v1"
}
] | 2021-11-15 | [
[
"Griebel",
"Matthias",
""
],
[
"Segebarth",
"Dennis",
""
],
[
"Stein",
"Nikolai",
""
],
[
"Schukraft",
"Nina",
""
],
[
"Tovote",
"Philip",
""
],
[
"Blum",
"Robert",
""
],
[
"Flath",
"Christoph M.",
""
]
] | We present deepflash2, a deep learning solution that facilitates the objective and reliable segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance. Thereby, deepflash2 addresses typical challenges that arise during training, evaluation, and application of deep learning models in bioimaging. The tool is embedded in an easy-to-use graphical user interface and offers best-in-class predictive performance for semantic and instance segmentation under economical usage of computational resources. |
1604.02404 | Keith Smith | Keith Smith, Hamed Azami, Mario A. Parra, Javier Escudero and John M.
Starr | Comparison of Network Analysis Approaches on EEG Connectivity in Beta
during Visual Short-Term Memory Binding Tasks | Proceeds from the IEEE Engineering in Medicine and Biology Conference
2015 | null | 10.1109/EMBC.2015.7318829 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyse the electroencephalogram signals in the beta band of working
memory representation recorded from young healthy volunteers performing several
different Visual Short-Term Memory (VSTM) tasks which have proven useful in the
assessment of clinical and preclinical Alzheimer's disease. We compare network
analysis using Maximum Spanning Trees (MSTs) with network analysis obtained
using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising
method of network analysis negating the more classical use of thresholds which
are so far chosen arbitrarily. However, we find that the threshold analyses
outperforms MSTs for detection of functional network differences. Particularly,
MSTs fail to find any significant differences. Further, the thresholds detect
significant differences between shape and shape-colour binding tasks when these
are tested in the left side of the display screen, but no such differences are
detected when these tasks are tested for in the right side of the display
screen. This provides evidence that contralateral activity is a significant
factor in sensitivity for detection of cognitive task differences.
| [
{
"created": "Fri, 8 Apr 2016 17:15:29 GMT",
"version": "v1"
}
] | 2016-04-11 | [
[
"Smith",
"Keith",
""
],
[
"Azami",
"Hamed",
""
],
[
"Parra",
"Mario A.",
""
],
[
"Escudero",
"Javier",
""
],
[
"Starr",
"John M.",
""
]
] | We analyse the electroencephalogram signals in the beta band of working memory representation recorded from young healthy volunteers performing several different Visual Short-Term Memory (VSTM) tasks which have proven useful in the assessment of clinical and preclinical Alzheimer's disease. We compare network analysis using Maximum Spanning Trees (MSTs) with network analysis obtained using 20% and 25% connection thresholds on the VSTM data. MSTs are a promising method of network analysis negating the more classical use of thresholds which are so far chosen arbitrarily. However, we find that the threshold analyses outperforms MSTs for detection of functional network differences. Particularly, MSTs fail to find any significant differences. Further, the thresholds detect significant differences between shape and shape-colour binding tasks when these are tested in the left side of the display screen, but no such differences are detected when these tasks are tested for in the right side of the display screen. This provides evidence that contralateral activity is a significant factor in sensitivity for detection of cognitive task differences. |
1906.04834 | Alexander Fisher | Alexander A. Fisher, Xiang Ji, Philippe Lemey, Marc A. Suchard | Relaxed random walks at scale | 18 pages, 4 figures | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relaxed random walk (RRW) models of trait evolution introduce branch-specific
rate multipliers to modulate the variance of a standard Brownian diffusion
process along a phylogeny and more accurately model overdispersed biological
data. Increased taxonomic sampling challenges inference under RRWs as the
number of unknown parameters grows with the number of taxa. To solve this
problem, we present a scalable method to efficiently fit RRWs and infer this
branch-specific variation in a Bayesian framework. We develop a Hamiltonian
Monte Carlo (HMC) sampler to approximate the high-dimensional, correlated
posterior that exploits a closed-form evaluation of the gradient of the trait
data log-likelihood with respect to all branch-rate multipliers simultaneously.
Our gradient calculation achieves computational complexity that scales only
linearly with the number of taxa under study. We compare the efficiency of our
HMC sampler to the previously standard univariable Metropolis-Hastings approach
while studying the spatial emergence of the West Nile virus in North America in
the early 2000s. Our method achieves an over 300-fold speed-increase over the
univariable approach. Additionally, we demonstrate the scalability of our
method by applying the RRW to study the correlation between five mammalian life
history traits in a phylogenetic tree with 3650 tips.
| [
{
"created": "Tue, 11 Jun 2019 21:35:30 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Nov 2019 05:12:53 GMT",
"version": "v2"
}
] | 2019-11-15 | [
[
"Fisher",
"Alexander A.",
""
],
[
"Ji",
"Xiang",
""
],
[
"Lemey",
"Philippe",
""
],
[
"Suchard",
"Marc A.",
""
]
] | Relaxed random walk (RRW) models of trait evolution introduce branch-specific rate multipliers to modulate the variance of a standard Brownian diffusion process along a phylogeny and more accurately model overdispersed biological data. Increased taxonomic sampling challenges inference under RRWs as the number of unknown parameters grows with the number of taxa. To solve this problem, we present a scalable method to efficiently fit RRWs and infer this branch-specific variation in a Bayesian framework. We develop a Hamiltonian Monte Carlo (HMC) sampler to approximate the high-dimensional, correlated posterior that exploits a closed-form evaluation of the gradient of the trait data log-likelihood with respect to all branch-rate multipliers simultaneously. Our gradient calculation achieves computational complexity that scales only linearly with the number of taxa under study. We compare the efficiency of our HMC sampler to the previously standard univariable Metropolis-Hastings approach while studying the spatial emergence of the West Nile virus in North America in the early 2000s. Our method achieves an over 300-fold speed-increase over the univariable approach. Additionally, we demonstrate the scalability of our method by applying the RRW to study the correlation between five mammalian life history traits in a phylogenetic tree with 3650 tips. |
2109.05753 | Deeptajyoti Sen Dr. | Deeptajyoti Sen and Sudeshna Sinha | Enhancement of Extreme Events through the Allee effect and its
Mitigation through Noise in a Three Species System | null | null | null | null | q-bio.PE nlin.CD | http://creativecommons.org/licenses/by/4.0/ | We consider the dynamics of a three-species system incorporating the Allee
Effect, focussing on its influence on the emergence of extreme events in the
system. First we find that under Allee effect the regular periodic dynamics
changes to chaotic. Further, we find that the system exhibits unbounded growth
in the vegetation population after a critical value of the Allee parameter. The
most significant finding is the observation of a critical Allee parameter
beyond which the probability of obtaining extreme events becomes non-zero for
all three population densities. Though the emergence of extreme events in the
predator population is not affected much by the Allee effect, the prey
population shows a sharp increase in the probability of obtaining extreme
events after a threshold value of the Allee parameter, and the vegetation
population also yields extreme events for sufficiently strong Allee effect.
Lastly we consider the influence of additive noise on extreme events. First, we
find that noise tames the unbounded vegetation growth induced by Allee effect.
More interestingly, we demonstrate that stochasticity drastically diminishes
the probability of extreme events in all three populations. In fact for
sufficiently high noise, we do not observe any more extreme events in the
system. This suggests that noise can mitigate extreme events, and has
potentially important bearing on the observability of extreme events in
naturally occurring systems.
| [
{
"created": "Mon, 13 Sep 2021 07:23:44 GMT",
"version": "v1"
}
] | 2021-09-14 | [
[
"Sen",
"Deeptajyoti",
""
],
[
"Sinha",
"Sudeshna",
""
]
] | We consider the dynamics of a three-species system incorporating the Allee Effect, focussing on its influence on the emergence of extreme events in the system. First we find that under Allee effect the regular periodic dynamics changes to chaotic. Further, we find that the system exhibits unbounded growth in the vegetation population after a critical value of the Allee parameter. The most significant finding is the observation of a critical Allee parameter beyond which the probability of obtaining extreme events becomes non-zero for all three population densities. Though the emergence of extreme events in the predator population is not affected much by the Allee effect, the prey population shows a sharp increase in the probability of obtaining extreme events after a threshold value of the Allee parameter, and the vegetation population also yields extreme events for sufficiently strong Allee effect. Lastly we consider the influence of additive noise on extreme events. First, we find that noise tames the unbounded vegetation growth induced by Allee effect. More interestingly, we demonstrate that stochasticity drastically diminishes the probability of extreme events in all three populations. In fact for sufficiently high noise, we do not observe any more extreme events in the system. This suggests that noise can mitigate extreme events, and has potentially important bearing on the observability of extreme events in naturally occurring systems. |
1909.01468 | Jacob Beal | Bryan A. Bartley, Jacob Beal, Jonathan R. Karr, Elizabeth A.
Strychalski | Organizing genome engineering for the gigabase scale | null | null | 10.1038/s41467-020-14314-z | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Engineering the entire genome of an organism enables large-scale changes in
organization, function, and external interactions, with significant
implications for industry, medicine, and the environment. Improvements to DNA
synthesis and organism engineering are already enabling substantial changes to
organisms with megabase genomes, such as Escherichia coli and Saccharomyces
cerevisiae. Simultaneously, recent advances in genome-scale modeling are
increasingly informing the design of metabolic networks. However, major
challenges remain for integrating these and other relevant technologies into
workflows that can scale to the engineering of gigabase genomes.
In particular, we find that a major under-recognized challenge is
coordinating the flow of models, designs, constructs, and measurements across
the large teams and complex technological systems that will likely be required
for gigabase genome engineering. We recommend that the community address these
challenges by 1) adopting and extending existing standards and technologies for
representing and exchanging information at the gigabase genomic scale, 2)
developing new technologies to address major open questions around data
curation and quality control, 3) conducting fundamental research on the
integration of modeling and design at the genomic scale, and 4) developing new
legal and contractual infrastructure to better enable collaboration across
multiple institutions.
| [
{
"created": "Tue, 3 Sep 2019 22:04:34 GMT",
"version": "v1"
}
] | 2020-03-25 | [
[
"Bartley",
"Bryan A.",
""
],
[
"Beal",
"Jacob",
""
],
[
"Karr",
"Jonathan R.",
""
],
[
"Strychalski",
"Elizabeth A.",
""
]
] | Engineering the entire genome of an organism enables large-scale changes in organization, function, and external interactions, with significant implications for industry, medicine, and the environment. Improvements to DNA synthesis and organism engineering are already enabling substantial changes to organisms with megabase genomes, such as Escherichia coli and Saccharomyces cerevisiae. Simultaneously, recent advances in genome-scale modeling are increasingly informing the design of metabolic networks. However, major challenges remain for integrating these and other relevant technologies into workflows that can scale to the engineering of gigabase genomes. In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will likely be required for gigabase genome engineering. We recommend that the community address these challenges by 1) adopting and extending existing standards and technologies for representing and exchanging information at the gigabase genomic scale, 2) developing new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing new legal and contractual infrastructure to better enable collaboration across multiple institutions. |
2309.16465 | Dominic Boutet | Dominic Boutet and Sylvain Baillet (Montreal Neurological Institute,
McGill University, Montreal QC, Canada) | A Metaheuristic for Amortized Search in High-Dimensional Parameter
Spaces | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parameter inference for dynamical models of (bio)physical systems remains a
challenging problem. Intractable gradients, high-dimensional spaces, and
non-linear model functions are typically problematic without large
computational budgets. A recent body of work in that area has focused on
Bayesian inference methods, which consider parameters under their statistical
distributions and therefore, do not derive point estimates of optimal parameter
values. Here we propose a new metaheuristic that drives dimensionality
reductions from feature-informed transformations (DR-FFIT) to address these
bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates
a gradient-free parameter search in high-dimensional spaces. We use artificial
neural networks to obtain differentiable proxies for the model's features of
interest. The resulting gradients enable the estimation of a local active
subspace of the model within a defined sampling region. This approach enables
efficient dimensionality reductions of highly non-linear search spaces at a low
computational cost. Our test data show that DR-FFIT boosts the performances of
random-search and simulated-annealing against well-established metaheuristics,
and improves the goodness-of-fit of the model, all within contained run-time
costs.
| [
{
"created": "Thu, 28 Sep 2023 14:25:14 GMT",
"version": "v1"
}
] | 2023-09-29 | [
[
"Boutet",
"Dominic",
"",
"Montreal Neurological Institute,\n McGill University, Montreal QC, Canada"
],
[
"Baillet",
"Sylvain",
"",
"Montreal Neurological Institute,\n McGill University, Montreal QC, Canada"
]
] | Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs. |
1706.06088 | Richard Betzel | Richard F. Betzel, John D. Medaglia, Ari E. Kahn, Jonathan Soffer,
Daniel R. Schonhaut, Danielle S. Bassett | Inter-regional ECoG correlations predicted by communication dynamics,
geometry, and correlated gene expression | 36 pages, 4 figures + 2 tables (main text), 14 figures + 7 tables
(supplementary materials) | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electrocorticography (ECoG) provides direct measurements of synchronized
postsynaptic potentials at the exposed cortical surface. Patterns of signal
covariance across ECoG sensors have been associated with diverse cognitive
functions and remain a critical marker of seizure onset, progression, and
termination. Yet, a systems level understanding of these patterns (or networks)
has remained elusive, in part due to variable electrode placement and sparse
cortical coverage. Here, we address these challenges by constructing
inter-regional ECoG networks from multi-subject recordings, demonstrate
similarities between these networks and those constructed from
blood-oxygen-level-dependent signal in functional magnetic resonance imaging,
and predict network topology from anatomical connectivity, interregional
distance, and correlated gene expression patterns. Our models accurately
predict out-of-sample ECoG networks and perform well even when fit to data from
individual subjects, suggesting shared organizing principles across persons. In
addition, we identify a set of genes whose brain-wide co-expression is highly
correlated with ECoG network organization. Using gene ontology analysis, we
show that these same genes are enriched for membrane and ion channel
maintenance and function, suggesting a molecular underpinning of ECoG
connectivity. Our findings provide fundamental understanding of the factors
that influence interregional ECoG networks, and open the possibility for
predictive modeling of surgical outcomes in disease.
| [
{
"created": "Mon, 19 Jun 2017 17:58:20 GMT",
"version": "v1"
}
] | 2017-06-20 | [
[
"Betzel",
"Richard F.",
""
],
[
"Medaglia",
"John D.",
""
],
[
"Kahn",
"Ari E.",
""
],
[
"Soffer",
"Jonathan",
""
],
[
"Schonhaut",
"Daniel R.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Electrocorticography (ECoG) provides direct measurements of synchronized postsynaptic potentials at the exposed cortical surface. Patterns of signal covariance across ECoG sensors have been associated with diverse cognitive functions and remain a critical marker of seizure onset, progression, and termination. Yet, a systems level understanding of these patterns (or networks) has remained elusive, in part due to variable electrode placement and sparse cortical coverage. Here, we address these challenges by constructing inter-regional ECoG networks from multi-subject recordings, demonstrate similarities between these networks and those constructed from blood-oxygen-level-dependent signal in functional magnetic resonance imaging, and predict network topology from anatomical connectivity, interregional distance, and correlated gene expression patterns. Our models accurately predict out-of-sample ECoG networks and perform well even when fit to data from individual subjects, suggesting shared organizing principles across persons. In addition, we identify a set of genes whose brain-wide co-expression is highly correlated with ECoG network organization. Using gene ontology analysis, we show that these same genes are enriched for membrane and ion channel maintenance and function, suggesting a molecular underpinning of ECoG connectivity. Our findings provide fundamental understanding of the factors that influence interregional ECoG networks, and open the possibility for predictive modeling of surgical outcomes in disease. |
2210.12385 | Jiayuan Ding | Dylan Molho, Jiayuan Ding, Zhaoheng Li, Hongzhi Wen, Wenzhuo Tang,
Yixin Wang, Julian Venegas, Wei Jin, Renming Liu, Runze Su, Patrick Danaher,
Robert Yang, Yu Leo Lei, Yuying Xie, Jiliang Tang | Deep Learning in Single-Cell Analysis | 77 pages, 11 figures, 15 tables, deep learning, single-cell analysis | null | null | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Single-cell technologies are revolutionizing the entire field of biology. The
large volumes of data generated by single-cell technologies are
high-dimensional, sparse, heterogeneous, and have complicated dependency
structures, making analyses using conventional machine learning approaches
challenging and impractical. In tackling these challenges, deep learning often
demonstrates superior performance compared to traditional machine learning
methods. In this work, we give a comprehensive survey on deep learning in
single-cell analysis. We first introduce background on single-cell technologies
and their development, as well as fundamental concepts of deep learning
including the most popular deep architectures. We present an overview of the
single-cell analytic pipeline pursued in research applications while noting
divergences due to data sources or specific applications. We then review seven
popular tasks spanning through different stages of the single-cell analysis
pipeline, including multimodal integration, imputation, clustering, spatial
domain identification, cell-type deconvolution, cell segmentation, and
cell-type annotation. Under each task, we describe the most recent developments
in classical and deep learning methods and discuss their advantages and
disadvantages. Deep learning tools and benchmark datasets are also summarized
for each task. Finally, we discuss the future directions and the most recent
challenges. This survey will serve as a reference for biologists and computer
scientists, encouraging collaborations.
| [
{
"created": "Sat, 22 Oct 2022 08:26:41 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Nov 2022 18:40:04 GMT",
"version": "v2"
}
] | 2022-11-08 | [
[
"Molho",
"Dylan",
""
],
[
"Ding",
"Jiayuan",
""
],
[
"Li",
"Zhaoheng",
""
],
[
"Wen",
"Hongzhi",
""
],
[
"Tang",
"Wenzhuo",
""
],
[
"Wang",
"Yixin",
""
],
[
"Venegas",
"Julian",
""
],
[
"Jin",
"Wei",
""
],
[
"Liu",
"Renming",
""
],
[
"Su",
"Runze",
""
],
[
"Danaher",
"Patrick",
""
],
[
"Yang",
"Robert",
""
],
[
"Lei",
"Yu Leo",
""
],
[
"Xie",
"Yuying",
""
],
[
"Tang",
"Jiliang",
""
]
] | Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations. |
2212.10567 | Abdul Wahab | Ehtisham Fazal and Muhammad Sohail Ibrahim and Seongyong Park and
Imran Naseem and Abdul Wahab | Anticancer Peptides Classification using Kernel Sparse Representation
Classifier | null | null | null | null | q-bio.QM cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | Cancer is one of the most challenging diseases because of its complexity,
variability, and diversity of causes. It has been one of the major research
topics over the past decades, yet it is still poorly understood. To this end,
multifaceted therapeutic frameworks are indispensable. \emph{Anticancer
peptides} (ACPs) are the most promising treatment option, but their large-scale
identification and synthesis require reliable prediction methods, which is
still a problem. In this paper, we present an intuitive classification strategy
that differs from the traditional \emph{black box} method and is based on the
well-known statistical theory of \emph{sparse-representation classification}
(SRC). Specifically, we create over-complete dictionary matrices by embedding
the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the
traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver
instead of the computationally expensive \emph{basis pursuit} solver in this
strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is
employed to cope with non-linearity and dimension reduction of the feature
space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is
used to balance the dictionary. The proposed method is evaluated on two
benchmark datasets for well-known statistical parameters and is found to
outperform the existing methods. The results show the highest sensitivity with
the most balanced accuracy, which might be beneficial in understanding
structural and chemical aspects and developing new ACPs. The Google-Colab
implementation of the proposed method is available at the author's GitHub page
(\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}).
| [
{
"created": "Mon, 19 Dec 2022 10:04:47 GMT",
"version": "v1"
}
] | 2022-12-22 | [
[
"Fazal",
"Ehtisham",
""
],
[
"Ibrahim",
"Muhammad Sohail",
""
],
[
"Park",
"Seongyong",
""
],
[
"Naseem",
"Imran",
""
],
[
"Wahab",
"Abdul",
""
]
] | Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC). Specifically, we create over-complete dictionary matrices by embedding the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver instead of the computationally expensive \emph{basis pursuit} solver in this strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available at the author's GitHub page (\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}). |
2312.00796 | Gustavo Sganzerla | Gustavo Sganzerla Martinez, Mansi Dutt, Anuj Kumar, David J Kelvin | Multiple Protein Profiler 1.0 (MPP): A webserver for predicting and
visualizing physiochemical properties of proteins at the proteome level | null | null | null | null | q-bio.BM cs.CE | http://creativecommons.org/licenses/by/4.0/ | Determining the physicochemical properties of a protein can reveal important
insights in their structure, biological functions, stability, and interactions
with other molecules. Although tools for computing properties of proteins
already existed, we could not find a comprehensive tool that enables the
calculations of multiple properties for multiple input proteins on the proteome
level at once. Facing this limitation, we have developed Multiple Protein
Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12
individual properties of multiple proteins in a significant manner. MPP
provides a tabular and graphic visualization of properties of multiple
proteins. The tool is freely accessible at
https://mproteinprofiler.microbiologyandimmunology.dal.ca/
| [
{
"created": "Fri, 17 Nov 2023 16:35:53 GMT",
"version": "v1"
}
] | 2023-12-05 | [
[
"Martinez",
"Gustavo Sganzerla",
""
],
[
"Dutt",
"Mansi",
""
],
[
"Kumar",
"Anuj",
""
],
[
"Kelvin",
"David J",
""
]
] | Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we have developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/ |
1511.09302 | Michael Courtney | Joshua Courtney and Michael Courtney | Evidence for Magnetoreception in Red Drum (Sciaenops ocellatus), Black
Drum (Pogonias cromis), and Sea Catfish (Ariopsis felis) | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past few decades, magnetoreception has been discovered in several
species of teleost and elasmobranch fishes by employing varied experimental
methods including conditioning experiments, observations of alignment with
external fields, and experiments with magnetic deterrents. Biogenic magnetite
has been confirmed to be an important receptor mechanism in some species, but
there is ongoing debate regarding whether other mechanisms are at work. This
paper presents evidence for magnetoreception in three additional species, red
drum (Sciaenops ocellatus), black drum (Pogonias cromis), and sea catfish
(Ariopsis felis), by employing experiments to test whether fish respond
differently to bait on a magnetic hook than on a control. In red drum, the
control hook outcaught the magnetic hook by 32 - 18 for chi-squared = 3.92 and
a P-value of 0.048. Black drum showed a significant attraction for the magnetic
hook, which prevailed over the control hook by 11 - 3 for chi-squared = 4.57
and a P-value of 0.033. Gafftopsail catfish (Bagre marinus) showed no
preference with a 31 - 35 split between magnetic hook and control for
chi-squared = 0.242 and a P-value of 0.623. In a sample of 100 sea catfish in
an analogous experiment using smaller hooks, the control hook was preferred
62-38 for chi-squared = 5.76 and a P-value of < 0.001. Such a simple method for
identifying magnetoreceptive species may quickly expand the number of known
magnetoreceptive species and allow for easier access to magnetoreceptive
species and thus facilitate testing of magnetoreceptive hypotheses.
| [
{
"created": "Mon, 30 Nov 2015 13:39:50 GMT",
"version": "v1"
}
] | 2015-12-01 | [
[
"Courtney",
"Joshua",
""
],
[
"Courtney",
"Michael",
""
]
] | Over the past few decades, magnetoreception has been discovered in several species of teleost and elasmobranch fishes by employing varied experimental methods including conditioning experiments, observations of alignment with external fields, and experiments with magnetic deterrents. Biogenic magnetite has been confirmed to be an important receptor mechanism in some species, but there is ongoing debate regarding whether other mechanisms are at work. This paper presents evidence for magnetoreception in three additional species, red drum (Sciaenops ocellatus), black drum (Pogonias cromis), and sea catfish (Ariopsis felis), by employing experiments to test whether fish respond differently to bait on a magnetic hook than on a control. In red drum, the control hook outcaught the magnetic hook by 32 - 18 for chi-squared = 3.92 and a P-value of 0.048. Black drum showed a significant attraction for the magnetic hook, which prevailed over the control hook by 11 - 3 for chi-squared = 4.57 and a P-value of 0.033. Gafftopsail catfish (Bagre marinus) showed no preference with a 31 - 35 split between magnetic hook and control for chi-squared = 0.242 and a P-value of 0.623. In a sample of 100 sea catfish in an analogous experiment using smaller hooks, the control hook was preferred 62-38 for chi-squared = 5.76 and a P-value of < 0.001. Such a simple method for identifying magnetoreceptive species may quickly expand the number of known magnetoreceptive species and allow for easier access to magnetoreceptive species and thus facilitate testing of magnetoreceptive hypotheses. |
2003.12068 | Robert Schaback | Robert Schaback | Modelling Recovered Cases and Death Probabilities for the COVID-19
Outbreak | 6 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | From March 23rd, the data for the recovered cases of COVID-19 are missing
from the standard repository maintained by the Johns Hopkins University in
collaboration with the WHO. But since data concerning recovered patients are
extremely important for modelling the COVID-19 outbreak, a method for
estimating the missing data is provided and tested. As a byproduct, it produces
estimates for the probabilities to die $k$ days after confirmation, or to
survive after $d$ days.
| [
{
"created": "Thu, 26 Mar 2020 17:29:30 GMT",
"version": "v1"
}
] | 2020-03-30 | [
[
"Schaback",
"Robert",
""
]
] | From March 23rd, the data for the recovered cases of COVID-19 are missing from the standard repository maintained by the Johns Hopkins University in collaboration with the WHO. But since data concerning recovered patients are extremely important for modelling the COVID-19 outbreak, a method for estimating the missing data is provided and tested. As a byproduct, it produces estimates for the probabilities to die $k$ days after confirmation, or to survive after $d$ days. |
2010.11592 | Leena Salmela | Miika Leinonen and Leena Salmela | Extraction of long k-mers using spaced seeds | IEEE/ACM Transactions on Computational Biology and Bioinformatics
(2021). LoMeX is freely available at https://github.com/Denopia/LoMeX | Leinonen, M., Salmela, L. (2021): Extraction of long k-mers using
spaced seeds. IEEE/ACM Transactions on Computational Biology and
Bioinformatics | 10.1109/TCBB.2021.3113131 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The extraction of k-mers from sequencing reads is an important task in many
bioinformatics applications, such as all DNA sequence analysis methods based on
de Bruijn graphs. These methods tend to be more accurate when the used k-mers
are unique in the analyzed DNA, and thus the use of longer k-mers is preferred.
When the read lengths of short read sequencing technologies increase, the error
rate will become the determining factor for the largest possible value of k.
Here we propose LoMeX which uses spaced seeds to extract long k-mers accurately
even in the presence of sequencing errors. Our experiments show that LoMeX can
extract long k-mers from current Illumina reads with a higher recall than a
standard k-mer counting tool. Furthermore, our experiments on simulated data
show that when the read length further increases, the performance of standard
k-mer counters declines, whereas LoMeX still extracts long k-mers successfully.
| [
{
"created": "Thu, 22 Oct 2020 10:47:35 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Oct 2020 12:45:27 GMT",
"version": "v2"
}
] | 2021-10-01 | [
[
"Leinonen",
"Miika",
""
],
[
"Salmela",
"Leena",
""
]
] | The extraction of k-mers from sequencing reads is an important task in many bioinformatics applications, such as all DNA sequence analysis methods based on de Bruijn graphs. These methods tend to be more accurate when the used k-mers are unique in the analyzed DNA, and thus the use of longer k-mers is preferred. When the read lengths of short read sequencing technologies increase, the error rate will become the determining factor for the largest possible value of k. Here we propose LoMeX which uses spaced seeds to extract long k-mers accurately even in the presence of sequencing errors. Our experiments show that LoMeX can extract long k-mers from current Illumina reads with a higher recall than a standard k-mer counting tool. Furthermore, our experiments on simulated data show that when the read length further increases, the performance of standard k-mer counters declines, whereas LoMeX still extracts long k-mers successfully. |
2308.05536 | Tiantian He | Tiantian He, Elinor Thompson, Anna Schroder, Neil P. Oxtoby, Ahmed
Abdulaal, Frederik Barkhof, Daniel C. Alexander | A coupled-mechanisms modelling framework for neurodegeneration | MICCAI 2023 | null | null | null | q-bio.QM cs.CE q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational models of neurodegeneration aim to emulate the evolving pattern
of pathology in the brain during neurodegenerative disease, such as Alzheimer's
disease. Previous studies have made specific choices on the mechanisms of
pathology production and diffusion, or assume that all the subjects lie on the
same disease progression trajectory. However, the complexity and heterogeneity
of neurodegenerative pathology suggests that multiple mechanisms may contribute
synergistically with complex interactions, meanwhile the degree of contribution
of each mechanism may vary among individuals. We thus put forward a
coupled-mechanisms modelling framework which non-linearly combines the
network-topology-informed pathology appearance with the process of pathology
spreading within a dynamic modelling system. We account for the heterogeneity
of disease by fitting the model at the individual level, allowing the
epicenters and rate of progression to vary among subjects. We construct a
Bayesian model selection framework to account for feature importance and
parameter uncertainty. This provides a combination of mechanisms that best
explains the observations for each individual from the ADNI dataset. With the
obtained distribution of mechanism importance for each subject, we are able to
identify subgroups of patients sharing similar combinations of apparent
mechanisms.
| [
{
"created": "Thu, 10 Aug 2023 12:34:25 GMT",
"version": "v1"
}
] | 2023-08-11 | [
[
"He",
"Tiantian",
""
],
[
"Thompson",
"Elinor",
""
],
[
"Schroder",
"Anna",
""
],
[
"Oxtoby",
"Neil P.",
""
],
[
"Abdulaal",
"Ahmed",
""
],
[
"Barkhof",
"Frederik",
""
],
[
"Alexander",
"Daniel C.",
""
]
] | Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer's disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. This provides a combination of mechanisms that best explains the observations for each individual from the ADNI dataset. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms. |
2009.13900 | John McBride | John M McBride and Tsvi Tlusty | Structural asymmetry along protein sequences and co-translational
folding | 10 pages, 5 figures | null | null | null | q-bio.BM physics.bio-ph q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | Proteins are translated from the N- to the C-terminus, raising the basic
question of how this innate directionality affects their evolution. To explore
this question, we analyze 16,200 structures from the protein data bank (PDB).
We find remarkable enrichment of $\alpha$-helices at the C terminus and
$\beta$-strands at the N terminus. Furthermore, this $\alpha$-$\beta$ asymmetry
correlates with sequence length and contact order, both determinants of folding
rate, hinting at possible links to co-translational folding (CTF). Hence, we
propose the 'slowest-first' scheme, whereby protein sequences evolved
structural asymmetry to accelerate CTF: the slowest of the
cooperatively-folding segments are positioned near the N terminus so they have
more time to fold during translation. A phenomenological model predicts that
CTF can be accelerated by asymmetry, up to double the rate, when folding time
is commensurate with translation time; analysis of the PDB reveals that
structural asymmetry is indeed maximal in this regime. This correspondence is
greater in prokaryotes, which generally require faster protein production.
Altogether, this indicates that accelerating CTF is a substantial evolutionary
force whose interplay with stability and functionality is encoded in sequence
asymmetry.
| [
{
"created": "Tue, 29 Sep 2020 09:47:57 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Oct 2020 06:37:43 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Mar 2021 11:32:23 GMT",
"version": "v3"
}
] | 2021-03-17 | [
[
"McBride",
"John M",
""
],
[
"Tlusty",
"Tsvi",
""
]
] | Proteins are translated from the N- to the C-terminus, raising the basic question of how this innate directionality affects their evolution. To explore this question, we analyze 16,200 structures from the protein data bank (PDB). We find remarkable enrichment of $\alpha$-helices at the C terminus and $\beta$-strands at the N terminus. Furthermore, this $\alpha$-$\beta$ asymmetry correlates with sequence length and contact order, both determinants of folding rate, hinting at possible links to co-translational folding (CTF). Hence, we propose the 'slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate CTF: the slowest of the cooperatively-folding segments are positioned near the N terminus so they have more time to fold during translation. A phenomenological model predicts that CTF can be accelerated by asymmetry, up to double the rate, when folding time is commensurate with translation time; analysis of the PDB reveals that structural asymmetry is indeed maximal in this regime. This correspondence is greater in prokaryotes, which generally require faster protein production. Altogether, this indicates that accelerating CTF is a substantial evolutionary force whose interplay with stability and functionality is encoded in sequence asymmetry. |
2309.10065 | Alec Reinhardt | Suprateek Kundu, Alec Reinhardt, Serena Song, Joo Han, M. Lawson
Meadows, Bruce Crosson, Venkatagiri Krishnamurthy | Bayesian longitudinal tensor response regression for modeling
neuroplasticity | 28 pages, 8 figures, 6 tables | null | null | null | q-bio.NC cs.LG eess.IV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A major interest in longitudinal neuroimaging studies involves investigating
voxel-level neuroplasticity due to treatment and other factors across visits.
However, traditional voxel-wise methods are beset with several pitfalls, which
can compromise the accuracy of these approaches. We propose a novel Bayesian
tensor response regression approach for longitudinal imaging data, which pools
information across spatially-distributed voxels to infer significant changes
while adjusting for covariates. The proposed method, which is implemented using
Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to
reduce dimensionality and preserve spatial configurations of voxels when
estimating coefficients. It also enables feature selection via joint credible
regions which respect the shape of the posterior distributions for more
accurate inference. In addition to group level inferences, the method is able
to infer individual-level neuroplasticity, allowing for examination of
personalized disease or recovery trajectories. The advantages of the proposed
approach in terms of prediction and feature selection over voxel-wise
regression are highlighted via extensive simulation studies. Subsequently, we
apply the approach to a longitudinal Aphasia dataset consisting of task
functional MRI images from a group of subjects who were administered either a
control intervention or intention treatment at baseline and were followed up
over subsequent visits. Our analysis revealed that while the control therapy
showed long-term increases in brain activity, the intention treatment produced
predominantly short-term changes, both of which were concentrated in distinct
localized regions. In contrast, the voxel-wise regression failed to detect any
significant neuroplasticity after multiplicity adjustments, which is
biologically implausible and implies lack of power.
| [
{
"created": "Tue, 12 Sep 2023 18:48:18 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Oct 2023 17:30:41 GMT",
"version": "v2"
}
] | 2023-10-19 | [
[
"Kundu",
"Suprateek",
""
],
[
"Reinhardt",
"Alec",
""
],
[
"Song",
"Serena",
""
],
[
"Han",
"Joo",
""
],
[
"Meadows",
"M. Lawson",
""
],
[
"Crosson",
"Bruce",
""
],
[
"Krishnamurthy",
"Venkatagiri",
""
]
] | A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially-distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power. |
2109.02124 | Lum Ramabaja | Lum Ramabaja | The Koha Code: A Biological Theory of Memory | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | This work introduces the Koha model, a new theory that aims to explain two
unresolved phenomena within biological neural networks: How information is
processed and stored within neural circuits, and how neurons learn to become
pattern detectors. In the Koha model, the dendritic spines of a neuron serve as
computational units that scan for precise spike patterns in their synaptic
inputs. The model proposes the existence of a temporal code within each
dendritic spine, which is used for the dampening or amplification of signals,
depending on the temporal information of incoming spike trains. Compelling
evidence is provided and a concrete process is described for how signal
filtration occurs within spine necks. A competitive learning algorithm is then
proposed that describes how neurons use their internal temporal codes to become
pattern detectors.
| [
{
"created": "Sun, 5 Sep 2021 16:57:43 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Sep 2021 16:03:39 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Sep 2021 17:55:15 GMT",
"version": "v3"
}
] | 2021-09-09 | [
[
"Ramabaja",
"Lum",
""
]
] | This work introduces the Koha model, a new theory that aims to explain two unresolved phenomena within biological neural networks: How information is processed and stored within neural circuits, and how neurons learn to become pattern detectors. In the Koha model, the dendritic spines of a neuron serve as computational units that scan for precise spike patterns in their synaptic inputs. The model proposes the existence of a temporal code within each dendritic spine, which is used for the dampening or amplification of signals, depending on the temporal information of incoming spike trains. Compelling evidence is provided and a concrete process is described for how signal filtration occurs within spine necks. A competitive learning algorithm is then proposed that describes how neurons use their internal temporal codes to become pattern detectors. |
2202.11745 | Abigail Plummer | Abigail Plummer, Mara Freilich, Roberto Benzi, Chang Jae Choi, Lisa
Sudek, Alexandra Z. Worden, Federico Toschi, and Amala Mahadevan | Oceanic frontal divergence alters phytoplankton competition and
distribution | 14 pages, 5 figures; SI: 13 pages, 13 figures | Journal of Geophysical Research: Oceans, 128, e2023JC019902 (2023) | 10.1029/2023JC019902 | null | q-bio.PE physics.ao-ph | http://creativecommons.org/licenses/by/4.0/ | Ecological interactions among phytoplankton occur in a moving fluid
environment. Oceanic flows can modulate the competition and coexistence between
phytoplankton populations, which in turn can affect ecosystem function and
biogeochemical cycling. We explore the impact of submesoscale velocity
gradients on phytoplankton ecology using observations, simulations, and theory.
Observations reveal that the relative abundance of Synechoccocus oligotypes
varies on 1--10 km scales at an ocean front with submesoscale velocity
gradients at the same scale. Simulations in realistic flow fields demonstrate
that regions of divergence in the horizontal flow field can substantially
modify ecological competition and dispersal on timescales of hours to days.
Regions of positive (negative) divergence provide an advantage (disadvantage)
to local populations, resulting in up to ~20% variation in community
composition in our model. We propose that submesoscale divergence is a
plausible contributor to observed taxonomic variability at oceanic fronts, and
can lead to regional variability in community composition.
| [
{
"created": "Wed, 23 Feb 2022 19:15:47 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Oct 2022 18:11:46 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Sep 2023 22:44:32 GMT",
"version": "v3"
}
] | 2023-09-27 | [
[
"Plummer",
"Abigail",
""
],
[
"Freilich",
"Mara",
""
],
[
"Benzi",
"Roberto",
""
],
[
"Choi",
"Chang Jae",
""
],
[
"Sudek",
"Lisa",
""
],
[
"Worden",
"Alexandra Z.",
""
],
[
"Toschi",
"Federico",
""
],
[
"Mahadevan",
"Amala",
""
]
] | Ecological interactions among phytoplankton occur in a moving fluid environment. Oceanic flows can modulate the competition and coexistence between phytoplankton populations, which in turn can affect ecosystem function and biogeochemical cycling. We explore the impact of submesoscale velocity gradients on phytoplankton ecology using observations, simulations, and theory. Observations reveal that the relative abundance of Synechoccocus oligotypes varies on 1--10 km scales at an ocean front with submesoscale velocity gradients at the same scale. Simulations in realistic flow fields demonstrate that regions of divergence in the horizontal flow field can substantially modify ecological competition and dispersal on timescales of hours to days. Regions of positive (negative) divergence provide an advantage (disadvantage) to local populations, resulting in up to ~20% variation in community composition in our model. We propose that submesoscale divergence is a plausible contributor to observed taxonomic variability at oceanic fronts, and can lead to regional variability in community composition. |
1511.04249 | Benjamin Pfeuty | Benjamin Pfeuty and Kunihiko Kaneko | Requirements for efficient cell-type proportioning: regulatory
timescales, stochasticity and lateral inhibition | null | null | 10.1088/1478-3975/13/2/026007 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The proper functioning of multicellular organisms requires the robust
establishment of precise proportions between distinct cell-types. This
developmental differentiation process typically involves intracellular
regulatory and stochastic mechanisms to generate cell-fate diversity as well as
intercellular mechanisms to coordinate cell-fate decisions at tissue level. We
thus surmise that key insights about the developmental regulation of cell-type
proportion can be captured by the modeling study of clustering dynamics in
population of inhibitory-coupled noisy bistable systems. This general class of
dynamical system is shown to exhibit a very stable two-cluster state, but also
frustrated relaxation, collective oscillations or steady-state hopping which
prevents from timely and reliably reaching a robust and well-proportioned
clustered state. To circumvent these obstacles or to avoid fine-tuning, we
highlight a general strategy based on dual-time positive feedback loops, such
as mediated through transcriptional versus epigenetic mechanisms, which
improves proportion regulation by coordinating early and flexible lineage
priming with late and firm commitment. This result sheds new light on the
respective and cooperative roles of multiple regulatory feedback, stochasticity
and lateral inhibition in developmental dynamics.
| [
{
"created": "Fri, 13 Nov 2015 11:46:19 GMT",
"version": "v1"
}
] | 2016-06-22 | [
[
"Pfeuty",
"Benjamin",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | The proper functioning of multicellular organisms requires the robust establishment of precise proportions between distinct cell-types. This developmental differentiation process typically involves intracellular regulatory and stochastic mechanisms to generate cell-fate diversity as well as intercellular mechanisms to coordinate cell-fate decisions at tissue level. We thus surmise that key insights about the developmental regulation of cell-type proportion can be captured by the modeling study of clustering dynamics in population of inhibitory-coupled noisy bistable systems. This general class of dynamical system is shown to exhibit a very stable two-cluster state, but also frustrated relaxation, collective oscillations or steady-state hopping which prevents from timely and reliably reaching a robust and well-proportioned clustered state. To circumvent these obstacles or to avoid fine-tuning, we highlight a general strategy based on dual-time positive feedback loops, such as mediated through transcriptional versus epigenetic mechanisms, which improves proportion regulation by coordinating early and flexible lineage priming with late and firm commitment. This result sheds new light on the respective and cooperative roles of multiple regulatory feedback, stochasticity and lateral inhibition in developmental dynamics. |
1207.7034 | Steven Kelk | Steven Kelk and Celine Scornavacca | Towards the fixed parameter tractability of constructing minimal
phylogenetic networks from arbitrary sets of nonbinary trees | have fixed a number of small typo's etc | null | null | null | q-bio.PE cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has remained an open question for some time whether, given a set of not
necessarily binary (i.e. "nonbinary") trees T on a set of taxa X, it is
possible to determine in time f(r).poly(m) whether there exists a phylogenetic
network that displays all the trees in T, where r refers to the reticulation
number of the network and m=|X|+|T|. Here we show that this holds if one or
both of the following conditions holds: (1) |T| is bounded by a function of r;
(2) the maximum degree of the nodes in T is bounded by a function of r. These
sufficient conditions absorb and significantly extend known special cases,
namely when all the trees in T are binary, or T contains exactly two nonbinary
trees. We believe this result is an important step towards settling the issue
for an arbitrarily large and complex set of nonbinary trees. For completeness
we show that the problem is certainly solveable in polynomial time.
| [
{
"created": "Mon, 30 Jul 2012 18:41:21 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Aug 2012 16:08:14 GMT",
"version": "v2"
}
] | 2012-08-03 | [
[
"Kelk",
"Steven",
""
],
[
"Scornavacca",
"Celine",
""
]
] | It has remained an open question for some time whether, given a set of not necessarily binary (i.e. "nonbinary") trees T on a set of taxa X, it is possible to determine in time f(r).poly(m) whether there exists a phylogenetic network that displays all the trees in T, where r refers to the reticulation number of the network and m=|X|+|T|. Here we show that this holds if one or both of the following conditions holds: (1) |T| is bounded by a function of r; (2) the maximum degree of the nodes in T is bounded by a function of r. These sufficient conditions absorb and significantly extend known special cases, namely when all the trees in T are binary, or T contains exactly two nonbinary trees. We believe this result is an important step towards settling the issue for an arbitrarily large and complex set of nonbinary trees. For completeness we show that the problem is certainly solveable in polynomial time. |
2305.07033 | Mohammadreza Esmaeilidehkordi | Mobina Naeemi, Mohamad Reza Esmaeili, Iraj Abedi | Attention U-net approach in predicting Intensity Modulated Radiation
Therapy dose distribution in brain glioma tumor | null | null | null | null | q-bio.QM eess.SP physics.med-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Today, intensity-modulated radiation therapy (IMRT) is one of the methods
used to treat brain tumors. In conventional treatment planning methods, after
identifying planning target volume (PTV), and organs at risk (OARs), and
determining the limitations for them to receive radiation, the dose
distribution is performed based on optimization algorithms, which is usually a
time-consuming method. In this article, artificial intelligence is used to
acquire the knowledge used in the treatment planning of past patients and to
plan for new patients to speed up the process of treatment planning and
determination of the appropriate dose distribution. In this paper, using deep
learning algorithms, two different approaches are studied to predict dose
distribution and compared with actual dose distributions. In the first method,
only the images containing PTV and the distribution of the corresponding doses
are used to train the convolutional neural network, but in the second one, in
addition to PTV, the contours of four OARs are also used to introduce the
network. The results show that the performance of both methods on test patients
have high accuracy and in comparison with each other almost have the same
results and high speed to design the dose distribution. Because the Only-PTV
method does not have the process of OARs identifying, applying it in designing
the dose distribution will be much faster than using the PTV-OARs method in the
whole of treatment planning.
| [
{
"created": "Wed, 10 May 2023 14:45:36 GMT",
"version": "v1"
}
] | 2023-05-15 | [
[
"Naeemi",
"Mobina",
""
],
[
"Esmaeili",
"Mohamad Reza",
""
],
[
"Abedi",
"Iraj",
""
]
] | Today, intensity-modulated radiation therapy (IMRT) is one of the methods used to treat brain tumors. In conventional treatment planning methods, after identifying planning target volume (PTV), and organs at risk (OARs), and determining the limitations for them to receive radiation, the dose distribution is performed based on optimization algorithms, which is usually a time-consuming method. In this article, artificial intelligence is used to acquire the knowledge used in the treatment planning of past patients and to plan for new patients to speed up the process of treatment planning and determination of the appropriate dose distribution. In this paper, using deep learning algorithms, two different approaches are studied to predict dose distribution and compared with actual dose distributions. In the first method, only the images containing PTV and the distribution of the corresponding doses are used to train the convolutional neural network, but in the second one, in addition to PTV, the contours of four OARs are also used to introduce the network. The results show that the performance of both methods on test patients have high accuracy and in comparison with each other almost have the same results and high speed to design the dose distribution. Because the Only-PTV method does not have the process of OARs identifying, applying it in designing the dose distribution will be much faster than using the PTV-OARs method in the whole of treatment planning. |
0707.2659 | Arunava Goswami | Dipankar Seth, Mritunjay Mandal, Nitai Debnath, Ayesha Rahman, N. K.
Sasmal, Sunit Mukhopadhyaya, and Arunava Goswami | Control of rodent sleeping sickness disease by surface functionalized
amorphous nanosilica | 02 pages, 01 table, 781 words | null | null | null | q-bio.MN q-bio.BM | null | Wild animals, pets, zoo animals and mammals of veterinary importance heavily
suffer from trypanosomiasis. Drugs with serious side effects are currently
mainstay of therapies used by veterinarians. Trypanosomiasis is caused by
Trypanosoma sp. leading to sleeping sickness in humans. Surface modified
(hydrophobic and lipophilic) amorphous nanoporous silica molecules could be
effectively used as therapeutic drug for combating trypanosomiasis. The
amorphous nanosilica was developed by top-down approach using volcanic soil
derived silica (Advasan; 50- 60 nm size with 3-10 nm inner pore size range) and
diatomaceous earth (FS; 60-80 nm size with 3-5 nm inner pore size range) as
source materials. According to WHO and USDA standards amorphous silica has long
been used as feed additives for several veterinary industries and considered to
be safe for human consumption. The basic mechanism of action of these
nanosilica molecules is mediated by the physical absorption of HDL components
in the lipophilic nanopores of nanosilica. This reduces the supply of the host
derived cholesterol, thus limiting the growth of the Trypanosoma sp. in vivo.
| [
{
"created": "Wed, 18 Jul 2007 07:38:06 GMT",
"version": "v1"
}
] | 2007-07-19 | [
[
"Seth",
"Dipankar",
""
],
[
"Mandal",
"Mritunjay",
""
],
[
"Debnath",
"Nitai",
""
],
[
"Rahman",
"Ayesha",
""
],
[
"Sasmal",
"N. K.",
""
],
[
"Mukhopadhyaya",
"Sunit",
""
],
[
"Goswami",
"Arunava",
""
]
] | Wild animals, pets, zoo animals and mammals of veterinary importance heavily suffer from trypanosomiasis. Drugs with serious side effects are currently mainstay of therapies used by veterinarians. Trypanosomiasis is caused by Trypanosoma sp. leading to sleeping sickness in humans. Surface modified (hydrophobic and lipophilic) amorphous nanoporous silica molecules could be effectively used as therapeutic drug for combating trypanosomiasis. The amorphous nanosilica was developed by top-down approach using volcanic soil derived silica (Advasan; 50- 60 nm size with 3-10 nm inner pore size range) and diatomaceous earth (FS; 60-80 nm size with 3-5 nm inner pore size range) as source materials. According to WHO and USDA standards amorphous silica has long been used as feed additives for several veterinary industries and considered to be safe for human consumption. The basic mechanism of action of these nanosilica molecules is mediated by the physical absorption of HDL components in the lipophilic nanopores of nanosilica. This reduces the supply of the host derived cholesterol, thus limiting the growth of the Trypanosoma sp. in vivo. |
2401.03483 | Afifurrahman Afifurrahman Afif | Afifurrahman, Mohd Hafiz Mohd, Farah Aini Abdullah | The interplay of common noise and finite pulses on biological neurons | 11 pages, 8 figures | null | 10.5614/cbms.2023.6.2.5 | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | The response of neurons is highly sensitive to the stimulus. The stimulus can
be associated with a direct injection in vitro experimentation (e.g., time
dependent and independent inputs); or post-synaptic potentials resulting from
the interaction of many neurons. A typical incoming stimulus resembles a noise
which in principle can be described as a random variable. In computational
neuroscience, the noise has been extensively studied for different setups. In
this study, we investigate the effect of noisy inputs in a minimal network of
two identical leaky integrate-and-fire (LIF) neurons interacting with finite
pulses. In particular, we consider a Gaussian white noise as a standard
function for stochastic modelling of neurons, while taking into account the
pulse width as an elementary component for the signal transmission. By
exploring the role of noise and finite pulses, the two neurons show a
synchronous spiking behaviour characterized by fluctuations in the interspike
intervals. Above some critical values the synchronous regime collapses onto
asynchronous dynamics. The abrupt change in such dynamics is accompanied by a
hysteresis, i.e., the coexistence of synchronous and asynchronous firing
behaviour.
| [
{
"created": "Sun, 7 Jan 2024 13:50:59 GMT",
"version": "v1"
}
] | 2024-01-09 | [
[
"Afifurrahman",
"",
""
],
[
"Mohd",
"Mohd Hafiz",
""
],
[
"Abdullah",
"Farah Aini",
""
]
] | The response of neurons is highly sensitive to the stimulus. The stimulus can be associated with a direct injection in vitro experimentation (e.g., time dependent and independent inputs); or post-synaptic potentials resulting from the interaction of many neurons. A typical incoming stimulus resembles a noise which in principle can be described as a random variable. In computational neuroscience, the noise has been extensively studied for different setups. In this study, we investigate the effect of noisy inputs in a minimal network of two identical leaky integrate-and-fire (LIF) neurons interacting with finite pulses. In particular, we consider a Gaussian white noise as a standard function for stochastic modelling of neurons, while taking into account the pulse width as an elementary component for the signal transmission. By exploring the role of noise and finite pulses, the two neurons show a synchronous spiking behaviour characterized by fluctuations in the interspike intervals. Above some critical values the synchronous regime collapses onto asynchronous dynamics. The abrupt change in such dynamics is accompanied by a hysteresis, i.e., the coexistence of synchronous and asynchronous firing behaviour. |
2308.09482 | Daniel Flam-Shepherd | Daniel Flam-Shepherd, Kevin Zhu and Al\'an Aspuru-Guzik | Atom-by-atom protein generation and beyond with language models | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Protein language models learn powerful representations directly from
sequences of amino acids. However, they are constrained to generate proteins
with only the set of amino acids represented in their vocabulary. In contrast,
chemical language models learn atom-level representations of smaller molecules
that include every atom, bond, and ring. In this work, we show that chemical
language models can learn atom-level representations of proteins enabling
protein generation unconstrained to the standard genetic code and far beyond
it. In doing so, we show that language models can generate entire proteins atom
by atom -- effectively learning the multiple hierarchical layers of molecular
information that define proteins from their primary sequence to their
secondary, and tertiary structure. We demonstrate language models are able to
explore beyond protein space -- generating proteins with modified sidechains
that form unnatural amino acids. Even further, we find that language models can
explore chemical space and protein space simultaneously and generate novel
examples of protein-drug conjugates. The results demonstrate the potential for
biomolecular design at the atom level using language models.
| [
{
"created": "Wed, 16 Aug 2023 17:56:17 GMT",
"version": "v1"
}
] | 2023-08-21 | [
[
"Flam-Shepherd",
"Daniel",
""
],
[
"Zhu",
"Kevin",
""
],
[
"Aspuru-Guzik",
"Alán",
""
]
] | Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary. In contrast, chemical language models learn atom-level representations of smaller molecules that include every atom, bond, and ring. In this work, we show that chemical language models can learn atom-level representations of proteins enabling protein generation unconstrained to the standard genetic code and far beyond it. In doing so, we show that language models can generate entire proteins atom by atom -- effectively learning the multiple hierarchical layers of molecular information that define proteins from their primary sequence to their secondary, and tertiary structure. We demonstrate language models are able to explore beyond protein space -- generating proteins with modified sidechains that form unnatural amino acids. Even further, we find that language models can explore chemical space and protein space simultaneously and generate novel examples of protein-drug conjugates. The results demonstrate the potential for biomolecular design at the atom level using language models. |
2111.04738 | Eduardo Conde-Sousa | Eduardo Conde-Sousa, Jo\~ao Vale, Ming Feng, Kele Xu, Yin Wang,
Vincenzo Della Mea, David La Barbera, Ehsan Montahaei, Mahdieh Soleymani
Baghshah, Andreas Turzynski, Jacob Gildenblat, Eldad Klaiman, Yiyu Hong,
Guilherme Aresta, Teresa Ara\'ujo, Paulo Aguiar, Catarina Eloy, Ant\'onio
Pol\'onia | HEROHE Challenge: assessing HER2 status in breast cancer without
immunohistochemistry or in situ hybridization | null | null | 10.3390/jimaging8080213 | null | q-bio.QM cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Breast cancer is the most common malignancy in women, being responsible for
more than half a million deaths every year. As such, early and accurate
diagnosis is of paramount importance. Human expertise is required to diagnose
and correctly classify breast cancer and define appropriate therapy, which
depends on the evaluation of the expression of different biomarkers such as the
transmembrane protein receptor HER2. This evaluation requires several steps,
including special techniques such as immunohistochemistry or in situ
hybridization to assess HER2 status. With the goal of reducing the number of
steps and human bias in diagnosis, the HEROHE Challenge was organized, as a
parallel event of the 16th European Congress on Digital Pathology, aiming to
automate the assessment of the HER2 status based only on hematoxylin and eosin
stained tissue sample of invasive breast cancer. Methods to assess HER2 status
were presented by 21 teams worldwide and the results achieved by some of the
proposed methods open potential perspectives to advance the state-of-the-art.
| [
{
"created": "Mon, 8 Nov 2021 13:39:41 GMT",
"version": "v1"
}
] | 2022-08-08 | [
[
"Conde-Sousa",
"Eduardo",
""
],
[
"Vale",
"João",
""
],
[
"Feng",
"Ming",
""
],
[
"Xu",
"Kele",
""
],
[
"Wang",
"Yin",
""
],
[
"Della Mea",
"Vincenzo",
""
],
[
"La Barbera",
"David",
""
],
[
"Montahaei",
"Ehsan",
""
],
[
"Baghshah",
"Mahdieh Soleymani",
""
],
[
"Turzynski",
"Andreas",
""
],
[
"Gildenblat",
"Jacob",
""
],
[
"Klaiman",
"Eldad",
""
],
[
"Hong",
"Yiyu",
""
],
[
"Aresta",
"Guilherme",
""
],
[
"Araújo",
"Teresa",
""
],
[
"Aguiar",
"Paulo",
""
],
[
"Eloy",
"Catarina",
""
],
[
"Polónia",
"António",
""
]
] | Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art. |
1503.02903 | Tomasz Rutkowski | Moonjeong Chang and Tomasz M. Rutkowski | Two-step Input Spatial Auditory BCI for Japanese Kana Characters | 7 pages, 2 figures, accepted for publication in Advances in Cognitive
Neurodynamics Volume 5 -- Proceedings of the 5th International Conference on
Cognitive Neurodynamics (ICCN 2015) | null | null | null | q-bio.NC cs.HC | http://creativecommons.org/licenses/by-nc-sa/3.0/ | We present an auditory stimulus optimization and a pilot study of a two-step
input speller application combined with a spatial auditory brain-computer
interface (saBCI) for paralyzed users. The application has been developed for
45, out of 48 defining the full set, Japanese kana characters in a two-step
input procedure setting for an easy-to-use BCI-speller interface. The user
first selects the representative letter of a subset, defining the second step.
In the second step, the final choice is made. At each interfacing step, the
choices are classified based on the P300 event related potential (ERP)
responses captured in the EEG, as in the classic oddball paradigm. The BCI
online experiment and EEG responses classification results of the pilot study
confirm the effectiveness of the proposed spelling method.
| [
{
"created": "Tue, 10 Mar 2015 13:51:34 GMT",
"version": "v1"
}
] | 2015-03-11 | [
[
"Chang",
"Moonjeong",
""
],
[
"Rutkowski",
"Tomasz M.",
""
]
] | We present an auditory stimulus optimization and a pilot study of a two-step input speller application combined with a spatial auditory brain-computer interface (saBCI) for paralyzed users. The application has been developed for 45, out of 48 defining the full set, Japanese kana characters in a two-step input procedure setting for an easy-to-use BCI-speller interface. The user first selects the representative letter of a subset, defining the second step. In the second step, the final choice is made. At each interfacing step, the choices are classified based on the P300 event related potential (ERP) responses captured in the EEG, as in the classic oddball paradigm. The BCI online experiment and EEG responses classification results of the pilot study confirm the effectiveness of the proposed spelling method. |
2102.04283 | Yingfang Yuan | Yingfang Yuan, Wenjun Wang, Wei Pang | A Systematic Comparison Study on Hyperparameter Optimisation of Graph
Neural Networks for Molecular Property Prediction | null | null | 10.1145/3449639.3459370 | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Graph neural networks (GNNs) have been proposed for a wide range of
graph-related learning tasks. In particular, in recent years, an increasing
number of GNN systems were applied to predict molecular properties. However, a
direct impediment is to select appropriate hyperparameters to achieve
satisfactory performance with lower computational cost. Meanwhile, many
molecular datasets are far smaller than many other datasets in typical deep
learning applications. Most hyperparameter optimization (HPO) methods have not
been explored in terms of their efficiencies on such small datasets in the
molecular domain. In this paper, we conducted a theoretical analysis of common
and specific features for two state-of-the-art and popular algorithms for HPO:
TPE and CMA-ES, and we compared them with random search (RS), which is used as
a baseline. Experimental studies are carried out on several benchmarks in
MoleculeNet, from different perspectives to investigate the impact of RS, TPE,
and CMA-ES on HPO of GNNs for molecular property prediction. In our
experiments, we concluded that RS, TPE, and CMA-ES have their individual
advantages in tackling different specific molecular problems. Finally, we
believe our work will motivate further research on GNN as applied to molecular
machine learning problems in chemistry and materials sciences.
| [
{
"created": "Mon, 8 Feb 2021 15:40:50 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Apr 2021 22:33:33 GMT",
"version": "v2"
},
{
"created": "Thu, 22 Apr 2021 00:46:07 GMT",
"version": "v3"
}
] | 2021-04-23 | [
[
"Yuan",
"Yingfang",
""
],
[
"Wang",
"Wenjun",
""
],
[
"Pang",
"Wei",
""
]
] | Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied to predict molecular properties. However, a direct impediment is to select appropriate hyperparameters to achieve satisfactory performance with lower computational cost. Meanwhile, many molecular datasets are far smaller than many other datasets in typical deep learning applications. Most hyperparameter optimization (HPO) methods have not been explored in terms of their efficiencies on such small datasets in the molecular domain. In this paper, we conducted a theoretical analysis of common and specific features for two state-of-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline. Experimental studies are carried out on several benchmarks in MoleculeNet, from different perspectives to investigate the impact of RS, TPE, and CMA-ES on HPO of GNNs for molecular property prediction. In our experiments, we concluded that RS, TPE, and CMA-ES have their individual advantages in tackling different specific molecular problems. Finally, we believe our work will motivate further research on GNN as applied to molecular machine learning problems in chemistry and materials sciences. |
2103.12132 | Alanna Hoyer-Leitzel | Alanna Hoyer-Leitzel and Sarah Iams | Impulsive fire disturbance in a savanna model: Tree-grass coexistence
states, multiple stable system states, and resilience | 22 pages, 9 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Savanna ecosystems are shaped by the frequency and intensity of regular
fires. We model savannas via an ordinary differential equation (ODE) encoding a
one-sided inhibitory Lotka-Volterra interaction between trees and grass. By
applying fire as a discrete disturbance, we create an impulsive dynamical
system that allows us to identify the impact of variation in fire frequency and
intensity. The model exhibits three different bistability regimes: between
savanna and grassland; two savanna states; and savanna and woodland. The
impulsive model reveals rich bifurcation structures in response to changes in
fire intensity and frequency -- structures that are largely invisible to
analogous ODE models with continuous fire. In addition, by using the amount of
grass as an example of a socially-valued function of the system state, we
examine the resilience of the social value to different disturbance regimes. We
find that large transitions ("tipping") in the valued quantity can be triggered
by small changes in disturbance regime.
| [
{
"created": "Mon, 22 Mar 2021 18:54:45 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Sep 2021 17:09:26 GMT",
"version": "v2"
}
] | 2021-09-13 | [
[
"Hoyer-Leitzel",
"Alanna",
""
],
[
"Iams",
"Sarah",
""
]
] | Savanna ecosystems are shaped by the frequency and intensity of regular fires. We model savannas via an ordinary differential equation (ODE) encoding a one-sided inhibitory Lotka-Volterra interaction between trees and grass. By applying fire as a discrete disturbance, we create an impulsive dynamical system that allows us to identify the impact of variation in fire frequency and intensity. The model exhibits three different bistability regimes: between savanna and grassland; two savanna states; and savanna and woodland. The impulsive model reveals rich bifurcation structures in response to changes in fire intensity and frequency -- structures that are largely invisible to analogous ODE models with continuous fire. In addition, by using the amount of grass as an example of a socially-valued function of the system state, we examine the resilience of the social value to different disturbance regimes. We find that large transitions ("tipping") in the valued quantity can be triggered by small changes in disturbance regime. |
0705.4328 | Frederick Matsen IV | Frederick A. Matsen, Elchanan Mossel and Mike Steel | Mixed-up trees: the structure of phylogenetic mixtures | null | null | null | null | q-bio.PE | null | In this paper we apply new geometric and combinatorial methods to the study
of phylogenetic mixtures. The focus of the geometric approach is to describe
the geometry of phylogenetic mixture distributions for the two state random
cluster model, which is a generalization of the two state symmetric (CFN)
model. In particular, we show that the set of mixture distributions forms a
convex polytope and we calculate its dimension; corollaries include a simple
criterion for when a mixture of branch lengths on the star tree can mimic the
site pattern frequency vector of a resolved quartet tree. Furthermore, by
computing volumes of polytopes we can clarify how ``common'' non-identifiable
mixtures are under the CFN model. We also present a new combinatorial result
which extends any identifiability result for a specific pair of trees of size
six to arbitrary pairs of trees. Next we present a positive result showing
identifiability of rates-across-sites models. Finally, we answer a question
raised in a previous paper concerning ``mixed branch repulsion'' on trees
larger than quartet trees under the CFN model.
| [
{
"created": "Wed, 30 May 2007 02:43:12 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Sep 2007 11:27:28 GMT",
"version": "v2"
},
{
"created": "Thu, 8 Nov 2007 18:40:45 GMT",
"version": "v3"
}
] | 2007-11-08 | [
[
"Matsen",
"Frederick A.",
""
],
[
"Mossel",
"Elchanan",
""
],
[
"Steel",
"Mike",
""
]
] | In this paper we apply new geometric and combinatorial methods to the study of phylogenetic mixtures. The focus of the geometric approach is to describe the geometry of phylogenetic mixture distributions for the two state random cluster model, which is a generalization of the two state symmetric (CFN) model. In particular, we show that the set of mixture distributions forms a convex polytope and we calculate its dimension; corollaries include a simple criterion for when a mixture of branch lengths on the star tree can mimic the site pattern frequency vector of a resolved quartet tree. Furthermore, by computing volumes of polytopes we can clarify how ``common'' non-identifiable mixtures are under the CFN model. We also present a new combinatorial result which extends any identifiability result for a specific pair of trees of size six to arbitrary pairs of trees. Next we present a positive result showing identifiability of rates-across-sites models. Finally, we answer a question raised in a previous paper concerning ``mixed branch repulsion'' on trees larger than quartet trees under the CFN model. |
1401.0160 | Nadav M. Shnerb | Haim Weissmann and Nadav M. Shnerb | Stochastic Desertification | null | null | 10.1209/0295-5075/106/28004 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The process of desertification is usually modeled as a first order
transition, where a change of an external parameter (e.g. precipitation) leads
to a catastrophic bifurcation followed by an ecological regime shift. However,
vegetation elements like shrubs and trees undergo a stochastic birth-death
process with an absorbing state; such a process supports a second order
continuous transition with no hysteresis. We present a numerical study of a
minimal model that supports bistability and catastrophic shift on spatial
domain with demographic noise and an absorbing state. When the external
parameter varies adiabatically the transition is continuous and the front
velocity renormalizes to zero at the extinction transition. Below the
transition one may identify three modes of desertification: accumulation of
local catastrophes, desert invasion and global collapse. A catastrophic regime
shift occurs as a dynamical hysteresis, when the pace of environmental
variations is too fast. We present some empirical evidence, suggesting that the
mid-holocene desertification of the Sahara was, indeed, continuous.
| [
{
"created": "Tue, 31 Dec 2013 15:31:44 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Weissmann",
"Haim",
""
],
[
"Shnerb",
"Nadav M.",
""
]
] | The process of desertification is usually modeled as a first order transition, where a change of an external parameter (e.g. precipitation) leads to a catastrophic bifurcation followed by an ecological regime shift. However, vegetation elements like shrubs and trees undergo a stochastic birth-death process with an absorbing state; such a process supports a second order continuous transition with no hysteresis. We present a numerical study of a minimal model that supports bistability and catastrophic shift on spatial domain with demographic noise and an absorbing state. When the external parameter varies adiabatically the transition is continuous and the front velocity renormalizes to zero at the extinction transition. Below the transition one may identify three modes of desertification: accumulation of local catastrophes, desert invasion and global collapse. A catastrophic regime shift occurs as a dynamical hysteresis, when the pace of environmental variations is too fast. We present some empirical evidence, suggesting that the mid-holocene desertification of the Sahara was, indeed, continuous. |
1806.01597 | Javier Buldu | David Papo and Javier M. Buld\'u | Brain synchronizability, a false friend | 4 pages, 1 figure | null | null | null | q-bio.NC nlin.AO physics.bio-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronization plays a fundamental role in healthy cognitive and motor
function. However, how synchronization depends on the interplay between local
dynamics, coupling and topology and how prone to synchronization a network with
given topological organization is are still poorly understood issues. To
investigate the synchronizability of both anatomical and functional brain
networks various studies resorted to the Master Stability Function (MSF)
formalism, an elegant tool which allows analysing the stability of synchronous
states in a dynamical system consisting of many coupled oscillators. Here, we
argue that brain dynamics does not fulfil the formal criteria under which
synchronizability is usually quantified and, perhaps more importantly, what
this measure itself quantifies refers to a global dynamical condition that
never holds in the brain (not even in the most pathological conditions), and
therefore no neurophysiological conclusions should be drawn based on it. We
discuss the meaning of synchronizability and its applicability to neuroscience
and propose alternative ways to quantify brain networks synchronization.
| [
{
"created": "Tue, 5 Jun 2018 10:22:05 GMT",
"version": "v1"
}
] | 2018-06-06 | [
[
"Papo",
"David",
""
],
[
"Buldú",
"Javier M.",
""
]
] | Synchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network with given topological organization is are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, what this measure itself quantifies refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization. |
1503.02777 | Lorenz K. Muller | Hesham Mostafa, Lorenz K. Muller, Giacomo Indiveri | Rhythmic inhibition allows neural networks to search for maximally
consistent states | null | Neural Computation 27:12, (2015), pg. 2510-2547 | 10.1162/NECO_a_00785 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits
yet its computational role still remains elusive. We show that a model of
Gamma-band rhythmic inhibition allows networks of coupled cortical circuit
motifs to search for network configurations that best reconcile external inputs
with an internal consistency model encoded in the network connectivity. We show
that Hebbian plasticity allows the networks to learn the consistency model by
example. The search dynamics driven by rhythmic inhibition enable the described
networks to solve difficult constraint satisfaction problems without making
assumptions about the form of stochastic fluctuations in the network. We show
that the search dynamics are well approximated by a stochastic sampling
process. We use the described networks to reproduce perceptual multi-stability
phenomena with switching times that are a good match to experimental data and
show that they provide a general neural framework which can be used to model
other 'perceptual inference' phenomena.
| [
{
"created": "Tue, 10 Mar 2015 05:48:52 GMT",
"version": "v1"
}
] | 2017-11-08 | [
[
"Mostafa",
"Hesham",
""
],
[
"Muller",
"Lorenz K.",
""
],
[
"Indiveri",
"Giacomo",
""
]
] | Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits yet its computational role still remains elusive. We show that a model of Gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search for network configurations that best reconcile external inputs with an internal consistency model encoded in the network connectivity. We show that Hebbian plasticity allows the networks to learn the consistency model by example. The search dynamics driven by rhythmic inhibition enable the described networks to solve difficult constraint satisfaction problems without making assumptions about the form of stochastic fluctuations in the network. We show that the search dynamics are well approximated by a stochastic sampling process. We use the described networks to reproduce perceptual multi-stability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework which can be used to model other 'perceptual inference' phenomena. |
1004.5275 | Uwe C. T\"auber | Qian He (Virginia Tech), Mauro Mobilia (U Leeds), and Uwe C. T\"auber
(Virginia Tech) | Spatial Rock-Paper-Scissors Models with Inhomogeneous Reaction Rates | 11 pages, revtex, 17 figures, to appear in Phys. Rev. E (2010) | Phys. Rev. E 82 (2010) 051909 | 10.1103/PhysRevE.82.051909 | null | q-bio.PE cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study several variants of the stochastic four-state rock-paper-scissors
game or, equivalently, cyclic three-species predator-prey models with conserved
total particle density, by means of Monte Carlo simulations on one- and
two-dimensional lattices. Specifically, we investigate the influence of spatial
variability of the reaction rates and site occupancy restrictions on the
transient oscillations of the species densities and on spatial correlation
functions in the quasi-stationary coexistence state. For small systems, we also
numerically determine the dependence of typical extinction times on the number
of lattice sites. In stark contrast with two-species stochastic Lotka-Volterra
systems, we find that for our three-species models with cyclic competition
quenched disorder in the reaction rates has very little effect on the dynamics
and the long-time properties of the coexistence state. Similarly, we observe
that site restriction only has a minor influence on the system's dynamical
properties. Our results therefore demonstrate that the features of the spatial
rock-paper-scissors system are remarkably robust with respect to model
variations, and stochastic fluctuations as well as spatial correlations play a
comparatively minor role.
| [
{
"created": "Thu, 29 Apr 2010 13:29:20 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Oct 2010 18:35:55 GMT",
"version": "v2"
}
] | 2010-11-13 | [
[
"He",
"Qian",
"",
"Virginia Tech"
],
[
"Mobilia",
"Mauro",
"",
"U Leeds"
],
[
"Täuber",
"Uwe C.",
"",
"Virginia Tech"
]
] | We study several variants of the stochastic four-state rock-paper-scissors game or, equivalently, cyclic three-species predator-prey models with conserved total particle density, by means of Monte Carlo simulations on one- and two-dimensional lattices. Specifically, we investigate the influence of spatial variability of the reaction rates and site occupancy restrictions on the transient oscillations of the species densities and on spatial correlation functions in the quasi-stationary coexistence state. For small systems, we also numerically determine the dependence of typical extinction times on the number of lattice sites. In stark contrast with two-species stochastic Lotka-Volterra systems, we find that for our three-species models with cyclic competition quenched disorder in the reaction rates has very little effect on the dynamics and the long-time properties of the coexistence state. Similarly, we observe that site restriction only has a minor influence on the system's dynamical properties. Our results therefore demonstrate that the features of the spatial rock-paper-scissors system are remarkably robust with respect to model variations, and stochastic fluctuations as well as spatial correlations play a comparatively minor role. |
1911.09309 | Zhijie Chen | Zhijie Chen, Junchi Yan, Longyuan Li and Xiaokang Yang | Decoding Spiking Mechanism with Dynamic Learning on Neuron Population | null | null | null | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A main concern in cognitive neuroscience is to decode the overt neural spike
train observations and infer latent representations under neural circuits.
However, traditional methods entail strong prior on network structure and
hardly meet the demand for real spike data. Here we propose a novel neural
network approach called Neuron Activation Network that extracts neural
information explicitly from single trial neuron population spike trains. Our
proposed method consists of a spatiotemporal learning procedure on sensory
environment and a message passing mechanism on population graph, followed by a
neuron activation process in a recursive fashion. Our model is aimed to
reconstruct neuron information while inferring representations of neuron
spiking states. We apply our model to retinal ganglion cells and the
experimental results suggest that our model holds a more potent capability in
generating neural spike sequences with high fidelity than the state-of-the-art
methods, as well as being more expressive and having potential to disclose
latent spiking mechanism. The source code will be released with the final
paper.
| [
{
"created": "Thu, 21 Nov 2019 06:56:57 GMT",
"version": "v1"
}
] | 2019-11-22 | [
[
"Chen",
"Zhijie",
""
],
[
"Yan",
"Junchi",
""
],
[
"Li",
"Longyuan",
""
],
[
"Yang",
"Xiaokang",
""
]
] | A main concern in cognitive neuroscience is to decode the overt neural spike train observations and infer latent representations under neural circuits. However, traditional methods entail strong prior on network structure and hardly meet the demand for real spike data. Here we propose a novel neural network approach called Neuron Activation Network that extracts neural information explicitly from single trial neuron population spike trains. Our proposed method consists of a spatiotemporal learning procedure on sensory environment and a message passing mechanism on population graph, followed by a neuron activation process in a recursive fashion. Our model is aimed to reconstruct neuron information while inferring representations of neuron spiking states. We apply our model to retinal ganglion cells and the experimental results suggest that our model holds a more potent capability in generating neural spike sequences with high fidelity than the state-of-the-art methods, as well as being more expressive and having potential to disclose latent spiking mechanism. The source code will be released with the final paper. |
1505.03964 | Gabriel Silva | Marius Buibas and Gabriel A. Silva | Algebraic identification of the effective connectivity of constrained
geometric network models of neural signaling | 9 pages | null | null | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cellular neural circuit and networks consisting of interconnected neurons and
glia are ulti- mately responsible for the information processing associated
with information processing in the brain. While there are major efforts aimed
at mapping the structural and (electro)physiological connectivity of brain
networks, such as the White House BRAIN Initiative aimed at the devel- opment
of neurotechnologies capable of high density neural recordings, theoretical and
compu- tational methods for analyzing and making sense of all this data seem to
be further behind. Here, we propose and provide a summary of an approach for
calculating effective connectivity from experimental observations of neuronal
network activity. The proposed method operates on network-level data, makes use
of all relevant prior knowledge, such as dynamical models of individual cells
in the network and the physical structural connectivity of the network, and is
broadly applicable to large classes of biological and non-biological networks.
| [
{
"created": "Fri, 15 May 2015 05:53:45 GMT",
"version": "v1"
}
] | 2015-05-18 | [
[
"Buibas",
"Marius",
""
],
[
"Silva",
"Gabriel A.",
""
]
] | Cellular neural circuit and networks consisting of interconnected neurons and glia are ulti- mately responsible for the information processing associated with information processing in the brain. While there are major efforts aimed at mapping the structural and (electro)physiological connectivity of brain networks, such as the White House BRAIN Initiative aimed at the devel- opment of neurotechnologies capable of high density neural recordings, theoretical and compu- tational methods for analyzing and making sense of all this data seem to be further behind. Here, we propose and provide a summary of an approach for calculating effective connectivity from experimental observations of neuronal network activity. The proposed method operates on network-level data, makes use of all relevant prior knowledge, such as dynamical models of individual cells in the network and the physical structural connectivity of the network, and is broadly applicable to large classes of biological and non-biological networks. |
2007.07500 | Leonardo Novelli | Leonardo Novelli and Joseph T. Lizier | Inferring network properties from time series using transfer entropy and
mutual information: validation of multivariate versus bivariate approaches | null | Network Neuroscience (2021) 5 (2): 373-404 | 10.1162/netn_a_00178 | null | q-bio.NC cs.IT cs.SI math.IT physics.data-an | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Functional and effective networks inferred from time series are at the core
of network neuroscience. Interpreting their properties requires inferred
network models to reflect key underlying structural features; however, even a
few spurious links can distort network measures, challenging functional
connectomes. We study the extent to which micro- and macroscopic properties of
underlying networks can be inferred by algorithms based on mutual information
and bivariate/multivariate transfer entropy. The validation is performed on two
macaque connectomes and on synthetic networks with various topologies (regular
lattice, small-world, random, scale-free, modular). Simulations are based on a
neural mass model and on autoregressive dynamics (employing Gaussian estimators
for direct comparison to functional connectivity and Granger causality). We
find that multivariate transfer entropy captures key properties of all networks
for longer time series. Bivariate methods can achieve higher recall
(sensitivity) for shorter time series but are unable to control false positives
(lower specificity) as available data increases. This leads to overestimated
clustering, small-world, and rich-club coefficients, underestimated shortest
path lengths and hub centrality, and fattened degree distribution tails.
Caution should therefore be used when interpreting network properties of
functional connectomes obtained via correlation or pairwise statistical
dependence measures, rather than more holistic (yet data-hungry) multivariate
models.
| [
{
"created": "Wed, 15 Jul 2020 06:09:02 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Nov 2020 03:30:28 GMT",
"version": "v2"
}
] | 2022-09-22 | [
[
"Novelli",
"Leonardo",
""
],
[
"Lizier",
"Joseph T.",
""
]
] | Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires inferred network models to reflect key underlying structural features; however, even a few spurious links can distort network measures, challenging functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all networks for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases. This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models. |
2204.00300 | Jiayang Chen | Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze
Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li | Interpretable RNA Foundation Model from Unannotated Data for Highly
Accurate RNA Structure and Function Predictions | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Non-coding RNA structure and function are essential to understanding various
biological processes, such as cell signaling, gene expression, and
post-transcriptional regulations. These are all among the core problems in the
RNA field. With the rapid growth of sequencing technology, we have accumulated
a massive amount of unannotated RNA sequences. On the other hand, expensive
experimental observatory results in only limited numbers of annotated data and
3D structures. Hence, it is still challenging to design computational methods
for predicting their structures and functions. The lack of annotated data and
systematic study causes inferior performance. To resolve the issue, we propose
a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million
non-coding RNA sequences through self-supervised learning. Within this
approach, we discover that the pre-trained RNA-FM could infer sequential and
evolutionary information of non-coding RNAs without using any labels.
Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the
downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and
evolution prediction, protein-RNA binding preference modeling, and gene
expression regulation modeling. The comprehensive experiments show that the
proposed method improves the RNA structural and functional modelling results
significantly and consistently. Despite only being trained with unlabelled
data, RNA-FM can serve as the foundational model for the field.
| [
{
"created": "Fri, 1 Apr 2022 09:10:31 GMT",
"version": "v1"
},
{
"created": "Fri, 13 May 2022 14:05:09 GMT",
"version": "v2"
},
{
"created": "Sat, 28 May 2022 14:27:17 GMT",
"version": "v3"
},
{
"created": "Sat, 16 Jul 2022 13:55:37 GMT",
"version": "v4"
},
{
"created": "Mon, 8 Aug 2022 00:49:01 GMT",
"version": "v5"
}
] | 2022-08-09 | [
[
"Chen",
"Jiayang",
""
],
[
"Hu",
"Zhihang",
""
],
[
"Sun",
"Siqi",
""
],
[
"Tan",
"Qingxiong",
""
],
[
"Wang",
"Yixuan",
""
],
[
"Yu",
"Qinze",
""
],
[
"Zong",
"Licheng",
""
],
[
"Hong",
"Liang",
""
],
[
"Xiao",
"Jin",
""
],
[
"Shen",
"Tao",
""
],
[
"King",
"Irwin",
""
],
[
"Li",
"Yu",
""
]
] | Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field. |
1704.07815 | Andrew Dittmore | Andrew Dittmore, Sumitabha Brahmachari, Yasuhara Takagi, John F.
Marko, and Keir C. Neuman | Supercoiling DNA locates mismatches | null | Phys. Rev. Lett. 119, 147801 (2017) | 10.1103/PhysRevLett.119.147801 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method of detecting sequence defects by supercoiling DNA with
magnetic tweezers. The method is sensitive to a single mismatched base pair in
a DNA sequence of several thousand base pairs. We systematically compare DNA
molecules with 0 to 16 adjacent mismatches at 1 M monovalent salt and 3.5 pN
force and show that, under these conditions, a single plectoneme forms and is
stably pinned at the defect. We use these measurements to estimate the energy
and degree of end-loop kinking at defects. From this, we calculate the relative
probability of plectoneme pinning at the mismatch under physiologically
relevant conditions. Based on this estimate, we propose that DNA supercoiling
could contribute to mismatch and damage sensing in vivo.
| [
{
"created": "Tue, 25 Apr 2017 17:48:20 GMT",
"version": "v1"
}
] | 2017-10-11 | [
[
"Dittmore",
"Andrew",
""
],
[
"Brahmachari",
"Sumitabha",
""
],
[
"Takagi",
"Yasuhara",
""
],
[
"Marko",
"John F.",
""
],
[
"Neuman",
"Keir C.",
""
]
] | We present a method of detecting sequence defects by supercoiling DNA with magnetic tweezers. The method is sensitive to a single mismatched base pair in a DNA sequence of several thousand base pairs. We systematically compare DNA molecules with 0 to 16 adjacent mismatches at 1 M monovalent salt and 3.5 pN force and show that, under these conditions, a single plectoneme forms and is stably pinned at the defect. We use these measurements to estimate the energy and degree of end-loop kinking at defects. From this, we calculate the relative probability of plectoneme pinning at the mismatch under physiologically relevant conditions. Based on this estimate, we propose that DNA supercoiling could contribute to mismatch and damage sensing in vivo. |
2212.10849 | Pavel Loskot | Pavel Loskot | A Query-Response Causal Analysis of Reaction Events in Biochemical
Reaction Networks | 7 figures and supplementary file included | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The stochastic kinetics of BRN are described by a chemical master equation
(CME) and the underlying laws of mass action. The CME must be usually solved
numerically by generating enough traces of random reaction events. The
resulting event-time series can be evaluated statistically to identify, for
example, the reaction clusters, rare reaction events, and the periods of
increased or steady-state activity. The aim of this paper is to newly exploit
the empirical statistics of the reaction events in order to obtain causally and
anti-causally related sub-sequences of reactions. This allows discovering some
of the causal dynamics of the reaction networks as well as uncovering their
more deterministic behaviors. In particular, it is proposed that the reaction
sub-sequences that are conditionally nearly certain or nearly uncertain can be
considered as being causally related or unrelated, respectively. Moreover,
since time-ordering of reactions is locally irrelevant, the reaction
sub-sequences can be transformed into the reaction event sets or multi-sets.
The appropriately defined distance metrics can be then used to define
equivalences between the reaction sub-sequences. The proposed framework for
identifying causally associated reaction sub-sequences has been implemented as
a computationally efficient query-response mechanism. The framework was
evaluated assuming five selected models of genetic reaction networks in seven
defined numerical experiments. The models were simulated in BioNetGen using
NFsim, which had to be modified to allow recording of the traces of reaction
events. The generated event time-series were analyzed by Python and Matlab
scripts. The whole process of data generation, analysis and visualization has
been nearly fully automated using shell scripts.
| [
{
"created": "Wed, 21 Dec 2022 08:50:23 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Jun 2023 08:27:08 GMT",
"version": "v2"
}
] | 2023-06-21 | [
[
"Loskot",
"Pavel",
""
]
] | The stochastic kinetics of BRN are described by a chemical master equation (CME) and the underlying laws of mass action. The CME must be usually solved numerically by generating enough traces of random reaction events. The resulting event-time series can be evaluated statistically to identify, for example, the reaction clusters, rare reaction events, and the periods of increased or steady-state activity. The aim of this paper is to newly exploit the empirical statistics of the reaction events in order to obtain causally and anti-causally related sub-sequences of reactions. This allows discovering some of the causal dynamics of the reaction networks as well as uncovering their more deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related or unrelated, respectively. Moreover, since time-ordering of reactions is locally irrelevant, the reaction sub-sequences can be transformed into the reaction event sets or multi-sets. The appropriately defined distance metrics can be then used to define equivalences between the reaction sub-sequences. The proposed framework for identifying causally associated reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The framework was evaluated assuming five selected models of genetic reaction networks in seven defined numerical experiments. The models were simulated in BioNetGen using NFsim, which had to be modified to allow recording of the traces of reaction events. The generated event time-series were analyzed by Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. |
2311.07609 | Siamak Pedrammehr Dr. | Mirsaeed Abdollahi, Ali Jafarizadeh, Amirhosein Ghafouri Asbagh, Navid
Sobhi, Keysan Pourmoghtader, Siamak Pedrammehr, Houshyar Asadi, Roohallah
Alizadehsani, Ru-San Tan, U. Rajendra Acharya | Artificial Intelligence in Assessing Cardiovascular Diseases and Risk
Factors via Retinal Fundus Images: A Review of the Last Decade | 41 pages, 5 figures, 3 tables, 114 references | null | null | null | q-bio.QM cs.CV eess.IV physics.med-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Background: Cardiovascular diseases (CVDs) are the leading cause of death
globally. The use of artificial intelligence (AI) methods - in particular, deep
learning (DL) - has been on the rise lately for the analysis of different
CVD-related topics. The use of fundus images and optical coherence tomography
angiography (OCTA) in the diagnosis of retinal diseases has also been
extensively studied. To better understand heart function and anticipate changes
based on microvascular characteristics and function, researchers are currently
exploring the integration of AI with non-invasive retinal scanning. There is
great potential to reduce the number of cardiovascular events and the financial
strain on healthcare systems by utilizing AI-assisted early detection and
prediction of cardiovascular diseases on a large scale. Method: A comprehensive
search was conducted across various databases, including PubMed, Medline,
Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library,
using specific keywords related to cardiovascular diseases and artificial
intelligence. Results: The study included 87 English-language publications
selected for relevance, and additional references were considered. This paper
provides an overview of the recent developments and difficulties in using
artificial intelligence and retinal imaging to diagnose cardiovascular
diseases. It provides insights for further exploration in this field.
Conclusion: Researchers are trying to develop precise disease prognosis
patterns in response to the aging population and the growing global burden of
CVD. AI and deep learning are revolutionizing healthcare by potentially
diagnosing multiple CVDs from a single retinal image. However, swifter adoption
of these technologies in healthcare systems is required.
| [
{
"created": "Sat, 11 Nov 2023 13:09:11 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Apr 2024 15:36:40 GMT",
"version": "v2"
}
] | 2024-04-30 | [
[
"Abdollahi",
"Mirsaeed",
""
],
[
"Jafarizadeh",
"Ali",
""
],
[
"Asbagh",
"Amirhosein Ghafouri",
""
],
[
"Sobhi",
"Navid",
""
],
[
"Pourmoghtader",
"Keysan",
""
],
[
"Pedrammehr",
"Siamak",
""
],
[
"Asadi",
"Houshyar",
""
],
[
"Alizadehsani",
"Roohallah",
""
],
[
"Tan",
"Ru-San",
""
],
[
"Acharya",
"U. Rajendra",
""
]
] | Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods - in particular, deep learning (DL) - has been on the rise lately for the analysis of different CVD-related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI-assisted early detection and prediction of cardiovascular diseases on a large scale. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: The study included 87 English-language publications selected for relevance, and additional references were considered. This paper provides an overview of the recent developments and difficulties in using artificial intelligence and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Conclusion: Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and deep learning are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required. |
1904.06377 | Martin Frasch | Colin Wakefield, Ben Janoschek, Yael Frank, Floyd Karp, Nicholas
Reyes, Jay Schulkin, Martin G. Frasch | Chronic stress may disrupt covariant fluctuations of vitamin D and
cortisol plasma levels in pregnant sheep during the last trimester: a
preliminary report | See also https://github.com/martinfrasch/stressed_sheep for data and
results | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Psychosocial stress during pregnancy is a known contributor to preterm birth,
but also has been increasingly appreciated as an in utero insult acting
long-term on prenatal and postnatal neurodevelopmental trajectories. These
events impact many information molecules, including both vitamin D and
cortisol. Both have been linked to low birth premature babies. Cortisol tends
to be further elevated in women, while vitamin D tends to be decreased from
their normal levels during pregnancy. One facilitates labor in part by
elevating placental CRH, the other by limiting CRH in placental tissue. Both
are linked to managing adversity. Studies in large animal models with high
resemblance to human physiology are sparse to model the changes induced by such
stress exposure. Using an established pregnant sheep model of stress during
human development, here we focused on measuring the changes in maternal Vitamin
D and cortisol responses due to chronic inescapable stress mimicking daily
challenges in the last trimester of human pregnancy. The present pilot data
show that chronic maternal stress during pregnancy results in endocrine and
metabolic chronic habituation paralleled by sensitization to acute stress
challenges. Chronic stress appears to disrupt a physiological relationship
between oscillations of vitamin D and cortisol. These speculations need to be
explored in future studies.
| [
{
"created": "Fri, 12 Apr 2019 19:04:49 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jul 2019 23:00:55 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Aug 2019 08:16:53 GMT",
"version": "v3"
}
] | 2019-08-13 | [
[
"Wakefield",
"Colin",
""
],
[
"Janoschek",
"Ben",
""
],
[
"Frank",
"Yael",
""
],
[
"Karp",
"Floyd",
""
],
[
"Reyes",
"Nicholas",
""
],
[
"Schulkin",
"Jay",
""
],
[
"Frasch",
"Martin G.",
""
]
] | Psychosocial stress during pregnancy is a known contributor to preterm birth, but also has been increasingly appreciated as an in utero insult acting long-term on prenatal and postnatal neurodevelopmental trajectories. These events impact many information molecules, including both vitamin D and cortisol. Both have been linked to low birth premature babies. Cortisol tends to be further elevated in women, while vitamin D tends to be decreased from their normal levels during pregnancy. One facilitates labor in part by elevating placental CRH, the other by limiting CRH in placental tissue. Both are linked to managing adversity. Studies in large animal models with high resemblance to human physiology are sparse to model the changes induced by such stress exposure. Using an established pregnant sheep model of stress during human development, here we focused on measuring the changes in maternal Vitamin D and cortisol responses due to chronic inescapable stress mimicking daily challenges in the last trimester of human pregnancy. The present pilot data show that chronic maternal stress during pregnancy results in endocrine and metabolic chronic habituation paralleled by sensitization to acute stress challenges. Chronic stress appears to disrupt a physiological relationship between oscillations of vitamin D and cortisol. These speculations need to be explored in future studies. |
1303.2398 | Chandra Wickramasinghe | N. C. Wickramasinghe, J. Wallis, D.H. Wallis and Anil Samaranayake | Fossil diatoms in a new carbonaceous meteorite | 14 pages, 7 figures plus an Appendix with 6 high resolution images of
diatoms in the meteorite | Journal of Cosmology, Vol 21(37), January 2013 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report the discovery for the first time of diatom frustules in a
carbonaceous meteorite that fell in the North Central Province of Sri Lanka on
29 December 2012. Contamination is excluded by the circumstance that the
elemental abundances within the structures match closely with those of the
surrounding matrix. There is also evidence of structures morphologically
similar to red rain cells that may have contributed to the episode of red rain
that followed within days of the meteorite fall. The new data on fossil diatoms
provide strong evidence to support the theory of cometary panspermia.
| [
{
"created": "Wed, 6 Mar 2013 21:14:38 GMT",
"version": "v1"
}
] | 2013-03-12 | [
[
"Wickramasinghe",
"N. C.",
""
],
[
"Wallis",
"J.",
""
],
[
"Wallis",
"D. H.",
""
],
[
"Samaranayake",
"Anil",
""
]
] | We report the discovery for the first time of diatom frustules in a carbonaceous meteorite that fell in the North Central Province of Sri Lanka on 29 December 2012. Contamination is excluded by the circumstance that the elemental abundances within the structures match closely with those of the surrounding matrix. There is also evidence of structures morphologically similar to red rain cells that may have contributed to the episode of red rain that followed within days of the meteorite fall. The new data on fossil diatoms provide strong evidence to support the theory of cometary panspermia. |
1112.4987 | Yuriy Pershin | M. Di Ventra and Y. V. Pershin | Biologically-Inspired Electronics with Memory Circuit Elements | To be published in "Advances in Neuromorphic Memristor Science and
Applications" (Springer), edited by R. Kozma, R. Pino, G. Pazienza | null | null | null | q-bio.NC cond-mat.mes-hall | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several abilities of biological systems, such as adaptation to natural
environment, or of animals to learn patterns when appropriately trained, are
features that are extremely useful, if emulated by electronic circuits, in
applications ranging from robotics to solution of complex optimization
problems, traffic control, etc. In this chapter, we discuss several examples of
biologically-inspired circuits that take advantage of memory circuit elements,
namely, electronic elements whose resistive, capacitive or inductive
characteristics depend on their past dynamics. We provide several illustrations
of what can be accomplished with these elements including learning circuits and
related adaptive filters, neuromorphic and cellular computing circuits, analog
massively-parallel computation architectures, etc. We also give examples of
experimental realizations of memory circuit elements and discuss opportunities
and challenges in this new field.
| [
{
"created": "Wed, 21 Dec 2011 11:07:16 GMT",
"version": "v1"
}
] | 2011-12-22 | [
[
"Di Ventra",
"M.",
""
],
[
"Pershin",
"Y. V.",
""
]
] | Several abilities of biological systems, such as adaptation to natural environment, or of animals to learn patterns when appropriately trained, are features that are extremely useful, if emulated by electronic circuits, in applications ranging from robotics to solution of complex optimization problems, traffic control, etc. In this chapter, we discuss several examples of biologically-inspired circuits that take advantage of memory circuit elements, namely, electronic elements whose resistive, capacitive or inductive characteristics depend on their past dynamics. We provide several illustrations of what can be accomplished with these elements including learning circuits and related adaptive filters, neuromorphic and cellular computing circuits, analog massively-parallel computation architectures, etc. We also give examples of experimental realizations of memory circuit elements and discuss opportunities and challenges in this new field. |
1607.01435 | Nora Molkenthin | Nora Molkenthin, Marc Timme | Scaling Laws in Spatial Network Formation | null | Phys. Rev. Lett. 117, 168301 (2016) | 10.1103/PhysRevLett.117.168301 | null | q-bio.MN nlin.AO physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometric constraints impact the formation of a broad range of spatial
networks, from amino acid chains folding to proteins structures to rearranging
particle aggregates. How the network of interactions dynamically self-organizes
in such systems is far from fully understood. Here, we analyze a class of
spatial network formation processes by introducing a mapping from geometric to
graph-theoretic constraints. Combining stochastic and mean field analyses
yields an algebraic scaling law for the extent (graph diameter) of the
resulting networks with system size, in contrast to logarithmic scaling known
for networks without constraints. Intriguingly, the exponent falls between that
of self-avoiding random walks and that of space filling arrangements,
consistent with experimentally observed scaling (of the spatial radius of
gyration) for protein tertiary structures.
| [
{
"created": "Tue, 5 Jul 2016 23:14:06 GMT",
"version": "v1"
}
] | 2016-10-19 | [
[
"Molkenthin",
"Nora",
""
],
[
"Timme",
"Marc",
""
]
] | Geometric constraints impact the formation of a broad range of spatial networks, from amino acid chains folding to proteins structures to rearranging particle aggregates. How the network of interactions dynamically self-organizes in such systems is far from fully understood. Here, we analyze a class of spatial network formation processes by introducing a mapping from geometric to graph-theoretic constraints. Combining stochastic and mean field analyses yields an algebraic scaling law for the extent (graph diameter) of the resulting networks with system size, in contrast to logarithmic scaling known for networks without constraints. Intriguingly, the exponent falls between that of self-avoiding random walks and that of space filling arrangements, consistent with experimentally observed scaling (of the spatial radius of gyration) for protein tertiary structures. |
2005.06199 | Nikolai Petrovsky | Sakshi Piplani, Puneet Kumar Singh, David A. Winkler, and Nikolai
Petrovsky | In silico comparison of spike protein-ACE2 binding affinities across
species; significance for the possible origin of the SARS-CoV-2 virus | Paper and supplementary data combined in one single pdf file | Scientific Reports. 2021 Jun 24;11(1):13063. PMID: 34168168 | 10.1038/s41598-021-92388-5. | null | q-bio.BM q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The devastating impact of the COVID-19 pandemic caused by SARS coronavirus 2
(SARS CoV 2) has raised important questions about viral origin, mechanisms of
zoonotic transfer to humans, whether companion or commercial animals can act as
reservoirs for infection, and why there are large variations in SARS-CoV-2
susceptibilities across animal species. Powerful in silico modelling methods
can rapidly generate information on newly emerged pathogens to aid
countermeasure development and predict future behaviours. Here we report an in
silico structural homology modelling, protein-protein docking, and molecular
dynamics simulation study of the key infection initiating interaction between
the spike protein of SARS-Cov-2 and its target, angiotensin converting enzyme 2
(ACE2) from multiple species. Human ACE2 has the strongest binding interaction,
significantly greater than for any species proposed as source of the virus.
Binding to pangolin ACE2 was the second strongest, possibly due to the
SARS-CoV-2 spike receptor binding domain (RBD) being identical to pangolin CoV
spike RDB. Except for snake, pangolin and bat for which permissiveness has not
been tested, all those species in the upper half of the affinity range (human,
monkey, hamster, dog, ferret) have been shown to be at least moderately
permissive to SARS-CoV-2 infection, supporting a correlation between binding
affinity and permissiveness. Our data indicates that the earliest isolates of
SARS-CoV-2 were surprisingly well adapted to human ACE2, potentially explaining
its rapid transmission.
| [
{
"created": "Wed, 13 May 2020 08:24:58 GMT",
"version": "v1"
},
{
"created": "Sat, 21 Nov 2020 08:02:11 GMT",
"version": "v2"
}
] | 2021-07-12 | [
[
"Piplani",
"Sakshi",
""
],
[
"Singh",
"Puneet Kumar",
""
],
[
"Winkler",
"David A.",
""
],
[
"Petrovsky",
"Nikolai",
""
]
] | The devastating impact of the COVID-19 pandemic caused by SARS coronavirus 2 (SARS CoV 2) has raised important questions about viral origin, mechanisms of zoonotic transfer to humans, whether companion or commercial animals can act as reservoirs for infection, and why there are large variations in SARS-CoV-2 susceptibilities across animal species. Powerful in silico modelling methods can rapidly generate information on newly emerged pathogens to aid countermeasure development and predict future behaviours. Here we report an in silico structural homology modelling, protein-protein docking, and molecular dynamics simulation study of the key infection initiating interaction between the spike protein of SARS-Cov-2 and its target, angiotensin converting enzyme 2 (ACE2) from multiple species. Human ACE2 has the strongest binding interaction, significantly greater than for any species proposed as source of the virus. Binding to pangolin ACE2 was the second strongest, possibly due to the SARS-CoV-2 spike receptor binding domain (RBD) being identical to pangolin CoV spike RDB. Except for snake, pangolin and bat for which permissiveness has not been tested, all those species in the upper half of the affinity range (human, monkey, hamster, dog, ferret) have been shown to be at least moderately permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and permissiveness. Our data indicates that the earliest isolates of SARS-CoV-2 were surprisingly well adapted to human ACE2, potentially explaining its rapid transmission. |
2005.05787 | Marta Antonelli Dr. | Marta C. Antonelli, Martin G. Frasch, Mercedes Rumi, Ritika Sharma,
Peter Zimmermann, Maria Sol Molinet and Silvia M. Lobmaier | Early Biomarkers and Intervention Programs for the Infant Exposed to
Prenatal Stress | 18 pages-Review | Curr Neuropharmacol 2021 | 10.2174/1570159X19666210125150955 | null | q-bio.QM q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Functional development of affective and reward circuits, cognition and
response inhibition later in life exhibits vulnerability periods during
gestation and early childhood. Extensive evidence supports the model that
exposure to stressors in the gestational period and early postnatal life
increases an individual's susceptibility to future impairments of functional
development. Recent versions of this model integrate epigenetic mechanisms of
the developmental response. Their understanding will guide the future treatment
of the associated neuropsychiatric disorders. A combination of non-invasively
obtainable physiological signals and epigenetic biomarkers related to the
principal systems of the stress response, the Hypothalamic-Pituitary axis (HPA)
and the Autonomic Nervous System (ANS), are emerging as the key predictors of
neurodevelopmental outcomes. Such electrophysiological and epigenetic
biomarkers can prove to timely identify children benefiting most from early
intervention programs. Such programs should ameliorate future disorders in
otherwise apparently healthy children. The recently developed Early
Family-Centered Intervention Programs aim to influence the care and stimuli
provided daily by the family and improving parent/child attachment, a key
element for healthy socio-emotional adult life. Although frequently
underestimated, such biomarker-guided early intervention strategy represents a
crucial first step in the prevention of future neuropsychiatric problems and in
reducing their personal and societal impact.
| [
{
"created": "Sat, 9 May 2020 13:55:55 GMT",
"version": "v1"
}
] | 2021-02-26 | [
[
"Antonelli",
"Marta C.",
""
],
[
"Frasch",
"Martin G.",
""
],
[
"Rumi",
"Mercedes",
""
],
[
"Sharma",
"Ritika",
""
],
[
"Zimmermann",
"Peter",
""
],
[
"Molinet",
"Maria Sol",
""
],
[
"Lobmaier",
"Silvia M.",
""
]
] | Functional development of affective and reward circuits, cognition and response inhibition later in life exhibits vulnerability periods during gestation and early childhood. Extensive evidence supports the model that exposure to stressors in the gestational period and early postnatal life increases an individual's susceptibility to future impairments of functional development. Recent versions of this model integrate epigenetic mechanisms of the developmental response. Their understanding will guide the future treatment of the associated neuropsychiatric disorders. A combination of non-invasively obtainable physiological signals and epigenetic biomarkers related to the principal systems of the stress response, the Hypothalamic-Pituitary axis (HPA) and the Autonomic Nervous System (ANS), are emerging as the key predictors of neurodevelopmental outcomes. Such electrophysiological and epigenetic biomarkers can prove to timely identify children benefiting most from early intervention programs. Such programs should ameliorate future disorders in otherwise apparently healthy children. The recently developed Early Family-Centered Intervention Programs aim to influence the care and stimuli provided daily by the family and improving parent/child attachment, a key element for healthy socio-emotional adult life. Although frequently underestimated, such biomarker-guided early intervention strategy represents a crucial first step in the prevention of future neuropsychiatric problems and in reducing their personal and societal impact. |
1709.05079 | Jeremy Sumner | Michael D. Woodhams, Jeremy G. Sumner, David A. Liberles, Michael A.
Charleston, Barbara R. Holland | Exploring the consequences of lack of closure in codon models | 15 pages; 3 figures; 5 tables | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Models of codon evolution are commonly used to identify positive selection.
Positive selection is typically a heterogeneous process, i.e., it acts on some
branches of the evolutionary tree and not others. Previous work on DNA models
showed that when evolution occurs under a heterogeneous process it is important
to consider the property of model closure, because non-closed models can give
biased estimates of evolutionary processes. The existing codon models that
account for the genetic code are not closed; to establish this it is enough to
show that they are not linear (meaning that the sum of two codon rate matrices
in the model is not a matrix in the model). This raises the concern that a
single codon model fit to a heterogeneous process might mis-estimate both the
effect of selection and branch lengths.
Codon models are typically constructed by choosing an underlying DNA model
(e.g., HKY) that acts identically and independently at each codon position, and
then applying the genetic code via the parameter $\omega$ to modify the rate of
transitions between codons that code for different amino acids. Here we use
simulation to investigate the accuracy of estimation of both the selection
parameter $\omega$ and branch lengths in cases where the underlying DNA process
is heterogeneous but $\omega$ is constant. We find that both $\omega$ and
branch lengths can be mis-estimated in these scenarios. Errors in $\omega$ were
usually less than 2% but could be as high as 17%. We also assessed if choosing
different underlying DNA models had any affect on accuracy, in particular we
assessed if using closed DNA models gave any advantage. However, a DNA model
being closed does not imply that the codon model constructed from it is closed,
and in general we found that using closed DNA models did not decrease errors in
the estimation of $\omega$.
| [
{
"created": "Fri, 15 Sep 2017 07:21:02 GMT",
"version": "v1"
}
] | 2017-09-18 | [
[
"Woodhams",
"Michael D.",
""
],
[
"Sumner",
"Jeremy G.",
""
],
[
"Liberles",
"David A.",
""
],
[
"Charleston",
"Michael A.",
""
],
[
"Holland",
"Barbara R.",
""
]
] | Models of codon evolution are commonly used to identify positive selection. Positive selection is typically a heterogeneous process, i.e., it acts on some branches of the evolutionary tree and not others. Previous work on DNA models showed that when evolution occurs under a heterogeneous process it is important to consider the property of model closure, because non-closed models can give biased estimates of evolutionary processes. The existing codon models that account for the genetic code are not closed; to establish this it is enough to show that they are not linear (meaning that the sum of two codon rate matrices in the model is not a matrix in the model). This raises the concern that a single codon model fit to a heterogeneous process might mis-estimate both the effect of selection and branch lengths. Codon models are typically constructed by choosing an underlying DNA model (e.g., HKY) that acts identically and independently at each codon position, and then applying the genetic code via the parameter $\omega$ to modify the rate of transitions between codons that code for different amino acids. Here we use simulation to investigate the accuracy of estimation of both the selection parameter $\omega$ and branch lengths in cases where the underlying DNA process is heterogeneous but $\omega$ is constant. We find that both $\omega$ and branch lengths can be mis-estimated in these scenarios. Errors in $\omega$ were usually less than 2% but could be as high as 17%. We also assessed if choosing different underlying DNA models had any affect on accuracy, in particular we assessed if using closed DNA models gave any advantage. However, a DNA model being closed does not imply that the codon model constructed from it is closed, and in general we found that using closed DNA models did not decrease errors in the estimation of $\omega$. |
1402.3304 | Evgeni Frenkel | Evgeni M. Frenkel, Benjamin H. Good, Michael M. Desai | The Fates of Mutant Lineages and the Distribution of Fitness Effects of
Beneficial Mutations in Laboratory Budding Yeast Populations | null | null | 10.1534/genetics.113.160069 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The outcomes of evolution are determined by which mutations occur and fix. In
rapidly adapting microbial populations, this process is particularly hard to
predict because lineages with different beneficial mutations often spread
simultaneously and interfere with one another's fixation. Hence to predict the
fate of any individual variant, we must know the rate at which new mutations
create competing lineages of higher fitness. Here, we directly measured the
effect of this interference on the fates of specific adaptive variants in
laboratory Saccharomyces cerevisiae populations and used these measurements to
infer the distribution of fitness effects of new beneficial mutations. To do
so, we seeded marked lineages with different fitness advantages into replicate
populations and tracked their subsequent frequencies for hundreds of
generations. Our results illustrate the transition between strongly
advantageous lineages which decisively sweep to fixation and more moderately
advantageous lineages that are often outcompeted by new mutations arising
during the course of the experiment. We developed an approximate likelihood
framework to compare our data to simulations and found that the effects of
these competing beneficial mutations were best approximated by an exponential
distribution, rather than one with a single effect size. We then used this
inferred distribution of fitness effects to predict the rate of adaptation in a
set of independent control populations. Finally, we discuss how our
experimental design can serve as a screen for rare, large-effect beneficial
mutations.
| [
{
"created": "Thu, 13 Feb 2014 21:05:28 GMT",
"version": "v1"
}
] | 2014-02-17 | [
[
"Frenkel",
"Evgeni M.",
""
],
[
"Good",
"Benjamin H.",
""
],
[
"Desai",
"Michael M.",
""
]
] | The outcomes of evolution are determined by which mutations occur and fix. In rapidly adapting microbial populations, this process is particularly hard to predict because lineages with different beneficial mutations often spread simultaneously and interfere with one another's fixation. Hence to predict the fate of any individual variant, we must know the rate at which new mutations create competing lineages of higher fitness. Here, we directly measured the effect of this interference on the fates of specific adaptive variants in laboratory Saccharomyces cerevisiae populations and used these measurements to infer the distribution of fitness effects of new beneficial mutations. To do so, we seeded marked lineages with different fitness advantages into replicate populations and tracked their subsequent frequencies for hundreds of generations. Our results illustrate the transition between strongly advantageous lineages which decisively sweep to fixation and more moderately advantageous lineages that are often outcompeted by new mutations arising during the course of the experiment. We developed an approximate likelihood framework to compare our data to simulations and found that the effects of these competing beneficial mutations were best approximated by an exponential distribution, rather than one with a single effect size. We then used this inferred distribution of fitness effects to predict the rate of adaptation in a set of independent control populations. Finally, we discuss how our experimental design can serve as a screen for rare, large-effect beneficial mutations. |
1606.03422 | Duc Nguyen | Duc Duy Nguyen, Guo-Wei Wei | The impact of surface area, volume, curvature and Lennard-Jones
potential to solvation modeling | 37 pages, 6 figures | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the impact of surface area, volume, curvature and
Lennard-Jones potential on solvation free energy predictions. Rigidity surfaces
are utilized to generate robust analytical expressions for maximum, minimum,
mean and Gaussian curvatures of solvent-solute interfaces, and define a
generalized Poisson-Boltzmann (GPB) equation with a smooth dielectric profile.
Extensive correlation analysis is performed to examine the linear dependence of
surface area, surface enclosed volume, maximum curvature, minimum curvature,
mean curvature and Gaussian curvature for solvation modeling. It is found that
surface area and surfaces enclosed volumes are highly correlated to each
others, and poorly correlated to various curvatures for six test sets of
molecules. Different curvatures are weakly correlated to each other for six
test sets of molecules, but are strongly correlated to each other within each
test set of molecules. Based on correlation analysis, we construct twenty six
nontrivial nonpolar solvation models. Our numerical results reveal that the
Lennard-Jones (LJ) potential plays a vital role in nonpolar solvation modeling,
especially for molecules involving strong van der Waals interactions. It is
found that curvatures are at least as important as surface area or surface
enclosed volume in nonpolar solvation modeling. In conjugation with the GPB
model, various curvature based nonpolar solvation models are shown to offer
some of the best solvation free energy predictions for a wide range of test
sets. For example, root mean square errors from a model constituting surface
area, volume, mean curvature and LJ potential are less than 0.42 kcal/mol for
all test sets.
| [
{
"created": "Fri, 10 Jun 2016 18:46:55 GMT",
"version": "v1"
}
] | 2016-06-13 | [
[
"Nguyen",
"Duc Duy",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | This paper explores the impact of surface area, volume, curvature and Lennard-Jones potential on solvation free energy predictions. Rigidity surfaces are utilized to generate robust analytical expressions for maximum, minimum, mean and Gaussian curvatures of solvent-solute interfaces, and define a generalized Poisson-Boltzmann (GPB) equation with a smooth dielectric profile. Extensive correlation analysis is performed to examine the linear dependence of surface area, surface enclosed volume, maximum curvature, minimum curvature, mean curvature and Gaussian curvature for solvation modeling. It is found that surface area and surfaces enclosed volumes are highly correlated to each others, and poorly correlated to various curvatures for six test sets of molecules. Different curvatures are weakly correlated to each other for six test sets of molecules, but are strongly correlated to each other within each test set of molecules. Based on correlation analysis, we construct twenty six nontrivial nonpolar solvation models. Our numerical results reveal that the Lennard-Jones (LJ) potential plays a vital role in nonpolar solvation modeling, especially for molecules involving strong van der Waals interactions. It is found that curvatures are at least as important as surface area or surface enclosed volume in nonpolar solvation modeling. In conjugation with the GPB model, various curvature based nonpolar solvation models are shown to offer some of the best solvation free energy predictions for a wide range of test sets. For example, root mean square errors from a model constituting surface area, volume, mean curvature and LJ potential are less than 0.42 kcal/mol for all test sets. |
q-bio/0602026 | Hidetsugu Sakaguchi | Hidetsugu Sakaguchi | Instability of synchronized motion in nonlocally coupled neural
oscillators | 8 pages, 9 figures | null | 10.1103/PhysRevE.73.031907 | null | q-bio.NC q-bio.QM | null | We study nonlocally coupled Hodgkin-Huxley equations with excitatory and
inhibitory synaptic coupling. We investigate the linear stability of the
synchronized solution, and find numerically various nonuniform oscillatory
states such as chimera states, wavy states, clustering states, and
spatiotemporal chaos as a result of the instability.
| [
{
"created": "Tue, 28 Feb 2006 03:34:10 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Sakaguchi",
"Hidetsugu",
""
]
] | We study nonlocally coupled Hodgkin-Huxley equations with excitatory and inhibitory synaptic coupling. We investigate the linear stability of the synchronized solution, and find numerically various nonuniform oscillatory states such as chimera states, wavy states, clustering states, and spatiotemporal chaos as a result of the instability. |
1202.0694 | Rudolf Hanel Dr | Rudolf Hanel, Manfred P\"ochacker, Manuel Sch\"olling, and Stefan
Thurner | A self-organized model for cell-differentiation based on variations of
molecular decay rates | 16 pages, 5 figures | null | 10.1371/journal.pone.0036679 | null | q-bio.MN cond-mat.other | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systemic properties of living cells are the result of molecular dynamics
governed by so-called genetic regulatory networks (GRN). These networks capture
all possible features of cells and are responsible for the immense levels of
adaptation characteristic to living systems. At any point in time only small
subsets of these networks are active. Any active subset of the GRN leads to the
expression of particular sets of molecules (expression modes). The subsets of
active networks change over time, leading to the observed complex dynamics of
expression patterns. Understanding of this dynamics becomes increasingly
important in systems biology and medicine. While the importance of
transcription rates and catalytic interactions has been widely recognized in
modeling genetic regulatory systems, the understanding of the role of
degradation of biochemical agents (mRNA, protein) in regulatory dynamics
remains limited. Recent experimental data suggests that there exists a
functional relation between mRNA and protein decay rates and expression modes.
In this paper we propose a model for the dynamics of successions of sequences
of active subnetworks of the GRN. The model is able to reproduce key
characteristics of molecular dynamics, including homeostasis, multi-stability,
periodic dynamics, alternating activity, differentiability, and self-organized
critical dynamics. Moreover the model allows to naturally understand the
mechanism behind the relation between decay rates and expression modes. The
model explains recent experimental observations that decay-rates (or turnovers)
vary between differentiated tissue-classes at a general systemic level and
highlights the role of intracellular decay rate control mechanisms in cell
differentiation.
| [
{
"created": "Fri, 3 Feb 2012 13:18:19 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Hanel",
"Rudolf",
""
],
[
"Pöchacker",
"Manfred",
""
],
[
"Schölling",
"Manuel",
""
],
[
"Thurner",
"Stefan",
""
]
] | Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation. |
2110.14853 | Cole Hurwitz | Cole Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G.
Perich, Lee E. Miller, Matthias H. Hennig | Targeted Neural Dynamical Modeling | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Latent dynamics models have emerged as powerful tools for modeling and
interpreting neural population activity. Recently, there has been a focus on
incorporating simultaneously measured behaviour into these models to further
disentangle sources of neural variability in their latent space. These
approaches, however, are limited in their ability to capture the underlying
neural dynamics (e.g. linear) and in their ability to relate the learned
dynamics back to the observed behaviour (e.g. no time lag). To this end, we
introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space
model that jointly models the neural activity and external behavioural
variables. TNDM decomposes neural dynamics into behaviourally relevant and
behaviourally irrelevant dynamics; the relevant dynamics are used to
reconstruct the behaviour through a flexible linear decoder and both sets of
dynamics are used to reconstruct the neural activity through a linear decoder
with no time lag. We implement TNDM as a sequential variational autoencoder and
validate it on simulated recordings and recordings taken from the premotor and
motor cortex of a monkey performing a center-out reaching task. We show that
TNDM is able to learn low-dimensional latent dynamics that are highly
predictive of behaviour without sacrificing its fit to the neural data.
| [
{
"created": "Thu, 28 Oct 2021 02:24:09 GMT",
"version": "v1"
}
] | 2021-10-29 | [
[
"Hurwitz",
"Cole",
""
],
[
"Srivastava",
"Akash",
""
],
[
"Xu",
"Kai",
""
],
[
"Jude",
"Justin",
""
],
[
"Perich",
"Matthew G.",
""
],
[
"Miller",
"Lee E.",
""
],
[
"Hennig",
"Matthias H.",
""
]
] | Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of neural variability in their latent space. These approaches, however, are limited in their ability to capture the underlying neural dynamics (e.g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e.g. no time lag). To this end, we introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space model that jointly models the neural activity and external behavioural variables. TNDM decomposes neural dynamics into behaviourally relevant and behaviourally irrelevant dynamics; the relevant dynamics are used to reconstruct the behaviour through a flexible linear decoder and both sets of dynamics are used to reconstruct the neural activity through a linear decoder with no time lag. We implement TNDM as a sequential variational autoencoder and validate it on simulated recordings and recordings taken from the premotor and motor cortex of a monkey performing a center-out reaching task. We show that TNDM is able to learn low-dimensional latent dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data. |
2003.08278 | Shi Gu | Shikuang Deng, Shi Gu | Controllability Analysis of Functional Brain Networks | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network control theory has recently emerged as a promising approach for
understanding brain function and dynamics. By operationalizing notions of
control theory for brain networks, it offers a fundamental explanation for how
brain dynamics may be regulated by structural connectivity. While powerful, the
approach does not currently consider other non-structural explanations of brain
dynamics. Here we extend the analysis of network controllability by formalizing
the evolution of neural signals as a function of effective inter-regional
coupling and pairwise signal covariance. We find that functional
controllability characterizes a region's impact on the capacity for the whole
system to shift between states, and significantly predicts individual
difference in performance on cognitively demanding tasks including those task
working memory, language, and emotional intelligence. When comparing
measurements from functional and structural controllability, we observed
consistent relations between average and modal controllability, supporting
prior work. In the same comparison, we also observed distinct relations between
controllability and synchronizability, reflecting the additional information
obtained from functional signals. Our work suggests that network control theory
can serve as a systematic analysis tool to understand the energetics of brain
state transitions, associated cognitive processes, and subsequent behaviors.
| [
{
"created": "Wed, 18 Mar 2020 15:36:39 GMT",
"version": "v1"
}
] | 2020-03-20 | [
[
"Deng",
"Shikuang",
""
],
[
"Gu",
"Shi",
""
]
] | Network control theory has recently emerged as a promising approach for understanding brain function and dynamics. By operationalizing notions of control theory for brain networks, it offers a fundamental explanation for how brain dynamics may be regulated by structural connectivity. While powerful, the approach does not currently consider other non-structural explanations of brain dynamics. Here we extend the analysis of network controllability by formalizing the evolution of neural signals as a function of effective inter-regional coupling and pairwise signal covariance. We find that functional controllability characterizes a region's impact on the capacity for the whole system to shift between states, and significantly predicts individual difference in performance on cognitively demanding tasks including those task working memory, language, and emotional intelligence. When comparing measurements from functional and structural controllability, we observed consistent relations between average and modal controllability, supporting prior work. In the same comparison, we also observed distinct relations between controllability and synchronizability, reflecting the additional information obtained from functional signals. Our work suggests that network control theory can serve as a systematic analysis tool to understand the energetics of brain state transitions, associated cognitive processes, and subsequent behaviors. |
2406.02014 | Wanli Ma | Wanli Ma and Xuegang Tang and Jin Gu and Ying Wang and Yuling Xia | Understanding Auditory Evoked Brain Signal via Physics-informed
Embedding Network with Multi-Task Transformer | null | null | null | null | q-bio.NC cs.LG cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the fields of brain-computer interaction and cognitive neuroscience,
effective decoding of auditory signals from task-based functional magnetic
resonance imaging (fMRI) is key to understanding how the brain processes
complex auditory information. Although existing methods have enhanced decoding
capabilities, limitations remain in information utilization and model
representation. To overcome these challenges, we propose an innovative
multi-task learning model, Physics-informed Embedding Network with Multi-Task
Transformer (PEMT-Net), which enhances decoding performance through
physics-informed embedding and deep learning techniques. PEMT-Net consists of
two principal components: feature augmentation and classification. For feature
augmentation, we propose a novel approach by creating neural embedding graphs
via node embedding, utilizing random walks to simulate the physical diffusion
of neural information. This method captures both local and non-local
information overflow and proposes a position encoding based on relative
physical coordinates. In the classification segment, we propose adaptive
embedding fusion to maximally capture linear and non-linear characteristics.
Furthermore, we propose an innovative parameter-sharing mechanism to optimize
the retention and learning of extracted features. Experiments on a specific
dataset demonstrate PEMT-Net's significant performance in multi-task auditory
signal decoding, surpassing existing methods and offering new insights into the
brain's mechanisms for processing complex auditory information.
| [
{
"created": "Tue, 4 Jun 2024 06:53:32 GMT",
"version": "v1"
}
] | 2024-06-05 | [
[
"Ma",
"Wanli",
""
],
[
"Tang",
"Xuegang",
""
],
[
"Gu",
"Jin",
""
],
[
"Wang",
"Ying",
""
],
[
"Xia",
"Yuling",
""
]
] | In the fields of brain-computer interaction and cognitive neuroscience, effective decoding of auditory signals from task-based functional magnetic resonance imaging (fMRI) is key to understanding how the brain processes complex auditory information. Although existing methods have enhanced decoding capabilities, limitations remain in information utilization and model representation. To overcome these challenges, we propose an innovative multi-task learning model, Physics-informed Embedding Network with Multi-Task Transformer (PEMT-Net), which enhances decoding performance through physics-informed embedding and deep learning techniques. PEMT-Net consists of two principal components: feature augmentation and classification. For feature augmentation, we propose a novel approach by creating neural embedding graphs via node embedding, utilizing random walks to simulate the physical diffusion of neural information. This method captures both local and non-local information overflow and proposes a position encoding based on relative physical coordinates. In the classification segment, we propose adaptive embedding fusion to maximally capture linear and non-linear characteristics. Furthermore, we propose an innovative parameter-sharing mechanism to optimize the retention and learning of extracted features. Experiments on a specific dataset demonstrate PEMT-Net's significant performance in multi-task auditory signal decoding, surpassing existing methods and offering new insights into the brain's mechanisms for processing complex auditory information. |
1412.2678 | Tyas Fiantoro | Tyas Pandu Fiantoro, Akhmad Kharis Nugroho | In-vitro iontophoresis of pinneal Sus scrofa skin and its transport flux
modelling as influenced by time and current density | 4 pages, 4 figures, infant version | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The purpose of this study was to enhance the existing time dependent flux
model for the transdermal iontophoretic transport of drugs. This study
evaluated the flux data as influenced by time and current density. In vitro
iontophoresis performed on the piglet (Sus scrofa) necropsy-taken medial scapha
pinneal skin that mounted in the U shaped sink chamber. Iontophoresis of
atenolol with a constant dose of 1000 ppm was implemented for 3 hours with
acceptor phase sampling every 30 minutes. Data were analised based on
exponential fitting of each current density value to produce a current density
dependent flux model. This model then combined with the time differential model
of flux to produce a flux model that takes account of both current density and
time.
| [
{
"created": "Mon, 8 Dec 2014 17:47:01 GMT",
"version": "v1"
}
] | 2014-12-09 | [
[
"Fiantoro",
"Tyas Pandu",
""
],
[
"Nugroho",
"Akhmad Kharis",
""
]
] | The purpose of this study was to enhance the existing time dependent flux model for the transdermal iontophoretic transport of drugs. This study evaluated the flux data as influenced by time and current density. In vitro iontophoresis performed on the piglet (Sus scrofa) necropsy-taken medial scapha pinneal skin that mounted in the U shaped sink chamber. Iontophoresis of atenolol with a constant dose of 1000 ppm was implemented for 3 hours with acceptor phase sampling every 30 minutes. Data were analised based on exponential fitting of each current density value to produce a current density dependent flux model. This model then combined with the time differential model of flux to produce a flux model that takes account of both current density and time. |
2311.00012 | Ariel Chernomoretz | Tom\'as Vega Waichman, M. Luz Vercesi, Ariel A. Berardino, Maximiliano
S. Beckel, Damiana Giacomini, Natal\'i B. Rasetto, Magal\'i Herrero, Daniela
J. Di Bella, Paola Arlotta, Alejandro F. Schinder and Ariel Chernomoretz | scX: A user-friendly tool for scRNA-seq exploration | 10 pages, 2 figures. Source code can be downloaded from
https://github.com/chernolabs/scX. User manual available at
https://chernolabs.github.io/scX/. Docker image available from dockerhub as
chernolabs/scx | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore
biological systems. Nevertheless, proficient expertise is essential for
handling and interpreting the data. In this paper, we present scX, an R package
built on the Shiny framework that streamlines the analysis, exploration, and
visualization of single-cell experiments. With an interactive graphic
interface, implemented as a web application, scX provides easy access to key
scRNAseq analyses, including marker identification, gene expression profiling,
and differential gene expression analysis. Additionally, scX seamlessly
integrates with commonly used single-cell Seurat and SingleCellExperiment R
objects, resulting in efficient processing and visualization of varied
datasets. Overall, scX serves as a valuable and user-friendly tool for
effortless exploration and sharing of single-cell data, simplifying some of the
complexities inherent in scRNAseq analysis.
| [
{
"created": "Tue, 31 Oct 2023 13:03:39 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Mar 2024 15:55:00 GMT",
"version": "v2"
}
] | 2024-03-08 | [
[
"Waichman",
"Tomás Vega",
""
],
[
"Vercesi",
"M. Luz",
""
],
[
"Berardino",
"Ariel A.",
""
],
[
"Beckel",
"Maximiliano S.",
""
],
[
"Giacomini",
"Damiana",
""
],
[
"Rasetto",
"Natalí B.",
""
],
[
"Herrero",
"Magalí",
""
],
[
"Di Bella",
"Daniela J.",
""
],
[
"Arlotta",
"Paola",
""
],
[
"Schinder",
"Alejandro F.",
""
],
[
"Chernomoretz",
"Ariel",
""
]
] | Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis. |
1301.5406 | Keith Bradnam | Keith R. Bradnam (1), Joseph N. Fass (1), Anton Alexandrov (36), Paul
Baranay (2), Michael Bechner (39), \.Inan\c{c} Birol (33), S\'ebastien
Boisvert, (11), Jarrod A. Chapman (20), Guillaume Chapuis (7,9), Rayan Chikhi
(7,9), Hamidreza Chitsaz (6), Wen-Chi Chou (14,16), Jacques Corbeil (10,13),
Cristian Del Fabbro (17), T. Roderick Docking (33), Richard Durbin (34), Dent
Earl (40), Scott Emrich (3), Pavel Fedotov (36), Nuno A. Fonseca (30,35),
Ganeshkumar Ganapathy (38), Richard A. Gibbs (32), Sante Gnerre (22),
\'El\'enie Godzaridis (11), Steve Goldstein (39), Matthias Haimel (30), Giles
Hall (22), David Haussler (40), Joseph B. Hiatt (41), Isaac Y. Ho (20), Jason
Howard (38), Martin Hunt (34), Shaun D. Jackman (33), David B Jaffe (22),
Erich Jarvis (38), Huaiyang Jiang (32), Sergey Kazakov (36), Paul J. Kersey
(30), Jacob O. Kitzman (41), James R. Knight (37), Sergey Koren (24,25),
Tak-Wah Lam (29), Dominique Lavenier (7,8,9), Fran\c{c}ois Laviolette (12),
Yingrui Li (28,29), Zhenyu Li (28), Binghang Liu (28), Yue Liu (32), Ruibang
Luo (28,29), Iain MacCallum (22), Matthew D MacManes (5), Nicolas Maillet
(8,9), Sergey Melnikov (36), Bruno Miguel Vieira (31), Delphine Naquin (8,9),
Zemin Ning (34), Thomas D. Otto (34), Benedict Paten (40), Oct\'avio S. Paulo
(31), Adam M. Phillippy (24,25), Francisco Pina-Martins (31), Michael Place
(39), Dariusz Przybylski (22), Xiang Qin (32), Carson Qu (32), Filipe J
Ribeiro (22), Stephen Richards (32), Daniel S. Rokhsar (20,21), J. Graham
Ruby (26,27), Simone Scalabrin (17), Michael C. Schatz (4), David C. Schwartz
(39), Alexey Sergushichev (36), Ted Sharpe (22), Timothy I. Shaw (14,15), Jay
Shendure (41), Yujian Shi (28), Jared T. Simpson (34), Henry Song (32), Fedor
Tsarev (36), Francesco Vezzi (19), Riccardo Vicedomini (17,18), Jun Wang
(28), Kim C. Worley (32), Shuangye Yin (22), Siu-Ming Yiu (29), Jianying Yuan
(28), Guojie Zhang (28), Hao Zhang (28), Shiguo Zhou (39), and Ian F. Korf
(1) ((1) UC Davis, (2) Yale University, (3) University of Notre Dame, (4)
Cold Spring Harbor Laboratory, (5) UC Berkeley, (6) Wayne State University,
(7) ENS Cachan/IRISA, (8) INRIA, (9) CNRS/Symbiose IRISA, (10) CHUQ Research
Center, (11) Laval University, (12) Laval University, (13) Laval University,
(14) University of Georgia, (15) University of Georgia, (16) Institute of
Aging Research, (17) University of Udine, (18) University of Udine, (19) KTH
Royal Institute of Technology, (20) DOE Joint Genome Institute, (21) UC
Berkeley, (22) Broad Institute, (23) New York Genome Center, (24) National
Biodefense Analysis and Countermeasures Center, (25) University of Maryland,
(26) UC San Francisco, (27) Howard Hughes Medical Institute, (28)
BGI-Shenzhen, (29) HKU-BGI Bioinformatics Algorithms and Core Technology
Research Laboratory, (30) EMBL-European Bioinformatics Institute, (31)
University of Lisbon, (32) Baylor College of Medicine, (33) British Columbia
Cancer Agency, (34) The Wellcome Trust Sanger Institute, (35) CRACS - INESC
TEC, (36) National Research University of Information Technology, (37) 454
Life Sciences, (38) Duke University Medical Center, (39) UW-Biotechnology
Center, (40) UC Santa Cruz, (41) University of Washington) | Assemblathon 2: evaluating de novo methods of genome assembly in three
vertebrate species | Additional files available at
http://korflab.ucdavis.edu/Datasets/Assemblathon/Assemblathon2/Additional_files/
Major changes 1. Accessions for the 3 read data sets have now been included
2. New file: spreadsheet containing details of all Study, Sample, Run, &
Experiment identifiers 3. Made miscellaneous changes to address reviewers
comments. DOIs added to GigaDB datasets | GigaScience 2:10 (2013) | 10.1186/2047-217X-2-10 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background - The process of generating raw genome sequence data continues to
become cheaper, faster, and more accurate. However, assembly of such data into
high-quality, finished genome sequences remains challenging. Many genome
assembly tools are available, but they differ greatly in terms of their
performance (speed, scalability, hardware requirements, acceptance of newer
read technologies) and in their final output (composition of assembled
sequence). More importantly, it remains largely unclear how to best assess the
quality of assembled genome sequences. The Assemblathon competitions are
intended to assess current state-of-the-art methods in genome assembly. Results
- In Assemblathon 2, we provided a variety of sequence data to be assembled for
three vertebrate species (a bird, a fish, and snake). This resulted in a total
of 43 submitted assemblies from 21 participating teams. We evaluated these
assemblies using a combination of optical map data, Fosmid sequences, and
several statistical methods. From over 100 different metrics, we chose ten key
measures by which to assess the overall quality of the assemblies. Conclusions
- Many current genome assemblers produced useful assemblies, containing a
significant representation of their genes, regulatory sequences, and overall
genome structure. However, the high degree of variability between the entries
suggests that there is still much room for improvement in the field of genome
assembly and that approaches which work well in assembling the genome of one
species may not necessarily work well for another.
| [
{
"created": "Wed, 23 Jan 2013 06:16:08 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Jun 2013 01:00:42 GMT",
"version": "v2"
},
{
"created": "Thu, 27 Jun 2013 16:00:29 GMT",
"version": "v3"
}
] | 2015-02-02 | [
[
"Bradnam",
"Keith R.",
""
],
[
"Fass",
"Joseph N.",
""
],
[
"Alexandrov",
"Anton",
""
],
[
"Baranay",
"Paul",
""
],
[
"Bechner",
"Michael",
""
],
[
"Birol",
"İnanç",
""
],
[
"Boisvert",
"Sébastien",
""
],
[
"Chapman",
"Jarrod A.",
""
],
[
"Chapuis",
"Guillaume",
""
],
[
"Chikhi",
"Rayan",
""
],
[
"Chitsaz",
"Hamidreza",
""
],
[
"Chou",
"Wen-Chi",
""
],
[
"Corbeil",
"Jacques",
""
],
[
"Del Fabbro",
"Cristian",
""
],
[
"Docking",
"T. Roderick",
""
],
[
"Durbin",
"Richard",
""
],
[
"Earl",
"Dent",
""
],
[
"Emrich",
"Scott",
""
],
[
"Fedotov",
"Pavel",
""
],
[
"Fonseca",
"Nuno A.",
""
],
[
"Ganapathy",
"Ganeshkumar",
""
],
[
"Gibbs",
"Richard A.",
""
],
[
"Gnerre",
"Sante",
""
],
[
"Godzaridis",
"Élénie",
""
],
[
"Goldstein",
"Steve",
""
],
[
"Haimel",
"Matthias",
""
],
[
"Hall",
"Giles",
""
],
[
"Haussler",
"David",
""
],
[
"Hiatt",
"Joseph B.",
""
],
[
"Ho",
"Isaac Y.",
""
],
[
"Howard",
"Jason",
""
],
[
"Hunt",
"Martin",
""
],
[
"Jackman",
"Shaun D.",
""
],
[
"Jaffe",
"David B",
""
],
[
"Jarvis",
"Erich",
""
],
[
"Jiang",
"Huaiyang",
""
],
[
"Kazakov",
"Sergey",
""
],
[
"Kersey",
"Paul J.",
""
],
[
"Kitzman",
"Jacob O.",
""
],
[
"Knight",
"James R.",
""
],
[
"Koren",
"Sergey",
""
],
[
"Lam",
"Tak-Wah",
""
],
[
"Lavenier",
"Dominique",
""
],
[
"Laviolette",
"François",
""
],
[
"Li",
"Yingrui",
""
],
[
"Li",
"Zhenyu",
""
],
[
"Liu",
"Binghang",
""
],
[
"Liu",
"Yue",
""
],
[
"Luo",
"Ruibang",
""
],
[
"MacCallum",
"Iain",
""
],
[
"MacManes",
"Matthew D",
""
],
[
"Maillet",
"Nicolas",
""
],
[
"Melnikov",
"Sergey",
""
],
[
"Vieira",
"Bruno Miguel",
""
],
[
"Naquin",
"Delphine",
""
],
[
"Ning",
"Zemin",
""
],
[
"Otto",
"Thomas D.",
""
],
[
"Paten",
"Benedict",
""
],
[
"Paulo",
"Octávio S.",
""
],
[
"Phillippy",
"Adam M.",
""
],
[
"Pina-Martins",
"Francisco",
""
],
[
"Place",
"Michael",
""
],
[
"Przybylski",
"Dariusz",
""
],
[
"Qin",
"Xiang",
""
],
[
"Qu",
"Carson",
""
],
[
"Ribeiro",
"Filipe J",
""
],
[
"Richards",
"Stephen",
""
],
[
"Rokhsar",
"Daniel S.",
""
],
[
"Ruby",
"J. Graham",
""
],
[
"Scalabrin",
"Simone",
""
],
[
"Schatz",
"Michael C.",
""
],
[
"Schwartz",
"David C.",
""
],
[
"Sergushichev",
"Alexey",
""
],
[
"Sharpe",
"Ted",
""
],
[
"Shaw",
"Timothy I.",
""
],
[
"Shendure",
"Jay",
""
],
[
"Shi",
"Yujian",
""
],
[
"Simpson",
"Jared T.",
""
],
[
"Song",
"Henry",
""
],
[
"Tsarev",
"Fedor",
""
],
[
"Vezzi",
"Francesco",
""
],
[
"Vicedomini",
"Riccardo",
""
],
[
"Wang",
"Jun",
""
],
[
"Worley",
"Kim C.",
""
],
[
"Yin",
"Shuangye",
""
],
[
"Yiu",
"Siu-Ming",
""
],
[
"Yuan",
"Jianying",
""
],
[
"Zhang",
"Guojie",
""
],
[
"Zhang",
"Hao",
""
],
[
"Zhou",
"Shiguo",
""
],
[
"Korf",
"Ian F.",
""
]
] | Background - The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results - In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions - Many current genome assemblers produced useful assemblies, containing a significant representation of their genes, regulatory sequences, and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another. |
2209.06504 | Lian Yang | Lian Yang, Ming Xiao, Xian Li, Ya-lan Wang | Clinicopathological correlation of p40/TTF1 co-expression in NSCLC and
review of related literature | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | TTF1 and {\Delta}Np63/p40 have been used to differentiate ADC and SQC in
hypofractionated NSCLC because of their sensitivity and specificity. There are
few cases where TTF1 and {\Delta}Np63/p40 are expressed together in the same
tumour cells, and little is known about the clinicopathological features,
treatment and prognosis of such cases. We investigated the electron microscopic
features, immunohistochemical expression and molecular variation of a case of
TTF1/p40 co-expressing NSCLC and reviewed and summarised the relevant
literature. Our patient was a 58-year-old male with a CT showing a left-sided
lung occupancy. As in all other cases reported in the literature, the tumour
showed a solid growth pattern with polygonal cells, eosinophilic cytoplasm and
clearly visible nuclear fission. Immunohistochemistry showed positive for
TTF-1, p40, CK5/6, CK7, P63 and p53, and negative for NapsinA and ALK. Electron
microscopy showed tumour cells characterised by bidirectional differentiation
of adenocytes and squamous cells, consistent with previous reports.
Second-generation sequencing suggested co-mutation of STK11/LKB1 and NF1 genes
in this case. Mutations in STK11/LKB1 and NF1 genes have been found in ADC and
SQC and are often associated with drug resistance and poor prognosis, but
STK11/NF1 co-mutation has not been reported and more cases are needed to reveal
the association. p40/TTF1 co-expression in NSCLC may be an under-recognised
variant of NSCLC The origin may be a double positive stem cell-like basal cell
located in the distal airway, with rapid clinical progression and poor
prognosis.
| [
{
"created": "Wed, 14 Sep 2022 09:03:08 GMT",
"version": "v1"
}
] | 2022-09-15 | [
[
"Yang",
"Lian",
""
],
[
"Xiao",
"Ming",
""
],
[
"Li",
"Xian",
""
],
[
"Wang",
"Ya-lan",
""
]
] | TTF1 and {\Delta}Np63/p40 have been used to differentiate ADC and SQC in hypofractionated NSCLC because of their sensitivity and specificity. There are few cases where TTF1 and {\Delta}Np63/p40 are expressed together in the same tumour cells, and little is known about the clinicopathological features, treatment and prognosis of such cases. We investigated the electron microscopic features, immunohistochemical expression and molecular variation of a case of TTF1/p40 co-expressing NSCLC and reviewed and summarised the relevant literature. Our patient was a 58-year-old male with a CT showing a left-sided lung occupancy. As in all other cases reported in the literature, the tumour showed a solid growth pattern with polygonal cells, eosinophilic cytoplasm and clearly visible nuclear fission. Immunohistochemistry showed positive for TTF-1, p40, CK5/6, CK7, P63 and p53, and negative for NapsinA and ALK. Electron microscopy showed tumour cells characterised by bidirectional differentiation of adenocytes and squamous cells, consistent with previous reports. Second-generation sequencing suggested co-mutation of STK11/LKB1 and NF1 genes in this case. Mutations in STK11/LKB1 and NF1 genes have been found in ADC and SQC and are often associated with drug resistance and poor prognosis, but STK11/NF1 co-mutation has not been reported and more cases are needed to reveal the association. p40/TTF1 co-expression in NSCLC may be an under-recognised variant of NSCLC The origin may be a double positive stem cell-like basal cell located in the distal airway, with rapid clinical progression and poor prognosis. |
2108.05964 | Enrico Catalano | Enrico Catalano | Biophysical interaction, nanotoxicology evaluation, and biocompatibility
and biosafety of metal nanoparticles | 27 pages | null | null | null | q-bio.QM physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nanotechnology has been one of the fastest growing fields in the last three
decades. Nanomaterials (sized 1-100 nm) has a wide spectrum of potential
applications in many fields, applied as coating materials or in treatment and
diagnosis. Nowadays, nanoparticles of both metallic and non-metallic origin are
under investigation and development for applications in various fields of
biology/therapeutics. Specifically, we show the correlations between the
physicochemistry and biophysical specificity of metal nanoparticles and their
uptake, transport, and biodistribution in cells, at the molecular, cellular,
and whole organism level. Physiologically important metals are present in the
human body with a wide range of biological activities. Some of these metals are
magnesium, chromium, manganese, iron, cobalt, copper, zinc, selenium and
molybdenum. Metals used in nanotechnology have to be biocompatible with the
human system in terms of absorption, assimilation, excretion, and side effects.
These metals are synthesized in the form of nanoparticles by different physical
and chemical methods. Nanotoxicological studies of metal nanoparticles are
intended to determine whether and to what extent their properties may pose a
threat to the environment and to human beings. An overview of metal and metal
oxide nanoparticles, their applications, and the potential for human exposure
is provided, and it is integrated by a discussion of general principles of
nanoparticle-induced toxicity and methods for toxicity testing of
nanomaterials. This review wants to focus on establishing metal nanoparticles
of physiological importance to be the best candidates for future
nanotechnological tools and medicines, owing to the acceptability and safety in
the human body. This can only be successful if these particles are synthesized
with a better biocompatibility and low or no toxicity.
| [
{
"created": "Wed, 4 Aug 2021 10:08:54 GMT",
"version": "v1"
}
] | 2021-08-16 | [
[
"Catalano",
"Enrico",
""
]
] | Nanotechnology has been one of the fastest growing fields in the last three decades. Nanomaterials (sized 1-100 nm) has a wide spectrum of potential applications in many fields, applied as coating materials or in treatment and diagnosis. Nowadays, nanoparticles of both metallic and non-metallic origin are under investigation and development for applications in various fields of biology/therapeutics. Specifically, we show the correlations between the physicochemistry and biophysical specificity of metal nanoparticles and their uptake, transport, and biodistribution in cells, at the molecular, cellular, and whole organism level. Physiologically important metals are present in the human body with a wide range of biological activities. Some of these metals are magnesium, chromium, manganese, iron, cobalt, copper, zinc, selenium and molybdenum. Metals used in nanotechnology have to be biocompatible with the human system in terms of absorption, assimilation, excretion, and side effects. These metals are synthesized in the form of nanoparticles by different physical and chemical methods. Nanotoxicological studies of metal nanoparticles are intended to determine whether and to what extent their properties may pose a threat to the environment and to human beings. An overview of metal and metal oxide nanoparticles, their applications, and the potential for human exposure is provided, and it is integrated by a discussion of general principles of nanoparticle-induced toxicity and methods for toxicity testing of nanomaterials. This review wants to focus on establishing metal nanoparticles of physiological importance to be the best candidates for future nanotechnological tools and medicines, owing to the acceptability and safety in the human body. This can only be successful if these particles are synthesized with a better biocompatibility and low or no toxicity. |
2402.07268 | Zehao Dong | Zehao Dong, Qihang Zhao, Philip R.O. Payne, Michael A Province, Carlos
Cruchaga, Muhan Zhang, Tianyu Zhao, Yixin Chen, Fuhai Li | Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker
Identification with PathFormer | null | null | 10.21203/rs.3.rs-3576068/v1 | null | q-bio.GN cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomarker identification is critical for precise disease diagnosis and
understanding disease pathogenesis in omics data analysis, like using fold
change and regression analysis. Graph neural networks (GNNs) have been the
dominant deep learning model for analyzing graph-structured data. However, we
found two major limitations of existing GNNs in omics data analysis, i.e.,
limited-prediction (diagnosis) accuracy and limited-reproducible biomarker
identification capacity across multiple datasets. The root of the challenges is
the unique graph structure of biological signaling pathways, which consists of
a large number of targets and intensive and complex signaling interactions
among these targets. To resolve these two challenges, in this study, we
presented a novel GNN model architecture, named PathFormer, which
systematically integrate signaling network, priori knowledge and omics data to
rank biomarkers and predict disease diagnosis. In the comparison results,
PathFormer outperformed existing GNN models significantly in terms of highly
accurate prediction capability ( 30% accuracy improvement in disease diagnosis
compared with existing GNN models) and high reproducibility of biomarker
ranking across different datasets. The improvement was confirmed using two
independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The
PathFormer model can be directly applied to other omics data analysis studies.
| [
{
"created": "Sun, 11 Feb 2024 18:23:54 GMT",
"version": "v1"
}
] | 2024-02-13 | [
[
"Dong",
"Zehao",
""
],
[
"Zhao",
"Qihang",
""
],
[
"Payne",
"Philip R. O.",
""
],
[
"Province",
"Michael A",
""
],
[
"Cruchaga",
"Carlos",
""
],
[
"Zhang",
"Muhan",
""
],
[
"Zhao",
"Tianyu",
""
],
[
"Chen",
"Yixin",
""
],
[
"Li",
"Fuhai",
""
]
] | Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction (diagnosis) accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability ( 30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies. |
q-bio/0603010 | Alexei Vazquez | Alexei Vazquez | Spreading dynamics on small-world networks with connectivity
fluctuations and correlations | 10 pages, 1 figure, RevTex. Phys. Rev. E (In press) | Phys. Rev. E 74, 056101 (2006) | 10.1103/PhysRevE.74.056101 | null | q-bio.PE cond-mat.dis-nn physics.soc-ph q-bio.QM | null | Infectious diseases and computer malwares spread among humans and computers
through the network of contacts among them. These networks are characterized by
wide connectivity fluctuations, connectivity correlations and the small-world
property. In a previous work [A. Vazquez, Phys. Rev. Lett. 96, 038702 (2006)] I
have shown that the connectivity fluctuations together with the small-world
property lead to a novel spreading law, characterized by an initial power law
growth with an exponent determined by the average node distance on the network.
Here I extend these results to consider the influence of connectivity
correlations which are generally observed in real networks. I show that
assortative and disassortative connectivity correlations enhance and diminish,
respectively, the range of validity of this spreading law. As a corollary I
obtain the region of connectivity fluctuations and degree correlations
characterized by the absence of an epidemic threshold. These results are
relevant for the spreading of infectious diseases, rumors, and information
among humans and the spreading of computer viruses, email worms and hoaxes
among computer users.
| [
{
"created": "Tue, 7 Mar 2006 18:55:41 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Sep 2006 15:42:41 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Vazquez",
"Alexei",
""
]
] | Infectious diseases and computer malwares spread among humans and computers through the network of contacts among them. These networks are characterized by wide connectivity fluctuations, connectivity correlations and the small-world property. In a previous work [A. Vazquez, Phys. Rev. Lett. 96, 038702 (2006)] I have shown that the connectivity fluctuations together with the small-world property lead to a novel spreading law, characterized by an initial power law growth with an exponent determined by the average node distance on the network. Here I extend these results to consider the influence of connectivity correlations which are generally observed in real networks. I show that assortative and disassortative connectivity correlations enhance and diminish, respectively, the range of validity of this spreading law. As a corollary I obtain the region of connectivity fluctuations and degree correlations characterized by the absence of an epidemic threshold. These results are relevant for the spreading of infectious diseases, rumors, and information among humans and the spreading of computer viruses, email worms and hoaxes among computer users. |
1801.06606 | Maurizio De Pitt\`a | Maurizio De Pitt\`a, Eshel Ben-Jacob, Hugues Berry | G protein-coupled receptor-mediated calcium signaling in astrocytes | 35 pages, 6 figures, 1 table, 3 appendices (book chapter) | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Astrocytes express a large variety of G~protein-coupled receptors (GPCRs)
which mediate the transduction of extracellular signals into intracellular
calcium responses. This transduction is provided by a complex network of
biochemical reactions which mobilizes a wealth of possible calcium-mobilizing
second messenger molecules. Inositol 1,4,5-trisphosphate is probably the best
known of these molecules whose enzymes for its production and degradation are
nonetheless calcium-dependent. We present a biophysical modeling approach based
on the assumption of Michaelis-Menten enzyme kinetics, to effectively describe
GPCR-mediated astrocytic calcium signals. Our model is then used to study
different mechanisms at play in stimulus encoding by shape and frequency of
calcium oscillations in astrocytes.
| [
{
"created": "Sat, 20 Jan 2018 00:15:08 GMT",
"version": "v1"
}
] | 2018-01-23 | [
[
"De Pittà",
"Maurizio",
""
],
[
"Ben-Jacob",
"Eshel",
""
],
[
"Berry",
"Hugues",
""
]
] | Astrocytes express a large variety of G~protein-coupled receptors (GPCRs) which mediate the transduction of extracellular signals into intracellular calcium responses. This transduction is provided by a complex network of biochemical reactions which mobilizes a wealth of possible calcium-mobilizing second messenger molecules. Inositol 1,4,5-trisphosphate is probably the best known of these molecules whose enzymes for its production and degradation are nonetheless calcium-dependent. We present a biophysical modeling approach based on the assumption of Michaelis-Menten enzyme kinetics, to effectively describe GPCR-mediated astrocytic calcium signals. Our model is then used to study different mechanisms at play in stimulus encoding by shape and frequency of calcium oscillations in astrocytes. |
2003.09976 | Connor Brennan | Connor Brennan and Alex Proekt | LOOPER: Inferring computational algorithms enacted by neuronal
population dynamics | 82 pages, 5 figures, 9 supplementary figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recording simultaneous activity of hundreds of neurons is now possible.
Existing methods can model such population activity, but do not directly reveal
the computations used by the brain. We present a fully unsupervised method that
models neuronal activity and reveals the computational strategy. The method
constructs a topological model of neuronal dynamics consisting of
interconnected loops. Transitions between loops mark computationally-salient
decisions. We accurately model activation of 100s of neurons in the primate
cortex during a working memory task. Dynamics of a recurrent neural network
(RNN) trained on the same task are topologically identical suggesting that a
similar computational strategy is used. The RNN trained on a modified dataset,
however, reveals a different topology. This topology predicts specific novel
stimuli that consistently elicit incorrect responses with near perfect
accuracy. Thus, our methodology yields a quantitative model of neuronal
activity and reveals the computational strategy used to solve the task.
| [
{
"created": "Sun, 22 Mar 2020 19:44:19 GMT",
"version": "v1"
}
] | 2020-03-24 | [
[
"Brennan",
"Connor",
""
],
[
"Proekt",
"Alex",
""
]
] | Recording simultaneous activity of hundreds of neurons is now possible. Existing methods can model such population activity, but do not directly reveal the computations used by the brain. We present a fully unsupervised method that models neuronal activity and reveals the computational strategy. The method constructs a topological model of neuronal dynamics consisting of interconnected loops. Transitions between loops mark computationally-salient decisions. We accurately model activation of 100s of neurons in the primate cortex during a working memory task. Dynamics of a recurrent neural network (RNN) trained on the same task are topologically identical suggesting that a similar computational strategy is used. The RNN trained on a modified dataset, however, reveals a different topology. This topology predicts specific novel stimuli that consistently elicit incorrect responses with near perfect accuracy. Thus, our methodology yields a quantitative model of neuronal activity and reveals the computational strategy used to solve the task. |
1704.01148 | T.R. Leffler | T.R. Leffler | A Model for Accounting for Qualia Within the Physical Framework of the
Natural Sciences | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper engages the hard problem of consciousness (Chalmers, 1995) by
examining the enigmatic relationship between the neural correlates of
consciousness (NCCs) and their associated qualia. The central premise is that
the NCCs have both quantifiable (physical) aspects and, distinctively from
other physical systems, also have in certain states unquantifiable aspects
(namely, qualia). The reconciliation of these apparently disparate aspects
within a physical framework has proven elusive, hence qualia's omission from
the physicalist account of the world (Jackson, 1982). This paper proposes a
novel model in which the duality - or "mixed quantifiability" - of the NCCs can
be mathematically represented via a specific category of equations - those
featuring singularities. Consequently, this implies that qualia correspond to
singularities in the mathematical representations of certain aspects of the
NCCs, thereby offering a model for accounting for qualia within the physical
framework of natural sciences. The proposed model may have been foreshadowed by
Srinivasa Ramanujan. In addition to its theoretical implications, it could have
practical implications, including for artificial intelligence (AI) and
artificial consciousness (AC).
| [
{
"created": "Tue, 4 Apr 2017 18:32:58 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Jul 2017 04:58:27 GMT",
"version": "v2"
},
{
"created": "Sat, 5 Jan 2019 18:58:53 GMT",
"version": "v3"
},
{
"created": "Sun, 25 Aug 2019 19:57:45 GMT",
"version": "v4"
},
{
"created": "Thu, 3 Aug 2023 02:19:02 GMT",
"version": "v5"
}
] | 2023-08-04 | [
[
"Leffler",
"T. R.",
""
]
] | This paper engages the hard problem of consciousness (Chalmers, 1995) by examining the enigmatic relationship between the neural correlates of consciousness (NCCs) and their associated qualia. The central premise is that the NCCs have both quantifiable (physical) aspects and, distinctively from other physical systems, also have in certain states unquantifiable aspects (namely, qualia). The reconciliation of these apparently disparate aspects within a physical framework has proven elusive, hence qualia's omission from the physicalist account of the world (Jackson, 1982). This paper proposes a novel model in which the duality - or "mixed quantifiability" - of the NCCs can be mathematically represented via a specific category of equations - those featuring singularities. Consequently, this implies that qualia correspond to singularities in the mathematical representations of certain aspects of the NCCs, thereby offering a model for accounting for qualia within the physical framework of natural sciences. The proposed model may have been foreshadowed by Srinivasa Ramanujan. In addition to its theoretical implications, it could have practical implications, including for artificial intelligence (AI) and artificial consciousness (AC). |
2206.06145 | Xizhe Zhang | Xizhe Zhang, Chunyu Pan, Xinru Wei, Meng Yu, Shuangjie Liu, Jun An,
Jieping Yang, Baojun Wei, Wenjun Hao, Yang Yao, Yuyan Zhu, Weixiong Zhang | Identification of cancer-keeping genes as therapeutic targets by finding
network control hubs | Contact the corresponding authors for supplementary material | null | null | null | q-bio.MN cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Finding cancer driver genes has been a focal theme of cancer research and
clinical studies. One of the recent approaches is based on network structural
controllability that focuses on finding a control scheme and driver genes that
can steer the cell from an arbitrary state to a designated state. While
theoretically sound, this approach is impractical for many reasons, e.g., the
control scheme is often not unique and half of the nodes may be driver genes
for the cell. We developed a novel approach that transcends structural
controllability. Instead of considering driver genes for one control scheme, we
considered control hub genes that reside in the middle of a control path of
every control scheme. Control hubs are the most vulnerable spots for
controlling the cell and exogenous stimuli on them may render the cell
uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied
them to a gene regulatory network of bladder cancer (BLCA). All the genes on
the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the
importance of these genes and the two pathways in cancer. A smaller set of 35
sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six
sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were
subjected to small-interferencing-RNA knockdown in four cell lines to validate
their effects on the proliferation or migration of cancer cells. Knocking down
RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor
xenografts in the mouse model. Combined, our results demonstrated the value of
CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an
effective means for studying and characterizing cancer etiology.
| [
{
"created": "Mon, 13 Jun 2022 13:29:12 GMT",
"version": "v1"
}
] | 2022-06-14 | [
[
"Zhang",
"Xizhe",
""
],
[
"Pan",
"Chunyu",
""
],
[
"Wei",
"Xinru",
""
],
[
"Yu",
"Meng",
""
],
[
"Liu",
"Shuangjie",
""
],
[
"An",
"Jun",
""
],
[
"Yang",
"Jieping",
""
],
[
"Wei",
"Baojun",
""
],
[
"Hao",
"Wenjun",
""
],
[
"Yao",
"Yang",
""
],
[
"Zhu",
"Yuyan",
""
],
[
"Zhang",
"Weixiong",
""
]
] | Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state. While theoretically sound, this approach is impractical for many reasons, e.g., the control scheme is often not unique and half of the nodes may be driver genes for the cell. We developed a novel approach that transcends structural controllability. Instead of considering driver genes for one control scheme, we considered control hub genes that reside in the middle of a control path of every control scheme. Control hubs are the most vulnerable spots for controlling the cell and exogenous stimuli on them may render the cell uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied them to a gene regulatory network of bladder cancer (BLCA). All the genes on the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the importance of these genes and the two pathways in cancer. A smaller set of 35 sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were subjected to small-interferencing-RNA knockdown in four cell lines to validate their effects on the proliferation or migration of cancer cells. Knocking down RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor xenografts in the mouse model. Combined, our results demonstrated the value of CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an effective means for studying and characterizing cancer etiology. |
1912.00866 | Huy Phi | Huy Phi, Sanjeev Janarthanan, Larry Zhang, Reza Hosseini Ghomi | Voice Biomarker Identification for Effects of Deep-Brain Stimulation on
Parkinson's Disease | 5 pages, including 3 tables, 2 figures, and references | null | null | null | q-bio.NC cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep-Brain Stimulation (DBS) is a therapy used in conjunction with medication
to help alleviate the motor symptoms of Parkinson's Disease (PD). However, the
monitoring and adjustment of DBS settings is tedious and expensive, requiring
long programming appointments every few months. We investigated the possible
correlation between PD motor score severity and digitally extracted patient
voice features to potentially aid clinicians in their monitoring and treatment
of PD with DBS, and eventually enable a closed-loop DBS system. 5 DBS PD
patients were enrolled. Voice samples were collected for various voice tasks
(single phoneme vocalization, free speech task, sentence reading task, counting
backward task, categorical fluency task) for DBS ON and OFF states. Motor
scores per the Unified Parkinson's Disease Rating Scale (UPDRS) were also
collected for DBS ON and OFF states. Voice samples were then analyzed to
extract voice features using publicly available voice feature library sets, and
statistically compared for DBS ON and OFF. Of the feature categories explored
(Acoustic, Prosodic, Linguistic) 6 features from the GeMAPS feature set for
acoustic features demonstrated significant differences with DBS ON and OFF
(p<0.05). Prosodic features such as pause length/percentage were found to be
negatively correlated with increased motor symptom severity. Non-significant
differences were found for linguistic features. These findings provide
preliminary evidence for acoustic and prosodic speech features to act as
potential biomarkers for PD disease severity in DBS patients. We hope to
explore further by expanding our data set, identifying other features, applying
machine learning models, and working towards a closed-loop DBS system that can
auto-tune itself based on changes in a patient's voice.
| [
{
"created": "Mon, 25 Nov 2019 07:46:27 GMT",
"version": "v1"
}
] | 2019-12-03 | [
[
"Phi",
"Huy",
""
],
[
"Janarthanan",
"Sanjeev",
""
],
[
"Zhang",
"Larry",
""
],
[
"Ghomi",
"Reza Hosseini",
""
]
] | Deep-Brain Stimulation (DBS) is a therapy used in conjunction with medication to help alleviate the motor symptoms of Parkinson's Disease (PD). However, the monitoring and adjustment of DBS settings is tedious and expensive, requiring long programming appointments every few months. We investigated the possible correlation between PD motor score severity and digitally extracted patient voice features to potentially aid clinicians in their monitoring and treatment of PD with DBS, and eventually enable a closed-loop DBS system. 5 DBS PD patients were enrolled. Voice samples were collected for various voice tasks (single phoneme vocalization, free speech task, sentence reading task, counting backward task, categorical fluency task) for DBS ON and OFF states. Motor scores per the Unified Parkinson's Disease Rating Scale (UPDRS) were also collected for DBS ON and OFF states. Voice samples were then analyzed to extract voice features using publicly available voice feature library sets, and statistically compared for DBS ON and OFF. Of the feature categories explored (Acoustic, Prosodic, Linguistic) 6 features from the GeMAPS feature set for acoustic features demonstrated significant differences with DBS ON and OFF (p<0.05). Prosodic features such as pause length/percentage were found to be negatively correlated with increased motor symptom severity. Non-significant differences were found for linguistic features. These findings provide preliminary evidence for acoustic and prosodic speech features to act as potential biomarkers for PD disease severity in DBS patients. We hope to explore further by expanding our data set, identifying other features, applying machine learning models, and working towards a closed-loop DBS system that can auto-tune itself based on changes in a patient's voice. |
2002.06401 | Jinzhi Lei | Yusong Ye, Zhuoqin Yang, Jinzhi Lei | DNA methylation heterogeneity induced by collaborations between
enhancers | 25 pages, 4 figures | Journal of Computational Biology, 2020 | 10.1089/cmb.2019.0413 | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | During mammalian embryo development, reprogramming of DNA methylation plays
important roles in the erasure of parental epigenetic memory and the
establishment of na\"{i}ve pluripogent cells. Multiple enzymes that regulate
the processes of methylation and demethylation work together to shape the
pattern of genome-scale DNA methylation and guid the process of cell
differentiation. Recent availability of methylome information from single-cell
whole genome bisulfite sequencing (scBS-seq) provides an opportunity to study
DNA methylation dynamics in the whole genome in individual cells, which reveal
the heterogeneous methylation distributions of enhancers in embryo stem cells
(ESCs). In this study, we developed a computational model of enhancer
methylation inheritance to study the dynamics of genome-scale DNA methylation
reprogramming during exit from pluripotency. The model enables us to track
genome-scale DNA methylation reprogramming at single-cell level during the
embryo development process, and reproduce the DNA methylation heterogeneity
reported by scBS-seq. Model simulations show that DNA methylation heterogeneity
is an intrinsic property driven by cell division along the development process,
and the collaboration between neighboring enhancers is required for
heterogeneous methylation. Our study suggest that the mechanism of genome-scale
oscillation proposed by Rulands et al. (2018) might not necessary to the DNA
methylation during exit from pluripotency.
| [
{
"created": "Sat, 15 Feb 2020 15:49:30 GMT",
"version": "v1"
}
] | 2020-04-20 | [
[
"Ye",
"Yusong",
""
],
[
"Yang",
"Zhuoqin",
""
],
[
"Lei",
"Jinzhi",
""
]
] | During mammalian embryo development, reprogramming of DNA methylation plays important roles in the erasure of parental epigenetic memory and the establishment of na\"{i}ve pluripogent cells. Multiple enzymes that regulate the processes of methylation and demethylation work together to shape the pattern of genome-scale DNA methylation and guid the process of cell differentiation. Recent availability of methylome information from single-cell whole genome bisulfite sequencing (scBS-seq) provides an opportunity to study DNA methylation dynamics in the whole genome in individual cells, which reveal the heterogeneous methylation distributions of enhancers in embryo stem cells (ESCs). In this study, we developed a computational model of enhancer methylation inheritance to study the dynamics of genome-scale DNA methylation reprogramming during exit from pluripotency. The model enables us to track genome-scale DNA methylation reprogramming at single-cell level during the embryo development process, and reproduce the DNA methylation heterogeneity reported by scBS-seq. Model simulations show that DNA methylation heterogeneity is an intrinsic property driven by cell division along the development process, and the collaboration between neighboring enhancers is required for heterogeneous methylation. Our study suggest that the mechanism of genome-scale oscillation proposed by Rulands et al. (2018) might not necessary to the DNA methylation during exit from pluripotency. |
1103.5301 | Harold Fellermann | Harold Fellermann and Steen Rasmussen | On the Growth Rate of Non-Enzymatic Molecular Replicators | Submitted to: Entropy | null | 10.3390/e13101882 | null | q-bio.BM cond-mat.stat-mech physics.bio-ph physics.chem-ph | http://creativecommons.org/licenses/by/3.0/ | It is well known that non-enzymatic template directed molecular replicators X
+ nO ---> 2X exhibit parabolic growth d[X]/dt = k [X]^{1/2}. Here, we analyze
the dependence of the effective replication rate constant k on hybridization
energies, temperature, strand length, and sequence composition. First we derive
analytical criteria for the replication rate k based on simple thermodynamic
arguments. Second we present a Brownian dynamics model for oligonucleotides
that allows us to simulate their diffusion and hybridization behavior. The
simulation is used to generate and analyze the effect of strand length,
temperature, and to some extent sequence composition, on the hybridization
rates and the resulting optimal overall rate constant k. Combining the two
approaches allows us to semi-analytically depict a fitness landscape for
template directed replicators. The results indicate a clear replication
advantage for longer strands at low temperatures.
| [
{
"created": "Mon, 28 Mar 2011 08:47:19 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Sep 2011 00:01:00 GMT",
"version": "v2"
}
] | 2015-05-27 | [
[
"Fellermann",
"Harold",
""
],
[
"Rasmussen",
"Steen",
""
]
] | It is well known that non-enzymatic template directed molecular replicators X + nO ---> 2X exhibit parabolic growth d[X]/dt = k [X]^{1/2}. Here, we analyze the dependence of the effective replication rate constant k on hybridization energies, temperature, strand length, and sequence composition. First we derive analytical criteria for the replication rate k based on simple thermodynamic arguments. Second we present a Brownian dynamics model for oligonucleotides that allows us to simulate their diffusion and hybridization behavior. The simulation is used to generate and analyze the effect of strand length, temperature, and to some extent sequence composition, on the hybridization rates and the resulting optimal overall rate constant k. Combining the two approaches allows us to semi-analytically depict a fitness landscape for template directed replicators. The results indicate a clear replication advantage for longer strands at low temperatures. |
1401.2668 | Jinbo Xu | Jianzhu Ma, Sheng Wang, Zhiyong Wang and Jinbo Xu | MRFalign: Protein Homology Detection through Alignment of Markov Random
Fields | Accepted by both RECOMB 2014 and PLOS Computational Biology | null | 10.1371/journal.pcbi.1003500 | null | q-bio.QM cs.CE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and
accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This
paper presents a new homology detection method MRFalign, consisting of three
key components: 1) a Markov Random Fields (MRF) representation of a protein
family; 2) a scoring function measuring similarity of two MRFs; and 3) an
efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning
two MRFs. Compared to HMM that can only model very short-range residue
correlation, MRFs can model long-range residue interaction pattern and thus,
encode information for the global 3D structure of a protein family.
Consequently, MRF-MRF comparison for remote homology detection shall be much
more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that
MRFalign outperforms several popular HMM or PSSM-based methods in terms of both
alignment accuracy and remote homology detection and that MRFalign works
particularly well for mainly beta proteins. For example, tested on the
benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM
succeed on 48% and 52% of proteins, respectively, at superfamily level, and on
15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign
succeeds on 57.3% and 42.5% of proteins at superfamily and fold level,
respectively. This study implies that long-range residue interaction patterns
are very helpful for sequence-based homology detection. The software is
available for download at http://raptorx.uchicago.edu/download/.
| [
{
"created": "Sun, 12 Jan 2014 20:41:08 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Jan 2014 01:55:17 GMT",
"version": "v2"
}
] | 2015-06-18 | [
[
"Ma",
"Jianzhu",
""
],
[
"Wang",
"Sheng",
""
],
[
"Wang",
"Zhiyong",
""
],
[
"Xu",
"Jinbo",
""
]
] | Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. |
2011.13012 | Babak Alipanahi | Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz, Justin Cosentino,
Zachary R. McCaw, Emanuel Schorsch, D. Sculley, Elizabeth H. Dorfman, Sonia
Phene, Naama Hammel, Andrew Carroll, Anthony P. Khawaja, Cory Y. McLean | Large-scale machine learning-based phenotyping significantly improves
genomic discovery for optic nerve head morphology | Includes Supplementary Information and Tables | null | null | null | q-bio.GN stat.AP | http://creativecommons.org/licenses/by/4.0/ | Genome-wide association studies (GWAS) require accurate cohort phenotyping,
but expert labeling can be costly, time-intensive, and variable. Here we
develop a machine learning (ML) model to predict glaucomatous optic nerve head
features from color fundus photographs. We used the model to predict vertical
cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for
glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR
identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$)
hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent
VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for
67,040 Europeans. The ML-based GWAS also identified 92 novel loci,
significantly expanding our understanding of the genetic etiologies of glaucoma
and VCDR. Pathway analyses support the biological significance of the novel
hits to VCDR, with select loci near genes involved in neuronal and synaptic
biology or known to cause severe Mendelian ophthalmic disease. Finally, the
ML-based GWAS results significantly improve polygenic prediction of VCDR and
primary open-angle glaucoma in the independent EPIC-Norfolk cohort.
| [
{
"created": "Wed, 25 Nov 2020 20:42:30 GMT",
"version": "v1"
}
] | 2020-11-30 | [
[
"Alipanahi",
"Babak",
""
],
[
"Hormozdiari",
"Farhad",
""
],
[
"Behsaz",
"Babak",
""
],
[
"Cosentino",
"Justin",
""
],
[
"McCaw",
"Zachary R.",
""
],
[
"Schorsch",
"Emanuel",
""
],
[
"Sculley",
"D.",
""
],
[
"Dorfman",
"Elizabeth H.",
""
],
[
"Phene",
"Sonia",
""
],
[
"Hammel",
"Naama",
""
],
[
"Carroll",
"Andrew",
""
],
[
"Khawaja",
"Anthony P.",
""
],
[
"McLean",
"Cory Y.",
""
]
] | Genome-wide association studies (GWAS) require accurate cohort phenotyping, but expert labeling can be costly, time-intensive, and variable. Here we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 92 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR, with select loci near genes involved in neuronal and synaptic biology or known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort. |
2403.11480 | Debanjali Bhattacharya Dr. | Debanjali Bhattacharya and Neelam Sinha | Towards understanding the nature of direct functional connectivity in
visual brain network | null | null | null | null | q-bio.NC cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent advances in neuroimaging have enabled studies in functional
connectivity (FC) of human brain, alongside investigation of the neuronal basis
of cognition. One important FC study is the representation of vision in human
brain. The release of publicly available dataset BOLD5000 has made it possible
to study the brain dynamics during visual tasks in greater detail. In this
paper, a comprehensive analysis of fMRI time series (TS) has been performed to
explore different types of visual brain networks (VBN). The novelty of this
work lies in (1) constructing VBN with consistently significant direct
connectivity using both marginal and partial correlation, which is further
analyzed using graph theoretic measures, (2) classification of VBNs as formed
by image complexity-specific TS, using graphical features. In image
complexity-specific VBN classification, XGBoost yields average accuracy in the
range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than
that using negative correlation. This result not only reflects the
distinguishing graphical characteristics of each image complexity-specific VBN,
but also highlights the importance of studying both positively correlated and
negatively correlated VBN to understand the how differently brain functions
while viewing different complexities of real-world images.
| [
{
"created": "Mon, 18 Mar 2024 05:03:07 GMT",
"version": "v1"
}
] | 2024-03-19 | [
[
"Bhattacharya",
"Debanjali",
""
],
[
"Sinha",
"Neelam",
""
]
] | Recent advances in neuroimaging have enabled studies in functional connectivity (FC) of human brain, alongside investigation of the neuronal basis of cognition. One important FC study is the representation of vision in human brain. The release of publicly available dataset BOLD5000 has made it possible to study the brain dynamics during visual tasks in greater detail. In this paper, a comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN). The novelty of this work lies in (1) constructing VBN with consistently significant direct connectivity using both marginal and partial correlation, which is further analyzed using graph theoretic measures, (2) classification of VBNs as formed by image complexity-specific TS, using graphical features. In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation. This result not only reflects the distinguishing graphical characteristics of each image complexity-specific VBN, but also highlights the importance of studying both positively correlated and negatively correlated VBN to understand the how differently brain functions while viewing different complexities of real-world images. |
1509.02312 | Nathana\"el Hoze | Nathanael Hoze and David Holcman | Recovering a stochastic process from noisy ensembles of many single
particle trajectories | null | Phys. Rev. E 92, 052109 (2015) | 10.1103/PhysRevE.92.052109 | null | q-bio.SC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recovering a stochastic process from noisy ensembles of single particle
trajectories (SPTs) is resolved here using the Langevin equation as a model.
The massive redundancy contained in SPTs data allows recovering local
parameters of the underlying physical model. We use several parametric and
non-parametric estimators to compute the first and second moment of the process
and to recover the local drift, its derivative and the diffusion tensor. Using
a local asymptotic expansion of the estimators and computing the empirical
transition probability function, we develop here a method to deconvolve the
instrumental from the physical noise. We use numerical simulations to explore
the range of validity for the estimators. The present analysis allows
characterizing what can exactly be recovered from the statistics of
super-resolution microscopy trajectories used in molecular trafficking and
underlying cellular function.
| [
{
"created": "Tue, 8 Sep 2015 10:29:17 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"Hoze",
"Nathanael",
""
],
[
"Holcman",
"David",
""
]
] | Recovering a stochastic process from noisy ensembles of single particle trajectories (SPTs) is resolved here using the Langevin equation as a model. The massive redundancy contained in SPTs data allows recovering local parameters of the underlying physical model. We use several parametric and non-parametric estimators to compute the first and second moment of the process and to recover the local drift, its derivative and the diffusion tensor. Using a local asymptotic expansion of the estimators and computing the empirical transition probability function, we develop here a method to deconvolve the instrumental from the physical noise. We use numerical simulations to explore the range of validity for the estimators. The present analysis allows characterizing what can exactly be recovered from the statistics of super-resolution microscopy trajectories used in molecular trafficking and underlying cellular function. |
1103.0907 | Tri Hieu Nim | Tri Hieu Nim, Le Luo, Marie-V\'eronique Cl\'ement, Jacob K. White,
Lisa Tucker-Kellogg | Estimating Reaction Rate Parameters in Cell Signaling Pathways Using
Extreme Decomposition and Belief Propagation Tailored for Data-Rich Cases | 11 pages, 9 figures, 1 table | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Modeling biological signaling networks using ordinary
differential equations (ODEs) has proven to be a powerful technique for
generating insight into cellular dynamics, but it typically requires estimating
rate parameters based on experimentally observed concentrations. New
measurement methods can measure concentrations for all molecular species in a
pathway, which creates a new opportunity to decompose the optimization of rate
parameters. Results: In contrast with conventional methods that minimize the
disagreement between simulated and observed concentrations, the BPPE method
fits a spline curve through the observed concentration points, and then matches
the derivatives of the spline-curve to the production and consumption of each
species. Whereas traditional methods follow the ODEs exactly and then attempt
to match the data, BPPE follows the data exactly and then attempts to match the
ODEs. The new objective function is an extreme decomposition of the problem
because each factor in the function is enforcing the equality of one ODE at one
timeslice. A "loopy belief propagation" algorithm solves this factorized
approximation of the parameter estimation problem providing systematic coverage
of the search space and unique asymptotic behavior; the run time is polynomial
in the number of molecules and timepoints, but exponential in the degree of the
biochemical network. The implementation is a global-local hybrid optimization,
and we compare with the performance of local, global, and hybrid methods. BPPE
is demonstrated for a novel model of Akt activation dynamics including
redox-mediated inactivation of PTEN. Availability: Software and supplementary
information are available at http://webbppe.nus.edu.sg:8080/opal2/WebBPPE .
Contact: LisaTK@nus.edu.sg . Keywords: probabilistic graphical models,
physico-chemical modeling, systems biology, signal transduction.
| [
{
"created": "Fri, 4 Mar 2011 14:28:06 GMT",
"version": "v1"
}
] | 2011-03-07 | [
[
"Nim",
"Tri Hieu",
""
],
[
"Luo",
"Le",
""
],
[
"Clément",
"Marie-Véronique",
""
],
[
"White",
"Jacob K.",
""
],
[
"Tucker-Kellogg",
"Lisa",
""
]
] | Motivation: Modeling biological signaling networks using ordinary differential equations (ODEs) has proven to be a powerful technique for generating insight into cellular dynamics, but it typically requires estimating rate parameters based on experimentally observed concentrations. New measurement methods can measure concentrations for all molecular species in a pathway, which creates a new opportunity to decompose the optimization of rate parameters. Results: In contrast with conventional methods that minimize the disagreement between simulated and observed concentrations, the BPPE method fits a spline curve through the observed concentration points, and then matches the derivatives of the spline-curve to the production and consumption of each species. Whereas traditional methods follow the ODEs exactly and then attempt to match the data, BPPE follows the data exactly and then attempts to match the ODEs. The new objective function is an extreme decomposition of the problem because each factor in the function is enforcing the equality of one ODE at one timeslice. A "loopy belief propagation" algorithm solves this factorized approximation of the parameter estimation problem providing systematic coverage of the search space and unique asymptotic behavior; the run time is polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. The implementation is a global-local hybrid optimization, and we compare with the performance of local, global, and hybrid methods. BPPE is demonstrated for a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN. Availability: Software and supplementary information are available at http://webbppe.nus.edu.sg:8080/opal2/WebBPPE . Contact: LisaTK@nus.edu.sg . Keywords: probabilistic graphical models, physico-chemical modeling, systems biology, signal transduction. |
2303.15482 | L Mahadevan | N. Charles, R. Chelakkot, M. Gazzola, B. Young, and L. Mahadevan | Non-planar snake gaits: from Stigmatic-starts to Sidewinding | Typos fixed and some new references added | null | null | null | q-bio.QM cond-mat.soft cs.RO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Of the vast variety of animal gaits, one of the most striking is the
non-planar undulating motion of a sidewinder. But non-planar gaits are not
limited to sidewinders. Here we report a new non-planar mode used as an escape
strategy in juvenile anacondas (Eunectes notaeus). In the S-start, named for
its eponymous shape, transient locomotion arises when the snake writhes and
bends out of the plane while rolling forward about its midsection without
slippage. To quantify our observations, we present a mathematical model for an
active non-planar filament that interacts anisotropically with a frictional
substrate and show that locomotion is due to a propagating localized pulse of a
topological quantity, the link density. A two-dimensional phase space
characterized by scaled body weight and muscular torque shows that relatively
light juveniles are capable of S-starts but heavy adults are not, consistent
with our experiments. Finally, we show that a periodic sequence of S-starts
naturally leads to a sidewinding gait. All together, our characterization of a
novel escape strategy in snakes highlights the role of topology in locomotion,
provides a phase diagram for mode feasibility as a function of body size, and
suggests a role for the S-start in the evolution of sidewinding.
| [
{
"created": "Sun, 26 Mar 2023 23:32:40 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Apr 2023 03:16:54 GMT",
"version": "v2"
}
] | 2023-04-06 | [
[
"Charles",
"N.",
""
],
[
"Chelakkot",
"R.",
""
],
[
"Gazzola",
"M.",
""
],
[
"Young",
"B.",
""
],
[
"Mahadevan",
"L.",
""
]
] | Of the vast variety of animal gaits, one of the most striking is the non-planar undulating motion of a sidewinder. But non-planar gaits are not limited to sidewinders. Here we report a new non-planar mode used as an escape strategy in juvenile anacondas (Eunectes notaeus). In the S-start, named for its eponymous shape, transient locomotion arises when the snake writhes and bends out of the plane while rolling forward about its midsection without slippage. To quantify our observations, we present a mathematical model for an active non-planar filament that interacts anisotropically with a frictional substrate and show that locomotion is due to a propagating localized pulse of a topological quantity, the link density. A two-dimensional phase space characterized by scaled body weight and muscular torque shows that relatively light juveniles are capable of S-starts but heavy adults are not, consistent with our experiments. Finally, we show that a periodic sequence of S-starts naturally leads to a sidewinding gait. All together, our characterization of a novel escape strategy in snakes highlights the role of topology in locomotion, provides a phase diagram for mode feasibility as a function of body size, and suggests a role for the S-start in the evolution of sidewinding. |
1809.10458 | Siwei Luo Mr. | Si-Wei Luo, Yi-Hua Jiang, Zhi Liang and Jia-Rui Wu | Protein token: a dynamic unit in protein interactions | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we introduced a new unit, named "protein token", as a dynamic
protein structural unit for protein-protein interactions. Unlike the
conventional structural units, protein token is not based on the sequential or
spatial arrangement of residues, but comprises remote residues involved in
cooperative conformational changes during protein interactions. Application of
protein token on Ras GTPases revealed various tokens present in the
superfamily. Distinct token combinations were found in H-Ras interacting with
its various regulators and effectors, directing to a possible clue for the
multiplexer property of Ras superfamily. Thus, this protein token theory may
provide a new approach to study protein-protein interactions in broad
applications.
| [
{
"created": "Thu, 27 Sep 2018 11:19:29 GMT",
"version": "v1"
}
] | 2018-09-28 | [
[
"Luo",
"Si-Wei",
""
],
[
"Jiang",
"Yi-Hua",
""
],
[
"Liang",
"Zhi",
""
],
[
"Wu",
"Jia-Rui",
""
]
] | In this study, we introduced a new unit, named "protein token", as a dynamic protein structural unit for protein-protein interactions. Unlike the conventional structural units, protein token is not based on the sequential or spatial arrangement of residues, but comprises remote residues involved in cooperative conformational changes during protein interactions. Application of protein token on Ras GTPases revealed various tokens present in the superfamily. Distinct token combinations were found in H-Ras interacting with its various regulators and effectors, directing to a possible clue for the multiplexer property of Ras superfamily. Thus, this protein token theory may provide a new approach to study protein-protein interactions in broad applications. |
0905.3502 | Anna Ochab-Marcinek | Anna Ochab-Marcinek | Extrinsic noise passing through a Michaelis-Menten reaction: A universal
response of a genetic switch | 25 pages, 9 figures, changed content, added figures, accepted for
publication in Journal of Theoretical Biology | null | 10.1016/j.jtbi.2009.12.028 | null | q-bio.CB q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of biochemical pathways usually focuses on a small section of a
protein interactions network. Two distinct sources contribute to the noise in
such a system: intrinsic noise, inherent in the studied reactions, and
extrinsic noise generated in other parts of the network or in the environment.
We study the effect of extrinsic noise entering the system through a nonlinear
uptake reaction which acts as a nonlinear filter. Varying input noise intensity
varies the mean of the noise after the passage through the filter, which
changes the stability properties of the system. The steady-state displacement
due to small noise is independent on the kinetics of the system but it only
depends on the nonlinearity of the input function.
For monotonically increasing and concave input functions such as the
Michaelis-Menten uptake rate, we give a simple argument based on the
small-noise expansion, which enables qualitative predictions of the
steady-state displacement only by inspection of experimental data: when weak
and rapid noise enters the system through a Michaelis-Menten reaction, then the
graph of the system's steady states vs. the mean of the input signal always
shifts to the right as noise intensity increases.
We test the predictions on two models of lac operon, where TMG/lactose uptake
is driven by a Michaelis-Menten enzymatic process. We show that as a
consequence of the steady state displacement due to fluctuations in
extracellular TMG/lactose concentration the lac switch responds in an
asymmetric manner: as noise intensity increases, switching off lactose
metabolism becomes easier and switching it on becomes more difficult.
| [
{
"created": "Thu, 21 May 2009 15:15:50 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Dec 2009 18:07:04 GMT",
"version": "v2"
}
] | 2010-01-29 | [
[
"Ochab-Marcinek",
"Anna",
""
]
] | The study of biochemical pathways usually focuses on a small section of a protein interactions network. Two distinct sources contribute to the noise in such a system: intrinsic noise, inherent in the studied reactions, and extrinsic noise generated in other parts of the network or in the environment. We study the effect of extrinsic noise entering the system through a nonlinear uptake reaction which acts as a nonlinear filter. Varying input noise intensity varies the mean of the noise after the passage through the filter, which changes the stability properties of the system. The steady-state displacement due to small noise is independent on the kinetics of the system but it only depends on the nonlinearity of the input function. For monotonically increasing and concave input functions such as the Michaelis-Menten uptake rate, we give a simple argument based on the small-noise expansion, which enables qualitative predictions of the steady-state displacement only by inspection of experimental data: when weak and rapid noise enters the system through a Michaelis-Menten reaction, then the graph of the system's steady states vs. the mean of the input signal always shifts to the right as noise intensity increases. We test the predictions on two models of lac operon, where TMG/lactose uptake is driven by a Michaelis-Menten enzymatic process. We show that as a consequence of the steady state displacement due to fluctuations in extracellular TMG/lactose concentration the lac switch responds in an asymmetric manner: as noise intensity increases, switching off lactose metabolism becomes easier and switching it on becomes more difficult. |
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