id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2004.07697 | Francesco Napolitano | Francesco Napolitano, Gennaro Gambardella, Diego Carrella, Xin Gao,
Diego di Bernardo | Computational Drug Repositioning and Elucidation of Mechanism of Action
of Compounds against SARS-CoV-2 | null | null | null | null | q-bio.GN q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The COVID-19 crisis called for rapid reaction from all the fields of
biomedical research. Traditional drug development involves time consuming
pipelines that conflict with the urgence of identifying effective therapies
during a health and economic emergency. Drug repositioning, that is the
discovery of new clinical applications for drugs already approved for different
therapeutic contexts, could provide an effective shortcut to bring COVID-19
treatments to the bedside in a timely manner. Moreover, computational
approaches can help accelerate the process even further. Here we present the
application of computational drug repositioning tools based on transcriptomics
data to identify drugs that are potentially able to counteract SARS-CoV-2
infection, and also to provide insights on their mode of action. We believe
that mucolytics and HDAC inhibitors warrant further investigation. In addition,
we found that the DNA Mismatch repair pathway is strongly modulated by drugs
with experimental in vitro activity against SARS-CoV-2 infection. Both full
results and methods are publicly available.
| [
{
"created": "Thu, 16 Apr 2020 15:11:08 GMT",
"version": "v1"
},
{
"created": "Mon, 4 May 2020 22:32:51 GMT",
"version": "v2"
}
] | 2020-05-06 | [
[
"Napolitano",
"Francesco",
""
],
[
"Gambardella",
"Gennaro",
""
],
[
"Carrella",
"Diego",
""
],
[
"Gao",
"Xin",
""
],
[
"di Bernardo",
"Diego",
""
]
] | The COVID-19 crisis called for rapid reaction from all the fields of biomedical research. Traditional drug development involves time consuming pipelines that conflict with the urgence of identifying effective therapies during a health and economic emergency. Drug repositioning, that is the discovery of new clinical applications for drugs already approved for different therapeutic contexts, could provide an effective shortcut to bring COVID-19 treatments to the bedside in a timely manner. Moreover, computational approaches can help accelerate the process even further. Here we present the application of computational drug repositioning tools based on transcriptomics data to identify drugs that are potentially able to counteract SARS-CoV-2 infection, and also to provide insights on their mode of action. We believe that mucolytics and HDAC inhibitors warrant further investigation. In addition, we found that the DNA Mismatch repair pathway is strongly modulated by drugs with experimental in vitro activity against SARS-CoV-2 infection. Both full results and methods are publicly available. |
2009.11599 | Michael Hendriksen | Michael Hendriksen, Julia A. Shore | Phylosymmetric algebras: mathematical properties of a new tool in
phylogenetics | 12 pages, 3 figures | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In phylogenetics it is of interest for rate matrix sets to satisfy closure
under matrix multiplication as this makes finding the set of corresponding
transition matrices possible without having to compute matrix exponentials. It
is also advantageous to have a small number of free parameters as this, in
applications, will result in a reduction of computation time. We explore a
method of building a rate matrix set from a rooted tree structure by assigning
rates to internal tree nodes and states to the leaves, then defining the rate
of change between two states as the rate assigned to the most recent common
ancestor of those two states. We investigate the properties of these matrix
sets from both a linear algebra and a graph theory perspective and show that
any rate matrix set generated this way is closed under matrix multiplication.
The consequences of setting two rates assigned to internal tree nodes to be
equal are then considered. This methodology could be used to develop
parameterised models of amino acid substitution which have a small number of
parameters but convey biological meaning.
| [
{
"created": "Thu, 24 Sep 2020 11:04:10 GMT",
"version": "v1"
}
] | 2020-09-25 | [
[
"Hendriksen",
"Michael",
""
],
[
"Shore",
"Julia A.",
""
]
] | In phylogenetics it is of interest for rate matrix sets to satisfy closure under matrix multiplication as this makes finding the set of corresponding transition matrices possible without having to compute matrix exponentials. It is also advantageous to have a small number of free parameters as this, in applications, will result in a reduction of computation time. We explore a method of building a rate matrix set from a rooted tree structure by assigning rates to internal tree nodes and states to the leaves, then defining the rate of change between two states as the rate assigned to the most recent common ancestor of those two states. We investigate the properties of these matrix sets from both a linear algebra and a graph theory perspective and show that any rate matrix set generated this way is closed under matrix multiplication. The consequences of setting two rates assigned to internal tree nodes to be equal are then considered. This methodology could be used to develop parameterised models of amino acid substitution which have a small number of parameters but convey biological meaning. |
2006.08532 | Karren Yang | Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay,
Tommi Jaakkola, Caroline Uhler | Improved Conditional Flow Models for Molecule to Image Synthesis | null | null | null | null | q-bio.BM cs.CV cs.LG eess.IV q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we aim to synthesize cell microscopy images under different
molecular interventions, motivated by practical applications to drug
development. Building on the recent success of graph neural networks for
learning molecular embeddings and flow-based models for image generation, we
propose Mol2Image: a flow-based generative model for molecule to cell image
synthesis. To generate cell features at different resolutions and scale to
high-resolution images, we develop a novel multi-scale flow architecture based
on a Haar wavelet image pyramid. To maximize the mutual information between the
generated images and the molecular interventions, we devise a training strategy
based on contrastive learning. To evaluate our model, we propose a new set of
metrics for biological image generation that are robust, interpretable, and
relevant to practitioners. We show quantitatively that our method learns a
meaningful embedding of the molecular intervention, which is translated into an
image representation reflecting the biological effects of the intervention.
| [
{
"created": "Mon, 15 Jun 2020 16:39:50 GMT",
"version": "v1"
}
] | 2020-06-16 | [
[
"Yang",
"Karren",
""
],
[
"Goldman",
"Samuel",
""
],
[
"Jin",
"Wengong",
""
],
[
"Lu",
"Alex",
""
],
[
"Barzilay",
"Regina",
""
],
[
"Jaakkola",
"Tommi",
""
],
[
"Uhler",
"Caroline",
""
]
] | In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-based generative model for molecule to cell image synthesis. To generate cell features at different resolutions and scale to high-resolution images, we develop a novel multi-scale flow architecture based on a Haar wavelet image pyramid. To maximize the mutual information between the generated images and the molecular interventions, we devise a training strategy based on contrastive learning. To evaluate our model, we propose a new set of metrics for biological image generation that are robust, interpretable, and relevant to practitioners. We show quantitatively that our method learns a meaningful embedding of the molecular intervention, which is translated into an image representation reflecting the biological effects of the intervention. |
2111.15275 | Claudius Gros | Claudius Gros | Emotions as abstract evaluation criteria in biological and artificial
intelligences | Frontiers in Computational Neuroscience (in press). arXiv admin note:
substantial text overlap with arXiv:1909.11700 | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological as well as advanced artificial intelligences (AIs) need to decide
which goals to pursue. We review nature's solution to the time allocation
problem, which is based on a continuously readjusted categorical weighting
mechanism we experience introspectively as emotions. One observes
phylogenetically that the available number of emotional states increases hand
in hand with the cognitive capabilities of animals and that raising levels of
intelligence entail ever larger sets of behavioral options. Our ability to
experience a multitude of potentially conflicting feelings is in this view not
a leftover of a more primitive heritage, but a generic mechanism for
attributing values to behavioral options that can not be specified at birth. In
this view, emotions are essential for understanding the mind.
For concreteness, we propose and discuss a framework which mimics emotions on
a functional level. Based on time allocation via emotional stationarity (TAES),
emotions are implemented as abstract criteria, such as satisfaction, challenge
and boredom, which serve to evaluate activities that have been carried out. The
resulting timeline of experienced emotions is compared with the `character' of
the agent, which is defined in terms of a preferred distribution of emotional
states. The long-term goal of the agent, to align experience with character, is
achieved by optimizing the frequency for selecting individual tasks. Upon
optimization, the statistics of emotion experience becomes stationary.
| [
{
"created": "Tue, 30 Nov 2021 10:49:04 GMT",
"version": "v1"
}
] | 2021-12-01 | [
[
"Gros",
"Claudius",
""
]
] | Biological as well as advanced artificial intelligences (AIs) need to decide which goals to pursue. We review nature's solution to the time allocation problem, which is based on a continuously readjusted categorical weighting mechanism we experience introspectively as emotions. One observes phylogenetically that the available number of emotional states increases hand in hand with the cognitive capabilities of animals and that raising levels of intelligence entail ever larger sets of behavioral options. Our ability to experience a multitude of potentially conflicting feelings is in this view not a leftover of a more primitive heritage, but a generic mechanism for attributing values to behavioral options that can not be specified at birth. In this view, emotions are essential for understanding the mind. For concreteness, we propose and discuss a framework which mimics emotions on a functional level. Based on time allocation via emotional stationarity (TAES), emotions are implemented as abstract criteria, such as satisfaction, challenge and boredom, which serve to evaluate activities that have been carried out. The resulting timeline of experienced emotions is compared with the `character' of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting individual tasks. Upon optimization, the statistics of emotion experience becomes stationary. |
2302.04223 | Florian Nill | Florian Nill | On the redundancy of birth and death rates in homogenous epidemic SIR
models | 9 pages, 3 figures, 2 tables. arXiv admin note: text overlap with
arXiv:2301.00159 | Fractal Fract. 2023, 7, 313 | 10.3390/fractalfract7040313 | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The dynamics of fractional population sizes y_i=Y_i/N in homogeneous
compartment models with time dependent total population N is analyzed. Assuming
constant per capita birth and death rates the vector field Y_i'=V_i(Y)
naturally projects to a vector field F_i(Y) tangent to the leaves of constant
population N. A universal formula for the projected field F_i is given. In this
way, in many SIR-type models with standard incidence all demographic parameters
become redundant for the dynamical system y_i'=F_i(y). They may be put to zero
by shifting remaining parameters appropriately. Normalizing eight examples from
the literature this way, they unexpectedly become isomorphic for corresponding
parameter ranges. Thus, some recently published results turn out to be already
covered by papers 20 years ago.
| [
{
"created": "Wed, 8 Feb 2023 17:46:16 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Feb 2023 15:33:11 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Feb 2023 16:42:28 GMT",
"version": "v3"
},
{
"created": "Fri, 14 Apr 2023 08:02:51 GMT",
"version": "v4"
}
] | 2023-04-17 | [
[
"Nill",
"Florian",
""
]
] | The dynamics of fractional population sizes y_i=Y_i/N in homogeneous compartment models with time dependent total population N is analyzed. Assuming constant per capita birth and death rates the vector field Y_i'=V_i(Y) naturally projects to a vector field F_i(Y) tangent to the leaves of constant population N. A universal formula for the projected field F_i is given. In this way, in many SIR-type models with standard incidence all demographic parameters become redundant for the dynamical system y_i'=F_i(y). They may be put to zero by shifting remaining parameters appropriately. Normalizing eight examples from the literature this way, they unexpectedly become isomorphic for corresponding parameter ranges. Thus, some recently published results turn out to be already covered by papers 20 years ago. |
1704.06014 | Kieran Fox | Kieran C.R. Fox, Kalina Christoff | Introduction: Toward an interdisciplinary science of spontaneous thought | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Enormous questions still loom for the emerging science of spontaneous
thought: what, exactly, is spontaneous thought? Why does our brain engage in
spontaneous forms of thinking, and when is this most likely to occur? And
perhaps the question most interesting and accessible from a scientific
perspective: how does the brain generate, elaborate, and evaluate its own
spontaneous creations? The central aim of this volume is to bring together
views from neuroscience, psychology, philosophy, phenomenology, history,
education, contemplative traditions, and clinical practice in order to begin to
address the ubiquitous but poorly understood mental phenomena that we
collectively call 'spontaneous thought.' Perhaps no other mental experience is
so familiar to us in daily life, and yet so difficult to understand and explain
scientifically. The present volume represents the first effort to bring such
highly diverse perspectives to bear on answering the what, when, why, and how
of spontaneous thought.
| [
{
"created": "Thu, 20 Apr 2017 04:58:42 GMT",
"version": "v1"
}
] | 2017-04-21 | [
[
"Fox",
"Kieran C. R.",
""
],
[
"Christoff",
"Kalina",
""
]
] | Enormous questions still loom for the emerging science of spontaneous thought: what, exactly, is spontaneous thought? Why does our brain engage in spontaneous forms of thinking, and when is this most likely to occur? And perhaps the question most interesting and accessible from a scientific perspective: how does the brain generate, elaborate, and evaluate its own spontaneous creations? The central aim of this volume is to bring together views from neuroscience, psychology, philosophy, phenomenology, history, education, contemplative traditions, and clinical practice in order to begin to address the ubiquitous but poorly understood mental phenomena that we collectively call 'spontaneous thought.' Perhaps no other mental experience is so familiar to us in daily life, and yet so difficult to understand and explain scientifically. The present volume represents the first effort to bring such highly diverse perspectives to bear on answering the what, when, why, and how of spontaneous thought. |
2406.10301 | Jagir Hussan R | Jagir R. Hussan | 12 Labours tools for developing Functional Tissue Units | null | null | null | null | q-bio.TO cs.MS math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A brief introduction of the technical approach to model FTUs as an aggregate
of cells, whose state transition dynamics are mathematically represented as
port-hamiltonians or Differential Algebraic equations is presented. A python
library and browser based tool to enable modellers to compose the FTU graph,
specify the cellular equations and the interconnection between the cells at the
level of physical quantities they exchange consistent with the technical
approach is discussed.
| [
{
"created": "Thu, 13 Jun 2024 22:55:40 GMT",
"version": "v1"
}
] | 2024-06-18 | [
[
"Hussan",
"Jagir R.",
""
]
] | A brief introduction of the technical approach to model FTUs as an aggregate of cells, whose state transition dynamics are mathematically represented as port-hamiltonians or Differential Algebraic equations is presented. A python library and browser based tool to enable modellers to compose the FTU graph, specify the cellular equations and the interconnection between the cells at the level of physical quantities they exchange consistent with the technical approach is discussed. |
1811.01373 | Renata Rychtarikova | K. Lonhus, D. \v{S}tys, M. Saberioon, R. Rycht\'arikov\'a | Segmentation of laterally symmetric overlapping objects: application to
images of collective animal behaviour | 17 pages, 4 figures | Symmetry 11(7), 866 (2019) | 10.3390/sym11070866 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video analysis is currently the main non-intrusive method for the study of
collective behavior. However, 3D-to-2D projection leads to overlapping of
observed objects. The situation is further complicated by the absence of stall
shapes for the majority of living objects. Fortunately, living objects often
possess a certain symmetry which was used as a basis for morphological
fingerprinting. This technique allowed us to record forms of symmetrical
objects in a pose-invariant way. When combined with image skeletonization, this
gives a robust, nonlinear, optimization-free, and fast method for detection of
overlapping objects, even without any rigid pattern. This novel method was
verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius
tetrazona) swimming in a reasonably small tank, which forced them to exhibit a
large variety of shapes. Compared with manual detection, the correct number of
objects was determined for up to almost $90 \%$ of overlaps, and the mean
Dice-Sorensen coefficient was around $0.83$. This implies that this method is
feasible in real-life applications such as toxicity testing.
| [
{
"created": "Sun, 4 Nov 2018 14:08:59 GMT",
"version": "v1"
},
{
"created": "Wed, 1 May 2019 17:32:18 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Jul 2019 10:38:30 GMT",
"version": "v3"
}
] | 2019-07-05 | [
[
"Lonhus",
"K.",
""
],
[
"Štys",
"D.",
""
],
[
"Saberioon",
"M.",
""
],
[
"Rychtáriková",
"R.",
""
]
] | Video analysis is currently the main non-intrusive method for the study of collective behavior. However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for up to almost $90 \%$ of overlaps, and the mean Dice-Sorensen coefficient was around $0.83$. This implies that this method is feasible in real-life applications such as toxicity testing. |
1106.0143 | Marianne Rooman | Jaroslav Albert, Marianne Rooman | Dynamic modeling of gene expression in prokaryotes: application to
glucose-lactose diauxie in Escherichia coli | 20 pages, 4 figures; Systems and Synthetic Biology 5 (2011) | null | null | null | q-bio.MN q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Coexpression of genes or, more generally, similarity in the expression
profiles poses an unsurmountable obstacle to inferring the gene regulatory
network (GRN) based solely on data from DNA microarray time series. Clustering
of genes with similar expression profiles allows for a course-grained view of
the GRN and a probabilistic determination of the connectivity among the
clusters. We present a model for the temporal evolution of a gene cluster
network which takes into account interactions of gene products with genes and,
through a non-constant degradation rate, with other gene products. The number
of model parameters is reduced by using polynomial functions to interpolate
temporal data points. In this manner, the task of parameter estimation is
reduced to a system of linear algebraic equations, thus making the computation
time shorter by orders of magnitude. To eliminate irrelevant networks, we test
each GRN for stability with respect to parameter variations, and impose
restrictions on its behavior near the steady state. We apply our model and
methods to DNA microarray time series' data collected on Escherichia coli
during glucose-lactose diauxie and infer the most probable cluster network for
different phases of the experiment.
| [
{
"created": "Wed, 1 Jun 2011 10:59:20 GMT",
"version": "v1"
}
] | 2011-06-02 | [
[
"Albert",
"Jaroslav",
""
],
[
"Rooman",
"Marianne",
""
]
] | Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view of the GRN and a probabilistic determination of the connectivity among the clusters. We present a model for the temporal evolution of a gene cluster network which takes into account interactions of gene products with genes and, through a non-constant degradation rate, with other gene products. The number of model parameters is reduced by using polynomial functions to interpolate temporal data points. In this manner, the task of parameter estimation is reduced to a system of linear algebraic equations, thus making the computation time shorter by orders of magnitude. To eliminate irrelevant networks, we test each GRN for stability with respect to parameter variations, and impose restrictions on its behavior near the steady state. We apply our model and methods to DNA microarray time series' data collected on Escherichia coli during glucose-lactose diauxie and infer the most probable cluster network for different phases of the experiment. |
2005.03499 | Youcef Mammeri | Youcef Mammeri | A reaction-diffusion system to better comprehend the unlockdown:
Application of SEIR-type model with diffusion to the spatial spread of
COVID-19 in France | arXiv admin note: text overlap with arXiv:2004.01805 | null | null | null | q-bio.PE math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A reaction-diffusion model was developed describing the spread of the
COVID-19 virus considering the mean daily movement of susceptible, exposed and
asymptomatic individuals. The model was calibrated using data on the confirmed
infection and death from France as well as their initial spatial distribution.
First, the system of partial differential equations is studied, then the basic
reproduction number, R0 is derived. Second, numerical simulations, based on a
combination of level-set and finite differences, shown the spatial spread of
COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are
compared according to variation of distancing, or partially spatial lockdown.
| [
{
"created": "Wed, 6 May 2020 09:33:39 GMT",
"version": "v1"
}
] | 2020-05-08 | [
[
"Mammeri",
"Youcef",
""
]
] | A reaction-diffusion model was developed describing the spread of the COVID-19 virus considering the mean daily movement of susceptible, exposed and asymptomatic individuals. The model was calibrated using data on the confirmed infection and death from France as well as their initial spatial distribution. First, the system of partial differential equations is studied, then the basic reproduction number, R0 is derived. Second, numerical simulations, based on a combination of level-set and finite differences, shown the spatial spread of COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are compared according to variation of distancing, or partially spatial lockdown. |
2405.17395 | Noga Mudrik | Noga Mudrik, Ryan Ly, Oliver Ruebel, Adam S. Charles | CrEIMBO: Cross Ensemble Interactions in Multi-view Brain Observations | null | null | null | null | q-bio.NC q-bio.QM stat.ML | http://creativecommons.org/licenses/by/4.0/ | Modern recordings of neural activity provide diverse observations of neurons
across brain areas, behavioral conditions, and subjects -- thus presenting an
exciting opportunity to reveal the fundamentals of brain-wide dynamics
underlying cognitive function. Current methods, however, often fail to fully
harness the richness of such data as they either provide an uninterpretable
representation (e.g., via "black box" deep networks) or over-simplify the model
(e.g., assume stationary dynamics or analyze each session independently). Here,
instead of regarding asynchronous recordings that lack alignment in neural
identity or brain areas as a limitation, we exploit these diverse views of the
same brain system to learn a unified model of brain dynamics. We assume that
brain observations stem from the joint activity of a set of functional neural
ensembles (groups of co-active neurons) that are similar in functionality
across recordings, and propose to discover the ensemble and their
non-stationary dynamical interactions in a new model we term CrEIMBO
(Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO
identifies the composition of the per-session neural ensembles through
graph-driven dictionary learning and models the ensemble dynamics as a latent
sparse time-varying decomposition of global sub-circuits, thereby capturing
non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits
while maintaining representation interpretability due to sharing sub-circuits
across sessions. CrEIMBO distinguishes session-specific from global
(session-invariant) computations by exploring when distinct sub-circuits are
active. We demonstrate CrEIMBO's ability to recover ground truth components in
synthetic data and uncover meaningful brain dynamics, capturing cross-subject
and inter- and intra-area variability, in high-density electrode recordings of
humans performing a memory task.
| [
{
"created": "Mon, 27 May 2024 17:48:32 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Mudrik",
"Noga",
""
],
[
"Ly",
"Ryan",
""
],
[
"Ruebel",
"Oliver",
""
],
[
"Charles",
"Adam S.",
""
]
] | Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects -- thus presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics underlying cognitive function. Current methods, however, often fail to fully harness the richness of such data as they either provide an uninterpretable representation (e.g., via "black box" deep networks) or over-simplify the model (e.g., assume stationary dynamics or analyze each session independently). Here, instead of regarding asynchronous recordings that lack alignment in neural identity or brain areas as a limitation, we exploit these diverse views of the same brain system to learn a unified model of brain dynamics. We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO identifies the composition of the per-session neural ensembles through graph-driven dictionary learning and models the ensemble dynamics as a latent sparse time-varying decomposition of global sub-circuits, thereby capturing non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits while maintaining representation interpretability due to sharing sub-circuits across sessions. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics, capturing cross-subject and inter- and intra-area variability, in high-density electrode recordings of humans performing a memory task. |
q-bio/0502020 | Gavin E. Crooks | Gavin E. Crooks, Richard E. Green and Steven E. Brenner | Pairwise alignment incorporating dipeptide covariation | null | Bioinformatics 21 3704-3710 (2005) | 10.1093/bioinformatics/bti616 | null | q-bio.BM q-bio.PE | null | Motivation: Standard algorithms for pairwise protein sequence alignment make
the simplifying assumption that amino acid substitutions at neighboring sites
are uncorrelated. This assumption allows implementation of fast algorithms for
pairwise sequence alignment, but it ignores information that could conceivably
increase the power of remote homolog detection. We examine the validity of this
assumption by constructing extended substitution matrixes that encapsulate the
observed correlations between neighboring sites, by developing an efficient and
rigorous algorithm for pairwise protein sequence alignment that incorporates
these local substitution correlations, and by assessing the ability of this
algorithm to detect remote homologies. Results: Our analysis indicates that
local correlations between substitutions are not strong on the average.
Furthermore, incorporating local substitution correlations into pairwise
alignment did not lead to a statistically significant improvement in remote
homology detection. Therefore, the standard assumption that individual residues
within protein sequences evolve independently of neighboring positions appears
to be an efficient and appropriate approximation.
| [
{
"created": "Sat, 19 Feb 2005 23:19:16 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Jul 2005 21:56:50 GMT",
"version": "v2"
}
] | 2014-05-27 | [
[
"Crooks",
"Gavin E.",
""
],
[
"Green",
"Richard E.",
""
],
[
"Brenner",
"Steven E.",
""
]
] | Motivation: Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrixes that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations, and by assessing the ability of this algorithm to detect remote homologies. Results: Our analysis indicates that local correlations between substitutions are not strong on the average. Furthermore, incorporating local substitution correlations into pairwise alignment did not lead to a statistically significant improvement in remote homology detection. Therefore, the standard assumption that individual residues within protein sequences evolve independently of neighboring positions appears to be an efficient and appropriate approximation. |
q-bio/0512019 | Kevin Woods | Niko Beerenwinkel, Colin N. Dewey, and Kevin M. Woods | Parametric inference of recombination in HIV genomes | 20 pages, 5 figures | null | null | null | q-bio.GN q-bio.QM | null | Recombination is an important event in the evolution of HIV. It affects the
global spread of the pandemic as well as evolutionary escape from host immune
response and from drug therapy within single patients. Comprehensive
computational methods are needed for detecting recombinant sequences in large
databases, and for inferring the parental sequences.
We present a hidden Markov model to annotate a query sequence as a
recombinant of a given set of aligned sequences. Parametric inference is used
to determine all optimal annotations for all parameters of the model. We show
that the inferred annotations recover most features of established hand-curated
annotations. Thus, parametric analysis of the hidden Markov model is feasible
for HIV full-length genomes, and it improves the detection and annotation of
recombinant forms.
All computational results, reference alignments, and C++ source code are
available at http://bio.math.berkeley.edu/recombination/.
| [
{
"created": "Thu, 8 Dec 2005 22:54:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Beerenwinkel",
"Niko",
""
],
[
"Dewey",
"Colin N.",
""
],
[
"Woods",
"Kevin M.",
""
]
] | Recombination is an important event in the evolution of HIV. It affects the global spread of the pandemic as well as evolutionary escape from host immune response and from drug therapy within single patients. Comprehensive computational methods are needed for detecting recombinant sequences in large databases, and for inferring the parental sequences. We present a hidden Markov model to annotate a query sequence as a recombinant of a given set of aligned sequences. Parametric inference is used to determine all optimal annotations for all parameters of the model. We show that the inferred annotations recover most features of established hand-curated annotations. Thus, parametric analysis of the hidden Markov model is feasible for HIV full-length genomes, and it improves the detection and annotation of recombinant forms. All computational results, reference alignments, and C++ source code are available at http://bio.math.berkeley.edu/recombination/. |
2004.04675 | Aresh Dadlani | Aresh Dadlani, Richard O. Afolabi, Hyoyoung Jung, Khosrow Sohraby,
Kiseon Kim | Deterministic Models in Epidemiology: From Modeling to Implementation | Tutorial | null | null | null | q-bio.PE math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The abrupt outbreak and transmission of biological diseases has always been a
long-time concern of humankind. For long, mathematical modeling has served as a
simple and yet efficient tool to investigate, predict, and control spread of
communicable diseases through individuals. A myriad of works on epidemic models
and their variants have been reported in the literature. For better prediction
of the dynamics of a particular disease, it is important to adopt the most
suitable model. In this paper, we study some of the widely-appreciated
deterministic epidemic models in which the population is divided into
compartments based on the health status of each individual. In particular, we
provide a demographic classification of such models and study each of them in
terms of mathematical formulation, near equilibrium point stability properties,
and disease outbreak threshold conditions (basic reproduction ratio).
Furthermore, we discuss the various influential factors that need to be
considered during epidemic modeling. The main objective of this article is to
provide a basic understanding of the mathematical complexity incurred in
deterministic epidemic models with the aid of graphical illustrations obtained
through implementation.
| [
{
"created": "Tue, 7 Apr 2020 20:54:27 GMT",
"version": "v1"
}
] | 2020-04-10 | [
[
"Dadlani",
"Aresh",
""
],
[
"Afolabi",
"Richard O.",
""
],
[
"Jung",
"Hyoyoung",
""
],
[
"Sohraby",
"Khosrow",
""
],
[
"Kim",
"Kiseon",
""
]
] | The abrupt outbreak and transmission of biological diseases has always been a long-time concern of humankind. For long, mathematical modeling has served as a simple and yet efficient tool to investigate, predict, and control spread of communicable diseases through individuals. A myriad of works on epidemic models and their variants have been reported in the literature. For better prediction of the dynamics of a particular disease, it is important to adopt the most suitable model. In this paper, we study some of the widely-appreciated deterministic epidemic models in which the population is divided into compartments based on the health status of each individual. In particular, we provide a demographic classification of such models and study each of them in terms of mathematical formulation, near equilibrium point stability properties, and disease outbreak threshold conditions (basic reproduction ratio). Furthermore, we discuss the various influential factors that need to be considered during epidemic modeling. The main objective of this article is to provide a basic understanding of the mathematical complexity incurred in deterministic epidemic models with the aid of graphical illustrations obtained through implementation. |
2312.06669 | Tingting Hou | Tingting Hou, Chang Jiang and Qing Lu | An Association Test Based on Kernel-Based Neural Networks for Complex
Genetic Association Analysis | 34 pages, 4 figures, 3 tables | null | null | null | q-bio.QM cs.LG stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of artificial intelligence, especially the progress of deep neural
networks, is expected to revolutionize genetic research and offer unprecedented
potential to decode the complex relationships between genetic variants and
disease phenotypes, which could mark a significant step toward improving our
understanding of the disease etiology. While deep neural networks hold great
promise for genetic association analysis, limited research has been focused on
developing neural-network-based tests to dissect complex genotype-phenotype
associations. This complexity arises from the opaque nature of neural networks
and the absence of defined limiting distributions. We have previously developed
a kernel-based neural network model (KNN) that synergizes the strengths of
linear mixed models with conventional neural networks. KNN adopts a
computationally efficient minimum norm quadratic unbiased estimator (MINQUE)
algorithm and uses KNN structure to capture the complex relationship between
large-scale sequencing data and a disease phenotype of interest. In the KNN
framework, we introduce a MINQUE-based test to assess the joint association of
genetic variants with the phenotype, which considers non-linear and
non-additive effects and follows a mixture of chi-square distributions. We also
construct two additional tests to evaluate and interpret linear and
non-linear/non-additive genetic effects, including interaction effects. Our
simulations show that our method consistently controls the type I error rate
under various conditions and achieves greater power than a commonly used
sequence kernel association test (SKAT), especially when involving non-linear
and interaction effects. When applied to real data from the UK Biobank, our
approach identified genes associated with hippocampal volume, which can be
further replicated and evaluated for their role in the pathogenesis of
Alzheimer's disease.
| [
{
"created": "Wed, 6 Dec 2023 05:02:28 GMT",
"version": "v1"
}
] | 2023-12-13 | [
[
"Hou",
"Tingting",
""
],
[
"Jiang",
"Chang",
""
],
[
"Lu",
"Qing",
""
]
] | The advent of artificial intelligence, especially the progress of deep neural networks, is expected to revolutionize genetic research and offer unprecedented potential to decode the complex relationships between genetic variants and disease phenotypes, which could mark a significant step toward improving our understanding of the disease etiology. While deep neural networks hold great promise for genetic association analysis, limited research has been focused on developing neural-network-based tests to dissect complex genotype-phenotype associations. This complexity arises from the opaque nature of neural networks and the absence of defined limiting distributions. We have previously developed a kernel-based neural network model (KNN) that synergizes the strengths of linear mixed models with conventional neural networks. KNN adopts a computationally efficient minimum norm quadratic unbiased estimator (MINQUE) algorithm and uses KNN structure to capture the complex relationship between large-scale sequencing data and a disease phenotype of interest. In the KNN framework, we introduce a MINQUE-based test to assess the joint association of genetic variants with the phenotype, which considers non-linear and non-additive effects and follows a mixture of chi-square distributions. We also construct two additional tests to evaluate and interpret linear and non-linear/non-additive genetic effects, including interaction effects. Our simulations show that our method consistently controls the type I error rate under various conditions and achieves greater power than a commonly used sequence kernel association test (SKAT), especially when involving non-linear and interaction effects. When applied to real data from the UK Biobank, our approach identified genes associated with hippocampal volume, which can be further replicated and evaluated for their role in the pathogenesis of Alzheimer's disease. |
q-bio/0403037 | John Hertz | A. Lerchner, G. Sterner, J. Hertz and M. Ahmadi | Mean field theory for a balanced hypercolumn model of orientation
selectivity in primary visual cortex | 20 pages, 9 figures | null | null | null | q-bio.NC | null | We present a complete mean field theory for a balanced state of a simple
model of an orientation hypercolumn. The theory is complemented by a
description of a numerical procedure for solving the mean-field equations
quantitatively. With our treatment, we can determine self-consistently both the
firing rates and the firing correlations, without being restricted to specific
neuron models. Here, we solve the analytically derived mean-field equations
numerically for integrate-and-fire neurons. Several known key properties of
orientation selective cortical neurons emerge naturally from the description:
Irregular firing with statistics close to -- but not restricted to -- Poisson
statistics; an almost linear gain function (firing frequency as a function of
stimulus contrast) of the neurons within the network; and a contrast-invariant
tuning width of the neuronal firing. We find that the irregularity in firing
depends sensitively on synaptic strengths. If Fano factors are bigger than 1,
then they are so for all stimulus orientations that elicit firing. We also find
that the tuning of the noise in the input current is the same as the tuning of
the external input, while that for the mean input current depends on both the
external input and the intracortical connectivity.
| [
{
"created": "Thu, 25 Mar 2004 19:42:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lerchner",
"A.",
""
],
[
"Sterner",
"G.",
""
],
[
"Hertz",
"J.",
""
],
[
"Ahmadi",
"M.",
""
]
] | We present a complete mean field theory for a balanced state of a simple model of an orientation hypercolumn. The theory is complemented by a description of a numerical procedure for solving the mean-field equations quantitatively. With our treatment, we can determine self-consistently both the firing rates and the firing correlations, without being restricted to specific neuron models. Here, we solve the analytically derived mean-field equations numerically for integrate-and-fire neurons. Several known key properties of orientation selective cortical neurons emerge naturally from the description: Irregular firing with statistics close to -- but not restricted to -- Poisson statistics; an almost linear gain function (firing frequency as a function of stimulus contrast) of the neurons within the network; and a contrast-invariant tuning width of the neuronal firing. We find that the irregularity in firing depends sensitively on synaptic strengths. If Fano factors are bigger than 1, then they are so for all stimulus orientations that elicit firing. We also find that the tuning of the noise in the input current is the same as the tuning of the external input, while that for the mean input current depends on both the external input and the intracortical connectivity. |
2303.05917 | Susan Martonosi | Abraham Holleran and Susan E. Martonosi and Michael Veatch | International Vaccine Allocation: An Optimization Framework | 32 pages, 5 figures | null | null | null | q-bio.PE math.OC | http://creativecommons.org/licenses/by/4.0/ | As observed during the global SARS-CoV-2 (COVID-19) pandemic, high-income
countries, such as the United States, may exhibit vaccine nationalism during a
pandemic: stockpiling doses of vaccine for their own citizens and being
reluctant to distribute doses of the vaccine to lower-income countries. While
many cite moral objections to vaccine nationalism, vaccine inequity during a
pandemic could possibly worsen the global effects of the pandemic, including in
the high-income countries themselves, through the evolution of new variants of
the virus. This paper uses the COVID-19 pandemic as a case study to identify
scenarios under which it might be in a high-income nation's own interest to
donate vaccine doses to another country before its own population has been
fully vaccinated. We develop an extended SEIR
(susceptible-exposed-infectious-recovered) epidemiological model embedded in an
optimization framework and examine scenarios involving a single donor and
multiple recipient (nondonor) geographic areas. We find that policies other
than donor-first can delay the emergence of a more-contagious variant compared
to donor-first, sometimes reducing donor-country deaths in addition to total
deaths. Thus, vaccine distribution is not a zero-sum game between donor and
nondonor countries: an optimization approach can achieve dramatic reduction in
total deaths with only a small increase (and occasionally even a decrease) in
donor-country deaths. We also identify realistic scenarios under which the
optimal policy has a switching form rather than adhering to a strict priority
order. The iterative linear programming approximation approach we develop can
help confirm those instances when a priority policy is optimal and, when not
optimal, can identify superior policies.
| [
{
"created": "Wed, 8 Mar 2023 22:08:47 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Sep 2023 15:51:50 GMT",
"version": "v2"
}
] | 2023-09-28 | [
[
"Holleran",
"Abraham",
""
],
[
"Martonosi",
"Susan E.",
""
],
[
"Veatch",
"Michael",
""
]
] | As observed during the global SARS-CoV-2 (COVID-19) pandemic, high-income countries, such as the United States, may exhibit vaccine nationalism during a pandemic: stockpiling doses of vaccine for their own citizens and being reluctant to distribute doses of the vaccine to lower-income countries. While many cite moral objections to vaccine nationalism, vaccine inequity during a pandemic could possibly worsen the global effects of the pandemic, including in the high-income countries themselves, through the evolution of new variants of the virus. This paper uses the COVID-19 pandemic as a case study to identify scenarios under which it might be in a high-income nation's own interest to donate vaccine doses to another country before its own population has been fully vaccinated. We develop an extended SEIR (susceptible-exposed-infectious-recovered) epidemiological model embedded in an optimization framework and examine scenarios involving a single donor and multiple recipient (nondonor) geographic areas. We find that policies other than donor-first can delay the emergence of a more-contagious variant compared to donor-first, sometimes reducing donor-country deaths in addition to total deaths. Thus, vaccine distribution is not a zero-sum game between donor and nondonor countries: an optimization approach can achieve dramatic reduction in total deaths with only a small increase (and occasionally even a decrease) in donor-country deaths. We also identify realistic scenarios under which the optimal policy has a switching form rather than adhering to a strict priority order. The iterative linear programming approximation approach we develop can help confirm those instances when a priority policy is optimal and, when not optimal, can identify superior policies. |
q-bio/0603033 | Chih-Yuan Tseng | Chih-Yuan Tseng, Chun-Ping Yu, and HC Lee | Integrity of H1 helix in prion protein revealed by molecular dynamic
simulations to be especially vulnerable to changes in the relative
orientation of H1 and its S1 flank | A major revision on statistical analysis method has been made. The
paper now has 23 pages, 11 figures. This work was presented at 2006 APS March
meeting session K29.0004 at Baltimore, MD, USA 3/13-17, 2006. This paper has
been accepted for pubcliation in European Biophysical Journal on Feb 2, 2009 | null | 10.1007/s00249-009-0414-4 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the template-assistance model, normal prion protein (PrPC), the pathogenic
cause of prion diseases such as Creutzfeldt-Jakob (CJD) in human, Bovine
Spongiform Encephalopathy (BSE) in cow, and scrapie in sheep, converts to
infectious prion (PrPSc) through an autocatalytic process triggered by a
transient interaction between PrPC and PrPSc. Conventional studies suggest the
S1-H1-S2 region in PrPC to be the template of S1-S2 $\beta$-sheet in PrPSc, and
the conformational conversion of PrPC into PrPSc may involve an unfolding of H1
in PrPC and its refolding into the $\beta$-sheet in PrPSc. Here we conduct a
series of simulation experiments to test the idea of transient interaction of
the template-assistance model. We find that the integrity of H1 in PrPC is
vulnerable to a transient interaction that alters the native dihedral angles at
residue Asn$^{143}$, which connects the S1 flank to H1, but not to interactions
that alter the internal structure of the S1 flank, nor to those that alter the
relative orientation between H1 and the S2 flank.
| [
{
"created": "Wed, 29 Mar 2006 07:11:33 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Feb 2009 16:55:16 GMT",
"version": "v2"
}
] | 2009-02-03 | [
[
"Tseng",
"Chih-Yuan",
""
],
[
"Yu",
"Chun-Ping",
""
],
[
"Lee",
"HC",
""
]
] | In the template-assistance model, normal prion protein (PrPC), the pathogenic cause of prion diseases such as Creutzfeldt-Jakob (CJD) in human, Bovine Spongiform Encephalopathy (BSE) in cow, and scrapie in sheep, converts to infectious prion (PrPSc) through an autocatalytic process triggered by a transient interaction between PrPC and PrPSc. Conventional studies suggest the S1-H1-S2 region in PrPC to be the template of S1-S2 $\beta$-sheet in PrPSc, and the conformational conversion of PrPC into PrPSc may involve an unfolding of H1 in PrPC and its refolding into the $\beta$-sheet in PrPSc. Here we conduct a series of simulation experiments to test the idea of transient interaction of the template-assistance model. We find that the integrity of H1 in PrPC is vulnerable to a transient interaction that alters the native dihedral angles at residue Asn$^{143}$, which connects the S1 flank to H1, but not to interactions that alter the internal structure of the S1 flank, nor to those that alter the relative orientation between H1 and the S2 flank. |
2203.03481 | David Calhas | David Calhas, Rui Henriques | EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode
Relationships | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Topographical structures represent connections between entities and provide a
comprehensive design of complex systems. Currently these structures are used to
discover correlates of neuronal and haemodynamical activity. In this work, we
incorporate them with neural processing techniques to perform regression, using
electrophysiological activity to retrieve haemodynamics. To this end, we use
Fourier features, attention mechanisms, shared space between modalities and
incorporation of style in the latent representation. By combining these
techniques, we propose several models that significantly outperform current
state-of-the-art of this task in resting state and task-based recording
settings. We report which EEG electrodes are the most relevant for the
regression task and which relations impacted it the most. In addition, we
observe that haemodynamic activity at the scalp, in contrast with sub-cortical
regions, is relevant to the learned shared space. Overall, these results
suggest that EEG electrode relationships are pivotal to retain information
necessary for haemodynamical activity retrieval.
| [
{
"created": "Mon, 7 Mar 2022 15:51:36 GMT",
"version": "v1"
}
] | 2022-03-08 | [
[
"Calhas",
"David",
""
],
[
"Henriques",
"Rui",
""
]
] | Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of neuronal and haemodynamical activity. In this work, we incorporate them with neural processing techniques to perform regression, using electrophysiological activity to retrieve haemodynamics. To this end, we use Fourier features, attention mechanisms, shared space between modalities and incorporation of style in the latent representation. By combining these techniques, we propose several models that significantly outperform current state-of-the-art of this task in resting state and task-based recording settings. We report which EEG electrodes are the most relevant for the regression task and which relations impacted it the most. In addition, we observe that haemodynamic activity at the scalp, in contrast with sub-cortical regions, is relevant to the learned shared space. Overall, these results suggest that EEG electrode relationships are pivotal to retain information necessary for haemodynamical activity retrieval. |
2001.09016 | Razvan Marinescu | Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E.
Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox,
Polina Golland, Stefan Klein, Daniel C. Alexander | TADPOLE Challenge: Accurate Alzheimer's disease prediction through
crowdsourced forecasting of future data | 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text
overlap with arXiv:1805.03909 | MICCAI Multimodal Brain Image Analysis Workshop, 2019 | 10.1007/978-3-030-32281-6_1 | null | q-bio.PE eess.IV stat.AP | http://creativecommons.org/licenses/by/4.0/ | The TADPOLE Challenge compares the performance of algorithms at predicting
the future evolution of individuals at risk of Alzheimer's disease. TADPOLE
Challenge participants train their models and algorithms on historical data
from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants
are then required to make forecasts of three key outcomes for ADNI-3 rollover
participants: clinical diagnosis, ADAS-Cog 13, and total volume of the
ventricles -- which are then compared with future measurements. Strong points
of the challenge are that the test data did not exist at the time of
forecasting (it was acquired afterwards), and that it focuses on the
challenging problem of cohort selection for clinical trials by identifying fast
progressors. The submission phase of TADPOLE was open until 15 November 2017;
since then data has been acquired until April 2019 from 219 subjects with 223
clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used
for the evaluation of the participants' predictions. Thirty-three teams
participated with a total of 92 submissions. No single submission was best at
predicting all three outcomes. For diagnosis prediction, the best forecast
(team Frog), which was based on gradient boosting, obtained a multiclass area
under the receiver-operating curve (MAUC) of 0.931, while for ventricle
prediction the best forecast (team EMC1), which was based on disease
progression modelling and spline regression, obtained mean absolute error of
0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was
considerably better than the benchmark mixed effects model (BenchmarkME),
provided to participants before the submission deadline. Further analysis can
help understand which input features and algorithms are most suitable for
Alzheimer's disease prediction and for aiding patient stratification in
clinical trials.
| [
{
"created": "Thu, 23 Jan 2020 16:06:12 GMT",
"version": "v1"
}
] | 2020-01-27 | [
[
"Marinescu",
"Razvan V.",
""
],
[
"Oxtoby",
"Neil P.",
""
],
[
"Young",
"Alexandra L.",
""
],
[
"Bron",
"Esther E.",
""
],
[
"Toga",
"Arthur W.",
""
],
[
"Weiner",
"Michael W.",
""
],
[
"Barkhof",
"Frederik",
""
],
[
"Fox",
"Nick C.",
""
],
[
"Golland",
"Polina",
""
],
[
"Klein",
"Stefan",
""
],
[
"Alexander",
"Daniel C.",
""
]
] | The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials. |
1406.4730 | Michael LeVine V | Michael V. LeVine, Jose Manuel Perez-Aguilar, Harel Weinstein | N-body Information Theory (NbIT) Analysis of Rigid-Body Dynamics in
Intracellular Loop 2 of the 5-HT2A Receptor | Proceedings International-Work Conference on Bioinformatics and
Biomedical Engineering 2014 (IWWBIO 2014) | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rigid-body motions of protein secondary structure are often implicated in
mecha-nisms of protein function. In GPCRs, evidence suggests that intracellular
loop 2 (IL2) contains a segment characterized as a helix when the activated
receptor trig-gers downstream signaling. However, neither experimental nor
computational methods are readily available to assess quantitatively the degree
of collective mo-tions in such secondary structure motifs of proteins. Here we
describe a new el-ement of our N-body Information Theory (NbIT) framework to
address this problem. To this end we introduce total intercorrelation, a
measure in infor-mation theory that can be used to describe n-body correlated
dynamics between multivariate distributions, such as 3-dimensional atomic
fluctuations in simula-tions of proteins. We also define two additional
measures, the rigid-body fraction and correlation order, which can be
determined from the decomposition of the configurational entropy. Using these
measures, we analyze the dynamics of IL2 in microsecond Molecular Dynamics
simulations of the 5-HT2A receptor to demonstrate the powerful features of the
new analysis techniques in studying the collective motion dynamics of secondary
structure motifs. The analysis reveals an intriguing difference in the extent
of correlated motions in the helical segment of IL2 in the presence and absence
of bound 5-HT, the endogenous agonist that ac-tivates the receptor and triggers
downstream signaling, suggesting that IL2 rigid-body motions can display
distinct behaviors that may discriminate functional mechanism of GPCRs.
| [
{
"created": "Wed, 18 Jun 2014 14:24:27 GMT",
"version": "v1"
}
] | 2014-06-19 | [
[
"LeVine",
"Michael V.",
""
],
[
"Perez-Aguilar",
"Jose Manuel",
""
],
[
"Weinstein",
"Harel",
""
]
] | Rigid-body motions of protein secondary structure are often implicated in mecha-nisms of protein function. In GPCRs, evidence suggests that intracellular loop 2 (IL2) contains a segment characterized as a helix when the activated receptor trig-gers downstream signaling. However, neither experimental nor computational methods are readily available to assess quantitatively the degree of collective mo-tions in such secondary structure motifs of proteins. Here we describe a new el-ement of our N-body Information Theory (NbIT) framework to address this problem. To this end we introduce total intercorrelation, a measure in infor-mation theory that can be used to describe n-body correlated dynamics between multivariate distributions, such as 3-dimensional atomic fluctuations in simula-tions of proteins. We also define two additional measures, the rigid-body fraction and correlation order, which can be determined from the decomposition of the configurational entropy. Using these measures, we analyze the dynamics of IL2 in microsecond Molecular Dynamics simulations of the 5-HT2A receptor to demonstrate the powerful features of the new analysis techniques in studying the collective motion dynamics of secondary structure motifs. The analysis reveals an intriguing difference in the extent of correlated motions in the helical segment of IL2 in the presence and absence of bound 5-HT, the endogenous agonist that ac-tivates the receptor and triggers downstream signaling, suggesting that IL2 rigid-body motions can display distinct behaviors that may discriminate functional mechanism of GPCRs. |
2201.09854 | Alice Baniel | Alice Baniel and Marie J. E. Charpentier | The social microbiome: the missing mechanism mediating the
sociality-fitness nexus? | 1 figure | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | In many social mammals, early life social adversity and social integration
largely predict individual health, lifespan and reproductive success. Efforts
in identifying the physiological mechanisms mediating the relationship between
the social environment and individual fitness have so far concentrated on
socially-induced stress, mediated by alterations in neuroendocrine signaling
and immune function. Here, we propose a much-needed alternative mechanism
relying on microbially-mediated effects: social relationships with
conspecifics, both in early life and adulthood, might strongly contribute both
to the transmission of beneficial microbes and to diversifying host
microbiomes. In turn, more valuable and diverse microbiomes would promote
pathogen resistance and optimal health and thus translate into positive fitness
outcomes. This mechanism relies on two emerging findings from empirical
studies, namely that microbiomes (i) are largely socially transmitted via
vertical and horizontal routes, and (ii) play a pervasive role in host
development, physiology, metabolism, and susceptibility to pathogens. We
suggest that the social transmission of microbiomes has the potential to
explain the sociality-fitness nexus, to a similar - or even higher - extent
than chronic social stress, in ways that have yet to be studied empirically in
social mammals.
| [
{
"created": "Mon, 24 Jan 2022 18:19:40 GMT",
"version": "v1"
}
] | 2022-01-25 | [
[
"Baniel",
"Alice",
""
],
[
"Charpentier",
"Marie J. E.",
""
]
] | In many social mammals, early life social adversity and social integration largely predict individual health, lifespan and reproductive success. Efforts in identifying the physiological mechanisms mediating the relationship between the social environment and individual fitness have so far concentrated on socially-induced stress, mediated by alterations in neuroendocrine signaling and immune function. Here, we propose a much-needed alternative mechanism relying on microbially-mediated effects: social relationships with conspecifics, both in early life and adulthood, might strongly contribute both to the transmission of beneficial microbes and to diversifying host microbiomes. In turn, more valuable and diverse microbiomes would promote pathogen resistance and optimal health and thus translate into positive fitness outcomes. This mechanism relies on two emerging findings from empirical studies, namely that microbiomes (i) are largely socially transmitted via vertical and horizontal routes, and (ii) play a pervasive role in host development, physiology, metabolism, and susceptibility to pathogens. We suggest that the social transmission of microbiomes has the potential to explain the sociality-fitness nexus, to a similar - or even higher - extent than chronic social stress, in ways that have yet to be studied empirically in social mammals. |
1409.2051 | Mike Steel Prof. | Sebastien Roch and Mike Steel | Likelihood-based tree reconstruction on a concatenation of alignments
can be positively misleading | 16 pages, 2 figures | Theoretical Population Biology, Volume 100, 2015, Pages 56-62 | 10.1016/j.tpb.2014.12.005. | null | q-bio.PE cs.CE math.PR math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reconstruction of a species tree from genomic data faces a double hurdle.
First, the (gene) tree describing the evolution of each gene may differ from
the species tree, for instance, due to incomplete lineage sorting. Second, the
aligned genetic sequences at the leaves of each gene tree provide merely an
imperfect estimate of the topology of the gene tree. In this note, we
demonstrate formally that a basic statistical problem arises if one tries to
avoid accounting for these two processes and analyses the genetic data directly
via a concatenation approach. More precisely, we show that, under the
multi-species coalescent with a standard site substitution model, maximum
likelihood estimation on sequence data that has been concatenated across genes
and performed under the incorrect assumption that all sites have evolved
independently and identically on a fixed tree is a statistically inconsistent
estimator of the species tree. Our results provide a formal justification of
simulation results described of Kubatko and Degnan (2007) and others, and
complements recent theoretical results by DeGorgio and Degnan (2010) and
Chifman and Kubtako (2014).
| [
{
"created": "Sat, 6 Sep 2014 19:51:05 GMT",
"version": "v1"
}
] | 2021-12-07 | [
[
"Roch",
"Sebastien",
""
],
[
"Steel",
"Mike",
""
]
] | The reconstruction of a species tree from genomic data faces a double hurdle. First, the (gene) tree describing the evolution of each gene may differ from the species tree, for instance, due to incomplete lineage sorting. Second, the aligned genetic sequences at the leaves of each gene tree provide merely an imperfect estimate of the topology of the gene tree. In this note, we demonstrate formally that a basic statistical problem arises if one tries to avoid accounting for these two processes and analyses the genetic data directly via a concatenation approach. More precisely, we show that, under the multi-species coalescent with a standard site substitution model, maximum likelihood estimation on sequence data that has been concatenated across genes and performed under the incorrect assumption that all sites have evolved independently and identically on a fixed tree is a statistically inconsistent estimator of the species tree. Our results provide a formal justification of simulation results described of Kubatko and Degnan (2007) and others, and complements recent theoretical results by DeGorgio and Degnan (2010) and Chifman and Kubtako (2014). |
1611.00836 | Osman Kahraman | Osman Kahraman, Peter D. Koch, William S. Klug, Christoph A.
Haselwandter | Bilayer-thickness-mediated interactions between integral membrane
proteins | null | Phys. Rev. E 93, 042410 (2016) | 10.1103/PhysRevE.93.042410 | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hydrophobic thickness mismatch between integral membrane proteins and the
surrounding lipid bilayer can produce lipid bilayer thickness deformations.
Experiment and theory have shown that protein-induced lipid bilayer thickness
deformations can yield energetically favorable bilayer-mediated interactions
between integral membrane proteins, and large-scale organization of integral
membrane proteins into protein clusters in cell membranes. Within the continuum
elasticity theory of membranes, the energy cost of protein-induced bilayer
thickness deformations can be captured by considering compression and expansion
of the bilayer hydrophobic core, membrane tension, and bilayer bending,
resulting in biharmonic equilibrium equations describing the shape of lipid
bilayers for a given set of bilayer-protein boundary conditions. Here we
develop a combined analytic and numerical methodology for the solution of the
equilibrium elastic equations associated with protein-induced lipid bilayer
deformations. Our methodology allows accurate prediction of thickness-mediated
protein interactions for arbitrary protein symmetries at arbitrary protein
separations and relative orientations. We provide exact analytic solutions for
cylindrical integral membrane proteins with constant and varying hydrophobic
thickness, and develop perturbative analytic solutions for non-cylindrical
protein shapes. We complement these analytic solutions, and assess their
accuracy, by developing both finite element and finite difference numerical
solution schemes. Taken together, the work presented here puts into place an
analytic and numerical framework which allows calculation of bilayer-mediated
elastic interactions between integral membrane proteins for the complicated
protein shapes suggested by structural biology and at the small protein
separations most relevant for the crowded membrane environments provided by
living cells.
| [
{
"created": "Wed, 2 Nov 2016 23:08:09 GMT",
"version": "v1"
}
] | 2016-11-04 | [
[
"Kahraman",
"Osman",
""
],
[
"Koch",
"Peter D.",
""
],
[
"Klug",
"William S.",
""
],
[
"Haselwandter",
"Christoph A.",
""
]
] | Hydrophobic thickness mismatch between integral membrane proteins and the surrounding lipid bilayer can produce lipid bilayer thickness deformations. Experiment and theory have shown that protein-induced lipid bilayer thickness deformations can yield energetically favorable bilayer-mediated interactions between integral membrane proteins, and large-scale organization of integral membrane proteins into protein clusters in cell membranes. Within the continuum elasticity theory of membranes, the energy cost of protein-induced bilayer thickness deformations can be captured by considering compression and expansion of the bilayer hydrophobic core, membrane tension, and bilayer bending, resulting in biharmonic equilibrium equations describing the shape of lipid bilayers for a given set of bilayer-protein boundary conditions. Here we develop a combined analytic and numerical methodology for the solution of the equilibrium elastic equations associated with protein-induced lipid bilayer deformations. Our methodology allows accurate prediction of thickness-mediated protein interactions for arbitrary protein symmetries at arbitrary protein separations and relative orientations. We provide exact analytic solutions for cylindrical integral membrane proteins with constant and varying hydrophobic thickness, and develop perturbative analytic solutions for non-cylindrical protein shapes. We complement these analytic solutions, and assess their accuracy, by developing both finite element and finite difference numerical solution schemes. Taken together, the work presented here puts into place an analytic and numerical framework which allows calculation of bilayer-mediated elastic interactions between integral membrane proteins for the complicated protein shapes suggested by structural biology and at the small protein separations most relevant for the crowded membrane environments provided by living cells. |
q-bio/0510023 | Dalius Balciunas | D. Balciunas | The equilibrium theory of life evolution | 4 pages | null | null | null | q-bio.PE | null | Apparent biodiversity on earth exists only if we compare different species
separated from their environments. Meanwhile coexisting species have to be
identical in terms of energetic interactions. Consider the biosphere as a
network of chemical reactions. This leads to the conclusion that forces which
drive life evolution may be found inside the process of the transformations of
molecules. From the thermodynamic point of view the system of reacting
particles reaches a steady state when the chemical potentials of reagents
become equal. Interactions between chemical compounds taking part in concurrent
reactions and the equilibration of their chemical potentials is the essence of
biological evolution and its only driving force.
| [
{
"created": "Wed, 12 Oct 2005 06:15:05 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Balciunas",
"D.",
""
]
] | Apparent biodiversity on earth exists only if we compare different species separated from their environments. Meanwhile coexisting species have to be identical in terms of energetic interactions. Consider the biosphere as a network of chemical reactions. This leads to the conclusion that forces which drive life evolution may be found inside the process of the transformations of molecules. From the thermodynamic point of view the system of reacting particles reaches a steady state when the chemical potentials of reagents become equal. Interactions between chemical compounds taking part in concurrent reactions and the equilibration of their chemical potentials is the essence of biological evolution and its only driving force. |
1712.02449 | Giuseppe Pica | Giuseppe Pica, Eugenio Piasini, Houman Safaai, Caroline A. Runyan,
Mathew E. Diamond, Tommaso Fellin, Christoph Kayser, Christopher D. Harvey,
Stefano Panzeri | Quantifying how much sensory information in a neural code is relevant
for behavior | null | Advances in Neural Information Processing Systems 30, 3689--3699,
2017 | null | null | q-bio.NC cs.IT math.IT physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Determining how much of the sensory information carried by a neural code
contributes to behavioral performance is key to understand sensory function and
neural information flow. However, there are as yet no analytical tools to
compute this information that lies at the intersection between sensory coding
and behavioral readout. Here we develop a novel measure, termed the
information-theoretic intersection information $I_{II}(S;R;C)$, that quantifies
how much of the sensory information carried by a neural response R is used for
behavior during perceptual discrimination tasks. Building on the Partial
Information Decomposition framework, we define $I_{II}(S;R;C)$ as the part of
the mutual information between the stimulus S and the response R that also
informs the consequent behavioral choice C. We compute $I_{II}(S;R;C)$ in the
analysis of two experimental cortical datasets, to show how this measure can be
used to compare quantitatively the contributions of spike timing and spike
rates to task performance, and to identify brain areas or neural populations
that specifically transform sensory information into choice.
| [
{
"created": "Wed, 6 Dec 2017 23:55:56 GMT",
"version": "v1"
}
] | 2017-12-08 | [
[
"Pica",
"Giuseppe",
""
],
[
"Piasini",
"Eugenio",
""
],
[
"Safaai",
"Houman",
""
],
[
"Runyan",
"Caroline A.",
""
],
[
"Diamond",
"Mathew E.",
""
],
[
"Fellin",
"Tommaso",
""
],
[
"Kayser",
"Christoph",
""
],
[
"Harvey",
"Christopher D.",
""
],
[
"Panzeri",
"Stefano",
""
]
] | Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow. However, there are as yet no analytical tools to compute this information that lies at the intersection between sensory coding and behavioral readout. Here we develop a novel measure, termed the information-theoretic intersection information $I_{II}(S;R;C)$, that quantifies how much of the sensory information carried by a neural response R is used for behavior during perceptual discrimination tasks. Building on the Partial Information Decomposition framework, we define $I_{II}(S;R;C)$ as the part of the mutual information between the stimulus S and the response R that also informs the consequent behavioral choice C. We compute $I_{II}(S;R;C)$ in the analysis of two experimental cortical datasets, to show how this measure can be used to compare quantitatively the contributions of spike timing and spike rates to task performance, and to identify brain areas or neural populations that specifically transform sensory information into choice. |
2306.09243 | Okezue Bell | Okezue Bell, Arthur Lee, Elizabeth Engle | On Selecting Distance Metrics in $n$-Dimensional Normed Vector Spaces of
Cells: A Novel Criterion and Similarity Measure Towards Efficient and
Accurate Omics Analysis | Author disagreement about the preprint timeline before further
testing and verification. All authors agree on withdrawal | null | null | null | q-bio.GN math.DG | http://creativecommons.org/licenses/by/4.0/ | Single-cell omics enable the profiles of cells, which contain large numbers
of biological features, to be quantified. Cluster analysis, a dimensionality
reduction process, is used to reduce the dimensions of the data to make it
computationally tractable. In these analyses, cells are represented as vectors
in $n$-Dimensional space, where each dimension corresponds to a certain cell
feature. The distance between cells is used as a surrogate measure of
similarity, providing insight into the cell's state, function, and genetic
mechanisms. However, as cell profiles are clustered in 3D or higher-dimensional
space, it remains unknown which distance metric provides the most accurate
spatiotemporal representation of similarity, limiting the interpretability of
the data. I propose and prove a generalized proposition and set of corollaries
that serve as a criterion to determine which of the standard distance measures
is most accurate for conveying cell profile heterogeneity. Each distance method
is evaluated via statistical, geometric, and topological proofs, which are
formalized into a set of criteria. In this paper, I present the putative,
first-ever method to elect the most accurate and precise distance metrics with
any profiling modality, which are determined to be the Wasserstein distance and
cosine similarity metrics, respectively, in general cases. I also identify
special cases in which the criterion may select non-standard metrics. Combining
the metric properties selected by the criterion, I develop a novel, custom,
optimal distance metric that demonstrates superior computational efficiency,
peak annotation, motif identification, and footprinting for transcription
factor binding sites when compared with leading methods.
| [
{
"created": "Tue, 13 Jun 2023 01:59:56 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jun 2024 04:54:35 GMT",
"version": "v2"
}
] | 2024-06-06 | [
[
"Bell",
"Okezue",
""
],
[
"Lee",
"Arthur",
""
],
[
"Engle",
"Elizabeth",
""
]
] | Single-cell omics enable the profiles of cells, which contain large numbers of biological features, to be quantified. Cluster analysis, a dimensionality reduction process, is used to reduce the dimensions of the data to make it computationally tractable. In these analyses, cells are represented as vectors in $n$-Dimensional space, where each dimension corresponds to a certain cell feature. The distance between cells is used as a surrogate measure of similarity, providing insight into the cell's state, function, and genetic mechanisms. However, as cell profiles are clustered in 3D or higher-dimensional space, it remains unknown which distance metric provides the most accurate spatiotemporal representation of similarity, limiting the interpretability of the data. I propose and prove a generalized proposition and set of corollaries that serve as a criterion to determine which of the standard distance measures is most accurate for conveying cell profile heterogeneity. Each distance method is evaluated via statistical, geometric, and topological proofs, which are formalized into a set of criteria. In this paper, I present the putative, first-ever method to elect the most accurate and precise distance metrics with any profiling modality, which are determined to be the Wasserstein distance and cosine similarity metrics, respectively, in general cases. I also identify special cases in which the criterion may select non-standard metrics. Combining the metric properties selected by the criterion, I develop a novel, custom, optimal distance metric that demonstrates superior computational efficiency, peak annotation, motif identification, and footprinting for transcription factor binding sites when compared with leading methods. |
1704.06086 | Yaguang Ren | Yaguang Ren, Sixi Chen, Mengmeng Ma, Congjie Zhang, Kejie Wang, Feng
Li, Wenxuan Guo, Jiatao Huang, Chao Zhang | Do ROS really slow down aging in C. elegans? | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The view that ROS slow down aging is getting popular. We here proposed an
idea that aging is slowed down by secondary responses rather than ROS.
| [
{
"created": "Thu, 20 Apr 2017 11:16:25 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jul 2017 03:55:58 GMT",
"version": "v2"
}
] | 2017-07-28 | [
[
"Ren",
"Yaguang",
""
],
[
"Chen",
"Sixi",
""
],
[
"Ma",
"Mengmeng",
""
],
[
"Zhang",
"Congjie",
""
],
[
"Wang",
"Kejie",
""
],
[
"Li",
"Feng",
""
],
[
"Guo",
"Wenxuan",
""
],
[
"Huang",
"Jiatao",
""
],
[
"Zhang",
"Chao",
""
]
] | The view that ROS slow down aging is getting popular. We here proposed an idea that aging is slowed down by secondary responses rather than ROS. |
1406.4205 | Peter F. Schultz | Peter F. Schultz and Dylan M. Paiton and Wei Lu and Garrett T. Kenyon | Replicating Kernels with a Short Stride Allows Sparse Reconstructions
with Fewer Independent Kernels | null | null | null | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In sparse coding it is common to tile an image into nonoverlapping patches,
and then use a dictionary to create a sparse representation of each tile
independently. In this situation, the overcompleteness of the dictionary is the
number of dictionary elements divided by the patch size. In deconvolutional
neural networks (DCNs), dictionaries learned on nonoverlapping tiles are
replaced by a family of convolution kernels. Hence adjacent points in the
feature maps (V1 layers) have receptive fields in the image that are
translations of each other. The translational distance is determined by the
dimensions of V1 in comparison to the dimensions of the image space. We refer
to this translational distance as the stride.
We implement a type of DCN using a modified Locally Competitive Algorithm
(LCA) to investigate the relationship between the number of kernels, the
stride, the receptive field size, and the quality of reconstruction. We find,
for example, that for 16x16-pixel receptive fields, using eight kernels and a
stride of 2 leads to sparse reconstructions of comparable quality as using 512
kernels and a stride of 16 (the nonoverlapping case). We also find that for a
given stride and number of kernels, the patch size does not significantly
affect reconstruction quality. Instead, the learned convolution kernels have a
natural support radius independent of the patch size.
| [
{
"created": "Tue, 17 Jun 2014 01:07:48 GMT",
"version": "v1"
}
] | 2014-06-18 | [
[
"Schultz",
"Peter F.",
""
],
[
"Paiton",
"Dylan M.",
""
],
[
"Lu",
"Wei",
""
],
[
"Kenyon",
"Garrett T.",
""
]
] | In sparse coding it is common to tile an image into nonoverlapping patches, and then use a dictionary to create a sparse representation of each tile independently. In this situation, the overcompleteness of the dictionary is the number of dictionary elements divided by the patch size. In deconvolutional neural networks (DCNs), dictionaries learned on nonoverlapping tiles are replaced by a family of convolution kernels. Hence adjacent points in the feature maps (V1 layers) have receptive fields in the image that are translations of each other. The translational distance is determined by the dimensions of V1 in comparison to the dimensions of the image space. We refer to this translational distance as the stride. We implement a type of DCN using a modified Locally Competitive Algorithm (LCA) to investigate the relationship between the number of kernels, the stride, the receptive field size, and the quality of reconstruction. We find, for example, that for 16x16-pixel receptive fields, using eight kernels and a stride of 2 leads to sparse reconstructions of comparable quality as using 512 kernels and a stride of 16 (the nonoverlapping case). We also find that for a given stride and number of kernels, the patch size does not significantly affect reconstruction quality. Instead, the learned convolution kernels have a natural support radius independent of the patch size. |
1905.02578 | Philippe Robert S. | Philippe Robert | Mathematical Models of Gene Expression | null | null | null | null | q-bio.MN math.PR q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we analyze the equilibrium properties of a large class of
stochastic processes describing the fundamental biological process within
bacterial cells, {\em the production process of proteins}. Stochastic models
classically used in this context to describe the time evolution of the numbers
of mRNAs and proteins are presented and discussed. An extension of these
models, which includes elongation phases of mRNAs and proteins, is introduced.
A convergence result to equilibrium for the process associated to the number of
proteins and mRNAs is proved and a representation of this equilibrium as a
functional of a Poisson process in an extended state space is obtained.
Explicit expressions for the first two moments of the number of mRNAs and
proteins at equilibrium are derived, generalizing some classical formulas.
Approximations used in the biological literature for the equilibrium
distribution of the number of proteins are discussed and investigated in the
light of these results. Several convergence results for the distribution of the
number of proteins at equilibrium are in particular obtained under different
scaling assumptions.
| [
{
"created": "Mon, 6 May 2019 14:25:40 GMT",
"version": "v1"
},
{
"created": "Mon, 13 May 2019 09:13:07 GMT",
"version": "v2"
},
{
"created": "Wed, 16 Oct 2019 09:06:11 GMT",
"version": "v3"
}
] | 2019-10-17 | [
[
"Robert",
"Philippe",
""
]
] | In this paper we analyze the equilibrium properties of a large class of stochastic processes describing the fundamental biological process within bacterial cells, {\em the production process of proteins}. Stochastic models classically used in this context to describe the time evolution of the numbers of mRNAs and proteins are presented and discussed. An extension of these models, which includes elongation phases of mRNAs and proteins, is introduced. A convergence result to equilibrium for the process associated to the number of proteins and mRNAs is proved and a representation of this equilibrium as a functional of a Poisson process in an extended state space is obtained. Explicit expressions for the first two moments of the number of mRNAs and proteins at equilibrium are derived, generalizing some classical formulas. Approximations used in the biological literature for the equilibrium distribution of the number of proteins are discussed and investigated in the light of these results. Several convergence results for the distribution of the number of proteins at equilibrium are in particular obtained under different scaling assumptions. |
1511.05519 | Alina Gavrilut | Maricel Agop, Alina Gavrilut, Gabriel Crumpei, Mitica Craus, Vlad
Birlescu | Brain dynamics through spectral-structural neuronal networks | arXiv admin note: text overlap with arXiv:0910.2741 by other authors | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Starting from the morphological-functional assumption of the fractal brain, a
mathematical model is given by activating brain non-differentiable dynamics
through the determinism-nondeterminism inference of the responsible mechanisms.
The postulation of a scale covariance principle in Schrodinger type
representation of the brain geodesics implies the spectral functionality of the
brain dynamics through mechanisms of tunelling, percolation etc., while in the
hydrodynamical type representation, it implies their structural functionality
through mechanisms of wave schock, solitons type etc. For external constraints
proportional with the states density, the fluctuations of the brain stationary
dynamics activate both the spectral neuronal networks and the structural ones
through a mapping principle of two distinct classes of cnoidal oscillation
modes. The spectral-structural compatibility of the neuronal networks generates
the communication codes of algebraic type, while the same compatibility on the
solitonic component induces a strange topology (the direct product of the
spectral topology and the structural one) that is responsible of the quadruple
law(for instance, the nucleotide base from the human DNA structure).
Implications in the elucidation of some neuropsychological mechanisms (memory
location and functioning, dementia etc.) are also presented.
| [
{
"created": "Mon, 16 Nov 2015 08:51:47 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"Agop",
"Maricel",
""
],
[
"Gavrilut",
"Alina",
""
],
[
"Crumpei",
"Gabriel",
""
],
[
"Craus",
"Mitica",
""
],
[
"Birlescu",
"Vlad",
""
]
] | Starting from the morphological-functional assumption of the fractal brain, a mathematical model is given by activating brain non-differentiable dynamics through the determinism-nondeterminism inference of the responsible mechanisms. The postulation of a scale covariance principle in Schrodinger type representation of the brain geodesics implies the spectral functionality of the brain dynamics through mechanisms of tunelling, percolation etc., while in the hydrodynamical type representation, it implies their structural functionality through mechanisms of wave schock, solitons type etc. For external constraints proportional with the states density, the fluctuations of the brain stationary dynamics activate both the spectral neuronal networks and the structural ones through a mapping principle of two distinct classes of cnoidal oscillation modes. The spectral-structural compatibility of the neuronal networks generates the communication codes of algebraic type, while the same compatibility on the solitonic component induces a strange topology (the direct product of the spectral topology and the structural one) that is responsible of the quadruple law(for instance, the nucleotide base from the human DNA structure). Implications in the elucidation of some neuropsychological mechanisms (memory location and functioning, dementia etc.) are also presented. |
2202.13365 | Jes\'us Fern\'andez-S\'anchez | Marta Casanellas and Jes\'us Fern\'andez-S\'anchez and Marina
Garrote-L\'opez and Marc Sabat\'e-Vidales | Designing weights for quartet-based methods when data is heterogeneous
across lineages | Main article: 25 pages, 6 figures, 4 tables; 1 appendix: 22 pages, 11
figures, 7 yables | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Homogeneity across lineages is a common assumption in phylogenetics according
to which nucleotide substitution rates remain constant in time and do not
depend on lineages. This is a simplifying hypothesis which is often adopted to
make the process of sequence evolution more tractable. However, its validity
has been explored and put into question in several papers. On the other hand,
dealing successfully with the general case (heterogeneity across lineages) is
one of the key features of phylogenetic reconstruction methods based on
algebraic tools.
The goal of this paper is twofold. First, we present a new weighting system
for quartets (ASAQ) based on algebraic and semi-algebraic tools, thus specially
indicated to deal with data evolving under heterogeneus rates. This method
combines the weights two previous methods by means of a test based on the
positivity of the branch length estimated with the paralinear distance. ASAQ is
statistically consistent when applied to GM data, considers rate and base
composition heterogeneity among lineages and does not assume stationarity nor
time-reversibility. Second, we test and compare the performance of several
quartet-based methods for phylogenetic tree reconstruction (namely, Quartet
Puzzling, Weight Optimization and Wilson's method) in combination with ASAQ
weights and other weights based on algebraic and semi-algebraic methods or on
the paralinear distance. These tests are applied to both simulated and real
data and support Weight Optimization with ASAQ weights as a reliable and
successful reconstruction method.
| [
{
"created": "Sun, 27 Feb 2022 13:42:52 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Casanellas",
"Marta",
""
],
[
"Fernández-Sánchez",
"Jesús",
""
],
[
"Garrote-López",
"Marina",
""
],
[
"Sabaté-Vidales",
"Marc",
""
]
] | Homogeneity across lineages is a common assumption in phylogenetics according to which nucleotide substitution rates remain constant in time and do not depend on lineages. This is a simplifying hypothesis which is often adopted to make the process of sequence evolution more tractable. However, its validity has been explored and put into question in several papers. On the other hand, dealing successfully with the general case (heterogeneity across lineages) is one of the key features of phylogenetic reconstruction methods based on algebraic tools. The goal of this paper is twofold. First, we present a new weighting system for quartets (ASAQ) based on algebraic and semi-algebraic tools, thus specially indicated to deal with data evolving under heterogeneus rates. This method combines the weights two previous methods by means of a test based on the positivity of the branch length estimated with the paralinear distance. ASAQ is statistically consistent when applied to GM data, considers rate and base composition heterogeneity among lineages and does not assume stationarity nor time-reversibility. Second, we test and compare the performance of several quartet-based methods for phylogenetic tree reconstruction (namely, Quartet Puzzling, Weight Optimization and Wilson's method) in combination with ASAQ weights and other weights based on algebraic and semi-algebraic methods or on the paralinear distance. These tests are applied to both simulated and real data and support Weight Optimization with ASAQ weights as a reliable and successful reconstruction method. |
2004.04529 | Xiaohui Chen | Xiaohui Chen, Ziyi Qiu | Scenario analysis of non-pharmaceutical interventions on global COVID-19
transmissions | published in Covid Economics: Vetted and Real-Time Papers, Centre for
Economic Policy Research (CEPR) | null | null | null | q-bio.PE physics.soc-ph q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a dynamic panel SIR (DP-SIR) model to investigate the
impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission
dynamics with panel data from 9 countries across the globe. By constructing
scenarios with different combinations of NPIs, our empirical findings suggest
that countries may avoid the lockdown policy with imposing school closure, mask
wearing and centralized quarantine to reach similar outcomes on controlling the
COVID-19 infection. Our results also suggest that, as of April 4th, 2020,
certain countries such as the U.S. and Singapore may require additional
measures of NPIs in order to control disease transmissions more effectively,
while other countries may cautiously consider to gradually lift some NPIs to
mitigate the costs to the overall economy.
| [
{
"created": "Tue, 7 Apr 2020 23:32:25 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Apr 2020 04:03:34 GMT",
"version": "v2"
},
{
"created": "Fri, 4 Jun 2021 01:34:29 GMT",
"version": "v3"
}
] | 2021-06-07 | [
[
"Chen",
"Xiaohui",
""
],
[
"Qiu",
"Ziyi",
""
]
] | This paper introduces a dynamic panel SIR (DP-SIR) model to investigate the impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission dynamics with panel data from 9 countries across the globe. By constructing scenarios with different combinations of NPIs, our empirical findings suggest that countries may avoid the lockdown policy with imposing school closure, mask wearing and centralized quarantine to reach similar outcomes on controlling the COVID-19 infection. Our results also suggest that, as of April 4th, 2020, certain countries such as the U.S. and Singapore may require additional measures of NPIs in order to control disease transmissions more effectively, while other countries may cautiously consider to gradually lift some NPIs to mitigate the costs to the overall economy. |
q-bio/0503009 | Michael Sadovsky | M.A. Makarova, M.G. Sadovsky | New symmetry in nucleotide sequences | 6 pages, 7 tables, 12 references | null | null | null | q-bio.GN q-bio.BM | null | Information valuable words are the strings with the significant deviation of
real frequency from the expected one. The expected frequency is determined
through the maximum entropy principle of the reconstructed (extended) frequency
dictionary of strings composed from the shorter words. The information valuable
words are found to be the complementary palindromes: they are read equally in
opposite directions, if nucleotides are changed for the complementary ones (A
<--> T; C <--> G) in one of them. Some properties of such symmetric words are
discussed.
| [
{
"created": "Sat, 5 Mar 2005 05:43:43 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Makarova",
"M. A.",
""
],
[
"Sadovsky",
"M. G.",
""
]
] | Information valuable words are the strings with the significant deviation of real frequency from the expected one. The expected frequency is determined through the maximum entropy principle of the reconstructed (extended) frequency dictionary of strings composed from the shorter words. The information valuable words are found to be the complementary palindromes: they are read equally in opposite directions, if nucleotides are changed for the complementary ones (A <--> T; C <--> G) in one of them. Some properties of such symmetric words are discussed. |
2307.07427 | R\'edoane Daoudi | R\'edoane Daoudi | Understanding AKT-mediated chemoresistance: the relationship between ion
channels and AKT activation | 16 pages, 1 figure | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Overcoming chemoresistance is a challenge for multiple chemotherapeutics
agents like cisplatin. ABC transporters such as MDR1 or MRPs and PI3K/AKT
pathway have been proposed as actors of chemoresistance in several cancers. In
this review we describe two downstream targets of Akt: c-myc and p53 in the
chemoresistance. We suggest a potential link between p53, c-myc and ABC
transporters expression. Consequently a link between Akt and ABC
transporters-mediated chemoresistance may exist. Finally we show that Akt
activation may be Orai-dependent and/or TRPC-dependent, suggesting that these
ion channels could constitute a therapeutic target in cancer.
| [
{
"created": "Thu, 13 Jul 2023 16:21:26 GMT",
"version": "v1"
}
] | 2023-07-17 | [
[
"Daoudi",
"Rédoane",
""
]
] | Overcoming chemoresistance is a challenge for multiple chemotherapeutics agents like cisplatin. ABC transporters such as MDR1 or MRPs and PI3K/AKT pathway have been proposed as actors of chemoresistance in several cancers. In this review we describe two downstream targets of Akt: c-myc and p53 in the chemoresistance. We suggest a potential link between p53, c-myc and ABC transporters expression. Consequently a link between Akt and ABC transporters-mediated chemoresistance may exist. Finally we show that Akt activation may be Orai-dependent and/or TRPC-dependent, suggesting that these ion channels could constitute a therapeutic target in cancer. |
1910.03927 | Diego Bonatto | Bianca de Paula Telini, Marcelo Menoncin and Diego Bonatto | Does inter-organellar proteostasis impact yeast quality and performance
during beer fermentation? | 56 pages, 16 figures | null | 10.3389/fgene.2020.00002 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During beer production, yeast generate ethanol that is exported to the
extracellular environment where it accumulates. Depending on the initial
carbohydrate concentration in the wort a high concentration of ethanol can be
achieved in beer, often higher than 20% (v/v). It is noteworthy that the
effects of elevated ethanol concentrations generated during beer fermentation
resemble those of heat shock stress, with similar responses observed in both
situations, such as the activation of proteostasis and protein quality control
mechanisms in different cell compartments, including endoplasmic reticulum
(ER), mitochondria, and cytosol. Despite the extensive published molecular and
biochemical data regarding the roles of proteostasis in different organelles of
yeast cells, little is known about how this mechanism impacts beer fermentation
and how different proteostasis mechanisms found in ER, mitochondria, and
cytosol communicate with each other during ethanol/fermentative stress.
Supporting this integrative view, transcriptome data analysis was applied using
publicly available information for a lager yeast strain grown under in beer
production conditions. The transcriptome data indicated upregulation of genes
that encode chaperones, co-chaperones, unfolded protein response elements in ER
and mitochondria, ubiquitin ligases, proteasome components, N-glycosylation
quality control pathway proteins, and components of processing bodies
(p-bodies) and stress granules (SGs) during lager beer fermentation. Thus, the
main purpose of this hypothesis and theory manuscript is to provide a concise
picture of how inter-organellar proteostasis mechanisms are connected with one
another and with biological processes that may modulate the viability and/or
vitality of yeast populations during HG/VHG beer fermentation and serial
repitching.
| [
{
"created": "Wed, 9 Oct 2019 12:21:00 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Dec 2019 12:39:40 GMT",
"version": "v2"
}
] | 2020-01-08 | [
[
"Telini",
"Bianca de Paula",
""
],
[
"Menoncin",
"Marcelo",
""
],
[
"Bonatto",
"Diego",
""
]
] | During beer production, yeast generate ethanol that is exported to the extracellular environment where it accumulates. Depending on the initial carbohydrate concentration in the wort a high concentration of ethanol can be achieved in beer, often higher than 20% (v/v). It is noteworthy that the effects of elevated ethanol concentrations generated during beer fermentation resemble those of heat shock stress, with similar responses observed in both situations, such as the activation of proteostasis and protein quality control mechanisms in different cell compartments, including endoplasmic reticulum (ER), mitochondria, and cytosol. Despite the extensive published molecular and biochemical data regarding the roles of proteostasis in different organelles of yeast cells, little is known about how this mechanism impacts beer fermentation and how different proteostasis mechanisms found in ER, mitochondria, and cytosol communicate with each other during ethanol/fermentative stress. Supporting this integrative view, transcriptome data analysis was applied using publicly available information for a lager yeast strain grown under in beer production conditions. The transcriptome data indicated upregulation of genes that encode chaperones, co-chaperones, unfolded protein response elements in ER and mitochondria, ubiquitin ligases, proteasome components, N-glycosylation quality control pathway proteins, and components of processing bodies (p-bodies) and stress granules (SGs) during lager beer fermentation. Thus, the main purpose of this hypothesis and theory manuscript is to provide a concise picture of how inter-organellar proteostasis mechanisms are connected with one another and with biological processes that may modulate the viability and/or vitality of yeast populations during HG/VHG beer fermentation and serial repitching. |
2008.03776 | Zhi Huang | Zhi Huang, Paul Salama, Wei Shao, Jie Zhang, Kun Huang | Low-Rank Reorganization via Proportional Hazards Non-negative Matrix
Factorization Unveils Survival Associated Gene Clusters | null | null | null | null | q-bio.QM cs.LG q-bio.GN stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the central goals in precision health is the understanding and
interpretation of high-dimensional biological data to identify genes and
markers associated with disease initiation, development, and outcomes. Though
significant effort has been committed to harness gene expression data for
multiple analyses while accounting for time-to-event modeling by including
survival times, many traditional analyses have focused separately on
non-negative matrix factorization (NMF) of the gene expression data matrix and
survival regression with Cox proportional hazards model. In this work, Cox
proportional hazards regression is integrated with NMF by imposing survival
constraints. This is accomplished by jointly optimizing the Frobenius norm and
partial log likelihood for events such as death or relapse. Simulation results
on synthetic data demonstrated the superiority of the proposed method, when
compared to other algorithms, in finding survival associated gene clusters. In
addition, using human cancer gene expression data, the proposed technique can
unravel critical clusters of cancer genes. The discovered gene clusters reflect
rich biological implications and can help identify survival-related biomarkers.
Towards the goal of precision health and cancer treatments, the proposed
algorithm can help understand and interpret high-dimensional heterogeneous
genomics data with accurate identification of survival-associated gene
clusters.
| [
{
"created": "Sun, 9 Aug 2020 17:59:30 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Sep 2020 07:52:45 GMT",
"version": "v2"
}
] | 2020-09-18 | [
[
"Huang",
"Zhi",
""
],
[
"Salama",
"Paul",
""
],
[
"Shao",
"Wei",
""
],
[
"Zhang",
"Jie",
""
],
[
"Huang",
"Kun",
""
]
] | One of the central goals in precision health is the understanding and interpretation of high-dimensional biological data to identify genes and markers associated with disease initiation, development, and outcomes. Though significant effort has been committed to harness gene expression data for multiple analyses while accounting for time-to-event modeling by including survival times, many traditional analyses have focused separately on non-negative matrix factorization (NMF) of the gene expression data matrix and survival regression with Cox proportional hazards model. In this work, Cox proportional hazards regression is integrated with NMF by imposing survival constraints. This is accomplished by jointly optimizing the Frobenius norm and partial log likelihood for events such as death or relapse. Simulation results on synthetic data demonstrated the superiority of the proposed method, when compared to other algorithms, in finding survival associated gene clusters. In addition, using human cancer gene expression data, the proposed technique can unravel critical clusters of cancer genes. The discovered gene clusters reflect rich biological implications and can help identify survival-related biomarkers. Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters. |
2012.05071 | Alastair Jamieson-Lane | Alastair Jamieson-Lane, Bernd Blasius | The Gossip Paradox: why do bacteria share genes? | 27 pages, 8 figures, 1 table | null | null | null | q-bio.PE math.DS q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacteria, in contrast to eukaryotic cells contain two types of genes:
chromosomal genes that are fixed to the cell, and plasmids that are mobile
genes, easily shared to other cells. The sharing of plasmid genes between
individual bacteria and between bacterial lineages has contributed vastly to
bacterial evolution, allowing specialized traits to `jump ship' between one
lineage or species and the next. The benefits of this generosity from the point
of view of both recipient and plasmid are generally understood, but come at the
expense of chromosomal genes in the donor cell, which share potentially
advantageous genes with their competition while receiving no benefit. Using
both continuous models and agent based simulations, we demonstrate that
`secretive' genes which restrict horizontal gene transfer are favored over wide
range of models and parameter values. Our findings lead to a peculiar paradox:
given the obvious benefits of keeping secrets, why do bacteria share
information so freely?
| [
{
"created": "Tue, 8 Dec 2020 06:27:28 GMT",
"version": "v1"
}
] | 2020-12-10 | [
[
"Jamieson-Lane",
"Alastair",
""
],
[
"Blasius",
"Bernd",
""
]
] | Bacteria, in contrast to eukaryotic cells contain two types of genes: chromosomal genes that are fixed to the cell, and plasmids that are mobile genes, easily shared to other cells. The sharing of plasmid genes between individual bacteria and between bacterial lineages has contributed vastly to bacterial evolution, allowing specialized traits to `jump ship' between one lineage or species and the next. The benefits of this generosity from the point of view of both recipient and plasmid are generally understood, but come at the expense of chromosomal genes in the donor cell, which share potentially advantageous genes with their competition while receiving no benefit. Using both continuous models and agent based simulations, we demonstrate that `secretive' genes which restrict horizontal gene transfer are favored over wide range of models and parameter values. Our findings lead to a peculiar paradox: given the obvious benefits of keeping secrets, why do bacteria share information so freely? |
1902.00060 | Peter Eastman | Peter Eastman and Vijay S. Pande | Predicting Toxicity from Gene Expression with Neural Networks | 12 pages, 2 figures, 4 tables | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We train a neural network to predict chemical toxicity based on gene
expression data. The input to the network is a full expression profile
collected either in vitro from cultured cells or in vivo from live animals. The
output is a set of fine grained predictions for the presence of a variety of
pathological effects in treated animals. When trained on the Open TG-GATEs
database it produces good results, outperforming classical models trained on
the same data. This is a promising approach for efficiently screening chemicals
for toxic effects, and for more accurately evaluating drug candidates based on
preclinical data.
| [
{
"created": "Thu, 31 Jan 2019 20:20:18 GMT",
"version": "v1"
}
] | 2019-02-04 | [
[
"Eastman",
"Peter",
""
],
[
"Pande",
"Vijay S.",
""
]
] | We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine grained predictions for the presence of a variety of pathological effects in treated animals. When trained on the Open TG-GATEs database it produces good results, outperforming classical models trained on the same data. This is a promising approach for efficiently screening chemicals for toxic effects, and for more accurately evaluating drug candidates based on preclinical data. |
2401.03036 | Rui Dilao | Catarina Dias and Rui Dil\~ao | Calibration of the distribution of the pair-rule proteins Even-skipped
and Fushi-tarazu in the Drosophila embryo | 15 pages, 10 figures | null | null | null | q-bio.QM q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | We modelled and calibrated the distributions of the seven-stripe patterns of
Even-skipped (\textit{Eve}) and Fushi-tarazu (\textit{Ftz}) pair-rule proteins
along the anteroposterior axis of the Drosophila embryo, established during
early development. The regulators of \textit{Eve} and \textit{Ftz} are
stripe-specific enhancers from the gap family of proteins and determine the
body structure of the \textit{Drosophila} larva. We achieved remarkable data
matching of the \textit{Eve} stripe pattern, and the calibrated model
reproduces gap gene mutation experiments. We have identified the putative
repressive combinations for five \textit{Eve} enhancers, and we have explored
the relationship between \textit{Eve} and \textit{Ftz} for complementary
patterns. Extended work inferring the Wingless (\textit{Wg}) fourteen stripe
pattern from \textit{Eve} and \textit{Ftz} enhancers has been proposed,
clarifying the hierarchical structure of \textit{Drosphila}'s genetic
expression network during early development before cellularisation.
| [
{
"created": "Fri, 5 Jan 2024 19:19:49 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Jan 2024 15:37:18 GMT",
"version": "v2"
}
] | 2024-01-29 | [
[
"Dias",
"Catarina",
""
],
[
"Dilão",
"Rui",
""
]
] | We modelled and calibrated the distributions of the seven-stripe patterns of Even-skipped (\textit{Eve}) and Fushi-tarazu (\textit{Ftz}) pair-rule proteins along the anteroposterior axis of the Drosophila embryo, established during early development. The regulators of \textit{Eve} and \textit{Ftz} are stripe-specific enhancers from the gap family of proteins and determine the body structure of the \textit{Drosophila} larva. We achieved remarkable data matching of the \textit{Eve} stripe pattern, and the calibrated model reproduces gap gene mutation experiments. We have identified the putative repressive combinations for five \textit{Eve} enhancers, and we have explored the relationship between \textit{Eve} and \textit{Ftz} for complementary patterns. Extended work inferring the Wingless (\textit{Wg}) fourteen stripe pattern from \textit{Eve} and \textit{Ftz} enhancers has been proposed, clarifying the hierarchical structure of \textit{Drosphila}'s genetic expression network during early development before cellularisation. |
1307.4628 | Sarabjeet Singh | Sarabjeet Singh, David J. Schneider and Christopher R. Myers | The structure of infectious disease outbreaks across the animal-human
interface | 18 pages (8 main text + 10 Appendix), 15 figures in total, currently
submitted to a journal for review | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the enormous relevance of zoonotic infections to world- wide public
health, and despite much effort in modeling individual zoonoses, a fundamental
understanding of the disease dynamics and the nature of outbreaks arising in
such systems is still lacking. We introduce a simple stochastic model of
susceptible-infected- recovered dynamics in a coupled animal-human
metapopulation, and solve analytically for several important properties of the
cou- pled outbreaks. At early timescales, we solve for the probability and time
of spillover, and the disease prevalence in the animal population at spillover
as a function of model parameters. At long times, we characterize the
distribution of outbreak sizes and the critical threshold for a large human
outbreak, both of which show a strong dependence on the basic reproduction
number in the animal population. The coupling of animal and human infection
dynamics has several crucial implications, most importantly al- lowing for the
possibility of large human outbreaks even when human-to-human transmission is
subcritical.
| [
{
"created": "Tue, 16 Jul 2013 16:05:33 GMT",
"version": "v1"
}
] | 2013-07-18 | [
[
"Singh",
"Sarabjeet",
""
],
[
"Schneider",
"David J.",
""
],
[
"Myers",
"Christopher R.",
""
]
] | Despite the enormous relevance of zoonotic infections to world- wide public health, and despite much effort in modeling individual zoonoses, a fundamental understanding of the disease dynamics and the nature of outbreaks arising in such systems is still lacking. We introduce a simple stochastic model of susceptible-infected- recovered dynamics in a coupled animal-human metapopulation, and solve analytically for several important properties of the cou- pled outbreaks. At early timescales, we solve for the probability and time of spillover, and the disease prevalence in the animal population at spillover as a function of model parameters. At long times, we characterize the distribution of outbreak sizes and the critical threshold for a large human outbreak, both of which show a strong dependence on the basic reproduction number in the animal population. The coupling of animal and human infection dynamics has several crucial implications, most importantly al- lowing for the possibility of large human outbreaks even when human-to-human transmission is subcritical. |
1908.11267 | Frederic Payan | Dominique Douguet (IPMC, UCA), Fr\'ed\'eric Payan (UCA) | SENSAAS (SENsitive Surface As A Shape): utilizing open-source algorithms
for 3D point cloud alignment of molecules | null | null | null | null | q-bio.BM eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open-source 3D data processing libraries originally developed for computer
vision and pattern recognition are used to align and compare molecular shapes
and sub-shapes. Here, a shape is represented by a set of points distributed on
the van der Waals surface of molecules. Each point is colored by its closest
atom, which itself belongs to a user defined class. The strength of this
representation is that it allows for comparisons of point clouds of different
kind of chemical entities: small molecules, peptides, proteins or cavities (the
negative image of the
| [
{
"created": "Mon, 19 Aug 2019 15:08:36 GMT",
"version": "v1"
}
] | 2019-08-30 | [
[
"Douguet",
"Dominique",
"",
"IPMC, UCA"
],
[
"Payan",
"Frédéric",
"",
"UCA"
]
] | Open-source 3D data processing libraries originally developed for computer vision and pattern recognition are used to align and compare molecular shapes and sub-shapes. Here, a shape is represented by a set of points distributed on the van der Waals surface of molecules. Each point is colored by its closest atom, which itself belongs to a user defined class. The strength of this representation is that it allows for comparisons of point clouds of different kind of chemical entities: small molecules, peptides, proteins or cavities (the negative image of the |
2201.03003 | Jon Bohlin | Jon Bohlin | A simple model describing evolution of genomic GC content with random
perturbations in asexually reproducing organisms | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | A model is presented relating the evolution of genomic GC content over time
to AT$\rightarrow$GC and GC$\rightarrow$AT mutation rates. By employing It\^o
calculus it is shown that if mutation rates in asexually reproducing organisms
are subject to random perturbations that can vary over time several
implications follow. For instance, an extra Brownian motion term appears
influencing nucleotide variability; the greater the variability of the random
perturbations on the mutation rates the stronger the impact of the Brownian
motion term. Reducing the influence of the random perturbations, to limit
fitness decreasing and deleterious mutations, will likely imply divesting
resources to genomic repair systems. The stable mutation rates seen in many
organisms could thus be an evolved strategy to reduce the influence of the
Brownian motion term. Furthermore, if change to genomic GC content, i.e. the GC
content of variable sites or single nucleotide polymorphisms (SNPs), is just as
likely to increase as to decrease, something that resembles knockout of repair
enzymes and removal of selective pressures seen in evolutionary laboratory
experiments, the species genome will likely decay unless infinite resources are
available. These implications are solely a consequence of allowing random
perturbations affect AT- and GC mutation rates and not obtainable using
standard non-stochastic methodology. Finally, a connection between the model
for genomic GC content evolution and the classical Luria-Delbr\"uck mutation
model is presented in a stochastic setting.
| [
{
"created": "Sun, 9 Jan 2022 13:05:00 GMT",
"version": "v1"
}
] | 2022-01-11 | [
[
"Bohlin",
"Jon",
""
]
] | A model is presented relating the evolution of genomic GC content over time to AT$\rightarrow$GC and GC$\rightarrow$AT mutation rates. By employing It\^o calculus it is shown that if mutation rates in asexually reproducing organisms are subject to random perturbations that can vary over time several implications follow. For instance, an extra Brownian motion term appears influencing nucleotide variability; the greater the variability of the random perturbations on the mutation rates the stronger the impact of the Brownian motion term. Reducing the influence of the random perturbations, to limit fitness decreasing and deleterious mutations, will likely imply divesting resources to genomic repair systems. The stable mutation rates seen in many organisms could thus be an evolved strategy to reduce the influence of the Brownian motion term. Furthermore, if change to genomic GC content, i.e. the GC content of variable sites or single nucleotide polymorphisms (SNPs), is just as likely to increase as to decrease, something that resembles knockout of repair enzymes and removal of selective pressures seen in evolutionary laboratory experiments, the species genome will likely decay unless infinite resources are available. These implications are solely a consequence of allowing random perturbations affect AT- and GC mutation rates and not obtainable using standard non-stochastic methodology. Finally, a connection between the model for genomic GC content evolution and the classical Luria-Delbr\"uck mutation model is presented in a stochastic setting. |
1305.1286 | Pawel Domagala | Pawel Domagala, Jolanta Hybiak, Violetta Sulzyc-Bielicka, Cezary
Cybulski, Janusz Rys, Wenancjusz Domagala | KRAS mutation testing in colorectal cancer as an example of the
pathologist's role in personalized targeted therapy: a practical approach | 20 pages, 2 figures, 2 tables, 140 references | Polish Journal of Pathology 2012 Nov;63(3):145-64 | 10.5114/PJP.2012.31499 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying targets for personalized targeted therapy is the pathologist's
domain and a treasure. For decades, pathologists have had to learn, understand,
adopt and implement many new laboratory techniques as they arrived on the
scene. Pathologists successfully integrate the results of those tests into
final pathology reports that were, and still are, the basis of clinical
therapeutic decisions. The molecular methods are different but no more
difficult to comprehend in the era of "kit procedures". Pathologists have the
knowledge and expertise to identify particular gene mutations using the
appropriate molecular tests currently available. This review focuses on the
most important recent developments in KRAS mutation testing in metastatic
colorectal cancer (CRC), and shows that a pathologist is involved in 10 stages
of this procedure. Recent studies have shown that highly sensitive, simple,
reliable and rapid assays may significantly improve the identification of CRC
patients resistant to anti-EGFR therapy. Thus, direct sequencing does not seem
to be an optimal procedure of KRAS testing for clinical purposes. Twelve
currently available high-sensitivity diagnostic assays (with the CE-IVD mark)
for KRAS mutation testing are briefly described and compared. The suggested
pathology report content for somatic mutation tests is described. In
conclusion, evidence is presented that sending away paraffin blocks with tumor
tissue for KRAS mutation testing may not be in the best interest of patients.
Instead, an evidence-based approach indicates that KRAS mutation testing should
be performed in pathology departments, only with the use of CE-IVD/FDA-approved
KRAS tests, and with the obligatory, periodic participation in the KRAS EQA
scheme organized by the European Society of Pathology as an independent
international body.
| [
{
"created": "Mon, 6 May 2013 19:48:18 GMT",
"version": "v1"
}
] | 2013-05-07 | [
[
"Domagala",
"Pawel",
""
],
[
"Hybiak",
"Jolanta",
""
],
[
"Sulzyc-Bielicka",
"Violetta",
""
],
[
"Cybulski",
"Cezary",
""
],
[
"Rys",
"Janusz",
""
],
[
"Domagala",
"Wenancjusz",
""
]
] | Identifying targets for personalized targeted therapy is the pathologist's domain and a treasure. For decades, pathologists have had to learn, understand, adopt and implement many new laboratory techniques as they arrived on the scene. Pathologists successfully integrate the results of those tests into final pathology reports that were, and still are, the basis of clinical therapeutic decisions. The molecular methods are different but no more difficult to comprehend in the era of "kit procedures". Pathologists have the knowledge and expertise to identify particular gene mutations using the appropriate molecular tests currently available. This review focuses on the most important recent developments in KRAS mutation testing in metastatic colorectal cancer (CRC), and shows that a pathologist is involved in 10 stages of this procedure. Recent studies have shown that highly sensitive, simple, reliable and rapid assays may significantly improve the identification of CRC patients resistant to anti-EGFR therapy. Thus, direct sequencing does not seem to be an optimal procedure of KRAS testing for clinical purposes. Twelve currently available high-sensitivity diagnostic assays (with the CE-IVD mark) for KRAS mutation testing are briefly described and compared. The suggested pathology report content for somatic mutation tests is described. In conclusion, evidence is presented that sending away paraffin blocks with tumor tissue for KRAS mutation testing may not be in the best interest of patients. Instead, an evidence-based approach indicates that KRAS mutation testing should be performed in pathology departments, only with the use of CE-IVD/FDA-approved KRAS tests, and with the obligatory, periodic participation in the KRAS EQA scheme organized by the European Society of Pathology as an independent international body. |
1203.4289 | Michael Hentrich | Michael Hentrich | Health Matters: Human Organ Donations, Sales, and the Black Market | 25 pages | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper I explore the human organ procurement system. Which is better
for saving lives and limiting black market use, the present altruistic system
of donations or a free and open sales market? I explain that there is a risk
with maintaining the present system, the altruistic vision, and that people may
die who might otherwise live if the sale of organs was permitted. But there is
no guarantee that permitting organ sales would effectively address the current
supply-side shortage and global use of the black market. In addition to
discussing the implications of these procurement systems, I look at methods to
increase organ donations and I explore the differences between presumed and
explicit consent. Ultimately, I conclude that the altruistic donation system,
bolstered by the addition of a policy of presumed consent and appropriate
financial incentives, is a better choice than a legal sales market in spite of
its shortcomings.
| [
{
"created": "Mon, 19 Mar 2012 23:54:54 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Mar 2012 00:13:39 GMT",
"version": "v2"
},
{
"created": "Mon, 26 Mar 2012 00:46:22 GMT",
"version": "v3"
},
{
"created": "Tue, 18 Sep 2012 18:58:04 GMT",
"version": "v4"
},
{
"created": "Tue, 13 Jan 2015 19:35:51 GMT",
"version": "v5"
}
] | 2015-01-14 | [
[
"Hentrich",
"Michael",
""
]
] | In this paper I explore the human organ procurement system. Which is better for saving lives and limiting black market use, the present altruistic system of donations or a free and open sales market? I explain that there is a risk with maintaining the present system, the altruistic vision, and that people may die who might otherwise live if the sale of organs was permitted. But there is no guarantee that permitting organ sales would effectively address the current supply-side shortage and global use of the black market. In addition to discussing the implications of these procurement systems, I look at methods to increase organ donations and I explore the differences between presumed and explicit consent. Ultimately, I conclude that the altruistic donation system, bolstered by the addition of a policy of presumed consent and appropriate financial incentives, is a better choice than a legal sales market in spite of its shortcomings. |
2012.00683 | Joseph Malinzi | Joseph Malinzi, Kevin Bosire Basita, Sara Padidar and Henry A. Adeola | Prospect for application of mathematical models in combination cancer
treatments | 28 Pages, 1 Figure, 3 Tables | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The long-term efficacy of targeted therapeutics for cancer treatment can be
significantly limited by the type of therapy and development of drug
resistance, inter alia. Experimental studies indicate that the factors
enhancing acquisition of drug resistance in cancer cells include cell
heterogeneity, drug target alteration, drug inactivation, DNA damage repair,
drug efflux, cell death inhibition, as well as microenvironmental adaptations
to targeted therapy, among others. Combination cancer therapies (CCTs) are
employed to overcome these molecular and pathophysiological bottlenecks and
improve the overall survival of cancer patients. CCTs often utilize multiple
combinatorial modes of action and thus potentially constitute a promising
approach to overcome drug resistance. Considering the colossal cost, human
effort, time and ethical issues involved in clinical drug trials and basic
medical research, mathematical modeling and analysis can potentially contribute
immensely to the discovery of better cancer treatment regimens. In this
article, we review mathematical models on CCTs developed thus far for cancer
management. Open questions are highlighted and plausible combinations are
discussed based on the level of toxicity, drug resistance, survival benefits,
preclinical trials and other side effects.
| [
{
"created": "Thu, 12 Nov 2020 12:39:50 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Feb 2021 09:11:06 GMT",
"version": "v2"
}
] | 2021-02-16 | [
[
"Malinzi",
"Joseph",
""
],
[
"Basita",
"Kevin Bosire",
""
],
[
"Padidar",
"Sara",
""
],
[
"Adeola",
"Henry A.",
""
]
] | The long-term efficacy of targeted therapeutics for cancer treatment can be significantly limited by the type of therapy and development of drug resistance, inter alia. Experimental studies indicate that the factors enhancing acquisition of drug resistance in cancer cells include cell heterogeneity, drug target alteration, drug inactivation, DNA damage repair, drug efflux, cell death inhibition, as well as microenvironmental adaptations to targeted therapy, among others. Combination cancer therapies (CCTs) are employed to overcome these molecular and pathophysiological bottlenecks and improve the overall survival of cancer patients. CCTs often utilize multiple combinatorial modes of action and thus potentially constitute a promising approach to overcome drug resistance. Considering the colossal cost, human effort, time and ethical issues involved in clinical drug trials and basic medical research, mathematical modeling and analysis can potentially contribute immensely to the discovery of better cancer treatment regimens. In this article, we review mathematical models on CCTs developed thus far for cancer management. Open questions are highlighted and plausible combinations are discussed based on the level of toxicity, drug resistance, survival benefits, preclinical trials and other side effects. |
2012.11713 | Albert Ammerman | Albert Ammerman | The Neolithic Transition in Europe at 50 Years | 32 pages, 4 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | One of the last chapters in the long course of human evolution was the shift
from hunting and gathering to the production of food or strategies of
subsistence based on farming and the herding of animals. In Southwest Asia, the
first steps towards the origins of agriculture began some 12,000 years ago and
then spread over most regions of Europe during the span of time from about
10,400 years ago (the start of the so-called PPNB on the island of Cyprus)
through around 6,000 years ago. The aim of this chapter is to provide an
overview on the research that we have done on the question of the Neolithic
transition in Europe, which began when Luca CavalliSforza, a leading figure in
the field of human population genetics, and I began to work in collaboration at
the University of Pavia in November of 1970. This draft forms the basis of my
paper as part of the Festschrift prepared for the 45th anniversary of Ryszard
Grygiel and Peter Bogucki's scientific cooperation.
| [
{
"created": "Mon, 21 Dec 2020 22:18:47 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Sep 2021 13:49:39 GMT",
"version": "v2"
}
] | 2021-09-07 | [
[
"Ammerman",
"Albert",
""
]
] | One of the last chapters in the long course of human evolution was the shift from hunting and gathering to the production of food or strategies of subsistence based on farming and the herding of animals. In Southwest Asia, the first steps towards the origins of agriculture began some 12,000 years ago and then spread over most regions of Europe during the span of time from about 10,400 years ago (the start of the so-called PPNB on the island of Cyprus) through around 6,000 years ago. The aim of this chapter is to provide an overview on the research that we have done on the question of the Neolithic transition in Europe, which began when Luca CavalliSforza, a leading figure in the field of human population genetics, and I began to work in collaboration at the University of Pavia in November of 1970. This draft forms the basis of my paper as part of the Festschrift prepared for the 45th anniversary of Ryszard Grygiel and Peter Bogucki's scientific cooperation. |
2305.19869 | Lyndon Duong | Lyndon R. Duong, Colin Bredenberg, David J. Heeger, Eero P. Simoncelli | Adaptive coding efficiency in recurrent cortical circuits via gain
control | 17 pages, 8 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Sensory systems across all modalities and species exhibit adaptation to
continuously changing input statistics. Individual neurons have been shown to
modulate their response gains so as to maximize information transmission in
different stimulus contexts. Experimental measurements have revealed
additional, nuanced sensory adaptation effects including changes in response
maxima and minima, tuning curve repulsion from the adapter stimulus, and
stimulus-driven response decorrelation. Existing explanations of these
phenomena rely on changes in inter-neuronal synaptic efficacy, which, while
more flexible, are unlikely to operate as rapidly or reversibly as single
neuron gain modulations. Using published V1 population adaptation data, we show
that propagation of single neuron gain changes in a recurrent network is
sufficient to capture the entire set of observed adaptation effects. We propose
a novel adaptive efficient coding objective with which single neuron gains are
modulated, maximizing the fidelity of the stimulus representation while
minimizing overall activity in the network. From this objective, we
analytically derive a set of gains that optimize the trade-off between
preserving information about the stimulus and conserving metabolic resources.
Our model generalizes well-established concepts of single neuron adaptive gain
control to recurrent populations, and parsimoniously explains experimental
adaptation data.
| [
{
"created": "Wed, 31 May 2023 14:06:01 GMT",
"version": "v1"
}
] | 2023-06-01 | [
[
"Duong",
"Lyndon R.",
""
],
[
"Bredenberg",
"Colin",
""
],
[
"Heeger",
"David J.",
""
],
[
"Simoncelli",
"Eero P.",
""
]
] | Sensory systems across all modalities and species exhibit adaptation to continuously changing input statistics. Individual neurons have been shown to modulate their response gains so as to maximize information transmission in different stimulus contexts. Experimental measurements have revealed additional, nuanced sensory adaptation effects including changes in response maxima and minima, tuning curve repulsion from the adapter stimulus, and stimulus-driven response decorrelation. Existing explanations of these phenomena rely on changes in inter-neuronal synaptic efficacy, which, while more flexible, are unlikely to operate as rapidly or reversibly as single neuron gain modulations. Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects. We propose a novel adaptive efficient coding objective with which single neuron gains are modulated, maximizing the fidelity of the stimulus representation while minimizing overall activity in the network. From this objective, we analytically derive a set of gains that optimize the trade-off between preserving information about the stimulus and conserving metabolic resources. Our model generalizes well-established concepts of single neuron adaptive gain control to recurrent populations, and parsimoniously explains experimental adaptation data. |
1308.4758 | Alan Veliz Cuba | Alan Veliz-Cuba and Ajit Kumar and Kresimir Josic | Piecewise linear and Boolean models of chemical reaction networks | This is an extension of previous work arXiv:1201.2072 | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Models of biochemical networks are frequently high-dimensional and complex.
Reduction methods that preserve important dynamical properties are therefore
essential in their study. Interactions between the nodes in such networks are
frequently modeled using a Hill function, $x^n/(J^n+x^n)$. Reduced ODEs and
Boolean networks have been studied extensively when the exponent $n$ is large.
However, the case of small constant $J$ appears in practice, but is not well
understood. In this paper we provide a mathematical analysis of this limit, and
show that a reduction to a set of piecewise linear ODEs and Boolean networks
can be mathematically justified. The piecewise linear systems have closed form
solutions that closely track those of the fully nonlinear model. On the other
hand, the simpler, Boolean network can be used to study the qualitative
behavior of the original system. We justify the reduction using geometric
singular perturbation theory and compact convergence, and illustrate the
results in networks modeling a genetic switch and a genetic oscillator.
| [
{
"created": "Thu, 22 Aug 2013 03:56:51 GMT",
"version": "v1"
}
] | 2013-08-23 | [
[
"Veliz-Cuba",
"Alan",
""
],
[
"Kumar",
"Ajit",
""
],
[
"Josic",
"Kresimir",
""
]
] | Models of biochemical networks are frequently high-dimensional and complex. Reduction methods that preserve important dynamical properties are therefore essential in their study. Interactions between the nodes in such networks are frequently modeled using a Hill function, $x^n/(J^n+x^n)$. Reduced ODEs and Boolean networks have been studied extensively when the exponent $n$ is large. However, the case of small constant $J$ appears in practice, but is not well understood. In this paper we provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. On the other hand, the simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in networks modeling a genetic switch and a genetic oscillator. |
1407.4658 | Florian Markowetz | Alex J. Cornish and Florian Markowetz | SANTA: quantifying the functional content of molecular networks | Accepted at PLoS Comp Bio | null | 10.1371/journal.pcbi.1003808 | null | q-bio.MN q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linking networks of molecular interactions to cellular functions and
phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial
statistics to assess the functional content of molecular networks. Based on the
guilt-by-association principle, our approach (called SANTA) quantifies the
strength of association between a gene set and a network, and functionally
annotates molecular networks like other enrichment methods annotate lists of
genes. As a general association measure, SANTA can (i) functionally annotate
experimentally derived networks using a collection of curated gene sets, and
(ii) annotate experimentally derived gene sets using a collection of curated
networks, as well as (iii) prioritize genes for follow-up analyses. We
exemplify the efficacy of SANTA in several case studies using the \emph{S.
cerevisiae} genetic interaction network and genome-wide RNAi screens in cancer
cell lines. Our theory, simulations and applications show that SANTA provides a
principled statistical way to quantify the association between molecular
networks and cellular functions and phenotypes. SANTA is available from
http://bioconductor.org/packages/release/bioc/html/SANTA.html.
| [
{
"created": "Thu, 17 Jul 2014 12:59:42 GMT",
"version": "v1"
}
] | 2015-06-22 | [
[
"Cornish",
"Alex J.",
""
],
[
"Markowetz",
"Florian",
""
]
] | Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets, and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the \emph{S. cerevisiae} genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html. |
1209.1445 | Ian Dworkin | Ian Dworkin, David Tack, Jarrod Hadfield | An experimental test for genetic constraints in Drosophila melanogaster | Major revision of manuscript to be submitted to Evolution. 24 Pages.
Two figures. Five tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In addition to natural selection, adaptive evolution requires genetic
variation to proceed. Yet the G-matrix may have limited 'genetic degrees of
freedom', with certain combinations of trait values unavailable to evolution.
Such limitations are often referred to as genetic constraints. Unfortunately,
clear predictions about when to expect constraints are rarely available.
Therefore, we developed an experimental system that provides specific
predictions regarding constraints. Such tests are important as disagreements
persist regarding the evidence for genetic constraints, possibly due to
differences in methodology, study system or both. Numerous measures of genetic
constraints have been suggested, and generally focus on whether some axes of G
have eigenvalues=~0, indicating a lack of genetic variance.The mutation
Ultrabithorax1 causes a mild homeotic transformation of segmental identity. We
predicted that this mutation would induce a genetic constraint due to this
homeosis. We measured genetic co-variation for a set of traits in a panel of
strains with and without Ubx1. As expected, Ubx1 induced homeotic
transformations, and altered patterns of allometry. Yet, no changes in
correlational structure nor in the distribution of eigenvalues of G were
observed. We discuss the role of using genetic manipulations to refine
hypotheses of constraints in natural systems.
| [
{
"created": "Fri, 7 Sep 2012 04:05:51 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Jan 2013 22:26:45 GMT",
"version": "v2"
}
] | 2013-01-22 | [
[
"Dworkin",
"Ian",
""
],
[
"Tack",
"David",
""
],
[
"Hadfield",
"Jarrod",
""
]
] | In addition to natural selection, adaptive evolution requires genetic variation to proceed. Yet the G-matrix may have limited 'genetic degrees of freedom', with certain combinations of trait values unavailable to evolution. Such limitations are often referred to as genetic constraints. Unfortunately, clear predictions about when to expect constraints are rarely available. Therefore, we developed an experimental system that provides specific predictions regarding constraints. Such tests are important as disagreements persist regarding the evidence for genetic constraints, possibly due to differences in methodology, study system or both. Numerous measures of genetic constraints have been suggested, and generally focus on whether some axes of G have eigenvalues=~0, indicating a lack of genetic variance.The mutation Ultrabithorax1 causes a mild homeotic transformation of segmental identity. We predicted that this mutation would induce a genetic constraint due to this homeosis. We measured genetic co-variation for a set of traits in a panel of strains with and without Ubx1. As expected, Ubx1 induced homeotic transformations, and altered patterns of allometry. Yet, no changes in correlational structure nor in the distribution of eigenvalues of G were observed. We discuss the role of using genetic manipulations to refine hypotheses of constraints in natural systems. |
1506.04923 | Kumar Sankar Ray | Kumar Sankar Ray and Mandrita Mondal | Logical Inference by DNA Strand Algebra | 18 pages, 10 figures | null | null | null | q-bio.BM cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on the concept of DNA strand displacement and DNA strand algebra we
have developed a method for logical inference which is not based on silicon
based computing. Essentially, it is a paradigm shift from silicon to carbon. In
this paper we have considered the inference mechanism, viz. modus ponens, to
draw conclusion from any observed fact. Thus, the present approach to logical
inference based on DNA strand algebra is basically an attempt to develop expert
system design in the domain of DNA computing. We have illustrated our
methodology with respect to worked out example. Our methodology is very
flexible for implementation of different expert system applications.
| [
{
"created": "Tue, 16 Jun 2015 11:19:55 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Ray",
"Kumar Sankar",
""
],
[
"Mondal",
"Mandrita",
""
]
] | Based on the concept of DNA strand displacement and DNA strand algebra we have developed a method for logical inference which is not based on silicon based computing. Essentially, it is a paradigm shift from silicon to carbon. In this paper we have considered the inference mechanism, viz. modus ponens, to draw conclusion from any observed fact. Thus, the present approach to logical inference based on DNA strand algebra is basically an attempt to develop expert system design in the domain of DNA computing. We have illustrated our methodology with respect to worked out example. Our methodology is very flexible for implementation of different expert system applications. |
2110.00170 | Sudesh Agrawal | Yanyue Ding, Sudesh K. Agrawal, Jincheng Cao, Lauren Meyers, John J.
Hasenbein | Surveillance Testing for Rapid Detection of Outbreaks in Facilities | 21 pages, 14 figures and 3 tables. Submitted to Health Care
Management Science | null | null | null | q-bio.PE stat.CO | http://creativecommons.org/licenses/by/4.0/ | This paper develops an agent-based disease spread model on a contact network
in an effort to guide efforts at surveillance testing in small to moderate
facilities such as nursing homes and meat-packing plants. The model employs
Monte Carlo simulations of viral spread sample paths in the contact network.
The original motivation was to detect COVID-19 outbreaks quickly in such
facilities, but the model can be applied to any communicable disease. In
particular, the model provides guidance on how many test to administer each day
and on the importance of the testing order among staff or workers.
| [
{
"created": "Fri, 1 Oct 2021 01:59:15 GMT",
"version": "v1"
}
] | 2021-10-04 | [
[
"Ding",
"Yanyue",
""
],
[
"Agrawal",
"Sudesh K.",
""
],
[
"Cao",
"Jincheng",
""
],
[
"Meyers",
"Lauren",
""
],
[
"Hasenbein",
"John J.",
""
]
] | This paper develops an agent-based disease spread model on a contact network in an effort to guide efforts at surveillance testing in small to moderate facilities such as nursing homes and meat-packing plants. The model employs Monte Carlo simulations of viral spread sample paths in the contact network. The original motivation was to detect COVID-19 outbreaks quickly in such facilities, but the model can be applied to any communicable disease. In particular, the model provides guidance on how many test to administer each day and on the importance of the testing order among staff or workers. |
1805.07322 | Rodrigo Cofre | Rodrigo Cofre, Cesar Maldonado, Fernando Rosas | Large Deviations Properties of Maximum Entropy Markov Chains from Spike
Trains | null | null | 10.3390/e20080573 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the maximum entropy Markov chain inference approach to
characterize the collective statistics of neuronal spike trains, focusing on
the statistical properties of the inferred model. We review large deviations
techniques useful in this context to describe properties of accuracy and
convergence in terms of sampling size. We use these results to study the
statistical fluctuation of correlations, distinguishability and irreversibility
of maximum entropy Markov chains. We illustrate these applications using simple
examples where the large deviation rate function is explicitly obtained for
maximum entropy models of relevance in this field.
| [
{
"created": "Fri, 18 May 2018 16:43:10 GMT",
"version": "v1"
}
] | 2018-08-15 | [
[
"Cofre",
"Rodrigo",
""
],
[
"Maldonado",
"Cesar",
""
],
[
"Rosas",
"Fernando",
""
]
] | We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field. |
2007.04381 | Piero Poletti | Piero Poletti, Marcello Tirani, Danilo Cereda, Filippo Trentini,
Giorgio Guzzetta, Valentina Marziano, Sabrina Buoro, Simona Riboli, Lucia
Crottogini, Raffaella Piccarreta, Alessandra Piatti, Giacomo Grasselli,
Alessia Melegaro, Maria Gramegna, Marco Ajelli and Stefano Merler | Infection fatality ratio of SARS-CoV-2 in Italy | null | Euro Surveill. 2020;25(31):pii=2001383 | 10.2807/1560-7917.ES.2020.25.31.2001383 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We analyzed 5,484 close contacts of COVID-19 cases from Italy, all of them
tested for SARS-CoV-2 infection. We found an infection fatality ratio of 2.2%
(95%CI 1.69-2.81%) and identified male sex, age >70 years, cardiovascular
comorbidities, and infection early in the epidemics as risk factors for death.
| [
{
"created": "Wed, 8 Jul 2020 19:16:08 GMT",
"version": "v1"
}
] | 2022-02-21 | [
[
"Poletti",
"Piero",
""
],
[
"Tirani",
"Marcello",
""
],
[
"Cereda",
"Danilo",
""
],
[
"Trentini",
"Filippo",
""
],
[
"Guzzetta",
"Giorgio",
""
],
[
"Marziano",
"Valentina",
""
],
[
"Buoro",
"Sabrina",
""
],
[
"Riboli",
"Simona",
""
],
[
"Crottogini",
"Lucia",
""
],
[
"Piccarreta",
"Raffaella",
""
],
[
"Piatti",
"Alessandra",
""
],
[
"Grasselli",
"Giacomo",
""
],
[
"Melegaro",
"Alessia",
""
],
[
"Gramegna",
"Maria",
""
],
[
"Ajelli",
"Marco",
""
],
[
"Merler",
"Stefano",
""
]
] | We analyzed 5,484 close contacts of COVID-19 cases from Italy, all of them tested for SARS-CoV-2 infection. We found an infection fatality ratio of 2.2% (95%CI 1.69-2.81%) and identified male sex, age >70 years, cardiovascular comorbidities, and infection early in the epidemics as risk factors for death. |
1503.04851 | Enrico Glerean | Enrico Glerean, Raj Kumar Pan, Juha Salmi, Rainer Kujala, Juha
Lahnakoski, Ulrika Roine, Lauri Nummenmaa, Sami Lepp\"am\"aki, Taina
Nieminen-von Wendt, Pekka Tani, Jari Saram\"aki, Mikko Sams, Iiro P.
J\"a\"askel\"ainen | Reorganization of functionally connected brain subnetworks in
high-functioning autism | null | null | null | null | q-bio.NC physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Previous functional connectivity studies have found both hypo-
and hyper-connectivity in brains of individuals having autism spectrum disorder
(ASD). Here we studied abnormalities in functional brain subnetworks in
high-functioning individuals with ASD during free viewing of a movie containing
social cues and interactions. Methods: Thirteen subjects with ASD and 13
matched-pair controls watched a 68 minutes movie during functional magnetic
resonance imaging. For each subject, we computed Pearson`s correlation between
haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the
whole-brain functional networks, we derived individual and group-level
subnetworks using graph theory. Scaled inclusivity was then calculated between
all subject pairs to estimate intersubject similarity of connectivity structure
of each subnetwork. Additional 27 individuals with ASD from the ABIDE
resting-state database were included to test the reproducibility of the
results. Results: Between-group differences were observed in the composition of
default-mode and a ventro-temporal-limbic (VTL) subnetwork. The VTL subnetwork
included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior
temporal gyri. Further, VTL subnetwork similarity between subject pairs
correlated significantly with similarity of symptom gravity measured with
autism quotient. This correlation was observed also within the controls, and in
the reproducibility dataset with ADI-R and ADOS scores. Conclusions:
Reorganization of functional subnetworks in individuals with ASD clarifies the
mixture of hypo- and hyper-connectivity findings. Importantly, only the
functional organization of the VTL subnetwork emerges as a marker of
inter-individual similarities that co-vary with behavioral measures across all
participants. These findings suggest a pivotal role of ventro-temporal and
limbic systems in autism.
| [
{
"created": "Mon, 16 Mar 2015 21:03:38 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Glerean",
"Enrico",
""
],
[
"Pan",
"Raj Kumar",
""
],
[
"Salmi",
"Juha",
""
],
[
"Kujala",
"Rainer",
""
],
[
"Lahnakoski",
"Juha",
""
],
[
"Roine",
"Ulrika",
""
],
[
"Nummenmaa",
"Lauri",
""
],
[
"Leppämäki",
"Sami",
""
],
[
"Wendt",
"Taina Nieminen-von",
""
],
[
"Tani",
"Pekka",
""
],
[
"Saramäki",
"Jari",
""
],
[
"Sams",
"Mikko",
""
],
[
"Jääskeläinen",
"Iiro P.",
""
]
] | Background: Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Methods: Thirteen subjects with ASD and 13 matched-pair controls watched a 68 minutes movie during functional magnetic resonance imaging. For each subject, we computed Pearson`s correlation between haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the whole-brain functional networks, we derived individual and group-level subnetworks using graph theory. Scaled inclusivity was then calculated between all subject pairs to estimate intersubject similarity of connectivity structure of each subnetwork. Additional 27 individuals with ASD from the ABIDE resting-state database were included to test the reproducibility of the results. Results: Between-group differences were observed in the composition of default-mode and a ventro-temporal-limbic (VTL) subnetwork. The VTL subnetwork included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior temporal gyri. Further, VTL subnetwork similarity between subject pairs correlated significantly with similarity of symptom gravity measured with autism quotient. This correlation was observed also within the controls, and in the reproducibility dataset with ADI-R and ADOS scores. Conclusions: Reorganization of functional subnetworks in individuals with ASD clarifies the mixture of hypo- and hyper-connectivity findings. Importantly, only the functional organization of the VTL subnetwork emerges as a marker of inter-individual similarities that co-vary with behavioral measures across all participants. These findings suggest a pivotal role of ventro-temporal and limbic systems in autism. |
2307.10500 | Viji Draviam | Binghao Chai, Christoforos Efstathiou, Haoran Yue and Viji M. Draviam | Opportunities and challenges for deep learning in cell dynamics research | null | null | null | null | q-bio.QM q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | With the growth of artificial intelligence (AI), there has been an increase
in the adoption of computer vision and deep learning (DL) techniques for the
evaluation of microscopy images and movies. This adoption has not only
addressed hurdles in quantitative analysis of dynamic cell biological
processes, but it has also started supporting advances in drug development,
precision medicine and genome-phenome mapping. Here we survey existing AI-based
techniques and tools, and open-source datasets, with a specific focus on the
computational tasks of segmentation, classification, and tracking of cellular
and subcellular structures and dynamics. We summarise long-standing challenges
in microscopy video analysis from the computational perspective and review
emerging research frontiers and innovative applications for deep
learning-guided automation for cell dynamics research.
| [
{
"created": "Wed, 19 Jul 2023 23:56:31 GMT",
"version": "v1"
}
] | 2023-07-21 | [
[
"Chai",
"Binghao",
""
],
[
"Efstathiou",
"Christoforos",
""
],
[
"Yue",
"Haoran",
""
],
[
"Draviam",
"Viji M.",
""
]
] | With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug development, precision medicine and genome-phenome mapping. Here we survey existing AI-based techniques and tools, and open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from the computational perspective and review emerging research frontiers and innovative applications for deep learning-guided automation for cell dynamics research. |
0909.0889 | Julie Gavard | Eva Maria Galan Moya (IC), Armelle Le Guelte (IC), Julie Gavard (IC) | PAKing up to the endothelium | Cell Signal (2009) epub ahead of print | null | 10.1016/j.cellsig.2009.08.006 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Angiogenesis recapitulates the growth of blood vessels that progressively
expand and remodel into a highly organized and stereotyped vascular network.
During adulthood, endothelial cells that formed the vascular wall retain their
plasticity and can be engaged in neo-vascularization in response to
physiological stimuli, such as hypoxia, wound healing and tissue repair,
ovarian cycle and pregnancy. In addition, numerous human diseases and
pathological conditions are characterized by an excessive, uncontrolled and
aberrant angiogenesis. The signalling pathways involving the small Rho GTPase,
Rac and its downstream effector the p21-activated serine/threonine kinase (PAK)
had recently emerged as pleiotropic modulators in these processes. Indeed, Rac
and PAK were found to modulate endothelial cell biology, such as sprouting,
migration, polarity, proliferation, lumen formation, and maturation.
Elucidating the Rac/PAK molecular circuitry will provide essential information
for the development of new therapeutic agents designed to normalize the blood
vasculature in human diseases.
| [
{
"created": "Fri, 4 Sep 2009 13:34:26 GMT",
"version": "v1"
}
] | 2009-09-07 | [
[
"Moya",
"Eva Maria Galan",
"",
"IC"
],
[
"Guelte",
"Armelle Le",
"",
"IC"
],
[
"Gavard",
"Julie",
"",
"IC"
]
] | Angiogenesis recapitulates the growth of blood vessels that progressively expand and remodel into a highly organized and stereotyped vascular network. During adulthood, endothelial cells that formed the vascular wall retain their plasticity and can be engaged in neo-vascularization in response to physiological stimuli, such as hypoxia, wound healing and tissue repair, ovarian cycle and pregnancy. In addition, numerous human diseases and pathological conditions are characterized by an excessive, uncontrolled and aberrant angiogenesis. The signalling pathways involving the small Rho GTPase, Rac and its downstream effector the p21-activated serine/threonine kinase (PAK) had recently emerged as pleiotropic modulators in these processes. Indeed, Rac and PAK were found to modulate endothelial cell biology, such as sprouting, migration, polarity, proliferation, lumen formation, and maturation. Elucidating the Rac/PAK molecular circuitry will provide essential information for the development of new therapeutic agents designed to normalize the blood vasculature in human diseases. |
1709.05058 | Nicholas Battista | Nicholas A. Battista, Julia E. Samson, Shilpa Khatri, Laura A. Miller | Under the sea: Pulsing corals in ambient flow | 7 pages, 9 Figures | null | 10.11145/texts.2017.11.197 | null | q-bio.QM physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While many organisms filter feed and exchange heat or nutrients in flow, few
benthic organisms also actively pulse to enhance feeding and exchange. One
example is the pulsing soft coral (Heteroxenia fuscescens). Pulsing corals live
in colonies, where each polyp actively pulses through contraction and
relaxation of their tentacles. The pulses are typically out of phase and
without a clear pattern. These corals live in lagoons and bays found in the Red
Sea and Indian Ocean where they at times experience strong ambient flows. In
this paper, $3D$ fluid-structure interaction simulations are used to quantify
the effects of ambient flow on the exchange currents produced by the active
contraction of pulsing corals. We find a complex interaction between the flows
produced by the coral and the background flow. The dynamics can either enhance
or reduce the upward jet generated in a quiescent medium. The pulsing behavior
also slows the average horizontal flow near the polyp when there is a strong
background flow. The dynamics of these flows have implications for particle
capture and nutrient exchange.
| [
{
"created": "Fri, 15 Sep 2017 04:49:46 GMT",
"version": "v1"
}
] | 2018-09-19 | [
[
"Battista",
"Nicholas A.",
""
],
[
"Samson",
"Julia E.",
""
],
[
"Khatri",
"Shilpa",
""
],
[
"Miller",
"Laura A.",
""
]
] | While many organisms filter feed and exchange heat or nutrients in flow, few benthic organisms also actively pulse to enhance feeding and exchange. One example is the pulsing soft coral (Heteroxenia fuscescens). Pulsing corals live in colonies, where each polyp actively pulses through contraction and relaxation of their tentacles. The pulses are typically out of phase and without a clear pattern. These corals live in lagoons and bays found in the Red Sea and Indian Ocean where they at times experience strong ambient flows. In this paper, $3D$ fluid-structure interaction simulations are used to quantify the effects of ambient flow on the exchange currents produced by the active contraction of pulsing corals. We find a complex interaction between the flows produced by the coral and the background flow. The dynamics can either enhance or reduce the upward jet generated in a quiescent medium. The pulsing behavior also slows the average horizontal flow near the polyp when there is a strong background flow. The dynamics of these flows have implications for particle capture and nutrient exchange. |
1709.01143 | Chrisovaladis Malesios | C. Malesios, N. Demiris, Z. Abas, K. Dadousis and T. Koutroumanidis | Modeling Sheep pox Disease from the 1994-1998 Epidemic in Evros
Prefecture, Greece | null | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sheep pox is a highly transmissible disease which can cause serious loss of
livestock and can therefore have major economic impact. We present data from
sheep pox epidemics which occurred between 1994 and 1998. The data include
weekly records of infected farms as well as a number of covariates. We
implement Bayesian stochastic regression models which, in addition to various
explanatory variables like seasonal and environmental/meteorological factors,
also contain serial correlation structure based on variants of the
Ornstein-Uhlenbeck process. We take a predictive view in model selection by
utilizing deviance-based measures. The results indicate that seasonality and
the number of infected farms are important predictors for sheep pox incidence.
| [
{
"created": "Mon, 28 Aug 2017 15:02:50 GMT",
"version": "v1"
}
] | 2017-09-06 | [
[
"Malesios",
"C.",
""
],
[
"Demiris",
"N.",
""
],
[
"Abas",
"Z.",
""
],
[
"Dadousis",
"K.",
""
],
[
"Koutroumanidis",
"T.",
""
]
] | Sheep pox is a highly transmissible disease which can cause serious loss of livestock and can therefore have major economic impact. We present data from sheep pox epidemics which occurred between 1994 and 1998. The data include weekly records of infected farms as well as a number of covariates. We implement Bayesian stochastic regression models which, in addition to various explanatory variables like seasonal and environmental/meteorological factors, also contain serial correlation structure based on variants of the Ornstein-Uhlenbeck process. We take a predictive view in model selection by utilizing deviance-based measures. The results indicate that seasonality and the number of infected farms are important predictors for sheep pox incidence. |
q-bio/0406007 | Nikolai Rulkov | Andrey L. Shilnikov and Nikolai F. Rulkov | Subthreshold oscillations in a map-based neuron model | To be published in Physics Letters A | null | 10.1016/j.physleta.2004.05.062 | null | q-bio.CB q-bio.NC | null | Self-sustained subthreshold oscillations in a discrete-time model of neuronal
behavior are considered. We discuss bifurcation scenarios explaining the birth
of these oscillations and their transformation into tonic spikes. Specific
features of these transitions caused by the discrete-time dynamics of the model
and the influence of external noise are discussed.
| [
{
"created": "Wed, 2 Jun 2004 23:45:48 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Shilnikov",
"Andrey L.",
""
],
[
"Rulkov",
"Nikolai F.",
""
]
] | Self-sustained subthreshold oscillations in a discrete-time model of neuronal behavior are considered. We discuss bifurcation scenarios explaining the birth of these oscillations and their transformation into tonic spikes. Specific features of these transitions caused by the discrete-time dynamics of the model and the influence of external noise are discussed. |
2406.15665 | Istvan Morocz | Istv\'an M\'orocz (1 and 6) and Mojtaba Jouzizadeh (2) and Amir H.
Ghaderi (3) and Hamed Cheraghmakani (4) and Seyed M. Baghbanian (4) and Reza
Khanbabaie (5) and Andrei Mogoutov (6) ((1) McGill University Montreal QC
Canada, (2) University of Ottawa Canada, (3) University of Calgary Canada,
(4) Mazandaran University of Medical Sciences Sari Iran, (5) University of
British Columbia Kelowna BC Canada, (6) Noisis Inc. Montreal QC Canada) | Brain states analysis of EEG data distinguishes Multiple Sclerosis | 9 pages, 3 figures | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Background: The treatment of multiple sclerosis implies, beside protecting
the body, the preserving of mental functions, considering how adverse cognitive
decay affects quality of life. However a cognitive assessment is nowadays still
realized with neuro-psychological tests without monitoring cognition on
objective neurobiological grounds whereas the ongoing neural activity is in
fact readily observable and readable.
Objective: The proposed method deciphers electrical brain states which as
multi-dimensional cognetoms discriminate quantitatively normal from
pathological patterns in the EEG signal.
Methods: Baseline recordings from a prior EEG study of 93 subjects, 37 with
MS, were analyzed. Spectral bands served to compute cognetoms and categorize
subsequent feature combination sets.
Results: Using cognetoms and spectral bands, a cross-sectional comparison
separated patients from controls with a precision of 82\% while using bands
alone arrived at 64\%. A few feature combinations were identified to drive this
distinction.
Conclusions: Brain states analysis distinguishes successfully controls from
patients with MS. Our results imply that this data-driven cross-sectional
comparison of EEG data may complement customary diagnostic methods in neurology
and psychiatry. However, thinking ahead in terms of quantitative monitoring of
disease time course and treatment efficacy, we hope having established the
analytic principles applicable to longitudinal clinical studies.
| [
{
"created": "Fri, 21 Jun 2024 21:47:36 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Mórocz",
"István",
"",
"1 and 6"
],
[
"Jouzizadeh",
"Mojtaba",
""
],
[
"Ghaderi",
"Amir H.",
""
],
[
"Cheraghmakani",
"Hamed",
""
],
[
"Baghbanian",
"Seyed M.",
""
],
[
"Khanbabaie",
"Reza",
""
],
[
"Mogoutov",
"Andrei",
""
]
] | Background: The treatment of multiple sclerosis implies, beside protecting the body, the preserving of mental functions, considering how adverse cognitive decay affects quality of life. However a cognitive assessment is nowadays still realized with neuro-psychological tests without monitoring cognition on objective neurobiological grounds whereas the ongoing neural activity is in fact readily observable and readable. Objective: The proposed method deciphers electrical brain states which as multi-dimensional cognetoms discriminate quantitatively normal from pathological patterns in the EEG signal. Methods: Baseline recordings from a prior EEG study of 93 subjects, 37 with MS, were analyzed. Spectral bands served to compute cognetoms and categorize subsequent feature combination sets. Results: Using cognetoms and spectral bands, a cross-sectional comparison separated patients from controls with a precision of 82\% while using bands alone arrived at 64\%. A few feature combinations were identified to drive this distinction. Conclusions: Brain states analysis distinguishes successfully controls from patients with MS. Our results imply that this data-driven cross-sectional comparison of EEG data may complement customary diagnostic methods in neurology and psychiatry. However, thinking ahead in terms of quantitative monitoring of disease time course and treatment efficacy, we hope having established the analytic principles applicable to longitudinal clinical studies. |
2105.12163 | Brinkley Raynor | Ricardo Castillo-Neyra, Bhaswar Bhattacharya, Aris Saxena, Brinkley
Raynor, Elvis Diaz, Gian Franco Condori, Maria Rieders, Michael Z. Levy | Optimizing the location of vaccination sites to stop a zoonotic epidemic | 21 pages, 2 tables, 5 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The mainstay of canine rabies control is fixed point mass dog vaccination
campaigns (MDVC). However, in some regions, ideal vaccination coverage in dogs
is not obtained due to low participation in the MDVC. Travel distance to the
vaccination sites has been identified as an important barrier to participation.
We aim to increase MDVC participation by optimally placing fixed point
vaccination locations to minimize walking distance to the nearest vaccination
location. We quantified participation probability based on walking distance to
the nearest vaccination point using a Poisson regression model. The regression
was fit with survey data collected from 2016-2019. We then used a computational
recursive interchange technique to solve the facility location problem to find
a set of optimal placements of fixed point vaccination locations. Finally, we
compared predicted participation of optimally placed vaccination sites to
historical participation data from surveys collected from 2016-2019. We
identified the p-median algorithm to solve the facility location problem as
ideal for fixed point vaccination placement. We found a predicted increase in
MDVC participation if vaccination locations are placed optimally. We also found
a more even vaccination coverage with optimized vaccination sites; however, the
workload in some optimized locations increased significantly. We developed a
data-driven computational algorithm to combat an ongoing rabies epidemic by
optimally using limited resources to maximize vaccination coverage. The main
positive effects we expect if this algorithm is to be implemented would be
increased overall vaccination coverage and increased spatial evenness of
coverage. A potential negative effect could be the presence of long waiting
lines as participation increases.
| [
{
"created": "Tue, 25 May 2021 18:41:16 GMT",
"version": "v1"
}
] | 2021-05-27 | [
[
"Castillo-Neyra",
"Ricardo",
""
],
[
"Bhattacharya",
"Bhaswar",
""
],
[
"Saxena",
"Aris",
""
],
[
"Raynor",
"Brinkley",
""
],
[
"Diaz",
"Elvis",
""
],
[
"Condori",
"Gian Franco",
""
],
[
"Rieders",
"Maria",
""
],
[
"Levy",
"Michael Z.",
""
]
] | The mainstay of canine rabies control is fixed point mass dog vaccination campaigns (MDVC). However, in some regions, ideal vaccination coverage in dogs is not obtained due to low participation in the MDVC. Travel distance to the vaccination sites has been identified as an important barrier to participation. We aim to increase MDVC participation by optimally placing fixed point vaccination locations to minimize walking distance to the nearest vaccination location. We quantified participation probability based on walking distance to the nearest vaccination point using a Poisson regression model. The regression was fit with survey data collected from 2016-2019. We then used a computational recursive interchange technique to solve the facility location problem to find a set of optimal placements of fixed point vaccination locations. Finally, we compared predicted participation of optimally placed vaccination sites to historical participation data from surveys collected from 2016-2019. We identified the p-median algorithm to solve the facility location problem as ideal for fixed point vaccination placement. We found a predicted increase in MDVC participation if vaccination locations are placed optimally. We also found a more even vaccination coverage with optimized vaccination sites; however, the workload in some optimized locations increased significantly. We developed a data-driven computational algorithm to combat an ongoing rabies epidemic by optimally using limited resources to maximize vaccination coverage. The main positive effects we expect if this algorithm is to be implemented would be increased overall vaccination coverage and increased spatial evenness of coverage. A potential negative effect could be the presence of long waiting lines as participation increases. |
0904.3396 | Mircea Andrecut Dr | M. Andrecut | Sparse Approximation of Computational Time Reversal Imaging | 21 pages, 8 figures | null | null | null | q-bio.OT q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational time reversal imaging can be used to locate the position of
multiple scatterers in a known background medium. Here, we discuss a sparse
approximation method for computational time-reversal imaging. The method is
formulated entirely in the frequency domain, and besides imaging it can also be
used for denoising, and to determine the magnitude of the scattering
coefficients in the presence of moderate noise levels.
| [
{
"created": "Wed, 22 Apr 2009 08:16:07 GMT",
"version": "v1"
}
] | 2009-04-23 | [
[
"Andrecut",
"M.",
""
]
] | Computational time reversal imaging can be used to locate the position of multiple scatterers in a known background medium. Here, we discuss a sparse approximation method for computational time-reversal imaging. The method is formulated entirely in the frequency domain, and besides imaging it can also be used for denoising, and to determine the magnitude of the scattering coefficients in the presence of moderate noise levels. |
1206.0324 | Swagatam Mukhopadhyay | Swagatam Mukhopadhyay, Pascal Grange, Anirvan M. Sengupta and Partha
P. Mitra | What does the Allen Gene Expression Atlas tell us about mouse brain
evolution? | 14 pages | null | null | null | q-bio.QM q-bio.GN q-bio.NC q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We use the Allen Gene Expression Atlas (AGEA) and the OMA ortholog dataset to
investigate the evolution of mouse-brain neuroanatomy from the standpoint of
the molecular evolution of brain-specific genes. For each such gene, using the
phylogenetic tree for all fully sequenced species and the presence of orthologs
of the gene in these species, we construct and assign a discrete measure of
evolutionary age. The gene expression profile of all gene of similar age,
relative to the average gene expression profile, distinguish regions of the
brain that are over-represented in the corresponding evolutionary timescale. We
argue that the conclusions one can draw on evolution of twelve major brain
regions from such a molecular level analysis supplements existing knowledge of
mouse brain evolution and introduces new quantitative tools, especially for
comparative studies, when AGEA-like data sets for other species become
available. Using the functional role of the genes representational of a certain
evolutionary timescale and brain region we compare and contrast, wherever
possible, our observations with existing knowledge in evolutionary
neuroanatomy.
| [
{
"created": "Fri, 1 Jun 2012 22:54:52 GMT",
"version": "v1"
}
] | 2012-06-05 | [
[
"Mukhopadhyay",
"Swagatam",
""
],
[
"Grange",
"Pascal",
""
],
[
"Sengupta",
"Anirvan M.",
""
],
[
"Mitra",
"Partha P.",
""
]
] | We use the Allen Gene Expression Atlas (AGEA) and the OMA ortholog dataset to investigate the evolution of mouse-brain neuroanatomy from the standpoint of the molecular evolution of brain-specific genes. For each such gene, using the phylogenetic tree for all fully sequenced species and the presence of orthologs of the gene in these species, we construct and assign a discrete measure of evolutionary age. The gene expression profile of all gene of similar age, relative to the average gene expression profile, distinguish regions of the brain that are over-represented in the corresponding evolutionary timescale. We argue that the conclusions one can draw on evolution of twelve major brain regions from such a molecular level analysis supplements existing knowledge of mouse brain evolution and introduces new quantitative tools, especially for comparative studies, when AGEA-like data sets for other species become available. Using the functional role of the genes representational of a certain evolutionary timescale and brain region we compare and contrast, wherever possible, our observations with existing knowledge in evolutionary neuroanatomy. |
2402.17810 | Qizhi Pei | Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua
Zhu, Shufang Xie, Tao Qin, Rui Yan | BioT5+: Towards Generalized Biological Understanding with IUPAC
Integration and Multi-task Tuning | Accepted by ACL 2024 (Findings) | null | null | null | q-bio.QM cs.AI cs.CE cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research trends in computational biology have increasingly focused on
integrating text and bio-entity modeling, especially in the context of
molecules and proteins. However, previous efforts like BioT5 faced challenges
in generalizing across diverse tasks and lacked a nuanced understanding of
molecular structures, particularly in their textual representations (e.g.,
IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework,
tailored to enhance biological research and drug discovery. BioT5+ incorporates
several novel features: integration of IUPAC names for molecular understanding,
inclusion of extensive bio-text and molecule data from sources like bioRxiv and
PubChem, the multi-task instruction tuning for generality across tasks, and a
numerical tokenization technique for improved processing of numerical data.
These enhancements allow BioT5+ to bridge the gap between molecular
representations and their textual descriptions, providing a more holistic
understanding of biological entities, and largely improving the grounded
reasoning of bio-text and bio-sequences. The model is pre-trained and
fine-tuned with a large number of experiments, including \emph{3 types of
problems (classification, regression, generation), 15 kinds of tasks, and 21
total benchmark datasets}, demonstrating the remarkable performance and
state-of-the-art results in most cases. BioT5+ stands out for its ability to
capture intricate relationships in biological data, thereby contributing
significantly to bioinformatics and computational biology. Our code is
available at \url{https://github.com/QizhiPei/BioT5}.
| [
{
"created": "Tue, 27 Feb 2024 12:43:09 GMT",
"version": "v1"
},
{
"created": "Fri, 31 May 2024 14:07:00 GMT",
"version": "v2"
}
] | 2024-06-03 | [
[
"Pei",
"Qizhi",
""
],
[
"Wu",
"Lijun",
""
],
[
"Gao",
"Kaiyuan",
""
],
[
"Liang",
"Xiaozhuan",
""
],
[
"Fang",
"Yin",
""
],
[
"Zhu",
"Jinhua",
""
],
[
"Xie",
"Shufang",
""
],
[
"Qin",
"Tao",
""
],
[
"Yan",
"Rui",
""
]
] | Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e.g., IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data. These enhancements allow BioT5+ to bridge the gap between molecular representations and their textual descriptions, providing a more holistic understanding of biological entities, and largely improving the grounded reasoning of bio-text and bio-sequences. The model is pre-trained and fine-tuned with a large number of experiments, including \emph{3 types of problems (classification, regression, generation), 15 kinds of tasks, and 21 total benchmark datasets}, demonstrating the remarkable performance and state-of-the-art results in most cases. BioT5+ stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology. Our code is available at \url{https://github.com/QizhiPei/BioT5}. |
1901.02919 | Jose A. Carrillo | Jose A. Carrillo, Hideki Murakawa, Makoto Sato, Hideru Togashi, Olena
Trush | A population dynamics model of cell-cell adhesion incorporating
population pressure and density saturation | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss several continuum cell-cell adhesion models based on the
underlying microscopic assumptions. We propose an improvement on these models
leading to sharp fronts and intermingling invasion fronts between different
cell type populations. The model is based on basic principles of localized
repulsion and nonlocal attraction due to adhesion forces at the microscopic
level. The new model is able to capture both qualitatively and quantitatively
experiments by Katsunuma et al. (2016) [J. Cell Biol. 212(5), pp. 561--575]. We
also review some of the applications of these models in other areas of tissue
growth in developmental biology. We finally explore the resulting qualitative
behavior due to cell-cell repulsion.
| [
{
"created": "Wed, 9 Jan 2019 20:21:06 GMT",
"version": "v1"
}
] | 2019-01-11 | [
[
"Carrillo",
"Jose A.",
""
],
[
"Murakawa",
"Hideki",
""
],
[
"Sato",
"Makoto",
""
],
[
"Togashi",
"Hideru",
""
],
[
"Trush",
"Olena",
""
]
] | We discuss several continuum cell-cell adhesion models based on the underlying microscopic assumptions. We propose an improvement on these models leading to sharp fronts and intermingling invasion fronts between different cell type populations. The model is based on basic principles of localized repulsion and nonlocal attraction due to adhesion forces at the microscopic level. The new model is able to capture both qualitatively and quantitatively experiments by Katsunuma et al. (2016) [J. Cell Biol. 212(5), pp. 561--575]. We also review some of the applications of these models in other areas of tissue growth in developmental biology. We finally explore the resulting qualitative behavior due to cell-cell repulsion. |
2305.11425 | Eric Mayor PhD | Eric Mayor | Neurotrophic Effects of Intermittent Fasting, Calorie Restriction and
Exercise: A Review and Annotated Bibliography | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | In the last decades, important progress has been achieved in the
understanding of the neurotrophic effects of intermittent fasting (IF), caloric
restriction (CR) and exercise. Improved neuroprotection, synaptic plasticity
and adult neurogenesis (NSPAN) are essential examples of these neurotrophic
effects. The importance in this respect of the metabolic switch from glucose to
ketone bodies as cellular fuel has been highlighted. More recently, calorie
restriction mimetics (CRMs; resveratrol and other polyphenols in particular)
have been investigated thoroughly in relation to NSPAN. In the narrative review
sections of this manuscript, recent findings on these essential functions are
synthesized and the most important molecules involved are presented. The most
researched signaling pathways (PI3K, Akt, mTOR, AMPK, GSK3$\beta$, ULK, MAPK,
PGC-1$\alpha$, NF-$\kappa$B, sirtuins, Notch, Sonic hedgehog and Wnt) and
processes (e.g., anti-inflammation, autophagy, apoptosis) that support or
thwart neuroprotection, synaptic plasticity and neurogenesis are then briefly
presented. This provides an accessible entry point to the literature. In the
annotated bibliography section of this contribution, brief summaries are
provided of about 30 literature reviews relating to the neurotrophic effects of
interest in relation to IF, CR, CRMs and exercise. Most of the selected reviews
address these essential functions from the perspective of healthier aging
(sometimes discussing epigenetic factors) and the reduction of the risk for
neurodegenerative diseases (Alzheimer's disease, Huntington's disease,
Parkinson's disease) and depression or the improvement of cognitive function.
| [
{
"created": "Wed, 3 May 2023 15:50:54 GMT",
"version": "v1"
},
{
"created": "Mon, 22 May 2023 07:22:52 GMT",
"version": "v2"
}
] | 2023-05-23 | [
[
"Mayor",
"Eric",
""
]
] | In the last decades, important progress has been achieved in the understanding of the neurotrophic effects of intermittent fasting (IF), caloric restriction (CR) and exercise. Improved neuroprotection, synaptic plasticity and adult neurogenesis (NSPAN) are essential examples of these neurotrophic effects. The importance in this respect of the metabolic switch from glucose to ketone bodies as cellular fuel has been highlighted. More recently, calorie restriction mimetics (CRMs; resveratrol and other polyphenols in particular) have been investigated thoroughly in relation to NSPAN. In the narrative review sections of this manuscript, recent findings on these essential functions are synthesized and the most important molecules involved are presented. The most researched signaling pathways (PI3K, Akt, mTOR, AMPK, GSK3$\beta$, ULK, MAPK, PGC-1$\alpha$, NF-$\kappa$B, sirtuins, Notch, Sonic hedgehog and Wnt) and processes (e.g., anti-inflammation, autophagy, apoptosis) that support or thwart neuroprotection, synaptic plasticity and neurogenesis are then briefly presented. This provides an accessible entry point to the literature. In the annotated bibliography section of this contribution, brief summaries are provided of about 30 literature reviews relating to the neurotrophic effects of interest in relation to IF, CR, CRMs and exercise. Most of the selected reviews address these essential functions from the perspective of healthier aging (sometimes discussing epigenetic factors) and the reduction of the risk for neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease) and depression or the improvement of cognitive function. |
2205.04871 | Tao Jia | Yunsong Luo, Wenyu Chen, Jiang Qiu, Tao Jia | Accelerated functional brain aging in major depressive disorder:
evidence from a large scale fMRI analysis of Chinese participants | 32 pages,13 figures | Transl Psychiatry 12, 397 (2022) | 10.1038/s41398-022-02162-y | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Major depressive disorder (MDD) is one of the most common mental health
conditions that has been intensively investigated for its association with
brain atrophy and mortality. Recent studies reveal that the deviation between
the predicted and the chronological age can be a marker of accelerated brain
aging to characterize MDD. However, current conclusions are usually drawn based
on structural MRI information collected from Caucasian participants. The
universality of this biomarker needs to be further validated by subjects with
different ethnic/racial backgrounds and by different types of data. Here we
make use of the REST-meta-MDD, a large scale resting-state fMRI dataset
collected from multiple cohort participants in China. We develop a stacking
machine learning model based on 1101 healthy controls, which estimates a
subject's chronological age from fMRI with promising accuracy. The trained
model is then applied to 1276 MDD patients from 24 sites. We observe that MDD
patients exhibit a $+4.43$ years ($\text{$p$} < 0.0001$, $\text{Cohen's $d$} =
0.35$, $\text{95\% CI}:1.86 - 3.91$) higher brain-predicted age difference
(brain-PAD) compared to controls. In the MDD subgroup, we observe a
statistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's
$d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-free
patients. The statistical relationship observed is further checked by three
different machine learning algorithms. The positive brain-PAD observed in
participants in China confirms the presence of accelerated brain aging in MDD
patients. The utilization of functional brain connectivity for age estimation
verifies existing findings from a new dimension.
| [
{
"created": "Sun, 8 May 2022 09:26:46 GMT",
"version": "v1"
}
] | 2022-10-18 | [
[
"Luo",
"Yunsong",
""
],
[
"Chen",
"Wenyu",
""
],
[
"Qiu",
"Jiang",
""
],
[
"Jia",
"Tao",
""
]
] | Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies reveal that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a $+4.43$ years ($\text{$p$} < 0.0001$, $\text{Cohen's $d$} = 0.35$, $\text{95\% CI}:1.86 - 3.91$) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's $d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension. |
2301.02554 | Siamak Mehrkanoon | Sheng Kuang, Henry C. Woodruff, Renee Granzier, Thiemo J.A. van
Nijnatten, Marc B.I. Lobbes, Marjolein L. Smidt, Philippe Lambin, Siamak
Mehrkanoon | MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain
Adaptation for Breast MRI Segmentation in Small Datasets | 17 pages, 8 figures | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Deep learning (DL) applied to breast tissue segmentation in magnetic
resonance imaging (MRI) has received increased attention in the last decade,
however, the domain shift which arises from different vendors, acquisition
protocols, and biological heterogeneity, remains an important but challenging
obstacle on the path towards clinical implementation. In this paper, we propose
a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA)
framework to address this issue in an unsupervised manner. Our approach
incorporates self-training with contrastive learning to align feature
representations between domains. In particular, we extend the contrastive loss
by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid
contrasts to better exploit the underlying semantic information of the image at
different levels. To resolve the data imbalance problem, we utilize a
category-wise cross-domain sampling strategy to sample anchors from target
images and build a hybrid memory bank to store samples from source images. We
have validated MSCDA with a challenging task of cross-domain breast MRI
segmentation between datasets of healthy volunteers and invasive breast cancer
patients. Extensive experiments show that MSCDA effectively improves the
model's feature alignment capabilities between domains, outperforming
state-of-the-art methods. Furthermore, the framework is shown to be
label-efficient, achieving good performance with a smaller source dataset. The
code is publicly available at \url{https://github.com/ShengKuangCN/MSCDA}.
| [
{
"created": "Wed, 4 Jan 2023 19:16:55 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Jun 2023 09:25:14 GMT",
"version": "v2"
}
] | 2023-06-09 | [
[
"Kuang",
"Sheng",
""
],
[
"Woodruff",
"Henry C.",
""
],
[
"Granzier",
"Renee",
""
],
[
"van Nijnatten",
"Thiemo J. A.",
""
],
[
"Lobbes",
"Marc B. I.",
""
],
[
"Smidt",
"Marjolein L.",
""
],
[
"Lambin",
"Philippe",
""
],
[
"Mehrkanoon",
"Siamak",
""
]
] | Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at \url{https://github.com/ShengKuangCN/MSCDA}. |
2005.07955 | Suban Sahoo | Seshu Vardhan and Suban K Sahoo | In silico ADMET and molecular docking study on searching potential
inhibitors from limonoids and triterpenoids for COVID-19 | null | null | 10.1016/j.compbiomed.2020.103936 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Virtual screening of phytochemicals was performed through molecular docking,
simulation, in silico ADMET and drug-likeness prediction to identify the
potential hits that can inhibit the effects of SARS-CoV-2. Considering the
published literature on medicinal importance, total 154 phytochemicals with
analogous structure from limonoids and triterpenoids were selected to search
potential inhibitors for the five therapeutic protein targets of SARS-CoV-2,
i.e., 3CLpro (main protease), PLpro (papain-like protease), SGp-RBD (spike
glycoprotein-receptor binding domain), RdRp (RNA dependent RNA polymerase) and
ACE2 (angiotensin-converting enzyme 2). The in silico computational results
revealed that the phytochemicals such as glycyrrhizic acid, limonin,
7-deacetyl-7-benzoylgedunin, maslinic acid, corosolic acid, obacunone and
ursolic acid were found to be effective against the target proteins of
SARS-CoV-2. The protein-ligand interaction study revealed that these
phytochemicals bind with the amino acid residues at the active site of the
target proteins. Therefore, the core structure of these potential hits can be
used for further lead optimization to design drugs for SARS-CoV-2. Also, the
medicinal plants containing these phytochemicals like licorice, neem, tulsi,
citrus and olives can be used to formulate suitable therapeutic approaches in
traditional medicines.
| [
{
"created": "Sat, 16 May 2020 11:22:59 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Jul 2020 12:15:53 GMT",
"version": "v2"
}
] | 2020-07-30 | [
[
"Vardhan",
"Seshu",
""
],
[
"Sahoo",
"Suban K",
""
]
] | Virtual screening of phytochemicals was performed through molecular docking, simulation, in silico ADMET and drug-likeness prediction to identify the potential hits that can inhibit the effects of SARS-CoV-2. Considering the published literature on medicinal importance, total 154 phytochemicals with analogous structure from limonoids and triterpenoids were selected to search potential inhibitors for the five therapeutic protein targets of SARS-CoV-2, i.e., 3CLpro (main protease), PLpro (papain-like protease), SGp-RBD (spike glycoprotein-receptor binding domain), RdRp (RNA dependent RNA polymerase) and ACE2 (angiotensin-converting enzyme 2). The in silico computational results revealed that the phytochemicals such as glycyrrhizic acid, limonin, 7-deacetyl-7-benzoylgedunin, maslinic acid, corosolic acid, obacunone and ursolic acid were found to be effective against the target proteins of SARS-CoV-2. The protein-ligand interaction study revealed that these phytochemicals bind with the amino acid residues at the active site of the target proteins. Therefore, the core structure of these potential hits can be used for further lead optimization to design drugs for SARS-CoV-2. Also, the medicinal plants containing these phytochemicals like licorice, neem, tulsi, citrus and olives can be used to formulate suitable therapeutic approaches in traditional medicines. |
2104.07165 | Radostin Simitev | Peter Mortensen, Hao Gao, Godfrey Smith, Radostin D. Simitev | Addendum: Action potential propagation and block in a model of atrial
tissue with myocyte-fibroblast coupling | Accepted in Mathematical Medicine and Biology: A Journal of the IMA
(ISSN (Online):1477-8602) on 2021-04-14 | null | 10.1093/imammb/dqab005 | null | q-bio.TO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analytical theory of our earlier study (Mortensen et al. (2021),
Mathematical Medicine and Biology, 38(1), pp. 106-131) is extended to address
the outstanding cases of fibroblast barrier distribution and myocyte strait
distribution. In particular, closed-form approximations to the resting membrane
potential and to the critical parameter values for propagation are derived for
these two non-uniform fibroblast distributions and are in good agreement with
numerical estimates.
| [
{
"created": "Wed, 14 Apr 2021 23:48:46 GMT",
"version": "v1"
}
] | 2021-04-16 | [
[
"Mortensen",
"Peter",
""
],
[
"Gao",
"Hao",
""
],
[
"Smith",
"Godfrey",
""
],
[
"Simitev",
"Radostin D.",
""
]
] | The analytical theory of our earlier study (Mortensen et al. (2021), Mathematical Medicine and Biology, 38(1), pp. 106-131) is extended to address the outstanding cases of fibroblast barrier distribution and myocyte strait distribution. In particular, closed-form approximations to the resting membrane potential and to the critical parameter values for propagation are derived for these two non-uniform fibroblast distributions and are in good agreement with numerical estimates. |
2208.04311 | Farzaneh Esmaili | Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Seyedehsamaneh
Shojaeilangari, Elham Yavari | A Review of Machine Learning and Algorithmic Methods for Protein
Phosphorylation Sites Prediction | 45 pages, 9 figures, 3 tables. arXiv admin note: text overlap with
arXiv:2108.04951 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Post-translational modifications (PTMs) have key roles in extending the
functional diversity of proteins and as a result, regulating diverse cellular
processes in prokaryotic and eukaryotic organisms. Phosphorylation modification
is a vital PTM that occurs in most proteins and plays a significant role in
many biological processes. Disorders in the phosphorylation process lead to
multiple diseases including neurological disorders and cancers. The purpose of
this review paper is to organize this body of knowledge associated with
phosphorylation site (p-site) prediction to facilitate future research in this
field. At first, we comprehensively reviewed all related databases and
introduced all steps regarding dataset creation, data preprocessing and method
evaluation in p-site prediction. Next, we investigated p-sites prediction
methods which fall into two computational groups: Algorithmic and Machine
Learning (ML). Additionally, it was shown that there are basically two main
approaches for p-sites prediction by ML: conventional and End-to-End deep
learning methods, which were given an overview for both of them. Moreover, this
study introduced the most important feature extraction techniques which have
mostly been used in p-site prediction. Finally, we created three test sets from
new proteins related to the 2022th released version of the dbPTM database based
on general and human species. Evaluation of the available online tools on the
test sets showed quite poor performance for p-sites prediction. Keywords:
Phosphorylation, Machine Learning, Deep Learning, Post Translation
Modification, Databases
| [
{
"created": "Thu, 4 Aug 2022 19:31:22 GMT",
"version": "v1"
}
] | 2022-08-10 | [
[
"Esmaili",
"Farzaneh",
""
],
[
"Pourmirzaei",
"Mahdi",
""
],
[
"Ramazi",
"Shahin",
""
],
[
"Shojaeilangari",
"Seyedehsamaneh",
""
],
[
"Yavari",
"Elham",
""
]
] | Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases including neurological disorders and cancers. The purpose of this review paper is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively reviewed all related databases and introduced all steps regarding dataset creation, data preprocessing and method evaluation in p-site prediction. Next, we investigated p-sites prediction methods which fall into two computational groups: Algorithmic and Machine Learning (ML). Additionally, it was shown that there are basically two main approaches for p-sites prediction by ML: conventional and End-to-End deep learning methods, which were given an overview for both of them. Moreover, this study introduced the most important feature extraction techniques which have mostly been used in p-site prediction. Finally, we created three test sets from new proteins related to the 2022th released version of the dbPTM database based on general and human species. Evaluation of the available online tools on the test sets showed quite poor performance for p-sites prediction. Keywords: Phosphorylation, Machine Learning, Deep Learning, Post Translation Modification, Databases |
q-bio/0607002 | Fail Gafarov M | Fail M. Gafarov | Self-wiring in neural nets of point-like cortical neurons fails to
reproduce cytoarchitectural differences | 13 pages, 5 figures | Journal of Integrative Neuroscience, Vol. 5, No. 2(2006) 159-169 | null | null | q-bio.NC cond-mat.dis-nn | null | We propose a model for description of activity-dependent evolution and
self-wiring between binary neurons. Specifically, this model can be used for
investigation of growth of neuronal connectivity in the developing neocortex.
By using computational simulations with appropriate training pattern sequences,
we show that long-term memory can be encoded in neuronal connectivity and that
the external stimulations form part of the functioning neocortical circuit. It
is proposed that such binary neuron representations of point-like cortical
neurons fail to reproduce cytoarchitectural differences of the neocortical
organization, which has implications for inadequacies of compartmental models.
| [
{
"created": "Tue, 4 Jul 2006 11:47:09 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Gafarov",
"Fail M.",
""
]
] | We propose a model for description of activity-dependent evolution and self-wiring between binary neurons. Specifically, this model can be used for investigation of growth of neuronal connectivity in the developing neocortex. By using computational simulations with appropriate training pattern sequences, we show that long-term memory can be encoded in neuronal connectivity and that the external stimulations form part of the functioning neocortical circuit. It is proposed that such binary neuron representations of point-like cortical neurons fail to reproduce cytoarchitectural differences of the neocortical organization, which has implications for inadequacies of compartmental models. |
2207.01586 | Zhihang Hu | Tao Shen, Zhihang Hu, Zhangzhi Peng, Jiayang Chen, Peng Xiong, Liang
Hong, Liangzhen Zheng, Yixuan Wang, Irwin King, Sheng Wang, Siqi Sun, and Yu
Li | E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D
Structure Prediction | null | null | null | null | q-bio.QM cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RNA structure determination and prediction can promote RNA-targeted drug
development and engineerable synthetic elements design. But due to the
intrinsic structural flexibility of RNAs, all the three mainstream structure
determination methods (X-ray crystallography, NMR, and Cryo-EM) encounter
challenges when resolving the RNA structures, which leads to the scarcity of
the resolved RNA structures. Computational prediction approaches emerge as
complementary to the experimental techniques. However, none of the \textit{de
novo} approaches is based on deep learning since too few structures are
available. Instead, most of them apply the time-consuming sampling-based
strategies, and their performance seems to hit the plateau. In this work, we
develop the first end-to-end deep learning approach, E2Efold-3D, to accurately
perform the \textit{de novo} RNA structure prediction. Several novel components
are proposed to overcome the data scarcity, such as a fully-differentiable
end-to-end pipeline, secondary structure-assisted self-distillation, and
parameter-efficient backbone formulation. Such designs are validated on the
independent, non-overlapping RNA puzzle testing dataset and reach an average
sub-4 \AA{} root-mean-square deviation, demonstrating its superior performance
compared to state-of-the-art approaches. Interestingly, it also achieves
promising results when predicting RNA complex structures, a feat that none of
the previous systems could accomplish. When E2Efold-3D is coupled with the
experimental techniques, the RNA structure prediction field can be greatly
advanced.
| [
{
"created": "Mon, 4 Jul 2022 17:15:35 GMT",
"version": "v1"
}
] | 2022-07-05 | [
[
"Shen",
"Tao",
""
],
[
"Hu",
"Zhihang",
""
],
[
"Peng",
"Zhangzhi",
""
],
[
"Chen",
"Jiayang",
""
],
[
"Xiong",
"Peng",
""
],
[
"Hong",
"Liang",
""
],
[
"Zheng",
"Liangzhen",
""
],
[
"Wang",
"Yixuan",
""
],
[
"King",
"Irwin",
""
],
[
"Wang",
"Sheng",
""
],
[
"Sun",
"Siqi",
""
],
[
"Li",
"Yu",
""
]
] | RNA structure determination and prediction can promote RNA-targeted drug development and engineerable synthetic elements design. But due to the intrinsic structural flexibility of RNAs, all the three mainstream structure determination methods (X-ray crystallography, NMR, and Cryo-EM) encounter challenges when resolving the RNA structures, which leads to the scarcity of the resolved RNA structures. Computational prediction approaches emerge as complementary to the experimental techniques. However, none of the \textit{de novo} approaches is based on deep learning since too few structures are available. Instead, most of them apply the time-consuming sampling-based strategies, and their performance seems to hit the plateau. In this work, we develop the first end-to-end deep learning approach, E2Efold-3D, to accurately perform the \textit{de novo} RNA structure prediction. Several novel components are proposed to overcome the data scarcity, such as a fully-differentiable end-to-end pipeline, secondary structure-assisted self-distillation, and parameter-efficient backbone formulation. Such designs are validated on the independent, non-overlapping RNA puzzle testing dataset and reach an average sub-4 \AA{} root-mean-square deviation, demonstrating its superior performance compared to state-of-the-art approaches. Interestingly, it also achieves promising results when predicting RNA complex structures, a feat that none of the previous systems could accomplish. When E2Efold-3D is coupled with the experimental techniques, the RNA structure prediction field can be greatly advanced. |
2306.13770 | Shengming Zhang | Shengming Zhang and Yizhou Sun | Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction
Prediction | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Drug-target interaction (DTI) prediction, which aims at predicting whether a
drug will be bounded to a target, have received wide attention recently, with
the goal to automate and accelerate the costly process of drug design. Most of
the recently proposed methods use single drug-drug similarity and target-target
similarity information for DTI prediction, which are unable to take advantage
of the abundant information regarding various types of similarities between
them. Very recently, some methods are proposed to leverage multi-similarity
information, however, they still lack the ability to take into consideration
the rich topological information of all sorts of knowledge bases where the
drugs and targets reside in. More importantly, the time consumption of these
approaches is very high, which prevents the usage of large-scale network
information. We thus propose a network-based drug-target interaction prediction
approach, which applies probabilistic soft logic (PSL) to meta-paths on a
heterogeneous network that contains multiple sources of information, including
drug-drug similarities, target-target similarities, drug-target interactions,
and other potential information. Our approach is based on the PSL graphical
model and uses meta-path counts instead of path instances to reduce the number
of rule instances of PSL. We compare our model against five methods, on three
open-source datasets. The experimental results show that our approach
outperforms all the five baselines in terms of AUPR score and AUC score.
| [
{
"created": "Sun, 25 Jun 2023 02:30:38 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"Zhang",
"Shengming",
""
],
[
"Sun",
"Yizhou",
""
]
] | Drug-target interaction (DTI) prediction, which aims at predicting whether a drug will be bounded to a target, have received wide attention recently, with the goal to automate and accelerate the costly process of drug design. Most of the recently proposed methods use single drug-drug similarity and target-target similarity information for DTI prediction, which are unable to take advantage of the abundant information regarding various types of similarities between them. Very recently, some methods are proposed to leverage multi-similarity information, however, they still lack the ability to take into consideration the rich topological information of all sorts of knowledge bases where the drugs and targets reside in. More importantly, the time consumption of these approaches is very high, which prevents the usage of large-scale network information. We thus propose a network-based drug-target interaction prediction approach, which applies probabilistic soft logic (PSL) to meta-paths on a heterogeneous network that contains multiple sources of information, including drug-drug similarities, target-target similarities, drug-target interactions, and other potential information. Our approach is based on the PSL graphical model and uses meta-path counts instead of path instances to reduce the number of rule instances of PSL. We compare our model against five methods, on three open-source datasets. The experimental results show that our approach outperforms all the five baselines in terms of AUPR score and AUC score. |
1907.06486 | Pierre Baudot | Pierre Baudot | The Poincar\'e-Boltzmann Machine: from Statistical Physics to Machine
Learning and back | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the computational methods of information cohomology
applied to genetic expression in and in the companion paper and proposes its
interpretations in terms of statistical physics and machine learning. In order
to further underline the Hochschild cohomological nature af information
functions and chain rules, following, the computation of the cohomology in low
degrees is detailed to show more directly that the $k$ multivariate
mutual-informations (I_k) are k-coboundaries. The k-cocycles condition
corresponds to I_k=0, generalizing statistical independence. Hence the
cohomology quantifies the statistical dependences and the obstruction to
factorization. The topological approach allows to investigate information in
the multivariate case without the assumptions of independent identically
distributed variables and without mean field approximations. We develop the
computationally tractable subcase of simplicial information cohomology
represented by entropy H_k and information I_k landscapes and their respective
paths. The I_1 component defines a self-internal energy U_k, and I_k,k>1
components define the contribution to a free energy G_k (the total correlation)
of the k-body interactions. The set of information paths in simplicial
structures is in bijection with the symmetric group and random processes,
provides a trivial topological expression of the 2nd law of thermodynamic. The
local minima of free-energy, related to conditional information negativity, and
conditional independence, characterize a minimum free energy complex. This
complex formalizes the minimum free-energy principle in topology, provides a
definition of a complex system, and characterizes a multiplicity of local
minima that quantifies the diversity observed in biology. I give an
interpretation of this complex in terms of frustration in glass and of Van Der
Walls k-body interactions for data points.
| [
{
"created": "Sat, 6 Jul 2019 18:26:19 GMT",
"version": "v1"
}
] | 2019-07-16 | [
[
"Baudot",
"Pierre",
""
]
] | This paper presents the computational methods of information cohomology applied to genetic expression in and in the companion paper and proposes its interpretations in terms of statistical physics and machine learning. In order to further underline the Hochschild cohomological nature af information functions and chain rules, following, the computation of the cohomology in low degrees is detailed to show more directly that the $k$ multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles condition corresponds to I_k=0, generalizing statistical independence. Hence the cohomology quantifies the statistical dependences and the obstruction to factorization. The topological approach allows to investigate information in the multivariate case without the assumptions of independent identically distributed variables and without mean field approximations. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes and their respective paths. The I_1 component defines a self-internal energy U_k, and I_k,k>1 components define the contribution to a free energy G_k (the total correlation) of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a trivial topological expression of the 2nd law of thermodynamic. The local minima of free-energy, related to conditional information negativity, and conditional independence, characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. I give an interpretation of this complex in terms of frustration in glass and of Van Der Walls k-body interactions for data points. |
0705.2747 | Vladimir Gubernov | A.V. Kolobov, V.V. Gubernov, A.A. Polezhaev | Autowaves in the model of avascular tumour growth | 9 pages, 7 figures | null | null | null | q-bio.TO nlin.PS | null | A mathematical model of infiltrative tumour growth taking into account cell
proliferation, death and motility is considered. The model is formulated in
terms of local cell density and nutrient (oxygen) concentration. In the model
the rate of cell death depends on the local nutrient level. Thus heterogeneous
nutrient distribution in tissue affects tumour structure and development. The
existence of automodel solutions is demonstrated and their properties are
investigated. The results are compared to the properties of the
Kolmogorov-Petrovskii-Piskunov and Fisher equations. Influence of the nutrient
distribution on the autowave speed selection as well as on the relaxation to
automodel solution is demonstrated. The model adequately describes the data,
observed in experiments.
| [
{
"created": "Fri, 18 May 2007 19:06:02 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Kolobov",
"A. V.",
""
],
[
"Gubernov",
"V. V.",
""
],
[
"Polezhaev",
"A. A.",
""
]
] | A mathematical model of infiltrative tumour growth taking into account cell proliferation, death and motility is considered. The model is formulated in terms of local cell density and nutrient (oxygen) concentration. In the model the rate of cell death depends on the local nutrient level. Thus heterogeneous nutrient distribution in tissue affects tumour structure and development. The existence of automodel solutions is demonstrated and their properties are investigated. The results are compared to the properties of the Kolmogorov-Petrovskii-Piskunov and Fisher equations. Influence of the nutrient distribution on the autowave speed selection as well as on the relaxation to automodel solution is demonstrated. The model adequately describes the data, observed in experiments. |
2009.14280 | Yusheng Jiao | Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess, Josh Merel and
Eva Kanso | Learning to swim in potential flow | null | Phys. Rev. Fluids 6, 050505 (2021) | 10.1103/PhysRevFluids.6.050505 | null | q-bio.QM cs.LG physics.flu-dyn q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fish swim by undulating their bodies. These propulsive motions require
coordinated shape changes of a body that interacts with its fluid environment,
but the specific shape coordination that leads to robust turning and swimming
motions remains unclear. To address the problem of underwater motion planning,
we propose a simple model of a three-link fish swimming in a potential flow
environment and we use model-free reinforcement learning for shape control. We
arrive at optimal shape changes for two swimming tasks: swimming in a desired
direction and swimming towards a known target. This fish model belongs to a
class of problems in geometric mechanics, known as driftless dynamical systems,
which allow us to analyze the swimming behavior in terms of geometric phases
over the shape space of the fish. These geometric methods are less intuitive in
the presence of drift. Here, we use the shape space analysis as a tool for
assessing, visualizing, and interpreting the control policies obtained via
reinforcement learning in the absence of drift. We then examine the robustness
of these policies to drift-related perturbations. Although the fish has no
direct control over the drift itself, it learns to take advantage of the
presence of moderate drift to reach its target.
| [
{
"created": "Wed, 30 Sep 2020 06:31:27 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Dec 2020 23:59:01 GMT",
"version": "v2"
}
] | 2021-05-19 | [
[
"Jiao",
"Yusheng",
""
],
[
"Ling",
"Feng",
""
],
[
"Heydari",
"Sina",
""
],
[
"Heess",
"Nicolas",
""
],
[
"Merel",
"Josh",
""
],
[
"Kanso",
"Eva",
""
]
] | Fish swim by undulating their bodies. These propulsive motions require coordinated shape changes of a body that interacts with its fluid environment, but the specific shape coordination that leads to robust turning and swimming motions remains unclear. To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control. We arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target. This fish model belongs to a class of problems in geometric mechanics, known as driftless dynamical systems, which allow us to analyze the swimming behavior in terms of geometric phases over the shape space of the fish. These geometric methods are less intuitive in the presence of drift. Here, we use the shape space analysis as a tool for assessing, visualizing, and interpreting the control policies obtained via reinforcement learning in the absence of drift. We then examine the robustness of these policies to drift-related perturbations. Although the fish has no direct control over the drift itself, it learns to take advantage of the presence of moderate drift to reach its target. |
1503.05869 | Alexander Gorban | Evgeny M. Mirkes, Thomas Walsh, Edward J. Louis, Alexander N. Gorban | Long and short range multi-locus QTL interactions in a complex trait of
yeast | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | We analyse interactions of Quantitative Trait Loci (QTL) in heat selected
yeast by comparing them to an unselected pool of random individuals. Here we
re-examine data on individual F12 progeny selected for heat tolerance, which
have been genotyped at 25 locations identified by sequencing a selected pool
[Parts, L., Cubillos, F. A., Warringer, J., Jain, K., Salinas, F., Bumpstead,
S. J., Molin, M., Zia, A., Simpson, J. T., Quail, M. A., Moses, A., Louis, E.
J., Durbin, R., and Liti, G. (2011). Genome research, 21(7), 1131-1138]. 960
individuals were genotyped at these locations and multi-locus genotype
frequencies were compared to 172 sequenced individuals from the original
unselected pool (a control group). Various non-random associations were found
across the genome, both within chromosomes and between chromosomes. Some of the
non-random associations are likely due to retention of linkage disequilibrium
in the F12 population, however many, including the inter-chromosomal
interactions, must be due to genetic interactions in heat tolerance. One region
of particular interest involves 3 linked loci on chromosome IV where the
central variant responsible for heat tolerance is antagonistic, coming from the
heat sensitive parent and the flanking ones are from the more heat tolerant
parent. The 3-locus haplotypes in the selected individuals represent a highly
biased sample of the population haplotypes with rare double recombinants in
high frequency. These were missed in the original analysis and would never be
seen without the multigenerational approach. We show that a statistical
analysis of entropy and information gain in genotypes of a selected population
can reveal further interactions than previously seen. Importantly this must be
done in comparison to the unselected population's genotypes to account for
inherent biases in the original population.
| [
{
"created": "Thu, 19 Mar 2015 18:31:08 GMT",
"version": "v1"
}
] | 2015-03-20 | [
[
"Mirkes",
"Evgeny M.",
""
],
[
"Walsh",
"Thomas",
""
],
[
"Louis",
"Edward J.",
""
],
[
"Gorban",
"Alexander N.",
""
]
] | We analyse interactions of Quantitative Trait Loci (QTL) in heat selected yeast by comparing them to an unselected pool of random individuals. Here we re-examine data on individual F12 progeny selected for heat tolerance, which have been genotyped at 25 locations identified by sequencing a selected pool [Parts, L., Cubillos, F. A., Warringer, J., Jain, K., Salinas, F., Bumpstead, S. J., Molin, M., Zia, A., Simpson, J. T., Quail, M. A., Moses, A., Louis, E. J., Durbin, R., and Liti, G. (2011). Genome research, 21(7), 1131-1138]. 960 individuals were genotyped at these locations and multi-locus genotype frequencies were compared to 172 sequenced individuals from the original unselected pool (a control group). Various non-random associations were found across the genome, both within chromosomes and between chromosomes. Some of the non-random associations are likely due to retention of linkage disequilibrium in the F12 population, however many, including the inter-chromosomal interactions, must be due to genetic interactions in heat tolerance. One region of particular interest involves 3 linked loci on chromosome IV where the central variant responsible for heat tolerance is antagonistic, coming from the heat sensitive parent and the flanking ones are from the more heat tolerant parent. The 3-locus haplotypes in the selected individuals represent a highly biased sample of the population haplotypes with rare double recombinants in high frequency. These were missed in the original analysis and would never be seen without the multigenerational approach. We show that a statistical analysis of entropy and information gain in genotypes of a selected population can reveal further interactions than previously seen. Importantly this must be done in comparison to the unselected population's genotypes to account for inherent biases in the original population. |
2208.02457 | Giorgio Kaniadakis | G. Kaniadakis | Novel predator-prey model admitting exact analytical solution | Typos corrected, 7 pages, 40 references, LATEX | Physical Review E 106, 044401 (2022) | 10.1103/PhysRevE.106.044401 | null | q-bio.PE cond-mat.stat-mech nlin.SI physics.bio-ph physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | The Lotka-Volterra predator-prey model still represents the paradigm for the
description of the competition in population dynamics. Despite its extreme
simplicity, it does not admit an analytical solution, and for this reason,
numerical integration methods are usually adopted to apply it to various fields
of science. The aim of the present work is to investigate the existence of new
predator-prey models sharing the broad features of the standard Lotka-Volterra
model and, at the same time, offer the advantage of possessing exact analytical
solutions. To this purpose, a general Hamiltonian formalism, which is suitable
for treating a large class of predator-prey models in population dynamics
within the same framework, has been developed as a first step. The only
existing model having the property of admitting a simple exact analytical
solution, is identified within the above class of models. The solution of this
special predator-prey model is obtained explicitly, in terms of known
elementary functions, and its main properties are studied. Finally, the
generalization of this model, based on the concept of power-law competition, as
well as its extension to the case of $N$-component competition systems, are
considered.
| [
{
"created": "Thu, 4 Aug 2022 04:59:31 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Oct 2022 19:22:03 GMT",
"version": "v2"
}
] | 2022-10-18 | [
[
"Kaniadakis",
"G.",
""
]
] | The Lotka-Volterra predator-prey model still represents the paradigm for the description of the competition in population dynamics. Despite its extreme simplicity, it does not admit an analytical solution, and for this reason, numerical integration methods are usually adopted to apply it to various fields of science. The aim of the present work is to investigate the existence of new predator-prey models sharing the broad features of the standard Lotka-Volterra model and, at the same time, offer the advantage of possessing exact analytical solutions. To this purpose, a general Hamiltonian formalism, which is suitable for treating a large class of predator-prey models in population dynamics within the same framework, has been developed as a first step. The only existing model having the property of admitting a simple exact analytical solution, is identified within the above class of models. The solution of this special predator-prey model is obtained explicitly, in terms of known elementary functions, and its main properties are studied. Finally, the generalization of this model, based on the concept of power-law competition, as well as its extension to the case of $N$-component competition systems, are considered. |
0802.3274 | Simone Pigolotti | Simone Pigolotti, Cristobal Lopez, Emilio Hernandez-Garcia, Ken Haste
Andersen | How Gaussian competition leads to lumpy or uniform species distributions | 11 pages, 3 figures, revised version | Theoretical Ecology: Volume 3, Issue 2 (2010), 89 | 10.1007/s12080-009-0056-2 | null | q-bio.PE nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central model in theoretical ecology considers the competition of a range
of species for a broad spectrum of resources. Recent studies have shown that
essentially two different outcomes are possible. Either the species surviving
competition are more or less uniformly distributed over the resource spectrum,
or their distribution is 'lumped' (or 'clumped'), consisting of clusters of
species with similar resource use that are separated by gaps in resource space.
Which of these outcomes will occur crucially depends on the competition kernel,
which reflects the shape of the resource utilization pattern of the competing
species. Most models considered in the literature assume a Gaussian competition
kernel. This is unfortunate, since predictions based on such a Gaussian
assumption are not robust. In fact, Gaussian kernels are a border case
scenario, and slight deviations from this function can lead to either uniform
or lumped species distributions. Here we illustrate the non-robustness of the
Gaussian assumption by simulating different implementations of the standard
competition model with constant carrying capacity. In this scenario, lumped
species distributions can come about by secondary ecological or evolutionary
mechanisms or by details of the numerical implementation of the model. We
analyze the origin of this sensitivity and discuss it in the context of recent
applications of the model.
| [
{
"created": "Fri, 22 Feb 2008 09:30:17 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Dec 2008 17:33:08 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Aug 2009 09:12:47 GMT",
"version": "v3"
}
] | 2010-04-05 | [
[
"Pigolotti",
"Simone",
""
],
[
"Lopez",
"Cristobal",
""
],
[
"Hernandez-Garcia",
"Emilio",
""
],
[
"Andersen",
"Ken Haste",
""
]
] | A central model in theoretical ecology considers the competition of a range of species for a broad spectrum of resources. Recent studies have shown that essentially two different outcomes are possible. Either the species surviving competition are more or less uniformly distributed over the resource spectrum, or their distribution is 'lumped' (or 'clumped'), consisting of clusters of species with similar resource use that are separated by gaps in resource space. Which of these outcomes will occur crucially depends on the competition kernel, which reflects the shape of the resource utilization pattern of the competing species. Most models considered in the literature assume a Gaussian competition kernel. This is unfortunate, since predictions based on such a Gaussian assumption are not robust. In fact, Gaussian kernels are a border case scenario, and slight deviations from this function can lead to either uniform or lumped species distributions. Here we illustrate the non-robustness of the Gaussian assumption by simulating different implementations of the standard competition model with constant carrying capacity. In this scenario, lumped species distributions can come about by secondary ecological or evolutionary mechanisms or by details of the numerical implementation of the model. We analyze the origin of this sensitivity and discuss it in the context of recent applications of the model. |
1802.03397 | Sagnik Bhattacharyya | Subhadip Paul, Satyam Mukherjee, Sagnik Bhattacharyya | Network organization of coopetitive genetic influences on cortical
morphologies | 32 pages, 3 tables, 2 figures; 1 supplementary method and 1
supplementary figure | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain can be represented as a network, where regions are the nodes and
relations between the regions are edges. Within a network, co-existence of
cooperative and competitive relationships between different nodes is called
coopetition. Inter-regional genetic influences on morphological phenotypes
(cortical thickness, surface area) of cortex display such coopetitive
relationships. Here, we have represented these genetic influences as a network
and shown that cooperative and competitive genetic influences on cortical
morphological phenotypes follow distinct organization principles. Utilizing the
theory of structural balance, we have shown that the pattern of collective
regulation of cortical morphological phenotypes by cooperative and competitive
genetic influences are overall bilaterally symmetric and such patterns of
collective genetic regulation are similar to the principal modes of population
variation of cortical morphological phenotypes. Finally, we have observed that
the maximally and minimally imbalanced regions corresponding to the collective
genetic regulation partially overlap with the cortical structural network hubs.
| [
{
"created": "Fri, 9 Feb 2018 17:38:57 GMT",
"version": "v1"
}
] | 2018-02-13 | [
[
"Paul",
"Subhadip",
""
],
[
"Mukherjee",
"Satyam",
""
],
[
"Bhattacharyya",
"Sagnik",
""
]
] | Brain can be represented as a network, where regions are the nodes and relations between the regions are edges. Within a network, co-existence of cooperative and competitive relationships between different nodes is called coopetition. Inter-regional genetic influences on morphological phenotypes (cortical thickness, surface area) of cortex display such coopetitive relationships. Here, we have represented these genetic influences as a network and shown that cooperative and competitive genetic influences on cortical morphological phenotypes follow distinct organization principles. Utilizing the theory of structural balance, we have shown that the pattern of collective regulation of cortical morphological phenotypes by cooperative and competitive genetic influences are overall bilaterally symmetric and such patterns of collective genetic regulation are similar to the principal modes of population variation of cortical morphological phenotypes. Finally, we have observed that the maximally and minimally imbalanced regions corresponding to the collective genetic regulation partially overlap with the cortical structural network hubs. |
2211.07024 | Ryuta Mizutani | Ryuta Mizutani, Rino Saiga, Yoshiro Yamamoto, Masayuki Uesugi, Akihisa
Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Vincent De Andrade,
Francesco De Carlo, Susumu Takekoshi, Chie Inomoto, Naoya Nakamura, Youta
Torii, Itaru Kushima, Shuji Iritani, Norio Ozaki, Kenichi Oshima, Masanari
Itokawa, and Makoto Arai | Structural aging of human neurons is the opposite of the changes in
schizophrenia | 24 pages, 5 figures. arXiv admin note: text overlap with
arXiv:2007.00212 | null | 10.1371/journal.pone.0287646 | null | q-bio.NC physics.bio-ph physics.med-ph q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human mentality develops with age and is altered in psychiatric disorders,
though their underlying mechanism is unknown. In this study, we analyzed
nanometer-scale three-dimensional structures of brain tissues of the anterior
cingulate cortex from eight schizophrenia and eight control cases. The
distribution profiles of neurite curvature of the control cases showed a trend
depending on their age, resulting in an age-correlated decrease in the standard
deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to
the control cases, the schizophrenia cases deviate upward from this
correlation, exhibiting a 60% higher neurite curvature compared with the
controls (p = 7.8 x 10^(-4)). The neurite curvature also showed a correlation
with a hallucination score (Pearson's r = 0.80, p = 1.8 x 10^(-4)), indicating
that neurite structure is relevant to brain function. We suggest that neurite
curvature plays a pivotal role in brain aging and can be used as a hallmark to
exploit a novel treatment of schizophrenia. This nano-CT paper is the result of
our decade-long analysis and is unprecedented in terms of number of cases.
| [
{
"created": "Sun, 13 Nov 2022 21:51:48 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Mizutani",
"Ryuta",
""
],
[
"Saiga",
"Rino",
""
],
[
"Yamamoto",
"Yoshiro",
""
],
[
"Uesugi",
"Masayuki",
""
],
[
"Takeuchi",
"Akihisa",
""
],
[
"Uesugi",
"Kentaro",
""
],
[
"Terada",
"Yasuko",
""
],
[
"Suzuki",
"Yoshio",
""
],
[
"De Andrade",
"Vincent",
""
],
[
"De Carlo",
"Francesco",
""
],
[
"Takekoshi",
"Susumu",
""
],
[
"Inomoto",
"Chie",
""
],
[
"Nakamura",
"Naoya",
""
],
[
"Torii",
"Youta",
""
],
[
"Kushima",
"Itaru",
""
],
[
"Iritani",
"Shuji",
""
],
[
"Ozaki",
"Norio",
""
],
[
"Oshima",
"Kenichi",
""
],
[
"Itokawa",
"Masanari",
""
],
[
"Arai",
"Makoto",
""
]
] | Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 x 10^(-4)). The neurite curvature also showed a correlation with a hallucination score (Pearson's r = 0.80, p = 1.8 x 10^(-4)), indicating that neurite structure is relevant to brain function. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia. This nano-CT paper is the result of our decade-long analysis and is unprecedented in terms of number of cases. |
1907.12309 | Sushrut Thorat | Sushrut Thorat, Giacomo Aldegheri, Marcel A. J. van Gerven, Marius V.
Peelen | Modulation of early visual processing alleviates capacity limits in
solving multiple tasks | Main paper - 4 pages, 2 figures; Appendix - 2 pages, 2 figures;
Published at the 2019 Conference on Cognitive Computational Neuroscience | null | 10.32470/CCN.2019.1229-0 | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by/4.0/ | In daily life situations, we have to perform multiple tasks given a visual
stimulus, which requires task-relevant information to be transmitted through
our visual system. When it is not possible to transmit all the possibly
relevant information to higher layers, due to a bottleneck, task-based
modulation of early visual processing might be necessary. In this work, we
report how the effectiveness of modulating the early processing stage of an
artificial neural network depends on the information bottleneck faced by the
network. The bottleneck is quantified by the number of tasks the network has to
perform and the neural capacity of the later stage of the network. The
effectiveness is gauged by the performance on multiple object detection tasks,
where the network is trained with a recent multi-task optimization scheme. By
associating neural modulations with task-based switching of the state of the
network and characterizing when such switching is helpful in early processing,
our results provide a functional perspective towards understanding why
task-based modulation of early neural processes might be observed in the
primate visual cortex
| [
{
"created": "Mon, 29 Jul 2019 09:56:40 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jul 2019 08:10:26 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Sep 2019 17:42:12 GMT",
"version": "v3"
}
] | 2019-09-24 | [
[
"Thorat",
"Sushrut",
""
],
[
"Aldegheri",
"Giacomo",
""
],
[
"van Gerven",
"Marcel A. J.",
""
],
[
"Peelen",
"Marius V.",
""
]
] | In daily life situations, we have to perform multiple tasks given a visual stimulus, which requires task-relevant information to be transmitted through our visual system. When it is not possible to transmit all the possibly relevant information to higher layers, due to a bottleneck, task-based modulation of early visual processing might be necessary. In this work, we report how the effectiveness of modulating the early processing stage of an artificial neural network depends on the information bottleneck faced by the network. The bottleneck is quantified by the number of tasks the network has to perform and the neural capacity of the later stage of the network. The effectiveness is gauged by the performance on multiple object detection tasks, where the network is trained with a recent multi-task optimization scheme. By associating neural modulations with task-based switching of the state of the network and characterizing when such switching is helpful in early processing, our results provide a functional perspective towards understanding why task-based modulation of early neural processes might be observed in the primate visual cortex |
2111.14501 | Yuki Koyanagi | J{\o}rgen Ellegaard Andersen, Hiroyuki Fuji and Yuki Koyanagi | Topology of protein metastructure and $\beta$-sheet topology | 21 pages, 20 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | We introduce a new, simplified model of proteins, which we call protein
metastructure. The metastructure of a protein carries information about its
secondary structure and $\beta$-strand conformations. Furthermore, protein
metastructure allows us to associate an object called a fatgraph to a protein,
and a fatgraph in turn gives rise to a topological surface. It becomes thus
possible to study the topological invariants associated to a protein. We
discuss the correspondence between protein metastructures and fatgraphs, and
how one can compute topological invariants, such as genus and the number of
boundary components, from fatgraphs. We then describe an algorithm for
generating likely candidate metastructures using the information obtained from
topology of protein fatgraphs. This algorithm is further developed to predict
$\beta$-sheet topology of proteins, with a possibility to combine it with an
existing algorithm. We demonstrate the algorithm on the data from PDB, and
improve the performance of and existing algorithm by combining with it.
| [
{
"created": "Mon, 29 Nov 2021 12:46:37 GMT",
"version": "v1"
}
] | 2021-11-30 | [
[
"Andersen",
"Jørgen Ellegaard",
""
],
[
"Fuji",
"Hiroyuki",
""
],
[
"Koyanagi",
"Yuki",
""
]
] | We introduce a new, simplified model of proteins, which we call protein metastructure. The metastructure of a protein carries information about its secondary structure and $\beta$-strand conformations. Furthermore, protein metastructure allows us to associate an object called a fatgraph to a protein, and a fatgraph in turn gives rise to a topological surface. It becomes thus possible to study the topological invariants associated to a protein. We discuss the correspondence between protein metastructures and fatgraphs, and how one can compute topological invariants, such as genus and the number of boundary components, from fatgraphs. We then describe an algorithm for generating likely candidate metastructures using the information obtained from topology of protein fatgraphs. This algorithm is further developed to predict $\beta$-sheet topology of proteins, with a possibility to combine it with an existing algorithm. We demonstrate the algorithm on the data from PDB, and improve the performance of and existing algorithm by combining with it. |
0811.1281 | Eytan Domany | Mark Koudritsky and Eytan Domany | Positional distribution of human transcription factor binding sites | 27 pages, 8 figures (already embedded in file) To appear in Nucleic
Acids Research | null | 10.1093/nar/gkn752 | null | q-bio.MN q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed a method for estimating the positional distribution of
transcription fac-tor (TF) binding sites using ChIP-chip data, and applied it
to recently published experiments on binding sites of nine TFs; OCT4, SOX2,
NANOG, HNF1A, HNF4A, HNF6, FOXA2, USF1 and CREB1. The data were obtained from a
genome-wide cov-erage of promoter regions from 8kb upstream of the
Transcription Start Site (TSS) to 2kb downstream. The number of target genes of
each TF ranges from few hundred to several thousand. We found that for each of
the nine TFs the estimated binding site distribution is closely approximated by
a mixture of two components: a narrow peak, localized within 300 base pairs
upstream of the TSS, and a distribution of almost uni-form density within the
tested region. Using Gene Ontology and Enrichment analysis, we were able to
associate (for each of the TFs studied) the target genes of both types of
binding with known biological processes. Most GO terms were enriched either
among the proximal targets or among those with a uniform distribution of
binding sites. For example, the three stemness-related TFs have several hundred
target genes that belong to "development" and "morphogenesis" whose binding
sites belong to the uniform dis-tribution.
| [
{
"created": "Sat, 8 Nov 2008 17:30:41 GMT",
"version": "v1"
}
] | 2008-11-11 | [
[
"Koudritsky",
"Mark",
""
],
[
"Domany",
"Eytan",
""
]
] | We developed a method for estimating the positional distribution of transcription fac-tor (TF) binding sites using ChIP-chip data, and applied it to recently published experiments on binding sites of nine TFs; OCT4, SOX2, NANOG, HNF1A, HNF4A, HNF6, FOXA2, USF1 and CREB1. The data were obtained from a genome-wide cov-erage of promoter regions from 8kb upstream of the Transcription Start Site (TSS) to 2kb downstream. The number of target genes of each TF ranges from few hundred to several thousand. We found that for each of the nine TFs the estimated binding site distribution is closely approximated by a mixture of two components: a narrow peak, localized within 300 base pairs upstream of the TSS, and a distribution of almost uni-form density within the tested region. Using Gene Ontology and Enrichment analysis, we were able to associate (for each of the TFs studied) the target genes of both types of binding with known biological processes. Most GO terms were enriched either among the proximal targets or among those with a uniform distribution of binding sites. For example, the three stemness-related TFs have several hundred target genes that belong to "development" and "morphogenesis" whose binding sites belong to the uniform dis-tribution. |
1912.00518 | Krzysztof Argasinski | Krzysztof Argasinski and Ryszard Rudnicki | Beyond classical Hamilton's Rule. State distribution asymmetry and the
dynamics of altruism | null | null | null | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes relationships between demographic and state-based
evolutionary game framework and inclusive fitness and Hamilton's rule. It is
shown that the classical Hamilton's rule (counterfactual method) combined with
demographic payoff functions, leads to easily testable models. It works well in
the case when the roles of donor and receiver are randomly drawn during each
interaction event. This is illustrated by the alarm call example. However, we
can imagine situations in which role-switching results from some external
mechanism. For example, fluxes of individuals between the border and the
interior of the habitat, when only border individuals may spot the threat and
warn their neighbors. To cover these cases, a new model is extended to the case
with explicit dynamics of the role distributions among carriers of different
strategies, driven by some general mechanisms. It is thereby shown that even in
the case when fluxes between roles are driven by neutral mechanisms acting in
the same way on all strategies, differences in mortality in the focal
interaction lead to different distributions of roles for different strategies.
This leads to a more complex rule for cooperation than the classical Hamilton's
rule. In addition to the classical cost and benefit, the new rule contains a
third component weighted by the difference in proportions of donors among
carriers of both strategies. Depending on the sign, this component can be
termed the survival surplus, or the sacrifice cost, when the receiver's
survival exceeds that of the donor. For the "surplus" case, cooperators can win
even in the case when the assortment mechanism is inefficient (i.e probability
of receiving help for noncooperators is slightly greater than for cooperators),
which is impossible in the classical Hamilton's rule.
| [
{
"created": "Sun, 1 Dec 2019 23:18:21 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Jun 2024 16:55:20 GMT",
"version": "v2"
}
] | 2024-06-27 | [
[
"Argasinski",
"Krzysztof",
""
],
[
"Rudnicki",
"Ryszard",
""
]
] | This paper analyzes relationships between demographic and state-based evolutionary game framework and inclusive fitness and Hamilton's rule. It is shown that the classical Hamilton's rule (counterfactual method) combined with demographic payoff functions, leads to easily testable models. It works well in the case when the roles of donor and receiver are randomly drawn during each interaction event. This is illustrated by the alarm call example. However, we can imagine situations in which role-switching results from some external mechanism. For example, fluxes of individuals between the border and the interior of the habitat, when only border individuals may spot the threat and warn their neighbors. To cover these cases, a new model is extended to the case with explicit dynamics of the role distributions among carriers of different strategies, driven by some general mechanisms. It is thereby shown that even in the case when fluxes between roles are driven by neutral mechanisms acting in the same way on all strategies, differences in mortality in the focal interaction lead to different distributions of roles for different strategies. This leads to a more complex rule for cooperation than the classical Hamilton's rule. In addition to the classical cost and benefit, the new rule contains a third component weighted by the difference in proportions of donors among carriers of both strategies. Depending on the sign, this component can be termed the survival surplus, or the sacrifice cost, when the receiver's survival exceeds that of the donor. For the "surplus" case, cooperators can win even in the case when the assortment mechanism is inefficient (i.e probability of receiving help for noncooperators is slightly greater than for cooperators), which is impossible in the classical Hamilton's rule. |
1105.4386 | Kazuhiro Takemoto | Kazuhiro Takemoto, Masanori Arita | Nested structure acquired through simple evolutionary process | 12 pages, 4 figures | J. Theor. Biol. 264, 782 (2010) | 10.1016/j.jtbi.2010.03.029 | null | q-bio.PE physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nested structure, which is non-random, controls cooperation dynamics and
biodiversity in plant-animal mutualistic networks. This structural pattern has
been explained in a static (non-growth) network models. However, evolutionary
processes might also influence the formation of such a structural pattern. We
thereby propose an evolving network model for plant-animal interactions and
show that non-random patterns such as nested structure and heterogeneous
connectivity are both qualitatively and quantitatively predicted through simple
evolutionary processes. This finding implies that network models can be
simplified by considering evolutionary processes, and also that another
explanation exists for the emergence of non-random patterns and might provide
more comprehensible insights into the formation of plant-animal mutualistic
networks from the evolutionary perspective.
| [
{
"created": "Mon, 23 May 2011 02:03:56 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Takemoto",
"Kazuhiro",
""
],
[
"Arita",
"Masanori",
""
]
] | Nested structure, which is non-random, controls cooperation dynamics and biodiversity in plant-animal mutualistic networks. This structural pattern has been explained in a static (non-growth) network models. However, evolutionary processes might also influence the formation of such a structural pattern. We thereby propose an evolving network model for plant-animal interactions and show that non-random patterns such as nested structure and heterogeneous connectivity are both qualitatively and quantitatively predicted through simple evolutionary processes. This finding implies that network models can be simplified by considering evolutionary processes, and also that another explanation exists for the emergence of non-random patterns and might provide more comprehensible insights into the formation of plant-animal mutualistic networks from the evolutionary perspective. |
0806.4154 | Le Zhang | L. Leon Chen, Le Zhang, Jeongah Yoon, and Thomas S. Deisboeck | Cancer Cell Motility: Optimizing Spatial Search Strategies | 31 pages, 8 figures | null | null | null | q-bio.CB q-bio.MN q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aberrantly regulated cell motility is a hallmark of cancer cells. A hybrid
agent-based model has been developed to investigate the synergistic and
antagonistic cell motility-impacting effects of three microenvironment
variables simultaneously: chemoattraction, haptotactic permission, and
biomechanical constraint or resistance. Reflecting distinct cell-specific
intracellular machinery, the cancer cells are modelled as processing a variety
of spatial search strategies that respond to these three influencing factors
with differential weights attached to each. While responding exclusively to
chemoattraction optimizes cell displacement effectiveness, incorporating
permission and resistance components becomes increasingly important with
greater distance to the chemoattractant source and/or after reducing the
ligand's effective diffusion coefficient. Extending this to a heterogeneous
population of cells shows that displacement effectiveness increases with clonal
diversity as characterized by the Shannon index. However, the resulting data
can be fit best to an exponential function, suggesting that there is a level of
population heterogeneity beyond which its added value to the cancer system
becomes minimal as directionality ceases to increase. Possible experimental
extensions and potential clinical implications are discussed.
| [
{
"created": "Wed, 25 Jun 2008 17:23:22 GMT",
"version": "v1"
}
] | 2008-06-26 | [
[
"Chen",
"L. Leon",
""
],
[
"Zhang",
"Le",
""
],
[
"Yoon",
"Jeongah",
""
],
[
"Deisboeck",
"Thomas S.",
""
]
] | Aberrantly regulated cell motility is a hallmark of cancer cells. A hybrid agent-based model has been developed to investigate the synergistic and antagonistic cell motility-impacting effects of three microenvironment variables simultaneously: chemoattraction, haptotactic permission, and biomechanical constraint or resistance. Reflecting distinct cell-specific intracellular machinery, the cancer cells are modelled as processing a variety of spatial search strategies that respond to these three influencing factors with differential weights attached to each. While responding exclusively to chemoattraction optimizes cell displacement effectiveness, incorporating permission and resistance components becomes increasingly important with greater distance to the chemoattractant source and/or after reducing the ligand's effective diffusion coefficient. Extending this to a heterogeneous population of cells shows that displacement effectiveness increases with clonal diversity as characterized by the Shannon index. However, the resulting data can be fit best to an exponential function, suggesting that there is a level of population heterogeneity beyond which its added value to the cancer system becomes minimal as directionality ceases to increase. Possible experimental extensions and potential clinical implications are discussed. |
2202.03096 | Eva Llabr\'es | Eva Llabr\'es, Elvira Mayol, N\'uria Marb\'a, Tom\'as Sintes | A mathematical model for inter-specific seagrass interactions:
reproducing field observations for C. nodosa and C. prolifera | version accepted in OIKOS | null | 10.1111/oik.09296 | null | q-bio.PE nlin.AO | http://creativecommons.org/publicdomain/zero/1.0/ | Seagrasses are vital organisms in coastal waters, and the drastic demise of
their population in the last decades has worrying implications for marine
ecosystems. Spatial models for seagrass meadows provide a mathematical
framework to study their dynamical processes and emergent collective behavior.
These models are crucial to predict the response of seagrasses to different
global warming scenarios, analyze the resilience of existing seagrass
distributions, and optimize restoration strategies. In this article, we propose
a model that includes interactions among different species based on the clonal
growth of seagrasses. We present a theoretical analysis of the model
considering the specific case of the seagrass-seaweed interaction between {\it
Cymodocea nodosa} and {\it Caulerpa prolifera}. Our simulations successfully
reproduce field observations of shoot densities in mixed meadows in the Ebro
River Delta in the Mediterranean Sea. Besides, the proposed model allows us to
investigate the possible underlying mechanisms that mediate the interaction
among the two macrophytes.
| [
{
"created": "Mon, 7 Feb 2022 12:14:29 GMT",
"version": "v1"
},
{
"created": "Sat, 13 May 2023 16:29:59 GMT",
"version": "v2"
}
] | 2023-05-16 | [
[
"Llabrés",
"Eva",
""
],
[
"Mayol",
"Elvira",
""
],
[
"Marbá",
"Núria",
""
],
[
"Sintes",
"Tomás",
""
]
] | Seagrasses are vital organisms in coastal waters, and the drastic demise of their population in the last decades has worrying implications for marine ecosystems. Spatial models for seagrass meadows provide a mathematical framework to study their dynamical processes and emergent collective behavior. These models are crucial to predict the response of seagrasses to different global warming scenarios, analyze the resilience of existing seagrass distributions, and optimize restoration strategies. In this article, we propose a model that includes interactions among different species based on the clonal growth of seagrasses. We present a theoretical analysis of the model considering the specific case of the seagrass-seaweed interaction between {\it Cymodocea nodosa} and {\it Caulerpa prolifera}. Our simulations successfully reproduce field observations of shoot densities in mixed meadows in the Ebro River Delta in the Mediterranean Sea. Besides, the proposed model allows us to investigate the possible underlying mechanisms that mediate the interaction among the two macrophytes. |
0909.0278 | Zhanghan Wu | Zhanghan Wu, Hong-Wei Wang, Weihua Mu, Zhongcan Ouyang, Eva Nogales,
Jianhua Xing | Simulations of tubulin sheet polymers as possible structural
intermediates in microtubule assembly | 43 pages, 13 figures. Submitted; PLoS ONE 2009 | PLoS ONE, 2009, 4(10): e7291. | 10.1371/journal.pone.0007291 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The microtubule assembly process has been extensively studied, but the
underlying molecular mechanism remains poorly understood. The structure of an
artificially generated sheet polymer that alternates two types of lateral
contacts and that directly converts into microtubules, has been proposed to
correspond to the intermediate sheet structure observed during microtubule
assembly. We have studied the self-assembly process of GMPCPP tubulins into
sheet and microtubule structures using thermodynamic analysis and stochastic
simulations. With the novel assumptions that tubulins can laterally interact in
two different forms, and allosterically affect neighboring lateral
interactions, we can explain existing experimental observations. At low
temperature, the allosteric effect results in the observed sheet structure with
alternating lateral interactions as the thermodynamically most stable form. At
normal microtubule assembly temperature, our work indicates that a class of
sheet structures resembling those observed at low temperature is transiently
trapped as an intermediate during the assembly process. This work may shed
light on the tubulin molecular interactions, and the role of sheet formation
during microtubule assembly.
| [
{
"created": "Tue, 1 Sep 2009 20:40:08 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Wu",
"Zhanghan",
""
],
[
"Wang",
"Hong-Wei",
""
],
[
"Mu",
"Weihua",
""
],
[
"Ouyang",
"Zhongcan",
""
],
[
"Nogales",
"Eva",
""
],
[
"Xing",
"Jianhua",
""
]
] | The microtubule assembly process has been extensively studied, but the underlying molecular mechanism remains poorly understood. The structure of an artificially generated sheet polymer that alternates two types of lateral contacts and that directly converts into microtubules, has been proposed to correspond to the intermediate sheet structure observed during microtubule assembly. We have studied the self-assembly process of GMPCPP tubulins into sheet and microtubule structures using thermodynamic analysis and stochastic simulations. With the novel assumptions that tubulins can laterally interact in two different forms, and allosterically affect neighboring lateral interactions, we can explain existing experimental observations. At low temperature, the allosteric effect results in the observed sheet structure with alternating lateral interactions as the thermodynamically most stable form. At normal microtubule assembly temperature, our work indicates that a class of sheet structures resembling those observed at low temperature is transiently trapped as an intermediate during the assembly process. This work may shed light on the tubulin molecular interactions, and the role of sheet formation during microtubule assembly. |
1106.2250 | Rhonda Dzakpasu | Mark Niedringhaus, Xin Chen, Katherine Conant, Rhonda Dzakpasu | Synaptic potentiation facilitates memory-like attractor dynamics in
cultured in vitro hippocampal networks | null | Niedringhaus M, Chen X, Conant K, Dzakpasu R (2013) Synaptic
Potentiation Facilitates Memory-like Attractor Dynamics in Cultured In Vitro
Hippocampal Networks. PLoS ONE 8(3): e57144 | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collective rhythmic dynamics from neurons is vital for cognitive functions
such as memory formation but how neurons self-organize to produce such activity
is not well understood. Attractor-based models have been successfully
implemented as a theoretical framework for memory storage in networks of
neurons. Activity-dependent modification of synaptic transmission is thought to
be the physiological basis of learning and memory. The goal of this study is to
demonstrate that using a pharmacological perturbation on in vitro networks of
hippocampal neurons that has been shown to increase synaptic strength follows
the dynamical postulates theorized by attractor models. We use a grid of
extracellular electrodes to study changes in network activity after this
perturbation and show that there is a persistent increase in overall spiking
and bursting activity after treatment. This increase in activity appears to
recruit more "errant" spikes into bursts. Lastly, phase plots indicate a
conserved activity pattern suggesting that the network is operating in a stable
dynamical state.
| [
{
"created": "Sat, 11 Jun 2011 15:49:44 GMT",
"version": "v1"
},
{
"created": "Tue, 10 Jan 2012 16:36:22 GMT",
"version": "v2"
}
] | 2013-03-22 | [
[
"Niedringhaus",
"Mark",
""
],
[
"Chen",
"Xin",
""
],
[
"Conant",
"Katherine",
""
],
[
"Dzakpasu",
"Rhonda",
""
]
] | Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological perturbation on in vitro networks of hippocampal neurons that has been shown to increase synaptic strength follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more "errant" spikes into bursts. Lastly, phase plots indicate a conserved activity pattern suggesting that the network is operating in a stable dynamical state. |
2206.04119 | Brian Trippe | Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara
Broderick, Regina Barzilay, Tommi Jaakkola | Diffusion probabilistic modeling of protein backbones in 3D for the
motif-scaffolding problem | Appearing in ICLR 2023. Code available:
github.com/blt2114/ProtDiff_SMCDiff | null | null | null | q-bio.BM cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Construction of a scaffold structure that supports a desired motif,
conferring protein function, shows promise for the design of vaccines and
enzymes. But a general solution to this motif-scaffolding problem remains open.
Current machine-learning techniques for scaffold design are either limited to
unrealistically small scaffolds (up to length 20) or struggle to produce
multiple diverse scaffolds. We propose to learn a distribution over diverse and
longer protein backbone structures via an E(3)-equivariant graph neural
network. We develop SMCDiff to efficiently sample scaffolds from this
distribution conditioned on a given motif; our algorithm is the first to
theoretically guarantee conditional samples from a diffusion model in the
large-compute limit. We evaluate our designed backbones by how well they align
with AlphaFold2-predicted structures. We show that our method can (1) sample
scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for
a fixed motif.
| [
{
"created": "Wed, 8 Jun 2022 18:35:08 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Mar 2023 00:22:03 GMT",
"version": "v2"
}
] | 2023-03-21 | [
[
"Trippe",
"Brian L.",
""
],
[
"Yim",
"Jason",
""
],
[
"Tischer",
"Doug",
""
],
[
"Baker",
"David",
""
],
[
"Broderick",
"Tamara",
""
],
[
"Barzilay",
"Regina",
""
],
[
"Jaakkola",
"Tommi",
""
]
] | Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to unrealistically small scaffolds (up to length 20) or struggle to produce multiple diverse scaffolds. We propose to learn a distribution over diverse and longer protein backbone structures via an E(3)-equivariant graph neural network. We develop SMCDiff to efficiently sample scaffolds from this distribution conditioned on a given motif; our algorithm is the first to theoretically guarantee conditional samples from a diffusion model in the large-compute limit. We evaluate our designed backbones by how well they align with AlphaFold2-predicted structures. We show that our method can (1) sample scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for a fixed motif. |
2007.14731 | Francesc Rossell\'o | Tom\'as M. Coronado, Arnau Mir, Francesc Rossell\'o | Squaring within the Colless index yields a better balance index | 31 pages | null | null | null | q-bio.PE cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Colless index for bifurcating phylogenetic trees, introduced by Colless
(1982), is defined as the sum, over all internal nodes $v$ of the tree, of the
absolute value of the difference of the sizes of the clades defined by the
children of $v$. It is one of the most popular phylogenetic balance indices,
because, in addition to measuring the balance of a tree in a very simple and
intuitive way, it turns out to be one of the most powerful and discriminating
phylogenetic shape indices. But it has some drawbacks. On the one hand,
although its minimum value is reached at the so-called maximally balanced
trees, it is almost always reached also at trees that are not maximally
balanced. On the other hand, its definition as a sum of absolute values of
differences makes it difficult to study analytically its distribution under
probabilistic models of bifurcating phylogenetic trees. In this paper we show
that if we replace in its definition the absolute values of the differences of
clade sizes by the squares of these differences, all these drawbacks are
overcome and the resulting index is still more powerful and discriminating than
the original Colless index.
| [
{
"created": "Wed, 29 Jul 2020 10:40:51 GMT",
"version": "v1"
}
] | 2020-07-30 | [
[
"Coronado",
"Tomás M.",
""
],
[
"Mir",
"Arnau",
""
],
[
"Rosselló",
"Francesc",
""
]
] | The Colless index for bifurcating phylogenetic trees, introduced by Colless (1982), is defined as the sum, over all internal nodes $v$ of the tree, of the absolute value of the difference of the sizes of the clades defined by the children of $v$. It is one of the most popular phylogenetic balance indices, because, in addition to measuring the balance of a tree in a very simple and intuitive way, it turns out to be one of the most powerful and discriminating phylogenetic shape indices. But it has some drawbacks. On the one hand, although its minimum value is reached at the so-called maximally balanced trees, it is almost always reached also at trees that are not maximally balanced. On the other hand, its definition as a sum of absolute values of differences makes it difficult to study analytically its distribution under probabilistic models of bifurcating phylogenetic trees. In this paper we show that if we replace in its definition the absolute values of the differences of clade sizes by the squares of these differences, all these drawbacks are overcome and the resulting index is still more powerful and discriminating than the original Colless index. |
0712.3786 | Michael Hagan | Michael F. Hagan | Controlling Viral Capsid Assembly with Templating | submitted to Phys. Rev. E | null | 10.1103/PhysRevE.77.051904 | null | q-bio.BM | null | We develop coarse-grained models that describe the dynamic encapsidation of
functionalized nanoparticles by viral capsid proteins. We find that some forms
of cooperative interactions between protein subunits and nanoparticles can
dramatically enhance rates and robustness of assembly, as compared to the
spontaneous assembly of subunits into empty capsids. For large core-subunit
interactions, subunits adsorb onto core surfaces en masse in a disordered
manner, and then undergo a cooperative rearrangement into an ordered capsid
structure. These assembly pathways are unlike any identified for empty capsid
formation. Our models can be directly applied to recent experiments in which
viral capsid proteins assemble around the functionalized inorganic
nanoparticles [Sun et al., Proc. Natl. Acad. Sci (2007) 104, 1354]. In
addition, we discuss broader implications for understanding the dynamic
encapsidation of single-stranded genomic molecules during viral replication and
for developing multicomponent nanostructured materials.
| [
{
"created": "Fri, 21 Dec 2007 19:56:41 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Hagan",
"Michael F.",
""
]
] | We develop coarse-grained models that describe the dynamic encapsidation of functionalized nanoparticles by viral capsid proteins. We find that some forms of cooperative interactions between protein subunits and nanoparticles can dramatically enhance rates and robustness of assembly, as compared to the spontaneous assembly of subunits into empty capsids. For large core-subunit interactions, subunits adsorb onto core surfaces en masse in a disordered manner, and then undergo a cooperative rearrangement into an ordered capsid structure. These assembly pathways are unlike any identified for empty capsid formation. Our models can be directly applied to recent experiments in which viral capsid proteins assemble around the functionalized inorganic nanoparticles [Sun et al., Proc. Natl. Acad. Sci (2007) 104, 1354]. In addition, we discuss broader implications for understanding the dynamic encapsidation of single-stranded genomic molecules during viral replication and for developing multicomponent nanostructured materials. |
0807.1954 | Ryota Kobayashi | Ryota Kobayashi | Influence of firing mechanisms on gain modulation | 7 pages, 4 figures, conference proceedings | null | 10.1088/1742-5468/2009/01/P01017 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We studied the impact of a dynamical threshold on the f-I curve-the
relationship between the input and the firing rate of a neuron-in the presence
of background synaptic inputs. First, we found that, while the leaky
integrate-and-fire model cannot reproduce the f-I curve of a cortical neuron,
the leaky integrate-and-fire model with dynamical threshold can reproduce it
very well. Second, we found that the dynamical threshold modulates the onset
and the asymptotic behavior of the f-I curve. These results suggest that a
cortical neuron has an adaptation mechanism and that the dynamical threshold
has some significance for the computational properties of a neuron.
| [
{
"created": "Sat, 12 Jul 2008 04:27:35 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Sep 2008 12:22:31 GMT",
"version": "v2"
},
{
"created": "Sun, 12 Oct 2008 04:59:52 GMT",
"version": "v3"
}
] | 2009-11-13 | [
[
"Kobayashi",
"Ryota",
""
]
] | We studied the impact of a dynamical threshold on the f-I curve-the relationship between the input and the firing rate of a neuron-in the presence of background synaptic inputs. First, we found that, while the leaky integrate-and-fire model cannot reproduce the f-I curve of a cortical neuron, the leaky integrate-and-fire model with dynamical threshold can reproduce it very well. Second, we found that the dynamical threshold modulates the onset and the asymptotic behavior of the f-I curve. These results suggest that a cortical neuron has an adaptation mechanism and that the dynamical threshold has some significance for the computational properties of a neuron. |
1804.01961 | Lucia Ballerini | Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez,
Francesca M. Chappell, Victor Gonz\'alez-Castro, Devasuda Anblagan, Samuel
Danso, Susana Mu\~noz Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom
MacGillivray, Emanuele Trucco, Joanna Wardlaw | Machine learning of neuroimaging to diagnose cognitive impairment and
dementia: a systematic review and comparative analysis | null | null | null | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | INTRODUCTION: Advanced machine learning methods might help to identify
dementia risk from neuroimaging, but their accuracy to date is unclear.
METHODS: We systematically reviewed the literature, 2006 to late 2016, for
machine learning studies differentiating healthy ageing through to dementia of
various types, assessing study quality, and comparing accuracy at different
disease boundaries.
RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs
healthy controls, used ADNI data, support vector machines and only T1-weighted
sequences. Accuracy was highest for differentiating AD from healthy controls,
and poor for differentiating healthy controls vs MCI vs AD, or MCI converters
vs non-converters. Accuracy increased using combined data types, but not by
data source, sample size or machine learning method.
DISCUSSION: Machine learning does not differentiate clinically-relevant
disease categories yet. More diverse datasets, combinations of different types
of data, and close clinical integration of machine learning would help to
advance the field.
| [
{
"created": "Thu, 5 Apr 2018 17:17:39 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Apr 2018 22:01:51 GMT",
"version": "v2"
}
] | 2018-04-13 | [
[
"Pellegrini",
"Enrico",
""
],
[
"Ballerini",
"Lucia",
""
],
[
"Hernandez",
"Maria del C. Valdes",
""
],
[
"Chappell",
"Francesca M.",
""
],
[
"González-Castro",
"Victor",
""
],
[
"Anblagan",
"Devasuda",
""
],
[
"Danso",
"Samuel",
""
],
[
"Maniega",
"Susana Muñoz",
""
],
[
"Job",
"Dominic",
""
],
[
"Pernet",
"Cyril",
""
],
[
"Mair",
"Grant",
""
],
[
"MacGillivray",
"Tom",
""
],
[
"Trucco",
"Emanuele",
""
],
[
"Wardlaw",
"Joanna",
""
]
] | INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs healthy controls, used ADNI data, support vector machines and only T1-weighted sequences. Accuracy was highest for differentiating AD from healthy controls, and poor for differentiating healthy controls vs MCI vs AD, or MCI converters vs non-converters. Accuracy increased using combined data types, but not by data source, sample size or machine learning method. DISCUSSION: Machine learning does not differentiate clinically-relevant disease categories yet. More diverse datasets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field. |
q-bio/0401021 | Arsen Grigoryan V. | V.F. Morozov, Shura Hayryan, E.Sh. Mamasakhlisov, A.V. Grigoryan, A.V.
Badasyan, Chin-Kun Hu | Helix-coil Transition in Closed Circular DNA | 16 pages, 4figures | null | 10.1016/j.physa.2004.09.037 | null | q-bio.BM | null | A simplified model for the closed circular DNA (ccDNA) is proposed to
describe some specific features of the helix-coil transition in such molecule.
The Hamiltonian of ccDNA is related to the one introduced earlier for the
linear DNA. The basic assumption is that the reduced energy of the hydrogen
bond is not constant through the transition process but depends effectively on
the fraction of already broken bonds. A transformation formula is obtained
which relates the temperature of ccDNA at a given degree of helicity during the
transition to the temperature of the corresponding linear chain at the same
degree of helicity. The formula provides a simple method to calculate the
melting curve for the ccDNA from the experimental melting curve of the linear
DNA with the same nucleotide sequence.
| [
{
"created": "Thu, 15 Jan 2004 13:28:03 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Morozov",
"V. F.",
""
],
[
"Hayryan",
"Shura",
""
],
[
"Mamasakhlisov",
"E. Sh.",
""
],
[
"Grigoryan",
"A. V.",
""
],
[
"Badasyan",
"A. V.",
""
],
[
"Hu",
"Chin-Kun",
""
]
] | A simplified model for the closed circular DNA (ccDNA) is proposed to describe some specific features of the helix-coil transition in such molecule. The Hamiltonian of ccDNA is related to the one introduced earlier for the linear DNA. The basic assumption is that the reduced energy of the hydrogen bond is not constant through the transition process but depends effectively on the fraction of already broken bonds. A transformation formula is obtained which relates the temperature of ccDNA at a given degree of helicity during the transition to the temperature of the corresponding linear chain at the same degree of helicity. The formula provides a simple method to calculate the melting curve for the ccDNA from the experimental melting curve of the linear DNA with the same nucleotide sequence. |
1609.03730 | Raunaq Pradhan Mr | Raunaq Pradhan, Yuanjin Zheng | A hierarchical neural stimulation model for pain relief by variation of
coil design parameters | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural stimulation represents a powerful technique for neural disorder
treatment. This paper deals with optimization of coil design parameters to be
used during stimulation for modulation of neuronal firing to achieve pain
relief. Pain mechanism is briefly introduced and a hierarchical stimulation
model from coil stimulation to neuronal firing is proposed. Electromagnetic
field distribution for circular, figure of 8 and Magnetic resonance coupling
figure of 8 coils are analyzed with respect to the variation of stimulation
parameters such as distance between coils, stimulation frequency, number of
turns and radius of coils. MRC figure of 8 coils were responsible for inducing
the maximum Electric field for same amount of driving current in coils.
Variation of membrane potential, ion channel conductance and neuronal firing
frequency in a pyramidal neuronal model due to magnetic and acoustic
stimulation are studied. The frequency of neuronal firing for cortical neurons
is higher during pain state, compared to no pain state. Lowest neuronal firing
frequency 18 Hz was found for MRC figure of 8 coils, compared to 30 Hz for
circular coils. Therefore, MRC figure of 8 coils are most effective for
modulation of neuronal firing, thereby achieving pain relief in comparison to
other coils considered in this study
| [
{
"created": "Tue, 13 Sep 2016 08:53:08 GMT",
"version": "v1"
}
] | 2016-09-14 | [
[
"Pradhan",
"Raunaq",
""
],
[
"Zheng",
"Yuanjin",
""
]
] | Neural stimulation represents a powerful technique for neural disorder treatment. This paper deals with optimization of coil design parameters to be used during stimulation for modulation of neuronal firing to achieve pain relief. Pain mechanism is briefly introduced and a hierarchical stimulation model from coil stimulation to neuronal firing is proposed. Electromagnetic field distribution for circular, figure of 8 and Magnetic resonance coupling figure of 8 coils are analyzed with respect to the variation of stimulation parameters such as distance between coils, stimulation frequency, number of turns and radius of coils. MRC figure of 8 coils were responsible for inducing the maximum Electric field for same amount of driving current in coils. Variation of membrane potential, ion channel conductance and neuronal firing frequency in a pyramidal neuronal model due to magnetic and acoustic stimulation are studied. The frequency of neuronal firing for cortical neurons is higher during pain state, compared to no pain state. Lowest neuronal firing frequency 18 Hz was found for MRC figure of 8 coils, compared to 30 Hz for circular coils. Therefore, MRC figure of 8 coils are most effective for modulation of neuronal firing, thereby achieving pain relief in comparison to other coils considered in this study |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.