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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2006.07209
|
Thomas Wieland
|
Thomas Wieland
|
Change points in the spread of COVID-19 question the effectiveness of
nonpharmaceutical interventions in Germany
|
Updated with German COVID-19 case data (RKI) from June 28, 2020
|
Saf. Sci. 131 (2020) 104924
|
10.1016/j.ssci.2020.104924
| null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aims: Nonpharmaceutical interventions against the spread of SARS-CoV-2 in
Germany included the cancellation of mass events (from March 8), closures of
schools and child day care facilities (from March 16) as well as a "lockdown"
(from March 23). This study attempts to assess the effectiveness of these
interventions in terms of revealing their impact on infections over time.
Methods: Dates of infections were estimated from official German case data by
incorporating the incubation period and an empirical reporting delay.
Exponential growth models for infections and reproduction numbers were
estimated and investigated with respect to change points in the time series.
Results: A significant decline of daily and cumulative infections as well as
reproduction numbers is found at March 8 (CI [7, 9]), March 10 (CI [9, 11] and
March 3 (CI [2, 4]), respectively. Further declines and stabilizations are
found in the end of March. There is also a change point in new infections at
April 19 (CI [18, 20]), but daily infections still show a negative growth. From
March 19 (CI [18, 20]), the reproduction numbers fluctuate on a level below
one. Conclusions: The decline of infections in early March 2020 can be
attributed to relatively small interventions and voluntary behavioural changes.
Additional effects of later interventions cannot be detected clearly.
Liberalizations of measures did not induce a re-increase of infections. Thus,
the effectiveness of most German interventions remains questionable. Moreover,
assessing of interventions is impeded by the estimation of true infection dates
and the influence of test volume.
|
[
{
"created": "Fri, 12 Jun 2020 14:13:32 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Jun 2020 21:52:35 GMT",
"version": "v2"
},
{
"created": "Mon, 6 Jul 2020 11:54:59 GMT",
"version": "v3"
}
] |
2020-08-27
|
[
[
"Wieland",
"Thomas",
""
]
] |
Aims: Nonpharmaceutical interventions against the spread of SARS-CoV-2 in Germany included the cancellation of mass events (from March 8), closures of schools and child day care facilities (from March 16) as well as a "lockdown" (from March 23). This study attempts to assess the effectiveness of these interventions in terms of revealing their impact on infections over time. Methods: Dates of infections were estimated from official German case data by incorporating the incubation period and an empirical reporting delay. Exponential growth models for infections and reproduction numbers were estimated and investigated with respect to change points in the time series. Results: A significant decline of daily and cumulative infections as well as reproduction numbers is found at March 8 (CI [7, 9]), March 10 (CI [9, 11] and March 3 (CI [2, 4]), respectively. Further declines and stabilizations are found in the end of March. There is also a change point in new infections at April 19 (CI [18, 20]), but daily infections still show a negative growth. From March 19 (CI [18, 20]), the reproduction numbers fluctuate on a level below one. Conclusions: The decline of infections in early March 2020 can be attributed to relatively small interventions and voluntary behavioural changes. Additional effects of later interventions cannot be detected clearly. Liberalizations of measures did not induce a re-increase of infections. Thus, the effectiveness of most German interventions remains questionable. Moreover, assessing of interventions is impeded by the estimation of true infection dates and the influence of test volume.
|
1902.09625
|
Pedro Constantino
|
Pedro H. Constantino and Yiannis N. Kaznessis
|
Moment Closure Stability Analysis of Stochastic Reaction Networks with
Oscillatory Dynamics
|
29 pages, 10 figures
| null | null | null |
q-bio.MN cond-mat.stat-mech
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Biochemical reactions with oscillatory behavior play an essential role in
synthetic biology at the microscopic scale. Although a robust stability theory
for deterministic chemical oscillators in the macroscopic limit exists, the
dynamical stability of stochastic oscillators is an object of ongoing research.
The Chemical Master Equation along with kinetic Monte Carlo simulations
constitute the most accurate approach to modeling microscopic systems. However,
because of the challenges of solving the fully probabilistic model, most
studies in stability analysis have focused on the description of externally
disturbed oscillators. Here we apply the Maximum Entropy Principle as closure
criterion for moment equations of oscillatory networks and perform the
stability analysis of the internally disturbed Brusselator network.
Particularly, we discuss the effects of kinetic and size parameters on the
dynamics of this stochastic oscillatory system with intrinsic noise. Our
numerical experiments reveal that changes in kinetic parameters lead to
phenomenological and dynamical Hopf bifurcations, while reduced system sizes in
the oscillatory region can reverse the stochastic Hopf dynamical bifurcations
at the ensemble level. This is a unique feature of the stochastic dynamics of
oscillatory systems, with an unknown parallel in the macroscopic limit.
|
[
{
"created": "Mon, 25 Feb 2019 21:25:37 GMT",
"version": "v1"
}
] |
2019-02-27
|
[
[
"Constantino",
"Pedro H.",
""
],
[
"Kaznessis",
"Yiannis N.",
""
]
] |
Biochemical reactions with oscillatory behavior play an essential role in synthetic biology at the microscopic scale. Although a robust stability theory for deterministic chemical oscillators in the macroscopic limit exists, the dynamical stability of stochastic oscillators is an object of ongoing research. The Chemical Master Equation along with kinetic Monte Carlo simulations constitute the most accurate approach to modeling microscopic systems. However, because of the challenges of solving the fully probabilistic model, most studies in stability analysis have focused on the description of externally disturbed oscillators. Here we apply the Maximum Entropy Principle as closure criterion for moment equations of oscillatory networks and perform the stability analysis of the internally disturbed Brusselator network. Particularly, we discuss the effects of kinetic and size parameters on the dynamics of this stochastic oscillatory system with intrinsic noise. Our numerical experiments reveal that changes in kinetic parameters lead to phenomenological and dynamical Hopf bifurcations, while reduced system sizes in the oscillatory region can reverse the stochastic Hopf dynamical bifurcations at the ensemble level. This is a unique feature of the stochastic dynamics of oscillatory systems, with an unknown parallel in the macroscopic limit.
|
2405.16524
|
Polina Lakrisenko
|
Polina Lakrisenko, Dilan Pathirana, Daniel Weindl, Jan Hasenauer
|
Exploration of methods for computing sensitivities in ODE models at
dynamic and steady states
| null | null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Estimating parameters of dynamic models from experimental data is a
challenging, and often computationally-demanding task. It requires a large
number of model simulations and objective function gradient computations, if
gradient-based optimization is used. The gradient depends on derivatives of the
state variables with respect to parameters, also called state sensitivities,
which are expensive to compute. In many cases, steady-state computation is a
part of model simulation, either due to steady-state data or an assumption that
the system is at steady state at the initial time point. Various methods are
available for steady-state and gradient computation. Yet, the most efficient
pair of methods (one for steady states, one for gradients) for a particular
model is often not clear. Moreover, depending on the model and the available
data, some methods may not be applicable or sufficiently robust. In order to
facilitate the selection of methods, we explore six method pairs for computing
the steady state and sensitivities at steady state using six real-world
problems. The method pairs involve numerical integration or Newton's method to
compute the steady-state, and -- for both forward and adjoint sensitivity
analysis -- numerical integration or a tailored method to compute the
sensitivities at steady-state. Our evaluation shows that the two method pairs
that combine numerical integration for the steady-state with a tailored method
for the sensitivities at steady-state were the most robust, and amongst the
most computationally-efficient. We also observed that while Newton's method for
steady-state computation yields a substantial speedup compared to numerical
integration, it may lead to a large number of simulation failures. Overall, our
study provides a concise overview across current methods for computing
sensitivities at steady state, guiding modelers to choose the right methods.
|
[
{
"created": "Sun, 26 May 2024 11:21:05 GMT",
"version": "v1"
}
] |
2024-05-28
|
[
[
"Lakrisenko",
"Polina",
""
],
[
"Pathirana",
"Dilan",
""
],
[
"Weindl",
"Daniel",
""
],
[
"Hasenauer",
"Jan",
""
]
] |
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. The gradient depends on derivatives of the state variables with respect to parameters, also called state sensitivities, which are expensive to compute. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point. Various methods are available for steady-state and gradient computation. Yet, the most efficient pair of methods (one for steady states, one for gradients) for a particular model is often not clear. Moreover, depending on the model and the available data, some methods may not be applicable or sufficiently robust. In order to facilitate the selection of methods, we explore six method pairs for computing the steady state and sensitivities at steady state using six real-world problems. The method pairs involve numerical integration or Newton's method to compute the steady-state, and -- for both forward and adjoint sensitivity analysis -- numerical integration or a tailored method to compute the sensitivities at steady-state. Our evaluation shows that the two method pairs that combine numerical integration for the steady-state with a tailored method for the sensitivities at steady-state were the most robust, and amongst the most computationally-efficient. We also observed that while Newton's method for steady-state computation yields a substantial speedup compared to numerical integration, it may lead to a large number of simulation failures. Overall, our study provides a concise overview across current methods for computing sensitivities at steady state, guiding modelers to choose the right methods.
|
2005.08443
|
Cameron Mura
|
Menuka Jaiswal, Saad Saleem, Yonghyeon Kweon, Eli J Draizen, Stella
Veretnik, Cameron Mura, Philip E. Bourne
|
Deep Learning of Protein Structural Classes: Any Evidence for an
'Urfold'?
|
6 pages, 3 figures, 1 table; IEEE SIEDS conference submission
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent computational advances in the accurate prediction of protein
three-dimensional (3D) structures from amino acid sequences now present a
unique opportunity to decipher the interrelationships between proteins. This
task entails--but is not equivalent to--a problem of 3D structure comparison
and classification. Historically, protein domain classification has been a
largely manual and subjective activity, relying upon various heuristics.
Databases such as CATH represent significant steps towards a more systematic
(and automatable) approach, yet there still remains much room for the
development of more scalable and quantitative classification methods, grounded
in machine learning. We suspect that re-examining these relationships via a
Deep Learning (DL) approach may entail a large-scale restructuring of
classification schemes, improved with respect to the interpretability of
distant relationships between proteins. Here, we describe our training of DL
models on protein domain structures (and their associated physicochemical
properties) in order to evaluate classification properties at CATH's
"homologous superfamily" (SF) level. To achieve this, we have devised and
applied an extension of image-classification methods and image segmentation
techniques, utilizing a convolutional autoencoder model architecture. Our DL
architecture allows models to learn structural features that, in a sense,
'define' different homologous SFs. We evaluate and quantify pairwise
'distances' between SFs by building one model per SF and comparing the loss
functions of the models. Hierarchical clustering on these distance matrices
provides a new view of protein interrelationships--a view that extends beyond
simple structural/geometric similarity, and towards the realm of
structure/function properties.
|
[
{
"created": "Mon, 18 May 2020 03:55:01 GMT",
"version": "v1"
}
] |
2020-05-19
|
[
[
"Jaiswal",
"Menuka",
""
],
[
"Saleem",
"Saad",
""
],
[
"Kweon",
"Yonghyeon",
""
],
[
"Draizen",
"Eli J",
""
],
[
"Veretnik",
"Stella",
""
],
[
"Mura",
"Cameron",
""
],
[
"Bourne",
"Philip E.",
""
]
] |
Recent computational advances in the accurate prediction of protein three-dimensional (3D) structures from amino acid sequences now present a unique opportunity to decipher the interrelationships between proteins. This task entails--but is not equivalent to--a problem of 3D structure comparison and classification. Historically, protein domain classification has been a largely manual and subjective activity, relying upon various heuristics. Databases such as CATH represent significant steps towards a more systematic (and automatable) approach, yet there still remains much room for the development of more scalable and quantitative classification methods, grounded in machine learning. We suspect that re-examining these relationships via a Deep Learning (DL) approach may entail a large-scale restructuring of classification schemes, improved with respect to the interpretability of distant relationships between proteins. Here, we describe our training of DL models on protein domain structures (and their associated physicochemical properties) in order to evaluate classification properties at CATH's "homologous superfamily" (SF) level. To achieve this, we have devised and applied an extension of image-classification methods and image segmentation techniques, utilizing a convolutional autoencoder model architecture. Our DL architecture allows models to learn structural features that, in a sense, 'define' different homologous SFs. We evaluate and quantify pairwise 'distances' between SFs by building one model per SF and comparing the loss functions of the models. Hierarchical clustering on these distance matrices provides a new view of protein interrelationships--a view that extends beyond simple structural/geometric similarity, and towards the realm of structure/function properties.
|
1504.06732
|
Paul Moore
|
P. J. Moore
|
A predictive coding account of OCD
|
arXiv admin note: substantial text overlap with arXiv:1503.00999
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a predictive coding account of obsessive-compulsive
disorder (OCD). We extend the predictive coding model to include the concept of
a 'formal narrative', or temporal sequence of cognitive states inferred from
sense data. We propose that human cognition uses a hierarchy of narratives to
predict changes in the natural and social environment. Each layer in the
hierarchy represents a distinct view of the world, but it also contributes to a
global unitary perspective. We suggest that the global perspective remains
intact in OCD but there is a dysfunction at a sub-linguistic level of
cognition. The consequent failure of recognition is experienced as the external
world being 'not just right', and its automatic correction is felt as
compulsion. A wide variety of symptoms and some neuropsychological findings are
thus explained by a single dysfunction. We conclude that the model provides a
deeper explanation for behavioural observations than current models, and that
it has potential for further development for application to neuropsychological
data.
|
[
{
"created": "Sat, 25 Apr 2015 14:40:07 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Jun 2015 14:15:16 GMT",
"version": "v2"
}
] |
2015-06-09
|
[
[
"Moore",
"P. J.",
""
]
] |
This paper presents a predictive coding account of obsessive-compulsive disorder (OCD). We extend the predictive coding model to include the concept of a 'formal narrative', or temporal sequence of cognitive states inferred from sense data. We propose that human cognition uses a hierarchy of narratives to predict changes in the natural and social environment. Each layer in the hierarchy represents a distinct view of the world, but it also contributes to a global unitary perspective. We suggest that the global perspective remains intact in OCD but there is a dysfunction at a sub-linguistic level of cognition. The consequent failure of recognition is experienced as the external world being 'not just right', and its automatic correction is felt as compulsion. A wide variety of symptoms and some neuropsychological findings are thus explained by a single dysfunction. We conclude that the model provides a deeper explanation for behavioural observations than current models, and that it has potential for further development for application to neuropsychological data.
|
2003.09861
|
Giulia Giordano
|
Giulia Giordano, Franco Blanchini, Raffaele Bruno, Patrizio Colaneri,
Alessandro Di Filippo, Angela Di Matteo, Marta Colaneri, and the COVID19
IRCCS San Matteo Pavia Task Force
|
A SIDARTHE Model of COVID-19 Epidemic in Italy
| null |
Nature Medicine, 26, pages 855-860 (2020)
|
10.1038/s41591-020-0883-7
| null |
q-bio.PE cs.SY eess.SY math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In late December 2019, a novel strand of Coronavirus (SARS-CoV-2) causing a
severe, potentially fatal respiratory syndrome (COVID-19) was identified in
Wuhan, Hubei Province, China and is causing outbreaks in multiple world
countries, soon becoming a pandemic. Italy has now become the most hit country
outside of Asia: on March 16, 2020, the Italian Civil Protection documented a
total of 27980 confirmed cases and 2158 deaths of people tested positive for
SARS-CoV-2. In the context of an emerging infectious disease outbreak, it is of
paramount importance to predict the trend of the epidemic in order to plan an
effective control strategy and to determine its impact. This paper proposes a
new epidemic model that discriminates between infected individuals depending on
whether they have been diagnosed and on the severity of their symptoms. The
distinction between diagnosed and non-diagnosed is important because
non-diagnosed individuals are more likely to spread the infection than
diagnosed ones, since the latter are typically isolated, and can explain
misperceptions of the case fatality rate and of the seriousness of the epidemic
phenomenon. Being able to predict the amount of patients that will develop
life-threatening symptoms is important since the disease frequently requires
hospitalisation (and even Intensive Care Unit admission) and challenges the
healthcare system capacity. We show how the basic reproduction number can be
redefined in the new framework, thus capturing the potential for epidemic
containment. Simulation results are compared with real data on the COVID-19
epidemic in Italy, to show the validity of the model and compare different
possible predicted scenarios depending on the adopted countermeasures.
|
[
{
"created": "Sun, 22 Mar 2020 11:17:18 GMT",
"version": "v1"
}
] |
2021-08-23
|
[
[
"Giordano",
"Giulia",
""
],
[
"Blanchini",
"Franco",
""
],
[
"Bruno",
"Raffaele",
""
],
[
"Colaneri",
"Patrizio",
""
],
[
"Di Filippo",
"Alessandro",
""
],
[
"Di Matteo",
"Angela",
""
],
[
"Colaneri",
"Marta",
""
],
[
"Force",
"the COVID19 IRCCS San Matteo Pavia Task",
""
]
] |
In late December 2019, a novel strand of Coronavirus (SARS-CoV-2) causing a severe, potentially fatal respiratory syndrome (COVID-19) was identified in Wuhan, Hubei Province, China and is causing outbreaks in multiple world countries, soon becoming a pandemic. Italy has now become the most hit country outside of Asia: on March 16, 2020, the Italian Civil Protection documented a total of 27980 confirmed cases and 2158 deaths of people tested positive for SARS-CoV-2. In the context of an emerging infectious disease outbreak, it is of paramount importance to predict the trend of the epidemic in order to plan an effective control strategy and to determine its impact. This paper proposes a new epidemic model that discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed is important because non-diagnosed individuals are more likely to spread the infection than diagnosed ones, since the latter are typically isolated, and can explain misperceptions of the case fatality rate and of the seriousness of the epidemic phenomenon. Being able to predict the amount of patients that will develop life-threatening symptoms is important since the disease frequently requires hospitalisation (and even Intensive Care Unit admission) and challenges the healthcare system capacity. We show how the basic reproduction number can be redefined in the new framework, thus capturing the potential for epidemic containment. Simulation results are compared with real data on the COVID-19 epidemic in Italy, to show the validity of the model and compare different possible predicted scenarios depending on the adopted countermeasures.
|
2406.03197
|
Filippo Agnesi
|
Filippo Agnesi, Lucia Carlucci, Gia Burjanadze, Fabio Bernini, Khatia
Gabisonia, John W Osborn, Silvestro Micera and Fabio A. Recchia
|
Complex hemodynamic responses to trans-vascular electrical stimulation
of the renal nerve in anesthetized pigs
|
5 pages, 2 tables, 5 figures
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The objective of this study was to characterize hemodynamic changes during
trans-vascular stimulation of the renal nerve and their dependence on
stimulation parameters. We employed a stimulation catheter inserted in the
right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic
and pulse blood pressure and heart rate were recorded during stimulations
delivered at different intravascular sites along the renal artery or while
varying stimulation parameters (amplitude, frequency, and pulse width). Blood
pressure changes during stimulation displayed a pattern more complex than
previously described in literature, with a series of negative and positive
peaks over the first two minutes, followed by a steady state elevation during
the remainder of the stimulation. Pulse pressure and heart rate only showed
transient responses, then they returned to baseline values despite constant
stimulation. The amplitude of the evoked hemodynamic response was roughly
linearly correlated with stimulation amplitude, frequency, and pulse width.
|
[
{
"created": "Wed, 29 May 2024 14:36:47 GMT",
"version": "v1"
}
] |
2024-06-06
|
[
[
"Agnesi",
"Filippo",
""
],
[
"Carlucci",
"Lucia",
""
],
[
"Burjanadze",
"Gia",
""
],
[
"Bernini",
"Fabio",
""
],
[
"Gabisonia",
"Khatia",
""
],
[
"Osborn",
"John W",
""
],
[
"Micera",
"Silvestro",
""
],
[
"Recchia",
"Fabio A.",
""
]
] |
The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width). Blood pressure changes during stimulation displayed a pattern more complex than previously described in literature, with a series of negative and positive peaks over the first two minutes, followed by a steady state elevation during the remainder of the stimulation. Pulse pressure and heart rate only showed transient responses, then they returned to baseline values despite constant stimulation. The amplitude of the evoked hemodynamic response was roughly linearly correlated with stimulation amplitude, frequency, and pulse width.
|
1406.5197
|
Danielle Bassett
|
Shi Gu, Fabio Pasqualetti, Matthew Cieslak, Scott T. Grafton, Danielle
S. Bassett
|
Controllability of Brain Networks
|
14 pages, 4 figures, supplementary materials
| null |
10.1038/ncomms9414
| null |
q-bio.NC cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cognitive function is driven by dynamic interactions between large-scale
neural circuits or networks, enabling behavior. Fundamental principles
constraining these dynamic network processes have remained elusive. Here we use
network control theory to offer a mechanistic explanation for how the brain
moves between cognitive states drawn from the network organization of white
matter microstructure. Our results suggest that densely connected areas,
particularly in the default mode system, facilitate the movement of the brain
to many easily-reachable states. Weakly connected areas, particularly in
cognitive control systems, facilitate the movement of the brain to
difficult-to-reach states. Areas located on the boundary between network
communities, particularly in attentional control systems, facilitate the
integration or segregation of diverse cognitive systems. Our results suggest
that structural network differences between the cognitive circuits dictate
their distinct roles in controlling dynamic trajectories of brain network
function.
|
[
{
"created": "Thu, 19 Jun 2014 20:05:48 GMT",
"version": "v1"
}
] |
2015-10-28
|
[
[
"Gu",
"Shi",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Cieslak",
"Matthew",
""
],
[
"Grafton",
"Scott T.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] |
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behavior. Fundamental principles constraining these dynamic network processes have remained elusive. Here we use network control theory to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily-reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between the cognitive circuits dictate their distinct roles in controlling dynamic trajectories of brain network function.
|
1010.4127
|
Pierre Sens
|
Pierre Sens and Matthew S. Turner
|
Microphase separation in nonequilibrium biomembranes
| null | null |
10.1103/PhysRevLett.106.238101
| null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Microphase separation of membrane components is thought to play an important
role in many physiological processes, from cell signaling to endocytosis and
cellular trafficking. Here, we study how variations in the membrane composition
can be driven by fluctuating forces. We show that the membrane steady state is
not only controlled by the strength of the forces and how they couple to the
membrane, but also by their dynamics: In a simple class of models this is
captured by a single a correlation time. We conclude that the coupling of
membrane composition to normal mechanical forces, such as might be exerted by
polymerizing cytoskeleton filaments, could play an important role in
controlling the steady state of a cell membrane that exhibits transient
microphase separation on lengthscales in the 10-100 nm regime.
|
[
{
"created": "Wed, 20 Oct 2010 08:40:50 GMT",
"version": "v1"
}
] |
2015-05-20
|
[
[
"Sens",
"Pierre",
""
],
[
"Turner",
"Matthew S.",
""
]
] |
Microphase separation of membrane components is thought to play an important role in many physiological processes, from cell signaling to endocytosis and cellular trafficking. Here, we study how variations in the membrane composition can be driven by fluctuating forces. We show that the membrane steady state is not only controlled by the strength of the forces and how they couple to the membrane, but also by their dynamics: In a simple class of models this is captured by a single a correlation time. We conclude that the coupling of membrane composition to normal mechanical forces, such as might be exerted by polymerizing cytoskeleton filaments, could play an important role in controlling the steady state of a cell membrane that exhibits transient microphase separation on lengthscales in the 10-100 nm regime.
|
0708.0186
|
Eduardo Candelario-Jalil
|
E. Candelario-Jalil, H. Slawik, I. Ridelis, A. Waschbisch, R.S.
Akundi, M. Hull, B.L. Fiebich
|
Regional distribution of the prostaglandin E2 receptor EP1 in the rat
brain: accumulation in Purkinje cells of the cerebellum
| null |
Journal of Molecular Neuroscience 27(3): 303-310 (2005)
| null | null |
q-bio.TO
| null |
Prostaglandin E2 (PGE2), is a major prostanoid produced by the activity of
cyclooxygenases (COX) in response to various physiological and pathological
stimuli. PGE2 exerts its effects by activating four specific E-type prostanoid
receptors (EP1, EP2, EP3, and EP4). In the present study, we analyzed the
expression of the PGE2 receptor EP1 (mRNA and protein) in different regions of
the adult rat brain (hippocampus, hypothalamus, striatum, prefrontal cerebral
cortex, parietal cortex, brain stem, and cerebellum) using reverse
transcription- polymerase chain reaction, Western blotting, and
immunohistochemical methods. On a regional basis, levels of EP1 mRNA were the
highest in parietal cortex and cerebellum. At the protein level, we found very
strong expression of EP1 in cerebellum, as revealed by Western blotting
experiments. Furthermore, the present study provides for the first time
evidence that the EP1 receptor is highly expressed in the cerebellum, where the
Purkinje cells displayed very high immunolabeling of their perikaryon and
dendrites, as observed in the immunohistochemical analysis. Results from the
present study indicate that the EP1 prostanoid receptor is expressed in
specific neuronal populations, which possibly determine the region-specific
response to PGE2.
|
[
{
"created": "Wed, 1 Aug 2007 16:02:56 GMT",
"version": "v1"
}
] |
2007-08-02
|
[
[
"Candelario-Jalil",
"E.",
""
],
[
"Slawik",
"H.",
""
],
[
"Ridelis",
"I.",
""
],
[
"Waschbisch",
"A.",
""
],
[
"Akundi",
"R. S.",
""
],
[
"Hull",
"M.",
""
],
[
"Fiebich",
"B. L.",
""
]
] |
Prostaglandin E2 (PGE2), is a major prostanoid produced by the activity of cyclooxygenases (COX) in response to various physiological and pathological stimuli. PGE2 exerts its effects by activating four specific E-type prostanoid receptors (EP1, EP2, EP3, and EP4). In the present study, we analyzed the expression of the PGE2 receptor EP1 (mRNA and protein) in different regions of the adult rat brain (hippocampus, hypothalamus, striatum, prefrontal cerebral cortex, parietal cortex, brain stem, and cerebellum) using reverse transcription- polymerase chain reaction, Western blotting, and immunohistochemical methods. On a regional basis, levels of EP1 mRNA were the highest in parietal cortex and cerebellum. At the protein level, we found very strong expression of EP1 in cerebellum, as revealed by Western blotting experiments. Furthermore, the present study provides for the first time evidence that the EP1 receptor is highly expressed in the cerebellum, where the Purkinje cells displayed very high immunolabeling of their perikaryon and dendrites, as observed in the immunohistochemical analysis. Results from the present study indicate that the EP1 prostanoid receptor is expressed in specific neuronal populations, which possibly determine the region-specific response to PGE2.
|
1211.6348
|
Fernando Fabian Montani
|
Fernando Montani, Elena Phoka, Mariela Portesi, Simon R. Schultz
|
Statistical modelling of higher-order correlations in pools of neural
activity
|
42 pages, 12 Figures; Submitted to Physica A
|
Physica A 392 (2013) 3066-3086
|
10.1016/j.physa.2013.03.012
| null |
q-bio.NC physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Simultaneous recordings from multiple neural units allow us to investigate
the activity of very large neural ensembles. To understand how large ensembles
of neurons process sensory information, it is necessary to develop suitable
statistical models to describe the response variability of the recorded spike
trains. Using the information geometry framework, it is possible to estimate
higher-order correlations by assigning one interaction parameter to each degree
of correlation, leading to a $(2^N-1)$-dimensional model for a population with
$N$ neurons. However, this model suffers greatly from a combinatorial
explosion, and the number of parameters to be estimated from the available
sample size constitutes the main intractability reason of this approach. To
quantify the extent of higher than pairwise spike correlations in pools of
multiunit activity, we use an information-geometric approach within the
framework of the extended central limit theorem considering all possible
contributions from high-order spike correlations. The identification of a
deformation parameter allows us to provide a statistical characterisation of
the amount of high-order correlations in the case of a very large neural
ensemble, significantly reducing the number of parameters, avoiding the
sampling problem, and inferring the underlying dynamical properties of the
network within pools of multiunit neural activity.
|
[
{
"created": "Tue, 27 Nov 2012 16:26:15 GMT",
"version": "v1"
}
] |
2013-05-30
|
[
[
"Montani",
"Fernando",
""
],
[
"Phoka",
"Elena",
""
],
[
"Portesi",
"Mariela",
""
],
[
"Schultz",
"Simon R.",
""
]
] |
Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a $(2^N-1)$-dimensional model for a population with $N$ neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from high-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of high-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.
|
1503.06726
|
Simon Tanaka Mr.
|
Simon Tanaka, David Sichau, Dagmar Iber
|
LBIBCell: A Cell-Based Simulation Environment for Morphogenetic Problems
|
15 pages, 23 pages with supplementary, 6 figures; Bioinformatics 2015
| null |
10.1093/bioinformatics/btv147
| null |
q-bio.QM q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The simulation of morphogenetic problems requires the simultaneous and
coupled simulation of signalling and tissue dynamics. A cellular resolution of
the tissue domain is important to adequately describe the impact of cell-based
events, such as cell division, cell-cell interactions, and spatially restricted
signalling events. A tightly coupled cell-based mechano-regulatory simulation
tool is therefore required.
We developed an open-source software framework for morphogenetic problems.
The environment offers core functionalities for the tissue and signalling
models. In addition, the software offers great flexibility to add custom
extensions and biologically motivated processes. Cells are represented as
highly resolved, massless elastic polygons; the viscous properties of the
tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used
to model the interaction between the viscous and elastic properties of the
cells, thus extending on the IBCell model. The fluid and signalling processes
are solved using the Lattice Boltzmann method. As application examples we
simulate signalling-dependent tissue dynamics.
|
[
{
"created": "Mon, 23 Mar 2015 16:59:14 GMT",
"version": "v1"
}
] |
2015-03-29
|
[
[
"Tanaka",
"Simon",
""
],
[
"Sichau",
"David",
""
],
[
"Iber",
"Dagmar",
""
]
] |
The simulation of morphogenetic problems requires the simultaneous and coupled simulation of signalling and tissue dynamics. A cellular resolution of the tissue domain is important to adequately describe the impact of cell-based events, such as cell division, cell-cell interactions, and spatially restricted signalling events. A tightly coupled cell-based mechano-regulatory simulation tool is therefore required. We developed an open-source software framework for morphogenetic problems. The environment offers core functionalities for the tissue and signalling models. In addition, the software offers great flexibility to add custom extensions and biologically motivated processes. Cells are represented as highly resolved, massless elastic polygons; the viscous properties of the tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used to model the interaction between the viscous and elastic properties of the cells, thus extending on the IBCell model. The fluid and signalling processes are solved using the Lattice Boltzmann method. As application examples we simulate signalling-dependent tissue dynamics.
|
q-bio/0401014
|
Mark Alber
|
Mark S. Alber, Yi Jiang and Maria A. Kiskowski
|
Lattice gas cellular automata model for rippling and aggregation in
myxobacteria
| null | null |
10.1016/j.physd.2003.11.012
| null |
q-bio.QM q-bio.OT
| null |
A lattice-gas cellular automaton (LGCA) model is used to simulate rippling
and aggregation in myxobacteria. An efficient way of representing cells of
different cell size, shape and orientation is presented that may be easily
extended to model later stages of fruiting body formation. This LGCA model is
designed to investigate whether a refractory period, a minimum response time, a
maximum oscillation period and non-linear dependence of reversals of cells on
C-factor are necessary assumptions for rippling. It is shown that a refractory
period of 2-3 minutes, a minimum response time of up to 1 minute and no maximum
oscillation period best reproduce rippling in the experiments of {\it
Myxoccoccus xanthus}. Non-linear dependence of reversals on C-factor is
critical at high cell density. Quantitative simulations demonstrate that the
increase in wavelength of ripples when a culture is diluted with non-signaling
cells can be explained entirely by the decreased density of C-signaling cells.
This result further supports the hypothesis that levels of C-signaling
quantitatively depend on and modulate cell density. Analysis of the
interpenetrating high density waves shows the presence of a phase shift
analogous to the phase shift of interpenetrating solitons. Finally, a model for
swarming, aggregation and early fruiting body formation is presented.
|
[
{
"created": "Fri, 9 Jan 2004 22:21:11 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"Alber",
"Mark S.",
""
],
[
"Jiang",
"Yi",
""
],
[
"Kiskowski",
"Maria A.",
""
]
] |
A lattice-gas cellular automaton (LGCA) model is used to simulate rippling and aggregation in myxobacteria. An efficient way of representing cells of different cell size, shape and orientation is presented that may be easily extended to model later stages of fruiting body formation. This LGCA model is designed to investigate whether a refractory period, a minimum response time, a maximum oscillation period and non-linear dependence of reversals of cells on C-factor are necessary assumptions for rippling. It is shown that a refractory period of 2-3 minutes, a minimum response time of up to 1 minute and no maximum oscillation period best reproduce rippling in the experiments of {\it Myxoccoccus xanthus}. Non-linear dependence of reversals on C-factor is critical at high cell density. Quantitative simulations demonstrate that the increase in wavelength of ripples when a culture is diluted with non-signaling cells can be explained entirely by the decreased density of C-signaling cells. This result further supports the hypothesis that levels of C-signaling quantitatively depend on and modulate cell density. Analysis of the interpenetrating high density waves shows the presence of a phase shift analogous to the phase shift of interpenetrating solitons. Finally, a model for swarming, aggregation and early fruiting body formation is presented.
|
1610.00121
|
Victor Solovyev
|
Victor Solovyev and Ramzan Umarov
|
Prediction of Prokaryotic and Eukaryotic Promoters Using Convolutional
Deep Learning Neural Networks
|
12 pages, 4 figures
| null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate computational identification of promoters remains a challenge as
these key DNA regulatory regions have variable structures composed of
functional motifs that provide gene specific initiation of transcription. In
this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence
characteristics of prokaryotic and eukaryotic promoters and build their
predictive models. We trained the same CNN architecture on promoters of four
very distant organisms: human, plant (Arabidopsis), and two bacteria
(Escherichia coli and Mycoplasma pneumonia). We found that CNN trained on
sigma70 subclass of Escherichia coli promoter gives an excellent classification
of promoters and non-promoter sequences (Sn=0.90, Sp=0.96, CC=0.84). The
Bacillus subtilis promoters identification CNN model achieves Sn=0.91, Sp=0.95,
and CC=0.86. For human and Arabidopsis promoters we employ CNNs for
identification of two well-known promoter classes (TATA and non-TATA
promoters). CNNs models nicely recognize these complex functional regions. For
human Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and
0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis
we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA)
promoters. Thus, the developed CNN models (implemented in CNNProm program)
demonstrated the ability of deep learning with grasping complex promoter
sequence characteristics and achieve significantly higher accuracy compared to
the previously developed promoter prediction programs. As the suggested
approach does not require knowledge of any specific promoter features, it can
be easily extended to identify promoters and other complex functional regions
in sequences of many other and especially newly sequenced genomes. The CNNProm
program is available to run at web server http://www.softberry.com.
|
[
{
"created": "Sat, 1 Oct 2016 11:24:47 GMT",
"version": "v1"
}
] |
2016-10-04
|
[
[
"Solovyev",
"Victor",
""
],
[
"Umarov",
"Ramzan",
""
]
] |
Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained the same CNN architecture on promoters of four very distant organisms: human, plant (Arabidopsis), and two bacteria (Escherichia coli and Mycoplasma pneumonia). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn=0.90, Sp=0.96, CC=0.84). The Bacillus subtilis promoters identification CNN model achieves Sn=0.91, Sp=0.95, and CC=0.86. For human and Arabidopsis promoters we employ CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNNs models nicely recognize these complex functional regions. For human Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models (implemented in CNNProm program) demonstrated the ability of deep learning with grasping complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.
|
1306.2167
|
Gergely J Sz\"oll\H{o}si
|
Gergely J. Sz\"oll\H{o}si and Wojciech Rosikiewicz and Bastien Boussau
and Eric Tannier and Vincent Daubin
|
Efficient Exploration of the Space of Reconciled Gene Trees
|
Manuscript accepted pending revision in Systematic Biology
| null | null | null |
q-bio.PE q-bio.BM q-bio.GN
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Gene trees record the combination of gene level events, such as duplication,
transfer and loss, and species level events, such as speciation and extinction.
Gene tree-species tree reconciliation methods model these processes by drawing
gene trees into the species tree using a series of gene and species level
events. The reconstruction of gene trees based on sequence alone almost always
involves choosing between statistically equivalent or weakly distinguishable
relationships that could be much better resolved based on a putative species
tree. To exploit this potential for accurate reconstruction of gene trees the
space of reconciled gene trees must be explored according to a joint model of
sequence evolution and gene tree-species tree reconciliation.
Here we present amalgamated likelihood estimation (ALE), a probabilistic
approach to exhaustively explore all reconciled gene trees that can be
amalgamated as a combination of clades observed in a sample of trees. We
implement ALE in the context of a reconciliation model, which allows for the
duplication, transfer and loss of genes. We use ALE to efficiently approximate
the sum of the joint likelihood over amalgamations and to find the reconciled
gene tree that maximizes the joint likelihood.
We demonstrate using simulations that gene trees reconstructed using the
joint likelihood are substantially more accurate than those reconstructed using
sequence alone. Using realistic topologies, branch lengths and alignment sizes,
we demonstrate that ALE produces more accurate gene trees even if the model of
sequence evolution is greatly simplified. Finally, examining 1099 gene families
from 36 cyanobacterial genomes we find that joint likelihood-based inference
results in a striking reduction in apparent phylogenetic discord, with 24%, 59%
and 46% percent reductions in the mean numbers of duplications, transfers and
losses.
|
[
{
"created": "Mon, 10 Jun 2013 11:33:28 GMT",
"version": "v1"
}
] |
2013-06-11
|
[
[
"Szöllősi",
"Gergely J.",
""
],
[
"Rosikiewicz",
"Wojciech",
""
],
[
"Boussau",
"Bastien",
""
],
[
"Tannier",
"Eric",
""
],
[
"Daubin",
"Vincent",
""
]
] |
Gene trees record the combination of gene level events, such as duplication, transfer and loss, and species level events, such as speciation and extinction. Gene tree-species tree reconciliation methods model these processes by drawing gene trees into the species tree using a series of gene and species level events. The reconstruction of gene trees based on sequence alone almost always involves choosing between statistically equivalent or weakly distinguishable relationships that could be much better resolved based on a putative species tree. To exploit this potential for accurate reconstruction of gene trees the space of reconciled gene trees must be explored according to a joint model of sequence evolution and gene tree-species tree reconciliation. Here we present amalgamated likelihood estimation (ALE), a probabilistic approach to exhaustively explore all reconciled gene trees that can be amalgamated as a combination of clades observed in a sample of trees. We implement ALE in the context of a reconciliation model, which allows for the duplication, transfer and loss of genes. We use ALE to efficiently approximate the sum of the joint likelihood over amalgamations and to find the reconciled gene tree that maximizes the joint likelihood. We demonstrate using simulations that gene trees reconstructed using the joint likelihood are substantially more accurate than those reconstructed using sequence alone. Using realistic topologies, branch lengths and alignment sizes, we demonstrate that ALE produces more accurate gene trees even if the model of sequence evolution is greatly simplified. Finally, examining 1099 gene families from 36 cyanobacterial genomes we find that joint likelihood-based inference results in a striking reduction in apparent phylogenetic discord, with 24%, 59% and 46% percent reductions in the mean numbers of duplications, transfers and losses.
|
1204.3123
|
Mustafa Barasa
|
Barasa Mustafa, Mwangi Irungu Michael, Mutiso Muli Joshua, Kagasi
Ambogo Esther, Ozwara Suba Hastings, Gicheru Muita Michael
|
Plasmodium knowlesi H strain pregnancy malaria immune responses in olive
baboons (Papio anubis)
|
Five pages, six figures;This study was funded by the research
capability strengthening WHO grant (Grant Number: A 50075) for malaria
research in Africa under the Multilateral Initiative on Malaria/Special
Programme for Research and Training in Tropical Diseases (WHO-MIM/TDR).The
Institute of Primate Research (IPR) in Nairobi (Kenya) provided the baboons
and laboratory facilities for the study
|
Int. J. Integ. Biol: Year 2010, Volume 10, Issue No. 1: 54 - 58
| null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Approximately 24 million pregnant women in Sub-Saharan Africa are at risk of
suffering from pregnancy malaria complications. Mechanisms responsible for
increased susceptibility to malaria in pregnant women are not fully understood.
Baboons are susceptible to Plasmodium knowlesi and their reproductive
physiology and host pathogen interactions are similar to those in humans,
making them attractive for development as a model for studying mechanisms
underlying pregnancy malaria. This study exploited the susceptibility of
baboons to Plasmodium knowlesi infection to characterize cytokine and
peripheral blood mononuclear cell recall proliferation responses underlying the
pathogenesis of pregnancy malaria in baboons infected with Plasmodium knowlesi.
The pregnancies of three time mated adult female baboons and their gestational
levels were confirmed by ultrasonography. On the 150th day of gestation, the
pregnant baboons together with four non pregnant controls were infected with
Plasmodium knowlesi H strain parasites. Collection of peripheral sera, and
mononuclear cells was then done on a weekly basis. Sera cytokine concentrations
were measured by Enzyme Linked Immunosorbent Assay (ELISA) using respective
enzyme conjugated antibodies. Peripheral blood mononuclear cell recall
proliferation assays were also done on a weekly basis. Results indicate that
pregnancy malaria in this model is associated with suppression of interferon
gamma and interleukin 6 (IL-6) responses. Tumour necrosis factor alpha
responses were upregulated while IL-4, IL-12 and recall proliferation responses
were not different from controls. These data to a great extent are consistent
with some findings from human studies, showing the feasibility of this model
for studying mechanisms underlying pregnancy malaria.
|
[
{
"created": "Sat, 14 Apr 2012 00:11:19 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Apr 2012 08:01:13 GMT",
"version": "v2"
}
] |
2012-04-18
|
[
[
"Mustafa",
"Barasa",
""
],
[
"Michael",
"Mwangi Irungu",
""
],
[
"Joshua",
"Mutiso Muli",
""
],
[
"Esther",
"Kagasi Ambogo",
""
],
[
"Hastings",
"Ozwara Suba",
""
],
[
"Michael",
"Gicheru Muita",
""
]
] |
Approximately 24 million pregnant women in Sub-Saharan Africa are at risk of suffering from pregnancy malaria complications. Mechanisms responsible for increased susceptibility to malaria in pregnant women are not fully understood. Baboons are susceptible to Plasmodium knowlesi and their reproductive physiology and host pathogen interactions are similar to those in humans, making them attractive for development as a model for studying mechanisms underlying pregnancy malaria. This study exploited the susceptibility of baboons to Plasmodium knowlesi infection to characterize cytokine and peripheral blood mononuclear cell recall proliferation responses underlying the pathogenesis of pregnancy malaria in baboons infected with Plasmodium knowlesi. The pregnancies of three time mated adult female baboons and their gestational levels were confirmed by ultrasonography. On the 150th day of gestation, the pregnant baboons together with four non pregnant controls were infected with Plasmodium knowlesi H strain parasites. Collection of peripheral sera, and mononuclear cells was then done on a weekly basis. Sera cytokine concentrations were measured by Enzyme Linked Immunosorbent Assay (ELISA) using respective enzyme conjugated antibodies. Peripheral blood mononuclear cell recall proliferation assays were also done on a weekly basis. Results indicate that pregnancy malaria in this model is associated with suppression of interferon gamma and interleukin 6 (IL-6) responses. Tumour necrosis factor alpha responses were upregulated while IL-4, IL-12 and recall proliferation responses were not different from controls. These data to a great extent are consistent with some findings from human studies, showing the feasibility of this model for studying mechanisms underlying pregnancy malaria.
|
2006.02459
|
Slobodan Vucetic
|
Benjamin Seibold, Zivjena Vucetic, Slobodan Vucetic
|
Quantitative Relationship between Population Mobility and COVID-19
Growth Rate based on 14 Countries
| null | null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This study develops a framework for quantification of the impact of changes
in population mobility due to social distancing on the COVID-19 infection
growth rate. Using the Susceptible-Infected-Recovered (SIR) epidemiological
model we establish that under some mild assumptions the growth rate of COVID-19
deaths is a time-delayed approximation of the growth rate of COVID-19
infections. We then hypothesize that the growth rate of COVID-19 infections is
a function of population mobility, which leads to a statistical model that
predicts the growth rate of COVID-19 deaths as a delayed function of population
mobility. The parameters of the statistical model directly reveal the growth
rate of infections, the mobility-dependent transmission rate, the
mobility-independent recovery rate, and the critical mobility, below which
COVID-19 growth rate becomes negative. We fitted the proposed statistical model
on publicly available data from 14 countries where daily death counts exceeded
100 for more than 3 days as of May 6th, 2020. The publicly available Google
Mobility Index (GMI) was used as a measure of population mobility at the
country level. Our results show that the growth rate of COVID-19 deaths can be
accurately estimated 20 days ahead as a quadratic function of the transit
category of GMI (adjusted R-squared = 0.784). The estimated 95% confidence
interval for the critical mobility is in the range between 36.1% and 47.6% of
the pre-COVID-19 mobility. This result indicates that a significant reduction
in population mobility is needed to reverse the growth of COVID-19 epidemic.
Moreover, the quantitative relationship established herein suggests that a
readily available, population-level metric such as GMI can be a useful
indicator of the course of COVID-19 epidemic.
|
[
{
"created": "Wed, 3 Jun 2020 18:10:38 GMT",
"version": "v1"
}
] |
2020-06-05
|
[
[
"Seibold",
"Benjamin",
""
],
[
"Vucetic",
"Zivjena",
""
],
[
"Vucetic",
"Slobodan",
""
]
] |
This study develops a framework for quantification of the impact of changes in population mobility due to social distancing on the COVID-19 infection growth rate. Using the Susceptible-Infected-Recovered (SIR) epidemiological model we establish that under some mild assumptions the growth rate of COVID-19 deaths is a time-delayed approximation of the growth rate of COVID-19 infections. We then hypothesize that the growth rate of COVID-19 infections is a function of population mobility, which leads to a statistical model that predicts the growth rate of COVID-19 deaths as a delayed function of population mobility. The parameters of the statistical model directly reveal the growth rate of infections, the mobility-dependent transmission rate, the mobility-independent recovery rate, and the critical mobility, below which COVID-19 growth rate becomes negative. We fitted the proposed statistical model on publicly available data from 14 countries where daily death counts exceeded 100 for more than 3 days as of May 6th, 2020. The publicly available Google Mobility Index (GMI) was used as a measure of population mobility at the country level. Our results show that the growth rate of COVID-19 deaths can be accurately estimated 20 days ahead as a quadratic function of the transit category of GMI (adjusted R-squared = 0.784). The estimated 95% confidence interval for the critical mobility is in the range between 36.1% and 47.6% of the pre-COVID-19 mobility. This result indicates that a significant reduction in population mobility is needed to reverse the growth of COVID-19 epidemic. Moreover, the quantitative relationship established herein suggests that a readily available, population-level metric such as GMI can be a useful indicator of the course of COVID-19 epidemic.
|
2109.12799
|
Cameron Zachreson
|
Cameron Zachreson, Freya M. Shearer, David J. Price, Michael J.
Lydeamore, Jodie McVernon, James McCaw, and Nicholas Geard
|
COVID-19 in low-tolerance border quarantine systems: impact of the Delta
variant of SARS-CoV-2
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In controlling transmission of COVID-19, the effectiveness of border
quarantine strategies is a key concern for jurisdictions in which the local
prevalence of disease and immunity is low. In settings like this such as China,
Australia, and New Zealand, rare outbreak events can lead to escalating
epidemics and trigger the imposition of large scale lockdown policies. Here, we
examine to what degree vaccination status of incoming arrivals and the
quarantine workforce can allow relaxation of quarantine requirements. To do so,
we develop and apply a detailed model of COVID-19 disease progression and
transmission taking into account nuanced timing factors. Key among these are
disease incubation periods and the progression of infection detectability
during incubation. Using the disease characteristics associated with the
ancestral lineage of SARS-CoV-2 to benchmark the level of acceptable risk, we
examine the performance of the border quarantine system for vaccinated
arrivals. We examine disease transmission and vaccine efficacy parameters over
a wide range, covering plausible values for the Delta variant currently
circulating globally. Our results indicate a threshold in outbreak potential as
a function of vaccine efficacy, with the time until an outbreak increasing by
up to two orders of magnitude as vaccine efficacy against transmission
increases from 70% to 90%. For parameters corresponding to the Delta variant,
vaccination is able to maintain the capacity of quarantine systems to reduce
case importation and outbreak risk, by counteracting the pathogen's increased
infectiousness. To prevent outbreaks, heightened vaccination in border
quarantine systems must be combined with mass vaccination. The ultimate success
of these programs will depend sensitively on the efficacy of vaccines against
viral transmission.
|
[
{
"created": "Mon, 27 Sep 2021 05:09:32 GMT",
"version": "v1"
}
] |
2021-09-28
|
[
[
"Zachreson",
"Cameron",
""
],
[
"Shearer",
"Freya M.",
""
],
[
"Price",
"David J.",
""
],
[
"Lydeamore",
"Michael J.",
""
],
[
"McVernon",
"Jodie",
""
],
[
"McCaw",
"James",
""
],
[
"Geard",
"Nicholas",
""
]
] |
In controlling transmission of COVID-19, the effectiveness of border quarantine strategies is a key concern for jurisdictions in which the local prevalence of disease and immunity is low. In settings like this such as China, Australia, and New Zealand, rare outbreak events can lead to escalating epidemics and trigger the imposition of large scale lockdown policies. Here, we examine to what degree vaccination status of incoming arrivals and the quarantine workforce can allow relaxation of quarantine requirements. To do so, we develop and apply a detailed model of COVID-19 disease progression and transmission taking into account nuanced timing factors. Key among these are disease incubation periods and the progression of infection detectability during incubation. Using the disease characteristics associated with the ancestral lineage of SARS-CoV-2 to benchmark the level of acceptable risk, we examine the performance of the border quarantine system for vaccinated arrivals. We examine disease transmission and vaccine efficacy parameters over a wide range, covering plausible values for the Delta variant currently circulating globally. Our results indicate a threshold in outbreak potential as a function of vaccine efficacy, with the time until an outbreak increasing by up to two orders of magnitude as vaccine efficacy against transmission increases from 70% to 90%. For parameters corresponding to the Delta variant, vaccination is able to maintain the capacity of quarantine systems to reduce case importation and outbreak risk, by counteracting the pathogen's increased infectiousness. To prevent outbreaks, heightened vaccination in border quarantine systems must be combined with mass vaccination. The ultimate success of these programs will depend sensitively on the efficacy of vaccines against viral transmission.
|
1302.1944
|
Anil Korkut
|
Anil Korkut, Wayne A Hendrickson
|
Stereochemistry of Polypeptide Conformation in Coarse Grained Analysis
|
12 pages, 3 figures, Postprint of book chapter submitted to the
Biomolecular Forms and Functions, M. Bansal and N. Srinivasan, Eds. copyright
(2013) [copyright World Scientific Publishing Company]
|
Biomolecular Forms and Functions, M. Bansal and N. Srinivasan,
Eds. IISc Press - World Scientific Publishing, Singapore, pp. 136-147 (2013)
| null | null |
q-bio.BM q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The conformations available to polypeptides are determined by the interatomic
forces acting on the peptide units, whereby backbone torsion angles are
restricted as described by the Ramachandran plot. Although typical proteins are
composed predominantly from {\alpha}-helices and {\beta}-sheets, they
nevertheless adopt diverse tertiary structure, each folded as dictated by its
unique amino-acid sequence. Despite such uniqueness, however, the functioning
of many proteins involves changes between quite different conformations. The
study of large-scale conformational changes, particularly in large systems, is
facilitated by a coarse-grained representation such as provided by virtually
bonded C{\alpha} atoms. We have developed a virtual atom molecular mechanics
(VAMM) force field to describe conformational dynamics in proteins and a
VAMM-based algorithm for computing conformational transition pathways. Here we
describe the stereochemical analysis of proteins in this coarse-grained
representation, comparing the relevant plots in coarse-grained conformational
space to the corresponding Ramachandran plots, having contoured each at levels
determined statistically from residues in a large database. The distributions
shown for an all-{\alpha} protein, two all-{\beta} proteins and one
{\alpha}+{\beta} protein serve to relate the coarse-grained distributions to
the familiar Ramachandran plot.
|
[
{
"created": "Fri, 8 Feb 2013 04:59:36 GMT",
"version": "v1"
}
] |
2013-02-11
|
[
[
"Korkut",
"Anil",
""
],
[
"Hendrickson",
"Wayne A",
""
]
] |
The conformations available to polypeptides are determined by the interatomic forces acting on the peptide units, whereby backbone torsion angles are restricted as described by the Ramachandran plot. Although typical proteins are composed predominantly from {\alpha}-helices and {\beta}-sheets, they nevertheless adopt diverse tertiary structure, each folded as dictated by its unique amino-acid sequence. Despite such uniqueness, however, the functioning of many proteins involves changes between quite different conformations. The study of large-scale conformational changes, particularly in large systems, is facilitated by a coarse-grained representation such as provided by virtually bonded C{\alpha} atoms. We have developed a virtual atom molecular mechanics (VAMM) force field to describe conformational dynamics in proteins and a VAMM-based algorithm for computing conformational transition pathways. Here we describe the stereochemical analysis of proteins in this coarse-grained representation, comparing the relevant plots in coarse-grained conformational space to the corresponding Ramachandran plots, having contoured each at levels determined statistically from residues in a large database. The distributions shown for an all-{\alpha} protein, two all-{\beta} proteins and one {\alpha}+{\beta} protein serve to relate the coarse-grained distributions to the familiar Ramachandran plot.
|
1810.11195
|
Tianming Wang
|
Hailong Dou, Haitao Yang, James L.D. Smith, Limin Feng, Tianming Wang,
Jianping Ge
|
Prey selection of Amur tigers in relation to the spatiotemporal overlap
with prey across the Sino-Russian border
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The endangered Amur tiger is confined primarily to a narrow area along the
border with Russia in Northeast China. Little is known about the foraging
strategies of this small subpopulation in Hunchun Nature Reserve on the Chinese
side of the border; at this location, the prey base and land use patterns are
distinctly different from those in the larger population of the Sikhote-Alin
Mountains of Russia. Using dietary analysis of scats and camera-trapping data
from Hunchun Nature Reserve, we assessed spatiotemporal overlap of tigers and
their prey and identified prey selection patterns to enhance understanding of
the ecological requirements of tigers in Northeast China. Results indicated
that wild prey constituted 94.9% of the total biomass consumed by tigers;
domestic livestock represented 5.1% of the diet. Two species, wild boar and
sika deer , collectively represented 83% of the biomass consumed by tigers.
Despite lower spatial overlap of tigers and wild boar compared to tigers and
sika deer, tigers preferentially preyed on boar, likely facilitated by high
temporal overlap in activity patterns. Tigers exhibit significant spatial
overlap with sika deer, likely favoring a high level of tiger predation on this
large-sized ungulate. However, tigers did not preferred roe deer (Capreolus
pygargus) and showed a low spatial overlap with roe deer. Overall, our results
suggest that tiger prey selection is determined by prey body size and also
overlap in tiger and prey use of time or space. Also, we suggest that
strategies designed to minimize livestock forays into forested lands may be
important for decreasing the livestock depredation by tigers. This study offers
a framework to simultaneously integrate food habit analysis with the
distribution of predators and prey through time and space to provide a
comprehensive understanding of foraging strategies of large carnivores.
|
[
{
"created": "Fri, 26 Oct 2018 05:56:36 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2019 09:33:09 GMT",
"version": "v2"
}
] |
2019-03-29
|
[
[
"Dou",
"Hailong",
""
],
[
"Yang",
"Haitao",
""
],
[
"Smith",
"James L. D.",
""
],
[
"Feng",
"Limin",
""
],
[
"Wang",
"Tianming",
""
],
[
"Ge",
"Jianping",
""
]
] |
The endangered Amur tiger is confined primarily to a narrow area along the border with Russia in Northeast China. Little is known about the foraging strategies of this small subpopulation in Hunchun Nature Reserve on the Chinese side of the border; at this location, the prey base and land use patterns are distinctly different from those in the larger population of the Sikhote-Alin Mountains of Russia. Using dietary analysis of scats and camera-trapping data from Hunchun Nature Reserve, we assessed spatiotemporal overlap of tigers and their prey and identified prey selection patterns to enhance understanding of the ecological requirements of tigers in Northeast China. Results indicated that wild prey constituted 94.9% of the total biomass consumed by tigers; domestic livestock represented 5.1% of the diet. Two species, wild boar and sika deer , collectively represented 83% of the biomass consumed by tigers. Despite lower spatial overlap of tigers and wild boar compared to tigers and sika deer, tigers preferentially preyed on boar, likely facilitated by high temporal overlap in activity patterns. Tigers exhibit significant spatial overlap with sika deer, likely favoring a high level of tiger predation on this large-sized ungulate. However, tigers did not preferred roe deer (Capreolus pygargus) and showed a low spatial overlap with roe deer. Overall, our results suggest that tiger prey selection is determined by prey body size and also overlap in tiger and prey use of time or space. Also, we suggest that strategies designed to minimize livestock forays into forested lands may be important for decreasing the livestock depredation by tigers. This study offers a framework to simultaneously integrate food habit analysis with the distribution of predators and prey through time and space to provide a comprehensive understanding of foraging strategies of large carnivores.
|
2203.09798
|
Francisco Rowe Dr
|
Niall Newsham, Francisco Rowe
|
Understanding the Trajectories of Population Decline Across Rural and
Urban Europe: A Sequence Analysis
|
24 pages, 5 tables, 5 figures
| null | null | null |
q-bio.PE stat.AP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Population decline is projected to become widespread in Europe, with the
continental population set to reverse its longstanding trajectory of growth
within the next five years. This represents unfamiliar demographic territory.
Despite this, literature on decline remains sparse and our understanding
porous. Particular epistemological deficiencies stem from a lack of both
cross-national and temporal analyses of population decline. This study seeks to
address these gapsthrough the novel application of sequence and cluster
analysis techniques to examine variations in population decline trajectories
since 2000 in 696 sub-national areas across 33 European territories. The
methodology allows for a holistic understanding of decline trajectories
capturing differences in the ordering, timing, magnitude and spatial structure
of population decline. We identify a typology of population decline
distinguishing seven distinct pathways to depopulation and chart their
geographies. Results revealed differentiated pathways of depopulation in
continental sub-regions, with consistent and rapid declines in the east,
persistent but moderate declines in central Europe, accelerating declines in
the south and decelerating population declines in the west. Results also
revealed differentiated patterns of depopulation across the rural-urban
continuum, with urban and populous areas experiencing deceleration in
population decline, while population decline accelerates or stabilises in rural
areas. Small and mid-sized areas displayed heterogeneous depopulation
trajectories, highlighting the importance of local contextual factors in
influencing trajectories of population decline.
|
[
{
"created": "Fri, 18 Mar 2022 08:29:57 GMT",
"version": "v1"
},
{
"created": "Tue, 10 May 2022 13:47:38 GMT",
"version": "v2"
}
] |
2022-05-11
|
[
[
"Newsham",
"Niall",
""
],
[
"Rowe",
"Francisco",
""
]
] |
Population decline is projected to become widespread in Europe, with the continental population set to reverse its longstanding trajectory of growth within the next five years. This represents unfamiliar demographic territory. Despite this, literature on decline remains sparse and our understanding porous. Particular epistemological deficiencies stem from a lack of both cross-national and temporal analyses of population decline. This study seeks to address these gapsthrough the novel application of sequence and cluster analysis techniques to examine variations in population decline trajectories since 2000 in 696 sub-national areas across 33 European territories. The methodology allows for a holistic understanding of decline trajectories capturing differences in the ordering, timing, magnitude and spatial structure of population decline. We identify a typology of population decline distinguishing seven distinct pathways to depopulation and chart their geographies. Results revealed differentiated pathways of depopulation in continental sub-regions, with consistent and rapid declines in the east, persistent but moderate declines in central Europe, accelerating declines in the south and decelerating population declines in the west. Results also revealed differentiated patterns of depopulation across the rural-urban continuum, with urban and populous areas experiencing deceleration in population decline, while population decline accelerates or stabilises in rural areas. Small and mid-sized areas displayed heterogeneous depopulation trajectories, highlighting the importance of local contextual factors in influencing trajectories of population decline.
|
1212.2617
|
Peter Richtarik
|
William Hulme, Peter Richt\'arik, Lynne McGuire and Alison Green
|
Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on
support vector machine classification of RT-QuIC data
|
32 pages, 12 figures, 1 table
| null | null | null |
q-bio.QM cs.LG stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work we study numerical construction of optimal clinical diagnostic
tests for detecting sporadic Creutzfeldt-Jakob disease (sCJD). A cerebrospinal
fluid sample (CSF) from a suspected sCJD patient is subjected to a process
which initiates the aggregation of a protein present only in cases of sCJD.
This aggregation is indirectly observed in real-time at regular intervals, so
that a longitudinal set of data is constructed that is then analysed for
evidence of this aggregation. The best existing test is based solely on the
final value of this set of data, which is compared against a threshold to
conclude whether or not aggregation, and thus sCJD, is present. This test
criterion was decided upon by analysing data from a total of 108 sCJD and
non-sCJD samples, but this was done subjectively and there is no supporting
mathematical analysis declaring this criterion to be exploiting the available
data optimally. This paper addresses this deficiency, seeking to validate or
improve the test primarily via support vector machine (SVM) classification.
Besides this, we address a number of additional issues such as i) early
stopping of the measurement process, ii) the possibility of detecting the
particular type of sCJD and iii) the incorporation of additional patient data
such as age, sex, disease duration and timing of CSF sampling into the
construction of the test.
|
[
{
"created": "Tue, 11 Dec 2012 20:33:16 GMT",
"version": "v1"
}
] |
2012-12-12
|
[
[
"Hulme",
"William",
""
],
[
"Richtárik",
"Peter",
""
],
[
"McGuire",
"Lynne",
""
],
[
"Green",
"Alison",
""
]
] |
In this work we study numerical construction of optimal clinical diagnostic tests for detecting sporadic Creutzfeldt-Jakob disease (sCJD). A cerebrospinal fluid sample (CSF) from a suspected sCJD patient is subjected to a process which initiates the aggregation of a protein present only in cases of sCJD. This aggregation is indirectly observed in real-time at regular intervals, so that a longitudinal set of data is constructed that is then analysed for evidence of this aggregation. The best existing test is based solely on the final value of this set of data, which is compared against a threshold to conclude whether or not aggregation, and thus sCJD, is present. This test criterion was decided upon by analysing data from a total of 108 sCJD and non-sCJD samples, but this was done subjectively and there is no supporting mathematical analysis declaring this criterion to be exploiting the available data optimally. This paper addresses this deficiency, seeking to validate or improve the test primarily via support vector machine (SVM) classification. Besides this, we address a number of additional issues such as i) early stopping of the measurement process, ii) the possibility of detecting the particular type of sCJD and iii) the incorporation of additional patient data such as age, sex, disease duration and timing of CSF sampling into the construction of the test.
|
q-bio/0703038
|
Zhong Li
|
Zhong Li, Aris Floratos, David Wang, Andrea Califano
|
A Pattern Discovery-Based Method for Detecting Multi-Locus Genetic
Association
|
49 pages, 4 tables, 4 figures
| null | null | null |
q-bio.GN q-bio.PE
| null |
Methods to effectively detect multi-locus genetic association are becoming
increasingly relevant in the genetic dissection of complex trait in humans.
Current approaches typically consider a limited number of hypotheses, most of
which are related to the effect of a single locus or of a relatively small
number of neighboring loci on a chromosomal region. We have developed a novel
method that is specifically designed to detect genetic association involving
multiple disease-susceptibility loci, possibly on different chromosomes. Our
approach relies on the efficient discovery of patterns comprising spatially
unrestricted polymorphic markers and on the use of appropriate test statistics
to evaluate pattern-trait association. Power calculations using multi-locus
disease models demonstrate significant gain of power by using this method in
detecting multi-locus genetic association when compared to a standard single
marker analysis method. When analyzing a Schizophrenia dataset, we confirmed a
previously identified gene-gene interaction. In addition, a less conspicuous
association involving different markers on the same two genes was also
identified, implicating genetic heterogeneity.
|
[
{
"created": "Fri, 16 Mar 2007 18:08:01 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Li",
"Zhong",
""
],
[
"Floratos",
"Aris",
""
],
[
"Wang",
"David",
""
],
[
"Califano",
"Andrea",
""
]
] |
Methods to effectively detect multi-locus genetic association are becoming increasingly relevant in the genetic dissection of complex trait in humans. Current approaches typically consider a limited number of hypotheses, most of which are related to the effect of a single locus or of a relatively small number of neighboring loci on a chromosomal region. We have developed a novel method that is specifically designed to detect genetic association involving multiple disease-susceptibility loci, possibly on different chromosomes. Our approach relies on the efficient discovery of patterns comprising spatially unrestricted polymorphic markers and on the use of appropriate test statistics to evaluate pattern-trait association. Power calculations using multi-locus disease models demonstrate significant gain of power by using this method in detecting multi-locus genetic association when compared to a standard single marker analysis method. When analyzing a Schizophrenia dataset, we confirmed a previously identified gene-gene interaction. In addition, a less conspicuous association involving different markers on the same two genes was also identified, implicating genetic heterogeneity.
|
1808.07154
|
Ralf Schwamborn
|
R. Schwamborn, T. K. Mildenberger, M. H. Taylor
|
Assessing sources of uncertainty in length-based estimates of body
growth in populations of fishes and macroinvertebrates with bootstrapped
ELEFAN
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The determination of rates of body growth is the first step in many aquatic
population studies and fisheries stock assessments. ELEFAN (Electronic LEngth
Frequency ANalysis) is a widely used method to fit a growth curve to
length-frequency distribution (LFD) data. However, up to now, it was not
possible to assess its accuracy or the uncertainty inherent of this method, or
to obtain confidence intervals for growth parameters within an unconstrained
search space. In this study, experiments were conducted to assess the precision
and accuracy of bootstrapped and single-fit ELEFAN-based curve fitting methods,
using synthetic LFDs with known input parameters and a real data set of Abra
alba shell lengths. The comparison of several types of bootstrap experiments
and their outputs (95% confidence intervals and confidence contour plots)
provided a first glimpse into the accuracy of modern ELEFAN-based fit methods.
The main components of uncertainty (precision and reproducibility of fit
algorithms, seed effects, sample size and matrix information content) could be
assessed from partial bootstraps. Uncertainty was mainly determined by LFD
matrix size, total number of non-zero bins and the sampling of large-sized
individuals. A new pseudo-Rsquared index for the goodness-of-fit of VBGF models
to LFD data is proposed. For a large, perfect synthetic data set,
pseudo-RsquaredPhi was very high (88 to 100%), indicating an excellent fit of
the VBGF model. The small Abra alba data set showed a low pseudo-RsquaredPhi,
from to 54% to 68%, indicating the need for more samples and a larger LFD data
matrix. New, robust, bootstrap-based methods for curve fitting are presented
and discussed. This study demonstrates a promising new path for length-based
analyses of growth and mortality in natural populations, which are the basis
for a new suite of methods that are included in the new fishboot package.
|
[
{
"created": "Tue, 21 Aug 2018 22:35:04 GMT",
"version": "v1"
}
] |
2018-08-23
|
[
[
"Schwamborn",
"R.",
""
],
[
"Mildenberger",
"T. K.",
""
],
[
"Taylor",
"M. H.",
""
]
] |
The determination of rates of body growth is the first step in many aquatic population studies and fisheries stock assessments. ELEFAN (Electronic LEngth Frequency ANalysis) is a widely used method to fit a growth curve to length-frequency distribution (LFD) data. However, up to now, it was not possible to assess its accuracy or the uncertainty inherent of this method, or to obtain confidence intervals for growth parameters within an unconstrained search space. In this study, experiments were conducted to assess the precision and accuracy of bootstrapped and single-fit ELEFAN-based curve fitting methods, using synthetic LFDs with known input parameters and a real data set of Abra alba shell lengths. The comparison of several types of bootstrap experiments and their outputs (95% confidence intervals and confidence contour plots) provided a first glimpse into the accuracy of modern ELEFAN-based fit methods. The main components of uncertainty (precision and reproducibility of fit algorithms, seed effects, sample size and matrix information content) could be assessed from partial bootstraps. Uncertainty was mainly determined by LFD matrix size, total number of non-zero bins and the sampling of large-sized individuals. A new pseudo-Rsquared index for the goodness-of-fit of VBGF models to LFD data is proposed. For a large, perfect synthetic data set, pseudo-RsquaredPhi was very high (88 to 100%), indicating an excellent fit of the VBGF model. The small Abra alba data set showed a low pseudo-RsquaredPhi, from to 54% to 68%, indicating the need for more samples and a larger LFD data matrix. New, robust, bootstrap-based methods for curve fitting are presented and discussed. This study demonstrates a promising new path for length-based analyses of growth and mortality in natural populations, which are the basis for a new suite of methods that are included in the new fishboot package.
|
q-bio/0606005
|
Yoram Burak
|
Yoram Burak, Ted Brookings, and Ila Fiete
|
Triangular lattice neurons may implement an advanced numeral system to
precisely encode rat position over large ranges
|
4 pages with one figure, and 2 pages of supplementary information
| null | null | null |
q-bio.NC
| null |
We argue by observation of the neural data that neurons in area dMEC of rats,
which fire whenever the rat is on any vertex of a regular triangular lattice
that tiles 2-d space, may be using an advanced numeral system to reversibly
encode rat position. We interpret measured dMEC properties within the framework
of a residue number system (RNS), and describe how RNS encoding -- which breaks
the non-periodic variable of rat position into a set of narrowly distributed
periodic variables -- allows a small set of cells to compactly represent and
efficiently update rat position with high resolution over a large range. We
show that the uniquely useful properties of RNS encoding still hold when the
encoded and encoding quantities are relaxed to be real numbers with built-in
uncertainties, and provide a numerical and functional estimate of the range and
resolution of rat positions that can be uniquely encoded in dMEC. The use of a
compact, `arithmetic-friendly' numeral system to encode a metric variable, as
we propose is happening in dMEC, is qualitatively different from all previously
identified examples of coding in the brain. We discuss the numerous
neurobiological implications and predictions of our hypothesis.
|
[
{
"created": "Sun, 4 Jun 2006 06:15:39 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Burak",
"Yoram",
""
],
[
"Brookings",
"Ted",
""
],
[
"Fiete",
"Ila",
""
]
] |
We argue by observation of the neural data that neurons in area dMEC of rats, which fire whenever the rat is on any vertex of a regular triangular lattice that tiles 2-d space, may be using an advanced numeral system to reversibly encode rat position. We interpret measured dMEC properties within the framework of a residue number system (RNS), and describe how RNS encoding -- which breaks the non-periodic variable of rat position into a set of narrowly distributed periodic variables -- allows a small set of cells to compactly represent and efficiently update rat position with high resolution over a large range. We show that the uniquely useful properties of RNS encoding still hold when the encoded and encoding quantities are relaxed to be real numbers with built-in uncertainties, and provide a numerical and functional estimate of the range and resolution of rat positions that can be uniquely encoded in dMEC. The use of a compact, `arithmetic-friendly' numeral system to encode a metric variable, as we propose is happening in dMEC, is qualitatively different from all previously identified examples of coding in the brain. We discuss the numerous neurobiological implications and predictions of our hypothesis.
|
1203.5914
|
Martin Burger
|
Michael Moeller and Martin Burger and Peter Dieterich and Albrecht
Schwab
|
A Framework for Automated Cell Tracking in Phase Contrast Microscopic
Videos based on Normal Velocities
| null | null | null | null |
q-bio.QM cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces a novel framework for the automated tracking of cells,
with a particular focus on the challenging situation of phase contrast
microscopic videos. Our framework is based on a topology preserving variational
segmentation approach applied to normal velocity components obtained from
optical flow computations, which appears to yield robust tracking and automated
extraction of cell trajectories. In order to obtain improved trackings of local
shape features we discuss an additional correction step based on active
contours and the image Laplacian which we optimize for an example class of
transformed renal epithelial (MDCK-F) cells. We also test the framework for
human melanoma cells and murine neutrophil granulocytes that were seeded on
different types of extracellular matrices. The results are validated with
manual tracking results.
|
[
{
"created": "Tue, 27 Mar 2012 10:05:19 GMT",
"version": "v1"
}
] |
2012-03-28
|
[
[
"Moeller",
"Michael",
""
],
[
"Burger",
"Martin",
""
],
[
"Dieterich",
"Peter",
""
],
[
"Schwab",
"Albrecht",
""
]
] |
This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example class of transformed renal epithelial (MDCK-F) cells. We also test the framework for human melanoma cells and murine neutrophil granulocytes that were seeded on different types of extracellular matrices. The results are validated with manual tracking results.
|
1408.2298
|
Atsushi Kamimura
|
Atsushi Kamimura and Kunihiko Kaneko
|
Transition to diversification by competition for resources in catalytic
reaction networks
|
18 pages, 13 figure, submitted for publication
| null | null | null |
q-bio.CB q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
All life, including cells and artificial protocells, must integrate diverse
molecules into a single unit in order to reproduce. Despite expected pressure
to evolve a simple system with the fastest replication speed, the mechanism by
which the use of the great variety of components, and the coexistence of
diverse cell-types with different compositions are achieved is as yet unknown.
Here we show that coexistence of such diverse compositions and cell-types is
the result of competitions for a variety of limited resources. We find that a
transition to diversity occurs both in chemical compositions and in protocell
types, as the resource supply is decreased, when the maximum inflow and
consumption of resources are balanced.
|
[
{
"created": "Mon, 11 Aug 2014 03:24:41 GMT",
"version": "v1"
}
] |
2014-08-12
|
[
[
"Kamimura",
"Atsushi",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] |
All life, including cells and artificial protocells, must integrate diverse molecules into a single unit in order to reproduce. Despite expected pressure to evolve a simple system with the fastest replication speed, the mechanism by which the use of the great variety of components, and the coexistence of diverse cell-types with different compositions are achieved is as yet unknown. Here we show that coexistence of such diverse compositions and cell-types is the result of competitions for a variety of limited resources. We find that a transition to diversity occurs both in chemical compositions and in protocell types, as the resource supply is decreased, when the maximum inflow and consumption of resources are balanced.
|
2003.12017
|
Ganesh Kumar M
|
Ganesh Kumar M, Soman K.P, Gopalakrishnan E.A, Vijay Krishna Menon,
Sowmya V
|
Prediction of number of cases expected and estimation of the final size
of coronavirus epidemic in India using the logistic model and genetic
algorithm
| null | null | null | null |
q-bio.PE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we have applied the logistic growth regression model and
genetic algorithm to predict the number of coronavirus infected cases that can
be expected in upcoming days in India and also estimated the final size and its
peak time of the coronavirus epidemic in India.
|
[
{
"created": "Thu, 26 Mar 2020 16:32:54 GMT",
"version": "v1"
}
] |
2020-03-27
|
[
[
"M",
"Ganesh Kumar",
""
],
[
"P",
"Soman K.",
""
],
[
"A",
"Gopalakrishnan E.",
""
],
[
"Menon",
"Vijay Krishna",
""
],
[
"V",
"Sowmya",
""
]
] |
In this paper, we have applied the logistic growth regression model and genetic algorithm to predict the number of coronavirus infected cases that can be expected in upcoming days in India and also estimated the final size and its peak time of the coronavirus epidemic in India.
|
1805.09603
|
Chen Beer
|
Chen Beer, Omri Barak
|
One step back, two steps forward: interference and learning in recurrent
neural networks
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Artificial neural networks, trained to perform cognitive tasks, have recently
been used as models for neural recordings from animals performing these tasks.
While some progress has been made in performing such comparisons, the evolution
of network dynamics throughout learning remains unexplored. This is paralleled
by an experimental focus on recording from trained animals, with few studies
following neural activity throughout training. In this work, we address this
gap in the realm of artificial networks by analyzing networks that are trained
to perform memory and pattern generation tasks. The functional aspect of these
tasks corresponds to dynamical objects in the fully trained network - a line
attractor or a set of limit cycles for the two respective tasks. We use these
dynamical objects as anchors to study the effect of learning on their
emergence. We find that the sequential nature of learning has major
consequences for the learning trajectory and its final outcome. Specifically,
we show that Least Mean Squares (LMS), a simple gradient descent suggested as a
biologically plausible version of the FORCE algorithm, is constantly obstructed
by forgetting, which is manifested as the destruction of dynamical objects from
previous trials. The degree of interference is determined by the correlation
between different trials. We show which specific ingredients of FORCE avoid
this phenomenon. Overall, this difference results in convergence that is orders
of magnitude slower for LMS. Learning implies accumulating information across
multiple trials to form the overall concept of the task. Our results show that
interference between trials can greatly affect learning, in a learning rule
dependent manner. These insights can help design experimental protocols that
minimize such interference, and possibly infer underlying learning rules by
observing behavior and neural activity throughout learning.
|
[
{
"created": "Thu, 24 May 2018 11:05:12 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Jan 2019 07:06:09 GMT",
"version": "v2"
},
{
"created": "Thu, 2 May 2019 14:32:44 GMT",
"version": "v3"
}
] |
2019-05-03
|
[
[
"Beer",
"Chen",
""
],
[
"Barak",
"Omri",
""
]
] |
Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has been made in performing such comparisons, the evolution of network dynamics throughout learning remains unexplored. This is paralleled by an experimental focus on recording from trained animals, with few studies following neural activity throughout training. In this work, we address this gap in the realm of artificial networks by analyzing networks that are trained to perform memory and pattern generation tasks. The functional aspect of these tasks corresponds to dynamical objects in the fully trained network - a line attractor or a set of limit cycles for the two respective tasks. We use these dynamical objects as anchors to study the effect of learning on their emergence. We find that the sequential nature of learning has major consequences for the learning trajectory and its final outcome. Specifically, we show that Least Mean Squares (LMS), a simple gradient descent suggested as a biologically plausible version of the FORCE algorithm, is constantly obstructed by forgetting, which is manifested as the destruction of dynamical objects from previous trials. The degree of interference is determined by the correlation between different trials. We show which specific ingredients of FORCE avoid this phenomenon. Overall, this difference results in convergence that is orders of magnitude slower for LMS. Learning implies accumulating information across multiple trials to form the overall concept of the task. Our results show that interference between trials can greatly affect learning, in a learning rule dependent manner. These insights can help design experimental protocols that minimize such interference, and possibly infer underlying learning rules by observing behavior and neural activity throughout learning.
|
1303.0256
|
Marcelo Tragtenberg Dr.
|
M. Girardi-Schappo, M. H. R. Tragtenberg, O. Kinouchi
|
A Brief History of Excitable Map-Based Neurons and Neural Networks
|
53 pages, 13 figures, submitted to Journal of Neuroscience Methods
|
Journal of Neuroscience Methods, Volume 220, Issue 2, 15 November
2013, Pages 116-130
|
10.1016/j.jneumeth.2013.07.014
| null |
q-bio.NC cond-mat.dis-nn nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This review gives a short historical account of the excitable maps approach
for modeling neurons and neuronal networks. Some early models, due to Pasemann
(1993), Chialvo (1995) and Kinouchi and Tragtenberg (1996), are compared with
more recent proposals by Rulkov (2002) and Izhikevich (2003). We also review
map-based schemes for electrical and chemical synapses and some recent findings
as critical avalanches in map-based neural networks. We conclude with
suggestions for further work in this area like more efficient maps,
compartmental modeling and close dynamical comparison with conductance-based
models.
|
[
{
"created": "Fri, 1 Mar 2013 19:25:54 GMT",
"version": "v1"
}
] |
2016-02-03
|
[
[
"Girardi-Schappo",
"M.",
""
],
[
"Tragtenberg",
"M. H. R.",
""
],
[
"Kinouchi",
"O.",
""
]
] |
This review gives a short historical account of the excitable maps approach for modeling neurons and neuronal networks. Some early models, due to Pasemann (1993), Chialvo (1995) and Kinouchi and Tragtenberg (1996), are compared with more recent proposals by Rulkov (2002) and Izhikevich (2003). We also review map-based schemes for electrical and chemical synapses and some recent findings as critical avalanches in map-based neural networks. We conclude with suggestions for further work in this area like more efficient maps, compartmental modeling and close dynamical comparison with conductance-based models.
|
1605.06790
|
Marisa Eisenberg
|
Elizabeth C. Lee, Michael R. Kelly, Brad M. Ochocki, Segun M.
Akinwumi, Karen E. S. Hamre, Joseph H. Tien, Marisa C. Eisenberg
|
Model distinguishability and inference robustness in mechanisms of
cholera transmission and loss of immunity
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mathematical models of cholera and waterborne disease vary widely in their
structures, in terms of transmission pathways, loss of immunity, and other
features. These differences may yield different predictions and parameter
estimates from the same data. Given the increasing use of models to inform
public health decision-making, it is important to assess distinguishability
(whether models can be distinguished based on fit to data) and inference
robustness (whether model inferences are robust to realistic variations in
model structure). We examined the effects of uncertainty in model structure in
epidemic cholera, testing a range of models based on known features of cholera
epidemiology. We fit to simulated epidemic and long-term data, as well as data
from the 2006 Angola epidemic. We evaluated model distinguishability based on
data fit, and whether parameter values and forecasts can accurately be inferred
from incidence data. In general, all models were able to successfully fit to
all data sets, even if misspecified. However, in the long-term data, the best
model fits were achieved when the loss of immunity form matched those of the
model that simulated the data. Two transmission and reporting parameters were
accurately estimated across all models, while the remaining showed broad
variation across the different models and data sets. Forecasting efforts were
not successful early, but once the epidemic peak had been achieved, most models
showed similar accuracy. Our results suggest that we are unlikely to be able to
infer mechanistic details from epidemic case data alone, underscoring the need
for broader data collection. Nonetheless, with sufficient data, conclusions
from forecasting and some parameter estimates were robust to variations in the
model structure, and comparative modeling can help determine how variations in
model structure affect conclusions drawn from models and data.
|
[
{
"created": "Sun, 22 May 2016 13:35:40 GMT",
"version": "v1"
}
] |
2016-05-24
|
[
[
"Lee",
"Elizabeth C.",
""
],
[
"Kelly",
"Michael R.",
""
],
[
"Ochocki",
"Brad M.",
""
],
[
"Akinwumi",
"Segun M.",
""
],
[
"Hamre",
"Karen E. S.",
""
],
[
"Tien",
"Joseph H.",
""
],
[
"Eisenberg",
"Marisa C.",
""
]
] |
Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and other features. These differences may yield different predictions and parameter estimates from the same data. Given the increasing use of models to inform public health decision-making, it is important to assess distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether model inferences are robust to realistic variations in model structure). We examined the effects of uncertainty in model structure in epidemic cholera, testing a range of models based on known features of cholera epidemiology. We fit to simulated epidemic and long-term data, as well as data from the 2006 Angola epidemic. We evaluated model distinguishability based on data fit, and whether parameter values and forecasts can accurately be inferred from incidence data. In general, all models were able to successfully fit to all data sets, even if misspecified. However, in the long-term data, the best model fits were achieved when the loss of immunity form matched those of the model that simulated the data. Two transmission and reporting parameters were accurately estimated across all models, while the remaining showed broad variation across the different models and data sets. Forecasting efforts were not successful early, but once the epidemic peak had been achieved, most models showed similar accuracy. Our results suggest that we are unlikely to be able to infer mechanistic details from epidemic case data alone, underscoring the need for broader data collection. Nonetheless, with sufficient data, conclusions from forecasting and some parameter estimates were robust to variations in the model structure, and comparative modeling can help determine how variations in model structure affect conclusions drawn from models and data.
|
1905.09888
|
Farzad Khalvati
|
Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas, Steven
Gallinger, Masoom A. Haider, Farzad Khalvati
|
Prognostic Value of Transfer Learning Based Features in Resectable
Pancreatic Ductal Adenocarcinoma
| null | null | null | null |
q-bio.QM cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers
with an extremely poor prognosis. Radiomics has shown prognostic ability in
multiple types of cancer including PDAC. However, the prognostic value of
traditional radiomics pipelines, which are based on hand-crafted radiomic
features alone is limited. Convolutional neural networks (CNNs) have been shown
to outperform these feature-based models in computer vision tasks. However,
training a CNN from scratch needs a large sample size which is not feasible in
most medical imaging studies. As an alternative solution, CNN-based transfer
learning has shown potential for achieving reasonable performance using small
datasets. In this work, we developed and validated a CNN-based transfer
learning approach for prognostication of PDAC patients for overall survival
using two independent resectable PDAC cohorts. The proposed deep transfer
learning model for prognostication of PDAC achieved the area under the receiver
operating characteristic curve of 0.74, which was significantly higher than
that of the traditional radiomics model (0.56) as well as a CNN model trained
from scratch (0.50). These results suggest that deep transfer learning may
significantly improve prognosis performance using small datasets in medical
imaging.
|
[
{
"created": "Thu, 23 May 2019 19:35:41 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Aug 2019 15:16:00 GMT",
"version": "v2"
}
] |
2019-08-22
|
[
[
"Zhang",
"Yucheng",
""
],
[
"Lobo-Mueller",
"Edrise M.",
""
],
[
"Karanicolas",
"Paul",
""
],
[
"Gallinger",
"Steven",
""
],
[
"Haider",
"Masoom A.",
""
],
[
"Khalvati",
"Farzad",
""
]
] |
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform these feature-based models in computer vision tasks. However, training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning has shown potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning approach for prognostication of PDAC patients for overall survival using two independent resectable PDAC cohorts. The proposed deep transfer learning model for prognostication of PDAC achieved the area under the receiver operating characteristic curve of 0.74, which was significantly higher than that of the traditional radiomics model (0.56) as well as a CNN model trained from scratch (0.50). These results suggest that deep transfer learning may significantly improve prognosis performance using small datasets in medical imaging.
|
1501.06149
|
Iain Johnston
|
Iain G. Johnston and Nick S. Jones
|
Closed-form stochastic solutions for non-equilibrium dynamics and
inheritance of cellular components over many cell divisions
| null | null |
10.1098/rspa.2015.0050
| null |
q-bio.QM stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stochastic dynamics govern many important processes in cellular biology, and
an underlying theoretical approach describing these dynamics is desirable to
address a wealth of questions in biology and medicine. Mathematical tools exist
for treating several important examples of these stochastic processes, most
notably gene expression, and random partitioning at single cell divisions or
after a steady state has been reached. Comparatively little work exists
exploring different and specific ways that repeated cell divisions can lead to
stochastic inheritance of unequilibrated cellular populations. Here we
introduce a mathematical formalism to describe cellular agents that are subject
to random creation, replication, and/or degradation, and are inherited
according to a range of random dynamics at cell divisions. We obtain
closed-form generating functions describing systems at any time after any
number of cell divisions for binomial partitioning and divisions provoking a
deterministic or random, subtractive or additive change in copy number, and
show that these solutions agree exactly with stochastic simulation. We apply
this general formalism to several example problems involving the dynamics of
mitochondrial DNA (mtDNA) during development and organismal lifetimes.
|
[
{
"created": "Sun, 25 Jan 2015 12:56:04 GMT",
"version": "v1"
}
] |
2016-02-17
|
[
[
"Johnston",
"Iain G.",
""
],
[
"Jones",
"Nick S.",
""
]
] |
Stochastic dynamics govern many important processes in cellular biology, and an underlying theoretical approach describing these dynamics is desirable to address a wealth of questions in biology and medicine. Mathematical tools exist for treating several important examples of these stochastic processes, most notably gene expression, and random partitioning at single cell divisions or after a steady state has been reached. Comparatively little work exists exploring different and specific ways that repeated cell divisions can lead to stochastic inheritance of unequilibrated cellular populations. Here we introduce a mathematical formalism to describe cellular agents that are subject to random creation, replication, and/or degradation, and are inherited according to a range of random dynamics at cell divisions. We obtain closed-form generating functions describing systems at any time after any number of cell divisions for binomial partitioning and divisions provoking a deterministic or random, subtractive or additive change in copy number, and show that these solutions agree exactly with stochastic simulation. We apply this general formalism to several example problems involving the dynamics of mitochondrial DNA (mtDNA) during development and organismal lifetimes.
|
1704.08585
|
G Ambika
|
K. P. Harikrishnan, Rinku Jacob, R. Misra, G. Ambika
|
Determining the minimum embedding dimension for state space
reconstruction through recurrence networks
|
13 pages, 8 figures, submitted to Pramana( J Phys)
|
Indian Academy of Sciences Conference Series (2017) 1:1
|
10.29195/iascs.01.01.0004
| null |
q-bio.NC nlin.CD physics.data-an
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The analysis of observed time series from nonlinear systems is usually done
by making a time-delay reconstruction to unfold the dynamics on a
multi-dimensional state space. An important aspect of the analysis is the
choice of the correct embedding dimension. The conventional procedure used for
this is either the method of false nearest neighbors or the saturation of some
invariant measure, such as, correlation dimension. Here we examine this issue
from a complex network perspective and propose a recurrence network based
measure to determine the acceptable minimum embedding dimension to be used for
such analysis. The measure proposed here is based on the well known
Kullback-Leibler divergence commonly used in information theory. We show that
the measure is simple and direct to compute and give accurate result for short
time series. To show the significance of the measure in the analysis of
practical data, we present the analysis of two EEG signals as examples.
|
[
{
"created": "Wed, 26 Apr 2017 02:20:18 GMT",
"version": "v1"
}
] |
2018-09-05
|
[
[
"Harikrishnan",
"K. P.",
""
],
[
"Jacob",
"Rinku",
""
],
[
"Misra",
"R.",
""
],
[
"Ambika",
"G.",
""
]
] |
The analysis of observed time series from nonlinear systems is usually done by making a time-delay reconstruction to unfold the dynamics on a multi-dimensional state space. An important aspect of the analysis is the choice of the correct embedding dimension. The conventional procedure used for this is either the method of false nearest neighbors or the saturation of some invariant measure, such as, correlation dimension. Here we examine this issue from a complex network perspective and propose a recurrence network based measure to determine the acceptable minimum embedding dimension to be used for such analysis. The measure proposed here is based on the well known Kullback-Leibler divergence commonly used in information theory. We show that the measure is simple and direct to compute and give accurate result for short time series. To show the significance of the measure in the analysis of practical data, we present the analysis of two EEG signals as examples.
|
2303.07250
|
Amit Samadder
|
Amit Samadder, Arnab Chattopadhyay, Sabyasachi Bhattacharya
|
The balance between contamination and predation determine species
existence in prey-predator dynamics with contaminated and uncontaminated prey
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In freshwater ecosystems, aquatic insects that ontogenetically shift their
habitat from aquatic to terrestrial play vital roles as prey subsidies that
move nutrients and energy from aquatic to terrestrial food webs. As a result,
these subsidies negatively affect alternative terrestrial prey by enhancing
predator density. However, these aquatic insects can also transport
contamination to the terrestrial community that is primarily produced in
aquatic ecosystems. Which can reduce insectivore abundance and biomass, lower
insectivore reproductive success, and increase predation pressure on
alternative prey with consequences for aquatic and terrestrial food webs.
Motivated by this, here we consider a prey-predator model where the predator
consumes contaminated and uncontaminated prey together. We find that, at a high
level of contamination, the vulnerability of contaminated prey and predator is
determined by predation preference. More specifically, a very low predation
preference towards contaminated prey ensures predator persistence, whereas a
low, high preference excludes the predator from the system. Interestingly,
either contaminated prey or the predator exist at intermediate predation
preference due to bi-stability. Furthermore, when there is no contamination in
one of the prey, the other prey can not coexist due to apparent competition for
a specific range of predation preferences. However, when sufficient
contamination exists in one prey, alternative uncontaminated prey coexists.
With this, contamination also stabilizes and destabilizes the three species
dynamics. Our result also indicates that if the intensity of the contamination
in predator reproduction is low, then contaminated prey is more susceptible to
the contamination.
|
[
{
"created": "Mon, 13 Mar 2023 16:24:08 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Mar 2023 12:50:47 GMT",
"version": "v2"
}
] |
2023-03-15
|
[
[
"Samadder",
"Amit",
""
],
[
"Chattopadhyay",
"Arnab",
""
],
[
"Bhattacharya",
"Sabyasachi",
""
]
] |
In freshwater ecosystems, aquatic insects that ontogenetically shift their habitat from aquatic to terrestrial play vital roles as prey subsidies that move nutrients and energy from aquatic to terrestrial food webs. As a result, these subsidies negatively affect alternative terrestrial prey by enhancing predator density. However, these aquatic insects can also transport contamination to the terrestrial community that is primarily produced in aquatic ecosystems. Which can reduce insectivore abundance and biomass, lower insectivore reproductive success, and increase predation pressure on alternative prey with consequences for aquatic and terrestrial food webs. Motivated by this, here we consider a prey-predator model where the predator consumes contaminated and uncontaminated prey together. We find that, at a high level of contamination, the vulnerability of contaminated prey and predator is determined by predation preference. More specifically, a very low predation preference towards contaminated prey ensures predator persistence, whereas a low, high preference excludes the predator from the system. Interestingly, either contaminated prey or the predator exist at intermediate predation preference due to bi-stability. Furthermore, when there is no contamination in one of the prey, the other prey can not coexist due to apparent competition for a specific range of predation preferences. However, when sufficient contamination exists in one prey, alternative uncontaminated prey coexists. With this, contamination also stabilizes and destabilizes the three species dynamics. Our result also indicates that if the intensity of the contamination in predator reproduction is low, then contaminated prey is more susceptible to the contamination.
|
2003.11008
|
Emanuele Olivetti
|
Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli,
Paolo Avesani, Filippo Arrigoni, Emanuele Olivetti
|
Automatic Tissue Segmentation with Deep Learning in Patients with
Congenital or Acquired Distortion of Brain Anatomy
| null | null | null | null |
q-bio.TO eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Brains with complex distortion of cerebral anatomy present several challenges
to automatic tissue segmentation methods of T1-weighted MR images. First, the
very high variability in the morphology of the tissues can be incompatible with
the prior knowledge embedded within the algorithms. Second, the availability of
MR images of distorted brains is very scarce, so the methods in the literature
have not addressed such cases so far. In this work, we present the first
evaluation of state-of-the-art automatic tissue segmentation pipelines on
T1-weighted images of brains with different severity of congenital or acquired
brain distortion. We compare traditional pipelines and a deep learning model,
i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional
pipelines completely fail to segment the tissues with strong anatomical
distortion. Surprisingly, the 3D U-Net provides useful segmentations that can
be a valuable starting point for manual refinement by
experts/neuroradiologists.
|
[
{
"created": "Tue, 24 Mar 2020 17:50:39 GMT",
"version": "v1"
}
] |
2020-03-25
|
[
[
"Amorosino",
"Gabriele",
""
],
[
"Peruzzo",
"Denis",
""
],
[
"Astolfi",
"Pietro",
""
],
[
"Redaelli",
"Daniela",
""
],
[
"Avesani",
"Paolo",
""
],
[
"Arrigoni",
"Filippo",
""
],
[
"Olivetti",
"Emanuele",
""
]
] |
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.
|
1706.03920
|
Bernhard Mehlig
|
M. Rafajlovic, D. Kleinhans, C. Gulliksson, J. Fries, D. Johansson, A.
Ardehed, L. Sundqvist, R. T. Pereyra, B. Mehlig, P. R. Jonsson and K.
Johannesson
|
Stochastic mechanisms forming large clones during colonisation of new
areas
|
33 pages, 5 figures, supplementary figures
|
J. Evol. Biol. 30 (2017) 1544
|
10.1111/jeb.13124
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In species reproducing both sexually and asexually clones are often more
common in recently established populations. Earlier studies have suggested that
this pattern arises from natural selection favouring asexual recruitment in
young populations. Alternatively, as we show here, this pattern may result from
stochastic processes during species-range expansions. We model a dioecious
species expanding into a new area in which all individuals are capable of both
sexual and asexual reproduction, and all individuals have equal survival rates
and dispersal distances. Even under conditions that eventually favour sexual
recruitment, colonisation starts with an asexual wave. Long after colonisation
is completed, a sexual wave erodes clonal dominance. If individuals reproduce
more than one season, and with only local dispersal, a few large clones
typically dominate for thousands of reproductive seasons. Adding occasional
long-distance dispersal, more dominant clones emerge, but they persist for a
shorter period of time. The general mechanism involved is simple: edge effects
at the expansion front favour asexual (uniparental) recruitment where potential
mates are rare. Specifically, our stochastic model makes detailed predictions
different from a selection model, and comparing these with empirical data from
a postglacially established seaweed species (Fucus radicans) shows that in this
case a stochastic mechanism is strongly supported.
|
[
{
"created": "Tue, 13 Jun 2017 06:45:57 GMT",
"version": "v1"
}
] |
2017-12-06
|
[
[
"Rafajlovic",
"M.",
""
],
[
"Kleinhans",
"D.",
""
],
[
"Gulliksson",
"C.",
""
],
[
"Fries",
"J.",
""
],
[
"Johansson",
"D.",
""
],
[
"Ardehed",
"A.",
""
],
[
"Sundqvist",
"L.",
""
],
[
"Pereyra",
"R. T.",
""
],
[
"Mehlig",
"B.",
""
],
[
"Jonsson",
"P. R.",
""
],
[
"Johannesson",
"K.",
""
]
] |
In species reproducing both sexually and asexually clones are often more common in recently established populations. Earlier studies have suggested that this pattern arises from natural selection favouring asexual recruitment in young populations. Alternatively, as we show here, this pattern may result from stochastic processes during species-range expansions. We model a dioecious species expanding into a new area in which all individuals are capable of both sexual and asexual reproduction, and all individuals have equal survival rates and dispersal distances. Even under conditions that eventually favour sexual recruitment, colonisation starts with an asexual wave. Long after colonisation is completed, a sexual wave erodes clonal dominance. If individuals reproduce more than one season, and with only local dispersal, a few large clones typically dominate for thousands of reproductive seasons. Adding occasional long-distance dispersal, more dominant clones emerge, but they persist for a shorter period of time. The general mechanism involved is simple: edge effects at the expansion front favour asexual (uniparental) recruitment where potential mates are rare. Specifically, our stochastic model makes detailed predictions different from a selection model, and comparing these with empirical data from a postglacially established seaweed species (Fucus radicans) shows that in this case a stochastic mechanism is strongly supported.
|
1903.07276
|
Nancy Forde
|
Michael W.H. Kirkness, Kathrin Lehmann and Nancy R. Forde
|
Mechanics and Structural Stability of the Collagen Triple Helix
|
Review article
| null | null | null |
q-bio.BM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The primary building block of the body is collagen, which is found in the
extracellular matrix and in many stress-bearing tissues such as tendon and
cartilage. It provides elasticity and support to cells and tissues while
influencing biological pathways including cell signaling, motility and
differentiation. Collagen's unique triple helical structure is thought to
impart mechanical stability. However, detailed experimental studies on its
molecular mechanics have been only recently emerging. Here, we review the
treatment of the triple helix as a homogeneous flexible rod, including bend
(standard worm-like chain model), twist, and stretch deformations, and the
assumption of backbone linearity. Additionally, we discuss protein-specific
properties of the triple helix including sequence dependence, and relate
single-molecule mechanics to collagen's physiological context.
|
[
{
"created": "Mon, 18 Mar 2019 07:00:50 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Jul 2019 18:59:36 GMT",
"version": "v2"
}
] |
2019-07-29
|
[
[
"Kirkness",
"Michael W. H.",
""
],
[
"Lehmann",
"Kathrin",
""
],
[
"Forde",
"Nancy R.",
""
]
] |
The primary building block of the body is collagen, which is found in the extracellular matrix and in many stress-bearing tissues such as tendon and cartilage. It provides elasticity and support to cells and tissues while influencing biological pathways including cell signaling, motility and differentiation. Collagen's unique triple helical structure is thought to impart mechanical stability. However, detailed experimental studies on its molecular mechanics have been only recently emerging. Here, we review the treatment of the triple helix as a homogeneous flexible rod, including bend (standard worm-like chain model), twist, and stretch deformations, and the assumption of backbone linearity. Additionally, we discuss protein-specific properties of the triple helix including sequence dependence, and relate single-molecule mechanics to collagen's physiological context.
|
2406.14246
|
Maren Philipps
|
Maren Philipps, Antonia K\"orner, Jakob Vanhoefer, Dilan Pathirana,
Jan Hasenauer
|
Non-Negative Universal Differential Equations With Applications in
Systems Biology
|
6 pages, This work has been submitted to IFAC for possible
publication. Initial submission was March 18, 2024
| null | null | null |
q-bio.QM cs.LG math.DS stat.ML
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Universal differential equations (UDEs) leverage the respective advantages of
mechanistic models and artificial neural networks and combine them into one
dynamic model. However, these hybrid models can suffer from unrealistic
solutions, such as negative values for biochemical quantities. We present
non-negative UDE (nUDEs), a constrained UDE variant that guarantees
non-negative values. Furthermore, we explore regularisation techniques to
improve generalisation and interpretability of UDEs.
|
[
{
"created": "Thu, 20 Jun 2024 12:14:09 GMT",
"version": "v1"
}
] |
2024-06-21
|
[
[
"Philipps",
"Maren",
""
],
[
"Körner",
"Antonia",
""
],
[
"Vanhoefer",
"Jakob",
""
],
[
"Pathirana",
"Dilan",
""
],
[
"Hasenauer",
"Jan",
""
]
] |
Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs.
|
1805.00757
|
Ricardo Ruiz Baier I
|
Ricardo Ruiz Baier, Alessio Gizzi, Alessandro Loppini, Christian
Cherubini, Simonetta Filippi
|
Modelling thermo-electro-mechanical effects in orthotropic cardiac
tissue
| null |
Communications in Computational Physics (2019)
|
10.4208/cicp.OA-2018-0253
| null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we introduce a new mathematical model for the active
contraction of cardiac muscle, featuring different thermo-electric and
nonlinear conductivity properties. The passive hyperelastic response of the
tissue is described by an orthotropic exponential model, whereas the ionic
activity dictates active contraction incorporated through the concept of
orthotropic active strain. We use a fully incompressible formulation, and the
generated strain modifies directly the conductivity mechanisms in the medium
through the pull-back transformation. We also investigate the influence of
thermo-electric effects in the onset of multiphysics emergent spatiotemporal
dynamics, using nonlinear diffusion. It turns out that these ingredients have a
key role in reproducing pathological chaotic dynamics such as ventricular
fibrillation during inflammatory events, for instance. The specific structure
of the governing equations suggests to cast the problem in mixed-primal form
and we write it in terms of Kirchhoff stress, displacements, solid pressure,
electric potential, activation generation, and ionic variables. We also propose
a new mixed-primal finite element method for its numerical approximation, and
we use it to explore the properties of the model and to assess the importance
of coupling terms, by means of a few computational experiments in 3D.
|
[
{
"created": "Wed, 2 May 2018 12:06:57 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Jul 2018 10:18:16 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Nov 2018 15:49:40 GMT",
"version": "v3"
}
] |
2019-05-02
|
[
[
"Baier",
"Ricardo Ruiz",
""
],
[
"Gizzi",
"Alessio",
""
],
[
"Loppini",
"Alessandro",
""
],
[
"Cherubini",
"Christian",
""
],
[
"Filippi",
"Simonetta",
""
]
] |
In this paper we introduce a new mathematical model for the active contraction of cardiac muscle, featuring different thermo-electric and nonlinear conductivity properties. The passive hyperelastic response of the tissue is described by an orthotropic exponential model, whereas the ionic activity dictates active contraction incorporated through the concept of orthotropic active strain. We use a fully incompressible formulation, and the generated strain modifies directly the conductivity mechanisms in the medium through the pull-back transformation. We also investigate the influence of thermo-electric effects in the onset of multiphysics emergent spatiotemporal dynamics, using nonlinear diffusion. It turns out that these ingredients have a key role in reproducing pathological chaotic dynamics such as ventricular fibrillation during inflammatory events, for instance. The specific structure of the governing equations suggests to cast the problem in mixed-primal form and we write it in terms of Kirchhoff stress, displacements, solid pressure, electric potential, activation generation, and ionic variables. We also propose a new mixed-primal finite element method for its numerical approximation, and we use it to explore the properties of the model and to assess the importance of coupling terms, by means of a few computational experiments in 3D.
|
1903.04925
|
Angana Chakraborty
|
Angana Chakraborty and Sanghamitra Bandyopadhyay
|
conLSH: Context based Locality Sensitive Hashing for Mapping of noisy
SMRT Reads
|
arXiv admin note: text overlap with arXiv:1705.03933
| null | null | null |
q-bio.GN cs.DS cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next
Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of
long and noisy reads demanding cutting edge research to get most out of it. To
deal with the high error probability of SMRT data, a novel contextual Locality
Sensitive Hashing (conLSH) based algorithm is proposed in this article, which
can effectively align the noisy SMRT reads to the reference genome. Here,
sequences are hashed together based not only on their closeness, but also on
similarity of context. The algorithm has $\mathcal{O}(n^{\rho+1})$ space
requirement, where $n$ is the number of sequences in the corpus and $\rho$ is a
constant. The indexing time and querying time are bounded by $\mathcal{O}(
\frac{n^{\rho+1} \cdot \ln n}{\ln \frac{1}{P_2}})$ and $\mathcal{O}(n^\rho)$
respectively, where $P_2 > 0$, is a probability value. This algorithm is
particularly useful for retrieving similar sequences, a widely used task in
biology. The proposed conLSH based aligner is compared with rHAT, popularly
used for aligning SMRT reads, and is found to comprehensively beat it in speed
as well as in memory requirements. In particular, it takes approximately
$24.2\%$ less processing time, while saving about $70.3\%$ in peak memory
requirement for H.sapiens PacBio dataset.
|
[
{
"created": "Mon, 11 Mar 2019 17:49:01 GMT",
"version": "v1"
}
] |
2019-03-13
|
[
[
"Chakraborty",
"Angana",
""
],
[
"Bandyopadhyay",
"Sanghamitra",
""
]
] |
Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of long and noisy reads demanding cutting edge research to get most out of it. To deal with the high error probability of SMRT data, a novel contextual Locality Sensitive Hashing (conLSH) based algorithm is proposed in this article, which can effectively align the noisy SMRT reads to the reference genome. Here, sequences are hashed together based not only on their closeness, but also on similarity of context. The algorithm has $\mathcal{O}(n^{\rho+1})$ space requirement, where $n$ is the number of sequences in the corpus and $\rho$ is a constant. The indexing time and querying time are bounded by $\mathcal{O}( \frac{n^{\rho+1} \cdot \ln n}{\ln \frac{1}{P_2}})$ and $\mathcal{O}(n^\rho)$ respectively, where $P_2 > 0$, is a probability value. This algorithm is particularly useful for retrieving similar sequences, a widely used task in biology. The proposed conLSH based aligner is compared with rHAT, popularly used for aligning SMRT reads, and is found to comprehensively beat it in speed as well as in memory requirements. In particular, it takes approximately $24.2\%$ less processing time, while saving about $70.3\%$ in peak memory requirement for H.sapiens PacBio dataset.
|
2011.08632
|
Thomas G\"otz
|
Thomas G\"otz, Silja Mohrmann, Robert Rockenfeller, Moritz Sch\"afer
and Karunia Putra Wijaya
|
Calculation of a local COVID-19 reproduction number for the northern
Rhineland-Palatinate
|
16 pages, 23 figures, in German
| null | null | null |
q-bio.PE physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Since the beginning of the corona pandemic in March 2020, various parameters
for describing the spread of the disease have been specified for Germany in
addition to the daily infection figures (new infections and total infections),
which are also used for political decisions. In addition to excess mortality
and the weekly incidence, these include the doubling time $T_2$ and the
reproduction number $R_t$. For the latter, various estimates can be found on
the website of the Robert-Koch-Institute, see \cite{EstR:RKI}, which are
calculated from the case numbers for all of Germany; local differences are not
taken into account here. In the present article, the calculations of the RKI on
a local level are examined using the example of northern Rhineland-Palatinate
and its districts. Here, not the reporting date but the onset of illness is
used as a reference for the calculation of $R_t$. For cases where the onset of
illness is not known, an adjusted generalized extreme value distribution (GEV)
is first fitted to the data for which the reporting delay (difference between
the onset of illness and the reporting date) is available and examined for
further characteristics such as local as well as demographic differences. This
GEV distribution is then used to calculate the reporting delays of incomplete
data points. The calculation of the daily value of $R_t$ between the end of
February and the end of October showed a similar course of the reproductive
rate compared to the nationwide figures. Expectably larger statistical
fluctuations were observed in the summer, mainly due to lower case numbers. The
values for northern Rhineland-Palatinate have been consistently above $1$ since
about mid-September. The calculations can also be transferred to other regions
and administrative districts.
|
[
{
"created": "Tue, 17 Nov 2020 13:52:15 GMT",
"version": "v1"
}
] |
2020-11-18
|
[
[
"Götz",
"Thomas",
""
],
[
"Mohrmann",
"Silja",
""
],
[
"Rockenfeller",
"Robert",
""
],
[
"Schäfer",
"Moritz",
""
],
[
"Wijaya",
"Karunia Putra",
""
]
] |
Since the beginning of the corona pandemic in March 2020, various parameters for describing the spread of the disease have been specified for Germany in addition to the daily infection figures (new infections and total infections), which are also used for political decisions. In addition to excess mortality and the weekly incidence, these include the doubling time $T_2$ and the reproduction number $R_t$. For the latter, various estimates can be found on the website of the Robert-Koch-Institute, see \cite{EstR:RKI}, which are calculated from the case numbers for all of Germany; local differences are not taken into account here. In the present article, the calculations of the RKI on a local level are examined using the example of northern Rhineland-Palatinate and its districts. Here, not the reporting date but the onset of illness is used as a reference for the calculation of $R_t$. For cases where the onset of illness is not known, an adjusted generalized extreme value distribution (GEV) is first fitted to the data for which the reporting delay (difference between the onset of illness and the reporting date) is available and examined for further characteristics such as local as well as demographic differences. This GEV distribution is then used to calculate the reporting delays of incomplete data points. The calculation of the daily value of $R_t$ between the end of February and the end of October showed a similar course of the reproductive rate compared to the nationwide figures. Expectably larger statistical fluctuations were observed in the summer, mainly due to lower case numbers. The values for northern Rhineland-Palatinate have been consistently above $1$ since about mid-September. The calculations can also be transferred to other regions and administrative districts.
|
1808.04443
|
Chuanqi Tan
|
Chuanqi Tan, Fuchun Sun, Wenchang Zhang, Shaobo Liu and Chunfang Liu
|
Spatial and Spectral Features Fusion for EEG Classification during Motor
Imagery in BCI
|
International Conference on Biomedical and Health Informatics (BHI
2017)
| null | null | null |
q-bio.QM eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Brain computer interface (BCI) is the only way for some special patients to
communicate with the outside world and provide a direct control channel between
brain and the external devices. As a non-invasive interface, the scalp
electroencephalography (EEG) has a significant potential to be a major input
signal for future BCI systems. Traditional methods only focus on a particular
feature in the EEG signal, which limits the practical applications of EEG-based
BCI. In this paper, we propose a algorithm for EEG classification with the
ability to fuse multiple features. First, use the common spatial pattern (CSP)
as the spatial feature and use wavelet coefficient as the spectral feature.
Second, fuse these features with a fusion algorithm in orchestrate way to
improve the accuracy of classification. Our algorithms are applied to the
dataset IVa from BCI complete \uppercase\expandafter{\romannumeral3}. By
analyzing the experimental results, it is possible to conclude that we can
speculate that our algorithm perform better than traditional methods.
|
[
{
"created": "Mon, 6 Aug 2018 07:52:06 GMT",
"version": "v1"
}
] |
2018-08-15
|
[
[
"Tan",
"Chuanqi",
""
],
[
"Sun",
"Fuchun",
""
],
[
"Zhang",
"Wenchang",
""
],
[
"Liu",
"Shaobo",
""
],
[
"Liu",
"Chunfang",
""
]
] |
Brain computer interface (BCI) is the only way for some special patients to communicate with the outside world and provide a direct control channel between brain and the external devices. As a non-invasive interface, the scalp electroencephalography (EEG) has a significant potential to be a major input signal for future BCI systems. Traditional methods only focus on a particular feature in the EEG signal, which limits the practical applications of EEG-based BCI. In this paper, we propose a algorithm for EEG classification with the ability to fuse multiple features. First, use the common spatial pattern (CSP) as the spatial feature and use wavelet coefficient as the spectral feature. Second, fuse these features with a fusion algorithm in orchestrate way to improve the accuracy of classification. Our algorithms are applied to the dataset IVa from BCI complete \uppercase\expandafter{\romannumeral3}. By analyzing the experimental results, it is possible to conclude that we can speculate that our algorithm perform better than traditional methods.
|
1601.03412
|
Jana Gevertz
|
Ami B. Shah, Katarzyna A. Rejniak, Jana L. Gevertz
|
Limiting the Development of Anti-Cancer Drug Resistance in a Spatial
Model of Micrometastases
|
25 pages, 8 figures
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While chemoresistance in primary tumors is well-studied, much less is known
about the influence of systemic chemotherapy on the development of drug
resistance at metastatic sites. In this work, we use a hybrid spatial model of
tumor response to a DNA damaging drug to study how the development of
chemoresistance in micrometastases depends on the drug dosing schedule. We
separately consider cell populations that harbor pre-existing resistance to the
drug, and those that acquire resistance during the course of treatment. For
each of these independent scenarios, we consider one hypothetical cell line
that is responsive to metronomic chemotherapy, and another that with high
probability cannot be eradicated by a metronomic protocol. Motivated by
experimental work on ovarian cancer xenografts, we consider all possible
combinations of a one week treatment protocol, repeated for three weeks, and
constrained by the total weekly drug dose. Simulations reveal a small number of
fractionated-dose protocols that are at least as effective as metronomic
therapy in eradicating micrometastases with acquired resistance (weak or
strong), while also being at least as effective on those that harbor weakly
pre-existing resistant cells. Given the responsiveness of very different
theoretical cell lines to these few fractionated-dose protocols, these may
represent more effective ways to schedule chemotherapy with the goal of
limiting metastatic tumor progression.
|
[
{
"created": "Wed, 13 Jan 2016 21:19:32 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Mar 2016 21:41:24 GMT",
"version": "v2"
}
] |
2016-03-04
|
[
[
"Shah",
"Ami B.",
""
],
[
"Rejniak",
"Katarzyna A.",
""
],
[
"Gevertz",
"Jana L.",
""
]
] |
While chemoresistance in primary tumors is well-studied, much less is known about the influence of systemic chemotherapy on the development of drug resistance at metastatic sites. In this work, we use a hybrid spatial model of tumor response to a DNA damaging drug to study how the development of chemoresistance in micrometastases depends on the drug dosing schedule. We separately consider cell populations that harbor pre-existing resistance to the drug, and those that acquire resistance during the course of treatment. For each of these independent scenarios, we consider one hypothetical cell line that is responsive to metronomic chemotherapy, and another that with high probability cannot be eradicated by a metronomic protocol. Motivated by experimental work on ovarian cancer xenografts, we consider all possible combinations of a one week treatment protocol, repeated for three weeks, and constrained by the total weekly drug dose. Simulations reveal a small number of fractionated-dose protocols that are at least as effective as metronomic therapy in eradicating micrometastases with acquired resistance (weak or strong), while also being at least as effective on those that harbor weakly pre-existing resistant cells. Given the responsiveness of very different theoretical cell lines to these few fractionated-dose protocols, these may represent more effective ways to schedule chemotherapy with the goal of limiting metastatic tumor progression.
|
1710.00869
|
Nick Wasylyshyn
|
Nick Wasylyshyn (1, 2), Brett Hemenway (2), Javier O. Garcia (1, 2),
Christopher N. Cascio (2), Matthew Brook O'Donnell (2), C. Raymond Bingham
(3), Bruce Simons-Morton (4), Jean M. Vettel (1, 2, 5), Emily B. Falk (2)
((1) US Army Research Laboratory, (2) University of Pennsylvania, (3)
University of Michigan Transportation Research Institute, (4) Eunice Kennedy
Shriver National Institute on Child Health and Human Development, (5)
University of California Santa Barbara)
|
Global Brain Dynamics During Social Exclusion Predict Subsequent
Behavioral Conformity
|
32 pages, 5 figures, 1 table
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Individuals react differently to social experiences; for example, people who
are more sensitive to negative social experiences, such as being excluded, may
be more likely to adapt their behavior to fit in with others. We examined
whether functional brain connectivity during social exclusion in the fMRI
scanner can be used to predict subsequent conformity to peer norms. Adolescent
males (N = 57) completed a two-part study on teen driving risk: a social
exclusion task (Cyberball) during an fMRI session and a subsequent driving
simulator session in which they drove alone and in the presence of a peer who
expressed risk-averse or risk-accepting driving norms. We computed the
difference in functional connectivity between social exclusion and social
inclusion from each node in the brain to nodes in two brain networks, one
previously associated with mentalizing (medial prefrontal cortex,
temporoparietal junction, precuneus, temporal poles) and another with social
pain (anterior cingulate cortex, anterior insula). Using cross-validated
machine learning, this measure of global network connectivity during exclusion
predicts the extent of conformity to peer pressure during driving in the
subsequent experimental session. These findings extend our understanding of how
global neural dynamics guide social behavior, revealing functional network
activity that captures individual differences.
|
[
{
"created": "Mon, 2 Oct 2017 19:08:41 GMT",
"version": "v1"
}
] |
2017-10-04
|
[
[
"Wasylyshyn",
"Nick",
""
],
[
"Hemenway",
"Brett",
""
],
[
"Garcia",
"Javier O.",
""
],
[
"Cascio",
"Christopher N.",
""
],
[
"O'Donnell",
"Matthew Brook",
""
],
[
"Bingham",
"C. Raymond",
""
],
[
"Simons-Morton",
"Bruce",
""
],
[
"Vettel",
"Jean M.",
""
],
[
"Falk",
"Emily B.",
""
]
] |
Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (anterior cingulate cortex, anterior insula). Using cross-validated machine learning, this measure of global network connectivity during exclusion predicts the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.
|
2309.07271
|
Yujiang Wang
|
Christopher Thornton, Mariella Panagiotopoulou, Fahmida A Chowdhury,
Beate Diehl, John S Duncan, Sarah J Gascoigne, Guillermo Besne, Andrew W
McEvoy, Anna Miserocchi, Billy C Smith, Jane de Tisi, Peter N Taylor, Yujiang
Wang
|
Diminished circadian and ultradian rhythms of human brain activity in
pathological tissue in vivo
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chronobiological rhythms, such as the circadian rhythm, have long been linked
to neurological disorders, but it is currently unknown how pathological
processes affect the expression of biological rhythms in the brain.
Here, we use the unique opportunity of long-term, continuous intracranially
recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian
(daily) and ultradian (minute to hourly) rhythms in different brain regions. We
show that functional circadian and ultradian rhythms are diminished in
pathological tissue, independent of regional variations. We further demonstrate
that these diminished rhythms are persistent in time, regardless of load or
occurrence of pathological events.
These findings provide evidence that brain pathology is functionally
associated with persistently diminished chronobiological rhythms in vivo in
humans, independent of regional variations or pathological events. Future work
interacting with, and restoring, these modulatory chronobiological rhythms may
allow for novel therapies.
|
[
{
"created": "Wed, 13 Sep 2023 19:21:16 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Aug 2024 17:55:13 GMT",
"version": "v2"
}
] |
2024-08-08
|
[
[
"Thornton",
"Christopher",
""
],
[
"Panagiotopoulou",
"Mariella",
""
],
[
"Chowdhury",
"Fahmida A",
""
],
[
"Diehl",
"Beate",
""
],
[
"Duncan",
"John S",
""
],
[
"Gascoigne",
"Sarah J",
""
],
[
"Besne",
"Guillermo",
""
],
[
"McEvoy",
"Andrew W",
""
],
[
"Miserocchi",
"Anna",
""
],
[
"Smith",
"Billy C",
""
],
[
"de Tisi",
"Jane",
""
],
[
"Taylor",
"Peter N",
""
],
[
"Wang",
"Yujiang",
""
]
] |
Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain. Here, we use the unique opportunity of long-term, continuous intracranially recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian (daily) and ultradian (minute to hourly) rhythms in different brain regions. We show that functional circadian and ultradian rhythms are diminished in pathological tissue, independent of regional variations. We further demonstrate that these diminished rhythms are persistent in time, regardless of load or occurrence of pathological events. These findings provide evidence that brain pathology is functionally associated with persistently diminished chronobiological rhythms in vivo in humans, independent of regional variations or pathological events. Future work interacting with, and restoring, these modulatory chronobiological rhythms may allow for novel therapies.
|
2303.14986
|
Niharika S. D'Souza
|
Niharika S. D'Souza and Archana Venkataraman
|
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery
from Functional Connectomics Manifolds
|
Accepted into IPMI 2023
| null | null | null |
q-bio.QM cs.LG cs.NE eess.SP q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Connectomics has emerged as a powerful tool in neuroimaging and has spurred
recent advancements in statistical and machine learning methods for
connectivity data. Despite connectomes inhabiting a matrix manifold, most
analytical frameworks ignore the underlying data geometry. This is largely
because simple operations, such as mean estimation, do not have easily
computable closed-form solutions. We propose a geometrically aware neural
framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic
mean of a collections of symmetric positive definite (SPD) matrices. The
mSPD-NN is comprised of bilinear fully connected layers with tied weights and
utilizes a novel loss function to optimize the matrix-normal equation arising
from Fr\'echet mean estimation. Via experiments on synthetic data, we
demonstrate the efficacy of our mSPD-NN against common alternatives for SPD
mean estimation, providing competitive performance in terms of scalability and
robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in
multiple experiments on rs-fMRI data and demonstrate that it uncovers stable
biomarkers associated with subtle network differences among patients with
ADHD-ASD comorbidities and healthy controls.
|
[
{
"created": "Mon, 27 Mar 2023 08:30:11 GMT",
"version": "v1"
}
] |
2023-03-28
|
[
[
"D'Souza",
"Niharika S.",
""
],
[
"Venkataraman",
"Archana",
""
]
] |
Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic mean of a collections of symmetric positive definite (SPD) matrices. The mSPD-NN is comprised of bilinear fully connected layers with tied weights and utilizes a novel loss function to optimize the matrix-normal equation arising from Fr\'echet mean estimation. Via experiments on synthetic data, we demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation, providing competitive performance in terms of scalability and robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in multiple experiments on rs-fMRI data and demonstrate that it uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls.
|
1605.07793
|
Mark Leake
|
Adam J. M. Wollman, Aisha H. Syeda, Peter McGlynn, Mark C. Leake
|
Single-molecule observation of DNA replication repair pathways in E.
coli
| null | null | null | null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The method of action of many antibiotics is to interfere with DNA replication
- quinolones trap DNA gyrase and topoisomerase proteins onto DNA while
metronidazole causes single and double stranded breaks in DNA. To understand
how bacteria respond to these drugs, it is important to understand the repair
processes utilised when DNA replication is blocked. We have used tandem lac
operators inserted into the chromosome bound by fluorescently labelled lac
repressors as a model protein block to replication in E. coli. We have used
dual-colour, alternating-laser, single-molecule narrowfield microscopy to
quantify the amount of operator at the block and simultaneously image
fluorescently labelled DNA polymerase. We anticipate use of this system as a
quantitative platform to study replication stalling and repair proteins.
|
[
{
"created": "Wed, 25 May 2016 09:21:23 GMT",
"version": "v1"
}
] |
2016-05-26
|
[
[
"Wollman",
"Adam J. M.",
""
],
[
"Syeda",
"Aisha H.",
""
],
[
"McGlynn",
"Peter",
""
],
[
"Leake",
"Mark C.",
""
]
] |
The method of action of many antibiotics is to interfere with DNA replication - quinolones trap DNA gyrase and topoisomerase proteins onto DNA while metronidazole causes single and double stranded breaks in DNA. To understand how bacteria respond to these drugs, it is important to understand the repair processes utilised when DNA replication is blocked. We have used tandem lac operators inserted into the chromosome bound by fluorescently labelled lac repressors as a model protein block to replication in E. coli. We have used dual-colour, alternating-laser, single-molecule narrowfield microscopy to quantify the amount of operator at the block and simultaneously image fluorescently labelled DNA polymerase. We anticipate use of this system as a quantitative platform to study replication stalling and repair proteins.
|
2311.17194
|
Qian Gao
|
Qian Gao (1), G. Gurdeniz (1), Giulia Pratico (1), Camilla T.
Damsgaard (1), Eduvigis Roldan-Marin (2), M. Pilar Cano (3), Concepcion
Sanchez-Moreno (2), Lars O. Dragsted (1) ((1) Department of Nutrition,
Exercise and Sports, University of Copenhagen, Copenhagen, Denmark (2)
Institute of Food Science, Technology and Nutrition (ICTAN), Spanish National
Research Council (CSIC), Madrid, Spain (3) Department of Biotechnology and
Food Microbiology, Institute of Food Science Research (CIAL) (CSIC-UAM),
Madrid, Spain)
|
Identification of urinary biomarkers of food intake for onion by
untargeted LC-MS metabolomics
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Scope: Biomarkers of food intake (BFIs) are useful tools for objective
assessment of food intake and compliance. The aim of this study was to discover
and identify urinary BFIs for onion. Methods and results: In a randomized
controlled cross-over trial, 6 overweight participants (age 24-62 years)
consumed meals with 20 g/d onion powder or no onion for 2 weeks. Untargeted
UPLC-qTOF-MS metabolic profiling analysis was performed on urine samples and
the profiles were analysed by multilevel-PLSDA, modified PLS, and nearest
shrunken centroid to select features associated with onion intake. Eight
biomarkers were tentatively identified; six of them originated from
S-substituted cysteine derivatives such as isoalliin and propiin, which are
considered the most specific for onion intake. Most of the biomarkers were
completely excreted within 24 hours and no accumulation was observed during 2
weeks indicating their ability to reflect only recent intake of onions.
Receiver-operator curves were made to evaluate the performance of individual
biomarkers for predicting onion intake. The area under the curve values for
these biomarkers ranged from 0.81 to 1. Conclusion: Promising biomarkers of
recent onion intake have been identified in human urine. Further studies with
complex diets are needed to validate the robustness of these biomarkers.
|
[
{
"created": "Tue, 28 Nov 2023 19:55:01 GMT",
"version": "v1"
}
] |
2023-11-30
|
[
[
"Gao",
"Qian",
""
],
[
"Gurdeniz",
"G.",
""
],
[
"Pratico",
"Giulia",
""
],
[
"Damsgaard",
"Camilla T.",
""
],
[
"Roldan-Marin",
"Eduvigis",
""
],
[
"Cano",
"M. Pilar",
""
],
[
"Sanchez-Moreno",
"Concepcion",
""
],
[
"Dragsted",
"Lars O.",
""
]
] |
Scope: Biomarkers of food intake (BFIs) are useful tools for objective assessment of food intake and compliance. The aim of this study was to discover and identify urinary BFIs for onion. Methods and results: In a randomized controlled cross-over trial, 6 overweight participants (age 24-62 years) consumed meals with 20 g/d onion powder or no onion for 2 weeks. Untargeted UPLC-qTOF-MS metabolic profiling analysis was performed on urine samples and the profiles were analysed by multilevel-PLSDA, modified PLS, and nearest shrunken centroid to select features associated with onion intake. Eight biomarkers were tentatively identified; six of them originated from S-substituted cysteine derivatives such as isoalliin and propiin, which are considered the most specific for onion intake. Most of the biomarkers were completely excreted within 24 hours and no accumulation was observed during 2 weeks indicating their ability to reflect only recent intake of onions. Receiver-operator curves were made to evaluate the performance of individual biomarkers for predicting onion intake. The area under the curve values for these biomarkers ranged from 0.81 to 1. Conclusion: Promising biomarkers of recent onion intake have been identified in human urine. Further studies with complex diets are needed to validate the robustness of these biomarkers.
|
1808.04322
|
Dong Xu
|
Chao Fang, Yi Shang and Dong Xu
|
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn
Prediction
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Beta-turn prediction is useful in protein function studies and experimental
design. Although recent approaches using machine-learning techniques such as
SVM, neural networks, and K-NN have achieved good results for beta-turn
pre-diction, there is still significant room for improvement. As previous
predictors utilized features in a sliding window of 4-20 residues to capture
interactions among sequentially neighboring residues, such feature engineering
may result in incomplete or biased features, and neglect interactions among
long-range residues. Deep neural networks provide a new opportunity to address
these issues. Here, we proposed a deep dense inception network (DeepDIN) for
beta-turn prediction, which takes advantages of the state-of-the-art deep
neural network design of the DenseNet and the inception network. A test on a
recent BT6376 benchmark shows that the DeepDIN outperformed the previous best
BetaTPred3 significantly in both the overall prediction accuracy and the
nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was
developed, which is the first beta-turn prediction tool utilizing deep neural
networks. The tool can be downloaded at
http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.
|
[
{
"created": "Mon, 13 Aug 2018 16:28:50 GMT",
"version": "v1"
}
] |
2018-08-14
|
[
[
"Fang",
"Chao",
""
],
[
"Shang",
"Yi",
""
],
[
"Xu",
"Dong",
""
]
] |
Beta-turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine-learning techniques such as SVM, neural networks, and K-NN have achieved good results for beta-turn pre-diction, there is still significant room for improvement. As previous predictors utilized features in a sliding window of 4-20 residues to capture interactions among sequentially neighboring residues, such feature engineering may result in incomplete or biased features, and neglect interactions among long-range residues. Deep neural networks provide a new opportunity to address these issues. Here, we proposed a deep dense inception network (DeepDIN) for beta-turn prediction, which takes advantages of the state-of-the-art deep neural network design of the DenseNet and the inception network. A test on a recent BT6376 benchmark shows that the DeepDIN outperformed the previous best BetaTPred3 significantly in both the overall prediction accuracy and the nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was developed, which is the first beta-turn prediction tool utilizing deep neural networks. The tool can be downloaded at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.
|
1701.08443
|
Sebastian Schreiber
|
Sebastian J. Schreiber
|
A dynamical trichotomy for structured populations experiencing positive
density-dependence in stochastic environments
| null |
(2017) In: Elaydi S., Hamaya Y., Matsunaga H., P\"otzsche C. (eds)
Advances in Difference Equations and Discrete Dynamical Systems. ICDEA 2016.
Springer Proceedings in Mathematics & Statistics, vol 212. Springer,
Singapore
|
10.1007/978-981-10-6409-8_3
| null |
q-bio.PE math.DS math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Positive density-dependence occurs when individuals experience increased
survivorship, growth, or reproduction with increased population densities.
Mechanisms leading to these positive relationships include mate limitation,
saturating predation risk, and cooperative breeding and foraging. Individuals
within these populations may differ in age, size, or geographic location and
thereby structure these populations. Here, I study structured population models
accounting for positive density-dependence and environmental stochasticity i.e.
random fluctuations in the demographic rates of the population. Under an
accessibility assumption (roughly, stochastic fluctuations can lead to
populations getting small and large), these models are shown to exhibit a
dynamical trichotomy: (i) for all initial conditions, the population goes
asymptotically extinct with probability one, (ii) for all positive initial
conditions, the population persists and asymptotically exhibits unbounded
growth, and (iii) for all positive initial conditions, there is a positive
probability of asymptotic extinction and a complementary positive probability
of unbounded growth. The main results are illustrated with applications to
spatially structured populations with an Allee effect and age-structured
populations experiencing mate limitation.
|
[
{
"created": "Sun, 29 Jan 2017 22:21:11 GMT",
"version": "v1"
}
] |
2019-02-12
|
[
[
"Schreiber",
"Sebastian J.",
""
]
] |
Positive density-dependence occurs when individuals experience increased survivorship, growth, or reproduction with increased population densities. Mechanisms leading to these positive relationships include mate limitation, saturating predation risk, and cooperative breeding and foraging. Individuals within these populations may differ in age, size, or geographic location and thereby structure these populations. Here, I study structured population models accounting for positive density-dependence and environmental stochasticity i.e. random fluctuations in the demographic rates of the population. Under an accessibility assumption (roughly, stochastic fluctuations can lead to populations getting small and large), these models are shown to exhibit a dynamical trichotomy: (i) for all initial conditions, the population goes asymptotically extinct with probability one, (ii) for all positive initial conditions, the population persists and asymptotically exhibits unbounded growth, and (iii) for all positive initial conditions, there is a positive probability of asymptotic extinction and a complementary positive probability of unbounded growth. The main results are illustrated with applications to spatially structured populations with an Allee effect and age-structured populations experiencing mate limitation.
|
2212.03859
|
Keith Li Chambers
|
Keith L Chambers, Mary R Myerscough and Helen M Byrne
|
A new lipid-structured model to investigate the opposing effects of LDL
and HDL on atherosclerotic plaque macrophages
|
44 pages, 13 figures
| null | null | null |
q-bio.CB q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Atherosclerotic plaques form in artery walls due to a chronic inflammatory
response driven by lipid accumulation. A key component of the inflammatory
response is the interaction between monocyte-derived macrophages and
extracellular lipid. Although concentrations of low-density lipoprotein (LDL)
and high-density lipoprotein (HDL) particles in the blood are known to affect
plaque progression, their impact on the lipid load of plaque macrophages
remains unexplored. In this paper, we develop a lipid-structured mathematical
model to investigate the impact of blood LDL/HDL levels on plaque composition,
and lipid distribution in plaque macrophages. A reduced subsystem, derived by
summing the equations of the full model, describes the dynamics of biophysical
quantities relating to plaque composition (e.g. total number of macrophages,
total amount of intracellular lipid). We also derive a continuum approximation
of the model to facilitate analysis of the macrophage lipid distribution. The
results, which include time-dependent numerical solutions and asymptotic
analysis of the unique steady state solution, indicate that plaque lipid
content is sensitive to the influx of LDL relative to HDL capacity. The
macrophage lipid distribution evolves in a wave-like manner towards an
equilibrium profile which may be monotone decreasing, quasi-uniform or
unimodal, attaining its maximum value at a non-zero lipid level. Our model also
reveals that macrophage uptake may be severely impaired by lipid accumulation.
We conclude that lipid accumulation in plaque macrophages may serve as a
partial explanation for the defective uptake of apoptotic cells (efferocytosis)
often reported in atherosclerotic plaques.
|
[
{
"created": "Wed, 7 Dec 2022 18:57:59 GMT",
"version": "v1"
}
] |
2022-12-08
|
[
[
"Chambers",
"Keith L",
""
],
[
"Myerscough",
"Mary R",
""
],
[
"Byrne",
"Helen M",
""
]
] |
Atherosclerotic plaques form in artery walls due to a chronic inflammatory response driven by lipid accumulation. A key component of the inflammatory response is the interaction between monocyte-derived macrophages and extracellular lipid. Although concentrations of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particles in the blood are known to affect plaque progression, their impact on the lipid load of plaque macrophages remains unexplored. In this paper, we develop a lipid-structured mathematical model to investigate the impact of blood LDL/HDL levels on plaque composition, and lipid distribution in plaque macrophages. A reduced subsystem, derived by summing the equations of the full model, describes the dynamics of biophysical quantities relating to plaque composition (e.g. total number of macrophages, total amount of intracellular lipid). We also derive a continuum approximation of the model to facilitate analysis of the macrophage lipid distribution. The results, which include time-dependent numerical solutions and asymptotic analysis of the unique steady state solution, indicate that plaque lipid content is sensitive to the influx of LDL relative to HDL capacity. The macrophage lipid distribution evolves in a wave-like manner towards an equilibrium profile which may be monotone decreasing, quasi-uniform or unimodal, attaining its maximum value at a non-zero lipid level. Our model also reveals that macrophage uptake may be severely impaired by lipid accumulation. We conclude that lipid accumulation in plaque macrophages may serve as a partial explanation for the defective uptake of apoptotic cells (efferocytosis) often reported in atherosclerotic plaques.
|
2111.07760
|
Rinaldo Schinazi
|
Rinaldo B. Schinazi
|
Evolutionary paths under catastrophes
| null | null | null | null |
q-bio.PE math.PR
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a model to study the impact of catastrophes on evolutionary
paths. If we do not allow catastrophes the number of changes in the maximum
fitness of a population grows logarithmically with respect to time. Allowing
catastrophes (no matter how rare) yields a drastically different behavior. When
catastrophes are possible the number of changes in the maximum fitness of the
population grows linearly with time. Moreover, the evolutionary paths are a lot
less predictable when catastrophes are possible. Our results can be seen as
supporting the hypothesis that catastrophes speed up evolution by disrupting
dominant species and creating space for new species to emerge and evolve.
|
[
{
"created": "Mon, 15 Nov 2021 13:58:35 GMT",
"version": "v1"
}
] |
2021-11-16
|
[
[
"Schinazi",
"Rinaldo B.",
""
]
] |
We introduce a model to study the impact of catastrophes on evolutionary paths. If we do not allow catastrophes the number of changes in the maximum fitness of a population grows logarithmically with respect to time. Allowing catastrophes (no matter how rare) yields a drastically different behavior. When catastrophes are possible the number of changes in the maximum fitness of the population grows linearly with time. Moreover, the evolutionary paths are a lot less predictable when catastrophes are possible. Our results can be seen as supporting the hypothesis that catastrophes speed up evolution by disrupting dominant species and creating space for new species to emerge and evolve.
|
1803.05357
|
Yasuaki Kobayashi
|
Yasuaki Kobayashi, Yusuke Yasugahira, Hiroyuki Kitahata, Mika
Watanabe, Ken Natsuga, Masaharu Nagayama
|
Interplay between epidermal stem cell dynamics and dermal deformations
|
10 pages, 8 figures
|
npj Computational Materials 4, 45 (2018)
|
10.1038/s41524-018-0101-z
| null |
q-bio.TO nlin.AO q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a particle-based model of self-replicating cells on a deformable
substrate composed of the dermis and the basement membrane and investigate the
relationship between dermal deformations and stem cell pattering on it. We show
that our model reproduces the formation of dermal papillae, protuberances
directing from the dermis to the epidermis, and the preferential stem cell
distributions on the tips of the dermal papillae, which the basic buckling
mechanism fails to explain. We argue that cell-type-dependent adhesion strength
of the cells to the basement membrane is crucial factors of these patterns.
|
[
{
"created": "Thu, 8 Mar 2018 07:50:29 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jun 2018 08:41:56 GMT",
"version": "v2"
}
] |
2018-08-22
|
[
[
"Kobayashi",
"Yasuaki",
""
],
[
"Yasugahira",
"Yusuke",
""
],
[
"Kitahata",
"Hiroyuki",
""
],
[
"Watanabe",
"Mika",
""
],
[
"Natsuga",
"Ken",
""
],
[
"Nagayama",
"Masaharu",
""
]
] |
We introduce a particle-based model of self-replicating cells on a deformable substrate composed of the dermis and the basement membrane and investigate the relationship between dermal deformations and stem cell pattering on it. We show that our model reproduces the formation of dermal papillae, protuberances directing from the dermis to the epidermis, and the preferential stem cell distributions on the tips of the dermal papillae, which the basic buckling mechanism fails to explain. We argue that cell-type-dependent adhesion strength of the cells to the basement membrane is crucial factors of these patterns.
|
q-bio/0505030
|
Guido Tiana
|
A. Amatori, G. Tiana, L. Sutto, J.Ferkinghoff-Borg, A. Trovato and R.
A. Broglia
|
Design of amino acid sequences to fold into C_alpha-model proteins
| null | null |
10.1063/1.1992447
| null |
q-bio.BM
| null |
In order to extend the results obtained with minimal lattice models to more
realistic systems, we study a model where proteins are described as a chain of
20 kinds of structureless amino acids moving in a continuum space and
interacting through a contact potential controlled by a 20x20 quenched random
matrix. The goal of the present work is to design and characterize amino acid
sequences folding to the SH3 conformation, a 60-residues recognition domain
common to many regulatory proteins. We show that a number of sequences can
fold, starting from a random conformation, to within a distance root mean
square deviation (dRMSD) of 2.6A from the native state. Good folders are those
sequences displaying in the native conformation an energy lower than a
sequence--independent threshold energy.
|
[
{
"created": "Mon, 16 May 2005 15:11:00 GMT",
"version": "v1"
}
] |
2009-11-11
|
[
[
"Amatori",
"A.",
""
],
[
"Tiana",
"G.",
""
],
[
"Sutto",
"L.",
""
],
[
"Ferkinghoff-Borg",
"J.",
""
],
[
"Trovato",
"A.",
""
],
[
"Broglia",
"R. A.",
""
]
] |
In order to extend the results obtained with minimal lattice models to more realistic systems, we study a model where proteins are described as a chain of 20 kinds of structureless amino acids moving in a continuum space and interacting through a contact potential controlled by a 20x20 quenched random matrix. The goal of the present work is to design and characterize amino acid sequences folding to the SH3 conformation, a 60-residues recognition domain common to many regulatory proteins. We show that a number of sequences can fold, starting from a random conformation, to within a distance root mean square deviation (dRMSD) of 2.6A from the native state. Good folders are those sequences displaying in the native conformation an energy lower than a sequence--independent threshold energy.
|
1310.3693
|
Andrea De Martino
|
Daniele De Martino, Fabrizio Capuani, Matteo Mori, Andrea De Martino,
Enzo Marinari
|
Counting and correcting thermodynamically infeasible flux cycles in
genome-scale metabolic networks
|
10 pages; see http://chimera.roma1.infn.it/SYSBIO/ for supporting
files
|
Metabolites 3:946 (2013)
|
10.3390/metabo3040946
| null |
q-bio.MN cond-mat.dis-nn cond-mat.stat-mech physics.bio-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Thermodynamics constrains the flow of matter in a reaction network to occur
through routes along which the Gibbs energy decreases, implying that viable
steady-state flux patterns should be void of closed reaction cycles.
Identifying and removing cycles in large reaction networks can unfortunately be
a highly challenging task from a computational viewpoint. We propose here a
method that accomplishes it by combining a relaxation algorithm and a Monte
Carlo procedure to detect loops, with ad hoc rules (discussed in detail) to
eliminate them. As test cases, we tackle (a) the problem of identifying
infeasible cycles in the E. coli metabolic network and (b) the problem of
correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions
for 15 human cell-type specific metabolic networks. Results for (a) are
compared with previous analyses of the same issue, while results for (b) are
weighed against alternative methods to retrieve thermodynamically viable flux
patterns based on minimizing specific global quantities. Our method on one hand
outperforms previous techniques and, on the other, corrects loopy solutions to
Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal
possible inconsistencies in model reconstructions.
|
[
{
"created": "Mon, 14 Oct 2013 14:26:35 GMT",
"version": "v1"
}
] |
2013-10-15
|
[
[
"De Martino",
"Daniele",
""
],
[
"Capuani",
"Fabrizio",
""
],
[
"Mori",
"Matteo",
""
],
[
"De Martino",
"Andrea",
""
],
[
"Marinari",
"Enzo",
""
]
] |
Thermodynamics constrains the flow of matter in a reaction network to occur through routes along which the Gibbs energy decreases, implying that viable steady-state flux patterns should be void of closed reaction cycles. Identifying and removing cycles in large reaction networks can unfortunately be a highly challenging task from a computational viewpoint. We propose here a method that accomplishes it by combining a relaxation algorithm and a Monte Carlo procedure to detect loops, with ad hoc rules (discussed in detail) to eliminate them. As test cases, we tackle (a) the problem of identifying infeasible cycles in the E. coli metabolic network and (b) the problem of correcting thermodynamic infeasibilities in the Flux-Balance-Analysis solutions for 15 human cell-type specific metabolic networks. Results for (a) are compared with previous analyses of the same issue, while results for (b) are weighed against alternative methods to retrieve thermodynamically viable flux patterns based on minimizing specific global quantities. Our method on one hand outperforms previous techniques and, on the other, corrects loopy solutions to Flux Balance Analysis. As a byproduct, it also turns out to be able to reveal possible inconsistencies in model reconstructions.
|
2310.09178
|
Simone Franchini Dr.
|
Giampiero Bardella, Simone Franchini, Liming Pan, Riccardo Balzan,
Surabhi Ramawat, Emiliano Brunamonti, Pierpaolo Pani, and Stefano Ferraina
|
Neural activity in quarks language: Lattice Field Theory for a network
of real neurons
|
79 pages, 20 figures
|
Entropy 2024, 26(6), 495
|
10.3390/e26060495
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Brain-computer interfaces surged extraordinary developments in recent years,
and a significant discrepancy now exists between the abundance of available
data and the limited headway made in achieving a unified theoretical framework.
This discrepancy becomes particularly pronounced when examining the collective
neural activity at the micro- and meso-scale, where a coherent formalization
that adequately describes neural interactions is still lacking. Here, we
introduce a mathematical framework to analyze systems of natural neurons and
interpret the related empirical observations in terms of lattice field theory,
an established paradigm from theoretical particle physics and statistical
mechanics. Our methods are tailored to interpret data from chronic neural
interfaces, especially spike rasters from measurements of single neurons
activity, and generalize the maximum entropy model for neural networks so that
also the time evolution of the system is taken into account. This is obtained
by bridging particle physics and neuroscience, paving the way to particle
physics-inspired models of neocortex.
|
[
{
"created": "Fri, 13 Oct 2023 15:14:23 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Dec 2023 09:27:45 GMT",
"version": "v2"
},
{
"created": "Sun, 24 Mar 2024 03:29:11 GMT",
"version": "v3"
}
] |
2024-06-07
|
[
[
"Bardella",
"Giampiero",
""
],
[
"Franchini",
"Simone",
""
],
[
"Pan",
"Liming",
""
],
[
"Balzan",
"Riccardo",
""
],
[
"Ramawat",
"Surabhi",
""
],
[
"Brunamonti",
"Emiliano",
""
],
[
"Pani",
"Pierpaolo",
""
],
[
"Ferraina",
"Stefano",
""
]
] |
Brain-computer interfaces surged extraordinary developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro- and meso-scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neurons activity, and generalize the maximum entropy model for neural networks so that also the time evolution of the system is taken into account. This is obtained by bridging particle physics and neuroscience, paving the way to particle physics-inspired models of neocortex.
|
2004.12979
|
Patricio Arru\'e Pa
|
Patricio Arrue, Nima Toosizadeh, Hessam Babaee, Kaveh Laksari
|
Low-rank representation of head impact kinematics: A data-driven
emulator
|
20 pages, 13 figures, 4 tables
|
Front. Bioeng. Biotechnol. (2020) 8:555493
|
10.3389/fbioe.2020.555493
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Head motion induced by impacts has been deemed as one of the most important
measures in brain injury prediction, given that the majority of brain injury
metrics use head kinematics as input. Recently, researchers have focused on
using fast approaches, such as machine learning, to approximate brain
deformation in real-time for early brain injury diagnosis. However, those
requires large number of kinematic measurements, and therefore data
augmentation is required given the limited on-field measured data available. In
this study we present a principal component analysis-based method that emulates
an empirical low-rank substitution for head impact kinematics, while requiring
low computational cost. In characterizing our existing data set of 537 head
impacts, consisting of 6 degrees of freedom measurements, we found that only a
few modes, e.g. 15 in the case of angular velocity, is sufficient for accurate
reconstruction of the entire data set. Furthermore, these modes are
predominantly low frequency since over 70% to 90% of the angular velocity
response can be captured by modes that have frequencies under 40Hz. We compared
our proposed method against existing impact parametrization methods and showed
significantly better performance in injury prediction using a range of
kinematic-based metrics -- such as head injury criterion and rotational injury
criterion (RIC) -- and brain tissue deformation-metrics -- such as brain angle
metric, maximum principal strain (MPS) and axonal fiber strains (FS). In all
cases, our approach reproduced injury metrics similar to the ground truth
measurements with no significant difference, whereas the existing methods
obtained significantly different (p<0.01) values as well as poor injury
classification sensitivity and specificity. This emulator will enable us to
provide the necessary data augmentation to build a head impact kinematic data
set of any size.
|
[
{
"created": "Mon, 27 Apr 2020 17:47:59 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Dec 2020 21:59:11 GMT",
"version": "v2"
}
] |
2020-12-11
|
[
[
"Arrue",
"Patricio",
""
],
[
"Toosizadeh",
"Nima",
""
],
[
"Babaee",
"Hessam",
""
],
[
"Laksari",
"Kaveh",
""
]
] |
Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real-time for early brain injury diagnosis. However, those requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g. 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% to 90% of the angular velocity response can be captured by modes that have frequencies under 40Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics -- such as head injury criterion and rotational injury criterion (RIC) -- and brain tissue deformation-metrics -- such as brain angle metric, maximum principal strain (MPS) and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p<0.01) values as well as poor injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size.
|
q-bio/0701040
|
Brigitte Gaillard
|
J.Y. Georges (DEPE-Iphc), A. Billes, S. Ferraroli (DEPE-Iphc), S.
Fossette (DEPE-Iphc), J. Fretey, D. Gr\'emillet (DEPE-Iphc), Y. Le Maho
(DEPE-Iphc), A. E. Myers, H. Tanaka, G. C. Hays
|
Meta-analysis of movements in Atlantic leatherback turtles during
nesting season : conservation implications
| null | null | null | null |
q-bio.PE
| null |
Despite decades of conservation efforts on the nesting beaches, the critical
status of leatherback turtles shows that their survival predominantly depends
on our ability to reduce at-sea mortality. Although areas where leatherbacks
meet fisheries have been identified during the long distance movements between
two consecutive nesting seasons, hotspots of lethal interactions are still
poorly defined within the nesting season, when individuals concentrate close to
land. Here we report movements of satellite-tracked gravid leatherback turtles
during the nesting season in Western Central Africa, South America and
Caribbean Sea, accounting for about 70% of the world population. We show that
during, and at the end of, the nesting season leatherback turtles have the
propensity to remain over the continental shelf, yet sometimes perform extended
movements and may even nest in neighbouring countries. Leatherbacks exploit
coastal commercial fishing grounds and face substantial accidental capture by
regional coastal fisheries (e.g. at least 10% in French Guiana). This
emphasises the need for regional conservation strategies to be developed at the
ocean scale, both at sea and on land, to ensure the survival of the last
leatherback turtles.
|
[
{
"created": "Thu, 25 Jan 2007 14:34:19 GMT",
"version": "v1"
}
] |
2016-08-14
|
[
[
"Georges",
"J. Y.",
"",
"DEPE-Iphc"
],
[
"Billes",
"A.",
"",
"DEPE-Iphc"
],
[
"Ferraroli",
"S.",
"",
"DEPE-Iphc"
],
[
"Fossette",
"S.",
"",
"DEPE-Iphc"
],
[
"Fretey",
"J.",
"",
"DEPE-Iphc"
],
[
"Grémillet",
"D.",
"",
"DEPE-Iphc"
],
[
"Maho",
"Y. Le",
"",
"DEPE-Iphc"
],
[
"Myers",
"A. E.",
""
],
[
"Tanaka",
"H.",
""
],
[
"Hays",
"G. C.",
""
]
] |
Despite decades of conservation efforts on the nesting beaches, the critical status of leatherback turtles shows that their survival predominantly depends on our ability to reduce at-sea mortality. Although areas where leatherbacks meet fisheries have been identified during the long distance movements between two consecutive nesting seasons, hotspots of lethal interactions are still poorly defined within the nesting season, when individuals concentrate close to land. Here we report movements of satellite-tracked gravid leatherback turtles during the nesting season in Western Central Africa, South America and Caribbean Sea, accounting for about 70% of the world population. We show that during, and at the end of, the nesting season leatherback turtles have the propensity to remain over the continental shelf, yet sometimes perform extended movements and may even nest in neighbouring countries. Leatherbacks exploit coastal commercial fishing grounds and face substantial accidental capture by regional coastal fisheries (e.g. at least 10% in French Guiana). This emphasises the need for regional conservation strategies to be developed at the ocean scale, both at sea and on land, to ensure the survival of the last leatherback turtles.
|
2304.05908
|
Moo K. Chung
|
Moo K. Chung, Tahmineh Azizi, Jamie L. Hanson, Andrew L. Alexander,
Richard J. Davidson, Seth D. Pollak
|
Altered Topological Structure of the Brain White Matter in Maltreated
Children through Topological Data Analysis
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Childhood maltreatment may adversely affect brain development and
consequently influence behavioral, emotional, and psychological patterns during
adulthood. In this study, we propose an analytical pipeline for modeling the
altered topological structure of brain white matter in maltreated and typically
developing children. We perform topological data analysis (TDA) to assess the
alteration in the global topology of the brain white-matter structural
covariance network among children. We use persistent homology, an algebraic
technique in TDA, to analyze topological features in the brain covariance
networks constructed from structural magnetic resonance imaging (MRI) and
diffusion tensor imaging (DTI). We develop a novel framework for statistical
inference based on the Wasserstein distance to assess the significance of the
observed topological differences. Using these methods in comparing maltreated
children to a typically developing control group, we find that maltreatment may
increase homogeneity in white matter structures and thus induce higher
correlations in the structural covariance; this is reflected in the topological
profile. Our findings strongly suggest that TDA can be a valuable framework to
model altered topological structures of the brain. The MATLAB codes and
processed data used in this study can be found at
https://github.com/laplcebeltrami/maltreated.
|
[
{
"created": "Wed, 12 Apr 2023 15:25:01 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Sep 2023 18:07:05 GMT",
"version": "v2"
},
{
"created": "Wed, 15 Nov 2023 02:12:50 GMT",
"version": "v3"
}
] |
2023-11-16
|
[
[
"Chung",
"Moo K.",
""
],
[
"Azizi",
"Tahmineh",
""
],
[
"Hanson",
"Jamie L.",
""
],
[
"Alexander",
"Andrew L.",
""
],
[
"Davidson",
"Richard J.",
""
],
[
"Pollak",
"Seth D.",
""
]
] |
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.
|
1108.1775
|
Natalia Denesyuk
|
Natalia A. Denesyuk and D. Thirumalai
|
Crowding Promotes the Switch from Hairpin to Pseudoknot Conformation in
Human Telomerase RNA
|
File "JACS_MAIN_archive_PDF_from_DOC.pdf" (PDF created from DOC)
contains the main text of the paper File JACS_SI_archive.tex + 7 figures are
the supplementary info
|
J. Am. Chem. Soc., 2011, 133 (31), pp 11858--11861
|
10.1021/ja2035128
| null |
q-bio.BM cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Formation of a pseudoknot in the conserved RNA core domain in the
ribonucleoprotein human telomerase is required for function. In vitro
experiments show that the pseudoknot (PK) is in equilibrium with an extended
hairpin (HP) structure. We use molecular simulations of a coarse-grained model,
which reproduces most of the salient features of the experimental melting
profiles of PK and HP, to show that crowding enhances the stability of PK
relative to HP in the wild type and in a mutant associated with dyskeratosis
congenita. In monodisperse suspensions, small crowding particles increase the
stability of compact structures to a greater extent than larger crowders. If
the sizes of crowders in a binary mixture are smaller than the unfolded RNA,
the increase in melting temperature due to the two components is additive. In a
ternary mixture of crowders that are larger than the unfolded RNA, which mimics
the composition of ribosome, large enzyme complexes and proteins in E. coli,
the marginal increase in stability is entirely determined by the smallest
component. We predict that crowding can restore partially telomerase activity
in mutants, which dramatically decrease the PK stability.
|
[
{
"created": "Mon, 8 Aug 2011 18:25:40 GMT",
"version": "v1"
}
] |
2011-08-09
|
[
[
"Denesyuk",
"Natalia A.",
""
],
[
"Thirumalai",
"D.",
""
]
] |
Formation of a pseudoknot in the conserved RNA core domain in the ribonucleoprotein human telomerase is required for function. In vitro experiments show that the pseudoknot (PK) is in equilibrium with an extended hairpin (HP) structure. We use molecular simulations of a coarse-grained model, which reproduces most of the salient features of the experimental melting profiles of PK and HP, to show that crowding enhances the stability of PK relative to HP in the wild type and in a mutant associated with dyskeratosis congenita. In monodisperse suspensions, small crowding particles increase the stability of compact structures to a greater extent than larger crowders. If the sizes of crowders in a binary mixture are smaller than the unfolded RNA, the increase in melting temperature due to the two components is additive. In a ternary mixture of crowders that are larger than the unfolded RNA, which mimics the composition of ribosome, large enzyme complexes and proteins in E. coli, the marginal increase in stability is entirely determined by the smallest component. We predict that crowding can restore partially telomerase activity in mutants, which dramatically decrease the PK stability.
|
1703.10627
|
Mans Henningson
|
M{\aa}ns Henningson and Sebastian Illes
|
Analysis and Modelling of Subthreshold Neural Multi-electrode Array Data
by Statistical Field Theory
|
27 pages, 13 figures
| null | null | null |
q-bio.NC cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous
neuronal network activity. The recorded signals comprise several distinct
components: Apart from artefacts without biological significance, one can
distinguish between spikes (action potentials) and subthreshold fluctuations
(local fields potentials). Here we aim to develop a theoretical model that
allows for a compact and robust characterization of subthreshold fluctuations
in terms of a Gaussian statistical field theory in two spatial and one temporal
dimension. What is usually referred to as the driving noise in the context of
statistical physics is here interpreted as a representation of the neural
activity. Spatial and temporal correlations of this activity give valuable
information about the connectivity in the neural tissue. We apply our methods
on a dataset obtained from MEA-measurements in an acute hippocampal brain slice
from a rat. Our main finding is that the empirical correlation functions indeed
obey the logarithmic behaviour that is a general feature of theoretical models
of this kind. We also find a clear correlation between the activity and the
occurence of spikes. Another important insight is the importance of correcly
separating out certain artefacts from the data before proceeding with the
analysis.
|
[
{
"created": "Thu, 30 Mar 2017 18:21:30 GMT",
"version": "v1"
}
] |
2017-04-03
|
[
[
"Henningson",
"Måns",
""
],
[
"Illes",
"Sebastian",
""
]
] |
Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artefacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aim to develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice from a rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behaviour that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurence of spikes. Another important insight is the importance of correcly separating out certain artefacts from the data before proceeding with the analysis.
|
1307.7302
|
Thomas Dean
|
Thomas Dean, Biafra Ahanonu, Mainak Chowdhury, Anjali Datta, Andre
Esteva, Daniel Eth, Nobie Redmon, Oleg Rumyantsev, Ysis Tarter
|
On the Technology Prospects and Investment Opportunities for Scalable
Neuroscience
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Two major initiatives to accelerate research in the brain sciences have
focused attention on developing a new generation of scientific instruments for
neuroscience. These instruments will be used to record static (structural) and
dynamic (behavioral) information at unprecedented spatial and temporal
resolution and report out that information in a form suitable for computational
analysis. We distinguish between recording - taking measurements of individual
cells and the extracellular matrix - and reporting - transcoding, packaging and
transmitting the resulting information for subsequent analysis - as these
represent very different challenges as we scale the relevant technologies to
support simultaneously tracking the many neurons that comprise neural circuits
of interest. We investigate a diverse set of technologies with the purpose of
anticipating their development over the span of the next 10 years and
categorizing their impact in terms of short-term [1-2 years], medium-term [2-5
years] and longer-term [5-10 years] deliverables.
|
[
{
"created": "Sat, 27 Jul 2013 20:25:00 GMT",
"version": "v1"
}
] |
2013-07-30
|
[
[
"Dean",
"Thomas",
""
],
[
"Ahanonu",
"Biafra",
""
],
[
"Chowdhury",
"Mainak",
""
],
[
"Datta",
"Anjali",
""
],
[
"Esteva",
"Andre",
""
],
[
"Eth",
"Daniel",
""
],
[
"Redmon",
"Nobie",
""
],
[
"Rumyantsev",
"Oleg",
""
],
[
"Tarter",
"Ysis",
""
]
] |
Two major initiatives to accelerate research in the brain sciences have focused attention on developing a new generation of scientific instruments for neuroscience. These instruments will be used to record static (structural) and dynamic (behavioral) information at unprecedented spatial and temporal resolution and report out that information in a form suitable for computational analysis. We distinguish between recording - taking measurements of individual cells and the extracellular matrix - and reporting - transcoding, packaging and transmitting the resulting information for subsequent analysis - as these represent very different challenges as we scale the relevant technologies to support simultaneously tracking the many neurons that comprise neural circuits of interest. We investigate a diverse set of technologies with the purpose of anticipating their development over the span of the next 10 years and categorizing their impact in terms of short-term [1-2 years], medium-term [2-5 years] and longer-term [5-10 years] deliverables.
|
1604.06300
|
Serge Sheremet'ev
|
Serge Sheremet'ev, Xenia Chebotareva
|
Current and Cretaceous-Cenozoic diversification of Angiosperms
|
51 pp with 18 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cretaceous-Cenozoic history of angiosperms led to the a certain character of
the distribution of taxa of different levels (the number of species and genera
in families, species/genera ratio in families, the number of species in the
genera). In most cases, these distributions are satisfactorily described by a
power law (Pareto distribution). In logarithmic coordinates power function is a
straight line. Empirical curves repeat this line is good enough, but on the
right side of the graph (at low volumes taxa), there is a marked deviation of
theoretical from the empirical curves. This suggests that the small volumes of
taxa should be greater for full compliance with the theoretical curves.
Modeling ratios among genera and species in families showed that only in the
case of dynamic extinction factor observed satisfactory agreement between
observed and calculated the number of species in a wide range of iterations.
This suggested that there was a differential extinction of species during the
evolution of angiosperms. This implies that the rate of extinction had to be
minimal in genera with a large number of species. On the contrary, extinction
rates may be increased by orders of magnitude with a decrease in the number of
species. As a result, large genera became getting bigger and small genera
become less. The species frequencies of distribution in the genera varied
according to a power law. The initial divergence of taxa volume, which led to
their further division into large and small, could be caused by the emergence
and expansion of herbs with their functional and adaptive capabilities.
|
[
{
"created": "Thu, 21 Apr 2016 13:37:11 GMT",
"version": "v1"
}
] |
2016-04-22
|
[
[
"Sheremet'ev",
"Serge",
""
],
[
"Chebotareva",
"Xenia",
""
]
] |
Cretaceous-Cenozoic history of angiosperms led to the a certain character of the distribution of taxa of different levels (the number of species and genera in families, species/genera ratio in families, the number of species in the genera). In most cases, these distributions are satisfactorily described by a power law (Pareto distribution). In logarithmic coordinates power function is a straight line. Empirical curves repeat this line is good enough, but on the right side of the graph (at low volumes taxa), there is a marked deviation of theoretical from the empirical curves. This suggests that the small volumes of taxa should be greater for full compliance with the theoretical curves. Modeling ratios among genera and species in families showed that only in the case of dynamic extinction factor observed satisfactory agreement between observed and calculated the number of species in a wide range of iterations. This suggested that there was a differential extinction of species during the evolution of angiosperms. This implies that the rate of extinction had to be minimal in genera with a large number of species. On the contrary, extinction rates may be increased by orders of magnitude with a decrease in the number of species. As a result, large genera became getting bigger and small genera become less. The species frequencies of distribution in the genera varied according to a power law. The initial divergence of taxa volume, which led to their further division into large and small, could be caused by the emergence and expansion of herbs with their functional and adaptive capabilities.
|
2211.16638
|
Justin Yeakel
|
Taran Rallings, Christopher P. Kempes, Justin D. Yeakel
|
On the dynamics of mortality and the ephemeral nature of mammalian
megafauna
|
10 pages, 5 figures, 1 table, 4 appendices, 8 supplementary figures
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Energy flow through consumer-resource interactions is largely determined by
body size. Allometric relationships govern the dynamics of populations by
impacting rates of reproduction, as well as alternative sources of mortality,
which have differential impacts on smaller to larger organisms. Here we derive
and investigate the timescales associated with four alternative sources of
mortality for terrestrial mammals: mortality from starvation, mortality
associated with aging, mortality from consumption by predators, and mortality
introduced by anthropogenic subsidized harvest. The incorporation of these
allometric relationships into a minimal consumer-resource model illuminates
central constraints that may contribute to the structure of mammalian
communities. Our framework reveals that while starvation largely impacts
smaller-bodied species, the allometry of senescence is expected to be more
difficult to observe. In contrast, external predation and subsidized harvest
have greater impacts on the populations of larger-bodied species. Moreover, the
inclusion of predation mortality reveals mass thresholds for mammalian
herbivores, where dynamic instabilities may limit the feasibility of megafaunal
populations. We show how these thresholds vary with alternative predator-prey
mass relationships, which are not well understood within terrestrial systems.
Finally, we use our framework to predict the harvest pressure required to
induce mass-specific extinctions, which closely align with previous estimates
of anthropogenic megafaunal exploitation in both paleontological and historical
contexts. Together our results underscore the tenuous nature of megafaunal
populations, and how different sources of mortality may contribute to their
ephemeral nature over evolutionary time.
|
[
{
"created": "Wed, 30 Nov 2022 00:08:45 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Aug 2023 21:46:54 GMT",
"version": "v2"
}
] |
2023-08-28
|
[
[
"Rallings",
"Taran",
""
],
[
"Kempes",
"Christopher P.",
""
],
[
"Yeakel",
"Justin D.",
""
]
] |
Energy flow through consumer-resource interactions is largely determined by body size. Allometric relationships govern the dynamics of populations by impacting rates of reproduction, as well as alternative sources of mortality, which have differential impacts on smaller to larger organisms. Here we derive and investigate the timescales associated with four alternative sources of mortality for terrestrial mammals: mortality from starvation, mortality associated with aging, mortality from consumption by predators, and mortality introduced by anthropogenic subsidized harvest. The incorporation of these allometric relationships into a minimal consumer-resource model illuminates central constraints that may contribute to the structure of mammalian communities. Our framework reveals that while starvation largely impacts smaller-bodied species, the allometry of senescence is expected to be more difficult to observe. In contrast, external predation and subsidized harvest have greater impacts on the populations of larger-bodied species. Moreover, the inclusion of predation mortality reveals mass thresholds for mammalian herbivores, where dynamic instabilities may limit the feasibility of megafaunal populations. We show how these thresholds vary with alternative predator-prey mass relationships, which are not well understood within terrestrial systems. Finally, we use our framework to predict the harvest pressure required to induce mass-specific extinctions, which closely align with previous estimates of anthropogenic megafaunal exploitation in both paleontological and historical contexts. Together our results underscore the tenuous nature of megafaunal populations, and how different sources of mortality may contribute to their ephemeral nature over evolutionary time.
|
1804.06050
|
Jingyi Jessica Li
|
Wei Vivian Li, Jingyi Jessica Li
|
Modeling and analysis of RNA-seq data: a review from a statistical
perspective
| null |
Quantitative Biology 6 (2018) 195-209
|
10.1007/s40484-018-0144-7
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Since the invention of next-generation RNA sequencing (RNA-seq)
technologies, they have become a powerful tool to study the presence and
quantity of RNA molecules in biological samples and have revolutionized
transcriptomic studies. The analysis of RNA-seq data at four different levels
(samples, genes, transcripts, and exons) involve multiple statistical and
computational questions, some of which remain challenging up to date.
Results: We review RNA-seq analysis tools at the sample, gene, transcript,
and exon levels from a statistical perspective. We also highlight the
biological and statistical questions of most practical considerations.
Conclusion: The development of statistical and computational methods for
analyzing RNA- seq data has made significant advances in the past decade.
However, methods developed to answer the same biological question often rely on
diverse statical models and exhibit different performance under different
scenarios. This review discusses and compares multiple commonly used
statistical models regarding their assumptions, in the hope of helping users
select appropriate methods as needed, as well as assisting developers for
future method development.
|
[
{
"created": "Tue, 17 Apr 2018 05:26:53 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Apr 2018 09:10:58 GMT",
"version": "v2"
},
{
"created": "Tue, 1 May 2018 15:01:45 GMT",
"version": "v3"
}
] |
2021-12-01
|
[
[
"Li",
"Wei Vivian",
""
],
[
"Li",
"Jingyi Jessica",
""
]
] |
Background: Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusion: The development of statistical and computational methods for analyzing RNA- seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.
|
2101.00500
|
Akke Mats Houben
|
Akke Mats Houben
|
Signal anticipation and delay in excitable media: group delay of the
FitzHugh-Nagumo model
|
9 pages, 6 figures
| null | null | null |
q-bio.NC nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An expression for the group delay of the FitzHugh-Nagumo model in response to
low amplitude input is obtained by linearisation of the cubic term of the
voltage equation around its stable fixed-point. It is found that a negative
group delay exists for low frequencies, indicating that the evolution of slowly
fluctuating signals are anticipated by the voltage dynamics. The effects of the
group delay for different types of signals are shown numerically for the
non-linearised FitzHugh-Nagumo model, and some observations on the signal
aspects that are anticipated are stated.
|
[
{
"created": "Sat, 2 Jan 2021 19:14:24 GMT",
"version": "v1"
}
] |
2021-01-05
|
[
[
"Houben",
"Akke Mats",
""
]
] |
An expression for the group delay of the FitzHugh-Nagumo model in response to low amplitude input is obtained by linearisation of the cubic term of the voltage equation around its stable fixed-point. It is found that a negative group delay exists for low frequencies, indicating that the evolution of slowly fluctuating signals are anticipated by the voltage dynamics. The effects of the group delay for different types of signals are shown numerically for the non-linearised FitzHugh-Nagumo model, and some observations on the signal aspects that are anticipated are stated.
|
1612.07468
|
Sang Kwan Choi
|
Sang Kwan Choi, Chaiho Rim and Hwajin Um
|
RNA substructure as a random matrix ensemble
|
8 pages, 12 figures; v2: data set and figure added, comments added,
references updated; v3: appendix and references added, few sentences
including abstract paraphrased for clarification, remarks added in the
conclusion; v4: published version
|
Phys. Rev. E 100, 062404 (2019)
|
10.1103/PhysRevE.100.062404
| null |
q-bio.QM cond-mat.stat-mech hep-th q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Combinatorial analysis of a certain abstract of RNA structures has been
studied to investigate their statistics. Our approach regards the backbone of
secondary structures as an alternate sequence of paired and unpaired sets of
nucleotides, which can be described by random matrix model. We obtain the
generating function of the structures using Hermitian matrix model with
Chebyshev polynomial of the second kind and analyze the statistics with respect
to the number of stems. To match the experimental findings of the statistical
behavior, we consider the structures in a grand canonical ensemble and find a
fugacity value corresponding to an appropriate number of stems.
|
[
{
"created": "Thu, 22 Dec 2016 07:25:43 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Feb 2017 04:55:00 GMT",
"version": "v2"
},
{
"created": "Wed, 17 Jul 2019 02:17:11 GMT",
"version": "v3"
},
{
"created": "Mon, 9 Mar 2020 08:44:44 GMT",
"version": "v4"
}
] |
2020-03-10
|
[
[
"Choi",
"Sang Kwan",
""
],
[
"Rim",
"Chaiho",
""
],
[
"Um",
"Hwajin",
""
]
] |
Combinatorial analysis of a certain abstract of RNA structures has been studied to investigate their statistics. Our approach regards the backbone of secondary structures as an alternate sequence of paired and unpaired sets of nucleotides, which can be described by random matrix model. We obtain the generating function of the structures using Hermitian matrix model with Chebyshev polynomial of the second kind and analyze the statistics with respect to the number of stems. To match the experimental findings of the statistical behavior, we consider the structures in a grand canonical ensemble and find a fugacity value corresponding to an appropriate number of stems.
|
1308.6014
|
Robert Rosenbaum
|
Robert Rosenbaum and Brent Doiron
|
Balanced networks of spiking neurons with spatially dependent recurrent
connections
| null | null |
10.1103/PhysRevX.4.021039
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Networks of model neurons with balanced recurrent excitation and inhibition
produce irregular and asynchronous spiking activity. We extend the analysis of
balanced networks to include the known dependence of connection probability on
the spatial separation between neurons. In the continuum limit we derive that
stable, balanced firing rate solutions require that the spatial spread of
external inputs be broader than that of recurrent excitation, which in turn
must be broader than or equal to that of recurrent inhibition. For finite size
networks we investigate the pattern forming dynamics arising when balanced
conditions are not satisfied. The spatiotemporal dynamics of balanced networks
offer new challenges in the statistical mechanics of complex systems.
|
[
{
"created": "Tue, 27 Aug 2013 23:39:34 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Nov 2013 18:14:06 GMT",
"version": "v2"
}
] |
2014-06-02
|
[
[
"Rosenbaum",
"Robert",
""
],
[
"Doiron",
"Brent",
""
]
] |
Networks of model neurons with balanced recurrent excitation and inhibition produce irregular and asynchronous spiking activity. We extend the analysis of balanced networks to include the known dependence of connection probability on the spatial separation between neurons. In the continuum limit we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. For finite size networks we investigate the pattern forming dynamics arising when balanced conditions are not satisfied. The spatiotemporal dynamics of balanced networks offer new challenges in the statistical mechanics of complex systems.
|
2103.08198
|
Li Xu
|
Li Xu, Denis Patterson, Ann Carla Staver, Simon Asher Levin, Jin Wang
|
Unifying deterministic and stochastic ecological dynamics via a
landscape-flux approach
| null | null |
10.1073/pnas.2103779118
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop a landscape-flux framework to investigate observed frequency
distributions of vegetation and the stability of these ecological systems under
fluctuations. The frequency distributions can characterize the
population-potential landscape related to the stability of ecological states.
We illustrate the practical utility of this approach by analyzing a
forest-savanna model. Savanna, and Forest states coexist under certain
conditions, consistent with past theoretical work and empirical observations.
However, a new Grassland state, unseen in the corresponding deterministic
model, emerges as an alternative quasi-stable state under fluctuations,
providing a novel theoretical basis for the appearance of widespread grasslands
in some empirical analyses. The ecological dynamics are determined by both the
population-potential landscape gradient and the steady-state probability flux.
The flux quantifies the net input/output to the ecological system and therefore
the degree of nonequilibriumness. Landscape and flux together determine the
transitions between stable states characterized by dominant paths and switching
rates. The intrinsic potential landscape admits a Lyapunov function, which
provides a quantitative measure of global stability. We find that the average
flux, entropy production rate, and free energy have significant changes near
bifurcations under both finite and zero fluctuation. These may provide both
dynamical and thermodynamic origins of the bifurcations. We identified the
variances in observed frequency time traces, fluctuations and time
irreversibility as kinematic measures for bifurcations. This new framework
opens the way to characterize ecological systems globally, to uncover how they
change among states, and to quantify the emergence of new quasi-stable states
under stochastic fluctuations.
|
[
{
"created": "Mon, 15 Mar 2021 08:09:31 GMT",
"version": "v1"
},
{
"created": "Sat, 27 Mar 2021 06:18:34 GMT",
"version": "v2"
}
] |
2022-10-12
|
[
[
"Xu",
"Li",
""
],
[
"Patterson",
"Denis",
""
],
[
"Staver",
"Ann Carla",
""
],
[
"Levin",
"Simon Asher",
""
],
[
"Wang",
"Jin",
""
]
] |
We develop a landscape-flux framework to investigate observed frequency distributions of vegetation and the stability of these ecological systems under fluctuations. The frequency distributions can characterize the population-potential landscape related to the stability of ecological states. We illustrate the practical utility of this approach by analyzing a forest-savanna model. Savanna, and Forest states coexist under certain conditions, consistent with past theoretical work and empirical observations. However, a new Grassland state, unseen in the corresponding deterministic model, emerges as an alternative quasi-stable state under fluctuations, providing a novel theoretical basis for the appearance of widespread grasslands in some empirical analyses. The ecological dynamics are determined by both the population-potential landscape gradient and the steady-state probability flux. The flux quantifies the net input/output to the ecological system and therefore the degree of nonequilibriumness. Landscape and flux together determine the transitions between stable states characterized by dominant paths and switching rates. The intrinsic potential landscape admits a Lyapunov function, which provides a quantitative measure of global stability. We find that the average flux, entropy production rate, and free energy have significant changes near bifurcations under both finite and zero fluctuation. These may provide both dynamical and thermodynamic origins of the bifurcations. We identified the variances in observed frequency time traces, fluctuations and time irreversibility as kinematic measures for bifurcations. This new framework opens the way to characterize ecological systems globally, to uncover how they change among states, and to quantify the emergence of new quasi-stable states under stochastic fluctuations.
|
2103.04162
|
Junqiu Wu
|
Ke Liu, Zekun Ni, Zhenyu Zhou, Suocheng Tan, Xun Zou, Haoming Xing,
Xiangyan Sun, Qi Han, Junqiu Wu and Jie Fan
|
Molecular modeling with machine-learned universal potential functions
| null | null | null | null |
q-bio.QM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular modeling is an important topic in drug discovery. Decades of
research have led to the development of high quality scalable molecular force
fields. In this paper, we show that neural networks can be used to train a
universal approximator for energy potential functions. By incorporating a fully
automated training process we have been able to train smooth, differentiable,
and predictive potential functions on large-scale crystal structures. A variety
of tests have also been performed to show the superiority and versatility of
the machine-learned model.
|
[
{
"created": "Sat, 6 Mar 2021 17:36:39 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Apr 2021 06:30:52 GMT",
"version": "v2"
}
] |
2021-04-20
|
[
[
"Liu",
"Ke",
""
],
[
"Ni",
"Zekun",
""
],
[
"Zhou",
"Zhenyu",
""
],
[
"Tan",
"Suocheng",
""
],
[
"Zou",
"Xun",
""
],
[
"Xing",
"Haoming",
""
],
[
"Sun",
"Xiangyan",
""
],
[
"Han",
"Qi",
""
],
[
"Wu",
"Junqiu",
""
],
[
"Fan",
"Jie",
""
]
] |
Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train a universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large-scale crystal structures. A variety of tests have also been performed to show the superiority and versatility of the machine-learned model.
|
2311.09140
|
Ying-Cheng Lai
|
Shirin Panahi, Younghae Do, Alan Hastings, and Ying-Cheng Lai
|
Rate-induced tipping in complex high-dimensional ecological networks
|
8 pages, 5 figures
| null | null | null |
q-bio.PE math.DS nlin.AO
|
http://creativecommons.org/licenses/by/4.0/
|
In an ecosystem, environmental changes as a result of natural and human
processes can cause some key parameters of the system to change with time.
Depending on how fast such a parameter changes, a tipping point can occur.
Existing works on rate-induced tipping, or R-tipping, offered a theoretical way
to study this phenomenon but from a local dynamical point of view, revealing,
e.g., the existence of a critical rate for some specific initial condition
above which a tipping point will occur. As ecosystems are subject to constant
disturbances and can drift away from their equilibrium point, it is necessary
to study R-tipping from a global perspective in terms of the initial conditions
in the entire relevant phase space region. In particular, we introduce the
notion of the probability of R-tipping defined for initial conditions taken
from the whole relevant phase space. Using a number of real-world, complex
mutualistic networks as a paradigm, we discover a scaling law between this
probability and the rate of parameter change and provide a geometric theory to
explain the law. The real-world implication is that even a slow parameter
change can lead to a system collapse with catastrophic consequences. In fact,
to mitigate the environmental changes by merely slowing down the parameter
drift may not always be effective: only when the rate of parameter change is
reduced to practically zero would the tipping be avoided. Our global dynamics
approach offers a more complete and physically meaningful way to understand the
important phenomenon of R-tipping.
|
[
{
"created": "Wed, 15 Nov 2023 17:32:08 GMT",
"version": "v1"
}
] |
2023-11-16
|
[
[
"Panahi",
"Shirin",
""
],
[
"Do",
"Younghae",
""
],
[
"Hastings",
"Alan",
""
],
[
"Lai",
"Ying-Cheng",
""
]
] |
In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we discover a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.
|
2306.11536
|
Yu Takagi
|
Yu Takagi, Shinji Nishimoto
|
Improving visual image reconstruction from human brain activity using
latent diffusion models via multiple decoded inputs
| null | null | null | null |
q-bio.NC cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The integration of deep learning and neuroscience has been advancing rapidly,
which has led to improvements in the analysis of brain activity and the
understanding of deep learning models from a neuroscientific perspective. The
reconstruction of visual experience from human brain activity is an area that
has particularly benefited: the use of deep learning models trained on large
amounts of natural images has greatly improved its quality, and approaches that
combine the diverse information contained in visual experiences have
proliferated rapidly in recent years. In this technical paper, by taking
advantage of the simple and generic framework that we proposed (Takagi and
Nishimoto, CVPR 2023), we examine the extent to which various additional
decoding techniques affect the performance of visual experience reconstruction.
Specifically, we combined our earlier work with the following three techniques:
using decoded text from brain activity, nonlinear optimization for structural
image reconstruction, and using decoded depth information from brain activity.
We confirmed that these techniques contributed to improving accuracy over the
baseline. We also discuss what researchers should consider when performing
visual reconstruction using deep generative models trained on large datasets.
Please check our webpage at
https://sites.google.com/view/stablediffusion-with-brain/. Code is also
available at https://github.com/yu-takagi/StableDiffusionReconstruction.
|
[
{
"created": "Tue, 20 Jun 2023 13:48:02 GMT",
"version": "v1"
}
] |
2023-06-21
|
[
[
"Takagi",
"Yu",
""
],
[
"Nishimoto",
"Shinji",
""
]
] |
The integration of deep learning and neuroscience has been advancing rapidly, which has led to improvements in the analysis of brain activity and the understanding of deep learning models from a neuroscientific perspective. The reconstruction of visual experience from human brain activity is an area that has particularly benefited: the use of deep learning models trained on large amounts of natural images has greatly improved its quality, and approaches that combine the diverse information contained in visual experiences have proliferated rapidly in recent years. In this technical paper, by taking advantage of the simple and generic framework that we proposed (Takagi and Nishimoto, CVPR 2023), we examine the extent to which various additional decoding techniques affect the performance of visual experience reconstruction. Specifically, we combined our earlier work with the following three techniques: using decoded text from brain activity, nonlinear optimization for structural image reconstruction, and using decoded depth information from brain activity. We confirmed that these techniques contributed to improving accuracy over the baseline. We also discuss what researchers should consider when performing visual reconstruction using deep generative models trained on large datasets. Please check our webpage at https://sites.google.com/view/stablediffusion-with-brain/. Code is also available at https://github.com/yu-takagi/StableDiffusionReconstruction.
|
2204.10073
|
Gabriel Palma
|
Gabriel R. Palma, Silvio S. Zocchi, Wesley A.C. Godoy and Jorge A.
Wiendl
|
New confidence interval methods for Shannon index
|
17 pages
| null | null | null |
q-bio.QM stat.AP
|
http://creativecommons.org/licenses/by/4.0/
|
Several factors affect the structure of communities, including biological,
physical and chemical phenomena, impacting the quantification of biodiversity,
measured by diversity indexes such as Shannon's entropy. Then, once a point
estimate is obtained, confidence intervals methods such as the bootstrap ones
are often used. These methods, however, can have different performances, which
many authors have revealed in the last decade. Furthermore, problems such as
the asymmetry of the distribution of estimates and the possibility of Shannon's
diversity index estimator bias can lead to incorrect recommendations to the
research community. Thus, we propose two methods and compare them with seven
others using their performances to face these problems. The first idea uses the
credible interval (CI) method to build a bootstrap confidence interval. The
second one starts by correcting the bias and then uses an asymptotic approach.
We considered 27 community structures representing scenarios with high
dominance, high codominance or moderate dominance, the number of species equal
to 4, 20 or 80 and 10, 50 or 500 individuals to compare their performances.
Then, we generated 1000 samples, built 95% confidence intervals, and calculated
the percentage of times they included the community diversity index (coverage
percentage) for each community structure. Our results showed the feasibility of
both proposed methods to estimate Shannon's diversity. The simulation study
revealed the bootstrap-t technique had the best performance, i.e., best
coverage percentage, compared with the other methods. Finally, we illustrate
the methodology by applying it to an original aphid and parasitoid species
dataset. We recommend the bootstrap-t when the community structure analysed is
similar to the simulated ones. Also, the methods provided high performance for
the high dominance scenarios.
|
[
{
"created": "Thu, 21 Apr 2022 13:08:35 GMT",
"version": "v1"
}
] |
2022-04-22
|
[
[
"Palma",
"Gabriel R.",
""
],
[
"Zocchi",
"Silvio S.",
""
],
[
"Godoy",
"Wesley A. C.",
""
],
[
"Wiendl",
"Jorge A.",
""
]
] |
Several factors affect the structure of communities, including biological, physical and chemical phenomena, impacting the quantification of biodiversity, measured by diversity indexes such as Shannon's entropy. Then, once a point estimate is obtained, confidence intervals methods such as the bootstrap ones are often used. These methods, however, can have different performances, which many authors have revealed in the last decade. Furthermore, problems such as the asymmetry of the distribution of estimates and the possibility of Shannon's diversity index estimator bias can lead to incorrect recommendations to the research community. Thus, we propose two methods and compare them with seven others using their performances to face these problems. The first idea uses the credible interval (CI) method to build a bootstrap confidence interval. The second one starts by correcting the bias and then uses an asymptotic approach. We considered 27 community structures representing scenarios with high dominance, high codominance or moderate dominance, the number of species equal to 4, 20 or 80 and 10, 50 or 500 individuals to compare their performances. Then, we generated 1000 samples, built 95% confidence intervals, and calculated the percentage of times they included the community diversity index (coverage percentage) for each community structure. Our results showed the feasibility of both proposed methods to estimate Shannon's diversity. The simulation study revealed the bootstrap-t technique had the best performance, i.e., best coverage percentage, compared with the other methods. Finally, we illustrate the methodology by applying it to an original aphid and parasitoid species dataset. We recommend the bootstrap-t when the community structure analysed is similar to the simulated ones. Also, the methods provided high performance for the high dominance scenarios.
|
1305.0159
|
Anthony J Cox
|
Lilian Janin and Giovanna Rosone and Anthony J. Cox
|
Adaptive reference-free compression of sequence quality scores
|
Accepted paper for HiTSeq 2013, to appear in Bioinformatics.
Bioinformatics should be considered the original place of publication of this
work, please cite accordingly
| null | null | null |
q-bio.GN cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motivation:
Rapid technological progress in DNA sequencing has stimulated interest in
compressing the vast datasets that are now routinely produced. Relatively
little attention has been paid to compressing the quality scores that are
assigned to each sequence, even though these scores may be harder to compress
than the sequences themselves. By aggregating a set of reads into a compressed
index, we find that the majority of bases can be predicted from the sequence of
bases that are adjacent to them and hence are likely to be less informative for
variant calling or other applications. The quality scores for such bases are
aggressively compressed, leaving a relatively small number at full resolution.
Since our approach relies directly on redundancy present in the reads, it does
not need a reference sequence and is therefore applicable to data from
metagenomics and de novo experiments as well as to resequencing data.
Results:
We show that a conservative smoothing strategy affecting 75% of the quality
scores above Q2 leads to an overall quality score compression of 1 bit per
value with a negligible effect on variant calling. A compression of 0.68 bit
per quality value is achieved using a more aggressive smoothing strategy, again
with a very small effect on variant calling.
Availability:
Code to construct the BWT and LCP-array on large genomic data sets is part of
the BEETL library, available as a github respository at
http://git@github.com:BEETL/BEETL.git .
|
[
{
"created": "Wed, 1 May 2013 12:51:10 GMT",
"version": "v1"
}
] |
2013-05-02
|
[
[
"Janin",
"Lilian",
""
],
[
"Rosone",
"Giovanna",
""
],
[
"Cox",
"Anthony J.",
""
]
] |
Motivation: Rapid technological progress in DNA sequencing has stimulated interest in compressing the vast datasets that are now routinely produced. Relatively little attention has been paid to compressing the quality scores that are assigned to each sequence, even though these scores may be harder to compress than the sequences themselves. By aggregating a set of reads into a compressed index, we find that the majority of bases can be predicted from the sequence of bases that are adjacent to them and hence are likely to be less informative for variant calling or other applications. The quality scores for such bases are aggressively compressed, leaving a relatively small number at full resolution. Since our approach relies directly on redundancy present in the reads, it does not need a reference sequence and is therefore applicable to data from metagenomics and de novo experiments as well as to resequencing data. Results: We show that a conservative smoothing strategy affecting 75% of the quality scores above Q2 leads to an overall quality score compression of 1 bit per value with a negligible effect on variant calling. A compression of 0.68 bit per quality value is achieved using a more aggressive smoothing strategy, again with a very small effect on variant calling. Availability: Code to construct the BWT and LCP-array on large genomic data sets is part of the BEETL library, available as a github respository at http://git@github.com:BEETL/BEETL.git .
|
1908.03686
|
Yiqiang Wang
|
Yiqiang Wang
|
Shuyi, A Name After Dendritic Cell-mediated Immunological Memory
|
6 pages; 2 figures, 13 references
| null | null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Immunological memory is a fundamental theory of modern immunology, which is
traditionally believed to be mediated only by B and T lymphocytes that
recognize antigen epitopes in a receptor-restricted manner. During the last
decade data accumulated to show that monocytes and macrophages, the two main
initiators of innate immune response, also built up a "memory" to antigens they
encountered, though in most concerned publications a different wording (i.e.
"train" or"educate") was utilized to describe this feature. More recently, Hole
et al demonstrated a "memory-like" response of dendritic cells (DCs). In brief,
if fungal-challenged mice could develop a protective immune response, DCs
immediately (in 3 weeks) isolated from those mice would manifest a
pro-inflammatory phenotype. Even after the mice were allowed to rest for 10
weeks, DCs from them still exhibited an enhanced immune activation profile in
their transcriptome and cytokine productions upon re-challenge with same
pathogens. Lastly, Hole showed that the "training" or memory-building in DCs
was achieved by histone modification. All above findings obtained in monocytes,
macrophages or DCs emphasized the necessity for rechecking the questions
whether antigen presenting cells (APCs) as a whole could be classified the
third class of cells that would mediate immunological memory. In this essay,
the author described the effort he made in late 1990s to identify dendtitic
cell-mediated memory, and how he named his daughter SHUYI to memorize that
hypothesis.
|
[
{
"created": "Sat, 10 Aug 2019 04:14:24 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Nov 2021 01:59:28 GMT",
"version": "v2"
}
] |
2023-09-27
|
[
[
"Wang",
"Yiqiang",
""
]
] |
Immunological memory is a fundamental theory of modern immunology, which is traditionally believed to be mediated only by B and T lymphocytes that recognize antigen epitopes in a receptor-restricted manner. During the last decade data accumulated to show that monocytes and macrophages, the two main initiators of innate immune response, also built up a "memory" to antigens they encountered, though in most concerned publications a different wording (i.e. "train" or"educate") was utilized to describe this feature. More recently, Hole et al demonstrated a "memory-like" response of dendritic cells (DCs). In brief, if fungal-challenged mice could develop a protective immune response, DCs immediately (in 3 weeks) isolated from those mice would manifest a pro-inflammatory phenotype. Even after the mice were allowed to rest for 10 weeks, DCs from them still exhibited an enhanced immune activation profile in their transcriptome and cytokine productions upon re-challenge with same pathogens. Lastly, Hole showed that the "training" or memory-building in DCs was achieved by histone modification. All above findings obtained in monocytes, macrophages or DCs emphasized the necessity for rechecking the questions whether antigen presenting cells (APCs) as a whole could be classified the third class of cells that would mediate immunological memory. In this essay, the author described the effort he made in late 1990s to identify dendtitic cell-mediated memory, and how he named his daughter SHUYI to memorize that hypothesis.
|
q-bio/0607024
|
Claire Christensen
|
C. Christensen, A. Gupta, C.D. Maranas and R. Albert
|
Large-scale inference and graph theoretical analysis of gene-regulatory
networks in B. stubtilis
|
22 pages, 4 figures, accepted for publication in Physica A (2006)
| null |
10.1016/j.physa.2006.04.118
| null |
q-bio.MN cond-mat.stat-mech q-bio.SC
| null |
We present the methods and results of a two-stage modeling process that
generates candidate gene-regulatory networks of the bacterium B. subtilis from
experimentally obtained, yet mathematically underdetermined microchip array
data. By employing a computational, linear correlative procedure to generate
these networks, and by analyzing the networks from a graph theoretical
perspective, we are able to verify the biological viability of our inferred
networks, and we demonstrate that our networks' graph theoretical properties
are remarkably similar to those of other biological systems. In addition, by
comparing our inferred networks to those of a previous, noisier implementation
of the linear inference process [17], we are able to identify trends in graph
theoretical behavior that occur both in our networks as well as in their
perturbed counterparts. These commonalities in behavior at multiple levels of
complexity allow us to ascertain the level of complexity to which our process
is robust to noise.
|
[
{
"created": "Tue, 18 Jul 2006 20:50:53 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Christensen",
"C.",
""
],
[
"Gupta",
"A.",
""
],
[
"Maranas",
"C. D.",
""
],
[
"Albert",
"R.",
""
]
] |
We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.
|
2204.12526
|
Sakira Hassan
|
Syeda Sakira Hassan, Rahul Mangayil, Tommi Aho, Olli Yli-Harja, Matti
Karp
|
Identification of feasible pathway information for c-di-GMP binding
proteins in cellulose production
| null |
EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65.
Springer, Singapore
| null | null |
q-bio.QM cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we utilize a machine learning approach to identify the
significant pathways for c-di-GMP signaling proteins. The dataset involves gene
counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024
bacterial genomes. Two novel approaches, Least absolute shrinkage and selection
operator (Lasso) and Random forests, have been applied for analyzing and
modeling the dataset. Both approaches show that bacterial chemotaxis is the
most essential pathway for c-di-GMP encoding domains. Though popular for
feature selection, the strong regularization of Lasso method fails to associate
any pathway to MshE domain. Results from the analysis may help to understand
and emphasize the supporting pathways involved in bacterial cellulose
production. These findings demonstrate the need for a chassis to restrict the
behavior or functionality by deactivating the selective pathways in cellulose
production.
|
[
{
"created": "Tue, 26 Apr 2022 18:22:13 GMT",
"version": "v1"
}
] |
2022-04-28
|
[
[
"Hassan",
"Syeda Sakira",
""
],
[
"Mangayil",
"Rahul",
""
],
[
"Aho",
"Tommi",
""
],
[
"Yli-Harja",
"Olli",
""
],
[
"Karp",
"Matti",
""
]
] |
In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasize the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.
|
1904.03290
|
Mike Steel Prof.
|
Mike Steel, Wim Hordijk, Stuart A. Kauffman
|
Dynamics of a birth-death process based on combinatorial innovation
|
21 pages, 4 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A feature of human creativity is the ability to take a subset of existing
items (e.g. objects, ideas, or techniques) and combine them in various ways to
give rise to new items, which, in turn, fuel further growth. Occasionally, some
of these items may also disappear (extinction). We model this process by a
simple stochastic birth--death model, with non-linear combinatorial terms in
the growth coefficients to capture the propensity of subsets of items to give
rise to new items. In its simplest form, this model involves just two
parameters $(P, \alpha)$. This process exhibits a characteristic 'hockey-stick'
behaviour: a long period of relatively little growth followed by a relatively
sudden 'explosive' increase. We provide exact expressions for the mean and
variance of this time to explosion and compare the results with simulations. We
then generalise our results to allow for more general parameter assignments,
and consider possible applications to data involving human productivity and
creativity.
|
[
{
"created": "Fri, 5 Apr 2019 21:38:14 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Aug 2019 20:16:43 GMT",
"version": "v2"
}
] |
2019-08-21
|
[
[
"Steel",
"Mike",
""
],
[
"Hordijk",
"Wim",
""
],
[
"Kauffman",
"Stuart A.",
""
]
] |
A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth--death model, with non-linear combinatorial terms in the growth coefficients to capture the propensity of subsets of items to give rise to new items. In its simplest form, this model involves just two parameters $(P, \alpha)$. This process exhibits a characteristic 'hockey-stick' behaviour: a long period of relatively little growth followed by a relatively sudden 'explosive' increase. We provide exact expressions for the mean and variance of this time to explosion and compare the results with simulations. We then generalise our results to allow for more general parameter assignments, and consider possible applications to data involving human productivity and creativity.
|
0911.2387
|
Sylvain Hanneton
|
Sylvain Hanneton (NPSM), Ana\"is Varenne (NPSM)
|
Coaching the Wii : evaluation of a physical training experiment assisted
by a video game
|
4 pages
|
"Haptic, Audio, Visual Environments and Games" (HAVE'09), Lecco :
Italy (2009)
| null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aging or sedentary behavior can decrease motor capabilities causing a loss of
autonomy. Prevention or readaptation programs that involve practice of physical
activities can be precious tools to fight against this phenomenon. ?Serious?
video game have the potential to help people to train their body mainly due to
the immersion of the participant in a motivating interaction with virtual
environments. We propose here to discuss the results of a preliminary study
that evaluated a training program using the well-known WiiFit game and Wii
balance board device in participants of different ages. Our results showed that
participants were satisfied with the program and that they progressed in their
level of performance. The most important observation of this study, however was
that the presence of a real human coach is necessary in particular for senior
participants, for security reasons but also to help them to deal with
difficulties with immersive situations.
|
[
{
"created": "Thu, 12 Nov 2009 13:31:33 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Hanneton",
"Sylvain",
"",
"NPSM"
],
[
"Varenne",
"Anaïs",
"",
"NPSM"
]
] |
Aging or sedentary behavior can decrease motor capabilities causing a loss of autonomy. Prevention or readaptation programs that involve practice of physical activities can be precious tools to fight against this phenomenon. ?Serious? video game have the potential to help people to train their body mainly due to the immersion of the participant in a motivating interaction with virtual environments. We propose here to discuss the results of a preliminary study that evaluated a training program using the well-known WiiFit game and Wii balance board device in participants of different ages. Our results showed that participants were satisfied with the program and that they progressed in their level of performance. The most important observation of this study, however was that the presence of a real human coach is necessary in particular for senior participants, for security reasons but also to help them to deal with difficulties with immersive situations.
|
2311.17785
|
Claire Guerrier
|
Claire Guerrier (LJAD, IRL CRM-CNRS), Tristan Dellazizzo Toth, Nicolas
Galtier, Kurt Haas
|
An Algorithm Based on a Cable-Nernst Planck Model Predicting Synaptic
Activity throughout the Dendritic Arbor with Micron Specificity
| null |
Neuroinformatics, 2023, 21 (1), pp.207-220
|
10.1007/s12021-022-09609-z
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent technological advances have enabled the recording of neurons in intact
circuits with a high spatial and temporal resolution, creating the need for
modeling with the same precision. In particular, the development of ultra-fast
two-photon microscopy combined with fluorescence-based genetically-encoded
Ca2+-indicators allows capture of full-dendritic arbor and somatic responses
associated with synaptic input and action potential output. The complexity of
dendritic arbor structures and distributed patterns of activity over time
results in the generation of incredibly rich 4D datasets that are challenging
to analyze (Sakaki, 2020). Interpreting neural activity from fluorescence-based
Ca2+ biosensors is challenging due to non-linear interactions between several
factors influencing intracellular calcium ion concentration and its binding to
sensors, including the ionic dynamics driven by diffusion, electrical gradients
and voltage-gated conductance.To investigate those dynamics, we designed a
model based on a Cable-like equation coupled to the Nernst-Planck equations for
ionic fluxes in electrolytes. We employ this model to simulate signal
propagation and ionic electrodiffusion across a dendritic arbor. Using these
simulation results, we then designed an algorithm to detect synapses from Ca2+
imaging datasets. We finally apply this algorithm to experimental
Ca2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al., 2019),
using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum
using fast random-access two-photon microscopy.Our model reproduces the
dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed
experimentally, and the resulting algorithm allows prediction of the location
of synapses across the dendritic arbor.Our study provides a way to predict
synaptic activity and location on dendritic arbors, from fluorescence data in
the full dendritic arbor of a neuron recorded in the intact and awake
developing vertebrate brain.
|
[
{
"created": "Wed, 29 Nov 2023 16:32:06 GMT",
"version": "v1"
}
] |
2023-11-30
|
[
[
"Guerrier",
"Claire",
"",
"LJAD, IRL CRM-CNRS"
],
[
"Toth",
"Tristan Dellazizzo",
""
],
[
"Galtier",
"Nicolas",
""
],
[
"Haas",
"Kurt",
""
]
] |
Recent technological advances have enabled the recording of neurons in intact circuits with a high spatial and temporal resolution, creating the need for modeling with the same precision. In particular, the development of ultra-fast two-photon microscopy combined with fluorescence-based genetically-encoded Ca2+-indicators allows capture of full-dendritic arbor and somatic responses associated with synaptic input and action potential output. The complexity of dendritic arbor structures and distributed patterns of activity over time results in the generation of incredibly rich 4D datasets that are challenging to analyze (Sakaki, 2020). Interpreting neural activity from fluorescence-based Ca2+ biosensors is challenging due to non-linear interactions between several factors influencing intracellular calcium ion concentration and its binding to sensors, including the ionic dynamics driven by diffusion, electrical gradients and voltage-gated conductance.To investigate those dynamics, we designed a model based on a Cable-like equation coupled to the Nernst-Planck equations for ionic fluxes in electrolytes. We employ this model to simulate signal propagation and ionic electrodiffusion across a dendritic arbor. Using these simulation results, we then designed an algorithm to detect synapses from Ca2+ imaging datasets. We finally apply this algorithm to experimental Ca2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al., 2019), using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum using fast random-access two-photon microscopy.Our model reproduces the dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed experimentally, and the resulting algorithm allows prediction of the location of synapses across the dendritic arbor.Our study provides a way to predict synaptic activity and location on dendritic arbors, from fluorescence data in the full dendritic arbor of a neuron recorded in the intact and awake developing vertebrate brain.
|
1902.05723
|
Iaroslav Ispolatov
|
Iaroslav Ispolatov, Evgeniia Alekseeva, and Michael Doebeli
|
Competition-driven evolution of organismal complexity
|
Open PDF with Acrobat to see movies, 22 pages, 9 figures
|
PLoS Comput Biol 15(10): e1007388.
https://doi.org/10.1371/journal.pcbi.1007388 (2019)
|
10.1371/journal.pcbi.1007388
| null |
q-bio.PE cond-mat.stat-mech nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Non-uniform rates of morphological evolution and evolutionary increases in
organismal complexity, captured in metaphors like "adaptive zones", "punctuated
equilibrium" and "blunderbuss patterns", require more elaborate explanations
than a simple gradual accumulation of mutations. Here we argue that non-uniform
evolutionary increases in phenotypic complexity can be caused by a
threshold-like response to growing ecological pressures resulting from
evolutionary diversification at a given level of complexity. Acquisition of a
new phenotypic feature allows an evolving species to escape this pressure but
can typically be expected to carry significant physiological costs. Therefore,
the ecological pressure should exceed a certain level to make such an
acquisition evolutionarily successful. We present a detailed quantitative
description of this process using a microevolutionary competition model as an
example. The model exhibits sequential increases in phenotypic complexity
driven by diversification at existing levels of complexity and the resulting
increase in competitive pressure, which can push an evolving species over the
barrier of physiological costs of new phenotypic features.
|
[
{
"created": "Fri, 15 Feb 2019 08:41:40 GMT",
"version": "v1"
}
] |
2020-07-01
|
[
[
"Ispolatov",
"Iaroslav",
""
],
[
"Alekseeva",
"Evgeniia",
""
],
[
"Doebeli",
"Michael",
""
]
] |
Non-uniform rates of morphological evolution and evolutionary increases in organismal complexity, captured in metaphors like "adaptive zones", "punctuated equilibrium" and "blunderbuss patterns", require more elaborate explanations than a simple gradual accumulation of mutations. Here we argue that non-uniform evolutionary increases in phenotypic complexity can be caused by a threshold-like response to growing ecological pressures resulting from evolutionary diversification at a given level of complexity. Acquisition of a new phenotypic feature allows an evolving species to escape this pressure but can typically be expected to carry significant physiological costs. Therefore, the ecological pressure should exceed a certain level to make such an acquisition evolutionarily successful. We present a detailed quantitative description of this process using a microevolutionary competition model as an example. The model exhibits sequential increases in phenotypic complexity driven by diversification at existing levels of complexity and the resulting increase in competitive pressure, which can push an evolving species over the barrier of physiological costs of new phenotypic features.
|
1604.04903
|
Thorsten Pr\"ustel
|
Thorsten Pr\"ustel and Martin Meier-Schellersheim
|
Path integral approach to theories of diffusion-influenced reactions
|
13 pages
| null |
10.1103/PhysRevE.96.022151
| null |
q-bio.QM cond-mat.stat-mech
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The path decomposition expansion represents the propagator of the
irreversible reaction as a convolution of the first-passage, last-passage and
rebinding time probability densities. Using path integral technique, we give an
elementary, yet rigorous, proof of the path decomposition expansion of the
Green's functions describing the non-reactive case and the irreversible
reaction of an isolated pair of molecules. To this end, we exploit the
connection between boundary value problems and interaction potential problems
with $\delta$- and $\delta'$-function perturbation. In particular, we employ a
known exact summation of a perturbation series to derive exact relations
between the Green's functions of the perturbed and unperturbed problem. Along
the way, we are able to derive a number of additional exact identities that
relate the propagators describing the free-space, the non-reactive as well as
the completely and partially reactive case.
|
[
{
"created": "Sun, 17 Apr 2016 18:05:02 GMT",
"version": "v1"
}
] |
2017-09-13
|
[
[
"Prüstel",
"Thorsten",
""
],
[
"Meier-Schellersheim",
"Martin",
""
]
] |
The path decomposition expansion represents the propagator of the irreversible reaction as a convolution of the first-passage, last-passage and rebinding time probability densities. Using path integral technique, we give an elementary, yet rigorous, proof of the path decomposition expansion of the Green's functions describing the non-reactive case and the irreversible reaction of an isolated pair of molecules. To this end, we exploit the connection between boundary value problems and interaction potential problems with $\delta$- and $\delta'$-function perturbation. In particular, we employ a known exact summation of a perturbation series to derive exact relations between the Green's functions of the perturbed and unperturbed problem. Along the way, we are able to derive a number of additional exact identities that relate the propagators describing the free-space, the non-reactive as well as the completely and partially reactive case.
|
2009.01923
|
Yaron Oz
|
Yaron Oz, Ittai Rubinstein, Muli Safra
|
Heterogeneity and Superspreading Effect on Herd Immunity
|
16 pages, 5 figures, includes population based simulations
| null | null | null |
q-bio.PE cond-mat.stat-mech physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We model and calculate the fraction of infected population necessary to reach
herd immunity, taking into account the heterogeneity in infectiousness and
susceptibility, as well as the correlation between those two parameters. We
show that these cause the effective reproduction number to decrease more
rapidly, and consequently have a drastic effect on the estimate of the
necessary percentage of the population that has to contract the disease for
herd immunity to be reached. We quantify the difference between the size of the
infected population when the effective reproduction number decreases below 1
vs. the ultimate fraction of population that had contracted the disease. This
sheds light on an important distinction between herd immunity and the end of
the disease and highlights the importance of limiting the spread of the disease
even if we plan to naturally reach herd immunity. We analyze the effect of
various lock-down scenarios on the resulting final fraction of infected
population. We discuss implications to COVID-19 and other pandemics and compare
our theoretical results to population-based simulations. We consider the
dependence of the disease spread on the architecture of the infectiousness
graph and analyze different graph architectures and the limitations of the
graph models.
|
[
{
"created": "Tue, 1 Sep 2020 09:43:38 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Oct 2020 18:03:04 GMT",
"version": "v2"
},
{
"created": "Fri, 15 Jan 2021 15:06:06 GMT",
"version": "v3"
}
] |
2021-01-19
|
[
[
"Oz",
"Yaron",
""
],
[
"Rubinstein",
"Ittai",
""
],
[
"Safra",
"Muli",
""
]
] |
We model and calculate the fraction of infected population necessary to reach herd immunity, taking into account the heterogeneity in infectiousness and susceptibility, as well as the correlation between those two parameters. We show that these cause the effective reproduction number to decrease more rapidly, and consequently have a drastic effect on the estimate of the necessary percentage of the population that has to contract the disease for herd immunity to be reached. We quantify the difference between the size of the infected population when the effective reproduction number decreases below 1 vs. the ultimate fraction of population that had contracted the disease. This sheds light on an important distinction between herd immunity and the end of the disease and highlights the importance of limiting the spread of the disease even if we plan to naturally reach herd immunity. We analyze the effect of various lock-down scenarios on the resulting final fraction of infected population. We discuss implications to COVID-19 and other pandemics and compare our theoretical results to population-based simulations. We consider the dependence of the disease spread on the architecture of the infectiousness graph and analyze different graph architectures and the limitations of the graph models.
|
2401.12498
|
Chuanbo Liu
|
Chuanbo Liu, Yu Fu, Lu Lin, Elliot L. Elson and Jin Wang
|
Understanding Cellular Noise with Optical Perturbation and Deep Learning
|
33 pages, 4 figures
| null | null | null |
q-bio.MN
|
http://creativecommons.org/licenses/by/4.0/
|
Noise plays a crucial role in the regulation of cellular and organismal
function and behavior.
Exploring noise's impact is key to understanding fundamental biological
processes, such as gene expression, signal transduction, and the mechanisms of
development and evolution.
Currently, a comprehensive method to quantify dynamical behavior of cellular
noise within these biochemical systems is lacking.
In this study, we introduce an optically-controlled perturbation system
utilizing the light-sensitive Phytochrome B (PhyB) from \textit{Arabidopsis
thaliana}, which enables precise noise modulation with high spatial-temporal
resolution.
Our system exhibits exceptional sensitivity to light, reacting consistently
to pulsed light signals, distinguishing it from other photoreceptor-based
promoter systems that respond to a single light wavelength.
To characterize our system, we developed a stochastic model for phytochromes
that accounts for photoactivation/deactivation, thermal reversion, and the
dynamics of the light-activated gene promoter system.
To precisely control our system, we determined the rate constants for this
model using an omniscient deep neural network that can directly map rate
constant combinations to time-dependent state joint distributions.
By adjusting the activation rates through light intensity and degradation
rates via N-terminal mutagenesis, we illustrate that out optical-controlled
perturbation can effectively modulate molecular expression level as well as
noise.
Our results highlight the potential of employing an optically-controlled gene
perturbation system as a noise-controlled stimulus source.
This approach, when combined with the analytical capabilities of a
sophisticated deep neural network, enables the accurate estimation of rate
constants from observational data in a broad range of biochemical reaction
networks.
|
[
{
"created": "Tue, 23 Jan 2024 05:48:20 GMT",
"version": "v1"
}
] |
2024-01-24
|
[
[
"Liu",
"Chuanbo",
""
],
[
"Fu",
"Yu",
""
],
[
"Lin",
"Lu",
""
],
[
"Elson",
"Elliot L.",
""
],
[
"Wang",
"Jin",
""
]
] |
Noise plays a crucial role in the regulation of cellular and organismal function and behavior. Exploring noise's impact is key to understanding fundamental biological processes, such as gene expression, signal transduction, and the mechanisms of development and evolution. Currently, a comprehensive method to quantify dynamical behavior of cellular noise within these biochemical systems is lacking. In this study, we introduce an optically-controlled perturbation system utilizing the light-sensitive Phytochrome B (PhyB) from \textit{Arabidopsis thaliana}, which enables precise noise modulation with high spatial-temporal resolution. Our system exhibits exceptional sensitivity to light, reacting consistently to pulsed light signals, distinguishing it from other photoreceptor-based promoter systems that respond to a single light wavelength. To characterize our system, we developed a stochastic model for phytochromes that accounts for photoactivation/deactivation, thermal reversion, and the dynamics of the light-activated gene promoter system. To precisely control our system, we determined the rate constants for this model using an omniscient deep neural network that can directly map rate constant combinations to time-dependent state joint distributions. By adjusting the activation rates through light intensity and degradation rates via N-terminal mutagenesis, we illustrate that out optical-controlled perturbation can effectively modulate molecular expression level as well as noise. Our results highlight the potential of employing an optically-controlled gene perturbation system as a noise-controlled stimulus source. This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.
|
1510.02989
|
Emmanuel Quansah Mr
|
Aladeen Basheer, Emmanuel Quansah, Schuman Bhowmick and Rana D.
Parshad
|
Prey cannibalism alters the dynamics of Holling-Tanner type
predator-prey models
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cannibalism, which is the act of killing and at least partial consumption of
conspecifics, is ubiquitous in nature. Mathematical models have considered
cannibalism in the predator primarily, and show that predator cannibalism in
two species ODE models provides a strong stabilizing effect. There is strong
ecological evidence that cannibalism exists among prey as well, yet this
phenomenon has been much less investigated. In the current manuscript, we
investigate both the ODE and spatially explicit forms of a Holling-Tanner
model, with ratio dependent functional response. We show that cannibalism in
the predator provides a stabilizing influence as expected. However, when
cannibalism in the prey is considered, we show that it cannot stabilise the
unstable interior equilibrium in the ODE case, but can destabilise the stable
interior equilibrium. In the spatially explicit case, we show that in certain
parameter regime, prey cannibalism can lead to pattern forming Turing dynamics,
which is an impossibility without it. Lastly we consider a stochastic prey
cannibalism rate, and find that it can alter both spatial patterns, as well as
limit cycle dynamics.
|
[
{
"created": "Sat, 10 Oct 2015 23:11:48 GMT",
"version": "v1"
}
] |
2015-10-13
|
[
[
"Basheer",
"Aladeen",
""
],
[
"Quansah",
"Emmanuel",
""
],
[
"Bhowmick",
"Schuman",
""
],
[
"Parshad",
"Rana D.",
""
]
] |
Cannibalism, which is the act of killing and at least partial consumption of conspecifics, is ubiquitous in nature. Mathematical models have considered cannibalism in the predator primarily, and show that predator cannibalism in two species ODE models provides a strong stabilizing effect. There is strong ecological evidence that cannibalism exists among prey as well, yet this phenomenon has been much less investigated. In the current manuscript, we investigate both the ODE and spatially explicit forms of a Holling-Tanner model, with ratio dependent functional response. We show that cannibalism in the predator provides a stabilizing influence as expected. However, when cannibalism in the prey is considered, we show that it cannot stabilise the unstable interior equilibrium in the ODE case, but can destabilise the stable interior equilibrium. In the spatially explicit case, we show that in certain parameter regime, prey cannibalism can lead to pattern forming Turing dynamics, which is an impossibility without it. Lastly we consider a stochastic prey cannibalism rate, and find that it can alter both spatial patterns, as well as limit cycle dynamics.
|
2406.09989
|
Zhichao Liang
|
Zhichao Liang, Guanyi Zhao, Yinuo Zhang, Weiting Sun, Jingzhe Lin,
Jialin Wang, Quanying Liu
|
Suppressing seizure via optimal electrical stimulation to the hub of
epileptic brain network
| null | null | null | null |
q-bio.NC cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The electrical stimulation to the seizure onset zone (SOZ) serves as an
efficient approach to seizure suppression. Recently, seizure dynamics have
gained widespread attendance in its network propagation mechanisms. Compared
with the direct stimulation to SOZ, other brain network-level approaches that
can effectively suppress epileptic seizures remain under-explored. In this
study, we introduce a platform equipped with a system identification module and
a control strategy module, to validate the effectiveness of the hub of the
epileptic brain network in suppressing seizure. The identified surrogate
dynamics show high predictive performance in reconstructing neural dynamics
which enables the model predictive framework to achieve accurate neural
stimulation. The electrical stimulation on the hub of the epileptic brain
network shows remarkable performance as the direct stimulation of SOZ in
suppressing seizure dynamics. Underpinned by network control theory, our
platform offers a general tool for the validation of neural stimulation.
|
[
{
"created": "Fri, 14 Jun 2024 12:54:12 GMT",
"version": "v1"
}
] |
2024-06-17
|
[
[
"Liang",
"Zhichao",
""
],
[
"Zhao",
"Guanyi",
""
],
[
"Zhang",
"Yinuo",
""
],
[
"Sun",
"Weiting",
""
],
[
"Lin",
"Jingzhe",
""
],
[
"Wang",
"Jialin",
""
],
[
"Liu",
"Quanying",
""
]
] |
The electrical stimulation to the seizure onset zone (SOZ) serves as an efficient approach to seizure suppression. Recently, seizure dynamics have gained widespread attendance in its network propagation mechanisms. Compared with the direct stimulation to SOZ, other brain network-level approaches that can effectively suppress epileptic seizures remain under-explored. In this study, we introduce a platform equipped with a system identification module and a control strategy module, to validate the effectiveness of the hub of the epileptic brain network in suppressing seizure. The identified surrogate dynamics show high predictive performance in reconstructing neural dynamics which enables the model predictive framework to achieve accurate neural stimulation. The electrical stimulation on the hub of the epileptic brain network shows remarkable performance as the direct stimulation of SOZ in suppressing seizure dynamics. Underpinned by network control theory, our platform offers a general tool for the validation of neural stimulation.
|
q-bio/0404030
|
Paul Higgs
|
Bin Tang, Philippe Boisvert and Paul G. Higgs
|
Comparison of tRNA and rRNA Phylogenies in Proteobacteria: Implications
for the Frequency of Horizontal Gene Transfer
| null | null | null | null |
q-bio.PE
| null |
The current picture of bacterial evolution is based largely on studies of 16S
rRNA. However, this is just one gene. It is known that horizontal gene transfer
can occur between bacterial species, although the frequency and implications of
this are not fully understood. If horizontal transfer were frequent, there
would be no single evolutionary tree for bacteria because each gene would
follow a different tree. We carried out phylogenetic analyses of rRNA and tRNA
genes from Proteobacteria (a diverse group for which many complete genome
sequences are available) using RNA-specific phylogenetic methods that account
for the conservation of the secondary structure. We compared trees for 16S rRNA
and 23S rRNA with those derived from concatenated alignments of 29 tRNA genes
that are found in all the genomes studied. The tRNA genes are scattered
throughout the genomes, and would not follow the same evolutionary history if
horizontal transfer were frequent. Nevertheless, the tRNA tree is consistent
with the rRNA tree in most respects. Minor differences can almost all be
attributed to uncertainty or unreliability of the phylogenetic method. We
therefore conclude that tRNA genes give a coherent picture of the phylogeny of
the organisms, and that horizontal transfer of tRNAs is too rare to obscure the
signal of the organismal tree. Some tRNA genes are not present in all the
genomes. We discuss possible explanations for the observed patterns of presence
and absence of genes: these involve gene deletion, gene duplication, and
mutations in the tRNA anticodons.
|
[
{
"created": "Fri, 23 Apr 2004 14:08:34 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Tang",
"Bin",
""
],
[
"Boisvert",
"Philippe",
""
],
[
"Higgs",
"Paul G.",
""
]
] |
The current picture of bacterial evolution is based largely on studies of 16S rRNA. However, this is just one gene. It is known that horizontal gene transfer can occur between bacterial species, although the frequency and implications of this are not fully understood. If horizontal transfer were frequent, there would be no single evolutionary tree for bacteria because each gene would follow a different tree. We carried out phylogenetic analyses of rRNA and tRNA genes from Proteobacteria (a diverse group for which many complete genome sequences are available) using RNA-specific phylogenetic methods that account for the conservation of the secondary structure. We compared trees for 16S rRNA and 23S rRNA with those derived from concatenated alignments of 29 tRNA genes that are found in all the genomes studied. The tRNA genes are scattered throughout the genomes, and would not follow the same evolutionary history if horizontal transfer were frequent. Nevertheless, the tRNA tree is consistent with the rRNA tree in most respects. Minor differences can almost all be attributed to uncertainty or unreliability of the phylogenetic method. We therefore conclude that tRNA genes give a coherent picture of the phylogeny of the organisms, and that horizontal transfer of tRNAs is too rare to obscure the signal of the organismal tree. Some tRNA genes are not present in all the genomes. We discuss possible explanations for the observed patterns of presence and absence of genes: these involve gene deletion, gene duplication, and mutations in the tRNA anticodons.
|
1912.10302
|
Polly Y. Yu
|
Bal\'azs Boros, Gheorghe Craciun, Polly Y. Yu
|
Weakly reversible mass-action systems with infinitely many positive
steady states
| null |
SIAM Journal on Applied Mathematics, 80(4):1936-1946, 2020
|
10.1137/19M1303034
| null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that weakly reversible mass-action systems can have a continuum of
positive steady states, coming from the zeroes of a multivariate polynomial.
Moreover, the same is true of systems whose underlying reaction network is
reversible and has a single connected component. In our construction, we relate
operations on the reaction network to the multivariate polynomial occurring as
a common factor in the system of differential equations.
|
[
{
"created": "Sat, 21 Dec 2019 17:31:27 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Sep 2020 16:27:09 GMT",
"version": "v2"
}
] |
2022-09-14
|
[
[
"Boros",
"Balázs",
""
],
[
"Craciun",
"Gheorghe",
""
],
[
"Yu",
"Polly Y.",
""
]
] |
We show that weakly reversible mass-action systems can have a continuum of positive steady states, coming from the zeroes of a multivariate polynomial. Moreover, the same is true of systems whose underlying reaction network is reversible and has a single connected component. In our construction, we relate operations on the reaction network to the multivariate polynomial occurring as a common factor in the system of differential equations.
|
1908.00572
|
Guo-Wei Wei
|
Rundong Zhao, Menglun Wang, Yiying Tong and Guo-Wei Wei
|
The de Rham-Hodge analysis and modeling of biomolecules
|
13 figures, one table
| null | null | null |
q-bio.BM math.AT math.DG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent years have witnessed a trend that advanced mathematical tools, such as
algebraic topology, differential geometry, graph theory, and partial
differential equations, have been developed for describing biological
macromolecules. These tools have considerably strengthened our ability to
understand the molecular mechanism of macromolecular function, dynamics and
transport from their structures. However, currently, there is no unified
mathematical theory to analyze, describe and characterize biological
macromolecular geometry, topology, flexibility and natural mode at a variety of
scales. We introduce the de Rham-Hodge theory, a landmark of 20th Century's
mathematics, as a unified paradigm for analyzing biological macromolecular
geometry and algebraic topology, for predicting macromolecular flexibility, and
for modeling macromolecular natural modes at a variety of scales. In this
paradigm, macromolecular geometric characteristic and topological invariants
are revealed by de Rham-Hodge spectral analysis. By using the Helmholtz-Hodge
decomposition, every macromolecular vector field is split into orthogonal
divergence-free, curl-free, and harmonic components with a distinct physical
interpretation. We organize the eigenvalues and eigenvectors of the 0-form
Laplace-de Rham operator into one of the most accurate protein B-factor
predictors. By combining the 1-form Laplace-de Rham operator and the
Helfrich-type curvature energy, we predict the natural modes of both X-ray
protein structures and cryo-EM maps. We construct accurate and efficient
three-dimensional discrete exterior calculus algorithms for the aforementioned
modeling and analysis of biological macromolecules. Using extensive
experiments, we validate that the proposed paradigm is one of the most
versatile and powerful tools for biological macromolecular studies.
|
[
{
"created": "Thu, 1 Aug 2019 18:37:13 GMT",
"version": "v1"
}
] |
2019-08-05
|
[
[
"Zhao",
"Rundong",
""
],
[
"Wang",
"Menglun",
""
],
[
"Tong",
"Yiying",
""
],
[
"Wei",
"Guo-Wei",
""
]
] |
Recent years have witnessed a trend that advanced mathematical tools, such as algebraic topology, differential geometry, graph theory, and partial differential equations, have been developed for describing biological macromolecules. These tools have considerably strengthened our ability to understand the molecular mechanism of macromolecular function, dynamics and transport from their structures. However, currently, there is no unified mathematical theory to analyze, describe and characterize biological macromolecular geometry, topology, flexibility and natural mode at a variety of scales. We introduce the de Rham-Hodge theory, a landmark of 20th Century's mathematics, as a unified paradigm for analyzing biological macromolecular geometry and algebraic topology, for predicting macromolecular flexibility, and for modeling macromolecular natural modes at a variety of scales. In this paradigm, macromolecular geometric characteristic and topological invariants are revealed by de Rham-Hodge spectral analysis. By using the Helmholtz-Hodge decomposition, every macromolecular vector field is split into orthogonal divergence-free, curl-free, and harmonic components with a distinct physical interpretation. We organize the eigenvalues and eigenvectors of the 0-form Laplace-de Rham operator into one of the most accurate protein B-factor predictors. By combining the 1-form Laplace-de Rham operator and the Helfrich-type curvature energy, we predict the natural modes of both X-ray protein structures and cryo-EM maps. We construct accurate and efficient three-dimensional discrete exterior calculus algorithms for the aforementioned modeling and analysis of biological macromolecules. Using extensive experiments, we validate that the proposed paradigm is one of the most versatile and powerful tools for biological macromolecular studies.
|
1504.03832
|
Laura Hindersin
|
Laura Hindersin and Arne Traulsen
|
Most undirected random graphs are amplifiers of selection for
Birth-death dynamics, but suppressors of selection for death-Birth dynamics
| null |
Hindersin, L. & Traulsen A. PLoS Comput. Biol. 2015;
11(11):e1004437
|
10.1371/journal.pcbi.1004437
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyze evolutionary dynamics on graphs, where the nodes represent
individuals of a population. The links of a node describe which other
individuals can be displaced by the offspring of the individual on that node.
Amplifiers of selection are graphs for which the fixation probability is
increased for advantageous mutants and decreased for disadvantageous mutants. A
few examples of such amplifiers have been developed, but so far it is unclear
how many such structures exist and how to construct them. Here, we show that
almost any undirected random graph is an amplifier of selection for Birth-death
updating, where an individual is selected to reproduce with probability
proportional to its fitness and one of its neighbors is replaced by that
offspring at random. If we instead focus on death-Birth updating, in which a
random individual is removed and its neighbors compete for the empty spot, then
the same ensemble of graphs consists of almost only suppressors of selection
for which the fixation probability is decreased for advantageous mutants and
increased for disadvantageous mutants. Thus, the impact of population structure
on evolutionary dynamics is a subtle issue that will depend on seemingly minor
details of the underlying evolutionary process.
|
[
{
"created": "Wed, 15 Apr 2015 09:14:44 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Oct 2016 14:52:53 GMT",
"version": "v2"
}
] |
2016-10-27
|
[
[
"Hindersin",
"Laura",
""
],
[
"Traulsen",
"Arne",
""
]
] |
We analyze evolutionary dynamics on graphs, where the nodes represent individuals of a population. The links of a node describe which other individuals can be displaced by the offspring of the individual on that node. Amplifiers of selection are graphs for which the fixation probability is increased for advantageous mutants and decreased for disadvantageous mutants. A few examples of such amplifiers have been developed, but so far it is unclear how many such structures exist and how to construct them. Here, we show that almost any undirected random graph is an amplifier of selection for Birth-death updating, where an individual is selected to reproduce with probability proportional to its fitness and one of its neighbors is replaced by that offspring at random. If we instead focus on death-Birth updating, in which a random individual is removed and its neighbors compete for the empty spot, then the same ensemble of graphs consists of almost only suppressors of selection for which the fixation probability is decreased for advantageous mutants and increased for disadvantageous mutants. Thus, the impact of population structure on evolutionary dynamics is a subtle issue that will depend on seemingly minor details of the underlying evolutionary process.
|
2009.04027
|
Sophia David
|
Sophia U. David (1), Sophie E. Loman (1), Christopher W. Lynn (1 and
2), Ann S. Blevins (1), Danielle S. Bassett (1-6) ((1) Department of
Bioengineering, School of Engineering & Applied Science, University of
Pennsylvania, Philadelphia, USA, (2) Department of Physics & Astronomy,
College of Arts & Sciences University of Pennsylvania, Philadelphia, USA, (3)
Department of Electrical & Systems Engineering, University of Pennsylvania,
Philadelphia, USA, (4) Department of Neurology, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, USA, (5) Department of Psychiatry,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA,
(6) Santa Fe Institute, Santa Fe, USA)
|
How We Learn About our Networked World
|
11 pages, 3 figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When presented with information of any type, from music to language to
mathematics, the human mind subconsciously arranges it into a network. A
network puts pieces of information like musical notes, syllables or
mathematical concepts into context by linking them together. These networks
help our minds organize information and anticipate what is coming. Here we
present two questions about network building. 1) Can humans more easily learn
some types of networks than others? 2) Do humans find some links between ideas
more surprising than others? The answer to both questions is "Yes," and we
explain why. The findings provide much-needed insight into the ways that humans
learn about the networked world around them. Moreover, the study paves the way
for future efforts seeking to optimize how information is presented to
accelerate human learning.
|
[
{
"created": "Tue, 8 Sep 2020 23:17:54 GMT",
"version": "v1"
}
] |
2020-09-10
|
[
[
"David",
"Sophia U.",
"",
"1 and\n 2"
],
[
"Loman",
"Sophie E.",
"",
"1 and\n 2"
],
[
"Lynn",
"Christopher W.",
"",
"1 and\n 2"
],
[
"Blevins",
"Ann S.",
"",
"1-6"
],
[
"Bassett",
"Danielle S.",
"",
"1-6"
]
] |
When presented with information of any type, from music to language to mathematics, the human mind subconsciously arranges it into a network. A network puts pieces of information like musical notes, syllables or mathematical concepts into context by linking them together. These networks help our minds organize information and anticipate what is coming. Here we present two questions about network building. 1) Can humans more easily learn some types of networks than others? 2) Do humans find some links between ideas more surprising than others? The answer to both questions is "Yes," and we explain why. The findings provide much-needed insight into the ways that humans learn about the networked world around them. Moreover, the study paves the way for future efforts seeking to optimize how information is presented to accelerate human learning.
|
1701.01975
|
Bar{\i}\c{s} Ekim
|
Bar{\i}\c{s} Ekim
|
A novel entropy-based hierarchical clustering framework for ultrafast
protein structure search and alignment
|
16 pages, 13 figures
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Identification and alignment of three-dimensional folding of proteins may
yield useful information about relationships too remote to be detected by
conventional methods, such as sequence comparison, and may potentially lead to
prediction of patterns and motifs in mutual structural fragments. With the
exponential increase of structural proteomics data, the methods that scale with
the rate of increase of data lose efficiency. Hence, new methods that reduce
the computational expense of this problem should be developed. We present a
novel framework through which we are able to find and align protein structure
neighbors via hierarchical clustering and entropy-based query search, and
present a web-based protein database search and alignment tool to demonstrate
the applicability of our approach. The resulting method replicates the results
of the current gold standard with a minimal loss in sensitivity in a
significantly shorter amount of time, while ameliorating the existing web
workspace of protein structure comparison with a customized and dynamic
web-based environment. Our tool serves as both a functional industrial means of
protein structure comparison and a valid demonstration of heuristics in
proteomics.
|
[
{
"created": "Sun, 8 Jan 2017 15:51:07 GMT",
"version": "v1"
}
] |
2017-01-10
|
[
[
"Ekim",
"Barış",
""
]
] |
Identification and alignment of three-dimensional folding of proteins may yield useful information about relationships too remote to be detected by conventional methods, such as sequence comparison, and may potentially lead to prediction of patterns and motifs in mutual structural fragments. With the exponential increase of structural proteomics data, the methods that scale with the rate of increase of data lose efficiency. Hence, new methods that reduce the computational expense of this problem should be developed. We present a novel framework through which we are able to find and align protein structure neighbors via hierarchical clustering and entropy-based query search, and present a web-based protein database search and alignment tool to demonstrate the applicability of our approach. The resulting method replicates the results of the current gold standard with a minimal loss in sensitivity in a significantly shorter amount of time, while ameliorating the existing web workspace of protein structure comparison with a customized and dynamic web-based environment. Our tool serves as both a functional industrial means of protein structure comparison and a valid demonstration of heuristics in proteomics.
|
1909.07508
|
Changbong Hyeon
|
D. Thirumalai and George H. Lorimer and Changbong Hyeon
|
Iterative Annealing Mechanism Explains the Functions of the GroEL and
RNA Chaperones
|
39 pages, 11 figures
| null | null | null |
q-bio.BM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular chaperones are ATP-consuming biological machines, which facilitate
the folding of proteins and RNA molecules that are kinetically trapped in
misfolded states for long times. Unassisted folding occurs by the kinetic
partitioning mechanism according to which folding to the native state, with low
probability as well as misfolding to one of the many metastable states, with
high probability, occur rapidly on similar time scales. GroEL is an all-purpose
stochastic machine that assists misfolded substrate proteins (SPs) to fold. The
RNA chaperones (CYT-19) help the folding of ribozymes that readily misfold.
GroEL does not interact with the folded proteins but CYT-19 disrupts both the
folded and misfolded ribozymes. Despite this major difference, the Iterative
Annealing Mechanism (IAM) quantitatively explains all the available
experimental data for assisted folding of proteins and ribozymes. Driven by ATP
binding and hydrolysis and GroES binding, GroEL undergoes a catalytic cycle
during which it samples three allosteric states, referred to as T (apo), R (ATP
bound), and R'' (ADP bound). In accord with the IAM predictions, analyses of
the experimental data shows that the efficiency of the GroEL-GroES machinery
and mutants is determined by the resetting rate $k_{R''\rightarrow T}$, which
is largest for the wild type GroEL. Generalized IAM accurately predicts the
folding kinetics of Tetrahymena ribozyme and its variants. Chaperones maximize
the product of the folding rate and the steady state native state fold by
driving the substrates out of equilibrium. Neither the absolute yield nor the
folding rate is optimized.
|
[
{
"created": "Mon, 16 Sep 2019 22:42:11 GMT",
"version": "v1"
}
] |
2019-09-18
|
[
[
"Thirumalai",
"D.",
""
],
[
"Lorimer",
"George H.",
""
],
[
"Hyeon",
"Changbong",
""
]
] |
Molecular chaperones are ATP-consuming biological machines, which facilitate the folding of proteins and RNA molecules that are kinetically trapped in misfolded states for long times. Unassisted folding occurs by the kinetic partitioning mechanism according to which folding to the native state, with low probability as well as misfolding to one of the many metastable states, with high probability, occur rapidly on similar time scales. GroEL is an all-purpose stochastic machine that assists misfolded substrate proteins (SPs) to fold. The RNA chaperones (CYT-19) help the folding of ribozymes that readily misfold. GroEL does not interact with the folded proteins but CYT-19 disrupts both the folded and misfolded ribozymes. Despite this major difference, the Iterative Annealing Mechanism (IAM) quantitatively explains all the available experimental data for assisted folding of proteins and ribozymes. Driven by ATP binding and hydrolysis and GroES binding, GroEL undergoes a catalytic cycle during which it samples three allosteric states, referred to as T (apo), R (ATP bound), and R'' (ADP bound). In accord with the IAM predictions, analyses of the experimental data shows that the efficiency of the GroEL-GroES machinery and mutants is determined by the resetting rate $k_{R''\rightarrow T}$, which is largest for the wild type GroEL. Generalized IAM accurately predicts the folding kinetics of Tetrahymena ribozyme and its variants. Chaperones maximize the product of the folding rate and the steady state native state fold by driving the substrates out of equilibrium. Neither the absolute yield nor the folding rate is optimized.
|
1102.3793
|
Arni S.R. Srinivasa Rao
|
Arni S.R. Srinivasa Rao and Thomas Kurien
|
Theoretical Framework and Empirical Modeling for Time Required to
Vaccinate a Population in an Epidemic
|
14 pages, 1 Table, 5 Figures (A preliminary draft)
|
Handbook of Statistics (2017), Volume 37
|
10.1016/bs.host.2017.07.005
| null |
q-bio.QM physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper describes a method to understand time required to vaccinate against
viruses in total as well as subpopulations. As a demonstration, a model based
estimate for time required to vaccinate H1N1 in India, given its administrative
difficulties is provided. We have proved novel theorems for the time functions
defined in the paper. Such results are useful in planning for future epidemics.
The number of days required to vaccinate entire high risk population in three
subpopulations (villages, tehsils and towns) are noted to be 84, 89 and 88
respectively. There exists state wise disparities in the health infrastructure
and capacities to deliver vaccines and hence national estimates need to be
re-evaluated based on individual performances in the states.
|
[
{
"created": "Fri, 18 Feb 2011 09:41:30 GMT",
"version": "v1"
}
] |
2021-06-15
|
[
[
"Rao",
"Arni S. R. Srinivasa",
""
],
[
"Kurien",
"Thomas",
""
]
] |
The paper describes a method to understand time required to vaccinate against viruses in total as well as subpopulations. As a demonstration, a model based estimate for time required to vaccinate H1N1 in India, given its administrative difficulties is provided. We have proved novel theorems for the time functions defined in the paper. Such results are useful in planning for future epidemics. The number of days required to vaccinate entire high risk population in three subpopulations (villages, tehsils and towns) are noted to be 84, 89 and 88 respectively. There exists state wise disparities in the health infrastructure and capacities to deliver vaccines and hence national estimates need to be re-evaluated based on individual performances in the states.
|
1801.09831
|
Sanjana Gupta
|
Sanjana Gupta, Liam Hainsworth, Justin S. Hogg, Robin E. C. Lee, and
James R. Faeder
|
Evaluation of Parallel Tempering to Accelerate Bayesian Parameter
Estimation in Systems Biology
| null | null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Models of biological systems often have many unknown parameters that must be
determined in order for model behavior to match experimental observations.
Commonly-used methods for parameter estimation that return point estimates of
the best-fit parameters are insufficient when models are high dimensional and
under-constrained. As a result, Bayesian methods, which treat model parameters
as random variables and attempt to estimate their probability distributions
given data, have become popular in systems biology. Bayesian parameter
estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample
model parameter distributions, but the slow convergence of MCMC sampling can be
a major bottleneck. One approach to improving performance is parallel tempering
(PT), a physics-based method that uses swapping between multiple Markov chains
run in parallel at different temperatures to accelerate sampling. The
temperature of a Markov chain determines the probability of accepting an
unfavorable move, so swapping with higher temperatures chains enables the
sampling chain to escape from local minima. In this work we compared the MCMC
performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on
six biological models of varying complexity. We found that for simpler models
PT accelerated convergence and sampling, and that for more complex models, PT
often converged in cases MH became trapped in non-optimal local minima. We also
developed a freely-available MATLAB package for Bayesian parameter estimation
called PTempEst (http://github.com/RuleWorld/ptempest), which is closely
integrated with the popular BioNetGen software for rule-based modeling of
biological systems.
|
[
{
"created": "Tue, 30 Jan 2018 02:45:59 GMT",
"version": "v1"
}
] |
2018-01-31
|
[
[
"Gupta",
"Sanjana",
""
],
[
"Hainsworth",
"Liam",
""
],
[
"Hogg",
"Justin S.",
""
],
[
"Lee",
"Robin E. C.",
""
],
[
"Faeder",
"James R.",
""
]
] |
Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTempEst (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.
|
2012.05454
|
Adrian Joseph Alva
|
Adrian Joseph Alva and Harjinder Singh
|
A minimal model for synaptic integration in simple neurons
|
25 pages, 8 figures
|
Physica D 426 (2021) 132988
|
10.1016/j.physd.2021.132988
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Synaptic integration is a prominent aspect of neuronal information
processing. The detailed mechanisms that modulate synaptic inputs determine the
computational properties of any given neuron. We study a simple model for the
summation of excitatory inputs from synapses and illustrate its use by
characterizing some functional properties of postsynaptic neurons. In this
regard, we study the response of postsynaptic neurons as defined by the model
to two well known noise driven processes: stochastic and coherence resonance.
The model requires a small number of parameters and is especially useful to
isolate the role of integration mechanisms that rely on summation of inputs
with little dendritic processing.
|
[
{
"created": "Thu, 10 Dec 2020 05:03:07 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Feb 2021 13:54:48 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Jul 2021 06:14:20 GMT",
"version": "v3"
}
] |
2021-07-15
|
[
[
"Alva",
"Adrian Joseph",
""
],
[
"Singh",
"Harjinder",
""
]
] |
Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neurons. In this regard, we study the response of postsynaptic neurons as defined by the model to two well known noise driven processes: stochastic and coherence resonance. The model requires a small number of parameters and is especially useful to isolate the role of integration mechanisms that rely on summation of inputs with little dendritic processing.
|
1706.08422
|
Edmund Barter
|
Edmund Barter and Thilo Gross
|
Spatial effects in meta-food-webs
|
15 pages, 8 figures
| null | null | null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In ecology it is widely recognised that many landscapes comprise a network of
discrete patches of habitat. The species that inhabit the patches interact with
each other through a foodweb, the network of feeding interactions. The
meta-foodweb model proposed by Pillai et al. combines the feeding relationships
at each patch with the dispersal of species between patches, such that the
whole system is represented by a network of networks. Previous work on
meta-foodwebs has focussed on landscape networks that do not have an explicit
spatial embedding, but in real landscapes the patches are usually distributed
in space. Here we compare the dispersal of a meta-foodweb on \ER networks, that
do not have a spatial embedding, and random geometric networks, that do have a
spatial embedding. We found that local structure and large network distances in
spatially embedded networks, lead to meso-scale patterns of patch occupation by
both specialist and omnivorous species. In particular, we found that spatial
separations make the coexistence of competing species more likely. Our results
highlight the effects of spatial embeddings for meta-foodweb models, and the
need for new analytical approaches to them.
|
[
{
"created": "Mon, 26 Jun 2017 14:56:01 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Aug 2017 09:47:35 GMT",
"version": "v2"
}
] |
2017-08-11
|
[
[
"Barter",
"Edmund",
""
],
[
"Gross",
"Thilo",
""
]
] |
In ecology it is widely recognised that many landscapes comprise a network of discrete patches of habitat. The species that inhabit the patches interact with each other through a foodweb, the network of feeding interactions. The meta-foodweb model proposed by Pillai et al. combines the feeding relationships at each patch with the dispersal of species between patches, such that the whole system is represented by a network of networks. Previous work on meta-foodwebs has focussed on landscape networks that do not have an explicit spatial embedding, but in real landscapes the patches are usually distributed in space. Here we compare the dispersal of a meta-foodweb on \ER networks, that do not have a spatial embedding, and random geometric networks, that do have a spatial embedding. We found that local structure and large network distances in spatially embedded networks, lead to meso-scale patterns of patch occupation by both specialist and omnivorous species. In particular, we found that spatial separations make the coexistence of competing species more likely. Our results highlight the effects of spatial embeddings for meta-foodweb models, and the need for new analytical approaches to them.
|
1009.4480
|
Adriaan (Ard) A. Louis
|
Thomas E. Ouldridge, Ard A. Louis, Jonathan P.K. Doye
|
Structural, mechanical and thermodynamic properties of a coarse-grained
DNA model
|
25 pages, 16 figures
|
J. Chem. Phys, 134, 085101 (2011)
|
10.1063/1.3552946
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We explore in detail the structural, mechanical and thermodynamic properties
of a coarse-grained model of DNA similar to that introduced in Thomas E.
Ouldridge, Ard A. Louis, Jonathan P.K. Doye, Phys. Rev. Lett. 104 178101
(2010). Effective interactions are used to represent chain connectivity,
excluded volume, base stacking and hydrogen bonding, naturally reproducing a
range of DNA behaviour. We quantify the relation to experiment of the
thermodynamics of single-stranded stacking, duplex hybridization and hairpin
formation, as well as structural properties such as the persistence length of
single strands and duplexes, and the torsional and stretching stiffness of
double helices. We also explore the model's representation of more complex
motifs involving dangling ends, bulged bases and internal loops, and the effect
of stacking and fraying on the thermodynamics of the duplex formation
transition.
|
[
{
"created": "Wed, 22 Sep 2010 21:14:06 GMT",
"version": "v1"
}
] |
2013-06-25
|
[
[
"Ouldridge",
"Thomas E.",
""
],
[
"Louis",
"Ard A.",
""
],
[
"Doye",
"Jonathan P. K.",
""
]
] |
We explore in detail the structural, mechanical and thermodynamic properties of a coarse-grained model of DNA similar to that introduced in Thomas E. Ouldridge, Ard A. Louis, Jonathan P.K. Doye, Phys. Rev. Lett. 104 178101 (2010). Effective interactions are used to represent chain connectivity, excluded volume, base stacking and hydrogen bonding, naturally reproducing a range of DNA behaviour. We quantify the relation to experiment of the thermodynamics of single-stranded stacking, duplex hybridization and hairpin formation, as well as structural properties such as the persistence length of single strands and duplexes, and the torsional and stretching stiffness of double helices. We also explore the model's representation of more complex motifs involving dangling ends, bulged bases and internal loops, and the effect of stacking and fraying on the thermodynamics of the duplex formation transition.
|
2110.09204
|
Andrea De Martino
|
A.R. Batista-Tomas, Andrea De Martino, Roberto Mulet
|
Path-integral solution of MacArthur's resource-competition model for
large ecosystems with random species-resources couplings
|
This article may be downloaded for personal use only. Any other use
requires prior permission of the author and AIP Publishing. This article
appeared in Chaos 31, 103113 (2021) and may be found at
https://aip.scitation.org/doi/full/10.1063/5.0046972
|
Chaos 31, 103113 (2021)
|
10.1063/5.0046972
| null |
q-bio.PE cond-mat.dis-nn cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We solve MacArthur's resource-competition model with random species-resource
couplings in the `thermodynamic' limit of infinitely many species and resources
using dynamical path-integrals a la De Domincis. We analyze how the steady
state picture changes upon modifying several parameters, including the degree
of heterogeneity of metabolic strategies (encoding the preferences of species)
and of maximal resource levels (carrying capacities), and discuss its
stability. Ultimately, the scenario obtained by other approaches is recovered
by analyzing an effective one-species-one-resource ecosystem that is fully
equivalent to the original multi-species one. The technique used here can be
applied for the analysis of other model ecosystems related to the version of
MacArthur's model considered here.
|
[
{
"created": "Mon, 18 Oct 2021 11:43:05 GMT",
"version": "v1"
}
] |
2021-10-19
|
[
[
"Batista-Tomas",
"A. R.",
""
],
[
"De Martino",
"Andrea",
""
],
[
"Mulet",
"Roberto",
""
]
] |
We solve MacArthur's resource-competition model with random species-resource couplings in the `thermodynamic' limit of infinitely many species and resources using dynamical path-integrals a la De Domincis. We analyze how the steady state picture changes upon modifying several parameters, including the degree of heterogeneity of metabolic strategies (encoding the preferences of species) and of maximal resource levels (carrying capacities), and discuss its stability. Ultimately, the scenario obtained by other approaches is recovered by analyzing an effective one-species-one-resource ecosystem that is fully equivalent to the original multi-species one. The technique used here can be applied for the analysis of other model ecosystems related to the version of MacArthur's model considered here.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.