pwc-archive/papers-with-abstracts huggingface dataset:
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction
Date: 2015-12-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Sensitivity, Time Series, Time Series Analysis

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into
time series representing neurophysiological activity in fixed frequency bands.
Using these time series, one can estimate frequency-band specific functional
connectivity between sensors or regions of interest, and thereby construct
functional brain networks that can be examined from a graph theoretic
perspective. Despite their common use, however, practical guidelines for the
choice of wavelet method, filter, and length have remained largely
undelineated. Here, we explicitly explore the effects of wavelet method (MODWT
vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least
Asymmetric, and Coiflet families), and wavelet length (2 to 24) - each
essential parameters in wavelet-based methods - on the estimated values of
network diagnostics and in their sensitivity to alterations in psychiatric
disease. We observe that the MODWT method produces less variable estimates than
the DWT method. We also observe that the length of the wavelet filter chosen
has a greater impact on the estimated values of network diagnostics than the
type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of
the method to detect differences between health and disease and tunes
classification accuracy. Collectively, our results suggest that the choice of
wavelet method and length significantly alters the reliability and sensitivity
of these methods in estimating values of network diagnostics drawn from graph
theory. They furthermore demonstrate the importance of reporting the choices
utilized in neuroimaging studies and support the utility of exploring wavelet
parameters to maximize classification accuracy in the development of biomarkers
of psychiatric disease and neurological disorders.
Symmetric and Asymmetric Tendencies in Stable Complex Systems
Date: 2016-08-23 00:00:00
Tasks: Diversity

A commonly used approach to study stability in a complex system is by
analyzing the Jacobian matrix at an equilibrium point of a dynamical system.
The equilibrium point is stable if all eigenvalues have negative real parts.
Here, by obtaining eigenvalue bounds of the Jacobian, we show that stable
complex systems will favor mutualistic and competitive relationships that are
asymmetrical (non-reciprocative) and trophic relationships that are symmetrical
(reciprocative). Additionally, we define a measure called the interdependence
diversity that quantifies how distributed the dependencies are between the
dynamical variables in the system. We find that increasing interdependence
diversity has a destabilizing effect on the equilibrium point, and the effect
is greater for trophic relationships than for mutualistic and competitive
relationships. These predictions are consistent with empirical observations in
ecology. More importantly, our findings suggest stabilization algorithms that
can apply very generally to a variety of complex systems.
Governing equations of tissue modelling and remodelling: A unified generalised description of surface and bulk balance
Date: 2016-02-22 00:00:00

Several biological tissues undergo changes in their geometry and in their
bulk material properties by modelling and remodelling processes. Modelling
synthesises tissue in some regions and removes tissue in others. Remodelling
overwrites old tissue material properties with newly formed, immature tissue
properties. As a result, tissues are made up of different "patches", i.e.,
adjacent tissue regions of different ages and different material properties,
within evolving boundaries. In this paper, generalised equations governing the
spatio-temporal evolution of such tissues are developed within the continuum
model. These equations take into account nonconservative, discontinuous surface
mass balance due to creation and destruction of material at moving interfaces,
and bulk balance due to tissue maturation. These equations make it possible to
model patchy tissue states and their evolution without explicitly maintaining a
record of when/where resorption and formation processes occurred. The time
evolution of spatially averaged tissue properties is derived systematically by
integration. These spatially-averaged equations cannot be written in closed
form as they retain traces that tissue destruction is localised at tissue
boundaries.
  The formalism developed in this paper is applied to bone tissues, which
exhibit strong material heterogeneities due to their slow mineralisation and
remodelling processes. Evolution equations are proposed in particular for
osteocyte density and bone mineral density. Effective average equations for
bone mineral density (BMD) and tissue mineral density (TMD) are derived using a
mean-field approximation. The error made by this approximation when remodelling
patchy tissue is investigated. The specific time signatures of BMD or TMD
during remodelling events may provide a way to detect these events occurring at
lower, unseen spatial resolutions from microCT scans.
Computational Crystallization
Date: 2015-12-24 00:00:00

Crystallization is a key step in macromolecular structure determination by
crystallography. While a robust theoretical treatment of the process is
available, due to the complexity of the system, the experimental process is
still largely one of trial and error. In this article, efforts in the field are
discussed together with a theoretical underpinning using a solubility phase
diagram. Prior knowledge has been used to develop tools that computationally
predict the crystallization outcome and define mutational approaches that
enhance the likelihood of crystallization. For the most part these tools are
based on binary outcomes (crystal or no crystal), and the full information
contained in an assembly of crystallization screening experiments is lost. The
potential of this additional information is illustrated by examples where new
biological knowledge can be obtained and where a target can be sub-categorized
to predict which class of reagents provides the crystallization driving force.
Computational analysis of crystallization requires complete and correctly
formatted data. While massive crystallization screening efforts are under way,
the data available from many of these studies are sparse. The potential for
this data and the steps needed to realize this potential are discussed.
Neurons as an Information-theoretic Engine
Date: 2017-12-27 00:00:00

We show that dynamical gain modulation of neurons' stimulus response is
described as an information-theoretic cycle that generates entropy associated
with the stimulus-related activity from entropy produced by the modulation. To
articulate this theory, we describe stimulus-evoked activity of a neural
population based on the maximum entropy principle with constraints on two types
of overlapping activities, one that is controlled by stimulus conditions and
the other, termed internal activity, that is regulated internally in an
organism. We demonstrate that modulation of the internal activity realises gain
control of stimulus response, and controls stimulus information. A cycle of
neural dynamics is then introduced to model information processing by the
neurons during which the stimulus information is dynamically enhanced by the
internal gain-modulation mechanism. Based on the conservation law for entropy
production, we demonstrate that the cycle generates entropy ascribed to the
stimulus-related activity using entropy supplied by the internal mechanism,
analogously to a heat engine that produces work from heat. We provide an
efficient cycle that achieves the highest entropic efficiency to retain the
stimulus information. The theory allows us to quantify efficiency of the
internal computation and its theoretical limit.
A mechanistic first--passage time framework for bacterial cell-division timing
Date: 2015-12-24 00:00:00

How exponentially growing cells maintain size homeostasis is an important
fundamental problem. Recent single-cell studies in prokaryotes have uncovered
the adder principle, where cells on average, add a fixed size (volume) from
birth to division. Interestingly, this added volume differs considerably among
genetically-identical newborn cells with similar sizes suggesting a stochastic
component in the timing of cell-division. To mechanistically explain the adder
principle, we consider a time-keeper protein that begins to get stochastically
expressed after cell birth at a rate proportional to the volume. Cell-division
time is formulated as the first-passage time for protein copy numbers to hit a
fixed threshold. Consistent with data, the model predicts that while the mean
cell-division time decreases with increasing size of newborns, the noise in
timing increases with size at birth. Intriguingly, our results show that the
distribution of the volume added between successive cell-division events is
independent of the newborn cell size. This was dramatically seen in
experimental studies, where histograms of the added volume corresponding to
different newborn sizes collapsed on top of each other. The model provides
further insights consistent with experimental observations: the distributions
of the added volume and the cell-division time when scaled by their respective
means become invariant of the growth rate. Finally, we discuss various
modifications to the proposed model that lead to deviations from the adder
principle. In summary, our simple yet elegant model explains key experimental
findings and suggests a mechanism for regulating both the mean and fluctuations
in cell-division timing for size control.
A Relaxed Drift Diffusion Model for Phylogenetic Trait Evolution
Date: 2015-12-29 00:00:00
Tasks: Bayesian Inference

Understanding the processes that give rise to quantitative measurements
associated with molecular sequence data remains an important issue in
statistical phylogenetics. Examples of such measurements include geographic
coordinates in the context of phylogeography and phenotypic traits in the
context of comparative studies. A popular approach is to model the evolution of
continuously varying traits as a Brownian diffusion process. However, standard
Brownian diffusion is quite restrictive and may not accurately characterize
certain trait evolutionary processes. Here, we relax one of the major
restrictions of standard Brownian diffusion by incorporating a nontrivial
estimable drift into the process. We introduce a relaxed drift diffusion model
for the evolution of multivariate continuously varying traits along a
phylogenetic tree via Brownian diffusion with drift. Notably, the relaxed drift
model accommodates branch-specific variation of drift rates while preserving
model identifiability. We implement the relaxed drift model in a Bayesian
inference framework to simultaneously reconstruct the evolutionary histories of
molecular sequence data and associated multivariate continuous trait data, and
provide tools to visualize evolutionary reconstructions. We illustrate our
approach in three viral examples. In the first two, we examine the
spatiotemporal spread of HIV-1 in central Africa and West Nile virus in North
America and show that a relaxed drift approach uncovers a clearer, more
detailed picture of the dynamics of viral dispersal than standard Brownian
diffusion. Finally, we study antigenic evolution in the context of HIV-1
resistance to three broadly neutralizing antibodies. Our analysis reveals
evidence of a continuous drift at the HIV-1 population level towards enhanced
resistance to neutralization by the VRC01 monoclonal antibody over the course
of the epidemic.
Risk Aversion in the Small and in the Large under Rank-Dependent Utility
Date: 2015-12-25 00:00:00

Under expected utility the local index of absolute risk aversion has played a
central role in many applications. Besides, its link with the "global" concepts
of the risk and probability premia has reinforced its attractiveness. This
paper shows that, with an appropriate approach, similar developments can be
achieved in the framework of Yaari's dual theory and, more generally, under
rank-dependent utility.
Unified Growth Theory Contradicted by the Economic Growth in Europe
Date: 2016-02-27 00:00:00

Historical economic growth in Western and Eastern Europe is analysed. These
regions should have produced the best and the most convincing confirmation of
the Unified Growth Theory because they, and in particular Western Europe, were
the centre of the Industrial Revolution, which according to Galor was the prime
engine of economic growth. However, the data for Western and Eastern Europe
show a remarkable disagreement with the Unified Growth Theory. There is no
connection, whatever, between the data and the Unified Growth Theory. The data
show that there was never a transition from stagnation to growth because there
was no stagnation. Industrial Revolution, which should have the strongest
influence in these regions, had absolutely no impact on changing the economic
growth trajectories. The alleged remarkable or stunning escape from Malthusian
trap did not happen because there was no trap. Unified Growth Theory does not
explain the mechanism of the economic growth because its explanations are based
on mythical features, which did not exist, the features contradicted by data.
This theory needs to be either thoroughly revised or most likely replaced by a
theory supported by a professional analysis of economic growth data.
Understanding Nucleic Acid Structural Changes by Comparing Wide-Angle X-ray Scattering (WAXS) Experiments to Molecular Dynamics Simulations
Date: 2016-05-15 00:00:00

Wide-angle x-ray scattering (WAXS) is emerging as a powerful tool for
increasing the resolution of solution structure measurements of biomolecules.
Compared to its better known complement, small angle x-ray scattering (SAXS),
WAXS targets higher scattering angles and can enhance structural studies of
molecules by accessing finer details of solution structures. Although the
extension from SAXS to WAXS is easy to implement experimentally, the
computational tools required to fully harness the power of WAXS are still under
development. Currently, WAXS is employed to study structural changes and ligand
binding in proteins; however the methods are not as fully developed for nucleic
acids. Here, we show how WAXS can qualitatively characterize nucleic acid
structures as well as the small but significant structural changes driven by
the addition of multivalent ions. We show the potential of WAXS to test
all-atom molecular dynamics (MD) simulations and to provide insight in
understanding how the trivalent ion cobalt(III) hexammine (CoHex) affects the
structure of RNA and DNA helices. We find that MD simulations capture the RNA
structural change that occurs due to addition of CoHex.
Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates
Date: 2016-10-25 00:00:00

As cells grow and divide under a given environment, they become crowded and
resources are limited, as seen in bacterial biofilms and multicellular
aggregates. These cells often show strong interactions through exchanging
chemicals, as in quorum sensing, to achieve mutualism. Here, to achieve stable
division of labor, three properties are required. First, isogenous cells
differentiate into several types. Second, this aggregate of distinct cell types
shows better growth than that of isolated cells, by achieving division of
labor. Third, this cell aggregate is robust in the number distribution of
differentiated cell types. We here address how cells acquire the ability of
cell differentiation and division of labor simultaneously, which is also
connected with the robustness of a cell society. For this purpose, we developed
a dynamical-systems model of cells consisting of chemical components with
intracellular catalytic reaction dynamics. The reactions convert external
nutrients into internal components for cellular growth, and the divided cells
interact via chemical diffusion. We found that cells sharing an identical
catalytic network spontaneously differentiate via induction from cell-cell
interactions, and then achieve division of labor, enabling a higher growth rate
than that in the unicellular case. This symbiotic differentiation emerged for a
class of reaction networks with limited resources and strong cell-cell
interactions. Then, robustness in the cell type distribution was achieved,
while instability of collective growth could emerge even among the cooperative
cells when the internal reserves of products were dominant. The present
mechanism is simple and general as a natural result of interacting cells with
resource limitation, and is consistent with the observed behaviors and forms of
several aggregates of unicellular organisms.
On the entropy of protein families
Date: 2015-12-26 00:00:00

Proteins are essential components of living systems, capable of performing a
huge variety of tasks at the molecular level, such as recognition, signalling,
copy, transport, ... The protein sequences realizing a given function may
largely vary across organisms, giving rise to a protein family. Here, we
estimate the entropy of those families based on different approaches, including
Hidden Markov Models used for protein databases and inferred statistical models
reproducing the low-order (1-and 2-point) statistics of multi-sequence
alignments. We also compute the entropic cost, that is, the loss in entropy
resulting from a constraint acting on the protein, such as the fixation of one
particular amino-acid on a specific site, and relate this notion to the escape
probability of the HIV virus. The case of lattice proteins, for which the
entropy can be computed exactly, allows us to provide another illustration of
the concept of cost, due to the competition of different folds. The relevance
of the entropy in relation to directed evolution experiments is stressed.
The maximum penalty criterion for ridge regression: application to the calibration of the force constant in elastic network models
Date: 2015-12-28 00:00:00
Tasks: regression

Multivariate regression is a widespread computational technique that may give
meaningless results if the explanatory variables are too numerous or highly
collinear. Tikhonov regularization, or ridge regression, is a popular approach
to address this issue. We reveal here a formal analogy between ridge regression
and statistical mechanics, where the objective function is comparable to a free
energy and the ridge parameter plays the role of temperature. This analogy
suggests two new criteria to select a suitable ridge parameter: the
specific-heat (Cv) and the maximum penalty (MP) fits. We apply these methods to
the calibration of the force constant in elastic network models (ENM). This key
parameter determines the amplitude of the predicted atomic fluctuations, and is
commonly obtained by fitting crystallographic B-factors. However, rigid-body
motions are often partially neglected in such fits, even though their
importance has been repeatedly stressed. Considering the full set of rigid-body
and internal degrees of freedom bears significant risks of overfitting, due to
strong correlations between explanatory variables, and requires thus careful
regularization. Using simulated data, we show that ridge regression with the Cv
or MP criterion markedly reduces the error of the estimated force constant, its
across-protein variation, and the number of proteins with unphysical values of
the fit parameters, in comparison with popular regularization schemes such as
generalized cross-validation. When applied to protein crystals, the new methods
provide a more robust calibration of ENM force constants, even though
rigid-body motions account on average for more than 80% of the amplitude of
B-factors. While MP emerges as the optimal choice for fitting crystallographic
B-factors, the Cv fit is more robust to the nature of the data, and is thus an
interesting candidate for other applications.
Design Principles of Pancreatic Islets: Glucose-dependent Coordination of Hormone Pulses
Date: 2015-12-28 00:00:00

Pancreatic islets are functional units involved in glucose homeostasis. The
multicellular system comprises three main cell types; $\beta$ and $\alpha$
cells reciprocally decrease and increase blood glucose by producing insulin and
glucagon pulses, while the role of $\delta$ cells is less clear. Although their
spatial organization and the paracrine/autocrine interactions between them have
been extensively studied, the functional implications of the design principles
are still lacking. In this study, we formulated a mathematical model that
integrates the pulsatility of hormone secretion and the interactions and
organization of islet cells and examined the effects of different cellular
compositions and organizations in mouse and human islets. A common feature of
both species was that islet cells produced synchronous hormone pulses under
low- and high- glucose conditions, while they produced asynchronous hormone
pulses under normal glucose conditions. However, the synchronous coordination
of insulin and glucagon pulses at low glucose was more pronounced in human
islets that had more $\alpha$ cells. When $\beta$ cells were selectively
removed to mimic diabetic conditions, the anti-synchronicity of insulin and
glucagon pulses was deteriorated at high glucose, but it could be partially
recovered when the re-aggregation of remaining cells was considered. Finally,
the third cell type, $\delta$ cells, which introduced additional complexity in
the multicellular system, prevented the excessive synchronization of hormone
pulses. Our computational study suggests that controllable synchronization is a
design principle of pancreatic islets.
Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information
Date: 2016-08-16 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we developed a model-based and a data-driven estimator for
directed information (DI) to infer the causal connectivity graph between
electrocorticographic (ECoG) signals recorded from brain and to identify the
seizure onset zone (SOZ) in epileptic patients. Directed information, an
information theoretic quantity, is a general metric to infer causal
connectivity between time-series and is not restricted to a particular class of
models unlike the popular metrics based on Granger causality or transfer
entropy. The proposed estimators are shown to be almost surely convergent.
Causal connectivity between ECoG electrodes in five epileptic patients is
inferred using the proposed DI estimators, after validating their performance
on simulated data. We then proposed a model-based and a data-driven SOZ
identification algorithm to identify SOZ from the causal connectivity inferred
using model-based and data-driven DI estimators respectively. The data-driven
SOZ identification outperforms the model-based SOZ identification algorithm
when benchmarked against visual analysis by neurologist, the current clinical
gold standard. The causal connectivity analysis presented here is the first
step towards developing novel non-surgical treatments for epilepsy.
Evolutionary simulations of autopoietic cells with cognition
Date: 2015-12-28 00:00:00

The minimal requirements for life are autopoiesis and cognition. We propose
autopoietic models with cognition and perform three classes of evolutionary
simulation. In our models the plasticity of the metabolic cycle and the
regulation function of the membrane are the bases for the cognition. The
cognitive cells show the adaptation and the evolution. The environment also
shows the evolution via the interaction with the system of cells. This is a
prototype of the co-evolution of the living system and its environment.