pwc-archive/papers-with-abstracts huggingface dataset:
Long-range dispersal, stochasticity and the broken accelerating wave of advance
Date: 2015-11-30 00:00:00

Rare long distance dispersal events are thought to have a disproportionate
impact on the spread of invasive species. Modelling using integrodifference
equations suggests that, when long distance contacts are represented by a
fat-tailed dispersal kernel, an accelerating wave of advance can ensue.
Invasions spreading in this manner could have particularly dramatic effects.
Recently, various authors have suggested that demographic stochasticity
disrupts wave acceleration. Integrodifference models have been widely used in
movement ecology, and as such a clearer understanding of stochastic effects is
needed. Here, we present a stochastic non-linear one-dimensional lattice model
in which demographic stochasticity and the dispersal regime can be
systematically varied. Extensive simulations show that stochasticity has a
profound effect on model behaviour, and usually breaks acceleration for
fat-tailed kernels. Exceptions are seen for some power law kernels, $K(l)
\propto |l|^{-\beta}$ with $\beta < 3$, for which acceleration persists despite
stochasticity. Such kernels lack a second moment and are important in
`accelerating' phenomena such as L\'{e}vy flights. Furthermore, for long-range
kernels the approach to the continuum limit behaviour as stochasticity is
reduced is generally slow. Given that real-world populations are finite,
stochastic models may give better predictive power when long-range dispersal is
important. Insights from mean-field models such as integrodifference equations
should be applied with caution in such circumstances.
Full-density multi-scale account of structure and dynamics of macaque visual cortex
Date: 2016-04-15 00:00:00
Tasks: Functional Connectivity

We present a multi-scale spiking network model of all vision-related areas of
macaque cortex that represents each area by a full-scale microcircuit with
area-specific architecture. The layer- and population-resolved network
connectivity integrates axonal tracing data from the CoCoMac database with
recent quantitative tracing data, and is systematically refined using dynamical
constraints. Simulations reveal a stable asynchronous irregular ground state
with heterogeneous activity across areas, layers, and populations. Elicited by
large-scale interactions, the model reproduces longer intrinsic time scales in
higher compared to early visual areas. Activity propagates down the visual
hierarchy, similar to experimental results associated with visual imagery.
Cortico-cortical interaction patterns agree well with fMRI resting-state
functional connectivity. The model bridges the gap between local and
large-scale accounts of cortex, and clarifies how the detailed connectivity of
cortex shapes its dynamics on multiple scales.
The role of master regulators in gene regulatory networks
Date: 2015-10-17 00:00:00

Gene regulatory networks present a wide variety of dynamical responses to
intrinsic and extrinsic perturbations. Arguably, one of the most important of
such coordinated responses is the one of amplification cascades, in which
activation of a few key-responsive transcription factors (termed master
regulators, MRs) lead to a large series of transcriptional activation events.
This is so since master regulators are transcription factors controlling the
expression of other transcription factor molecules and so on. MRs hold a
central position related to transcriptional dynamics and control of gene
regulatory networks and are often involved in complex feedback and feedforward
loops inducing non-trivial dynamics. Recent studies have pointed out to the
myocyte enhancing factor 2C (MEF2C, also known as MADS box transcription
enhancer factor 2, polypeptide C) as being one of such master regulators
involved in the pathogenesis of primary breast cancer. In this work, we perform
an integrative genomic analysis of the transcriptional regulation activity of
MEF2C and its target genes to evaluate to what extent are these molecules
inducing collective responses leading to gene expression deregulation and
carcinogenesis. We also analyzed a number of induced dynamic responses, in
particular those associated with transcriptional bursts, and nonlinear
cascading to evaluate the influence they may have in malignant phenotypes and
cancer.
Statistical Thermodynamic Foundation for the Origin and Evolution of Life
Date: 2015-11-30 00:00:00

In this paper we review and extend our earlier recent work on thermostated
systems. A description of nano-biological systems by Markov chains in
coordinate space in the strongly overdamped limit is presented.
Characterization of the most probable path is given and a new formula for the
probability of this special path is provided from recursion formulae. The
deterministic limit is derived and the significance of Lagrange multipliers
introduced when constructing the most probable path is elucidated. The
characterization of the generation of path entropy by the most probable path is
given an equivalent interpretation relating to the rate of entropy production
by the most probable path. The paper concludes with an account of the
biological implications. Here we address why the origin of life and its
subsequent evolution took place, not the particular chemical details of how it
happened.
Implementation of The Double-Centroid Reduced Representation of Proteins and its Application to the Prediction of Ligand Binding Sites and Protein-Protein Interaction Partners Using FORTRAN 77/90 Language
Date: 2015-11-30 00:00:00

Transformation of protein 3D structures from their all-atom representation
(AAR) to the double-centroid reduced representation (DCRR) is a prerequisite to
the implementation of both the tetrahedral three-dimensional search motif (3D
SM) method for predicting specific ligand binding sites (LBS) in proteins, and
the 3D interface search motif tetrahedral pair (3D ISMTP) method for predicting
binary protein-protein interaction (PPI) partners (Reyes, V.M., 2015a & c,
2015b, 2009a, b & c). Here we describe results demonstrating the efficacy of
the set of FORTRAN 77 and 90 source codes used in the transformation from AAR
to DCRR and the implementation of the 3D SM and 3D ISMTP methods. Precisely, we
show here the construction of the 3D SM for the biologically important ligands,
GTP and sialic acid, from a training set composed of experimentally solved
structures of proteins complexed with the pertinent ligand, and their
subsequent use in the screening for potential receptor proteins of the two
ligands. We also show here the construction of the 3D ISMTP for the binary
complexes, RAC:P67PHOX and KAP:phospho-CDK2, from a training set composed of
the experimentally solved complexes, and their subsequent use in the screening
for potential protomers of the two complexes. The 15 FORTRAN programs used in
the AAR to DCRR transformation and the implementation of the two said methods
are: get_bbn.f, get_sdc.f, res2cm_bbn.f, res2cm_sdc.f, nrst_ngbr.f,
find_Hbonds.f, find_VDWints.f, find_clusters.f90, find_trees.f90,
find_edgenodes.f90, match_nodes.f, fpBS.f90, Gen_Chain_Separ.f,
remove_H_atoms.f and resd_num_reduct.f. Two flowcharts - one showing how to
implement the tetrahedral 3D SM method to find LBSs in proteins, and another
how to implement the 3D ISMTP method to find binary PPI partners - are
presented in our two companion papers (Fig. 2, Reyes, V.M., 2015a, Fig. 1 & 2,
Reyes, V.M., 2015c).
Some Examples of Contextuality in Physics: Implications to Quantum Cognition
Date: 2015-06-16 00:00:00

Contextuality, the impossibility of assigning a single random variable to
represent the outcomes of the same measurement procedure under different
experimental conditions, is a central aspect of quantum mechanics. Thus
defined, it appears in well-known cases in quantum mechanics, such as the
double-slit experiment, the Bell-EPR experiment, and the Kochen-Specker
theorem. Here we examine contextuality in such cases, and discuss how each of
them bring different conceptual issues when applied to quantum cognition. We
then focus on the shortcomings of using quantum probabilities to describe
social systems, and explain how negative quasi-probability distributions may
address such limitations.
Evolution arrests invasions of cooperative populations
Date: 2015-12-11 00:00:00

Population expansions trigger many biomedical and ecological transitions,
from tumor growth to invasions of non-native species. Although population
spreading often selects for more invasive phenotypes, we show that this outcome
is far from inevitable. In cooperative populations, mutations reducing
dispersal have a competitive advantage. Such mutations then steadily accumulate
at the expansion front bringing invasion to a halt. Our findings are a rare
example of evolution driving the population into an unfavorable state and could
lead to new strategies to combat unwelcome invaders. In addition, we obtain an
exact analytical expression for the fitness advantage of mutants with different
dispersal rates.
Observations on recent progress in the field of timing and time perception
Date: 2015-11-30 00:00:00
Tasks: Articles

Time is an important dimension of brain function, but little is still known
about the underlying cognitive principles and neurobiological mechanisms. The
field of timing and time perception has witnessed rapid growth and
multidisciplinary interest in the recent years with the advent of modern
neuroimaging, neurophysiological and optogenetic tools. In this article, I
review the literature from the last ten years (2005-2015) using a data mining
approach and highlight the most significant empirical as well as review
articles based on the number of citations (a minimum of 100 citations). Such
analysis provides a unique perspective on the current state-of-the-art in the
field and highlights subtopics in the field that have received considerable
attention, and those that have not. The objective of the article is to present
an objective summary of the current progress in the field of timing and time
perception and provide a valuable and accessible resource summarizing the most
cited articles for new as well current investigators in the field.
A Framework for Analyzing Stochastic Jumps in Finance based on Belief and Knowledge
Date: 2016-02-18 00:00:00

We introduce a formal language IE that is a variant of the language PAL
developed in [van Benthem 2011] by adding a belief operator and a common belief
operator,specializing to stochastic analysis. A constant symbol in the language
denotes a stochastic process so that we can represent several financial events
as formulae in the language, which is expected to be clues of analyzing the
moments that some stochastic jumps such as financial crises occur based on
knowledge and belief of individuals or those shared within groups of
individuals. In order to represent beliefs, we use sigma-complete Boolean
algebras as generalized sigma-algebras. We use the representation for
constructing a model in which the interpretations of the formulae written in
the language IE reside. The model also uses some new categories for integrating
several components appeared in the theory into one.
Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity
Date: 2016-05-30 00:00:00

Network analysis is a powerful way of modeling chromatin interactions.
Assortativity is a network property used in social sciences to identify factors
affecting how people establish social ties. We propose a new approach, using
chromatin assortativity to integrate the epigenomic landscape of a specific
cell type with its chromatin interaction network and thus investigate which
proteins or chromatin marks mediate genomic contacts. We use high-resolution
Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse
embryonic stem cells to investigate promoter-centered chromatin interaction
networks and calculate the presence of specific epigenomic features in the
chromatin fragments constituting the nodes of the network. We estimate the
association of these features to the topology of four chromatin interaction
networks and identify features localized in connected areas of the network.
Polycomb Group proteins and associated histone marks are the features with the
highest chromatin assortativity in promoter-centred networks. We then ask which
features distinguish contacts amongst promoters from contacts between promoters
and other genomic elements. We observe higher chromatin assortativity of the
actively elongating form of RNA Polymerase 2 (RNAPII) compared to inactive
forms only in interactions between promoters and other elements. Contacts among
promoters, and between promoters and other elements have different
characteristic epigenomic features. We identify a possible role for the
elongating form of RNAPII in mediating interactions among promoters, enhancers
and transcribed gene bodies. Our approach facilitates the study of multiple
genome-wide epigenomic profiles, considering network topology and allowing the
comparison of chromatin interaction networks.
A network epidemic model with preventive rewiring: comparative analysis of the initial phase
Date: 2016-10-18 00:00:00

This paper is concerned with stochastic SIR and SEIR epidemic models on
random networks in which individuals may rewire away from infected neighbors at
some rate $\omega$ (and reconnect to non-infectious individuals with
probability $\alpha$ or else simply drop the edge if $\alpha=0$), so-called
preventive rewiring. The models are denoted SIR-$\omega$ and SEIR-$\omega$, and
we focus attention on the early stages of an outbreak, where we derive
expression for the basic reproduction number $R_0$ and the expected degree of
the infectious nodes $E(D_I)$ using two different approximation approaches. The
first approach approximates the early spread of an epidemic by a branching
process, whereas the second one uses pair approximation. The expressions are
compared with the corresponding empirical means obtained from stochastic
simulations of SIR-$\omega$ and SEIR-$\omega$ epidemics on Poisson and
scale-free networks. Without rewiring of exposed nodes, the two approaches
predict the same epidemic threshold and the same $E(D_I)$ for both types of
epidemics, the latter being very close to the mean degree obtained from
simulated epidemics over Poisson networks. Above the epidemic threshold,
pairwise models overestimate the value of $R_0$ computed from simulations,
which turns out to be very close to the one predicted by the branching process
approximation. When exposed individuals also rewire with $\alpha > 0$ (perhaps
unaware of being infected), the two approaches give different epidemic
thresholds, with the branching process approximation being more in agreement
with simulations.
Implementation of the Tangent Sphere and Cutting Plane Methods in the Quantitative Determination of Ligand Binding Site Burial Depths in Proteins Using FORTRAN 77/90 Language
Date: 2015-11-30 00:00:00

Ligand burial depth is an indicator of protein flexibility, as the extent of
receptor conformational change required to bind a ligand in general varies
directly with its depth of burial. In a companion paper (Reyes, V.M. 2015a), we
report on the Tangent Sphere (TS) and Cutting Plane (CP) methods --
complementary methods to quantify, independent of protein size, the degree of
ligand burial in a protein receptor. In this report, we present results that
demonstrate the effectiveness of a set of FORTRAN 77 and 90 source codes used
in the implementation of the two related procedures, as well as the precise
implementation of the procedures. Particularly, we show here that application
of the TS and CP methods on a theoretical model protein in the form of a
spherical grid of points accurately portrays the behavior of the TS and CP
indices, the predictive parameters obtained from the two methods. We also show
that results of the implementation of the TS and CP methods on six protein
receptors (Laskowski et al. 1996) are inagreement with their findings regarding
cavity sizes in these proteins. The six FORTRAN programs we present here are:
find_molec_centr.f, tangent_sphere.f, find_CP_coeffs.f, CPM_Neg_Side.f,
CPM_Pos_Side.f and CPM_Zero_Side.f. The first program calculates the x-, y- and
z-coordinates of the molecular geometric centroid of the protein (global
centroid, GC), the center of the TS. Its radius is the distance between the GC
and the local centroid (LC), the centroid of the bound ligand or a portion of
its binding site. The second program finds the number of protein atoms inside,
outside and on the TS. The third determines the four coefficients A, B, C and D
of the equation of the CP, Ax + By + Cz + D = 0. The CP is tangent to the TS at
GC. The fourth, fifth and sixth programs determine the number of protein atoms
lying on the negative side, positive side, and on the CP.
Implementation of the Spherical Coordinate Representation of Protein 3D Structures and its Applications Using FORTRAN 77/90 Language
Date: 2015-11-30 00:00:00

We previously described the representation of protein 3D structures in
spherical coordinates (rho, phi, theta) and two of its applications: separation
of the outer layer (OL) from the inner core (IC) of proteins, and assessment of
protein surface protrusions and invaginations (Reyes, V.M., 2011& 2009). Here
we present results demonstrating the performance success of the FORTRAN 77 and
90 programs used in the implementation of the two said applications, and how to
implement both applications. In particular, we show here data that demonstrate
the success of our OL-IC separation procedure using a subset of the Laskowski
et al. (1996) dataset. Using a theoretical model protein in the form of a
scalene ellipsoid grid of points with and without an artificially constructed
protrusion or invagination, we also show results demonstrating that protrusions
and invaginations on the protein surface maybe predicted. The nine programs we
present here and their respective functions are: find_molec_centr.f: finds the
x-, y- and z-coordinates of the protein molecular geometric centroid,
cart2sphere_degrees.f90: converts PDB protein coordinates to spherical, with
phi and theta in degrees, cart2sphere_radians.f90: does the same thing as the
second program, but with phi and theta in radians, spher2cart_degrees.f90:
converts the coordinates from spherical back to PDB, where input phi and theta
are in degrees, spher2cart_radians.f90: does the same thing as the fourth
program, but with phi and theta in radians, find_rho_cutoff.f: determines the
rho cut-off for finding the boundary between OL and IC,
phi6_theta8_binning.f90: performs the binning of phi in six- and theta in
eight-degree increments, phi10_theta10_binning.f90: performs the binning of phi
and theta both in ten-degree increments, and bin_rho.f90: performs the binning
of rho values for plotting the frequency distribution of maximum rho values.
Understanding of Genetic Code Degeneracy and New Way of Classifying of Protein Family: A Mathematical Approach
Date: 2015-11-30 00:00:00
Tasks: Drug Design

The genetic code is the set of rules by which information encoded in genetic
material (DNA or RNA sequences) is translated into proteins (amino acid
sequences) by living cells. The code defines a mapping between tri-nucleotide
sequences, called codons, and amino acids. Since there are 20 amino acids and
64 possible tri-nucleotide sequences, more than one among these 64 triplets can
code for a single amino acid which incorporates the problem of degeneracy. This
manuscript explains the underlying logic of degeneracy of genetic code based on
a mathematical point of view using a parameter named Impression. Classification
of protein family is also a long standing problem in the field of Bio-chemistry
and Genomics. Proteins belonging to a particular class have some similar
bio-chemical properties which are of utmost importance for new drug design.
Using the same parameter Impression and using graph theoretic properties we
have also devised a new way of classifying a protein family.
Valid population inference for information-based imaging: From the second-level $t$-test to prevalence inference
Date: 2016-08-10 00:00:00
Tasks: valid

In multivariate pattern analysis of neuroimaging data, 'second-level'
inference is often performed by entering classification accuracies into a
$t$-test vs chance level across subjects. We argue that while the
random-effects analysis implemented by the $t$-test does provide population
inference if applied to activation differences, it fails to do so in the case
of classification accuracy or other 'information-like' measures, because the
true value of such measures can never be below chance level. This constraint
changes the meaning of the population-level null hypothesis being tested, which
becomes equivalent to the global null hypothesis that there is no effect in any
subject in the population. Consequently, rejecting it only allows to infer that
there are some subjects in which there is an information effect, but not that
it generalizes, rendering it effectively equivalent to fixed-effects analysis.
This statement is supported by theoretical arguments as well as simulations. We
review possible alternative approaches to population inference for
information-based imaging, converging on the idea that it should not target the
mean, but the prevalence of the effect in the population. One method to do so,
'permutation-based information prevalence inference using the minimum
statistic', is described in detail and applied to empirical data.
Infomap Bioregions: Interactive mapping of biogeographical regions from species distributions
Date: 2016-11-30 00:00:00
Tasks: Vector Graphics

Biogeographical regions (bioregions) reveal how different sets of species are
spatially grouped and therefore are important units for conservation,
historical biogeography, ecology and evolution. Several methods have been
developed to identify bioregions based on species distribution data rather than
expert opinion. One approach successfully applies network theory to simplify
and highlight the underlying structure in species distributions. However, this
method lacks tools for simple and efficient analysis. Here we present Infomap
Bioregions, an interactive web application that inputs species distribution
data and generates bioregion maps. Species distributions may be provided as
georeferenced point occurrences or range maps, and can be of local, regional or
global scale. The application uses a novel adaptive resolution method to make
best use of often incomplete species distribution data. The results can be
downloaded as vector graphics, shapefiles or in table format. We validate the
tool by processing large datasets of publicly available species distribution
data of the world's amphibians using species ranges, and mammals using point
occurrences. We then calculate the fit between the inferred bioregions and WWF
ecoregions. As examples of applications, researchers can reconstruct ancestral
ranges in historical biogeography or identify indicator species for targeted
conservation.
Bond Graph Modelling of Chemoelectrical Energy Transduction
Date: 2017-04-19 00:00:00

Energy-based bond graph modelling of biomolecular systems is extended to
include chemoelectrical trans- duction thus enabling integrated
thermodynamically-compliant modelling of chemoelectrical systems in general and
excitable membranes in particular.
  Our general approach is illustrated by recreating a well-known model of an
excitable membrane. This model is used to investigate the energy consumed
during a membrane action potential thus contributing to the current debate on
the trade-off between the speed of an action potential event and energy
consumption. The influx of Na+ is often taken as a proxy for energy
consumption; in contrast, this paper presents an energy based model of action
potentials. As the energy based approach avoids the assumptions underlying the
proxy approach it can be directly used to compute energy consumption in both
healthy and diseased neurons.
  These results are illustrated by comparing the energy consumption of healthy
and degenerative retinal ganglion cells using both simulated and in vitro data.