pwc-archive/papers-with-abstracts huggingface dataset:
Mechanisms of vortices termination in the cardiac muscle
Date: 2017-02-15 00:00:00

We propose a solution to a long standing problem: how to terminate multiple
vortices in the heart, when the locations of their cores and their critical
time windows are unknown. We scan the phases of all pinned vortices in parallel
with electric field pulses (E-pulses). We specify a condition on pacing
parameters that guarantees termination of one vortex. For more than one vortex
with significantly different frequencies, the success of scanning depends on
chance, and all vortices are terminated with a success rate of less than one.
We found that a similar mechanism terminates also a free (not pinned) vortex. A
series of about 500 experiments with termination of ventricular fibrillation by
E-pulses in pig isolated hearts is evidence that pinned vortices, hidden from
direct observation, are significant in fibrillation. These results form a
physical basis needed for the creation of new effective low energy
defibrillation methods based on the termination of vortices underlying
fibrillation.
Network approach integrates 3D structural and sequence data to improve protein structural comparison
Date: 2017-02-27 00:00:00

Initial protein structural comparisons were sequence-based. Since amino acids
that are distant in the sequence can be close in the 3-dimensional (3D)
structure, 3D contact approaches can complement sequence approaches.
Traditional 3D contact approaches study 3D structures directly. Instead, 3D
structures can be modeled as protein structure networks (PSNs). Then, network
approaches can compare proteins by comparing their PSNs. Network approaches may
improve upon traditional 3D contact approaches. We cannot use existing PSN
approaches to test this, because: 1) They rely on naive measures of network
topology. 2) They are not robust to PSN size. They cannot integrate 3) multiple
PSN measures or 4) PSN data with sequence data, although this could help
because the different data types capture complementary biological knowledge. We
address these limitations by: 1) exploiting well-established graphlet measures
via a new network approach, 2) introducing normalized graphlet measures to
remove the bias of PSN size, 3) allowing for integrating multiple PSN measures,
and 4) using ordered graphlets to combine the complementary PSN data and
sequence data. We compare both synthetic networks and real-world PSNs more
accurately and faster than existing network, 3D contact, or sequence
approaches. Our approach finds PSN patterns that may be biochemically
interesting.
Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index
Date: 2016-05-24 00:00:00
Tasks: BIG-bench Machine Learning

Financial Times Series such as stock price and exchange rates are, often,
non-linear and non-stationary. Use of decomposition models has been found to
improve the accuracy of predictive models. The paper proposes a hybrid approach
integrating the advantages of both decomposition model (namely, Maximal Overlap
Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR)
to predict the National Stock Exchange Fifty Index. In first phase, the data is
decomposed into a smaller number of subseries using MODWT. In next phase, each
subseries is predicted using machine learning models (i.e., ANN and SVR). The
predicted subseries are aggregated to obtain the final forecasts. In final
stage, the effectiveness of the proposed approach is evaluated using error
measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR)
are compared with ANN and SVR models and, it was observed that the return on
investment obtained based on trading rules using predicted values of MODWT-SVR
model was higher than that of Buy-and-hold strategy.
Directly measuring single molecule heterogeneity using force spectroscopy
Date: 2016-05-24 00:00:00

One of the most intriguing results of single molecule experiments on proteins
and nucleic acids is the discovery of functional heterogeneity: the observation
that complex cellular machines exhibit multiple, biologically active
conformations. The structural differences between these conformations may be
subtle, but each distinct state can be remarkably long-lived, with random
interconversions between states occurring only at macroscopic timescales,
fractions of a second or longer. Though we now have proof of functional
heterogeneity in a handful of systems---enzymes, motors, adhesion
complexes---identifying and measuring it remains a formidable challenge. Here
we show that evidence of this phenomenon is more widespread than previously
known, encoded in data collected from some of the most well-established single
molecule techniques: AFM or optical tweezer pulling experiments. We present a
theoretical procedure for analyzing distributions of rupture/unfolding forces
recorded at different pulling speeds. This results in a single parameter,
quantifying the degree of heterogeneity, and also leads to bounds on the
equilibration and conformational interconversion timescales. Surveying ten
published datasets, we find heterogeneity in five of them, all with
interconversion rates slower than 10 s$^{-1}$. Moreover, we identify two
systems where additional data at realizable pulling velocities is likely to
find a theoretically predicted, but so far unobserved cross-over regime between
heterogeneous and non-heterogeneous behavior. The significance of this regime
is that it will allow far more precise estimates of the slow conformational
switching times, one of the least understood aspects of functional
heterogeneity.
Modeling delayed processes in biological systems
Date: 2016-05-24 00:00:00

Delayed processes are ubiquitous in biological systems and are often
characterized by delay differential equations (DDEs) and their extension to
include stochastic effects. DDEs do not explicitly incorporate intermediate
states associated with a delayed process but instead use an estimated average
delay time. In an effort to examine the validity of this approach, we study
systems with significant delays by explicitly incorporating intermediate steps.
We show by that such explicit models often yield significantly different
equilibrium distributions and transition times as compared to DDEs with
deterministic delay values. Additionally, different explicit models with
qualitatively different dynamics can give rise to the same DDEs revealing
important ambiguities. We also show that DDE-based predictions of oscillatory
behavior may fail for the corresponding explicit model.
From Local Chaos to Critical Slowing Down: A Theory of the Functional Connectivity of Small Neural Circuits
Date: 2016-05-26 00:00:00
Tasks: Functional Connectivity

Functional connectivity is a fundamental property of neural networks that
quantifies the segregation and integration of information between cortical
areas. Due to mathematical complexity, a theory that could explain how the
parameters of mesoscopic networks composed of a few tens of neurons affect the
functional connectivity is still to be formulated. Yet, many interesting
problems in neuroscience involve the study of networks composed of a small
number of neurons. Based on a recent study of the dynamics of small neural
circuits, we combine the analysis of local bifurcations of multi-population
neural networks of arbitrary size with the analytical calculation of the
functional connectivity. We study the functional connectivity in different
regimes, showing that external stimuli cause the network to switch from
asynchronous states characterized by weak correlation and low variability
(local chaos), to synchronous states characterized by strong correlations and
wide temporal fluctuations (critical slowing down). Local chaos typically
occurs in large networks, but here we show that it can also be generated by
strong stimuli in small neural circuits. On the other side, critical slowing
down is expected to occur when the stimulus moves the network close to a local
bifurcation. In particular, strongly positive correlations occur at the
saddle-node and Andronov-Hopf bifurcations of the network, while strongly
negative correlations occur when the network undergoes a spontaneous
symmetry-breaking at the branching-point bifurcations. These results prove that
the functional connectivity of firing-rate network models is strongly affected
by the external stimuli even if the anatomical connections are fixed, and
suggest an effective mechanism through which biological networks can
dynamically modulate the encoding and integration of sensory information.
Linear Credit Risk Models
Authors: Damien Ackerer, Damir Filipović
Date: 2016-05-24 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a novel class of credit risk models in which the drift of the survival process of a firm is a linear function of the factors. The prices of defaultable bonds and credit default swaps (CDS) are linear-rational in the factors. The price of a CDS option can be uniformly approximated by polynomials in the factors. Multi-name models can produce simultaneous defaults, generate positively as well as negatively correlated default intensities, and accommodate stochastic interest rates. A calibration study illustrates the versatility of these models by fitting CDS spread time series. A numerical analysis validates the efficiency of the option price approximation method.
Communities as cliques
Date: 2016-05-24 00:00:00
Tasks: Diversity

High-diversity assemblages are very common in nature, and yet the factors
allowing for the maintenance of biodiversity remain obscure. The competitive
exclusion principle and May's complexity-diversity puzzle both suggest that a
community can support only a small number of species, turning the spotlight at
the dynamics of local patches or islands, where stable and uninvadable (SU)
subsets of species play a crucial role. Here we map the community SUs question
to the geometric problem of finding maximal cliques of the corresponding graph.
We solve for the number of SUs as a function of the species richness in the
regional pool, $N$, showing that this growth is subexponential, contrary to
long-standing wisdom. We show that symmetric systems relax rapidly to an SU,
where the system stays until a regime shift takes place. In asymmetric systems
the relaxation time grows much faster with $N$, suggesting an excitable
dynamics under noise.
Transcriptional Similarity in Couples Reveals the Impact of Shared Environment and Lifestyle on Gene Regulation through Modified Cytosines
Date: 2016-05-24 00:00:00

Gene expression is a complex and quantitative trait that is influenced by
both genetic and non-genetic regulators including environmental factors.
Evaluating the contribution of environment to gene expression regulation and
identifying which genes are more likely to be influenced by environmental
factors are important for understanding human complex traits. We hypothesize
that by living together as couples, there can be commonly co-regulated genes
that may reflect the shared living environment (e.g., diet, indoor air
pollutants, behavioral lifestyle). The lymphoblastoid cell lines (LCLs) derived
from unrelated couples of African ancestry (YRI, Yoruba people from Ibadan,
Nigeria) from the International HapMap Project provided a unique model for us
to characterize gene expression pattern in couples by comparing gene expression
levels between husbands and wives. Strikingly, 778 genes were found to show
much smaller variances in couples than random pairs of individuals at a false
discovery rate (FDR) of 5%. Since genetic variation between unrelated family
members in a general population is expected to be the same assuming a
random-mating society, non-genetic factors (e.g., epigenetic systems) are more
likely to be the mediators for the observed transcriptional similarity in
couples. We thus evaluated the contribution of modified cytosines to those
genes showing transcriptional similarity in couples as well as the
relationships these CpG sites with other gene regulatory elements, such as
transcription factor binding sites (TFBS). Our findings suggested that
transcriptional similarity in couples likely reflected shared common
environment partially mediated through cytosine modifications.
Eco-evolutionary dynamics of social dilemmas
Date: 2016-05-24 00:00:00

Social dilemmas are an integral part of social interactions. Cooperative
actions, ranging from secreting extra-cellular products in microbial
populations to donating blood in humans, are costly to the actor and hence
create an incentive to shirk and avoid the costs. Nevertheless, cooperation is
ubiquitous in nature. Both costs and benefits often depend non-linearly on the
number and types of individuals involved -- as captured by idioms such as `too
many cooks spoil the broth' where additional contributions are discounted, or
`two heads are better than one' where cooperators synergistically enhance the
group benefit. Interaction group sizes may depend on the size of the population
and hence on ecological processes. This results in feedback mechanisms between
ecological and evolutionary processes, which jointly affect and determine the
evolutionary trajectory. Only recently combined eco-evolutionary processes
became experimentally tractable in microbial social dilemmas. Here we analyse
the evolutionary dynamics of non-linear social dilemmas in settings where the
population fluctuates in size and the environment changes over time. In
particular, cooperation is often supported and maintained at high densities
through ecological fluctuations. Moreover, we find that the combination of the
two processes routinely reveals highly complex dynamics, which suggests common
occurrence in nature.
Hybrid Markov-mass action law for cell activation by rare binding events
Date: 2016-05-24 00:00:00

The binding of molecules, ions or proteins to specific target sites is a
generic step for cell activation. However, this step relies on rare events
where stochastic particles located in a large bulk are searching for small and
often hidden targets and thus remains difficult to study. We present here a
hybrid discrete-continuum model where the large ensemble of particles is
described by mass-action laws. The rare discrete binding events are modeled by
a Markov chain for the encounter of a finite number of small targets by few
Brownian particles, for which the arrival time is Poissonian. This model is
applied for predicting the time distribution of vesicular release at neuronal
synapses that remains elusive. This release is triggered by the binding of few
calcium ions that can originate either from the synaptic bulk or from the
transient entry through calcium channels. We report that the distribution of
release time is bimodal although triggered by a single fast action potential:
while the first peak follows a stimulation, the second corresponds to the
random arrival over much longer time of ions located in the bulk to small
binding targets. To conclude, the present multiscale stochastic chemical
reaction modeling allows studying cellular events based on integrating discrete
molecular events over various time scales.
Games of multicellularity
Date: 2016-05-25 00:00:00

Evolutionary game dynamics are often studied in the context of different
population structures. Here we propose a new population structure that is
inspired by simple multicellular life forms. In our model, cells reproduce but
can stay together after reproduction. They reach complexes of a certain size,
n, before producing single cells again. The cells within a complex derive
payoff from an evolutionary game by interacting with each other. The
reproductive rate of cells is proportional to their payoff. We consider all
two-strategy games. We study deterministic evolutionary dynamics with
mutations, and derive exact conditions for selection to favor one strategy over
another. Our main result has the same symmetry as the well-known sigma
condition, which has been proven for stochastic game dynamics and weak
selection. For a maximum complex size of n=2 our result holds for any intensity
of selection. For n > 2 it holds for weak selection. As specific examples we
study the prisoner's dilemma and hawk-dove games. Our model advances
theoretical work on multicellularity by allowing for frequency-dependent
interactions within groups.
A maximum-caliber approach to predicting perturbed folding kinetics due to mutations
Date: 2016-05-25 00:00:00
Tasks: Protein Folding

We present a maximum-caliber method for inferring transition rates of a
Markov State Model (MSM) with perturbed equilibrium populations, given
estimates of state populations and rates for an unperturbed MSM. It is similar
in spirit to previous approaches but given the inclusion of prior information
it is more robust and simple to implement. We examine its performance in simple
biased diffusion models of kinetics, and then apply the method to predicting
changes in folding rates for several highly non-trivial protein folding systems
for which non-native interactions play a significant role, including (1)
tryptophan variants of GB1 hairpin, (2) salt-bridge mutations of Fs peptide
helix, and (3) MSMs built from ultra-long folding trajectories of FiP35 and GTT
variants of WW domain. In all cases, the method correctly predicts changes in
folding rates, suggesting the wide applicability of maximum-caliber approaches
to efficiently predict how mutations perturb protein conformational dynamics.
Designing a single-molecule biophysics tool for characterizing DNA damage for techniques that kill infectious pathogens through DNA damage effects
Date: 2016-05-25 00:00:00

Antibiotics such as the quinolones and fluoroquinolones kill bacterial
pathogens ultimately through DNA damage. They target the essential type IIA
topoisomerases in bacteria by stabilising the normally transient double strand
break state which is created to modify the supercoiling state of the DNA. Here
we discuss the development of these antibiotics and their method of action.
Existing methods for DNA damage visualisation, such as the comet assay and
immunofluorescence imaging can often only be analysed qualitatively and this
analysis is subjective. We describe a putative single-molecule fluorescence
technique for quantifying DNA damage via the total fluorescence intensity of a
DNA origami tile fully saturated with an intercalating dye, along with the
optical requirements for how to implement these into a light microscopy imaging
system capable of single-molecule millisecond timescale imaging. This system
promises significant improvements in reproducibility of the quantification of
DNA damage over traditional techniques.
Force spectroscopy in studying infection
Date: 2016-05-25 00:00:00
Tasks: Drug Design

Biophysical force spectroscopy tools - for example optical tweezers, magnetic
tweezers, atomic force microscopy, - have been used to study elastic,
mechanical, conformational and dynamic properties of single biological
specimens from single proteins to whole cells to reveal information not
accessible by ensemble average methods such as X-ray crystallography, mass
spectroscopy, gel electrophoresis and so on. Here we review the application of
these tools on a range of infection-related questions from antibody-inhibited
protein processivity to virus-cell adhesion. In each case we focus on how the
instrumental design tailored to the biological system in question translates
into the functionality suitable for that particular study. The unique insights
that force spectroscopy has gained to complement knowledge learned through
population averaging techniques in interrogating biomolecular details prove to
be instrumental in therapeutic innovations such as those in structure-based
drug design.
Single-molecule observation of DNA replication repair pathways in E. coli
Date: 2016-05-25 00:00:00

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.
Using Fluorescence Recovery After Photobleaching (FRAP) to study dynamics of the Structural Maintenance of Chromosome (SMC) complex in vivo
Date: 2016-05-25 00:00:00

The SMC complex, MukBEF, is important for chromosome organization and
segregation in Escherichia coli. Fluorescently tagged MukBEF forms distinct
spots (or 'foci') in the cell, where it is thought to carry out most of its
chromosome associated activities. This chapter outlines the technique of
Fluorescence Recovery After Photobleaching (FRAP) as a method to study the
properties of YFP-tagged MukB in fluorescent foci. This method can provide
important insight into the dynamics of MukB on DNA and be used to study its
biochemical properties in vivo.
Single molecule narrowfield microscopy of protein-DNA binding dynamics in glucose signal transduction of live yeast cells
Date: 2016-05-25 00:00:00

Single-molecule narrowfield microscopy is a versatile tool to investigate a
diverse range of protein dynamics in live cells and has been extensively used
in bacteria. Here, we describe how these methods can be extended to larger
eukaryotic, yeast cells, which contain sub-cellular compartments. We describe
how to obtain single-molecule microscopy data but also how to analyse these
data to track and obtain the stoichiometry of molecular complexes diffusing in
the cell. We chose glucose mediated signal transduction of live yeast cells as
the system to demonstrate these single-molecule techniques as transcriptional
regulation is fundamentally a single molecule problem - a single repressor
protein binding a single binding site in the genome can dramatically alter
behaviour at the whole cell and population level.
Risk Arbitrage and Hedging to Acceptability under Transaction Costs
Date: 2020-04-15 00:00:00

The classical discrete time model of proportional transaction costs relies on
the assumption that a feasible portfolio process has solvent increments at each
step. We extend this setting in two directions, allowing for convex transaction
costs and assuming that increments of the portfolio process belong to the sum
of a solvency set and a family of multivariate acceptable positions, e.g. with
respect to a dynamic risk measure. We describe the sets of superhedging prices,
formulate several no (risk) arbitrage conditions and explore connections
between them. In the special case when multivariate positions are converted
into a single fixed asset, our framework turns into the no good deals setting.
However, in general, the possibilities of assessing the risk with respect to
any asset or a basket of the assets lead to a decrease of superhedging prices
and the no arbitrage conditions become stronger. The mathematical technique
relies on results for unbounded and possibly non-closed random sets in
Euclidean space.