pwc-archive/papers-with-abstracts huggingface dataset:
The Transcription Factor E4F1 Coordinates CHK1-Dependent Checkpoint and Mitochondrial Functions
Date: 2015-10-14 00:00:00

Recent data support the notion that a group of key transcriptional regulators
involved in tumorigenesis, including MYC, p53, E2F1, and BMI1, share an
intriguing capacity to simultaneously regulate metabolism and cell cycle. Here,
we show that another factor, the multifunctional protein E4F1, directly
controls genes involved in mitochondria functions and cell-cycle checkpoints,
including Chek1, a major component of the DNA damage response. Coordination of
these cellular functions by E4F1 appears essential for the survival of
p53-deficient transformed cells. Acute inactivation of E4F1 in these cells
results in CHK1-dependent checkpoint deficiency and multiple mitochondrial
dysfunctions that lead to increased ROS production, energy stress, and
inhibition of de novo pyrimidine synthesis. This deadly cocktail leads to the
accumulation of uncompensated oxidative damage to proteins and extensive DNA
damage, ending in cell death. This supports the rationale of therapeutic
strategies simultaneously targeting mitochondria and CHK1 for selective killing
of p53-deficient cancer cells.
New thoughts on an old riddle: what determines genetic diversity within and between species?
Date: 2015-10-21 00:00:00
Tasks: Diversity

The question of what determines genetic diversity both between and within
species has long remained unsolved by the modern evolutionary theory (MET).
However, it has not deterred researchers from producing interpretations of
genetic diversity by using MET. We here examine the two key experimental
observations of genetic diversity made in the 1960s, one between species and
the other within a population of a species, that directly contributed to the
development of MET. The interpretations of these observations as well as the
assumptions by MET are widely known to be inadequate. We review the recent
progress of an alternative framework, the maximum genetic diversity (MGD)
hypothesis, that uses axioms and natural selection to explain the vast majority
of genetic diversity as being at optimum equilibrium that is largely determined
by organismal complexity. The MGD hypothesis fully absorbs the proven virtues
of MET and considers its assumptions relevant only to a much more limited
scope. This new synthesis has accounted for the much overlooked phenomenon of
progression towards higher complexity, and more importantly, been instrumental
in directing productive research into both evolutionary and biomedical
problems.
Cooperation, competition and the emergence of criticality in communities of adaptive systems
Date: 2016-03-22 00:00:00

The hypothesis that living systems can benefit from operating at the vicinity
of critical points has gained momentum in recent years. Criticality may confer
an optimal balance between exceedingly ordered and too noisy states. We here
present a model, based on information theory and statistical mechanics,
illustrating how and why a community of agents aimed at understanding and
communicating with each other converges to a globally coherent state in which
all individuals are close to an internal critical state, i.e. at the borderline
between order and disorder. We study --both analytically and computationally--
the circumstances under which criticality is the best possible outcome of the
dynamical process, confirming the convergence to critical points under very
generic conditions. Finally, we analyze the effect of cooperation (agents try
to enhance not only their fitness, but also that of other individuals) and
competition (agents try to improve their own fitness and to diminish those of
competitors) within our setting. The conclusion is that, while competition
fosters criticality, cooperation hinders it and can lead to more ordered or
more disordered consensual solutions.
A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins
Date: 2015-10-20 00:00:00

Bacteriocins are peptide-derived molecules produced by bacteria, whose
recently-discovered functions include virulence factors and signalling
molecules as well as their better known roles as antibiotics. To date, close to
five hundred bacteriocins have been identified and classified. Recent
discoveries have shown that bacteriocins are highly diverse and widely
distributed among bacterial species. Given the heterogeneity of bacteriocin
compounds, many tools struggle with identifying novel bacteriocins due to their
vast sequence and structural diversity. Many bacteriocins undergo
post-translational processing or modifications necessary for the biosynthesis
of the final mature form. Enzymatic modification of bacteriocins as well as
their export is achieved by proteins whose genes are often located in a
discrete gene cluster proximal to the bacteriocin precursor gene, referred to
as \textit{context genes} in this study. Although bacteriocins themselves are
structurally diverse, context genes have been shown to be largely conserved
across unrelated species. Using this knowledge, we set out to identify new
candidates for context genes which may clarify how bacteriocins are
synthesized, and identify new candidates for bacteriocins that bear no sequence
similarity to known toxins. To achieve these goals, we have developed a
software tool, Bacteriocin Operon and gene block Associator (BOA) that can
identify homologous bacteriocin associated gene clusters and predict novel
ones. We discover that several phyla have a strong preference for bactericon
genes, suggesting distinct functions for this group of molecules. Availability:
https://github.com/idoerg/BOA
Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation
Date: 2016-04-10 00:00:00

Two-locus sampling probabilities have played a central role in devising an
efficient composite likelihood method for estimating fine-scale recombination
rates. Due to mathematical and computational challenges, these sampling
probabilities are typically computed under the unrealistic assumption of a
constant population size, and simulation studies have shown that resulting
recombination rate estimates can be severely biased in certain cases of
historical population size changes. To alleviate this problem, we develop here
new methods to compute the sampling probability for variable population size
functions that are piecewise constant. Our main theoretical result, implemented
in a new software package called LDpop, is a novel formula for the sampling
probability that can be evaluated by numerically exponentiating a large but
sparse matrix. This formula can handle moderate sample sizes ($n \leq 50$) and
demographic size histories with a large number of epochs ($\mathcal{D} \geq
64$). In addition, LDpop implements an approximate formula for the sampling
probability that is reasonably accurate and scales to hundreds in sample size
($n \geq 256$). Finally, LDpop includes an importance sampler for the posterior
distribution of two-locus genealogies, based on a new result for the optimal
proposal distribution in the variable-size setting. Using our methods, we study
how a sharp population bottleneck followed by rapid growth affects the
correlation between partially linked sites. Then, through an extensive
simulation study, we show that accounting for population size changes under
such a demographic model leads to substantial improvements in fine-scale
recombination rate estimation. LDpop is freely available for download at
https://github.com/popgenmethods/ldpop
Motion Blur Filtering: A Statistical Approach for Extracting Confinement Forces and Diffusivity from a Single Blurred Trajectory
Date: 2016-05-18 00:00:00

Single Particle Tracking (SPT) can aid in understanding complex
spatio-temporal processes. However, quantifying diffusivity and forces from
individual live cell trajectories is complicated by inter- & intra-trajectory
kinetic heterogeneity, thermal fluctuations, and statistical temporal
dependence inherent to the underlying molecule's time correlated confined
dynamics experienced in the cell. Experimental artifacts such as localization
uncertainty and motion blur also obscure the data. We introduce a new maximum
likelihood estimation (MLE) technique that decouples the above noise sources
and systematically treats temporal correlation via a likelihood function
(permitting more reliable extraction of effective forces from position vs. time
data). Our estimator is demonstrated to be consistent over a wide range of
exposure times, diffusion coefficients, and confinement "radii". The algorithm
and corresponding software can reliably extract motion parameters independent
of exposure time in trajectories exhibiting confined and/or non-stationary
dynamics and will aid in directly comparing trajectories obtained from
different imaging modalities.
Proton Conducting Graphene Oxide Coupled Neuron Transistors for Brain-Inspired Cognitive Systems
Date: 2015-10-21 00:00:00

Neuron is the most important building block in our brain, and information
processing in individual neuron involves the transformation of input synaptic
spike trains into an appropriate output spike train. Hardware implementation of
neuron by individual ionic/electronic hybrid device is of great significance
for enhancing our understanding of the brain and solving sensory processing and
complex recognition tasks. Here, we provide a proof-of-principle artificial
neuron based on a proton conducting graphene oxide (GO) coupled oxide-based
electric-double-layer (EDL) transistor with multiple driving inputs and one
modulatory input terminal. Paired-pulse facilitation, dendritic integration and
orientation tuning were successfully emulated. Additionally, neuronal gain
control (arithmetic) in the scheme of rate coding is also experimentally
demonstrated. Our results provide a new-concept approach for building
brain-inspired cognitive systems.
Single-molecule studies of the dynamics and interactions of bacterial OXPHOS complexes
Date: 2015-10-21 00:00:00

Although significant insight has been gained into biochemical, genetic and
structural features of oxidative phosphorylation (OXPHOS) at the single-enzyme
level, relatively little was known of how the component complexes function
together in time and space until recently. Several pioneering single-molecule
studies have emerged over the last decade in particular, which have illuminated
our knowledge of OXPHOS, most especially on model bacterial systems. Here, we
discuss these recent findings of bacterial OXPHOS, many of which generate
time-resolved information of the OXPHOS machinery with the native physiological
context intact. These new investigations are transforming our knowledge of not
only the molecular arrangement of OXPHOS components in live bacteria, but also
of the way components dynamically interact with each other in a functional
state. These new discoveries have important implications towards putative
supercomplex formation in bacterial OXPHOS in particular.
Mathematics of Predicting Growth
Date: 2015-10-20 00:00:00

Mathematical methods of analysis of data and of predicting growth are
discussed. The starting point is the analysis of the growth rates, which can be
expressed as a function of time or as a function of the size of the growing
entity. Application of these methods is illustrated using the world economic
growth but they can be applied to any other type of growth.
Stochastic model of endosomal escape of Influenza virus
Date: 2015-10-21 00:00:00

Influenza viruses enter a cell via endocytosis after binding to the surface.
During the endosomal journey, acidification triggers a conformational change of
the virus spike protein hemagglutinin (HA) that results in escape of the viral
genome from the endosome to the cytoplasm. A quantitative understanding of the
processes involved in HA mediated fusion with the endosome is still missing. We
develop here a stochastic model to estimate the change of conformation of HAs
inside the endosome nanodomain. Using a Markov-jump process to model the
arrival of protons to HA binding sites, we compute the kinetics of their
accumulation and the mean first time for HAs to be activated. This analysis
reveals that HA proton binding sites possess a high chemical barrier, ensuring
a stability of the spike protein at sub-acidic pH. Finally, we predict that
activating more than 3 adjacent HAs is necessary to prevent a premature fusion.
The role of membrane curvature for the wrapping of nanoparticles
Date: 2015-10-22 00:00:00

Cellular internalization of nanoparticles requires the full wrapping of the
nanoparticles by the cell membrane. This wrapping process can occur
spontaneously if the adhesive interactions between the nanoparticles and the
membranes are sufficiently strong to compensate for the cost of membrane
bending. In this article, we show that the membrane curvature prior to wrapping
plays a key role for the wrapping process, besides the size and shape of the
nanoparticles that have been investigated in recent years. For membrane
segments that initially bulge away from nanoparticles by having a mean
curvature of the same sign as the mean curvature of the particle surface, we
find strongly stable partially wrapped states that can prevent full wrapping.
For membrane segments that initially bulge towards the nanoparticles, in
contrast, partially wrapped states can constitute a significant energetic
barrier for the wrapping process.
Decomposing the site frequency spectrum: the impact of tree topology on neutrality tests
Date: 2017-01-12 00:00:00

We investigate the dependence of the site frequency spectrum (SFS) on the
topological structure of genealogical trees. We show that basic population
genetic statistics - for instance estimators of $\theta$ or neutrality tests
such as Tajima's $D$ - can be decomposed into components of waiting times
between coalescent events and of tree topology. Our results clarify the
relative impact of the two components on these statistics. We provide a
rigorous interpretation of positive or negative values of an important class of
neutrality tests in terms of the underlying tree shape. In particular, we show
that values of Tajima's $D$ and Fay and Wu's $H$ depend in a direct way on a
peculiar measure of tree balance which is mostly determined by the root balance
of the tree. We present a new test for selection in the same class as Fay and
Wu's $H$ and discuss its interpretation and power. Finally, we determine the
trees corresponding to extreme expected values of these neutrality tests and
present formulae for these extreme values as a function of sample size and
number of segregating sites.
Liquid-theory analogy of direct-coupling analysis of multiple-sequence alignment and its implications for protein structure prediction
Date: 2015-11-10 00:00:00
Tasks: Multiple Sequence Alignment, Prediction, Protein Structure Prediction

The direct-coupling analysis is a powerful method for protein contact
prediction, and enables us to extract "direct" correlations between distant
sites that are latent in "indirect" correlations observed in a protein
multiple-sequence alignment. I show that the direct correlation can be obtained
by using a formulation analogous to the Ornstein-Zernike integral equation in
liquid theory. This formulation intuitively illustrates how the indirect or
apparent correlation arises from an infinite series of direct correlations, and
provides interesting insights into protein structure prediction.
A Link between Sequential Semi-anonymous Nonatomic Games and their Large Finite Counterparts
Date: 2016-06-22 00:00:00
Tasks: Form

We show that equilibria of a sequential semi-anonymous nonatomic game (SSNG)
can be adopted by players in corresponding large but finite dynamic games to
achieve near-equilibrium payoffs. Such equilibria in the form of random
state-to-action rules are parsimonious in form and easy to execute, as they are
both oblivious of past history and blind to other players' present states. Our
transient results can be extended to a stationary case, where the finite
counterparts are special discounted stochastic games. The kind of equilibria we
adopt for SSNG are similar to distributional equilibria that are well
understood in literature, and they themselves are shown to exist.
Liquidity, risk measures, and concentration of measure
Date: 2015-10-27 00:00:00

Expanding on techniques of concentration of measure, we develop a
quantitative framework for modeling liquidity risk using convex risk measures.
The fundamental objects of study are curves of the form $(\rho(\lambda
X))_{\lambda \ge 0}$, where $\rho$ is a convex risk measure and $X$ a random
variable, and we call such a curve a \emph{liquidity risk profile}. The shape
of a liquidity risk profile is intimately linked with the tail behavior of the
underlying $X$ for some notable classes of risk measures, namely shortfall risk
measures. We exploit this link to systematically bound liquidity risk profiles
from above by other real functions $\gamma$, deriving tractable necessary and
sufficient conditions for \emph{concentration inequalities} of the form
$\rho(\lambda X) \le \gamma(\lambda)$, for all $\lambda \ge 0$. These
concentration inequalities admit useful dual representations related to
transport inequalities, and this leads to efficient uniform bounds for
liquidity risk profiles for large classes of $X$. On the other hand, some
modest new mathematical results emerge from this analysis, including a new
characterization of some classical transport-entropy inequalities. Lastly, the
analysis is deepened by means of a surprising connection between time
consistency properties of law invariant risk measures and the tensorization of
concentration inequalities.
Resilience, reactivity and variability : A mathematical comparison of ecological stability measures
Date: 2015-10-24 00:00:00

In theoretical studies, the most commonly used measure of ecological
stability is resilience: ecosystems asymptotic rate of return to equilibrium
after a pulse-perturbation $-$or shock. A complementary notion of growing
popularity is reactivity: the strongest initial response to shocks. On the
other hand, empirical stability is often quantified as the inverse of temporal
variability, directly estimated on data, and reflecting ecosystems response to
persistent and erratic environmental disturbances. It is unclear whether and
how this empirical measure is related to resilience and reactivity. Here, we
establish a connection by introducing two variability-based stability measures
belonging to the theoretical realm of resilience and reactivity. We call them
intrinsic, stochastic and deterministic invariability; respectively defined as
the inverse of the strongest stationary response to white-noise and to
single-frequency perturbations. We prove that they predict ecosystems worst
response to broad classes of disturbances, including realistic models of
environmental fluctuations. We show that they are intermediate measures between
resilience and reactivity and that, although defined with respect to persistent
perturbations, they can be related to the whole transient regime following a
shock, making them more integrative notions than reactivity and resilience. We
argue that invariability measures constitute a stepping stone, and discuss the
challenges ahead to further unify theoretical and empirical approaches to
stability.
To know or not to know? How looking at payoffs signals selfish behavior
Date: 2016-03-16 00:00:00

In daily life, subjects often face a social dilemma in two stages. In Stage
1, they recognize the social dilemma structure of the decision problem at hand
(a tension between personal interest and collective interest); in Stage 2, they
have to choose between gathering additional information to learn the exact
payoffs corresponding to each of the two options or making a choice without
looking at the payoffs. While previous theoretical research suggests that the
mere act of considering one's strategic options in a social dilemma will be met
with distrust, no experimental study has tested this hypothesis. What does
"looking at payoffs" signal in observers? Do observers' beliefs actually match
decision makers' intentions? Experiment 1 shows that the actual action of
looking at payoffs signals selfish behavior, but it does not actually mean so.
Experiments 2 and 3 show that, when the action of looking at payoffs is
replaced by a self-report question asking the extent to which participants look
at payoffs in their everyday lives, subjects in high looking mode are indeed
more selfish than those in low looking mode, and this is correctly predicted by
observers. These results support Rand and colleagues' Social Heuristics
Hypothesis and the novel "cooperate without looking" model by Yoeli, Hoffman,
and Nowak. However, Experiment 1 shows that actual looking may lead to
different results, possibly caused by the emergence of a moral cleansing
effect.
Comparative Document Analysis for Large Text Corpora
Authors: Ren Xiang, Lv Yuanhua, Wang Kuansan, Han Jiawei
Date: 2015-10-25 00:00:00
Tasks: Articles

This paper presents a novel research problem on joint discovery of
commonalities and differences between two individual documents (or document
sets), called Comparative Document Analysis (CDA). Given any pair of documents
from a document collection, CDA aims to automatically identify sets of quality
phrases to summarize the commonalities of both documents and highlight the
distinctions of each with respect to the other informatively and concisely. Our
solution uses a general graph-based framework to derive novel measures on
phrase semantic commonality and pairwise distinction}, and guides the selection
of sets of phrases by solving two joint optimization problems. We develop an
iterative algorithm to integrate the maximization of phrase commonality or
distinction measure with the learning of phrase-document semantic relevance in
a mutually enhancing way. Experiments on text corpora from two different
domains---scientific publications and news---demonstrate the effectiveness and
robustness of the proposed method on comparing individual documents. Our case
study on comparing news articles published at different dates shows the power
of the proposed method on comparing document sets.
Coherent CVA and FVA with Liability Side Pricing of Derivatives
Date: 2015-10-25 00:00:00

This article presents FVA and CVA of a bilateral derivative in a coherent
manner, based on recent developments in fair value accounting and ISDA
standards. We argue that a derivative liability, after primary risk factors
being hedged, resembles in economics an issued variable funding note, and
should be priced at the market rate of the issuer's debt. For the purpose of
determining the fair value, the party on the liability side is economically
neutral to make a deposit to the other party, which earns his current debt rate
and effectively provides funding and hedging for the party holding the
derivative asset. The newly derived partial differential equation for an option
discounts the derivative's receivable part with counterparty's curve and
payable part with own financing curve. The price difference from the
counterparty risk free price, or total counterparty risk adjustment, is
precisely defined by discounting the product of the risk free price and the
credit spread at the local liability curve. Subsequently the adjustment can be
broken into a default risk component -- CVA and a funding component -- FVA,
consistent with a simple note's fair value treatment and in accordance with the
usual understanding of a bond's credit spread consisting of a CDS spread and a
basis. As for FVA, we define a cost -- credit funding adjustment (CFA) and a
benefit -- debit funding adjustment (DFA), in parallel to CVA and DVA and
attributed to counterparty's and own funding basis. This resolves a number of
outstanding FVA debate issues, such as double counting, violation of the law of
one price, misuse of cash flow discounting, and controversial hedging of own
default risk. It also allows an integrated implementation strategy and reuse of
existing CVA infrastructure.