pwc-archive/papers-with-abstracts huggingface dataset:
The Computation of Cyclic Peptide with Prolin-Prolin Bond as Fusion Inhibitor of DENV Envelope Protein through Molecular Docking and Molecular Dynamics Simulation
Date: 2015-10-27 00:00:00
Tasks: Molecular Docking

A disease that caused by dengue virus (DENV) has become the major health
problem of the world. Nowadays, no effective treatment is available to overcome
the disease due to the level of dengue virus pathogeneses. A novel treatment
method such as antiviral drug is highly necessary for coping with the dengue
disease. Envelope protein is one of the non-structural proteins of DENV, which
engaged in the viral fusion process. The fusion process is mediated by the
conformational change in the protein structure from dimer to trimer state. The
previous research showed the existing cavity on the dimer structure of the
envelope protein. The existing ligand could get into cavity of the envelope
protein, stabilize the dimer structure or hamper the transition of dimer
protein into trimer. In this fashion, the fusion process can be prevented. The
aim of this research is designing the cyclic peptide with prolin-prolin bond as
fusion inhibitor of DENV envelope protein through molecular docking and
molecular dynamics simulation. The screening of 3,883 cyclic peptides, each of
them connected by prolin-prolin bond, through molecular docking resulted in
five best ligands. The pharmacological and toxicity character of these five
ligands were analised in silico. The result showed that PYRRP was the best
ligand. PAWRP was also chosen as the best ligand because it showed good
affinity with protein cavity. Stability of ligand-protein complex was analyzed
by molecular dynamics simulation. The result showed that PYRRP ligand was able
to support the stability of DENV envelope protein dimer structure at 310 K and
312 K. While PAWRP ligand actively formed complex with the DENV envelope
protein at 310 K compared to 312 K. Thus the PYRRP ligand has a potential to be
developed as DENV fusion inhibitor.
Mutation, Sexual Reproduction and Survival in Dynamic Environments
Date: 2016-04-23 00:00:00

A new approach to understanding evolution [Val09], namely viewing it through
the lens of computation, has already started yielding new insights, e.g.,
natural selection under sexual reproduction can be interpreted as the
Multiplicative Weight Update (MWU) Algorithm in coordination games played among
genes [CLPV14]. Using this machinery, we study the role of mutation in changing
environments in the presence of sexual reproduction. Following [WVA05], we
model changing environments via a Markov chain, with the states representing
environments, each with its own fitness matrix. In this setting, we show that
in the absence of mutation, the population goes extinct, but in the presence of
mutation, the population survives with positive probability. On the way to
proving the above theorem, we need to establish some facts about dynamics in
games. We provide the first, to our knowledge, polynomial convergence bound for
noisy MWU in a coordination game. Finally, we also show that in static
environments, sexual evolution with mutation converges, for any level of
mutation.
LSV models with stochastic interest rates and correlated jumps
Date: 2016-11-23 00:00:00

Pricing and hedging exotic options using local stochastic volatility models
drew a serious attention within the last decade, and nowadays became almost a
standard approach to this problem. In this paper we show how this framework
could be extended by adding to the model stochastic interest rates and
correlated jumps in all three components. We also propose a new fully implicit
modification of the popular Hundsdorfer and Verwer and Modified Craig-Sneyd
finite-difference schemes which provides second order approximation in space
and time, is unconditionally stable and preserves positivity of the solution,
while still has a linear complexity in the number of grid nodes.
Hybrid scheme for modeling local field potentials from point-neuron networks
Date: 2016-01-20 00:00:00

Due to rapid advances in multielectrode recording technology, the local field
potential (LFP) has again become a popular measure of neuronal activity in both
basic research and clinical applications. Proper understanding of the LFP
requires detailed mathematical modeling incorporating the anatomical and
electrophysiological features of neurons near the recording electrode, as well
as synaptic inputs from the entire network. Here we propose a hybrid modeling
scheme combining the efficiency of commonly used simplified point-neuron
network models with the biophysical principles underlying LFP generation by
real neurons. The scheme can be used with an arbitrary number of point-neuron
network populations. The LFP predictions rely on populations of
network-equivalent, anatomically reconstructed multicompartment neuron models
with layer-specific synaptic connectivity. The present scheme allows for a full
separation of the network dynamics simulation and LFP generation. For
illustration, we apply the scheme to a full-scale cortical network model for a
$\sim$1 mm$^2$ patch of primary visual cortex and predict laminar LFPs for
different network states, assess the relative LFP contribution from different
laminar populations, and investigate the role of synaptic input correlations
and neuron density on the LFP. The generic nature of the hybrid scheme and its
publicly available implementation in \texttt{hybridLFPy} form the basis for LFP
predictions from other point-neuron network models, as well as extensions of
the current application to larger circuitry and additional biological detail.
Statistically-Consistent k-mer Methods for Phylogenetic Tree Reconstruction
Date: 2016-01-14 00:00:00
Tasks: Multiple Sequence Alignment

Frequencies of $k$-mers in sequences are sometimes used as a basis for
inferring phylogenetic trees without first obtaining a multiple sequence
alignment. We show that a standard approach of using the squared-Euclidean
distance between $k$-mer vectors to approximate a tree metric can be
statistically inconsistent. To remedy this, we derive model-based distance
corrections for orthologous sequences without gaps, which lead to consistent
tree inference. The identifiability of model parameters from $k$-mer
frequencies is also studied. Finally, we report simulations showing the
corrected distance out-performs many other $k$-mer methods, even when sequences
are generated with an insertion and deletion process. These results have
implications for multiple sequence alignment as well, since $k$-mer methods are
usually the first step in constructing a guide tree for such algorithms.
Local equilibrium in bird flocks
Date: 2016-06-27 00:00:00

The correlated motion of flocks is an instance of global order emerging from
local interactions. An essential difference with analogous ferromagnetic
systems is that flocks are active: animals move relative to each other,
dynamically rearranging their interaction network. The effect of this
off-equilibrium element is well studied theoretically, but its impact on actual
biological groups deserves more experimental attention. Here, we introduce a
novel dynamical inference technique, based on the principle of maximum entropy,
which accodomates network rearrangements and overcomes the problem of slow
experimental sampling rates. We use this method to infer the strength and range
of alignment forces from data of starling flocks. We find that local bird
alignment happens on a much faster timescale than neighbour rearrangement.
Accordingly, equilibrium inference, which assumes a fixed interaction network,
gives results consistent with dynamical inference. We conclude that bird
orientations are in a state of local quasi-equilibrium over the interaction
length scale, providing firm ground for the applicability of statistical
physics in certain active systems.
Exact numerical calculation of fixation probability and time on graphs
Date: 2016-11-11 00:00:00

The Moran process on graphs is a popular model to study the dynamics of
evolution in a spatially structured population. Exact analytical solutions for
the fixation probability and time of a new mutant have been found for only a
few classes of graphs so far. Simulations are time-expensive and many
realizations are necessary, as the variance of the fixation times is high. We
present an algorithm that numerically computes these quantities for arbitrary
small graphs by an approach based on the transition matrix. The advantage over
simulations is that the calculation has to be executed only once. Building the
transition matrix is automated by our algorithm. This enables a fast and
interactive study of different graph structures and their effect on fixation
probability and time. We provide a fast implementation in C with this note
https://github.com/hindersin/efficientFixation. Our code is very flexible, as
it can handle two different update mechanisms (Birth-death or death-Birth), as
well as arbitrary directed or undirected graphs.
The Ramachandran number: an order parameter for protein geometry
Date: 2015-11-24 00:00:00

Three-dimensional protein structures usually contain regions of local order,
called secondary structure, such as $\alpha$-helices and $\beta$-sheets.
Secondary structure is characterized by the local rotational state of the
protein backbone, quantified by two dihedral angles called $\phi$ and $\psi$.
Particular types of secondary structure can generally be described by a single
(diffuse) location on a two-dimensional plot drawn in the space of the angles
$\phi$ and $\psi$, called a Ramachandran plot. By contrast, a
recently-discovered nanomaterial made from peptoids, structural isomers of
peptides, displays a secondary-structure motif corresponding to two regions on
the Ramachandran plot [Mannige et al., Nature 526, 415 (2015)]. In order to
describe such `higher-order' secondary structure in a compact way we introduce
here a means of describing regions on the Ramachandran plot in terms of a
single Ramachandran number, ${\mathcal{R}}$, which is a structurally meaningful
combination of $\phi$ and $\psi$. We show that the potential applications of
${\mathcal{R}}$ are numerous: it can be used to describe the geometric content
of protein structures, and can be used to draw diagrams that reveal, at a
glance, the frequency of occurrence of regular secondary structures and
disordered regions in large protein datasets. We propose that ${\mathcal{R}}$
might be used as an order parameter for protein geometry for a wide range of
applications.
Semantic processing of EHR data for clinical research
Authors: Sun Hong, Depraetere Kristof, De Roo Jos, Mels Giovanni, De Vloed Boris, Twagirumukiza Marc, Colaert Dirk
Date: 2015-11-10 00:00:00

There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.
Ultrasensitivity and sharp threshold theorems for multisite systems
Date: 2016-04-29 00:00:00

We study the ultrasensitivity of multisite binding processes where ligand
molecules can bind to several binding sites, considering more particularly
recent models involving complex chemical reactions in phosphorylation systems
such as allosteric phosphorylation processes, or substrate-catalyst chain
reactions and nucleosome mediated cooperativity. New statistics based formulas
for the Hill coefficient and the effective Hill coefficient are provided and
necessary conditions for a system to be ultrasensitive are exhibited. We then
assume that the binding process is described by a density dependent birth and
death process. We provide precise large deviation results for the steady state
distribution of the process, and show that switch-like ultrasensitive responses
are strongly related to the multi-stability of the associated dynamical system.
Ultrasensitivity occurs if and only if the entropy of the dynamical system has
more than one global minimum for some critical ligand concentration. In this
case, the Hill coefficient is proportional to the number of binding sites, and
the systems is highly ultrasensitive. We also discuss the interpretation of an
extension $I_q$ of the effective Hill coefficient $I_{0.9}$ for which we
recommend the computation of a broad range of values of $q$ instead of just the
standard one corresponding to the 10% to 90% variation in the dose-response. It
is shown that this single choice can sometimes mislead the conclusion by not
detecting ultrasensitivity. This new approach allows a better understanding of
multisite ultrasensitive systems and provides new tools for the design of such
systems.
On the C-property and $w^*$-representations of risk measures
Date: 2016-09-21 00:00:00

We identify a large class of Orlicz spaces $X$ for which the topology
$\sigma(X,X_n^\sim)$ fails the C-property introduced in [7]. We also establish
a variant of the C-property and use it to prove a $w^*$-representation theorem
for proper convex increasing functionals on dual Banach lattices that satisfy a
suitable version of Delbaen's Fatou property. Our results apply, in particular,
to risk measures on all Orlicz spaces over $[0,1]$ which is not $L_1[0,1]$.
Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics
Date: 2015-11-10 00:00:00

Dysregulation in signal transduction pathways can lead to a variety of
complex disorders, including cancer. Computational approaches such as network
analysis are important tools to understand system dynamics as well as to
identify critical components that could be further explored as therapeutic
targets. Here, we performed perturbation analysis of a large-scale signal
transduction model in extracellular environments that stimulate cell death,
growth, motility, and quiescence. Each of the model's components was perturbed
under both loss-of-function and gain-of-function mutations. We identified the
most and least influential components based on the magnitude of their influence
on the rest of the system. Based on the premise that the most influential
components might serve as better drug targets, we characterized them for
biological functions, housekeeping genes, essential genes, and druggable
proteins. Moreover, known cancer drug targets were also classified in
influential components based on the affected components in the network.
Additionally, the systemic perturbation analysis of the model revealed a
network motif of most influential components which affect each other.
Furthermore, our analysis predicted novel combinations of cancer drug targets
with various effects on other most influential components. We found that the
combinatorial perturbation consisting of PI3K inactivation and overactivation
of IP3R1 can lead to increased activity levels of apoptosis-related components
and tumor suppressor genes, suggesting that this combinatorial perturbation may
lead to a better target for decreasing cell proliferation and inducing
apoptosis. Lastly, our results suggest that systematic perturbation analyses of
large-scale computational models may serve as an approach to prioritize and
assess signal transduction components in order to identify novel drug targets
in complex disorders.
Diffusion-like recommendation with enhanced similarity of objects
Authors: An Ya-Hui, Dong Qiang, Sun Chong-Jing, Nie Da-Cheng, Fu Yan
Date: 2018-10-11 00:00:00
Tasks: Diversity

In last decades, diversity and accuracy have been regarded as two important
measures in evaluating a recommendation model. However, a clear concern is that
a model focusing excessively on one measure will put the other one at risk,
thus it is not easy to greatly improve diversity and accuracy simultaneously.
In this paper, we propose to enhance the Resource-Allocation (RA) similarity in
resource transfer equations of diffusion-like models, by giving a tunable
exponent to the RA similarity, and traversing the value of the exponent to
achieve the optimal recommendation results. In this way, we can increase the
recommendation scores (allocated resource) of many unpopular objects.
Experiments on three benchmark data sets, MovieLens, Netflix, and RateYourMusic
show that the modified models can yield remarkable performance improvement
compared with the original ones.
Environmental unpredictability and inbreeding depression select for mixed dispersal syndromes
Date: 2015-11-11 00:00:00

Mixed dispersal syndromes have historically been regarded as bet-hedging
mechanisms that enhance survival in unpredictable environments, ensuring that
some propagules stay in the maternal environment while others can potentially
colonize new sites. However, this entails paying the costs of both dispersal
and non-dispersal. Propagules that disperse are likely to encounter unfavorable
conditions for establishment, while non-dispersing propagules might form
populations of close relatives burdened with inbreeding. Here, we investigate
the conditions under which mixed dispersal syndromes emerge and are
evolutionarily stable, taking into account the risks of both environmental
unpredictability and inbreeding. Using mathematical and computational modeling
we show that high dispersal propensity is favored whenever temporal
environmental unpredictability is low and inbreeding depression high, whereas
mixed dispersal syndromes are adaptive under conditions of high environmental
unpredictability, but more particularly if also inbreeding depression is small.
Although pure dispersers can be selected for under some circumstances, mixed
dispersal provides the optimal strategy under most parameterizations of our
models, indicating that this strategy is likely to be favored under a wide
variety of conditions. Furthermore, populations exhibiting any single phenotype
go inevitably extinct when environmental and genetic costs are high, whilst
mixed strategies can maintain viable populations even under such conditions.
Our models support the hypothesis that the interplay between inbreeding
depression and environmental unpredictability shapes dispersal syndromes, often
resulting in mixed strategies. Moreover, mixed dispersal seems to facilitate
persistence whenever conditions are critical or nearly critical for survival.
Foundations for Wash Sales
Date: 2016-06-12 00:00:00

Consider an ephemeral sale-and-repurchase of a security resulting in the same
position before the sale and after the repurchase. A sale-and-repurchase is a
wash sale if these transactions result in a loss within $\pm 30$ calendar days.
Since a portfolio is essentially the same after a wash sale, any tax advantage
from such a loss is not allowed. That is, after a wash sale a portfolio is
unchanged so any loss captured by the wash sale is deemed to be solely for tax
advantage and not investment purposes.
  This paper starts by exploring variations of the birthday problem to model
wash sales. The birthday problem is: Determine the number of independent and
identically distributed random variables required so there is a probability of
at least 1/2 that two or more of these random variables share the same outcome.
This paper gives necessary conditions for wash sales based on variations on the
birthday problem. This allows us to answer questions such as: What is the
likelihood of a wash sale in an unmanaged portfolio where purchases and sales
are independent, uniform, and random? This paper ends by exploring the
Littlewood-Offord problem as it relates capital gains and losses with wash
sales.
Mathematical toy model inspired by the problem of the adaptive origins of the sexual orientation continuum
Date: 2016-09-15 00:00:00
Tasks: Diversity

Same-sex sexual behavior is ubiquitous in the animal kingdom, but its
adaptive origins remain a prominent puzzle. Here I suggest the possibility that
same-sex sexual behavior arises as a consequence of the competition between an
evolutionary drive for a wide diversity in traits, which improves the
adaptability of a species, and a drive for sexual dichotomization of traits,
which promotes opposite-sex attraction and increases the rate of reproduction.
A simple analytical "toy model" is proposed for describing this tradeoff. The
model exhibits a number of interesting features, and suggests a simple
mathematical form for describing the sexual orientation continuum.
Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation
Authors: Shi Chuan, Liu Jian, Zhuang Fuzhen, Yu Philip S., Wu Bin
Date: 2015-11-12 00:00:00
Tasks: Attribute

Recently, there is a surge of social recommendation, which leverages social
relations among users to improve recommendation performance. However, in many
applications, social relations are absent or very sparse. Meanwhile, the
attribute information of users or items may be rich. It is a big challenge to
exploit these attribute information for the improvement of recommendation
performance. In this paper, we organize objects and relations in recommendation
system as a heterogeneous information network, and introduce meta path based
similarity measure to evaluate the similarity of users or items. Furthermore, a
matrix factorization based dual regularization framework SimMF is proposed to
flexibly integrate different types of information through adopting the
similarity of users and items as regularization on latent factors of users and
items. Extensive experiments not only validate the effectiveness of SimMF but
also reveal some interesting findings. We find that attribute information of
users and items can significantly improve recommendation accuracy, and their
contribution seems more important than that of social relations. The
experiments also reveal that different regularization models have obviously
different impact on users and items.
Deleveraging, short sale constraints and market crash
Date: 2015-11-12 00:00:00

In this paper, we develop a theory of market crashes resulting from a
deleveraging shock. We consider two representative investors in a market
holding different opinions about the public available information. The
deleveraging shock forces the high confidence investors to liquidate their
risky assets to pay back their margin loans. When short sales are constrained,
the deleveraging shock creates a liquidity vacuum in which no trades can occur
between the two representative investors until the price drop to a threshold
below which low confidence investors take over the reduced demands. There are
two roles short sellers could play to stabilize the market. First, short
sellers provide extra supply in a bullish market so that the price of the asset
is settled lower than otherwise. Second, short sellers catch the falling price
earlier in the deleveraging process if they are previously allowed to hold a
larger short position. We apply our model to explain the recent deleveraging
crisis of the Chinese market with great success.