pwc-archive/papers-with-abstracts huggingface dataset:
Genetic variation in human drug-related genes
Date: 2017-06-26 00:00:00
Tasks: Decision Making

Variability in drug efficacy and adverse effects are observed in clinical
practice. While the extent of genetic variability in classical pharmacokinetic
genes is rather well understood, the role of genetic variation in drug targets
is typically less studied. Based on 60,706 human exomes from the ExAC dataset,
we performed an in-depth computational analysis of the prevalence of
functional-variants in in 806 drug-related genes, including 628 known drug
targets. We find that most genetic variants in these genes are very rare (f <
0.1%) and thus likely not observed in clinical trials. Overall, however, four
in five patients are likely to carry a functional-variant in a target for
commonly prescribed drugs and many of these might alter drug efficacy. We
further computed the likelihood of 1,236 FDA approved drugs to be affected by
functional-variants in their targets and show that the patient-risk varies for
many drugs with respect to geographic ancestry. A focused analysis of
oncological drug targets indicates that the probability of a patient carrying
germline variants in oncological drug targets is with 44% high enough to
suggest that not only somatic alterations, but also germline variants carried
over into the tumor genome should be included in therapeutic decision-making.
Spatial effects in meta-food-webs
Date: 2017-08-10 00:00:00

In ecology it is widely recognised that many landscapes comprise a network of
discrete patches of habitat. The species that inhabit the patches interact with
each other through a foodweb, the network of feeding interactions. The
meta-foodweb model proposed by Pillai et al. combines the feeding relationships
at each patch with the dispersal of species between patches, such that the
whole system is represented by a network of networks. Previous work on
meta-foodwebs has focussed on landscape networks that do not have an explicit
spatial embedding, but in real landscapes the patches are usually distributed
in space. Here we compare the dispersal of a meta-foodweb on \ER networks, that
do not have a spatial embedding, and random geometric networks, that do have a
spatial embedding. We found that local structure and large network distances in
spatially embedded networks, lead to meso-scale patterns of patch occupation by
both specialist and omnivorous species. In particular, we found that spatial
separations make the coexistence of competing species more likely. Our results
highlight the effects of spatial embeddings for meta-foodweb models, and the
need for new analytical approaches to them.
A Partial Solution to Continuous Blotto
Date: 2017-09-14 00:00:00

This paper analyzes the structure of mixed-strategy equilibria for Colonel
Blotto games, where the outcome on each battlefield is a polynomial function of
the difference between the two players' allocations. This paper severely
reduces the set of strategies that needs to be searched to find a Nash
equilibrium. It finds that there exists a Nash equilibrium where both players'
mixed strategies are discrete distributions, and it places an upper bound on
the number of points in the supports of these discrete distributions.
Learning to make external sensory stimulus predictions using internal correlations in populations of neurons
Date: 2017-06-26 00:00:00

To compensate for sensory processing delays, the visual system must make
predictions to ensure timely and appropriate behaviors. Recent work has found
predictive information about the stimulus in neural populations early in vision
processing, starting in the retina. However, to utilize this information, cells
downstream must in turn be able to read out the predictive information from the
spiking activity of retinal ganglion cells. Here we investigate whether a
downstream cell could learn efficient encoding of predictive information in its
inputs in the absence of other instructive signals, from the correlations in
the inputs themselves. We simulate learning driven by spiking activity recorded
in salamander retina. We model a downstream cell as a binary neuron receiving a
small group of weighted inputs and quantify the predictive information between
activity in the binary neuron and future input. Input weights change according
to spike timing-dependent learning rules during a training period. We
characterize the readouts learned under spike timing-dependent learning rules,
finding that although the fixed points of learning dynamics are not associated
with absolute optimal readouts, they convey nearly all the information conveyed
by the optimal readout. Moreover, we find that learned perceptrons transmit
position and velocity information of a moving bar stimulus nearly as
efficiently as optimal perceptrons. We conclude that predictive information is,
in principle, readable from the perspective of downstream neurons in the
absence of other inputs, and consequently suggests that bottom-up prediction
may play an important role in sensory processing.
Queueing Analysis of a Chagas Disease Control Campaign
Date: 2017-06-27 00:00:00
Tasks: Management

A critical component of preventing the spread of vector borne diseases such
as Chagas disease are door-to-door campaigns by public health officials that
implement insecticide application in order to eradicate the vector infestation
of households. The success of such campaigns depends on adequate household
participation during the active phase as well as on sufficient follow-up during
the surveillance phase when newly infested houses or infested houses that had
not participated in the active phase will receive treatment. Queueing models
which are widely used in operations management give us a mathematical
representation of the operational efforts needed to contain the spread of
infestation. By modeling the queue as consisting of all infested houses in a
given locality, we capture the dynamics of the insect population due to
prevalence of infestation and to the additional growth of infestation by
redispersion, i.e. by the spread of infestation to previously uninfested houses
during the wait time for treatment. In contrast to traditional queueing models,
houses waiting for treatment are not known but must be identified through a
search process by public health workers. Thus, both the arrival rate of houses
to the queue as well as the removal rate from the queue depend on the current
level of infestation. We incorporate these dependencies through a load
dependent queueing model which allows us to estimate the long run average rate
of removing houses from the queue and therefore the cost associated with a
given surveillance program. The model is motivated by and applied to an ongoing
Chagas disease control campaign in Arequipa, Peru.
Correlates of severity of disease in Macaca mulatta infected with Plasmodium cynomolgi
Date: 2017-06-30 00:00:00

Characterization of host responses associated with severe malaria through an
integrative approach is necessary to understand the dynamics of a
\textit{Plasmodium cynomolgi} infection. In this study, we conducted temporal
immune profiling, cytokine profiling and transcriptomic analysis of five
\textit{Macaca mulatta} infected with \textit{P. cynomolgi}. This experiment
resulted in two severe infections, and two mild infections. Our analysis
reveals that differential transcriptional up-regulation of genes linked with
response to pathogen-associated molecular pattern (PAMP) and pro-inflammatory
cytokines is characteristic of hosts experiencing severe malaria. Furthermore,
our analysis discovered associations of transcriptional differential regulation
unique to severe hosts with specific cellular and cytokine responses. The
combined data provide a molecular and cellular basis for the development of
severe malaria during \textit{P. cynomolgi} infection.
Magnetic properties of photosynthetic materials - a nano scale study
Date: 2017-06-10 00:00:00
Tasks: Attribute, Blocking

Photosynthetic materials form the basis of quantum biology. An important
attribute of quantum biology is correlation and coherence of spin states. Such
correlated spin states are targets of static magnetic field. In this paper, we
report magnetic properties and spectroscopically realizable static magnetic
field effect in photosynthetic materials. Two classes of nano-scale assembly of
chlorophyll (NC) are used for such a study. Magnetic measurements are made
using a superconducting quantum interference device (SQUID). Both ferromagnetic
and superparamagnetic states are observed in NC along with a blocking
temperature around 250 K. Low temperature quantum (liquid nitrogen)
spectroscopy is employed to see how optical transitions are affected in
presence of static magnetic field. Plausible practical application aspects of
magnetic properties of this optically active material are discussed in the
text.
Adhesion and volume constraints via nonlocal interactions lead to cell sorting
Date: 2017-06-27 00:00:00

We demonstrate how concepts of statistical mechanics of interacting particles
can have important implications in the choice of interaction potentials to
model qualitative properties of cell aggregates in theoretical biology. We
illustrate this by showing cell sorting phenomena for cell groups with
different adhesiveness parameters: ranging from well-mixed cells aggregates to
full segregation of cell type passing through engulfment via adhesiveness
tuning.
Structured Recommendation
Authors: Chen Dawei, Xie Lexing, Menon Aditya Krishna, Ong Cheng Soon
Date: 2017-06-27 00:00:00
Tasks: Recommendation Systems, Structured Prediction, valid

Current recommender systems largely focus on static, unstructured content. In
many scenarios, we would like to recommend content that has structure, such as
a trajectory of points-of-interests in a city, or a playlist of songs. Dubbed
Structured Recommendation, this problem differs from the typical structured
prediction problem in that there are multiple correct answers for a given
input. Motivated by trajectory recommendation, we focus on sequential
structures but in contrast to classical Viterbi decoding we require that valid
predictions are sequences with no repeated elements. We propose an approach to
sequence recommendation based on the structured support vector machine. For
prediction, we modify the inference procedure to avoid predicting loops in the
sequence. For training, we modify the objective function to account for the
existence of multiple ground truths for a given input. We also modify the
loss-augmented inference procedure to exclude the known ground truths.
Experiments on real-world trajectory recommendation datasets show the benefits
of our approach over existing, non-structured recommendation approaches.
Population-specific design of de-immunized protein biotherapeutics
Date: 2017-06-28 00:00:00
Tasks: Combinatorial Optimization

Immunogenicity is a major problem during the development of biotherapeutics
since it can lead to rapid clearance of the drug and adverse reactions. The
challenge for biotherapeutic design is therefore to identify mutants of the
protein sequence that minimize immunogenicity in a target population whilst
retaining pharmaceutical activity and protein function. Current approaches are
moderately successful in designing sequences with reduced immunogenicity, but
do not account for the varying frequencies of different human leucocyte antigen
alleles in a specific population and in addition, since many designs are
non-functional, require costly experimental post-screening. Here we report a
new method for de-immunization design using multi-objective combinatorial
optimization that simultaneously optimizes the likelihood of a functional
protein sequence at the same time as minimizing its immunogenicity tailored to
a target population. We bypass the need for three-dimensional protein structure
or molecular simulations to identify functional designs by automatically
generating sequences using probabilistic models that have been used previously
for mutation effect prediction and structure prediction. As proof-of-principle
we designed sequences of the C2 domain of Factor VIII and tested them
experimentally, resulting in a good correlation with the predicted
immunogenicity of our model.
Semantic Web Prefetching Using Semantic Relatedness between Web pages
Authors: Parmar Jyotsna, Jyoti
Date: 2017-06-28 00:00:00
Tasks: Descriptive

Internet as become the way of life in the fast growing digital life.Even with
the increase in the internet speed, higher latency time is still a challenge.
To reduce latency, caching and pre fetching techniques can be used. However,
caching fails for dynamic websites which keeps on changing rapidly. Another
technique is web prefetching, which prefetches the web pages that the user is
likely to request for in the future. Semantic web prefetching makes use of
keywords and descriptive texts like anchor text, titles, text surrounding
anchor text of the present web pages for predicting users future requests.
Semantic information is embedded within the web pages during their designing
for the purpose of reflecting the relationship between the web pages. The
client can fetch this information from the server. However, this technique
involves load on web designers for adding external tags and on server for
providing this information along with the desired page, which is not desirable.
This paper is an effort to find the semantic relation between web pages using
the keywords provided by the user and the anchor texts of the hyperlinks on the
present web page.It provides algorithms for sequential and similar semantic
relations. These algorithms will be implemented on the client side which will
not cause overhead on designers and load on server for semantic information.
Simulation Study of Two Measures of Integrated Information
Date: 2017-06-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

Background: Many authors have proposed Quantitative Theories of Consciousness
(QTC) based on theoretical principles like information theory, Granger
causality and complexity. Recently, Virmani and Nagaraj (arXiv:1608.08450v2
[cs.IT]) noted the similarity between Integrated Information and
Compression-Complexity, and on this basis, proposed a novel measure of network
complexity called Phi-Compression Complexity (Phi-C or $\Phi^C$). Their
computer simulations using Boolean networks showed that $\Phi^C$ compares
favorably to Giulio Tononi et al's Integrated Information measure $\Phi$ 3.0
and exhibits desirable mathematical and computational characteristics. Methods:
In the present work, $\Phi^C$ was measured for two types of simulated networks:
(A) Networks representing simple neuronal connectivity motifs (presented in
Fig.9 of Tononi and Sporns, BMC Neuroscience 4(1), 2003); (B) random networks
derived from Erd\"os-R \'enyi G(N, p)graphs. Code for all simulations was
written in Python 3.6, and the library NetworkX was used to simulate the
graphs. Results and discussions summary: In simulations A, for the same set of
networks, $\Phi^C$ values differ from the values of IIT 1.0 $\Phi$ in a
counter-intuitive manner. It appears that $\Phi^C$ captures some invariant
aspects of the interplay between information integration, network topology,
graph composition and node entropy. While Virmani and Nagaraj
(arXiv:1608.08450v2 [cs.IT]) sought to highlight the correlations between
$\Phi^C$ and IIT $\Phi$, the results of simulations A highlight the differences
between the two measures in the way they capture the integrated information. In
simulations B, the results of simulations A are extended to the more general
case of random networks. In the concluding section we outline the novel aspects
of this paper, and our ongoing and future research.
Asymptotics for the Discrete-Time Average of the Geometric Brownian Motion and Asian Options
Date: 2017-06-29 00:00:00

The time average of geometric Brownian motion plays a crucial role in the
pricing of Asian options in mathematical finance. In this paper we consider the
asymptotics of the discrete-time average of a geometric Brownian motion sampled
on uniformly spaced times in the limit of a very large number of averaging time
steps. We derive almost sure limit, fluctuations, large deviations, and also
the asymptotics of the moment generating function of the average. Based on
these results, we derive the asymptotics for the price of Asian options with
discrete-time averaging in the Black-Scholes model, with both fixed and
floating strike.
Hilbert transform, spectral filters and option pricing
Date: 2020-01-15 00:00:00

We show how spectral filters can improve the convergence of numerical schemes
which use discrete Hilbert transforms based on a sinc function expansion, and
thus ultimately on the fast Fourier transform. This is relevant, for example,
for the computation of fluctuation identities, which give the distribution of
the maximum or the minimum of a random path, or the joint distribution at
maturity with the extrema staying below or above barriers. We use as examples
the methods by Feng and Linetsky (2008) and Fusai, Germano and Marazzina (2016)
to price discretely monitored barrier options where the underlying asset price
is modelled by an exponential L\'evy process. Both methods show exponential
convergence with respect to the number of grid points in most cases, but are
limited to polynomial convergence under certain conditions. We relate these
rates of convergence to the Gibbs phenomenon for Fourier transforms and achieve
improved results with spectral filtering.
New approaches for CMOS-based devices for large-scale neural recording
Date: 2017-06-30 00:00:00

Extracellular, large scale in vivo recording of neural activity is mandatory
for elucidating the interaction of neurons within large neural networks at the
level of their single unit activity. Technological achievements in MEMS-based
multichannel electrode arrays offer electrophysiological recording capabilities
that go far beyond those of classical wire electrodes. Despite their impressive
channel counts, recording systems with modest interconnection overhead have
been demonstrated thanks to the hybrid integration of CMOS circuitry for signal
preprocessing and data handling. The number of addressable channels is
increased even further by a switch matrix for electrode selection co-integrated
along the slender probe shafts. When realized by IC fabrication technologies,
these probes offer highest recording site densities along the entire shaft
length.
A Stochastic Model for the Formation of Spatial Methylation Patterns
Date: 2017-07-10 00:00:00

DNA methylation is an epigenetic mechanism whose important role in
development has been widely recognized. This epigenetic modification results in
heritable changes in gene expression not encoded by the DNA sequence. The
underlying mechanisms controlling DNA methylation are only partly understood
and recently different mechanistic models of enzyme activities responsible for
DNA methylation have been proposed. Here we extend existing Hidden Markov
Models (HMMs) for DNA methylation by describing the occurrence of spatial
methylation patterns over time and propose several models with different
neighborhood dependencies. We perform numerical analysis of the HMMs applied to
bisulfite sequencing measurements and accurately predict wild-type data. In
addition, we find evidence that the enzymes' activities depend on the left 5'
neighborhood but not on the right 3' neighborhood.
Regret-based Selection for Sparse Dynamic Portfolios
Date: 2017-07-23 00:00:00

This paper considers portfolio construction in a dynamic setting. We specify
a loss function comprised of utility and complexity components with an unknown
tradeoff parameter. We develop a novel regret-based criterion for selecting the
tradeoff parameter to construct optimal sparse portfolios over time.
Connecting macroscopic dynamics with microscopic properties in active microtubule network contraction
Date: 2017-06-30 00:00:00

The cellular cytoskeleton is an active material, driven out of equilibrium by
molecular motor proteins. It is not understood how the collective behaviors of
cytoskeletal networks emerge from the properties of the network's constituent
motor proteins and filaments. Here we present experimental results on networks
of stabilized microtubules in Xenopus oocyte extracts, which undergo
spontaneous bulk contraction driven by the motor protein dynein, and
investigate the effects of varying the initial microtubule density and length
distribution. We find that networks contract to a similar final density,
irrespective of the length of microtubules or their initial density, but that
the contraction timescale varies with the average microtubule length. To gain
insight into why this microscopic property influences the macroscopic network
contraction time, we developed simulations where microtubules and motors are
explicitly represented. The simulations qualitatively recapitulate the
variation of contraction timescale with microtubule length, and allowed stress
contributions from different sources to be estimated and decoupled.
Computing Biomolecular System Steady-states
Date: 2017-12-22 00:00:00

A new approach to computing the equilibria and steady-states of biomolecular
systems modelled by bond graphs is presented. The approach is illustrated using
a model of a biomolecular cycle representing a membrane transporter and a model
of the mitochondrial electron transport chain.
Implications of the Trivers-Willard Sex Ratio Hypothesis for Avian Species and Poultry Production, And a Summary of the Historic Context of this Research
Date: 2017-08-30 00:00:00

At a theoretical level, the Trivers-Willard Sex Ratio Hypothesis applies to
both avian species and mammals. This article, however, conjectures that at the
statistical level, sex ratio effects are likely to produce sharper numerical
variations among birds than among mammals. We explain this greater statistical
variation should likely have beneficial implications for increasing the
efficiency of world-wide poultry egg (and perhaps also meat) production.
Curvature-based Methods for Brain Network Analysis
Date: 2019-05-13 00:00:00

The human brain forms functional networks on all spatial scales. Modern fMRI
scanners allow to resolve functional brain data in high resolutions, allowing
to study large-scale networks that relate to cognitive processes. The analysis
of such networks forms a cornerstone of experimental neuroscience. Due to the
immense size and complexity of the underlying data sets, efficient evaluation
and visualization remain a challenge for data analysis. In this study, we
combine recent advances in experimental neuroscience and applied mathematics to
perform a mathematical characterization of complex networks constructed from
fMRI data. We use task-related edge densities [Lohmann et al., 2016] for
constructing networks of task-related changes in synchronization. This
construction captures the dynamic formation of patterns of neuronal activity
and therefore represents efficiently the connectivity structure between brain
regions. Using geometric methods that utilize Forman-Ricci curvature as an
edge-based network characteristic [Weber et al., 2017], we perform a
mathematical analysis of the resulting complex networks. We motivate the use of
edge-based characteristics to evaluate the network structure with geometric
methods. The geometric features could aid in understanding the connectivity and
interplay of brain regions in cognitive processes.