pwc-archive/papers-with-abstracts huggingface dataset:
Reconstructing antibody repertoires from error-prone immunosequencing datasets
Date: 2017-04-24 00:00:00
Tasks: Benchmarking

Transforming error-prone immunosequencing datasets into antibody repertoires
is a fundamental problem in immunogenomics, and a prerequisite for studies of
immune responses. Although various repertoire reconstruction algorithms were
released in the last three years, it remains unclear how to benchmark them and
how to assess the accuracy of the reconstructed repertoires. We describe a
novel IgReC algorithm for constructing antibody repertoires from
high-throughput immunosequencing datasets and a new framework for assessing the
quality of reconstructed repertoires. Benchmarking IgReC against the existing
antibody repertoire reconstruction tools has demonstrated that it results in
highly accurate repertoire reconstructions. Surprisingly, antibody repertoires
constructed by IgReC from barcoded immunosequencing datasets in blind mode
(without using unique molecular identifiers information) improved upon the
repertoires constructed by the state-of-the-art tools that use barcoding. This
finding suggests that IgReC may alleviate the need to generate repertoires
using the barcoding technology (the workhorse of current immunogenomics
efforts) because our computational approach to error correction of
immunosequencing data ends up being nearly as powerful as the experimental
approach based on barcoding.
Value-at-Risk Diversification of $\alpha$-stable Risks: The Tail-Dependence Puzzle
Date: 2017-04-24 00:00:00

We consider the problem of risk diversification of $\alpha$-stable heavy
tailed risks. We study the behaviour of the aggregated Value-at-Risk, with
particular reference to the impact of different tail dependence structures on
the limits to diversification. We confirm the large evidence of sub-additivity
violations, particularly for risks with low tail index values and positive
dependence. So, reinsurance strategies are not allowed to exploit
diversification gains, or only a very limited amount of them. Concerning the
impact of tail dependence, we find the peculiar results that for high tail
dependence levels the limits to diversification are uniformly lower for all the
levels of dependence, and for all levels of $\alpha<2$. The result is confirmed
as we move towards extreme points in the tail: in this case, we show that at
some point in the tail the aggregated VaR becomes additive above some level of
dependence, but this critical dependence level is lower for copulas with lower
tail dependence.
Environmental accounting for ecosystem conservation: Linking societal and ecosystem metabolisms
Date: 2017-04-26 00:00:00

This paper proposes an approach to environmental accounting useful for
studying the feasibility of socio-economic systems in relation to the external
constraints posed by ecological compatibility. The approach is based on a
multi-scale analysis of the metabolic pattern of ecosystems and societies and
it provides an integrated characterization of the resulting interaction. The
text starts with a theoretical part explaining (i) the implicit epistemological
revolution implied by the notion of ecosystem metabolism and the fund-flow
model developed by Georgescu-Roegen applied to environmental accounting, and
(ii) the potentials of this approach to create indicators to assess ecological
integrity and environmental impacts. This revolution also makes it possible to
carry out a multi-scale integrated assessment of ecosystem and societal
metabolisms at the territorial level. In the second part, two applications of
this approach using an indicator of the negentropic cost show the possibility
to characterize in quantitative and qualitative terms degrees of alteration
(crop cultivation, tree plantations)for different biomes (tropical and boreal
forests). Also, a case study for land use scenarios has been included. The
proposed approach represents an integrated multi-scale tool for the analysis of
nature conservation scenarios and strategies.
A mapping of the stochastic Lotka-Volterra model to models of population genetics and game theory
Date: 2017-04-25 00:00:00

The relationship between the M-species stochastic Lotka-Volterra competition
(SLVC) model and the M-allele Moran model of population genetics is explored
via timescale separation arguments. When selection for species is weak and the
population size is large but finite, precise conditions are determined for the
stochastic dynamics of the SLVC model to be mappable to the neutral Moran
model, the Moran model with frequency-independent selection and the Moran model
with frequency-dependent selection (equivalently, a game-theoretic formulation
of the Moran model). We demonstrate how these mappings can be used to calculate
extinction probabilities and the times until a species' extinction in the SLVC
model.
Quantifying Hidden Architectural Patterns in Metaplastic Tumors by Calculating the Quadrant-Slope Index (QSI)
Date: 2017-04-25 00:00:00

The Quadrant-Slope Index (QSI) method was created in order to detect subtle
patterns of organization in tumor images that have metaplastic elements, such
as streams of spindle cells [1]. However, metaplastic tumors also have nuclei
that may be aligned like a stream but are not obvious to the pathologist
because the shape of the cytoplasm is unclear. The previous method that I
developed, the Nearest-Neighbor Angular Profile (N-NAP) method [2], is good for
detecting subtle patterns of order based on the assumption that breast tumor
cells are attempting to arrange themselves side-by-side (like bricks), as in
the luminal compartment of a normal mammary gland [3]. However, this assumption
is not optimal for detecting cellular arrangements that are head-to-tail, such
as in streams of spindle cells. Metaplastic carcinomas of the breast (i.e.
basal-like breast cancers, triple-negative breast cancers) are believed to be
derived from the stem or progenitor cells that reside in the
basal/myoepithelial compartment of the normal mammary gland [Reviewed in 3].
Epithelial cells in the basal/myoepithelial compartment arrange themselves in
an head-to-tail fashion, forming a net that surrounds the luminal compartment
[3,4]. If cancer cells in a metaplastic tumor are trying to be normal, the
optimal way to detect subtle regions of them attempting to be ordered normally
should highlight the head-to-tail alignment of cells.
Consistency of Regions of Interest as nodes of functional brain networks measured by fMRI
Date: 2017-04-25 00:00:00
Tasks: Time Series, Time Series Analysis

The functional network approach, where fMRI BOLD time series are mapped to
networks depicting functional relationships between brain areas, has opened new
insights into the function of the human brain. In this approach, the choice of
network nodes is of crucial importance. One option is to consider fMRI voxels
as nodes. This results in a large number of nodes, making network analysis and
interpretation of results challenging. A common alternative is to use
pre-defined clusters of anatomically close voxels, Regions of Interest (ROIs).
This approach assumes that voxels within ROIs are functionally similar. Because
these two approaches result in different network structures, it is crucial to
understand what happens to network connectivity when moving from the voxel
level to the ROI level. We show that the consistency of ROIs, defined as the
mean Pearson correlation coefficient between the time series of their voxels,
varies widely in resting-state experimental data. Therefore the assumption of
similar voxel dynamics within each ROI does not generally hold. Further, the
time series of low-consistency ROIs may be highly correlated, resulting in
spurious links in ROI-level networks. Based on these results, we recommend that
averaging BOLD signals over anatomically defined ROIs should be carefully
considered.
User Profile Based Research Paper Recommendation
Authors: Sahijwani Harshita, Dasgupta Sourish
Date: 2017-04-25 00:00:00
Tasks: Recommendation Systems

We design a recommender system for research papers based on topic-modeling.
The users feedback to the results is used to make the results more relevant the
next time they fire a query. The user's needs are understood by observing the
change in the themes that the user shows a preference for over time.
Supercoiling DNA locates mismatches
Date: 2017-04-25 00:00:00

We present a method of detecting sequence defects by supercoiling DNA with
magnetic tweezers. The method is sensitive to a single mismatched base pair in
a DNA sequence of several thousand base pairs. We systematically compare DNA
molecules with 0 to 16 adjacent mismatches at 1 M monovalent salt and 3.5 pN
force and show that, under these conditions, a single plectoneme forms and is
stably pinned at the defect. We use these measurements to estimate the energy
and degree of end-loop kinking at defects. From this, we calculate the relative
probability of plectoneme pinning at the mismatch under physiologically
relevant conditions. Based on this estimate, we propose that DNA supercoiling
could contribute to mismatch and damage sensing in vivo.
Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes
Date: 2017-04-25 00:00:00
Tasks: Model Selection

Containing the recent West African outbreak of Ebola virus (EBOV) required
the deployment of substantial global resources. Operationally, health workers
and surveillance teams treated cases, collected genetic samples, and tracked
case contacts. Despite the substantial progress in analyzing and modeling EBOV
epidemiological data, a complete characterization of the spatiotemporal spread
of Ebola cases remains a challenge. In this work, we offer a novel perspective
on the EBOV epidemic that utilizes virus genome sequences to inform
population-level, spatial models. Calibrated to phylogenetic linkages, these
dynamic spatial models provide unique insight into the disease mobility of EBOV
in Sierra Leone. Further, we developed a model selection framework that
identifies important epidemiological variables influencing the spatiotemporal
propagation of EBOV. Consistent with other investigations, our results show
that the spread of EBOV during the beginning and middle portions of the
epidemic strongly depended on the size of and distance between populations. Our
analysis also revealed a substantial decline in the dependence on population
size at the end of the epidemic, coinciding with the large-scale intervention
campaign: Operation Western Area Surge. More generally, we believe this
framework, pairing molecular diagnostics with dynamic models, has the potential
to be a powerful forecasting tool along with offering operationally-relevant
guidance for surveillance and sampling strategies during an epidemic.
Ranking in evolving complex networks
Authors: Liao Hao, Mariani Manuel Sebastian, Medo Matus, Zhang Yi-Cheng, Zhou Ming-Yang
Date: 2017-04-26 00:00:00
Tasks: Traffic Prediction

Complex networks have emerged as a simple yet powerful framework to represent
and analyze a wide range of complex systems. The problem of ranking the nodes
and the edges in complex networks is critical for a broad range of real-world
problems because it affects how we access online information and products, how
success and talent are evaluated in human activities, and how scarce resources
are allocated by companies and policymakers, among others. This calls for a
deep understanding of how existing ranking algorithms perform, and which are
their possible biases that may impair their effectiveness. Well-established
ranking algorithms (such as the popular Google's PageRank) are static in nature
and, as a consequence, they exhibit important shortcomings when applied to real
networks that rapidly evolve in time. The recent advances in the understanding
and modeling of evolving networks have enabled the development of a wide and
diverse range of ranking algorithms that take the temporal dimension into
account. The aim of this review is to survey the existing ranking algorithms,
both static and time-aware, and their applications to evolving networks. We
emphasize both the impact of network evolution on well-established static
algorithms and the benefits from including the temporal dimension for tasks
such as prediction of real network traffic, prediction of future links, and
identification of highly-significant nodes.
Beyond the network of plants volatile organic compounds
Date: 2017-04-26 00:00:00
Tasks: Community Detection

Plants emission of volatile organic compounds (VOCs) is involved in a wide
class of ecological functions, as VOCs play a crucial role in plants
interactions with biotic and abiotic factors. Accordingly, they vary widely
across species and underpin differences in ecological strategy. In this paper,
VOCs spontaneously emitted by 109 plant species (belonging to 56 different
families) have been qualitatively and quantitatively analysed in order to
classify plants species. By using bipartite networks methodology, based on
recent advancements in Complex Network Theory, and through the application of
complementary classical and advanced community detection algorithms, the
possibility to classify species according to chemical classes such as terpenes
and sulfur compounds is suggested. This indicates complex network analysis as
an advantageous methodology to uncover plants relationships also related to the
way they react to the environment where they evolve and adapt.
Constraint-based inverse modeling of metabolic networks: a proof of concept
Date: 2017-04-26 00:00:00

We consider the problem of inferring the probability distribution of flux
configurations in metabolic network models from empirical flux data. For the
simple case in which experimental averages are to be retrieved, data are
described by a Boltzmann-like distribution ($\propto e^{F/T}$) where $F$ is a
linear combination of fluxes and the `temperature' parameter $T\geq 0$ allows
for fluctuations. The zero-temperature limit corresponds to a Flux Balance
Analysis scenario, where an objective function ($F$) is maximized. As a test,
we have inverse modeled, by means of Boltzmann learning, the catabolic core of
Escherichia coli in glucose-limited aerobic stationary growth conditions.
Empirical means are best reproduced when $F$ is a simple combination of biomass
production and glucose uptake and the temperature is finite, implying the
presence of fluctuations. The scheme presented here has the potential to
deliver new quantitative insight on cellular metabolism. Our implementation is
however computationally intensive, and highlights the major role that effective
algorithms to sample the high-dimensional solution space of metabolic networks
can play in this field.
High-Frequency Jump Analysis of the Bitcoin Market
Date: 2017-06-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We use the database leak of Mt. Gox exchange to analyze the dynamics of the
price of bitcoin from June 2011 to November 2013. This gives us a rare
opportunity to study an emerging retail-focused, highly speculative and
unregulated market with trader identifiers at a tick transaction level. Jumps
are frequent events and they cluster in time. The order flow imbalance and the
preponderance of aggressive traders, as well as a widening of the bid-ask
spread predict them. Jumps have short-term positive impact on market activity
and illiquidity and see a persistent change in the price.
Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms
Date: 2017-04-27 00:00:00
Tasks: Translation

Electronic Health Records (EHR) are data generated during routine clinical
care. EHR offer researchers unprecedented phenotypic breadth and depth and have
the potential to accelerate the pace of precision medicine at scale. A main EHR
use-case is creating phenotyping algorithms to define disease status, onset and
severity. Currently, no common machine-readable standard exists for defining
phenotyping algorithms which often are stored in human-readable formats. As a
result, the translation of algorithms to implementation code is challenging and
sharing across the scientific community is problematic. In this paper, we
evaluate openEHR, a formal EHR data specification, for computable
representations of EHR phenotyping algorithms.
Optimal excess-of-loss reinsurance and investment problem for an insurer with default risk under a stochastic volatility model
Date: 2017-04-26 00:00:00

In this paper, we study an optimal excess-of-loss reinsurance and investment
problem for an insurer in defaultable market. The insurer can buy reinsurance
and invest in the following securities: a bank account, a risky asset with
stochastic volatility and a defaultable corporate bond. We discuss the optimal
investment strategy into two subproblems: a pre-default case and a post-default
case. We show the existence of a classical solution to a pre-default case via
super-sub solution techniques and give an explicit characterization of the
optimal reinsurance and investment policies that maximize the expected CARA
utility of the terminal wealth. We prove a verification theorem establishing
the uniqueness of the solution. Numerical results are presented in the case of
the Scott model and we discuss economic insights obtained from these results.
Novel Nongenomic Signaling by Glucocorticoid May Involve Changes to Liver Membrane Order in Rainbow Trout
Date: 2017-04-26 00:00:00

Stress-induced glucocorticoid elevation is a highly conserved response among
vertebrates. This facilitates stress adaptation and the mode of action involves
activation of the intracellular glucocorticoid receptor leading to the
modulation of target gene expression. However, this genomic effect is slow
acting and, therefore, a role for glucocorticoid in the rapid response to
stress is unclear. Here we show that stress levels of cortisol, the primary
glucocorticoid in teleosts, rapidly fluidizes rainbow trout (Oncorhynchus
mykiss) liver plasma membranes in vitro. This involved incorporation of the
steroid into the lipid domains, as cortisol coupled to a membrane impermeable
peptide moiety, did not affect membrane order. Studies confirmed that cortisol,
but not sex steroids, increases liver plasma membrane fluidity. Atomic force
microscopy revealed cortisol mediated changes to membrane surface topography
and viscoelasticity confirming changes to membrane order. Treating trout
hepatocytes with stress levels of cortisol led to the modulation of cell
signaling pathways, including the phosphorylation status of putative PKA, PKC
and AKT substrate proteins within 10 minutes. The phosphorylation by protein
kinases in the presence of cortisol was consistent with that seen with benzyl
alcohol, a known membrane fluidizer. Our results suggest that biophysical
changes to plasma membrane properties, triggered by stressor induced
glucocorticoid elevation, act as a nonspecific stress response and may rapidly
modulate acute stress-signaling pathways.
How the connectivity structure of neuronal networks influences responses to oscillatory stimuli
Date: 2017-04-26 00:00:00

Propagation of oscillatory signals through the cortex and coherence is shaped
by the connectivity structure of neuronal circuits. This study systematically
investigates the network and stimulus properties that shape network responses.
The results show how input to a cortical column model of the primary visual
cortex excites dynamical modes determined by the laminar pattern. Stimulating
the inhibitory neurons in the upper layer reproduces experimentally observed
resonances at gamma frequency whose origin can be traced back to two anatomical
sub-circuits. We develop this result systematically: Initially, we highlight
the effect of stimulus amplitude and filter properties of the neurons on their
response to oscillatory stimuli. Subsequently, we analyze the amplification of
oscillatory stimuli by the effective network structure. We demonstrate that the
amplification of stimuli, as well as their visibility in different populations,
can be explained by specific network patterns. Inspired by experimental results
we ask whether the anatomical origin of oscillations can be inferred by
applying oscillatory stimuli. We find that different network motifs can
generate similar responses to oscillatory input, showing that resonances in the
network response cannot, straightforwardly, be assigned to the motifs they
emerge from. Applying the analysis to a spiking model of a cortical column, we
characterize how the dynamic mode structure, which is induced by the laminar
connectivity, processes external input. In particular, we show that a stimulus
applied to specific populations typically elicits responses of several
interacting modes. The resulting network response is therefore composed of a
multitude of contributions and can therefore neither be assigned to a single
mode nor do the observed resonances necessarily coincide with the intrinsic
resonances of the circuit.
The use of quantum dots to amplify antigen detection
Date: 2017-04-27 00:00:00

Proposal to develop an Improved immunological assay employing primary IgG
antibodies and secondary IgM antibodies labeled with quantum dots to amplify
antigen detection.
Economic Neutral Position: How to best replicate not fully replicable liabilities
Date: 2019-07-21 00:00:00
Tasks: Position

Financial undertakings often have to deal with liabilities of the form
'non-hedgeable claim size times value of a tradeable asset', e.g. foreign
property insurance claims times fx rates. Which strategy to invest in the
tradeable asset is risk minimal? We generalize the Gram-Charlier series for the
sum of two dependent random variable, which allows us to expand the capital
requirements based on value-at-risk and expected shortfall. We derive a stable
and fairly model independent approximation of the risk minimal asset allocation
in terms of the claim size distribution and the moments of asset return. The
results enable a correct and easy-to-implement modularization of capital
requirements into a market risk and a non-hedgeable risk component.
Evolution of moments and correlations in non-renewal escape-time processes
Date: 2017-04-27 00:00:00

The theoretical description of non-renewal stochastic systems is a challenge.
Analytical results are often not available or can only be obtained under strong
conditions, limiting their applicability. Also, numerical results have mostly
been obtained by ad-hoc Monte--Carlo simulations, which are usually
computationally expensive when a high degree of accuracy is needed. To gain
quantitative insight into these systems under general conditions, we here
introduce a numerical iterated first-passage time approach based on solving the
time-dependent Fokker-Planck equation (FPE) to describe the statistics of
non-renewal stochastic systems. We illustrate the approach using
spike-triggered neuronal adaptation in the leaky and perfect integrate-and-fire
model, respectively. The transition to stationarity of first-passage time
moments and their sequential correlations occur on a non-trivial timescale that
depends on all system parameters. Surprisingly this is so for both single
exponential and scale-free power-law adaptation. The method works beyond the
small noise and timescale separation approximations. It shows excellent
agreement with direct Monte Carlo simulations, which allows for the computation
of transient and stationary distributions. We compare different methods to
compute the evolution of the moments and serial correlation coefficients (SCC),
and discuss the challenge of reliably computing the SCC which we find to be
very sensitive to numerical inaccuracies for both the leaky and perfect
integrate-and-fire models. In conclusion, our methods provide a general picture
of non-renewal dynamics in a wide range of stochastic systems exhibiting short
and long-range correlations.