pwc-archive/papers-with-abstracts huggingface dataset:
In silico tumor control induced via alternating immunostimulating and immunosuppressive phases
Date: 2015-07-22 00:00:00

Despite recent advances in the field of Oncoimmunology, the success potential
of immunomodulatory therapies against cancer remains to be elucidated. One of
the reasons is the lack of understanding on the complex interplay between tumor
growth dynamics and the associated immune system responses. Towards this goal,
we consider a mathematical model of vascularized tumor growth and the
corresponding effector cell recruitment dynamics. Bifurcation analysis allows
for the exploration of model's dynamic behavior and the determination of these
parameter regimes that result in immune-mediated tumor control. Here, we focus
on a particular tumor evasion regime that involves tumor and effector cell
concentration oscillations of slowly increasing and decreasing amplitude,
respectively. Considering a temporal multiscale analysis, we derive an
analytically tractable mapping of model solutions onto a weakly negatively
damped harmonic oscillator. Based on our analysis, we propose a theory-driven
intervention strategy involving immunostimulating and immunosuppressive phases
to induce long-term tumor control.
Alexandria: Extensible Framework for Rapid Exploration of Social Media
Authors: Heath Fenno F. III, Hull Richard, Khabiri Elham, Riemer Matthew, Sukaviriya Noi, Vaculin Roman
Date: 2015-07-23 00:00:00

The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.
Continuous-Time Mean-Variance Portfolio Selection with Constraints on Wealth and Portfolio
Date: 2015-07-24 00:00:00

We consider continuous-time mean-variance portfolio selection with bankruptcy
prohibition under convex cone portfolio constraints. This is a long-standing
and difficult problem not only because of its theoretical significance, but
also for its practical importance. First of all, we transform the above problem
into an equivalent mean-variance problem with bankruptcy prohibition without
portfolio constraints. The latter is then treated using martingale theory. Our
findings indicate that we can directly present the semi-analytical expressions
of the pre-committed efficient mean-variance policy without a viscosity
solution technique but within a general framework of the cone portfolio
constraints. The numerical simulation also sheds light on results established
in this paper.
Interpreting the dependence of mutation rates on age and time
Date: 2015-07-24 00:00:00

Mutations can arise from the chance misincorporation of nucleotides during
DNA replication or from DNA lesions that are not repaired correctly. We
introduce a model that relates the source of mutations to their accumulation
with cell divisions, providing a framework for understanding how mutation rates
depend on sex, age and absolute time. We show that the accrual of mutations
should track cell divisions not only when mutations are replicative in origin
but also when they are non-replicative and repaired efficiently. One
implication is that the higher incidence of cancer in rapidly renewing tissues,
an observation ascribed to replication errors, could instead reflect exogenous
or endogenous mutagens. We further find that only mutations that arise from
inefficiently repaired lesions will accrue according to absolute time; thus, in
the absence of selection on mutation rates, the phylogenetic "molecular clock"
should not be expected to run steadily across species.
The long-tail distribution function of mutations in bacteria
Date: 2015-07-24 00:00:00

Levy flights in the space of mutations model time evolution of bacterial DNA.
Parameters in the model are adjusted in order to fit observations coming from
the Long Time Evolution Experiment with E. Coli.
Energy Minimization of Discrete Protein Titration State Models Using Graph Theory
Date: 2016-04-17 00:00:00

There are several applications in computational biophysics which require the
optimization of discrete interacting states; e.g., amino acid titration states,
ligand oxidation states, or discrete rotamer angles. Such optimization can be
very time-consuming as it scales exponentially in the number of sites to be
optimized. In this paper, we describe a new polynomial-time algorithm for
optimization of discrete states in macromolecular systems. This algorithm was
adapted from image processing and uses techniques from discrete mathematics and
graph theory to restate the optimization problem in terms of "maximum
flow-minimum cut" graph analysis. The interaction energy graph, a graph in
which vertices (amino acids) and edges (interactions) are weighted with their
respective energies, is transformed into a flow network in which the value of
the minimum cut in the network equals the minimum free energy of the protein,
and the cut itself encodes the state that achieves the minimum free energy.
Because of its deterministic nature and polynomial-time performance, this
algorithm has the potential to allow for the ionization state of larger
proteins to be discovered.
Simultaneous regulation of cell size and chromosome replication in bacteria
Date: 2015-07-24 00:00:00

Bacteria are able to maintain a narrow distribution of cell sizes by
regulating the timing of cell divisions. In rich nutrient conditions, cells
divide much faster than their chromosomes replicate. This implies that cells
maintain multiple rounds of chromosome replication per cell division by
regulating the timing of chromosome replications. Here, we show that both cell
size and chromosome replication may be simultaneously regulated by the
long-standing initiator accumulation strategy. The strategy proposes that
initiators are produced in proportion to the volume increase and is accumulated
at each origin of replication, and chromosome replication is initiated when a
critical amount per origin has accumulated. We show that this model maps to the
incremental model of size control, which was previously shown to reproduce
experimentally observed correlations between various events in the cell cycle
and explains the exponential dependence of cell size on the growth rate of the
cell. Furthermore, we show that this model also leads to the efficient
regulation of the timing of initiation and the number of origins consistent
with existing experimental results.
Optimal management of a stochastically varying population when policy adjustment is costly
Date: 2015-07-24 00:00:00
Tasks: Management

Ecological systems are dynamic and policies to manage them need to respond to
that variation. However, policy adjustments will sometimes be costly, which
means that fine-tuning a policy to track variability in the environment very
tightly will only sometimes be worthwhile. We use a classic fisheries
management question -- how to manage a stochastically varying population using
annually varying quotas in order to maximize profit -- to examine how costs of
policy adjustment change optimal management recommendations. Costs of policy
adjustment (here changes in fishing quotas through time) could take different
forms. For example, these costs may respond to the size of the change being
implemented, or there could be a fixed cost any time a quota change is made. We
show how different forms of policy costs have contrasting implications for
optimal policies. Though it is frequently assumed that costs to adjusting
policies will dampen variation in the policy, we show that certain cost
structures can actually increase variation through time. We further show that
failing to account for adjustment costs has a consistently worse economic
impact than would assuming these costs are present when they are not.
High Performance Computing of Gene Regulatory Networks using a Message-Passing Model
Date: 2015-07-24 00:00:00
Tasks: C++ code, Computational Efficiency

Gene regulatory network reconstruction is a fundamental problem in
computational biology. We recently developed an algorithm, called PANDA
(Passing Attributes Between Networks for Data Assimilation), that integrates
multiple sources of 'omics data and estimates regulatory network models. This
approach was initially implemented in the C++ programming language and has
since been applied to a number of biological systems. In our current research
we are beginning to expand the algorithm to incorporate larger and most diverse
data-sets, to reconstruct networks that contain increasing numbers of elements,
and to build not only single network models, but sets of networks. In order to
accomplish these "Big Data" applications, it has become critical that we
increase the computational efficiency of the PANDA implementation. In this
paper we show how to recast PANDA's similarity equations as matrix operations.
This allows us to implement a highly readable version of the algorithm using
the MATLAB/Octave programming language. We find that the resulting M-code much
shorter (103 compared to 1128 lines) and more easily modifiable for potential
future applications. The new implementation also runs significantly faster,
with increasing efficiency as the network models increase in size. Tests
comparing the C-code and M-code versions of PANDA demonstrate that this
speed-up is on the order of 20-80 times faster for networks of similar
dimensions to those we find in current biological applications.
How to predict the consequences of a tick value change? Evidence from the Tokyo Stock Exchange pilot program
Date: 2015-07-25 00:00:00

The tick value is a crucial component of market design and is often
considered the most suitable tool to mitigate the effects of high frequency
trading. The goal of this paper is to demonstrate that the approach introduced
in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the
consequences of a tick value change on the microstructure of an asset. To that
purpose, we analyze the pilot program on tick value modifications started in
2014 by the Tokyo Stock Exchange in light of this methodology. We focus on
forecasting the future cost of market and limit orders after a tick value
change and show that our predictions are very accurate. Furthermore, for each
asset involved in the pilot program, we are able to define (ex ante) an optimal
tick value. This enables us to classify the stocks according to the relevance
of their tick value, before and after its modification.
Phylogenetic effective sample size
Date: 2016-05-11 00:00:00

In this paper I address the question - how large is a phylogenetic sample I
propose a definition of a phylogenetic effective sample size for Brownian
motion and Ornstein-Uhlenbeck processes - the regression effective sample size.
I discuss how mutual information can be used to define an effective sample size
in the non-normal process case and compare these two definitions to an already
present concept of effective sample size (the mean effective sample size).
Through a simulation study I find that the AICc is robust if one corrects for
the number of species or effective number of species. Lastly I discuss how the
concept of the phylogenetic effective sample size can be useful for
biodiversity quantification, identification of interesting clades and deciding
on the importance of phylogenetic correlations.
Forecasting Leading Death Causes in Australia using Extended CreditRisk$+$
Date: 2015-07-26 00:00:00
Tasks: Time Series Analysis

Recently we developed a new framework in Hirz et al (2015) to model
stochastic mortality using extended CreditRisk$^+$ methodology which is very
different from traditional time series methods used for mortality modelling
previously. In this framework, deaths are driven by common latent stochastic
risk factors which may be interpreted as death causes like neoplasms,
circulatory diseases or idiosyncratic components. These common factors
introduce dependence between policyholders in annuity portfolios or between
death events in population. This framework can be used to construct life tables
based on mortality rate forecast. Moreover this framework allows stress testing
and, therefore, offers insight into how certain health scenarios influence
annuity payments of an insurer. Such scenarios may include improvement in
health treatments or better medication. In this paper, using publicly available
data for Australia, we estimate the model using Markov chain Monte Carlo method
to identify leading death causes across all age groups including long term
forecast for 2031 and 2051. On top of general reduced mortality, the proportion
of deaths for certain certain causes has changed massively over the period 1987
to 2011. Our model forecasts suggest that if these trends persist, then the
future gives a whole new picture of mortality for people aged above 40 years.
Neoplasms will become the overall number-one death cause. Moreover, deaths due
to mental and behavioural disorders are very likely to surge whilst deaths due
to circulatory diseases will tend to decrease. This potential increase in
deaths due to mental and behavioural disorders for older ages will have a
massive impact on social systems as, typically, such patients need long-term
geriatric care.
Non-hereditary Minimum Deep Coalescence trees
Date: 2015-07-26 00:00:00

One of the goals of phylogenetic research is to find the species tree
describing the evolutionary history of a set of species. But the trees derived
from geneti data with the help of tree inference methods are gene trees that
need not coincide with the species tree. This can for example happen when
so-called deep coalescence events take place. It is also known that species
trees can differ from their most likely gene trees. Therefore, as a means to
find the species tree, it has been suggested to use subtrees of the gene trees,
for example triples, and to puzzle them together in order to find the species
tree. In this paper, we will show that this approach may lead to wrong trees
regarding the minimum deep coalescence criterion (MDC). In particular, we
present an example in which the optimal MDC tree is unique, but none of its
triple subtrees fulfills the MDC criterion. In this sense, MDC is a
non-hereditary tree reconstruction method.
Model Risk Analysis via Investment Structuring
Date: 2015-07-28 00:00:00
Tasks: model

"What are the origins of risks?" and "How material are they?" -- these are
the two most fundamental questions of any risk analysis. Quantitative
Structuring -- a technology for building financial products -- provides
economically meaningful answers for both of these questions. It does so by
considering risk as an investment opportunity. The structure of the investment
reveals the precise sources of risk and its expected performance measures
materiality. We demonstrate these capabilities of Quantitative Structuring
using a concrete practical example -- model risk in options on vol-targeted
indices.
Intracellular impedance measurements reveal non-ohmic properties of the extracellular medium around neurons
Date: 2015-11-13 00:00:00

The electrical properties of extracellular space around neurons are important
to understand the genesis of extracellular potentials, as well as for
localizing neuronal activity from extracellular recordings. However, the exact
nature of these extracellular properties is still uncertain. We introduce a
method to measure the impedance of the tissue, and which preserves the intact
cell-medium interface, using whole-cell patch-clamp recordings in vivo and in
vitro. We find that neural tissue has marked non-ohmic and frequency-filtering
properties, which are not consistent with a resistive (ohmic) medium, as often
assumed. In contrast, using traditional metal electrodes provides very
different results, more consistent with a resistive medium. The amplitude and
phase profiles of the measured impedance are consistent with the contribution
of ionic diffusion. We also show that the impact of such frequency-filtering
properties is possibly important on the genesis of local field potentials, as
well as on the cable properties of neurons. The present results show non-ohmic
properties of the extracellular medium around neurons, and suggest that source
estimation methods, as well as the cable properties of neurons, which all
assume ohmic extracellular medium, may need to be re-evaluated.
Modelling the progression of atrial fibrillation: A stochastic individual-based approach
Date: 2015-07-28 00:00:00
Tasks: Rhythm

We propose a stochastic individual-based model of the progression of atrial
fibrillation (AF). The model operates at patient level over a lifetime and is
based on elements of the physiology and biophysics of AF, making contact with
existing mechanistic models. The outputs of the model are times when the
patient is in normal rhythm and AF, and we carry out a population-level
analysis of the statistics of disease progression. While the model is stylised
at present and not directly predictive, future improvements are proposed to
tighten the gap between existing mechanistic models of AF, and epidemiological
data, with a view towards model-based personalised medicine.
Application of Kullback-Leibler divergence for short-term user interest detection
Authors: Borisyak Maxim, Zykov Roman, Noskov Artem
Date: 2015-07-27 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Classical approaches in recommender systems such as collaborative filtering
are concentrated mainly on static user preference extraction. This approach
works well as an example for music recommendations when a user behavior tends
to be stable over long period of time, however the most common situation in
e-commerce is different which requires reactive algorithms based on a
short-term user activity analysis. This paper introduces a small mathematical
framework for short-term user interest detection formulated in terms of item
properties and its application for recommender systems enhancing. The framework
is based on the fundamental concept of information theory --- Kullback-Leibler
divergence.
Detecting concentration changes with cooperative receptors
Date: 2015-07-27 00:00:00

Cells constantly need to monitor the state of the environment to detect
changes and timely respond. The detection of concentration changes of a ligand
by a set of receptors can be cast as a problem of hypothesis testing, and the
cell viewed as a Neyman-Pearson detector. Within this framework, we investigate
the role of receptor cooperativity in improving the cell's ability to detect
changes. We find that cooperativity decreases the probability of missing an
occurred change. This becomes especially beneficial when difficult detections
have to be made. Concerning the influence of cooperativity on how fast a
desired detection power is achieved, we find in general that there is an
optimal value at finite levels of cooperation, even though easy discrimination
tasks can be performed more rapidly by noncooperative receptors.
Analysis of Pain Hemodynamic Response Using Near-Infrared Spectroscopy (NIRS)
Date: 2015-07-24 00:00:00

Despite recent advances in brain research, understanding the various signals
for pain and pain intensities in the brain cortex is still a complex task due
to temporal and spatial variations of brain hemodynamics. In this paper we have
investigated pain based on cerebral hemodynamics via near-infrared spectroscopy
(NIRS). This study presents a pain stimulation experiment that uses three
acupuncture manipulation techniques to safely induce pain in healthy subjects.
Acupuncture pain response was presented and hemodynamic pain signal analysis
showed the presence of dominant channels and their relationship among
surrounding channels, which contribute the further pain research area.
Computational principles of biological memory
Date: 2015-07-27 00:00:00

Memories are stored, retained, and recollected through complex, coupled
processes operating on multiple timescales. To understand the computational
principles behind these intricate networks of interactions we construct a broad
class of synaptic models that efficiently harnesses biological complexity to
preserve numerous memories. The memory capacity scales almost linearly with the
number of synapses, which is a substantial improvement over the square root
scaling of previous models. This was achieved by combining multiple dynamical
processes that initially store memories in fast variables and then
progressively transfer them to slower variables. Importantly, the interactions
between fast and slow variables are bidirectional. The proposed models are
robust to parameter perturbations and can explain several properties of
biological memory, including delayed expression of synaptic modifications,
metaplasticity, and spacing effects.
Codon Bias Patterns of $E.coli$'s Interacting Proteins
Date: 2015-07-28 00:00:00

Synonymous codons, i.e., DNA nucleotide triplets coding for the same amino
acid, are used differently across the variety of living organisms. The
biological meaning of this phenomenon, known as codon usage bias, is still
controversial. In order to shed light on this point, we propose a new codon
bias index, $CompAI$, that is based on the competition between cognate and
near-cognate tRNAs during translation, without being tuned to the usage bias of
highly expressed genes. We perform a genome-wide evaluation of codon bias for
$E.coli$, comparing $CompAI$ with other widely used indices: $tAI$, $CAI$, and
$Nc$. We show that $CompAI$ and $tAI$ capture similar information by being
positively correlated with gene conservation, measured by ERI, and
essentiality, whereas, $CAI$ and $Nc$ appear to be less sensitive to
evolutionary-functional parameters. Notably, the rate of variation of $tAI$ and
$CompAI$ with ERI allows to obtain sets of genes that consistently belong to
specific clusters of orthologous genes (COGs). We also investigate the
correlation of codon bias at the genomic level with the network features of
protein-protein interactions in $E.coli$. We find that the most densely
connected communities of the network share a similar level of codon bias (as
measured by $CompAI$ and $tAI$). Conversely, a small difference in codon bias
between two genes is, statistically, a prerequisite for the corresponding
proteins to interact. Importantly, among all codon bias indices, $CompAI$ turns
out to have the most coherent distribution over the communities of the
interactome, pointing to the significance of competition among cognate and
near-cognate tRNAs for explaining codon usage adaptation.