pwc-archive/papers-with-abstracts huggingface dataset:
Identifying anatomical origins of coexisting oscillations in the cortical microcircuit
Date: 2016-05-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Oscillations are omnipresent in neural population signals, like multi-unit
recordings, EEG/MEG, and the local field potential. They have been linked to
the population firing rate of neurons, with individual neurons firing in a
close-to-irregular fashion at low rates. Using a combination of mean-field and
linear response theory we predict the spectra generated in a layered
microcircuit model of V1, composed of leaky integrate-and-fire neurons and
based on connectivity compiled from anatomical and electrophysiological
studies. The model exhibits low- and high-gamma oscillations visible in all
populations. Since locally generated frequencies are imposed onto other
populations, the origin of the oscillations cannot be deduced from the spectra.
We develop an universally applicable systematic approach that identifies the
anatomical circuits underlying the generation of oscillations in a given
network. Based on a theoretical reduction of the dynamics, we derive a
sensitivity measure resulting in a frequency-dependent connectivity map that
reveals connections crucial for the peak amplitude and frequency of the
observed oscillations and identifies the minimal circuit generating a given
frequency. The low-gamma peak turns out to be generated in a sub-circuit
located in layer 2/3 and 4, while the high-gamma peak emerges from the
inter-neurons in layer 4. Connections within and onto layer 5 are found to
regulate slow rate fluctuations. We further demonstrate how small perturbations
of the crucial connections have significant impact on the population spectra,
while the impairment of other connections leaves the dynamics on the population
level unaltered. The study uncovers connections where mechanisms controlling
the spectra of the cortical microcircuit are most effective.
Universalized Prisoner's Dilemma With Risk
Date: 2015-09-15 00:00:00

In this paper I present a mathematically novel approach to the Prisoner's
Dilemma. I do so by first defining recursively a distinct action type, what I
call 'universalizing', that I add to the original prisoner's dilemma. Such a
modified version of the Prisoner's Dilemma provides a very food productive
model of the choices that would be made in a prisoner's dilemma by agents who
trust each other. As I show, players playing a universalized prisoner's dilemma
get as far out of the dilemma as is mathematically possible. I then add the
concept of risk to the universalized version of prisoner's dilemma. Doing so
provide a model that is sensitive to the trustworthiness of the agents in any
prisoner's dilemma. As I show, with no risk, agents get out of the prisoners
dilemma; and with maximal risk, the succumb to it. succumb to it.
Exploring the isotopic niche: isotopic variance, physiological incorporation, and the temporal dynamics of foraging
Date: 2016-01-25 00:00:00

Consumer foraging behaviors are dynamic, changing in response to prey
availability, seasonality, competition, and even the consumer's physiological
state. The isotopic composition of a consumer is a product of these factors as
well as the isotopic 'landscape' of its prey, i.e. the isotopic mixing space.
Here we build a mechanistic framework that links the ecological and
physiological processes of an individual consumer to the isotopic distribution
that describes its diet, and ultimately to the isotopic composition of its own
tissues, defined as its 'isotopic niche'. By coupling these processes, we
systematically investigate under what conditions the isotopic niche of a
consumer changes as a function of both the geometric properties of its mixing
space and foraging strategies that may be static or dynamic over time. Results
of our derivations reveal general insight into the conditions impacting
isotopic niche width as a function of consumer specialization on prey, as well
as the consumer's ability to transition between diets over time. We show
analytically that moderate specialization on isotopically unique prey can serve
to maximize a consumer's isotopic niche width, while temporally dynamic diets
will tend to result in peak isotopic variance during dietary transitions. We
demonstrate the relevance of our theoretical findings by examining a marine
system composed of nine invertebrate species commonly consumed by sea otters.
In general, our analytical framework highlights the complex interplay of mixing
space geometry and consumer dietary behavior in driving expansion and
contraction of the isotopic niche. Because this approach is established on
ecological mechanism, it is well-suited for enhancing the ecological
interpretation, and uncovering the root causes, of observed isotopic data.
Hyperbolic Growth of the World Population in the Past 12,000 Years
Date: 2016-01-20 00:00:00

Data describing the growth of the world population in the past 12,000 years
are analysed. It is shown that, if unchecked, population does not increase
exponentially but hyperbolically. This analysis reveals three
approximately-determined episodes of hyperbolic growth: 10,000-500 BC, AD
500-1200 and AD 1400-1950, representing a total of about 89% of the past 12,000
years. It also reveals three demographic transitions: 500 BC-AD 500, AD
1200-1400 and AD 1950-present, representing the remaining 11% of the past
12,000 years. The first two transitions were between sustained hyperbolic
trajectories. The current transition is to an unknown trajectory. There was
never any form of dramatic transition from stagnation to growth, described
often as a takeoff, because there was no stagnation in the growth of the world
population. Correct understanding of the historical growth of human population
is essential in the correct interpretation of the historical growth of income
per capita.
SWS promoting MHb-IPN-MRN circuit opposes the theta promoting circuit, active wake and REM sleep
Date: 2016-06-27 00:00:00
Tasks: Hippocampus, Rhythm

Hippocampus was by multisynaptic axonal tracts linked to medial habenula in
my DTI study. These tracts linked hippocampus to septum, and amygdala to BNST.
Septal and BNST axons passed by anteromedial thalamic nucleus to MHb, which
projected to pineal gland. Question is what is the MHb doing and why it
receives information from hippocampus via septum? This study explores the MHb
connectivity, predicts its functional role and how it is linked to memory.
Combination of known findings about the septum and MHb connectivity and
function led to this circuit-based idea that posterior septum activates MHb,
MHb activates IPN,and IPN stimulates MRN and its serotonin release. Proposed
idea is that this MHb-IPN-MRN circuit promotes slow wave sleep (SWS), high
serotonin and low acetylcholine state. My prediction is that this SWS-promoting
circuit reciprocally suppresses the theta oscillations promoting circuit,
linked to high acetylcholine levels in brain, and formed by supramamillary area
projections to the medial septum (MS) that induces theta rhythm in hippocampus
and other theta-coupled regions. The MHb-IPN-MRN pathway likely inhibits,
possibly reciprocally,some regions that have stimulating input to the
theta-generating SUM and MS, such as the wake promoting nucleus incertus,
posterior and lateral hypothalamus and LDT, but also the REM sleep inducing
neurons in LDT and PnO. Thus, proposed SWS- promoting circuit attenuates the
output of theta-promoting regions, both the active wake-on and REM-on regions.
The theta rhythm in wake state is linked to recording and binding information
with their spatio-temporal and relational context by hippocampus, while the SWS
supports rest and replay of hippocampaly stored information and their cortical
reactivations, e. g. in retrosplenial cortex linked to autobiographic memory or
in prefrontal cortex that can combine information from any source.
Channel Capacity of Coding System on Tsallis Entropy and q-Statistics
Date: 2017-12-12 00:00:00

The field of information science has greatly developed, and applications in
various fields have emerged. In this paper, we evaluated the coding system in
the theory of Tsallis entropy for transmission of messages and aimed to
formulate the channel capacity by maximization of the Tsallis entropy within a
given condition of code length. As a result, we obtained a simple relational
expression between code length and code appearance probability and,
additionally, a generalized formula of the channel capacity on the basis of
Tsallis entropy statistics. This theoretical framework may contribute to data
processing techniques and other applications.
3D RNA and functional interactions from evolutionary couplings
Date: 2016-04-21 00:00:00
Tasks: Prediction, Protein Structure Prediction

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences
far outpaces research on their structure and functional interactions. We mine
the evolutionary sequence record to derive precise information about function
and structure of RNAs and RNA-protein complexes. As in protein structure
prediction, we use maximum entropy global probability models of sequence
co-variation to infer evolutionarily constrained nucleotide-nucleotide
interactions within RNA molecules, and nucleotide-amino acid interactions in
RNA-protein complexes. The predicted contacts allow all-atom blinded 3D
structure prediction at good accuracy for several known RNA structures and
RNA-protein complexes. For unknown structures, we predict contacts in 160
non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings
help identify important functional interactions, e.g., at switch points in
riboswitches and at a complex nucleation site in HIV. Aided by accelerating
sequence accumulation, evolutionary coupling analysis can accelerate the
discovery of functional interactions and 3D structures involving RNA.
Real-time shape approximation and 5-D fingerprinting of single proteins
Date: 2015-08-29 00:00:00

This work exploits the zeptoliter sensing volume of electrolyte-filled
nanopores to determine, simultaneously and in real time, the approximate shape,
volume, charge, rotational diffusion coefficient, and dipole moment of
individual proteins. We have developed the theory for a quantitative
understanding and analysis of modulations in ionic current that arise from
rotational dynamics of single proteins as they move through the electric field
inside a nanopore. The resulting multi-parametric information raises the
possibility to characterize, identify, and quantify individual proteins and
protein complexes in a mixture. This approach interrogates single proteins in
solution and determines parameters such as the approximate shape and dipole
moment, which are excellent protein descriptors and cannot be obtained
otherwise from single protein molecules in solution. Taken together, this
five-dimensional characterization of biomolecules at the single particle level
has the potential for instantaneous protein identification, quantification, and
possibly sorting with implications for structural biology, proteomics,
biomarker detection, and routine protein analysis.
Repeated transcranial direct current stimulation (tDCS) in nondepressed reduces levels of reported negative affects from daily stressors
Date: 2016-02-10 00:00:00

Negative emotional responses to the daily life stresses have cumulative
effects which, in turn, impose wide-ranging negative constraints on emotional
well being and neurocognitive performance (Kalueff:2007cp, Charles:2013eq,
Nadler:2010hk). Crucial cognitive functions such as memory and problem solving,
as well more short term emotional responses (e.g., anticipation of- and
response to- monetary rewards or losses) are influenced by mood. The negative
impact of these behavioural responses is felt at the individual level, but it
also imposes major economic burden on modern healthcare systems. Although much
research have been undertaken to understand the underlying mechanisms of
depressed mood and design efficient treatment pathways, comparatively little
was done to characterize mood modulations that remain within the boundaries of
a healthy mental functioning. In one placebo-controlled experiments, we applied
daily prefrontal transcranial Direct Current Stimulation (tDCS) at five points
in time, and found reliable improvements on self-reported mood evaluation. We
replicated this finding in an independent double-blinded placebo-controlled
experiment and showed that stimulation over a shorter period of time (3 days)
is sufficient to create detectable mood improvements. Taken together, our data
show that repeated bilateral prefrontal tDCS can reduce psychological distress
in nondepressed individuals.
Information processing occurs via critical avalanches in a model of the primary visual cortex
Date: 2016-11-18 00:00:00

We study a new biologically motivated model for the Macaque monkey primary
visual cortex which presents power-law avalanches after a visual stimulus. The
signal propagates through all the layers of the model via avalanches that
depend on network structure and synaptic parameter. We identify four different
avalanche profiles as a function of the excitatory postsynaptic potential. The
avalanches follow a size-duration scaling relation and present critical
exponents that match experiments. The structure of the network gives rise to a
regime of two characteristic spatial scales, one of which vanishes in the
thermodynamic limit.
Tumor-induced neoneurogenesis and perineural tumor growth: a mathematical approach
Date: 2015-07-13 00:00:00
Tasks: Blocking

Primary tumors infrequently lead to demise of cancer patients; instead,
mortality and a significant degree of morbidity result from the growth of
secondary tumors in distant organs (metastasis). It is well-known that
malignant tumors induce the formation of a lymphatic and a blood vascular
network around themselves. A similar but far less studied process occurs in
relation to the nervous system and is referred to as \emph{neoneurogenesis}; in
fact, recent studies have demonstrated that tumors initiate their own
innervation. However, the relationship between tumor progression and the
nervous system is still poorly understood. This process is most likely
regulated by a multitude of factors in the tumor-nerve microenvironment and it
is therefore important to study the interactions between the nervous system and
tumor cells through mathematical/computational modelling: this may reveal the
most significant factors of the plethora of interacting elements regulating
neoneurogenesis. The present work is a first attempt to model the
neurobiological aspect of cancer development through a (simple) system of
differential equations. The model confirms the experimental observations that a
tumor is able to promote nerve formation/elongation around itself, and that
high levels of nerve growth factor (NGF) and axon guidance molecules (AGMs) are
recorded in the presence of a tumor. Our results also reflect the observation
that high stress levels (represented by higher norepinephrine release by
sympathetic nerves) contribute to tumor development and spread, indicating a
mutually beneficial relationship between tumor cells and neurons. The model
predictions suggest novel therapeutic strategies, aimed at blocking the stress
effects on tumor growth and dissemination.
Universal portfolios in stochastic portfolio theory
Date: 2016-12-12 00:00:00

Consider a family of portfolio strategies with the aim of achieving the
asymptotic growth rate of the best one. The idea behind Cover's universal
portfolio is to build a wealth-weighted average which can be viewed as a
buy-and-hold portfolio of portfolios. When an optimal portfolio exists, the
wealth-weighted average converges to it by concentration of wealth. Working
under a discrete time and pathwise setup, we show under suitable conditions
that the distribution of wealth in the family satisfies a pathwise large
deviation principle as time tends to infinity. Our main result extends Cover's
portfolio to the nonparametric family of functionally generated portfolios in
stochastic portfolio theory and establishes its asymptotic universality.
Kinetic Analysis of Protein Assembly on the Basis of the Center Manifold around the Critical Point
Date: 2015-10-10 00:00:00

Protein assembly plays an important role in the regulation of biological
systems. The cytoskeleton assembly activity is provided by the binding
cofactors GTP (guanidine triphosphate) or ATP(adenosine triphosphate) to
monomeric protein, and is initiated by assembling the monomeric proteins. The
binding GTP or ATP is hydrolyzed to GDP (guanidine diphosphate) or ADP
(adenosine diphosphate) by the monomeric enzymatic activity. This self-limited
assembly is characteristic of the cytoskeleton. To quantitatively evaluate the
assembly kinetics, we propose a nonlinear and non-equilibrium kinetic model,
with the nonlinearity provided by the fluctuation in monomer concentrations
during the diffusion. Numerical simulations suggest that the assembly and
disassembly oscillates in a chaos-like manner. We use a kinetic analysis of the
center manifold around the critical point to show that minimal increases in
ATP/GTP concentrations may lead to some attenuation in the amplification of
these fluctuations. The present model and our application of center manifold
theory illustrate a unique feature of protein assemblies, and our stability
analysis provides an analytical methodology for the biological reaction system.
Prey cannibalism alters the dynamics of Holling-Tanner type predator-prey models
Date: 2015-10-10 00:00:00

Cannibalism, which is the act of killing and at least partial consumption of
conspecifics, is ubiquitous in nature. Mathematical models have considered
cannibalism in the predator primarily, and show that predator cannibalism in
two species ODE models provides a strong stabilizing effect. There is strong
ecological evidence that cannibalism exists among prey as well, yet this
phenomenon has been much less investigated. In the current manuscript, we
investigate both the ODE and spatially explicit forms of a Holling-Tanner
model, with ratio dependent functional response. We show that cannibalism in
the predator provides a stabilizing influence as expected. However, when
cannibalism in the prey is considered, we show that it cannot stabilise the
unstable interior equilibrium in the ODE case, but can destabilise the stable
interior equilibrium. In the spatially explicit case, we show that in certain
parameter regime, prey cannibalism can lead to pattern forming Turing dynamics,
which is an impossibility without it. Lastly we consider a stochastic prey
cannibalism rate, and find that it can alter both spatial patterns, as well as
limit cycle dynamics.
Assessing the Value of Peer-Produced Information for Exploratory Search
Authors: Santos-Neto Elizeu, Figueiredo Flavio, Oliveira Nigini, Andrade Nazareno, Almeida Jussara, Ripeanu Matei
Date: 2015-10-11 00:00:00
Tasks: TAG

Tagging is a popular feature that supports several collaborative tasks,
including search, as tags produced by one user can help others finding relevant
content. However, task performance depends on the existence of 'good' tags. A
first step towards creating incentives for users to produce 'good' tags is the
quantification of their value in the first place. This work fills this gap by
combining qualitative and quantitative research methods. In particular, using
contextual interviews, we first determine aspects that influence users'
perception of tags' value for exploratory search. Next, we formalize some of
the identified aspects and propose an information-theoretical method with
provable properties that quantifies the two most important aspects (according
to the qualitative analysis) that influence the perception of tag value: the
ability of a tag to reduce the search space while retrieving relevant items to
the user. The evaluation on real data shows that our method is accurate: tags
that users consider more important have higher value than tags users have not
expressed interest.
Mining Interesting Trivia for Entities from Wikipedia
Authors: Prakash Abhay
Date: 2015-10-11 00:00:00

Trivia is any fact about an entity, which is interesting due to any of the
following characteristics - unusualness, uniqueness, unexpectedness or
weirdness. Such interesting facts are provided in 'Did You Know?' section at
many places. Although trivia are facts of little importance to be known, but we
have presented their usage in user engagement purpose. Such fun facts generally
spark intrigue and draws user to engage more with the entity, thereby promoting
repeated engagement. The thesis has cited some case studies, which show the
significant impact of using trivia for increasing user engagement or for wide
publicity of the product/service.
  In this thesis, we propose a novel approach for mining entity trivia from
their Wikipedia pages. Given an entity, our system extracts relevant sentences
from its Wikipedia page and produces a list of sentences ranked based on their
interestingness as trivia. At the heart of our system lies an interestingness
ranker which learns the notion of interestingness, through a rich set of
domain-independent linguistic and entity based features. Our ranking model is
trained by leveraging existing user-generated trivia data available on the Web
instead of creating new labeled data for movie domain. For other domains like
sports, celebrities, countries etc. labeled data would have to be created as
described in thesis. We evaluated our system on movies domain and celebrity
domain, and observed that the system performs significantly better than the
defined baselines. A thorough qualitative analysis of the results revealed that
our engineered rich set of features indeed help in surfacing interesting trivia
in the top ranks.
Inferring disease correlation from healthcare data
Authors: Priyadarshini Gargi, Anand Ashish
Date: 2015-10-11 00:00:00
Tasks: Clinical Knowledge

Electronic Health Records maintained in health care settings are a potential
source of substantial clinical knowledge. The massive volume of data,
unstructured nature of records and obligatory requirement of domain
acquaintance together pose a challenge in knowledge extraction from it. The aim
of this study is to overcome this challenge with a methodical analysis,
abstraction and summarization of such data. This is an attempt to explain
clinical observations through bio-medical and genomic data. Discharge summaries
of obesity patients were processed to extract coherent patterns. This was
supported by Machine Learning and Natural Language Processing based
technologies and concept mapping tool along with biomedical, clinical and
genomic knowledge bases. Semantic relations between diseases were extracted and
filtered through Chi square test to remove spurious relations. The remaining
relations were validated against biomedical literature and gene interaction
networks. A collection of binary relations of diseases was derived from the
data. One set implied co-morbidity while the other set contained diseases which
are risk factors of others. Validation against bio-medical literature increased
the prospect of correlation between diseases. Gene interaction network revealed
that the diseases are related and their corresponding genes are in close
proximity. Conclusion: This study focuses on deducing meaningful relations
between diseases from discharge summaries. For analytical purpose, the scope
has been limited to a few common, well-researched diseases. It can be extended
to incorporate relatively unknown, complex diseases and discover new traits to
help in clinical assessments.