pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of the True Evolutionary Distance under the Fragile Breakage Model
Date: 2017-05-25 00:00:00

The ability to estimate the evolutionary distance between extant genomes
plays a crucial role in many phylogenomic studies. Often such estimation is
based on the parsimony assumption, implying that the distance between two
genomes can be estimated as the rearrangement distance equal the minimal number
of genome rearrangements required to transform one genome into the other.
However, in reality the parsimony assumption may not always hold, emphasizing
the need for estimation that does not rely on the rearrangement distance. The
distance that accounts for the actual (rather than minimal) number of
rearrangements between two genomes is often referred to as the true
evolutionary distance. While there exists a method for the true evolutionary
distance estimation, it however assumes that genomes can be broken by
rearrangements equally likely at any position in the course of evolution. This
assumption, known as the random breakage model, has recently been refuted in
favor of the more rigorous fragile breakage model postulating that only certain
"fragile" genomic regions are prone to rearrangements.
  We propose a new method for estimating the true evolutionary distance between
two genomes under the fragile breakage model. We evaluate the proposed method
on simulated genomes, which show its high accuracy. We further apply the
proposed method for estimation of evolutionary distances within a set of five
yeast genomes and a set of two fish genomes.
Functional brain modules reconfigure at multiple scales across the human lifespan
Date: 2015-10-27 00:00:00

The human brain is a complex network of interconnected brain regions
organized into functional modules with distinct roles in cognition and
behavior. An important question concerns the persistence and stability of these
modules over the human lifespan. Here we use graph-theoretic analysis to
algorithmically uncover the brain's intrinsic modular organization across
multiple spatial scales ranging from small communities comprised of only a few
brain regions to large communities made up of many regions. We find that at
coarse scales modules become progressively more segregated, while at finer
scales segregation decreases. Module composition also exhibits scale-specific
and age-dependent changes. At coarse scales, the module assignments of regions
normally associated with control, default mode, attention, and visual networks
are highly flexible. At fine scales the most flexible regions are associated
with the default mode network. Finally, we show that, with age, some regions in
the default mode network, specifically retrosplenial cortex, maintain a greater
proportion of functional connections to their own module, while regions
associated with somatomotor and saliency/ventral attention networks distribute
their links more evenly across modules.
Asian option as a fixed-point
Date: 2018-04-24 00:00:00

We characterize the price of an Asian option, a financial contract, as a
fixed-point of a non-linear operator. In recent years, there has been interest
in incorporating changes of regime into the parameters describing the evolution
of the underlying asset price, namely the interest rate and the volatility, to
model sudden exogenous events in the economy. Asian options are particularly
interesting because the payoff depends on the integrated asset price. We study
the case of both floating- and fixed-strike Asian call options with arithmetic
averaging when the asset follows a regime-switching geometric Brownian motion
with coefficients that depend on a Markov chain. The typical approach to
finding the value of a financial option is to solve an associated system of
coupled partial differential equations. Alternatively, we propose an iterative
procedure that converges to the value of this contract with geometric rate
using a classical fixed-point theorem.
"Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets
Date: 2015-10-28 00:00:00

We introduce the Speculative Influence Network (SIN) to decipher the causal
relationships between sectors (and/or firms) during financial bubbles. The SIN
is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of
regime-switching between a normal market phase represented by a geometric
Brownian motion (GBM) and a bubble regime represented by the stochastic
super-exponential Sornette-Andersen (2002) bubble model. The calibration of the
HMM provides the probability at each time for a given security to be in the
bubble regime. Conditional on two assets being qualified in the bubble regime,
we then use the transfer entropy to quantify the influence of the returns of
one asset $i$ onto another asset $j$, from which we introduce the adjacency
matrix of the SIN among securities. We apply our technology to the Chinese
stock market during the period 2005-2008, during which a normal phase was
followed by a spectacular bubble ending in a massive correction. We introduce
the Net Speculative Influence Intensity (NSII) variable as the difference
between the transfer entropies from $i$ to $j$ and from $j$ to $i$, which is
used in a series of rank ordered regressions to predict the maximum loss
(\%{MaxLoss}) endured during the crash. The sectors that influenced other
sectors the most are found to have the largest losses. There is a clear
prediction skill obtained by using the transfer entropy involving industrial
sectors to explain the \%{MaxLoss} of financial institutions but not vice
versa. We also show that the bubble state variable calibrated on the Chinese
market data corresponds well to the regimes when the market exhibits a strong
price acceleration followed by clear change of price regimes. Our results
suggest that SIN may contribute significant skill to the development of general
linkage-based systemic risks measures and early warning metrics.
Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification
Date: 2016-09-14 00:00:00

Motivation: Prediction of phenotypes from high-dimensional data is a crucial
task in precision biology and medicine. Many technologies employ genomic
biomarkers to characterize phenotypes. However, such elements are not
sufficient to explain the underlying biology. To improve this, pathway analysis
techniques have been proposed. Nevertheless, such methods have shown lack of
accuracy in phenotypes classification. Results: Here we propose a novel
methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the
analysis of signaling pathways, which has built on top of the work of Tarca et
al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such
as microRNAs, and their interactions with genes. The method takes as input the
expression values of genes and/or microRNAs and returns a list of pathways
sorted according to their deregulation degree, together with the corresponding
statistical significance (p-values). Our analysis shows that MITHrIL
outperforms its competitors even in the worst case. In addition, our method is
able to correctly classify sets of tumor samples drawn from TCGA. Availability:
MITHrIL is freely available at the following URL:
http://alpha.dmi.unict.it/mithril/
Computer-Suported Risk Identification for the Holistic Management of Risks
Date: 2015-10-28 00:00:00
Tasks: Management

Risk is part of the fabric of every business; surprisingly, there is little
work on establishing best practices for systematic, repeatable risk
identification, arguably the first step of any risk management process. In this
paper, we present a proposal that constitutes a more holistic risk management
approach, a methodology for computer-supported risk identification is proposed
that may lead to more consistent (objective, repeatable) risk analysis.
Cortical composition hierarchy driven by spine proportion economical maximization or wire volume minimization
Date: 2015-10-28 00:00:00

The structure and quantitative composition of the cerebral cortex are
interrelated with its computational capacity. Empirical data analyzed here
indicate a certain hierarchy in local cortical composition. Specifically,
neural wire, i.e., axons and dendrites take each about 1/3 of cortical space,
spines and glia/astrocytes occupy each about $(1/3)^{2}$, and capillaries
around $(1/3)^{4}$. Moreover, data analysis across species reveals that these
fractions are roughly brain size independent, which suggests that they could be
in some sense optimal and thus important for brain function. Is there any
principle that sets them in this invariant way? This study first builds a model
of local circuit in which neural wire, spines, astrocytes, and capillaries are
mutually coupled elements and are treated within a single mathematical
framework. Next, various forms of wire minimization rule (wire length, surface
area, volume, or conduction delays) are analyzed, of which, only minimization
of wire volume provides realistic results that are very close to the empirical
cortical fractions. As an alternative, a new principle called "spine economy
maximization" is proposed and investigated, which is associated with
maximization of spine proportion in the cortex per spine size that yields
equally good but more robust results. Additionally, a combination of wire cost
and spine economy notions is considered as a meta-principle, and it is found
that this proposition gives only marginally better results than either pure
wire volume minimization or pure spine economy maximization, but only if spine
economy component dominates. In sum, these results suggest that the new spine
economy principle may be important for brain evolutionary design in a broader
context.
A general approximation for the dynamics of quantitative traits
Date: 2016-03-14 00:00:00

Selection, mutation and random drift affect the dynamics of allele
frequencies and consequently of quantitative traits. While the macroscopic
dynamics of quantitative traits can be measured, the underlying allele
frequencies are typically unobserved. Can we understand how the macroscopic
observables evolve without following these microscopic processes? The problem
has previously been studied by analogy with statistical mechanics: the allele
frequency distribution at each time is approximated by the stationary form,
which maximizes entropy. We explore the limitations of this method when
mutation is small ($4N\!\mu<1$) so that populations are typically close to
fixation and we extend the theory in this regime to account for changes in
mutation strength. We consider a single diallelic locus under either
directional selection, or with over-dominance, and then generalise to multiple
unlinked biallelic loci with unequal effects. We find that the maximum entropy
approximation is remarkably accurate, even when mutation and selection change
rapidly.
T cell equation as a conceptual model of T cell responses for maximizing the efficacy of cancer immunotherapy
Date: 2017-09-16 00:00:00

Following antigen stimulation, the net outcomes of a T cell response are
shaped by integrated signals from both positive co-stimulatory and negative
regulatory molecules. Recently, the blockade of negative regulatory molecules
(i.e. immune checkpoint signals) demonstrates therapeutic effects in treatment
of human cancer, but only in a fraction of cancer patients. Since this therapy
is aimed to enhance T cell responses to cancers, here we devised a conceptual
model by integrating both positive and negative signals in addition to antigen
stimulation. A digital range of adjustment of each signal is formulated in our
model for prediction of a final T cell response. This model allows us to
evaluate strategies in order to enhance antitumor T cell responses. Our model
provides a rational combination strategy for maximizing the therapeutic effects
of cancer immunotherapy.
Bioinformatic Scaling of Allosteric Interactions in Biomedical Isozymes
Date: 2015-10-19 00:00:00

Allosteric (long-range) interactions can be surprisingly strong in proteins
of biomedical interest. Here we use bioinformatic scaling to connect prior
results on nonsteroidal anti-inflammatory drugs to promising new drugs that
inhibit cancer cell metabolism. Many parallel features are apparent, which
explain how even one amino acid mutation, remote from active sites, can alter
medical results. The enzyme twins involved are cyclooxygenase (aspirin) and
isocitrate dehydrogenase (IDH). The IDH results are accurate to 1% and are
overdetermined by adjusting a single bioinformatic scaling parameter. It
appears that the final stage in optimizing protein functionality may involve
leveling of the hydrophobic cutoffs of the arms of conformational hydrophilic
hinges.
Klout Score: Measuring Influence Across Multiple Social Networks
Authors: Rao Adithya, Spasojevic Nemanja, Li Zhisheng, DSouza Trevor
Date: 2015-10-28 00:00:00

In this work, we present the Klout Score, an influence scoring system that
assigns scores to 750 million users across 9 different social networks on a
daily basis. We propose a hierarchical framework for generating an influence
score for each user, by incorporating information for the user from multiple
networks and communities. Over 3600 features that capture signals of
influential interactions are aggregated across multiple dimensions for each
user. The features are scalably generated by processing over 45 billion
interactions from social networks every day, as well as by incorporating
factors that indicate real world influence. Supervised models trained from
labeled data determine the weights for features, and the final Klout Score is
obtained by hierarchically combining communities and networks. We validate the
correctness of the score by showing that users with higher scores are able to
spread information more effectively in a network. Finally, we use several
comparisons to other ranking systems to show that highly influential and
recognizable users across different domains have high Klout scores.
Gold, currencies and market efficiency
Date: 2015-10-29 00:00:00

Gold and currency markets form a unique pair with specific interactions and
dynamics. We focus on the efficiency ranking of gold markets with respect to
the currency of purchase. By utilizing the Efficiency Index (EI) based on
fractal dimension, approximate entropy and long-term memory on a wide portfolio
of 142 gold price series for different currencies, we construct the efficiency
ranking based on the extended EI methodology we provide. Rather unexpected
results are uncovered as the gold prices in major currencies lay among the
least efficient ones whereas very minor currencies are among the most efficient
ones. We argue that such counterintuitive results can be partly attributed to a
unique period of examination (2011-2014) characteristic by quantitative easing
and rather unorthodox monetary policies together with the investigated illegal
collusion of major foreign exchange market participants, as well as some other
factors discussed in some detail.
Flexible Traversal Through Diverse Brain States Underlies Executive Function in Normative Neurodevelopment
Date: 2015-10-29 00:00:00

Adolescence is marked by rapid development of executive function. Mounting
evidence suggests that executive function in adults may be driven by dynamic
control of neurophysiological processes. Yet, how these dynamics evolve over
adolescence and contribute to cognitive development is unknown. Using a novel
dynamic graph approach in which each moment in time is a node and the
similarity in brain states at two different times is an edge, we identify two
primary brain states reminiscent of intrinsic and task-evoked systems. We
demonstrate that time spent in these two states increases over development, as
does the flexibility with which the brain switches between them. Increasing
time spent in primary states and flexibility among states relates to increased
executive performance over adolescence. Indeed, flexibility is increasingly
advantageous for performance toward early adulthood. These findings demonstrate
that brain state dynamics underlie the development of executive function during
the critical period of adolescence.
Competition among eggs shifts to cooperation along a sperm supply gradient in an external fertilizer
Date: 2015-12-11 00:00:00

Competition among gametes for fertilization imposes strong selection. For
external fertilizers, this selective pressure extends to eggs for which
spawning conditions can range from sperm limitation (competition among eggs) to
sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing
fertilization models ignore dynamics that can alter the functional nature of
gamete interactions. These factors include attraction of sperm to eggs, egg
crowding effects or other nonlinearities in per capita rates of sperm-egg
interaction. Such processes potentially allow egg concentrations to drastically
affect viable fertilization probabilities. I experimentally tested whether such
egg effects occur using the urchin $\textit{Strongylocentrotus purpuratus}$ and
parameterized a newly derived model of fertilization dynamics and existing
models modified to include such interactions. The experiments revealed that at
low sperm concentrations, eggs compete for sperm while at high sperm
concentrations eggs cooperatively reduce abnormal fertilization (a proxy for
polyspermy). I show that these observations are consistent with declines in the
per capita rate at which sperm and eggs interact as eggs increase in density.
The results suggest a fitness trade-off of egg release during spawning: as
sperm range from scarce to superabundant, interactions among eggs transition
from highly competitive to cooperative in terms of viable fertilization
probabilities.
Antisocial pool rewarding does not deter public cooperation
Date: 2015-10-29 00:00:00

Rewarding cooperation is in many ways expected behaviour from social players.
However, strategies that promote antisocial behaviour are also surprisingly
common, not just in human societies, but also among eusocial insects and
bacteria. Examples include sanctioning of individuals who behave prosocially,
or rewarding of freeriders who do not contribute to collective enterprises. We
therefore study the public goods game with antisocial and prosocial pool
rewarding in order to determine the potential negative consequences on the
effectiveness of positive incentives to promote cooperation. Contrary to a
naive expectation, we show that the ability of defectors to distribute rewards
to their like does not deter public cooperation as long as cooperators are able
to do the same. Even in the presence of antisocial rewarding the spatial
selection for cooperation in evolutionary social dilemmas is enhanced. Since
the administration of rewards to either strategy requires a considerable degree
of aggregation, cooperators can enjoy the benefits of their prosocial
contributions as well as the corresponding rewards. Defectors when aggregated,
on the other hand, can enjoy antisocial rewards, but due to their lack of
contributions to the public good they ultimately succumb to their inherent
inability to secure a sustainable future. Strategies that facilitate the
aggregation of akin players, even if they seek to promote antisocial behaviour,
thus always enhance the long-term benefits of cooperation.
AIDE: An Automated Sample-based Approach for Interactive Data Exploration
Authors: Dimitriadou Kyriaki, Papaemmanouil Olga, Diao Yanlei
Date: 2015-10-29 00:00:00
Tasks: Management

In this paper, we argue that database systems be augmented with an automated
data exploration service that methodically steers users through the data in a
meaningful way. Such an automated system is crucial for deriving insights from
complex datasets found in many big data applications such as scientific and
healthcare applications as well as for reducing the human effort of data
exploration. Towards this end, we present AIDE, an Automatic Interactive Data
Exploration framework that assists users in discovering new interesting data
patterns and eliminate expensive ad-hoc exploratory queries.
  AIDE relies on a seamless integration of classification algorithms and data
management optimization techniques that collectively strive to accurately learn
the user interests based on his relevance feedback on strategically collected
samples. We present a number of exploration techniques as well as optimizations
that minimize the number of samples presented to the user while offering
interactive performance. AIDE can deliver highly accurate query predictions for
very common conjunctive queries with small user effort while, given a
reasonable number of samples, it can predict with high accuracy complex
disjunctive queries. It provides interactive performance as it limits the user
wait time per iteration of exploration to less than a few seconds.
Detection of contamination in noninvasive prenatal fetal gender test
Date: 2015-10-30 00:00:00

The risk of false positive results in noninvasive prenatal diagnosis focused
on fetal gender and RhD status determination could be a problem in clinical
routine. This is because these tests are based on detection of presence of DNA
sequences with high population frequency and so there is the risk of sample
contamination during sample collection and processing. In our study the
different fragmentation of fetal and maternal DNA molecules present in maternal
circulation was utilized in identification of contaminated samples.
Amplification of Y-chromosome specific assays different in size was tested on
circulating DNA samples.
  Of the four tested assays two shorter (84 and 177 bp) showed expected qPCR
efficiency and have comparable amplification profiles. The difference in Ct
values between these two assays was found to be statistically significant in
comparison of fetal male and normal male samples (p<0.0001) as well as in
blinded pilot study performed on 10 artificially contaminated and 10
non-contaminated samples (F=34.4, p<0.0001) that were all identified correctly.
  Our results showed that differently sized assays performed well in detection
of external contamination of samples in noninvasive prenatal fetal gender test
and could be of help in clinical laboratories to minimize the risk of false
positive results.
Multiple sequence alignment for short sequences
Date: 2015-11-15 00:00:00
Tasks: Multiple Sequence Alignment

Multiple sequence alignment (MSA) has been one of the most important problems
in bioinformatics for more decades and it is still heavily examined by many
mathematicians and biologists. However, mostly because of the practical
motivation of this problem, the research on this topic is focused on aligning
long sequences. It is understandable, since the sequences that need to be
aligned (usually DNA or protein sequences) are generally quite long (e. g., at
least 30-40 characters). Nevertheless, it is a challenging question that
exactly where MSA starts to become a real hard problem (since it is known that
MSA is NP-complete [2]), and the key to answer this question is to examine
short sequences. If the optimal alignment for short sequences could be
determined in polynomial time, then these results may help to develop faster or
more accurate heuristic algorithms for aligning long sequences. In this work,
it is shown that for length-1 sequences using arbitrary metric, as well as for
length-2 sequences using unit metric, the optimum of the MSA problem can be
achieved by the trivial alignment.