pwc-archive/papers-with-abstracts huggingface dataset:
A Stochastic Multiscale Model that Explains the Segregation of Axonal Microtubules and Neurofilaments in Neurological Diseases
Date: 2015-04-23 00:00:00

The organization of the axonal cytoskeleton is a key determinant of the
normal function of an axon, which is a long thin projection away from a neuron.
Under normal conditions two axonal cytoskeletal polymers microtubules and
neurofilaments align longitudinally in axons and are interspersed in axonal
cross-sections. However, in many neurotoxic and neurodegenerative disorders,
microtubules and neurofilaments segregate apart from each other, with
microtubules and membranous organelles clustered centrally and neurofilaments
displaced to the periphery. This striking segregation precedes abnormal and
excessive neurofilament accumulation in these diseases, which in turn leads to
focal axonal swellings. While neurofilament accumulation suggests the
impairment of neurofilament transport along axons, the underlying mechanism of
their segregation from microtubules remains poorly understood for over 30
years. To address this question, we developed a stochastic multiscale model for
the cross-sectional distribution of microtubules and neurofilaments in axons.
The model describes microtubules, neurofilaments and organelles as interacting
particles in a 2D cross-section, and incorporates the stochastic interactions
these particles through molecular motors. Simulations of the model demonstrate
that organelles can pull nearby microtubules together, and in the absence of
neurofilament transport, this mechanism gradually segregates microtubules from
neurofilaments on a time scale of hours, similar to that observed in toxic
neuropathies. This suggests that the microtubule-neurofilament segregation is
simply a consequence of the selective impairment of neurofilament transport.
The model generates the experimentally testable prediction that the rate and
extent of segregation will be dependent on the sizes of the moving organelles
as well as the density of their traffic.
Improved Core Genes Prediction for Constructing well-supported Phylogenetic Trees in large sets of Plant Species
Date: 2015-04-23 00:00:00

The way to infer well-supported phylogenetic trees that precisely reflect the
evolutionary process is a challenging task that completely depends on the way
the related core genes have been found. In previous computational biology
studies, many similarity based algorithms, mainly dependent on calculating
sequence alignment matrices, have been proposed to find them. In these kinds of
approaches, a significantly high similarity score between two coding sequences
extracted from a given annotation tool means that one has the same genes. In a
previous work article, we presented a quality test approach (QTA) that improves
the core genes quality by combining two annotation tools (namely NCBI, a
partially human-curated database, and DOGMA, an efficient annotation algorithm
for chloroplasts). This method takes the advantages from both sequence
similarity and gene features to guarantee that the core genome contains correct
and well-clustered coding sequences (\emph{i.e.}, genes). We then show in this
article how useful are such well-defined core genes for biomolecular
phylogenetic reconstructions, by investigating various subsets of core genes at
various family or genus levels, leading to subtrees with strong bootstraps that
are finally merged in a well-supported supertree.
Transitions in the Stock Markets of the US, UK, and Germany
Date: 2015-04-23 00:00:00

In an analysis of the US, the UK, and the German stock market we find a
change in the behavior based on the stock's beta values. Before 2006 risky
trades were concentrated on stocks in the IT and technology sector. Afterwards
risky trading takes place for stocks from the financial sector. We show that an
agent-based model can reproduce these changes. We further show that the initial
impulse for the transition might stem from the increase of high frequency
trading at that time.
Lead-Lag Relationship using a Stop-and-Reverse-MinMax Process
Date: 2015-04-23 00:00:00

The intermarket analysis, in particular the lead-lag relationship, plays an
important role within financial markets. Therefore a mathematical approach to
be able to find interrelations between the price development of two different
financial underlyings is developed in this paper. Computing the differences of
the relative positions of relevant local extrema of two charts, i.e., the local
phase shifts of these underlyings, gives us an empirical distribution on the
unit circle. With the aid of directional statistics such angular distributions
are studied for many pairs of markets. It is shown that there are several very
strongly correlated underlyings in the field of foreign exchange, commodities
and indexes. In some cases one of the two underlyings is significantly ahead
with respect to the relevant local extrema, i.e., there is a phase shift
unequal to zero between these two underlyings.
Predicting genetic interactions from Boolean models of biological networks
Date: 2015-04-23 00:00:00

Genetic interaction can be defined as a deviation of the phenotypic
quantitative effect of a double gene mutation from the effect predicted from
single mutations using a simple (e.g., multiplicative or linear additive)
statistical model. Experimentally characterized genetic interaction networks in
model organisms provide important insights into relationships between different
biological functions. We describe a computational methodology allowing to
systematically and quantitatively characterize a Boolean mathematical model of
a biological network in terms of genetic interactions between all loss of
function and gain of function mutations with respect to all model phenotypes or
outputs. We use the probabilistic framework defined in MaBoSS software, based
on continuous time Markov chains and stochastic simulations. In addition, we
suggest several computational tools for studying the distribution of double
mutants in the space of model phenotype probabilities. We demonstrate this
methodology on three published models for each of which we derive the genetic
interaction networks and analyze their properties. We classify the obtained
interactions according to their class of epistasis, dependence on the chosen
initial conditions and phenotype. The use of this methodology for validating
mathematical models from experimental data and designing new experiments is
discussed.
Entrainment in up and down states of neural populations: non-smooth and stochastic models
Date: 2015-04-23 00:00:00
Tasks: Sensitivity

We study the impact of noise on a neural population rate model of up and down
states. Up and down states are typically observed in neuronal networks as a
slow oscillation, where the population switches between high and low firing
rates (Sanchez-Vivez and McCormick, 2000). A neural population model with spike
rate adaptation is used to model such slow oscillations, and the timescale of
adaptation determines the oscillation period. Furthermore, the period depends
non-monotonically on the background tonic input driving the population, having
long periods for very weak and very strong stimuli. Using both linearization
and fast-slow timescale separation methods, we can compute the phase
sensitivity function of the slow oscillation. We find that the phase response
is most strongly impacted by perturbations to the adaptation variable. Phase
sensitivity functions can then be utilized to quantify the impact of noise on
oscillating populations. Noise alters the period of oscillations by speeding up
the rate of transition between the up and down states. When common noise is
presented to two distinct populations, their transitions will eventually become
entrained to one another through stochastic synchrony.
Testing the performance of technical trading rules in the Chinese market
Date: 2015-04-24 00:00:00

Technical trading rules have a long history of being used by practitioners in
financial markets. Their profitable ability and efficiency of technical trading
rules are yet controversial. In this paper, we test the performance of more
than seven thousands traditional technical trading rules on the Shanghai
Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and
Shanghai Shenzhen 300 Index (SHSZ 300) from April 8, 2005 through June 30, 2013
to check whether an effective trading strategy could be found by using the
performance measurements based on the return and Sharpe ratio. To correct for
the influence of the data-snooping effect, we adopt the Superior Predictive
Ability test to evaluate if there exists a trading rule that can significantly
outperform the benchmark. The result shows that for SSCI, technical trading
rules offer significant profitability, while for SHSZ 300, this ability is
lost. We further partition the SSCI into two sub-series and find that the
efficiency of technical trading in sub-series, which have exactly the same
spanning period as that of SHSZ 300, is severely weakened. By testing the
trading rules on both indexes with a five-year moving window, we find that the
financial bubble from 2005 to 2007 greatly improve the effectiveness of
technical trading rules. This is consistent with the predictive ability of
technical trading rules which appears when the market is less efficient.
Thermodynamics of Error Correction
Date: 2015-12-11 00:00:00

Information processing at the molecular scale is limited by thermal
fluctuations. This can cause undesired consequences in copying information
since thermal noise can lead to errors that can compromise the functionality of
the copy. For example, a high error rate during DNA duplication can lead to
cell death. Given the importance of accurate copying at the molecular scale, it
is fundamental to understand its thermodynamic features. In this paper, we
derive a universal expression for the copy error as a function of entropy
production and {\cred work dissipated by the system during wrong
incorporations}. Its derivation is based on the second law of thermodynamics,
hence its validity is independent of the details of the molecular machinery, be
it any polymerase or artificial copying device. Using this expression, we find
that information can be copied in three different regimes. In two of them, work
is dissipated to either increase or decrease the error. In the third regime,
the protocol extracts work while correcting errors, reminiscent of a Maxwell
demon. As a case study, we apply our framework to study a copy protocol
assisted by kinetic proofreading, and show that it can operate in any of these
three regimes. We finally show that, for any effective proofreading scheme,
error reduction is limited by the chemical driving of the proofreading
reaction.
The dichotomy structure of Y chromosome Haplogroup N
Date: 2015-04-24 00:00:00

Haplogroup N-M231 of human Y chromosome is a common clade from Eastern Asia
to Northern Europe, being one of the most frequent haplogroups in Altaic and
Uralic-speaking populations. Using newly discovered bi-allelic markers from
high-throughput DNA sequencing, we largely improved the phylogeny of Haplogroup
N, in which 16 subclades could be identified by 33 SNPs. More than 400 males
belonging to Haplogroup N in 34 populations in China were successfully
genotyped, and populations in Northern Asia and Eastern Europe were also
compared together. We found that all the N samples were typed as inside either
clade N1-F1206 (including former N1a-M128, N1b-P43 and N1c-M46 clades), most of
which were found in Altaic, Uralic, Russian and Chinese-speaking populations,
or N2-F2930, common in Tibeto-Burman and Chinese-speaking populations. Our
detailed results suggest that Haplogroup N developed in the region of China
since the final stage of late Paleolithic Era.
FermiKit: assembly-based variant calling for Illumina resequencing data
Date: 2015-04-24 00:00:00

Summary: FermiKit is a variant calling pipeline for Illumina data. It de novo
assembles short reads and then maps the assembly against a reference genome to
call SNPs, short insertions/deletions (INDELs) and structural variations (SVs).
FermiKit takes about one day to assemble 30-fold human whole-genome data on a
modern 16-core server with 85GB RAM at the peak, and calls variants in half an
hour to an accuracy comparable to the current practice. FermiKit assembly is a
reduced representation of raw data while retaining most of the original
information.
  Availability and implementation: https://github.com/lh3/fermikit
  Contact: hengli@broadinstitute.org
Efficient Network Structures with Separable Heterogeneous Connection Costs
Date: 2015-12-11 00:00:00
Tasks: Clustering

We introduce a heterogeneous connection model for network formation to
capture the effect of cost heterogeneity on the structure of efficient
networks. In the proposed model, connection costs are assumed to be separable,
which means the total connection cost for each agent is uniquely proportional
to its degree. For these sets of networks, we provide the analytical solution
for the efficient network and discuss stability impli- cations. We show that
the efficient network exhibits a core-periphery structure, and for a given
density, we find a lower bound for clustering coefficient of the efficient
network.
benchNGS : An approach to benchmark short reads alignment tools
Date: 2015-04-24 00:00:00

In the last decade a number of algorithms and associated software have been
developed to align next generation sequencing (NGS) reads with relevant
reference genomes. The accuracy of these programs may vary significantly,
especially when the NGS reads are quite different from the available reference
genome. We propose a benchmark to assess accuracy of short reads mapping based
on the pre-computed global alignment of related genome sequences.
  In this paper we propose a benchmark to assess accuracy of the short reads
mapping based on the pre-computed global alignment of closely related genome
sequences. We outline the method and also present a short report of an
experiment performed on five popular alignment tools based on the pairwise
alignments of Escherichia coli O6 CFT073 genome with genomes of seven other
bacteria.
The influence of the migration network topology on the stability of a small food web
Date: 2015-04-25 00:00:00

The stability of ecosystems as well as the relation between topology and
dynamics on multilayer networks are important questions that are usually
discussed in separate communities. Here, we combine these two topics by
investigating the influence of the topology of the migration network on the
stability of a four-species foodweb module on six patches. The parameters are
chosen such that the dynamics on an isolated patch have a periodic attractor
with all four species present as well as an attractor where the prey that is
preferred by the top predator dies out. The stability measure used here is
robustness, which is the average proportion of surviving species in the system,
and which shows a complex dependence on the migration rate. We use principal
component analysis to quantify the migration network structure in terms of the
most relevant network measures, and we evaluate correlations between these
measures and characteristics of the robustness curves. Our most important
findings are that higher connectivity of the migration network leads to a
larger maximum robustness, that a broad distribution of connectivities favors
extinction of the preferred prey at intermediate migration rates, and that
migration topologies with a larger betweenness centrality are more prone to
extinction of the preferred prey at the onset of synchronization. Our study
thus demonstrates a strong correlation between dynamical robustness and spatial
topology and can serve as an example for similar studies in other types of
multilayer networks.
Google matrix of the world network of economic activities
Date: 2015-04-25 00:00:00

Using the new data from the OECD-WTO world network of economic activities we
construct the Google matrix $G$ of this directed network and perform its
detailed analysis. The network contains 58 countries and 37 activity sectors
for years 1995 and 2008. The construction of $G$, based on Markov chain
transitions, treats all countries on equal democratic grounds while the
contribution of activity sectors is proportional to their exchange monetary
volume. The Google matrix analysis allows to obtain reliable ranking of
countries and activity sectors and to determine the sensitivity of
CheiRank-PageRank commercial balance of countries in respect to price
variations and labor cost in various countries. We demonstrate that the
developed approach takes into account multiplicity of network links with
economy interactions between countries and activity sectors thus being more
efficient compared to the usual export-import analysis. The spectrum and
eigenstates of $G$ are also analyzed being related to specific activity
communities of countries.
Finding a Mate With No Social Skills
Date: 2015-04-26 00:00:00

Sexual reproductive behavior has a necessary social coordination component as
willing and capable partners must both be in the right place at the right time.
While there are many known social behavioral adaptations to support solutions
to this problem, we explore the possibility and likelihood of solutions that
rely only on non-social mechanisms. We find three kinds of social organization
that help solve this social coordination problem (herding, assortative mating,
and natal philopatry) emerge in populations of simulated agents with no social
mechanisms available to support these organizations. We conclude that the
non-social origins of these social organizations around sexual reproduction may
provide the environment for the development of social solutions to the same and
different problems.
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
Authors: Grycuk Rafal, Gabryel Marcin, Scherer Rafal, Voloshynovskiy Sviatoslav
Date: 2015-04-26 00:00:00
Tasks: Clustering, Management, Retrieval

In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and $K$-means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.
The Social Cost of Carbon with Economic and Climate Risks
Date: 2015-04-28 00:00:00
Tasks: Uncertainty Quantification

There is great uncertainty about future climate conditions and the
appropriate policies for managing interactions between the climate and the
economy. We develop a multidimensional computational model to examine how
uncertainties and risks in the economic and climate systems affect the social
cost of carbon (SCC)---that is, the present value of the marginal damage to
economic output caused by carbon emissions. The SCC is substantially increased
by economic and climate risks at both current and future times. Furthermore,
the SCC is itself a stochastic process with significant variation; for example,
the basic elements of risk incorporated into our model cause the SCC in 2100 to
be, with significant probability, ten times what it would be without those
risks. We have only imprecise information about what parameter values are best
for approximating reality. To deal with this parametric uncertainty we perform
extensive uncertainty quantification and show that these findings are robust
for a wide range of alternative specifications. More generally, this work shows
that large-scale computing can enable economists to examine substantially more
complex and realistic models for the purposes of policy analysis.
WHOSE - A Tool for Whole-Session Analysis in IIR
Authors: Hienert Daniel, van Hoek Wilko, Weber Alina, Kern Dagmar
Date: 2015-04-27 00:00:00
Tasks: Information Retrieval, Retrieval

One of the main challenges in Interactive Information Retrieval (IIR)
evaluation is the development and application of re-usable tools that allow
researchers to analyze search behavior of real users in different environments
and different domains, but with comparable results. Furthermore, IIR recently
focuses more on the analysis of whole sessions, which includes all user
interactions that are carried out within a session but also across several
sessions by the same user. Some frameworks have already been proposed for the
evaluation of controlled experiments in IIR, but yet no framework is available
for interactive evaluation of search behavior from real-world information
retrieval (IR) systems with real users. In this paper we present a framework
for whole-session evaluation that can also utilize these uncontrolled data
sets. The logging component can easily be integrated into real-world IR systems
for generating and analyzing new log data. Furthermore, due to a supplementary
mapping it is also possible to analyze existing log data. For every IR system
different actions and filters can be defined. This allows system operators and
researchers to use the framework for the analysis of user search behavior in
their IR systems and to compare it with others. Using a graphical user
interface they have the possibility to interactively explore the data set from
a broad overview down to individual sessions.
Beyond two-stage models for lung carcinogenesis in the Mayak workers: Implications for Plutonium risk
Date: 2015-04-27 00:00:00

Mechanistic multi-stage models are used to analyze lung-cancer mortality
after Plutonium exposure in the Mayak-workers cohort, with follow-up until
2008. Besides the established two-stage model with clonal expansion, models
with three mutation stages as well as a model with two distinct pathways to
cancer are studied. The results suggest that three-stage models offer an
improved description of the data. The best-fitting models point to a mechanism
where radiation increases the rate of clonal expansion. This is interpreted in
terms of changes in cell-cycle control mediated by bystander signaling or
repopulation following cell killing. No statistical evidence for a two-pathway
model is found. To elucidate the implications of the different models for
radiation risk, several exposure scenarios are studied. Models with a radiation
effect at an early stage show a delayed response and a pronounced drop-off with
older ages at exposure. Moreover, the dose-response relationship is strongly
nonlinear for all three-stage models, revealing a marked increase above a
critical dose.
A test of multiple correlation temporal window characteristic of non-Markov processes
Date: 2015-04-27 00:00:00

We introduce a sensitive test of memory effects in successive events. The
test consists of a combination K of binary correlations at successive times. K
decays monotonically from K = 1 for uncorrelated events as a Markov process;
whereas memory effects provide a temporal window with K > 1. For a monotonic
memory fading, K < 1 always. Here we report evidence of a K > 1 temporal window
in cognitive tasks consisting of the visual identification of the front face of
the Necker cube after a previous presentation of the same. The K > 1 behaviour
is maximal at an inter-measurement time {\tau} around 2 sec with inter-subject
differences. The K > 1 persists over a time window of 1 sec around {\tau};
outside this window the K < 1 behaviour is recovered. The universal occurrence
of a K > 1 window in pairs of successive perceptions suggests that, at variance
with single visual stimuli eliciting a suitable response, a pair of stimuli
shortly separated in time displays mutual correlations.