pwc-archive/papers-with-abstracts huggingface dataset:
The use of the multi-cumulant tensor analysis for the algorithmic optimisation of investment portfolios
Date: 2016-08-25 00:00:00

The cumulant analysis plays an important role in non Gaussian distributed
data analysis. The shares' prices returns are good example of such data. The
purpose of this research is to develop the cumulant based algorithm and use it
to determine eigenvectors that represent investment portfolios with low
variability. Such algorithm is based on the Alternating Least Square method and
involves the simultaneous minimisation 2'nd -- 6'th cumulants of the
multidimensional random variable (percentage shares' returns of many
companies). Then the algorithm was tested during the recent crash on the Warsaw
Stock Exchange. To determine incoming crash and provide enter and exit signal
for the investment strategy the Hurst exponent was calculated using the local
DFA. It was shown that introduced algorithm is on average better that benchmark
and other portfolio determination methods, but only within examination window
determined by low values of the Hurst exponent. Remark that the algorithm of is
based on cumulant tensors up to the 6'th order calculated for a
multidimensional random variable, what is the novel idea. It can be expected
that the algorithm would be useful in the financial data analysis on the world
wide scale as well as in the analysis of other types of non Gaussian
distributed data.
Towards protein-protein docking with significant structural changes using CABS-dock
Date: 2016-05-30 00:00:00
Tasks: Drug Discovery, Molecular Docking

The protein-protein interactions (PPIs) are crucial for understanding the
majority of cellular processes. PPIs play important role in gene transcription
regulation, cellular signaling, molecular basis of immune response and more.
Moreover, a disruption of hese mechanisms is frequently postulated as a
possible cause of diseases such as Alzheimer's or cancer. For many of
biologically relevant cases the structure of protein-protein complexes remain
unknown. Therefore computational techniques, including molecular docking, have
become a valuable part of drug discovery pipelines. Unfortunately, none of the
widely used protein-protein docking tools is free from serious limitations.
Typically, in docking simulations the protein flexibility is either completely
neglected or very limited. Additionally, some knowledge of the approximate
location and/or the shape of the active site is also required. Such limitations
arise mostly from the enormous number of degrees of freedom of protein-protein
systems. In this paper, an efficient computational method for protein-protein
docking is proposed and initially tested on a single docking case. The proposed
method is based on a two-step procedure. In the first step, CABS-dock web
server for protein-peptide docking is used to dock a peptide, which is the
appropriate protein fragment responsible for the protein-protein interaction,
to the other protein partner. During peptide docking, no knowledge about the
binding site, nor the peptide structure, is used and the peptide is allowed to
be fully flexible. In the second step, the docked peptide is used in the
structural adjustment of protein complex partners. The proposed method allowed
us to obtain a high accuracy model, therefore it provides a promising framework
for further advances.
Flexible protein-peptide docking using CABS-dock with knowledge about the binding site
Date: 2016-05-30 00:00:00

Despite considerable efforts, structural prediction of protein-peptide
complexes is still a very challenging task, mainly due to two reasons: high
flexibility of the peptides and transient character of their interactions with
proteins. Recently we have developed an automated web server CABS-dock
(http://biocomp.chem.uw.edu.pl/CABSdock), which conducts flexible
protein-peptide docking without any knowledge about the binding site. Our
method allows for full flexibility of the peptide, whereas the flexibility of
the receptor is restricted to near native conformations considering the main
chain, and full flexibility of the side chains. Performance of the CABS-dock
server was thoroughly tested on a benchmark of 171 test cases, both bound and
unbound. Evaluation of the obtained results showed overall good performance of
the method, especially that no information of the binding site was used. From
unsuccessful experiments we learned that the accuracy of docking might be
significantly improved, if only little information of the binding site was
considered. In fact, in real-life applications user typically has access to
some data indicating the location and/or structure of the binding site. In the
current work, we test and demonstrate the performance of the CABS-dock server
with two new features. The first one allows to utilize the knowledge about
receptor residue(s) constituting the binding site, and the second one allows to
enforce the desired secondary structure on the peptide structure. Based on the
given example, we observe significant improvement of the docking accuracy in
comparison to the default CABS-dock mode.
Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity
Date: 2016-05-30 00:00:00
Tasks: Diagnostic

Scale-free networks (SFN) arise from simple growth processes, which can
encourage efficient, centralized and fault tolerant communication (1). Recently
its been shown that stable network hub structure is governed by a phase
transition at exponents (>2.0) causing a dramatic change in network structure
including a loss of global connectivity, an increasing minimum dominating node
set, and a shift towards increasing connectivity growth compared to node
growth. Is this SFN shift identifiable in atypical brain activity? The Pareto
Distribution (P(D)~D^-\b{eta}) on the hub Degree (D) is a signature of
scale-free networks. During resting-state, we assess Degree exponents across a
large range of neurotypical and atypical subjects. We use graph complexity
theory to provide a predictive theory of the brain network structure. Results.
We show that neurotypical resting-state fMRI brain activity possess scale-free
Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as
well as in 60 different individuals (1.8 se .02). We also show that 60
individuals with Autistic Spectrum Disorder, and 60 individuals with
Schizophrenia have significantly higher (>2.0) scale-free exponents (2.4 se
.03, 2.3 se .04), indicating more fractionated and less controllable dynamics
in the brain networks revealed in resting state. Finally we show that the
exponent values vary with phenotypic measures of atypical disease severity
indicating that the global topology of the network itself can provide specific
diagnostic biomarkers for atypical brain activity.
Highly flexible protein-peptide docking using CABS-dock
Date: 2016-12-31 00:00:00
Tasks: Molecular Docking

Protein-peptide molecular docking is a difficult modeling problem. It is even
more challenging when significant conformational changes that may occur during
the binding process need to be predicted. In this chapter, we demonstrate the
capabilities and features of the CABS-dock server for flexible protein-peptide
docking. CABS-dock allows highly efficient modeling of full peptide flexibility
and significant flexibility of a protein receptor. During CABS-dock docking,
the peptide folding and binding process is explicitly simulated and no
information about the peptide binding site or its structure, is used. This
chapter presents a successful CABS-dock use for docking a potentially
therapeutic peptide to a protein target. Moreover, simulation contact maps, a
new CABS-dock feature, are described and applied to the docking test case.
Finally, a tutorial for running CABS-dock from the command line or command line
scripts is provided. The CABS-dock web server is available from
http://biocomp.chem.uw.edu.pl/CABSdock/
A Metric for Evaluating and Comparing Closed-Loop Deep Brain Stimulation Algorithms
Date: 2016-05-19 00:00:00

Objective: Closed-loop deep brain stimulation (DBS) may improve current
clinical DBS treatment for neurological movement disorders, but control
algorithms may perform differently across patients. New metrics are needed for
comparing and evaluating closed-loop algorithm performance that address the
specific needs of closed-loop neuromodulation controllers. Approach: A metric
is proposed for system performance that includes normalized terms that can be
used to compare algorithm performance for a patient. This metric was evaluated
using two closed-loop control algorithms that were tested in patients with
Parkinson's Disease (PD) who experience rest tremor. Main Results: The metric's
resulting balance between tremor treatment and power savings varied on a per
patient and algorithm basis. This was expected given how each trial resulted in
a variable reduction in stimulation power at the cost of additional tremor for
the patient when compared to open-loop stimulation. Significance: The proposed
metric will aid in clinical evaluation of new algorithms and provide a
benchmark for future system designers. This will be important given the growing
potential applications of dynamically adjusted neural stimulation.
ClinicalTrials.gov Identifier: NCT02384421.
Effects of cell cycle noise on excitable gene circuits
Date: 2016-05-30 00:00:00

We assess the impact of cell cycle noise on gene circuit dynamics. For
bistable genetic switches and excitable circuits, we find that transitions
between metastable states most likely occur just after cell division and that
this concentration effect intensifies in the presence of transcriptional delay.
We explain this concentration effect with a 3-states stochastic model. For
genetic oscillators, we quantify the temporal correlations between daughter
cells induced by cell division. Temporal correlations must be captured properly
in order to accurately quantify noise sources within gene networks.
Fast-slow bursters in the unfolding of a high codimension singularity and the ultra-slow transitions of classes
Date: 2016-05-30 00:00:00

Bursting is a phenomenon found in a variety of physical and biological
systems. For example, in neuroscience, bursting is believed to play a key role
in the way information is transferred in the nervous system. In this work, we
propose a model that, appropriately tuned, can display several types of
bursting behaviors. The model contains two subsystems acting at different
timescales. For the fast subsystem we use the planar unfolding of a high
codimension singularity. In its bifurcation diagram, we locate paths that
underly the right sequence of bifurcations necessary for bursting. The slow
subsystem steers the fast one back and forth along these paths leading to
bursting behavior. The model is able to produce almost all the classes of
bursting predicted for systems with a planar fast subsystems. Transitions
between classes can be obtained through an ultra-slow modulation of the model's
parameters. A detailed exploration of the parameter space allows predicting
possible transitions. This provides a single framework to understand the
coexistence of diverse bursting patterns in physical and biological systems or
in models.
Document Retrieval on Repetitive String Collections
Authors: Gagie Travis, Hartikainen Aleksi, Karhu Kalle, Kärkkäinen Juha, Navarro Gonzalo, Puglisi Simon J., Sirén Jouni
Date: 2017-05-18 00:00:00
Tasks: Retrieval

Most of the fastest-growing string collections today are repetitive, that is,
most of the constituent documents are similar to many others. As these
collections keep growing, a key approach to handling them is to exploit their
repetitiveness, which can reduce their space usage by orders of magnitude. We
study the problem of indexing repetitive string collections in order to perform
efficient document retrieval operations on them. Document retrieval problems
are routinely solved by search engines on large natural language collections,
but the techniques are less developed on generic string collections. The case
of repetitive string collections is even less understood, and there are very
few existing solutions. We develop two novel ideas, {\em interleaved LCPs} and
{\em precomputed document lists}, that yield highly compressed indexes solving
the problem of document listing (find all the documents where a string
appears), top-$k$ document retrieval (find the $k$ documents where a string
appears most often), and document counting (count the number of documents where
a string appears). We also show that a classical data structure supporting the
latter query becomes highly compressible on repetitive data. Finally, we show
how the tools we developed can be combined to solve ranked conjunctive and
disjunctive multi-term queries under the simple tf-idf model of relevance. We
thoroughly evaluate the resulting techniques in various real-life
repetitiveness scenarios, and recommend the best choices for each case.
A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting
Date: 2016-05-31 00:00:00
Tasks: Bayesian Inference, parameter estimation, State Space Models

This paper explores and develops alternative statistical representations and
estimation approaches for dynamic mortality models. The framework we adopt is
to reinterpret popular mortality models such as the Lee-Carter class of models
in a general state-space modelling methodology, which allows modelling,
estimation and forecasting of mortality under a unified framework. Furthermore,
we propose an alternative class of model identification constraints which is
more suited to statistical inference in filtering and parameter estimation
settings based on maximization of the marginalized likelihood or in Bayesian
inference. We then develop a novel class of Bayesian state-space models which
incorporate apriori beliefs about the mortality model characteristics as well
as for more flexible and appropriate assumptions relating to heteroscedasticity
that present in observed mortality data. We show that multiple period and
cohort effect can be cast under a state-space structure. To study long term
mortality dynamics, we introduce stochastic volatility to the period effect.
The estimation of the resulting stochastic volatility model of mortality is
performed using a recent class of Monte Carlo procedure specifically designed
for state and parameter estimation in Bayesian state-space models, known as the
class of particle Markov chain Monte Carlo methods. We illustrate the framework
we have developed using Danish male mortality data, and show that incorporating
heteroscedasticity and stochastic volatility markedly improves model fit
despite an increase of model complexity. Forecasting properties of the enhanced
models are examined with long term and short term calibration periods on the
reconstruction of life tables.
Theory on the mechanism of site-specific DNA-protein interactions in the presence of traps
Date: 2016-05-31 00:00:00

The speed of site-specific binding of transcription factor (TFs) proteins
with genomic DNA seems to be strongly retarded by the randomly occurring
sequence traps. Traps are those DNA sequences sharing significant similarity
with the original specific binding sites. It is an intriguing question how the
naturally occurring TFs and their specific binding sites are designed to manage
the retarding effects of such randomly occurring traps. We develop a simple
random walk model on the site-specific binding of TFs with genomic DNA in the
presence of sequence traps. Our dynamical model predicts that (a) the retarding
effects of traps will be minimum when the traps are arranged around the
specific binding site such that there is a negative correlation between the
binding strength of TFs with traps and the distance of traps from the specific
binding site and (b) the retarding effects of sequence traps can be appeased by
the condensed conformational state of DNA. Our computational analysis results
on the distribution of sequence traps around the putative binding sites of
various TFs in mouse and human genome clearly agree well the theoretical
predictions. We propose that the distribution of traps can be used as an
additional metric to efficiently identify the specific binding sites of TFs on
genomic DNA.
Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks
Date: 2016-05-31 00:00:00

Most disorders are caused by a combination of multiple genetic and/or
environmental factors. If two diseases are caused by the same molecular
mechanism, they tend to co-occur in patients. Here we provide a quantitative
method to disentangle how much genetic or environmental risk factors contribute
to the pathogenesis of 358 individual diseases, respectively. We pool data on
genetic, pathway-based, and toxicogenomic disease-causing mechanisms with
disease co-occurrence data obtained from almost two million patients. From this
data we construct a multilayer network where nodes represent disorders that are
connected by links that either represent phenotypic comorbidity of the patients
or the involvement of a certain molecular mechanism. From the similarity of
phenotypic and mechanism-based networks for each disorder we derive measure
that allows us to quantify the relative importance of various molecular
mechanisms for a given disease. We find that most diseases are dominated by
genetic risk factors, while environmental influences prevail for disorders such
as depressions, cancers, or dermatitis. Almost never we find that more than one
type of mechanisms is involved in the pathogenesis of diseases.
A signal of competitive dominance in mid-latitude herbaceous plant communities
Authors: Jose A. Capitan, Sara Cuenda, Alejandro Ordoñez, David Alonso
Date: 2016-05-31 00:00:00
Tasks: Clustering

Understanding the main determinants of species coexistence across space and time is a central question in ecology. However, ecologists still know little about the scales and conditions at which biotic interactions matter and how these interact with the environment to structure species assemblages. Here we use recent theory developments to analyze plant distribution and trait data across Europe and find that plant height clustering is related to both evapotranspiration and gross primary productivity. This clustering is a signal of interspecies competition between plants, which is most evident in mid-latitude ecoregions, where conditions for growth (reflected in actual evapotranspiration rates and gross primary productivities) are optimal. Away from this optimum, climate severity likely overrides the effect of competition, or other interactions become increasingly important. Our approach bridges the gap between species-rich competition theories and large-scale species distribution data analysis.
Mutations as Levy flights
Authors: Dario Leon, Augusto Gonzalez
Date: 2016-05-31 00:00:00

Data from a long time evolution experiment with Escherichia Coli and from a large study on copy number variations in subjects with european ancestry are analyzed in order to argue that mutations can be described as Levy flights in the mutation space. These Levy flights have at least two components: random single-base substitutions and large DNA rearrangements. From the data, we get estimations for the time rates of both events and the size distribution function of large rearrangements.
Prediction of allosteric sites and mediating interactions through bond-to-bond propensities
Date: 2016-05-31 00:00:00
Tasks: quantile regression

Allosteric regulation is central to many biochemical processes. Allosteric
sites provide a target to fine-tune protein activity, yet we lack computational
methods to predict them. Here, we present an efficient graph-theoretical
approach for identifying allosteric sites and the mediating interactions that
connect them to the active site. Using an atomistic graph with edges weighted
by covalent and non-covalent bond energies, we obtain a bond-to-bond propensity
that quantifies the effect of instantaneous bond fluctuations propagating
through the protein. We use this propensity to detect the sites and
communication pathways most strongly linked to the active site, assessing their
significance through quantile regression and comparison against a reference set
of 100 generic proteins. We exemplify our method in detail with three
well-studied allosteric proteins: caspase-1, CheY, and h-Ras, correctly
predicting the location of the allosteric site and identifying key allosteric
interactions. Consistent prediction of allosteric sites is then attained in a
further set of 17 proteins known to exhibit allostery. Because our propensity
measure runs in almost linear time, it offers a scalable approach to
high-throughput searches for candidate allosteric sites.
Mechanics and polarity in cell motility
Date: 2016-03-31 00:00:00

The motility of a fish keratocyte on a flat substrate exhibits two distinct
regimes: the non-migrating and the migrating one. In both configurations the
shape is fixed in time and, when the cell is moving, the velocity is constant
in magnitude and direction. Transition from a stable configuration to the other
one can be produced by a mechanical or chemotactic perturbation. In order to
point out the mechanical nature of such a bistable behaviour, we focus on the
actin dynamics inside the cell using a minimal mathematical model. While the
protein diffusion, recruitment and segregation govern the polarization process,
we show that the free actin mass balance, driven by diffusion, and the
polymerized actin retrograde flow, regulated by the active stress, are
sufficient ingredients to account for the motile bistability. The length and
velocity of the cell are predicted on the basis of the parameters of the
substrate and of the cell itself. The key physical ingredient of the theory is
the exchange among actin phases at the edges of the cell, that plays a central
role both in kinematics and in dynamics.
Endogenous Formation of Limit Order Books: Dynamics Between Trades
Date: 2017-06-19 00:00:00

In this work, we present a continuous-time large-population game for modeling
market microstructure betweentwo consecutive trades. The proposed modeling
framework is inspired by our previous work [23]. In this framework, the Limit
Order Book (LOB) arises as an outcome of an equilibrium between multiple agents
who have different beliefs about the future demand for the asset. The agents'
beliefs may change according to the information they observe, triggering
changes in their behavior. We present an example illustrating how the proposed
models can be used to quantify the consequences of changes in relevant
information signals. If these signals, themselves, depend on the LOB, then, our
approach allows one to model the "indirect" market impact (as opposed to the
"direct" impact that a market order makes on the LOB, by eliminating certain
limit orders). On the mathematical side, we formulate the proposed modeling
framework as a continuum-player control-stopping game. We manage to split the
equilibrium problem into two parts. The first one is described by a
two-dimensional system of Reflected Backward Stochastic Differential Equations
(RBSDEs), whose solution components reflect against each other. The second one
leads to an infinite-dimensional fixed-point problem for a discontinuous
mapping. Both problems are non-standard, and we prove the existence of their
solutions in the paper.