pwc-archive/papers-with-abstracts huggingface dataset:
PeppyChains: Simplifying the assembly of 3D-printed generic protein models
Date: 2015-02-16 00:00:00

Peppytides is a coarse-grained, accurate, physical model of the polypeptide
chain. I have shared instructions to make your own polypeptide chain and STL
files of Peppytides in MAKE magazine in Jan 2014 issue. However, Peppytides
involves a lot of steps and assembly of units. People were asking me, 'Is there
any easier way to make these models?'. I have been at several workshops,
hands-on sessions, talks and a science events with Peppytides. From the
feedback that I got everywhere, most of the makers, teachers and students want
something that is 3D-Print-&-Go, or at least easier to make, even at the cost
of some features.
  I have designed a new version of the model, named PeppyChains, in which the
backbone chain of the model can be 3d-printed as a single unit. PeppyChains
design eliminates the assembling of parts to form the backbone chain, but with
the cost of losing the ability to use bias-magnets to favor certain phi/psi
angles in the backbone. I have been demonstrating PeppyChains at various talks
and workshops along with Peppytides, and I have received requests for making
the STL file public. On popular demand, here I am sharing this STL and
providing the step-by-step instructions to make PeppyChains. Just add the
hydrogen-bond magnets, paint and color-code the atoms, and you are ready to go!
No drilling. No time consuming assembly.
Empowering cash managers to achieve cost savings by improving predictive accuracy
Date: 2016-05-13 00:00:00
Tasks: Management

Cash management is concerned with optimizing the short-term funding
requirements of a company. To this end, different optimization strategies have
been proposed to minimize costs using daily cash flow forecasts as the main
input to the models. However, the effect of the accuracy of such forecasts on
cash management policies has not been studied. In this article, using two real
data sets from the textile industry, we show that predictive accuracy is highly
correlated with cost savings when using daily forecasts in cash management
models. A new method is proposed to help cash managers estimate if efforts in
improving predictive accuracy are proportionally rewarded by cost savings. Our
results imply the need for an analysis of potential cost savings derived from
improving predictive accuracy. From that, the search for better forecasting
models is in place to improve cash management.
Diversity of emergent dynamics in competitive threshold-linear networks
Authors: Katherine Morrison, Anda Degeratu, Vladimir Itskov, Carina Curto
Date: 2016-05-14 00:00:00
Tasks: Diversity

Threshold-linear networks consist of simple units interacting in the presence of a threshold nonlinearity. Competitive threshold-linear networks have long been known to exhibit multistability, where the activity of the network settles into one of potentially many steady states. In this work, we find conditions that guarantee the absence of steady states, while maintaining bounded activity. These conditions lead us to define a combinatorial family of competitive threshold-linear networks, parametrized by a simple directed graph. By exploring this family, we discover that threshold-linear networks are capable of displaying a surprisingly rich variety of nonlinear dynamics, including limit cycles, quasiperiodic attractors, and chaos. In particular, several types of nonlinear behaviors can co-exist in the same network. Our mathematical results also enable us to engineer networks with multiple dynamic patterns. Taken together, these theoretical and computational findings suggest that threshold-linear networks may be a valuable tool for understanding the relationship between network connectivity and emergent dynamics. The new Matlab package CTLN Basic 2.0 can be used to reproduce the simulations in Figures 1-13. The package is available at https://github.com/nebneuron/CTLN-Basic-2.0
Role of phytochemicals in the chemoprevention of tumors
Date: 2016-05-15 00:00:00

Phytochemicals are plant-derived secondary metabolites, which may exert many
biological activities in humans, including anticancer properties. Although
recent findings appear to support their role in cancer prevention and
treatment, this issue is still controversial. Anti-cancer activity of
phytochemicals mainly depends on their multi-target mechanism of action,
including antimutagenic, antioxidant and antiproliferative activities.
Furthermore, they may modulate the host immune response to cancer, reducing
inflammatory microenvironment and enhancing lymphocyte onco-surveillance. Since
carcinogenesis is multi-factorial and involves several signaling pathways,
multi-targeted agents as phytochemicals may represent promising anticancer
compounds. This narrative review aims to analyze the current literature on
phytochemicals highlighting their specific targets on carcinogenic molecular
pathways and their chemopreventive role. A full comprehension of their activity
at molecular and cellular levels will contribute for a better understanding of
phytochemical clinical efficacy, thus promoting the identification of new
effective plant-derived therapeutics.
On the Optimal Dividend Problem in the Dual Model with Surplus-Dependent Premiums
Date: 2016-05-15 00:00:00

This paper concerns the dual risk model, dual to the risk model for insurance
applications, where premiums are surplus-dependent. In such a model premiums
are regarded as costs, while claims refer to profits. We calculate the mean of
the cumulative discounted dividends paid until ruin, if the barrier strategy is
applied. We formulate associated Hamilton-Jacobi-Bellman equation and identify
sufficient conditions for a barrier strategy to be optimal. Some numerical
examples are provided when profits have exponential law.
Learning zero-cost portfolio selection with pattern matching
Date: 2016-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

We consider and extend the adversarial agent-based learning approach of
Gy{\"o}rfi {\it et al} to the situation of zero-cost portfolio selection
implemented with a quadratic approximation derived from the mutual fund
separation theorems. The algorithm is applied to daily sampled sequential
Open-High-Low-Close data and sequential intraday 5-minute bar-data from the
Johannesburg Stock Exchange (JSE). Statistical tests of the algorithms are
considered. The algorithms are directly compared to standard NYSE test cases
from prior literature. The learning algorithm is used to select parameters for
agents (or experts) generated by pattern matching past dynamics using a simple
nearest-neighbour search algorithm. It is shown that there is a speed advantage
associated with using an analytic solution of the mutual fund separation
theorems. It is argued that the expected loss in performance does not undermine
the potential application to intraday quantitative trading and that when
transactions costs and slippage are considered the strategies can still remain
profitable when unleveraged. The paper demonstrates that patterns in financial
time-series on the JSE can be systematically exploited in collective but that
this does not imply predictability of the individual asset time-series
themselves.
An end-to-end assembly of the Aedes aegypti genome
Date: 2016-06-13 00:00:00

We present an end-to-end genome assembly of a female Aedes aegypti mosquito,
which spreads viral diseases such as yellow fever, dengue, chikungunya, and
Zika to humans. The assembly is based on an earlier genome published in 2007
and improved in 2013. The new assembly has a scaffold N50 of 419Mb, with 96.9%
of the ungapped sequence anchored to chromosomes. We used the new assembly to
examine the conservation of A. aegypti chromosomes. Our results suggest that
synteny is strongly conserved between Ae. aegypti and An. gambiae. Comparison
to D. melanogaster highlights the extent to which the identity of entire
chromosome arms is preserved across dipterans.
A Bayesian approach to parameter identification with an application to Turing systems
Date: 2016-05-16 00:00:00

We present a Bayesian methodology for infinite as well as finite dimensional
parameter identification for partial differential equation models. The Bayesian
framework provides a rigorous mathematical framework for incorporating prior
knowledge on uncertainty in the observations and the parameters themselves,
resulting in an approximation of the full probability distribution for the
parameters, given the data. Although the numerical approximation of the full
probability distribution is computationally expensive, parallelised algorithms
can make many practically relevant problems computationally feasible. The
probability distribution not only provides estimates for the values of the
parameters, but also provides information about the inferability of parameters
and the sensitivity of the model. This information is crucial when a
mathematical model is used to study the outcome of real-world experiments.
Keeping in mind the applicability of our approach to tackle real-world
practical problems with data from experiments, in this initial proof of concept
work, we apply this theoretical and computational framework to parameter
identification for a well studied semilinear reaction-diffusion system with
activator-depleted reaction kinetics, posed on evolving and stationary domains.
MODA: MOdule Differential Analysis for weighted gene co-expression network
Date: 2016-05-16 00:00:00

Gene co-expression network differential analysis is designed to help
biologists understand gene expression patterns under different condition. By
comparing different gene co-expression networks we may find conserved part as
well as condition specific set of genes. Taking the network as a collection as
modules, we use a sample-saving method to construct condition-specific gene
co-expression network, and identify differentially expressed subnetworks as
conserved or condition specific modules which may be associated with biological
processes. We have implemented the method as an R package which establishes a
pipeline from expression profile to biological explanations. The usefulness of
the method is also demonstrated by synthetic data as well as Daphnia magna gene
expression data under different environmental stresses.
Pairwise maximum-entropy models and their Glauber dynamics: bimodality, bistability, non-ergodicity problems, and their elimination via inhibition
Date: 2016-05-16 00:00:00

Pairwise maximum-entropy models have been used in recent neuroscientific
literature to predict the activity of neuronal populations, given only the
time-averaged correlations of the neuron activities. This paper provides
evidence that the pairwise model, applied to experimental recordings, predicts
a bimodal distribution for the population-averaged activity, and for some
population sizes the second mode peaks at high activities, with 90% of the
neuron population active within time-windows of few milliseconds. This
bimodality has several undesirable consequences: 1. The presence of two modes
is unrealistic in view of observed neuronal activity. 2. The prediction of a
high-activity mode is unrealistic on neurobiological grounds. 3. Boltzmann
learning becomes non-ergodic, hence the pairwise model found by this method is
not the maximum entropy distribution; similarly, solving the inverse problem by
common variants of mean-field approximations has the same problem. 4. The
Glauber dynamics associated with the model is either unrealistically bistable,
or does not reflect the distribution of the pairwise model. This bimodality is
first demonstrated for an experimental dataset comprising 159 neuron activities
recorded from the motor cortex of macaque monkey. Using a reduced
maximum-entropy model, evidence is then provided that this bimodality affects
typical neural recordings of population sizes of a couple of hundreds or more
neurons. As a way to eliminate the bimodality and its ensuing problems, a
modified pairwise model is presented, which -- most important -- has an
associated pairwise Glauber dynamics. This model avoids bimodality thanks to a
minimal asymmetric inhibition. It can be interpreted as a
minimum-relative-entropy model with a particular prior, or as a maximum-entropy
model with an additional constraint.
Improved RNA pseudoknots prediction and classification using a new topological invariant
Date: 2016-05-16 00:00:00
Tasks: ARC

We propose a new topological characterization of RNA secondary structures
with pseudoknots based on two topological invariants. Starting from the classic
arc-representation of RNA secondary structures, we consider a model that
couples both I) the topological genus of the graph and II) the number of
crossing arcs of the corresponding primitive graph. We add a term proportional
to these topological invariants to the standard free energy of the RNA
molecule, thus obtaining a novel free energy parametrization which takes into
account the abundance of topologies of RNA pseudoknots observed in RNA
databases.
The topology of card transaction money flows
Date: 2016-02-23 00:00:00

Money flow models are essential tools to understand different economical
phenomena, like saving propensities and wealth distributions. In spite of their
importance, most of them are based on synthetic transaction networks with
simple topologies, e.g. random or scale-free ones, as the characterisation of
real networks is made difficult by the confidentiality and sensitivity of money
transaction data. Here we present an analysis of the topology created by real
credit card transactions from one of the biggest world banks, and show how
different distributions, e.g. number of transactions per card or amount, have
nontrivial characteristics. We further describe a stochastic model to create
transactions data sets, feeding from the obtained distributions, which will
allow researchers to create more realistic money flow models.
Stochastic Effects in a Discretized Kinetic Model of Economic Exchange
Date: 2016-11-25 00:00:00

Linear stochastic models and discretized kinetic theory are two complementary
analytical techniques used for the investigation of complex systems of economic
interactions. The former employ Langevin equations, with an emphasis on stock
trade; the latter is based on systems of ordinary differential equations and is
better suited for the description of binary interactions, taxation and welfare
redistribution. We propose a new framework which establishes a connection
between the two approaches by introducing stochastic effects into the kinetic
model based on Langevin and Fokker-Planck formalisms. Numerical simulations of
the resulting model indicate positive correlations between the Gini index and
the total wealth, that suggests a growing inequality with increasing income.
Further analysis shows a simultaneous decrease in inequality as social mobility
increases in presence of a conserved total wealth, in conformity with economic
data.
Quantum theory of securities price formation in financial markets
Date: 2016-05-18 00:00:00

We develop a theory of securities price formation and dynamics based on
quantum approach and without presuming any similarities with quantum mechanics.
Disorder introduced by trading environment leads to probability distribution of
returns that is not a smooth curve, but a speckle-pattern fluctuating in both
price coordinate and time. This means that any given return can at times
acquire a substantial probability of occurring while remaining low on average
in time. Still, due to local character of order interaction during price
formation the distribution width grows smoothly, has a minimum value at small
time scale and a square root behavior at large time scale. Examples of
calibration to market data, both intraday and daily, are provided.
Abasy Atlas: A comprehensive inventory of systems, global network properties and systems-level elements across bacteria
Date: 2016-05-16 00:00:00

The availability of databases electronically encoding curated regulatory
networks and of high-throughput technologies and methods to discover regulatory
interactions provides an invaluable source of data to understand the principles
underpinning the organization and evolution of these networks responsible for
cellular regulation. Nevertheless, data on these sources never goes beyond the
regulon level despite the fact that regulatory networks are complex
hierarchical-modular structures still challenging our understanding. This
brings the necessity for an inventory of systems across a large range of
organisms, a key step to rendering feasible comparative systems biology
approaches. In this work, we take the first step towards a global understanding
of the regulatory networks organization by making a cartography of the
functional architectures of diverse bacteria. Abasy (Across-bacteria systems)
Atlas provides a comprehensive inventory of annotated functional systems,
global network properties, and systems-level elements (global regulators,
modular genes shaping functional systems, basal machinery genes, and
intermodular genes) predicted by the natural decomposition approach for
reconstructed and meta-curated regulatory networks across a large range of
bacteria, including pathogenically and biotechnologically relevant organisms.
The meta-curation of regulatory datasets provides the most complete and
reliable set of regulatory interactions currently available. Abasy Atlas
contains systems and system-level elements for 50 regulatory networks
comprising 78,649 regulatory interactions covering 42 bacteria in nine taxa,
containing 3,708 regulons and 1,776 systems. All this brings together a large
corpus of data that will surely inspire studies to generate hypothesis
regarding the principles governing the evolution and organization of systems
and the functional architectures controlling them.
Wrong-Way Risk Models: A Comparison of Analytical Exposures
Date: 2016-05-17 00:00:00

In this paper, we compare static and dynamic (reduced form) approaches for
modeling wrong-way risk in the context of CVA. Although all these approaches
potentially suffer from arbitrage problems, they are popular (respectively) in
industry and academia, mainly due to analytical tractability reasons. We
complete the stochastic intensity models with another dynamic approach,
consisting in the straight modeling of the survival (Az\'ema supermartingale)
process using the $\Phi$-martingale. Just like the other approaches, this
method allows for automatic calibration to a given default probability curve.
We derive analytically the positive exposures $V^+_t$ "conditional upon
default" associated to prototypical market price processes of FRA and IRS in
all cases. We further discuss the link between the "default" condition and
change-of-measure techniques. The expectation of $V^+_t$ conditional upon
$\tau=t$ is equal to the unconditional expectation of $V^+_t\zeta_t$. The
process $\zeta$ is explicitly derived in the dynamic approaches: it is proven
to be positive and to have unit expectation. Unfortunately however, it fails to
be a martingale, so that Girsanov machinery cannot be used. Nevertheless, the
expectation of $V^+_t\zeta_t$ can be computed explicitly, leading to analytical
expected positive exposure profiles in the considered examples.
Multistep Model for Predicting Upper-Limb 3D Isometric Force Application from Pre-Movement Electrocorticographic Features
Date: 2016-05-17 00:00:00

Neural correlates of movement planning onset and direction may be present in
human electrocorticography in the signal dynamics of both motor and non-motor
cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov
model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA),
and LASSO regression to extract direction-sensitive planning information and
movement onset in an upper-limb 3D isometric force task in a human subject.
This mode achieves a relatively high true positive force-onset prediction rate
of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in
predicting one of six planned 3D directions of isometric force using
pre-movement signals. We also find direction-distinguishing information up to
400ms before force onset in the pre-movement signals, captured by electrodes
placed over the limb-ipsilateral dorsal premotor regions. This approach can
contribute to more accurate decoding of higher-level movement goals, at earlier
timescales, and inform sensor placement. Our results also contribute to further
understanding of the spatiotemporal features of human motor planning.
Variable Synaptic Strengths Controls the Firing Rate Distribution in Feedforward Neural Networks
Date: 2017-08-11 00:00:00

Heterogeneity of firing rate statistics is known to have severe consequences
on neural coding. Recent experimental recordings in weakly electric fish
indicate that the distribution-width of superficial pyramidal cell firing rates
(trial- and time-averaged) in the electrosensory lateral line lobe (ELL)
depends on the stimulus, and also that network inputs can mediate changes in
the firing rate distribution across the population. We previously developed
theoretical methods to understand how two attributes (synaptic and intrinsic
heterogeneity) interact and alter the firing rate distribution in a population
of integrate-and-fire neurons with random recurrent coupling. Inspired by our
experimental data, we extend these theoretical results to a delayed feedforward
spiking network that qualitatively capture the changes of firing rate
heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous
neural attributes alter firing rate heterogeneity, accounting for the effect
with various sensory stimuli. The model predicts how the strength of the
effective network connectivity is related to intrinsic heterogeneity in such
delayed feedforward networks: the strength of the feedforward input is
positively correlated with excitability (threshold value for spiking) with low
firing rate heterogeneity and is negatively correlated with excitability with
high firing rate heterogeneity. We also show how our theory can be use to
predict effective neural architecture. We demonstrate that neural attributes do
not interact in a simple manner but rather in a complex stimulus-dependent
fashion to control neural heterogeneity and discuss how it can ultimately shape
population codes.
Establishment and stability of the latent HIV-1 DNA reservoir
Date: 2016-05-24 00:00:00

HIV-1 infection currently cannot be cured because the virus persists as
integrated proviral DNA in long-lived cells despite years of suppressive
antiretroviral therapy (ART). To characterize establishment, turnover, and
evolution of viral DNA reservoirs we deep-sequenced the p17gag region of the
HIV-1 genome from samples obtained after 3-18 years of suppressive ART from 10
patients. For each of these patients, whole genome deep-sequencing data of
HIV-1 RNA populations before onset of ART were available from 6-12 longitudinal
plasma samples spanning 5-8 years of untreated infection. This enabled a
detailed analysis of the dynamics and origin of proviral DNA during ART. A
median of 14% (range 0-42%) of the p17gag DNA sequences were overtly defective
due to G-to-A hypermutation. The remaining sequences were remarkably similar to
previously observed RNA sequences and showed no evidence of evolution over many
years of suppressive ART. Most sequences from the DNA reservoirs were very
similar to viruses actively replicating in plasma (RNA sequences) shortly
before start of ART. The results do not support persistent HIV-1 replication as
a mechanism to maintain the HIV-1 reservoir during suppressive therapy. Rather,
the data indicate that viral DNA variants are turning over as long as patients
are untreated and that suppressive ART halts this turnover.