pwc-archive/papers-with-abstracts huggingface dataset:
Computational Analysis of Amoeboid Swimming at Low Reynolds Number
Date: 2015-07-22 00:00:00

Recent experimental work has shown that eukaryotic cells can swim in a fluid
as well as crawl on a substrate. We investigate the swimming behavior of
Dictyostelium discoideum amoebae who swim by initiating traveling protrusions
at the front that propagate rearward. In our model we prescribe the velocity at
the surface of the swimming cell, and use techniques of complex analysis to
develop 2D models that enable us to study the fluid-cell interaction. Shapes
that approximate the protrusions used by Dictyostelium discoideum can be
generated via the Schwarz-Christoffel transformation, and the boundary- value
problem that results for swimmers in the Stokes flow regime is then reduced to
an integral equation on the boundary of the unit disk. We analyze the swimming
characteristics of several varieties of swimming Dictyostelium discoideum
amoebae, and discuss how the slenderness of the cell body and the shapes of the
protrusion effect the swimming of these cells. The results may provide guidance
in designing low Reynolds number swimming models.
A Method for Modeling Growth of Organs and Transplants Based on the General Growth Law: Application to the Liver in Dogs and Humans
Date: 2015-09-10 00:00:00

Understanding biological phenomena requires a systemic approach that
incorporates different mechanisms acting on different spatial and temporal
scales, since in organisms the workings of all components, such as organelles,
cells, and organs interrelate. This inherent interdependency between diverse
biological mechanisms, both on the same and on different scales, provides the
functioning of an organism capable of maintaining homeostasis and physiological
stability through numerous feedback loops. Thus, developing models of organisms
and their constituents should be done within the overall systemic context of
the studied phenomena. We introduce such a method for modeling growth and
regeneration of livers at the organ scale, considering it a part of the overall
multi-scale biochemical and biophysical processes of an organism. Our method is
based on the earlier discovered general growth law, postulating that any
biological growth process comprises a uniquely defined distribution of
nutritional resources between maintenance needs and biomass production. Based
on this law, we introduce a liver growth model that allows to accurately
predicting the growth of liver transplants in dogs and liver grafts in humans.
Using this model, we find quantitative growth characteristics, such as the time
point when the transition period after surgery is over and the liver resumes
normal growth, rates at which hepatocytes are involved in proliferation, etc.
We then use the model to determine and quantify otherwise unobservable
metabolic properties of livers.
Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling
Date: 2015-09-10 00:00:00

We propose a method for finding metabolic parameters of cells, organs and
whole organisms, which is based on the earlier discovered general growth law.
Based on the obtained results and analysis of available biological models, we
propose a general framework for modeling biological phenomena and discuss how
it can be used in Virtual Liver Network project. The foundational idea of the
study is that growth of cells, organs, systems and whole organisms, besides
biomolecular machinery, is influenced by biophysical mechanisms acting at
different scale levels. In particular, the general growth law uniquely defines
distribution of nutritional resources between maintenance needs and biomass
synthesis at each phase of growth and at each scale level. We exemplify the
approach considering metabolic properties of growing human and dog livers and
liver transplants. A procedure for verification of obtained results has been
introduced too. We found that two examined dogs have high metabolic rates
consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per
day, and verified this using the proposed verification procedure. We also
evaluated consumption rate of nutrients in human livers, determining it to be
about 0.088 gram of nutrients per cubic centimeter of liver per day for males,
and about 0.098 for females. This noticeable difference can be explained by
evolutionary development, which required females to have greater liver
processing capacity to support pregnancy. We also found how much nutrients go
to biomass synthesis and maintenance at each phase of liver and liver
transplant growth. Obtained results demonstrate that the proposed approach can
be used for finding metabolic characteristics of cells, organs, and whole
organisms, which can further serve as important inputs for many applications in
biology (protein expression), biotechnology (synthesis of substances), and
medicine.
A Hedged Monte Carlo Approach to Real Option Pricing
Date: 2015-09-11 00:00:00
Tasks: Management

In this work we are concerned with valuing optionalities associated to invest
or to delay investment in a project when the available information provided to
the manager comes from simulated data of cash flows under historical (or
subjective) measure in a possibly incomplete market. Our approach is suitable
also to incorporating subjective views from management or market experts and to
stochastic investment costs. It is based on the Hedged Monte Carlo strategy
proposed by Potters et al (2001) where options are priced simultaneously with
the determination of the corresponding hedging. The approach is particularly
well-suited to the evaluation of commodity related projects whereby the
availability of pricing formulae is very rare, the scenario simulations are
usually available only in the historical measure, and the cash flows can be
highly nonlinear functions of the prices.
Stimuli reduce the dimensionality of cortical activity
Date: 2016-03-16 00:00:00

The activity of ensembles of simultaneously recorded neurons can be
represented as a set of points in the space of firing rates. Even though the
dimension of this space is equal to the ensemble size, neural activity can be
effectively localized on smaller subspaces. The dimensionality of the neural
space is an important determinant of the computational tasks supported by the
neural activity. Here, we investigate the dimensionality of neural ensembles
from the sensory cortex of alert rats during period of ongoing (inter-trial)
and stimulus-evoked activity. We find that dimensionality grows linearly with
ensemble size, and grows significantly faster during ongoing activity compared
to evoked activity. We explain these results using a spiking network model
based on a clustered architecture. The model captures the difference in growth
rate between ongoing and evoked activity and predicts a characteristic scaling
with ensemble size that could be tested in high-density multi-electrode
recordings. Moreover, the model predicts the existence of an upper bound on
dimensionality. This upper bound is inversely proportional to the amount of
pair-wise correlations and, compared to a homogeneous network without clusters,
it is larger by a factor equal to the number of clusters. The empirical
estimation of such bounds depends on the number and duration of trials.
Together, these results provide a framework to analyze neural dimensionality in
alert animals, its behavior under stimulus presentation, and its theoretical
dependence on ensemble size, number of clusters, and pair-wise correlations in
spiking network models.
An Analytical Approach for Insulin-like Growth Factor Receptor 1 and Mammalian Target of Rapamycin Blockades in Ewing Sarcoma
Authors: Romeil Sandhu, Salah-Eddine Lamhamedi-Cherradi, Sarah Tannenbaum, Joseph Ludwig, Allen Tannenbaum
Date: 2015-09-11 00:00:00

We present preliminary results that quantify network robustness and fragility of Ewing sarcoma (ES), a rare pediatric bone cancer that often exhibits de novo or acquired drug resistance. By identifying novel proteins or pathways susceptible to drug targeting, this formalized approach promises to improve preclinical drug development and may lead to better treatment outcomes. Toward that end, our network modeling focused upon the IGF-1R-PI3K-Akt-mTOR pathway, which is of proven importance in ES. The clinical response and proteomic networks of drug-sensitive parental cell lines and their drug-resistant counterparts were assessed using two small molecule inhibitors for IGF-1R (OSI-906 and NVP-ADW-742) and an mTOR inhibitor (mTORi), MK8669, such that protein-to-protein expression networks could be generated for each group. For the first time, mathematical modeling proves that drug resistant ES samples exhibit higher degrees of overall network robustness (e.g., the ability of a system to withstand random perturbations to its network configuration) to that of their untreated or short-term (72-hour) treated samples. This was done by leveraging previous work, which suggests that Ricci curvature, a key geometric feature of a given network, is positively correlated to increased network robustness. More importantly, given that Ricci curvature is a local property of the system, it is capable of resolving pathway fragility. In this note, we offer some encouraging yet limited insights in terms of system-level robustness of ES and lay the foundation for scope of future work in which a complete study will be conducted.
Integrated probability of coronary heart disease subject to the -308 tumor necrosis factor-alpha SNP: a Bayesian meta-analysis
Date: 2015-09-12 00:00:00

We present a meta-analysis of independent studies on the potential
implication in the occurrence of coronary heart disease (CHD) of the
single-nucleotide polymorphism (SNP) at the -308 position of the tumor necrosis
factor alpha (TNF-alpha) gene. We use Bayesian analysis to integrate
independent data sets and to infer statistically robust measurements of
correlation. Bayesian hypothesis testing indicates that there is no preference
for the hypothesis that the -308 TNF-alpha SNP is related to the occurrence of
CHD, in the Caucasian or in the Asian population, over the null hypothesis. As
a measure of correlation, we use the probability of occurrence of CHD
conditional on the presence of the SNP, derived as the posterior probability of
the Bayesian meta-analysis. The conditional probability indicates that CHD is
not more likely to occur when the SNP is present, which suggests that the -308
TNF-alpha SNP is not implicated in the occurrence of CHD.
Production Function of the Mining Sector of Iran
Date: 2015-09-21 00:00:00
Tasks: Econometrics, Time Series, Time Series Analysis

The purpose of this study is to estimate the production function and examine
the structure of production in the mining sector of Iran. Several studies have
already been conducted in estimating production functions of various economic
sectors; however, less attention has been paid to mining sectors. After
examining the stationarity of variables using augmented Dickey-Fuller and
Phillips-Perron tests, this study estimated the production function of the
mining sector of Iran under different scenarios using the co-integration method
and time-series data for 1976-2006. The unconstrained Cobb-Douglas production
function in Tinbergen form provided better results in terms of theoretical
foundations of economics, statistics, and econometrics. These results show that
the structure of the mining sector of Iran is both capital-intensive and
labour-intensive. Based on the findings of this study, the elasticity of
production with respect to capital and labour have been 0.44 and 0.41,
respectively. In addition, the coefficient of time variable, as an indicator of
technological progress in the production process, is statistically significant
representing a positive effect of technological changes on the output of Iran's
mining sector.
Two different modes of oscillation in a gene transcription regulatory network with interlinked positive and negative feedback loops
Date: 2015-09-12 00:00:00

We study the oscillatory behaviour of a gene regulatory network with
interlinked positive and negative feedback loop. Frequency and amplitude are
two important properties of oscillation. Studied network produces two different
modes of oscillation. In one mode (mode 1) frequency remains constant over a
wide range amplitude and in other mode (mode 2) the amplitude of oscillation
remains constant over a wide range of frequency. Our study reproduces both
features of oscillations in a single gene regulatory network and show that the
negative plus positive feedback loops in gene regulatory network offer
additional advantage. We identified the key parameters/variables responsible
for different modes of oscillation. The network is flexible in switching
between different modes by choosing appropriately the required
parameters/variables.
Ecological-economic modelling of interactions between wild and commercial bees and pesticide use
Date: 2015-09-12 00:00:00
Tasks: Management

The decline in extent of wild pollinators in recent years has been partly
associated with changing farm practices and in particular with increasing
pesticide use. In this paper we combine ecological modelling with economic
analysis of a single farm output under the as- sumption that both pollination
and pest control are essential inputs. We show that the drive to increase farm
output can lead to a local decline in the wild bee population. Commercial bees
are often considered an alternative to wild pollinators, but we show that their
intro- duction can lead to further decline and finally local extinction of wild
bees. The transitions between different outcomes are characterised by threshold
behaviour and are potentially difficult to predict and detect in advance. Small
changes in economic parameters (input prices) and ecological parameters (wild
bees carrying capacity and effect of pesticides on bees) can move the
economic-ecological system beyond the extinction threshold. We also show that
increasing the pesticide price or decreasing the commercial bee price might
lead to re-establishment of wild bees following their local extinction. Thus,
we demonstrate the importance of combining ecological modelling with economics
to study the provision of ecosystem services and to inform sustainable
management of ecosystem service providers.
Accelerating Scientific Publication in Biology
Date: 2015-09-12 00:00:00

Scientific publications enable results and ideas to be transmitted throughout
the scientific community. The number and type of journal publications also have
become the primary criteria used in evaluating career advancement. Our analysis
suggests that publication practices have changed considerably in the life
sciences over the past thirty years. More experimental data is now required for
publication, and the average time required for graduate students to publish
their first paper has increased and is approaching the desirable duration of
Ph.D. training. Since publication is generally a requirement for career
progression, schemes to reduce the time of graduate student and postdoctoral
training may be difficult to implement without also considering new mechanisms
for accelerating communication of their work. The increasing time to
publication also delays potential catalytic effects that ensue when many
scientists have access to new information. The time has come for life
scientists, funding agencies, and publishers to discuss how to communicate new
findings in a way that best serves the interests of the public and the
scientific community.
Structural symmetry in evolutionary games
Date: 2015-09-12 00:00:00

In evolutionary game theory, an important measure of a mutant trait
(strategy) is its ability to invade and take over an otherwise-monomorphic
population. Typically, one quantifies the success of a mutant strategy via the
probability that a randomly occurring mutant will fixate in the population.
However, in a structured population, this fixation probability may depend on
where the mutant arises. Moreover, the fixation probability is just one
quantity by which one can measure the success of a mutant; fixation time, for
instance, is another. We define a notion of homogeneity for evolutionary games
that captures what it means for two single-mutant states, i.e. two
configurations of a single mutant in an otherwise-monomorphic population, to be
"evolutionarily equivalent" in the sense that all measures of evolutionary
success are the same for both configurations. Using asymmetric games, we argue
that the term "homogeneous" should apply to the evolutionary process as a whole
rather than to just the population structure. For evolutionary matrix games in
graph-structured populations, we give precise conditions under which the
resulting process is homogeneous. Finally, we show that asymmetric matrix games
can be reduced to symmetric games if the population structure possesses a
sufficient degree of symmetry.
Geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences
Date: 2016-06-16 00:00:00

Questions in computational molecular biology generate various discrete
optimization problems, such as DNA sequence alignment and RNA secondary
structure prediction. However, the optimal solutions are fundamentally
dependent on the parameters used in the objective functions. The goal of a
parametric analysis is to elucidate such dependencies, especially as they
pertain to the accuracy and robustness of the optimal solutions. Techniques
from geometric combinatorics, including polytopes and their normal fans, have
been used previously to give parametric analyses of simple models for DNA
sequence alignment and RNA branching configurations. Here, we present a new
computational framework, and proof-of-principle results, which give the first
complete parametric analysis of the branching portion of the nearest neighbor
thermodynamic model for secondary structure prediction for real RNA sequences.
Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus
Date: 2015-11-25 00:00:00

Inspired by Adam Smith and Friedrich Hayek, many economists have postulated
the existence of invisible forces that drive economic markets. These market
forces interact in complex ways making it difficult to visualize or understand
the interactions in every detail. Here I show how these forces can transcend a
zero-sum game and become a win-win business interaction, thanks to emergent
social synergies triggered by division of labor. Computer simulations with the
model Sociodynamica show here the detailed dynamics underlying this phenomenon
in a simple virtual economy. In these simulations, independent agents act in an
economy exploiting and trading two different goods in a heterogeneous
environment. All and each of the various forces and individuals were tracked
continuously, allowing to unveil a synergistic effect on economic output
produced by the division of labor between agents. Running simulations in a
homogeneous environment, for example, eliminated all benefits of division of
labor. The simulations showed that the synergies unleashed by division of labor
arise if: Economies work in a heterogeneous environment; agents engage in
complementary activities whose optimization processes diverge; agents have
means to synchronize their activities. This insight, although trivial if viewed
a posteriori, improve our understanding of the source and nature of synergies
in real economic markets and might render economic and natural sciences more
consilient.
Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics
Date: 2015-12-23 00:00:00
Tasks: Anatomy, Efficient Exploration, Functional Connectivity

Loss of cortical integration and changes in the dynamics of
electrophysiological brain signals characterize the transition from wakefulness
towards unconsciousness. The common mechanism underlying these observations
remains unknown. In this study we arrive at a basic model, which explains these
empirical observations based on the theory of phase transitions in complex
systems. We studied the link between spatial and temporal correlations of
large-scale brain activity recorded with functional magnetic resonance imaging
during wakefulness, propofol-induced sedation and loss of consciousness, as
well as during the subsequent recovery. We observed that during unconsciousness
activity in frontal and thalamic regions exhibited a reduction of long-range
temporal correlations and a departure of functional connectivity from the
underlying anatomical constraints. These changes in dynamics and
anatomy-function coupling were correlated across participants, suggesting that
temporal complexity and an efficient exploration of anatomical connectivity are
inter-related phenomena. A model of a system exhibiting a phase transition
reproduced our findings, as well as the diminished sensitivity of the cortex to
external perturbations during unconsciousness. This theoretical framework
unifies different empirical observations about brain activity during
unconsciousness and predicts that the principles we identified are universal
and independent of the causes behind loss of awareness.
An Introduction to Business Mathematics
Date: 2015-09-16 00:00:00
Tasks: Management

These lecture notes provide a self-contained introduction to the mathematical
methods required in a Bachelor degree programme in Business, Economics, or
Management. In particular, the topics covered comprise real-valued vector and
matrix algebra, systems of linear algebraic equations, Leontief's stationary
input-output matrix model, linear programming, elementary financial
mathematics, as well as differential and integral calculus of real-valued
functions of one real variable. A special focus is set on applications in
quantitative economical modelling.
Orthologs from maxmer sequence context
Date: 2015-11-22 00:00:00

Context-dependent identification of orthologs customarily relies on conserved
gene order or whole-genome sequence alignment. It is shown here that
short-range context--as short as single maximal matches--also provides an
effective means to identify orthologs within whole genomes. On pristine
(un-repeatmasked) mammalian whole-genome assemblies we perform a genome
"intersection" that in general consumes less than one thirtieth of the
computation time required by commonly used methods for whole-genome alignment,
and we extract "non-embedded maximal matches," maximal matches that are not
embedded into other maximal matches, as potential orthologs. An ortholog
identified via non-embedded maximal matches is analogous to a "positional
ortholog" or a "primary ortholog" as defined in previous literature; such
orthologs constitute homologs derived from the same direct ancestor whose
ancestral positions in the genome are conserved. At the nucleotide level,
non-embedded maximal matches recapitulate most exact matches identified by a
Lastz net alignment. At the gene level, reciprocal best hits of genes
containing non-embedded maximal matches recover one-to-one orthologs annotated
by Ensembl Compara with high selectivity and high sensitivity; these reciprocal
best hits additionally include putatively novel orthologs not found in Ensembl
(e.g. over two thousand for human/chimpanzee). The method is especially
suitable for genome-wide identification of orthologs.