pwc-archive/papers-with-abstracts huggingface dataset:
Non-selective evolution of growing populations
Date: 2015-07-28 00:00:00

Non-selective effects, like genetic drift, are an important factor in modern
conceptions of evolution, and have been extensively studied for constant
population sizes. Here, we consider non-selective evolution in the case of
growing populations that are of small size and have varying trait compositions
(e.g. after a population bottleneck). We find that, in these conditions,
populations never fixate to a trait, but tend to a random limit composition,
and that the distribution of compositions 'freezes' to a steady state This
final state is crucially influenced by the initial conditions. We obtain these
findings from a combined theoretical and experimental approach, using multiple
mixed subpopulations of two Pseudomonas putida strains in non-selective growth
conditions as model system. The experimental results for the population
dynamics match the theoretical predictions based on the P\'olya urn model for
all analyzed parameter regimes. In summary, we show that exponential growth
stops genetic drift. This result contrasts with previous theoretical analyses
of non-selective evolution (e.g. genetic drift), which investigated how traits
spread and eventually take over populations (fixate). Moreover, our work
highlights how deeply growth influences non-selective evolution, and how it
plays a key role in maintaining genetic variability. Consequently, it is of
particular importance in life-cycles models of periodically shrinking and
expanding populations.
Flow similarity, stochastic branching, and quarter power scaling in plants
Date: 2015-07-24 00:00:00

The origin of allometric scaling patterns that are multiples of 1/4 has long
fascinated biologists. While not universal, scaling relationships with
exponents that are close to multiples of 1/4 are common and have been described
in all major clades. Foremost among these relationships is the 3/4 scaling of
metabolism with mass which underpins the 1/4 power dependence of biological
rates and times. Several models have been advanced to explain the underlying
mechanistic drivers of such patterns, but questions regarding a disconnect
between model structures and empirical data have limited their widespread
acceptance. Notable among these is a fractal branching model which predicts
power law scaling of both metabolism and physical dimensions. While a power law
is a useful first approximation to many datasets, non-linearity in some large
data compilations suggest the possibility of more complex or alternative
mechanisms. Here, we first show that quarter power scaling can be derived using
only the preservation of volume flow rate and velocity as model constraints.
Applying our model to the specific case of land plants, we show that
incorporating biomechanical principles and allowing different parts of plant
branching networks to be optimized to serve different functions predicts
non-linearity in allometric relationships, and helps explain why interspecific
scaling exponents covary along a fractal continuum. We also demonstrate that
while branching may be a stochastic process, due to the conservation of volume,
data may still be consistent with the expectations for a fractal network when
one examines subtrees within a tree. Data from numerous sources at the level of
plant shoots, stems, petioles, and leaves show strong agreement with our model
predictions. This novel theoretical framework provides an easily testable
alternative to current general models of plant metabolic allometry.
Evaluating the importance of environmental factors on the spatial distribution of livestock settlements in the Monte desert with a Monte Carlo based model: Settlement Dynamics in Drylands (SeDD)
Date: 2015-07-28 00:00:00

In the Monte desert, increasing population density, changing land rights and
infrastructure may encourage livestock activity, with unknown consequences on
ecosystems. Factors that influence livestock settlement distribution may affect
ecosystem degradation. We hypothesize that surface and groundwater availability
influence livestock settlements distribution. We evaluated this hypothesis with
a Monte Carlo based model to simulate Settlement Dynamics in Drylands (SeDD),
which calculates probabilities on a gridded region based on six environmental
factors: groundwater depth, vegetation type, proximity to rivers, paved roads,
old river beds, and existing settlements. A parameter sweep, including millions
of simulations, was run with combinations of parameters related to these
factors. The sets of parameter values that minimized the residuals between
simulations and observations indicated the relative importance of each factors
on settlement distribution. Distances to rivers and old river beds were
critical to explain the current distribution of settlements, while vegetation,
paved roads, and water table depths were not important. Spatial distribution of
simulated vegetation, which included degradation around livestock settlements,
generally agreed with remotely sensed vegetation classes. The model could be a
useful tool to evaluate the effects of land use changes (water provision, river
flows), on settlement distribution and vegetation degradation in arid
environments.
Analytical properties of a three-compartmental dynamical demographic model
Date: 2015-07-29 00:00:00

The three-compartmental demographic model by Korotaeyv-Malkov-Khaltourina,
connecting population size, economic surplus, and educational level, is
considered from the point of view of dynamical systems theory. It is shown that
there exist two integrals of motion, which enable the system to be reduced to
one non-linear ordinary differential equation. The study of its structure
provides analytical criteria for the dominance ranges of the dynamics of
Malthus and Kremer. Additionally, the particular ranges of parameters enable
the derived general ordinary differential equations to be reduced to the models
of Gompertz and Thoularis-Wallace.
Coherent organization in gene regulation: a study on six networks
Date: 2015-07-29 00:00:00

Structural and dynamical fingerprints of evolutionary optimization in
biological networks are still unclear. We here analyze the dynamics of genetic
regulatory networks responsible for the regulation of cell cycle and cell
differentiation in three organisms or cell types each, and show that they obey
a version of Hebb's rule which we term as coherence. More precisely, we find
that simultaneously expressed genes with a common target are less likely to
conflict at the attractors of the regulatory dynamics. We then investigate the
dependence of coherence on structural parameters, such as the mean number of
inputs per node and the activatory/repressory interaction ratio, as well as on
dynamically determined quantities, such as the basin size and the number of
expressed genes.
A Network-Aware Approach for Searching As-You-Type in Social Media (Extended Version)
Authors: Lagrée Paul, Cautis Bogdan, Vahabi Hossein
Date: 2015-07-29 00:00:00
Tasks: Retrieval

We present in this paper a novel approach for as-you-type top-$k$ keyword
search over social media. We adopt a natural "network-aware" interpretation for
information relevance, by which information produced by users who are closer to
the seeker is considered more relevant. In practice, this query model poses new
challenges for effectiveness and efficiency in online search, even when a
complete query is given as input in one keystroke. This is mainly because it
requires a joint exploration of the social space and classic IR indexes such as
inverted lists. We describe a memory-efficient and incremental prefix-based
retrieval algorithm, which also exhibits an anytime behavior, allowing to
output the most likely answer within any chosen running-time limit. We evaluate
it through extensive experiments for several applications and search scenarios,
including searching for posts in micro-blogging (Twitter and Tumblr), as well
as searching for businesses based on reviews in Yelp. They show that our
solution is effective in answering real-time as-you-type searches over social
media.
Improving Reachability and Navigability in Recommender Systems
Authors: Lamprecht Daniel, Strohmaier Markus, Helic Denis
Date: 2015-07-29 00:00:00
Tasks: Recommendation Systems

In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks.
Non-Compositional Term Dependence for Information Retrieval
Authors: Lioma Christina, Simonsen Jakob Grue, Larsen Birger, Hansen Niels Dalum
Date: 2015-07-29 00:00:00
Tasks: Information Retrieval, Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are
too heavily dependent on each other to be treated as a bag of words, and to
adapt the indexing and ranking accordingly. Dependent terms are predominantly
identified using lexical frequency statistics, assuming that (a) if terms
co-occur often enough in some corpus, they are semantically dependent; (b) the
more often they co-occur, the more semantically dependent they are. This
assumption is not always correct: the frequency of co-occurring terms can be
separate from the strength of their semantic dependence. E.g. "red tape" might
be overall less frequent than "tape measure" in some corpus, but this does not
mean that "red"+"tape" are less dependent than "tape"+"measure". This is
especially the case for non-compositional phrases, i.e. phrases whose meaning
cannot be composed from the individual meanings of their terms (such as the
phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction
between the frequency and strength of term dependence in IR, we present a
principled approach for handling term dependence in queries, using both lexical
frequency and semantic evidence. We focus on non-compositional phrases,
extending a recent unsupervised model for their detection [21] to IR. Our
approach, integrated into ranking using Markov Random Fields [31], yields
effectiveness gains over competitive TREC baselines, showing that there is
still room for improvement in the very well-studied area of term dependence in
IR.
Levy model of cancer
Authors: Roberto Herrero, Dario A. Leon, Augusto Gonzalez
Date: 2015-07-29 00:00:00
Tasks: model

A small portion of a tissue defines a microstate in gene expression space. Mutations, epigenetic events or external factors cause microstate displacements which are modeled by combining small independent gene expression variations and large Levy jumps, resulting from the collective variations of a set of genes. The risk of cancer in a tissue is estimated as the microstate probability to transit from the normal to the tumor region in gene expression space. The formula coming from the contribution of large Levy jumps seems to provide a qualitatively correct description of the lifetime risk of cancer, and reveals an interesting connection between the risk and the way the tissue is protected against infections.
Epistasis and the structure of fitness landscapes: are experimental fitness landscapes compatible with Fisher's Geometric model?
Date: 2016-05-17 00:00:00

The fitness landscape defines the relationship between genotypes and fitness
in a given environment, and underlies fundamental quantities such as the
distribution of selection coefficient, or the magnitude and type of epistasis.
A better understanding of variation of landscape structure across species and
environments is thus necessary to understand and predict how populations will
adapt. An increasing number of experiments investigates the properties of
fitness landscapes by identifying mutations, constructing genotypes with
combinations of these mutations, and measuring the fitness of these genotypes.
Yet these empirical landscapes represent a very small sample of the vast space
of all possible genotypes, and this sample is often biased by the protocol used
to identify mutations. Here we develop a rigorous statistical framework based
on Approximate Bayesian Computation to address these concerns, and use this
flexible framework to fit a broad class of phenotypic fitness models (including
Fisher's model) to 26 empirical landscapes representing 9 diverse biological
systems. In spite of uncertainty due to the small size of most published
empirical landscapes, the inferred landscapes have similar structure in similar
biological systems. Surprisingly, goodness of fit tests reveal that this class
of phenotypic models, which has been successful so far in interpreting
experimental data, is a plausible model in only 3 out of 9 biological systems.
More precisely, although Fisher's model was able to explain several statistical
properties of the landscapes - including mean and standard deviation of
selection and epistasis coefficients -, it was often unable to explain the full
structure of fitness landscapes.
Stimulus-Dependent Frequency Modulation of Information Transmission in Neural Systems
Date: 2015-02-13 00:00:00

Neural oscillations are universal phenomena and can be observed at different
levels of neural systems, from single neuron to macroscopic brain. The
frequency of those oscillations are related to the brain functions. However,
little is know about how the oscillating frequency of neural system affects
neural information transmission in them. In this paper, we investigated how the
signal processing in single neuron is modulated by subthreshold membrane
potential oscillation generated by upstream rhythmic neural activities. We
found that the high frequency oscillations facilitate the transferring of
strong signals, whereas slow oscillations the weak signals. Though the capacity
of information convey for weak signal is low in single neuron, it is greatly
enhanced when weak signals are transferred by multiple pathways with different
oscillation phases. We provided a simple phase plane analysis to explain the
mechanism for this stimulus-dependent frequency modulation in the leakage
integrate-and-fire neuron model. Those results provided a basic understanding
of how the brain could modulate its information processing simply through
oscillating frequency.
Energy-efficient population coding constrains network size of a neuronal array system
Date: 2015-07-29 00:00:00

Here, we consider the open issue of how the energy efficiency of neural
information transmission process in a general neuronal array constrains the
network size, and how well this network size ensures the neural information
being transmitted reliably in a noisy environment. By direct mathematical
analysis, we have obtained general solutions proving that there exists an
optimal neuronal number in the network with which the average coding energy
cost (defined as energy consumption divided by mutual information) per neuron
passes through a global minimum for both subthreshold and superthreshold
signals. Varying with increases in background noise intensity, the optimal
neuronal number decreases for subthreshold and increases for suprathreshold
signals. The existence of an optimal neuronal number in an array network
reveals a general rule for population coding stating that the neuronal number
should be large enough to ensure reliable information transmission robust to
the noisy environment but small enough to minimize energy cost.
A risk analysis for a system stabilized by a central agent
Date: 2015-08-17 00:00:00
Tasks: Math

We formulate and analyze a multi-agent model for the evolution of individual
and systemic risk in which the local agents interact with each other through a
central agent who, in turn, is influenced by the mean field of the local
agents. The central agent is stabilized by a bistable potential, the only
stabilizing force in the system. The local agents derive their stability only
from the central agent. In the mean field limit of a large number of local
agents we show that the systemic risk decreases when the strength of the
interaction of the local agents with the central agent increases. This means
that the probability of transition from one of the two stable quasi-equilibria
to the other one decreases. We also show that the systemic risk increases when
the strength of the interaction of the central agent with the mean field of the
local agents increases. Following the financial interpretation of such models
and their behavior given in our previous paper (Garnier, Papanicolaou and Yang,
SIAM J. Fin. Math. 4, 2013, 151-184), we may interpret the results of this
paper in the following way. From the point of view of systemic risk, and while
keeping the perceived risk of the local agents approximately constant, it is
better to strengthen the interaction of the local agents with the central agent
than the other way around.
Modeling Spatial Invasion of Ebola in West Africa
Date: 2016-05-25 00:00:00
Tasks: Epidemiology

The 2014-2015 Ebola Virus Disease (EVD) epidemic in West Africa was the
largest ever recorded, representing a fundamental shift in Ebola epidemiology
with unprecedented spatiotemporal complexity. We developed spatial transmission
models using a gravity-model framework to explain spatiotemporal dynamics of
EVD in West Africa at both the national and district-level scales, and to
compare effectiveness of local interventions (e.g. local quarantine) and
long-range interventions (e.g. border-closures). Incorporating spatial
interactions, the gravity model successfully captures the multiple waves of
epidemic growth observed in Guinea. Model simulations indicate that
local-transmission reductions were most effective in Liberia, while long-range
transmission was dominant in Sierra Leone. The model indicates the presence of
spatial herd protection, wherein intervention in one region has a protective
effect on surrounding regions. The district-level intervention analysis
indicates the presence of intervention-amplifying regions, which provide
above-expected levels of reduction in cases and deaths beyond their borders.
The gravity-modeling approach accurately captured the spatial spread patterns
of EVD at both country and district levels, and helps to identify the most
effective locales for intervention. This model structure and intervention
analysis provides information that can be used by public health policymakers to
assist planning and response efforts for future epidemics.
Measuring temporal turnover in ecological communities
Date: 2016-04-21 00:00:00

Range migrations in response to climate change, invasive species and the
emergence of novel ecosystems highlight the importance of temporal turnover in
community composition as a fundamental part of global change in the
Anthropocene. Temporal turnover is usually quantified using a variety of
metrics initially developed to capture spatial change. However, temporal
turnover is the consequence of unidirectional community dynamics resulting from
processes such as population growth, colonisation and local extinction. Here,
we develop a framework based on community dynamics, and propose a new temporal
turnover measure. A simulation study and an analysis of an estuarine fish
community both clearly demonstrate that our proposed turnover measure offers
additional insights relative to spatial-context-based metrics. Our approach
reveals whether community turnover is due to shifts in community composition or
in community abundance, and identifies the species and/or environmental factors
that are responsible for any change.
An automated workflow for parallel processing of large multiview SPIM recordings
Date: 2015-08-11 00:00:00

Multiview light sheet fluorescence microscopy (LSFM) allows to image
developing organisms in 3D at unprecedented temporal resolution over long
periods of time. The resulting massive amounts of raw image data requires
extensive processing interactively via dedicated graphical user interface (GUI)
applications. The consecutive processing steps can be easily automated and the
individual time points can be processed independently, which lends itself to
trivial parallelization on a high performance cluster (HPC). Here we introduce
an automated workflow for processing large multiview, multi-channel,
multi-illumination time-lapse LSFM data on a single workstation or in parallel
on a HPC. The pipeline relies on snakemake to resolve dependencies among
consecutive processing steps and can be easily adapted to any cluster
environment for processing LSFM data in a fraction of the time required to
collect it.
A modeling approach to study the effect of cell polarization on keratinocyte migration
Date: 2015-07-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The skin forms an efficient barrier against the environment, and rapid
cutaneous wound healing after injury is therefore essential. Healing of the
uppermost layer of the skin, the epidermis, involves collective migration of
keratinocytes, which requires coordinated polarization of the cells. To study
this process, we developed a model that allows analysis of live-cell images of
migrating keratinocytes in culture based on a small number of parameters,
including the radius of the cells, their mass and their polarization. This
computational approach allowed the analysis of cell migration at the front of
the wound and a reliable identification and quantification of the impaired
polarization and migration of keratinocytes from mice lacking fibroblast growth
factors 1 and 2 an established model of impaired healing. Therefore, our
modeling approach is suitable for large-scale analysis of migration phenotypes
of cells with specific genetic defects or upon treatment with different
pharmacological agents.