pwc-archive/papers-with-abstracts huggingface dataset:
Salt Effects on the Thermodynamics of a Frameshifting RNA Pseudoknot under Tension
Date: 2016-06-21 00:00:00

Because of the potential link between -1 programmed ribosomal frameshifting
and response of a pseudoknot (PK) RNA to force, a number of single molecule
pulling experiments have been performed on PKs to decipher the mechanism of
programmed ribosomal frameshifting. Motivated in part by these experiments, we
performed simulations using a coarse-grained model of RNA to describe the
response of a PK over a range of mechanical forces ($f$s) and monovalent salt
concentrations ($C$s). The coarse-grained simulations quantitatively reproduce
the multistep thermal melting observed in experiments, thus validating our
model. The free energy changes obtained in simulations are in excellent
agreement with experiments. By varying $f$ and $C$, we calculated the phase
diagram that shows a sequence of structural transitions, populating distinct
intermediate states. As $f$ and $C$ are changed, the stem-loop tertiary
interactions rupture first, followed by unfolding of the $3^{\prime}$-end
hairpin ($\textrm{I}\rightleftharpoons\textrm{F}$). Finally, the
$5^{\prime}$-end hairpin unravels, producing an extended state
($\textrm{E}\rightleftharpoons\textrm{I}$). A theoretical analysis of the phase
boundaries shows that the critical force for rupture scales as $\left(\log
C_{\textrm{m}}\right)^{\alpha}$ with $\alpha=1\,(0.5)$ for
$\textrm{E}\rightleftharpoons\textrm{I}$
($\textrm{I}\rightleftharpoons\textrm{F}$) transition. This relation is used to
obtain the preferential ion-RNA interaction coefficient, which can be
quantitatively measured in single-molecule experiments, as done previously for
DNA hairpins. A by-product of our work is the suggestion that the frameshift
efficiency is likely determined by the stability of the $5^{\prime}$-end
hairpin that the ribosome first encounters during translation.
A hybrid algorithm for coupling PDE and compartment-based dynamics
Date: 2016-04-28 00:00:00

Stochastic simulation methods can be applied successfully to model exact
spatio-temporally resolved reaction-diffusion systems. However, in many cases,
these methods can quickly become extremely computationally intensive with
increasing particle numbers. An alternative description of many of these
systems can be derived in the diffusive limit as a deterministic, continuum
system of partial differential equations. Although the numerical solution of
such partial differential equations is, in general, much more efficient than
the full stochastic simulation, the deterministic continuum description is
generally not valid when copy numbers are low and stochastic effects dominate.
Therefore, to take advantage of the benefits of both of these types of models,
each of which may be appropriate in different parts of a spatial domain, we
have developed an algorithm that can be used to couple these two types of model
together. This hybrid coupling algorithm uses an overlap region between the two
modelling regimes. By coupling fluxes at one end of the interface and using a
concentration-matching condition at the other end, we ensure that mass is
appropriately transferred between PDE- and compartment-based regimes. Our
methodology gives notable reductions in simulation time in comparison with
using a fully stochastic model, whilst maintaining the important stochastic
features of the system and providing detail in appropriate areas of the domain.
We test our hybrid methodology robustly by applying it to several biologically
motivated problems including diffusion and morphogen gradient formation. Our
analysis shows that the resulting error is small, unbiased and does not grow
over time.
Matrix Factorization Method for Decentralized Recommender Systems
Authors: Zheng Wenjie
Date: 2016-04-28 00:00:00
Tasks: Recommendation Systems

Decentralized recommender system does not rely on the central service
provider, and the users can keep the ownership of their ratings. This article
brings the theoretically well-studied matrix factorization method into the
decentralized recommender system, where the formerly prevalent algorithms are
heuristic and hence lack of theoretical guarantee. Our preliminary simulation
results show that this method is promising.
Individual structural connectivity defines propagation networks in partial epilepsy
Date: 2016-04-28 00:00:00
Tasks: Diffusion  MRI

Neural network oscillations are a fundamental mechanism for cognition,
perception and consciousness. Consequently, perturbations of network activity
play an important role in the pathophysiology of brain disorders. When
structural information from non-invasive brain imaging is merged with
mathematical modeling, then generative brain network models constitute
in-silico platforms for the exploration of causal mechanisms of brain function
and clinical hypothesis testing. We here demonstrate along the example of
drug-resistant epilepsy that patient-specific virtual brain models derived from
diffusion MRI have sufficient predictive power to improve diagnosis and surgery
outcome. In partial epilepsy, seizures originate in a local network, the
so-called epileptogenic zone, before recruiting other close or distant brain
regions. We create personalized large-scale brain networks for 15 patients and
simulate the individual seizure propagation patterns. Model validation is
performed against the presurgical stereotactic electroencephalography (SEEG)
data and the standard-of-care clinical evaluation. We demonstrate that the
individual brain models account for the temporal variability in patient seizure
propagation patterns and explain the variability in postsurgical success. Our
results show that individual variations in structural connectivity, when linked
with mathematical dynamic models, have the capacity to explain changes in
spatiotemporal organization of brain dynamics as observed in network-based
brain disorders, thus opening up avenues towards discovery of novel clinical
interventions.
Hilbert Exclusion: Improved Metric Search through Finite Isometric Embeddings
Authors: Connor Richard, Cardillo Franco Alberto, Vadicamo Lucia, Rabitti Fausto
Date: 2016-04-28 00:00:00

Most research into similarity search in metric spaces relies upon the
triangle inequality property. This property allows the space to be arranged
according to relative distances to avoid searching some subspaces. We show that
many common metric spaces, notably including those using Euclidean and
Jensen-Shannon distances, also have a stronger property, sometimes called the
four-point property: in essence, these spaces allow an isometric embedding of
any four points in three-dimensional Euclidean space, as well as any three
points in two-dimensional Euclidean space. In fact, we show that any space
which is isometrically embeddable in Hilbert space has the stronger property.
This property gives stronger geometric guarantees, and one in particular, which
we name the Hilbert Exclusion property, allows any indexing mechanism which
uses hyperplane partitioning to perform better. One outcome of this observation
is that a number of state-of-the-art indexing mechanisms over high dimensional
spaces can be easily extended to give a significant increase in performance;
furthermore, the improvement given is greater in higher dimensions. This
therefore leads to a significant improvement in the cost of metric search in
these spaces.
An Explicit Formula for Likelihood Function for Gaussian Vector Autoregressive Moving-Average Model Conditioned on Initial Observables with Application to Model Calibration
Date: 2016-04-29 00:00:00

We derive an explicit formula for likelihood function for Gaussian VARMA
model conditioned on initial observables where the moving-average (MA)
coefficients are scalar. For fixed MA coefficients the likelihood function is
optimized in the autoregressive variables $\Phi$'s by a closed form formula
generalizing regression calculation of the VAR model with the introduction of
an inner product defined by MA coefficients. We show the assumption of scalar
MA coefficients is not restrictive and this formulation of the VARMA model
shares many nice features of VAR and MA model. The gradient and Hessian could
be computed analytically. The likelihood function is preserved under the root
invertion maps of the MA coefficients. We discuss constraints on the gradient
of the likelihood function with moving average unit roots. With the help of FFT
the likelihood function could be computed in $O((kp+1)^2T +ckT\log(T))$ time.
Numerical calibration is required for the scalar MA variables only. The
approach can be generalized to include additional drifts as well as integrated
components. We discuss a relationship with the Borodin-Okounkov formula and the
case of infinite MA components.
Paradoxical signaling regulates structural plasticity in dendritic spines
Date: 2016-04-29 00:00:00

Transient spine enlargement (3-5 min timescale) is an important event
associated with the structural plasticity of dendritic spines. Many of the
molecular mechanisms associated with transient spine enlargement have been
identified experimentally. Here, we use a systems biology approach to construct
a mathematical model of biochemical signaling and actin-mediated transient
spine expansion in response to calcium-influx due to NMDA receptor activation.
We have identified that a key feature of this signaling network is the
paradoxical signaling loop. Paradoxical components act bifunctionally in
signaling networks and their role is to control both the activation and
inhibition of a desired response function (protein activity or spine volume).
Using ordinary differential equation (ODE)-based modeling, we show that the
dynamics of different regulators of transient spine expansion including CaMKII,
RhoA, and Cdc42 and the spine volume can be described using paradoxical
signaling loops. Our model is able to capture the experimentally observed
dynamics of transient spine volume. Furthermore, we show that actin remodeling
events provide a robustness to spine volume dynamics. We also generate
experimentally testable predictions about the role of different components and
parameters of the network on spine dynamics.
Gompertzian population growth under some deterministic and stochastic jump schedules
Date: 2016-04-29 00:00:00
Tasks: Unity

Many cell populations, exemplified by certain tumors, grow approximately
according to a Gompertzian growth model which has a slower approach to an upper
limit than that of logistic growth. Certain populations of animals and other
organisms have also recently been analyzed with the Gompertz model. This
article addresses the question of how long it takes to reduce the population
from one level to a lower one under a schedule of sudden decrements, each of
which removes a given fraction of the cell mass or population. A deterministic
periodic schedule is first examined and yields exact results for the
eradication or extinction time which is defined as that required to reduce the
number of cells to less than unity. The decrements in cell mass at each hit
could correspond to an approximation to reduction of a tumor by external beam
radiation therapy. The effects of variations in magnitude of successive
decrements, the time interval between them, the initial population size and the
intrinsic growth rate are calculated and results presented graphically.
  With a schedule governed by a Poisson process, the number of organisms or
cells satisfies a stochastic differential equation whose solution sample paths
have downward jumps as random times. The moments of the exit time then satisfy
a system of recurrent differential-difference equations. A simple
transformation results in a simpler system which has been studied both
analytically and numerically in the context of interspike intervals of a model
neuron. Results are presented for the mean eradication time for various
frequencies and magnitudes of hits and for various eventual and initial
population sizes. The standard deviation of the eradication time is also
investigated.
A new structural stochastic volatility model of asset pricing and its stylized facts
Date: 2016-04-29 00:00:00

Building on a prominent agent-based model, we present a new structural
stochastic volatility asset pricing model of fundamentalists vs. chartists
where the prices are determined based on excess demand. Specifically, this
allows for modelling stochastic interactions between agents, based on a herding
process corrected by a price misalignment, and incorporating strong noise
components in the agents' demand. The model's parameters are estimated using
the method of simulated moments, where the moments reflect the basic properties
of the daily returns of a stock market index. In addition, for the first time
we apply a (parametric) bootstrap method in a setting where the switching
between strategies is modelled using a discrete choice approach. As we
demonstrate, the resulting dynamics replicate a rich set of the stylized facts
of the daily financial data including: heavy tails, volatility clustering, long
memory in absolute returns, as well as the absence of autocorrelation in raw
returns, volatility-volume correlations, aggregate Gaussianity, concave price
impact and extreme price events.
How did prebiotic polymers become informational foldamers?
Date: 2016-04-28 00:00:00

A mystery about the origins of life is which molecular structures $-$ and
what spontaneous processes $-$ drove the autocatalytic transition from simple
chemistry to biology? Using the HP lattice model of polymer sequence spaces
leads to the prediction that random sequences of hydrophobic ($H$) and polar
($P$) monomers can collapse into relatively compact structures, exposing
hydrophobic surfaces, acting as primitive versions of today's protein
catalysts, elongating other such HP polymers, as ribosomes would now do. Such
foldamer-catalysts form an autocatalytic set, growing short chains into longer
chains that have particular sequences. The system has capacity for the
multimodality: ability to settle at multiple distinct quasi-stable states
characterized by different groups of dominating polymers. This is a testable
mechanism that we believe is relevant to the early origins of life.
The puzzle that just isn't
Date: 2016-04-29 00:00:00

In his stimulating article on the reasons for two puzzling observations about
the behaviour of interest rates, exchange rates and the rate of inflation,
Charles Engel (2016) puts forward an explanation that rests on the concept of a
non-pecuniary liquidity return on assets. Albeit intriguing the analysis
struggles to account for a number of facts which are familiar to participants
of the foreign exchange and bond markets. Reconciling these facts in
conjunction with a careful dissection of the "puzzle" to begin with, shows that
the forward premium puzzle just does not exist, at least not in its canonical
form.
Universal Indexes for Highly Repetitive Document Collections
Authors: Claude Francisco, Fariña Antonio, Martínez-Prieto Miguel A., Navarro Gonzalo
Date: 2016-05-24 00:00:00

Indexing highly repetitive collections has become a relevant problem with the
emergence of large repositories of versioned documents, among other
applications. These collections may reach huge sizes, but are formed mostly of
documents that are near-copies of others. Traditional techniques for indexing
these collections fail to properly exploit their regularities in order to
reduce space.
  We introduce new techniques for compressing inverted indexes that exploit
this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar
compression of the differential inverted lists, instead of the usual practice
of gap-encoding them. We show that, in this highly repetitive setting, our
compression methods significantly reduce the space obtained with classical
techniques, at the price of moderate slowdowns. Moreover, our best methods are
universal, that is, they do not need to know the versioning structure of the
collection, nor that a clear versioning structure even exists.
  We also introduce compressed self-indexes in the comparison. These are
designed for general strings (not only natural language texts) and represent
the text collection plus the index structure (not an inverted index) in
integrated form. We show that these techniques can compress much further, using
a small fraction of the space required by our new inverted indexes. Yet, they
are orders of magnitude slower.
An ecological resilience perspective on cancer: insights from a toy model
Date: 2016-08-31 00:00:00

In this paper we propose an ecological resilience point of view on cancer.
This view is based on the analysis of a simple ODE model for the interactions
between cancer and normal cells. The model presents two regimes for tumor
growth. In the first, cancer arises due to three reasons: a partial corruption
of the functions that avoid the growth of mutated cells, an aggressive
phenotype of tumor cells and exposure to external carcinogenic factors. In this
case, treatments may be effective if they drive the system to the basin of
attraction of the cancer cure state. In the second regime, cancer arises
because the repair system is intrinsically corrupted. In this case, the
complete cure is not possible since the cancer cure state is no more stable,
but tumor recurrence may be delayed if treatment is prolongued. We review three
indicators of the resilience of a stable equilibrium, related with size and
shape of its basin of attraction: latitude, precariousness and resistance. A
novel method to calculate these indicators is proposed. This method is simpler
and more efficient than those currently used, and may be easily applied to
other population dynamics models. We apply this method to the model and
investigate how these indicators behave with parameters changes. Finally, we
present some simulations to illustrate how the resilience analysis can be
applied to validated models in order to obtain indicators for personalized
cancer treatments.
  Keywords: Tumor growth; Chemotherapy; Basins of Attraction; Regime shifts;
Critical transitions
deBInfer: Bayesian inference for dynamical models of biological systems in R
Date: 2016-10-15 00:00:00
Tasks: Bayesian Inference

1. Understanding the mechanisms underlying biological systems, and
ultimately, predicting their behaviours in a changing environment requires
overcoming the gap between mathematical models and experimental or
observational data. Differential equations (DEs) are commonly used to model the
temporal evolution of biological systems, but statistical methods for comparing
DE models to data and for parameter inference are relatively poorly developed.
This is especially problematic in the context of biological systems where
observations are often noisy and only a small number of time points may be
available. 2. The Bayesian approach offers a coherent framework for parameter
inference that can account for multiple sources of uncertainty, while making
use of prior information. It offers a rigorous methodology for parameter
inference, as well as modelling the link between unobservable model states and
parameters, and observable quantities. 3. We present deBInfer, a package for
the statistical computing environment R, implementing a Bayesian framework for
parameter inference in DEs. deBInfer provides templates for the DE model, the
observation model and data likelihood, and the model parameters and their prior
distributions. A Markov chain Monte Carlo (MCMC) procedure processes these
inputs to estimate the posterior distributions of the parameters and any
derived quantities, including the model trajectories. Further functionality is
provided to facilitate MCMC diagnostics, the visualisation of the posterior
distributions of model parameters and trajectories, and the use of compiled DE
models for improved computational performance. 4. The templating approach makes
deBInfer applicable to a wide range of DE models. We demonstrate its
application to ordinary and delay DE models for population ecology.
A framework for philosophical biology
Date: 2016-04-29 00:00:00
Tasks: Philosophy

Advances in biology have mostly relied on theories that were subsequently
revised, expanded or eventually refuted using experimental and other means.
Theoretical biology used to primarily provide a basis to rationally examine the
frameworks within which biological experiments were carried out and to shed
light on overlooked gaps in understanding. Today, however, theoretical biology
has generally become synonymous with computational and mathematical biology.
This could in part be explained by a relatively recent tendency in which a
"data first", rather than a "theory first", approach is preferred. Moreover,
generating hypotheses has at times become procedural rather than theoretical.
This situation leaves our understanding enmeshed in data, which should be
disentangled from much noise. Given the many unresolved questions in biology
and medicine, it seems apt to revive the role of pure theory in the biological
sciences. This paper makes the case for a "philosophical biology"
(philbiology), distinct from but quite complementary to philosophy of biology
(philobiology), which would entail biological investigation through
philosophical approaches. Philbiology would thus be a reincarnation of
theoretical biology, adopting the true sense of the word "theory" and making
use of a rich tradition of serious philosophical approaches in the natural
sciences. A philbiological investigation, after clearly defining a given
biological problem, would aim to propose a set of empirical questions, along
with a class of possible solutions, about that problem. Importantly, whether or
not the questions can be tested using current experimental paradigms would be
secondary to whether the questions are inherently empirical or not. The final
goal of a philbiological investigation would be to develop a theoretical
framework that can lead observational and/or interventional experimental
studies of the defined problem.
Relationship Queries on Large graphs using Pregel
Authors: Agarwal Puneet, Ramanath Maya, Shroff Gautam
Date: 2016-04-30 00:00:00
Tasks: Graph Mining

Large-scale graph-structured data arising from social networks, databases,
knowledge bases, web graphs, etc. is now available for analysis and mining.
Graph-mining often involves 'relationship queries', which seek a ranked list of
interesting interconnections among a given set of entities, corresponding to
nodes in the graph. While relationship queries have been studied for many
years, using various terminologies, e.g., keyword-search, Steiner-tree in a
graph etc., the solutions proposed in the literature so far have not focused on
scaling relationship queries to large graphs having billions of nodes and
edges, such are now publicly available in the form of 'linked-open-data'. In
this paper, we present an algorithm for distributed keyword search (DKS) on
large graphs, based on the graph-parallel computing paradigm Pregel. We also
present an analytical proof that our algorithm produces an optimally ranked
list of answers if run to completion. Even if terminated early, our algorithm
produces approximate answers along with bounds. We describe an optimized
implementation of our DKS algorithm along with time-complexity analysis.
Finally, we report and analyze experiments using an implementation of DKS on
Giraph the graph-parallel computing framework based on Pregel, and demonstrate
that we can efficiently process relationship queries on large-scale subsets of
linked-open-data.