pwc-archive/papers-with-abstracts huggingface dataset:
Fluctuation analysis in nonstationary conditions: single Ca channel current in cortical pyramidal neurons
Date: 2017-08-29 00:00:00

Fluctuation analysis is a method which allows measurement of the single
channel current of ion channels even when it is too small to be resolved
directly with the patch clamp technique. This is the case for voltage-gated
Ca2+ channels (VGCCs). They are present in all mammalian central neurons,
controlling presynaptic release of transmitter, postsynaptic signaling and
synaptic integration. The amplitudes of their single channel currents in a
physiological concentration of extracellular Ca2+, however, are small and not
well determined. But measurement of this quantity is essential for estimating
numbers of functional VGCCs in the membrane and the size of channel-associated
Ca2+ signaling domains, and for understanding the stochastic nature of Ca2+
signaling. Here, we recorded the VGCC current in nucleated patches from layer 5
pyramidal neurons in rat neocortex, in physiological external Ca2+ (1-2 mM).
The ensemble-averaging of current responses required for conventional
fluctuation analysis proved impractical because of the rapid rundown of VGCC
currents. We therefore developed a more robust method, using mean current
fitting of individual current responses and band-pass filtering. Furthermore,
voltage ramp stimulation proved useful. We validated the accuracy of the method
by analyzing simulated data. At an external Ca2+ concentration of 1 mM, and a
membrane potential of -20 mV, we found that the average single channel current
amplitude was about 0.04 pA, increasing to 0.065 pA at 2 mM external Ca2+, and
0.12 pA at 5 mM. The relaxation time constant of the fluctuations was in the
range 0.2-0.8 ms. The results are relevant to understanding the stochastic
properties of dendritic Ca2+ spikes in neocortical layer 5 pyramidal neurons.
With the reported method, single channel current amplitude of native VGCCs can
be resolved accurately despite conditions of unstable rundown.
Extracellular stimulation of nerve cells with electric current spikes induced by voltage steps
Date: 2016-02-27 00:00:00

A new stimulation paradigm is presented for the stimulation of nerve cells by
extracellular electric currents. In the new paradigm stimulation is achieved
with the current spike induced by a voltage step whenever the voltage step is
applied to a live biological tissue. By experimental evidence and theoretical
arguments, it is shown that this spike is well suited for the stimulation of
nerve cells. Stimulation of the human tongue is used for proof of principle.
Charge injection thresholds are measured for various voltages. The time-profile
of the current spike used in the experiment has a half-width of about 1
microsecond. The decay of the spike is non-exponential. The spike has at least
three distinctly different phases. A Maxwell phase is followed by a
charge-rearrangement phase. Charging of cell membranes is completed in a third
phase. All three phases contribute to depolarization or hyperpolarization of
cell membranes. Due to the short duration of the spike the charge transfer is
very small. The activation time (time of no return) of nerve cell membranes
leading to an action potential is measured and found to be unexpectedly short.
It can become as short as 3 microseconds for a voltage step of 10 V or higher.
A Mathematical Model of Foreign Capital Inflow
Date: 2017-05-20 00:00:00
Tasks: model

The paper models foreign capital inflow from the developed to the developing
countries in a stochastic dynamic programming (SDP) framework. Under some
regularity conditions, the existence of the solutions to the SDP problem is
proved and they are then obtained by numerical technique because of the
non-linearity of the related functions. A number of comparative dynamic
analyses explore the impact of parameters of the model on dynamic paths of
capital inflow, interest rate in the international loan market and the exchange
rate.
Unbiased estimation of risk
Date: 2017-08-24 00:00:00

The estimation of risk measures recently gained a lot of attention, partly
because of the backtesting issues of expected shortfall related to
elicitability. In this work we shed a new and fundamental light on optimal
estimation procedures of risk measures in terms of bias. We show that once the
parameters of a model need to be estimated, one has to take additional care
when estimating risks. The typical plug-in approach, for example, introduces a
bias which leads to a systematic underestimation of risk. In this regard, we
introduce a novel notion of unbiasedness to the estimation of risk which is
motivated by economic principles. In general, the proposed concept does not
coincide with the well-known statistical notion of unbiasedness. We show that
an appropriate bias correction is available for many well-known estimators. In
particular, we consider value-at-risk and expected shortfall (tail
value-at-risk). In the special case of normal distributions, closed-formed
solutions for unbiased estimators can be obtained. We present a number of
motivating examples which show the outperformance of unbiased estimators in
many circumstances. The unbiasedness has a direct impact on backtesting and
therefore adds a further viewpoint to established statistical properties.
Small-time asymptotics for basket options -- the bi-variate SABR model and the hyperbolic heat kernel on $\mathbb{H}^3$
Date: 2016-04-19 00:00:00

We compute a sharp small-time estimate for the price of a basket call under a
bi-variate SABR model with both $\beta$ parameters equal to $1$ and three
correlation parameters, which extends the work of Bayer,Friz&Laurence [BFL14]
for the multivariate Black-Scholes flat vol model. The result follows from the
heat kernel on hyperbolic space for $n=3$ combined with the Bellaiche [Bel81]
heat kernel expansion and Laplace's method, and we give numerical results which
corroborate our asymptotic formulae. Similar to the Black-Scholes case, we find
that there is a phase transition from one "most-likely" path to two most-likely
paths beyond some critical $K^*$.
Testing boundaries of applicability of quantum probabilistic formalism to modeling of cognition
Date: 2016-02-11 00:00:00

Recently the mathematical formalism of quantum mechanics, especially methods
of quantum probability theory, started to be widely used in a variety of
applications outside of physics, e.g., cognition and psychology as well as
economy and finances. To distinguish such models from genuine quantum physical
models, they often called quantum-like (although often people simply speak
about, e.g., "quantum cognition"). These novel applications generate a number
of foundational questions. Nowadays we can speak about a new science -
foundations of quantum-like modeling. At the first stage this science was
mainly about comparison of classical and quantum models, mainly in the
probabilistic setting. It was found that statistical data from cognitive
psychology violate some basic constraints posed on data by classical
probability theory (Kolmogorov, 1933); in particular, the constraints given by
the formula of total probability and Bell's type inequalities. Recently another
question attracted some attention. In spite of real success in applications,
there are no reason to believe that the quantum probability would cover
completely all problems of, e.g., cognition. May be more general probability
models have to be explored. A similar problem attracted a lot of attention in
foundations of quantum physics culminating in a series of experiments to check
Sorkin's equality for the triple-slit experiment by Weihs' group. In this note
we present a similar test in the cognitive experimental setting. Performance of
this test would either give further confirmation of the adequacy of the quantum
probability model to cognitive applications or rejection of the conventional
quantum model. Thus this note opens the door for a series of exciting
experimental tests for the quantum-like model of cognition.
Calibrar: an R package for fitting complex ecological models
Authors: Ricardo Oliveros-Ramos, Yunne-Jai Shin
Date: 2016-03-10 00:00:00
Tasks: parameter estimation

The fitting or parameter estimation of complex ecological models is a challenging optimisation task, with a notable lack of tools for fitting complex, long runtime or stochastic models. calibrar is an R package that is dedicated to the fitting of complex models to data. It is a generic tool that can be used for any type of model, especially those with non-differentiable objective functions and long runtime, including Individual Based Models. calibrar supports multiple phases and constrained optimisation, includes 18 optimisation algorithms, including derivative-based and heuristic ones. It supports any type of parallelization, the restart of interrupted optimisations for long runtime models and the combination of different optimisation methods during the multiple phases of the calibration. User-level expertise in R is necessary to handle calibration experiments with calibrar, but there is no need to modify the model's code, which can be programmed in any language. It implements maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs. For more experienced users, calibrar allows the implementation of user-defined objective functions. The package source code is fully accessible and can be installed directly from CRAN.
Decoded fMRI neurofeedback can induce bidirectional behavioral changes within single participants
Date: 2016-03-10 00:00:00

Studies using real-time functional magnetic resonance imaging (rt-fMRI) have
recently incorporated the decoding approach, allowing for fMRI to be used as a
tool for manipulation of fine-grained neural activity. Because of the
tremendous potential for clinical applications, certain questions regarding
decoded neurofeedback (DecNef) must be addressed. Neurofeedback effects can
last for months, but the short- to mid-term dynamics are not known.
Specifically, can the same subjects learn to induce neural patterns in two
opposite directions in different sessions? This leads to a further question,
whether learning to reverse a neural pattern may be less effective after
training to induce it in a previous session. Here we employed a
within-subjects' design, with subjects undergoing DecNef training sequentially
in opposite directions (up or down regulation of confidence judgements in a
perceptual task), with the order counterbalanced across subjects. Behavioral
results indicated that the manipulation was strongly influenced by the order
and direction of neurofeedback. We therefore applied nonlinear mathematical
modeling to parametrize four main consequences of DecNef: main effect of change
in behavior, strength of down-regulation effect relative to up-regulation,
maintenance of learning over sessions, and anterograde learning interference.
Modeling results revealed that DecNef successfully induced bidirectional
behavioral changes in different sessions. Furthermore, up-regulation was more
sizable, and the effect was largely preserved even after an interval of
one-week. Lastly, the second week effect was diminished as compared to the
first week effect, indicating strong anterograde learning interference. These
results suggest reinforcement learning characteristics of DecNef, and provide
important constraints on its application to basic neuroscience, occupational
and sports trainings, and therapies.
Using numerical plant models and phenotypic correlation space to design achievable ideotypes
Date: 2017-06-19 00:00:00
Tasks: Management

Numerical plant models can predict the outcome of plant traits modifications
resulting from genetic variations, on plant performance, by simulating
physiological processes and their interaction with the environment.
Optimization methods complement those models to design ideotypes, i.e. ideal
values of a set of plant traits resulting in optimal adaptation for given
combinations of environment and management, mainly through the maximization of
a performance criteria (e.g. yield, light interception). As use of simulation
models gains momentum in plant breeding, numerical experiments must be
carefully engineered to provide accurate and attainable results, rooting them
in biological reality. Here, we propose a multi-objective optimization
formulation that includes a metric of performance, returned by the numerical
model, and a metric of feasibility, accounting for correlations between traits
based on field observations. We applied this approach to two contrasting
models: a process-based crop model of sunflower and a functional-structural
plant model of apple trees. In both cases, the method successfully
characterized key plant traits and identified a continuum of optimal solutions,
ranging from the most feasible to the most efficient. The present study thus
provides successful proof of concept for this enhanced modeling approach, which
identified paths for desirable trait modification, including direction and
intensity.
Coupling volume-excluding compartment-based models of diffusion at different scales: Voronoi and pseudo-compartment approaches
Date: 2016-05-24 00:00:00
Tasks: Benchmarking, Blocking

Numerous processes across both the physical and biological sciences are
driven by diffusion. Partial differential equations (PDEs) are a popular tool
for modelling such phenomena deterministically, but it is often necessary to
use stochastic models to accurately capture the behaviour of a system,
especially when the number of diffusing particles is low. The stochastic models
we consider in this paper are `compartment-based': the domain is discretized
into compartments, and particles can jump between these compartments.
Volume-excluding effects (crowding) can be incorporated by blocking movement
with some probability.
  Recent work has established the connection between fine-grained models and
coarse-grained models incorporating volume exclusion, but only for uniform
lattices. In this paper we consider non-uniform, hybrid lattices that
incorporate both fine- and coarse-grained regions, and present two different
approaches to describing the interface of the regions. We test both techniques
in a range of scenarios to establish their accuracy, benchmarking against
fine-grained models, and show that the hybrid models developed in this paper
can be significantly faster to simulate than the fine-grained models in certain
situations, and are at least as fast otherwise.
Mechanical Bounds to Transcriptional Noise
Date: 2016-03-10 00:00:00

Over the last several decades it has been increasingly recognized that
stochastic processes play a central role in transcription. Though many
stochastic effects have been explained, the source of transcriptional bursting
(one of the most well-known sources of stochasticity) has continued to evade
understanding. Recent results have pointed to mechanical feedback as the source
of transcriptional bursting but a reconciliation of this perspective with
preexisting views of transcriptional regulation is lacking. In this letter we
present a simple phenomenological model which is able to incorporate the
traditional view of gene expression within a framework with mechanical limits
to transcription. Our model explains the emergence of universal properties of
gene expression, wherein the lower limit of intrinsic noise necessarily rises
with mean expression level.
Inference of internal stress in a cell monolayer
Date: 2016-03-11 00:00:00

We combine traction force data with Bayesian inversion to obtain an absolute
estimate of the internal stress field of a cell monolayer. The method, Bayesian
inversion stress microscopy (BISM), is validated using numerical simulations
performed in a wide range of conditions. It is robust to changes in each
ingredient of the underlying statistical model. Importantly, its accuracy does
not depend on the rheology of the tissue. We apply BISM to experimental
traction force data measured in a narrow ring of cohesive epithelial cells, and
check that the inferred stress field coincides with that obtained by direct
spatial integration of the traction force data in this quasi-one-dimensional
geometry.
Discrete-Time Quadratic Hedging of Barrier Options in Exponential L\'{e}vy Model
Date: 2016-03-11 00:00:00

We examine optimal quadratic hedging of barrier options in a discretely
sampled exponential L\'{e}vy model that has been realistically calibrated to
reflect the leptokurtic nature of equity returns. Our main finding is that the
impact of hedging errors on prices is several times higher than the impact of
other pricing biases studied in the literature.
Non-systemic transmission of tick-borne diseases: a network approach
Date: 2016-03-11 00:00:00

Tick-Borne diseases can be transmitted via non-systemic (NS) transmission.
This occurs when tick gets the infection by co-feeding with infected ticks on
the same host resulting in a direct pathogen transmission between the vectors,
without infecting the host. This transmission is peculiar, as it does not
require any systemic infection of the host. The NS transmission is the main
efficient transmission for the persistence of the Tick-Borne Encephalitis virus
in nature. By describing the heterogeneous ticks aggregation on hosts through a
\hyphenation{dynamical} bipartite graphs representation, we are able to
mathematically define the NS transmission and to depict the epidemiological
conditions for the pathogen persistence. Despite the fact that the underlying
network is largely fragmented, analytical and computational results show that
the larger is the variability of the aggregation, and the easier is for the
pathogen to persist in the population.
Intervality and coherence in complex networks
Date: 2016-03-11 00:00:00

Food webs -- networks of predators and prey -- have long been known to
exhibit "intervality": species can generally be ordered along a single axis in
such a way that the prey of any given predator tend to lie on unbroken compact
intervals. Although the meaning of this axis -- identified with a "niche"
dimension -- has remained a mystery, it is assumed to lie at the basis of the
highly non-trivial structure of food webs. With this in mind, most trophic
network modelling has for decades been based on assigning species a niche value
by hand. However, we argue here that intervality should not be considered the
cause but rather a consequence of food-web structure. First, analysing a set of
$46$ empirical food webs, we find that they also exhibit {\it predator}
intervality: the predators of any given species are as likely to be contiguous
as the prey are, but in a different ordering. Furthermore, this property is not
exclusive of trophic networks: several networks of genes, neurons, metabolites,
cellular machines, airports, and words are found to be approximately as
interval as food webs. We go on to show that a simple model of food-web
assembly which does not make use of a niche axis can nevertheless generate
significant intervality. Therefore, the niche dimension (in the sense used for
food-web modelling) could in fact be the consequence of other, more fundamental
structural traits, such as trophic coherence. We conclude that a new approach
to food-web modelling is required for a deeper understanding of ecosystem
assembly, structure and function, and propose that certain topological features
thought to be specific of food webs are in fact common to many complex
networks.
Adjoint Multi-Start Based Estimation of Cardiac Hyperelastic Material Parameters using Shear Data
Date: 2016-03-11 00:00:00
Tasks: parameter estimation

Cardiac muscle tissue during relaxation is commonly modelled as a
hyperelastic material with strongly nonlinear and anisotropic stress response.
Adapting the behavior of such a model to experimental or patient data gives
rise to a parameter estimation problem which involves a significant number of
parameters. Gradient-based optimization algorithms provide a way to solve such
nonlinear parameter estimation problems with relatively few iterations, but
require the gradient of the objective functional with respect to the model
parameters. This gradient has traditionally been obtained using finite
differences, the calculation of which scales linearly with the number of model
parameters, and introduces a differencing error. By using an automatically
derived adjoint equation, we are able to calculate this gradient more
efficiently, and with minimal implementation effort. We test this adjoint
framework on a least squares fitting problem involving data from simple shear
tests on cardiac tissue samples. A second challenge which arises in
gradient-based optimization is the dependency of the algorithm on a suitable
initial guess. We show how a multi-start procedure can alleviate this
dependency. Finally, we provide estimates for the material parameters of the
Holzapfel and Ogden strain energy law using finite element models together with
experimental shear data.
Initial validation for the estimation of resting-state fMRI effective connectivity by a generalization of the correlation approach
Date: 2017-01-24 00:00:00
Tasks: Functional Connectivity, Prediction

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study
human brain networks. Network functional connectivity is often estimated by
calculating the timeseries correlation between blood-oxygen-level dependent
(BOLD) signal from different regions of interest. However, standard correlation
cannot characterize the direction of information flow between regions. In this
paper, we introduce and test a new concept, prediction correlation, to estimate
effective connectivity in functional brain networks from rs-fMRI. In this
approach, the correlation between two BOLD signals is replaced by a correlation
between one BOLD signal and a prediction of this signal via a causal system
driven by another BOLD signal. Three validations are described: (1) Prediction
correlation performed well on simulated data where the ground truth was known,
and outperformed four other methods. (2) On simulated data designed to display
the "common driver" problem, prediction correlation did not introduce false
connections between non-interacting driven ROIs. (3) On experimental data,
prediction correlation recovered the previously identified network organization
of human brain. Prediction correlation scales well to work with hundreds of
ROIs, enabling it to assess whole brain interregional connectivity at the
single subject level. These results provide an initial validation that
prediction correlation can capture the direction of information flow and
estimate the duration of extended temporal delays in information flow between
regions of interest based on BOLD signal. This approach not only maintains the
high sensitivity to network connectivity provided by the correlation analysis,
but also performs well in the estimation of causal information flow in the
brain.
Assessing Executive Function Using a Computer Game: Computational Modeling of Cognitive Processes
Date: 2016-03-12 00:00:00

Early and reliable detection of cognitive decline is one of the most
important challenges of current healthcare. In this project we developed an
approach whereby a frequently played computer game can be used to assess a
variety of cognitive processes and estimate the results of the pen-and-paper
Trail-Making Test (TMT) - known to measure executive function, as well as
visual pattern recognition, speed of processing, working memory, and
set-switching ability. We developed a computational model of the TMT based on a
decomposition of the test into several independent processes, each
characterized by a set of parameters that can be estimated from play of a
computer game designed to resemble the TMT. An empirical evaluation of the
model suggests that it is possible to use the game data to estimate the
parameters of the underlying cognitive processes and using the values of the
parameters to estimate the TMT performance. Cognitive measures and trends in
these measures can be used to identify individuals for further assessment, to
provide a mechanism for improving the early detection of neurological problems,
and to provide feedback and monitoring for cognitive interventions in the home.