pwc-archive/papers-with-abstracts huggingface dataset:
Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving
Date: 2016-06-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity

Conventional neuroimaging analyses have revealed the computational
specificity of localized brain regions, exploiting the power of the subtraction
technique in fMRI and event-related potential analyses in EEG. Moving beyond
this convention, many researchers have begun exploring network-based
neurodynamics and coordination between brain regions as a function of
behavioral parameters or environmental statistics; however, most approaches
average evoked activity across the experimental session to study task-dependent
networks. Here, we examined on-going oscillatory activity and use a methodology
to estimate directionality in brain-behavior interactions. After source
reconstruction, activity within specific frequency bands in a priori regions of
interest was linked to continuous behavioral measurements, and we used a
predictive filtering scheme to estimate the asymmetry between brain-to-behavior
and behavior-to-brain prediction. We applied this approach to a simulated
driving task and examine directed relationships between brain activity and
continuous driving behavior (steering or heading error). Our results indicated
that two neuro-behavioral states emerge in this naturalistic environment: a
Proactive brain state that actively plans the response to the sensory
information, and a Reactive brain state that processes incoming information and
reacts to environmental statistics.
From Conformational Spread to Allosteric and Cooperative models of E. coli flagellar motor
Date: 2016-09-29 00:00:00

Escherichia coli swims using flagella activated by rotary motors. The
direction of rotation of the motors is indirectly regulated by the binding of a
single messenger protein. The conformational spread model has been shown to
accurately describe the equilibrium properties as well as the dynamics of the
flagellar motor. In this paper we study this model from an analytic point of
view. By exploiting the separation of timescales observed in experiments, we
show how to reduce the conformational spread model to a coarse-grained,
cooperative binding model. We show that this simplified model reproduces very
well the dynamics of the motor switch.
Evidence for a postreproductive phase in female false killer whales Pseudorca crassidens
Date: 2017-02-10 00:00:00

A substantial period of life after reproduction ends, known as
postreproductive lifespan (PRLS), is at odds with classical life history
theory. Prolonged PRLS has been confirmed in only two non-human mammals, both
odontocete cetaceans. We investigate the evidence for PRLS in a third species,
the false killer whale, Pseudorca crassidens, using a quantitative measure of
PRLS and morphological evidence from reproductive tissue. We examined specimens
from false killer whales from combined strandings (South Africa, 1981) and
harvest (Japan 1979-80) and found morphological evidence of changes in the
activity of the ovaries in relation to age. Ovulation had ceased in 50% of
whales over 45 years, and all whales over 55 years old had ovaries classified
as postreproductive. We also calculated a measure of PRLS, known as
postreproductive representation (PrR) as an indication of the effect of
inter-population demographic variability. PrR for the combined sample was 0.14,
whereas the mean of the simulated distribution for PrR under the null
hypothesis of no PRLS was 0.02. The 99th percentile of the simulated
distribution was 0.08 and no simulated value exceeded 0.13. These results
suggest that PrR was convincingly different from the measures simulated under
the null hypothesis. We found morphological and statistical evidence for PRLS
in South African and Japanese pods of false killer whales, suggesting that this
species is the third non-human mammal in which this phenomenon has been
demonstrated in wild populations. Our estimates for PrR in false killer whales
(0.12-0.37) spanned the single values available for the short-finned pilot
whale (0.28) and the killer whale (0.22) and are comparable to estimates for
historical or hunter-gather human populations (0.3-0.47).
An observation of circular RNAs in bacterial RNA-seq data
Date: 2016-06-14 00:00:00

Circular RNAs (circRNAs) are a class of RNA with an important role in micro
RNA (miRNA) regulation recently discovered in Human and various other
eukaryotes as well as in archaea. Here, we have analyzed RNA-seq data obtained
from {\it Enterococcus faecalis} and {\it Escherichia coli} in a way similar to
previous studies performed on eukaryotes. We report observations of circRNAs in
RNA-seq data that are reproducible across multiple experiments performed with
different protocols or growth conditions.
The essential role of time in network-based recommendation
Authors: Vidmer Alexandre, Medo Matus
Date: 2016-06-15 00:00:00

Random walks on bipartite networks have been used extensively to design
personalized recommendation methods. While aging has been identified as a key
component in the growth of information networks, most research has focused on
the networks' structural properties and neglected the often available time
information. Time has been largely ignored both by the investigated
recommendation methods as well as by the methodology used to evaluate them. We
show that this time-unaware approach overestimates the methods' recommendation
performance. Motivated by microscopic rules of network growth, we propose a
time-aware modification of an existing recommendation method and show that by
combining the temporal and structural aspects, it outperforms the existing
methods. The performance improvements are particularly striking in systems with
fast aging.
Invariant recognition drives neural representations of action sequences
Date: 2017-04-20 00:00:00
Tasks: Action Recognition, Object Recognition

Recognizing the actions of others from visual stimuli is a crucial aspect of
human visual perception that allows individuals to respond to social cues.
Humans are able to identify similar behaviors and discriminate between distinct
actions despite transformations, like changes in viewpoint or actor, that
substantially alter the visual appearance of a scene. This ability to
generalize across complex transformations is a hallmark of human visual
intelligence. Advances in understanding motion perception at the neural level
have not always translated in precise accounts of the computational principles
underlying what representation our visual cortex evolved or learned to compute.
Here we test the hypothesis that invariant action discrimination might fill
this gap. Recently, the study of artificial systems for static object
perception has produced models, CNNs, that achieve human level performance in
complex discriminative tasks. Within this class of models, architectures that
better support invariant object recognition also produce image representations
that match those implied by human and primate neural data. However, whether
these models produce representations of action sequences that support
recognition across complex transformations and closely follow neural
representations remains unknown. Here we show that spatiotemporal CNNs
appropriately categorize video stimuli into actions, and that deliberate model
modifications that improve performance on an invariant action recognition task
lead to data representations that better match human neural recordings. Our
results support our hypothesis that performance on invariant discrimination
dictates the neural representations of actions computed by human visual cortex.
Characterization of the Community Structure of Large-Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
Authors: Eduardo C. Padovani
Date: 2016-06-15 00:00:00

One of the main goals of neuroscience is to understand how an organism's cognitive capacities or physiological states are potentially related to brain activities that involve the interaction of multiple brain structures and cortical areas. A key feature of functional brain networks is that they are modularly structured, and this modular architecture is regarded as accounting for a range of properties and functional dynamics. In the neurobiological context, communities may indicate brain regions involved in the same activity, representing neural-segregated processes. Several studies have demonstrated the modular character of the organization of brain activities. However, empirical evidence regarding its dynamics and relation to different levels of consciousness has not yet been reported. In this context, this research sought to characterize the community structure of functional brain networks during an anesthetic induction process. The experiment was based on intracranial recordings of the neural activities of an old-world macaque of the species Macaca fuscata during a Ketamine-Medetomidine anesthetic induction process. Changes were observed within approximately one and a half minutes after administering the anesthetics, revealing a transition in the community structure. The awake state was characterized by large clusters involving the frontal and parietal regions. In contrast, the anesthetized state was marked by the presence of communities in the primary visual and motor cortices, while the areas of the secondary associative cortex were the most affected. The results report the influence of general anesthesia on the structure of functional clusters, contributing to understanding some novel aspects of the neural correlates of consciousness.
Local Operators in Kinetic Wealth Distribution
Date: 2016-05-30 00:00:00

The statistical mechanics approach to wealth distribution is based on the
conservative kinetic multi-agent model for money exchange, where the local
interaction rule between the agents is analogous to the elastic particle
scattering process. Here, we discuss the role of a class of conservative local
operators, and we show that, depending on the values of their parameters, they
can be used to generate all the relevant distributions. We also show
numerically that in order to generate the power-law tail an heterogeneous risk
aversion model is required. By changing the parameters of these operators one
can also fine tune the resulting distributions in order to provide support for
the emergence of a more egalitarian wealth distribution.
Note on level r consensus
Date: 2016-05-23 00:00:00

We show that the hierarchy of level $r$ consensus partially collapses. In
particular, any profile $\pi\in \mathcal{P}$ that exhibits consensus of level
$(K-1)!$ around $\succ_0$ in fact exhibits consensus of level $1$ around
$\succ_0$.
Effect of NaCl on Pseudomonas biofilm viscosity by continuous, non-intrusive microfluidic-based approach
Date: 2016-06-15 00:00:00

A method combining video imaging in parallel microchannels with a
semi-empirical mathematical model provides non-intrusive, high-throughput
measurements of time-varying biofilm viscosity. The approach is demonstrated
for early growth Pseudomonas sp. biofilms exposed to constant flow streams of
nutrient solutions with different ionic strengths. The ability to measure
viscosities at early growth stages, without inducing a shear-thickening
response, enabled measurements that are among the lowest reported to date. In
addition, good time resolution enabled the detection of a rapid thickening
phase, which occurred at different times after the exponential growth phase
finished, depending on the ionic strength. The technique opens the way for a
combinatorial approach to beter understand the complex dynamical response of
biofilm mechanical properties under well-controlled physical, chemical and
biological growth conditions and time-limited perturbations.
On the optimality of grid cells
Date: 2016-06-15 00:00:00
Tasks: Sensitivity

Grid cells, discovered more than a decade ago [5], are neurons in the brain
of mammals that fire when the animal is located near certain specific points in
its familiar terrain. Intriguingly, these points form, for a single cell, a
two-dimensional triangular grid, not unlike our Figure 3. Grid cells are widely
believed to be involved in path integration, that is, the maintenance of a
location state through the summation of small displacements. We provide
theoretical evidence for this assertion by showing that cells with grid-like
tuning curves are indeed well adapted for the path integration task. In
particular we prove that, in one dimension under Gaussian noise, the
sensitivity of measuring small displacements is maximized by a population of
neurons whose tuning curves are near-sinusoids -- that is to say, with peaks
forming a one-dimensional grid. We also show that effective computation of the
displacement is possible through a second population of cells whose sinusoid
tuning curves are in phase difference from the first. In two dimensions, under
additional assumptions it can be shown that measurement sensitivity is
optimized by the product of two sinusoids, again yielding a grid-like pattern.
We discuss the connection of our results to the triangular grid pattern
observed in animals.
Temporal and Semantic Effects on Multisensory Integration
Date: 2016-06-15 00:00:00

How do we integrate modality-specific perceptual information arising from the
same physical event into a coherent percept? One possibility is that observers
rely on information across perceptual modalities that shares temporal structure
and/or semantic associations. To explore the contributions of these two factors
in multisensory integration, we manipulated the temporal and semantic
relationships between auditory and visual information produced by real-world
events, such as paper tearing or cards being shuffled. We identified distinct
neural substrates for integration based on temporal structure as compared to
integration based on event semantics. Semantically incongruent events recruited
left frontal regions, while temporally asynchronous events recruited right
frontal cortices. At the same time, both forms of incongruence recruited
subregions in the temporal, occipital, and lingual cortices. Finally, events
that were both temporally and semantically congruent modulated activity in the
parahippocampus and anterior temporal lobe. Taken together, these results
indicate that low-level perceptual properties such as temporal synchrony and
high-level knowledge such as semantics play a role in our coherent perceptual
experience of physical events.
Animated 3D Human Models for Use in Person Recognition Experiments
Date: 2016-06-15 00:00:00
Tasks: Person Recognition

The development of increasingly realistic experimental stimuli and task
environments is important for understanding behavior outside the laboratory. We
report a process for generating 3D human model stimuli that combines commonly
used graphics software and enables the flexible generation of animated human
models while providing parametric control over individualized identity
features. Our approach creates novel head models using FaceGen Modeller,
attaches them to commercially-purchased 3D avatar bodies in 3D Studio Max, and
generates Cal3D human models that are compatible with many virtual 3D
environments. Stimuli produced by this method can be embedded as animated 3D
avatars in interactive simulations or presented as 2D images embedded in scenes
for use in traditional laboratory experiments. The inherent flexibility in this
method makes the stimuli applicable to a broad range of basic and applied
research questions in the domain of person perception. We describe the steps of
the stimulus generation process, provide an example of their use in a
recognition memory paradigm, and highlight the adaptability of the method for
related avenues of research.
Optimal Liquidation under Partial Information with Price Impact
Date: 2019-06-26 00:00:00

We study the optimal liquidation problem in a market model where the bid
price follows a geometric pure jump process whose local characteristics are
driven by an unobservable finite-state Markov chain and by the liquidation
rate. This model is consistent with stylized facts of high frequency data such
as the discrete nature of tick data and the clustering in the order flow. We
include both temporary and permanent effects into our analysis. We use
stochastic filtering to reduce the optimal liquidation problem to an equivalent
optimization problem under complete information. This leads to a stochastic
control problem for piecewise deterministic Markov processes (PDMPs). We carry
out a detailed mathematical analysis of this problem. In particular, we derive
the optimality equation for the value function, we characterize the value
function as continuous viscosity solution of the associated dynamic programming
equation, and we prove a novel comparison result. The paper concludes with
numerical results illustrating the impact of partial information and price
impact on the value function and on the optimal liquidation rate.
Comparing Single Molecule Tracking and correlative approaches: an application to the datasets recently presented in Nature Methods by Chenuard et al
Date: 2016-06-16 00:00:00

Recent efforts to survey the numerous softwares available to perform single
molecule tracking (SMT) highlighted a significant dependence of the outcomes on
the specific method used, and the limitation encountered by most techniques to
capture fast movements in a crowded environment. Other approaches to identify
the mode and rapidity of motion of fluorescently labeled biomolecules, that do
not relay on the localization and linking of the images of isolated single
molecules are, however, available.This direct comparison shows that correlative
imaging analysis approaches complement effectively current SMT methods in
circumstances when, due to either the density of the sample, the low signal to
noise ratio or molecular blinking, trajectory linking does not allow to capture
long-range or fast motion.
Recent advancement in Next Generation Sequencing techniques and its computational analysis
Date: 2017-03-24 00:00:00

Next Generation Sequencing (NGS), a recently evolved technology, have served
a lot in the research and development sector of our society. This novel
approach is a newbie and has critical advantages over the traditional Capillary
Electrophoresis (CE) based Sanger Sequencing. The advancement of NGS has led to
numerous important discoveries, which could have been costlier and time taking
in case of traditional CE based Sanger sequencing. NGS methods are highly
parallelized enabling to sequence thousands to millions of molecules
simultaneously. This technology results into huge amount of data, which need to
be analysed to conclude valuable information. Specific data analysis algorithms
are written for specific task to be performed. The algorithms in group, act as
a tool in analysing the NGS data. Analysis of NGS data unravels important clues
in quest for the treatment of various life-threatening diseases; improved crop
varieties and other related scientific problems related to human welfare. In
this review, an effort was made to address basic background of NGS
technologies, possible applications, computational approaches and tools
involved in NGS data analysis, future opportunities and challenges in the area.
Folding and Stabilization of Native-Sequence-Reversed Proteins
Date: 2016-06-16 00:00:00
Tasks: Protein Design, Protein Folding, Protein Structure Prediction

Though the problem of sequence-reversed protein folding is largely
unexplored, one might speculate that reversed native protein sequences should
be significantly more foldable than purely random heteropolymer sequences. In
this article, we investigate how the reverse-sequences of native proteins might
fold by examining a series of small proteins of increasing structural
complexity ({\alpha}-helix, \b{eta}-hairpin, {\alpha}-helix bundle, and
{\alpha}/\b{eta}-protein). Employing a tandem protein structure prediction
algorithmic and molecular dynamics simulation approach, we find that the
ability of reverse sequences to adopt native-like folds is strongly in
influenced by protein size and the flexibility of the native hydrophobic core.
For \b{eta}-hairpins with reverse-sequences that fail to fold, we employ a
simple mutational strategy for guiding stable hairpin formation that involves
the insertion of amino acids into the \b{eta}-turn region. This systematic look
at reverse sequence duality sheds new light on the problem of protein
sequence-structure mapping and may serve to inspire new protein design and
protein structure prediction protocols.
Explicit solutions for continuous time mean-variance portfolio selection with nonlinear wealth equations
Date: 2016-06-17 00:00:00

This paper concerns the continuous time mean-variance portfolio selection
problem with a special nonlinear wealth equation. This nonlinear wealth
equation has a nonsmooth coefficient and the dual method developed in [6] does
not work. We invoke the HJB equation of this problem and give an explicit
viscosity solution of the HJB equation. Furthermore, via this explicit
viscosity solution, we obtain explicitly the efficient portfolio strategy and
efficient frontier for this problem. Finally, we show that our nonlinear wealth
equation can cover three important cases.
A novel method linking neural connectivity to behavioral fluctuations: Behavior-Regressed Connectivity
Date: 2017-10-10 00:00:00
Tasks: Functional Connectivity

Background: During an experimental session, behavioral performance
fluctuates, yet most neuroimaging analyses of functional connectivity derive a
single connectivity pattern. These conventional connectivity approaches assume
that since the underlying behavior of the task remains constant, the
connectivity pattern is also constant.
  New Method: We introduce a novel method, behavior-regressed connectivity
(BRC), to directly examine behavioral fluctuations within an experimental
session and capture their relationship to changes in functional connectivity.
This method employs the weighted phase lag index (WPLI) applied to a window of
trials with a weighting function. Using two datasets, the BRC results are
compared to conventional connectivity results during two time windows: the one
second before stimulus onset to identify predictive relationships, and the one
second after onset to capture task-dependent relationships.
  Results: In both tasks, we replicate the expected results for the
conventional connectivity analysis, and extend our understanding of the
brain-behavior relationship using the BRC analysis, demonstrating
subject-specific connectivity patterns that correspond to both positive and
negative relationships with behavior.
  Comparison with Existing Method(s): Conventional connectivity analyses assume
a consistent relationship between behaviors and functional connectivity, but
the BRC method examines performance variability within an experimental session
to understand dynamic connectivity and transient behavior.
  Conclusion: The BRC approach examines connectivity as it covaries with
behavior to complement the knowledge of underlying neural activity derived from
conventional connectivity analyses. Within this framework, BRC may be
implemented for the purpose of understanding performance variability both
within and between participants.