pwc-archive/papers-with-abstracts huggingface dataset:
Zinc oxide induces the stringent response and major reorientations in the central metabolism of Bacillus subtilis
Date: 2018-01-26 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Microorganisms, such as bacteria, are one of the first targets of
nanoparticles in the environment. In this study, we tested the effect of two
nanoparticles, ZnO and TiO2, with the salt ZnSO4 as the control, on the
Gram-positive bacterium Bacillus subtilis by 2D gel electrophoresis-based
proteomics. Despite a significant effect on viability (LD50), TiO2 NPs had no
detectable effect on the proteomic pattern, while ZnO NPs and ZnSO4
significantly modified B. subtilis metabolism. These results allowed us to
conclude that the effects of ZnO observed in this work were mainly attributable
to Zn dissolution in the culture media. Proteomic analysis highlighted twelve
modulated proteins related to central metabolism: MetE and MccB (cysteine
metabolism), OdhA, AspB, IolD, AnsB, PdhB and YtsJ (Krebs cycle) and XylA,
YqjI, Drm and Tal (pentose phosphate pathway). Biochemical assays, such as free
sulfhydryl, CoA-SH and malate dehydrogenase assays corroborated the observed
central metabolism reorientation and showed that Zn stress induced oxidative
stress, probably as a consequence of thiol chelation stress by Zn ions. The
other patterns affected by ZnO and ZnSO4 were the stringent response and the
general stress response. Nine proteins involved in or controlled by the
stringent response showed a modified expression profile in the presence of ZnO
NPs or ZnSO4: YwaC, SigH, YtxH, YtzB, TufA, RplJ, RpsB, PdhB and Mbl. An
increase in the ppGpp concentration confirmed the involvement of the stringent
response during a Zn stress. All these metabolic reorientations in response to
Zn stress were probably the result of complex regulatory mechanisms including
at least the stringent response via YwaC.
Quantifying Health Shocks Over the Life Cycle
Date: 2018-01-26 00:00:00

We first show (1) the importance of investigating health expenditure process
using the order two Markov chain model, rather than the standard order one
model, which is widely used in the literature. Markov chain of order two is the
minimal framework that is capable of distinguishing those who experience a
certain health expenditure level for the first time from those who have been
experiencing that or other levels for some time. In addition, using the model
we show (2) that the probability of encountering a health shock first de-
creases until around age 10, and then increases with age, particularly, after
age 40, (3) that health shock distributions among different age groups do not
differ until their percentiles reach the median range, but that above the
median the health shock distributions of older age groups gradually start to
first-order dominate those of younger groups, and (4) that the persistency of
health shocks also shows a U-shape in relation to age.
Ordered Kripke Model, Permissibility, and Convergence of Probabilistic Kripke Model
Date: 2018-01-26 00:00:00
Tasks: model

We define a modification of the standard Kripke model, called the ordered
Kripke model, by introducing a linear order on the set of accessible states of
each state. We first show this model can be used to describe the lexicographic
belief hierarchy in epistemic game theory, and perfect rationalizability can be
characterized within this model. Then we show that each ordered Kripke model is
the limit of a sequence of standard probabilistic Kripke models with a modified
(common) belief operator, in the senses of structure and the
(epsilon-)permissibilities characterized within them.
Rational Models for Inflation-Linked Derivatives
Authors: Henrik Dam, Andrea Macrina, David Skovmand, David Sloth
Date: 2018-01-26 00:00:00
Tasks: Management

We construct models for the pricing and risk management of inflation-linked derivatives. The models are rational in the sense that linear payoffs written on the consumer price index have prices that are rational functions of the state variables. The nominal pricing kernel is constructed in a multiplicative manner that allows for closed-form pricing of vanilla inflation products suchlike zero-coupon swaps, year-on-year swaps, caps and floors, and the exotic limited-price-index swap. We study the conditions necessary for the multiplicative nominal pricing kernel to give rise to short rate models for the nominal interest rate process. The proposed class of pricing kernel models retains the attractive features of a nominal multi-curve interest rate model, such as closed-form pricing of nominal swaptions, and it isolates the so-called inflation convexity-adjustment term arising from the covariance between the underlying stochastic drivers. We conclude with examples of how the model can be calibrated to EUR data.
Who is the infector? Epidemic models with symptomatic and asymptomatic cases
Date: 2018-01-26 00:00:00

What role do asymptomatically infected individuals play in the transmission
dynamics? There are many diseases, such as norovirus and influenza, where some
infected hosts show symptoms of the disease while others are asymptomatically
infected, i.e. do not show any symptoms. The current paper considers a class of
epidemic models following an SEIR (Susceptible $\to$ Exposed $\to$ Infectious
$\to$ Recovered) structure that allows for both symptomatic and asymptomatic
cases. The following question is addressed: what fraction $\rho$ of those
individuals getting infected are infected by symptomatic (asymptomatic) cases?
This is a more complicated question than the related question for the beginning
of the epidemic: what fraction of the expected number of secondary cases of a
typical newly infected individual, i.e. what fraction of the basic reproduction
number $R_0$, is caused by symptomatic individuals? The latter fraction only
depends on the type-specific reproduction numbers, while the former fraction
$\rho$ also depends on timing and hence on the probabilistic distributions of
latent and infectious periods of the two types (not only their means). Bounds
on $\rho$ are derived for the situation where these distributions (and even
their means) are unknown. Special attention is given to the class of Markov
models and the class of continuous-time Reed-Frost models as two classes of
distribution functions. We show how these two classes of models can exhibit
very different behaviour.
Ictal and Post Ictal Impaired Consciousness due to Enhanced Mutual Information in Temporal Lobe Epilepsy
Date: 2018-01-26 00:00:00

Seizure and synchronization are related to each other in complex manner.
Altered synchrony has been implicated in loss of consciousness during partial
seizures. However, the mechanism of altered consciousness following termination
of seizures has not been studied well. In this work we used bivariate mutual
information as a measure of synchronization to understand the neural correlate
of altered consciousness during and after termination of mesial temporal lobe
onset seizures. First, we have compared discrete bivariate mutual information
(MI) measure with amplitude correlation (AC), phase synchronization (PS),
nonlinear correlation and coherence, and established MI as a robust measure of
synchronization. Next, we have extended MI to more than two signals by
principal component method. The extended MI was applied on intracranial
electroencephalogram (iEEG) before, during and after 23 temporal lobe seizures
recorded from 11 patients. The analyses were carried out in delta, theta,
alpha, beta and gamma bands. In 77% of the complex partial seizures MI was
higher towards the seizure offset than in the first half of the seizure in the
seizure onset zone (SOZ) channels in beta and gamma bands, whereas MI remained
higher in the beginning or in the middle of the seizure than towards the offset
across the least involved channels in the same bands. Synchronization seems
built up outside the SOZ, gradually spread and culminated in SOZ and remained
high beyond offset leading to impaired consciousness in 82% of the complex
partial temporal lobe seizures. Consciousness impairment was scored according
to a method previously applied to assess the same in patients with temporal
lobe epilepsy during seizure.
Nonseparable Sample Selection Models with Censored Selection Rules
Date: 2020-09-29 00:00:00

We consider identification and estimation of nonseparable sample selection
models with censored selection rules. We employ a control function approach and
discuss different objects of interest based on (1) local effects conditional on
the control function, and (2) global effects obtained from integration over
ranges of values of the control function. We derive the conditions for the
identification of these different objects and suggest strategies for
estimation. Moreover, we provide the associated asymptotic theory. These
strategies are illustrated in an empirical investigation of the determinants of
female wages in the United Kingdom.
On a capital allocation principle coherent with the Solvency 2 standard formula
Date: 2018-01-26 00:00:00

Solvency II Directive 2009/138/EC requires an insurance and reinsurance
undertakings assessment of a Solvency Capital Requirement by means of the
so-called "Standard Formula" or by means of partial or full internal models.
Focusing on the first approach, the bottom-up aggregation formula proposed by
the regulator permits a capital reduction due to diversification effect,
according to the typical subadditivity property of risk measures. However, once
the overall capital has been assessed no specific allocation formula is
provided or required in order to evaluate the contribution of each risk source
on the overall SCR. The aim of this paper is to provide a closed formula for
capital allocation fully coherent with the Solvency II Capital Requirement
assessed by means of Standard Formula. The solution proposed permits a top-down
approach to assess the allocated SCR among the risks considered in the
multilevel aggregation scheme established by Solvency II. Besides, we
demonstrate that the allocation formula here proposed is consistent with the
Euler's allocation principle
Parametric Modeling of Non-Stationary Signals
Date: 2018-01-27 00:00:00

Parametric modeling of non-stationary signals is addressed in this article.
We present several models based on the characteristic features of the modeled
signal, together with the methods for accurate estimation of model parameters.
Non-stationary signals, viz. transient system response, speech phonemes, and
electrocardiograph signal are fitted by these feature-based models.
Inter-cellular Interactions and Patterns: Vertebrate Development and Embryonic Stem Cells
Date: 2018-01-27 00:00:00

Development from egg to embryo to adult is a fascinating instance of
biological self-organization for which genetics has supplied us with a parts
list. It remains to find the principles organizing the assembly of those parts.
In the last decade embryonic stem cells (ESC) have provided the material from
which to build the mammalian embryo. This review, for a quantitative audience,
explains why colonies of ESC are an ideal system with which to peal back the
multiple layers of regulation that make embryonic development such a robust
process. It formed the basis of a presentation at the 27th Solvay Conference on
the Physics of Living Matter 2017 edited by Boris Shraiman.
Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD
Date: 2018-04-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Posttraumatic stress disorder (PTSD) is a chronic and disabling
neuropsychiatric disorder characterized by insufficient top-down modulation of
the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback
(rtfMRI-nf) is an emerging method with potential for modifying the
amygdala-prefrontal interactions. We report the first controlled emotion
self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf
of the amygdala activity. PTSD patients in the experimental group (EG, n=20)
learned to upregulate BOLD activity of the left amygdala (LA) using rtfMRI-nf
during a happy emotion induction task. PTSD patients in the control group (CG,
n=11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf
training sessions, and EEG recordings were performed simultaneously with fMRI.
PTSD severity was assessed using the Clinician-Administered PTSD Scale (CAPS).
The EG participants showed a significant reduction in total CAPS ratings,
including significant reductions in avoidance and hyperarousal symptoms.
Overall, 80% of the EG participants demonstrated clinically meaningful
reductions in CAPS ratings, compared to 38% in the CG. During the first
session, fMRI connectivity of the LA with the orbitofrontal cortex and the
dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this
enhancement significantly and positively correlated with initial CAPS ratings.
Left-lateralized enhancement in upper alpha EEG coherence also exhibited a
significant positive correlation with the initial CAPS. Reduction in PTSD
severity between the first and last rtfMRI-nf sessions significantly correlated
with enhancement in functional connectivity between the LA and the left DLPFC.
Our results demonstrate that the rtfMRI-nf of the amygdala activity has the
potential to correct the amygdala-prefrontal functional connectivity
deficiencies specific to PTSD.
Optimal Energy Management Strategies in Wireless Data and Energy Cooperative Communications
Date: 2018-01-28 00:00:00
Tasks: energy management, Management

This paper presents a new cooperative wireless communication network strategy
that incorporates energy cooperation and data cooperation. The model
establishment, design goal formulations, and algorithms for throughput
maximization of the proposed protocol are presented and illustrated using a
three-node network with two energy harvesting (EH) user nodes and a destination
node. Transmission models are established from the performance analysis for a
total of four scenarios. Based on the models, we seek to find optimal energy
management strategies by jointly optimizing time allocation for each user,
power allocations over these time intervals, and data throughputs at user nodes
so as to maximize the sum-throughput or, alternatively, the minimum throughput
of the two users in all scenarios. An accelerated Newton barrier algorithm and
an alternative algorithm based on local quadratic approximation of the
transmission models are developed to solve the aforementioned optimization
problems. Numerical experiments under practical settings provide supportive
observations to our performance analysis.
Mitigating Pilot Contamination in Multi-cell Hybrid Millimeter Wave Systems
Date: 2018-01-28 00:00:00

In this paper, we investigate the system performance of a multi-cell
multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple-output
(MIMO) network adopting the channel estimation algorithm proposed in [1] for
channel estimation. Due to the reuse of orthogonal pilot symbols among
different cells, the channel estimation is expected to be affected by pilot
contamination, which is considered as a fundamental performance bottleneck of
conventional multicell MU massive MIMO networks. To analyze the impact of pilot
contamination on the system performance, we derive the closed-form
approximation expression of the normalized mean squared error (MSE) of the
channel estimation performance. Our analytical and simulation results show that
the channel estimation error incurred by the impact of pilot contamination and
noise vanishes asymptotically with an increasing number of antennas equipped at
each radio frequency (RF) chain deployed at the desired BS. Thus, pilot
contamination is no longer the fundamental problem for multi-cell hybrid mmWave
systems.
Noise-induced Mixing and Multimodality in Reaction Networks
Date: 2018-01-28 00:00:00

We analyze a class of chemical reaction networks under mass-action kinetics
and involving multiple time-scales, whose deterministic and stochastic models
display qualitative differences. The networks are inspired by gene-regulatory
networks, and consist of a slow-subnetwork, describing conversions among the
different gene states, and fast-subnetworks, describing biochemical
interactions involving the gene products. We show that the long-term dynamics
of such networks can consist of a unique attractor at the deterministic level
(unistability), while the long-term probability distribution at the stochastic
level may display multiple maxima (multimodality). The dynamical differences
stem from a novel phenomenon we call noise-induced mixing, whereby the
probability distribution of the gene products is a linear combination of the
probability distributions of the fast-subnetworks which are `mixed' by the
slow-subnetworks. The results are applied in the context of systems biology,
where noise-induced mixing is shown to play a biochemically important role,
producing phenomena such as stochastic multimodality and oscillations.
Stability, convergence, and sensitivity analysis of the Filament Based Lamellipodium Model and the corresponding FEM
Date: 2018-01-28 00:00:00
Tasks: Sensitivity

This paper focuses on the study of the Filament Based Lamellipodium Model
(FBLM) and the corresponding Finite Element Method (FEM) from a numerical point
of view. We study fundamental numerical properties of the FEM and justify the
further use of the FBLM. We exhibit that the FEM satisfies a timestep stability
condition that is consistent with the nature of the problem. We propose a
particular strategy to automatically adapt the time step of the method. We show
that the FEM convergences with respect to the (two-dimensional) space
discretization in a series of characteristic and representative experiments. We
embed and couple the FBLM with a complex extracellular environment comprised of
chemical and haptic components and study their combined time evolution. Under
this prism, we study the sensitivity of the FBLM on several of its controlling
parameters and discuss their influence in the development of the model.
Adaptation of olfactory receptor abundances for efficient coding
Date: 2019-01-22 00:00:00
Tasks: Diversity

Olfactory receptor usage is highly heterogeneous, with some receptor types
being orders of magnitude more abundant than others. We propose an explanation
for this striking fact: the receptor distribution is tuned to maximally
represent information about the olfactory environment in a regime of efficient
coding that is sensitive to the global context of correlated sensor responses.
This model predicts that in mammals, where olfactory sensory neurons are
replaced regularly, receptor abundances should continuously adapt to odor
statistics. Experimentally, increased exposure to odorants leads variously, but
reproducibly, to increased, decreased, or unchanged abundances of different
activated receptors. We demonstrate that this diversity of effects is required
for efficient coding when sensors are broadly correlated, and provide an
algorithm for predicting which olfactory receptors should increase or decrease
in abundance following specific environmental changes. Finally, we give simple
dynamical rules for neural birth and death processes that might underlie this
adaptation.
A representative agent model based on risk-neutral prices
Date: 2018-01-28 00:00:00

In this paper, we determine a representative agent model based on
risk-neutral information. The main idea is that the pricing kernel is
transition independent, which is supported by the well-known capital asset
pricing theory. Determining the representative agent model is closely related
to the eigenpair problem of a second-order differential operator. The purpose
of this paper is to find all such eigenpairs which are financially or
economically meaningful. We provide a necessary and sufficient condition for
the existence of such pairs, and prove that that all the possible eignepairs
can be expressed as a one-parameter family. Finally, we find a representative
agent model derived from the eigenpairs.
Towards monitoring conformational changes of the GPCR neurotensin receptor 1 by single-molecule FRET
Date: 2018-01-28 00:00:00

Neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor that is
important for signaling in the brain and the gut. Its agonist ligand
neurotensin (NTS), a 13-amino-acid peptide, binds with nanomolar affinity from
the extracellular side to NTSR1 and induces conformational changes that trigger
intracellular signaling processes. Our goal is to monitor the conformational
dynamics of single fluorescently labeled NTSR1. For this, we fused the
fluorescent protein mNeonGreen to the C terminus of NTSR1, purified the
receptor fusion protein from E. coli membranes, and reconstituted NTSR1 into
liposomes with E. coli polar lipids. Using single-molecule anisotropy
measurements, NTSR1 was found to be monomeric in liposomes, with a small
fraction being dimeric and oligomeric, showing homoFRET. Similar results were
obtained for NTSR1 in detergent solution. Furthermore, we demonstrated agonist
binding to NTSR1 by time-resolved single-molecule F\"orster resonance energy
transfer (smFRET), using neurotensin labeled with the fluorophore ATTO594.
Benchmarking Clinical Decision Support Search
Authors: Nguyen Vincent, Karimi Sarvnaz, Falamaki Sara, Paris Cecile
Date: 2018-01-29 00:00:00
Tasks: Articles, Benchmarking, Diversity

Finding relevant literature underpins the practice of evidence-based
medicine. From 2014 to 2016, TREC conducted a clinical decision support track,
wherein participants were tasked with finding articles relevant to clinical
questions posed by physicians. In total, 87 teams have participated over the
past three years, generating 395 runs. During this period, each team has
trialled a variety of methods. While there was significant overlap in the
methods employed by different teams, the results were varied. Due to the
diversity of the platforms used, the results arising from the different
techniques are not directly comparable, reducing the ability to build on
previous work. By using a stable platform, we have been able to compare
different document and query processing techniques, allowing us to experiment
with different search parameters. We have used our system to reproduce leading
teams runs, and compare the results obtained. By benchmarking our indexing and
search techniques, we can statistically test a variety of hypotheses, paving
the way for further research.
Ensemble Neural Relation Extraction with Adaptive Boosting
Authors: Yang Dongdong, Wang Senzhang, Li Zhoujun
Date: 2018-04-28 00:00:00
Tasks: Relation, Relation Extraction, Sentence

Relation extraction has been widely studied to extract new relational facts
from open corpus. Previous relation extraction methods are faced with the
problem of wrong labels and noisy data, which substantially decrease the
performance of the model. In this paper, we propose an ensemble neural network
model - Adaptive Boosting LSTMs with Attention, to more effectively perform
relation extraction. Specifically, our model first employs the recursive neural
network LSTMs to embed each sentence. Then we import attention into LSTMs by
considering that the words in a sentence do not contribute equally to the
semantic meaning of the sentence. Next via adaptive boosting, we build
strategically several such neural classifiers. By ensembling multiple such LSTM
classifiers with adaptive boosting, we could build a more effective and robust
joint ensemble neural networks based relation extractor. Experiment results on
real dataset demonstrate the superior performance of the proposed model,
improving F1-score by about 8% compared to the state-of-the-art models.