pwc-archive/papers-with-abstracts huggingface dataset:
Glucose availability but not changes in pancreatic hormones sensitizes hepatic AMPK activity during nutritional transition in rodents
Authors: Camille Huet, Nadia Boudaba, Bruno Guigas, Benoit Viollet, Marc Foretz
Date: 2020-03-20 00:00:00

The cellular energy sensor AMP-activated protein kinase (AMPK) is a metabolic regulator that mediates adaptation to nutritional variations in order to maintain a proper energy balance in cells. We show here that suckling-weaning and fasting-refeeding transitions in rodents are associated with changes in AMPK activation and the cellular energy state in the liver. These nutritional transitions were characterized by a metabolic switch from lipid to glucose utilization, orchestrated by modifications in glucose levels and the glucagon:insulin ratio in the bloodstream. We therefore investigated the respective roles of glucose and pancreatic hormones on AMPK activation in mouse primary hepatocytes. We found that glucose starvation transiently activates AMPK, whereas changes in glucagon and insulin levels had no impact on AMPK. Challenge of hepatocytes with metformin-induced metabolic stress strengthened both AMPK activation and cellular energy depletion limited-glucose conditions, whereas neither glucagon nor insulin altered AMPK activation. Although both insulin and glucagon induced AMPK$\alpha$ phosphorylation at its Ser-485/491 residue, they did not affect its activity. Finally, the decrease in cellular ATP levels in response to an energy stress was additionally exacerbated under fasting conditions and by AMPK deficiency in hepatocytes, revealing metabolic inflexibility and emphasizing the importance of AMPK for maintaining hepatic energy charge. Our results suggest that nutritional changes (i.e. glucose availability), rather than the related hormonal changes (i.e. the glucagon:insulin ratio), sensitize AMPK activation to the energetic stress induced by the dietary transition during fasting. This effect is critical for preserving the cellular energy state in the liver.
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
Authors: Xiyi Wei, Yu-Tian Xiao, Jian Wang, Rui Chen, Wei zhang, Yue Yang, Daojun Lv, Chao Qin, Di Gu, Bo Zhang, Weidong Chen, Jianquan Hou, Ninghong Song, Guohua Zeng, Shancheng Ren
Date: 2020-03-30 00:00:00

Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences. Methods: We performed a systematic review to collate data from observational studies examining associations of sex differences with clinical outcomes of COVID-19. PubMed, Web of Science and four preprint servers were searched for relevant studies. Data were extracted and analyzed using meta-analysis where possible, with summary data presented otherwise. Publicly available bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data were analyzed to explore the potential mechanisms underlying the observed association. Results: 39 studies met inclusion criteria, representing 77932 patients, of which 41510 (53.3%) were males. Men were at a markedly increased risk of developing severe cases compared with women. Furthermore, the pooled odds ratio (OR) of mortality for male group compared with the female group indicated significant higher mortality rate for male. Data from scRNA-seq suggest that men have a higher amount of ACE2-expressing pulmonary alveolar type II cells than women. Sex-based immunological differences exist. The expression of androgen receptor (AR) is positively correlated with ACE2, and there is evidence that AR may directly regulate the expression of ACE2. Conclusions: This meta-analysis detected an increased severity and mortality rate in the male populations with COVID-19, which might be attributable to the sex-based differences in cellular compositions and immunological microenvironments of the lung. The host cell receptor ACE2 is likely regulated by AR signaling pathway, which is identified as a potential target for prevention and treatment of SARS-Cov-2 infections in men.
Solvable delay model for epidemic spreading: the case of Covid-19 in Italy
Date: 2020-09-10 00:00:00
Tasks: Epidemiology

We study a simple realistic model for describing the diffusion of an
infectious disease on a population of individuals. The dynamics is governed by
a single functional delay differential equation, which, in the case of a large
population, can be solved exactly, even in the presence of a time-dependent
infection rate. This delay model has a higher degree of accuracy than that of
the so-called SIR model, commonly used in epidemiology, which, instead, is
formulated in terms of ordinary differential equations. We apply this model to
describe the outbreak of the new infectious disease, Covid-19, in Italy, taking
into account the containment measures implemented by the government in order to
mitigate the spreading of the virus and the social costs for the population.
Relative Arbitrage: Sharp Time Horizons and Motion by Curvature
Authors: Martin Larsson, Johannes Ruf
Date: 2020-03-30 00:00:00

We characterize the minimal time horizon over which any equity market with $d \geq 2$ stocks and sufficient intrinsic volatility admits relative arbitrage with respect to the market portfolio. If $d \in \{2,3\}$, the minimal time horizon can be computed explicitly, its value being zero if $d=2$ and $\sqrt{3}/(2\pi)$ if $d=3$. If $d \geq 4$, the minimal time horizon can be characterized via the arrival time function of a geometric flow of the unit simplex in $\mathbb R^d$ that we call the minimum curvature flow.
Stochastic single-particle based simulations of cellular signaling embedded into computational models of cellular morphology
Date: 2020-03-30 00:00:00
Tasks: Diversity

Cells exhibit a wide variety of different shapes. This diversity poses a
challenge for computational approaches that attempt to shed light on the role
cell geometry plays in regulating cell physiology and behavior. The simulation
platform Simmune is capable of embedding the computational representation of
signaling pathways into realistic models of cellular morphology. However,
Simmune's current approach to account for the cell geometry is limited to
deterministic models of reaction-diffusion processes, thus providing a
coarse-grained description that ignores stochastic local fluctuations. Here we
present an extension of Simmune that removes these limitations by employing an
alternative computational representation of cellular geometry that is smooth
and grid-free. These features make it possible to incorporate a fully
stochastic, spatially resolved description of the cellular biochemistry. The
alternative computational representation is compatible with Simmune's current
approach for specifying molecular interactions. This means that a modeler using
the approach needs to create a model of cellular biochemistry and morphology
only once to be able to use it for both, deterministic and stochastic
simulations.
Structural analysis of SARS-CoV-2 and prediction of the human interactome
Authors: Andrea Vandelli, Michele Monti, Edoardo Milanetti, Riccardo Delli Ponti, Gian Gaetano Tartaglia
Date: 2020-03-30 00:00:00

Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2500 coronaviruses and computed >100000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We found that the 3 and 5 prime ends are the most structured elements in the viral genome and the 5 prime end has the strongest propensity to associate with human proteins. The domain encompassing nucleotides 23000-24000 is highly conserved both at the sequence and structural level, while the region upstream varies significantly. These two sequences code for a domain of the viral protein Spike S that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) and has the potential to bind sialic acids. Our predictions indicate that the first 1000 nucleotides in the 5 prime end can interact with proteins involved in viral RNA processing such as double-stranded RNA specific editases and ATP-dependent RNA-helicases, in addition to other high-confidence candidate partners. These interactions, previously reported to be also implicated in HIV, reveal important information on host-virus interactions. The list of transcriptional and post-transcriptional elements recruited by SARS-CoV-2 genome provides clues on the biological pathways associated with gene expression changes in human cells.
Genomics-guided molecular maps of coronavirus targets in human cells: a path toward the repurposing of existing drugs to mitigate the pandemic
Authors: Gennadi Glinsky
Date: 2020-03-30 00:00:00

Human genes required for SARS-CoV-2 entry into human cells, ACE2 and FURIN, were employed as baits to build genomics-guided maps of up-stream regulatory elements, their expression and functions in human body, including pathophysiologically-relevant cell types. Genes acting as repressors and activators of the ACE2 and FURIN genes were identified based on the analyses of gene silencing and overexpression experiments as well as relevant transgenic mouse models. Panels of repressors (VDR; GATA5; SFTPC; HIF1a) and activators (HMGA2; INSIG1) were then employed to identify existing drugs that could be repurposed to mitigate the coronavirus infection. Present analyses identify Vitamin D and Quercetin as promising pandemic mitigation agents. Gene expression profiles of Vitamin D and Quercetin activities and their established safety records as over-the-counter medicinal substances suggest that they may represent viable candidates for further assessment and considerations of their potential as coronavirus pandemic mitigation agents. Notably, gene set enrichment analyses and expression profiling experiments identify multiple drugs, most notably testosterone, dexamethasone, and doxorubicin, smoking, and many disease conditions that appear to act as putative coronavirus infection-promoting agents. Discordant patterns of Testosterone versus Estradiol impacts on SCARS-CoV-2 targets suggest a plausible molecular explanation of the apparently higher male mortality during coronavirus pandemic.
A generalized Hausdorff distance based quality metric for point cloud geometry
Date: 2020-03-30 00:00:00

Reliable quality assessment of decoded point cloud geometry is essential to
evaluate the compression performance of emerging point cloud coding solutions
and guarantee some target quality of experience. This paper proposes a novel
point cloud geometry quality assessment metric based on a generalization of the
Hausdorff distance. To achieve this goal, the so-called generalized Hausdorff
distance for multiple rankings is exploited to identify the best performing
quality metric in terms of correlation with the MOS scores obtained from a
subjective test campaign. The experimental results show that the quality metric
derived from the classical Hausdorff distance leads to low objective-subjective
correlation and, thus, fails to accurately evaluate the quality of decoded
point clouds for emerging codecs. However, the quality metric derived from the
generalized Hausdorff distance with an appropriately selected ranking,
outperforms the MPEG adopted geometry quality metrics when decoded point clouds
with different types of coding distortions are considered.
DC-DistADMM: ADMM Algorithm for Constrained Distributed Optimization over Directed Graphs
Authors: Vivek Khatana, Murti V. Salapaka
Date: 2020-03-30 00:00:00
Tasks: Distributed Optimization

This article reports an algorithm for multi-agent distributed optimization problems with a common decision variable, local linear equality and inequality constraints and set constraints with convergence rate guarantees. \textcolor{black}{The algorithm accrues all the benefits of the Alternating Direction Method of Multipliers (ADMM) approach}. It also overcomes the limitations of existing methods on convex optimization problems with linear inequality, equality and set constraints by allowing directed communication topologies. Moreover, the algorithm can be synthesized distributively. The developed algorithm has: (i) a $O(1/k)$ rate of convergence, where $k$ is the iteration counter, when individual functions are convex but not-necessarily differentiable, and (ii) a geometric rate of convergence to any arbitrary small neighborhood of the optimal solution, when the objective functions are smooth and restricted strongly convex at the optimal solution. The efficacy of the algorithm is evaluated by a comparison with state-of-the-art constrained optimization algorithms in solving a constrained distributed $\ell_1$-regularized logistic regression problem, and unconstrained optimization algorithms in solving a $\ell_1$-regularized Huber loss minimization problem. Additionally, a comparison of the algorithm's performance with other algorithms in the literature that utilize multiple communication steps is provided.
The D model for deaths by COVID-19
Date: 2020-03-30 00:00:00
Tasks: model

We present a simple analytical model to describe the fast increase of deaths
produced by the corona virus (COVID-19) infections. The 'D' (deaths) model
comes from a simplified version of the SIR (susceptible-infected-recovered)
model known as SI model. It assumes that there is no recovery. In that case the
dynamical equations can be solved analytically and the result is extended to
describe the D-function that depends on three parameters that we can fit to the
data. Results for the data from Spain, Italy and China are presented. The model
is validated by comparing with the data of deaths in China, which are well
described. This allows to make predictions for the development of the disease
in Spain and Italy.
Phase response reconstruction for non-minimum phase systems using frequency-domain magnitude values
Authors: Taco Boland, Rik Naus, Peter Zwamborn
Date: 2020-03-30 00:00:00

It is often more complicated to measure the phase response of a large system than the magnitude. In that case, one can attempt to use the Kramers-Kronig (KK) relations for magnitude and phase, which relates magnitude and phase analytically. The advantage is that then only the magnitude of the frequency response needs to be measured. We show that the KK relations for magnitude and phase may yield invalid results when the transfer function has zeros located in the right half of the complex s-plane, i.e. the system is non-minimum phase. In this paper we propose a method which enables to determine these zeros, by using specific excitation signals and measuring the resulting time responses of the system. The method is verified using blind tests among the authors. When the locations of the zeros in the right half of the complex s-plane are known, modified KK relations can be successfully applied to non-minimum phase systems. We demonstrate this by computing the phase response of the electric field, excited by a point dipole source inside a closed cavity with Perfect Electrically Conducting (PEC) walls. Also, the effects of simulated measurement noise are considered for this example.
Specification tests for generalized propensity scores using double projections
Authors: Pedro H. C. Sant'Anna, Xiaojun Song
Date: 2020-03-30 00:00:00

This paper proposes a new class of nonparametric tests for the correct specification of models based on conditional moment restrictions, paying particular attention to generalized propensity score models. The test procedure is based on two different projection arguments, leading to test statistics that are suitable to setups with many covariates, and are (asymptotically) invariant to the estimation method used to estimate the nuisance parameters. We show that our proposed tests are able to detect a broad class of local alternatives converging to the null at the usual parametric rate and illustrate its attractive power properties via simulations. We also extend our proposal to test parametric or semiparametric single-index-type models.
Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework
Authors: Benjamin Avanzi, Greg Taylor, Bernard Wong, Alan Xian
Date: 2020-03-31 00:00:00

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement. Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems. This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.
Robust predictive model for Carriers, Infections and Recoveries (CIR): predicting death rates for CoVid-19 in Spain
Authors: Efren M. Benavides
Date: 2020-03-31 00:00:00

This article presents a new model to predict the evolution of infective diseases under uncertainty or low-quality information, just as it has happened in the initial scenario during the CoVid-19 spread in China and Europe. The model has been used to predict the death rate in Spain but can be used to predict the demand of ICUs or mechanical ventilators under different restraint policies. The main novelty of the model is that it keeps track of the date of infection of a single individual and uses stochastic distributions to aggregate individuals who share the same date of infection. In addition, it uses two types of infections, mild and serious, with a different recovery time. These features are implemented in a set of differential equations which determine the number of Carriers, Infections, Recoveries, Hospitalized and Deaths. Comparison with real data shows good agreement.
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios
Date: 2020-03-31 00:00:00
Tasks: Time Series Analysis

The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives,
while the transmission/infection and recovery dynamics of COVID-19 remain
obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered
(SEIR) model was proposed and applied to fit and then predict the time series
of COVID-19 evolution observed in the last three months (till 3/22/2020) in
various provinces and metropolises in China. The model results revealed the
space dependent transmission/infection rate and the significant spatiotemporal
variation in the recovery rate, likely due to the continuous improvement of
screening techniques and public hospital systems, as well as full city
lockdowns in China. The validated SEIR model was then applied to predict
COVID-19 evolution in United States, Italy, Japan, and South Korea which have
responded differently to monitoring and mitigating COVID-19 so far, although
these predictions contain high uncertainty due to the intrinsic change of the
maximum infected population and the infection/recovery rates within the
different countries. In addition, a stochastic model based on the random walk
particle tracking scheme, analogous to a mixing-limited bimolecular reaction
model, was developed to evaluate non-pharmaceutical strategies to mitigate
COVID-19 spread. Preliminary tests using the stochastic model showed that
self-quarantine may not be as efficient as strict social distancing in slowing
COVID-19 spread, if not all of the infected people can be promptly diagnosed
and quarantined.
Indoor Distance Estimation using LSTMs over WLAN Network
Date: 2020-03-31 00:00:00

The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy
degradation and are almost unavailable in indoor environments. Indoor
positioning systems (IPS) based on WiFi signals have been gaining popularity.
However, owing to the strong spatial and temporal variations of wireless
communication channels in the indoor environment, the achieved accuracy of
existing IPS is around several tens of centimeters. We present the detailed
design and implementation of a self-adaptive WiFi-based indoor distance
estimation system using LSTMs. The system is novel in its method of estimating
with high accuracy the distance of an object by overcoming possible causes of
channel variations and is self-adaptive to the changing environmental and
surrounding conditions. The proposed design has been developed and physically
realized over a WiFi network consisting of ESP8266 (NodeMCU) devices. The
experiment were conducted in a real indoor environment while changing the
surroundings in order to establish the adaptability of the system. We introduce
and compare different architectures for this task based on LSTMs, CNNs, and
fully connected networks (FCNs). We show that the LSTM based model performs
better among all the above-mentioned architectures by achieving an accuracy of
5.85 cm with a confidence interval of 93% on the scale of (4.14 m * 2.86 m). To
the best of our knowledge, the proposed method outperforms other methods
reported in the literature by a significant margin.
Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants
Authors: Meiyun Xia, Pengfei Xu, Yuanbin Yang, Wenyu Jiang, Zehua Wang, Xiaolei Gu, Mingxi Yang, Deyu Li, Shuyu Li, Guijun Dong, Ling Wang, Daifa Wang
Date: 2020-03-31 00:00:00
Tasks: Functional Connectivity

Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently. In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory, involving healthy adults. Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation, as neurofeedback, yielding a strong targeting effect. In the experiment, 10 participants (test group) received three cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the control group. Frontoparietal FC was significantly increased in the test group (p D 0.03), and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: p D 0.0005, reaction time of 3-back test: p D 0.0009). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective, and widespread training approach for cognitive function enhancement.
Asymptomatic infectives and $R_0$ for COVID
Date: 2020-04-12 00:00:00

We discuss how the presence of a large set of asymptomatic infectives changes
our estimate of the COVID-19 basic reproduction number, also known as $R_0$.