Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation of an Attenuated Chimeric Bat Influenza A Virus Live-Vaccine Prototype

Recurring influenza A virus outbreaks in livestock, particularly in swine and chickens, pose a constant threat to humans. Live attenuated influenza vaccines (LAIVs) might be a potent tool to prevent epizootic outbreaks and the resulting human IAV infections; however, LAIVs have several disadvantages, especially in terms of reassortment with circulating IAVs. ABSTRACT Recurring epizootic influenza A virus (IAV) infections in domestic livestock such as swine and poultry are associated with a substantial economic burden and pose a constant threat to human health. Therefore, universally applicable and safe animal vaccines are urgently needed. We recently demonstrated that a reassortment-incompatible chimeric bat H17N10 virus harboring the A/swan/Germany/R65/2006 (H5N1) surface glycoproteins hemagglutinin (HA) and neuraminidase (NA) can be efficiently used as a modified live influenza vaccine (MLIV). To ensure vaccine safety and, thus, improve the applicability of this novel MLIV for mammalian usage, we performed consecutive passaging in eggs and chickens. Following passaging, we identified mutations in the viral polymerase subunits PB2 (I382S), PB1 (Q694H and I695K), and PA (E141K). Strikingly, recombinant chimeric viruses encoding these mutations showed no growth deficiencies in avian cells but displayed impaired growth in human cells and mice. Homologous prime-boost immunization of mice with one of these avian-adapted chimeric viruses, designated rR65mono/H17N10EP18, elicited a strong neutralizing antibody response and conferred full protection against lethal highly pathogenic avian influenza virus (HPAIV) H5N1 challenge infection. Importantly, the insertion of the avian-adaptive mutations into the conventional avian-like A/SC35M/1980 (H7N7) and prototypic human A/PR/8/34 (H1N1) viruses led to attenuated viral growth in human cells and mice. Collectively, our data show that the polymerase mutations identified here can be utilized to further improve the safety of our novel H17N10-based MLIV candidates for future mammalian applications. IMPORTANCE Recurring influenza A virus outbreaks in livestock, particularly in swine and chickens, pose a constant threat to humans. Live attenuated influenza vaccines (LAIVs) might be a potent tool to prevent epizootic outbreaks and the resulting human IAV infections; however, LAIVs have several disadvantages, especially in terms of reassortment with circulating IAVs. Notably, the newly identified bat influenza A viruses H17N10 and H18N11 cannot reassort with conventional IAVs. Chimeric bat influenza A viruses encoding surface glycoproteins of conventional IAV subtypes might thus function as safe and applicable modified live influenza vaccines (MLIVs).
Influence of Local Land-Surface Processes on the Indian Monsoon: A Numerical Study

Abstract Twelve July integrations were made with the GLAS (Goddard Laboratory for Atmospheres) GCM (General Circulation Model) to investigate the influence of changes in the land-surface fluxes over the Indian subcontinent on the monsoon circulation and rainfall. The runs consist of an ensemble of three integrations for each of four separate cases: i) a control, ii) an experiment with increased land-surface albodo, iii) an experiment with increased land-surface albedo and reduced land-surface roughness, and iv) an experiment with increased land-surface albedo, reduced surface roughness and no evapotranspiration. All the prescribed land-surface anomalies were limited to the Indian region. An intercomparison of the ensemble means of monthly fields produced by the experiments with those of the control showed that the Indian Monsoon was significantly weakened by both the increase of surface albedo and by the reduction in surface roughness. Higher surface albedo reduced the monsoon rainfall in conformity with .
Sex, puberty, and the gut microbiome.

In humans, the gut microbiome is strongly implicated in numerous sex-specific physiological processes and diseases. Given this, it is important to understand how sex differentiation of the gut microbiome occurs and how these differences contribute to host health and disease. While it is commonly believed that the gut microbiome stabilizes after 3 years of age, our review of the literature found considerable evidence that the gut microbiome continues to mature during and after puberty in a sex-dependent manner. We also review the intriguing, though sparse, literature on potential mechanisms by which host sex may influence the gut microbiome, and vice versa, via sex steroids, bile acids, and the immune system. We conclude that the evidence for the existence of a sex-specific gut microbiome is strong but that there is a dearth of research on how host-microbe interactions lead to this differentiation. Finally, we discuss the types of future studies needed to understand the processes driving the maturation of sex-specific microbial communities and the interplay between gut microbiota, host sex, and human health.
Implications of DNA Methylation in Lewy Body Pathology

Numerous shared genetic risk loci in Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) have been elucidated by large-scale genome-wide association studies, offering new insights into the etiology of the diseases. Emerging evidence suggests that epigenetic mechanisms play an essential role in the development of these synucleinopathies. A recent study by Pihlstrøm and colleagues explored the role of DNA methylation changes in PD and DLB. The authors conducted a methylome-wide association study using 322 superior frontal gyrus gray matter tissue samples from donors with Braak Lewy body stages 0 to 6 and identified 24 5’-C-phosphate-G-30 (CpG) probes linked to Braak α-synuclein stage at a false discovery rate (FDR) <0.05. The results were validated in 200 additional donor samples using comparable inclusion criteria and processing workflow. DNA methylation differences across Braak Lewy body stages for the 24 FDR-significant sites were strongly correlated between the discovery and the validation cohorts. In addition, four DNA methylation sites, including cg07107199 (annotated to TMCC2), cg14511218 (positioned downstream of SFMBT2), cg09985192 (annotated to AKAP6), and cg04011470 (annotated to PHYHIP), were significantly replicated. Through meta-analysis, these four DNA methylation sites showed the strongest association signal, and the cg03318382 (positioned upstream of SEPTIN5) emerged with the fifth strongest P value. The last falls within the 3 Mb commonly deleted region 22q11.2 associated with earlyonset PD. Finally, they assessed genotype data for cismethylation quantitative trait loci within 500 kb of the Braak stage-associated CpGs but found no genetic variants significantly associated withmethylation levels for any of the probes. This study presents a well-powered, two-stage, epigenomewide association study of Lewy body pathology in the postmortem human frontal cortex, establishing significant methylation differences associated with Braak Lewy body stages. One particular limitation of the study is that the analyses were restricted to a single brain region, so the methylation sites identified by the two-stage approach may not correlate with other brain regions. In this regard, previous work has demonstrated brain regional variation in differential DNA methylation. Lastly, it is worth mentioning that DNA methylation can be influenced by environmental exposures. Future meta-analyses with larger sample sizes will be a useful strategy to disentangle the epigenetic architecture underlying Lewy neuropathology. The investigation of DNA methylation can be used to complement other genomic approaches, such as genomewide association studies and gene expression studies, and might provide important contributions to understand the disease mechanisms and eventually develop disease-modifying therapies for synucleinopathies.
Modeling studies and numerical analyses of coupled PDEs system in electrohydrodynamics

MODELING STUDIES AND NUMERICAL ANALYSES OF COUPLED PDES SYSTEM IN ELECTROHYDRODYNAMICS by Yuzhou Sun Dr. Pengtao Sun, Examination Committee Chair Associate Professor of Mathematics University of Nevada, Las Vegas, USA Electrohydrodynamics (EHD) is the term used for the hydrodynamics coupled with electrostatics, whose governing equations consist of the electrostatic potential (Poisson) equation, the ionic concentration (Nernst-Planck) equations, and NavierStokes equations for an incompressible, viscous dielectric liquid. In this dissertation, we focus on a specific application of EHD fuel cell dynamics in the field of renewable and clean energy, study its traditional model and attempt to develop a new fuel cell model based on the traditional EHD model. Meanwhile, we develop a series of efficient and robust numerical methods for these models, and carry out their numerical analyses on the approximation accuracy. In particular, we analyze the error estimates of finite element method for a simplified 2D isothermal steady state two-phase transport model of Proton Exchange Membrane Fuel Cell (PEMFC) as well as its transient version. On the aspect of hydrodynamics arising in the fuel cell system, the fluid flow through the open channels and porous media at the same time, both Navier-Stokes equations and Darcys law are involved in the fluid domains, leading to a Navier-Stokes-Darcy coupling problem. In this dissertation, we study a one-continuum model approach, so-called Brinkman model, to overcome this problem iii in a more efficient way. To develop a new fuel cell model based on EHD theory, in addition to the two-phase transport model of fuel cells, we carry out numerical analyses for Poisson-Nernst-Planck (PNP) equations using both standard FEM and mixed FEM, which are the essential governing equations involved by EHD model. Finally, we are able to further extend the traditional fuel cell model to more general cases in view of EHD characteristics, and develop a new fuel cell model by appropriately combining PNP equations with the traditional fuel cell model. We conduct the error analysis for PNP-Brinkman system in this dissertation.
Prevalence and correlates of pre-diabetes in adults of mixed ethnicities in the South African population: A systematic review and meta-analysis

Introduction Pre-diabetes is a metabolic condition characterised by moderate glycaemic dysregulation and is a frontline risk factor for multiple metabolic complications such as type 2 diabetes mellitus. To the best of our knowledge, this will be the first systematic review and meta-analysis focusing on generating a comprehensive pooling of studies reporting on pre-diabetes prevalence in South Africa. Therefore, the review’s purpose will be to screen and select reports that can be used to synthesise and provide the best estimate prevalence of pre-diabetes and its associated correlates in the South African population. Methods and analysis To determine the prevalence and correlates of pre-diabetes in South Africa, we searched PubMed, Web of Science, Google scholar and African Journal online for published or unpublished studies reporting the prevalence of pre-diabetes in South Africa starting from the year 2000 to 2020. Studies were assessed for eligibility by checking if they met the inclusion criteria. Results & conclusion The total number of studies deemed eligible is 13 and from these studies, an overall prevalence of pre-diabetes was reported to be 15,56% in the South African population. Hypertension, obesity and sedentary lifestyle were the common correlates recorded for the population of interest. Therefore, the review highlights the disturbingly high prevalence of pre-diabetes in South Africa and necessitates further investigations into the possible genetics, biochemical and hormonal changes in pre-diabetes. Ethics and dissemination The review will not require ethics clearance because non-identifiable data will be used. The review outcomes will give insight into the current burden that pre-diabetes has in South Africa. PROSPERO registration number CRD42020182430
Virome Analysis of Normal and Growth Retardation Disease-Affected Macrobrachium rosenbergii

Growth retardation disease (GRD) has seriously affected the development and economic growth of the M. rosenbergii aquaculture industry. Our virome analysis showed that diverse viral sequences were present in M. rosenbergii, significantly expanding our knowledge of viral diversity in M. rosenbergii. ABSTRACT The giant freshwater prawn, Macrobrachium rosenbergii, is an important aquaculture species in China. Growth retardation disease (GRD) is a common contagious disease in M. rosenbergii, resulting in slow growth and precocious puberty in prawns, and has caused growing economic losses in the M. rosenbergii industry. To investigate the viral diversity of M. rosenbergii and identify potentially high-risk viruses linked to GRD, virome analysis of the GRD-affected and normal M. rosenbergii was carried out using next-generation sequencing (NGS). A total of 327 contigs (>500 bp) were related to viral sequences belonging to 23 families/orders and a group of unclassified viruses. The majority of the viral contigs in M. rosenbergii belonged to the order Picornavirales, with the Solinviviridae family being the most abundant in both the diseased and normal groups. Furthermore, 16 RNA viral sequences with nearly complete genomes were characterized and phylogenetically analyzed, belonging to the families Solinviviridae, Flaviviridae, Polycipiviridae, Marnaviridae, and Dicistroviridae as well as three new clades of the order Picornavirales. Notably, the cross-species transmission of a picorna-like virus was observed between M. rosenbergii and plants. The “core virome” seemed to be present in the diseased and normal prawns. Still, a clear difference in viral abundance was observed between the two groups. These results showed that the broad diversity of viruses is present in M. rosenbergii and that the association between viruses and disease of M. rosenbergii needs to be further investigated. IMPORTANCE Growth retardation disease (GRD) has seriously affected the development and economic growth of the M. rosenbergii aquaculture industry. Our virome analysis showed that diverse viral sequences were present in M. rosenbergii, significantly expanding our knowledge of viral diversity in M. rosenbergii. Some differences in viral composition were noted between the diseased and normal prawns, indicating that some viruses become more abundant in occurrences or outbreaks of diseases. In the future, more research will be needed to determine which viruses pose a risk for M. rosenbergii. Our study provides important baseline information contributing to disease surveillance and risk assessment in M. rosenbergii aquaculture.
Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features

ABSTRACT Background Drug development productivity has been declining lately due to elevated costs and reduced discovery rates. Therefore, pharmaceutical companies have been seeking alternative ways to determine and evaluate drug candidates. Research design and methods In this work, we proposed a new computational approach to directly predict the regulatory approval of drug candidates, and implemented it as a method called ‘DrugApp. ’ To accomplish this task, we employed multiple types of features including molecular and physicochemical properties of drug candidates, together with clinical trial and patent-related features, which are then processed by random forest classifiers to train our disease group-specific approval prediction models. Results Our evaluations indicated DrugApp has a high and robust prediction performance. Within a use-case study, we showed our method can predict phase IV trial drugs that are later withdrawn from the market due to severe side effects. Finally, we used DrugApp models to forecast the approval of drug candidates that are currently in phases I/II/III of clinical trials. Conclusions We hope that our study will aid the research community in terms of evaluating and improving the process of drug development. The datasets, source code, results, and pre-trained models of DrugApp are freely available at https://github.com/HUBioDataLab/DrugApp.
A Contour-Based Recovery Of Optical Flows By Iterative Improvement

We present an iterative algorithm for the computation of motion parameters which characterize the motion in the image plane, given a sequence of contours which change in time due to motion in space. Our method is based on the observation due to Waxman and Ullman (1985) that the second-order polynomial approximation of optical flow in the image coordinates provides sufficient information for 3-D motion computation. The use of an explicit flow model enables us to improve normal flow estimates through an iterative process. The algorithm has been tested on the synthetic time-varying images. The optical flows recovered from this scheme are accurate enough to be used for 3-D structure and motion computation. We also discuss the (in)variance of the zerocrossings of the Laplacian operating on the image intensities with respect to motion. The change of perspective view due to relative motion results certain zerocrossings not being preserved as image evolves, thereby deteriorating the accuracy of 2-D motion obtained by analyzing such zero-crossing contours. We present a theorem which relates true local measurement of zerocrossings to the tolerance of optical flow vector. Such analysis suggests an effective way to prevent "unstable" zerocrossings from being used for the matching process of motion computation.
Taming a Generative Model

Generative models are becoming ever more powerful, being able to synthesize highly realistic images. We pro-pose an algorithm for taming these models — changing the probability that the model will produce a speciﬁc image or image category. We consider generative models that are powered by normalizing ﬂows, which allows us to reason about the exact generation probability likelihood for a given image. Our method is general purpose, and we exemplify it using models that generate human faces, a subdomain with many interesting privacy and bias considerations. Our method can be used in the context of privacy, e.g., removing a speciﬁc person from the output of a model, and also in the context of de-biasing by forcing a model to output speciﬁc image categories according to a given target distribution. Our method uses a fast ﬁne-tuning process without retraining the model from scratch, achieving the goal in less than 1% of the time taken to initially train the generative model. We evaluate qualitatively and quantitatively, to examine the success of the taming process and output quality. Our code is publicly available at https://github.com/ ShimonMalnick/Taming_a_Generative_Model .
Dirac Kondo effect under magnetic catalysis

We develop a mean-field theory of a novel Kondo effect emerging in systems without a Fermi surface, which instead emerges under strong magnetic fields. We determine the magnitude of the Kondo condensate which is a particle pairing composed of conducting Dirac fermions and localized impurities. We focus on the competition between the Kondo effect and the energy gap formation that stems from the pairing among the Dirac fermions leading to the dynamical chiral symmetry breaking. We find that this competition induces a quantum critical point. We also investigate finite-temperature effects. This system at vanishing fermion density can be studied with Monte Carlo lattice simulations which do not suffer from the sign problem.
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring

Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future. One of its main challenges is forecasting the sequence of activities that will occur from a given point in time -- suffix prediction -- . Most approaches to the suffix prediction problem learn to predict the suffix by learning how to predict the next activity only, not learning from the whole suffix during the training phase. This paper proposes a novel architecture based on an encoder-decoder model with an attention mechanism that decouples the representation learning of the prefixes from the inference phase, predicting only the activities of the suffix. During the inference phase, this architecture is extended with a heuristic search algorithm that improves the selection of the activity for each index of the suffix. Our approach has been tested using 12 public event logs against 6 different state-of-the-art proposals, showing that it significantly outperforms these proposals.
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications

This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of US macro and financial indicators, where the homoskedasticity assumption is relaxed to allow for time-varying volatility. In particular, we exploit the mixture representation of the multivariate asymmetric Laplace likelihood and the Cholesky-type decomposition of the scale matrix to introduce stochastic volatility and GARCH processes, and we provide an efficient MCMC to estimate them. The proposed models outperform the homoskedastic benchmark mainly when predicting the distribution's tails. We provide a model combination using a quantile score-based weighting scheme, which leads to improved performances, notably when no single model uniformly outperforms the other across quantiles, time, or variables.
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users’ energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.
On the Pontrjagin classes of spray manifolds

Locally projectively flat metrics (or sprays) form a rich class of metrics (or sprays) in Finsler and spray geometry. The characterization of such metrics is the Hilbert Fourth Problem in the regular case. In this paper we study the Pontrjagin classes of a manifold given a spray structure, and show that a manifold equipped with a locally projectively flat Finsler metric (or spray) has zero Pontrjagin classes.