Miracle-dz/UniFAR-FTUs huggingface dataset:
Coinfection and nonrandom recombination drive the evolution of swine enteric coronaviruses

ABSTRACT Coinfection with multiple viruses is a common phenomenon in clinical settings and is a crucial driver of viral evolution. Although numerous studies have demonstrated viral recombination arising from coinfections of different strains of a specific species, the role of coinfections of different species or genera during viral evolution is rarely investigated. Here, we analyzed coinfections of and recombination events between four different swine enteric coronaviruses that infect the jejunum and ileum in pigs, including porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), and a deltacoronavirus, porcine deltacoronavirus (PDCoV). Various coinfection patterns were observed in 4,468 fecal and intestinal tissue samples collected from pigs in a 4-year survey. PEDV/PDCoV was the most frequent coinfection. However, recombination analyses have only detected events involving PEDV/TGEV and SADS-CoV/TGEV, indicating that inter-species recombination among coronaviruses is most likely to occur within the same genus. We also analyzed recombination events within the newly identified genus Deltacoronavirus and found that sparrows have played a unique host role in the recombination history of the deltacoronaviruses. The emerging virus PDCoV, which can infect humans, has a different recombination history. In summary, our study demonstrates that swine enteric coronaviruses are a valuable model for investigating the relationship between viral coinfection and recombination, which provide new insights into both inter- and intraspecies recombination events among swine enteric coronaviruses, and extend our understanding of the relationship between coronavirus coinfection and recombination.
The integration of e-learning in the Algerian tertiary level: The case of Biskra University

In this new Age of the digital technology, it is of tremendous importance for higher education in Algeria to work hard for the purpose to narrow the huge gap between the reality they face and live in and the potential of the new technologies in order to keep abreast with the standard norms of modern education. Undoubtedly, some uses of the digital technologies are quite promising but require substantial reorganisation and rethinking of the roles of the individuals and institutions. In the sphere of this problematic, in the present work, we purport to explain the gap between the existing, prevailing reality of the Algerian universities and the expectations of the effects of the digital technologies in the academic world of higher education. In precise terms, this paper will tackle the question of to which extent the Algerian universities are prepared to implement and use the new technologies as part of their current practices for the purposes they can serve, and discuss the challenges and problems the institutions in higher education are facing to place information and communication technologies as central to teaching and learning.
Understanding Human Leishmaniasis: The Need for an Integrated Approach

M. Hide,1 B. Bucheton,1 S. Kamhawi,2 R. Bras-Goncalves,3 S. Sundar,4 J.-L. Lemesre,3 and A. -L. Ba~nuls1 1Genetique et Evolution des Maladies Infectieuses (UMR CNRS/IRD 2724), IRD de Montpellier, 911 av Agropolis BP 64501, 34394 Montpellier Cedex 5, France 2Laboratory of Parasitic Diseases, National Institutes of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA 3Pathogenie des Trypanosomatides (UR008), IRD de Montpellier, 911 av Agropolis BP 64501, 34394 Montpellier Cedex 5, France 4Infectious Diseases Research Laboratory, Department of Medicine, Institute of Medical Sciences, Banaras Hindu University, 6 SK Gupta, Nagar, Lanka,Varanasi 221005, India
A Map Matching Algorithm in GPS-based Car Navigation System

Along with development of the GPS navigation system, one of important performance parameters to evaluate a navigation system is how to match GPS track with the road in electronic map accurately. Now many systems use the shortest distance algorithm to match with the electronic map. But GPS's error, city environment and road complexity often lead to inaccurate matching. To solve this problem, this paper presents a GPS-navigation-system-based combination map matching algorithm that combines the shortest distance algorithm and the assistant map matching algorithm which based the experience of the GPS navigation system. Then we have tested the correctness of the algorithm by actual running, it can make GPS track and the electronic map to match accurately and reliably.
Fabric: a retrospective on evolving SDN

MPLS was an attempt to simplify network hardware while improving the flexibility of network control. Software-Defined Networking (SDN) was designed to make further progress along both of these dimensions. While a significant step forward in some respects, it was a step backwards in others. In this paper we discuss SDN's shortcomings and propose how they can be overcome by adopting the insight underlying MPLS. We believe this hybrid approach will enable an era of simple hardware and flexible control.
The Effects of Varying Dietary Carbohydrate and Fat Content on Survival in a Murine LNCaP Prostate Cancer Xenograft Model

Purpose: Numerous dietary factors elevate serum levels of insulin and insulin-like growth factor I (IGF-I), both potent prostate cancer mitogens. We tested whether varying dietary carbohydrate and fat, without energy restriction relative to comparison diets, would slow tumor growth and reduce serum insulin, IGF-I, and other molecular mediators of prostate cancer in a xenograft model. Experimental Design: Individually caged male severe combined immunodeficient mice (n = 130) were randomly assigned to one of three diets (described as percent total calories): very high-fat/no-carbohydrate ketogenic diet (NCKD: 83% fat, 0% carbohydrate, 17% protein), low-fat/high-carbohydrate diet (LFD: 12% fat, 71% carbohydrate, 17% protein), or high-fat/moderate-carbohydrate diet (MCD: 40% fat, 43% carbohydrate, 17% protein). Mice were fed to maintain similar average body weights among groups. Following a preliminary feeding period, mice were injected with 1 × 106 LNCaP cells (day 0) and sacrificed when tumors were ≥1,000 mm3. Results: Two days before tumor injection, median NCKD body weight was 2.4 g (10%) and 2.1 g (8%) greater than the LFD and MCD groups, respectively (P < 0.0001). Diet was significantly associated with overall survival (log-rank P = 0.004). Relative to MCD, survival was significantly prolonged for the LFD (hazard ratio, 0.49; 95% confidence interval, 0.29-0.79; P = 0.004) and NCKD groups (hazard ratio, 0.59; 95% confidence interval, 0.37-0.93; P = 0.02). Median serum insulin, IGF-I, IGF-I/IGF binding protein-1 ratio, and IGF-I/IGF binding protein-3 ratio were significantly reduced in NCKD relative to MCD mice. Phospho-AKT/total AKT ratio and pathways associated with antiapoptosis, inflammation, insulin resistance, and obesity were also significantly reduced in NCKD relative to MCD tumors. Conclusions: These results support further preclinical exploration of carbohydrate restriction in prostate cancer and possibly warrant pilot or feasibility testing in humans.
Learning Temporal Distribution and Spatial Correlation for Universal Moving Object Segmentation

Universal moving object segmentation aims to provide a general model for videos from all types of natural scenes, as previous approaches are usually effective for speciﬁc or similar scenes. In this paper, we propose a method called L earning T emporal Distribution and S patial Correlation (LTS) that has the potential to be a general so-lution for universal moving object segmentation. In the proposed approach, the distribution from temporal pixels is ﬁrst learned by our Defect Iterative Distribution Learning (DIDL) network for a scene-independent segmentation. Then, the Stochastic Bayesian Reﬁnement (SBR) Network, which learns the spatial correlation, is proposed to improve the binary mask generated by the DIDL network. Beneﬁt-ing from the scene independence of the temporal distribution and the accuracy improvement resulting from the spatial correlation, the proposed approach performs well for almost all videos from diverse and complex natural scenes with ﬁxed parameters. Comprehensive experiments on standard datasets including LASIESTA, CDNet2014, BMC, SBMI2015 and 128 real world videos demonstrate the superiority of proposed approach compared to state-of-the-art methods with or without the use of deep learning networks. To the best of our knowledge, this work has high potential to be a general solution for moving object segmentation in real world environments. The code and real-world videos will be made public after the paper is accepted.
Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach

With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring, and biological information management systems, uncertainty-aware query processing has received a significant amount of research effort from the database community in recent years. In this paper, we investigate a new type of query in the context of uncertain databases, namely uncertain top-k influential sites query (UTkIS query for short), which can be applied in a wide range of application areas such as marketing analysis and mobile services. Since it is not so straightforward to precisely define the semantics of top-k query with uncertain data, in this paper we introduce a novel and more intuitive formulation of the query on the basis of expected rank semantics. To address the efficiency issue caused by possible worlds exploration, we propose effective pruning rules and a divide-and-conquer paradigm such that the number of candidates as well as the number of possible worlds to be considered can be significantly reduced. Finally, we conduct extensive experiments on real data sets to verify the effectiveness and efficiency of the new methods proposed in this paper.
A Built-In IDDQ Testing Circuit*

Although IDDQ testing has become a widely accepted defect detection technique for CMOS ICs, its effectiveness in very deep submicron technologies is threatened by the increased transistor leakage current. In this paper, a built-in IDDQ testing circuit is presented, that aims to extend the viability of IDDQ testing in future technologies and first experimental results are discussed.
Recursive Bayesian Regression Modeling and Learning

This paper presents a new Bayesian regression and learning algorithm for adaptive pattern classification. Our aim is to continuously update regression parameters to meet nonstationary environments for real-world applications. Here, a kernel regression model is used to represent two-class data. The initial regression parameters are estimated by maximizing the likelihood of training data. To activate online learning, we properly express the randomness of regression parameters as a conjugate prior, which is a normal-gamma distribution. When new adaptation data are enrolled, we can accumulate sufficient statistics and come up with a new normal-gamma distribution as the posterior distribution. We therefore exploit a recursive Bayesian algorithm for online regression and learning. Regression parameters are incrementally adapted to the newest environments. Robustness of classification rule is assured using online regression parameters. In the experiments on face recognition, the proposed regression algorithm outperforms support vector machine and relevance vector machine for different numbers of adaptation data.
Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.
A phase field approach to the fluid filled fracture surrounded by a poroelastic medium ∗

In this paper we present an incremental formulation of the phase field model for a fluid filled crack in a poroelastic medium. The mathematical model represents a linear elasticity system with fading elastic moduli as the crack grows, that is coupled with an elliptic variational inequality for the phase field variable. The convex constraint of the variational inequality assures the irreversibility and entropy compatibility of the crack formation. We construct a finite dimensional approximation and demonstrate its solvability. Using compactness and monotonicity arguments we prove that solutions to the discretized problem converge to a solution of the incremental problem. We present a discretization technique ∗A.M. would like to thank Institute for Computational Engineering and Science (ICES), UT Austin for hospitality during his sabbatical stay from February to July, 2013. The research by M. F. Wheeler was partially supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences through DOE Energy Frontier Research Center: The Center for Frontiers of Subsurface Energy Security (CFSES) under Contract No. DE-FG02-04ER25617, MOD. 005. †E-mail: Andro.Mikelic@univ-lyon1.fr
Université de Montréal

SOL1018 (3 crédits) Initiation à la recherche sociologique Hiver 2022 Horaire : mercredi de 8 h 30 à 11 h 30 Salle de cours : Pour les séances en présentiel B-3280 – Pavillon 3200, Jean-Brillant ; en ligne pour le reste des séances Kaveh Boveiri kaveh.boveiri@umontreal.ca Bureau : C-5147 Pav. Lionel-Groulx Disponibilités : Après le cours et sur rendez-vous Auxiliaire d’enseignement : Amani Braa amani.braa@umontreal.ca
Web Data Commons - Extracting Structured Data from Two Large Web Corpora

More and more websites embed structured data describing for instance products, people, organizations, places, events, resumes, and cooking recipes into their HTML pages using encoding standards such as Microformats, Microdatas and RDFa. The Web Data Commons project extracts all Microformat, Microdata and RDFa data from the Common Crawl web corpus, the largest and most up-todata web corpus that is currently available to the public, and provides the extracted data for download in the form of RDF-quads. In this paper, we give an overview of the project and present statistics about the popularity of the different encoding standards as well as the kinds of data that are published using each format.
Robust Data Detection for the Photon-Counting Free-Space Optical System With Implicit CSI Acquisition and Background Radiation Compensation

Since atmospheric turbulence and pointing errors cause signal intensity fluctuations and the background radiation surrounding the free-space optical photon-counting receiver contributes an undesired noisy component, the receiver requires accurate channel state information (CSI) and background information to adjust the detection threshold. In most previous studies, for CSI acquisition, pilot symbols were employed, which leads to a reduction of spectral and energy efficiency; and an impractical assumption that the background radiation component is perfectly known was made. In this paper, we develop an efficient and robust sequence receiver, which acquires the CSI and the background information implicitly and requires no knowledge about the channel model information. It is robust since it can automatically estimate the CSI and background component and detect the data sequence accordingly. Its decision metric has a simple form and involves no integrals, and thus, can be easily evaluated. A Viterbi-type trellis-search algorithm is adopted to improve the search efficiency, and a selective-store strategy is adopted to overcome a potential error floor problem as well as to increase the memory efficiency. To further simplify the receiver, a decision-feedback symbol-by-symbol receiver is proposed as an approximation of the sequence receiver. By simulations and theoretical analysis, we show that the performance of both the sequence receiver and the symbol-by-symbol receiver, approaches that of detection with perfect knowledge of the CSI and background radiation, as the length of the window for forming the decision metric increases.
Condition and population structure of the round goby (Neogobius melanostomus Pallas, 1811) in Szczecin Lagoon in 2010–2014

Abstract The round goby (Neogobius melanostomus Pallas, 1811) is one of the most invasive fish species in the world, including Poland. 300 fish of this species were collected in the waters of Szczecin lagoon between 2010 and 2014 and examination of the size, sex and age structures of the population and of the condition of the fish was performed. Total length and standard length of all the collected fish amounted to 149.2 mm (±42.21) and 128.1 mm (±38.65), respectively. The age structure of the fish consisted of nine generations, with clear prevalence of the fish aged 3+ and 4+. Mean values of condition factors for the whole fish sample amounted to 0.20 (±0.02). However, no statistically significant differences in particular years were observed (ANOVA, p > 0.05), but the condition of the fish between 2010 and 2011 was slightly higher than between 2013 and 2014. The obtained results indicate that the Szczecin Lagoon environment provides this species with favourable living conditions and it can be stipulated that the quantity of individuals of this fish species will increase.
Implicit and Explicit Camera Calibration: Theory and Experiments

By implicit camera calibration, we mean the process of calibrating a camera without explicitly computing its physical parameters. Implicit calibration can be used for both three-dimensional (3-D) measurement and generation of image coordinates. In this paper, we present a new implicit model based on the generalized projective mappings between the image plane and two calibration planes. The back-projection and projection processes are modelled separately to ease the computation of distorted image coordinates from known world points. A set of constraints of perspectivity is derived to relate the transformation parameters of the two calibration planes. Under the assumption of the radial distortion model, we present a computationally efficient method for explicitly correcting the distortion of image coordinates in frame buffer without involving the computation of camera position and orientation. By combining with any linear calibration techniques, this method makes explicit the camera physical parameters. Extensive experimental comparison of our methods with the classic photogrammetric method and Tsai's (1986) method in the aspects of 3-D measurement (both absolute and relative errors), the prediction of image coordinates, and the effect of the number of calibration points, is made using real images from 15 different depth values. >
S100B Engages RAGE or bFGF/FGFR1 in Myoblasts Depending on Its Own Concentration and Myoblast Density. Implications for Muscle Regeneration

In high-density myoblast cultures S100B enhances basic fibroblast growth factor (bFGF) receptor 1 (FGFR1) signaling via binding to bFGF and blocks its canonical receptor, receptor for advanced glycation end-products (RAGE), thereby stimulating proliferation and inhibiting differentiation. Here we show that upon skeletal muscle injury S100B is released from myofibers with maximum release at day 1 post-injury in coincidence with satellite cell activation and the beginning of the myoblast proliferation phase, and declining release thereafter in coincidence with reduced myoblast proliferation and enhanced differentiation. By contrast, levels of released bFGF are remarkably low at day 1 post-injury, peak around day 5 and decline thereafter. We also show that in low-density myoblast cultures S100B binds RAGE, but not bFGF/FGFR1 thereby simultaneously stimulating proliferation via ERK1/2 and activating the myogenic program via p38 MAPK. Clearance of S100B after a 24-h treatment of low-density myoblasts results in enhanced myotube formation compared with controls as a result of increased cell numbers and activated myogenic program, whereas chronic treatment with S100B results in stimulation of proliferation and inhibition of differentiation due to a switch of the initial low-density culture to a high-density culture. However, at relatively high doses, S100B stimulates the mitogenic bFGF/FGFR1 signaling in low-density myoblasts, provided bFGF is present. We propose that S100B is a danger signal released from injured muscles that participates in skeletal muscle regeneration by activating the promyogenic RAGE or the mitogenic bFGF/FGFR1 depending on its own concentration, the absence or presence of bFGF, and myoblast density.
Sex differences in age development of a mouse inbred strain: body composition, adipocyte size and organ weights of liver, heart and muscles

The mouse inbred strain HLG has only recently been submitted for international registration. It has been widely used in radiological studies. Since these mice tend to become obese, they may be of interest for studies concerning fat metabolism. Therefore, the present ageing study examines the sex-specific development of body weight, body composition, adipocyte size as well as the organ weight of liver, heart and thigh muscles of the inbred strain HLG (54 males/53 virgin females) between day 40 and 360 of life. The body weight development showed a distinct sexual dimorphism, quickly increasing in the case of males but comparatively protracted in females until maturity. Significantly higher values for body and fat-free dry weight as well as body water were found in males of all ages. Body fat and adipocyte diameter were only higher in the 80 and 120 day-old males. Thus, the increase in female body weight resulted largely through a rise in body fat as additionally evidenced by the sex-specific correlation of characteristics. A 2-fold analysis of variance showed sex differences in the relative weights of liver and muscle, but not of the heart. A sex-specific close correlation was observed between body and liver weights in males until day 180. Correlations between body fat and other characteristics were only rarely found. Differences in coefficients of variation largely depended on the characteristics. A higher variability was also found for some characteristics in the youngest groups (40 days). A sex-specific difference was only seen in the case of body fat content for 60 day-old mice (female>male).