Miracle-dz/UniFAR-FTUs huggingface dataset:
The SAFRAN‐ISBA‐MODCOU hydrometeorological model applied over France

The hydrometeorological model SIM consists in a meterological analysis system (SAFRAN), a land surface model (ISBA) and a hydrogeological model (MODCOU). It generates atmospheric forcing at an hourly time step, and it computes water and surface energy budgets, the river ow at more than 900 rivergauging stations, and the level of several aquifers. SIM was extended over all of France in order to have a homogeneous nation-wide monitoring of the water resources: it can therefore be used to forecast flood risk and to monitor drought risk over the entire nation. The hydrometeorologival model was applied over a 10-year period from 1995 to 2005. In this paper the databases used by the SIM model are presented, then the 10-year simulation is assessed by using the observations of daily stream-flow, piezometric head, and snow depth. This assessment shows that SIM is able to reproduce the spatial and temporal variabilities of the water fluxes. The efficiency is above 0.55 (reasonable results) for 66 % of the simulated rivergages, and above 0.65 (rather good results) for 36 % of them. However, the SIM system produces worse results during the driest years, which is more likely due to the fact that only few aquifers are simulated explicitly. The annual evolution of the snow depth is well reproduced, with a square correlation coeficient around 0.9 over the large altitude range in the domain. The stream ow observations were used to estimate the overall error of the simulated latent heat ux, which was estimated to be less than 4 %.
THE MICROSCOPIC DYNAMICS OF A SPATIAL ECOLOGICAL MODEL

The evolution of states of a spatial ecological model is stud- ied. The model describes an infinite population of point entities placed in R d which reproduce themselves at distant points (disperse) and die with rate that includes a competition term. The system's states are probability measures on the space of configurations of entities, and their evolution is described by means of a hierarchical chain of equations for the corresponding correlation functions derived from the Fokker-Planck equation for measures. Under natural conditions imposed on the model parameters it is proved that the correlation functions evolve in a scale of Banach spaces in such a way that each correlation function corre- sponds to a unique sub-Poissonian state. Some further properties of the evolution of states constructed in this way are also described.
“Double‐hit” chronic lymphocytic leukemia: An aggressive subgroup with 17p deletion and 8q24 gain

Chronic lymphocytic leukemia (CLL) with 17p deletion (17p‐) is associated with a lack of response to standard treatment and thus the worst possible clinical outcome. Various chromosomal abnormalities (including unbalanced translocations, deletions, ring chromosomes and isochromosomes) result in the loss of 17p and one copy of the TP53 gene. The objective of the present study was to determine whether the type of chromosomal abnormality leading to 17p‐ and the additional aberrations influenced the prognosis in a series of 195 patients with 17p‐CLL. Loss of 17p resulted primarily from an unbalanced translocation (70%) with several chromosome partners (the most frequent being chromosome 18q), followed by deletion 17p (23%), monosomy 17 (8%), isochromosome 17q [i(17q)] (5%) and a ring chromosome 17 (2%). In a univariate analysis, monosomy 17, a highly complex karyotype (≥5 abnormalities), and 8q24 gain were associated with poor treatment‐free survival, and i(17q) (P = .04), unbalanced translocations (P = .03) and 8q24 gain (P = .001) were significantly associated with poor overall survival. In a multivariate analysis, 8q24 gain remained a significant predictor of poor overall survival. We conclude that 17p deletion and 8q24 gain have a synergistic impact on outcome, and so patients with this “double‐hit” CLL have a particularly poor prognosis. Systematic, targeting screening for 8q24 gain should therefore be considered in cases of 17p‐ CLL.
A Metabolomic Approach Applied to a Liquid Chromatography Coupled to High-Resolution Tandem Mass Spectrometry Method (HPLC-ESI-HRMS/MS): Towards the Comprehensive Evaluation of the Chemical Composition of Cannabis Medicinal Extracts.

INTRODUCTION Cannabis sativa L. is a powerful medicinal plant and its use has recently increased for the treatment of several pathologies. Nonetheless, side effects, like dizziness and hallucinations, and long-term effects concerning memory and cognition, can occur. Most alarming is the lack of a standardised procedure to extract medicinal cannabis. Indeed, each galenical preparation has an unknown chemical composition in terms of cannabinoids and other active principles that depends on the extraction procedure. OBJECTIVE This study aims to highlight the main differences in the chemical composition of Bediol® extracts when the extraction is carried out with either ethyl alcohol or olive oil for various times (0, 60, 120 and 180 min for ethyl alcohol, and 0, 60, 90 and 120 min for olive oil). METHODOLOGY Cannabis medicinal extracts (CMEs) were analysed by liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS) using an untargeted metabolomics approach. The data sets were processed by unsupervised multivariate analysis. RESULTS Our results suggested that the main difference lies in the ratio of acid to decarboxylated cannabinoids, which dramatically influences the pharmacological activity of CMEs. Minor cannabinoids, alkaloids, and amino acids contributing to this difference are also discussed. The main cannabinoids were quantified in each extract applying a recently validated LC-MS and LC-UV method. CONCLUSIONS Notwithstanding the use of a standardised starting plant material, great changes are caused by different extraction procedures. The metabolomics approach is a useful tool for the evaluation of the chemical composition of cannabis extracts. Copyright © 2017 John Wiley & Sons, Ltd.
Short-Term Exercise Training Alters Leukocyte Chemokine Receptors in Obese Adults

Obesity is characterized by chronic low-grade inflammation driven by activation and tissue infiltration of circulating leukocytes. Although exercise has anti-inflammatory effects, the impact of exercise on mediators of leukocyte migration is unclear. Purpose To determine the impact of high-intensity interval training (HIIT) versus moderate-intensity continuous training (MICT), in the absence of weight/fat loss, on circulating chemokines and leukocyte chemokine receptors. Methods Thirty-seven inactive obese adults were randomized to 2 wk (10 sessions) of HIIT or MICT with fasting blood samples collected before and after training. Plasma concentration of C-C motif chemokine ligand 2 (CCL2; also known as monocyte chemoattractant protein-1), CCL3 (also known as macrophage inflammatory protein-1alpha), and C-X-C motif ligand 8 (CXCL8; also known as interleukin-8) were determined and the chemokine receptors CCR2, CCR5, and CXCR2 were measured on monocytes, neutrophils, and T cells. Results MICT reduced the percentage of monocytes positive for CCR2 and reduced surface protein expression of CXCR2 on monocytes (both P < 0.05), whereas HIIT increased CCR5 surface protein expression and percentage CCR5 positive monocytes and neutrophils (all P < 0.05) along with increasing the percentage of T cells that were positive for CCR5 (P < 0.05). There were no significant changes in circulating chemokines, percent body fat or visceral adipose tissue. Conclusions Exercise, in the absence of weight/fat loss and without changes in circulating chemokines, has direct effects on leukocytes in obese adults with HIIT and MICT resulting in different responses. MICT may reduce monocyte migration potential through downregulation of CCR2 and CXCR2, whereas HIIT may increase potential for CCR5-mediated monocyte, neutrophil, and T-cell infiltration. The impact of different exercise protocols on leukocyte trafficking to tissues in obesity warrants further research.
Deposited in DRO : 24 February 2017 Version of attached le : Published Version Peer-review status of attached le : Peer-reviewed Citation for published item :

Standard object recognition procedures assess animals’ memory through their spontaneous exploration of novel objects or novel configurations of objects with other aspects of their environment. Such tasks are widely used in memory research, but also in pharmaceutical companies screening new drug treatments. However, behaviour in these tasks may be driven by influences other than novelty such as stress from handling which can subsequently influence performance. This extra-experimental variance means that large numbers of animals are required to maintain power. In addition, accumulation of data is time consuming as animals typically perform only one trial per day. The present study aimed to explore how effectively recognition memory could be tested with a new continual trials apparatus which allows for multiple trials within a session and reduced handling stress through combining features of delayed nonmatching-to-sample and spontaneous object recognition tasks. In this apparatus Lister hooded rats displayed performance significantly above chance levels in object recognition tasks (Experiments 1 and 2) and in tasks of object-location (Experiment 3) and object-in-context memory (Experiment 4) with data from only five animals or fewer per experimental group. The findings indicated that the results were comparable to those of previous reports in the literature and maintained statistical power whilst using less than a third of the number of animals typically used in spontaneous recognition paradigms. Overall, the results highlight the potential benefit of the continual trials apparatus to reduce the number of animals used in recognition memory tasks. © 2012 Elsevier B.V.
On comparison of deep learning architectures for distant speech recognition

Deep Learning technologies are becoming the major approaches for natural signal and information processings, including for speech recognition. Many architectures for deep learning have been proposed for automatic speech recognition (ASR). In this paper, we investigate the robustness of various deep learning architectures: DBN-DNN (Deep Belief Network Deep Neural Network), LSTM (Long Short Term Memory), TDNN (Time Delay Neural Network), and CNN (Convolutional Neural Network), for distant speech recognition. The architectures are evaluated on Meeting Recorder Digits (MRD) set of Aurora-5 dataset, a corpus of real recordings on reverberant conditions. Experimental results show that CNN consistently offer the best performance on two most commonly used features in ASR, i.e. Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP).
Classification of students results using KNN and ANN

It is a primary concern to observe the student's academic performance for high learning. This paper proposes a method for student's results classification centered on the clustering of data and agreeing on their performance level. It is based on two traditional evolutionary algorithms. This system evaluates KNN and ANN using given data set for a student result classification by using multilayer perceptron. Data from educational boards of Pakistan is being used for the evaluation. The proposed system will be useful for academic planners for making an effective resolution.
Sexual differentiation in the developing mouse brain: contributions of sex chromosome genes

Neural sexual differentiation begins during embryogenesis and continues after birth for a variable amount of time depending on the species and brain region. Because gonadal hormones were the first factors identified in neural sexual differentiation, their role in this process has eclipsed investigation of other factors. Here, we use a mouse with a spontaneous translocation that produces four different unique sets of sex chromosomes. Each genotype has one normal X‐chromosome and a unique second sex chromosome creating the following genotypes: XY*x, XX, XY*, XXY*. This Y* mouse line is used by several laboratories to study two human aneuploid conditions: Turner and Klinefelter syndromes. As sex chromosome number affects behavior and brain morphology, we surveyed brain gene expression at embryonic days 11.5 and 18.5 to isolate X‐chromosome dose effects in the developing brain as possible mechanistic changes underlying the phenotypes. We compared gene expression differences between gonadal males and females as well as individuals with one vs. two X‐chromosomes. We present data showing, in addition to genes reported to escape X‐inactivation, a number of autosomal genes are differentially expressed between the sexes and in mice with different numbers of X‐chromosomes. Based on our results, we can now identify the genes present in the region around the chromosomal break point that produces the Y* model. Our results also indicate an interaction between gonadal development and sex chromosome number that could further elucidate the role of sex chromosome genes and hormones in the sexual differentiation of behavior.
New Constructions of Binary and Ternary Locally Repairable Codes Using Cyclic Codes

New constructions of binary and ternary locally repairable codes (LRCs) using cyclic codes and their concatenation are proposed. The proposed binary LRCs with <inline-formula> <tex-math notation="LaTeX">$d=4$ </tex-math></inline-formula> and some <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> and with <inline-formula> <tex-math notation="LaTeX">$d\ge 5$ </tex-math></inline-formula> and some <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> are shown to be optimal in terms of the upper bounds. In addition, the similar method of the binary case is applied to construct the ternary LRCs with good parameters.
DMMLN: A deep multi-task and metric learning based network for video classification

In this paper we propose a novel deep multi-task and metric learning based network for video classification. Specifically, given the video sequence, features are extracted from each frame using a CNN-based temporal segment network which can model spatial information as well as long-term temporal dynamics in the video. The features from the first step are then aggregated using temporal pooling method to give a global representation for the complete video to deal with the problem that local frames may not contain the information indicated by the global video labels. Then we propose a self-adaptive margin based deep metric learning method to learn intra-class variations and inter-class similarities paying more attention to closer negative samples. Using a multi-task learning method, the temporal segment network, temporal pooling layer, deep metric learning and classification are jointly trained to get a global optimization network. Experiments on UCF101 and HMDB51 datasets show that this approach achieves the state-of-the-art performance.
Rewritable data insertion in encrypted JPEG using coefficient prediction method

This paper proposes a data insertion method using DCT coefficient prediction algorithm in encrypted JPEG image. First, a sequence of quantized AC coefficients are removed, and the remaining nonzero coefficients are shifted to the left, hence ensuring sufficient room for data insertion. Next, the external data is mapped to a set of Huffman codewords, and the external data is inserted into the image by introducing the corresponding Huffman codewords. At the receiver's side, the hidden data can be extracted by referring to the pre-defined table without the need to decrypt the image. To restore the image, the decrypted JPEG image is segmented using the Superpixel function, and further processed using the prediction algorithm for recovering the removed AC coefficients. Experimental results show the recovered images resemble their original counterparts. In the best case scenario, up to 19% compression gain is achieved while 24, 576 bits of external data can be inserted into the image.
Understanding evolutionary dynamics in online social networks

This paper reports the evolutionary dynamics of a social network service. The sequences of reposting behaviours on a social network service gathered over two years are studied to determine the driving factors for the evolution of the network using the Hawkes process. The results show that the endogenous feedback mechanism, rather than exogenous events, plays a significant role in the growth of the service's size and complexity and that it is the evolutionary engine of the system.
Delayed effect of wheat starch in foods on the intraoral demineralization of enamel.

There is considerable evidence for an association between dental caries and food starches. However, the intraoral utilization of starch may be quite complex, giving rise to conflicting results. Demineralization induced by unsweetened cookies was examined in an intraoral model system that utilized palatal appliances containing blocks of bovine enamel. The enamel surfaces were covered with either a filter paper disc to trap sugars or a layer of Streptococcus mutans to metabolize the sugars and bring about enamel demineralization. Demineralization was determined as an increase in porosity with respect to iodide ions (delta Ip). Measurements revealed a rapid elevation and maintenance of high levels of maltose in the plaque space after ingestion of the unsweetened or sweet cookies. Entrapped food particles appeared to serve as a reservoir of maltose. Unsweetened cookies brought about enamel demineralization, but the pH of the streptococcal plaque fell slowly, and the initiation of demineralization was delayed. Thus, delta Ip and plaque pH were -0.3 +/- 1.3 U and 6.1 +/- 0.3, respectively, after 15 min. The delay was shown to be related to the need to induce the acidogenic streptococci to metabolize maltose. Once induced, delta Ip rose rapidly and reached a maximum at 45 min. Sweet cookies released sucrose and maltose and brought about a rapid onset of demineralization. In summary, the data demonstrated (1) that maltose was released rapidly from unsweetened cookie particles and diffused into the plaque space of the model system and (2) that maltose-dependent demineralization of enamel required time for the induction of the streptococcal cells. (ABSTRACT TRUNCATED AT 250 WORDS)
Are Perceived Prevalences of Infection also Biased and How? Lessons from Large Epidemics of Mosquito-Borne Diseases in Tropical Regions

Background. Although people are likely to underestimate the frequencies of risks to health from common diseases and overestimate those from rare diseases, we still do not know much about reasons for this systematic bias, which is also referred to as “primary bias” in the literature. In this study, we take advantage of a series of large epidemics of mosquito-borne diseases to examine the accuracy of judgments of risk frequencies. In this aim, we assessed the perceived v. observed prevalence of infection by Zika, chikungunya or dengue fever during these outbreaks, as well as their variations among different subpopulations and epidemiological settings. Methods. We used data drawn from 4 telephone surveys, conducted between 2006 and 2016, among representative samples of the adult population in tropical regions (Reunion, Martinique, and French Guiana). The participants were asked to estimate the prevalence of these infections by using a natural frequency scale. Results. The surveys showed that 1) most people greatly overestimated the prevalence of infection by arbovirus, 2) these risk overestimations fell considerably as the actual prevalence of these diseases increased, 3) the better-educated and male participants consistently yielded less inaccurate risk estimates across epidemics, and 4) these biases in the perception of prevalence of these infectious diseases are relatively well predicted by the probability weighting function developed in the field of behavioral decision making. Conclusions. These findings suggest that the primary bias, which has been found in laboratory experiments to characterize a variety of probabilistic judgments, equally affects perception of prevalence of acute infectious diseases in epidemic settings. They also indicate that numeracy may play a considerable role in people’s ability to transform epidemiological observations from their social environment to more accurate risk estimates.
Mining important nodes in complex networks using nonlinear PCA

An important problem in the analysis of complex networks is to mine the top k important nodes. The existing literature offers several metrics, also called centrality measures which estimates importance using the structural properties of node, namely, degree, closeness, betweenness, eigenvector centrality, Pagerank etc. Though there exists plenty of centrality measures, none of them emphasizes the non-linearity of data. In the current study we propose a non-linear principle component analysis based approach to identify the top k important nodes. The proposed method is evaluated based on the saturation time and the fraction of infected nodes during a susceptible-infected propagation. The experiment on synthetic as well as real life data sets show that the developed method is competitive with the state-of-the-art.
Antibodies Directed toward Neuraminidase N1 Control Disease in a Mouse Model of Influenza

ABSTRACT There is increasing evidence to suggest that antibodies directed toward influenza A virus (IAV) neuraminidase (NA) are an important correlate of protection against influenza in humans. Moreover, the potential of NA-specific antibodies to provide broader protection than conventional hemagglutinin (HA) antibodies has been recognized. Here, we describe the isolation of two monoclonal antibodies, N1-7D3 and N1-C4, directed toward the N1 NA. N1-7D3 binds to a conserved linear epitope in the membrane-distal, carboxy-terminal part of the NA and reacted with the NA of seasonal H1N1 isolates ranging from 1977 to 2007 and the 2009 H1N1pdm virus, as well as A/Vietnam/1194/04 (H5N1). However, N1-7D3 lacked NA inhibition (NI) activity and the ability to protect BALB/c mice against a lethal challenge with a range of H1N1 viruses. Conversely, N1-C4 bound to a conformational epitope that is conserved between two influenza virus subtypes, 2009 H1N1pdm and H5N1 IAV, and displayed potent in vitro antiviral activity mediating both NI and plaque size reduction. Moreover, N1-C4 could provide heterosubtypic protection in BALB/c mice against a lethal challenge with 2009 H1N1pdm or H5N1 virus. Glutamic acid residue 311 in the NA was found to be critical for the NA binding and antiviral activity of monoclonal antibody N1-C4. Our data provide further evidence for cross-protective epitopes within the N1 subtype and highlight the potential of NA as an important target for vaccine and therapeutic approaches. IMPORTANCE Influenza remains a worldwide burden on public health. As such, the development of novel vaccines and therapeutics against influenza virus is crucial. Human challenge studies have recently highlighted the importance of antibodies directed toward the viral neuraminidase (NA) as an important correlate of reduced influenza-associated disease severity. Furthermore, there is evidence that anti-NA antibodies can provide broader protection than antibodies toward the viral hemagglutinin. Here, we describe the isolation and detailed characterization of two N1 NA-specific monoclonal antibodies. One of these monoclonal antibodies broadly binds N1-type NAs, and the second displays NA inhibition and in vitro and in vivo antiviral activity against 2009 H1N1pdm and H5N1 influenza viruses. These two new anti-NA antibodies contribute to our understanding of the antigenic properties and protective potential of the influenza virus NA antigen.