Compressible primitive equation: formal derivation and stability of weak solutions

We present a formal derivation of a simplified version of Compressible Primitive Equations (CPEs) for atmosphere modeling. They are obtained from $3$-D compressible Navier-Stokes equations with an \emph{anisotropic viscous stress tensor} where viscosity depends on the density. We then study the stability of the weak solutions of this model by using an intermediate model, called model problem, which is more simple and practical, to achieve the main result

Effet du sel sur le comportement des jeunes plants de palmier à huile (Elaeis guineensis Jacq.) en Basse Casamance

En Casamance, les palmeraies occupent une place primordiale dans l’équilibre écologique mais la pression anthropique, la baisse de la pluviométrie et la salinisation des sols ont affecté cet écosystème. Le présent travail a été entrepris pour étudier le comportement des jeunes plants d’Elaeis guineensis en milieu salin. Le dispositif expérimental comprend quatre parcelles élémentaires en blocs randomisés répétées quatre fois. Les plants ont été maintenus en conditions naturelles et irrigués avec une gamme de solution de NaCl (0 ; 4 ; 10 ; 35 g.l-1). Après 6 mois de traitement, les résultats ont montré une hauteur moyenne T0 (230,44) ; T1 (109,13) ; T2 (90,81) et T3 (78,125). Le taux de survie a diminué dans le temps. Il a été de 79,6% et 50,1% à trois mois pour T2 et T3 respectivement. Aucune mortalité n’a été observée pour T1. A 4 mois, il a été de 36 % pour T2 et 0% pour T3. A 5 mois, les premières mortalités ont été enregistrées pour T1 (5,3%). Cependant, aucune différence significative n’a été observée entre T0 (100%) et T1 (94,7%). Le nombre moyen de feuilles par plant a été de T0 (5,75); T1 (5,5) ; T2 (4) et T3 (3,2).Mots clés : Elaeis guineensis Jacq, Basse Casamance, jeunes plants, sel, concentration, comportement

Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013

International audienceIn the post-genomic era, a holistic understanding of biological systems and pro- cesses, in all their complexity, is critical in comprehending nature’s choreogra- phy of life. As a result, bioinformatics involving its two main disciplines, namely, the life sciences and the computational sciences, is fast becoming a very promis- ing multidisciplinary research field. With the ever-increasing application of large- scale high-throughput technologies, such as gene or protein microarrays and mass spectrometry methods, the enormous body of information is growing rapidly. Bioinformaticians are posed with a large number of difficult problems to solve, arising not only due to the complexities in acquiring the molecular information but also due to the size and nature of the generated data sets and/or the limi- tations of the algorithms required for analyzing these data. The recent advance- ments in computational and information-theoretic techniques are enabling us to conduct various in silico testing and screening of many lab-based experiments be- fore these are actually performed in vitro or in vivo. These in silico investigations are providing new insights for interpreting and establishing new direction for a deeper understanding. Among the various advanced computational methods cur- rently being applied to such studies, the pattern recognition techniques are mostly found to be at the core of the whole discovery process for apprehending the under- lying biological knowledge. Thus, we can safely surmise that the ongoing bioin- formatics revolution may, in future, inevitably play a major role in many aspects of medical practice and/or the discipline of life sciences.The aim of this conference on Pattern Recognition in Bioinformatics (PRIB) is to provide an opportunity to academics, researchers, scientists, and industry professionals to present their latest research in pattern recognition and compu- tational intelligence-based techniques applied to problems in bioinformatics and computational biology. It also provides them with an excellent forum to interact with each other and share experiences. The conference is organized jointly by the Nice Sophia Antipolis University, France, and IAPR (International Association for Pattern Recognition) Bioinformatics Technical Committee (TC-20).This volume presents the proceedings of the 8th IAPR International Confer- ence on Pattern Recognition in Bioinformatics (PRIB 2013), held in Nice, June 17–19, 2013. It includes 25 technical contributions that were selected by the In- ternational Program Committee from 43 submissions. Each of these rigorously reviewed papers was presented orally at PRIB 2013. The proceedings consists of five parts:Part I Bio-Molecular Networks and Pathway Analysis Part II Learning, Classification, and ClusteringPart III Data Mining and Knowledge DiscoveryPart IV Protein: Structure, Function, and Interaction Part V Motifs, Sites, and Sequences AnalysisPart I of the proceedings contains six chapters on “Bio-Molecular Networks and Pathway Analysis.” Rahman et al. propose a fast agglomerative cluster- ing method for protein complex discovery. A new criterion is introduced that combines an edge clustering coefficient and an edge clustering value, allowing us to decide when a node can be added to the current cluster. Maduranga et al. use the well-known random forest method to predict GRNs. The problem of in- ferring GRNs from (limited) time-series data is recast as a number of regression problems, and the random forest approach is used here to fit a model to this. Winterbach et al. evaluate how well topological signatures in protein interaction networks predict protein function. They compare several complex signatures and their own simple signature. They find that network topology is only a weak predictor of function and the simple signature performs on par with the more sophisticated ones. De Ridder et al. propose an approach for identifying putative cancer pathways. This approach relies on expression profiling tumors that are induced by retroviral insertional mutagenesis. This provides the opportunity to search for associations between tumor-initiating events (the viral insertion sites) and the consequent transcription changes, thus revealing putative regulatory in- teractions. An important advantage is that the selective pressure exerted by the tumor growth is exploited to yield a relatively small number of loci that are likely to be causal for tumor formation. Ochs et al. apply outlier statistics, gene set analysis, and top scoring pair methods to identify deregulated pathways in can- cer. Analysis of the results on pediatric acute myeloid leukemia data indicate the effectiveness of the proposed methodology. Pizzuti et al. present some variants of RNSC (restricted neighborhood search clustering) for prediction of protein com- plexes that are based on new score functions and evolutionary computation. It is shown via computational experiments that the proposed methods have better prediction accuracies (in F-measure) than the basic RNSC algorithm.Part II of the proceedings contains three chapters on “Learning, Classifica- tion, and Clustering.” Marchiori addresses a limitation of the RELIEF feature weighting algorithm that maximizes the sample margin over the entire training set, or the sum of the possibly competing feature weights. Her work proposes, instead, a conditional weighting algorithm (CCFW) and classifier (CCWNN) to improve feature weighting and classification. Mundra et al. propose a sample se- lection criterion using a modified logistic regression loss function and a backward elimination based gene ranking algorithm. On the basis of the classifier margin for sample points, points on or within the margin are more important than those outside, the sample selection criterion based on T-score is proposed. Li et al. describe a generalization of sparse matrix factorization (SMF) algorithms and showcase a few very concisely described applications in bioinformatics. The main merit of the work is the fact that a unified representation for SMF algorithms is proposed, as well as an optimization algorithm to solve this problem.Part III of the proceedings contains six chapters on “Data Mining and Knowl- edge Discovery.” Hsu et al. consider prediction of RNA secondary structure in the “triple helix” setting for which they argue existing methods are inade- quate. Their approach uses a Simple Tree Adjoining Grammar (STAG) coupledwith maximum likelihood estimation (MLE), implemented via an efficient dy- namic programming formulation. Higgs et al. present an algorithm for generating near-native protein models. It combines a fragment feature-based resampling algorithm with a local optimization method that performed best, for protein structure prediction (PSP), among a set of five optimization techniques. Com- putational experiments show that the use of local optimization is beneficial in terms of both RMSD and TM score. Spirov et al. discuss a method for trans- formation of variables, in order to normalize Drosophila oocyte images acquired via confocal microscopy. The paper describes an interesting problem, namely, the experimental determination of intrinsic Drosophila embryo coordinates, and proposes an approach using evolutionary computation by genetic algorithms. Rezaeian et al. propose a novel and flexible hierarchical framework to select dis- criminative genes and predict breast tumor subtypes simultaneously. Dai et al. tackle an important problem in drug-target interaction research and present an interesting application of machine learning methods to the analysis of drugs. Gritsenko et al. make an adaptation of their previously developed protocol for building and evaluating predictors, in order to introduce a framework that en- ables forward engineering in biology. An experimental test is performed in the biological field of codon optimization and the results obtained are comparable with those produced by the reference tool JCat.Part IV of the proceedings contains six chapters on “Protein: Structure, Func- tion, and Interaction.” Xiong et al. propose an active learning-based approach for protein function prediction. The novelty of the proposal is the use of a pre- processing phase that uses spectral clustering before selecting candidates for labeling with graph centrality metrics. Experimental results show that cluster- ing reveals a valid pre-processing step for the active learning method. Gehrmann et al. address the problem of integrating multiple sources of evidence to predict protein functions. The paper proposes to use a conditional random field (CRF) to represent protein functions as random variables to be predicted and different sources of evidence as conditioning variables. Inference and learning algorithms based on MCMC are described and the proposed method is applied to a yeast dataset. Dehzangi et al. describe a new approach to protein fold recognition, a problem that has been widely studied over the past decade. The main contribu- tion is the proposal of a new set of global protein features based on evolutionary consensus sequences and predicted secondary structure, and local features based on distributions and auto covariances of these features over segments. An RBF SVM using these features is applied to two benchmark datasets in an extensive comparison with a number of existing methods and is demonstrated to work well. Dehzangi et al. present a novel approach to using features extracted from the position specific scoring matrix (PSSM) to predict the structural class of a protein. The authors propose two new sets of features: a global one based on the consensus sequence of a PSSM and a local one that takes the auto-covariance in sequence segments into account. The features extracted are used to train an RBF SVM and are shown to lead to good results (better than other state-of-the-art algorithms) on two benchmarks. Chiu et al. discuss a new method for detecting associated sites in aligned sequence ensembles. The main idea is derived from the concept of granular computing, where information is extracted at different levels of granularity or resolution. The experimentation was focused on p53 and it has been demonstrated that the extracted association patterns are useful in discov- ering sites with some structural and functional properties of a protein molecule. Tung presents a new method for predicting the potential hepatocarcinogenicity of non-genotoxic chemicals. The proposed method based on chemical–protein interactions and interpretable decision tree is compared with other data-mining approaches and shows very good performances in both accuracy and simplicity of the found model.Part V of the proceedings contains four chapters on “Motifs, Sites, and Se- quences Analysis.” Pathak et al. present an algorithm that exploits structural information for reducing false positives in motifs prediction. They tested the validity of the algorithm using the minimotifs stored in the MnM database. Lacroix et al. present a workflow for the prediction of the effects of residue sub- stitution on protein stability. The workflow integrates eight algorithms that use delta-delta-G as a measure of stability. The workflow is designed to populate the online resource SPROUTS. A use case of the workflow is presented using the PDB entry 1enh. Malhotra et al. present an algorithm for inferring haplotypes of virus populations from k-mer counts obtained from next-generation sequencing (NGS) data. The algorithm takes as input read counts for a set of k-mers and produces as output a predicted number of haplotypes, their relative frequen- cies and, for reads covering SNPs, can assign reads to a haplotype. The novel feature of the algortihm is that it does not rely on having a reference genome. The authors report that it performs well on synthetic data compared with the existing algorithm ShoRAH, which relies on a reference genome. Comin et al. discuss and improve the Entropic Profile method introduced in the literature for detecting conservation in genome sequences. The authors propose a linear-time linear-space algorithm that captures the importance of given regions with re- spect to the whole genome, suitable for large genomes and for the discovery of motifs with unbounded length.Many have contributed directly or indirectly toward the organization and success of the PRIB 2013 conference. We would like to thank all the individ- uals and institutions, especially the authors for submitting the papers and the sponsors for generously providing financial support for the conference. We are very grateful to IAPR for the sponsorship. Our gratitude goes to the Nice Sophia Antipolis University, Nice, France, and IAPR (International Association for Pat- tern Recognition) Bioinformatics Technical Committee (TC-20) for supporting the conference in many ways.We would like to express our gratitude to all PRIB 2013 International Pro- gram Committee members for their objective and thorough reviews of the sub- mitted papers. We fully appreciate the PRIB 2013 Organizing Committee for their time, efforts, and excellent work. We would also like to thank the Nice Sophia Antipolis University for hosting the symposium and providing technical support. We sincerely thank the EDSTIC doctoral school for providing grants toa number of students attending the conference. We also thank “Region PACA” and the University of Salerno (Italy) for partially funding the invited speakers. Last, but not least, we wish to convey our sincere thanks to Springer forproviding excellent professional support in preparing this volume

Birth season and environmental influences on blood leucocyte and lymphocyte subpopulations in rural Gambian infants

BACKGROUND: In rural Gambia, birth season predicts infection-related adult mortality, providing evidence that seasonal factors in early life may programme immune development. This study tested whether lymphocyte subpopulations assessed by automated full blood count and flow cytometry in cord blood and at 8, 16 and 52 weeks in rural Gambian infants (N = 138) are affected by birth season (DRY = Jan-Jun, harvest season, few infections; WET = Jul-Dec, hungry season, many infections), birth size or micronutrient status. RESULTS: Geometric mean cord and postnatal counts were higher in births occurring in the WET season with both season of birth and season of sampling effects. Absolute CD3+, CD8+, and CD56+ counts, were higher in WET season births, but absolute CD4+ counts were unaffected and percentage CD4+ counts were therefore lower. CD19+ counts showed no association with birth season but were associated with concurrent plasma zinc status. There were no other associations between subpopulation counts and micronutrient or anthropometric status. CONCLUSION: These results demonstrate a seasonal influence on cell counts with a disproportionate effect on CD8+ and CD56+ relative to CD4+ cells. This seasonal difference was seen in cord blood (indicating an effect in utero) and subsequent samples, and is not explained by nutritional status. These findings are consistent with the hypothesis than an early environmental exposure can programme human immune development

Severe pre-eclampsia: epidemiological, diagnostic, therapeutic and prognostic aspects at Hospital Principal Dakar from January 2019 to December 2020

Background: Preeclampsia, major public health problem, is one of the leading causes of maternal and infant mortality. It is increasingly frequent in our referral health centers, especially in its severe form. Methods: Retrospective descriptive and analytical study about severe preeclampsia at the Maternity of Hospital Principal Dakar, from 01 January 2019 to 31 December 2020. Results: Frequency of severe preeclampsia was 3.09%. Medical evacuation (70.59%) was the most frequent mode of admission. Patients were in average 29.8 years and primipare. Personal medical history was dominated by high blood pressure (16.29%). The average gestational age was 34+2 days, but pregnancy was carried to term by the majority of patients. Functional signs were dominated by headache (40.65%). Blood pressure was greater than or equal to 160/90 mmHg (90.32%). Hyperuricemia was the most frequent biological anomaly after proteinuria (45.1%). Complications were dominated by retroplacental hematoma (4.49%) and intrauterine growth retardation (IUGR) (28.48%). Calcium channel blockers (81.88%) were the main antihypertensive agents administered. Caesarean section was the most common delivery method (80.46%). The maternal prognosis was good, with no maternal deaths recorded. Perinatal mortality was 173.9%. Conclusions: Preeclampsia remains a fearsome pregnancy’s pathology. Raising awareness of pregnant women during ANC on the risks of pre-eclampsia, retraining of health personnel, close and early monitoring of women at risk and management in a multidisciplinary setting help to improve the maternal-fetal prognosis.

Parametrizations of Inclusive Cross Sections for Pion Production in Proton-Proton Collisions

Accurate knowledge of cross sections for pion production in proton-proton collisions finds wide application in particle physics, astrophysics, cosmic ray physics and space radiation problems, especially in situations where an incident proton is transported through some medium, and one requires knowledge of the output particle spectrum given the input spectrum. In such cases accurate parametrizations of the cross sections are desired. In this paper we review much of the experimental data and compare to a wide variety of different cross section parametrizations. In so doing, we provide parametrizations of neutral and charged pion cross sections which provide a very accurate description of the experimental data. Lorentz invariant differential cross sections, spectral distributions and total cross section parametrizations are presented.Comment: 32 pages with 15 figures. Published in Physical Review D62, 094030. File includes 6 tex files. The main file is paper.tex which has include statements refering to the rest. figures are in graphs.di

Simonkolleite nano-platelets : synthesis and temperature effect on hydrogen gas sensing properties

Please read abstract in article.The African Laser Centre “ALC”-Pretoria, the Abdus Salam ICTP-Trieste, the Nanosciences African Network “NANOAFNET”-Cape Town, iThemba LABS-National Research Foundation of South Africa and the French-South Africa as well as the Japan-South Africa bilateral cooperation programmes.http://www.elsevier.com/locate/apsuscnf201

Exploring links between greenspace and sudden unexpected death: A spatial analysis

Greenspace has been increasingly recognized as having numerous health benefits. However, its effects are unknown concerning sudden unexpected death (SUD), commonly referred to as sudden cardiac death, which constitutes a large proportion of mortality in the United States. Because greenspace can promote physical activity, reduce stress and buffer air pollutants, it may have beneficial effects for people at risk of SUD, such as those with heart disease, hypertension, and diabetes mellitus. Using several spatial techniques, this study explored the relationship between SUD and greenspace. We adjudicated 396 SUD cases that occurred from March 2013 to February 2015 among reports from emergency medical services (EMS) that attended out-of-hospital deaths in Wake County (central North Carolina, USA). We measured multiple greenspace metrics in each census tract, including the percentages of forest, grassland, average tree canopy, tree canopy diversity, near-road tree canopy and greenway density. The associations between SUD incidence and these greenspace metrics were examined using Poisson regression (non-spatial) and Bayesian spatial models. The results from both models indicated that SUD incidence was inversely associated with both greenway density (adjusted risk ratio [RR] = 0.82, 95% credible/ confidence interval [CI]: 0.69–0.97) and the percentage of forest (adjusted RR = 0.90, 95% CI: 0.81–0.99). These results suggest that increases in greenway density by 1 km/km2 and in forest by 10% were associated with a decrease in SUD risk of 18% and 10%, respectively. The inverse relationship was not observed between SUD incidence and other metrics, including grassland, average tree canopy, near-road tree canopy and tree canopy diversity. This study implies that greenspace, specifically greenways and forest, may have beneficial effects for people at risk of SUD. Further studies are needed to investigate potential causal relationships between greenspace and SUD, and potential mechanisms such as promoting physical activity and reducing stress

Carbon nanoparticles in lateral flow methods to detect genes encoding virulence factors of Shiga toxin-producing Escherichia coli

The use of carbon nanoparticles is shown for the detection and identification of different Shiga toxin-producing Escherichia coli virulence factors (vt1, vt2, eae and ehxA) and a 16S control (specific for E. coli) based on the use of lateral flow strips (nucleic acid lateral flow immunoassay, NALFIA). Prior to the detection with NALFIA, a rapid amplification method with tagged primers was applied. In the evaluation of the optimised NALFIA strips, no cross-reactivity was found for any of the antibodies used. The limit of detection was higher than for quantitative PCR (q-PCR), in most cases between 104 and 105 colony forming units/mL or 0.1–0.9 ng/μL DNA. NALFIA strips were applied to 48 isolates from cattle faeces, and results were compared to those achieved by q-PCR. E. coli virulence factors identified by NALFIA were in very good agreement with those observed in q-PCR, showing in most cases sensitivity and specificity values of 1.0 and an almost perfect agreement between both methods (kappa coefficient larger than 0.9). The results demonstrate that the screening method developed is reliable, cost-effective and user-friendly, and that the procedure is fast as the total time required is <1 h, which includes amplification