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Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

This paper invests in FPGA technology to control the speed of an autonomous car using fuzzy logic. For that purpose, we propose a co-design based on a novel fuzzy controller IP. It was developed using the hardware language VHDL and driven by the Zynq processor through an SDK software design written in C. The proposed IP acts according to the ambient temperature and the presence or absence of an obstacle and its distance from the car. The partitioning of the co-design tasks divides them into hardware and software parts. The simulation results of the fuzzy IP and those of the complete co-design implementation on a Xilinx Zynq board showed the effectiveness of the proposed controller to meet the target constraints and generate suitable PWM signals. The proposed hardware architecture based on 6-LUT blocks uses 11 times fewer logic resources than other previous similar designs. Also, it can be easily updated when new constraints on the system are to be considered, which makes it suitable for many related applications. Fuzzy computing was accelerated thanks to the use of digital signal processing blocks that ensure parallel processing. Indeed, a complete execution cycle takes only 7 us.

Background: Type 2 diabetes mellitus (T2DM) is a metabolic syndrome that affects a large proportion of the population, if not well controlled; this will lead to serious metabolic problems, including atherosclerosis, predominantly coronary artery disease (CAD).Irisin is a peptide hormone, secreted mainly by the heart and skeletal muscle. It has a role in converting white adipose tissue to brown adipose tissue. It is one of the novel biochemical markers that link diabetes with CAD.Objective : To explore the relationship between serum Irisin level and duration of diabetes, in cases of presence and absence of CAD, As well as the possibility of using it as a marker for the assessment of the severity of the disease.Method: One hundred sixty-one volunteers aged [(40-60 year), body mass index (20- 25Kg/m2)], with normal blood pressure. They divided into six groups, that distributed as [(Ia = control (negative catheterization without DM), Ib = control (apparently healthy), IIa = DM (with negative catheterization) IIb = DM (diagnosed by history and clinical examination), IIIa = CAD (without DM, positive catheterization), IIIb = CAD + DM (positive catheterization)]. The diabetic groups with and without CAD had been divided depending on the duration of the diabetic onset into three periods ( 10 years). The parameters that measured were FPG, HbA1c and fasting serum (Irisin, lipid profile).Results: The present findings showed the Means (± SD) value of Irisin levels was a significant decrease in (IIa, IIb, IIIa, IIIb ) groups as compared with control groups (Ia, Ib). In addition, there is an inverse relationship between serum Irisin and the duration of DM in the total DM groups (IIa +IIb) and the CAD + DM group (IIIb). Moreover, higher statistical decrease in mean serum level of Irisin with duration of DM was found in CAD + DM group as compared with the total DM group. Also, there was a significant decrease in mean serum level of HDL-C for (IIa, IIb, IIIa, IIIb) groups than in (Ia, Ib) groups. Besides, there was a significant decrease in the mean of serum HDL level in CAD groups (IIIa, IIIb) than in DM groups (IIa, IIb). While the means of FPG level, HbA1c, serum cholesterol level, were significantly elevated in groups (IIa, IIb, IIIb) as compared with (Ia, Ib) groups. Also, there was a significant increase in the mean serum levels of triglyceride, VLDL-C and LDL-C for (IIa, IIb, IIIa, IIIb ) groups than in the control groups.Conclusion: Irisin was lower among patients with long-standing diabetes (with or without CAD) as compared to those with short duration of T2DM that can be included as a marker for assessment the severity of diabetes and prediction of CAD.

International audience; Rapid relaxation devices developed by private companies propose rapid solutions to fight against stress or anxiety. However, there have been insufficient scientific studies on these devices. In a previous article, we evaluated the variation of 15 physiological and psychological parameters before and after relaxation in 4 groups of participants using 3 different rapid (15 minute) relaxation devices with sensorial immersion and a control group using no device. This pilot study included 40 participants, 12 males and 28 females, aged 27-68 years old with an average of 42.7 ± 11.5 years old and showed that some parameters were more relevant for the analysis of these relaxation devices and suggested some differences in the relaxation processes between devices. We hypothesized that by analyzing physiological parameters recorded during the rapid relaxation process in the same population, we could unravel the previously observed pre-post treatment variations. The measurements included brain wave electroencephalography (Muse2 EEG) recordings, respiration rhythm (mechanical abdominal movements) and heart rate variability parameters (PPG signals). The objective of the study was to identify the physiological parameters recorded during relaxation of interest to discriminate the groups and to study the effects of the devices on these parameters. The EEG recordings showed differences in dominant waves between groups. In addition, the Be-Breathe intervention group exhibited a decreased respiration rate compared to the control group, and a simultaneous increase in heart rate variability parameters, while other groups showed less significant variations in their respiration or heart rate variables, which was confirmed by a k-means cluster analysis. We discuss how these variations observed during rapid relaxation could contribute to the differences that we previously observed pre and post relaxation interventions. Finally, we built a model to determine which parameters discriminate best between the groups.; Les dispositifs de relaxation rapide développés par des entreprises privées proposent des solutions rapides pour lutter contre le stress ou l'anxiété. Cependant, les études scientifiques sur ces dispositifs sont insuffisantes. Dans un article précédent, nous avons évalué la variation de 15 paramètres physiologiques et psychologiques avant et après la relaxation dans 4 groupes de participants utilisant 3 différents appareils de relaxation rapide (15 minutes) avec immersion sensorielle et un groupe témoin n'utilisant aucun appareil. Cette étude pilote a inclus 40 participants, 12 hommes et 28 femmes, âgés de 27 à 68 ans avec une moyenne de 42,7 ± 11,5 ans et a montré que certains paramètres étaient plus pertinents pour l'analyse de ces dispositifs de relaxation et suggéraient des différences dans le processus de la relaxation entre les appareils. Nous avons émis l'hypothèse qu'en analysant les paramètres physiologiques enregistrés au cours du processus de relaxation rapide dans la même population, nous pourrions démêler les variations pré-post-traitement précédemment observées. Les mesures comprenaient des enregistrements d'électroencéphalographie des ondes cérébrales (Muse2 EEG), du rythme respiratoire (mouvements abdominaux mécaniques) et des paramètres de variabilité de la fréquence cardiaque (signaux PPG). L'objectif de l'étude était d'identifier les paramètres physiologiques enregistrés lors de la relaxation qui pourraiet être d'intérêt pour discriminer les groupes et étudier les effets des dispositifs sur ces paramètres. Les enregistrements EEG ont montré des différences dans les ondes dominantes entre les groupes. De plus, le groupe d'intervention Be-Breathe a présenté une diminution de la fréquence respiratoire par rapport au groupe témoin et une augmentation simultanée des paramètres de variabilité de la fréquence cardiaque, tandis que d'autres groupes ont montré des variations moins significatives de leurs variables de respiration ou de fréquence cardiaque, ce qui a été confirmé par un Analyse de cluster k-means. Nous discutons de la façon dont ces variations observées lors d'une relaxation rapide pourraient contribuer aux différences que nous avons précédemment observées avant et après les interventions de relaxation. Enfin, nous avons construit un modèle pour déterminer quels paramètres permettent le mieux de discriminer les groupes.

International audience; In developed societies, the number of people diagnosed with chronic stress-related illnesses has risen rapidly in recent years. To meet the increasing demand for relaxation and well-being, several companies have developed relaxation installations to be used within business premises or in public locations. The effects of such devices on physiological and psychological parameters have not been scientifically tested yet. This pilot study (N=40) evaluates the variations of 4 physiological and 11 psychological parameters on four different groups, three of them using a different rapid (15 minute) sensorial immersion relaxation devices and a control group using no device. The objective of the study was to identify the psychological and psychological parameters of interest and to study the effects of the devices on these parameters. Physiological parameters measured included heart rate, blood pressure, SpO2 and posture. Psychological parameters included an anxiety survey and four numerical scales to evaluate well-being, energy gain, and subjective muscular and nervous relaxation. We also used cognitive tests and verbatim reports. We identified significant physiological and psychological parameters that can be of use for evaluating rapid relaxation devices (particularly mean blood pressure, posture, subjective muscular and nervous relaxation and some of the cognitive test results). Interestingly, the parameters variations differed between groups. This study paves the way for further analysis of relaxation devices and suggests that rapid sensorial immersion relaxation devices can be of use in stressful environments. Each device could particularly help specific users, depending upon their needs.; Dans les sociétés développées, le nombre de personnes diagnostiquées avec des maladies chroniques liées au stress a augmenté ces dernières années. Pour répondre à la demande croissante de détente et de bien-être, plusieurs entreprises ont développé des installations de détente à utiliser dans des locaux commerciaux ou dans des lieux publics. Les effets de ces dispositifs sur les paramètres physiologiques et psychologiques n'ont pas encore été testés scientifiquement. Cette étude pilote (N = 40) évalue les variations de 4 paramètres physiologiques et 11 paramètres psychologiques sur quatre groupes différents, trois d'entre eux utilisant un autre appareil de relaxation sensorielle par immersion rapide (15 minutes) et un groupe témoin sans appareil. L'objectif de l'étude était d'identifier les paramètres psychologiques et psychologiques d'intérêt et d'étudier les effets des appareils sur ces paramètres. Les paramètres physiologiques mesurés comprenaient la fréquence cardiaque, la pression artérielle, la SpO2 et la posture. Les paramètres psychologiques comprenaient une enquête sur l'anxiété et quatre échelles numériques pour évaluer le bien-être, le gain d'énergie et la relaxation musculaire et nerveuse subjective. Nous avons également utilisé des tests cognitifs et des rapports verbatim. Nous avons identifié des paramètres physiologiques et psychologiques significatifs pouvant être utiles pour évaluer les dispositifs de relaxation rapide (notamment la pression artérielle moyenne, la posture, la relaxation musculaire et nerveuse subjective et certains résultats des tests cognitifs). Fait intéressant, les variations des paramètres différaient entre les groupes. Cette étude ouvre la voie à une analyse plus approfondie des dispositifs de relaxation et suggère que les dispositifs de relaxation par immersion sensorielle rapide peuvent être utiles dans des environnements stressants. Chaque appareil pourrait particulièrement aider des utilisateurs spécifiques, en fonction de leurs besoins.

International audience; Due to the recent advancement in genomic sequencing technologies, the number of protein sequences in public databases is growing exponentially. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. The May 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 158 million protein sequences. For the complete exploitation of this huge knowledge base, protein sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. However, there is only about half a million sequences (UniprotKB/SwissProt) are reviewed and functionally annotated by expert curators using information extracted from the published literature and computational analyses. The manual annotation by experts are expensive, slow and insufficient to fill the gap between the annotated and unannotated protein sequences. In this paper, we present an automatic functional annotation technique using neural network based based word embedding exploiting domain and family information of proteins. Domains are the most conserved regions in protein sequences and constitute the building blocks of 3D protein structures. To do the experiment, we used fastText a , a library for learning of word embeddings and text classification developed by Facebook's AI Research lab. The experimental results show that domain embeddings perform much better than k-mer based word embeddings. a https://github.com/facebookresearch/fasttext

International audience; Ionizing-radiation-resistant bacteria (IRRB) could be used for bioremediation of radioactive wastes and in the therapeutic industry. Limited computational works are available for the prediction of bacterial ionizing radiation resistance (IRR). In this work, we present ABClass, an in silico approach that predicts if an unknown bacterium belongs to IRRB or ionizing-radiation-sensitive bacteria (IRSB). This approach is based on a multiple instance learning (MIL) formulation of the IRR prediction problem. It takes into account the relation between semantically related instances across bags. In ABClass, a preprocessing step is performed in order to extract substructures/motifs from each set of related sequences. These motifs are then used as attributes to construct a vector representation for each set of sequences. In order to compute partial prediction results, a discriminative classifier is applied to each sequence of the unknown bag and its correspondent related sequences in the learning dataset. Finally, an aggregation method is applied to generate the final result. The algorithm provides good overall accuracy rates. ABClass can be downloaded at the following link: http://homepages.loria.fr/SAridhi/software/MIL/.

The Tunisian Dorsal backland is the Eastern Atlas side of maghrebides. Field data of Fahs area allowed us to develop new interpretations and to characterize the main structural features of the studied devices (Jebel Rouas and Ruissate). Heritage of Zaghouan accident, Triassic salt movements and strike-direction of major synsedimentary faults are the principal causes and results of the skinned and superimposed geometric architecture, generated by the reversed extensional (Jurassic-Cretaceous) tectonics. The actual geometry of Jebel Rouas and Ruissate represents a fault propagation fold, affecting Jurassic and Cretaceous sets. The backland of this thrust fault defines an imbrications structures of Barremian series. Tectonic records activities show the existence of angular unconformities (Oligocene and Eocene series on the Cretaceous sets considered as bedrock), slumps, tectonic breccias and synsedimentary faults are all of them controlled by a deep major accident; N-S to NE-SW and NW-SE. Features of the study area are probably related first; to the blockage of Zaghouan thrust oriented NE-SW in the foreland; then, to the intense halokinetic activity, which facilitates the layers displacement acting as decollment level. The detailed structural and stratigraphic study of Fahs area and its neighbors shows the presence of an intense tangential tectonic during upper Miocene, affecting Meso-Cenozoic sets, because all the structures involved are sealed by Oligocene and Miocene thinned series. This is accentuated by the existence of different sets of decollment at different depths, which are represented by a displacement to the SE through the backland of the Tunisian Dorsal. We define these features as an imbrication and thrusting Out of sequence system.

Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the understanding of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on structural clustering. Yet, these algorithms have been evaluated on small graph database. In this paper, we propose DSCAN, a novel distributed structural graph clustering algorithm. We present an implementation of DSCAN on top of BLADYG, a distributed graph processing framework. We experimentally show that DSCAN significantly outperforms existing clustering algorithm in terms of scalability and performance in the case of large graphs.

BACKGROUND: The occurrence of multiple supernumerary teeth in individuals without any associated syndrome is rare. Supernumerary teeth may occur in any region of the dental arch and are frequently observed in the maxillary region. But the occurrence of distomolars is rare, particularly mandibular distomolars are extremely rare. CASES PRESENTATION: In this paper, we present a series of case reports of maxillary and mandibular distomolars. CONCLUSION: The occurrence of distomolars is rare, but when detected patients should be kept under observation.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

International audience; In the last few years, we have seen that many applications or computer problems are mobilized as a graph since this data structure gives a particular handling for some use cases such as social networks, bioinformatics, road networks and communication networks. Despite its importance, the graph processing remains a challenge when dealing with large graphs. In this context, several solutions and works have been proposed to support large graph processing and storage. Nevertheless, new needs are emerging to support the dynamism of the graph (Dynamic Graph) and properties variation of the graph during the time (temporal graph). In this paper, we first present the concepts of dynamic and temporal graphs. Secondly, we show some frameworks that treat static, dynamic and temporal graphs. Finally, we propose a new framework based on the limits of the frameworks study.

This work is dedicated to the memory of David W. Ritchie, who recently passed away.; International audience; Background: Thanks to recent developments in genomic sequencing technologies, the number of protein sequences in public databases is growing enormously. To enrich and exploit this immensely valuable data, it is essential to annotate these sequences with functional properties such as Enzyme Commission (EC) numbers, for example. The January 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 140 million protein sequences. However, only about half of a million of these (UniprotKB/SwissProt) have been reviewed and functionally annotated by expert curators using data extracted from the literature and computational analyses. To reduce the gap between the annotated and unannotated protein sequences, it is essential to develop accurate automatic protein function annotation techniques. Results: In this work, we present GrAPFI (Graph-based Automatic Protein Function Inference) for automatically annotating proteins with EC number functional descriptors from a protein domain similarity graph. We validated the performance of GrAPFI using six reference proteomes in UniprotKB/SwissProt, namely Human, Mouse, Rat, Yeast, E. Coli and Arabidopsis thaliana. We also compared GrAPFI with existing EC prediction approaches such as ECPred, DEEPre, and SVMProt. This shows that GrAPFI achieves better accuracy and comparable or better coverage with respect to these earlier approaches. Conclusions: GrAPFI is a novel protein function annotation tool that performs automatic inference on a network of proteins that are related according to their domain composition. Our evaluation of GrAPFI shows that it gives better performance than other state of the art methods. GrAPFI is available at https://gitlab.inria.fr/bsarker/bmc_grapfi.git as a stand alone tool written in Python.

Additional file 1 Additional figures and tables referenced in the article.

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory.

International audience; During the last decade, cloud computing became a natural choice to host and provide various computing resources as on-demand services. The correct characterization and management of cloud environment objects (clouds, data centers, providers, services, data, users, etc.) is the first step towards effective provisioning and integration of cloud services. However, cloud computing environment is often subject to uncertainty. This could be attributed to the incompleteness and imprecision of cloud available information, as well as the highly changing conditions. The purpose of this survey is to study, criticize and classify the already existing works that deal with uncertainty in the cloud. We present a taxonomy on the uncertainty in the cloud and we study how such concept was tackled by researchers in cloud environments. Finally, we identify the challenges and the requirements to deal with uncertain data in the cloud, as well as the future directions.

International audience; Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch.

National audience; In Multiple Instance Learning (MIL) problem for sequence data, the learning data consist of a set of bags where each bag contains a set of instances/sequences. In some real world applications such as bioinformatics comparing a random couple of sequences makes no sense. In fact, each instance of each bag may have structural and/or functional relationship with other instances in other bags. Thus, the classification task should take into account the relation between semantically related instances across bags. In this paper, we present ABClass, a novel MIL approach for sequence data classification. Each sequence is represented by one vector of attributes extracted from the set of related instances. For each sequence of the unknown bag, a discriminative classifier is applied in order to compute a partial classification result. Then, an aggregation method is applied in order to generate the final result. We applied ABClass to solve the problem of bacterial Ionizing Radiation Resistance (IRR) prediction. The experimental results were satisfactory.; Dans le cas du problème de l'apprentissage multi-instances (MI) pour les séquences, les données d'apprentissage consistent en un ensemble de sacs où chaque sac contient un ensemble d'instances/séquences. Dans certaines applications du monde réel, comme la bioinformatique, comparer un couple aléatoire de séquences n'a aucun sens. En fait, chaque instance de chaque sac peut avoir une relation structurelle et/ou fonctionnelle avec d'autres instances dans d'autres sacs. Ainsi, la tâche de classification doit prendre en compte la relation entre les instances sémantiquement liées à travers les sacs. Dans cet article, nous présentons ABClass, une nouvelle approche de classification MI des séquences. Chaque séquence est représentée par un vecteur d'attributs extraits à partir de l'en-semble des instances qui lui sont liées. Pour chaque séquence du sac à prédire, un classifieur discriminant est appliqué afin de calculer un résultat de classification partiel. Ensuite, une méthode d'agrégation est appliquée afin de générer le résultat final. Nous avons appliqué ABClass pour résoudre le problème de la prédiction de la résistance aux rayonnements ionisants (RRI) chez les bactéries. Les résultats expérimentaux sont satisfaisants.

The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on improving as much as possible the accuracy by recommending familiar items (i.e., popular items over the group). Yet, this may reduce the number of items that could be recommended and consequently weakens the chances of recommending novel items. To address this problem, it is desirable to consider recommendation coverage when selecting the appropriate group. This could help in simultaneously making both accurate and diverse recommendations. In this paper, we propose to focus mainly on the growing of the large search space of users’ profiles and to use an evolutionary multi-objective optimization-based recommendation system to pull up a group of profiles that maximizes both similarity with the active user and diversity between its members. In such manner, the recommendation system will provide high performances in terms of both accuracy and diversity. The experimental results on the Movielens benchmark and on a real-world insurance dataset show the efficiency of our approach in terms of accuracy and diversity compared to state-of-the-art competitors.

International audience; Ionizing-radiation-resistant bacteria (IRRB) could be used for biore-mediation of radioactive wastes and in the therapeutic industry. Limited computational works are available for the prediction of bacterial ionizing radiation resistance (IRR). In this chapter, we present some works that study the causes of the high resistance of IRRB to ionizing radiation. Then we focus on presenting in silico approaches that use protein sequences of bacteria in order to predict if an unknown bacterium belongs to IRRB or ionizing-radiation-sensitive bacteria (IRSB). These approaches formulate the problem of predicting bacterial IRR as a multiple instance learning (MIL) problem where bacteria represent the bags and * Corresponding Author: manel.zoghlami@gmail.com. 2 Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri et al. primary structure of basal DNA repair proteins of each bacterium represent the instances inside the bags. We also present a formulation of the problem of MIL in sequence data and explain how it could be used to solve the problem of IRR prediction in bacteria. A brief comparison of the presented approaches is provided.

Recently, increasingly large amounts of data are generated from a variety of sources.Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

The chapter presents the recent studies focusing on optimizing the efficiency of air-conditioning (AC) systems using solar energy. For this purpose, several advanced AC plants (absorption, adsorption, and desiccant) are designed. Their technology and components are described in this chapter. It also discusses the energy intake of the solar energy use in air-conditioning, especially in rural regions where the electricity shortage is frequent, as well as the reduction of the energy costs and the pollution rate. A comparison between solar AC systems and traditional AC systems at the level of the designs, costs, and effectiveness is made at the end of the chapter.

Recently, graph mining approaches have become very popular, especially in certain domains such as bioinformatics, chemoinformatics and social networks. One of the most challenging tasks is frequent subgraph discovery. This task has been highly motivated by the tremendously increasing size of existing graph databases. Due to this fact, there is an urgent need of efficient and scaling approaches for frequent subgraph discovery. In this paper, we propose a novel approach for large-scale subgraph mining by means of a density-based partitioning technique, using the MapReduce framework. Our partitioning aims to balance computational load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases.

This paper presents a study on the use of ice to improve the energy efficiency of a domestic refrigerator by applying a pseudo bond graph model that describes the thermal transfers sustained by a quantity of ice introduced inside the cavity of refrigeration. The use of ice resulted in a global energy saving of 4.68%. The effect of ice was found to be more significant during the transitional regime. It reduced the response time to reach the stable average temperature from 15 h to only 3.5 h compared to when not using ice. This achievement did not cost additional electrical power, but rather allowed a saving of electrical energy of 76.73%. However, during the steady state, a reduction in the energy efficiency was noted. An improvement in the cooling by keeping the temperature inside the refrigerator more homogeneous is also proved. The model has two inputs: the outside temperature, and the modulated temperature of the evaporator. This latter determines the functioning of the compressor cycle. The model describes the thermal transfers by natural convection inside the refrigerator. Two experiments were carried out to make a performance comparison and to prove the influence of ice in cooling and energy saving. We used real measurements to modulate the evaporator temperature source in the pseudo bond graph model. The simulation results show the effectiveness of the proposed approach. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Colloque avec actes et comité de lecture. Internationale.; International audience; Le présent travail a pour objectif de présenter une étude de sensibilité des modèles numériques réalisés avec le code ABAQUS, vis-à-vis de la variation des paramètres du procédé d'emboutissage, ainsi que l'effet d'orientation initiale du renfort, le type (coque ou membrane) et la taille du maillage. La simulation de la mise en forme est réalisée à l'échelle macroscopique en considérant le renfort comme un milieu continu. Cette approche continue s'appuie sur une loi de comportement hypoélastique qui a l'aptitude de suivre la rotation des fibres (directions d'anisotropie) au cours de la mise en forme. Cette loi de comportement est implémentée dans le code de calcul des éléments finis ABAQUS /explicit en utilisant une subroutine VUMAT.

International audience; Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in several scenarios including human disease and drug discovery. In this age of rapid and affordable biological sequencing, the number of sequences accumulating in databases is rising with an increasing rate. This presents many challenges for biologists and computer scientists alike. In order to make sense of this huge quantity of data, these sequences should be annotated with functional properties. UniProtKB consists of two components: i) the UniProtKB/Swiss-Prot database containing protein sequences with reliable information manually reviewed by expert bio-curators and ii) the UniProtKB/TrEMBL database that is used for storing and processing the unknown sequences. Hence, for all proteins we have available the sequence along with few more information such as the taxon and some structural domains. Pairwise similarity can be defined and computed on proteins based on such attributes. Other important attributes, while present for proteins in Swiss-Prot, are often missing for proteins in TrEMBL, such as their function and cellular localization. The enormous number of protein sequences now in TrEMBL calls for rapid procedures to annotate them automatically. In this work, we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation approach for large-scale annotation of proteins. To do this, the functional annotations of reviewed proteins are used to predict those of non-reviewed proteins using label propagation on a graph representation of the protein database. DistNBLP is built on top of the "akka" toolkit for building resilient distributed message-driven applications.

International audience; The GO ontology is widely used for functional annotation of genes and proteins. It describes biological processes (BP), molecular function (MF), and cellular components (CC) in three distinct hierarchical controlled vocabularies. At the molecular level, functions are often performed by highly conserved parts of proteins, identified by sequence or structure alignments and classified into domains or families (SCOP, CATH, PFAM, TIGRFAMs, etc.). The InterPro database provides a valuable integrated classification of protein sequences and domains which is linked to nearly all existing other classifications. Interestingly, several InterPro families have been manually annotated with GO terms using expert knowledge and the literature. However, the list of such annotations is incomplete (only 20% of Pfam domains and families possess MF GO functional annotation). We therefore developed the GODomainMiner approach to expand the available functional annotations of protein domains and families. Based on our ECDomainMiner approach, we use the respective associations of protein sequences with GO terms and protein domains to infer direct associations between GO terms and protein domains. Finally, we used our calculated GO-Domain associations to devise a systematic way, called AutoProf-Annotator, to generate high confidence rules for protein sequence (or structure) annotation.

ResumeCet article propose une methodologie d’etude de sensibilite pour cerner l’incertitude d’estimation de la periode de retour d’une crue maximale annuelle exceptionnelle connue seulement par la cote des plus hautes eaux. Deux autres contraintes ont preside au choix methodologique. Tout d’abord, nous ne disposions pas des debits maximaux de crue a la station (hors ceux de la crue annuelle) et nous disposions d’une courte serie des debits maximaux annuels. L’etude a ainsi ete focalisee sur les debits moyens journaliers dont la serie est complete avec une seule lacune qui est celle de la crue qui nous interesse. La taille de la serie des debits moyens journaliers la plus complete etant assez moderee, un modele statistique a depassement de seuil POT (Peaks Over Threshold) a ete adopte. A partir de la laisse de crue, le debit maximal de la crue non jaugee a ete estime par cinq approches differentes (methodes d’extrapolation par regression statistique a partir des parametres hydrauliques de la station, deux ...

In the North African Atlas, Triassic sedimentary rocks exhibit a variety of deformation styles that are caused by the interaction between halokinesis, tectonics and sedimentation. This paper aims to present an integrated study documenting the occurrence of salt-tectonic processes in the Maknassy-Mezzouna orogenic system. The interpretation of both, field mapping and seismic data suggests the existence of a complex tectonic evolution of these Triassic evaporites , with the occurrence of Mesozoic extensional/transtensional and Tertiary compressional/transpressional tectonic regime accompanied by sedimentary loading. This evolution took place along three major events of either lateral or vertical migration of Triassic evaporites. The first one is tectonic-driven and is related to the extensional/transtensional tectonic regime, which occurred during the break-up of Pangea and rifting of Neotethyan Ocean in the Early Jurassic . The second remobilization of salt Triassic rock developed during the Cretaceous. The initial mobilization of the Triassic salt was probably induced by sedimentary loading with a limited contribution of tectonic forces. During its third stage of evolution, salt Triassic rocks evolve into salt sheets due to the shortening deformation occurred during Tertiary and Quaternary in relationship with the closure of the Neotethyan realm. Salt migration controls subsidence , generates depocenter shifts and causes thickness variation of the entire post-Triassic sedimentary sequence . The signature of salt Triassic syn-sedimentary control is confirmed by thickness reduction and abrupt variation of structural dip that affects all the sedimentary pile in the flanks of salt sheets. In seismic sections the syn-sedimentary salt tectonic control is observed by geometries like pinching out, thickness reduction, onlap termination geometry as well as unconformities identified along the flanks of the salt sheets.