dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Global proteomics profiles distinguish testes, ovaries and embryos in Gammarus fossarum. In order to have a systemic view of the molecular processes in the reproductive organs and in the embryonic development of the sentinel species G. fossarum, we analyzed proteomics data obtained from individually sampled testes and ovaries at different maturation status and from individually sampled embryos obtained at different developmental stages (see Material and Methods section). Sample clustering and Principal Component Analysis (PCA) based on raw data did not change after filtering and normalization (Fig. 1A ,C for raw data, Fig. 1B, D  for normalized samples). The proteomes of the testes and ovaries were clearly separated and the difference across tissues accounted for up to 72% of the variance in the data (Fig.
Module protein composition and hub proteins provide new insights in the reproductive biology and embryonic developmental processes in Gammarus fossarum. We looked at the predicted biological role of the different proteins that clustered into the same co-expression module in order to investigate the biological significance of the identified co-expressed modules. We used the output of the NCBInr and Swissprot BLAST results to associate each protein to one category of the following molecular functions or protein families: the Large Lipid Transport Protein superfamily which included both Vitellogenin-like proteins (Vtg-like) and clottable protein-like (CPs-like), cell cycle and division, actin-myosin families, energy metabolism, RNA processing (including proteins regulating the transcriptional process, RNA splicing and mRNA transport to cytoplasm), protein synthesis (including riboproteins and factors involved in protein translation), apoptosis (including both potential activators and inhibitors), and haemocyanin-like proteins.
Interestingly, we found two taxonomically-restricted proteins in the yellow module and in the blue module (coded by the contigs 201834_fr4 and 67263_fr2) that showed high intramodular connectivity and module membership, characteristics of a hub role in their respective modules. These proteins do not show any significant homology (E-value > 0. 001) with proteins currently available in the NCBInr and Swissprot databases. This result suggests that protein with key roles in the organogenesis and oocyte maturation may have evolved divergently in amphipods and it shows the strength of co-expression network analysis to get new molecular insights in non-model organisms. Finally, the two modules correlated with the amphipod testes showed two distinct functional profiles. The brown module was highly enriched in proteins annotated as members of the actin or myosin families (84%, Table  1  and Fig.
Discussion In this study, we used for the first time a co-expression network analysis to investigate the protein organization of the reproductive organs and embryos in the freshwater amphipod G. fossarum using shotgun proteomics data. We showed that WGCNA was well suited to extract biological information using shotgun proteomics data from a non-model organism. In our case study that considered contrasted biological samples at different maturation or developmental stages, this methodology allowed us to identify different modules of co-expressed proteins correlated with the spermatogenesis cycle, and some specific physiological stage of ovary maturation or embryonic development, shedding new light on the molecular physiology of the reproductive system in G. fossarum. We found one module (named as yellow) correlated with embryos, notably with the mid-developmental stages S3 and S4.
fossarum, is characterized by the activation of factors involved in RNA processing, such as RNA editing or RNA splicing control. Interestingly, mechanisms involved in the regulation of alternative splicing have been identified in evolutionarily distant metazoans, such as the fruit fly and the mouse which are well characterized models  23, 24. Our data-driven approach enabled us to identify two proteins with hub properties, 201834_fr4 in the yellow module and 67263_fr2 in the blue module, that had no significant homology among the NCBInr and Swissprot databases by sequence similarity search (E-value < 0. 001). This result highlights the potential of co-expression network analyses in identifying taxon-restricted proteins with key roles in molecular physiology processes. Moreover, the observation that one of these proteins is involved in organogenesis is in line with the recent proposal, based on the comparison of the developmental transcriptomes of 10 different species, of a divergent mid-development transition that uses species-specific functions in embryo development for defining the phyletic body plan  25.
Simulation Setup In this research, we employ a powerful and accurate simulation for cloud computing known as Cloud Sim simulator. Some features of the simulators include flexibility, graphical output, tables and diagrams information report and so on. In order to evaluate the proposed approach, the first step is to simulate cloud datacenter circle, then, VMs' interface and finally duty lists. In this study, the input simulation scenario includes a data center with 10 physical host, 10V Ms and also 10 duties with 150, 000MIPS length. Every physical host comprises a processor that will randomly select. Also cloudsim simulator  [32] , Netbean software and Java library is used to implement the scenario. Table  4  shows the simulation parameter setting and required features.
VI. RESULTS We show in Fig. 3  the measured auto-correlation functions of the redMaGiC galaxy sample and its cross-correlation with the SPT+Planck CMB lensing map. The small-scale data points shown with faded symbols are the scales removed by the scale cuts as discussed in Sec. IV B. The theoretical predictions assuming the fiducial cosmology listed in Table  II  are shown as the black lines. In the following sections we extract cosmological information using these measurements. We note that we made few modifications in Sec. VI A and VI B after we unblinded the data. We first added constraints on the galaxy bias from w δ g κ CMB (θ) alone with the cosmology fixed to Planck best-fit values (instead of DES-Y1). The motivation for this was to allow us to directly compare the constraints on galaxy bias when varying over the cosmological parameters, but combining with the Planck baseline likelihood.
The D G estimator We first measure the linear growth function D(z) using the D G estimator. We compute D G for all five tomographic bins, applying the conservative angular scale cuts listed in Sec. IV B, and additionally removing scales above 100 (see Sec. IV D 2 for details). The results are shown in Fig. 5. In addition, we also calculate the best-fit amplitude A D by combining all the bins, from which we obtain A D = 1. 16 +0. 20  -0. 20 , which agrees with the fiducial ΛCDM expectation of A D = 1. In comparing the result presented here (using 1289 deg 2 ) with that of G16 that used DES-SV data covering 140 deg 2 , we see that we obtain similar constraining power. This is due to: 1. The conservative scale cuts imposed in this study to minimize w δ g κ tSZ (θ) bias. 2. The lower number density of redMaGiC than the galaxy sample used by G16, in exchange for better photo-z errors.
ACKNOWLEDGEMENTS This paper has gone through internal review by the DES collaboration. YO acknowledges funding from the  Kavli Foundation , the  Natural Sciences and Engineering Research Council of Canada ,  Canadian Institute for Advanced Research , and  Canada Research Chairs program. EB is partially supported by the  US Department of Energy  grant  de-sc0007901. CC was supported in part by the  Kavli Institute for Cosmologi-cal Physics at the University of Chicago  through grant  NSF PHY-1125897  and an endowment from  Kavli Foundation  and its founder Fred Kavli. CR acknowledges support from a  Australian Research Council   Future Fellowship  ( FT150100074 ). BB is supported by the  Fermi Research Alliance, LLC  under Contract No. De-AC02-07CH11359  with the  United States Department of Energy.
The McGill authors acknowledge funding from the  Natural Sciences and Engineering Research Council of Canada ,  Canadian Institute for Advanced Research , and  Canada Research Chairs program. The  South Pole Telescope program  is supported by the  National Science Foundation  through grant  PLR-1248097. Partial support is also provided by the  NSF Physics Frontier Center  grant  PHY-0114422  to the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Kavli Foundation , and the  Gordon and Betty Moore Foundation  through Grant  GBMF#947  to the  University of Chicago. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration.
Based in part on observations at  Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Computations were made on the supercomputer Guillimin from  McGill University , managed by  Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the  Canada Foundation for Innovation (CFI) , the  ministère de l'Économie, de la science et de l'innovation du Québec (MESI)  and the  Fonds de recherche du Québec -Nature et technologies (FRQ-NT). This research is part of the Blue Waters sustained-petascale computing project, which is supported by the  National Science Foundation  (awards  OCI-0725070  and  ACI-1238993 ) and the state of  Illinois. Blue Waters  is a joint effort of the  University of Illinois at Urbana-Champaign  and its  National Center for Supercomputing Applications. We acknowledge the use of many python packages: Astropy, a community-developed core Python package for Astronomy [53], CAMB [34,54], Chain consumer 11 , Cosmo-SIS 12 , HEALPix [55], IPython [56], Matplotlib [57],  NumPy & SciPy  [58,59], Quicklens 13 and TreeCorr [60].
*exp(2*%i*k2s*z))'; end z=0:1e-9:1800e-9; //from 0 to 1800 nm (space domain for integration) intS(k)=0. 5*pe2*(1/r02-r02). *inttrap(z,defor3); //Trapezoidal integration end t=0:1e-12:6000e-12; //from 0 to 6 ns (time domain) //Transient reflectivity variation (empiric exponential decay added) drr=real(intS')+50*exp(-t/(20e-12)); The numerical results obtained from the simulation are in strong accordance with experimental dataset (Figure  7 ), considering the experimental set-up described in Figure  2. A good correlation is found concerning FFT spectral components (Figure  8 ). Possible slight frequency shifts can be due to experimental uncertainity related to the value of the angle of incidence θ (experimental set-up), as this parameter could impact the Brillouin signal.
Acknowledgments: Experimental works were all performed in  Condensed State Physics Laboratory (LPEC/CNRS ),  72085 Le Mans Cedex 09, France. The author would like to thank  Pascal Ruello  ( CNRS I3M , France),  Vitali Gusev  ( CNRS LAUM, France ),  Ayman Al Falou  from  IEEE/OSA  for scientific discussions, and  C. Thomas  from  Rennes 1 university (France ) for advice.
The data to be analysed with respect to three perspectives We took advantage of a platform, AnA. doc (Alturkmani, Daubias, Loisy, Messaoui, & Trouche to be published), which had been developed for the analysis of teachers' interactions with curriculum resources. With this platform, researchers were encouraged to share their data related to different situations of teachers' working with resources (e. g. lesson preparation, lesson implementation, or reflection/revision). The shared data were organized following a given model: teacher self-presentation; presentation of the context of their work (including their educational backgrounds and professional experiences); videos of the situation; and the resources used/ designed in this situation. Hence, the methodological choices presented in the following apply to the analyses of existing data.
Three methodological perspectives We present here the methodological perspectives associated with DAD, ATD, and CHAT. DAD has developed a specific methodology, based on reflective investigation  (Gueudet & Trouche 2012a ): following teacher's interactions with resources, beyond the direct observation by the researcher, involving teachers closely in the data collection, and accessing their "thinking" through reflective analysis and stimulated recall. The AnA. doc platform had been set up in this spirit, giving access not only to the video/s of the situation/s at stake and to the associated resources, but also to the teachers' self presentations, providing elements of reflective analysis. By combining these diverse sources, we inferred elements of structure of the teacher's resource system (static point of view), and of schemes developed over the documentational genesis (dynamic point of view).
Training the End Users According to Sutcliffe & Ryan  [8] , one of the four techniques of the SCRAM method for requirements elicitation and validation is providing a designed artifact which users can react to, like using prototypes or conducting concept demonstrator sessions. What is presented in a demonstrator session is called a demonstrator script and its nature can vary: Røkke et al. [10]  mention that it could be a prototype-simulation or even a prospective design, and the session can be interactive (with the participants using the system) or simply a presentation showing how to use the system. In any case, what is important is to generate a debate to get feedback from the participants and observe their reactions. Sutcliffe & Ryan  [8]  state that the demonstrator has limited functionality and interactivity, and it is intended to illustrate a typical user task.
). In addition, we generated the concept demonstrator script with two goals: to get feedback from the users but also to familiarize them with the interactions offered by the system before they had actually to use it in a different domain. In the subsections below, we detail how we generated the demonstrator and how we designed the demonstration session. The demonstrator script was designed to show the use of GRUS  [7]  to make a multicriteria decision in the field of Agriculture with participants who are experts in the domain of the problem. As GRUS allows users to use predefined process and even create new ones, we choose to show its usage with the same kind of process that will be later used (after the demonstration) by the participants to solve another problem in a different domain. The process we choose was a multicriteria evaluation process, which involves: 1) defining the meeting parameters, 2) defining the criteria and alternatives, 3) multicriteria evaluation, 4) direct choice and 5) reporting.
One of the sessions run on Ubuntu using the SimpleScreenRecorder tool 1  , three on a Windows platform with the Debut video capture application 2  , and one in MacOS using the QuickTime-integrated functionality  3. The software used to produce the video was Kdenlive  4  , an open-source multi-track video editor. We also defined guidelines to process the individual videos: 1. Split the steps. Identify each step of the process and render it in a separated file. 2. Split delays and remove them. If there are delays (e. g. , the user is thinking), split the video to separate such sections and remove them (so the videos look fluent). 3. Accelerate slow actions. If there are slow sections in the video that cannot be split and removed, apply the speed filter to such section. 4. Zoom in. Apply the zoom effect when the host resolution was too high and form controls look so small, compared to the other sessions.
Acknowledgements Authors of this publication acknowledge the contribution of the Project  691249 ,  RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems  (www. ruc-aps. eu),funded by the  European Union  under their funding scheme  H2020-MSCA-RISE-2015 This work is partially supported by the project  ANR-17-CE04-0012 VGI4bio.
AI is a set of analysis methods which permits to extract relevant information from any dataset. AI uses data mining techniques to get sense for the data. a set of techniques that can be used to explore treatment and outcome questions in large clinical databases and help develop algorithms and guidelines for problems where controlled data are difficult to obtain. The data mining process includes several steps, including data capture, data selection, data processing, and machine learning. Abdullah et al. reported that combining self-reported data with data from several smartphone sensors and communication patterns resulted in reliable prediction of the Social Rhythm Metric, a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder  [25]  Voice features from phone calls can also be collected and analyzed during everyday life in naturalistic settings.
Secondary outcomes Secondary outcomes are: Evaluate appetite using the Council of Nutrition Appetite Questionnaire (CNAQ) score with EMA questions. Sleep duration before and after a suicide attempt in intervention group during the 6-month follow-up period. Electrodermal activity captured by waerables during EMA assessment in the intervention group. Pooled EMA data Suicidal events and eating disorders in control and intervention group during the 6-month follow-up period.
EB2 app The eB2 app collects data from inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. Also, using Google Play Services, the app is able to access detailed activity information and nearby location data. The app was developed to run in background and the user only interacts with the app for the initial configuration. Moreover, it was designed with battery safe considerations like non-continuous recording schedule, automatic sleep/wake function, and it additionally notifies.
Data collection and follow-up Baseline assessment The baseline enrolment questionnaire includes the patient's basic demographic information including age, gender, marital status, employment status, country of birth, language spoken at home, level of education and whether they had a regular general practitioner. Medical treatments at inclusion, as well as any changes in these treatments during follow-up, will be collected and analyzed as potential confounders. The treatments will be classified according to the classification established by the World Health Organization (ATC index). The entire lifetime diagnosis of mental disorders axis I and II according to DSM-5 will be based on existing clinical evaluation protocols at inclusion: Mini International Neuropsychiatric Interview 7. 0. 0. Participants will be evaluated for depressive symptomatology  [32] , to detect the possibility of a manic episode  [33]  and to evaluate their global functioning  [34].
Outcomes measures We will use complementary strategies to obtain the primary outcome measures and assess adverse events. Assessments will be carried out by trained clinicians (Psychologist, Psychiatrist or Psychiatrist Residents up to second year) at outpatient clinics and emergency departments. Researchers will check the hospital attendance data especially the emergency records. The assessment of suicidal behaviors will include at the inclusion and completion of follow-up: The Columbia Suicide Severity Rating Scale a scale that showed good sensitivity and specificity to identify the most at risk subjects  [35]. If a suicide attempt has occurred, the suicide intent scale  [36]  and the Risk Rescue Rating Scale.
2 ). EB2 is designed to collect information about the users' physical and physiological activity level. This application has been developed for the Android mobile platform and works in combination with the arm Band. SmartPhone and the arm Band communicate via Bluetooth 4. 0. This application obtains information from the user by accessing the built-in sensors in the host Android SmartPhone and the arm Band. Through the process of data logging the Sensor Logger APP records both the information from the SmartPhone and arm Band. This double recording of information by the Sensor Logger APP allows it to obtain enough information from inertial sensors to determine the user's activity level during the recording process  [39]. The heart rate signal and Galvanic skin response are also recorded to determine the emotional state of the user.
Sample size We propose to recruit 1000 patients from two sites and expect a total of 100 suicide re-attempts during the follow-up period. We plan to recruit 500 patients per site (total of 1000) during 18 months with a follow-up period of 6 months. Based on our suicidal behavior monitoring pilot data (see above), we have calculated power in multivariate logit regression models, assuming a type I error of 0. 05 and two-sided comparison. Using the effect sizes and standard deviations from our pilot data we expect our sample 1000 attempters and 100 reattempters during the follow-up) to provide 80% power to detect significant changes in death desire, suicidal ideation or suicide attempt related with sleep and appetite.
Data analysis We will carry out descriptive analyses of all data collected separately by site and treatment. We will calculate means, variances and empirical quartiles, and assess the distribution and skewness of the data. If groups differ significantly on covariates then these scores will additionally be included as covariates in multivariate regression models. We will use 3 multi-level logit regression to account for multiple observations per individual, to identify individual-level (sleep, appetite, socio-demographic, clinical data, treatment data) and site-level characteristics associated with death desire, suicidal ideation or suicide attempt. We will use hazards models to relate covariate characteristics (sleep, appetite, socio-demographic, clinical data, treatment data) with time to suicide re-attempt during the follow-up period.
Data privacy Data privacy is a serious concern in the e-health research area. Our system complies with the highest level of security regarding patients' data. The eB2 app captures data from smartphones, which possibly was a deterrent for patients to accept the app  [40]. In a preliminary study, patients were aware of the general approach of our method and were not very concerned about sharing their personal data since it was anonymized at the smartphone. Another major concern regarding personal electronic data is data security  [41]. In order to preserve the patient's privacy and reduce the risk associated to a non-legitimate access, all the sensible information stored in the eB2 server was hashed and anonymized. Phone numbers, email addresses, Bluetooth, and Wi-Fi MAC addresses were hashed using the SHA-1 algorithm and the location was transformed using a non-invertible function.