dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of splice mutations was predicted on the major transcript with the Alamut Visual Software (Interactive Software, Rouen, France), which contains the algorithms SpliceSiteFinder, MaxEntScan, NNSPLICE, GeneSplicer, Human Splicing Finder, ESE-Finder, and RESCUE-ESE. Mutation data from all TCGA series were retrieved from the GDC portal in day 29th of September 2017. Frequencies of germline mutations in general population were retrieved from the ExAC database (access on 12 October 2017). Clonal evolution. Clonal evolution was inferred with Pyclone 28. Parameters used to define clusters of SNVs were: (i) at least five SNVs and (ii) mean cancer cell fraction of at least 10% in one sample. Blood cytometry. The whole blood was washed in PBS 1X with 0. 4 g/l human albumin. Samples were stained with master mix of antibodies for analysis on CantoII or LSRFortessa flow cytometers (BD) before lysis of red cells and fixation (BD FacsTM Lysing solution 10X).
Data were analyzed using Flow Jo software (Tree Star). The anti-human antibodies used were CD8b PC5 (2ST8. 5H7; Beckman Coulter), TCR γδ FITC (11F2), (BD Biosciences), CD45RA PC7 (HI100 eBiosciences), CD4 PE TX (S3. 5 Invitrogen), CD3 Alexa700 (UCHT1), CCR7 BV421 (G043H7), CD27 BV605 (O323), and CD127 BV650 (A019D5)-all from Biolegend. Immunohistochemistry. Immunohistochemistry (IHC) was performed on FFPE sections (4 μm in thickness) for CD3 (Dako, A0452, 1/200), CD4 (Dako 4B12 IR649, 1/100), CD8 (Dako C8/144B, IS623, undiluted), PD-L1 (Dako clone 22C3), MLH1 (Dako ES05 1/50), MSH2 (Dako FE11 1/50), MSH6 (Dako EP49 1/50), PMS2 (BD Phamingen A16-4 1/50). PD-L1 immunostaining with Dako clone 22C3 was performed by Merck Research Laboratory, Palo Alto, CA. IC (lymphocytic) infiltrates were scored according to the method of Rothermel et al.
Acknowledgements Supported by funding from the  European Commission  under the  Horizon 2020 program  ( U. M. Cure/L. Mobuchon ; project number:  667787 ), the  INCa/ITMO/AVIESAN  PhD fellowship program " Formation à la recherche translationelle " ( M. Rodrigues ), the  Cancéropôle Ile de-France , the  INCa   PRT-K14   UMCURE , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Ligue Nationale Contre le Cancer (Labellisation) , and the  Institut Curie. The  Institut Curie ICGex NGS  platform is funded by the  EQUIPEX "investissements d'avenir" program  and  ANR10-INBS-09-08  from the  Agence Nationale de le Recherche. We thank the patients and their family members. We also acknowledge support from the  Institut Curie  for sample collection, banking, and processing: the  Biological Resource Center and its members (O.
From these cases, various data concerning sociodemographic conditions, death scene, personal and family medical history, parental behaviours, sleep environment, clinical examinations, biological and imagery investigations and autopsy are systematically collected. These data will be complemented as of 2018 with a biobank of diverse biological samples (blood, hair, urine, faeces and cerebrospinal fluid), with other administrative healthrelated data (health claim reimbursements and hospital admissions) and socioenvironmental data. Insights from exploratory descriptive statistics and thematic analysis will be combined for the design of targeted strategies to effectively reduce preventable infant deaths. Ethics and dissemination The French sudden unexpected infant death registry (Observatoire National des Morts Inattendues du Nourrisson registry;OMIN) was approved in 2015 by the French Data Protection Authority in clinical research (Commission Nationale de l'Informatique et des Libertés: number 915273) and by an independent ethics committee (Groupe Nantais d'Ethique dans le Domaine de la Santé: number 2015-01-27).
dEsIgn study design and population The French SUID registry is an observational prospective registry that over at least a 10-year period (2015-2025) aims to include all SUID cases occurring in the French metropolitan territory plus two overseas islands: La Martinique (Caribbean Sea) and La Réunion (Indian Ocean) (figure  1 ). Thirty-five French SUID referral centres are participating in this registry. Because in France, a SUID case is legally defined as the sudden unexpected death of a child <2 years old, 7 all children younger than 2 years dying in the context of SUID are eligible for the registry. Once all the persons who have parental authority (often one or both parents) are informed that participating is voluntary, data for all children for whom all the persons who have parental authority give informed written consent are included.
data collection The OMIN is based on a continuous and prospective record of prespecified and standardised information concerning the examinations that should be performed with a SUID case as recommended by the French national health authority. 7 The data collection depends on the SUID case trajectory after death as well as the willingness of parents with parental authority to participate. Data are gathered from two different sources: (1) the mobile intensive care unit (MICU) initially in charge of the SUID case on the death scene and (2) the SUID referral centre performing the postmortem exploration to identify the cause of death (figure  2 ). Additionally, other health-related and socioenvironmental data will be secondary linked as of 2018 to the case in the OMIN database. To ensure that all SUID cases are recorded in the registry, data from the MICU concerning dead children transported directly to the mortuary and not under the control of a referral centre are also collected.
In case of forensic investigations (not performed by the referral centres) requested by the Court of Justice, only the MICU data are available. Data collected during the forensic investigation are recorded the second time when the legal procedures are finalised. A summary of the timeline and sources of data collection are presented in figure  3. data from MICu and suId referral centres Data collected by MICU and SUID referral centres are related to the social and demographic status of children and their parents, personal and family medical history, antenatal and current parental behaviours, child feeding, death scene, usual and death sleep environment, nature and results of clinical examinations at the death scene and the referral centre, nature and results of additional clinical examinations performed in the referral centre, and classification of SUID cases by the medical team based on a national classification and that from Fleming et al 1 (table  1 ).
Ten aliquots for urine, one for CSF and three for faeces (2 in bottle brusches) will be kept. A lock of hair and two faeces bottlebrushes will be additionally stored. Standardised procedures for biological sample collection will be used, including standardised blood sampling, transport from each site to the central laboratory (<24 hours), aliquoting in cryotubes and storage temperature (ambient temperature, -80°C or -196°C depending on the nature of the biological sample). For timely follow-up of collected biological samples as well as their availability, a central database will be created.
Linkage with secondary sources of data The completion of a death certificate by a physician is compulsory in France on the occurrence of death. Data from this certificate are gathered by the French registry of death causes (CépiDc) for the whole French population, by gender, age, country of birth and underlying cause of death coded according to the International Classification of Diseases, 10th Revision (ICD-10 discharge diagnosis codes). Legal authorisation was obtained from CépiDc to gather information concerning all deaths in children <2 years old for calculating the exhaustiveness of inclusions in the French SUID registry. Similarly, health-related information concerning infants and their mothers will be gathered as of 2018 in the French SUID registry from the French national health insurance information system (Système national d'information inter-régimes de l'Assurance maladie, SNIIR-AM) and the French hospital discharge database (Programme de Médicalisation des Systèmes d'Information, PMSI).
SNIIR-AM covers the entire French population (65. 3 million inhabitants) and contains exhaustive data on all reimbursements for health-related expenditures  8 9  including medicinal products such as drugs and outpatient medical and nursing care prescribed or performed by healthcare professionals. PMSI provides detailed medical information on all admissions to French public and private hospitals, including dates of hospital admission and hospital discharge, ICD-10 discharge diagnosis codes and medical procedures during the hospital stay. Data in PMSI and SNIIR-AM were recently made publicly available for pharmacological and epidemiological studies. 10 11 o allow for case-control studies to identify risk factors for SUID, data access will be requested in the near future from the Étude longitudinale française depuis l'enfance (ELFE) cohort to use included participants as a control group.
ELFE was initiated in 2011 and is a nationally representative cohort of 20 000 children followed from Open Access control of data completeness and validity. Automatic data quality controls are performed periodically to control for missing data and value ranges. Once data from the CépiDc are available, recorded data will be compared with those from death certificates to estimate the exhaustiveness of the inclusions in the database. Finally, linkages with previously described secondary data sources will be planned once a year. In this registry, the number of SUID cases occurring in the French territory per year will determine the recruitment size. On the basis of the referral centres' experience as well as the last national estimation performed in 2014 by the CépiDc,  15  we expect to recruit at least 300 SUID cases each year.
A final sample size of about 3000 cases is thus expected for a study period of 10 years. CIs, means, SDs and frequency distributions will be calculated for all measures. Available data for cases with parental refusals and without a SUID referral centre in charge will be compared with cases with a SUID referral centre in charge. Survival analyses will be used to analyse time-to-event data such as death to identify factors associated with early or late SUID. Multivariable binomial regressions will be used to compare subgroups of SUID children or control children. Multilevel multivariate statistical modelling will also be used to simultaneously study individual and higher-level predictors of outcomes (such as place of residence characteristics). To counteract the potential problem of multiple comparisons, adjustment for statistical significance will be performed when needed.
Data are available for analysis after validation by the scientific committee according to a validated chart of data access. Interested researchers have to comply with the French legislation (ie, apply for authorisation from the Commission Nationale de l'Informatique et des Libertés for treatment of personal health data). Research projects have also to be approved by an independent ethics committee. Scientific use of health insurance claims data requires individual accreditation from the data owner, the French national health insurance. Results will be discussed with associations of families affected by SUID, caregivers, funders of the registry, medical societies and researchers and will be submitted to international peer-reviewed journals and presented at international conferences. dIsCussIon The systematic collection of a large variety of socioenvironmental, behavioural, clinical, imaging and autopsy data simultaneously with biological samples and administrative data concerning both the children and their mothers appeared critical to study or sustainably prevent SUIDs in the post 'back-to-sleep' era.
To our knowledge, this is the first prospective registry specifically designed to respond to this issue even if large population-based registries with less variety of collected data already exist in other countries. 16 17 Because collaboration with such countries is imperative to share best practices, monitor progress and achieve statistical power for future investigations,  5  the OMIN involves as far as possible standardised data to facilitate international collaborations such as meta-analyses or original or replication studies. To be fully operational and to respond to its objectives, the registry will have to manage several challenges. The first will be to recruit a control population simultaneously with SUID cases for risk-factor studies. Indeed, although the already existing ELFE cohort is a potential way to recruit control children, several exposures are lacking in this cohort and the risk of biased selections may not be ascertained from this data source.
The second challenge is to ensure the exhaustiveness of including all SUID cases occurring in the French territory. One French referral centres currently do not participate in the OMIN. Also, preliminary tests seem to indicate a possible underinclusion in several participating centres. Data from the CépiDc, when available, will help better quantify the magnitude of this potential bias and implement corrective measures. The third challenge will be to sustainably maintain both the mobilisation of all medical and health actors and caregivers as well as public and private funding, which are essential elements for the success of this project. In the short term, this registry has the potential to provide an effective response to the SUID major public health issue by identifying underlying mechanisms that can be prevented and by sharing high-quality data to inform best practices and the accurate classification of SUIDs.
1 • latitude by 0. 2 • longitude to 2 • latitude by 4 • longitude in steps of 0. 1 • latitude by 0. 2 • longitude. Third, apart from the total snowfall amount, individual snowfall events recorded by both the MRRs and CloudSat are investigated. Individual CloudSat overpasses in the grid box over the station are averaged and compared to measurements of the MRRs. This analysis is executed using different spatial resolutions (varying from 0. 1 • latitude by 0. 2 • longitude to 2 • latitude by 4 • longitude) in order to investigate the effect of the spatial resolution on how well surface snowfall amounts match. Lastly, a comparison between the MRRs, CloudSat and ERA-Interim reanalysis  (Dee et al. , 2011)  is executed for the three stations. ERA-Interim reanalysis data are generally considered one of the best reanalysis products regarding snowfall over Antarctica but are still very biased  (Bromwich et al.
Acknowledgements. We thank the two reviewers for their comments, which substantially improved the quality of this paper. This work was supported by the  Belgian Science Policy Office (BELSPO ; grant number  BR/143/A2/AEROCLOUD ) and the  Research Foundation Flanders (FWO ; grant number  G0C2215N ). Edited by:  Philip Marsh Reviewed  by:  Florentin Lemonnier  and one anonymous referee.
Data availability. CloudSat data are freely available via the Cloud-Sat Data Processing Center (http://www. cloudsat. cira. colostate. edu/, Wood et al. , 2013Wood et al. ,  , 2014)). Data from the Micro Rain Radar at the Princess Elisabeth station can be obtained from the database at http://www. aerocloud. be (Souverijns et al. , 2018). Data from the Micro Rain Radar at the Dumont D'Urville station are available at https://doi. org/10. 1594/PANGAEA. 882565 (Berne et al. , 2017) , while data from the Micro Rain Radar at the Mario Zucchelli station will be made publicly available as soon as possible (contact person: claudio. scarchilli@enea. it). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-12-3775-2018-supplement.
OTUs presenting less than 10 sequences across all samples were removed. Samples were normalized to 15,000 high quality sequences. Sequences are available at this link: https://doi. org/10. 15454/E6YRKX. Community diversity was estimated using the Shannon diversity index. A R script was used to perform a hierarchical clustering using hclust function from the vegan R-package. Dissimilarities inputs for clustering were obtained using the function vegdist based on the dissimilarity Bray-Curtis indices. Principal coordinate analysis (PCoA) was conducted to determine microbial community differences across insects classified by type of reactors. A Hellinger transformation had previously been carried out on the sequencing data using the vegan package and PCoA were conducted on the R-package Mixomics given the Hellinger distance matrix.
The xylanase and CMCase activities of beetle gut microbiomes measured in the present study were quite the same at both initial and final stage. One hypothesis could be that beetle larvae were harvested in dead pine trunks (wood eater). Therefore, the difference between initial and final activity is lower since at initial stage a high potential to degrade xylan and cellulose was already detected. Enzymatic activity, which represents a global degradation rate, was standardized by the 16S rRNA copies present in the sample which correspond to the witness of microbial biomass quantity. In this case, locust microbiomes exhibited the highest specific enzyme activities at initial and final stage whereas chafer guts microbiota still had the lowest specific enzyme activities (data not shown). It is striking to note that the specific enzyme activity decreased considerably between the beginning and the end of the experiment.
Distribution and Bacterial Community Structures Across Samples Before and After Wheat Straw Fermentation in Batch Experiments Microbiomes from the insect gut s introduced in the bioreactors were sequenced using the MiSeq Illumina technology. The taxonomic assignment showed that 5 phyla were represented (Figure  3 ) including Actinobacteria, Firmicutes, Fusobateria, Bacteroidetes and Proteobacteria. The 3 dominant phyla, i. e. , Bacteroidetes, Firmicutes and Proteobacteria, were identified in 4 of 5 insect gut microbiomes, which is consistent with data obtained in another study across 21 taxonomic orders of insects  (Yun et al. , 2014). Actinobacteria and Fusobacteria phyla were poorly represented and Actinobacteria were found only in cockroach and chafer microbiota. Shannon diversity index was used to estimate the diversity of the community (Figure  4 ).
Basic statistics and model fit The mean and standard deviation was estimated for every batch-sex group dataset, and its deviation from the normal distribution was tested by means of the Shapiro-Wilk test. The fit of the data to the probability density function of the mixture model was tested by means of the Kolmogornov-Smirnov test. All the statistic computations were done using the base package of R  [51]. The significance level was set to α = 0. 05.
Partitioning of the variance A number of pupae equal to the minimal sample size was randomly selected for each batch-sex group per species/strain. This was performed in order to get a balanced factorial design dataset. A linear model was fit for each batch by means of ordinary least squares. The model included sex and batch as fixed factors, and their interaction. The partitioning of variance was assessed through ANOVA. All statistical analyses were done using the base package of R  [51].
Results Table  1  summarizes the statistics of the pupal size of the species and strains used in the present study. In total, 7733 pupae were analyzed. The majority of the batches were not significantly different from a normal distribution, and the mixture of two Gaussian distributions fitted well to all batches of all the species/strains used when male and female pupae data are combined. Table  2  presents the significance of the goodness-of-fit between the models and the data. None of the samples was significantly different from a Gaussian mixture distribution. Only two batches separated by sex showed significant departures from normality: An. arabiensis females of the batch 3, and Ae. albopictus Rimini males of batch 3. Figure  3  shows the histograms and fitted models for the three batches of each species/strain studied.
Acknowledgements We would like to thank the staff members of the  Insect Pest Control Laboratory of the Joint FAO/IAEA Division ,  Antonios Augustinos ,  Nanwintoum S. Bimbile-Somda ,  Danilo Carvalho ,  Anna Konczal ,  Hamidou Maïga ,  Wadaka Mamai ,  Gulizar Pillwax  and  Asha Wijegunawardana , for their great support during the preparation of the samples used in this study. We are particularly grateful to  Ángela Aguado  and  José Félix Descalzo  for their essential assistance. We thank to  Enrique Torrente  and  Jose Antonio Herrador  their indispensable support to carry out this research. This study was supported by the  International Atomic Energy  research contact no. 17939  as part of the  Coordinated Research Project  " Exploring  genetic, molecular, mechanical and behavioural methods of sex separation in mosquitoes".
F v ) through L u, j (resp. L v, j ). L u, j is the recirculation width at the interface and is estimated as L u, j = (L u,L + L u,R )/2 where the subscript L (resp. R ) stands for the cell on the left (resp. right) side of the interface. The definition of φ allows writing L u = (1 -φ) × L j leading to: L u, j = (L u,L + L u,R ) 2 = � 1 - φ L + φ R 2 � × L j L v, j = φ L + φ R 2 × L j (4) With such considerations, (3) can be written either for the 1D or the 2D cells as : U n+1 {1D,2D},i = U n {1D,2D},i -∆t          1 A i � j∈Γ {1D,2D},v P i, j L v, j F n v, j + � j∈Γ {1D,2D} P i, j L j F n j + � j∈Γ {1D,2D},u P i, j L u, j F n u, j + S(U n i )          (5) In the numerical tool Flood1D2D, the fluxes are computed using the PorAS Riemann solver  [2]  based on U L and U R , the hydraulic variables in the left-hand and right-hand cells of the interface.
| High-resolution mitochondrial respiration rate Rates of oxygen consumption (JO 2 in pmol O 2 /s) were measured using an Oxygraph 2-k high-resolution respirometer equipped with two measurement chambers and then analysed using datlab software (Oroboros Instruments, Innsbruck Austria). The oxygen electrodes were first calibrated at two points: air-saturated buffer and zero oxygen. The air saturation calibration was achieved by adding respiratory buffer and then allowing oxygen concentration to stabilize, while the zero oxygen calibration was achieved by adding saturating dithionite. Oxygen flux was corrected for instrumental background oxygen flux  (Pesta & Gnaiger, 2012). Part of the liver homogenate from each fish was added to one of the two measurement chambers of an oxygraph immediately following preparation; both fish from each processing pair were measured in parallel.
Third, we tested for food treatment differences in respiration rates scaled up to the whole-liver level (pmol O 2 s -1 mg -1 liver × liver size in mg) as the size of organs relative to body size can also change substantially following fasting  (Secor & Carey, 2016). Finally, we analysed the effect of food treatment on membrane potential and MitoP/MitoB ratio using LMM approaches. The models included the membrane potential or MitoP/MitoB ratio as the dependent variable and food treatment as factorial predictor. Analyses are based on a sample size of 10-12 fish per group and were performed in IBM SPSS Statistics 21 (SPSS Inc. , Chicago, IL, USA); the level of significance was set to p < 0. 05, and all means are presented ± SE.