dataesr/scientific-paragraphs-categorization huggingface dataset:
F I G U R E 3 Monthly peaks in the seasonal timing of abundance of (a) late stage (CV) and adult Calanus finmarchicus, and (b) juvenile (CI-CIV) Calanus spp. during four historical time periods 1961-1974 (blue dotted line), 1978-1988 (red dot-dash line), 1992-2000 (yellow dashed line), 2001-2013 (thin solid purple line), and the mean of all years (thick solid black line). Data are from the Gulf of Maine Continuous Plankton Recorder (CPR) dataset, and analysis is after Pershing et al. , 2005. See Supporting Information Appendix S2 for additional details [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E 4 (a) Mean larval abundance (number 10 m -2 ) and (b) phenological shifts in occurrence of 19 ichthyoplankton taxa occurring in Georges Bank and Gulf of Maine regions as reported from.
birds in databases maintained by state, federal (U. S. Fish and Wildlife Service), academic (e. g. , University of New Brunswick), and nonprofit organizations (e. g. , National Audubon Seabird Restoration Program) in the GoM. Observations include chick provisioning, hatching, productivity, chick condition, and return rates that can be used to address current management and conservation concerns related to phenology (Kress et al. , 2016;  Northeast Fish & Wildlife   Diversity Technical Committee, 2016). For non-breeding seabirds that utilize GoM habitats (e. g. , shearwaters), phenology data may be limited and difficult to acquire because these species are rarely observed from land, and spatial distributions over open ocean habitats are patchy and unpredictable. A major knowledge gap for this group is how environmental and ecological conditions on and total distances to overwintering grounds influence the timing of movements to and from the GoM.
This has to be done without prior selection for each substance, each exposure pathway, each toxicity endpoint. Basic NIs can be summed in different ways to produce intermediate indicators, relevant for a category of toxicity, an exposure pathway or a toxicity endpoint. A second level of integration is proposed to obtain the final indicator, the global noxiousness indicator. In a second part we applied our approach to a High Level radioactive Waste, a mixture of radionuclides and stable metals ideal to test our indicator. The selection of substances was then done according to the availability of the data required by the indicator calculation, our guideline being to privilege recognized and homogeneous sources of data. The framework presented in this paper to address the risk assessment of mixtures of stable and radioactive substances for both man and the environment was conceived to meet an operational need for comparison between complex source terms, i.
But its use will require some care. For example, the duration of what is similarly called "whole life", a shared notion used in the derivation of ED50 values, differs slightly between chemotoxicity (70 years) and radiotoxicity (86 years). The issue of how comparable the various types of toxicity benchmarks (i. e. for radioactive or stable substance) are, must also be solved. Some homogenization work would also be probably necessary to obtain consistent datasets regardless the exposure pathway (ingestion, inhalation) and the pathology (cancer, non-cancer).
The previous approach has then been applied to human health in a similar however a bit more complex way. In addition to the category of effects (cancer; non-cancer) and type of toxicity (radiological; chemical) the noxiousness has to be characterised also with regard to exposures pathways. Indeed the ED50 values for people required by the method are usually reported for a given exposure pathway. A look in some relevant databases showed that data are easily available mainly for ingestion or inhalation. We ackownledged that other exposure pathways such as irradiation at distance or dermal adsorption could be of importance. However we considered that an objective of feasibility demonstration as pursued here could be satisfied by the immediate availability of ingestion and inhalation data. Moreover reducing the application to these two exposures pathways allowed a certain homogeneity in the data required for human and ecosystem health by using a same internationaly recognised source.
Data sources Toxicity data were collected from aknowledged sources for both human and ecosystems, ensuring consistence by using as long as possible common supports. For chemicals, the databases associated with the version 2. 0 of the tool USETOX were then consulted for humans (ED50; Fantke et al, 2017) and ecosystems (EC50; Hugonnet et al, 2014). For radionuclides, the FREDERICA database  (Copplestone et al, 2008)  and the derived dose-effect relationships (Garnier-Laplace et al, 2010) underpinned the reasoning for ecosystems. For humans, data were retrieved from the UNSCEAR report (UNSCEAR, 1982) and the paper from  Edwards and Lloyd (1998). Data regarding the parameterisation of transfer in the environment were found in the ERICA Tool  (Brown et al, 2008)  specifically for wildlife and in some IAEA reports  (IAEA, 2001 (IAEA, , 2010) )  for humans.
Gene ontology assessment demonstrated major changes in biological process pathway expression for many skeletal and renal pathways, as well as sensory pathways, specifically relating to the eye (Fig  5A ). In addition, several gene sets relating to organ development were different between Pre-rRNA species were evidenced with 5'ITS1 and ITS2 probes, revealing precursors to the 18S rRNA (component of the small ribosomal subunit) and the 5. 8S and 28S rRNAs (components of the large ribosomal subunit), respectively. (B) Pre-rRNAs were quantified and log 2 ratios of product to precursor pre-rRNA species, or 18S to 28S rRNAs, were calculated and normalized to the mean value obtained for 3 healthy controls (controls 1-3). The data were obtained from three independent experiments (± S. E. M. ). https://doi.
Hbs1L-deficient fibroblasts had increased translation efficiency of ribosomal RNA Patient-derived fibroblasts were grown to near confluence and then treated with cycloheximide to pause translation. mRNA not being actively translated was degraded, and then the ribosomes were removed from the remaining mRNA. The remaining mRNA that had been bound to ribosomes was then sequenced using next generation sequencing. The principle component analysis and expression heatmaps demonstrated excellent reproducibility within biological replicates and marked variance between patient fibroblasts and controls (S3 Fig). Ontology analysis of biological processes for the Ribo-Seq data were different than the mRNA-Seq, however skeletal system development and morphogenesis genes remained significantly different in both datasets (Fig  6A ).
We used Ribo-Seq to assess whether there was clustering of ribosomes at a particular region of the mRNA sequences. For instance, if there is a defect in non-stop decay increased ribosome coverage at the 5' end of the transcript would be expected. Ribosomal footprint analysis showed no difference in the ribosomal footprint in patient versus control fibroblasts. Hbs1 has been shown in yeast and plant systems to function with Pelota in NSD  [8] [9] [10]. The footprinting is an indirect assessment of NSD and supports the shRNA data showing no differences in NSD. This difference might be due to differences in the importance of the Hbs1LV1/V2 isoforms for NSD and NGD in the human compared with yeast. However, it is also possible that it is a function of footprinting strategy, as others have recently shown with an epidermal Pelota-deficient model that shorter length sequencing (20-21 nt) revealed differences in footprinting where standard length footprints (28-34 nt) did not  [38].
Experimental replicates for RT-PCR contained 80 ng of human or mouse cDNA combined with appropriate primers (described in S6 Table ) and Fast SYBR Green (Invitrogen) according to the manufacturer's instructions. Reactions were run under standard conditions using the ABI Step One Plus qPCR machine (Thermo Fisher Scientific, Waltham, MA, USA) and analyzed using the StepOne Real-Time PCR System (Thermo Fisher Scientific). Tissue from E9. 5 embryos was homogenized and total RNA was extracted as above. 200 ng of extracted RNA was used in a 10 μl reaction using the TaqMan RNA-to-CT One-Step Kit (Applied Biosystems, Foster City, CA, USA). TaqMan probe Mm03807425_g1 (Applied Biosystems), flanking the Hbs1l splice acceptor region between exons 4 and 5 (Ensembl Transcript ID: ENSMUST00000219915. 1), was used with primer-limited B2m endogenous control (Applied Biosystems) in a multiplex reaction.
After UV cross-linking, pre-hybridization of the membrane was performed for 1 hour at 45˚C in 6X SSC, 5X Denhardt's solution, 0. 5% SDS, 0. 9 g/ml tRNA. The 5'-radiolabeled oligonucleotide probe was incubated overnight at 4˚C. The sequences of the RNA probes were: 5'-ITS1 (5'-CCTCGCCCTCCGGGCTCCGTTAATGATC-3'), ITS2 (ITS2b: 5'-CTGCGAGGGA ACCCCCAGCCGCGCA-3' and ITS2d/e: 5'-GCGCGACGGCGGACGACACCGCGGCGTC-3'), 18S (5'-TTTACTTCCTCTAGATAGTCAAGTTCGACC-3'), 28S (5'-CCCGTTCCCTTGG CTGTGGTTTCGCTAGATA-3'). Membranes were subjected to two ten-minute washes in 2X SSC with 0. 1% SDS and one ten-minute wash in 1X SSC with 0. 1% SDS and then exposed. Signals were acquired with a Typhoon Trio PhosphorImager (GE Healthcare, Boston, MA, USA) and quantified using the MultiGauge software (Fujifilm, Tokyo, Japan). The assay was repeated using siRNA-treated HeLa cells.
Gene ontology analyses For ontology analyses, ranked gene sets were uploaded to GOrilla gene ontology assignment software (http://cbl-gorilla. cs. technion. ac. il) and output parameter was selected as process or component depending on the gene list uploaded (biological process versus cellular component)  [48, 49]. Outputs from GOrilla were then uploaded to REViGO (http://revigo. irb. hr) and the default medium allowed similarity was used  [50].
Statistical approach Data are reported as mean ± standard error of the mean (SEM) unless indicated otherwise. Protein expression data derived from Western blots were normalized to the loading control and then calculated as fold change relative to the expression level of the experimental control(s). Relative quantification of gene expression data derived from RT-PCR experiments was performed using the 2 -ΔΔCT method. rRNA and pre-rRNA expression data derived from Northern blots were calculated as log 2 ratios of product to precursor pre-rRNA species and 18S to 28S rRNAs relative to three control cell lines. The single-source nature of our biological sample representing human Hbs1L-deficiency precluded the use of tests of statistical significance in some of our human experimental analyses, though several control samples and a minimum of three technical replicates for each sample were used for all human cell line experiments to increase the power.
When replicates were sufficient, as for polysome profiling of fibroblast cultures, we used paired t test to compare patient to control replicates. Statistical analyses for RNA Seq and RiboSeq analyses were calculated using the Github GSEA package in R. Gene ontology significance in GOrilla was assessed using the minimum-hypergeometric (mHG) statistic in R and p values were assigned to be >10 ^-3. For mouse phenotyping, morphological and X-ray data were analyzed using the Fisher Exact Test framework, and body compositional, hematological, and clinical chemistry data were analyzed using the Mixed Model framework (linear mixed-effects model, equation with or without weight). Fertility results were supplied as data.
Acknowledgments The authors would like to thank the patient and her parents for their commitment to this research and to finding a cause and a cure for this disorder. In addition, we thank the  Wellcome Sanger Institute Mouse Genetics Project (Sanger MGP)  and its funders for providing the mutant mouse line (Hbs1l-KO). Funding and associated primary phenotypic information may be found at www. sanger. ac. uk/mouseportal.
AO received funding for this project from the  Marshall Klaus Award of the American Academy of Pediatrics Section on Neonatal Perinatal Medicine. This work was performed through the  Gene Discovery Core of The Manton Center for Orphan Disease Research at Boston Children's Hospital  and made possible by  National Institute of Health (NIH)  grants  R01 AR068429  (PBA),  R01 GM065204  (VNG),  R01 CA080946  (VNG),  NIH   DK117149  (VNG) and  U19 HD077671  (PBA). MFOD and PEG were supported under the frame of E-Rare-2,  ERA-Net for Research on Rare Diseases  (# ANR-15-RAR3-0007-04  in France) and by  Agence National de la Recherche  (projects # ANR-RIBOMAN-ANR15-CE12-0001-02  and # ANR-DBA-Multigenes-ANR-15-AAPgenerique-CE12 ). EH and BS were supported by  CNRS ,  Inserm ,  University of Strasbourg ,  Ligue Labellise  ´e equipe Labellisee 2017, and  ANR-15-CE17-0017.
e. , no price-supply interaction is modelled between farm level behaviour in IFM-CAP and the market level response in CAPRI) and (iv) IFM-CAP price and yield effects are determined by the accuracy of the CAPRI model simulations and consistency in CAP assumptions between the two models. IFM-CAP is calibrated for the base year 2012 using cross-sectional analysis (i. e. multiple observations) and the Highest Posterior Density (HPD) approach using prior information relating to NUTS2 10 supply elasticities and dual values of resources (e. g. land rental prices). The calibration to the exogenous supply elasticities is performed in a non-myopic way, i. e. , we consider the effects of changing dual values on the simulation response (for more details see  Louhichi et al. 2018). The primary data source used to parameterize IFM-CAP are individual farm-level data (83,292 farms observations for the base-year 2012) from the Farm Accountancy Data Network (FADN) database complemented by other external EU-wide data sources such as the European Farm Structure Survey (FSS), the CAPRI model database  (Britz and Witzke, 2014)  and Eurostat (for more details see  Louhichi et al.
Baseline The Baseline represents the pre-reform DPs considering the future development of the farming sector. Four main assumptions are adopted in order to construct the IFM-CAP Baseline: (i) a continuation of the pre-reform DPs up to 2025; (ii) an exogenous adjustment of Baseline output prices and yields using their changes from the CAPRI Baseline between 2025 and base year (2012); (iii) an adjustment of input costs to account for improvement in farm efficiency approximated by total factor productivity (EC 2016b); and (iv) an assumed inflation rate of 1. 9% per year for input costs (as in the CAPRI model Baseline). All other model parameters (e. g. farm resource endowments) are assumed to remain unchanged up to 2025. The pre-reform DPs up to 2025 are constructed based on FADN data for the year 2012.
For the DDPs the use of individual farm level data is important in order to capture the farm level heterogeneity of the payments as determined by the implemented scheme in a given MS (Table  1 ). This is particularly important for MS that implemented historical or static hybrid SPS models in the pre-reform period, where the hectare value of DDPs varies between farms within a region or MS. Further, under the SPS model farmers are allocated entitlements that give the right to receive payments if each entitlement is accompanied by one hectare of eligible land. The number of entitlements at farm level used in IFM-CAP for the pre-reform period is available from the FADN. For CDPs, we consider the average payment per crop/livestock unit in each NUTS2 region based on the 2012 FADN data. This assumption is applied to be able to generate the value of CDP for alternative activities when they are not observed in the base year.
Background: The national ranking examination (NRE) marks the end of the second cycle (6th university year) of French medical studies and ranks students allowing them to choose their specialty and city of residency. We studied the potential predictive factors of success at the 2015 NRE by students attending a French School of Medicine. Methods: From March 2016 to March 2017, a retrospective study of factors associated with the 2015 NRE success was conducted and enrolled 242 students who attended their sixth year at the school of medicine of Reims. Demographic and academic data collected by a home-made survey was studied using univariate and then multivariate analysis by generalized linear regression with a threshold of p < 0. 05 deemed significant. Results: The factors independently associated with a better ranking at the NRE were the motivation for the preparation of the NRE (gain of 3327 ± 527 places, p < 0.
Data management Demographic, academic and results data of the white test examinations organized by the private NRE training conferences (Conférence Hermès, SASU, 11 rue de la vistule, Paris; Hippocrate-ECN, Faculté Dauphine, Place du Maréchal de Lattre de Tassigny, Paris; Conf-raphael, 10 rue Castex, Paris; Med XL / Conf +, 15 Rue Saint Bernard, Paris; Conférence Khalifa, Établissement d'enseignement supérieur libre, 134 Rue de Grenelle, Paris) were collected respectively according to the data published publicly by the school of medicine, by the Official Journal of the French Republic and finally by the private training organizations on their respective websites. Data not publicly available was collected using a self-survey written in Word format (Microsoft, Redmond, USA) and delivered by email with the study's letter of intent to inform the students concerned about the nature and purpose of the study, but also the confidential and anonymous treatment of all data collected, the prior declaration to the french national commission of informatics and liberties (CNIL) and finally the possibility left to everyone to opt out of the study.
This study has several limitations, the first one is related to its retrospective nature and the fact that students filled in a self-survey that could have a memory bias. The second one is that the response rate to the self-survey represents 66% of the 2015 NRE cohort of the medical school studied. Nevertheless, the study of the judgment criterion (NRE rank) of respondents to the survey against nonresponders is almost identical, which makes it reasonable to assume that the sample surveyed seems representative of the entire student population. The third one is that the measurement of student motivation was performed by a 3-items Likert-scale. Taking into account the inherently subjective nature of this measure, it corresponds in practice to a decision that is easily expressed by students. This measure is certainly methodologically questionable, but nevertheless it has the advantage of being a very easy collected objective data by the students themselves.
One of those protocols, named CAROLéLISA (Autograft of Human Ovarian Tissue: Efficiency and Safety), is led by Professor Catherine Poirot in the unit of reproduction biology of Assistance Publique-Hôpitaux de Paris in Paris. The first patient was included in June 2010 and the cohort currently comprises 40 subjects. Professor Bruno Salle and Dr Jacqueline Lornage are leading a protocol in the university hospital Lyon-Bron, Lyon, but no details of the study procedures are available. The DATOR study (Development of Ovarian Tissue Autograft in Order to Restore Ovarian Function) (NCT02846064) was launched in 2013 with the aims of assessing the safety and efficacy of ovarian tissue autotransplantation in terms of restoration of ovarian function and fertility. This evaluation will take into account graft survival in the short-to-medium term.
Follow-Up in the Development of Ovarian Tissue Autograft in Order to Restore Ovarian Function Study To collect outcome data to evaluate the success of the autograft, monthly follow-up is performed for 1 year after the transplant or until the patient becomes pregnant. The monthly follow-up includes clinical, biological, and ultrasound examination. The clinical evaluation is performed by the gynecologists and biologists of the ART unit in the participating center. They investigate for clinical signs of recovery of ovarian function (onset of spontaneous menstrual cycles or disappearance of hot flushes). Biological follow-up includes hormone tests (FSH, LH, AMH, inhibin B, estradiol, and progesterone). Normalization of endogenous gonadotropins is also investigated. While awaiting this stage, hormone replacement therapy and administration of vitamin are pursued.
RenderX All analyses will be performed using SAS version 9. 4 (SAS Institute Inc). Continuous variables will be presented as mean (SD) and median (interquartile range). Categorical variables will be presented as number and percentage. Some potential prognostic factors will be measured at baseline and will be compared between the not restored fertility and restored fertility groups using the Pearson chi-square test or Fisher exact test for categorical variables and the Student t test or analysis of variance for normally distributed quantitative variables. The Mann-Whitney U and Kruskal-Wallis tests will be used for comparison of nonnormally distributed variables and semiquantitative variables. Multivariate analysis (logistic or Cox regression) will be used if we have a sufficient power for this analysis.
Acknowledgments This project is funded by a  Young Researcher grant  from the  University Hospital of Besancon, France. The DATOR Group consists of  Frédérique Dijoud ,  Germain Agnani ,  Pierre-Simon Rorhlich ,  Christophe Piguet ,  Brigitte Mignot ,  Eric Deconink ,  Christophe Borg ,  Francine Arbez Gindre ,  Paul Barrière ,  Patrick Bastit ,  Julie Blessmann Roset ,  Frédérique Carre Pigeon ,  Blandine Courbière ,  Christine Decanter ,  Marie-Catherine Diligent ,  Olivier Graesslin ,  Aurore Guennifey ,  Sylviane Hennebicq ,  Claude Hocke ,  Pascale Hoffmann ,  Isabelle Koscinski ,  Florence Leperlier ,  Brigitte Leroy ,  Loïc Marpeau ,  Catherine Metzler Guillemain ,  Aline Papaxanthos ,  Jean Parinaud ,  Jean-Christophe Pech ,  Anne Perdrix ,  Olivier Pirrello ,  Marius Teletin ,  Pascal Piver ,  Benoît Resch ,  Nathalie Rives ,  Jacques Villière ,  Denis Vinatier ,  Annie Zaccabri ,  Roger Leandri ,  Carole Fajau Prevot ,  Florence Eustache ,  Michael Grynberg ,  Rajeev Ramanah ,  Florence Brugnon ,  Anne-Sophie Greneau ,  Elodie Adda Herzog ,  Valérie Scarabin Carre ,  Nelly Achour Frydman ,  Pascale Lagre ,  Beatrice Delepine ,  Ghassan Harika ,  Rachel Levy ,  Nathalie Chabbert Buffet ,  Marcos Ballester ,  Cécile Mezan de Malartic ,  Bénédicte Paillusson , and  Florence Boitrelle.
Character assessment All measurements were taken by FG and MR. Measurements of randomly chosen vouchers were measured by both persons and the entire dataset only put together after these measures did not differ. Measures were taken with a digital caliper (accuracy ± 0. 1 mm) and a measuring ocular on a Leica MZ95 dissecting microscope (accuracy ± 0. 02 mm). The following measurements were taken: TL (total length), BL (body length), TAL (tail length), BH (body height at the point of the spiracle insertion), BW (maximum body width), TMW (tail muscle width at tail base), TMH (maximum tail muscle height), VFH (maximum height of ventral fin), DFH (maximum height of dorsal fin), MTH (maximum tail height), ED (horizontal eye diameter), ND (horizontal nostril diameter), IOD (interorbital distance), IND (inter-nasal distance), SN (nostril-snout distance), EN (eye-nostril distance), ES (eye-snout distance), MW (mouth width), SP (spiracle length) and SSD (snout-spiracle distance).