dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and Conclusion We produced a global synthesis of all available atmospheric EC measurements of air-water CO 2 exchange (FCO 2 ) over shallow, seagrass-dominated environments. At most sites, the absolute magnitude of FCO 2 was as large or larger than published "blue carbon" burial rates (CBR). Elsewhere, CO 2 fluxes in excess of organic carbon storage have been reported for Japanese seagrasses  (Kuwae & Hori, 2019) , but the present study demonstrates that this is a global, not regional phenomenon. At seagrass meadows functioning as net sources of CO 2 to the atmosphere (BA, OES), FCO 2 was between 44 (OES) -115 (BA)% of global average CBR (0. 13 µmol m -2 s -1 ). Assuming minimal lateral exchange, this effectively converted BA from a net carbon sink into a net carbon source. Datasets for both BA and OES contain substantial and representative measurements during all seasons (Figure  5b ), indicating that while there is substantial seasonal variability  (Rutgersson et al.
Data Availability Statement Data are published openly at https://doi. org/10. 6084/m9. figshare. 12161478. v1 for BA, and 10. 5281/zenodo. 3372787 for EES (Barreras-Apodaca & Sánchez-Mejía, 2019). The remaining datasets for FU, FK, AR, and OES are available under previous publications  Tokoro et al. (2014) ,  Polsenaere et al. (2012), and Rutgersson et al. (2020).
Transcriptomic Profiling of Muscle Aging and Comparison with Other Databases Whole genome expression profiling indicated that 479 genes were differentially expressed between YO and old (EL and/or SX) muscles (Table  S1 ). Previous transcriptome databases established for human muscle aging  [29] [30] [31] [32] [42] [43] [44] [45] [46]  sharply differed with regard to age, gender, or health status. We nonetheless tested the significance of the overlap between these databases and our list to eventually identify a common signature, despite major health divergences. Among our 479 genes, 241 have been reported in previous transcriptomic databases (all comparisons are shown in Table  S2 ). This analysis indicates that, despite diverse clinical backgrounds, the genes most commonly associated with aging were related to muscle regeneration (CDKN1A/p21, HOXB2, DMRT2), neurite outgrowth (LGI1, SCN4B, FEZ2), stress signaling (ATP1B4, GADD45G, C21orf7), angiogenesis (KLF5, CRIM1), and energy metabolism (NT5C2, PPARGC1A/PGC1α).
Transcriptomic Analysis of Muscle Healthy Aging in Men Comparison of whole-genome expression profiling between healthy YO and EL muscles indicated that 328 genes were significantly associated with healthy aging alone. Of these, 103 were upregulated and 225 were downregulated with age (Figure  1A ), and the full list of differentially expressed genes is given in Table  S1. top GO biological processes. The lists of genes upregulated (upper) and downregulated (lower) with aging were analyzed for enrichment of specific gene ontology (GO) terms. (C) Protein-protein interaction network represented by all genes differentially expressed with aging. The interaction map was generated using STRING with high confidence of 0. 7 and all criteria for linkage (co-occurrence, co-expression, experiments, neighborhood, databases, text-mining, and homology).
Instead of testing differential expression of individual genes (ORA), we also focused on differential expression of pathway-based sets of genes and conducted functional class scoring (FCS) using GeneTrail2  [50]. The FCS comparison of the entire lists of YO versus EL transcripts (Table  S4 ) confirmed ORA analyses, with enriched apoptosis and immune response and depleted protein catabolism in EL muscle. FCS further revealed in EL depleted GO biological processes reflecting protein synthesis and mitochondrial metabolisms and upregulated GO reflecting ECM, fat cell, TNF, and TGFβ signaling. TNF signaling is central for the immune response, and TGFβ signaling impairs muscle regeneration and favors ECM fibrosis. In all, these observations are in agreement with our previous histological studies showing that healthy aging is associated with atrophy of type II glycolytic fibers, with a slight accumulation of lipid droplets and with fibrosis of perimysial ECM  [17, 22].
Microarray Hybridization and Transcriptomic Analysis Independent RNA isolations were carried out for each biopsy, but a sufficient amount of high quality RNA could not be obtained for 4 YO, 2 EL and 3 SX samples. Independent hybridization reactions were then performed to generate data sets for 11 YO, 13 EL and 6 SX muscles. TissueRuptor and RNeasy Fibrous Tissue Mini Kit (Qiagen) were used to isolate muscle RNA. Integrity of RNA samples was verified using an Agilent Bioanalyser with the RNA 6000 Nano labchip ® kit (Agilent). Concentration and purity were determined with a Nanodrop ND-1000 spectrophotometer. The depletion of the ribosomal fractions was processed using 2 µg of total RNA and magnetic beads from Ribominus Kit (Invitrogen). Biotinylated single stranded cDNA were prepared from 400 ng of depleted total RNA according to the Affymetrix WT protocol.
Following fragmentation and terminal labeling, 5. 5 µg of single stranded cDNA was hybridized for 16 h at 45 • C and 60 rpm on GeneChip ® Human Exon 1. 0 ST Array in the Affymetrix Oven 645. GeneChips were washed and stained in the Affymetrix Fluidics Station 450 with Hybridization Wash and Stain Affymetrix kit. GeneChips were scanned using the Affymetrix GeneChip Scanner 3000 7G, and data were generated with Affymetrix Expression Console v 1. 2. 1 software using RMA algorithm. Data were processed using GeneSpring GX 13 (Agilent) and analyzed by one-way ANOVA followed by pairwise comparisons using Tukey HSD post-hoc test. Benjamini-Hochberg multiple testing correction was performed to identify differentially expressed entities with a significance threshold set at p < 0. 05. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [80] and are accessible through GEO Series accession number GSE136344 (https://www.
ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE136344; release date 14 July 2021). Biological replicates showed Pearson's correlation coefficients above 0. 95, indicating high reproducibility of the gene expression data (Supplementary Materials Figure  S1 ). To confirm microarray analyses, quantitative real time PCR (qRT-PCR) was carried out for EEF1A1, TSC22D1, KCNMA1, MYOZ2, LRP1B, SLC43A2, PDLIM1, PER3, MYLIP, KLF15, GPCPD1, and IGFN1 mRNAs and 18S RNA as housekeeper. Reverse transcription of total RNA was performed using the QuantiTect Reverse Transcription kit (Qiagen) and qPCR using the FastStart DNA Master SYBR Green I kit (Roche), a CFX96 thermocycler (Biorad), and the comparative CT method. qRT-PCR validated microarray analyses and a Pearson's correlation of 0. 96 (p < 4. 5 × 10 -13 ) was observed between the fold changes from the microarray and the qRT-PCR analyses (Figure  S2 ).
We tested the significance of the overlap between our set of differentially expressed genes with other databases using a hypergeometric test (http://nemates. org/MA/progs/ overlap_stats. html, access on 15 April 2021) and a background of 18,738 genes. For overrepresentation analysis (ORA), the lists of genes up-and downregulated with aging or metabolic syndrome were analyzed for enrichment of specific Gene Ontology (GO) terms using Genomatix software suite v3. 2 and adjusted for multiple testing correction according to  [81]. The protein-protein interaction network represented by differentially expressed genes was investigated using the Search Tool for Retrieval of Interacting Genes (STRING, version 11. 0) database (https://string-db. org, access on 15 April 2021)  [82]. STRING analysis options were based on 'evidence' mode, high confidence (0.
The gels were stained for 72 h with 0. 06% (w/v) Coomassie Blue G-250 added to this last solution. Gels were scanned using the ImageScanner and LabScan-v. 5 software (Amersham), and protein spots were analyzed and matched between all gels (duplicates and conditions) using Progenesis SameSpot software (Non Linear Dynamics). Proteins with significant changed abundance were picked for tryptic digestion from gels, and a single spot from the most representative gel was used for each identification.
The gradient consisted of 4-50% (v/v) acetonitrile in 0. 5% (v/v) formic acid at a flow rate of 300 nL/min for 30 min. The eluate was electrosprayed into an LTQ Velos (Thermo Fisher Scientific, Waltham, MA, USA) through a nanoelectrospray ion source. The LTQ Velos was operated in a CID top 10 mode. Raw files were processed using version 2. 4 of Thermo Proteome Discoverer. For protein identification, the NCBInr suscrofa protein database (41,000 seq) was combined with the sequences of human keratin contaminants. Peptide mass tolerance was set to 1. 5 Da, and fragment mass tolerance was set to 0. 8 Da. Two missed cleavages were allowed. Variable modifications were methionine oxidation (M) and carbamidomethylation (C) of cysteine. Protein identification was validated when at least three peptides originating from one protein showed significant identification Mascot scores (p < 0.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/ijms22084205/s1, Figure  S1 : Microarray biological replicates; Figure  S2 : Correlation between microarray and qRT-PCR; Figure  S3 : Representative 2DGE and differentially expressed proteins; Figure  S4 : Correlations between mRNA and protein levels; Table  S1 : Muscle genes differentially expressed with healthy aging and/or metabolic syndrome in old men; Table  S2 : Comparison with other transcriptome datasets; Table  S3 : Over-representation analysis (ORA) for muscle genes up/downregulated with healthy aging; Table  S4 : Functional class scoring (FCS); Table  S5 : Positional gene enrichment (PGE) for healthy aging; Table  S6 : Over-representation analysis (ORA) for muscle genes up/downregulated with metabolic syndrome in old men; Table  S7 : Positional gene enrichment (PGE) for metabolic syndrome in old men.
Acknowledgments: The authors want to express special thanks to those individuals who volunteered to participate in this study. The authors are also grateful to  Professor Philippe Courpron  for his constant support and his central role in initiating this work. The authors would like to thank  Veronique Pantesco  ( IRB, CHRU Montpellier ) for technical assistance in transcriptomic analysis. The authors thank the  Fondation pour l'Université de Lyon  for assistance with obtaining reagents.
Funding: This research was funded by the  European Commission MyoAge  ( EC Fp7 CT-223756 ),  Caisse d'Epargne Rhone Alpes  ( CERA Sarcopenie n • 30 ),  Fonds Européens de Développement Régional (FEDER) , and  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). MG was supported by a postgraduate fellowship from  Région Auvergne  and  FEDER.
Background: Anorexia Nervosa (AN) is a highly prevalent disease in adolescents and young adults (AYAs). The quality of initial inpatient medical management in a patient with severe malnutrition due to AN is crucial to prevent further medical instability. This review aims to inventory evidence regarding initial refeeding and management of AYAs with AN in an inpatient setting, in order to avoid medical complications. Methods: A systematic review will be conducted using PubMed, MEDLINE, All EBM Reviews, Embase, PsycINFO, Cochrane Database and CINAHL. The search strategy consists of terms related to anorexia, hospitalization and Pediatrics. Randomized controlled trials, case-control studies, cross-sectional and cohort studies will be included. Participants will include adolescents and adults 18-24 years old diagnosed with AN and meeting criteria for severe disease.
Participants We will include studies that had a study population of children and/or adolescents (< 18 years old) and/or young adults, defined as adults between the age of 19 and 24 years old. In studies addressing both adults and adolescents, we will include them only if data for adolescents and adults younger than 25 years old are reported separately from adults older than 25 years old. If separate data for adolescents and young adults, either reported separately or as a combined group distinct from older adults, are not available in a study that fulfills all the other inclusion criteria, we will try to contact the author to obtain data on this specific age group. Gender will not be an exclusion criterion and we will include studies considering all gendered patients. The diagnosis of AN will be based on established criteria from the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) or fourth-text revised edition (DSM-IVR) or from the International Classification of Diseases (ICD-10).
Search strategy Literature search strategies will be developed using medical subject headings (MeSH) and text words related to Anorexia. Systematic searches of the databases PubMed (NLM), Ovid Medline, Ovid All EBM Reviews, Ovid Embase, Ovid PsycINFO, Cochrane Database and EBSCO CINAHL will be completed by a librarian of our Hospital with special training and skills in literature searches. A draft PubMed search strategy is included in Additional file 1. Once the PubMed strategy is finalized, it will be adapted to the syntax and subject headings of the other databases. If further information is needed for some identified studies, we will contact the authors in order to try to obtain adequate information. In order to ensure that all relevant material has been identified, we will also scan the reference lists of included studies or relevant reviews identified through the search.
Study selection Literature search results will be imported into a commercial reference management software package (End-Note version X8. 2, 2018, Clarivate Analytics) and all duplicates will be excluded. One author will review the title and abstract of all the references to identify potentially eligible studies. We will obtain full text articles for all titles that appear to meet inclusion criteria or where there is any uncertainty. The full articles will be examined by two authors independently to decide whether they meet the inclusion criteria. We will seek additional information from study authors where useful to resolve eligibility. We will resolve disagreements through discussion. If agreement is not achieved between the two authors through discussion, another co-author will act as a third-party to reached a final decision on eligibility.
Data extraction For data extraction, we will use a standardized form. This form will be developed by the systematic review team by adapting the Cochrane data collection form  [14]  for intervention review to the specific requirements of this study. To ensure consistency across reviewers, we will conduct a calibration exercise before starting the review. Data collection will be performed by one author of the systematic review. Data extracted will consists of: Study results: findings related to the primary and secondary outcomes as described in the previous section. A second author will review all the data collection forms completed and resolve disagreements by discussion with the first author. Once more, if agreement is not achieved between the two authors through discussion, another co-author will act as a third-party to achieve a final decision.
Risk of bias Since we are planning to include RCTs and nonrandomized studies, such as cohort studies and casecontrol studies, we will be using two different tools to evaluate the risk of bias in our systematic review. For RCTs, we will assess the possible risk of bias for each study using the Cochrane Collaboration tool for assessing the risk of bias  [15]. We will collect information for all items included in this tool: sequence generation, allocation concealment, blinding, incomplete outcome data and selective outcome reporting. From the extracted information, we will rate each of these categories as "high risk" or "low risk". If there is insufficient information in one category to evaluate the risk, we will classify the risk as "unclear risk". The judgement on risk will be performed by one author using this tool.
Data synthesis Since available literature on Anorexia Nervosa in general is principally composed of non-randomized studies, we expect to include only a few, if any, RCTs in this systematic review. We do not expect to have enough RCTs to conduct a meta-analysis. We will use a systematic narrative synthesis instead. In the synthesis, we will provide information in the text and tables to summarize and explain the characteristics and findings of the included studies. We will present all results on our first objective, which was to the effects of initial medical management of adolescents and young adults admitted with severe Anorexia Nervosa on medical stability and complications. We will synthesize data obtained into main and additional outcomes. We hope to be able to present separate data for these two age groups, but if only combined information is obtained, we might need to present data without this distinction between the two groups.
Parsimony analysis Using TNT  (Goloboff and Catalano 2016) , we conducted a parsimony analysis on 74 taxa of Old World rhinos (plus Tapirus terrestris as an outgroup), using the data matrix of  Geraads et al. (2016) , with some corrections, deletions of characters whose coding is too subjective, and addition of the few post-cranial characters that can be observed in Eoazara xerrii gen. et sp. nov. at Skoura (SOM 1, Supplementary Online Material available at http://app. pan. pl/SOM/ app66-Geraads_Zouhri_SOM. pdf). Still, the results must be regarded with caution because; while most researchers would probably agree on the scoring of some characters, that of many others is far from straightforward. In addition, as already pointed out  (Heissig 1981; Geraads et al. 2016)  homoplastic evolution is widespread in rhinocerotids.
Acknowledgements Special thanks to  Serge Xerri  ( Rabat, Morocco ), whose generousness allowed Eoazara xerrii gen. et sp. nov. to be known to science, and to  David Lefèvre  ( Université de Montpellier, France ) and  Jean-Paul Raynal  ( Université de Bordeaux, France ) for support to DG in Morocco. Thanks to  Madelaine Böhme  ( Eberhard-Karls-University of Tübingen, Germany ),  Emma Mbua  and  Fredrick Kyalo Manthi  (both  National Museums of Kenya, Nairobi ), and  Emmanuel Robert  ( Université de Lyon, France ) for granting access to collections, and to  Lilian Cazes  and  Philippe Loubry  (both  Centre de Recherche en Paléontologie-Paris, France ) for the photos. We thank the editor  Olivier Lambert  ( Institut Royal des Sciences Naturelles de Belgique, Brussels, Belgium ),  Oscar Sanisidro  ( Universidad de Alcalá de Henares, Madrid, Spain ), and an anonymous reviewer for their constructive comments;  Naoto Handa  ( Museum of Osaka University, Osaka, Japan ) and two anonymous reviewers also provided helpful comments on a previous version of this manuscript.
Dataset Description Our research dataset included work items and their conversations between 2011 and 2016, over a total of 612 iterations. We analyzed the discussions for all completed work items of all types 2 that had at least fifteen comments in their discussion threads. We chose fifteen comments as it is a reasonable number to obtain insights on the progression of work on the work items. Since we are focusing mainly on comments data, discussion with very few comments are not significant to highlight all the development stages (elicitation, implementation, clarification,. ) in a work item's lifecycle. We also ruled out WIs with a particular statusinvalid, abandoned, suspended -that indicated the work item did not follow the normal development process. This filtering resulted in a total of 125 late work items and a total of 2655 associated comments.
Dataset Overview The Estimate attribute (Table  1 ) gives the estimated duration required for WI implementation. This information is not frequently provided in our dataset. Only 26% (32) of non-late and 16%  (20)  of late WIs have this information. Severity describes the importance of a WI and Priority its implementation emergency level. Depending on these attributes values, a sensitive WI is likely to get more attention and resource for its implementation speedup. However, in our dataset, 2 With the exception of Tasks, which did not contain any implementation comments  a comparison of these attributes distributions between late and non-late WIs (Tables  4  and 5 ) highlights no significant differences, i. e. both sets have more or less the same distributions. Therefore, they can be discarded as the attributes that discriminate late WIs from non-late ones.
Work Items Management The Created By attribute (Table  1 ) identifies the user who created the WI and provides information related to its goals, while the Owned By identifies the user who is going to drive or coordinate the WI implementation to resolution. The Resolved By identified the user who closed WI as resolved. This attribute has always the same value as the owner in our dataset, for instance the owner closes the WI when it is implemented. The Subscribed By attribute holds a list of collaborating users to implement WI requirements. Plan Items do not have a creator. They only have owners. Because a plan item is a high level description of a feature, its implementation involves the collaboration of teams working on dependent work items such as Stories, Enhancements, Defects or Tasks. A development "team leader" coordinates work 7 J o u r n a l P r e -p r o o f of a team of developers to deliver its assigned requirements.
Further, there are Project Leaders who lead the work at each geographical location. With respect to the workflow around a WI implementation, typically a WI is delegated to the appropriate developers, i. e. , the creator of WI is different from the owner (@UserX != @UserY). For example when a @UserX spots a defect and creates a WI to report it and assigns it to @UserY (more capable to handle the WI); or when a Plan Item is decomposed into stories and assigned to the appropriate developers/team. Some WIs in our dataset indicate selfmanagement, i. e. the owner of the WI is also the creator (@UserX = @UserY), and it is typical of WIs of the type Enhancements. In our dataset 36% of non-late and 27% of late WIs are in this situation (Table  6 ).
2. 1). This analysis was conducted on both the late and non-late WIs. Throughout this analysis we interacted closely with an IBM development team leader involved in the Jazz project during the iterations in our dataset to validate our understanding of the WIs, their context of implementation and details of the WI discussions. To answer RQ1, we first analyzed the late WIs clusters and searched for patterns that might explain reasons for delays in the WI implementation. For RQ2, we then contrasted the properties of the late WI with that of the non-late WI clusters to further our understanding of reasons for delay in WI implementation. We illustrate this staged analysis process in Fig. 2  and explain it in detail in the following subsections.
Hay  [26] , recommends a two-step process beginning with basic coding in order to distinguish overall topics, followed by a more in depth, interpretive code in which more specific trends and patterns can be interpreted. While qualitative coding often is done manually by the researchers, contemporary natural language processing tools such as Weka  [25]  can be used to automatically categorize pieces of text into predefined categories. Such tools can help speed up coding processes that use a single pieces of text as an objects 9 J o u r n a l P r e -p r o o f for topic identification  [40, 51]. This is the case for a priori -based coding where categories are available or can be easily deducted, as can be the case for, e. g. , survey or interview transcript data  [31]. In our analysis, we applied a conventional content analysis technique  [28]  to examine, review the conversations, and identify the main discussion topics as codes.