dataesr/scientific-paragraphs-categorization huggingface dataset:
Results During the pilot study, data from five of the six SAMU structures of PACA region were structured enough to be analyzed. On the study period, almost 2,400,000 files were recorded and 89% contain medical information. Data completeness was high (87%) and stable during the whole period. The annual rate of SAMU solicitation was 16 for 100 inhabitants at the regional scale. 15% of the records were opened only for medical advice. In contrast, patients were evacuated directly in intensive care unit in 9. 5% of cases without ED admission. Coding quality depended on the existence and the use of official thesauri and varied widely among SAMU structures. Despite coding variations, SAMU indicators for winter epidemics were significantly correlated with ED and SOS Médecins indicators. Respectively with ED flu, bronchiolitis and gastroenteritis indicators, the strongest correlations were found for SAMU lower respiratory infection (0.
Conclusions This pilot study demonstrated the feasibility to collect daily SAMU activity data. The key strengths of SAMU data were a large geographic coverage, the subsidiarity with SurSaUD® system data sources, the follow-up of prehospital activity and for patients directly admitted into an intensive care unit. Some limitations were highlighted related to differences in coding practices especially for medical diagnosis. The generalization of this study will require the standardization of coding practices and homogenization of thesaurus. The implementation of the national SAMU information system should allow in a very next future to widely progressing on these topics.
PD-OPA1 Mutant iPSC-Derived NPCs Exhibit Morphological and Functional Mitochondrial Alterations To investigate the pathological bases of the OPA1 mutations leading to PD, skin fibroblasts were obtained from two individuals manifesting PD symptoms, one for each family, and reprogrammed into iPSCs with a non-integrating system based on Sendai viruses expressing the Yamanaka genes (CytoTune-iPS). For the subsequent functional studies, iPSC lines were initially selected based on the correct colony morphology, expression of crucial pluripotency genes, and silencing of fibroblast-specific genes (Figures  S1A-S1AO ). Then, iPSCs were assessed for multi-lineage cell differentiation competence as validated by the generation in vitro of somatic cells for the three germinal layers (data not shown). Moreover, Sanger sequencing confirmed the presence of the p.
To this end, mtDNA from iPSC lines derived from both OPA1-mutant PD patients (PD-OPA1 iPSCs) and normal donors was fully sequenced and analyzed. Among the six different iPSC lines, G488R #34 and G488R #37 carried a homoplastic disruptive mutation in the ND5 gene and were therefore discarded (data not shown). Conversely, PD-OPA1 iPSC lines G488R #12, G488R #22, and A495V #72 and normal donor (ND) #37 and ND #68 did present few synonymous mutations without any predictable functional significance (Figure  S2A ). Hence, we focused on these five lines for the following studies. Immunoblotting analysis showed that OPA1 protein levels were reduced in all the PD-OPA1 iPSC lines, suggesting that the missense mutations lead to protein instability and increased protein degradation  (Figures S2B and S2C).
The mitochondrial size was calculated by measuring the perpendicular axis with respect to the larger diameter for all the mitochondria (200 mitochondria in total) and relative quantifications (J). Data are mean ± SEM, n = 3 independent experiments. **p < 0. 01, ***p < 0. 001 for ND NPCs and p < 0. 01, p < 0. 001 for OPA1-complemented NPCs. Statistical analysis was performed using one-way ANOVA followed by Tukey post-test. Scale bars, 100 mm (A-G), 500 nm (I). ND NPCs, which was significantly fragmented in the PD-OPA1 NPCs. This aberrant morphology was normalized in the OPA1-complemented patient NPCs, indicating that this defect was directly caused by OPA1 gene alterations (Figures  1E-1H ). On the same line, quantitative measurement of transmission electron microscopy images confirmed that OPA1-mutant mitochondria were reduced in length with a tendency to present a round-shaped morphology (Figures  1I  and 1J ).
Mitotracker Green and Orange NPCs from patients and controls were incubated with 50 nM of Mitotracker Green and Mitotracker Orange (Molecular Probes) for 30 min at 37 C, washed with PBS, and acquired by confocal microscope (Leica TCS SP5, Germany). For the analysis, the samples were acquired on LSR-Fortessa (BD) flow cytometer. All data were analyzed using FCS Ex4press 6 Flow (De Novo Software) and expressed as mean fluorescence intensity.
ACKNOWLEDGMENTS We are thankful to  Drs. B. Garavaglia ,  L. Muzio ,  S. Levi ,  D. Zacchetti ,  P. Santambrogio , and  M. Bacigaluppi  for sharing of reagents,  J. L. Lanciego  for human nigral tissue samples,  C. Peron  for technical help, and  D. Bonanomi ,  E. Cattaneo ,  A. Gritti ,  A. Menegon , and all members of the  Broccoli lab  for helpful discussion. We acknowledge the anonymous reviewers for their insightful comments. We acknowledge the  FRACTAL core facility  for expert supervision in flow cytometry. This work was supported by the  European Research Council  (AdERC # 340527 ) and  Regione Lombardia  (iPSLight # 227333  and GenePark # 149065 ).
Introduction A fundamental part of innate immune responses is the regulated expression of defence genes. In both vertebrates and many invertebrates, including Drosophila, two of the key regulators controlling innate immunity are the Rel-homology domain (RHD) protein NF-κB and its protein partner IκB  [1]. Across many species, NF-κB functions in concert with members of the conserved Akirin family (InterPro: IPR024132) to govern the expression of defence genes  [2]. More specifically, in vertebrates, Akirin2 bridges NF-κB and the SWI/SNF chromatin-remodelling complex, by interacting with IκB-z and the BRG1-Associated Factor 60 (BAF60) proteins, downstream of Toll-like receptor (TLR) signalling  [3, 4]. In insects, an equivalent complex (including Relish and the Brahma-associated proteins BAP55 and BAP60 in Drosophila) governs antimicrobial peptide (AMP) gene expression upon infection by Gram-negative bacteria  [4] [5] [6].
Mass spectrometry data analysis Raw data files were processed by MaxQuant software package (version 1. 5. 5. 0)  [66]  using Andromeda search engine  [67]. Spectral data were searched against a target-decoy database consisting of the forward and reverse sequences of WormPep release WS254 (28,071 entries), UniProt E. coli K-12 proteome release 2016_02 (4,314 entries) and a list of 245 common contaminants. Trypsin/P specificity was selected. Carbamidomethylation of cysteine was chosen as fixed modification. Oxidation of methionine and acetylation of the protein N-terminus were set as variable modifications. A maximum of 2 missed cleavages were allowed. The minimum peptide length was set to be 7 amino acids. At least one unique peptide was required for each protein group. False discovery rate (FDR) was set to 1% for both peptide and protein identifications.
Protein quantification was performed using the LFQ label-free quantification algorithm  [68]. Minimum LFQ ratio count was set to one. Both the unique and razor peptides were used for protein quantification. The "match between runs" option was used for transferring identifications between measurement runs allowing a maximal retention time window of 0. 7 min. All raw mass spectrometry data have been deposited in the PRIDE repository with the dataset identifier PXD008074. Statistical data analysis was performed using R statistical software. Only proteins quantified in at least two out of the three GFP pull-down replicates (or two out of two GFP pull-downs for the experiment using infected worms) were included in the analysis. LFQ intensities were log2-transformed. Imputation for missing values was performed for each pull-down replicate in Perseus  [69]  software (version 1.
5. 5. 0) using a normal distribution to simulate low intensity values below the noise level (width = 0. 3; shift = 1. 8). The LFQ abundance ratio was then calculated for each protein between the GFP pull-downs and the controls. Significance of the enrichment was measured by an independent-sample Student's t test assuming equal variances. Specific interaction partners were then determined in a volcano plot where a combined threshold (hyperbolic curve) was set based on a modified t-statistic (t(SAM, significance analysis of microarrays); s 0 = 1. 5, t 0 = 0. 9 * 1. 5)  [70, 71]. Proteins cross-reactive to the anti-GFP antibody were identified by a pull-down experiment using the non-transgenic rde-1 strain and were filtered out from the AKIR-1 protein interactor dataset.
Acknowledgments We thank  F. Palladino  and  R. Waterston  for discussion and sharing data before publication,  A. Boned  for her contribution,  L. Spinelli  for statistics advice,  F. Muller ,  A. Pani  and  C. Wicky  for the generous gift of reagents and  C. Maynard  for critical reading of the manuscript. We acknowledge the PICSL imaging facility of the  CIML (ImagImm) , member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). Worm sorting was performed using the facilities of the  French National Functional Genomics platform , supported by the  GIS IBiSA and Labex INFORM. Some nematode strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ).
This work was supported by institutional grants from the  Institut national de la santeet de la recherche medicale (inserm. fr) , Centre national de la recherche scientifique  (cnrs. fr)and  Aix-Marseille University  (univ-amu. fr)to the CIML, and the  Agence Nationale de la Recherche  (anr. fr) program grants ( ANR-12-BSV3-0001-01 ,  ANR-16-CE15-0001-01 ,  ANR-11-LABX-0054  ( Labex INFORM ) and  ANR-11-IDEX-0001-02  (A Ã MIDEX)) to JJE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study design and data collection Data from an observational cohort study known as the ECMO-CGR study was retrospectively analyzed  [17]  (S1 Table ). ECMO-CGR study was conducted from January 2010 to December 2016 in a single center: a French university hospital with a 23-bed mixed medical/surgical ICU and a cardiac surgery unit. All patients hospitalized in ICU who underwent ECMO during their stay were consecutively included. The exclusion criteria in the present study were age below 18 years, treatment by VV-ECMO, and ECMO duration less than two days. This present study was approved by the Ethical Committee of the French Intensive Care Society, which waived the need for informed consent because of the observational and retrospective nature of the study (reference SRLF "CE-SRLF17-18"). Reporting of this study complied with the Strengthening the Reporting of Observational studies in Epidemiology recommendations  [18].
Adult offspring (F 1 females + F 1 males) was counted after 6 additional days (i. e. days 14 and 17, respectively) and used to estimate juvenile mortality (JM = [total number of eggs -number of unhatched eggs -number of F 1 adults]/ total number of eggs), F 1 sex ratio (SR = number of F 1 males/number of F 1 adults) and the number of viable offspring (VO = total number of adult offspring per female per treatment observed at the end of the experiment on each plant). The entire experiment was repeated three months later (hereafter called blocks 1 and 2) except for replicates involving tomato plants. Indeed, given a very high proportion of drowned females (88 ± 3. 3 %; data not shown) and because the surviving females laid on average less than 1 egg per day (0. 32 ± 0. 05; data not shown) on this plant, subsequent traits could not be measured and we decided to exclude it from this experiment.
For all analyses, maximal models were simplified by sequentially eliminating non-significant terms to establish a minimal model  (Crawley, 2007) , and the significance of the explanatory variables was established using χ 2 -tests or F-tests to account for overdispersion  (Bolker, 2008). The significant values given in the text are for the minimal model, while non-significant values correspond to those obtained before deletion of the variable from the model  (Crawley, 2007). Full datasets are given in Additional files 2 and 3.
ACKNOWLEDGMENTS We thank  M. Bakırdöven ,  J. Denoyelle ,  L. Rodrigues , and  Inês Santos  for their help in spider-mite collection. We also thank  IS  for the maintenance of the plants and mite populations,  Miguel Cruz ,  Nelson Martins ,  Jordi Moya Laraño  and  Susana Verala  for advices in statistical analysis.
FUNDING This work was funded by an  FCT-ANR  project  ( FCT-ANR//BIA-EVF/0013/2012 ) to SM and  Isabelle Olivieri  and by a  FCT-Tubitak project  ( FCT-TUBITAK/0001/2014 ) to SM and  Ibrahim Cakmak. FZ and DG were funded through  FCT Post-Doc  ( SFRH/BPD/125020/2016 ) and PhD ( PD/BD/114010/2015 ) fellowships, respectively. Funding agencies did not participate in the design or analysis of experiments.
To address this, the GA4GH and IRDiRC organizations sponsored the creation of the Automatable Discovery and Access Matrix (ADA-M, read simply as "Adam"). ADA-M is a comprehensive information model that provides the basis for producing structured metadata "Profiles" of regulatory conditions, thereby enabling efficient application of those conditions across regulatory spheres. Widespread use of ADA-M will aid researchers in globally searching and prescreening potential data and/or biospecimen resources for compatibility with their research plans in a responsible and efficient manner, increasing likelihood of timely DAC approvals while also significantly reducing time and effort DACs, RECs, and IRBs spend evaluating resource requests and research proposals. Extensive online documentation, software support, video guides, and an Application Programming Interface (API) for ADA-M have been made available.
This challenge becomes even greater as items are reused, widely shared, and recombined, potentially in automated settings. In late 2015, members of the Global Alliance for Genomics and Health (GA4GH) and the International Rare Diseases Research Consortium (IRDiRC) organizations came together to discuss ways to improve resource sharing, especially biomedical data. This inspired the formation of the Automatable Discovery and Access (ADA) Task Team. The team comprised over 50 volunteer members from academia, industry, and the not-for-profit sectors, and was co-chaired by Anthony Brookes (University of Leicester) and John Wilbanks (Sage Bionetworks). The group worked together to create a much needed information model (a metadata standard) for consent and data use conditions. The product of this effort is the Automatable Discovery and Access Matrix (ADA-M, read simply as "Adam"), released online in late 2016 as a version 1.
ADA-M, AN AUTOMATABLE DISCOVERY AND ACCESS METADATA STANDARD ADA-M was created to help increase the efficiency of resource discovery and access, by promoting responsible recording, versioning, communication, querying, and actioning of resource sharing plans. Datasets provide the clearest example for how resource sharing can benefit from the use of ADA-M, not least because the need to share data is so great and growing rapidly. The ADA-M metadata structure is flexible and can refer to any form of data asset, such as single data elements, individual records, full datasets, summarized forms of data, metadata, or even whole dataset collections and databases. For illustrative purposes we will focus primarily on data, yet it should not be forgotten that the ADA-M can equally benefit biospecimen resource sharing among biobanks and their users.
ADA-M's objective is very straightforward. It seeks only to address the particular unmet need for a standardized format for regulatory metadata. It is not a project that aims to itself collect or store information about the conditions of use of data, nor does it try to influence whether or not any data should be collected or made available for sharing under any specific conditions. But by providing a consistent model for how to digitally structure the information about consent and other conditions of use regarding a given dataset, ADA-M will facilitate the responsible and efficient discovery and access of those data. The computer-readable metadata placed in ADA-M format in no way control or modify the data use requirements that pertain to data or other assets, but merely reflect them and make these requirements more accessible.
Hence, the mandate of ADA-M is not to interfere in any way with the objectives of particular research projects, but only to help those projects function more efficiently in ICTenabled contexts. In short, there remains a clear boundary between ADA-M's purpose and the myriad objectives which drive the collection and use of biological data and resources. By providing a practical structure for metadata by which any number of sharing conditions can be stated, making them unambiguous, computer-readable, and directly available for digital communication, searching, and automation activities, ADA-M fosters data sharing in accordance with the foundational principles of the GA4GH's Human Rights-Based Framework for Responsible Sharing of Genomic and Health-Related Data 2 (specifically Article 27, "Everyone has the right freely to participate in the cultural life of the community, to enjoy the arts and to share in scientific advancement and its benefits".
See http://www. un. org/en/universal-declaration-human-rights/index. Accessed 30 May 2018). To this end it is designed to be compatible with and work alongside other standardization efforts, software, and computing platforms, intended to help harmonize, rather than compete with, other initiatives. An initial version of ADA-M was subjected to extensive alphatesting over 4 months, by consultation with a wide range of individuals and groups. Collaborative cross-mapping exercises were also undertaken, using datasets managed by EGA, the Broad Institute's DUOS system, a database of consent clauses, and Consent Codes 3 (see Appendix 2 of the Guidance Document, "ADA-M v1. 0 Structure" at: https://www. ga4gh. org/docs/ ga4ghtoolkit/regulatoryandethics/ ADAM_GuidanceDocument_15Dec2016_Final_v2. pdf.
Accessed 30 May 2018). Based on feedback received, iterative rounds of improvement and retesting were undertaken. The task team proposed, debated, and democratically decided a series of progressive changes and improvements. The resulting version of ADA-M was released online in late 2016, since then it has been adopted in whole or in part by the Australian Genomics Health Alliance, the European Solve-RD project, members of the Health Data Research UK institute, and the US Broad Institute's DUOS project, while in parallel it is being actively evaluated by the EU biobanking Infrastructure "BBMRI", by Genomics England Ltd. , by the Canadian Care4Rare-SOLVE project, and by the UK Tissue Directory and Coordination Center. Other expressions of interest have been received, leading ADA-M to recently be incorporated into several new funding proposals.
Such projects manage many and various datasets for a range of different purposes. Using the ADA-M model to structure regulatory metadata about their datasets will help them establish ICT-enabled ways to track and relay information about the conditions of use stipulated by, for instance, research subjects, journals, host institutes, biobanks, or granting agencies. These metadata, transformed into the ADA-M structure, constitute what we call an ADA-M "Profile". How each project goes about interpreting source documents and collecting these elements that will make up the metadata, how they technically convert the regulatory information into a Profile, what granularity of information is placed in the Profile (even just a single use condition), whether this is done in real time by researchers or by a subsequent steward or consortia, and whether they wish to place the resulting (and possibly constantly evolving) Profiles into a centralized database or keep them alongside the referenced datasets, is all completely up to them to decide.
Fourth, it enables detailed computer-based discovery (querying) of the sharing rules that apply to a given resource. This is especially helpful in federated architectures that necessarily need to employ interoperable metadata models. Computerized discovery helps to ensure time and effort is not wasted by DACs and potential users who discover resources, make requests, and evaluate requests only to eventually find that the proposed research is incompatible with one or more use conditions in the sharing plan. Fifth, it establishes a standard way to process resource access requests using scalable automated systems, or to connect and harmonize such systems, and thereby alleviate complete reliance on DACs, which are not easily scalable. Specifically, once an ADA-M Profile has been created for a given dataset, it allows the custodian to electronically and reliably check regulatory parameters within the Profile against requests made to access the data.
Aligning consent language, types of consent, and conditions of use to the ADA-M elements will often entail a process of interpretation of prior documentation, codifications, statements, and intentions. Interpretation of such matters has always been a critical and subjective step in data stewardship and oversight, essential for ensuring responsible use of data. It is not the goal of ADA-M to guide or control this step of interpreting the primary information, although we do anticipate that the widespread use of ADA-M would result in increased Fig. 1  "The ADA-M Archetype": The different combinations of numbers and shapes illustrate how an ADA-M metadata profile can accommodate many different permutations of regulatory factors and considerations, so that even highly complex information retains the standardized structure necessary for interoperable ICT contexts consistency in this function.
ADA-M merely captures the final result in a clear, comprehensive, and standardized format which can then be used to ensure the referenced data are used responsibly and efficiently. A completed ADA-M Profile could be anything from a very simple document comprising only the required Header sections and one Permission or Term concept entered (below), through to a highly detailed exposé of all aspects of a data sharing plan, including lots of free text explanations. It can therefore be made to suit requirements for different kinds of research projects, different types of researchers, and for research done in different regulatory regions. The Profile then allows data with specific use conditions to be efficiently matched up with appropriate research projects, enabling more fluid and effective sharing and availability of data.
THE STRUCTURE OF ADA-M Full details of the ADA-M structure are provided online and a detailed Guidance Document is provided as Supplementary Data (see the Guidance Document at https://www. ga4gh. org/docs/ ga4ghtoolkit/regulatoryandethics/ ADAM_GuidanceDocument_15Dec2016_Final_v2. pdf). In brief, ADA-M v1. 0 includes: 1. A HEADER section to capture contextual information about the ADA-M Profile itself and some basic statements about the data or other resource to which the Profile refers. 2. A MAIN BODY section that specifies and organizes regulatory concepts into 42 distinct categories that were felt to meet current needs (i. e. , non-directive aspects of data use, that may or may not be employed to define acceptable use and conditions of use in any one setting), grouped into three sections, namely, "Permissions" (mainly relevant to consent), "Terms" (typically relating to legal/contractual matters), and "Meta-Conditions" (over-arching topics).