dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential gene expression analyses RNA-Seq of the 22 different transcriptome libraries resulted in a total of 806 million paired end raw reads, which was reduced to 667 million paired end reads after quality clipping. These were assembled into a backbone assembly of 26,064 contigs with an average length of 1011 bp and a median length of 815 bp (fully described in  [24] ). As the aim of this study was to understand the molecular mechanisms underlying adaptation to longterm exposure to heat stress in Pecten maximus, a very stringent strategy was adopted to identify the consistently differentially expressed transcripts. This was performed, by taking into account those displaying a ratio (treatment/ control) of at least 2 in all the last three time points. 1. 67 % of the transcriptome (531 transcripts from 26,064 contigs) showed significant differential expression at the elevated temperatures with putative function for 177 transcripts, assigned by Blast sequence similarity searching against the NCBI non-redundant database, using an E-value cut off of 1e -10 (summarised in Additional file 1: Tables  1  and 2 , for 25 °C and 21 °C, respectively).
Differentially expressed proteins Using 2-DE, a mean proteome content of 956 spots was observed. Analyses revealed that 24 protein spots (2. 5 % of the observable proteome) were significantly differentially expressed at 25 °C, at one or other of the time points (T4: 27 days or T7: 56 days), of which 12 were upregulated and 12 were down-regulated (Table  1 , 3). All of these 24 protein spots were subjected to trypsin digestion and Matrix-Assisted Laser Desorption Ionization Time-Of-Flight tandem mass spectrometry (MALDI TOF-TOF) analysis. This led to the identification of nine proteins using sequence similarity searching (Table  2 ). Of these nine putatively annotated proteins, five were identified from ESTs sequenced in the RNAseq experiments described in this study, and the other four proteins from the scallop hemocytes ESTs  [28]  used to build our database.
Network analysis All of the 177 transcripts putatively annotated using the initial Blast sequence similarity searching were used in further searches against the UniProt human database and the results (short names of genes) were entered into the STRING program. In all, 132 genes had homologs in human, and network analysis highlighted relationships between 35 of them (Fig. 4 ), among which only one, titin, was relied to the 21 °C condition, the other sequences were all from the 25 °C samplings. The main result showed a central node around glyceraldehyde-3phosphate dehydrogenase (GAPDH: 10 interactants), which showed weaker links to nodes centered around the c-JUN pathway (8 interactants), calreticulin (CALR: 7 interactants), 90 kDa heat shock protein (HSP90: 5 interactants) and ankyrin (ANK1:3 interactants).
5 × 10 6 μm 3. mL -  of microalgae  [71]. This ensured that the scallops were fed ad libitum irrespective of the temperature and the number of individuals in the tank. The scallops were initially left at the ambient temperature of 15. 1 ± 0. 2 °C for 16 days to allow the animals to acclimatise to the rearing conditions and to identify any damaged or unhealthy individuals prior to experimentation. Then, one tank was maintained as a control at approximately 15 °C (15. 1 ± 0. 2 °C), while the temperature was increased at 1 °C/day to reach 21 °C (21. 4 ± 0. 2 °C) and 25 °C (25. 2 ± 0. 9 °C) in the two other tanks. During the experiment, the temperature was recorded every 15 min in the three tanks using button temperature data loggers (Signatrol SL52T Button Data Logger). Additional environmental parameters were also monitored: salinity, pH, O 2 and ammonia.
RNA-Seq data sets The RNA libraries yielded a total of 806 million paired end reads. Raw reads were filtered and trimmed using the FASTX-toolkit (Version 0. 0. 13 from Assaf Gordon Hannon lab) and rRNA contamination removed using ribopicker  [75]  and cutadapt (Version 1. 1;  [76] ), with a final quality check performed using fastQC (Version 0. 10. 0; http:// www. bioinformatics. bbsrc. ac. uk/projects/fastqc/). The contigs were assembled using SOAPdenovo  [77]  and a kmer size of 89 was used to construct the initial de novo transcriptome assembly, resulting in 1,311,367 contigs, which were further assembled with CAP3  [78]. Redundancy was determined by self-Blasting and the use of CD-HIT (95 % similarity  [79] ). De novo assembly produced a total number of 26,064 contigs  [24]. The transcripts were then processed through the Blast2GO pipeline to produce putative annotations and functional classifications based on Blastx results against the GenBank NR database release 190.
Differential proteomics analysis Images were aligned and spots were detected and quantified using the Progenesis SameSpots software (version 3. 3, Nonlinear Dynamics) applying the automated algorithm. All detected spots were manually carefully checked and artifact spots were removed. Data were exported as volume raw values and statistical analyzes were conducted in R  [81]  using the packages prot2D  [82]  and Limma  [83]. Data were normalized (quantile normalization) and the samples from the 25 °C treatment were paired-compared using moderated t-test to the samples from 15 °C treatment (control) for each sampling date (with 6 replicates per group). A global correction by false discovery rate (fdr;  [84] ) was used, in order to take into account multiple comparisons issues and paired-comparison correction.
1 % in water), and mixed with the α-cyano-4-hydroxycinnamic acid (HCCA, 10 mg. ml -1 of a ACN/TFA/water (60/4/36:v/v/v) solution), and spotted on a polished steel target using the dried droplet method. Peptides were then analyzed by Matrix-Assisted Laser Desorption Ionization Time-Of-Flight tandem mass spectrometry (MALDI TOF-TOF) in positive ion reflector mode, using an Autoflex III (Bruker Daltonics) mass spectrometer. The FlexControl software (v3. 0, Bruker Daltonics) was set up to acquire successively PMF spectra and MS/MS from the dominant peaks. Mass spectra were analyzed with FlexAnalysis (v 3. 0; Bruker Daltonics) by applying the following conditions: TopHat algorithm for baseline subtraction, Savitzky-Golay analysis for smoothing (0. 2 m/z; number of cycles: 1) and SNAP algorithm for peaks detection (signal-to-noise ratio: 6 for MS and 1.
5 for MS/MS). The charge state of the peptides was assumed to be +1. Fragments of porcine trypsin were used for internal mass calibration. Proteins were subsequently identified with PEAKS (v. 5. 2, Bioinformatics Solutions) using MS/MS-based identification and de novo sequencing. The search parameters against a custom-made database were set as follows: carbamidomethylation of cysteine was set as a fixed modification, oxidation of methionine and phosphorylation of serine, threonine and tyrosine were set as variable modifications, one missing cleavage during trypsin digestion was allowed and the tolerance for precursor-ion mass tolerance was set to 1 Da. The database was constructed by combining P. maximus sequences from two sources: sequences acquired during this study  [24]  and sequences from a recent transcriptomic analysis of P.
maximus hemocytes cells  [28]. Overall, the database included a total of 252,888 P. maximus expressed sequence tags (ESTs). Protein identification was considered as unambiguous when a minimum of two peptides matched with a minimum score of 20. False discovery rates were also estimated using a reverse database as decoy. EST database sequences were annotated by sequence similarity searches against a non-redundant database using the Blast algorithm from NCBI (http://www. ncbi. nlm. nih. gov/BLAST).
Network analysis To better understand the relationships between the proteins and genes identified in this study, network analyses using the String 9. 05 algorithm  [86]  (http://string-db. org) were performed. STRING is a database of known and predicted protein interactions, which includes both direct (physical) and indirect (functional) associations. STRING quantitatively integrates interaction data and calculate a score for each protein interaction based on the genomic context, high-throughput experiments, co-expression and literature. This kind of network analysis is strongly dependent on the quality of the used database, and is thus much more efficient using databases from the most studied model organisms. Hence, the putative human homologues of the genes and proteins up-or down-regulated in this study were identified and used as the input data for the STRING program.
Acknowledgements This research was granted by the  Région Bretagne , i. e. the  Pemadapt  project (ref. 6368) and a  doctoral fellowship  to  S. A. ( Protmar  project, ref. 6197). MSC, MAST and LSP were financed by core  NERC  funding to the  British Antarctic Survey Polar Sciences for Planet Earth programme. COMANCHE  ( ANR-2010-STRA-010 ) and  LabexMER  ( ANR-10-LABX-19 ) programs from the  French National Research Agency (ANR) , and a  NERC   Theme Leader Award  to LSP ( NE/G018 ), also supported our research. For temperatures in the bay of Brest, we used data provided by the  "Service d'Observation en Milieu Littoral", INSU-CNRS ,  SOMLIT-Brest, IUEM/UBO.
Background: Potentially avoidable hospitalizations represent an indirect measure of access to effective primary care. However many approaches have been proposed to measure them and results may differ considerably. This work aimed at examining the agreement between the Weissman and Ansari approaches in order to measure potentially avoidable hospitalizations in France. Methods: Based on the 2012 French national hospital discharge database (Programme de Médicalisation des Systèmes d'Information), potentially avoidable hospitalizations were measured using two approaches proposed by Weissman et al. and by Ansari et al. Age-and sex-standardised rates were calculated in each department. The two approaches were compared for diagnosis groups, type of stay, severity, age, sex, and length of stay. Results: The number and age-standardised rate of potentially avoidable hospitalizations estimated by the Weissman  et al.
Therefore, different methods have been used, that vary in terms of conditions and ICD codes. In France, Gusmano et al. [31, 32]  used the Weissman et al. approach  [4] , but this method does not encompass all potentially avoidable conditions. As an example, status asthmaticus, the most severe PAH in asthma, is not coded (ICD10 code J46), whereas it is coded in other studies  [23]. Moreover, COPD (J20, J41, J42, J43, J44, J47  [23] ), the most common cause of death due to respiratory diseases  [33, 34]  and a common cause of PAH  [5] , is not even considered by Weissman et al. [4]. The current study aimed at estimating PAH in France in 2012 using the French hospital discharge database (Programme de Médicalisation des Systèmes d'Information; PMSI). Firstly, the Weissman et al. approach  [4] , already tested in France, was compared with the more recent Ansari one  [23]  at the French national and departmental levels.
Study population The data for all patients hospitalized in France in 2012 were collected from the national administrative database, the PMSI. French public and private hospitals are financed through a Diagnosis-Related Group (DRG)-based prospective payment system  [35, 36]. The PMSI is the national discharge database and includes all hospital discharges from all public and private hospitals in France. The quality of this database is deemed well, especially since 2007  [37]. It centralises data by a diagnosis that is encoded according to ICD-10, medical procedure, age, residence and French diagnosis-related groups of patients admitted to all hospitals (public and private). The reliability and validity of the PMSI database have already been demonstrated for various acute and chronic conditions  [38] [39] [40] [41].
Hospital discharges are classified in diagnosis groups (catégories majeures diagnostiques, CMD) and then in DRGs according to ICD-10 principal and secondary diagnosis codes, surgical and non-surgical procedures (Classification Commune des Actes Medicaux, CCAM)  [42]  and age. We extracted discharge data for acute hospital stays in medicine, surgery and obstetrics/gynaecology. Discharges for foreign patients were excluded. This research was approved by the Commission Nationale Informatique et Liberte, an independent ethic committee (www. cnil. fr; approval number DE-2013-118). Access to the PMSI database is free for researchers after approval by the CNIL. Written informed consent from patients is not required by French law for such studies.
Strengths and limitations This study is based on the nation-wide, all-payers, and public and private hospitals discharge database. Hence it can be considered as exhaustive and representative. By definition, DRGs classify cases according to principal and secondary diagnoses, patient age and sex, the presence of co-morbidities and complications as well as the procedures performed. The PMSI is based on DRGs allowing an exhaustive patient case classification system (i. e. the system of diagnosis-related groupings). The PMSI is standardized and exhaustive but there are some limitations. Inconsistency may occur due to variability in coding in different health institutions because of ignorance or misinterpretation of coding rules. Since 2009, army hospitals have been included, and the definition of primary diagnosis has changed.
2, Principal diagnosis only K26. 4, K26. 5, K26. 6, K27. 0, K27. 1, K27. 2, K27. 4, K27. 5, K27. 6, K28. 0, K28. 1, K28. 2, K28. 4, K28. 5, K28. 6 Cellulitis L03, L04, L08, L98. 0, L88, L98. 3 Principal diagnosis only, exclude cases with procedure codes Pelvic inflammatory disease N70, N73, N74 Principal diagnosis only Ear, nose and throat infections H66, H67, J02, J03, J06, J31. 2 Principal diagnosis only Dental conditions K02, K03, K04, K05, K06, K08, K09. 8, K09. 9, K12, K13 Principal diagnosis only Convulsions and epilepsy O15, G40, G41, R56 Principal diagnosis only Gangrene R02 Principal diagnosis only.
The only testimony to the use of nipah leaves for writing material is De Clercq  (1927, as quoted in   Van der Molen 1983:89) , who states that he heard that formerly, in the hinterlands of South Sumatra, and perhaps up to the time of his report, nipah were used for writing ephemeral love letters. Beyond this, the literature is silent on the characteristics of this rare writing material. There are, to be sure, a small number of articles or catalogue notes that discuss nipah, but they are limited to the investigation of available nipah manuscripts; the writing support is in such sources categorized as nipah without further question. The processing and preparation of nipah leaves for writing also remains an uninvestigated question, as does their use. The first notice mentioning the existence of manuscripts written on palm leaves from the Priangan area was published by  Netscher (1853:474) , although he still designated the writing material of the Arjunawiwāha codex (later acquired by the Bataviaasch Genootschap [hereafter bg] with accession number l 641) as lontarblad (lontar leaf).
The number of image analysis tools supporting the extraction of architectural features of root systems has increased in recent years. These tools offer a handy set of complementary facilities, yet it is widely accepted that none of these software tools is able to extract in an efficient way the growing array of static and dynamic features for different types of images and species. We describe the Root System Markup Language (RSML), which has been designed to overcome two major challenges: (1) to enable portability of root architecture data between different software tools in an easy and interoperable manner, allowing seamless collaborative work; and (2) to provide a standard format upon which to base central repositories that will soon arise following the expanding worldwide root phenotyping effort.
RSML follows the XML standard to store two-or three-dimensional image metadata, plant and root properties and geometries, continuous functions along individual root paths, and a suite of annotations at the image, plant, or root scale at one or several time points. Plant ontologies are used to describe botanical entities that are relevant at the scale of root system architecture. An XML schema describes the features and constraints of RSML, and opensource packages have been developed in several languages (R, Excel, Java, Python, and C#) to enable researchers to integrate RSML files into popular research workflow.
Arabidopsis [Arabidopsis thaliana]) have often neglected detailed root architecture, using mainly global estimators, such as total root length or the maximal depth of the root system convex hull  (Galkovskyi et al. , 2012). However, an increasing number of research questions now require a precise quantification of root 1 This work was supported by the Belgian Science Policy Interuniversity Attraction Poles Program (grant no. P7/29 to G. L. and X. D. ), by the Fonds National de la Recherche Scientifique (to G. L. and F. M. ), by the French National Research Agency (HydroRoot project, grant no. ANR-11-BSV6-0018 to C. P. and C. G. ), by the European Union FP7-KBBE-2011-5 project (EURoot: Enhancing Resource Uptake from Roots under Stress in Cereal Crops to P. N. , T. P. P. , M. P. P. , and X. D.
However, this flexibility may induce additional complexity when exchanging data between different groups of scientists using different protocols and software within a specific domain. For each new protocol, for example, a modeler must define the number of scales, their meaning, the name of the attributes, and how to encode them. While OpenAlea provides software solutions to manage this complexity, external software must implement and manage this complexity in their own programming environment and language. Now, a new step must be taken to further improve the ability of researchers to acquire plant architecture data using different software and to exchange and share these data. To achieve this, the research community needs to agree on a common biological language to build up shared databases and quantitative tools and to compare hypotheses and approaches.
Description of the RSML Format The RSML format defines an XML file in which the topological, geometrical, and numerical information describing a root system is stored. In practice, the RSML format is split into metadata and the scene elements (Fig. 2 ). The metadata store experimental and technical information relevant to the scene, while the scene itself contains the root system representation. Supplementary data can be stored using properties (at the scene, plant, or root level), functions along the root axes, and annotations. A brief outline of the main components of the RSML format is presented in the following sections. The RSML Web site (http://rootsystemml. github. io/) provides technical specifications of the format as well as RSML examples and related image files that illustrate the possibilities offered by the RSML format.
Metadata The metadata specify the experimental context in which an RSML file was constructed. They also provide a concise description of the file content, allowing documents to be quickly scanned, filtered, or classified without reading the entirety of each file. Included in the metadata element are a unique identifier for the scene, file information, and the realworld unit and resolution for the root conversion of pixel data. The ,software. and ,last-modified. elements allow tracking of changes when multiple software tools have handled the same document. The ,property. and ,function. definitions describe the associated properties or functions that will appear in the document. If the RSML file is one of many in a time series data set, a ,time-sequence. element identifies this set and the position of the file in the sequence.
Scene and Topology The scene represents a single image, possibly within a series of 2D or 3D images, that will contain at least one root system. Within the scene, there is at least one plant containing at least one root. Finally, roots may also contain additional child roots (i. e. lateral roots). RSML documents mimic this structure (Fig. 2 ): the scene element will contain one or more plant elements, which in turn will contain one or more root elements. Further levels of root elements are used to show lateral roots of higher orders (Fig. 2C ). Therefore, the topological aspects of the root system, such as the connections between a primary root and its child lateral roots, are represented through the nested structure of the scene element in the RSML document itself. All geometry and other measurements are stored as data attached to the relevant elements throughout the document (Fig.
2C ). In order to ensure a uniform naming of the different root types and terms across different files, root type descriptors used in RSML refer to the Plant Ontology Database  (Plant Ontology Consortium, 2002; Avraham et al. , 2008) , a plant ontology widely accepted within the plant community. The current root type terminology in the Plant Ontology Database is presented in Table  II. This list is not exhaustive and might be extended with other terms from the Plant Ontology Database.
Inside the geometrical description of a root, a polyline element contains an ordered list of points that provide the end points of the successive segments that make up the root. Each point element contains x and y attributes for 2D scenes and an optional z attribute for 3D architectures. All geometric units in RSML are given in pixel coordinates, referring directly to the image associated with this root system. RSML metadata can be used to provide the scaling necessary to convert into real-world units. It may be the case that the conversion to polyline form comes at the expense of accuracy in a given software package. Should a certain application store geometry in spline form, for example, the conversion to a polyline will only approximate the curve. In this case, additional geometries are permitted alongside, but not instead of, the polyline.
Properties Many aspects of root systems cannot be linked directly to root geometry and are instead related to botanical entities. For example, while diameter is intrinsically linked to a position along the root, qualitative (long or short root, dead root) or global (length, insertion angle, or position) information is better attached to the whole root as a property. A ,property. element contained within a scene, plant, or root element specifies a measurement of that object. Properties can take one of numerous data types, allowing binary, integers, real types, or text values. As properties may be specific to the generating software, the meaning of a property can be supplied within the document metadata. Properties also might be used for a more efficient parsing of the RSML document, enabling analysis tools to directly retrieve these precalculated properties without having to compute them from the topological and geometrical information.
Root Development Root developmental processes (e. g. growth rate) are often analyzed using time-lapse image sequences. Preserving time series information is an important factor in many root phenotyping applications and requires maintaining an explicit link between successive images of the same plant. The RSML format allows images of a time-lapse sequence to be linked through the use of the ,time-sequence. element in the metadata. The ,index. element indicates its position in the time series. Software that provides an explicit mapping between geometries in a time series can use the metadata to indicate this. Some software use such information to calculate a change in parameters over time (e. g. elongation rate). Others (SmartRoot and RootSystemAnalyser) use previous time-step information to initialize subsequent reconstructions, improving root-tracing efficiency by focusing on incremental information in the following image.