dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA barcoding. -DNA barcoding, the analysis of a standardized segment of the mitochondrial cytochrome c oxidase subunit I (COI) gene, was performed on a representative selection of specimens (171, selected by diversity criteria). Tissues were sent to the Canadian Center for DNA Barcoding (CCDB) at the University of Guelph for DNA extraction, polymerase chain reaction (PCR) and sequencing. PCR was performed using the PCR primers C_ LepFolF/C_LepFolR. Data are currently managed under projects: "Mantodea of Gabon -Project 1 [ECOTROP 2014]", "Mantodea of Gabon -Project 2 [ECOTROP 2011]", "DNA Barcoding Mantodea -Collection N. Moulin" at Barcode of Life Data Systems (BOLD, Biodiversity Institute of Ontario, Canada; boldsystems. org). Kimura-2-parameter (K2P) distances were calculated using the BOLD 4.
Acknowledgements I wish to thank the  Scientific Comittee of "SANGHA Biodiversité en Terre Pygmée" Expedition :  Prof. Michel Dron  ( Institute of Plant Biology, Paris-Saclay ),  Prof. Jean Laurent Syssa-Magale  ( Faculty of Science, Bangui ),  Dr. Thierry Rousselin  ( Geo212 Company, Paris ),  Dr. Jean-Marc Seng  ( Biotransfer Company, Montreuil ),  Dr. Christian Jost  (Research Center on animal cognition),  Mr. Jean-Pierre Vidon  ( Ambassador of France in Bangui ),  Mr. Daniel Emery Dédé  ( Deputy, President of Production Committee, Natural Resources and Environment, Chairman of the Parliamentary Group of Friendship-Central Africa Republic-Nigeria ),  Mr. Karim Mekassoua  ( Minister of State Sangha-Mbaere, CAR ),  Mr. Gustave Doungoube  and  Bob Konzi-Sarambo  ( Minister of Environment and Ecology of CAR ),  Dr.
Jean-Louis Fijalkowski. This study has been partly funded by the  Grant Germaine Cousin  (2012, 2014) from the  Société Entomologique de France , by the  Crowdfunding KissKissBankBank of April 2015 "A la recherche de la Biodiversité des Mantes d'Afrique" (Actions Biodiversité NGO ,  Sergej Buchet  and  Viviane Calaora ,  Alain Duhamel ,  Patrick Moulin  and  Colette Moulin-Berger ), by a parternship with the engineering company " Alise Environnement ". DNA barcoding was supported by the  International Barcode of Life  project led by  Paul Hebert  at  Biodiversity Institute of Ontario (Guelph, Canada ). Further, we wish to thank the managers and contributors of the  Mantodea Species File (MSF , http://mantodea. speciesfile. org)for the great and useful work they carry out for dictyopterists worldwide, and  Roger Roy  ( Museum National d'Histoire Naturelle, Paris ) for his trust and his help in identifying some specimens.
sample size calculation The sample size was calculated from data obtained from the NOCETER trial, which took place in 11 French maternity units and evaluated cervical ripening among women with PP and a Bishop score <6. 12  It finally reported a caesarean rate of 27%; 17. 7% of the treatment group had caesareans for non-reassuring FHR. Accordingly, we hypothesise that the caesarean rate for fetal distress will be 17. 7% in the pharmacological group and that mechanical cervical ripening will reduce the rate to 12%. To detect a reduction from 17. 7% to 12% of the main outcome (caesarean for non-reassuring FHR) with a power of 80% and a two-tailed type I error of 5%, we need to include a total of 1220 women (610 in each group). data collection Data will be collected from the medical records by clinical research assistants and anonymised.
Acknowledgements The authors thank  Aurélie Darmaillacq  (clinical research associate) and  Elie Guichard  for their constructive work during the trial preparation. contributors CD, FP, ALG and BG conceived and designed the trial. CD and FP wrote the manuscript. FP and CD will be the principal investigators and will recruit patients and conduct the trial. ALG and BG planned the statistical analysis. All authors have read and approved the final manuscript.
e. mentions of clinical entities) or that may be derived from text data (e. g. relations between entities identified in clinical texts). Addressing the unification of information models is out of our scope here. Our goal is to put forth a representation scheme that will support secondary use of EHR data for conducting a large variety of retrospective studies. More specifically, we aim to support information extraction from clinical narratives in order to answer clinical questions such as: "What is the prevalence of incidental findings in patients with suspected thromboembolic disease?", "What is the contribution of CT venography in the diagnosis of thromboembolic disease?" or "What are the types and grades of toxicities experienced by colon cancer patients receiving FOLFOX therapy?". Simple and fast low-level annotations have already yielded good results in mining large datasets, as exemplified in LePendu et al.
Temporal relations (or temporal links, TLINKs) of seven types were annotated and finally merged into three (before, overlap and after). Two types of section times were labelled (admission and discharge). The 2012 data consisted of 310 discharge summaries (178,000 tokens). The THYME (Temporal Histories of Your Medical Events) project  [47]  is an ongoing effort to annotate temporal information in clinical data. The data reported in  [48]  consists of 1251 de-identified clinical notes on colon and brain cancer. The annotation guidelines  [49]  do not define specific entity types. Any event or state relevant to the patient's clinical timeline was annotated (procedures, diseases, diagnoses, patient complaints or states). The annotation scheme regarding event attributes is rich. The DocTimeRel attribute encodes the temporal relation between the event in question and the time when the record was authored (before, after, overlap or before/overlap).
Caveats and lessons learned from existing frameworks The review of prior work on existing framework and their use for annotating clinical narratives highlights specific features of frameworks and their applications that must be taken into account for the use of existing frameworks and annotated datasets, or the design of new material: • Category definition. The definition of some concept categories varies between frameworks. One prominent category that exhibits definition variation is a category refered to as Disorders or Medical problems. One source of One source of definition variation is whether any medical condition experienced by patients should be included in a broad category of whether diagnosed Diseases should be separated from the clinical Signs or Symptoms leading to the diagnosis.
Acknowledgements The authors would like to thank  Cyril Grouin ,  Thierry Hamon  and  Pierre Zweigenbaum  for fruitful discussions on the design and representation of the scheme. The authors also thank the  Biomedical Informatics Department at the Rouen University Hospital  for providing access to the LERUDI corpus for this work. The authors specially wish to acknowledge  Dr. Griffon  for his critical appraisal of the annotation scheme and its application to the corpus.
Despite its economic importance, studies on SKJ habitat preferences are scarce, especially in the AO and IO. However, there is a considerable amount of purse seine data available for analysis that could offer interesting insights. Habitat analyses can range from a simple examination of the relationship between presence and SST  (Andrade, 2003)  to more complex investigations that derive SKJ feeding habitats as input data for spatial population models (using temperature and oxygen in APECOSM,  Dueri et al. , 2012 ; using a biogeochemical model in SEAPODYM,  Lehodey et al. , 1998). Using generalized additive models to associate SST, chlorophyll-a concentrations (CHL), sea surface height anomalies (SSHa), and eddy kinetic energy at weekly and monthly time scales,  Mugo et al. (2010)  undertook a more detailed analysis of SKJ habitat in the North Pacific Ocean.
MATERIALS AND METHODS Our ENM was composed of a five-step methodology (Figure  1 ) that aimed to: (1) identify the main behaviors and ecological traits of SKJ based on current literature and expert knowledge; (2) collect and process gridded data (presence-only and environmental variables) for defined study areas; (3) perform a cluster analysis to identify a set of variable thresholds that characterized SKJ feeding ecology; (4) derive the habitat model equation that classified the degree to which each portion (i. e. , a model grid cell) of the study area was either suitable or unsuitable habitat (deterministic environmental envelope) on a daily basis; and (5) perform, by geographical area and fishing mode, a seasonal and inter-annual analysis using the distance of presence data to the closest favorable feeding habitat as a metric.
Skipjack Tuna Presence-Only Data In this study, SKJ presence data points were defined as purse seine sets in which SKJ was identified, regardless of its quantity or proportion to other species. Presence data was obtained from European Union (EU) tropical tuna purse seine fleet logbooks operating in the AO and IO. A total of 155,064 SKJ presence-only data points (with precise Global Positioning System locations-GPS) were collected for the Spanish fleet  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  and French fleet (1997-2015) (Figure  2 ). Redundancy filtering ensured that observations collected on the same day were separated by more than 2. 3 km i. e. , about half the width of a model cell.
Chlorophyll-a Data Daily CHL (mg. m -3 ) data were obtained from SeaWiFS (1998-2010; 1/12 • resolution) and  MODIS-Aqua (2003 -2015 ; 1/24 • resolution) ocean color sensors using the OCI algorithm (Hu et al. , 2012). Both products were extracted from the NASA portal (https://oceancolor. gsfc. nasa. gov/cgi/l3). Using this data at a daily time scale, meso-scale CHL fronts were identified. Daily CHL data were pre-processed using iterations of a median filter in order to recover missing data on the edge of the valid data, followed by a Gaussian smoothing procedure (see  Druon et al. , 2012 for details). Chlorophyll-a fronts were derived from the daily CHL data using an edge-detection algorithm. This approach was shown to perform better than the histogram methods for detecting horizontal gradients, given clear viewing conditions  (Ullman and Cornillon, 2000).
Step 3-Characterization of SKJ Feeding Habitat In step three, we considered the environmental variability of each covariate, with a view to identifying the threshold values that characterize favorable vs. unfavorable feeding habitat. These analyses were carried out using the 1997-2014 abiotic data and the 1998-2015 CHL data. The links between each environmental variable and tuna presence were analyzed for each ocean basin using cluster analysis  (Hartigan, 1975; Berthold et al. , 2010). The cluster analysis approach is particularly well suited for identifying habitats that are marginally represented by the data and may otherwise be interpreted as outliers by other statistical methods. In our analysis, we used daily or 3-day mean values of CHL and gradCHL and daily interpolated values from monthly means for SST, SSS, MLD, O 2 , SSC, and SSHa.
5-Comparison between Fishing Activity and Feeding Habitat Model performance was estimated by computing the distances between the presence data and the closest favorable feeding habitat (as defined by the 3-day mean composite data) for the period between 1998 and 2014. We then compared the distribution of these distances between the two fishing modes and ocean basins. A more detailed monthly analysis of these distances was also performed to investigate seasonal links between the number of fishing sets and the size of the favorable feeding habitat. Mean annual SKJ catch rates (t. d -1 ) were computed for the EU and associated purse seine fleets as the ratio between annual catch and annual fishing effort (expressed in fishing days;  Chassot et al. , 2015). Temporal correlations between annual catch rates and favorable feeding habitat size were investigated at the ocean basin scale using Spearman's rank correlation coefficients.
Habitat Modeling and Parameterization SKJ can occur in a broad range of biotic and abiotic oceanographic conditions. However, in this study we assumed that given SKJ is an income breeder, it will always tend to seek out favorable feeding grounds. Thus, except during brief migrations periods, SKJ presence can be used to identify the existence of nearby favorable feeding habitat. The cluster analysis described a wide range of suitable trophic conditions for SKJ to feed, from the oligotrophic conditions in some regions of the Western IO and equatorial AO to the eutrophic conditions in the upwelling areas of the tropical AO (Table  1 , Figures SI-2, SI-3). The threshold values for CHL and gradCHL were found to be of the same order of magnitude between the MODIS and SeaWiFS data and the differences are likely attributable to differences in data resolution and the optical characteristics of the sensors.
The favorable CHL range was found to be between about 0. 12 and 5 mg. m -3 in both oceans while the minimum and intermediate gradCHL values were found to be around 7. 10 -3 and 5. 10 -2 mg. m -3. km -  , respectively. Due to the cloud coverage in both oceans, <18% of the presence data could be associated with the closest 3-day (±1 day of the observation) composite favorable feeding habitat for CHL and 14% for gradCHL. For SST and SSS, favorable feeding habitat was characterized by the relatively large ranges of 21. 6-30. 0 • C and 30. 3-36. 2 PSU, respectively. The SSHa distribution that describe 90% of the presence data ranged from a minimum of -0. 2 m (in the AO) to a maximum of 0. 67 m (in the IO) while the MLD range was 6-158 m. Finally, the minimum O 2 value observed was 196 mmol. m -3 (4.
8 (p < 0. 001). Despite probable differences in fishing effort and fishing efficiency, total catches also showed substantial correlation with feeding habitat size (r = 0. 7, p < 0. 001). Habitat size, catch rates and total catches showed markedly lower levels from 2007. From 2009 onwards, the fraction of SKJ caught (in weight) on dFAD The drifting fish aggregating device associated sets (dFAD) presence data (red crosses) and the free-swimming school sets (pink circles) were overlaid with their respective numbers of presence data. The monthly ranges and years presented in these figures were chosen specifically to show stark contrasts in terms of fishing mode and habitat size i. e. , small (A,C,E,G) vs. large (B,D,F,H). Favorable feeding habitat is defined by the presence of favorable environmental variable values and expressed as a frequency of occurrence.
Modeling Methods The application of a common model parameterization across ocean basins and the substantial volume of data used in this study enabled us to successfully identify the ecological niche of SKJ in the Eastern Central AO and Western IO. In this study, we were fortunate enough to obtain presence data that covered a wide geographical extent and data for an extensive range of environmental conditions. Moreover, relatively long time-series  (1998-2014/2015)  were available for these data sets. This allowed us to derive a robust ENM for SKJ. We assumed that large productive fronts are more resilient than smaller features and thus, more able to sustain well-developed food chains on which SKJ can feed  (Olson et al. , 1994). Thus, in this study, the linear function linking daily favorable feeding habitat values with the size of gradCHL values provided an estimate of feeding capacity across productive fronts of differing sizes.
In order to identify and set the model parameterization, we conducted separate cluster analysis on the biotic and abiotic variables. We undertook these analyses separately because there were fewer CHL-related data points available (a 2. 5fold difference in the IO and an 8-fold difference in the AO) than data for the abiotic variables. The use of a cluster analysis that combined both sets of variables would not have accounted for as much abiotic variability. By using a clustering method, we ensured that environmental characteristics that may be important for SKJ but under-represented in the presence data were captured. This was essential for correctly identifying their overall ecological niche. Furthermore, the model parameterization excluded the biotic cluster that represented near-null gradCHL values to ensure that the resultant habitat, in combination with the abiotic preferences (established from the cluster analysis of the abiotic variables), only described the feeding behavior.
6-25. 9 • C and Gabon values are 22. 6-27. 7 • C). Thus, productivity hotspots for adult SKJ feeding in the AO may actually be unfavorable for larvae given their temperature requirements for survival. SKJ larvae were FIGURE 4 | Monthly boxplots of the distances between skipjack tuna (Katsuwonus pelamis; SKJ) presence data and the closest favorable feeding habitat boundary for free-swimming school sets (FSC; upper panels) and drifting fish aggregating device associated sets (dFADs; lower panels) in (A) the Eastern Central Atlantic Ocean and (B) the Western Indian Ocean from 1998 to 2014. Negative values correspond to presence data points observed inside the favorable feeding habitat. The width of the boxes is proportional to the monthly number of sets while the box length corresponds to the interquartile range (median value in red, whiskers cover 99.
SKJ grow quickly and mature early  (Grande et al. , 2014; Murua et al. , 2017). Despite the difficulties associated in obtaining absolute age estimates and the fact that there can be notable inter-individual variability in growth, mark-recapture data suggests that SKJ are capable of rapid growth in the first months of life, resulting in maturity at around 6 months of age  (Eveson et al. , 2015; Murua et al. , 2017). The overall size of favorable feeding habitat in the IO may, therefore, be interpreted as an indicator of the carrying capacity of the environment to sustain the growth of SKJ populations. This may have important implications from a management perspective, especially for a species that experiences intense fishing pressures. In the AO, we did not observe this relationship. Here, catch rates have increased by ca.
ACKNOWLEDGMENTS The authors would like to particularly thank the following people and organizations for their contributions to this paper. NASA Ocean Biology (OB. DAAC), Greenbelt, MD, USA , and the  EU-Copernicus Marine Environment Monitoring Service  for the quality and availability of their ocean color and ocean modeling products, respectively. ORTHONGEL, ANABAC, OPAGAC and all past and current personnel involved in the data collection and management of purse seine fisheries data funded by the  European Union  through the  Data Collection Framework (Reg. 199/2008  and  665/2008 ). The staff at the "Observatoire Thonier" in the  Research Unit MARBEC (IRD/Ifremer/UM/CNRS ) and the  IEO tuna research group. This is contribution number  827  of  AZTI, Marine Research Division. The authors are thankful to  John Casey ,  Jane Alpine , and two reviewers for helping in editing the paper and for providing valuable comments.
The protein samples were loaded (20 µg per lane) and separated on 12% SDS-PAGE gels. The fluorescently labeled proteins were detected by a ProXPRESS 2D proteomic Imaging System (PerkinElmer, Villebon sur Yvette, France) with excitation wavelength at 532 nm and emission wavelength at 580 nm. Signals were quantified with ImageJ software. Gels were stained with Coomassie Brilliant Blue stain (0. 5 g CBB G250, 10% acetic acid, 45% methanol in ultra-pure water (v/v)), destained (10% acetic acid, 40% methanol in ultra-pure water (v/v)), and scanned to control the protein quantity after electrophoresis. To identify previously detected active proteases, protein extracts were labeled with the biotinylated-tagged probes, DCG04 (PLCP labeling) or FP-biotin (SH labeling), and pull-downs of biotinylated proteins were performed with a modified protocol from  Poret et al.
The fragmentation data were interpreted using Mass Hunter software (version B. 03. 01, Agilent Technologies, Les Ulis, France). For protein identification, MS/MS peak lists were extracted, converted into mzdata. xml format files, and compared with the protein database (NCBInr-Viridiplantae) using the MASCOT Daemon search engine (version 2. 1. 3; Matrix Science, London, UK). The searches were performed with no fixed modification and with variable modifications for the oxidation of methionine, and with a maximum of one missed cleavage site. MS/MS spectra were searched with a mass tolerance of 20 ppm for precursor ions and 0. 6 Da for MS/MS fragments. Only peptides matching an individual ion score >41 were considered. Proteins with two or more unique peptides matching the protein sequence were considered as a positive identification.
[37]au et al. (2012)Ps and SPs labeled with the biotin-tagged probe DCG04 and FP, respectively, in senescing cotyledons of oilseed rape (cv. Ténor) after five days of nitrate limitation by LC-MS/MS. The assigned protein with the best match is provided alongside the Uniprot accession number (if available) or NCBI/GenBank accession number. Proteases were classified according toRichau et al. (2012)[37]for CPs and MEROPS database for SPs.
(  2012 ) proposed a software-based memory throttling mechanism to explicitly limit the memory request rate of each core and thereby control the memory interference. They also developed analytical solutions to compute proper throttling parameters that ensure the schedulability of critical tasks while minimising the performance impact of throttling. Kelter et al. (2014)  analysed the maximum bus arbitration delays for multicore systems sharing a TDMA bus and using both (private) L1 and (shared) L2 instruction and data caches. Dasari et al. (2016)  proposed a general framework to compute the maximum interference caused by the shared memory bus and its impact on the execution time of the tasks running on the cores. This method is more complex than the one proposed in this paper, and may be more accurate when it estimates the delay due to the shared bus; however, it assumes partitioned caches and therefore does not take cacherelated effects into account, which makes it less general than the framework proposed here.
This was followed by work on resource adaptive arbiters  (Schranzhofer et al. 2011). Their work assumes a task model where each task consists of sequences of super-blocks, themselves divided into phases that represent implicit communication (fetching or writing of data to/from memory), computation (processing the data), or both. In contrast to the techniques presented in this paper, their approach requires major program intervention and compiler assistance to prefetch data. Adopting a similar model,  Pellizzoni et al. (2010)  compute an upper bound on the contention delay incurred by periodic tasks, for systems comprising any number of cores and peripheral buses sharing a single main memory. Their method does not cater for sporadic tasks and does not apply to systems with shared caches. In addition it relies on accurate profiling of cache utilization, suitable assignment of the TDMA timeslots to the tasks' super-blocks, and imposes a restriction on where the tasks can be pre-empted.
2010 ) to provide traces. The traces for the benchmarks were generated using the gem5 instruction set simulator  (Binkert et al. 2011 ) and contain statically linked library calls. As the benchmark code corresponds to independent tasks, no data is shared between the tasks. Table  2  shows information for all 39 benchmark programs used to provide traces including the total number of instructions (which is equal to the processor demand), the number of read/write operations, the memory demand, and the maximum number of UCBs and ECBs on the reference multicore architecture. Each benchmark is assigned only one trace, which is sufficient due to the simple structure of the benchmark suite: The benchmarks are either singlepath by design or the input is provided as part of the benchmark suite. Despite the rather simple structure of the benchmarks, the tasks show a strong variation in processor and memory demand.