dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw data of horizontal and vertical gaze-holding of a patient with upbeat nystagmus (e). Horizontal (upper trace) and vertical (lower trace) eye movements. To investigate which saccadic parameters most reflect neurological status, correlation analysis between clinical rating scales and saccadic measures was performed. PV of downward saccades and duration of vertical saccades showed the strongest associations with all applied clinical parameters (Table  2 ; Figures  2A-C ). Other measures of downward vertical saccades, such as duration, amplitude, and slopes, also showed variable but significant associations with clinical scores. This was also true for all measures of vertical saccades and all clinical measures. Slopes of horizontal and vertical upward saccades showed no significant relationships with any of the clinical measures, but the IQ score (vertical slopes) was highly correlated with all clinical rating scales (Table  2 ).
METHOD A literature search was made up until March 2018 using the following databases: Pubmed, ScienceDirect, Scopus, SportsDiscus and Web of Science. Search terms were used to restrict studies to those involving humans under hypoxic conditions, including a combination of either "altitude" or "hypoxia" or "oxygen fraction" or "oxygen delivery" and each of the following: "exercise, " "exercise training, " "Live Low-Train High, " "intermittent hypoxia, " "incremental, " "GXT, " "graded, " "heart rate, " "exhaustion, " "patient, " "disease. " Each title, abstract and full text were assessed for relevance to the topic and selected if they met the following inclusion criteria: involving humans; normoxia and hypobaric or normobaric hypoxia conditions; hypoxic level expressed as altitude (m) and/or inspired oxygen fraction (%).
From these, 1,991 publications were excluded. Finally HR max data were collated from 81 publications involving healthy subjects and 5 publications involving patients (Supplementary Table  S1 ). In five of the reviewed articles, the data were not reported in the text/Tables but in figures. In these cases, the data were extracted using the Digitizelt software (v2. 2, BormiSoft2, Braunschweig, Germany). Thirteen publications with healthy subjects and 9 with patients compared exercise training in hypoxia vs. exercise training in normoxia matching the intensity with HR (Supplementary Table  S2 ). Due to the lack of standardization of study designs (the most important of which being the hypoxic stimulus, but also the type of disease, the age or gender of participants, the type of incremental test), a systematic assessment of hypoxic intervention was not done.
Finally, the slides were washed, mounted with fluorescence mounting medium (DAKO) and analyzed using a Zeiss Axiovert 200 microscope (Zeiss) with the VisiView software (Visitron Systems, Puchheim, Germany). To exclude unspecific, doxorubicin-mediated fluorescence signals, negative controls (in the absence of anti-Ago2 and anti-Ki67 antibodies) of medium-or doxorubicin-treated cells were performed. At the appropriate device settings, all negative controls were virtually free of red or green fluorescence signals (data not shown).
Acknowledgements Martin Röcken  and  Thomas Wieder  share senior authorship. The authors would like to thank  S. Weidemann  and  V. Galinat  for expert technical assistance. This work was supported by the  Wilhelm Sander Foundation  ( 2012. 056. 3  to M. R. ),  German Cancer Aid  (application numbers 109037 and  110664  to M. R. ), and  German Research Association  ( TRR-SFB 156 ,  DFG RO 764/14-1  and  DFG RO 764/15-2  to M. R. and  DFG WI 1279/4-1  to T. W. ). The group of O. B. is supported by grants from  ANR-BMFT ,  Fondation ARC pour la recherche sur le Cancer, INSERM , and the  National Cancer Institute of the National Institutes of Health  under Award Number  R01CA136533. O. B. is a CNRS Research Director  DR2. We acknowledge support by  Deutsche Forschungsgemeinschaft and open Access Publishing Fund of University of Tübingen.
Sample materials used for this study included archival FFPE tumor samples (slides only) from consented subjects enrolled in the FIRE-3 (NCT00433927) and PICCOLO (ISRCTN93248876) prospective, randomized clinical trials, and tumor samples (shavings only) received by Laboratoire CERBA from patients being tested as a part of routine care. Laboratoire CERBA is authorized by the French Ministry of Health to use residual testing material for scientific purposes (N°AC-2015-2418). Characteristics associated with individuals whose FFPE tumor samples were used for this study are shown in Supplementary Tables  1  and 2.
Sample preparation and RNA extraction Total RNA was extracted from the tumor sample using a dedicated kit design for FFPE samples and subsequent quantitative polymerase chain reaction (qPCR) reactions (Table  1 ). All kits were used in accordance with manufacturer's instructions. Studies demonstrated that the miRNeasy FFPE, AllPrep DNA/RNA FFPE, and QIAsymphony RNA kits (Qiagen, Hilden, Germany) and Maxwell 16 LEV RNA FFPE kit (Promega, Madison, WI, USA) are compatible with the miR-31-3p quantification method described below (data not shown). Following total RNA extraction, quantity and quality evaluations were performed using capillary electrophoretic RNA qualification with a Fragment Analyzer (AATI, Evry, France), Bioanalyzer (Agilent, Les Ulis, France), Experion (Bio-Rad Laboratories, Hercules, CA, USA) and/or via UV absorbance with a NanoDrop Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
miR-calibrator selection and miR-calibrator expression stability A comprehensive literature review and microarray studies of FFPE mCRC tumor samples were performed to identify an appropriate miRNA to use for the normalization of miRNA RT-qPCR data obtained from the miR-31-3p expression quantification test. The reference gene selected had the lowest standard deviation for the average cycle threshold (Ct) value for a single quantity of human colorectal total RNA tested. After identification of the miR-calibrator, its stability was then qualified by testing 189 FFPE mCRC tumor samples from 160 slides and 29 shavings.
Data analysis Data analysis for instruments from Applied Biosystems was performed using the instrument's qPCR software set at automatic mode as the baseline. The threshold was manually configured to 0. 2 for both miR-31-3p and the miR-calibrator (Y-axis is logarithmic scale). Data analysis for the LightCycler 480 Instrument II was performed using the second derivative method with the baseline and threshold set automatically. Expression levels were normalized using the Human Brain Total RNA via the ∆∆Ct method. The difference between miR-31-3p Ct and the miR-calibrator Ct for each sample was compared with the difference between miR-31-3p Ct and the miR-calibrator Ct of the Human Brain Total RNA standard. This method enabled the determination of the relative quantification (RQ) for miR-31-3p expression.
miR-calibrator selection and expression stability Following a literature review and analysis of microarray data from FFPE mCRC tumor samples, several potential miRNA reference candidates were identified. Validation via RT-qPCR testing was performed (data not shown) to ensure stability of the reference miRNAs across differing mCRC tumor tissue samples. This included 160 FFPE tumor samples obtained from slides and 29 samples from shavings. The most stable miRNA reference (miR-calibrator) was selected based on the results of these studies. The miR-calibrator selected had the lowest standard deviation of all miRNA reference candidates tested, with an SD of 0. 72 for FFPE slides and 0. 99 for FFPE shavings. Figure  1  shows the Ct distribution for the selected miR-calibrator obtained from FFPE samples from both slides and shavings.
Discussion Our data demonstrate that the quantification of miR-31-3p expression using the standardized RT-qPCR assay described above is feasible, sensitive, specific, and reproducible. The assay was developed and validated to enable the relative quantification of miR-31-3p expression in FFPE tumor samples from primary tumors of patients with mCRC. The performance of the assay was also validated in 5 separate lab settings using differing qPCR systems and lab technicians under a variety of operating conditions. Our results demonstrate that there was no significant bias in test results between the differing qPCR platforms tested. Based on these findings, running this miR-31-3p expression quantification assay on the systems tested is acceptable for clinical practice. Acceptable RT-qPCR results were obtained from FFPE tissue samples obtained from both slides and shavings with the amount of material required to perform the assay in line with what is typically available for clinical testing from the intended patient population.
4). This combination experimentsethermodynamic model allows us to give a possible explanation of the existence of bistability phenomena in ultrasmall SCO nanoparticles. Most of these thermodynamic approaches emphasize the primary role of structural and elastic properties for the understanding of cooperative mechanisms in SCO materials. If investigations of crystallographic properties are routine and X-ray diffraction experiments are common characterization techniques to probe of mechanical/elastic properties remains scarce and experimental techniques, which allow a systematic determination of such properties, are still lacking. It would be important to further probe elastic and vibrational properties of SCO materials and nanomaterials to constitute a database of elastic moduli of molecular materials and coordination networks.
All of these new techniques combined with the old ones will undoubtedly make it possible to complete the lack of characterization of the mechanical properties of SCO materials. The access to a database listing the thermal and mechanical properties of SCO materials may succeed in the prediction of the effects of the different physical ingredients on the observed equilibrium properties  [135]. However, the thermodynamic aspects of nonequilibrium kinetic processes or spatiotemporal dynamics (nucleation and domain growth mechanisms) need the knowledge of thermomechanical properties of SCO solids of which we currently have only orders of magnitude. In particular, the spatiotemporal evolution of the spin transition seems to be very sensitive to heat exchange with the external environment  [136]  and the experimental determinations of the thermal conductivity, the heat and the radiative transfer coefficients become unavoidable for the building of a thermomechanical model in the framework of the local equilibrium approximations.
Complementary base pairing of sRNAs with homologous mRNA sequences guides a group of proteins, called the RNA induced silencing complex (RISC), that mediate mRNA downloaded from Rfam (version 13. 0) and S. sclerotiorum non-coding RNAs downloaded from Rfam were discarded sequentially. Only reads that passed all of these steps and that were subsequently exactly mapped to the reference genome of S. sclerotiorum 1980  (30)  were kept. The same procedure was followed for P. vulgaris using sRNA sequences predicted for this species obtained by  (33). For differential expression analysis, the two filtering procedures for the two different hosts were both applied to the in vitro sample to create two different filtered in vitro datasets for analysis. This controlled for differences in filtering procedures that may cause artificial observation of changes in sRNA expression.
Test for over-representation of functional domains To test for over-representation of Gene Ontology (GO) terms among A. thaliana predicted targets of S. sclerotiorum sRNAs, we used the R bioconductor package TopGO  (37). We performed the test separately for the molecular function and biological process categories of GO terms. In each instance, we used terms with 5 or more annotated genes. P-values for GO and PFAM enrichment were obtained using Fisher's exact test and adjusted with the Benjamini-Hochberg correction  (38). We also predicted targets in 10 different host transcriptomes from Phytozome. These included all species highlighted in supplementary figure  1. In all of these species, we performed an over-representation test of PFAM domains. To do this, we first created a list of all PFAM domains within the predicted annotations.
Messenger RNA sequencing and differential expression analysis The expression of A. thaliana Col-0 genes during S. sclerotiorum infection corresponds to expression at the edge of necrotic lesions relative to non-challenged plants as reported in  (26) , data downloaded from the NCBI Gene Expression Omnibus (accession GSE106811). To determine expression of A. thaliana genes during B. cinerea infection, we obtained a dataset from  (39)  from the NCBI sequence read archive (https://www. ncbi. nlm. nih. gov/sra). The accessions were SRX1705130, SRX1705129 and SRX1705128, mock inoculated plants and SRX1728, SRX1729, SRX1730, 24 hours post inoculation with B. cinerea. DESeq2 version 1. 10. 1 was used to assess log2(fold change) between the mock-inoculated and B. cinerea infected samples.
Predicted plant targets of Sclerotinia sclerotiorum sRNAs are enriched for immunity-related functional domains Several Ascomycete fungi produce sRNAs capable of modulating gene expression in their host plants  (41). To identify predicted targets of the 374 highly abundant S. sclerotiorum sRNAs in plant genomes, we used psRNATarget  (35). We performed both GO term and PFAM domain enrichment analyses using Fisher's exact test. We predicted in the A. thaliana genome 1,368 sRNA target genes with an (E) value ≤ 3, among which 408 had an (E) value ≤ 2. 5. Among gene functions strongly enriched in sRNA targets (Figure  4 , Supplementary Tables  1  and 2 ), several were related to signalling (e. g. GO:0007165, GO:0019199, PF00069 and PF03107) and specifically plant immunity signalling (GO:0019199 and PF13855).
Annotations related to plant defense responses (GO:0006952, GO:0052542, and PF12662), were also enriched. Other annotations enriched among predicted S. sclerotiorum sRNA targets included ontologies and domains related to hormone metabolism (GO:0009737, GO:0046345, GO:0008742) and redox metabolism (GO:0055114, GO:0019825, PF00175). S. sclerotiorum is able to infect hundreds of plant species, including carrot (Daucus carota), tomato (Solanum lycopersicum), rapeseed (Brassica rapa) and soybean (Glycine max). To determine the range of potential targets of S. sclerotiorum sRNAs in diverse host species, we predicted the targets of the 374 abundant S. sclerotiorum sRNAs in the genomes of 10 plant species using psRNATarget and analysed PFAM domains enriched for sRNA target genes. We found that all of these genomes exhibited numerous potential sRNA targets, on average 521 per host genome (Supplementary Table  3 ).
A total of 98 non-redundant PFAM domains were enriched in S. sclerotiorum sRNA target genes in at least one plant species (Fisher's exact test P < 1e -04 ) (Figure  4B ). A number of these PFAM domains also indicated possible roles in plant immunity, such as PF13855, PF00931, PF01453, PF00305. Twenty PFAM domains were enriched among S. sclerotiorum sRNA targets in most plant species (Figure  4B , blue), notably including domains related to redox functions, secretion and transport and transcription. Despite being identified using the same set of 374 S. sclerotiorum sRNAs, different functional terms were over-represented in different host species. In particular, twelve PFAM domains were strongly enriched among S. sclerotiorum sRNA targets in a few species only (Figure  4B , red), such as PF00931 in legumes, PF00350 in Brassicaceae and potato, and domains related to the biosynthesis of secondary metabolites (PF01255 and PF14226) in Solanaceae.
FUNDING This research was supported by funding from the  Grains Research and Development Corporation and Curtin University  [grant number  CUR00023  to MCD], the  European Research Council  [grant number  ERC-StG-336808  to SR] and the  French Laboratory of Excellence TULIP  [grant numbers  ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ]. This study also benefited from support by the  "visiting scientist" program  of the  TULIP Labex.
Reference database We set up a sequence reference database for the most common animal prey taxa encountered in our sampled fields. For this, we took advantage of (i) the arthropod specimens accidentally trapped alongside the carabid beetles within the pitfalls; (ii) on-purpose field sampling sessions for collecting the most common mollusk species in our fields. All specimens were preserved in 96° ethanol and if possible, further identified at the finest taxonomic level. The list of the referenced taxa and their taxonomic identifications could be found in Appendix S1 (Supporting information). DNA was extracted from each specimen individually. For arthropods, we use a protocol aiming at preserving the general morphology of the specimen, while small pieces of tissue were cut off for mollusks.
Bioinformatic analyses Raw output sequences were analysed with the OBITools pipeline. First, we used the illuminapairend function for assembling, for each read, the forward and the reverse ends of the pair-end sequencing in one single consensus sequence (threshold quality score: ≥40). Second, consensus sequences were assigned to samples by identifying the forward and reverse primers and tag combinations using the ngsfilter function. All sequences that did not match perfectly with tag sequences (0 mismatch) or that had more than three mismatches on primer sequences were discarded. Third, all strictly identical sequences were clustered together (information about their distribution among samples was kept) using the obiuniq function. Forth, using the obigrep function we discarded all sequences shorter than 10 base pairs (bp) as well as all sequences occurring less than once over the entire dataset (i.
e. singletons). Fifth, using the obiclean function that models the production of errors during the PCR (cf  Boyer et al. 2016) , we removed all sequences prospectively resulting from PCR errors according to the procedure described in  Giguet-Covex et al. (2014). Finally, all curated sequences were taxonomically assigned using the ecoTag program and the EMBL sequence reference database (release 123; http://www. embl. de/). A unique taxon was assigned to each sequence. When several matches between the query sequence and the reference database were possible, the sequence was assigned to the taxon corresponding to the last common ancestor node of all the taxa in the NCBI taxonomic tree that best matched against the query sequence. A species name was accepted only if the identity score strictly equaled 1.
We used this information to deduce differences in the food web that were only due to crop plant. Using a General Linear Model (GLM) for each resource category, we tested whether the probability of consumption of a resource by an individual was significantly influenced by (i) the sampling field location, (ii) the crop plant, (iii) the species to which it belongs: Resource ~ Crop + Species + Field, family = "binomial" with 2 modalities for the crop plant, 14 for the carabid species and 3 for the field location. We run a best model selection using the function 'step' (option 'both direction') of the R package 'stats' (https://stat. ethz. ch/R-manual/R-devel/library/stats/html/stats-package. html). Because of our unbalanced data among crop types and field locations (i. e. some species being represented by more individuals than others), we also conducted a sensibility analysis by running again the model selection (as explained earlier) several times, with one particular species removed at each time.
Reference database We successfully sequenced 291 specimens of which 265 were taxonomically assigned at least to the order level with 96% being assigned to the species level (cf Appendix S1). References specimens encompassed 8 orders of arachnids, insects and mollusks. We didn't have any prior taxonomic reference for the mollusk specimens based on morphological identifications. This was also the case for 22 arachnid and 5 coleopteran specimens. We therefore considered that the taxonomic assignment of their corresponding barcode sequences captured their true taxonomic identity. For arachnids, we observed a mismatch between molecular and morphological taxonomic assignments for four specimens at the species level, and for one specimen at the genus level, which corresponds to 6% of all referenced specimens.
Acknowledgements We thank all the field assistants and students from the  UMR IGEPP  who occasionally gave a hand with the collection and the sorting of the carabid beetles:  Frédéric Hamelin ,  Nolwenn Génuit ,  Mael Dugué ,  Kévin Tougeron ,  Théo Vantsteenkeste ,  Frédérique Mahéo ,  Lucie Mieuzet ,  Nathalie Leterme ,  Jean-Francois Le Gallic ,  Bernard Chaubet  and  Sarah Polin. We also thank the  LTER area 'Armorique'  for providing infrastructures as well as the network of farmers that kindly allowed us to sample in their fields. Finally, we warmly acknowledge all the help we received from colleagues during the constitution of the prey reference database:  Jean-Yves Rasplus ,  Astrid Cruaud ,  Jean-Claude Streito ,  Isabelle Meusnier ,  Maxime Galan  and  Gwenaelle Genson  ( UMR CBGP, INRA Montpellier );  Julien Pétillon  and  Armelle Ansart  ( UMR ECOBIO, Université de Rennes 1 );  Jean-Luc Roger  ( SAD Paysage, INRA, Rennes );  Marylin Roncoroni  ( CEBC Chizé );  Nadya I.
Nikolova  ( Biodiversity Institute of Ontario, Canada ). The authors of this study received funding from the  Agence Nationale de la Recherche  (' Landscaphid'  project,  ANR-09-STRA-05 ) and  INRA department "Santé des Plantes et Environnement". Stefaniya Kamenova  was partially funded by the  Région Poitou-Charentes (France)  during her PhD thesis.
For the tumour carrying capacity K we focus on the data in  Chen et al. (2011) ,   A2 , and for the initial conditions listed in Table  A1. Sub-panel (a)  shows the tumour-immune-virus dynamics. Sub-panel (b) shows log (x v (t)) at four specific days: t = 7, t = 14, t = 21 and t = 28. Sub-panel (c) shows the percentages of M1 and M2 macrophages in the system, at four specific days: t = 7, t = 14, t = 21 and t = 28. Notes: The two horizontal upper lines describe the tumour detection threshold of ≈ 10 7 cells/vol (see  Friberg & Mattson, 1997)  and tumour carrying capacity K = 3. 3 × 10 9. where the maximum recorded tumour volume in the absence of any treatment was 3300 mm 3. Assuming that a volume of 1000 mm 3 contains approximately 10 9 tumour cells  (Friberg & Mattson, 1997) , we obtain a carrying capacity K = 3.
Longitudinal data (e. g. tag-recapture) and hierarchical models greatly facilitate the estimation of individual and group (i. e. sex, year-of-birth cohort) variation in vital rates, life-history traits and fitness  [15]. Our goal is to provide a general framework for the fine-grained estimation of vital rates and their determinants-including extreme climate events-when there is substantial individual and shared variation in those traits, using a freshwater salmonid population as a case study. Specifically, we combined a long-term tag-recapture dataset and a general statistical and modelling framework to estimate variation in vital rates among years, groups and individuals of a population of brown trout Salmo trutta L. living in Upper Volaja (Western Slovenia). Then, we tested hypotheses on the determinants of variation to understand how that variation influences the population dynamics of the species and its future under scenarios of climate change.