dataesr/scientific-paragraphs-categorization huggingface dataset:
The time of exploration was assessed as in the NOR test. For the training, 1-, and 24-h test, a DI was calculated using the equation: DI ¼ 100 NLE À FLE TE where NLE is the novel location exploration, FLE is the familiar location exploration, and TE is the total time exploring the two objects. We set a threshold for object interaction in NOR and NOL tests. If animals explored objects <20 s during the training phase, the test was discontinued. The fear conditioning test was performed in fear boxes (Panlab) controlled by the Freezing software (Panlab). One day before the training session, the mice were habituated for 3 h to the fear conditioning room. In the training session, the mice were introduced into the fear box and allowed to explore it for 4 min before being presented with a 80-db 28,000-Hz tone for 28 s (CS).
Freezing was again measured before (to assess fear generalization) and during the presentation of the tone (to assess cued fear memory). Statistical methods. Statistical analyses were performed using the SigmaStat package of SigmaPlot 12. 5 software. All data were averaged and expressed as the mean ± s. e. m. in scatter plots. In box plots, the data are represented as the median and mean (black and white horizontal lines within the box, respectively), the first and third quartiles (bottom and top of the box, respectively), the 10th and 90th percentiles (whiskers above and below the box, respectively), and as raw data (colored dots over the boxplots). Student's t test was used for statistical comparisons between two groups after checking for a normal distribution of the data. Oneway analysis of variance (ANOVA) followed by Holm-Sidak post hoc test was used to compare the differences between the genotypes when data followed a normal distribution.
Acknowledgements We thank  Dr. MI Aller  and  M. Linares  for helping with mice genotyping. The authors gratefully acknowledge the financial support received from the  Spanish Agency of Research (AEI)  under the grant  BFU2015-64656-R  (to J. L. ), co-financed by the  European Regional Development Fund (ERDF) , and the " Severo Ochoa" Program for Centres of Excellence in R&D  ( SEV-2013-0317  and  SEV-2017-0723 ). This work was also supported by the  Generalitat Valenciana  through the program  PrometeoII/2015/012  (to J. L. ), the  FPI fellowship program  (to S. V. and A. G. ), and the  SyMBaD -Marie Curie ITN  (agreement #  238608 ) (to W. M. ).
Background According to the International Transport Forum  (ITF 2015) , the number of road fatalities decreased by about 42% between 2000 and 2013 in the countries of the International Road Traffic and Accident Database. Mortality among light vehicle users declined by 54% in the same period, while the number of users of motorized two-wheelers (MTW) killed fell by only 22%. The result is an increase in the proportion of fatalities concerning MTW users relative to other road users. In France, they represented 21% of road deaths (18% for motorcyclists and 3% for moped riders) and 30% of those injured and hospitalized (21 and 9% respectively) in 2016  (ONISR 2017). Many studies of the injury severity of crashes have examined the patterns and risk factors of MTW injuries as well as effective prevention programs for reducing them  (Ankarath et al.
Statistical analysis Drivers wearing a full-face helmet and those wearing other types of helmet were compared in terms of the characteristics of the driver and vehicle, and the protective equipment worn, using the chi 2 test. Univariate analyses were carried out to test the association between the occurrence of a facial injury or of a non-facial head injury and the type of helmet, as well as other factors listed above, by using logistic regression. Variables with p-value < 0. 2 were selected for multivariate analysis, except for type of helmet, which was systematically included. All of the analyses were carried out using SAS version 9. 4 (surveyfreq, surverylogistic procedures), taking into account corrective weightings for non-response.
Deformation of Miocene sediments have provided critical timing constraints used to derive the relationship between the pre-Miocene shortening from the subsequent Miocene extension and latest Miocene-Recent inversion. These constraints have been used to derive a relative chronology of deformation events, which was further interpreted in a regional context. In a first stage, the measured kinematic structures were grouped at local scale by calculating paleostress directions for the same deformation event  (Figures 4, 6, 8, and 9 , see also the supporting information). Structures were plotted in the Win-Tensor software  (Delvaux & Sperner, 2003) , by using a combination of PBT-(p, b and t kinematic axis, see  Turner, 1953)  and direct inversion methods  (Angelier, 1984) , 10. 1029/2018TC005066.
Acknowledgments This study has been financed by  IFP Energies Nouvelles ,  Rueil Malmaison ,  France  and the  University of Utrecht, Netherlands. Bruno Tomljenović (University of Zagreb)  is acknowledged for his support in the field of Croatia and Bosnia and  Herzegovina. Matija Vukovski  ( University of Zagreb ) is acknowledged for his efforts and help in the field and providing us with additional samples from the  Livno-Tomislavgrad Basin. We would like to thank  Hazim Hrvatović  ( Federal Institute of Geology, Sarajevo ) for his logistic support in the field of Bosnia and Herzegovina. We would like to convey our thank to  Mark Handy  and  Kamil Ustaszewski  for their great support and discussions. Laszlo Csontos  and  Marko Vrabec  are gratefully acknowledged for their suggestions that have significantly improved the quality of the original manuscript.
K. ) is surrounded by oppositely charged ions called the fixed layer. Outside the fixed layer, there are varying compositions of ions of opposite polarities, forming a cloudlike area. This area is called the diffuse double layer, and the whole area is electrically neutral. When a voltage is applied to the solution in which the particles are dispersed, particles are attracted to the electrode of the opposite polarity, accompanied by the fixed layer and part of the diffuse double layer, or internal side of the sliding surface. This system uses dispersion technology software (DTS v4. 20), which changes to the ζ measuring mode, and the sample for which ζ is measured is contained in disposable capillary cuvettes (DTS1060) equipped with electrodes. Each determination was performed in triplicate.
Cell cycle analysis for viability and cytotoxicity Cell cycle was analyzed by flow cytometry after the addition of propidium iodide. Cells non treated control, treated with Nano miRs  (200c, 221, 222, 346, and 1195 ) and empty Nano negative control (same composition as the suspension or vehicle with no miRs)were washed with cold PBS, resuspended in PBS, fixed by addition of equal volume of cold absolute ethanol and incubated for two or more hours at 4 °C. Cells were then washed with cold PBS, stained with propidium iodide (0. 1% Triton X-100 and 0. 2 mg/mL DNAse-free RNAse A in cold PBS) and stored at -80 °C before FACS for data acquisition by flow cytometry. Real-time qRT-PCR miR expression was analyzed by RT-qPCR in total RNA samples using methods described previously  [7]. Per sample, 10 ng to 1 μg was subjected to reverse transcription using TaqMan RNA and MicroRNA Reverse Transcription Kits (Applied Biosystems, Grand Island, NY), respectively.
Small RNA and RNA sequence preprocessing and filtering data Each sequencing run used a 150 or 300 cycle NextSeq 500 v1 or v2 High Output Sequencing Kit, and corresponding High Output Flow Cell. Illumina libraries were quantified using qRT-PCR. Loading concentrations varied between 0. 8 and 1. 7 pM. The paired-end library was enriched before being sequenced on the Illumina HiSeq at the WIBR and MIT BioMicro Center. The location of each barcode was recorded, and this allowed for the post-sequencing identification of each population. Accession number GSE125000.
Statistical analysis For all experiments, statistical analysis was first performed using one-way analysis of variance (ANOVA) to determine statistical significance between groups for each endpoint assessed. To assess the in vitro oncolytic effects, statistical significance between groups was calculated with multiple comparisons between treatment groups and controls evaluated using Student's t-test. All p-values < 0. 05 were considered statistically significant. For mRNA NGS data analysis, principal component analysis (PCA) was used in the unsupervised analysis to reduce the dimensions of large data sets and to explore sample classes arising naturally based on the expression profile. Our NGS data (Accession number GSE125000) analysis pipeline is based on the Tuxedo software package, which is a combination of open-source software and implements peer-reviewed statistical methods.
In addition, we employed specialized software developed internally at Exiqon to interpret and improve the readability of the final results. The components of our NGS data analysis pipeline for RNA-Seq include Bowtie2 (v. 2. 2. 2)  [35] , TopHat (v2. 0. 11)  [36]  and Cufflinks (v2. 2. 1)  [37, 38].
It does ensure, however, that a group of differentially expressed genes with similar functionality are significantly over-represented. The expected values represent an estimate of the number of transcripts associated with the given significant GO term at random among all the annotated differentially expressed genes. The significant (observed) values represent the number of differentially expressed transcripts associated with that particular GO term in the sample (real) dataset. Annotations represent the total number of genes associated with that GO term in the sample dataset, which means that the reference background could potentially have a larger number of annotations. The q value represents the test statistics for the given GO term whether or not it is significantly enriched ability of mouse lung non-metastatic (KW-634) cells transfected with Nano miR-200c were significantly decreased when compared cells treated with miR-200c alone and the controls (Fig.
(iii) For a given species S, the relation between one of its metabolic pathways and its genome takes the form of a correspondence function associating genes to reactions: for any given reaction r, the correspondence function returns the set of genes of species S that code for enzymes catalyzing reaction r (Fig. 1c ). This information can be found in a knowledge base such as KEGG (Kyoto Encyclopedia of Genes and Genomes)  [26]  which, for a given species, contains information on its metabolic pathways, the reactions that the species performs, and the genes associated to these reactions. The method we propose, CoMetGeNe, requires two input graphs possessing the same vertex set. Thus, an additional undirected graph is constructed as described in  [27]  such that it reflects gene neighborhood with respect to the reactions that the gene products catalyze (Fig.
Pipeline Trail finding (the HNET algorithm) and trail grouping are implemented in the form of CoMetGeNe, a Python 2. 7 pipeline available under a MIT license at https:// cometgene. lri. fr. CoMetGeNe is not compatible with Python 3. The following Python libraries are required: NetworkX (version ≥ 1. 10 and ≤ 2. 2) for graph handling, and lxml (version ≥ 3. 5. 0) for XML parsing. An Internet connection is mandatory for automatic data retrieval. Pipeline usage is detailed in Additional file 3.
Trail finding The script CoMetGeNe. py offers a convenient command-line interface for Finding metabolic and genomic patterns for a single species. The only required information is the species to be analyzed (designated by its three-or four-letter KEGG identifier  [31] ) and the directory where metabolic pathways of the species in question will be stored. Optionally, the gap parameters δ D and δ G can be specified (their default value being 0), as well as an output file for the results. An important speedup is attained if CoMetGeNe is ran in parallel using the provided script CoMetGeNe_launcher. py. Restrictions inherent to KEGG limit pathway and genomic information retrieval to 3 and 2 threads, respectively. Trail finding in CoMetGeNe can, however, take full advantage of the maximum number of physical threads.
See Results for CoMetGeNe run times. A potential caveat when running CoMetGeNe in parallel is that KEGG may block concurrent downloads when using a fast Internet connection. Another potential caveat is that the machine may run out of memory on very large datasets (hundreds or thousands of species). In both cases, a possible workaround consists in adjusting parameters for CoMetGeNe_launcher. py. In the latter case, the dataset may be split into several smaller batches. For more details, see the "Trail finding" page on the CoMetGeNe website (https://cometgene. lri. fr/tfinding. html). Storing metabolic pathways and genomic information for a given species allows CoMetGeNe to perform trail finding without re-downloading the same data for subsequent executions, e. g. when CoMetGeNe is ran for the same species but with different gap parameters.
CoMetGeNe uses a configurable timeout (defaulting to 5 min) for analyzing a given metabolic If the timeout is reached without producing any result, the pathway in question is "blacklisted" for the current species and set of gap parameters. This prevents CoMetGeNe from further attempting to analyze the given pathway for subsequent executions if the gap parameters increase. For example, a pathway that is blacklisted for (δ D = 2, δ G = 2) will not be further analyzed for (δ D , δ G ) ∈ {(2, 3), (3, 2), (3, 3)}. The blacklist is stored locally as a text file. Blacklisted pathways are computationally prohibitive due to the exponential number of enumerable paths. However, blacklisted pathways only amount to 3. 3% of our dataset (121 out of 3709 pathways).
Trail grouping Once CoMetGeNe results are available for several species, trail grouping can be performed in order to identify conserved metabolic and genomic patterns for several organisms (see Finding conserved metabolic and genomic patterns across multiple species). The script grouping. py provides this functionality and offers the possibility to save tables T r S and T g S in CSV format. Three binary files are created when grouping trails by either reactions or genes. They contain pathway data, genomic information, and parsed CoMetGeNe results that can be reused when choosing another species as reference.
The number of trails per species varies between 19 and 501, with an average of 201 trails. Table  3  shows trail span distribution (recall that the span of a trail represents the number of distinct reactions in the trail). The majority of trails are short, consisting of up to three distinct reactions. Other trails, however, have as many as 35 unique reactions, e. g. for the fatty acid biosynthesis pathway in Bifidobacterium breve (bbv) and Streptococcus pneumoniae (snd), see Additional file 7 for the full list of reactions. See Additional file 6 for statistics per species on the number of trails, as well as the average and median trail span. The trail finding run time for CoMetGeNe for the whole dataset of 50 bacterial species (Table  2 ) was under 4 hours and 30 min when using 8 threads (see Additional file 8 for execution times per species).
The trail finding run time does not take into account the time required to automatically retrieve data from KEGG, as this is dependent upon the Internet connection speed and upon the number and size of the selected genomes. In our experimental setup, metabolic pathways and genomic information were retrieved in 12 and 76 min, respectively. When each of the  species in the dataset is taken in turn as reference species, trail grouping by reactions and by genes takes approximately one hour in total. Thus, data retrieval from KEGG for our bacterial panel (Table  2 ), followed by trail finding and trail grouping, amounted to approximately 7 hours. Available software for detecting metabolic reactions being catalyzed by products of neighboring genes is scarce. The C3Part/Isofun package  [32]  implements the methods proposed in  [19] [20] [21].
It takes as input a file in extended DIMACS format describing the layered multigraph  [21] , and outputs connected components common to both layers in the multigraph. Unlike C3Part/Isofun, the CoMetGeNe software does not require the user to prepare any input files as they are extracted automatically from KEGG, thus rendering CoMetGeNe extremely simple to use. In order to evaluate C3Part/Isofun, we constructed the input files corresponding to the pathways and genome of Escherichia coli. Since C3Part/Isofun produces undirected subgraphs whereas CoMetGeNe outputs trails, comparing the two programs is not straightforward. We therefore investigated whether the reaction sets corresponding to CoMetGeNe trails may be found among the results of C3Part/Isofun and vice versa. CoMetGeNe and C3Part/Isofun extracted 114 trails and 65 subgraphs, respectively.
The density of crosses in T g eco (Additional file 10) confirms that the trail in Fig. 3a  is frequently found for the species in the dataset, albeit with varying degrees of conservation of gene neighborhood. This finding represents a positive control, being consistent with the fact that most bacteria possess peptidoglycan cell walls. Cells with dot symbols (. ) in T g eco (Fig. 3c  and Additional file 10) do not allow to distinguish between non neighboring and missing genes. However, Fig. 3d  identifies species with missing reactions (in gray in the figure) with respect to E. coli: Geobacter sulfurreducens (gsu), Staphylococcus aureus (sau), Mycoplasma pneumoniae (mpn), Fimbriimonas ginsengisoli (fgi), Rhodopirellula baltica (rba), and Opitutus terrae (ote). The remaining species perform all the reactions but do not necessarily have contiguous genes coding for the required enzymes.
Although the STRING database  [42]  reports that fusions of murC and ddl occur frequently in the Chlamydiae phylum, it does not appear to be the case for OP10G_4784 due to missing Mur ligase domains and different sequence size with respect to murC-ddl fusions in Chlamydiae. Interestingly, a Mur ligase catalytic domain is reported for the short neighboring gene OP10G_4785, also annotated as a UDP-N-acetylmuramate-L-alanine ligase. A KEGG ortholog search for OP10G_4785 reveals longer murC ortholog sequences in other species. Two hypotheses are therefore possible: (i) the activity EC 6. 3. 2. 8 is performed jointly by products of genes OP10G_4784 and OP10G_4785 in F. ginsengisoli (fgi), or (ii) the open reading frame for OP10G_4784 was incorrectly predicted, the ddl coding sequence erroneously including a Mur_ligase_C domain that may in fact belong to OP10G_4785.
R. baltica (rba), as other Planctomycetes, has been thought to be lacking peptidoglycan  [43]. Consistent with annotations in KEGG reflecting the existing genome annotations, CoMetGeNe only identifies one reaction among the six in the trail in Fig. 3a  as being present in R. baltica. In addition, no peptidoglycan biosynthesis genes are currently listed in the STRING database  [42]  for other Planctomycetes beside members of the Planctomyces genus. However, Jeske et al. [44]  have biochemically demonstrated that sugar and peptide components of peptidoglycan are present in Planctomycetes. The study also uses an in silico approach to identify candidate peptidoglycan biosynthesis genes in R. baltica and other Planctomycetes. The fact that the findings of this study are yet to be reflected in existing annotations indicates the difficulty of validating proposed gene function.
We propose the candidate murC gene in O. terrae to be Oter_2637, following a protein BLAST  [39]  for the E. coli MurC query sequence (WP_012375453 with 29% identity, 94% query cover, E-value 5e-41). 3a  for other explanations. b Group of homologous genes involved in the trail in a, present in various species. Eleven of the species in the dataset (highlighted in gray) either do not have functionally similar genes to b0003, or are not contiguous with genes functionally similar to b0002 and b0004. α-, β-, γ -, and δ-proteobacteria are highlighted in pink; Terrabacteria, in brown; Sphingobacteria (FCB bacteria), in yellow; and Planctobacteria (PVC bacteria), in light green. c Group of reactions defining the trail in a. The cells highlighted in gray correspond to the three species among the ones highlighted in gray in b that do not perform reaction R01771 (catalyzed by the product of gene b0003 in E.
Uncovering unexpected gene ordering patterns Figures  4b  and 4c  respectively show the corresponding grouping by genes and by reactions for E. coli as reference species and 30 other bacteria from the dataset (trail grouping for the full dataset is available in Additional files 12 and 13). In the case of the 11 species highlighted in gray in Fig. 4b , functionally similar genes to b0003 are not neighbors of functionally similar genes to b0002 and b0004. The relevant genomic context for these species and two additional ones, Denitrovibrio acetiphilus (dap) and Rhodopirellula baltica (rba), is shown in Fig. 5. Figure  4c  shows that, of the species highlighted in gray in Fig. 4b , Caldisericum exile (cex), Gemmatimonas aurantiaca (gau), and Bacteroides fragilis (bfr) do not perform reaction R01771 (EC 2.
Genome fragmentation in the case of eukaryotes with respect to bacteria accounts for the differences in trail detection. Whereas the organization of prokaryotic genes into operons has long been known and studied, CoMetGeNe does not focus specifically on operons. It uncovers them if the resulting proteins are involved in consecutive steps in a metabolic pathway, but it also uncovers genes that are adjacent to operons if the proteins they encode belong to the same trail of reactions. For example, CoMetGeNe identifies a trail of six reactions for E. coli in the valine, leucine, and isoleucine biosynthesis pathway (eco00290 in KEGG) representing the conversion of threonine into leucine (data not shown). This trail involves five genes of E. coli, four of which constitute the ilvMEDA region of the ilvLGMEDA operon.
Data analysis. Processed slides were scanned in a GenePix 4000B (Molecular Devices, USA) with the PMT settings 440 and 310 at 635 and 532 nm and saved as TIF files. Images were processed in GenePix Pro software v6. 0. 1. 27 (Axon Instruments) and saved as comma-delimited text files. Digital fluorescence units (DFUs) were calculated for each spot by subtracting local background from the median fluorescence value of the spot. One pad on each microarray was used as a control, containing reagents and no serum, the results of which were subtracted from all other pads to account for any auto-fluorescence or non-specific binding. Clinically healthy and IBH horses were used as control. PLS toolbox (version 5. 8. 3, Eigenvector Research Inc. , USA) running on a MATLAB platform (MathWorks, Cambridge, UK) was used to carry out principal component analysis and partial least squares discriminant analysis (PLS-DA) was used as a classifier which enabled construction of the predictive mathematical models  7.
Acknowledgements This work was supported by the  Morris Animal Foundation  (Grant No. :  D16EQ-039 ), the  Swiss National Science Foundation  (Grant No. :  310030-160196/1 ),  Stiftung Forschung  für das Pferd, the  Royal Agricultural University , the Fred and  Marjorie Sainsbury Charitable Trust  and  HAYGAIN. The authors would like to thank the  Equine Respiratory Tissue Bank  for the supply of Canadian samples.