dataesr/scientific-paragraphs-categorization huggingface dataset:
The high and reliable seed production determines that the consequences of failure to establish in these species are less dire  [17]. Does the absence of a relationship between average estimates of dormancy release in Medicago and macroclimatic clines within our dataset suggest that selection does not operate on dormancy traits? Taking into account plasticity of dormancy release between temperature treatments revealed that physical dormancy plasticity (PI PY ) increases with increasing aridity (2D, Figures  3  and 4 ; Supplementary data Figure  S3 ). It follows, therefore, that more plastic behavior can potentially distribute germination across the year and act as a within-season bet-hedging strategy  [48] , suggesting that in more unpredictable environments genetic components of phenotypic variance may be lower and thus a reduced evolutionary response to selection would be possible  [24].
This needs to be considered in follow up studies. Our analysis was also likely impacted by several factors inherent to the available Medicago set. At first, there is substantial imprecision in the GPS localization of the origin of some accessions  [55]  leading consequently to incorrectly extracted environmental factors  [56] [57] [58]. Secondly, there is geographical bias towards the western part of the Mediterranean with underrepresented parts of the native species range, such as Italy, Adriatic Sea coast, Turkey, Lebanon, and Israel. In addition, the characteristics of WorldClim data (averages in terms of time and also space) mask the micro-ecological pattern and, in geographically complex regions, environmental conditions change considerably over short spatial scales, such that neighboring populations can be subject to different selective pressures, as found in the study of seed dormancy of Swedish A.
Seeds were monitored at 24 h intervals for total of 88 days. After each scoring, the plates were randomly relocated within chambers. Germinated seeds (e. g. , when the radicle protruded from the testa) were removed. At the end of the testing, remaining seeds were scarified and let germinate to verify their viability. This typically was over 98%. Although we selected macroscopically intact seeds for experiments, we cannot exclude some microscopic cracks on seeds resulting from mechanical damage during threshing. We observed that in the course of the first hours of seed germination experiments, a certain proportion of the seeds imbibe. Therefore, we subtracted the first day imbibition value from the analysis. In 2016, 2017, and 2018, a total of 147, 74, and 130 accessions were tested (Supplementary data Figure  S8 ).
Extraction of Environmental Variables and Spatial Accuracy Due to different spatial accuracies of accessions and in order to minimize the spatial error caused by imprecise coordinates, we developed a geoprocessing model in the ArcGIS PRO environment  [83]. The model automated the calculation of mean values of selected variables from within a 5 km buffer around each collection site in order to smooth the uncertainty caused by imprecise localization. The WorldClim database version 2. 0  [84]  was used to extract climatic data  (period 1970-2000)  from GeoTIFF rasters in the WGS-84 coordinate system (EPSG: 4326) with a spatial resolution of 30 arc-seconds (~1 km). Bioclimatic variables (BIO1-BIO19) were derived from the monthly temperature and rainfall values  [85] , and represent annual trends (e.
Data were interpolated in spatial resolution of 2. 5 arc-minutes. Temperature data were converted to degrees of Celsius (BIO1, 5, and 10). The resulting BIO12 (1981-2010) describes the annual mean of specific humidity instead of cumulative annual rainfall. Twelve month means (BIO1, 5, 10, and 12) for each site were calculated as follows: IV = [(90th percentile -10th percentile)/50th percentile] * 10 (1) The different IV classes are low (IV <0. 50), low-moderate (0. 50-0. 75), moderate (0. 75-1. 00), moderate-high (1. 00-1. 25), high (1. 25-1. 50), very high (1. 50-2. 00), and extreme (IV >2. 00). Soil data were extracted from the SoilGrids database  [88]. SoilGrids prediction models are fitted using over 230,000 soil profile observations from the World Soil Information Service (WoSIS)database and a series of environmental covariates.
Testing of Relationships Among Dormancy Traits, Geography and Environmental Variables The matrix of environmental variables was checked for the presence of the multicollinearity using VIF. The reduced set of environmental variables (with VIF <15), including 14 bioclimatic variables and eight soil variables, was used in all further analyses. For each pair of variables, bivariate scatter plots together with fitted locally weighted smoothing were displayed and Pearson's Correlation Coefficient was calculated using the library Performance Analytics in R  [90]. The matrix of the reduced set of environmental variables was analyzed by principal component analysis (PCA;  [91] ) using  Canoco 5. 10 [92]  to find the main environmental gradients within the dataset. Several precipitation variables were log(x+1) transformed and subsequently each variable was standardized to zero mean and unit variance before PCA.
Genome-Wide Association Analysis Genome-wide association analysis was performed on seven seed dormancy traits (FPYD 25 , FPYD 35 , AUC 25 , AUC 35 , AUC 35-25 , PI PY , PI AUC ) and three bioclimatic variables (BIO1, BIO9, BIO12) on 178 accessions. Prior to GWA analyses, normal distribution of each trait was checked using the Shapiro-Wilk test. Two contrasted algorithms were used to test markers-traits associations: Efficient Mixed Model Association (EMMA), a classical mixed linear model (MLM) for single locus GWAS  [97] , and FarmCPU, a multi-locus method combining the fixed effect model and random effect model iteratively in order to improve the statistical power of MLM methods  [98]. Both algorithms were implemented in the R package rMVP using default parameters (P-value threshold 0. 01) and run using a Single Nucleotide Polymorphism (SNP) dataset containing 5.
85 million SNPs remapped in the Medicago genome v. 5  [99]. Population structure, calculated using STRUCTURE by Bonhomme et al. [100] , was used as a covariable. Normal distribution, QQ plots, and single/multiple Manhattan plots were performed using R package rMVP. Medicago genome version 5. 0 of A17 genotype  [101]  was used to search for the encoded genes within the region of 10 kb from detected SNP. Transposable elements were excluded from the search. To link identified QTNs with putative causal gene by considering the linkage disequilibrium (LD), we selected all SNPs correlated (r 2 > 0. 7) with the top identified QTNs within a 15kb genomic range, corresponding to the average LD block size present in the Medicago Hapmap population  [100, 102] , and we listed gene IDs closely related to these SNPs.
Independent variables and moderators In this study, we distinguish four alliance types: pure explorative, market search, technology search and pure exploitative. Similarly to previous research (e. g. ,  Rothermael and Deeds, 2004) , we used a publicly available data source, Factiva, to track the alliances in which the ASOs engaged. VC-backed firms are typically well covered in the press; therefore, relying on public sources such as Factiva should not induce severe selection bias. For each company in the sample, a search was conducted using the query: "Company name" and "university of origin" 5  and ("partner" or "license" or "cooperate" or "seller" or "distributor" or "collaborate" or "collaboration" or "cooperation" or "alliance" or "partnership"). The date range for the query was the year of foundation until the year of exit or 2013, whichever was earlier.
Materials and Methods Broccoli is a pipeline written in Python 3 that requires the ete3 library  (Huerta-Cepas et al. 2010). It is composed of four steps as summarized in figure  1A  and described below. The rationale of Broccoli is that, since single gene trees are expected to be too inaccurate to directly infer orthology relationships, as many trees as the number of sequences will be inferred (Steps 1 and 2) and orthology will be inferred from the consensus of information extracted from these multiple trees using a network analysis (Step 3). Step 1: kmer Preclustering The objective is to simplify proteomes without loss of information and therefore to decrease the computational time of Steps 2 and 3. Broccoli first converts the protein names into unique identifiers. The proteome of each species is then independently clustered using kmers of amino acids.
Performance Tests The paraBench data set was built from an in-house collection of phylogenetic markers. The data set and the performance metrics are fully described in supplementary material 2, Supplementary Material online. As opposed to the benchmark of orthologous pairs, the performance metrics were calculated considering all possible pairs within each orthologous group. The data set, reference clustering, python script to compute the performance metrics and the results obtained in this study are all available at https://github. com/rderelle/paraBench. In addition, the Quest for Orthologs (QfO) benchmark 2018 data set was downloaded from the EBI ftp server, then analyzed by Broccoli using the "not_same_sp" option and by varying the r threshold value using the "-ratio_ortho" option. The resulting sets of orthologous pairs were submitted to the OpenEBench website to run the QfO benchmark.
Benchmarks of Broccoli under default parameters are available online at https://orthology. benchmarkservice. org, and other benchmark outputs are available to download in the Zenodo research data archive https://zenodo. org/record/ 3710751. The versions of the pipelines and programs, and command lines, used in this study are indicated in supplementary material 3, Supplementary Material online. All benchmark outputs and QfO raw results are available to download in the Zenodo research data archive at https://zenodo. org/record/ 3710751.
Running Time Analyses The running time analyses based on fungal data sets were performed using 4 CPUs of an Intel Xeon Gold 6248 processor and 40 GB of RAM memory. The fungal data sets correspond to those used in  Emms and Kelly (2019) , with one modification performed on the 64 species data set (see readme file in the Zenodo archive). All data sets are available to download in the Zenodo research data archive at https://zenodo. org/ record/3710751. The QfO 2018 runtimes were measured using 8 CPUs of the same processor and 60 GB of RAM memory.
Benchmark of Orthologous Pairs We tested the orthologous pairs predicted by Broccoli, with its r threshold (Step 4) ranging from 0. 3 to 0. 7 in 0. 1 increment (including the default value of 0. 5), by using the large-scale Quest for Orthologs 2018 benchmark data set (referred thereafter as QfO 2018 data set)  (Altenhoff et al. 2016; Forslund et al. 2018 ). This benchmark includes three groups of tests: 1) pairs of orthologs are compared with manually curated sets of gene phylogenies (using the TreeFam-A and SwissTree databases), 2) the function of orthologs are compared with each other using the Gene ontology and Enzyme classification, assuming that orthologous genes should have the same function in different species, and 3) the phylogenetic relationships of orthologous proteins are compared with reference species trees (the species Tree Discordance Benchmark, STDB, and its generalized version GSTDB), assuming that the gene trees should mirror the species trees.
All benchmark values and Orthologous pairs produced by Broccoli showed a strong agreement with the reference gene-phylogenies databases. When compared with the TreeFam-A database, only results produced by Broccoli were located on the Pareto frontier (fig. 3A ), which is defines by the set of methods that are not outperformed by any other method in both recall (X axis) and precision (Y axis)  (Altenhoff et al. 2016). As expected, results corresponding to r ¼ 0. 7 (i. e. , stringent threshold) scored the highest precision of all methods and results corresponding to r ¼ 0. 3 (i. e. , relaxed threshold) were the most sensitive of all methods. In the case of the SwissTree database, a small reference data set compared with the TreeFam-A database, all results were located on or closed to the Pareto frontier (supplementary material 4, Supplementary Material online).
Chimeric Proteins In the absence of a specific gene-fusion benchmark, it is difficult to assess the quality of the predictions made by Broccoli. Nevertheless, a total of 1,675 proteins were predicted to be the result of gene-fusion events from the QfO 2018 data set (representing 0. 2% of all proteins; list available in supplementary material 3, Supplementary Material online and in the Zenodo research data archive). The number of chimeric proteins per species was highly heterogeneous, ranging from 0 (Thermodesulfovibrio yellowstonii and Giardia intestinalis) to 223 (Zea mays; supplementary material 3, Supplementary Material online). Four species of this data set showed particularly high numbers of chimeric proteins (namely Z. mays, Phytophthora ramorum, Branchiostoma floridae, and Monosiga brevicolis).
We hypothesize that these high prevalences are the consequences of errors in the gene prediction of these genomes. Broccoli was able to identify chimeric proteins that resulted from the combination of up to six orthologous groups and genes-fusions events shared by up to 16 proteins. The latter case corresponds to the pentafunctional arom proteins present in multiple eukaryotic lineages  (Richards et al. 2006). However, Broccoli failed to recover some well-known gene fusion events such as the fusion of the dihydrofolate reductase and thymidylate synthase proteins present in "unikonts" and absent in most "bikonts"  (Cavalier-Smith 2003) , and the fusion of two tRNA synthetases shared by most metazoan species  (Ray et al. 2011). Finally, we carefully compared the Pfam domain contents of the seven Caenorhabditis elegans chimeric proteins identified by Broccoli in the QfO 2018 data set to those of their respective orthologous groups.
Running Time Analyses Considering the large number of phylogenetic analyses performed by  Broccoli (e. g. , 658, 421  phylogenies for the QfO 2018 data set), it is expected to be several orders of magnitude slower than distance-based pipelines. We compared the runtimes of Broccoli to those of the two fastest distance-based algorithms Sonicparanoid and OrthoFinder2 (Emms and Kelly 2019) using data sets composed of 4 to 64 fungal proteomes. In these tests, the runtimes of Broccoli were found between those of Sonicparanoid and OrthoFinder2 (fig. 4 ). Regarding the two extremes of the speed spectrum, OrthoFinder2 with the MSA option was by far the slowest pipeline, and Sonicparanoid, which only performs half of similarity searches  (Cosentino and Iwasaki 2019) , was found to be the fastest for every data set.
MBE With a small subset of proteins being assigned to several orthologous groups, the clustering generated by Broccoli lies between classical gene classifications and protein domain subdivisions (e. g. , Pfam database;  El-Gebali et al. 2019). This fast and precise identification of chimeric proteins alongside their corresponding orthologous groups represents a promising avenue that should facilitate evolutionary studies of gene-fusion events (see also  Pathmanathan et al. 2018 ). However, future work is still required to widen the search of chimeric protein in the orthology network as the set of chimeric proteins currently identified by Broccoli appears to be incomplete. Given the large variety of analyses performed by this pipeline (kmer clustering, phylogenetic analyses, and network analysis), there are combinations of parameters that have not been tested, and parts that have not been fully optimized (e.
Acknowledgments The authors thank  Franz Lang  and  Luisa Orsini  for their valuable comments on an earlier version of the article. The authors also wish to thank  Benjamin Buchfink  for implementing new output fields in Diamond, and  Toni Gabald  on for his seminal work on the phylogeny-based approach. Broccoli was developed as part of the DeepEuk collaborative project. R. D. was supported by a  NERC   Highlight Topic Grant  ( NE/ N006216/1 ). J. K. C. was supported by  UK NERC  award Cracking the  Code of Adaptive Evolution  ( NE/N016777/1 ).
Creation of clinical case templates The medical team built clinical case templates based on the preestablished learning goals. These templates were designed to be (i) generic, to allow adjustment for a large range of medical cases, (ii) realistic, to motivate students, (iii) short, to make it possible for students to play "anywhere anytime", (iv) varied, to ensure that the game did not become monotonous. These templates were then implemented in the game. When the medical team wishes to add a new clinical case, one of these templates is completed (Table  1 ), and the clinical case is automatically saved in the game database, without the need for reprogramming.
Technical implementation AntibioGame® is implemented in PHP 7. 0 and MySQL 5. 7. It is a web application available for personal computers and smartphones. Firefox Mozilla is required for its optimal use. Various tests were performed during the development process, to check software quality and playability. First, all functions were tested by three students from different backgrounds (video games, computer sciences, and biology). This made it possible to fix 47 problems (e. g. interaction problem with some icons). Second, playability was assessed by seven people from different backgrounds (video games, medical). This made it possible to fix 22 problems (e. g. incomprehensibility of some icons, leading to the addition of information bubbles). Third, medical relevance was assessed by the educational team of the university, including two specialists in antibiotics, the educational director, and the head of a hospital infectious diseases department.
Acknowledgments We thank all the medical students for taking the time to participate in this study. We thank  Karny BEDIKIAN ,  Françoise JAUREGUY ,  Dr Warda KHAMIS ,  Prof. Bertrand DEGOS  and  Prof. Jean-Ralph ZAHAR , for encouraging medical students to participate in this study. We thank  Dr Hugues CORDEL  and  Thomas PLANQUES  for their advice. We thank  Bernard VIRGINIE  for his technical support. We thank  Giani PETRI  for his advice regarding the use of the MEEGA + scale.
Loci have a distribution of initial intensity, and undergo photobleaching. Tracks were analysed using a custom written Matlab R2018a script, as previously described  27. This was further adapted for the shorter trajectories of 40 images in our experiments. Briefly, the tracking consists of three main steps: (1) localisation of candidate particles. The aim of this step is to obtain an estimate of the particle localisation; (2) subpixel resolution detection of the position. Using the original unprocessed images, the regions around the candidate particles are fitted to a 2D Gaussian; (3) linking of the trajectories. In this step, the positions detected along the different time frames are assembled to reconstruct the particles trajectories. Analysis of trajectories to extract the mobility parameters.
This tethering can arise from interactions of the locus with other chromosomes or cellular substructures, such as the membrane. These interactions cannot be measured directly but they can be inferred from SPTs. (4) The effective diffusion coefficient D c reflects the second-order statistical properties of a trajectory. This diffusion coefficient accounts for local crowding that may vary along the trajectory. Calibration of the foci. In order to estimate the copy number of fluorophores, we implemented a custom intensity calibration method in MATLAB (R2018a), based on the principle described in ref. 61. The calibration method aims at estimating the ratio between the intensity of foci and the number of GFP molecules by exploiting the intrinsic fluctuations of intensity generated by the random photobleaching process.
This process also allowed to verify that the bleaching contribution is indeed the dominant contribution in the empirical variance. As an additional pre-processing step, we estimate and subtract background intensity by extrapolating the intensity at which no bleaching is observed (Supplementary Fig. 3a ). Statistics and reproducibility. Each strain was tested at least three times independently, and the data were pooled in the same data file after checking that the results obtained with each replicate were comparable. The final numbers of foci used for data analysis are indicated in Supplementary Data 1 and in the legends of the figures. Supplementary Data 2 shows the median for each dataset. To compare distributions, a two-sample Kolmogorov-Smirnov test was used and p values are indicated in the text and Supplementary Data 3.
After this time, the supernatant was removed and reactions were quickly washed with 15 μL RB2, then resuspended in 15 μL RB2 + 0. 1% SDS. After 10 min, the supernatant was deposited on a 0. 8% agarose gel. DNA was visualised with Sybr-Green (Life technologies). Multiplexed tethered particle motion (TPM). The overall TPM procedure, including data analysis, has been described previously  38, 62. DNAs were obtained as follows: PCR with F2060 and R2016 on pBR328 for 0matS-DNA (2588 bp); PCR with F2060 and R2016 on pTOC7 for 1matS-DNA (2506 bp); PCR with F1201 and R1201 on pTOC6 for 2matS-DNA (2443 bp). Fifty picomolar DNA were incubated with 50 pM anti-digoxigenin-covered beads (antibody: Roche, #11093274910 and carboxylate-modified beads F1-XC030, Merck-Estapor) in Buffer A (PBS 1X, 1 mg mL -1 Pluronic F-127, 0.
ZapB fractions were pooled and frozen. Pull-down experiments with ZapB. This experiment is performed like other pulldown experiments but with different MatP concentrations (from 1 to 6 μM) with or without ZapB (from 2 to 20 μM). Each reaction was done in double in order to be analysed by classical agarose gel electrophoresis or SDS-PAGE. For western-blot analysis, reactions were analysed by SDS-PAGE and transferred to polyvinylidene difluoride membranes (Bio-Rad) using the Trans-Blot ® Turbo TM transfer system (Bio-Rad). Membranes were blocked 1 h at RT or overnight at 4 °C in 5% non-fat powder milk in PBS containing 0. 05% Tween 20. Antibodies were anti-His (1:1000) (Tanaka, #631210). Blots were developed by chemiluminescence using Clarity western ECL substrate (Bio-Rad), visualised with the Chemidoc TM Touch imaging system (Bio-Rad) and analysed with Image Lab software (Bio-Rad).
Acknowledgements We would like to thank  M. Cosentino Lagomarsino ,  A. Javer ,  J. Kotar ,  M. Panlilio ,  M. Wlodarski , for useful discussions and help with experimental setups. We are also grateful to  F. Boccard  and  O. Espeli  for the kind gifts of bacterial strains and plasmids. Research in the FC group is founded by the  CNRS  and the  Université Paul Sabatier (UPS Toulouse ). Research in PC lab was supported by  HFSP  ( RGY0070/2014 ) and  UKRI  grant  EP/T002778/1.
9 mmol/L) at 12 months. Secondary efficacy outcomes include between group differences in stimulated C-peptide AUC at 24 months, time spent in target glucose range, glucose variability, hypoglycaemia and hyperglycaemia as recorded by periodically applied masked continuous glucose monitoring devices, total, basal and bolus insulin dose, and change in body weight. Cognitive, emotional and behavioural characteristics of participants and parents will be evaluated, and a cost-utility analysis performed to support adoption of CL as a standard treatment modality following diagnosis of T1D. Ethics and dissemination Ethics approval has been obtained from Cambridge East Research Ethics Committee. The results will be disseminated by peer-reviewed publications and conference presentations. trial registration number NCT02871089; Pre-results.