dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The data are presented as mean ± standard error (SE). The effects of diet and acclimation temperature on SGR and lipid content in fish muscles were tested using a two-way ANOVA with n = the number of fish replicates for each dietary condition Statistica software vs. 7 (StatSoft, Maison-Alfort, France). Prior to ANOVA analyses, normality distribution and homeodasticity were controlled using Kolmogorov-Smirnov and the Levene test, respectively (Statistica software vs. 7; StatSoft, Maison-Alfort, France). The effects of acclimation temperature, food (considered as a categorical covariate), challenge temperature (fixed effect), and factor interactions on ecophysiological variables (i. e. MO 2max , MO 2min , AS, U max , NCOT, EPOC and f v max ) have been tested by applying multiple linear mixed effects models, where individual fish electronic identity has been considered a random factor.
Transactions are extremely important from an epidemiological point of view because they form a dynamic contact structure among agents that can support disease transmission. In our model, the frequency of trade, i. e. , of transactions, is limited by a coefficient κ compounding multiple constraints referred to as trade friction. Causes of such friction include the search for a trading partner and the logistics of stock delivery. κ can be easily derived from available trade flow datasets such as trade of livestock (see SI for an application to exchanges of swine and cattle in France). We believe that the notion of frictional dynamical transactions is an improvement over existing market models (see SI for details) that may help to better understand the interactions between trade and disease epidemiology for many infectious diseases.
Data analyses To parameterize our FTM model and estimate the range of friction encompassing real markets, we analyse trading datasets of two livestock markets: cattle (BDNI dataset) and swine (BDPorc dataset). The BDNI and BDPorc datasets are respectively managed by the French ministry in charge of Agriculture and the French professional union BDPorc. Each dataset details movements of living animals occurring in France among all economic agents involved in the supply chain, from strictly breeding farms to slaughterhouses with various categories of wholesalers in between (e. g. breeding-fattening farms, strictly fattening farms, markets, dealers). To estimate our model parameters, we extract and reconstruct from a subset of each dataset a table of individual transactions detailing: the pair of supplier-demander involved during each transaction, the date and the associated volume of goods exchanged (see SI for details on data subset included in our study).
B Model parametrization and estimation of friction from French cattle and swine livestock exchange data B. 1 Presentation of the datasets To parameterize the FTM model and estimate the range of friction encompassing real markets, we analyze trading datasets of two livestock markets: cattle (BDNI dataset) and swine (BDPorc dataset). The BDNI and BDPorc datasets are respectively managed by the French ministry in charge of Agriculture (http:// agriculture. gouv. fr/identification-et-tracabilite) and the French professional union BDPorc (http://www. bdporc. com/). Each dataset details movements of living animals occurring in France among all economic agents involved in the supply chain, from strictly breeding farms to slaughterhouses with various categories of wholesalers in between (e. g. breeding-fattening farms, strictly fattening farms, markets, dealers).
B. 2 Extraction of individual transactions from the datasets To estimate the parameters of the FTM model, we need to extract for each dataset a table of individual transactions detailing: the pair of supplier-demander involved during each transaction, the date and the associated volume of goods exchanged. We only extract such detailed transactions for a subset of the datasets. We focus on cattle data for civil year 2009 and pig data for civil year 2010. Following the literature on networks of livestock exchanges in France  [Rautureau et al. , 2011 [Rautureau et al. , , 2012]] , we neglect imports, exports and movements of animals to slaughterhouses and rendering plants. Imports and exports are already accounted for in the FTM model as free parameters (E S for supply and E D for demand, with E > 0 for imports and E < 0 for exports).
B. 2. 2 Extraction of transactions from the BDPorc dataset (swine) Swine traceability is only known at the scale of batches of pigs transported by the same truck during a round of transportation. A round is a movement of swine involving the same truck that starts a journey empty, loads pigs from one or more holdings and finishes the journey empty after successful delivery to one or more holdings. Loading and delivery events may occur several times and in any order during a given round. Inferring detailed transactions from the BDPorc dataset hence requires some reconstruction hypotheses that are detailed here. The inference problem is the following: we know how much each agent is contributing, with whom and when it is interacting, but we do not know which pigs precisely reach a given destination.
Acknowledgments Many thanks to  Samuel Alizon ,  Hugues Beyler ,  Caroline Bidot ,  Pauline Ezanno ,  Yann Kervinio ,  Julien Fosse ,  Etienne Geoffroy ,  Natacha Go ,  Bhagat Lal Dutta ,  Francois Moutou ,  Emilie Moyne ,  Marco Pautasso  and  Stéphane Robin  for helpful comments and insights. We are grateful to the  French Ministry in charge of Agriculture  and to the professional union BDPorc for granting us access to the cattle and swine datasets respectively. EV, HM and MML would like to thank the  French Ministries in charge of Agriculture and Environment  and the  INRA MIA Department  for financial and operational support. JANF and CAG were funded by  DEFRA  and  USDA.
0 and 2. 3 mg ml Ϫ1 , respectively, and stored at 4 °C. Proteins were centrifuged (10,000 ϫ g for 5 min) just before the experiment. Size exclusion chromatography was carried out on an Alliance 2695 HPLC system (Waters) using a Silica Gel KW804 column (Shodex) equilibrated in 10 mM Tris, pH 7. 5, and 150 mM NaCl at a flow of 0. 5 ml/min. Detection was performed using a triple-angle light scattering detector (Mini-DAWN TREOS, Wyatt Technology), a quasi-elastic light scattering instrument (DynaPro, Wyatt Technology), and a differential refractometer (Optilab rEX, Wyatt Technology). Crystallization, Data Collection, and Processing-Crystallization and data collection of pro-Monalysin and tantalum derivative were performed as described previously  (22). The cleaved Monalysin crystals were obtained at room temperature from drops in which pro-Monalysin was co-crystalized with 1.
ϳ220 CCD images were collected under low dose conditions using a Tecnai F30 microscope with a field emission gun operated at 300 kV (IGBMC, Strasbourg, France) and a FEI Eagle 4Kx4K CCD camera at a magnification of 59,000ϫ (1. 92 Å/pixel at the specimen level), and over a range of nominal defocus values comprised between Ϫ2 and Ϫ3. 5 m. Defocus estimation and contrast transfer function (CTF) correction were carried out using FINDCTF2D (TIGRIS software from Timothy Grant, Imperial College London). 50,000 particles were automatically selected using the program Boxer from the EMAN2 package  (35) , extracted into boxes of 144,144, and combined into a dataset stack (Fig. 1A ). The dataset was pretreated using the SPIDER package  (36)  and submitted to maximum likelihood classification and alignment (37) using the Xmipp package  (38).
The initial map was built from a visually selected class average representing a side-view imposing D9 symmetry (Fig. 1, B  and C ). The initial structure was then refined by three-dimensional maximum likelihood refinement and further refined by projection matching with an angular sampling of 5°using SPIDER. After refinement, a final structure was obtained at a resolution of approximately 17 Å as estimated by Fourier shell correlation and the half-bit threshold criterion (39) (Fig. 1D ). The final reconstruction was built with ϳ12,000 particles after removal of most particles in a top-view orientation to compensate for their large over-representation and reduce preferential views effects and three-dimensional distortions. Molecular graphics and analyses were performed with the UCSF Chimera package (Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIGMS P41-GM103311 through the National Institutes of Health)).
This N terminus stretch buries 568 Å 2 of solvent-accessible surface on the protein core. A structural similarity search within the Protein Data Bank using the pairwise structural comparison server DALI (41) of the Monalysin structure gave no statistically significant similarities, the highest Z-score being of 4. 7 for an insecticidal crystal protein (Protein Data Bank (PDB) 4JP0) with a core r. m. s. d. of 4. 6 Å. A protein sequence similarity retrieval using the BLAST program uncovered three genes with more than 30% of sequence identity with pro-Monalysin, from Cystobacter fuscus (48% sequence identity), Pseudomonas putida (35%), and Pseudomonas sp. (33%). They are annotated as hypothetical proteins and hence remain with unknown function. The two last proteins are shorter because they lack the N terminus (Fig.
Acknowledgments-We thank the  European Synchrotron Research Facility (ESRF, Grenoble, France)  and the  Soleil synchrotron (Saint-Aubin, France) , and in particular the staffs of beamlines ID23 and Proxima1, respectively, for their assistance. Note Added in Proof-The legend for Fig. 1B was not correct in the version of this article that was published on April 5, 2015 as a Paper in Press. This error has been corrected. This correction does not change the interpretation of the results or the conclusions.
* This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  Grant  ANR-10-INSB-05-01  and by Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI). The atomic coordinates and structure factors (codes  4MKO  and  4MKQ ) have been deposited in the Protein Data Bank (http://wwpdb. org/). The EM structure of Monalysin was deposited at the  Electron Microscopy Data Bank (EMDB)  with accession number  EMD-2698. 1 Both authors contributed equally to this work.
Animals and housing Data were collected on four commercial Danish Holstein Friesian dairy farms with a herd size above 200 cows. The cows were hornless and were kept in loose-housing systems with access to cubicles. They were milked twice a day in a milking parlour. No cows included in the study had access to pasture during the study. Routine claw trimming was performed three times a year. Herd level milk yield ranged from 8496 to 11 656 kg milk per cow per year.
Accelerometers By random selection 200 cows on each farm were equipped with three-dimensional accelerometers (IceTag3D, IceRobotics, Edinburgh, UK) on a hind leg. Cows which were equipped with an accelerometer and that left the herd were not replaced by other cows, but IceTags not in use after approximately half a year after the start of the period of data collection were attached to new cows once. Thereafter, the IceTags were not moved to new cows. For safety reasons, the accelerometers were attached in the milking parlour to the hind leg closest to the technician, so ∼50% of the cows wore the accelerometer on their left leg and 50% on their right leg. The accelerometers recorded three-dimensional acceleration data 16 times/s. The accelerometer software (IceTagAnalyzer, IceRobotics) computes the number of steps made, and a motion index (MI) per second, that is, the total acceleration within the second.
The IceTag accelerometer and software have been validated earlier  (Nielsen et al. , 2010). The accelerometer data were transferred automatically to a local computer at each milking. Data from the local computer was transferred to a central database via the internet. Accelerometer data processing Data were collected with a prototype of a system for downloading data, and data were missing for some days and some cows or sometimes for part of the day. Accelerometer data were cleansed according to the following criteria: days missing more than 10 min of data were disregarded. To remain in the data set, cows were required to have ⩾30 consecutive days of data. Furthermore, cows missing >1 period of at least 7 days, and cows missing a period of ⩾120 consecutive days were disregarded. After the application of these criteria, a total of 80 417 days remained in the data set.
Another advantage of this is that steps per daily walking minute is uncorrelated with walking duration. From these data, <1% of days (i. e. 307) were removed because either MI standing or MI lying exceeded 10 000 g/day or because MI walking exceeded 25 000 g/day (deemed as outliers based on box-plots). Deriving a data set based on LS In the processed data, there were 959 LS observations in 401 lactations from 348 cows. The number of cows, lactations, observers and the distribution of LS observations by LS level is reported by farm in Table  1. Due to only 23 observations of LS5, LS5 were pooled with LS4. LS may change rapidly, therefore we focussed on days with accelerometer data measured during the week immediately before an LS. Weeks were defined as the 8 days starting 7 days before each LS assessment and ending on the day the LS was assessed.
Acknowledgements V. M. Thorup  has received the support of the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills' fellowship (under grant  agreement  n°267196 ). This study was part of the  ERA-net  project  DAIRY-ICT. The project was financially supported by the  Ministry of Food, Agriculture and Fisheries of Denmark , the  Danish Cattle Federation and Aarhus University. The authors wish to thank the following technicians at  Aarhus University, Department of Animal Science , for performing the LS assessments:  J. M. Obidah ,  P. Poulsen  and  N. H. Thomsen.
In order to achieve these outcomes, molecular typing assays must possess the discriminatory power required to distinguish between related and nonrelated bacterial isolates, have high reproducibility, and be easy to perform. Furthermore, the results generated need to be easy to interpret, portable and allow inter-laboratory comparison. All typing results, especially during an outbreak, must be able to correlate with epidemiological data for accurate interpretation  [6] [7] [8]. In Alberta, Canada, all E. coli O157:H7 are routinely typed by pulsed-field gel electrophoresis (PFGE) and multilocus variable-number tandem repeat analysis (MLVA) under surveillance practices. In July 2014, a unique cluster of E. coli O157:H7 was identified by PFGE with concordant MLVA analysis. The subsequent molecular and epidemiological investigation revealed that the cluster was associated with one of the largest human E.
Molecular detection of Escherichia coli O157:H7 outbreaks in Alberta Following established protocols from frontline microbiology diagnostic laboratories for enteric bacteria isolation, all presumptive E. coli O157:H7 isolates are forwarded to the Alberta Provincial Laboratory for Public Health (ProvLab) for serotype confirmation and molecular typing. Routinely, all E. coli O157:H7 isolates are subjected to PFGE and MLVA using standardised PulseNet protocols (www. pulsenetinternational. org). For E. coli, XbaI endonuclease is the primary restriction enzyme used for chromosomal DNA digestion and is followed by secondary enzyme digestion with BlnI. Images (tagged image file format) of the PFGE profiles for all isolates are uploaded to the PNC Public Health Agency of Canada National Microbiology Laboratory in Winnipeg, Manitoba (PHAC-NML) secure national database for national pattern designation.
Genome sequencing and assembly WGS of isolates was performed at the PHAC-NML Core Genomics facility. Sample libraries were prepared using Nextera XT library preparation kit (Illumina, Inc. , San Diego, CA, US). Sequencing was performed on the Illumina MiSeq platform with the MiSeq Reagent Kit V2 to achieve an average genome coverage greater than 50x for all isolates. Raw sequence reads are available under National Center for Biotechnology Information (NCBI) Bioproject PRJNA291542. Sequencing reads were de novo assembled into contigs using SPAdes  [17]  and annotated with Prokka  [18]. SPAdes-assembled contigs smaller than 1 kb were removed from the analysis.
Core single nucleotide variant data preprocessing, quality control and data reduction All read data available for each genome were processed using the following steps: (i) FFastq files were converted to Sanger quality encoding Fastq format, (ii) all Fastq files for each isolate were concatenated into one Fastq file per isolate. The concatenated Fastq files were next subjected to a quality control step using a custom Perl script that trims the reads up to a maximum of 10 bases on either end of the reads if the average base call quality in that region was below 25. Next, reads less than 36 bp in length and reads with an average base quality call below 25 were discarded from the analysis. Following the quality control step, all data for each isolate were reduced to a maximum of 200 x coverage (estimated based on the total bp length of the E.
Core single nucleotide variant calling Core genome analysis was performed using the PHAC-NML bioinformatics custom Single Nt Variant Phylogenomic pipeline (SNVPhyl)  [19]  consisting of open-source software and custom Perl scripts. Briefly, sequencing reads were mapped against the complete reference genome, E. coli O157:H7 strain Sakai using SMALT v. 0. 7. 0. 1  [20]  with a k-mer size of 13, a step size 6, and a minimum alignment fraction of 0. 5. Variants were called using FreeBayes v0. 9. 8  [21]  with variant reporting for all variants, no complex variants, minimum mapping quality of 30, minimum base quality of 30, minimum alternate fraction of variant bases in agreement of 75%, and minimum coverage of 20 reads at every position in the reference sequence.
k-mer clustering The frequencies of all nt sequences of predefined lengths (k-mers) in the entire genome of each isolate was compared with the frequency of k-mers in all other isolates to determine a k-mer phylogeny tree. The alignment-free feature frequency profile (FFP) method  [28]  was used at a k-mer length of 25 nt and the SPAdes assembled contigs for each isolate. The optimal k-mer length was determined using the ffpvoacb utility part of the FFP package and the ffpreprof -e32 to determine the upper bound and the ffpvprof -f 2 to determine the lower bound. Phylogenetic trees were inferred from the resulting divergence distance matrix using the neighbour-joining method installed in the Phylip  [29]  package.
Cluster 74 included three pork isolates (14-6540, 14-6549 and 14-7569) and 71 clinical isolates with the following laboratory accession numbers: 14-5190 to 91, 14-5196, 14-5369, 14-5371, 14-5373, 14-5375 to 76, 14-5379 to 83, 14-5385 to 87, 14-5393 to 5399, 14-5401 to 03, 14-5493, 14-5495 to 500, 14-5600, 14-5603 to 07, 14-5609 to 11, 14-5613, 14-5615 and 16, 14-5619 to 21, 14-5625 to 26, 14-5761 to 67, 14-5886 to 88, 14-5891, 14-6200, 14-6370 to 71, 14-6373, 14-6541, 14-6543-45, 14-6548, 14-7104, 14-7109. Cluster 23 included one pork isolate (14-6539) and 22 clinical isolates: 14-5192, 14-5198, 14-5200, 14-5390, 14-5492, 14-5501, 14-5601 to 02, 14-5608, 14-5612, 14-5617, 14-5622, 14-5770, 14-5890, 14-5892, 14-6101, 14-6229 to 30, 14-6365, 14-6368, 14-7106, 14-7813.
Unlike MLVA and/or PFGE using PulseNet standardised methods, both k-mer and core SNV methods do not generate a numerical 'barcode' for the organisms, which makes it difficult to compare isolates from different laboratories. Therefore, a large shared and curated database will likely be required to generate cluster identifiers that could act as the organism's 'barcode'. Currently, PulseNet networks worldwide conduct timely surveillance and facilitate outbreak detection. For WGS to be used in this manner, standardised protocols, quality validation metrics and a robust method to determine isolate relatedness will need to be established. Published reports use different sequencing platforms and bioinformatics pipelines to assemble and analyse WGS data, prohibiting direct comparisons between studies.
Head-to-head comparison of these different methods will help determine the appropriate standard(s). The inter-laboratory comparison of WGS data also requires communal database of isolates, which requires adequate computing infrastructure and secure electronic networks capable of transmitting large datasets. Other considerations encompass the ethical, legal or political barriers to sharing complete genomics data between various health authorities. In conclusion, WGS holds significant potential to replace current gold-standard typing methods such as PFGE for the routine surveillance and detection of enteric outbreaks. This shift will be driven by the advantages offered by WGS such as increased discriminatory power and genetic resolution. However, before this technology can be widely implemented, certain barriers remain to be addressed such as initial capital expense, computing infrastructure, and validated automated, user-friendly WGS analysis software.
Acknowledgements ProvLab & PHAC-NML laboratory technicians who performed the PFGE, MLVA and laboratory work associated with WGS. Natalie Knox ,  Aaron Petkau ,  Philip Mabon ,  Morag Graham , and Gary van Domselaar at  PHAC-NML  who developed the bioinformatics pipeline. The  Canada-Alberta Partners in Food Safety Outbreak Investigation Coordinating Committee  who coordinated and carried out the outbreak investigations outside the laboratory. Alberta Health for patient demographic information.
Solution and Refinement of the Fab410-BfAChE Complex-Initial phases were obtained by molecular replacement with the AMoRe program package (51) using as search models the structures of apo-mAChE (Protein Data Bank (PDB) code 1J06 (  18 )) and antibody HFE7A (PDB code 1IQW (  52 )). Various normal mode-perturbed models of the Fab template harboring a range of elbow angles were generated using the ElNemo server  (53). Among them, five models were selected based on their lowest correlation coefficient and R-factor values during molecular replacement searches and subsequently used as a Phaser ensemble  (54). This procedure yielded a final solution with a log-likelihood gain score of 2455 in the 40 -2. 75-Å resolution range. The model was improved by manual adjustment with the graphics program Coot (55) and refined with REFMAC  (56)  and autoBUSTER  (57) , including non-crystallographic symmetry restraints and translation/libration/screw refinement with each AChE subunit and Fab410 variable and constant domain defining a translation/libration/screw group.
The molecular surface area buried to a 1. 4-Å-radius probe at the Fab410-BfAChE complex interface was calculated using PISA  (60). Tunnels and channels in AChE were computed with CAVER 3. 0  (61). Structural superimposition, based on the respective AChE subunits, of the Fab410-BfAChE complex with the Fas2-mAChE complex (PDB code 1KU6 (  14 ); root mean square deviation value, 0. 83 Å for 516 C␣ atoms), propidium-mAChE complex (PDB code 1N5R (  18  Theoretical Modeling of the Fab410-EeAChE Complex-A previously designed model of the EeAChE subunit devoid of the EeAChE-specific insert Ile 418 -Gln 446 and C-terminal tailed peptide Ala 576 -Leu 610  (32)  and the present, randomly oriented Fab410 structure were used as templates. The model of the Fab410-EeAChE complex was generated by flexible docking using HADDOCK 2.