dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence alignments and clade selection We aligned sequences using MAFFT  (Katoh et al. 2002) v7. 453. We removed sequences from the alignment that either were duplicated, short (< 70% of the total sequence length), or indicative of containing sequencing or assembly errors. We used an alignment of all H9NX sequences available on GISAID and our newly generated sequences to estimated preliminary phylogenies with Fasttree v2. 1. 11 (Price et al. 2010). All but seven HA H9 sequences from Bangladesh, including all sequences generated in this study, fell within a monophyletic clade containing H9N2 virus sequences sampled from poultry in India and Bangladesh (n = 333). We retained sequences from that monophyletic clade where exact sampling dates and at least district-level location data were known, resulting in 298 HA sequences in the H9N2 virus alignment (Bangladesh: n = 284; India: n = 14).
Molecular clock phylogenies We investigated the introduction date of H9N2 to sampled locations using the Bayesian phylogenetic package BEAST  (Drummond & Rambaut 2007; Suchard et al. 2018) v1. 10. 4. First, we compared four possible models: pairwise combinations of two molecular clock models (uncorrelated lognormal relaxed clock  (Drummond et al. 2006 ) and strict clock  (Marco A. R. Ferreira 2008) ) and two coalescent models (constant size  (Griffiths & Tavaré 1994; Kingman 1982 ), and Bayesian skygrid  (Gill et al. 2013 )), all using an SRD06 substitution model  (Shapiro et al. 2006). We executed multiple MCMC chains comprising 100 million steps and sampling every 10,000 steps. As identified using path-sampling  (Lartillot & Philippe 2006 ) and stepping-stone-sampling analyses  (Fan et al.
2011; Xie et al. 2011 ), the best model used the uncorrelated relaxed clock  (Drummond et al. 2006 ) and a Bayesian skygrid coalescent prior  (Gill et al. 2013). The posterior tree distributions for H9N2 and H5NX HA alignments were each obtained from two parallel MCMC chains with 250 million steps, sampling every 25,000 steps. We assessed the convergence of each run using Tracer  (Rambaut et al. 2018) v1. 7. 1 (http://tree. bio. ed. ac. uk/software/tracer/), and confirmed the presence of appropriate parameter convergence by visually inspecting and then log combining multiple parallel runs. We summarised the information on maximum clade credibility (MCC) trees using TreeAnnotator  (Drummond & Rambaut 2007) v1. 10. 4 , with the first 10% discarded as burn-in. We repeated all BEAST analyses for the NA sequence alignments using similar methods and parameters.
We used a generalised linear model (GLM) extension of the skygrid coalescent model  (Dellicour et al. 2020; Gill et al. 2016 ) (henceforth, "skygrid-GLM") to determine whether the effective population size (N e ) of the H9N2 HA lineage was associated with the tonnage of chicken or duck meat production in Bangladesh. Data were available yearly for each predictor from FAOSTAT (Food and Agriculture Organization of the United Nations n. d. ) statistical database. To provide correspondence with the estimation of virus effective population size at two-month intervals, we used the "zoo" package  (Zeileis & Grothendieck 2005 ) in R (RStudio Team 2020) v4. 1. 2 to estimate values for each predictor every two months between 2000 and 2020 using linear interpolation. We then conducted the same analysis for the H5 HA dataset.
Next, to quantify lineage diversity within and between markets, we used the adjusted Rand index  (Rand 1971 ) by means of the "phyclust" package  (Chen 2011 ) in R (RStudio Team 2020) v4. 1. 2 to determine the similarity of clustering by market and clade in the cross-sectional H9N2 dataset. To determine if there was a significant difference in the observed clustering pattern to that expected based on random viral movement, we compared the estimated median adjusted Rand index with a median adjusted Rand index calculated by permuting the market location 10,000 times while controlling for the city. We further generated binary adjacency networks in R (RStudio Team 2020) v4. 1. 2, in which edges linked market nodes if they shared a genetically-defined clade. To further test whether AIV clades are randomly distributed across Downloaded from https://academic.
H9N2 dispersal between different chicken types To determine whether H9N2 virus genetic diversity is structured according to Bangladesh's three main chicken types (broiler, sunali, deshi) and two main cities (Dhaka, Chattogram), we used Bayesian Tip-association Significance Testing (BaTS)  (Parker et al. 2008 ) v1. 0. We removed tips in each tree in the HA H9N2 posterior tree distribution when tip information on either chicken type and sampling city was unavailable, using the R (RStudio Team 2020) v4. 1. 2 library, 'ape'  (Paradis & Schliep 2019)  package. This generated a distribution of downsampled posterior trees each containing 70 tips, corresponding to those sequences in the cross-sectional dataset (Table  1 ). The first BaTS analyses we performed assessed if sequences tended to cluster based on the sampling city (Dhaka, Chattogram).
These production areas are the set of Upazilas (sub-district) where farms supplying markets within a city are located, which were previously computed from reconstructed transaction networks based on traders' interviews as described in  Moyen et al. (2021)   (Moyen et al. 2021). Descriptions for all GLM predictors included are summarised in greater detail in Table  S5. We completed four separate MCMC analyses of 100 million steps sampling every 10,000 steps using the HA H9N2 empirical tree distribution downsampled to tips from the cross-sectional dataset (i. e. , those tips with all appropriate metadata). The first model, termed model A, used all covariates except the chicken type and city similarity matrices. Models B and C were the same as model A, but excluded the number of sequences or the weekly sales number, respectively.
Dynamics of avian influenza virus in Bangladesh All but seven H9N2 sequences from Bangladesh (of which six were sampled from ducks) fell within a H9NX monophyletic clade containing virus sequences sampled from poultry in India and Bangladesh, and this clade (n = 298 sequences with appropriate metadata) was therefore selected for further analysis. All sequences in this clade that had a respective NA sequence were identified as being H9N2 subtype, thus strongly indicating that any HA-only sequences (i. e. , cross-sectional dataset) were also H9N2  (Price et al. 2010). The TMRCA of all Bangladeshi HA sequences in this major H9N2 clade was  Several clades in the HA H9N2 tree contain sequences from only Dhaka or from only Chattogram, suggesting that LPAIV transmission might occur preferentially between birds sold in each city (Figure  1A ).
Distribution of virus clades across LBMs We investigated whether individual H9N2 HA clades tended to be randomly distributed across LBMs in a city or associated with specific markets. We quantified the diversity of viruses within and between markets in a city by employing the adjusted Rand index to measure the similarity of clustering by market and virus clade for the cross-sectional H9N2 dataset. We found no significant difference (p = 0. 993) between the median adjusted Rand index calculated from the empirical and permuted data (Table  S6 ). Therefore, differences in viral genetic diversity between and within markets were not significantly different. The binary adjacency network analysis on the same cross-sectional H9N2 dataset found no significant difference (p = 0. 881) between the median network density estimated from the permuted data and the median network density calculated from the empirical data (Table  S7  & Figure  2 ).
2021). Although the lack of wild bird sequences in our datasets prevents any robust conclusions of the contribution of wild birds to H9N2 mobility here, if wild birds played an important role in such viral movements we may expect Bangladeshi sequences to be more frequently intermixed with samples from locations linked to the country via wild migration routes (e. g. , China, Mongolia)  (Lycett et al. 2019; Tian et al. 2015). H9N2 viruses detected within one market are often interspersed phylogenetically with those from other markets, and genetically-different viruses from this subtype appear randomly distributed across markets. This may indicate strong epidemiological connections between these markets leading to frequent virus spread between them or from shared sources, as suggested by the highly-connected trading network captured by  Moyen et al.
(2018)   (Moyen et al. 2018). Our observations suggest that viral genetic diversity within a given LBM might not be hugely dissimilar to viral genetic diversity across all markets in a city, meaning surveillance of only a few markets within a city may be sufficient to capture viral diversity. This pattern could be generated in several ways, including frequent introduction of viral clades to LBMs (as suggested by  (Moyen et al. 2021 )) followed by limited intramarket persistence. However, we could not test this or other hypotheses regarding lineage introduction and persistence here because many markets in our dataset were represented by only one sequence. Our analyses indicate that the H9N2 viruses generally cluster more frequently by chicken type in Chattogram than in Dhaka, suggesting that it may be necessary to consider sub-national variation in production and trading processes when evaluating AIV dispersal.
Although H9 and H5 subtypes have been repeatedly detected in LBMs in both Dhaka and Chattogram  (Biswas et al. 2018; Hassan et al. 2020; Kim et al. 2018; Rimi et al. 2019; Sayeed et al. 2017) , the relatively lower number of Chattogram sequences may somewhat reflect both the higher number of live birds traded in Dhaka than in Chattogram  (Moyen et al. 2021)  as well as the greater quantity of mixed bird-type markets in Dhaka than Chattogram, which are generally associated with higher prevalence of infection  (Kim et al. 2018). Biased sampling over space and time is less problematic for our analyses based on the cross-sectional dataset than those based on all publicly available data, as the cross-sectional dataset was generated through the same observational study conducted in both Chattogram and Dhaka.
2018 ) spanning only two months, we could not describe and explore seasonal variations in H9N2 infection patterns in such analyses. The dataset used for this analysis also contained only HA H9 sequences, meaning that we could not determine whether other segments or AIV subtypes may be similarly shaped by poultry trading practices in Bangladesh. Improving insight into how viruses spread at a range of spatial scales could help guide improvements in AIV control. Our study shows the importance of recording accurate information on chicken type, and highlights the need for greater surveillance on farms to understand viral epidemiology in Bangladesh. Our results suggest that nationally uniform interventions to reduce AIV prevalence may be unlikely to provide optimal effectiveness. Instead, actions should be tailored to the specific local structural characteristics of the poultry trading network and AIV dispersal patterns, but could be made more efficient through targeted surveillance of a small number of key sites  (Moyen et al.
Acknowledgements We thank  Dr Natalie Moyen  for providing production area overlap distance matrices. Thanks to  Professor Richard Webby  and his team at  St. Jude Research Center of Excellence for Influenza Research and Surveillance (SJCEIRS)  for collecting and sharing H9 sequence metadata (accessions in Table S4). Thanks to  Susan Collins  for technical assistance in laboratory work. We acknowledge the use of sequences from both the GISAID EpiFlu database and the IRD animal surveillance database metadata and provide accession numbers and acknowledgements in the supplementary electronic material (Table S2 andTables S3, respectively). interdisciplinary research hubs funded under the  UK government's Grand Challenge Research Fund Interdisciplinary Research Hub initiative.
Study positive samples using 4 previously described [8] but primers for HA1 and 6 for genomes not yet reported. HA2 (HA1: F308, R308, F754,R754; HA2: F723,R723,F531,R531,F29 9,R299), and were sequenced using Sanger sequencing (www. dnaseq. co. uk). Longitudinal dataset: A RNA was extracted using Host species, H9N2; 11 HA longitudinal study into AIV QIAamp Viral RNA Mini Kit market location, sequences (accession prevalence in live bird markets (Qiagen) [27] then reverse sampling date. numbers: Table S1). in Dhaka and Chattogram, transcribed and amplified H5NX; 24 HA during which 60 live birds and using RT-PCR. Positive sequences (accession any observed dead birds were samples underwent whole numbers: Table S1), 10 sampled monthly in two genome sequencing (WGS) NA sequences markets (one in Dhaka and at APHA using an Illumina (accession numbers: one in Chattogram).
Regarding the mlsA genes, the overexpression of the LM module in glucose medium was also confirmed by proteomics data indicating an overproduction of MlsA1 protein by a factor of 3. 81. These results indicated that mycolactone biosynthesis machinery seems to be overproduced in glucose or maltose-supplemented medium, while the production of its metabolite is reduced in both growth conditions. [9, 10]. The six genes coding for proteins involved in mycolactone synthesis are located on the giant plasmid named pMUM001. The genes mlsA1 (50,973 bp) and mlsA2 (7,233 bp) encode modular type I polyketide synthase (PKS) required for the biosynthesis of the mycolactone core (highlighted in orange). The side chain enzyme is encoded by mlsB gene (42,393 bp) in pink. The mls genes encode 11 different functional domains that are repeated along the gene.
Acknowledgments We thank  Dr. Michael McNeil  ( Colorado State University ) for his help with LC-MS analyses. We acknowledge  Jerome Roux  for their technical assistance (SCAHU, Angers). We would like to thank  Romain Mallet  ( SCIAM, Univ. Angers, France ) for his useful assistance to carry out the microscopy analysis. We acknowledge  Dr. Martin Barrios-Llerena  and  Dr. Thierry Le Bihan  ( SynthSys, University of Edinburgh ) for their grateful help in bioinformatics analysis and  Pr Jean-Philippe Bouchara  for stimulating discussions.
This study was supported by the  Fondation Raoul Follereau  (http://www. raoul-follereau. org/), the  Institut National de la Santeet de la Recherche Medicale (Inserm, Programme Inserm Avenir) (http://www. inserm. fr/); Agence Nationale de la Recherche  ( ANR 11 CEPL 007  ( EXTRA-MU ))(http://www. agencenationale-recherche. fr/),institutional grants from the  University of Angers  (http://www. univ-angers. fr/), Region Pays de la Loire  ( ARMINA  project)(http://www. paysdelaloire. fr/). CD is funded by post-doctoral grants from  Conseil General Maine-et-Loire  (http://www. cg49. fr/)and  Angers Loire Metropole  (http://www. angersloiremetropole. fr/) and  Axa Research Fund  (http://www. axa-research. org/) and  ANR 11 CEPL 007  ( EXTRA-MU ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Stereotactic brain biopsy of the lesions identified a modified acid-fast variable branching filamentous bacterium that had morphology consistent with Nocardia species. The final Nocardia species culture was obtained by inoculating a Columbia agar plate containing 5% sheep blood (bioMérieux) with pus from the abscess. The culture was incubated at 37°C in a 5% carbon dioxide atmosphere. The definite identification was based on the partial sequencing of the 16S ribosomal ribonucleic acid gene (over a length of 1439 nucleotides) as previously described  [10]. The sequencing results showed a 100% sequence similarity with the N. wallacei reference sequence (GenBank accession number GQ853074. 1). No other organisms were isolated from the patient sample. The antibiotic susceptibility of the isolate was assessed on Mueller-Hinton agar (bioMérieux) using the disk method (Mast Diagnostics).
Racing conditions Data were recorded during 6 different endurance races (2012, April to October). The races were classified as International Equestrian competitions of around 130 km to be covered in a day, with positive/negative changes varying from 6504 m to 63344 m. Each race was subdivided in 4 loops (L1-L4) varying from 20 to 42 km. They included roads and forest track conditions. Veterinary inspections 'Vet Gates' took place before and after the race as well as between the loops. To be ranked, the horse had to pass in each of these inspections.
Data acquisition Two tri-axial (3D) accelerometer data loggers -LocometrixH, (henceforth Locom, INRA, SGQA, France) and EquimetrixH (henceforth Equim, Centaure Metrix, France) -were used to record rider and horse accelerations, respectively (full scale of measurements: Locom = 66 g; Equim = 610 g; sensitivity = 16. 4 mV/g; resonance frequency = 550 Hz). Conforming to the recommended instructions for their utilization, the accelerometers were rigidly fixed on rider and horse bodies as follows: Locom was fixed onto a neoprene kidney belt worn by the rider, and Equim was secured inside a modified girth strap of the saddle and adjusted under the caudal part of the sternum  [9]. In these locations, the accelerometers recorded the vertical (dorsoventral for the horse vs. craniocaudal for the rider), the antero-posterior (fore-aft) and mediolateral (side to side) accelerations of the horse and rider.
Accelerometric data processing and analysis along the vertical axis The synchronized 3D acceleration signals of the six horses and riders were processed for each stride (defined as a full cycle of limb motion) by signal analysis procedures developed under a scientific software environment (Matlab 7). Their respective 3D displacements were obtained by double integration using cumulative trapezoidal numerical integration. A Butterworth bandpass filter (1-12 Hz) was used to remove the drift (low frequency) created by the double integration as well as any perturbation associated to a brisk movement. This filter was applied with a zero-time lag Butherworth filter to keep the signal without any phase shift. The analysis of the horse-rider interactions concentrated on their displacements along their vertical axis: dorsoventral (DV) for the horse vs.
For each Lissajous plot, the horse's DV and rider's CC displacements per stride were time normalized and combined with each other. To allow subsequent differentiation of the riding techniques within each gait, these data were not normalized in amplitude. Since the spatial coordinates of the horse and rider were unknown in Galilean frame, their displacements were centered on their respective average position. As illustrated in Figure  2 , Lissajous plots (i. e. , displacementdisplacement plots) were used to characterize the horse-rider spatio-temporal relationship during each stride. The different patterns of horse-rider coordination were identified according to classic profiles of Lissajous plots. Lissajous profiles close to oblique line (45u to the right) reflected quite perfect in-phase vertical horse and rider's displacements along the stride (panel A).
Statistical analysis Separate between subjects one-way ANOVAs were carried out on the mean values of each dyads in the 4 gait-technique combinations (within subject) to compare: 1) a angles for the Lissajous' plots corresponding to the different patterns, 2) mean RP at the lower reversal point of the cycle and 3) Standard Deviation of RP. Newman-Keuls Post-hoc tests were used to identify the significant differences between patterns. All data were analyzed using dedicated software package (STATISTICA, version 10, stat-Soft, Inc. Tulsa, Oklaoma, USA). In all tests and comparisons, p-values less than 0. 05 were considered significant.
Discussion As a prerequisite, the present work confirmed that combination of accelerometric data recorded by two classic devices -one located on horse (Equim) and the other on rider (Locom) -allowed identification of the horse's gait patterns and riding techniques, respectively. Indeed, by a double integration of the accelerometric data, we obtained the changes in relative position along time of the horse and rider in real endurance racing condition. Thus, the present study extends the findings of those carried out in dressage  [5]  by characterizing horse-rider coordination patterns during a natural, endurance race situation. This was achieved using simple representations capturing the spatio-temporal relations between the horse and the rider over repeated strides that is, Lissajous plots and relative phase analysis at specific points of movement cycle.
Gel analysis Protein spots were visualized with a Typhoon™ 9400 Series Variable Mode Imager (Amersham Pharmacia Biotech). The resulting gel images were processed using DeCyder Differential Analysis Software v5. 02 (Amersham Pharmacia Biotech). Protein spots were detected using the Differential In-gel Analysis (DIA) mode of 'DeCyder'. The Biological Variation Analysis (BVA) mode allowed inter-gel matching on the basis of the in-gel standards (Cy2). Spot intensities were normalized to the internal standard. For each spot, averages and standard deviations of protein abundance were compared between the profiles of B. suis grown in rich medium and cultivated under starvation conditions. The Student's t-test was applied to each set of matched spots. Significantly regulated proteins (p-value ≤ 0.
The desalted and concentrated peptides were eluted from the ZipTips™ on the MALDI targets with matrix solution (0. 1% trifluoroacetic acid (TFA)/80% acetonitrile, equally mixed with 2,5-dihydroxybenzoic acid: 2-hydroxy-5-methoxybenzoic acid, 9:1). For analysis of the tryptic peptides, MALDI-TOF mass spectrometry was carried out using the Voyager-DE™ STR Biospectrometry Workstation (Applied Biosystems). The spectra were acquired in a positive reflectron mode (20 kV) and collected within the mass range of 700 to 4,200 Da. The autolytic fragments of trypsin acted as internal calibrants. The peptide mass fingerprint spectra were processed with the Data Explorer v4. 9 Software (AB Sciex). The baseline was corrected, whenever necessary, with the following parameters: peak width 32, flexibility 0 and degree 0.
01. To detect peaks the parameters valley to baseline, 50% centroid, an S/N threshold of 15, and a noise window width (m/z) of 1 were used. The S/N was recalculated from the cluster area and the threshold for peak detection was set to 20. No deisotoping was performed. Peak lists were filtered for monoisotopic masses and the charge state 1+. Both monoisotopic peptide masses and signal heights were used to query an in-house Brucella suis database using the search engine Mascot v2. 1. 04 (Matrix Science) in order to obtain corresponding amino acid sequences. All sequences currently available from NCBI (http://www. ncbi. nlm. nih. gov) were entered in the in-house database.
Statistical Analysis All data were analyzed using PASW Statistics 18 (version 18. 0. 2, SPSS Inc. , Chicago, IL). Data for bird performance [BW, feed conversion ratio (FCR), and feed intake] and physical characteristics of the diets (length and diameter) were analyzed using the GLM procedure including the effect of group in the model (2 levels: C or M). Mortality and data from scan sampling were analyzed using the chi-squared test. The numbers of geese lying or standing inside or outside the building were pooled together, so that only 3 behavior patterns were analyzed: feeding, drinking, and other (lying inside + standing inside + outside the building). Values of different observations per day were pooled within each of periods: period 1 from 42 to 55 d (ad libitum feeding access), period 2 from 56 to 62 d (2 + 2 h feeding access), period 3 from 63 to 70 d (2 h feeding access), period 4 from 71 to 94 d (1 h feeding access), and period 5 from 95 to 97 d (3 h feeding access).