dataesr/scientific-paragraphs-categorization huggingface dataset:
The cone and desolvation gas flow rates were respectively 60 and 800 L. h À1. MS/MS data were acquired in daughter scan mode in both positive and negative-ion mode electrospray. The fragmentation for the 28 analytes was performed on both molecular ions, [M+H] + and [M-H] À , using cone voltage of 30 V and a collision energy ramp from 5 to 60 eV.
Strain collection across East Asia We collected 357 H. pylori isolates from China, Japan and South Korea, isolated between 1999 and 2018, including 11 provinces of China and six regions of Japan (Tables  S1, S2 , Fig. 1 ). We used data from 2014 to show regional risk levels in China and compared it with data from Japan and Korea  [8, 9, [11] [12] [13] [14]. Our sample of 77 Yunnan isolates includes strains from four ethnic minorities and the Han majority individuals.
Genome sequencing Genomic DNA was extracted using the Qiagen DNeasy Mini Kit and genomes were sequenced using Illumina or PacBio sequencers. For Illumina sequencing, paired-end libraries were created and sequenced using the Illumina Hiseq and Miseq platform. For PacBio sequencing, quality assessment of genomic DNA, SMRTbell library preparation and data evaluation were performed, and sequenced using the PacBio RS II platform. Sequences were deposited into GenBank with BioProject number PRJNA482300. Combining publicly available genomes (available in January 2018 when the study started) with these newly sequenced genomes, we collected a dataset consisting of 381 H. pylori genomes for further analysis (Table  S2 ).
For China southeast, only those from Fujian Changle that clustered into a singular clade were defined as a subgroup, 'Fuijan'. Subgroups with mixed ethnicities or high levels of admixture were not used in the calculations. For each SNP site, we calculated a fixation index (Fst) using PopGenome R package  [21]  for each subgroup such that Fst (sg1)=pairwise calculation of Fst of sg1 versus all other subgroups. We also compared isolates from China with those from Japan and South Korea and the two lower-incidence regions, Yunnan and Okinawa, versus high incidence regions (the remainder of hspEAsia strains).
Acknowledgements We thank  Jonas Korlach  and  Primo Baybayan  for help in SMRT sequencing. We would like to thank the staff of  Comparative Genomics Laboratory at National Institute for Genetics, Mishima, Japan  for supporting genome sequencing. We thank  Dr. Chao Yang ,  Professor Yujun Cui  and  Ruifu Yang  for the discussions about population genetics of H. pylori, and  M. Zwama  for discussion on HefC,  Mizuki Ohno  and  Yosuke Kawai  for discussion on human genetics.
Funding information This work was supported by a grant from the  State Key Laboratory of Infectious Disease Prevention and Control (SKLID)  ( 2014SKLID102 ) of the  Chinese Centre for Disease Control and Prevention. Supported by  National Science and Technology Major Project  ( 2018Z×10712-001 ) and a joint project ' Isolation and sequence analysis of Helicobacter pylori strains collected from investigations on carriage rate  '. K. T. was supported by  Swedish Society for Medical research (SSMF). Parts of the bioinformatic analyses were performed on resources provided by  Swedish National Infrastructure for Computing (SNIC)  through  Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX)  under projects  snic2018-8-24/ uppstore2017270 , partially funded by the  Swedish Research Council  through grant agreement no.
Rhythm Analyses Using the covered distance and food intake of each cat every 10 min, the same rhythm parameters were calculated and compared as in our previous study  [29]. The period (duration of a full cycle, ≈24 h for a circadian rhythm) and amplitude (a measure of how much of the activity or food intake that does occur is actually rhythmic) were measured using the periodogram of the rhythm, calculated by the Clocklab software (v. 2. 72, Actimetrics, Wilmette, IL, USA, extension of Matlab v. R2013a, Mathworks, Natick, MA, USA). The Interdaily Stability, Intradaily Variability and onset of the least active 5 h (L5) were calculated by the ActiWatch software (v. 7. 31, CamNtech Ltd. , Papworth Everard, UK). Interdaily Stability quantifies the degree of resemblance between activity patterns of individual days, Intradaily Variability quantifies the fragmentation of periods of rest and activity and L5 onset yields the time when starts the sequence of the 5 least active hours in the 24-h average activity profile.
A strong and robust rhythm was characterised by high amplitude and Interdaily Stability, but low Intradaily Variability. In order to establish if the cats tend to consume/be active more during night or day hours, we assessed their consumption/activity by hour and by day according to the light condition (night hours vs. day hours), using sunrise and sunset hours as phase references. An individual was considered to show a bimodal pattern when the magnitude of the 12 h-peak was equal or superior to half of the 24 h-peak (highest one) on its periodogram. Finally, we used the 10 min periods data to calculate how many times the cats moved and ate in an average day, in both housing conditions.
Acknowledgments: The authors express their gratitude to the directors and managers of the facilities where the studies were held:  Thierry Bedossa  and  Kosma Brijatoff  for the shelter AVA,  Sandrine Michel  and  Pascal Boisot  for the cattery of  Royal Canin. We are indebted to  Benjamin Allouche  and  Morgan Brault  for their expert technical support and acknowledge  Hubert Peyre  and  Bruno Venco  for their assistance. We also wish to thank the animal technicians and trainees for their involvement, help and cooperation during the protocols.
Data Database description. As a part of their activities of disease surveillance and control, the French Ministry of Agriculture has established a database on Swine owners and movements since 2009 (BDporc). This database is managed by different professionals in the pig industry and contains information about the pigs holdings in France, as well as livestock movements among them. For this study we used i) the 2012-2014 data pre-processed for a previous analysis  [8] , this dataset includes farms, slaughterhouses, gathering centers, traders, markets, stopping points and agricultural cooperatives (companies), ii) annual farm exports recorded from 2015 to 2020, iii) slaughterhouses, rendering plants and gathering centers information exported from the database in March 2021. Holdings. The details of the holdings were based on different exports from the database.
In the databases, swine holdings are reported with their own coordinates. Two holdings more than 500 meters apart were considered as different holdings, even if they belonged to the same farm/owner. The datasets include a set of elements characterizing the agricultural activities of each establishment. These characteristics include: i) the owner identification, ii) the holding identification, iii) the geographical information including address, post code/city, country and GPS coordinates, iv) the production type, v) the main husbandry activity, vi) the reared species, vii) the eventual outdoor areas, viii) the respective sizes of reproduction, post weaning and fattening areas and ix) the eventual company identification (see details in Table  1 ). Slaughterhouses, rendering plants and gathering centers information contained identification number, address, city and owner.
Movements. Movement information is continuously recorded by transporters, farmers and administrations: every time an animal is moved from one holding to another the transport's ID number, places of origin, staging and destination, number and type of animals transported (piglet, pig, barrow, sow or cull), and the date are recorded. This information can be aggregated to establish the flows of animals between different holdings. Used datasets were extracted from the annual pig movements database from 2017 to 2020. Data cleaning and pre-processing. The different datasets recording holdings information were cross-referenced by saving the most recent record when an holding appeared in several databases. Geographical coordinates were checked and, when missing, town center coordinates were used using OpenStreetMap Nominatim via the R package tmaptools  [26].
Holdings without coordinates or town information were removed. The type of some farms was adapted according to the size of the reported areas (e. g. a finisher reporting post-weaning animals was changed to post-weaner/finisher). The status of multiplier and nucleus was used to define the type of farm independently of the type of farm initially declared. The movement database included both national and international movements. However, exports and imports of foreign animals were recorded at country level without specific geographical coordinates. Therefore, movements from/to foreign countries were only used for descriptive purposes and were removed from further analysis. Movements for which essential information was missing (e. g. transport identification numbers, holdings identification numbers or animal category) were also removed from the analysis.
Reared species Pigs or wild boars Batch-rearing systems � (BRS) 4-batchs, 5-batchs, 7-batchs, 10-batchs, 20-batchs � Also called batch management production systems, it is characterized by the number of batch reared simultaneously and the duration of weaning. It defines the within-farm dynamics and structure. As it was not available in the database, it was estimated from the import and export frequencies and the size of the holdings. This approximation was justified by the proportion of farms in each BRS in the total industry. https://doi. org/10. 1371/journal. pone. 0266457. t001 from/to the overseas territories might depend on a different agro-economic and social systems than on the mainland, they were also removed. The movement and holdings databases were cross-referenced. Holdings involved in at least one loading or unloading event during the period under study (2017-2019) were considered active.
Only active holdings and movements between them were kept in the analysis. By looking for consistency between the types of animals shipped and the types of farms, inconsistent movements were removed (e. g. movements of barrows from farrowers were removed as well as movements of piglets from finishers). Finally, as the movement database is composed of ordered loading and unloading events, only movements with at least one loading event and one unloading event starting with a loading were kept. To simplify the analysis, the transit of trucks without any loading or unloading event was not taken into account, thus focusing on diseases transmitted by direct contact. For each transportation, the records were divided according to the types of animals transported. For each type of animal, directed movements were created from the loading farms to the unloading farms when the loading event took place before the unloading events, as described in the method for the animal introduction model  [8].
Exploratory analysis. Animal movements were analysed using complex network analysis (CNA) tools. The analysis was performed using R software  [27]  and more specifically the igraph package  [28]. Several directed and unweighted networks were built using specific subsets of movements depending on the objectives. The general description of the network was carried out for the whole period 2017-2020. To serve epidemiological concerns, the CNA was then focused on industrial pig production only including commercial farms and slaughterhouses. The network indicators, described in Table  2 , were calculated to describe the general structure of the network in relation to our epidemiological concerns. The industry being composed of disconnected clusters (connected components), the closeness was not considered.
ERGM is a class of statistical model for network analysis and simulation. It aims to analyse the effect of the network topology and the characteristics of its nodes and/or links on the presence (and absence) of network ties (movements in our case) between 2 nodes (pig holdings in our case). Based on those features, ERGMs models the network, identifying the probability distribution of any network so that large samples of networks can be generated. Model selection. The analysis was performed using the R software  [27]  and more specifically the statnet and ERGM packages  [32, 33]. A set of explaining variables was selected for each ERGM by sequential tests, following a bidirectional stepwise procedure. Starting with a forward approach from the simplest model including only edges as a predictor, the addition of each network statistic was tested sequentially, to identify a relevant subset of predictors (network statistics) for each final model.
Data Information were gathered on a total of 28,061 pig holdings and 1,426,865 transportation composed of 3,606,086 loading and unloading records occurring between 01/01/2017 and 31/12/ 2020. In total, 372,608 loading/unloading records (10%) were removed from the initial records for one or several of the following reasons: related to international transportation (25,073 records-1%), related to direct selling, household slaughter, unmarked animals (14,982 records-0%), missing site or transport identification (15,643 records-0%), related to an unrecorded holding (63,256 records-2%), related to removal of animals (death or missing) (148,063 records-4%), missing loading or unloading record (108,668 records-3%), related to one of the 303 holdings belonging to overseas territories (29,223 records-1%).
The increasing number of active facilities observed between 2017 and 2019 was unexpected and could be related to recent efforts from database managers to inform and register all facilities/actors, as well as the cumulative amount of record over the years reducing the number of sites removed from the analysis because of missing critical information. On the other hand, there was a sharp decrease in the number of active holdings in 2020. In 2020, the European pig production was strongly affected by both the Covid-19 crisis and the African Swine Fever pandemic  [38] , therefore some of the changes occurring in 2020 could be linked to those health crises  [39] [40] [41]. The overall decrease in the number of movements and more generally in the number of transported animals confirms the general tendency observed over 2014-2016.
The distances calculated from our datasets were smaller to those generally observed in other European countries such as Germany, Bulgaria or Spain than in 2014/2016  [25, 44, 45]. This may reflect a change in the network structure favoring short-distance exchanges. As recommended by other studies, using effective distances or travel-time instead of Euclidean distance could provide more accuracy on the actual distance traveled by trucks  [46]. The annual number of active holdings (nodes) and movements (edges) were quite well represented by semestrial networks and no seasonal variation was detected. For all sub-networks, the centralities were similar, whether by month, semester or year, suggesting that the network structure was stable over time and that relatively short periods remain relevant to study network patterns.
The period of one semester was chosen in order to allow the observation of several production cycles, (the growth period lasts 180 days) and the potential multiplicity of trading partners during the study period. This choice was all the more relevant as this period is commonly used in trade network analysis  [3, 6, 48]. The high number of selected variables in all three ERGMs illustrates the inherent complexity of the trading patterns. Nevertheless among the multiplicity of selected variables, the most impacting ones are particularly relevant and expected. In the sows network, the influence of breeders (NU and MU) was clearly highlighted, as well as their privileged connection to FF. However, the position of FI as main senders in between-farm movements is less consensual and might be due to misinterpretation of some records in the database.
In the growing pigs and piglets networks, as expected farms organized as a 10-BRS or a 20-BRS were more involved in the movements. The movements of piglets and growing pigs seemed to be privileged between farms with the same BRS, that could be explain by a synchronisation of the production stages. In the sows networks model, expect for the connection from 10-BRS to 20-BRS that was weakly significant, all other variables related to BRS had negative estimates limiting the implication of 10-BRS and 20-BRS as senders. Also, in all models, the variables associated to the 5-BRS had negative estimates. Since BRS is estimated from movement frequency and farm size, these results should be viewed with caution, and should be confirmed using data with a validated BRS. In both piglets and growing pigs networks, the traditional production systems (without outdoor areas) were more involved in the movements especially as senders in the growing pigs networks.
Raw data are confidential and cannot be shared publicly. Data were collected through the National Swine Identification Database owned by BDporc. BDPorc, 43, rue Sedaine, CS 91115, 75538 PARIS cedex 11 -FRANCE (contact@bdporc. fr). Aggregated data were provided within the manuscript and its Supporting information files. The R package 'SwineNet' including code and example dataset is in final phase of development and will soon be available publicly on GitHub (https://github. com/ Pachka/SwineNet).
, 2018 , Gioia et al. , 2019). A bovine brucellosis control and eradication plan are currently being developed in Ecuador. This plan seeks to implement vaccination, biosafety, and epidemiological surveillance strategies  (Agrocalidad, 2008). To strengthen sanitary surveillance and control systems, the World Organization of Animal Health (OIE) has been promoting the implementation of records to identify cattle and their movements between farms  (OIE, Chapter 7. 3, Terrestrial Code, 2009) , which have been implemented in many countries. Computer development has significantly contributed to information management as it facilitates the storage and handling of vast amounts of data. Livestock trading systems can be represented through networks based on a database of animal movements recording their origin and destination.
, 2017). In Ecuador, the issuance of the Organic Law of Animal Health 2004 (Ley Orgánica de Sanidad  Animal, 2004)  and the subsequent launch of the Foot-and-Mouth Disease Eradication Program in 2010  (Agrocalidad, 2011)  gave way to the structuring and improvement of national databases on cattle census and movements. In several Latin American countries, SNA tools were used to describe and analyze the cattle movement networks in Argentina  (Aznar et al. , 2011) ,  Uruguay (VanderWaal et al. , 2016) , and Brazil  (Júnior et al. , 2017). This work have provided information to strengthen epidemiological surveillance in those countries. More generally, the integration of cattle trade data in epidemiological studies and the use of SNA have allowed studying the relationship between animal movements and the spread of infectious diseases of cattle.
Oppositely, bacterial diseases of cattle such as bovine tuberculosis or bovine brucellosis often evolve slowly in affected hosts, and spread slowly inside and between herds. In the case of bovine brucellosis, infected animals are mostly adult females. Therefore, the relevant network for the study of disease spread is different for FMD (the whole cattle trade network) and for bovine brucellosis (the cow trade network), and these two networks might have different properties. The objectives of this study were to build and compare two distinct networks to address, in Ecuador: i) the spread of an FMD serotype/ topotype not included in the current vaccines being used, and ii) bovine brucellosis spread through cow network. Cattle movements were studied at the yearly and monthly scales using data for 2017 and 2018.
Data In Ecuador, Agrocalidad is the official institution that has developed two databases in the context of the FMD control program set up in 2010. One database contains information on the cattle inventory by farm collected during the FMD vaccination campaigns carried out every six months throughout the country. This database comprises information on farm location specifying three administrative units (from the smallest to the largest: parish, canton, province), cattle farm type (beef, dairy, mixed) and number of cattle by category  (calf, heifer, cow, bull). For this study, we used data for January 2017 and January 2018 to create farm and cattle density maps. The second database gathers all information on cattle movements including the identification numbers of origin and destination holdings, date of movement, geographical origin and destination specifying the three administrative units aforementioned, type of holding at origin (farm or market) type of holding at destination (farm, market, slaughterhouse) and number and category of moved cattle (cows, bulls, steers, calves).
Data for movements in 2017 and 2018 were extracted to construct the movement networks. Cleaning of the dataset from the movement database was performed to eliminate duplicated data and to homogenize administrative unit names. For this last operation, we used names approved by the Ecuadorian Institute of Statistics and Census of Ecuador (INEC). As we found inconsistencies on the use of farm identification codes, we decided to aggregate data at the parish level to conduct the analysis. Farm and cattle numbers recorded in the inventory database were aggregated by parish taking farm type into account. All the cleaning and data processing were performed using the R 4. 0. 3 software (R Core Team, 2020). The resulting dataset was imported into QGIS version 3. 10  (Sutton, 2004)  to create farm and cattle density maps.
The Jaccard index (JI), which allows the measurement of the similarity between two data sets, was used to compare the nodes and links included in each network from month to month and from year to year. It takes values between 0 (no elements in common) and 1 (the samples are identical). A function to estimate the JI was created using the R 4. 0. 3 software (R Core Team, 2020). The number and size of the strongly (SC) and weakly (WC) connected components were determined for both the global and cow networks at the annual scale. The components are sub-networks where all nodes are connected. They can be interpreted as areas of a network where connectivity is exceptionally high. In SC, all nodes are mutually accessible by directed links, while in WC, all nodes are linked without taking into account the direction of the links  (Robinson and Chistley, 2007).
The scalefree networks are characterized by the presence of hubs, which are responsible for several striking properties for propagation of infections  (Meloni et al. , 2009). A small-world network represents a situation where most nodes have close neighbors, but a few nodes have very distant neighbors  (Watts and Strogatz, 1998). To detect the presence of scale-free and small-world properties in our networks, we plotted their degree distributions on a logarithmic scale, and generated random networks having the same numbers of nodes and links, to compare their clustering coefficient and average path length values with those of our networks. Networks analyses were performed using the Igraph package for R 4. 0. 3. (Gábor  Csárdi and Tamas Nepusz, 2005).