dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2 IME_oriTs Identified in S. salivarius Strains L11, L25 and L45 Are Able to Excise from the ChromosomeIn both L11 and L25 S. salivarius strains, an IME_oriT is integrated into oriT of an ICE of the ICESt3 family, which is integrated in the 3 end of fda. In S. salivarius L45, another IME_oriT is integrated in oriT of an ICESt3-related ICE integrated in the 3 end of rpsI. Excision of these three IMEs and of their host ICEs was tested by PCR for wild type strains. All the six integrative elements (IMEs and ICEs) were found to excise (Figure2, data shown for elements of strain L25).
Data collection. Video and audio recordings of the animals were taken simultaneously over an average period of 19 observation days at each zoo. All individuals were recorded using focal animal sampling  56   Video recordings were made using a digital camcorder (Sony DCR-SR36E, Sony Corporation, Tokyo, Japan). Audio recordings were made using an omni-directional microphone (Sennheiser MKH 8020, Sennheiser electronic GmbH & Co. KG, Wedemark-Wennebostel, Germany; flat frequency response from 10-20,000 Hz ± 5db) that was equipped with a wind shield and a boom pole. The microphone was connected to Vocal and behavioural analysis. Video recordings were synchronised with respective audio recordings and analysed using the Observer XT software (version 12, Noldus Information Technology, Netherlands  57  ).
The analysis was conducted by three different observers (CL: Erfurt, Gelsenkirchen; BC: Osnabrück, Hodenhagen, Schwerin; JJ: Augsburg, Münster). The Cohen's Kappa coefficient was determined among the observers by comparing 15 pilot observations (total of 100 min). All Ƙ values were ≥ 0. 95, indicating a high interrater reliability  58. Vocalisations were detected by auditory identification and categorised according to the literature  46, 52, 53. For each vocalisation, the respective call type and sender were noted. Vocalisations that due to ambient noises could not be reliably assigned a call type or a sender were excluded from the analysis. For a sufficient data basis, only call types that were uttered by at least 50% of adult senders were considered for further analysis. Consequently, the following four call types were included (Fig.
The best fitting model (final model) was determined via backward stepwise elimination procedure ('car' package, ' Anova' function). If the interaction term was not significant, only the main factors remained in the final model. To investigate significant effects of the main factor dyad type, a comparison across the three dyad types was conducted ('lsmeans' package, 'lsmeans' function, 'Tukey' adjustment for multiple comparisons). If an interaction term turned out to be significant, a break down analysis was performed by splitting the dataset according to dyad type. In the result section, only final models were reported along with slope β values indicating the coherence between call rate and the quality of social interaction or association strength. ethical statement. The article contains only observational data of zoo animals during their daily routine.
Acknowledgements We are very grateful to  Carina Limburg  for her continuing perseverance in observation coding. We thank  Zoo Osnabrück ,  Zoo Augsburg ,  Zoopark Erfurt ,  Serengeti-Park Hodenhagen ,  ZOOM Gelsenkirchen ,  Zoo Schwerin  and  Zoo Münster  for permitting us to work in their facilities and their kind support during this study. We would like to particularly thank  Dr. Michael Boeer ,  Thomas Lipp ,  Tina Risch ,  Daniela Lahn ,  Wolf-Dietrich Gürtler ,  Sabrina Höft  and  Dr. Dirk Wewers  and all involved animal keepers for their patience and encouragement. Last but not least, we would like to thank  Louise Huber-Fennell  as well as  Frances Sherwood-Brock  for proof-reading the English language in this manuscript. This research was funded by the  Serengeti-Park Stiftung  and the  Deutsche Forschungsgemeinschaft  ( SCHE 1927/2-1 ).
Strain identification and molecular epidemiology MALDI-TOF mass spectrometry is revolutionizing the clinical diagnostic laboratory, but not all machines can identify Brucella. We developed a spectral database allowing Brucella to be identified by the bioMérieux VITEK system  [7]  and a safe, rapid protocol for solvent inactivation before analysis. Solvent inactivated bacteria are stable for several days, allowing transport to a centre with a machine  [8]. Multilocus sequence typing (MLST) and Multiple-Locus Variable number tandem repeat Analysis (MLVA) is now used throughout the world for molecular epidemiology. These studies are showing how B. melitensis and B. abortus strains have been transported across the world by animal trade and will be important in the future control programs  [9, 10].
ACKNOWLEDGMENTS We thank the laboratory technicians for their expert assistance. We also thank all bacteriologists and clinicians who sent isolates to the  French National Reference  Centre for Mycobacteria. Part of this work was presented, in part, at the 49 th Union World Conference on Lung Health in 2018 at the  Hague, Netherlands , and at the Mycobactéries meeting in 2018 in Saint-Quentin en Yvelines, France.
Data Collection and Selection Available Data. Software backups of the farm management system were collected on, respectively, 34 and 42 Belgian and British farms that use an automated milking system (AMS). From this database, 27 farms were selected based on (1) the accessibility and reliability of at least 5 yr of contiguous data and (2) the availability of daily milk yield at individual cow level. The time period covered by these data varied between 2005 and 2019. All 27 farms had AMS either from Lely (Lely Industries N. V. , Maasluis, the Netherlands; n = 16) or from DeLaval (DeLaval International, Tumba, Sweden; n = 11). On average, 2. 4 lactations were recorded per cow's life. All farms had intensive production systems, with cows kept indoors and fed with both forage and concentrates. Other management practices differed among herds but were not further documented in the software backup files of the farm management system.
All data tables were extracted from the restored backup files of the AMS software system using SQL Server Management Studio  (Microsoft Corp. , Redmond, WA). The further data mining, pre-processing, and merging of these data tables and the rest of the analyses described below were performed in Matlab R2017a (The MathWorks Inc. , Natick, MA). Both the full data set of 27 farms all having daily milk records (data set 1, DS1) and a subset of 13 farms also having daily activity data available (data set 2, DS2, all milked by a Lely AMS) were used for this study. An overview of the characteristics of both data sets is given in Table  1. Cow Selection. After extraction of data tables, individual cows on each farm were selected based on the availability of sensor data for their entire lifetime production.
Because exact culling dates were not always available, we elected to apply a criterion to discriminate between cows that likely had been dried off toward the end of the time span covered by the data set available for that farm (not to be included in the analysis) or removed from the herd (to be included in the analysis). For each farm, the 95% confidence interval (CI) of the average dry period length was calculated. If the last milk record was before the end of the data set minus the upper 95% CI boundary, that cow had a 97. 5% chance of having been removed from the herd, and she was included. Accordingly, only cows that met this criterion and for which the date of first calving was within the time span of the available data for that farm were selected. An overview of the characteristics of this selection is provided in Table  1.
Data set 1 consisted of 3,754 unique cows and 9,395 unique lactations, and DS2 included 2,075 cows with 5,286 lactations. Per farm, respectively, 24 to 308 cows (139 ± 82, mean ± SD) with, in total, 44 to 799 lactations (348 ± 229), and 57 to 308 cows (160 ± 84) with 113 to 799 lactations (407 ± 264) were selected for DS1 and DS2. Sensor Data. The milk yield sensor data were recorded by the AMS using ICAR-approved milk meters as integrated in the Lely and DeLaval robots. The available activity sensor data were recorded by Lely neckmounted activity sensors and consisted of raw 2-hourly measures of acceleration, but no further details of the individual sensor systems were available. For this study, the 2-hourly measures were summed up per day (midnight to midnight) to obtain time series of single daily activity records.
Sensor Features The time series data of 2 sensors were included in this study: (1) milk meter sensors from which daily milk yields were calculated and (2) activity sensors from which the 2-hourly raw data were aggregated into daily activity records. Sensor features were calculated for each of the cows for which the first lactation was longer than 200 d, because 200 d is enough to grasp a good image of the time series dynamics, as the second part of the lactation curve after the peak can be estimated by a linear function  (Wood, 1967). Only the data of the first 305 d of the first lactation were included for the calculations. Milk Yield. As explained in greater detail in Appendix A, in total 30 milk yield sensor features (SF) were calculated based on the daily milk yield dynamics. Moreover, a methodology was developed to calculate SF from the dynamics of the lactation curves using both the theoretical shape and the deviations from this theoretical shape as proxies for the cow's physiological status.
Model Evaluation The initial model fit at farm level was evaluated using the RMSE (i. e. , the RMSE TR , RMSE_training, calculated on the training set) and the adjusted R 2 , R adj 2 , calculated as shown in Equation [3]: R SSE SSTO n k adj 2 1 1 1 1 1 1 = - --                    - ( ) -- ( ) , [3] with SSE the residual sum of squares of the regression, SSTO the total sum of squares (i. e. , the mean value of the outcome RR scaled ), n the number of data points of each farm, and k the number of SF retained in the final model for that farm. To evaluate classification performance and discriminate between high-and low-resilience cows (which is of practical relevance), the cows of each farm were divided into 3 different categories based on their ranking: high-(H, top third), moderate-(M, middle third), and low-(L, bottom third) resilience animals.
Sensor Feature Definition and Overview The present study shows an example of how realfarm high-frequency sensor data can be used beyond monitoring and detection applications  (Boichard and Brochard, 2012). We used the longitudinal, high-granularity milk yield and activity data of respectively 27 and 13 commercial dairy operations with an AMS to calculate biologically meaningful features of the cows. This is a unique data set, not only because of its commercial nature (as opposed to research farm data) but also because these high-frequency time series allowed for the inclusion of the dynamics of milk yield and daily activity. The availability of at least 5 years of successive measurements per farm uniquely permitted us to study the accumulated effect of health and fertility traits of many animals over their entire lifetime.
Predicting Lifetime Resilience Ranking from Both Yield and Activity Features Pearson Linear Correlations. Data set 2 consisted of 13 farms for which, besides milk yield features (n = 30 features), activity features (n = 15 features) could also be calculated. These features included both general characteristics of daily activity (skewness, variability, absolute daily level) and specific features associated with short-term and longer-period activity changes. Farm-individual Pearson correlation coefficients  (Figure 4)  between the activity features and the RR varied between ρ = -0. 41 and ρ = 0. 44, and only the number of short activity peaks in SF34 was consistently associated with a higher ranking [lower number of peaks is associated with a higher resilience, ρ = 0. 29 ± 0. 10 (range: 0. 16 to 0.
ACKNOWLEDGMENTS Ines Adriaens received funding from the  Research Foundation Flanders (Brussels, Belgium)  through grants No. 11ZG916N  and  V423719N  and from a  Katholieke Universiteit Leuven (Belgium)  postdoctoral mandate grant No. PDM/19/132. The data from this study were collected in the format of farm software back-up files by the authors, and the resulting database used is owned by the authors. Part of the data were collected in the context of the  VLAIO LA-trajectory "MastiMan ," grant No. HBC. 2016. 0774by  Igor Van den Brulle  and  Sofie Piepers  from the  University of Ghent, Flanders (Belgium; Department of Reproduction, Obstetrics and Herd Health, M-Team Mastitis and Milk Quality Research Unit ). This work is part of the  GenTORE  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  ( Paris, France ), under grant agreement No.
1 ). Only vaccination strategies and no other type of control strategies (i. e. culling, biosecurity, movement restriction, etc. ) are compared. The implementation of the tool requires five steps: 1) modelling of the poultry production networks, 2) definition of vaccination strategies to be tested based on the poultry production networks; 3) simulation of vaccination strategies within the networks to generate the outputs in terms of vaccination coverage, immunity levels and duration of immunity; 4) spatial analysis of the immunity level distribution and 5) comparative cost-benefit analysis of the different strategies. Steps 1-4 are performed using specific scripts built in the EVACS "RStudio" project previously developed using the "RStudio" software version 1. 1 ("R" version 3. 5. 1); step 5 is performed using the EVACs "cost-benefit analysis" Excel spread sheet  (Microsoft Excel 2007).
Data requirement and collection In order to model the poultry production networks in France, data on the poultry production organisation and census were collected for each production sector (layers or meat) and species (chicken, ducks …. ) including: the number of birds and farm per type of production (grandparents, breeders, free-range production, indoor production); the level of integration (integrated with or without hatchery or independent); the type and volume of movements of birds, eggs or day-old birds between production types. Data were collected both from a public database for the national poultry census per production sector and production type  (Agreste, 2018)  and from a private database for day-old bird flows from hatcheries (Ceva Poultry database). In addition, interviews with representatives of most French poultry production sectors were performed.
After this data collection and collation phase, a participatory workshop was organised with these representatives to validate the modelled networks. To model the immunity within the poultry production networks, data were collected on: the type of vaccines used, the vaccination coverage per production type, the efficacy of the vaccine used (in terms of seroconversion and duration of protection), the number of vaccine doses administered and vaccination schedule (time interval between doses). To perform the spatial analysis the number of heads of the different poultry production types and sectors (grandparents, breeders, indoor production, free-range production) per region were collected  (Agreste, 2018). To perform the cost-benefit analysis, data on the vaccination cost (i. e. cost of vaccine dose, vaccination implementation costs) and on the production values (i.
Data analysis All the data collected were entered in a database developed with Excel software  (Microsoft Excel 2007). The EVACs tool was applied using "RStudio" software version 1. 1 ("R" version 3. 5. 1). The network script is using "igraph" and "sna" packages  (Butts, 2016). The immunity modelling script is a stochastic simulation model using gamma distribution and sensitivity analysis, and "igraph" and "MASS" packages  (Gábor, 2018; Ripley et al. , 2018). The spatial analysis script also uses "raster" and "rgeos" packages to generate maps  (Bivand et al. , 2018; Hijmans et al. , 2017). The cost-benefit analysis uses an Excel spreadsheet (Microsoft Excel 2007).
Step 1: poultry production network modelling The "network modelling" R script of the EVACs tool was used to conduct the network analysis  (Peyre et al. , 2016). The aim of the network modelling step is to characterise the poultry production networks and to identify the main type of farms (i. e. nodes of the social network analysis) and bird flow between the farms (e. g. day-old birds). Production network models were developed for each of the major French poultry production sectors in France (i. e. broiler, layer, fattening duck, meat duck, turkey and guinea fowl). Attribute tables were used to generate networks based on: the type of production (grandparents, breeders, free-range production, indoor production) for farms and hatcheries, the integration level (integrated with or without hatchery or independent), and the number of birds (heads) on the farms.
Step 3: Estimation of the efficacy of the vaccination strategies The "immunity modelling" R script of the EVACs tool was used to estimate the efficacy of the different vaccination strategies for each type of production (network nodes) in terms of: vaccination coverage (proportion of birds in the entire poultry population which have been vaccinated); immunity level (proportion of birds with a protective seroconversion level) and duration of the immunity (proportion of weeks where more than 70 % of birds had a protective seroconversion level)  (Peyre et al. , 2016). The parameters used for the model are described in Table  3. As the vaccination would be mandatory if applied in France, the vaccination coverage at farm level (% of vaccinated farms) was considered maximum (100 %). Due to practical aspects, the vaccination coverage at bird level (% of vaccinated birds in a vaccinated farm) was considered better with hatchery vaccination (mean = 98 %, IC 95 %=[95, 99]%) as compared with farm vaccination (mean = 95 %, IC 95 %=[90, 98]%).
Grandparents and breeders Free-range Indoor Farm integration level Not applicable Integrated Independent Integrated Independent (  Peyre et al. , 2016). As no AI vaccination is currently performed in France, data on vaccine efficacy were collected from the literature. The same vaccine efficacy was applied in the model for both vaccination types based on literature data  (Peyre et al. , 2016). P 1 I b I - - - P 2 I R R - - P 3 I R R R Int H R Int H P 4 I R R I - P 5 I I - I - P 6 I I I I I P 7 I R R R The vaccination coverage was considered sufficient above 80 % of the entire targeted population  (Bouma et al. , 2009). The immunity level was considered to be protective above 60 % based on the R0 estimations previously reported  (Fine et al. , 2011; Garske et al. , 2007; Tiensin et al. , 2007).
Step 4: spatial analysis section The "spatial analysis" R script of the EVACs tool was used to map the distribution of the immunity levels according to the different vaccination strategies  (Peyre et al. , 2016). Poultry census data at the region level  (Agreste, 2018)  were used for the spatial analysis. Data were aggregated according to the production types (grandparents, breeders, indoor production, free-range production) and production sectors.
Some of these residues are also conserved in the TIR-domain of human SARM1 and plant RUN1, in which the NAD + hydrolytic activity was recently described  [10, 11]. To decipher the effects of the mutations on BtpB properties, we mapped the corresponding residues on the BtpA-TIR domain structure (PDB: 4LZP)  [30] , as BtpB structure is not yet solved. As seen in Fig  4A , none of the residues mutated belonged to the TIR-TIR interface. S162 (S149 in BtpA) and F163 (H150 in BtpA) belong to the βA strand. Y225 (F208 in BtpA) and Q226 (F209 in BtpA) are part of the small helix αC. Mutations of these residues are likely to disrupt the inner core and thus destabilize the whole structure. Mutation of BtpB S201P (S185 in BtpA) is likely to perturb the NAD+ catalytic site. In the recent crystal structure of NADP + -bound RUN1-Tir domain  [11]  (PDB: 6O0W), the substrate lies in a pocket formed by the BB-loop and the loop containing the conserved catalytic WxxxE motif  [19]  (S5B Fig).
1% 0. 0046 ( �� ) for BtpB-TIR. (B) Cellular NAD + levels measured by mass spectrometry, standardized as a NAD + /extract protein ratio, from YPH499 cells transformed with the same plasmids as in A. Graph shows NAD + /protein ratio as a percentage of the empty vector control cells NAD + /protein ratio. Data correspond to means ± standard deviation of four different transformants and statistical comparison was done with one-way ANOVA with p-values referred to vector <0. 0001 ( ��� ) for BtpA-TIR, BtpB and BtpB-TIR, and 0. 0134 ( � ) for BtpA. https://doi. org/10. 1371/journal. ppat. 1007979. g003 ± 7. 7 for BtpA-TIR vs. 26% ± 12 for BtpA-TIR E217A), these structures were larger and more intense for the mutant than for the wild type version (Fig  4F ). Importantly, these results indicate that, as observed for BtpB, the catalytic E217 residue is essential for toxicity in yeast but still allows assembly of the BtpA TIR domain into ordered structures.
Cells were then incubated with EGF conjugated with Alexa Fluor 555 for 10 minutes and the percentage of infected cells showing uptake of this fluorescent marker quantified by microscopy. Counts correspond to individual microscopy fields, with a total of at least 200 cells counted for each, from three independent experiments. Mock infected cells are included as a control. Data correspond to means ± standard deviation and statistical comparison was done with one-way ANOVA test, with no statistical significance observed. https://doi. org/10. 1371/journal. ppat. 1007979. g007 (A) HeLa cells were infected for 48 h with either wild-type B. abortus, or strains lacking btpA, btpB or both genes. Mock infected cells are also included. Total NAD levels were measured using a colorimetric assay and data correspond to means ± standard deviation from five independent experiments and statistical comparison was done with a one-way ANOVA test, with statistical significance indicated in the graph.
Antibodies were used as follows: As primary antibody, monoclonal rat anti-alpha-tubulin (Serotec, YOL1/34) at 1:500 dilution; as secondary antibody, Alexa Fluor 594 anti-rat dye (Life Technologies) at 1:1000 dilution. DAPI was added at 1:1000 for nuclear labelling. Cells were examined in Eclipse TE2000U microscope (Nikon, Tokyo, Japan) and digital images were acquired with an Orca C4742-95-12ER charge-coupled-device camera (Hamamatsu Photonics, Hamamatsu City, Japan) and processed by HCImage and ImageJ software.
Statistical analysis and software All data sets were tested for normality using Shapiro-Wilkinson test. When a normal distribution was confirmed a One-Way ANOVA test with a Tukey correction was used for statistical comparison of multiple data sets and Students t-test for two sample comparison. For data sets that did not show normality, a Kruskall-Wallis test was applied, with Dunn's correction, or Mann-Whitney U-test for two sample comparison. 3D protein images were generated using PyMOL (Scho ¨dinger), taking advantage of previously published structural data of BtpA (PDB: 4LZP) and RUN1-TIR + NADP + (PDB: 6O0W).
Acknowledgments We wish to acknowledge the  Genomics and Mass Spectrometry Units at Universidad Complutense de Madrid (UCM)  for sequencing and NAD + analyses, respectively. We would like to thank  Thomas Henry  ( CIRI, Lyon ) for proving us with iBMDM and  Diego Comerci  ( CONI-CET, Argentina ) for helping us with the complementation constructs. We also thank  Lucia Sastre  for technical assistance in molecular cloning and yeast growth assays and members of  U3 Research Lab  for help and discussion.