dataesr/scientific-paragraphs-categorization huggingface dataset:
The sample was kept between 0 and 4 °C during the transfer to the lab within 18 h. Twelve and nine indicators were analysed on fresh milk within 48 h after sampling and on frozen milk samples (À20 °C), respectively (Table  S2 ). Some of the analyses were performed by private laboratories, using mostly routine methods. The others were performed by research laboratories using reference methods (e. g. , gas chromatography for milk fatty acids, FA). When data were missing (some analyses were not carried out because sampling and analysis of bulk tank milks occurred before the assessment model had been finalized), they were approximated by existing equations or assigned a unique mean value for all the farms obtained from the literature or databases (Table  S2 ). The two assessment models were then applied to those 30 farms.
Interpretation of indicators For a majority of indicators, the thresholds were defined during the consultation process. However, for most of the nutritional and health indicators no consensus was reached and experts were only able to define the global sense of interpretation (e. g. , lower is better) but not the thresholds. These were therefore defined from the analysis of a database of around 1 300 individuals  (Coppa et al. , 2013; Chassaing et al. , 2016) : the 0/10 score was defined as the 2. 5th percentile and the 10/10 score as the 97. 5th percentile of the values of this database for indicators interpreted as 'higher is better' (and the opposite for 'lower is better' indicators), and then discussed and validated with the experts (Table  S3 ).
A database was therefore used to define these thresholds values. This means that they do not have a direct nutritional or health meaning (no thresholds for which the indicator would become dangerous), even if the general orientation of the interpretation is known. It will be possible to review this point in step with the future evolution of scientific knowledge on the contribution of dairy products to dietary recommendations and requirements  (Martin et al. , 2019). In addition, the indicators used to assess nutritional quality were selected among those providing at least 5% of needs by the products concerned (UHT milk and cheese) thus depending on the current, geographically targeted (France) consumption of these products. As this consumption can vary according to place and time, it had to be possible to modulate and update the assessment.
A low but real discriminating power The assessment was performed on 30 farms. Overall quality scores on the 30 farms presented a narrow dispersion, which could question the power of the assessment to discriminate the bulk tank milks of the different farms in terms of intrinsic quality. However, some indicators could not be measured (alcohol test, Dornic acidity, levels of Pseudomonas and E. coli) and the corresponding missing values were replaced by the same mean value extracted from databases or literature (Table  S2 ) for all the farms, independently of the season, which could explain this narrow dispersion. Despite this limitation, the assessment was able to discriminate the different milks in terms of intrinsic quality at the seasonal level as it showed significant differences between indoor and grazing period quality scores of the cheese and UHT milk assessments.
Acknowledgements The authors would like to thank the experts who participated in the focus groups for the development of the assessments, interviewers and samplers who went to the farms to collect milk and information, as well as the French farmers who collaborated in the samplings and surveys. Authors would also like to acknowledge the help they received when drafting this article from the UMRH Scientific Writing Workshop chaired by  Isabelle Veissier  and  Isabelle Ortigues-Marty. The authors also thank  MetaForm Langues  ( Clermont-Ferrand, France ) for checking and amending the English text.
In two previous studies, a methodology was developed to predict a wide range of behaviours in dairy cows at pasture from accelerometer data every 10 s, including grazing, walking, resting and ruminating both in lying and standing position  (Riaboff et al. , 2019 (Riaboff et al. , , 2020a)). These predicted behaviours can be combined with the cow position using GPS data collected on animals  (Riaboff et al. , 2020b). Behavioural and movement variables can then be calculated from the predicted behaviours and GPS position. In this study, we propose to identify behavioural variables predicted from accelerometer data as well as movement variables computed from GPS data that could be used to discriminate lameness scores in pasture-based systems, using a Partial Least Squares Discriminant Analysis (PLS-DA).
Materials and methods An overview of the main steps in the Materials and methods section is provided in Fig. 1. First, accelerometer and GPS data were obtained from 68 dairy cows from three pasture-based farms. For each cow, a lameness score was collected twice a week over the course of the experiment (Section 2. 1). Prediction of behaviours was carried out using accelerometer data and combined with the position of dairy cows using GPS data (Section 2. 2). For each cow, a lameness score was assigned for each day the cow was fitted with the accelerometer and GPS device. For each day the cow was fitted, the lameness score was the score from the closest scoring session. Behavioural and movement variables were then computed for each cow on each day she was equipped after identifying behavioural bouts within each outdoor period (Section 2.
1. 3. 1. The distribution of lameness scores in each farm is provided in Appendix A. As parity may impact behaviour, we tried to balance the dataset in terms of parity. For this purpose, we chose the animals so that each lame cow had an equivalent sound cow with the same parity (1, 2 or ≥ 3) and with a difference in days in milk of less than 20 days. Based on these criteria, 13/54 cows were equipped in the first farm, 27/71 cows in the second farm, and 26/61 in the last one, giving a total of 68 different cows used for the study. Among the equipped cows, one cow in Farm 1 and eight cows in Farm 3 showed signs of heat on one day each during the experiment (AMS data). Details on the animals selected in each farm are provided in Table  1.
Description of sensors An RF-Track datalogger (RF-Track, Rennes, France) comprising an LSM9DS1 three-axis accelerometer (STMicroelectronics, Geneva, Switzerland) ±2 g and a GPS sensor (part number EVA-7M-0, μ-Blox, Thalwil, Switzerland) with a static position error estimated at ± 1. 72 m was used. The sampling rate was 59. 5 Hz and 1 Hz for the accelerometer and GPS data, respectively. Sensors were powered with two 3. 7 V lithium batteries (2. 6 Ah). A secure digital card was used for data storage. The dataloggers were 98. 2 mm x 51. 60 mm x 36. 0 mm in size and weighed 250 g. The dataloggers were attached to a collar and positioned on the right side of the cow's neck. A counter-weight of 500 g was added to prevent the collars from turning around and they were tightly adjusted. The x-axis detected the up-down direction, the y-axis detected the backward-forward direction and the z-axis detected the left-right direction.
Accelerometer and GPS data collection. In each farm, the selected cows were continuously equipped with the device for a minimum of two days and a maximum of six days, the latter corresponding to the battery life of the sensors. Data were downloaded after each continuous period and batteries were recharged for eight hours before equipping the cows for the next period. Four, two and five continuous periods were carried out, leading to 21, 12 and 25 days of experiment in the first, the second and the third farm, respectively. In this way, accelerometer and GPS data were collected on a total of 58 different days (Table  1 ).
-Walking: moving from one location to another without lowering the head to ground level. -Ruminating while lying: lying down with regurgitating ruminal bolus before chewing and then re-swallowing. -Ruminating while standing: standing with regurgitating ruminal bolus before chewing and then re-swallowing. -Resting while lying: lying down without rumination. -Resting while standing: standing without movement or rumination. The methodology developed was thus applied in this study to predict the behaviours as illustrated in Fig. 3. Raw signal accelerometer sequences collected on the 68 cows over the 58 days of the experiment were divided into segments (windows) of 10 s, without data in common between two consecutive windows (without overlap). Sixty-one features were then calculated in each window.
This pre-processing step was performed in Matlab R2018a. The eXtreme Gradient Boosting model fitted by  Riaboff et al. (2020a)  was then directly used to predict the behaviours from the calculated features using the xgboost package  (Chen et al. , 2018)  in R 4. 0. 0 (R Core  Team, 2020). The Hidden Markov Model (HMM)-based Viterbi algorithm reported by  Riaboff et al. (2020a)  was then applied with the R package HMM  (Himmelmann, 2010)  to smooth the predicted behaviours in successive windows from the same cow over the entire experiment. Each 10 s window of behaviour was finally associated with the position (longitude and latitude) of the cow, based on the time of the GPS and the accelerometer, which was previously synchronised. In this way, each 10 s window of behaviour was associated with the GPS coordinates of the first sample of the window and those of the last sample of the window.
Identification of behavioural bouts within outdoor periods. The aim of this step was to identify behavioural bouts within each outdoor period, i. e. sessions during which an animal expressed the same behaviour continuously, with only brief interruptions. The principle used to identify behavioural bouts within each outdoor period is illustrated in Fig. 4. For this purpose, we first identified periods spent outdoors for each cow from GPS data and the coordinates of the barn (French National Geographic Institute; "Géoportail", 2006), using the R package sp  (Pebesma and Bivan, 2005). We then applied Lavielle segmentation  (Lavielle, 1999)  on the "indoor-outdoor" periods time series in order to smooth potential GPS errors (Fig. 4  a) using the R package adehabitatLT  (Calenge, 2006). This method finds the best segmentation of a time series given a fixed maximum number of segments by minimising a contrast function (contrast between the actual time series and the segmented time series).
Calculation of movement and behavioural variables. The behavioural bouts previously obtained and the corresponding positions of dairy cows on pasture were used to compute different variables. These variables are presented in Table  3. The following behavioural variables were computed for each cow on each day she was equipped: the overall time spent on pasture (1), the time-budgets (2), the number of bouts (3), and the mean duration of bouts (4). The variables (2) to (4) were calculated for the six predicted behaviours described in Section 2. 2, as well as for the following grouped behaviours: • Ruminating: grouping of "ruminating while lying" and "ruminating while standing" behaviours • Resting: grouping of "resting while lying" and "resting while standing" behaviours • Lying: grouping of behavioural variables from "ruminating while lying" and "resting while lying" • Standing: grouping of behavioural variables from "ruminating while standing" and "resting while standing" The following movement variables were also computed from the GPS data and behavioural bouts: the number of round-trips between the pasture and the barn (5), the overall distance travelled (6), the overall dispersion (7), the distance travelled during grazing (8), the distance travelled during walking (9), the ratio of the distance travelled during grazing over the distance travelled during grazing and walking (10), the speed of walking (11), and the mean duration between the time the cow enters the pasture and the time she lies down (12).
Weighting and standardisation. As explained in section 2. 1, 68 cows from three commercial farms were used in this study, and data were collected over 58 days, which resulted in a considerable variation between cows (time spent outdoors, etc. ) and between days (pasture access, area of pasture, etc. ), thus preventing comparison of the behavioural variables from one cow to another and from one day to the next. Consequently, both behavioural and movement variables were corrected to make the observations comparable thereafter. A weighting was thus applied depending on the variable, using:     -The time spent outside by the cow on a given day (Time out ). -The maximum time out recorded on a given day among all the cows equipped on that day (Time out,max ). -The distance travelled by the cow on a given day (Distance).
Dataset cleaning The resulting dataset was then cleaned to avoid including unusable observations and highly correlated variables in the analysis to follow. First, observations for which the nearest scoring session was more than three days away (the cow having missed a scoring session; Section 2. 3. 1) were removed from the dataset, corresponding to 14 observations (1. 5 % of the dataset). Missing values corresponding to observations in which the cow did not go to the pasture on a given day (Section 2. 3. 2. 2) were also removed from the dataset. This corresponded to the deletion of 88 observations (9. 3 % of the dataset). Details on the observations removed are provided in Appendix E a. This resulted in a dataset of 863 observations (Appendix E b). The coefficient of correlations between each behavioural and movement variable were calculated using the R package corrplot  (Wei, 2017).
Identification of discriminating variables in lameness score with a PLS-DA A one-way Analysis of Variance followed by a Tukey test was applied in a preliminary step, and showed that the group LS = 0 was never significantly different from the group LS = 1 for any of the variables studied (Appendix G). For this reason, we combined LS = 0 and LS = 1 within the same group "LS0-1" before applying the PLS-DA. We applied a PLS-DA model because (i) it is a Machine Learning method adapted to perform a first analysis on a large dataset about which we have limited a priori knowledge, and (ii) it is particularly relevant to identify the variables that have contributed to discrimination between groups using the Variable Importance in Projection (VIP) metric  (Tenenhaus, 1998). PLS-DA is a multivariate projection method for modelling a relationship between the quantitative variables X and a dummy matrix Y  (Barker and Rayens, 2003).
The target is to find PLS-components which both restore the variance of the matrix X and maximise the separation between the classes of Y. In this study, the independent variables X were the behavioural and movement variables, and the dummy matrix Y was a three-column matrix, representing the groups "LS0_1", "LS2" and "LS3". For each column in Y, each sample was assigned to 0 or 1 depending on the group to which it belonged. VIPs were then used to identify variables of X which are important in determining class membership of Y, also called discriminant variables  (Chong and Jun, 2005). A more detailed description of the PLS-DA and the definition of VIP are provided in Appendix H. The PLS-DA and VIP analysis were performed with the R package plsdeplot  (Sanchez, 2016).
5  b; Table  4 ). All of these results suggest a more pronounced exploratory dynamic in sound and slightly asymmetric gait cows than in lame animals, especially during grazing behaviour. Nevertheless, walking time did not discriminate between lameness scores (Fig. 5 c ), which is in accordance with the study by  Beer et al. (2016)  in indoor-based systems. However, speed has not emerged as a discriminant variable in our study, contrary to what was observed in indoor-based systems  (Beer et al. , 2016; Blackie et al. , 2011). It should be noted that in our study, speed was measured by averaging the distances travelled calculated from the GPS data across all the 10 s windows in which the walking behaviour was predicted. It is therefore possible that the chosen window size was too small to calculate a representative walking speed.
Limits and perspectives The discriminant behavioural and movement variables identified can be used to enhance the performance of lameness detection systems in pasture-based farming, but the creation of a high-performance prediction model based exclusively on these variables seems difficult to achieve at the moment. The two main reasons for this are (i) the poor performance of the PLS-DA model (Q2cum: 0. 11; criterion of validity of the predictive model: Q2cum > 0. 5  (Tenenhaus, 1998) ), and (ii) the need for the animals to go on pasture to collect data. Concerning the first point (i), other statistical models should be used to confirm our results. In our study, we chose a machine learning method (PLS-DA) to identify discriminant variables because (i) we had a large dataset of 863 observations (Appendix E b) and 37 innovative variables for which we had limited a priori knowledge, and (ii) cows were not followed long enough to ensure sufficient transitions between scores, especially for lameness score 3, nor were there enough animals (Appendix E b) to be able to distinguish the random cow effect from the fixed lameness effect in a probabilistic model.
3 % of the data were discarded for this reason; Section 2. 3. 3). The behavioural and movement variables calculated in this way are therefore relevant in addition to other variables measured on the animals in the barn, but are not sufficient to develop a prediction model based solely on these variables. Furthermore, from a technological point of view, the device used to automatically report GPS and accelerometer data is not usable in its current state for a field application. The major drawback is the battery life, limited to six days, which requires the sensors to be removed every six days for recharging. As the sampling rate of both the GPS (1 Hz) and the accelerometer (59. 5 Hz) sensors are high, a possible solution is to (i) reduce the sampling rate of the GPS sensor to compute the movement variables with the desired minimum precision, and (ii) to reduce the sampling rate of the accelerometer sensor to find an appropriate tradeoff between battery life and the performance of the prediction of behaviours.
Acknowledgments: The authors are much indebted to all participants in the Cost action IPEMA, particularly to the following people forming the Core group of the action who were involved in the management of the action, preparation of the webinar and answers to the questions (by alphabetical order of surname):  Marijke Aluwé  ( ILVO, Belgium ),  Gé Backus  ( Connecting Agri&Food, Netherlands ),  Giuseppe Bee  ( Agroscope, Swizerland ),  Eberhard von Borell  ( Martin Luther University Halle-Wittenberg, Germany ),  Marjeta Čandek-Potokar  and  Martin Škrlep  (KIS, Slovenia),  Olena Doran  ( UWE, UK ),  Catherine Larzul  ( INRAE, France ),  Liliana Tudoreanu  ( University of Bucharest, Romania ) and  Mandes Verhaag  ( Thünen Institute of Farm Economics, Germany ). Special attention is drawn on the memory of  Ulrike Weiler  ( University of Hohenheim, Germany ), who chaired the Cost action IPEMA until her untimely death in July 2020.
Statistical Methods Statistical analyses were conducted using the MedCalc R statistical software (Ostend, Belgium). The threshold for Sox2 positivity (>42%) was calculated according to receiver-operating characteristic (ROC) curves that best discriminated between cats that died due to their mammary carcinoma and cats that did not die from cancer within 2 years post-diagnosis. Comparisons between Sox2-positive and Sox2-negative FMCs were done using Fisher's exact tests for categorical variables with two categories, chi-squared tests for categorical variables with more than two categories, or one-way analyses of variance for continuous variables. Univariate survival analyses relied on the Kaplan-Meier method and log-rank tests, while Cox proportional-hazards models were used for multivariate survival analyses.
The negative association between Sox2 and FOXA1 has been demonstrated in human breast and lung cancers, in which Sox2 represses FOXA1 gene expression  (37, 38). Considering that PGR (the PR-encoding gene) is a direct ER target gene  (39) , and FOXA1 is a critical pioneer factor for ER  (40) , it can be hypothesized that Sox2 altered ER transcriptional activity in FMCs of the present study as it does in breast cancers  (41). In triple-negative FMCs, Sox2 showed a positive association with AR expression. This is consistent with data obtained at the mRNA level using publicly available gene expression databases: SOX2 and AR are positively correlated (r = 0. 03, p = 0. 0007) in breast cancers, although the correlation is not significant in triple-negative breast cancers in particular (r = 0. 06, p = 0.
ACKNOWLEDGMENTS We would like to thank  Dr. Delphine Loussouarn  (MD, pathologist,  University Hospital of Nantes, France)  who participated in the histopathological and immunohistochemical evaluation of the feline mammary carcinomas included in this study;  Prof. Mario Campone  ( Integrated Center for Oncology Nantes/Angers, France ) for his input in comparative oncology;  Dr. Floriane Morio  and  Dr. Clotilde de Brito , who helped in collecting the clinical and follow-up data;  Dr. Jean-Loïc Le Net ,  Dr. Virginie Théau ,  Dr. Pierre Lagourette ,  Dr. Olivier Albaric  and  Dr. Sophie Labrut , the veterinary pathologists who performed the initial diagnoses of feline mammary carcinomas, as well as  Mr. Bernard Fernandez ,  Mrs. Florence Lezin , and  Mrs. Catherine Guéreaud , the technicians in histopathology who made the slides.
Two previous studies of this Group analyzed incidence of MAC pulmonary disease between 2000 and 2002 and incidence of NTM-PD in HIV-negative patients from 2001 to 2003  [7, 8]. Patients ≥ 18 years-old were included from January 2009 through December 2014 in 18 French Hospitals belonging to the MycoMed network. All patients with at least one positive respiratory sample with NTM were eligible but only patients meeting the 2007 ATS/IDSA criteria after reviewing medical records were included. Patients with cystic fibrosis were excluded from the study. We have chosen 2007 ATS/IDSA criteria instead of 2017 BTS and 2020 ATS/IDSA/ERS/ESCMID, as our last was included in 2015, before publications of these 2 recent guidelines  [2, 9]. Demographic, bacteriological and clinical and radiological data were obtained from medical records.
Results During the period study, 568 NTM-PD were included. Among them 91 were excluded because of lack of radiologic data (87 cases) or identification of two different NTM species (4 cases). Overall, the remaining 477 cases (53. 3% of males) met the 2007 ATS/IDSA criteria for NTM-PD, and were included. The median age was 65 years [range. MAC comprised the majority (286/477, 60%) of all NTM findings (M. avium, n = 149; M. intracellulare, n = 134; MAC without subspecie identification, n = 3), followed by, M. xenopi (94/477, 19. 7%), M. kansasii (27/477, 5. 7%), M. abscessus complex (18/477,3. 8%) M. fortuitum complex (17/477, 3. 6%) (M. fortuitum (n = 13; M. chelonae, n = 4). Other unfrequent NTM species were identified in 5. 4% (26/477) of cases: M. simiae (n = 10), M. malmoense (n = 4), M.