dataesr/scientific-paragraphs-categorization huggingface dataset:
F identified at least 8 of 10 positive cats that would develop azotemia within 12 months. The decreased sensitivity meant that only 4 of 10 cats at risk would be detected, and the decrease in NPV to 79% meant that 2 in 10 cats predicted to be negative would be false negatives. The choice of strategy for setting the decision threshold should be guided by the purpose of the test and the population characteristics of the cats being screened. It may be more important for a screening test that predicts the future development of a disease to favor the detection of potential positives over false negatives, especially where the recommended action for positive individuals is not harmful, and in the case of more frequent health checks, is relatively inexpensive. Phase 1 of the modeling process designed the model using well characterized cats from a clinical research database (RVC1 dataset).
T A B L E 3 Number of control (negative) and case (positive) cat visits used to build and test (phase 1 and 3) and test (phase 2) the submodels Submodel M0 was based on each clinic visit assessing the status of a cat with respect to a diagnosis of chronic kidney disease (CKD). Submodels M3-M12+ were for the prediction of the CKD status of negative cats in time frames out to 3, 6, 9, 12 months, and more than 12 months after M0. Phase 1 Phase 2 Phase 3 Submodel Negative control Positive case Negative control Positive case Negative control Positive case M0 521 151 8314 890 8835 1041 M3 514 7 8109 205 8623 212 M6 490 31 7899 415 8389 446 M9 471 50 7598 716 8069 766 M12 456 65 7350 964 7806 1029 M12+ 447 74 4281 4033 4728 4107 Notes: T A B L E 4 Baseline characteristics of cats in the datasets used for modeling RCV1 RCV2 Banfield Total number of cats, n 218 60 3486 Intact males, n (%) 2 (0.
Study Our study was performed in Nice Teaching Hospital, in Southeast France, during 1999-2019. A total of 50 isolates of Nocardia species were collected from 36 patients. The major types of infections were cutaneous infections (58%), pulmonary infections (33%) and bacteremia (9%). During the study period, all the isolates were identified to the species level using 16S rDNA gene sequencing as previously described  [8]. For the two N. takedensis strains isolated from patient 1 and patient 2, blast analysis of the 1435 nucleotides amplicon both showed 99. 6% identity to the 16S rDNA sequence of N. takedensis CIP108681 T. Phylogenetic positions of the two clinical strains within the genus Nocardia are shown in Fig. 1. The tree was obtained by neighbor-joining method using MEGA X software as previously described  [9].
) Fig. 1  Phylogenetic tree based on the 16S rDNA sequence of Nocardia strains isolated from patients 1 and 2. Phylogenetic positions of the Nocardia strains isolated from patient 1 and patient 2 within the genus Nocardia based on the 16S rDNA sequences. The tree was obtained by Neighbor-joining method using MEGA X software  [9]. Scale bar (= 0. 01) indicates accumulated changes per nucleotide. The numbers next to the branches are the percentages determined in bootstrap analysis greater than 70% (1,000 replicates). The number in parentheses are the accession numbers of species in GenBank. completely recovered  [5, 6, 12, 13]. Therefore, we assume that this molecule could be used as an empirical antibiotic therapy in case of cutaneous Nocardiosis due to N. takedensis, before an AST is available.
FECRT 95% confidence intervals were estimated by 1000 bootstraps using the fecrtCI() function as implemented in the eggCounts package v2. 1-2  [12]. To account for sex-and age-specific variation in Parascaris spp. egg excretion dynamics, bootstrapping was performed at the farm level across ages and sexes, and within farm for each age class and sex. The respective effects of these three factors on FECR were also estimated by a marginal modelling approach as previously described  [13] , using the geeM package v. 0. 10. 1  [14]  assuming a negative binomial distribution for the level of EPG. Under this model, EPG counts are modelled by the sum of environmental effects (sex, age, stud farm), a binary variable coding for the treatment day (accounting for the treatment-associated change in EPG level), and their respective interactions that permits an estimation of the contribution of environmental factors to FECRs.
Behavioural Observations Reaction to novelty. Immediately following the introduction of the enrichment in the pens (D0), piglets were video recorded for 5 min (camera: Panasonic HC-250EB-K, Panasonic ® , Currys PC world, Carlow, Ireland; fixed on a tripod). The latency for the first piglet to touch either of the objects was recorded. In addition, the number of interactions with both enrichment objects was recorded at the pen level. Videos were analysed using The Observer XT software (Noldus, Wageningen, The Netherlands) using all occurrence continuous sampling. Routine observations. Prior to weaning, direct behavioural observations were carried out from the side of the pen on D1, D5, D8, D12 and D14 of the experiment. Post-weaning, observations were carried out on D1, D3, D5 and D15 after the day of weaning.
Data were collected on a recording sheet using all occurrence continuous sampling, at pen level. For all observation days, seven 3-minutes observation sessions were conducted for each pen, one each hour between 09:00 and 16:00. The order of observation was randomised for each recording day, and all occurrences of all piglets behaviours were recorded. Both general behaviours, which could be carried out in all locations in pen, and specific object-orientated behaviours were recorded, as described in the ethogram in Table  1. All negative behaviours (biting, belly-nosing and aggression) were also consolidated into a single measure of negative behaviour. The same ethogram was used, both pre-and post-weaning, other than suckling and udder massage, which were only possible pre-weaning. Table  1.
Statistical Analysis Statistical analyses were performed using SAS 9. 4 (SAS Inst. Inc. , Cary, CA, USA). The experimental unit for the analysis was the litter. General linear models (GLM) and generalized linear mixed models (GLMM) were fitted using the Residual Pseudo Likelihood approximation method. Differences were considered statistically significant when alpha was ≤0. 05, and tendencies were determined when alpha was between 0. 05 and 0. 1. For all measures, an overall treatment effect was initially investigated, as well as a comparison between the control and diversity treatments considered together. The separate analysis then investigated differences in interactions with the enrichment materials, depending on the type provided and its location in pen. As the experiment did not include a full cross-over design, we were unable to investigate the interaction between the two.
Interaction with Enrichment Prior to analysis, the data were averaged across the 7 observation periods per day. A general linear model (Proc Mixed) was used to investigate the interaction with enrichment across the three treatment groups. Data were averaged for each recording day. Treatment, day and their interaction and replicate were fixed effects in the models. The number of piglets in the litter was included as a covariate, and the day was considered a repeated effect. The sow was considered a random effect. For the second analysis, the total of all behaviours, as well as the instances of oral, shaking and nosing behaviours, was investigated using another general linear model. Fixed effects were the location in pen, the material used, both the material and location nested within the treatment group, day and relevant interactions.
Group size, individuals or locations within the dolphinaria were alternated periodically to promote variability. All 16 individuals participated in the study. Finally, Zoomarine Roma (Pomezia, Italy) had 11 dolphins; 10 participated in the study subdivided in three groups (Table  1 ). Each group was kept in one to two pools, varying through the days of observation. The study group therefore included a total of 43 bottlenose dolphins (32 adults: 18 females and 14 males, and 11 juveniles: 5 females and 6 males). In each facility, data were gathered during a time period consisting of three consecutive weeks in February 2020. Two facilities (Barcelona Zoo and Oceanogràfic) were open to the public and the latter had three public presentations per day, while the other three facilities were closed to visitors during the study period.
For their analysis, we applied generalised linear mixed-effects models (GLMM) for binomial data (with a logit link) using the R package lme4  [63]. The dependent variable "positive responses to commands" was a count of the occurrence of behaviours and thus was analysed by a GLMM for Poisson distributed data with a square-root link (R package lme4). The dependent variable "willingness to participate WtP", an ordinal index on a Likert scale (zero to four; see details above), was analysed by a cumulative-link mixed-effects model (with a logit link) using the R package ordinal  [64]. All of these models included the identity of the dolphin as a random factor to account for individual-based repeated measurements. All models also included the identity of the facility and the identity of the trainer as further random factors to account for effects of same origin  [65].
Acknowledgements In addition to the authors of this paper, the following also participated in the forum and supported its key conclusions:  Philippe Azam ,  Solvay ,  Paul Benndorf ,  Henkel ,  Ulla Nøhr Dalberg ,  Novozymes ,  Christian Desaintes ,  European Commission, DG Research ,  Stephane Dhalluin ,  L'Oreal ,  Dorothea Eigler ,  Evonik ,  Kevan Gartland ,  Sumitomo Chemical (UK)  plc,  Peter Griem ,  Symrise ,  Kamila Gromek ,  GSK, Herve Groux, ImmunoSearch ,  Gunilla Grundstrom ,  Senzagen ,  David John ,  Animal Health Europe ,  Susanne Kolle ,  BASF ,  Martina Klaric ,  Cosmetics Europe ,  Gavin Maxwell ,  Unilever ,  Tim Rowan , EPAA consultant,  Kathleen Vriens ,  J&J ,  Annette Wilschut ,  DSM ,  Zvonar Zvonimir , EPAA. EPAA thanks all the participants of the forum for their active contribution to the event.
Statistical analysis Data analysis was carried out using SAS 9. 4 software (Copyright (c) 2002-2012 by SAS Institute Inc. , Cary, NC, USA). The significance threshold was fixed at 5%, which is a standard threshold. The reliability between the two observers who assessed the video recordings was calculated using the CORR procedure and Spearman's or Pearson's correlation coefficients, depending on the normality of data (normality was verified using the UNIVARIATE procedure): Spearman's correlation coefficient was used when normality was not verified for at least one variable, and Pearson's correlation coefficient, when normality was verified for both variables. This methodology was selected according to a study by Martin and Bateson  [38]. Comparisons of the strange person test results after the Play and Control sessions were performed using mixed models, and the pen was considered a random effect.
The following parameters were studied: latency to approach the person (duration in seconds), duration for which the pig was distant from the person (duration in seconds), duration of direct contact (duration in seconds), the need to separate the pig (in frequency) and duration of tail movement (duration in seconds). For the continuous variables (latency to approach the person, duration for which the pig was distant from the person, duration of direct contact and duration of tail movement), the distribution of the variable and the model residuals were analysed using the UNIVARIATE procedure to determine whether the assumption of normality was met. If normality was verified, the raw data were used for the model using a general linear mixed model with the MIXED procedure. If the data for the variable did not follow a normal distribution, other continuous distributions were tested with the UNIVARIATE procedure.
23 mol L -1 NaCl, 25 mmol L -1 Tris-HCl, pH 7. 4, and heated in a water bath at 75 °C for 1 h. The soluble fraction was separated from the insoluble fraction by filtration (pleated filters in cotton cellulose, VWR 512-0206; VWR Scientific, Radnor, PA, USA) and discarded. Insoluble fraction was hydrolysed according to the same method as for TCol. For TCol and ICol data were expressed as mg of hydroxyproline per g of dry matter [mg OH-pro g -1 dry matter (DM)]. The soluble collagen was determined as: soluble collagen (SCol) = (TCol -ICol)/TCol) × 100. For CLs, 600 μL of 6 mol L -1 NaOH and 600 μL of 1 mol L -1 Tris were added to 600 μL of acid supernatant. Final pH was adjusted between 7 and 8 by adding some drops of 6 mol L -1 HCl or NaOH. Pyridinoline cross-links (pyridinoline + deoxypyridinoline) were determined by the enzyme-linked immunoassay Metra Pyd EIA kit (Quidel Corporation, San Diego, CA, USA) according to the manufacturer and adapted by our group to the muscular tissue.
Intramuscular fat content Total lipid content of LT, RA, SM and ST was estimated by near infrared spectroscopy (NIRS) according to the procedure previously described by Guy et al. 19  Briefly, muscle samples (about 5 g of muscle lyophilized powder) were scanned in a circular cup (diameter 50 mm, depth 10 mm) (Part number IH -0307; NIR-Systems; Infrasoft International, State College, PA, USA), compressed and sealed with a disposable paper-backed wrap. Samples were scanned in reflectance mode (400-2500 nm) in a NIRS 6500 scanning monochromator (NIRSystems, Silver Spring, MD, USA) using ISI software, version 3. 01 (Infrasoft Internationa) equipped with a spinning module. Reflectance data were recorded at 2-nm intervals and stored as log (1/reflectance). Then the reflectance data were exported into WinISI II version 1.
60 (Infrasoft International), which was used to estimate IMF values. A NIRS model was used to analyse the data based on spectra and biochemical analyses performed on 48 samples used in the current study. For the biochemical analyses, muscle dry matter was assayed gravimetrically after drying at 80 °C for 48 h. Then, total lipids were extracted by mixing 6 g of muscle powder with chloroform-methanol. 20  Each sample was measured in triplicate and data were expressed in g per 100 g of dry matter (g 100 g -1 DM). The statistical parameters (coefficient of determination and standard error of prediction) of the prediction model were 0. 92 and 1. 19 g 100 g -1 of fresh matter, respectively.
Statistical analysis The statistical analyses were performed using XLSTAT 2017. 19. 4 (AddinSoft, Paris, France). First, normal distribution and homogeneity of the dataset was tested by Shapiro-Wilk test (P > 0. 05). It should be noted that there is scarcity of publications studying the muscle components and sensory quality traits of dairy animals. Accordingly, although experimental design was not developed to compare dairy and beef, the differences in sensory tenderness scores among muscles (M) and animal types (AT) (dairy and beef) were investigated using a generalized linear model at a significance level of 5%. The differences in biochemical composition between the M and AT are presented elsewhere. 16 or any comparisons including interactions, a Student-Newman-Keuls test was used to separate the least-squares means (LSmeans).
The overall KMO value of the PCA was 0. 72, which is acceptable. 24 -scores were computed in a stepwise manner using multivariate regression analyses to consider the fixed effects of M and to avoid the effects as a result of other factors of variation present in the database by taking into account the differences in the number of animals for LT and RA muscle compared to SM and ST. Zscores represent the deviation of each trait observation relative to the mean of the corresponding animal irrespective of the breed, experiment and other confounded effects and were calculated using PROC STANDARD of SAS (SAS Institute Inc. , Cary, NC, USA), which standardizes data to a mean of 0 and a SD of 1. Subsequently, and using the Z-scores, Pearson correlations were performed to investigate simple relationships between muscle characteristics and sensory tenderness.
ACKNOWLEDGEMENTS The authors acknowledge the help of the staffs of  Experimental Unit (UERT)  of  INRAE , Theix for animal management and slaughtering, as well as the 'Biomarkers' and 'Dinamic' teams, especially  David Chadeyron ,  Blandine Fréchet ,  Arnaud Delavaud  and  Fabienne Picard , for their technical assistance. The authors are further thankful to the  French Livestock Institute Meat Quality Team , namely  Laurent Allais ,  Catherine Malayrat ,  Chloé Flattard  and  Valérie Hardit , for muscle sampling and sensorial analyses. We also thank  Terrena and Elivia  who allowed this project to happen and for sampling the muscles. This study was funded by the financial support of 'régions  Bretagne and Pays de la Loire '.
Acknowledgments: The authors would like to acknowledge the staff of the  Avian Experimental Unit (ANSES SELEAC, 22440 Ploufragan, France)  for rearing pullets and laying hens and taking care of the animals and for their assistance in experiments,  A. Huneau  and  C. Lucas  ( EPISABE Unit, ANSES, 22440 Ploufragan, France ) for their statistical advices, and  M. Le Houérou  and  A. Bauller , students involved in the study. The authors are grateful to  L. Bignon , co-initiator of the project and  C. Leterrier  ( INRA, 37380 Nouzilly, France ) for helpful discussions. Additionally, the authors wish to thank  CPE Expansion and Craig Stevens, MA, ELS  for the English language review.
After the last incubation, the cells were washed in Perm/Wash buffer included in the buffer set and analyzed in 200 µL of the same buffer by a FACSCanto II flow cytometer (BD Biosciences, San Jose, CA, United States). At least 2 × 10 5 lymphocytes were recorded per sample. The obtained FCM data were further analyzed by FlowJo software version 10. 5. 3 (FlowJo LLC, Ashland, OR, United States).
Precision feeding (PF) with the daily mixing of 2 diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared with a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, 1 of 2,511 gestations (farm A) and 1 of 2,528 gestations (farm B), reporting sows' characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF.
Development of the DSS General approach. In practice, the DSS will send daily instructions to the automatic feeder about the daily quantity of each diet to deliver to each sow to constitute their rations. Two diets will be available with different nutrient contents and mixed in the feeder based on the DSS instructions. Decisions will rely on a database containing rules about the on-farm general nutritional strategy, the initial status of the animals, i. e. , age, parity, body weight (BW) and backfat thickness (BT) at insemination, and real-time data measured on farm (Figure  1 ). The farm strategy will be described using the profile of average characteristics of sows and their litters, per parity, calibrated with InraPorc  (Dourmad et al. , 2008) , and the objectives of BW and BT after farrowing.
Simulations The Python (Python version 3. 7. 2, Python Software Foundation, Beaverton, Oregon) model used for the development of the DSS, and the present simulations are composed of 3 classes (feed, environment, and sow), and 1 gestation function. The gestation function calculates, for each day and each sow, the growth of the different body compartments and the nutrients requirements (Figure  1 ). The sow class inherits the attributes of the feed and environment classes. The inputs for the feed class corresponds to the dietary ME, SID AA, and STTD mineral contents. The inputs for the environment class are the housing system (e. g. , in our case group-housed) and the room temperature (e. g. , in our case 18 °C, thermoneutral condition). The inputs of the sow class are the identification number, age, parity, litter size (LS) of the studied gestation, average litter birth BW, individual sow's BW and BT at insemination, individual sow's estimated BW after farrowing, and average target BT after farrowing.
Two feeding strategies were simulated with the DSS with a constant feeding plan per sow all along the gestation. A CF strategy, performed with a control diet (C) containing 4. 8 g SID Lys/kg and 2. 5 g STTD-P/kg, was compared with a PF strategy, performed with 2 diets formulated for low (diet L: 3. 0 g SID Lys/kg, 2. 0 g STTD-P/kg) or high (diet H, 6. 5 g SID Lys/kg and 3. 3 g STTD-P/ kg) AA and mineral contents. The 3 diets contained 13 MJ ME/kg. Diets L and H were formulated at least cost using average prices of feed ingredients observed in France over the first semester 2019  (IFIP, 2019). Diet C was obtained from a mixture of diets L (48. 6%) and H (51. 4%). Simulations were run with the DSS to compare the 2 feeding strategies in terms of nutrient intake, excretion, efficiency of retention, and feed costs with 2 datasets collected in 2 experimental farms.
The database used for the evaluation of the DSS Two datasets were obtained from crossbred Large White × Landrace sows from 2 different lines, in 2 experimental farms (2,511 gestations for farm A in Canada and 2,528 gestations for farm B in France). Each dataset contained the BW and BT of sows measured individually after insemination, as well as characteristics of litter (LS and litter birth BW). An individualized target of BW (BWt) after farrowing was determined for each sow regarding its age at farrowing (AF) using a specific equation calibrated for each farm: Farm A (Weibull function,  Dourmad et al. , 2008) : BWt = 275 × (1 -exp (-3. 824/1,000 × AF 0. 9801 )) Farm B (Brody model,  Quiniou, 2019) : BWt = 331. 4 × (1 -0. 821 × exp (-2. 121/1,000 × AF)) The objective of BT after farrowing was fixed for all sows at 18 mm in farm A and 20 mm in farm B in accordance with the actual practices of each farm.
Model and DSS adjustment in practice In this study, the objective of BW at farrowing depended on the age of the sow at insemination, and on a relationship between AF and BW previously characterized in each farm. To better deal with inter-individual variability, it would be interesting to consider the change of BW with age of each animal during its lifespan to better specify the objective of BW at the next farrowing. This implies to develop a system allowing the DSS to adapt progressively to individual trajectory of BW. New analytic software has being developed to estimate automatically and on real-time individual BW via video recording  (Kashiha, 2014; Pezzuolo, 2018). This would be a practical solution to collect daily individual BW on farm, replacing the scales that are difficult to calibrate or even not available in practice.
Ambient temperature of the gestating room could also be recorded daily and considered in the requirements calculations. The DSS is already designed to take temperature into account. When temperature drops below the low critical temperature, ME requirement for maintenance increases by 10 kJ/kg BW -0. 75 /d/°C for group-housed sows. Therefore, it only needs daily data to run, easily collected with a simple thermometer in the sows' room. Similarly, the physical activity of each sow could be recorded daily and integrated in the model to adjust daily nutrient requirements. Accelerometers are being developed and can be fixed on the ear on the sow  (Marcon et al. , 2017; Scheel et al. , 2017). They are able to assess the number of movements per hour but also the postures (standing, lying, and walking).