dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensitivity of metabolisable energy utilisation and standardised ileal digestible lysine requirement to temperature The results of the sensitivity analysis of ME utilisation and SID lysine requirement to ambient temperature are presented in Fig. 1  (simulation results are available in the database, refer to the ''Specification Table "). The values are expressed as a percentage of the value obtained for the reference temperature of 22 °C (upper limit of the thermo-neutral zone). When ambient temperature increased, ME intake decreased, with 30 % reduction for 32 °C compared to 22 °C. Conversely, at 18 °C, ME intake increased by 10 % compared to 22 °C. Although ME requirement for milk production decreased when temperature increased, ME deficit increased with temperature, with about 40 % increase for 32 °C compared to 22 °C (Fig.
Since the lactating sow spends most of her time lying, floor cooling, which increases   conduction heat loss, has also been shown to be very efficient in increasing feed intake for milk production  (Silva et al. , 2009a). The database used in the present study refers only to conventional housing systems, with very limited possibilities for sows to adapt their behaviour in order to increase latent heat loss from the skin. It may thus be expected that the relationships obtained from the present meta-analysis might overestimate the magnitude of the sow's response to heat stress in other housing conditions where latent heat loss may be increased by water evaporation from the skin. The second criterion with the highest response to temperature is feed intake (À36 % between 22 and 32 °C). The reduction in ME intake results in a decrease in the heat increment associated with the metabolic use of ME.
Reference of publications used in the database. the effects of the evolution of ambient temperature according to the month, on average lactation ADFI, LGR and standardised ileal digestible (SID) lysine requirement of primiparous and multiparous sows. Different datasets of daily outdoor temperature were used: for Rennes (West France) and Toulouse (South France), and for years 2001-2005 (observed data) and years 2046-2050 (data of temperature expected due to climate change (from Climator Project, 2010). A total of 360 simulations were performed: 10 years Â 12 months Â 3 sow parities.
3 1. 58 0. 1 8. 6 Piglets Born alive per litter 25 114 10. 6 1. 14 7. 60 13. 90 Weaned per litter 30 134 9. 43 0. 98 6. 22 12. 00 Birth weight, kg 38 147 1. 43 0. 11 1. 01 1. 85 Weaning weight, kg 29 147 7. 07 1. 20 4. 67 10. 21 Litter weight gain, kg/d 31 140 2. 20 0. 61 1. 31 4. 00 Piglet weight gain, g/d 30 134 231 54 164 461 1 Av = Average, SD = Standard deviation, Min = Minimum, Max = Maximum. 2 Number of individual values available in the database for a given criteria. 3 Metabolisable energy.
However, no consensus regarding the number of individuals to be included for confirmatory analysis is available, which lead to variability in the number of contributions per farm. A farm was defined as the group of animals from which pig serum was sampled. It is thus an epidemiological unit. Altogether, the available data resulted from convenience sampling, allowing descriptive statistical analysis. Serum samples were analysed by the Laboratoire des Leptospires (Marcy-l'Étoile, France) between October 2007 and April 2017 using the MAT to detect antibodies against a large panel of serogroups. Over the ten-year duration of the survey, the panel of serogroups varied slightly. However, 19,395 sera sampled between January 2011 and January 2017 were tested for the following serogroups, with their serovars in parentheses: Australis (munchen, australis, and bratislava), Autumnalis (autumnalis and bim), Ballum (ballum), Bataviae (bataviae), Icterohaemorrhagiae (icterohaemorrhagiae and copenhageni), Grippotyphosa (grippotyphosa and vanderhoedoni), Panama (panama and mangus), Pomona (pomona and mozdok), Sejroe (sejroe, saxkoebing, hardjo, and wolffi) and Tarassovi (tarassovi).
For instance, a farm with two pigs that were seroreactive against the serogroup Australis would be associated with that serogroup. A farm with one pig seroreactive against the serogroup Australis and one pig seroreactive against the serogroups Icterohaemorrhagiae and Panama would be associated with the serogroups Australis, Icterohaemorrhagiae and Panama. To describe the spatial serogroup distribution, mainland France was studied at the "département" scale (France is divided into 96 "départements", administrative units similar to counties in the United States). The tested pigs in each "département" were visualized in Quantum GIS (QGIS version 2. 18) with the background map from IGN GEOFLA ®. Open access pig density data were used for pig distribution mapping  [28]. To explore the temporal variability of the serogroups, the numbers of swine sera positive for a given serogroup weighted by the values defined above were retrieved for each year.
Because the data were count data, we used a log link function with a Poisson distribution. The model also included the number of seropositive individuals per farm as an offset variable. We followed the methods of Hardin & Hilbe  [30] , who recommended an unstructured working correlation matrix for complete datasets with few observations (three herein) by panels. PROC GENMOD was used to fit the GEE models (SAS Institute Inc. 2012. SAS/STAT Software, Version 9. 4. Cary, NC).
The serogroups obtained at the farm level are shown in Table  1. More than 97. 5% of the farms (n = 1,087) were associated with at least Australis and/or Icterohaemorrhagiae, and 50% (n = 561) were exclusively associated with Australis and/or Icterohaemorrhagiae. Fig. 2  The spatial distribution of the swine tested and the seropositivity against Australis, Icterohaemorrhagiae per "département" (administrative district) in mainland France The temporal distribution of the dataset, including seropositive vs. seronegative pigs and farms, is displayed in Table  2. Considering the temporal variability of the serogroups among years (Table  3 ), the fit of the model was adequate, as shown by the result that only seven Pearson residuals were larger than 3 (6 were less than 3. 84 and one was 8. 82, out of 3,342 observations) and that there was no overdispersion (normalized Pearson chi-square = 0.
A recent study in Italy that included pigs with clinical suspicion of leptospirosis was implemented using an eight-serovar panel; seven serovars were the same as those in our panel, and their study identified Australis and Pomona as the most frequently detected serogroups  [19]. However, caution is warranted in interpreting this consistency because the previous study do not report how the MAT results with cross-seroreactivity were managed, and the approach may have differed from that in the present study. These observations support the need for a standardized approach for MAT surveillance data analysis. Previous studies have described a poor correlation between the presence of antibodies and the carrier state  [25, 32]. Among 22 Australis-infected sows, six had titres above 1:100, which is the limit of positivity recommended by the OIE for screening and diagnosis  [12, 25].
Even when a threshold of positivity of 1:100 is applied, recently infected, chronically infected or previously exposed individuals cannot be clearly distinguished  [4]. However, because we were chiefly interested in the distribution of the Leptospira serogroups at a large temporal scale, any evidence of past infection was relevant to the description of the serogroup distribution in pigs. In addition, the serogroups Australis and Icterohaemorrhagiae were predominant in each year from 2011 to 2016 in our dataset, which suggests that the misclassification of past or current infections would have limited effects on our results. Most of the samples were obtained from Brittany, which is an intensive pig farming area compared with other regions in France. However, the serogroup distribution was similar among regions.
Conclusions The analysis of data from diagnostic laboratories is useful for obtaining an indication of the circulating Leptospira serogroups in a region or a country and over time. Our results suggested that over the study period, most Leptospira infections in swine in France originated from only two serogroups, Australis and Icterohaemorrhagiae, out of the ten used for the laboratory analysis. This information should be considered to support future prophylactic measures. In Europe, numerous laboratories have published interpreted MAT results. However, there is no consensus regarding serovars to be included in the panel, the positive threshold and serogroup determination in the case of cross-reactions, leading to the incomparability of results among studies. Therefore, this is an appeal to veterinary laboratories in Western Europe to standardize their approach to MAT surveillance data analysis.
With the appreciation that behavior represents the integration and complexity of the nervous system, neurobehavioral phenotyping and assessment has seen a renaissance over the last couple of decades, resulting in a robust database on rodent performance within various testing paradigms, possible associations with human disorders, and therapeutic interventions. The interchange of data across behavior and other test modalities and multiple model systems has advanced our understanding of fundamental biology and mechanisms associated with normal functions and alterations in the nervous system. While there is a demonstrated value and power of neurobehavioral assessments for examining alterations due to genetic manipulations, maternal factors, early development environment, the applied use of behavior to assess environmental neurotoxicity continues to come under question as to whether behavior represents a sensitive endpoint for assessment.
This has produced a robust database on rodent performance within various testing paradigms, possible associations with human disorders, and therapeutic interventions. Thus, multiple tracts for behavioral assessment have evolved including, phenotyping of genetic manipulation, readouts of specific disorders, neurotoxicity evaluations of pharmaceutical or chemical agents, and understanding underlying mechanisms associated with sensory, motor, cognitive/learning performance, and memory. That drugs or chemicals in the environment might adversely affect the nervous system has been a general concern for years with emphasis on the vulnerability of the developing nervous system. Epidemiological literature on childhood effects of neurotoxicants is often difficult to assess due to the complex nature of brain functions, the multiple factors that influence brain development, and exposure to multiple environmental factors.
Within a session, habituation normally occurs within the first 10 trials and over 4-5 days for across sessions  (Valsamis and Schmid, 2011; Pilz et al. , 2014). Prepulse startle inhibition (PPI) describes the phenomenon in which a weak initial stimulus (prepulse) inhibits the startle response that is elicited by a strong stimulus. Recent articles on procedural methods and optimization considerations for ASR and PPI are available  (Valsamis and Schmid, 2011; Shoji and Miyakawa, 2018; Hormigo et al. , 2019; Miller et al. , 2021). The primary data collected is on the strength of the reflex response, therefore attention is required for optimizing the system (restraint and sensitivity) for the age and weight of the animal. In addition to strength of the response, the time to respond is recorded.
For example, log transforms of daily latency in the MWM can allow use of parametric methods (i. e. , ANOVA, RMANOVA). Other assumptions include normality of observations and homogeneity of variance. Reporting of the data should include details of the testing paradigm and all relevant data, not only those found statistically significant. With the available computer software, multiple redundant endpoints may be collected, some which are relevant to the study and some that provide an internal quality control of the paradigm and data. Representations of the distribution of data can be accomplished by providing all individual data points, using violin plots, or representing variance as a 95% confidence interval. Such representation aids in distinguishing between statistical significance and biological relevance.
Data was collected using a commercial device (42 × 42 cm) with photocell detectors (0. 32 cm diameter) spaced 5 cm from floor and 1. 27 cm linearly apart around the chamber. Horizontal activity photocells were set at 1 inch from arena floor and rearing photocells empirically determined and set at 4 inches. Individual animal time series were examined for extremely high or low performing animals. To meet the normality and homogeneity of variance assumptions of parametric tests. Activity measures over epochs were analyzed using a repeated measured analysis of variance (RM ANOVA) main effects of time (epochs), group, and the interaction between factors with an autocorrelated [AR(1)] error structure reflecting correlation between consecutive epochs. The Total Session Activity data suggests that Groups B and C were less active than Group A.
77-5. 05 fold coverage (Table  S6 ) were analyzed using two complementary methods: pseudo-haploidization following (21) resulting in 4,833,570 nucleotide transversions, and genotype imputation following (38), at 7,161,029 polymorphic sites present at more than 5% frequency in modern donkeys  (38, 39). Imputation accuracy was confirmed by high consistency rates between imputed and observed genotypes following down-sampling of high-coverage modern genomes, and downstream analyses largely consistent with those based on pseudo-haploidized data (Fig. S6 , S7, S9, S10, S11, S12, S14, Table  S6 ). The three oldest samples from our dataset consist of donkeys from Anatolia (Acemhöyük, Turkey), radiocarbon dated to 2,564-2,039 BCE. Their age and phylogenetic placement within Clade B confirm an early expansion out of Africa by ~2,500 BCE, in agreement with SMC++ time estimates (Fig.
Romans bred improved donkeys for producing mules essential for their military power and economy Beyond documenting domestication history at the global scale, our genomic dataset also included 3 jennies (females) and 6 jacks (males) from the same archaeological site (Boinville-en-Woëvre) (Fig. 3A ). These were found in a dedicated farming area of a Roman villa, providing insights into local management practices in Roman Northern France (200-500 CE). One jack (GVA349) appeared particularly inbred with long ROH indicative of recent consanguinity (Fig. 4A ) and was genetically related to four jacks and one jenny (family group GVA1, including GVA125, GVA347, GVA348, GVA349, GVA353, and GVA354; Table  S10 ). Additionally, two jennies showed genetic relatedness coefficients equivalent to full siblings (family group GVA2 GVA355 and GVA358; Table  S10 ).
Further genomic studies in other regions would also largely benefit the understanding of donkey diversity and history. Resolving the genetic structure of equine remains from the 3 rd millennia BCE of southwest Asia will be challenging due to postmortem DNA decay, but essential to map the geographic range of the divergent lineage identified here (MV242), as well as to understand dispersal mechanisms in greater detail. The same holds true for Chalcolithic and Bronze Age Europe, which remain genetically undocumented in our dataset, and onwards. Developing genetic knowledge of ancient European donkeys will further clarify patterns of exchange across the Mediterranean region, including during and after Roman times, as revealed in this study. It will also provide insights into the dispersal mechanisms underpinning the genetically supported presence of donkey remains in Portugal ~2,200 BCE  (33).
Archaeological samples and context (Provenance) The following section describes the archaeological contexts associated with all ancient donkeys sequenced in this study. The full name of each site is composed of the modern country where the excavation site lays followed by the age in Before Common Era (BCE) or Common Era (CE) as estimated from radiocarbon dating or inferred from archaeological context. The accession number and associated metadata for each ancient donkey genome included in this paper can be found in Table  S2. • TUK_2564-2039BCE: Acemhöyük, Turkey (samples: AC14380, AC14415, MV051). Acemhöyük is a large mound site located in the Aksaray province of central Turkey representing an important urban center in the Early and Middle Bronze Age (EBA and MBA, ~2,800-1,700 BCE). The site is located at an elevation of approximately 950 m above sea level on the alluvial fan of the Melendiz river near the central Anatolian Great Salt Lake (Tüz Gölü).
Specimen AC14380 (derived from mekan C) was recovered on the 23 rd of July, 2012. The specimen AC14415 was recovered on the 24 th of July 2015; while specimen MV051 (recorded as specimen AC13084 in the Acemhöyük zooarchaeological database) was recovered on August 17 th , 2012. All of these specimens derive from deposits representing multiple complete or partial donkey burials located in close proximity to the level XI city wall (MNI of 8 donkeys recovered from this area). They were recovered from shallow deposits directly under the remains of structures associated with the modern village, which currently surrounds the mound and were initially thought to be modern pits related to the disposal of donkey remains. However, it became clear that these donkey burials, as well as others in adjacent areas DB/50 (MNI=3) and DB/48 (MNI=3) are associated with the EBA occupation of the city.
Subject to trimming, the software mapDamage2 (24) was used to check for the presence of nucleotide mis-incorporation profiles characteristic of ancient DNA data at the library level, randomly selecting 100,000 reads. We observed the expected increase of C to T (G to A) misincorporation rates at read starts (read ends) for both USER™-treated and non-USER™-treated data, although of lower magnitude for the former, as expected. Furthermore, genomic positions preceding read starts were higher in purines in non-USER™ read alignments, consistently with post-mortem DNA fragmentation being depurination-driven. In USER™-treated read alignments, these positions were enriched in cytosine residues, in line with the excision of deaminated cytosines by the sequential activities of Uracil DNA glycosylase and Endonuclease VIII enzymes present in the USER™ mix.
• Generation of the recombination map and phasing of modern individuals We selected 25 donkeys to generate a recombination map for all autosomal variants that passed QC filters (n=13,013,551). In order to select individuals that provided a representative subset of all subpopulations, we constructed a Principal Component Analysis (PCA) using PLINK (version 1. 9) (63) with all domestic donkey samples (n=206). We finally selected 25 domestic donkeys representing the different geographical locations sampled, so no two individuals were chosen from the same country. In order to prevent selecting individuals with high levels of inbreeding, we estimated levels of inbreeding as runs of homozygosity (ROH) across all autosomes using PLINK (version 1. 9)  (63). Considering that the data used to generate the recombination map were unimputed, we also selected individuals with the lowest proportion of missing SNPs (Table  S1 ).
However, the absence of the other two African wild ass subspecies in the dataset (E. a. africanus or E. a. atlanticus) makes it impossible to determine which of these subspecies is genetically closest to the donkey. Interestingly, the 7 kiangs in the dataset separated into two clusters which diverged on the PC2 axis only, which may represent two different subspecies of kiang that have previously been found to be genetically distinct  (22). Of the two publicly available samples labelled as "Asiatic Wild Ass" (Accession numbers: AW_1 (SRS3167373) and AW_2 (SRS3167374)), one clustered with a group of kiangs and the other was most genetically similar to E. hemionus. The PCA including only domestic donkeys and their closest relative showed E. a. som as divergent from the domestic donkeys but closest to East African donkeys (Fig.
Inclusion of these individuals resulted in strong migration edges to the outgroup and each other, making it impossible to see admixture between other groups of donkeys. We grouped the remaining donkeys into subpopulations based on their geographical location, and then thinned the variants using the "--indep-pairwise 50 10 0. 2 --maf 0. 05" parameters in PLINK (632,429 variants remaining after pruning). We estimated the optimal number of migration edges using a mixed linear model implemented in the optM R package (https://cran. r-project. org/web/packages/OptM/index. html). Using the tree with the optimal number of migration edges (m=3), we estimated bootstrap confidence intervals for each node using modified scripts from the BITE package with 100 pseudoreplicates (122) (Fig. 1C ). The Treemix analyses showed distinctive population sub-structuring within domestic donkeys from different geographical locations, with two main branches forming between African (Clade A) and African and non-African donkeys (Clade B), with further differentiation of Asian and European donkeys into separate clusters.
775). The same rate of regression from Oman and Ethiopia further suggests that donkeys expanded out from a single source in Africa into the Arabian Peninsula and then into Eurasia. To determine the demographic history and split timing of donkey subpopulations, we selected 4 main subpopulations based on Treemix modelling, ADMIXTURE and PCA analysis comprising of individuals from the Horn of Africa + Kenya (Horn+Ken), western Africa (WAfrica), Asia and Europe. We selected three individuals from each subpopulation and converted the variants in the VCF file to SMC++ format, masking regions with wild introgression and tandem repeats using the "vcf2smc" function in the SMC++ package (version 1. 15. 4)  (28). We then constructed pseudo-bootstrap replicates of each file by randomly resampling 90% of each chromosome in chunks with 10 replicates based on a modified script from MSMC2 package  (126, 127) , which was developed and implemented by Zheng and colleagues  (127).
Imputation of ancient genomes We imputed the ancient genomes based on the pipeline developed and tested by Hui and colleagues  (38). In line with this method, we created a reference panel consisting of all modern domestic donkeys (n=206) and variants with a MAF >=0. 05. We selected only ancient donkeys with a genome coverage of over 0. 75X as candidates for imputation (n=31 individuals, Fig. 3A , Table  S2 ). Before imputation we pseudo-haploidized the ancient individuals using the "dohaplo" flag in ANGSD, conditioning only on positions found in the modern reference panel. We then projected the ancient individuals onto the PCA of modern domesticates using the "lsqproject" function in the smartpca program from the EIGENSOFT package (version 6. 1. 4) (26, 57) (Fig. S7 ). We found that all ancient individuals clustered closely with the modern domesticates, indicating that they have a similar genetic makeup and that the reference panel of modern variants can be used for the imputation of the ancient samples.
2, 0. 5, 0. 5, 0. 9, 0. 92, 0. 94, 0. 96 and 0. 99) from ten modern individuals with the lowest rates of missing SNPs (pre-phasing and excluding the donkey that was used for the reference genome). We then re-imputed the variants for these individuals using the same imputation pipeline as outlined above. and after filtering, compared them with the original variants for the same sample to measure the accuracy of imputing samples with different rates of missingness (Fig. S8 ). Based on this imputation accuracy test, we predicated that all samples have an overall imputation accuracy between 98. 1% and 98. 6% (Fig. S8 , Table  S6 ). After imputation, we projected the ancient, imputed samples onto the PCA with the nonimputed, pseudo-haploidized data for the same ancient donkeys and the modern donkeys used in the reference dataset (Fig.