dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING We received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie action, Innovative Training Network: Fun-HoMic ; grant N •  812969. CdE received funding from the  French Government 'Investissement d'Avenir' program (Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ,  ANR-10-LABX-62-IBEID ), the  Agence Nationale de la Recherche  ( ERA-Net Infect-ERA, FUNCOMPATH ,  ANR-14-IFEC-0004 ), the  EU   Horizon2020 consortium  ' Host-Directed Medicine in invasive FUNgal infections'-HDM-FUN  (Grant Agreement 847507). SLL and CdE received funding from the  Swiss National Science Foundation  ( Sinergia program , # CRSII5 173863 ). BIOASTER received funding from the  French Government 'Investissement d'Avenir' program  (Grant No.
ANR-10-AIRT-03 ). MSG was supported by a  Humboldt Research Fellowship for Postdoctoral Researchers  by the  Alexander von Humboldt-Foundation  and the  Deutsche Forschungsgemeinschaft (DFG)   Emmy Noether Program  (project no. 434385622/GR 5617/1-1 ). BH was supported by the  Deutsche Forschungsgemeinschaft (DFG)  project  Hu 532/20-1 , project  C1  within the  Collaborative Research Centre (CRC) / Transregio 124 FungiNet and the Balance of the Microverse Cluster under Germany´s Excellence Strategy-EXC 2051-Project-ID 390713860 , the  EU Horizon2020 consortium  ' Host-Directed Medicine in invasive FUNgal infections'-HDM-FUN  (Grant Agreement  847507 ), the  Leibniz Association Campus InfectoOptics   SAS-2015-HKI-LWC  and the  Wellcome Trust  ( 215599/Z/19/Z ). IDJ was supported by the  Deutsche Forschungsgemeinschaft (DFG)  project  C5  within the  Collaborative Research Centre (CRC) / Transregio 124 FungiNet and the Balance of the Microverse Cluster under Germany´s Excellence Strategy-EXC 2051-Project-ID 390713860 , the  Leibniz Association Campus InfectoOptics   SAS-2015-HKI-LWC  and the  Wellcome Trust  (Grant  215599/Z/19/Z ).
Importantly, sera from patients with the autoimmune disease SLE did not exhibit increased levels of antibodies to gingipain domains, indicating that the antibody reactivities detected were not the result of nonspecific binding (Supplemental Figure  2 ). We then sought to confirm the ELISA reactivities by Western blotting. Serum samples from five control subjects that scored negative in the ELISA assays were found negative for HA1, HA4, and CD gingipain domains, indicating a strong positive correlation between the ELISA and the Western blotting data (Supplemental Figure  3 ). Five sera from RA patients and five sera from PD patients were also screened by Western blotting. There was generally a strong correlation between ELISA and Western positivity. For RA sera, there was a positive correlation between anti-HA4 reactivity levels measured by ELISA and band intensity revealed by Western, and three samples that scored negative in ELISA were positive for HA1 by Western blotting.
1. 3. Strategies to improve feed efficiency Genetics and microbiota. Feed efficiency is one of the most important selection criteria in breeding programs, as it affects total cost of pig production and environmental footprint  (Kanis et al. , 2005; Reckmann et al. , 2016). Thus, FE and related traits are a major target for genomic selection, a growing and promising method. Until recently, FCR and RFI were the two main traits used to evaluate FE, as described in the first part of this review. With the development of new technology and automatic data recording, recent studies have been looking for new traits related to FE like, for example, feed intake and feeding behavior (daily occupation time, daily feeder visit, and daily feeding rate) recorded daily and individually. Major quantitative trait loci for feed intake and for feeding behavior traits have been identified on different chromosomes as well as the positional and functional candidate genes  (Reyer et al.
LC/MS/MS Conditions The UPLC MS/MS system was composed of a 1260 binary pump with an autosampler coupled to an Agilent 6410 triple quad (Santa Clara, CA, USA). Analytes were separated using a Poroshell 120 column (3. 0 × 50 mm, 2. 7 µm). Chromatograms were analyzed using MassHunter quantitative analysis software from Agilent. Detection was conducted after positive electrospray ionization with the following source parameters: gas temperature: 300 • C; gas flow: 10 L/min; nebulizer: 25 psi; capillary voltage: 4000 V. The optimized multiple reaction monitoring (MRM) conditions used for each analyte are listed in Table  S1. The most abundant transition was chosen for MRM quantitation of ZEN, ZAN, α-ZOL, β-ZOL, α-ZAL, and β-ZAL, while two other transitions were used as qualifiers. The quantifier used for [ 13 C 18 ]-ZEN was the ion with a mass weight of 199.
4. 3). Therefore, the number of individuals included in the food CPP analyses was 19 (8 low-and 11 high-ranging chickens). Using the remaining dataset, differences in range visits and distance indexes were still significant between the ranging groups (see Table  1b  for results). During the tests of the social CPP, inconsistency in the results of the first two testing days led us to submit all individuals to two additional days of conditioning, after which we then proceeded to perform six days of testing. Eleven individuals (5 low-and 6 high-ranging) were excluded as they exceeded our pre-set limit of 50% of the test trials without moving (see Section 2. 4. 3). The number of individuals included in the social CPP analysis, therefore, was 20 (11 low-and 9 high-ranging chickens). All statistical analyses were performed using IBM SPSS Statistics 21.
Perturbations that result in an increase in feed intake (e. g. , cold stress, immunocastration or providing a diet with low-energy content) are not considered here, but the proposed method is generic and can be adapted to account for these types of perturbations. It is hypothesized that the observed cumulative feed intake (CFI) of a pig is the combination of a target trajectory curve (i. e. , the amount of feed a pig desires to consume in a non-perturbed condition) and a change in feed intake due to perturbations. During a perturbation, the feed intake of the pig will deviate from the target trajectory but, once the perturbing factor is over, the pig will strive to increase its feed intake through compensatory feed intake to rejoin the target trajectory of CFI (target CFI). The data analysis procedure includes two main steps.
An auto-correlation test was used as a selection criterion to temporarily remove data with negative residuals from the dataset. The fitting procedure was then repeated on the resulting dataset until the autocorrelation of the residuals was no longer significant. Compared to fitting the curve to the original dataset of CFI, this procedure results in moving the CFI curve upwards, while fitting the model to fewer observations compared to the original dataset. Preliminary analysis indicated that the absence of auto-correlation could be achieved only if very few data remained. This appears to be due to small oscillations in CFI that are not necessarily the result of perturbations. To ensure that the target CFI is estimated with a reasonable number of observations, the data elimination procedure was terminated when at least 20 observations were remaining.
In short, the parameters of the target CFI were estimated by repeatedly fitting the reparametrized equation (  1 ) to CFI data and temporarily eliminating observations with negative residuals until there was no auto-correlation among the residuals or when at least 20 observations remained. Deviations from the target CFI correspond to potential perturbations. As indicated earlier, small oscillations in feed intake patterns exist. The aim here is to detect the most important deviations that are the result of perturbations. These perturbations can then be characterized by the duration and magnitude of the deviation from the target CFI. A deviation was considered as a perturbation if it lasted at least 5 days, to ensure that a reasonable number of data were used to estimate the model parameters.
The magnitude was determined by calculating the maximum reduction of a deviation from the target trajectory. Because CFI is increasing continuously, deviations from the target CFI were expressed as a percentage and an arbitrary value of 5% was set as a threshold value to identify a perturbation. To identify perturbations among all the deviations from the target CFI, a B-spline function of order 6 was fitted to the difference between the observed CFI and the target CFI. Any period during which the observed data deviated from the target CFI for more than 5 days and for more than 5% was considered to be the result of a perturbation. The interest of using a B-spline function is its high flexibility and its smoothing properties that allow to capture small deviations from the target CFI  (Ramsay and Silverman, 2007).
However, the method is generic in that the different elements can be changed and adapted as judged necessary. Data source used for model calibration Data were collected from an experimental farm of INRA in Le Magneraud (Charente-Maritime, France). Five pigs from the same batch (i. e. , they were born on the same farm and approximately on the same day) were chosen to demonstrate the procedure. The pigs entered the same growing facility at 68 days of age and stayed there until reaching their slaughter weights (124 kg on average). Feed was provided ad libitum. Feed intake was recorded on a daily basis using the singleplace Acema 64 electronic feeder (Acemo, Pontivy, France) as described by  Labroue et al. (1994). Because CFI is sensitive to missing data (e. g. , due to loss of a radio-frequency identification (RFID) ear tag or malfunctioning of the feeder), a procedure to deal with missing data was developed (Supplementary Material S2; an example of missing data estimation by the procedure is shown in Figure  S1 ).
Statistical analysis All statistical and optimization procedures were performed using R software version 3. 5. 0 (http://cran. r-project. org/). To account for scale differences in the target CFI (reparametrized equation (  1 )), a weighted regression procedure was applied using (1/CFI) 2 as statistical weight. The optimization was performed using the non-linear function 'nlsLM' of the package 'minpack. lm'. The structural identifiability of reparametrized equation (1) and equations (2a) and (2b) was tested using the software DAISY  (Bellu et al. , 2007). All equations were structurally identifiable, meaning that the parameter estimation problem is well posed and it is theoretically possible to estimate uniquely the model parameters given the available measurements (Mu ñoz-Tamayo et al. , 2018).
The process was terminated when 37 CFI observations remained, even though auto-correlation still existed. Further application of the procedure would result in fewer than 20 remaining observations. Estimated parameters of the target CFI are indicated in Table  1. Figure  3  shows the target CFI against observed CFI, illustrating that the observed CFI deviated from the target CFI from 100 to 150 days of age and, to a lesser extent after 185 days of age. Figure  4  illustrates the differences between the observed and target CFI. Not considering the data of the first week, three deviations from the target CFI were detected. Given the selection criteria for a perturbation, only one deviation was considered as a perturbation. The analysis using the B-spline function indicated that it lasted from 96 to 168 days of age and the maximum deviation was 9.
In an analysis of different growth functions, Schulin-Zeuthen et al. (2008)  indicated that BW could very well be described by a monomolecular function of CFI, basically indicating that animals grow because they eat and, at maturity, they eat for maintenance. It is interesting to note that for all five animals used in this study, a significant auto-correlation remained for CFI unless the procedure was allowed to proceed beyond the limit of 20 remaining observations. The presence of auto-correlation in CFI data indicates that there are patterns in feed intake that cannot be captured by a polynomial model with (potentially) four parameters. Although the choice of 20 observations was arbitrary, it is a compromise between the number of remaining data and the presence of auto-correlation in the target CFI.
This would allow quantifying differences in the responses of individual pigs to a common perturbing factor through the resistance and resilience traits k1 and k2. Certainly, this has a great potential in animal breeding to estimate heritabilities and to evaluate relationships between performance and robustness traits  (Guy et al. , 2012; Hermesch et al. , 2015). Elements of the data analysis procedure proposed in this study can also be used in precision livestock farming. For example, it could be used as an early warning system if deviations in feed intake occur relative to the target CFI. Likewise, specific management strategies (e. g. , in terms of nutrition, medication or care) may be given to animals that deviate from their target CFI to limit the impact of the (known or unknown) perturbing factors and to facilitate the recovery of the animals so that they can regain their target CFI.
Acknowledgements This study has been funded by the  European Union 's  H2020  project  Feed-a-Gene  under grant agreement no. 633531. Hieu Nguyen-Ba  also received funding from the  INRA meta-programme Adaptation of Agriculture and Forests to Climate Change (INRA-ACCAF)  and from the  French Digital Agricultural Convergence Lab (#DigitAg)  for his PhD work. Feed intake response of pigs to perturbations.
2a  and b ). Culture on Sabouraud's agar incubated at 35°C resulted in the isolation and identification of multi-sensitive Aspergillus fumigatus (MIC to amphotericin B 0. 125 mg/L, itraconazole 0. 25 mg/L, voriconazole 0. 094 mg/L, caspofungin 0. 004 mg/L, posaconazole 0. 032 mg/L (ETEST®; Biomerieux, France). Quantitative PCR for Aspergillus DNA targeting the mitocondrial tRNA gene was positive. Finally, analysis of the ethmoid biopsy by conventional PCR targeting the sequences ITS-1 (5'-TCCGATAGGTGAACCTGCGG-3') and ITS-4 (3'-TCCTCCGCTTATTGATATGA-5') followed by sequencing of the amplification products showed, after analysis of the nucleotide sequence using BLAST software (https://blast. ncbi. nlm. nih. gov/Blast. cgi), 100% identity with the sequence WM 03. 448 from A. fumigatus strain MITS 21 ISHAM-ITS.
Following the development of liposomal amphotericin B-related nephrotoxicity, voriconazole was started for a total duration of 1 year with regular monitoring of plasma levels (1. 28 to 4. 91 mg/L). The patient is currently stable, but remains blind in the right eye and sequelae of paresthesia. This sequelae paresthesia maybe also related to the neurotoxicity of voriconazole as previously described  (6). During the treatment, a repeat imaging was performed and has shown progressive disappearance of rhino-orbito cerebral lesion. An RMI brain imaging performed two years after initiation of antifungal treatment revealed total disappearance of cerebral lesions (data not shown).
In our patient, analysis of the ethmoid biopsy by qPCR permitted the diagnosis of Aspergillus infection. The identification of A. fumigatus was confirmed by culture and the analysis of ITS sequences. Anatomo-pathological analysis confirmed the invasive character of the infection. These data suggest the interest of using molecule diagnosis in invasive and deep forms of aspergillosis. Early management of ROA requires systematic surgical resection associated with a systemic antifungal drug administered long-term for 12-18 months  (19). Although voriconazole is the drug of choice for this clinical form of aspergillosis, due to its good diffusion into bone, amphotericin B is often used to broaden the antifungal spectrum  (5, 20). The use of voriconazole as monotherapy without surgical resection was shown to be effective in a recent report  (13).
Dynamic of piglet interaction with the rope Five naïve litters of 28-day-old piglets (n=57) and five naïve litters of 21-day-old piglets (n=57) were randomly selected. Prior rope presentation, all piglets were individually identified by number marking between the shoulder blades. These litters were video recorded for the full 30 min of rope presentation and behavior of the piglets was analysed. The observation period was divided into 10-s intervals by a single observer and each piglet was scored as either interacting with the rope or not. Rope interaction was defined as chewing the rope or trying to catch it. The obtained data were used to describe (1) the percentage of piglets interacting with the rope over the 30 min observation period, (2) the latency before the first interaction for each piglet, (3) the total duration of piglet contact with the rope for each piglet.
The HPV vaccine is based on the immunogenic properties of icosahedral virus like particles (VLP) produced by recombinantly expressing the major HP protein, L1, for each type of virus, in saccharomyces cerevisae yeast. The strategy was to use in vivo in early development, gather data and then transition to in vitro before approval. To measure potency, an in vitro relative potency (IVRP) assay was developed, a sandwich enzyme immunoassay measuring the amount of antibodies bound to neutralizing epitopes for each HPV type. The IVRP was used both in monovalent bulks and on final container samples. The assay proved able to provide a direct comparison between the antigen content of each VLP type in a given test sample and the content of a batch that was shown to be efficacious in humans, and its results also did show correlation with immunogenicity measured through a traditional mouse assay, so its result is considered predictive of immunogenicity in humans.
Laura Viviani presented the efforts of the Humane Society International (HSI) and the activities initiatedpromotion of global regulatory alignment for the deletion of the general safety test  [3]  and the use of non-animal based methods to replace the rabbit pyrogenicity test through the engagement of key regulatory and industry stakeholders of various countries. Ms. Viviani also presented the project of a dedicated database for non-animal based methods, their implementation status and the current existing collaboration opportunities. Speaking on behalf of the Bill & Melinda Gates Foundation (the foundation), Gautam Sanyal gave an overview of the foundation's strong commitment to delivery of life saving vaccines to the developing world at an affordable cost. The Foundation actively supports efforts to (a) accelerate development timelines, (b) reduce cost of manufacturing, (c) secure supply for GAVI, the Global Alliance for Vaccines and Immunizations, and (d) ensure appropriate product profiles, including new combinations and novel vaccine formulations, as needed for different geographies.
Acknowledgements IABS wishes to acknowledge the scientific committee, the speakers, and the participants for their contributions to the organization and the productive discussion had during the conference. The conference was generously sponsored by the  Bill & Melinda Gates Foundation  and by the  Humane Society International. The  VAC2VAC  project received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement  115924. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA.
GenBank nucleotide accession numbers are indicated in Supplementary Figure  S1. omp2 sequences were aligned using BioNumerics version 7. 6. 3 (Applied Maths, Belgium) with default parameters and the multiple alignment was used to produce a Maximum Parsimony tree. Comparative Omp2 protein analyses were done using a previously published Omp2a and Omp2b predicted and functionally validated topology model  (Paquet et al. , 2001). Whole genome SNP (wgSNP) analysis was done as previously described  (Vergnaud et al. , 2018). Full genome sequences and assemblies were converted into artificial reads. SNPs were identified by mapping sequencing reads on a reference genome within BioNumerics. Assembly GCF_000007125 (B. melitensis 16 M) was used as reference. The Bio-Neighbor joining algorithm 1 www. ebi.
RESULTS AND DISCUSSION Global View of omp2 Gene Diversity in Brucella spp. As shown in Figures  1, 2  and Supplementary Figure  S1 , omp2 sequence diversity analysis allowed to separate the non-classical Brucella spp. of this study in nine distinct groups (A to I), that correlated perfectly well with other previously published molecular methods such as MLSA, MLVA, IS711 profiling, or whole genome phylogeny (Figure  3 ). A second observation was a relative lower nucleotide sequence identity between their respective omp2a and omp2b gene copy ranging from 78. 9 to FIGURE 1 | Brucella spp. omp2 clustering analysis, constructed using the omp2a and omp2b sequences of the non-classical and classical Brucella species of this study (GenBank accession numbers are in Supplementary Figure  S1 ). The strains are represented by circles, increasing in size for strains with identical sequences.
Bioinformatics and data analysis The quality control check on raw reads in FASTQ format were processed using FASTQC and the Q20, Q30 and GC contents of the clean data were calculated. The Salmon software (version 0. 9. 1) was used for transcript quantification  [23]. NCBI RefSeq reference transcript of the Capra hircus genome (assembly ARS1) was used to build the index within Salmon. The reads from each sample were mapped to the same index and quantified. Unix commands were used to obtain corresponding gene and transcript identifiers from the NCBI RefSeq annotation of the Capra hircus (ARS1). Using these identifiers, the tximport (version 1. 8. 0) package was used to import data into the R software (v3. 5. 1) and summarize the TPM estimates obtained from the Salmon tool of all samples at the gene level  [24].
This process produced a global count file on which the statistical analyses were performed. A threshold of greater than or equal to 5 counts across samples was applied in order to remove genes showing low expression. Partial least squares discriminant analysis (PLSDA) had been conducted using the mixomics package within R  [25]. In this analysis, x was the matrix of gene expression values (count table) and the classes of y were given as resistant and susceptible. Each row of the x matrix represented the gene expression values for a sample, and each column corresponded to a gene. Differentially expressed genes (DEG) of read counts were identified using the Bioconductor package DESeq2 within R  [26]. Ten comparisons were performed; three comparing day 0 with day 8, 15 or 35 post-infection in the susceptible group, another three comparing the same days in the resistant group and four comparing samples from resistant versus susceptible animals at day 0, 8, 15 and 35 post-infection.
To account for multiple testing, genes were filtered using a Benjamini and Hochberg false discovery rate (FDR) of < 0. 001. Final DEG were determined on the basis of their fold change values to be log 2 ≥ 1. 0 for up-regulated genes and ≤ -1. 0 for down-regulated genes. Gene ontology (GO) analysis for the biological processes was performed to identify the biological function classification of the genes, which describes properties of genes and their products. DEG are functionally grouped into the biological processes looking for significantly enriched functions compared to the human genomic background due to the lack of goat (C. hircus) GO data. GO enrichment analysis and GO annotations plotting were performed using the clus-terProfiler R package  [27]. All enriched GO terms that possessed a p-value < 0.