dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses Continuous variables are shown as mean ± SEM. Normal distribution was verified by means of the Kolmogorov-Smirnov test. Normally distributed data were compared using a one-way analysis of variance (ANOVA) test followed by a Dunnett's multiple comparison post-hoc analysis. Non-parametric data were analyzed using the Kruskal Wallis test followed by Mann Whitney U test. Statistical significance was accepted at p < 0. 05. Analyses were performed using GraphPad Prism 5. 0 (GraphPad Software Inc).
Funding: This research was funded by "Instituto de Salud Carlos III-FEDER", Miguel Servet Fellowship contract CP13/00221. N. L. -A. reports a grant from G3 Pharmaceuticals. P. R. and F. J. are supported by FIGHT-HF (reference: ANR-15-RHU-0004). F. J. is supported by the MIRCOV grant (reference: ANR-COVID-Resilience-2021). L. M. is supported by a PFIS (FI19/00302) grant and E. J. (CD19/00251) is supported by a Sara Borrell grant, both from "Instituto de Salud Carlos III".
Simple Summary: Nutritional manipulation of livestock diets has long been considered asa tool for the control of infectious diseases. The objective of this literature review is to provide a comprehensive and critical view of the studies that have investigated the interactions between synthetic amino acids supplementation and immune response against infectious diseases in livestock. We performed a literature search in two main databases, PubMed and Web of Science. Based on our criteria for eligibility of the research articles, we selected 58 studies. Most of the studies selected focus on poultry and three amino acids that are all associated with a significant improvement in host response: methionine, threonine and arginine. The most described immune mechanisms associated with synthetic amino acids supplementation were directed against intracellular pathogens.
Materials and Methods The study methodology was based on the guideline of "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement"  [20]. The literature search was conducted using the Web of Science and PubMed electronic databases. The search strategy is described in Figure  1  and the search terms used are in Table  1. *The use of truncation (*) allows a query to be made only on the root of the word. Furthermore, the reference lists from selected articles provide additional articles. Studies were closely evaluated and selected for inclusion if they were performed using livestock to measure the effect of dietary supplementation (above the National Research Council, NRC requirement). They had to have at least one particular synthetic amino acid on the expression of at least one mediator of the immune response following an immune challenge, and measure at least one production trait.
Study Selection and Characteristics A total of 2322 results were provided by the Web of Science and PubMed databases (Figure  1  and Table  1 ). After excluding duplicates and screening titles and abstracts, 77 full-text research articles were further evaluated for relevance. Finally, 58 research articles were included in the qualitative synthesis. All included research articles were in English and published between 1969 and 2020. Six papers were not found using the bibliographical research strategy described but were located in the citations of the selected articles.
Statistical tests Each experiment was repeated independently a minimum of three times (unless otherwise indicated), error bars represent the standard error of the mean of replicate experiments. Data were analyzed using appropriate statistical tests as indicated in figure legends using the GraphPad Prism software. Significance tests were performed using the Mann-Whitney test. For experiments with more than two conditions, significance was tested using ANOVA test followed by post hoc Dunnett's multiple comparison tests. P values of < 0. 05 = *, < 0. 01 = **, and < 0. 001 = ***.
Acknowledgements We thank the  VDRC in Vienna , the  Bloomington Stock Center  ( NIH P40OD018537 ),  Brian McCabe , and  Gabor Juhas  for the fly stocks. We thank the  BIOP platform (EPFL)  and the  BioEM platform (EPFL)  for help with electron microscopy experiments. This work was supported by the  Sinergia  grant  CRSII5_186397. We thank  Jean-Philippe Boquete  for his precious technical support, we thank  Hannah Westlake  for critical reading of the manuscript, and  Dominique Soldati  ( University of Geneva ) and  Brian McCabe  ( EPFL ) for stimulating discussion.
An overview of the declarations of P-F conflicts of interests is given in the Methods Report. Overall, 40 members of the expert panel (62. 5%) declared that they had no P-F COIs. For the 2021 update of the guideline, the same key questions were used as those developed for the version of the guideline published in 2018. Details on the processes used to develop these questions are available in the Methods Report of the latter. 7 The key questions were translated into the PICO format, which specifies the intervention, comparison and outcome used to assess efficacy and safety. Systematic searches for randomized controlled trials and clinical controlled trials were undertaken in three databases on 15 May 2020. The search identified a total of 2053 records. Two independent reviewers evaluated the literature and extracted eligible data.
Study population This retrospective single-center study included 43 patients [Table  1 ] treated with anti-PD-1 (nivolumab or pembrolizumab) for metastatic melanoma in the dermatology department of the university hospital (Lyon Sud, Hospices Civils de Lyon, Lyon, France) between December 2013 and April 2019. Written informed consent was obtained from participants ant the study was approved by a regional review board (Comité de Protection des Personnes Ile de France XI, Saint-Germain-en-Laye, France, number 12027) and is registered in ClinicalTrial. gov (Mel-Base, NCT02828202). Each patient was examined prior to the first injection of anti-PD-1 (V1), which was the first line of treatment, and after 6 months of treatment (V2) after which they were classified as either patients with treatment-sensitive tumor (complete or partial response) or patients with treatment-resistant tumor.
Statistical analysis Log 10 transformed TTV load format was used for analysis (log 10 copy/mL). A Mann-Whitney test was used to compare TTV viral load levels between groups and a p-value < 0. 05 was considered to indicate statistical significance. Data were analyzed and plotted using GraphPad Prism software (version 5; GraphPad software, La Jolla, CA, USA) and R (version 3. 5. 1).
abortus. To confirm the identification of isolates 6 and 11 as well as to complete the identification of isolate 7, we performed MLVA based on 16 loci  [5, 6]  in the following order: panel 1, Bruce06 -Bruce08 -Bruce11 -Bruce12 -Bruce42 -Bruce43-Bruce45 -Bruce55; panel 2a, Bruce18 -Bruce19 -Bruce21; and panel 2b, Bruce04 -Bruce07 -Bruce09 -Bruce16 -Bruce30. B. melitensis 16M was used as the reference strain for comparison and verification of test quality. In addition, MLST was performed based on 9 loci  [7] : gap -aroA -glk -dnaK -gyrB -trpE -cobQ -int-hyp (orf1)-omp25. Moreover, isolates 6 and 7 were subjected to wholegenome shotgun sequencing using the Illumina NexteraXT system (protocol 150,319,425,031,942, revision C), which has been deposited in DDBJ/ENA/GenBank under accession.
The DISI cohort included one death patient in each of the influenza+ and influenza-groups (#11 and #28, respectively). The remaining cohort was admitted to the Respiratory/General Ward with one influenza+ patient requiring ICU (#62). 53 patients presented with ILI, while 6 influenza+ and 1 influenza-patient presented with pneumonia (Supplementary Table  1 , Supplementary Data 1). We successfully recalled 80% (35/44) of influenza+ patients at a median of 41 days after disease onset and 75% of our influenzapatients (15/20, median 39 days). Overall, influenza+ patients were predominantly (55%) infected with the H3N2 influenza A virus (IAV) subtype (Fig. 1b  and Supplementary Table  1 ), followed by the co-circulating IAV 2009 pandemic H1N1 (pH1N1)-like strain (16%) and two co-circulating influenza B virus (IBV) strains (Phuket/3073/2013 (9%) and Brisbane/60/ 2008 (7%)), representing the dominant strains for each year in Australia.
IAV-infected patients (median 58 yrs, n = 34) were significantly older than IBV-infected (45 yrs, n = 10) and influenza-patients (47 yrs, n = 20) (Fig. 1c ). Apart from age, patient demographics in influenza+ and influenza-groups were well-matched, with 86% and 70%, respectively, having one or more high-risk conditions for severe influenza disease. Both groups had a median of 4 days in hospital (Supplementary Table  1 , Supplementary Data 1). Time in hospital was significantly lower for seasonal influenza+ patients compared to the more severely-ill H7N9 cohort  18  with a median 14 days for recovery (n = 12, p < 0. 0001, Kruskal-Wallis test) and a median of 33 days for those who died (n = 6, p = 0. 0437, Kruskal-Wallis test) (Fig. 1d ). The lower hospital burden reflects the lower severity of seasonal influenza compared to avian A/H7N9 disease.
^MAIT cells were defined by the MR1-5-OP-RU-tetramer and anti-TRAV1-2 antibody. e Frequency (mean, SD) of influenza+ (Flu+) patient cells expressing total cytotoxic molecules at acute (includes n = 16 at V1 and n = 11 at subsequent visits i. e. , V2, V3, or V4) and follow-up timepoints (n = 19), in comparison to healthy donors (n = 20, except for MAIT subset where n = 12). f Representative FACS plots of CD8 + T cells from influenza+ patient and individual frequencies (mean, SD) of granzymes (A, B, K and M) and perforin staining for each cell subset. Dataset n numbers are the same as in (e). g Pie charts representing the average fractions of cells co-expressing different cytotoxic molecules (slices) and the combinations of granzymes and perforin molecules (arcs). Statistical significance (0.
Methods Study participants and design. The DISI cohort enrolled consenting adult patients admitted to The Alfred hospital during the 2014-2017 peak influenza seasons with ILI. We recruited 44 influenza-positive patients and 20 influenzanegative patients with other respiratory diseases. Bloods were collected within 24-72 h of hospital admission (Visit 1, V1), every 2-5 days until discharge (V2, V3, V4 etc), then followed up ~30 days later. Nasal swabs were collected at V1 and V2 timepoints while in hospital. Clinical data collection included vaccination status, sequential SOFA score  29  as a measure of disease severity, and any significant risk factors (Supplementary Table  1 , Supplementary Data 1). The H7N9-infected hospitalized patient cohort (n = 18) has previously been described  18. Healthy adults vaccinated in 2015 (TIV, n = 16) and 2016 (QIV, n = 26) have previously been described in detail  4  , where blood samples were collected prior to vaccination (day -1 or 0) and on days 7, 14 and 28 following vaccination.
PBMCs were isolated by Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density-gradient centrifugation and cryopreserved. DNA was extracted from the granulocyte layer using a QIAamp DNA Mini Kit (Qiagen, Hilden, Germany, #51306) and sent to the Victorian Transplantation and Immunogenetics Service (Australian Red Cross Lifeblood, West Melbourne, Victoria, Australia) for HLA class I and class II molecular typing using the Luminex platform and microsphere technology (One Lambda, Canoga Park, CA, USA), with LABType SSO HLA typing kits (One Lambda, #RSSO1E47, #RSSO2345). Nasal swab samples were kept cold during transport and stored at -80 °C within 4 h. In cases where fewer than the total number of donors were analyzed (Supplementary Data 7), DISI patients were selected for influenza-specific cellular assays based on their influenza status and remaining sample availability, such as the class I and II TAME experiments which were carried out on IAV + patients and relied on HLA type and tetramer availability.
Influenza A or B multiplex RT-PCR reactions were set up using 3 µl of RNA and 3 µl of primer cocktail (Supplementary Table  5 ) with SuperScript III one-step RT-PCR system with Platinum Taq high-fidelity DNA polymerase (Invitrogen, #12574030). PCR products were further fragmented to 200 bp with Ion Xpress Plus fragment library kit (Life Fig. 7  Analyses of immune responses and clinical and genetic host factors. a,b Unsupervised heatmaps of immune, clinical and genetic parameters in influenza+ patients at (a), acute and (b), convalescent timepoints. Scale visualizes each variable using the Empirical Percentile Transformation. A value of 1 means that the measure was higher than 100% of the other samples for that measure. Each variable was transformed independently. Interactive heatmaps are shown in Supplementary Data 2-6 for influenza+, influenza-and combined datasets.
Statistically significant p values are shown (0. 0001>p<0. 05). Technologies, #4471269) and then ligated to Ion Xpress barcode adapters 1-96 (Life Technologies, #4474517), then samples with different barcodes were pooled. AMPure XP reagent (Agencourt, #A63881) was used to clean up the library pool and quantified with the Ion library quantitation kit (Life Technologies, #4468802), 10 pM of final library concentration was used to prepare template-positive Ion Sphere particles on the Ion OneTouch 2 instrument (Life Technologies), Ion 316 Chip version 2 (Life Technologies, #4488149) was used for sequencing on the Ion Torrent PGM. FluLINE pipeline was used for data analysis to produce consensus sequence  52. HA phylogenetic analysis with neighbor-joining trees were done using the Geneious 9. 0.
PBMCs were cell surface stained in 50 ul with panels 1 and 3 (Supplementary Data 8) for 30 min on ice, fixed then permabilised and stained with intracellular antibodies from panels 1 and 3 using the BD Cytofix/Cytoperm Kit (#554714) according to manufacturer's instructions. PBMCs were cell surface stained with panel 2 (Supplementary Data 8) following TAME as detailed below. TruCount/MultiTest samples were acquired on a BD CantoII to achieve 3000 bead events (~30,000 lymphocytes). All other samples were acquired on a BD LSRII Fortessa collecting all events. Flow cytometry data were analyzed using FlowJo v10 software. Influenza virus infection assay. Thawed PBMCs (<1. 5e6) from DISI influenza+ patients were cultured overnight at 37 °C/5% CO 2 with their cognate live egggrown virus (MOI = 4), essentially as described  4, 12  , for a total of 22 h before cells were harvested, then cell surface and intracellularly stained with Panel 1a or 1b (Supplementary Data 8) using the BD Cytofix/Cytoperm Kit (#554714) according to manufacturer's instructions.
Tetramers restricted to the same HLA were added together at the same dilution. Unenriched, enriched and flow-through fractions were cell surface stained with Panel 2a (influenza+) or 2b (influenza-, follow-up samples only) (Supplementary Data 8) for 30 min on ice, fixed with 1% PFA, then acquired by flow cytometry. Panel 2b samples were fixed and permeabilized using the eBioscience TM Foxp3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific, Carlsbad, CA, USA, #00-5523-00). Samples with cell events just below 10, especially for tetramer + CD4 + T cells, were not further characterized phenotypically using these staining panels. All events were collected on a BD LSRII Fortessa. Expression of cytolyic molecules. Flow-through fractions of DISI patients following TAME or thawed PBMCs from healthy donors (Supplementary Table  3 ) were stained with Panel 3 (Supplementary Data 8) for measuring granzymes (A, B, K and M) and perforin using the Foxp3/Transcription Factor Staining Buffer Set.
All events were collected on a BD LSRII Fortessa. Statistical analyses. Statistical significance was assessed using GraphPad Prism v8 software unless stated otherwise. Mann-Whitney (unpaired) and Wilcoxin (paired) tests were two-tailed. Friedman (matched) and Kruskal-Wallis (unmatched) tests were used to compare more than two groups. Tukey's multiple comparison test compared row means between more than two groups. Partial correlation plots (Fig. 1f ) showing significant (FDR-adjusted p value < 0. 05) colored squares were partialed to account for the variance caused by log 10 (age) and the sampling days after disease onset. To build antibody landscapes (Fig. 2f ), local regression (LOESS) was applied in R 3. 5. 3  55. LOESS with 95% confidence intervals was also used to plot Figs. 3c, 5c and 5d.
Linear relationships between day of sample and percent of IFN-γ producing cells were assessed with ordinary least squares (lm function) in R with 95% confidence intervals shaded in gray and correlation coefficients (r 2 ) reported in Fig. 5d. Correlations (Fig. 3g -i, Supplementary Fig. 4 ) were assessed using Spearman's correlation coefficient (r s ). Polyfunctionality pie charts (Fig. 5g ) were generated using Pestle v1. 8 and Spice v5 software  56.
Acknowledgements We thank  Jill Garlick ,  Janine Roney , and the research nurses at the  Alfred Hospital ,  Nicola Bird ,  Kim Harland ,  Louise Carolan  and  Bernadette McCudden  for their technical assistance. Thank you to  Professors David Fairlie  and  Jim McCluskey  for the MR1-5'OP-RU tetramer. We also thank the study participants for providing blood for research purposes. This work was supported by The  Australian National Health and Medical Research Council (NHMRC)   NHMRC Program Grant  ( 1071916 ) to K. K. , S. J. T. , N. L. G. , A. K. , D. C. J. , L. E. B. , W. C. , and a  NIAID   UO1  grant  1U01AI144616-01  " Dissection of Influenza Vaccination  and  Infection for Childhood Immunity" (DIVINCI)  to K. K. and P. G. T. THON is supported by the  NHMRC   EL1  Fellowship (# 1194036 ).
A field study team visited farms four times over the production cycle to collect data on commercial feed products used by week. A total of 338 flocks raised in these farms were investigated. Of the 115 farms, 44 completed 1 cycle (38. 3. 4%), 25 (21. 7%) 2 cycles, 8 (7. 0%) 3 cycles, 11 (9. 6%) 4 cycles, 12 (10. 4%) 5 cycles, and 15 (13. 0%) more than 5 cycles. The median flock size at restocking was 303. A total of 6,041 weeks of data were collected. The median duration of these native meat chicken production cycles was  [19] [20] [21]  weeks. All farm visits were conducted from October 2016 to Oct 2019.
Data analyses We calculated consumption of each AAI included in commercial feed by week by relating the amounts of AAI (mg) to the weight of birds at the time of consumption (standard weight of the flock) (kg) (mg/kg live chicken) for all weeks (n) over the flock's life duration (Expression 1). mg=kg chicken at time of consumption ¼ P n k¼1 AAI used ðmgÞ in week k Standard weight of the flock ðkgÞ at week k Weekly consumption of AAIs in feed was calculated by multiplying weekly feed consumption by the AAIs concentration indicated in that feed. The feed consumption was estimated from unpublished data related to native Vietnamese layer pullets, where 443g of feed were consumed by 1 kg of live chicken per week. The denominator (total weight of the flock at week k) was calculated from the number of chickens present in the flock multiplied by an estimated (standard) weight.
The latter was based on weekly weight data from 10 randomly selected chickens from 11 representative flocks, collected from week 1 until week 22 of their production cycle  [19]. The concentration (strength) of AAI in each feed product was obtained from its label. However, information for a number of feed products contained uncertain information in their labels, concerning the identity of the AAI and the amounts included. For feed products with AAI content ambiguously labeled (i. e. indicating inclusion of one of >1 listed AAIs), the amount of each AAI was calculated by assigning each antimicrobial a probability corresponding being included (probability = 1), and not being included (probability = 0). For products indicating their AAIs concentration as a range, lowest and highest estimates were calculated for each antimicrobial.
Presumably all of these environmental lineages phylogenetically assigned to the short-branch microsporidians are likewise parasitic; it is unknown, though, who are their potential microbial-or macro-organismic hosts, or where to even begin to look for them in environments as species-rich as tropical forests. A novel approach to evaluating the diversity of protistan parasites and their hosts in metabarcoding datasets was recently demonstrated  [14]. Using linear regression models, Singer et al. [14]  showed that the abundances of apicomplexans and their metazoan hosts positively correlated across alpine sites in Switzerland. That type of analysis is dependent in part, though, on knowing what are the potential hosts through previous observations. Another approach to unravel potential host-parasite relationships when the hosts are unknown is to use co-occurrence network analyses.
Although network analyses based on co-occurrences do not confirm biotic interactions  [15] , co-occurrence network can highlight potentially interesting taxonomic groups as potential hosts. We used a co-occurrence network buil t fr om metabarcoding data from Mahé et al. [1]. Briefly, the data came from soils collected in lowland rainforests in Costa Rica, Panama, and Ecuador. The soils were amplified using broad eukaryotic primers for the V4 region of SSU-rRNA  [16]  and sequenced with Illumina MiSeq. After initial cleaning steps, reads were clustered into operational taxonomic units (OTUs) with swarm  [17]  and taxonomically assigned using the PR 2 database  [18]. Most of the OTUs were assigned to different protistan taxa, while others were assigned to Fungi and Metazoa. From this original data, refinements of the taxonomic assignments placed 974 OTUs into the short-branch microsporidia  [5].
We calculated the richness of these OTUs with vegan v. 2. 5-6  [19] , and compared the exclusive and shared OTUs by country with a Venn diagram  [20]  in R v. 3. 6. 3  [21]. Representative sequences from all eukaryotic OTUs were used to construct a co-occurrence network with the NetworkNullHPC script (https://github. com/lentendu/ NetworkNullHPC). In this network, the OTUs are represented as nodes, and a statistically significant Spearman correlation between two OTUs is represented by an edge between them. The network contains only OTUs with a significant co-occurrence with at least one other OTU, using a set of null models following Connor et al. [22]. The resulting co-occurrence matrix was analyzed in R with tidyverse v. 1. 3. 0  [23]  and igraph v. 1. 2. 4. 2  [24] , then explored and visualized with Gephi v.
S1 ). The co-occurrence network consisted of 14,329 edges involving 368 nodes (approximately 2. 40% of all OTUs in the dataset). Costa Rica had the highest richness of short-branch Microsporidia, and the highest number of exclusive OTUs (Figs. S2  & S3 ). However, just 15 widespread microsporidian OTUs were present in the network, corresponding to approximately 1. 54% of all their OTUs in the dataset (Fig. 1 ). Of these OTUs, 11 had a closest taxonomic assignment to the Paramicrosporidium, and four to the Mitosporidium, although the OTUs likely form independent environmental lineages (Table  S1 ). Filtering the co-occurrences for correlations only associated with these 15 short-branch microsporidian OTUs resulted in 1223 edges involving 244 nodes, with 768 edges belonging to OTUs assigned to the "paramicrosporidium" and 455 edges to OTUs assigned to the "mitosporidium" (Fig.