dataesr/scientific-paragraphs-categorization huggingface dataset:
Trends by patient characteristics Trend analyses by patient characteristics were restricted to dataset B in order to study a stable cohort of laboratories and a defined population under surveillance. Between 2005 and 2018, the MRSA percentage decreased significantly in all age groups (p = 0. 003 for infants, age < 1 year; p < 0. 001 for all other age groups) (Figure  2 ). During the same period, the number of MRSA BSI decreased significantly in all age groups except for patients 80 years and older, while the number of MSSA increased significantly in all age groups except for infants. Comparing 2005 with 2018, the number of MRSA BSI in the six age groups (< 1 year, 1-24 years, 25-49 years, 50-64 years, 65-79 years and ≥ 80 years) decreased by 18%, 44%, 50%, 39%, 34% and 3%, respectively. Conversely, the number of MSSA BSI increased by 9%, 26%, 38%, 64%, 109% and 164%, respectively.
Discussion Data from EARS-Net showed a statistically significant increase in the number of S. aureus BSI in the EU/EEA between 2005 and 2018. This trend is supported by  other published studies  [17] [18] [19] [20] , which indicates that the health burden of BSI caused by S. aureus is growing in Europe. Our analysis showed that the increase in S. aureus BSI was mainly the result of an increase in the number of MSSA BSI, while the number and the percentages of MRSA BSI decreased overall for the EU/EEA during the same period. As a result, the MSSA-to-MRSA ratio increased both when considering all S. aureus BSI reported to EARSS/EARS-Net during the study period (dataset A) or when restricting the analysis to only those laboratories which consistently reported data during the same period (dataset B).
A successful MSSA clone was described as having specific dynamics of colonisation, persistence and transmission, thus allowing its international spread although it is not multidrug-resistant  [24]. Another noteworthy element is the possible increasing role of MSSA as a healthcareassociated pathogen, especially for community-onset but healthcare-associated BSI acquired in places such as wound care clinics, nursing homes and dialysis centres  [22, 25]. Information on whether a BSI was healthcare-associated is not available from EARS-Net, as it performs laboratory-based surveillance that does not collect clinical or epidemiological data on infection. Finally, interventions based on screening and isolation of positive patients have focused on MRSA, while to control MSSA, it is necessary to act effectively on nonspecific measures such as hand hygiene  [26].
Our study has several limitations. The ascertainment of BSI cases depends on the propensity to perform blood cultures  [27]. Because of the response to empiric therapy with first-line antimicrobials, MSSA BSI are less likely to be ascertained when there is a low propensity to perform blood cultures which are reserved for cases such as treatment failure  [6]. Therefore, an increasing blood culture rate could lead to a more pronounced increase in the ascertainment of BSI caused by MSSA than those caused by MRSA  [6, 17, 27]. The increasing propensity to perform blood cultures in the period from 2005 to 2018, as observed in the EARS-Net dataset, makes it more difficult to interpret trends. However, given that the increase in the number of MSSA BSI over time was confirmed even after adjusting for the blood culture set rate, this confounding factor would only partially explain the observed trend for MSSA BSI, which we therefore considered real.
This result is further supported by the data from outpatients, a context in which the implementation of stewardship activities promoting sampling of blood cultures is less likely and where the increase in MSSA BSI was greater than the average increase for the whole study sample. Another potential issue that could have affected the results is the general tendency to centralise laboratories in large units, leading to a possible increase over time in the coverage of the laboratories considered in dataset B. However, in such a situation we would expect an increasing trend for both MRSA and MSSA BSI, since the variation in the number of reported MRSA and MSSA BSI should be proportional to the catchment population. Therefore, based on our results, it is unlikely that the coverage of laboratories that consistently reported data to EARS-Net increased substantially during the study period.
) and from 23. 5% in 2005 to 10. 4% in 2018 (p < 0. 001) for consistently reporting laboratories (dataset B). The EU/EEA population-weighted MRSA percentage significantly decreased from 28. 6% in 2005 to 16. 4% in 2018 (p < 0. 001) for all laboratories (dataset A) and from 30. 2% in 2005 to 16. 3% in 2018 (p < 0. 001) based on consistently reporting laboratories (dataset B). The MSSA-to-MRSA ratio increased from 3. 1 to 6. 1 for dataset A and from 3. 3 to 8. 6 for dataset B.
in 2005 to 72,085 in 2018 (Table). When restricted to only laboratories that continuously reported data during 2005 to 2018, 258,488 BSI cases (41,033 MRSA and 217,415 MSSA) were reported from 285 laboratories in 25 countries (dataset B, with Table Bloodstream infections caused by meticillin-resistant (n = 96,918) and meticillin-susceptible Staphylococcus aureus (n = 477,033), number of reporting laboratories and countries, EU/EEA, 2005-2018.
in 2005 to 72,085 in 2018 (Table). When restricted to only laboratories that continuously reported data during 2005 to 2018, 258,488 BSI cases (41,033 MRSA and 217,415 MSSA) were reported from 285 laboratories in 25 countries (dataset B, with Table Bloodstream infections caused by meticillin-resistant (n = 96,918) and meticillin-susceptible Staphylococcus aureus (n = 477,033), number of reporting laboratories and countries, EU/EEA, 2005-2018 Year Countries n Laboratories n Total n n Staphylococcus aureus BSI MRSA Crude % Population-weighted % MSSA n All laboratories (dataset A) 2005 29 664 27,125 6,615 24. 4 28. 6 20,510 2006 29 650 28,287 6,551 23. 2 27. 3 21,736 2007 29 643 30,027 6,605 22. 0 23. 6 23,422 2008 29 637 29,339 5,981 20. 4 23. 5 23,358 2009 28 643 30,729 5,969 19. 4 23. 5 24,760 2010 29 673 32,961 6,149 18.
7 22. 6 26,812 2011 30 685 35,712 5,863 16. 4 21. 8 29,849 2012 30 714 36,988 6,019 16. 3 21. 5 30,969 2013 30 725 40,968 6,673 16. 3 20. 5 34,295 2014 30 714 40,906 6,545 16. 0 19. 6 34,361 2015 30 738 45,509 7,313 16. 1 19. 0 38,196 2016 30 805 57,387 8,005 13. 9 17. 7 49,382 2017 30 853 65,928 8,500 12. 9 16. 8 57,428 2018 30 884 72,085 10,130 14. 1 16. 4 61,955 Laboratories that consistently reported data (dataset B) 2005 25 285 15,192 3,570 23. 5 30. 2 11,622 2006 25 285 15,954 3,617 22. 7 28. 8 12,337 2007 25 285 16,761 3,787 22. 6 25. 6 12,974 2008 25 285 16,307 3,253 19. 9 24. 1 13,054 2009 25 285 16,232 3,069 18. 9 25. 1 13,163 2010 25 285 15,987 2,868 17. 9 23. 0 13,119 2011 25 285 17,229 2,673 15. 5 20. 7 14,556 2012 25 285 17,622 2,740 15. 5 22. 8 14,882 2013 25 285 19,507 2,587 13.
3 20. 0 16,920 2014 25 285 18,833 2,573 13. 7 20. 3 16,260 2015 25 285 20,432 2,692 13. 2 18. 9 17,740 2016 25 285 21,711 2,547 11. 7 17. 6 19,164 2017 25 285 22,802 2,577 11. 3 17. 0 20,225 2018 25 285 23,879 2,480 10. 4 16. 3 21,399 BSI: bloodstream infections; EEA: European Economic Area; EU: European Union; MRSA: meticillin-resistant Staphylococcus aureus; MSSA: meticillin-susceptible S. aureus. Results from all laboratories, 30 EU/EEA countries (dataset A) and from laboratories in 25 EU/EEA countries, which consistently reported data during the study period (dataset B).
Calculations: Growth and Nutritional Parameters 2. 5. 1. Relative Daily Growth Rate (RGR), K1 and K2 The relative daily growth rate (RGR) was expressed as a percentage of BWi and calculated as RGR i = 100 × ((BWf -BWi)/(d × BWi)), where BWi and BWf are the initial and final body-weight, respectively, and d is the number of days between measurements. For each modality (treatment and ration size), RGR was first determined at an individual level (tagged specimens) and then at tank levels based on individual's RGR, thereby removing any mortality from the dataset and providing a more robust estimation of RGR. The gross feed conversion efficiency (K1) expresses the capacity to convert feed into body tissues. The net feed conversion efficiency (K2) provides a measure of the capacity to convert the amount of food available for growth, which is equal to the amount of feed consumed in excess of the maintenance ration (Rm).
Statistics Statistical analyses were conducted using R software  [61]. Prior to analysis, all data were systematically checked for normal distribution and variance homogeneity. The percentage survival rates were normalized using an arcsine transformation before analysis. Data on survival rates were tested using a one-way analysis of variance followed by a Student's multiple comparison t-test to determine differences among ration sizes and treatments. The effects of treatments and ration size on the physiological parameters studied were assessed by a two-way analysis of variances followed by a pairwise comparisons using Fisher's Protected Least Significant Difference (PLSD). For each parameter, four samples per tank were assayed. The effect of dietary treatment, ration size and their interaction on BWf and RGR were tested using individual shrimp data by a two-way nested analysis of variance in order to take into account a possible significant random tank effect.
The two patients carry biallelic mutations of TLR3 or IFIH1 We searched the WES data of 15 EVE patients for relatively rare (minor allele frequency [MAF] <0. 01 in the Genome Aggregation Database [gnomAD] and in our in-house WES database containing ∼10,000 exomes) nonsynonymous variants with a combined annotation-dependent depletion (CADD) score >10, in genes related to type I IFN immunity and previously shown to underlie other severe viral infections in humans  (Casanova and Abel, 2020) , with an autosomal recessive (AR) mode of inheritance. We excluded variants of genes with a gene damage index >13. 83, the gene damage index cutoff for AR human disease-causing gene prediction  (Itan et al. , 2015). The trio WES design made it possible for us to select homozygous or compoundheterozygous variants with full penetrance  (Casanova et al.
Statistical analysis When applicable, results are presented as mean ± SD. Mean values were compared between control cells and cells from the patients by a one-way ANOVA multiple comparison test or twotailed Student's t test or paired samples t test. When indicated, linear mixed models were used for log-transformed relative values to account for repeated measurements. Statistical analysis was performed in SPSS 19. 0. Statistical significance was denoted with ns, P > 0. 05; *, P < 0. 05; **, P < 0. 01; and ***, P < 0. 001 in the corresponding figures. Online supplemental material Fig. S1  shows biallelic TLR3 or IFIH1 mutations in two patients with EVE. Fig. S2  shows the function of P1's TLR3 mutants in vitro. Fig. S3  shows ISG induction in TLR3-or MDA5-deficient fibroblasts following EV30 or EV71 infection.
Acknowledgments We thank our patients and their families for participating in this study. We thank the members of both branches of the Chen et al. Journal of Experimental Medicine 13 of 17 IEIs of TLR3 and MDA5 in enterovirus encephalitis https://doi. org/10. 1084/jem. 20211349Downloaded from http://rupress. org/jem/article-pdf/ 218/12/e20211349/1425024/jem_20211349. pdf by  Bibliotheque Interuniversitaire Paris user on 26 January 2022 Laboratory of Human Genetics of Infectious Diseases  for helpful discussions;  Tatiana Kochetkov  for technical assistance; and  Yelena Nemirovskaya  for administrative assistance. This work was funded in part by the  National Center for Advancing Translational Sciences/National Institutes of Health/ Clinical and Translational Science Award program  (grant  UL1TR001866 ); grants from the  Agence Nationale de la Recherche  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 );  Agence Nationale de la Recherche  grants  SEAeHostFactors  ( ANR-18-CE15-0020-02 ),  GENVIR  ( ANR-20-CE93-003 ), and  CNSVIRGEN  ( ANR-19-CE15-0009-01 ); the  Rockefeller University ;  Institut National de la Santé et de la Recherche Médicale; University of Paris ; and the  St.
For flow cytometry analysis, acquisition and data analysis were conducted on a LSR Fortessa cytometer (BD Biosciences) and FlowJo v10. 7 software. For Next Generation Sequencing, cells were sorted using Influx Cell Sorter (BD Biosciences) and BD FACSTM v1. 2. 0. 142 software (BD Biosciences). We used the following antibodies from BD Biosciences or eBiosciences: CD11b-PE-Cy7, CD19-PE-Texas Red, CD25-APC, CD253-APC, CD274-PE, CD3e-PE-Cy7, CD4efluor 450, CD45-BV711, CD8b-FITC, F4/80-Alexa-eFluor 647, FoxP3-PE, Granzyme B-PE, Ly6G-FITC, MHC Class II-APC efluor 780, NK1. 1-FITC, PD1-APC. Live and dead cells were distinguished using the LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (Life technologies). The gating strategy is shown in Fig. S1. Library preparation, sequencing and data analyses. RNA was assessed on a Tapestation (Agilent).
DNA libraries were prepared according to the manufacturer's instructions (Illumina). Each library was sequenced on the Illumina NextSeq 500 device and a High Output 75 cycles cartridge with a capacity of 400 million readings. Expression analysis was conducted following these guidelines (https:// doi. org/ 10. 1038/ nprot. 2016. 095). Reads were first mapped to the mouse genome (GCF_000001635, v24) using Hisat2 (2. 1. 0) (https:// doi. org/ 10. 1038/ nmeth. 3317), counted as reads per gene with Stringtie (1. 3. 5) (https:// doi. org/ 10. 1038/ nbt. 3122), and then analyzed using the statistical algorithm DESeq2 (1. 22. 2) (https:// doi. org/ 10. 1186/ s13059-014-0550-8). All samples used for host transcriptome analysis have at least 1,379,285 transcripts per sample with an average of 1,773,354 accross all samples.
Acknowledgements This work was supported by the  INSERM , the  Université Paris-Est Créteil , the  Institut Mondor de Recherche Biomédicale (IMRB)  and by grants of the  Fondation ARC  and the  Association Française pour l'Etude du Foie. The authors thank  Noburu Mizushima  for providing Atg5 flox/flox mice,  Christophe Boucly ,  Marjorie Collery ,  Alice Da Silva ,  Damien Fois  and  Florine Weber  for their help during in vivo experiments,  Aurélie Guguin ,  Adeline Henri  and  Odile Ruckebush  for their expertise in flow cytometry and  Xavier Ducrouy  for his expertise in confocal microscopy.
The following secondary antibodies were used (1:400; Thermo Fisher Scientific): Alexa Fluor 488 goat anti-chicken (#A-11039), Alexa Fluor 546 goat anti-rat (#A-11081), Alexa Fluor 488 goat anti-rabbit (#A-11008), and Alexa Fluor 488 goat anti-guinea pig (#A-11073). Fluorescence images were acquired by sequential scanning using a Leica SP5 DS (20× and 40× objectives) and processed with Fiji 39 Software. Antibody staining quantification. For quantification of AdipoR staining in the APNs, stacks were taken for each cluster of Apn in each lobe from top to bottom limit of the staining. Slices of the whole stack were used to evaluate the fluorescence intensity using Fiji 39 (StagReg, Time Series Analyser V3. 0, and Roi Manager plugins). NFAT quantification. We used the calcium reporter UAS-CaLexA (calciumdependent nuclear import of LexA, lexAop-CD8-GFP-2A-CD8-GFP; UAS-mLexA-VP16-NFAT, and LexAop-rCD2-GFP), allowing Ca ++ -dependent activation of GFP transcription  11.
Data were presented as mean values ± SEM. P values are indicated in all panels. a. u. arbitrary units. Source data are provided as a Source Data file. for 20 min; Merck, #i9278). For sequential incubations, after 30 min of incubation with JHa (or EtOH as a control), insulin (or H2O, as a control) was added for 30 min. The incubation mix was then removed and tissues were fixed and processed as described above. Chicken anti-GFP antibody (1:10,000, Abcam #mAb13970) was used as primary. Images were obtained with Leica SP5 (objective ×40) and processed with Fiji Software  39. For quantification of fluorescence, an average of independent measurements per fat bodies of the indicated genotypes was performed. Brains from Apn > CalexA larvae grown in 0xS were dissected in Schneider medium (Gibco TM #21020024) with FBS and incubated overnight at 18 °C with 20 ul of the hemolymph form control (lpp > ) or test (lpp > grp78-Ri) larvae grown in 0×S or 4×S.
Eluted proteins were digested with sequencing-grade trypsin (Promega) and analyzed by nanoLC-MS/MS on a QExactive+ mass spectrometer coupled to an EASY-nanoLC-1000 (Thermo Fisher Scientific) as described previously  48. Data were searched against the Flybase database (release r6. 16) with a decoy strategy. Peptides were identified with Mascot algorithm (version 2. 5, Matrix Science, London, UK) and data were further imported into Proline 1. 4 software (http:// proline. profiproteomics. fr/). Proteins were validated on Mascot pretty rank equal to 1, and 1% FDR on both peptide spectrum matches (PSM score) and protein sets (protein set score). The total number of MS/MS fragmentation spectra was used to quantify each protein from at least three independent biological replicates. This spectral count was submitted to a negative-binomial test using an edgeR GLM regression through R (R v3.
2. 5). For each identified protein, an adjusted P value corrected by Benjamini-Hochberg was calculated, as well as a protein fold change (FC). We used a in-house developped R script, called IPinquiry, freely available under Github (https://github. com/hzuber67/IPinquiry4). Fig. 4  Central AdipoR function and JH signaling control peripheral insulin sensitivity. a GFP localization at the membrane in tGPH fat body explants -/+ incubation with human insulin, as a marker for insulin response. Fat bodies are stained with anti-GFP. (minimum n = 8 in two independent experiments). a′ Quantification of membrane GFP staining in individual cells of larval fat bodies explants wo/insulin (black) or w/insulin incubation (red) from (a) Apn > wo/ins n = 36, w/ins n = 44; Apn > AdipoR-Ri wo/ins n = 44, w/ins n = 44).
Statistical significance was tested using ordinary two-way ANOVA with Sidak's multiple comparisons test. Data were presented as mean values ± SEM. P values are indicated in all panels. a. u. arbitrary units. Source data are provided as a Source Data file. RNAi screen of candidate genes. Candidate genes were selected on the enrichment value of the MS results. A blind RNAi screen was performed using two independent lines for each candidate (VDRC KK/GD and TRIP, if available). The RNAi were expressed with ppl > (expressed in the gut and the fat body, both tissues assumed to be the two most important secretory tissues of the larva). Pupal lethality was checked as a read-out. Top candidate genes of the MS were screened in priority and among them, ones known to encode secreted or putative secreted proteins.
A non-exhaustive list of candidates is presented in Fig. S2c. S1. Software. Mascot Algorithm v2. 5 (Matrix Science, London, UK) and Proline software v1. 4 (http://proline. profiproteomics. fr) were used for the mass spectrometry. GraphPad Prism V9 was used for statistical analysis and graphical representation of the data. Fiji (ImageJ version 2. 0. 0-RC-69/1. 52n) was used for Image treatment from microscopy and western blot. Affinity Photo v. 1. 8. 5 and Affinity Designer v. 1. 9. 3 (Serif, Europe) were used for Figures elaboration.
Acknowledgements We thank  T. Pihl  for help with the experiments and laboratory members for discussions; We thank  I. Miguel-Aliaga ,  K. Masuyama , the  Bloomington Stock Center , and the  Vienna Drosophila RNAi Center  for fly stocks. Funding is from  Inserm ,  CNRS , the  Fondation ARC pour la recherche sur le cancer  (grant no  PGA120150202355  to P. L. ), the  European Research Council  ( ERC Advanced  grant no  268813  to P. L. ).
Data availability The processed mass spectrometry data are available at PRIDE repository: ProteomeXchange accession: PXD013689 Project Webpage: http://www. ebi. ac. uk/pride/archive/projects/PXD013689 FTP Download: ftp://ftp. pride. ebi. ac. uk/pride/data/archive/2021/06/PXD013689/ Source data are provided with this paper.
Majority of studies dedicated to observe humoral response after bacterial exposure focused on already known AMPs such as lysozyme, galliomycin, gallerimycin, cecropin  (Mukherjee et al. , 2010; Insua et al. , 2013; Andrejko et al. , 2014). Recently, a quantitative shotgun proteomics was successfully realized in response to a S. aureus infection demonstrating a rapid and coordinated humoral immune response  (Sheehan et al. , 2019). Still using a proteomic approach, these authors have mapped the proteome of both infected larvae and a fungal pathogen. By this dual approach, they were able to describe the genesis of the infection, from host-pathogen recognition to the process of melanization and encapsulation  (Sheehan et al. , 2020). In 2020, the Antimicrobial Peptide Database reported about 310 AMPS in insects thus suggesting that the 20 or so peptides identified in G.
mellonella constitute only a tiny part of the humoral repertory, and that further studies are required to get more information about this humoral response  (Sowa-Jasiłek et al. , 2020). At last, G. mellonella genome is now sequenced, but it is still not entirely analyzed and annotated (accession number: NTHM00000000)  (Lange et al. , 2018). The genome sequenced was obtained from the isolate FT-Tue with a total of 2,141,900 reads and 20,638,932,410 bases. Moreover, a G. mellonella transcriptome database is now available (https://www. unigiessen. de/fbz/fb08/Inst/bioinformatik/Research/Supplements/ galleria)  (Mannala et al. , 2017). These preliminary data might allow the implementation of a shared-file database from genomic, transcriptomic and proteomic projects.
balamuthi complemented by transcriptome sequencing, and report on analyses of the data aimed at the following key tasks: 1) to characterize the changes associated with the transition of the Archamoebae stem lineage to the anaerobic lifestyle; 2) to define features of free-living ancestors of Entamoeba that may have facilitated evolution of parasitism in the Entamoeba lineage; and 3) to identify innovations that appeared separately in free-living and parasitic Archamoebae as adaptations to two fundamentally different anaerobic environments. Mastigamoeba balamuthi Genome. doi:10. 1093/molbev/msab020.
balamuthi genome and its structural RNA genes are provided in supplementary Results and Discussion and table  S3 , Supplementary Material online. A Survey of the Mastigamoeba Genome Illuminates the Character of the LCAA To trace gene losses and gains that led to diversification of free-living M. balamuthi and parasitic Entamoeba species, we reconstructed gene histories within Archamoebae and their relatives. First, we collected conserved orthologous groups (OGs) in the predicted proteomes of eight amoebozoans (M. balamuthi, E. histolytica, Entamoeba moshkovskii, Entamoeba invadens, Dictyostelium discoideum, Acytostelium subglobosum, Protostelium aurantium, and Acanthamoeba castellanii) and four opisthokonts (Homo sapiens, Amphimedon queenslandica, Saccharomyces cerevisiae, and Batrachochytrium dendrobatidis) by searching the EggNOG database of OGs using a profile hidden Markov model (HMM)-based search.