dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and population For ART uptake, we used an ecological design involving aggregate secondary programme data and all PLHIV newly diagnosed before (2014 to June 2016) and during "Treat All" (July 2016 to 2018) in Harare were the study population. For time to ART initiation and retention, we used a cohort design involving patient wise secondary programme data. We included all PLHIV newly initiated on ART from 50 electronic patient management system (ePMS) sites in Harare before (April-June 2015) and during "Treat All" (April-June 2017).
Adherence to CPT was used to assess the likelihood that the patient would adhere to ART. Patients were followed up monthly initially and then every three months. Viral load testing targeted those suspected of HIV treatment failure  13. A patient with confirmed virologic, immunologic or clinical failure were switched to second line ART 12. Data were routinely recorded in the ART register, patient OI/ART care booklet. The patient level data was also routinely captured electronically in the 50 ePMS sites. If a patient officially transfers-out to another ART clinic, they are issued with a transfer-out letter for presentation at the receiving ART clinic and they maintain their ART number. In other instances, patients may unofficially transfer out to another health facility without being assigned a transfer-out letter and are therefore declared lost to follow-up if they exceed 90 days after their last scheduled clinic or drug pick-up visit and all phone-call or physical follow-ups attempts by community health workers to trace them back to care have been futile.
Data variables and sources of data For ART uptake, we extracted quarterly aggregate number of new HIV diagnoses and new ART initiations from the District Health Information System 2 (DHIS 2). For time to ART initiation and retention, the source of patient level data was the ePMS and included OI/ART number; ART site; date of HIV diagnosis; date of ART initiation; date of birth; baseline characteristics and outcome at three, six and 12 months -alive and on treatment, death, loss to follow up, stopped ART, transferred out; and date of ART outcome. Operational definition of outcomes has been depicted in Table  1.
Ethics We obtained ethics approval from the Union Ethics Advisory Group (EAG), Paris, France (EAG number: 47/19, 29 April 2019) and the Medical Research Council of Zimbabwe (MRCZ), Harare (MRCZ/E/245, 19 August 2019). As this study involved review of existing programme records, we sought and obtained waiver of written informed consent from the ethics committees. Secondary data were extracted after obtaining administrative approval from the concerned authorities.
Response: Thank you very much. We agree with you. But as discussed before, data on ART adherence is collected but is unreliable as this is based on self-reports and pill count. Adherence would best be measured by VL testing which was not routinely available in the period prior to HIV treat All. Hence we were not in a position to compare the same before and during Treat All. Also there were large instances of missing data of CD4 count (mentioned as a limitation in second last paragraph of discussion).
Response: Thank you very much. We agree with the reviewer. Index tracing and testing will help achieving the first (90) or the first (95) of the UNAIDS target. This deals with what proportion of expected HIV positive should be diagnosed. Our study dealt (objective on ART uptake) with the second 90 or 95 which states what percentage of diagnosed PLHIV should be started on ART. We hope this is fine. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.
7% DNDiFEX006 NCT02184689 (II/III: 2014-2017) Efficacy and safety of fexinidazole in children (≥6 years and <15; ≥20 kg) with open-label and single arm g-HAT of any stage: a prospective, multicenter, 125 (95% CI [96. 2%-99. 7%]) and in children of 97. 6% (95% CI [93. 1%-99. 5%] at 12 months post-treatment. [24] cohort study, plug-in to the pivotal study DNDiFEX09HAT NCT03025789 \(IIIb:2016-ongoing).
In contrast, COHVAC participants were volunteers for ANRS HIV preventive vaccine trials who were selected for trials because they were in good health and had a very low-risk profile for HIV infection. At enrolment in the COHVAC cohort, a low percentage (< 10%) of participants reported unprotected sex with partners of unknown HIV status  [17]. Cisgender participants from the IPERGAY and COHVAC studies who did not acquire HIV nor HCV or HBV and with available frozen samples during follow-up were eligible to be controls. For IPERGAY participants, the selection was restricted to European individuals, whereas no data on ethnicity or geographical origin was collected in the COHVAC cohort. IPERGAY and COHVAC participants were matched 1:1 with PRIMO participants for sex and age (by five-year strata of current age).
Acknowledgements We thank all the patients and physicians who participated in the  ANRS PRIMO cohort , the  ANRS COHVAC cohort  and the  ANRS IPER-GAY  trial. We also thank  Prof. Patrice Th  erond and  Dr. Claudine Cosson  from the  Department of Biochemistry of Bicetre Hospital  for CRP measurements. This work was supported by the  French Agency for Research on AIDS and Viral Hepatitis (ANRS)  and a doctoral grant from  Paris-Saclay University  to  Sophie Novelli.
Acknowledgments The authors are grateful to the provincial AIDS and STI control coordination and various actors in the field who facilitated this work. The authors also thank  Dr. Etienne Mpoyi  ( WHO Contact Person ),  Dr. Willy Mvita  (Manager of the  HIV  Project at the  Cellule d'Appui et de Gestion Financière [CAGF] ),  Dr. If Malaba  ( Health Laboratories Chief Manager at the DRC Ministry of Public Health ), and  Ms. Bintou Touré-Fadiga  ( Disease Fund Manager [HIV/TB ] at the  Global Fund to Fight AIDS, Tuberculosis, and Malaria ). Financial support. This work was supported by the  Global Fund to Fight AIDS, Tuberculosis, and Malaria  and the  Joint United Nations Program on HIV/AIDS. The funders played a role in data collection and analysis. The funders were not involved in the decision to publish or preparation of the manuscript.
First, we defined an interval that encompassed the PCSK9 gene boundaries (AE20 kb) on chromosome 1 based on the reference genome built 37 from the Ensembl database (Chr1; position 55485221-55550525; http://grch37. ensembl. org). Then, we selected all the SNPs comprised between these boundaries in the 2 chips. Twenty-four SNPs were selected from the Global Screening Array chip and 12 from the Exome chip. However, 3 markers were duplicated between the 2 chips, and 1 of each was excluded. Hence, 33 SNPs were selected. After the quality control steps, 1 SNP was excluded for monomorphism (ie, minor allele frequency=0); no SNP had >1% of missing data (ie, all SNPs had a call rate >0. 99), and no SNP deviated from the Hardy-Weinberg equilibrium at a threshold of P<1. 10 À8. We also excluded 7 SNPs that are rare (minor allele frequency <0.
We also tested the association of the genetic alleles on top of the clinical model plus PCSK9 and LDLR. The stability of this model was also confirmed using "partialing-out cross-fit" estimators controlling for all the variables with a P<0. 01 from Table  1  with overlapping results (data not shown). Since PCSK9 and LDLR proteins were measured using normalized protein expression) values on a log 2 scale, the odds ratio (OR) for each protein estimates the increase in the odds of carotid plaques associated with a doubling in the protein concentration. A P<0. 05 was considered statistically significant. The analyses were performed using STATA version 15 software (Stata Statistical Software, Release 15, StataCorp LP, College Station, TX). The genetic analyses were performed using R (version 3.
Acknowledgments We acknowledge  Robert Olaso  and  his lab "Production Platforms in Human Genomics" at the Centre National de Recherche en G enomique Humaine  for genotyping data production, and  Anne Boland  at the  Centre National de Recherche en G enomique Humaine  for management of the genotyping study. We are highly grateful to the  Vandoeuvre-L es Nancy Centre de M edecine Pr eventive  staff, and to  Dr Sophie Visvikis-Siest  ( Inserm U1122 ) who managed the STANISLAS Cohort for the first 3 visits. The authors deeply thank the.
Sources of Funding This biomarker study was funded by the  French National Research Agency Fighting Heart Failure  ( ANR-15-RHU-0004 ) and  FEDER Lorraine , and all coauthors are supported by the  French PIA  project " Lorraine Universit e d'Excellence" GEE-NAGE  ( ANR-15-IDEX-04-LUE ) programs, and the  Contrat de Plan Etat R egion Lorraine  and  FEDER IT2MP.
Based on our data, the heterogeneity was only found for the carriage isolates. Our data suggest that at least two virulence phenotypes of serotype 9 isolates exist: clinical isolates that are virulent and avirulent carriage isolates. To confirm there are differences in virulence among serotype 9 isolates, four isolates were tested for virulence under experimental conditions. Two carrier isolates, one clinical isolate, and one laboratory strain that clustered among the clinical isolates were tested for virulence by infecting CDCD piglets intravenously with a high dose of each of the isolates. The survival curves clearly showed there was a significant difference in virulence between carriage isolates and clinical isolates. Although the carriage isolates could induce S. suis specific disease symptoms like lameness, 80%-90% of the piglets survived the infection, whereas the piglets infected with the clinical isolates all died within 2 days postinfection.
However, up to 85% of the healthy controls and 95% of the diseased piglets were also colonised by serotype 9 in the intestine, whereas the other serotypes did not colonise at all in the intestine or only in a limited number of animals (Table  A2 ). Faecal samples were also screened for presence of serotypes 1, 2, 7, and 9. Whereas none of the piglets were positive for serotypes 1 or 2, a few animals were positive for serotype 7 and a considerable number of animals were positive for serotype 9 in faeces. Although only one farm was sampled in this study, the data suggest that serotype 9 is a better coloniser of the porcine intestine than the other serotypes. This suggestion was supported by the data published by Ferrando et al. [54]  that showed that serotype 9 isolates can adhere to porcine intestinal cells more efficiently that the other serotypes.
(2009). Positive samples of heat-inactivated parasites were used to evaluate the efficiency of the markers according to  Morga et al. (2009). Analysis of the histograms was done with Flowing Software version 2. 5 (Turku Centre for Technology, Finland). Results were expressed as fold change variations between control and challenge parasites (ratio challenge/control). Data are presented as standard error of the mean ± SEM for all studies. Mean and SD were calculated for each triplicate. Significance was concluded at p < 0. 05. The analyses were performed using the software SPSS version 12 (IBM Analytics) using ANOVA one factor analysis and post hoc proofs based on HSD Tukey test.
Gel pieces were then incubated for 15 min in linear gradient of solvent B in 108 min (solvent A was 0. 1% formic acid in 5% ACN, and solvent B was 0. 1% formic acid in 80% ACN). The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 1. 8-kV needle voltage and a 44-V capillary voltage. Data were acquired in a data-dependent mode alternating an FTMS scan survey over the range m/z 350-1600 and twelve ion trap MS/MS scans with higher-energy collision-induced dissociation (HCD) as activation mode. MS/MS spectra were acquired using a 2-m/z unit ion isolation window and normalized collision energy of 27. Mono-charged ions and unassigned charge-state ions were rejected from fragmentation. Dynamic exclusion duration was set to 30 s. Fessart et al. (2014).
Database search and result processing Gene Ontology (GO) data of the 1498 differentially expressed proteins (DEPs) from O. edulis and C. gigas were obtained using the application PANNZER2  (Törönen et al. 2018). The GO accession numbers were then uploaded into the REVIGO application  (Supek et al. 2011)  to classify the proteins according to their molecular function using the semantic similarity measure from Resnik (normalized) against whole Uniprot database.
Protein expression statistical analysis Data are presented as standard error of the mean ± SEM for all studies. Statistical differences between C. gigas and O. edulis data were analyzed using an ANOVA with GraphPad Prism 4 software. A p value of 0. 05 or lower with a fold change greater than 2 or lower than 0. 5 was considered significant. Differences in protein and peptide number were evaluated by the software SPSS version 12 (IBM Analytics) using Student t-test for independent samples. Significance was concluded at p < 0. 01.
virginica (Pales  Espinosa et al. 2016). The C. virginica study reported several proteins involved in immune recognition, immune activation and cell signaling, microbe neutralization or nonspecific response to stress. We identified more than 2,400 different proteins in C. gigas samples and approximately 1353 in O. edulis samples. This important difference could be explained by the low number of O. edulis genome data in public databases. Moreover, only four reference proteomes from molluscs are available in Uniprot database: one gastropod (Lottia gigantea), two bivalves (C. gigas and Patinopecten yessoensis) and one cephalopod (Octopus bimaculoides), which limit the protein identification of O. edulis peptides. As many as 1,320 proteins were identified in both species with at least two entries in our constructed database.
Data were searched by SEQUEST through Proteome Discoverer 2. 3 (Thermo Fisher Scientific Inc. ) against a NCBI protein database restricted to the Ostreidae family (140,823 entries -April 2019). Database included 75,152 entries from C. gigas, 60,643 from C. virginica, 996 from C. sikamea, 494 from C. ariakensis, 448 from C. angulata, 278 from O. edulis and 2,812 from other oyster species. The search parameters were established according to.
Acknowledgements The  Région of Poitou Charentes  and  IFREMER  supported this research. The authors thank the team of the hatchery facilities of  Ifremer La Tremblade  for their help in the maintenance of oysters. This work was also supported by the project  POCI-01-0145-FEDER-030232 , co-financed by  COMPETE 2020, Portugal 2020  and the  European Union  through the  ERDF , and by the  FCT  through national funds and strategical project  UID/Multi/04423/2019.
Statistical analysis The statistical analysis and graphical representation were performed using the software package SPSS (Statistical Package for the Social Sciences), version 26 for Windows. Today, the program is used in different area like marketing, experimental research, education, health. SPSS use different procedures like descriptive, comparative, reductive, modeling which some we use in our article: descriptive, frequency and comparative analysis. Also, Chi square test was used to calculate p-value and determine the confidence interval. Descriptive statistics (95% Confidence Interval) were calculated for each variable as appropriate. Mann-Whitney U statistics for two independent variables. A p-value of less than 0. 05 was regarded as statistically significant and a strong relation between independent variables which we consider in this article.
(2007)  on a group of 350 children and newborn babies and by  Warrunee et al. (2007)  on 1920 children show no significant differences regarding the gender of the people infected with Giardia spp. Also, a series of previous studies show a higher incidence in males than in females  (Wansaas et al. , 2009; Mellingen et al. , 2010; Escobedo et al. , 2011; Asher et al. , 2014; Ramos et al. , 2014; Tamer et al. , 2008; Tamer et al. , 2015). The data we collected in our study show a higher incidence of infection with Giardia spp. in people who come from urban areas, than in people from rural areas (63. 70%, compared to 36. 30%). Our data are similar to those presented by  Neghina et al. (2013) , in a study conducted in Timis County, Romania, on a 14 years period of time. A study conducted by  Korzeniewski et al.
P. V. was supported by the  Austrian Science Fund  (FWF; projects P32470-B and  F4704-B20 ). J. G. is supported by the  Charles and Ann Johnson Foundation. D. D. M. is supported by the  Division of Intramural Research, National Institute of Allergy and Infectious Diseases. Disclosure of potential conflict of interest: P. Valent received consultancy honoraria from Blueprint,  Novartis ,  Deciphera ,  Celgene , and  Incyte  and a research grant from  Pfizer. C. Akin  received consultancy honoraria from Blueprint and  Novartis  and research grant from  Blueprint  and is an investigator in a clinical trial for Blueprint. M. Niedoszytko received consultancy honoraria from  Novartis  and AB Science and is an investigator in clinical trials for  Novartis and AB Science. I. Alvarez-Twose  received consultancy honoraria from  Novartis  and  Blueprint.
Epigenetic Initiation of the Th17 Differentiation Program The regulation of gene expression generally involves pioneer transcription factors which induce chromatin remodeling, allowing accessibility of cis regulatory elements to secondary transcription factors. Depending of the cytokinic environment surrounding naive CD4 T cells, different STAT transcription factors can be activated and then can bind to specific loci to regulate the expression of genes involved in T helper differentiation. The STAT transcription factor family is also responsible for the remodeling of the epigenetic landscape  [8]. In Th17 cells, the activation of STAT3 by IL-6 positively regulates the permissive marks H3K4me3 on the Il-17 locus (Figure  1  With meta-analysis of multiple RNAseq and transcription factor genome occupancy datasets validated by in vitro experiments, Ciofani et al.
Patient Population The study design and primary results for the 1-year follow-up have been previously reported. 9 Briefly, we prospectively included consecutive PLHIV with a first episode of ACS in 23 different coronary intensive care units in France between September 2003 and March 2006. The last patient was followed up until April 2009. Two HIV-patients were enrolled and matched for age (±5 years), sex, and type of ACS (ST-segment-elevation myocardial infarction [STEMI], non-ST-segmentelevation myocardial infarction [NSTEMI], or unstable angina) in the same center a maximum of 6 months after enrollment of each PLHIV. Patient characteristics, cardiovascular risk factors and therapies, medical histories, and baseline clinical data were collected prospectively (clinical visits with a cardiologist at months 1, 6, 12, 18, 24, and 36 after the first episode of ACS).
2 versus 8. 2 per 100 person-years after 5-year follow-up). 20 wo studies have reported that PLHIV have a higher rate of recurrent ACS (including STEMI, NSTEMI, and unstable angina) compared with HIV-controls, but showed that long-term cardiovascular and overall mortality did not differ. 8, 9 One study found that PLHIV had a higher risk of death during the first year after an ACS. 14 owever, 2 other studies  10, 15  found that the long-term prognosis after ACS was similar for PLHIV and HIVpatients, without an increased risk of recurrent ACS. Possible rationale to explain these discrepancies is discussed below. First, there were differences in study design (eg, prospective data collection and retrospective database collection) and definitions of ACS (eg, only STEMI and NSTEMI  8, 10, 14, 15  or unstable angina).
parva and T. annulata, a total of 109 protein motifs, present in these 400 proteins, satisfied the required criteria (Fig. 1a, b ; Supplementary Dataset). This list is critically dependent on a recently generated T. parva genome annotation, since the coding sequences of 101 of these 400 genes were altered in the new annotation (Tretina et al. , submitted)   9. Interestingly, we identified 15 T. parva proteins that contain a short linear motif predicted to interact with the well-studied host tumor suppressor Retinoblastoma-1 protein (RB) (Fig. 1c , Supplementary Table  1 ). Along with p107 and p130, RB is a pocket family protein that plays a critical role in mammalian cell cycle regulation by inhibiting E2F transcription factor activity, an important regulator of cell proliferation and survival  10.
As expected, the LXCXE peptide increased E2F activity in T. parva-infected B cells, as well as in T. annulata-infected B cells and macrophages. In contrast, the mutated LXNXE peptide did not show the same reporter activity as the LXCXE peptide (Fig. 3b, d, e ), providing direct functional evidence that the parasite-derived LXCXE motif can induce E2F activity in Theileria-infected leukocytes. Since the LXCXE peptide can induce E2F activity, we investigated whether it could, on its own, increase bovine leukocyte proliferation. To extend our observations to T. annulata-infected leukocytes, we incubated T. annulata-infected B cells with WT or mutated LXNXE peptides and counted cell numbers at 24 h and 48 h. The addition of these peptides had no effect on cell viability in any of the experimental groups (data not shown).
Acknowledgements We are grateful to  Dr. Bret A. Hassel  for manuscript feedback, and  Linda Hamburg  for her contributions to the mouse and laboratory work. This work was supported in part by the  United States Department of Agriculture, Agricultural Research Service (USDA-ARS)  ( 58-5348-4-013 ), by the  Norman Borlaug Commemorative Research Initiative , an initiative between the Feed the  Future program  of the  U. S. Agency for International Development (USAID)  and  USDA-ARS  ( 58-5348-2-117  F), by the  Department for International Development of the United Kingdom , by the  Bill and Melinda Gates Foundation  ( OPP1078791 ), and by the  National Institute of Allergy and Infectious Diseases  ( T32 AI007540-14 , on Immunity and Infection). This work was also supported by the grant  Labex ParaFrap  ( ANR-11-LABX-0024 ) and core funding from  INSERM  and the  CNRS  awarded to GL and a  CRG4 grant  ( URF/1/2610-01-01 ) from the  Office for Sponsored Research (OSR) in King Abdullah University of Science and Technology (KAUST)  award to AP and GL and the faculty baseline fund ( BAS/1/1020-01-01 ) awarded to AP.
| Immune cell type estimation Gene expression profiling was performed previously  (Boldrup, Gu, et al. , 2017; Gu et al. , 2019) , and data were available for 25 of the 101 patients. Briefly, RNA was extracted from tumor biopsies collected before treatment of the patients and processed for gene expression profiling using Illumina HumanHT-12 v4 Expression BeadChip (Illumina Inc. )  (Boldrup, Gu, et al. , 2017; Gu et al. , 2019). Raw data are available from ArrayExpress (accession numbers E-MTAB-4678 and E-MTAB-5534). To investigate the relationship between PD-L1 mRNA levels and immune infiltration, we applied the xCell method (https://xcell. ucsf. edu/) (Aran, Hu, & Butte, 2017)  to estimate immune cell types according to gene expression profiling data. The generated xCell scores of 34 immune cell types were used for cell quantification.