dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors thank the following investigators at the participating regional centers:  Professor Maxime Dougados  ( Cochin B, Paris ),  Professor Andr e Kahan  ( Cochin A, Paris ),  Professor Olivier Meyer  ( Bichat, Paris ),  Professor Pierre Bourgeois  ( La Piti e Salpêtriere, Paris ),  Professor Francis Berenbaum  ( Saint Antoine Paris ),  Professor Pascal Claudepierre  ( Cr eteil ),  Professor Maxime Breban  ( Boulogne Billancourt ),  Dr. Bernadette Saint-Marcoux  ( Aulnay-sous-Bois ),  Professor Philippe Goupille  (Tours),  Professor Jean-Francis Maillefert  (Dijon),  Dr. Xavier Pu  echal and  Dr. Emmanuel Dernis  ( Le Mans ),  Professor Daniel Wendling  ( Besancon ),  Professor Bernard Combe  (Montpellier),  Professor Liana Euller-Ziegler  (Nice),  Professor Philippe Orcel ,  Dr.
This study was conducted under the umbrella of the  French Society of Rheumatology and INSERM. The Devenir des Spondylarthropathies Indiffer erenci ees R ecentes  (DESIR) cohort was sponsored by the  D epartement de la Recherche Clinique et du D eveloppement de l'Assistance Publique-Hôpitaux de Paris  ( AP-HP ). An unrestricted grant from  Pfizer  was allocated for the 10-year followup of the recruited patients. Database management of the DESIR cohort is performed within the  Department of Epidemiology and Biostatistics ,.
objective To assess follow-up and programmatic outcomes of HIV-exposed infants at Martin Preuss Centre, Lilongwe, from 2012 to 2014. methods Retrospective cohort study using routinely collected HIV-exposed infant data. Data were analysed using frequencies and percentages in Stata v. 13. results Of 1035 HIV-exposed infants registered 2012-2014, 79% were available to be tested for HIV and 76% were HIV-tested either with DNA-PCR or rapid HIV test serology by 24 months of age. Sixty-five infants were found to be HIV-positive and 43% were started on antiretroviral therapy (ART) at different ages from 6 weeks to 24 months. Overall, 48% of HIV-exposed infants were declared lost-to-follow-up in the database. Of these, 69% were listed for tracing; of these, 78% were confirmed as lost-to-follow-up through patient charts; of these, 51% were traced; and of these, 62% were truly not in care, the remainder being wrongly classified.
Back-to-care (B2C) programme The Malawi National ART/PMTCT guidelines for clinical management of HIV stress the need for active followup for both HIV-infected persons and HIV-exposed infants  [8, 9]. However, in routine practice, there is either no or minimal active follow-up of HIV-infected adults, children or exposed infants once they have discontinued their HIV care because of facility or individual level reasons. In the event of mother and/or infants missing their follow-up appointment for at least 2 months after their scheduled appointment, and if they have previously consented, they are traced by the B2C staff using various tracing modalities. The B2C programme, which started in 2006 with funding from International Epidemiologic Database to Evaluate AIDS in Southern Africa (IeDEA-SA)  [16] , employs six persons who are all trained lay-workers.
The aim of the B2C programme is to improve patient retention at Lighthouse clinics  [16]. The tracers use three modalities of tracing: sending short messaging services (SMS) on mobile phones, making voice phone calls and going on field visits to clients' homes or other preferred locations. If patients have a mobile phone, standardised text messages are sent reminding them to come back to care or to contact the B2C staff. After 2 days, if they still have not come to the clinic or made further communication with the clinic staff, voice calls are made on their phones. If the phone calls are unsuccessful, either because the patient cannot be reached or the patient still does not come back to the clinic, then trained field tracers make a field visit. In order to trace HIV-exposed infants who are lostto-follow-up, a list of HIV-exposed infants who missed their scheduled clinic appointments by at least 56 days is generated using HIV-exposed infant follow-up data which is entered in the Microsoft Access database.
Data variables, sources of data, data collection tools and data validation The data variables included: year of registration; infant age; infant sex; mother's ART status; infant breast feeding status; documented guardian; agreement or not by the guardian to have the infant actively followed up in case of non-attendance at clinic; for the infants and children -HIV testing by DNA or HIV rapid serology; HIVpositive status; ART initiation; cumulative programmatic outcomes by 30 June, 2015; for children lost-to-followup (defined as an HIV-exposed infant who did not come for review for at least 56 days after the scheduled clinic appointment) -HIV-exposed infants listed for tracing; tracing carried out; true outcomes of those traced; and for those not in care, the reasons for not being in care. The sources of data were the customised Microsoft Access database for the HIV-exposed infants, the HIVexposed infant treatment cards, patient files and the tracing forms.
Baseline characteristics of HIV-exposed infants There were 1035 HIV-exposed infants registered at MPC over the 3 years, with a median interquartile range (IQR) age at registration of 32 weeks (10-59 weeks). Baseline characteristics are shown in Table  1. There were increasing numbers of children registered each year from 2012 to 2014. In those with documentation, key findings were that the majority of infants were registered after 6 weeks of age, most mothers were alive and on ART, mixed feeding was the predominant method and in most cases the guardian of the child was the mother. The majority of guardians agreed that they and their infants could be actively followed up in case of non-attendance at MPC. For all individual field categories, there were missing data ranging from 2% to 36%. HIV testing, HIV-positive results and initiation of ART Cumulative numbers of children HIV tested, found to be HIV-positive and started on ART at various ages are shown in Table  2.
Programme outcomes of early infant diagnosis and outcomes of tracing through back-to-care programme The cumulative programmatic outcomes of children in the 3-year cohort censored on 30 June, 2015, are shown in Table  3. Altogether, there were 385 (37%) children who had either completed their 24-month care and were discharged HIV-negative to the community or were still retained in care, on or off ART. Of all the children, 48% were declared lost-to-follow-up in the database. Of the remainder, 67 (7%) were transferred to another clinic with their current status unknown, six children were known to have died and in 76 (7%) there was no documentation of outcome. Of the 501 children declared lost-to-follow-up in the electronic database, 69% were listed for tracing, 271 (78%) of these had confirmation from patient records that they were indeed lost-to-follow-up and 138 (51%) of these were actively followed up by tracing (Table  4 ).
Discussion This study examined the outcomes of a large cohort of HIV-exposed infants at MPC in Lilongwe where the number of HIV-exposed infants was substantial and increasing year by year. There were a number of important findings. First, while a substantial proportion of infants and children were HIV tested, <20% received an appropriate HIV test (DNA-PCR) at 6 weeks of age mainly because children were enrolled too late into the programme. This is a missed opportunity for preventing infection and starting ART early in life. Furthermore, according to the records, <50% of the children who were diagnosed HIV-positive between 6 weeks and 24 months of age were initiated on ART, so missed opportunities are being perpetuated. Second, retention in care was poor with nearly half of all infants being recorded in the database as lost-to-follow-up.
However, when the B2C programme was implemented, there were many gaps and mistakes observed in the documentation of routine service delivery and loss to follow-up of infants. The tracing lists were incomplete, the confirmation of 'lost-to-followup' was incorrect when checked against cards and files and about 40% of the children who had been declared lost-to-follow-up after this checking had been done actually had a different outcome when traced by phone or field visits. Thus, the actual loss to follow-up was smaller than that recorded in the database. Third, the main documented reasons for infants not being in care were mothers or guardians forgetting or not being available for the clinic appointments as well as transport expenses. Finally, tracing was successful in bringing a third of the children who were documented as not in care back to MPC for follow-up.
However, this is still less than the overall B2C success rate for patients on ART which is at around twothirds  [16]. The strengths of this study were the large sample size and its conduct within the routine service delivery setting, which potentially allows the data to improve operational practice and inform policy change. We also adhered to the STROBE guidelines for conducting and reporting on observational studies  [18]. The major weakness was missing or incorrect data. Some of the baseline characteristics were missing in up to one-third of infants and the tracing lists, which are so important to activate tracing activities, did not capture all patients declared lost-to-follow-up in the database. Where tracing lists were produced, they were quite frequently wrong when checked against cards and files.
Having a mother on ART is associated with a significant reduction in loss to follow-up vs. not having a mother on ART  [24] , and thus efforts to ensure linkage of testing with treatment in the Option B+ programme are likely to reduce losses to follow-up amongst HIV-exposed children. Male involvement in a variety of PMTCT interventions has been shown to reduce loss to follow-up  [25]. Good documentation and management of patient records, accompanied with active tracing and follow-up of patients has been reducing losses to follow-up, improving retention in care and HIV testing uptake  [16, 26]. There are therefore some important practical implications arising from this study for facilities that are similar to MPC with regard to context and resource. First, documentation at MPC needs to improve as currently the database is over-reporting the loss to follow-up.
This will require a more pro-active and systematic approach to supervision and checking of the current database from cards and patient files and ensuring that the electronic database comprehensively captures and links all components of PMTCT. Second, more concerted efforts are needed to enrol HIV-exposed infants into the MPC programme before or at 6 weeks of age so that DNA-PCR testing can be done in a timely way, and subsequently the infants confirmed to be HIV-infected should start ART immediately as per national and international guidelines  [2, 8, 9]. A point of care test (not necessarily done at birth) would improve testing rates and perhaps retention in care  [27]. Finally, given that about one-third of infants who were not in care at the time of tracing were in fact brought †There were 155 children who were lost-to-follow-up who did not appear in the line list of those who needed to be traced.
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and Med ecins sans Fronti eres. The specific SORT IT programme which resulted in this publication was jointly developed and implemented by: the  Operational Research Unit (LUXOR), M edecins Sans Fronti eres, Brussels Operational Center, Luxembourg; the Centre for Operational Research, The Union, Paris, France; the Centre for International Health, University of Bergen, Norway ; the  Institute of Tropical Medicine, Antwerp, Belgium ; and Partners in Health,  USA.
Differential gene expression analysis in E. muelleri The premise of our approach was that the transcripts which show some degree of choanocyte-specific or choanocyte-enriched expression under normal conditions should exhibit lower expression levels in HU-treated sponges that lack choanocytes. Between 21. 1 and 29. 3 million reads were sequenced from each of six biological replicates (three control samples and three HUtreated samples). Reads were mapped to clusters using Corset, and transcript diversity was further reduced to 30,033 (from 85,971) using EdgeR to remove clusters with fewer than 1 counts per million (cpm) in at least three biological replicates (recommended by the EdgeR manual). Of these, 2940 clusters showed at least twofold (log FC < -1) lower expression in HU-treated samples than in untreated control samples, with a false discovery rate (FDR)-corrected p < 0.
05. In comparison, analysis of these same data using Kallisto/Sleuth, and applying the same significance criteria, resulted in 1062 transcripts with significantly lower expression levels in HU-treated samples than in untreated control samples. Kallisto differs from read counting methods in that it pseudoaligns reads to a set of transcripts without assigning each read to a specific set of coordinates within the transcript. Furthermore, the speed of Kallisto allows for the use of bootstrapping to test the uncertainty in transcript abundance estimates, which can stem from high similarity among transcript isoforms  [21]. Sleuth is a companion program that uses Kallisto results, including an error model incorporating Kallisto's bootstrapping, to differentiate between true biological expression differences and variation resulting from sources of experimental noise  [22].
Error correction We conducted read error correction on trimmed reads using Reptile v1. 1  [51]  following the steps described by the authors, with modifications described below. We began by using the "fastq-converter. pl" script to convert from FASTQ and to discard reads with more than 1 ambiguous character (N) in any window of 13 bases. We chose the character "a" as the target to convert ambiguous bases in reads surviving this filter, as all of the characters in our input reads were in upper case (A, C, G, or T); thus, we could later recognize ambiguous bases converted in this step. Next, we tuned parameters using the "seq-analy" utility included in the software package, again following the instructions provided by the authors, in four steps: (1) Running "seq-analy" with default settings.
In some extreme cases, every single base within a sequence read was identified as a target for error correction. We reasoned that this was an unintended consequence of the iteration-to-exhaustion approach taken by Reptile (step 2d of the algorithm described in section 3. 1 of the Reptile manuscript). Therefore, we designed a custom Perl script to correct sporadic errors but not clustered errors. We began by grouping each read according to the total number of errors identified within the read. For each group, we built a distribution of the number of other errors identified adjacent to each error within the same k-mer window. For sporadic errors, this number should be close to 0, but for clustered errors, the number could be up to the k-mer size minus one. There was a clear pattern within each of these distributions, with a number of errors identified with no neighbors, a smaller number identified with 1 neighbor, and an increasing number beginning at 2 or more neighbors.
Identification and removal of cross-contamination Cross-contamination within a sequencing lane is a known phenomenon, and it is estimated to cause incorrect assignment of roughly 0. 5 % of index pairs within the same lane  [53]. The transcriptome of E. muelleri was sequenced together in the same lane as an undescribed species of Oscarella (in preparation), so we designed a procedure to identify and eliminate cross-contaminated contigs present in the de novo assemblies. To identify putatively cross-contaminated contigs, we ran the blastn program from the BLAST package version 2. 2. 26  [54]  with an expectation value of 1 × 10 -10 to match contigs from each species against the other species. In order to separate a contaminating sequence from a truly homologous one, we were forced to make heuristic decisions in choosing cutoffs for two properties of the BLAST hits: percent match and match length.
Prediction of amino acid sequences from assembled transcripts and elimination of redundant transcripts We used Transdecoder release 2012-08-15 [http://transdecoder. github. io/] to predict amino acid sequences from decontaminated assembled transcripts, with a minimum protein sequence length of 50, resulting in 101,671 predicted proteins. We noticed that many of the resulting predicted proteins coming from different contigs within a species were completely identical along their entire length. Furthermore, we also observed many contigs whose predicted proteins were a subset of the predicted proteins from another contig. For example, contig 1 could have predicted proteins A and B, and contig 2 could have two predicted proteins exactly matching A and B, and a third predicted protein C. Using a custom Perl script, we removed both types of redundancy (exact matches and subsets) from the data set of predicted proteins, and we also removed the contigs from which they were predicted.
Measurement of expression levels and elimination of noise transcripts To estimate expression levels, we remapped sequence reads to the decontaminated, non-redundant, Tandem Repeats masked contigs using the Burroughs-Wheeler Aligner, BWA, version 0. 7. 5a  [56]. We ran BWA "mem" with the "-a" option to report all equally best hits. All other BWA parameter values were set to their defaults. We converted BWA output to BAM format using SAMtools version 0. 1. 18 [57]. We then ran eXpress version 1. 4. 0  [58]  on the resulting BAM files with the option "-rfstranded" (for strand-specific reads) in order to estimate expression levels, in FPKM, for each contig. All other parameter values were left at their defaults. We examined the distribution of FPKM values across contigs (data not shown) and found the peak of the distribution at values near 1, with steep decreases in the number of contigs at values two orders of magnitude both lower and higher.
Differential gene expression analysis in E. muelleri Two independent methods were used to examine differential gene expression in control versus HU-treated samples: Corset (v1. 03) combined with EdgeR (v3. 2;  [63] ), versus Kallisto  [21]  combined with Sleuth  [22]. Corset is designed to cluster RNA transcripts that presumably derive from a single genomic DNA locus  [64]. Corsetmapped reads were analyzed with experimental groups identified (-g) to more efficiently split differentially expressed paralogs. Differential gene expression analysis was conducted on Corset count data using edgeR, a bioconductor package in R  [63]. Count data were filtered to remove transcripts with fewer than 1 cpm in at least three samples; this approach is recommended by the EdgeR manual. Data were normalized and GLMTagwise dispersion estimated.
GLM testing was conducted using a design matrix to correct for batch effects (~location + treatment), and transcripts were considered to be differentially expressed if they had an FDR corrected (Benjamini-Hochberg) p value <0. 05 and |log FC| > 1. Kallisto count data were analyzed by Sleuth using default parameters and results visualized using Shiny v 0. 13. 0 (http://shiny. rstudio. com). Transcripts were considered to be differentially expressed if they had an FDR corrected (Bonferroni) p < 0. 05 and |log FC| > 1. Differentially expressed transcripts were annotated to reflect whether they were less expressed in EdgeR, Sleuth, or both analyses. We ranked transcripts according to three evidence categories: (I) significantly differentially expressed in each of EdgeR and Sleuth analysis of E.
Funding This research was supported by a grant from the  University of Denver Faculty Research Fund  ( 89504-143221  to S. A. N. ), a  National Defense Science and Engineering Graduate Fellowship  from the  United States Department of Defense  (D. J. R. ), a  National Science Foundation Central Europe Summer Research Institute  Fellowship (D. J. R. ), a  Chang-Lin Tien Fellowship in Environmental Sciences and Biodiversity  (D. J. R. ), a  Postdoctoral fellowship  from the  Conseil Regional de Bretagne  (D. J. R. ), a grant from the  French Government  " Investissements d' Avenir" program OCEANOMICS  ( ANR-11-BTBR-0008 , D. J. R. ), and a grant from  United States National Science Foundation  ( IOS1257967 , L. W. -a grant to  Athula H. Wikramanayake ).
Estimation of mortality and excess length of stay Two important characteristics of this dataset were the inclusion of time-varying exposures (BSI, surgery and ICU admission) and competing risks (death and discharge alive). We adopted the multistate model illustrated in Figure  1  to explicitly account for these characteristics  [4]. Patients entered the initial state on admission to acute care and exited by entering one of two competing absorbing states (hospital death or discharge alive), with or without passing through one of two intermediate states (susceptible or non-susceptible BSI). Admissions were artificially right-censored at day 45 to reduce the influence of outliers. We reasoned that patients with such prolonged admissions were likely to remain hospitalised for other reasons not influenced by BSI.
In addition, daily risk (or hazard) of death, as estimated here, can be expected to be smaller than the cumulative risks reported in the review. Although seemingly in contrast to older literature, our findings are consistent with another recent, large European multicentre study that found that meticillin resistance had no significant impact on mortality (adjusted hazard ratio (aHR), 1. 1; 95% CI: 0. 7-1. 8) or excess LOS (0. 6 days; 95% CI: -3. 7 to 5. 3), whereas third-generation cephalosporin resistance increased both risk of death (aHR: 2. 9; 95% CI: 1. 2-6. 9) and excess LOS (5. 0 days; 95% CI: 0. 4-10. 2)  [22, 23]. A similarly modest impact of AMR has been reported in the European ICU setting  [24]. These data should be interpreted within the context of the study design. The dataset was extracted retrospectively from existing databases.
Concerns regarding the quality of ICD coding data have been well described  [25] , although the Charlson comorbidity index derived from administrative databases has elsewhere proven superior to chart review  [26]. We relied on routine antimicrobial susceptibility results performed by local laboratories using guidelines from five different organisations. However, for MRSA and 3GCRE, there should not be a major misclassification bias. We were unable to detect community-onset healthcare-acquired infection,  b Estimated for a hospital with 450,000 bed-days annually (95% CrI not displayed because precision from the study cohort is such that no additional uncertainty is added to the model). c Employs the bed-day valuation derived from a contingent valuation survey that estimated the opportunity cost of each bed-day consumed by a patient with BSI.
Acknowledgements We gratefully acknowledge  Professor Hajo Grundmann  and  Dr Marlieke de Kraker  for their contribution to the design of this study, and thank all individuals involved with data extraction at each participating site for their collaboration. This work was performed during the PhD of A. S. at the  Department of Medicine, University of Melbourne. This was an investigator-initiated project supported by an unrestricted research grant provided by  Pfizer Europe. The sponsor had no role in study design, in the collection, analysis, and interpretation of data, in the writing of the report or in the decision to submit the paper for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.