dataesr/scientific-paragraphs-categorization huggingface dataset:
Search strategy and selection criteria In this systematic review and individual participant data meta-analysis, studies were eligible for inclusion if they were randomised controlled trials that compared ADT plus docetaxel (intervention) with ADT alone (comparator). They should have aimed to randomly assign people who were either diagnosed with de-novo (ie, synchronous), metastatic, hormone-sensitive prostate cancer or who developed metastatic, hormone-sensitive prostate cancer after previously being diagnosed with localised disease (ie, metachronous), and who were either starting or responding to first-line hormone therapy. Docetaxel could have been co-administered with supportive treatments. We did not limit inclusion criteria by outcomes assessed in the trials. Eligible trials were identified through systematic searches conducted routinely for the STOPCAP programme of systematic reviews and meta-analyses in metastatic, hormone-sensitive prostate cancer, using an approach that has been reported previously.
1, 14, 15  The literature review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. In brief, we ran comprehensive search strategies (appendix pp 2-5) for MEDLINE (from database inception to March 31, 2022), Embase (from database inception to March 31, 2022), and the Cochrane Central Register of Controlled Trials (from database inception to March 31, 2022). We also regularly searched the clinical trials register ClinicalTrials. gov from database inception to March 28, 2023, and screened the proceedings and abstracts of relevant conferences (from Jan 1, 1990, to Dec 31, 2022) using a range of relevant search terms (including "metastatic hormone-sensitive prostate cancer", "chemotherapy", and "docetaxel") and by manually searching reference lists of relevant trial reports and review articles.
Eligibility was determined by three authors (LHR, SB, and CLV) with any conflicts resolved by consensus, involving other members of the core research team or advisory group as needed. We sought individual participant data from study investigators or relevant repositories for all randomly assigned patients from eligible trials. These data were collected according to a detailed data dictionary (available on request to the corresponding author, CLV). In summary, we requested information on baseline characteristics including age, height, weight, WHO performance status, prostate-specific antigen (PSA) measurements, Gleason score, and Tumour, Node, Metastasis (TNM) stage at the time of metastatic diagnosis, along with date of randomisation and treatment allocations, date of diagnosis (both for previous localised disease, if applicable, and of metastatic disease), and number and location of metastases.
We also sought information on treatments, including the type and duration of ADT, dosing schedules, and dates of commencement, and on progression and survival outcomes. We requested data based on the most recent follow-up to maximise information, and therefore power, and to allow reporting of outcomes in the longer term. Individual participant data from each trial were assessed for completeness and harmonised as far as possible, and the variables were checked for consistency, validity, and range by two authors (LHR and PJG), in discussion with core research team members (CLV, DJF, and JFT). Any issues arising were queried and resolved through contact with the trial teams. Procedures for secure transfer and storage of deidentified individual participant data were set out in an approved ethics application (UCL Research Ethics Committee Project identifier 14095/001).
See Online for appendix For the protocol and statistical analysis plan see http://www. stopcapm1. org/protocol/ For each trial, the risk of bias (low risk, some concerns, or high risk) was assessed using version 2 of the Cochrane Risk of Bias Assessment Tool. 16  These assessments were based on the main outcomes of overall survival, progression-free survival, and failure-free survival, and for the randomisation process, deviations from the intended intervention, missing outcome data, and measurement of the outcome. The selection of the reported trial result domain is not relevant for an individual participant data meta-analysis but was included for completeness. The assessments were based on information from trial protocols and manuscripts, information from trialists, and direct checks of the individual participant data.
If evidence of non-proportional hazards was detected, we used the HR and log-rank test as the primary estimate of effect and statistical test but placed greater inferential emphasis on absolute outcome differences at 5 years. To estimate absolute survival, we fitted a flexible parametric survival model 29 to all data, adjusting for trial membership and the core (imputed) covariate set. When analysing treatment-covariate interactions, models were formulated to avoid aggregation bias. 30  Time-varying effects were placed on trial membership and treatment parameters (including treatment interactions where appropriate) to allow trialspecific baseline hazard functions and to account for non-proportionality of hazards between treatment groups. Smoothed patient-averaged (marginal) survival curves were estimated using regression standardisation  29, 31  across observed covariate values, using the standsurv package  32  in Stata, enabling absolute differences with 95% CIs to be derived.
We used Cox regression to estimate treatment-bycovariate interactions within each trial, adjusting for the key covariates described above. Within-trial interaction HRs were combined using a fixed-effect, two-stage metaanalysis. 30, 35  Subgroup-specific HRs were also estimated on the basis of within-trial information to avoid aggregation bias. 36  If multiple covariates were found to interact significantly (p<0•10) with the effects of docetaxel on progression-free survival, we assessed the strength of each such interaction effect independently of the others in an exploratory analysis, by fitting a one-stage metaanalysis model to all trial data simultaneously. We used an adjusted Cox regression containing main effects of treatment and of each covariate found to modify treatment, plus treatment-interaction terms for these covariates, structured appropriately to avoid aggregation bias.
26  Additionally, if two or more interactions of particular significance or clinical interest were identified, we derived a new covariate formed from the crosstabulation of the two subgroup variables and estimated treatment effects within each such category. To account for missing data imputation, imputed categorical covariates were treated as continuous within the model, but subsequent testing and effect estimation was performed conditionally on the set of observed (nonimputed) covariate values. We also did exploratory statistical hypothesis testing based on Cox regression models to test whether any identified treatment-covariate interactions were explained by the effects of any other covariate or combination of covariates. To estimate the absolute survival difference at 5 years within covariate subgroups, we again fitted a flexible parametric model with an appropriate one-stage modelling structure to prevent aggregation bias.
Results We identified five eligible trials; we purposely did not seek individual participant data from two unpublished trials (Pedley et al  37  and NCT00796458), previously identified through our systematic review of aggregate data,  1  because we estimated they would comprise only around 2% of known randomly assigned participants and would therefore contribute very little to the evidence base. However, we sought and obtained data on all 2261 participants included in the three largest trials of docetaxel-GETUS-15, 4,5 CHAARTED, 2 and STAMPEDE  7, 20  -comprising an estimated 98% of all participants randomly assigned in relevant trials (appendix p 52). The trials recruited patients between October, 2004, and March, 2013, and accrued 385 patients (GETUG-AFU15), 790 patients (CHAARTED), and 1086 metastatic patients (STAMPEDE) to eligible comparison groups (table  1 ).
The median follow-up across trials and across all participants was 72 months (IQR 55-85). Data supplied correspond to the most upto-date reported analysis and follow-up for each trial. For each trial, risk of bias was judged to be low, both for individual domains and overall, for each of the main outcomes (appendix pp 7-46, 53). Data on age, WHO performance status, alkaline phosphatase concentration, PSA concentration, Gleason sum score, clinical T stage, nodal involvement, disease status at the time of randomisation, location of metastases, disease volume, and risk were supplied for all three trials. A large proportion of data were missing on alkaline phosphatase, clinical T stage, and nodal metastases in each of the three trials. Furthermore, there was a higher proportion of missing stage data in patients with metachronous (30% missing stage data) than for those with synchronous (19% missing stage data).
The adjusted analysis showed clear evidence of a relative benefit of adding docetaxel to ADT (HR 0•79, 95% CI 0•70-0•88; p<0•0001), with little evidence of statistical heterogeneity (p=0•32, I²=13%; appendix p 55). Absolute survival benefit of docetaxel was 11% (95% CI 7-15), increasing 5-year survival from 39% (36-41) with ADT alone to 49% (46-52) with ADT plus docetaxel (appendix p 56). Planned sensitivity analyses gave similar results (appendix p 47). Data on progression-free survival were available for all 2261 participants, and 1624 events were reported across the three trials. Radiological progression (with or without a clinical progression) was the most common first event reported (1105 [68%] of 1624), followed by similar numbers of clinical progressions (264 [16%]) and deaths without evidence of progression (255 [16%]; appendix p 57).
Acknowledgments This study was funded by the  UK Research and Innovation Medical Research Council  (grant number  MC_UU_00004/06 , to support CLV, DJF, LHR, ER, SB, JFT, IRW, and  MKBP ) and by  Prostate Cancer UK  (grant number  RIA 16-ST2-020 , awarded to JFT, to support DJF, LHR, PJG, and ER). PJG is partly supported by the  UK National Institute for Health Research and Care's Development and   Skills Enhancement Award  ( NIHR301653 ). This manuscript was prepared using data from datasets ( NCT00309985  D1-D12; specifically, D3, D5, D7, D8, and D10) from the NCTN/NCORP Data Archive of the  National Cancer Institute (NCI)  National Clinical Trials Network (NCTN). Data were originally collected from the clinical trial (CHAARTED, NCT00309985). All analyses and conclusions in this manuscript are the sole responsibility of the authors and do not necessarily reflect the opinions or views of the clinical trial investigators, the NCTN, the NCORP, or the  NCI.
05) than those in CFPPA (65 ± 11 %) and ZSA (71 ± 7 %). Human MeHg exposure pathways were calculated based on MeHg isotope data and discussed in detail below. Human hair in the four study areas showed positive δ 202 Hg MeHg and Δ 199 Hg MeHg values (Fig. 2 , Table  S4 ). The Δ 199 Hg MeHg values in hair averaged at 0. 12 ± 0. 04 ‰ in MMA, 0. 40 ± 0. 36 ‰ in GMA, 0. 58 ± 0. 23 ‰ in CFPPA and 0. 67 ± 0. 14 ‰ in ZSA, and these values were within the range of fish and rice in the four study areas (Fig. 2 ). Therefore, the binary mixing isotope model described in Section 2. 4 was used for quantifying the relative contributions of rice and fish consumption to human MeHg exposure. The Δ 199 Hg MeHg values rather than Δ 199 Hg THg in rice, fish and hair samples were directly used as model input, which shielded the disturbance of IHg.
4 )  (Wang et al. , 2021). The dietary model ignored individual differences on daily rice intake, and the data obtained from the questionnaire may cause large uncertainties in the estimation. As well, the bio-accessibilities of MeHg in rice and fish were not considered in the dietary model, and the simulation experiment found that the bio-accessibility of rice and fish MeHg averaged at 41 ± 9 % and 61 ± 14 %, respectively  (Gong et al. , 2018) , indicating that traditional dietary model overestimated human MeHg exposure from rice consumption. In contrast, the binary mixing model based on MIF data can directly evaluate human MeHg exposure sources without these interfering factors. Additionally, the contribution ratios calculated by Δ 199 Hg MeHg values were much different from the results (Table  S6 ) deduced by Δ 199 Hg THg , illustrating IHg induced uncertainties when using THg isotopes to identify MeHg exposure pathways.
We do this by finding the value of log g f for which the respective line in the solar-flux spectrum is best fitted by the synthetic spectrum. The collisional broadening due to neutral hydrogen, and in some cases charged particles, is important for strong lines with damping wings. For collisional broadening due to hydrogen, where possible, we have used parameters from the ABO theory  (Anstee & O'Mara 1991 , 1995; Barklem & O'Mara 1997; Barklem et al. 1998 ) taken from the spectral synthesis code BSYN based on routines from MARCS  (Gustafsson et al. 2008). For the Mg i lines at 21 059. 75 Å and the multiplet at 21 060. 71-21 061. 095 Å, the parameters used in Paper I for collisional broadening due to hydrogen were σ(v = 10 4 m s -1 ) = 2927 a 2 0 , with velocity parameter α = 1. 313 3. These values were calculated using the impulse approximation  (Kaulakys 1985 (Kaulakys , 1991;; Hoang Binh & van Regemorter 1995; Osorio et al.
Further, for the 21059 Å line, data for Stark broadening were extracted from the Stark-B database  (Sahal-Bréchot et al. 2015) , the data originating from  Dimitrijevic & Sahal-Bréchot (1996). Combining the data for broadening due to electrons (e) and protons (p) with the assumption N p = N e , and converting to the format conventionally used in many spectrum-synthesis codes for quadratic Stark broadening, we find that log Γ/N e = -2. 56 at T = 10 000 K, where Γ is the full-width at half maximum in rad s -1 and N e is in cm -3. We find no data for the 21060. 71-21061. 095 Å multiplet. As this multiplet is analogous to the 21059 Å transition but in the triplet term system, we assume the Stark broadening for this multiplet to be the same as the line in the singlet system. The combination of the increased collisional broadening due to hydrogen and the strong Stark broadening significantly improved the agreement with the observed spectrum in the Sun.
Spectral synthesis We used Spectroscopy Made Easy (SME version 583,  Valenti & Piskunov 1996; Piskunov & Valenti 2017)  for our spectral analysis. For a given set of stellar parameters, in order to generate synthetic spectra, SME interpolates a model atmosphere from a grid of spherically symmetric MARCS models computed using LTE. To account for non-LTE effects, SME applies departure coefficients by interpolating in grids. In this study, we used this type of non-LTE grid for which the departure coefficients were computed using the MPI-parallelised non-LTE radiative transfer code Balder. This code is based on the code Multi3D  (Leenaarts & Carlsson 2009) , which calculates the level populations for model atoms based on model atmospheres, forcing a statistical equilibrium for all levels in the ambient radiation field.
References. (1) Kurucz (  2007 ), (2) BSYN based on routines from MARCS code  Gustafsson et al. (2008) , (3) see text, (4) Brault & Noyes (1983), (5)   Civiš et al. (   2013 ), (  6 ) Kurucz (  2014 ), (7) manual entry by NIST lookup, ( * ) astrophysical estimate. In the case of magnesium, we used the line at 21059. 757 Å and the multiplet lines at 21060. 710-21060. 896 Å. As shown in Fig. 2 , these lines are wide and very well defined and are therefore good candidates for the abundance determination as we have precise broadening values. Moreover, due to the proximity of these spectral features, we fitted all these lines simultaneously using one line mask. Compared to the other elements of this study, there are only a few Ca lines available and these all lie on the edge of one of the detector chips.
Dynamical analysis While our sample size is relatively modest, we undertook a basic dynamical analysis in order to ensure membership of the inner Galactic bulge. Therefore, astrometric parameters are necessary as initial conditions to study dynamics. Among the 72 stars, 63 were cross-matched with Gaia EDR3 (Gaia Collaboration 2016, 2021) in order to obtain their proper motions. To ensure reliable values, a proper motion error limit is set at 0. 3 mas yr -1. In addition, updated distances were determined (see Sect. 4. 2) and radial velocities were measured using the strong and clear Mg lines. We used the software package AGAMA  (Vasiliev 2019 ) to compute the orbits of our stars up to 10 Gyr. As we are studying inner bulge stars, we used a combination of three gravitational potentials: a disc, a halo, and a bulge/bar.
Acknowledgements. We are grateful to the referee for her/his useful comments. We would like to thank the  IGRINS team  and especially  Dr. G. Mace  for excellent help and support. Further, we would like to acknowledge to enlightening discussions with  Dr. A. Amarsi  about NLTE calculations. This work used the  Immersion Grating Infrared Spectrometer (IGRINS)  that was developed under a collaboration between the  University of Texas at Austin  and the  Korea Astronomy and Space Science Institute (KASI)  with the financial support A94, page 17 of 18 Nieuwmunster, N. , et al. : A&A 671, A94 (2023) of the  Mt. Cuba Astronomical Foundation , of the  US National Science Foundation  under grants  AST-1229522  and  AST-1702267 , of the  McDonald Observatory  of the  University of Texas at Austin , of the Korean GMT Project of KASI, and  Gemini Observatory.
Strain selection and typing In total, we studied 4716 Shigella reference strains and clinical isolates distributed in two datasets: a development dataset and a validation dataset (Supplementary Data 1). The development dataset was used to develop ShigaPass, a tool predicting Shigella serotypes from various genetic structures: the rfb gene cluster; POAC genes; seven HK genes (from the EnteroBase E. coli/Shigella MLST scheme; https://enterobase. warwick. ac. uk/species/index/ecoli  [32, 33] ); the fliC gene; and CRISPR spacers. This dataset contained data for 440 Shigella reference strains and clinical isolates selected from the French National Reference Centre for E. coli, Shigella and Salmonella (FNRC-ESS) collection, Institut Pasteur, Paris, and four S. flexneri 5b strains retrieved from EnteroBase  [33] , to ensure that the strains and isolates studied were representative of the Shigella population, covering all four serogroups and 59 of the 60 serotypes (the serotype not covered being S.
flexneri 1d) (Table  S1 ). The strains from the development dataset were sequenced on various Illumina sequencing platforms (HiSeq, NovaSeq 6000, NextSeq 500) (Supplementary Data 1). In addition, data for a collection of 45 EIEC strains representative of the various previously described EIEC clusters  [10, [34] [35] [36] , 70 E. coli strains including 68 E. coli strains from the E. coli reference (ECOR) collection representative of the diversity of E. coli  [7, 37] , and 20 non-Shigella/E. coli strains were included; these sequences were selected from the FNRC-ESS or downloaded from NCBI and used as a negative control for the development dataset (Table  S1 ). The validation dataset consisted of 4276 clinical isolates of Shigella used to validate ShigaPass. Between January 2017 and September 2021 (when phenotypic typing was stopped), 4489 clinical Shigella isolates were received, serotyped and sequenced (NextSeq 500) at the FNRC-ESS, which is responsible for the microbiological surveillance of Shigella in France through a voluntary laboratory-based network of ~1000 clinical laboratories located in mainland France and its overseas territories.
We included 4276/4489 (95. 2 %) of these isolates in the validation dataset. These 4276 isolates were those for which the genomic sequences obtained had passed the quality control criteria of EnteroBase (sequence length: 3. 7-6. 4 Mb, number of contigs: ≤800, N50: >20 kb, proportion of N's: <3 %, species assignment according to Kraken: >70 % contigs assigned)  [33] , and which had not already been used in the development dataset. A collection of non-Shigella isolates (n=28) was also included in the development dataset, as a negative control (Table  S1 ). All the reference strains and clinical isolates originating from the FNRC-ESS were characterized by sero-agglutination and a panel of biochemical tests, according to the standard protocols previously described  [38]. These seroagglutination serotyping results are considered the reference results to which in silico serotyping results are compared.
DNA extraction and sequencing Genomic DNA was extracted with the Wizard Genomic DNA Kit (Promega), the Maxwell 16 cell DNA purification kit (Promega) or the MagNA Pure DNA isolation kit (Roche Molecular Systems), in accordance with the manufacturers' instructions. WGS was performed with various Illumina platforms for the strains and isolates of the development dataset (HiSeq, NovaSeq 6000, NextSeq 500). For the validation dataset, WGS was performed as part of routine procedures at the FNRC-ESS, and at the Mutualized Platform for Microbiology (P2M) at Institut Pasteur, Paris. The libraries were prepared with the Nextera XT kit (Illumina) and sequencing was performed with the NextSeq 500 system (Supplementary Data 1). All reads were filtered with FqCleanER version 3. 0 (https://gitlab. pasteur.
ShigaPass databases ShigaPass predicts Shigella serotypes from WGS assemblies based on the variability of rfb gene cluster DNA sequences, POAC genes, MLST sequence type (ST), fliC alleles and CRISPR spacers. The complete and draft assemblies of the genomes studied are queried against these different databases with blast+ (blastn) version 2. 12  [43]. The resulting hits are filtered to match the thresholds selected for sequence identity and coverage (Table  S2 ), and the best-match hits are identified for each marker. The rfb database contained the k-mer sequences of 42 of the 44 previously described Shigella O-antigen (rfb) gene clusters (Table  S3 )  [7]. These rfb sequences were trimmed into 150 bp long k-mers with a sliding window of 50 bp, to facilitate detection with an automated blast pipeline.
The mean number of k-mers per rfb was 170, with the number of k-mers ranging from 83 to 294. The rfb k-mers used in ShigaPass are publicly available from GitHub (https://github. com/imanyass/ShigaPass/tree/main/SCRIPT/ ShigaPass_DataBases/RFB). The same k-mer strategy was used to construct the POAC database containing the sequences of the previously published POAC genes (gtrI, gtrIc, gtrII, gtrIV, gtrV, gtrX, oac, oac1b and optII) found in S. flexneri serotypes 1-5, X and Y (Table  S4 )  [2, 20, 21, [23] [24] [25] [26]. The POAC k-mers used in ShigaPass are publicly available from: https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ ShigaPass_DataBases/RFB/POAC-genes_150-mers. fasta. The MLST database for seven E. coli/Shigella HK genes (adk, fumC, gyrB, icd, mdh, recA and purA) was downloaded from EnteroBase (June 2020) (https://enterobase.
warwick. ac. uk/species/index/ecoli)  [33]  and MLST profiles were determined by ShigaPass according to Achtman's E. coli/Shigella MLST typing scheme  [32]. The fliC database consisted of 59 fliC sequences. In silico and conventional PCR were performed to extract the fliC gene from the genomes of the development dataset (Supplementary Material section 'Characterization of fliC alleles and fliC phylogeny')  [44]. The fliC sequences used in ShigaPass are publicly available from: https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ ShigaPass_DataBases/FLIC/. The CRISPR database consisted of 22 spacer sequences obtained by in silico PCR targeting the CRISPR locus located downstream from the iap gene (iap CRISPR) in the genomes of the development dataset (Supplementary Material section 'Determination of CRISPR profiles')  [45, 46].