dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcomes We do not have a single primary outcome, as this is not meaningful for a feasibility study. Our key outcomes to inform a definitive trial are rates of referral, recruitment, therapy attendance, adherence to medication, and completion of follow-up appointments and questionnaires. The acceptability of treatment will be determined by measuring discontinuation rates and through data collected within a nested qualitative study (see below). We have specific red/amber/green progression criteria that have been agreed with our iDMC, TSC and funder. Green would mean progression to a full trial is possible without the need for any substantial changes to the design or the way it was delivered; amber would mean we may need more resource to recruit, and ways of improving retention and compliance; and red would mean there is substantial doubt that the definitive study is feasible at an affordable cost.
We collected basic health economics data about services used by participants, using an economic patient questionnaire adapted from previous studies conducted by the authors  [38, 39]  and the EuroQol five dimension, five level scale (EQ-5D-5 L) health status questionnaire  [40]. This information will inform the design of the economic component of a definitive trial. We designed a variable-length follow-up period, whereby participants recruited in the first 10 months were offered assessments at 3, 6 and 12 months and those recruited thereafter were only offered assessments up to the end of treatment (6 months, the proposed timing for the primary outcome). RAs collected all outcome measures at baseline and at the 3-month, 6-month and 12-month follow-up assessments. RAs underwent inhouse training in administering all the measures to establish sufficient inter-rater reliability, including watching and scoring role-play and videos.
Data management All of the information collected about the participants is strictly confidential. All participants' personal identifiable data (PID) are kept securely in databases, on secure NHS computer drives that are only accessible to the research team and are protected by a password known only by study staff. All RAs' computers are also individually passwordprotected. Paper copies of research data (i. e. assessment packs) are anonymised with a trial identification (ID) number and kept in a locked, filing cabinet separate to the storage of PID (such as referral forms and letters about the participant). Participants were made aware that although their data are strictly confidential and not shared outside of the research team, confidentiality could be broken in cases where participants were deemed to pose a risk to themselves or to others.
All research data entered on the secure web-based platform created by CHaRT were completely anonymised. To ensure the accuracy of the data entry for the proposed primary outcome measure (PANSS), RAs checked entries for every participant by comparing PANSS scores in the paper assessment files against the scores entered into the CHaRT platform. Data were checked once all possible assessments for each time point were completed. An error rate of 2% or less was deemed acceptable. If the error rate had been above 2% the trial statistician and methodologist would have advised on further data checking, although this was not necessary due to the accuracy of the data entry. The final trial dataset is managed and held by our CTU, CHaRT. Requests for access to the dataset will be considered in the first instance by the CI and then the CTU.
We will use descriptive statistics to summarise the key indicators of success of the trial, including participant recruitment; checks for absence of selective recruitment of participants; baseline balance and participant flow. We will report data in line with the consolidated standards of reporting trials (CON-SORT) 2010 extension statement for pilot and feasibility trials  [46]. Important summary statistics will be the number of participants referred through case managers and mental health staff, number of referrals found to be eligible and number of consenting individuals and recruited individuals to each arm. Numbers for discontinuation from the allocated interventions, withdrawal of consent and failure to provide follow-up outcome data will also be generated. We will also report the proportion of participants who received their allocated intervention compared to the proportion who did not, and the proportion of participants offered a move to the combined therapy arm due to deterioration.
We will report our feasibility results (recruitment, retention, adherence) overall, in order to inform decisions about the viability of a future definitive trial. However, we will also report our descriptive results and 95% confidence intervals on outcome measures by group. We will describe actual treatment received and treatment compliance (to account for departures from the randomised interventions). We will also report descriptive statistics for the components of psychological intervention received, including number of sessions and milestones achieved. To inform a phase III trial we will conduct the following analysis to ensure the data conform to the assumptions of the tests that will be conducted at that stage: measures proposed as the primary (PANSS) and secondary outcomes (QPR) for the phase III study will be analysed by analysis of repeated measures using a mixed effects model to account for the discrete timing of the follow-up assessments.
The CYP interviews were conducted with participants from all treatment arms within the trial after 6 month assessments; this allowed us to explore people's experiences of receiving the treatments and of participating in the trial. Based on our previous work we expected thematic saturation would be achieved within 15-20 CYP interviews  [47] , 15-20 family members/carers  [48]  and 15-20 clinicians. All interviews were audio-recorded and transcribed verbatim at which point any identifying information (names and places) was removed. Data are currently being thematically analysed  [49]. This method results in a rich and accessible account of qualitative data through the researcher making sense of the data and reporting themes that emerge  [49]. Data were coded systematically and iteratively, and were organised within Nvivo qualitative data analysis Software version 11  [50].
Moreover, we go beyond UK Health Research Authority (HRA) requirements for review of SAEs and in addition to review by the CI, the iDMC chairperson provided an independent review of whether each SAE is trial-related or not. In addition, our trial included a bespoke measure of potentially unwanted effects of trial participation. In summary, APs and psychological interventions (PI), specifically, family intervention and cognitive behaviour therapy are recommended treatments for FEP in adolescents but evidence about relative efficacy/acceptability is limited. If PI were non-inferior to APs, within an acceptable margin, this could be a major advance in treating a vulnerable group with high sensitivity to APs. The MAPS trial provides crucial data on the feasibility and acceptability of conducting a phase III trial of these treatments and the conditions that would be required to conduct such a trial.
Acknowledgements Thank you to all the participants, family members/carers who agreed to take part in the trial and clinicians who have supported participant enrolment into the trial. This study was supported by the  National Institute for Health Research (NIHR) , the  NIHR MindTech MedTech Co-operative , the  NIHR Nottingham Biomedical Research Centre  and the  Clinical Research Network-Mental Health. MBi is part funded by  NIHR CLAHRC West Midlands. We are grateful to the  Psychosis Research Unit (PRU) Service User Reference Group (SURG)  for their consultation regarding the design of the study and contribution to the developments of study-related materials. We are grateful to our Independent Trial Steering Committee and Independent Data Monitoring Committee for providing oversight of the trial.
Given that there are dramatic changes in the MHC peptide processing and presentation pathway upon cytokine signaling, we set out to profile the immunopeptidome landscape after 24 h of stimulation (Figure  2A ). A549 cells were stimulated with TNFα and IFNγ (denoted as T+I) or left untreated (UT) and HLA-bound peptides were isolated and analyzed by LC-MS/MS and the MaxQuant software tool (51) as previously described  (42). Following filtering and processing steps (detailed in the materials and methods), we identified an average of 3,444 peptides in the untreated samples and 6,582 peptides in the treated samples (Figure  2B ). Biological replicates showed 0. 56-0. 91 spearman correlation (Figure  2C ), while the stimulated and untreated samples did not correlate with one another, rho = -0. 02 to 0.
The Immunopeptidome Correlates With Protein Half-Life but Not Cellular Protein Abundance Previous studies suggested that HLA molecules are more likely to present peptides derived from proteins with high turnover rate  (13, 15). To examine this property in our data, we extracted the published half-lives of the presented proteins that we identified from the database of Larance et al. (55)  which includes a measure of degradation, or protein turnover ratio, for the proteome. Indeed, we found that the mean protein turnover ratio for the parent proteins of the immunopeptidome is lower than that of the cellular proteome (Figure  3C ; Mann-Whitney * p = 0. 0182). Likewise, using the GeneCards Suite Human Integrated Protein Expression Database (HIPED) (56), we found that cellular proteins, which are presented, had lower abundance (on average) than the proteins that are not   3D ; Mann-Whitney * * * * P < 0.
In this fraction of cellular proteins, the correlation with presented protein abundance increases to r = 0. 33 (Supplementary Figure  6E ). To rule out that the lack of correlation is due to the different dynamic ranges of the immunopeptidome and cellular proteome, we ranked the fold changes of all the proteins in both datasets. Still, the analysis showed no correlation in the changes observed in the immunopeptidome and cellular proteome (Supplementary Figure  6F ). We note that this observation may be due to the specific time points at which we examined the data, which may not capture a biological correlation between these two processes. Nevertheless, our results indicate that the half-life of proteins, rather than their abundance, may be an important determinant in the regulation of the immunopeptidome.
Identification of HLA Peptides The LC-MS/MS analyses of the HLA and the tryptic peptides were performed with a Q-Exactive-Plus mass spectrometer fitted with either with Easy nLC 1000 capillary HPLC (Thermo-Fisher Scientific) or with Ultimate 3000 RSLC nanocapillary UHPLC (Thermo-Fisher Scientific). The reversed phase chromatographies were performed with home-made 30 cm long, 75 µm inner diameter, packed with 3. 5 µm silica ReproSil-Pur C18-AQ resin (Dr. Maisch GmbH, Ammerbuch-Entringen, Germany), as in Ishihama et al. (87). The HLA peptides were eluted using a linear gradient of 5-28% of acetonitrile in 0. 1% formic acid, at a flow rate of 0. 15 µl/min for 2 h. Data was acquired using a data-dependent "top 10" method, fragmenting the peptides by higher-energy collisional dissociation (HCD).
The nanoUPLC was coupled online through a nanoESI emitter (10 µm tip; New Objective; Woburn, MA, USA) to a quadrupole orbitrap mass spectrometer (Q Exactive Plus, Thermo Scientific) using a FlexIon nanospray apparatus (Thermo). Data was acquired in data dependent acquisition (DDA) mode, using a Top10 method. MS1 resolution was set to 70,000 (at 400 m/z), mass range of 300-1,650m/z, AGC of 3e6 and maximum injection time was set to 50 ms. MS2 resolution was set to 17,500, quadrupole isolation 1. 7 m/z, AGC of 1e5, dynamic exclusion of 60 s and maximum injection time of 60 ms.
Mass Spectrometry Data Analysis Peptides were identified and quantified by the MaxQuant software [version 1. 6. 0. 16  (51) ] with the following parameters: unspecific enzyme, minimum peptide length for unspecific search of 8, maximum peptide length for unspecific search of 25, and match between runs enabled. Methionine oxidation and N-acetylation were accepted as variable modifications. A false discovery rate (FDR) of 1% was applied for peptide identification. For the analysis of tryptic digests, the MaxQuant default parameters were set. Masses were searched against the human proteome database from the Uniprot/Swiss-Prot (last update on 2. 2018). The MS proteomics data of the cellular proteome and immunopeptidome have been deposited to the ProteomeXchange Consortium via the PRIDE (88) partner repository with the dataset identifiers PXD009935 and PXD009936.
Label-Free Quantitation and Bioinformatics Analysis Using Python 3. 6, peptides identified though MaxQuant  (50)  were initially filtered to remove reverse sequences and known MS contaminants. We identified 7,524 unique peptides across both treatments with an average of 3,508 and 6,816 peptides in the untreated and stimulated triplicate samples, respectively. To reduce ambiguity, we allowed peptides that had at least two valid LFQ intensities out of three independent biological replicates in at least one treatment group, and included razor peptides, which belong to a unique MaxQuant "protein group. " Following this filtering step we were left with an average of 3,444 peptides in the untreated samples and 6,582 peptides in the samples. Finally, missing peptide intensity values were imputed to randomly chosen values from a normal distribution.
The identified HLA peptides were subject to Gibbs clustering using the default parameters for MHC I peptides  (90, 91). The haplotype of A549 cells was previously reported in  (92). Identified peptides were assigned to their best fit MHC using NetMHC 4. 0 for the HLA haplotypes, which are present in A549 and for which information is available in NetMHC  (57, 58). The sequence logo of peptides bound by each A549 haplotype were determined through Gibbs clustering of all of the peptides annotated as binding to that haplotype in the IEDB ]Immune Epitope Database and Analysis Resource;  (93) ]. All motifs (with the exception of Figure  5A ) were generated by Seq2Logo 2 using default parameters except for the logo type, which was set to Shannon  (94). The comparison motif in Figure  5A  was generated using Two Sample logo  (95).
Default parameter were used with the exception of bonferroni correction which was enabled. Standard scores were calculated as follows: Whole protein turnover rates were those published by Larance et al. (55). They are presented as log2 transformed ratios between the abundance following treatment with cycloheximide (CHX) and DMSO. Because CHX inhibits protein synthesis, general protein abundance decreases resulting in negative fold changes. Protein abundance data was mined from the GeneCards Suite Human Integrated Protein Expression Database (HIPED)  (56) , unifying protein abundance data from 4 proteomics sources, ProteomicsDB, MOPED, PaxDb, and MaxQB. We utilized HIPED's integrated proteomes of 69 normal human anatomical entities (tissues, in-vivo cells and body fluids). The mined protein abundance PPM (parts per million) values were normalized by log10 transformation.
All statistical analyses were performed using either GraphPad Prism or MATLAB 2016a. Pathway enrichment was done using Panther 13. 1 [Protein Analysis Through Evolutionary Relationships (54)] using the GO-slim biological processes annotation collection. Protein schematics were made using the start and end position of the presented peptides aligned to the protein sequence. Only peptides with a log10 transformed intensity >6 are displayed. z = x stimulated -x unstimulated.
TCGA Data Analysis TCGA data was mined using the xenaPython package in Python 3. 6. The results shown in this analysis are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome. nih. gov/. The full lung carcinoma cohort (both lung adenocarcinoma [LUAD] and lung squamous cell carcinoma [LUSC] designations) was used for this study. One sample (TCGA-63-A5MY-01) was removed as an outlier because the expression of HLA-B was more than 3 interquartile ranges below quartile Q1 leaving a total of n = 1,128 samples for the study. The Tumor Inflammation Signature, or TIS was calculated as the weighted linear sum of the expression levels of 18 genes (Supplementary Table  3 ) as developed by Ayer et al. and  Danaher et al. (65, 96). Expression levels of the three HLA genes were normalized to the mean expression for that gene in the cohort and log2 transformed.
The turnover ratio was here applied to the subset of proteins which contain presented peptides (presented proteome) and those which are only found in the cellular lysate (cellular proteome). Error bars indicate confidence intervals on the mean (Mann-Whitney *p = 0. 0182) (D) Protein abundance was inferred from the GeneCards Suite Human Integrated Protein Expression Database (HIPED) (56) and applied to the subset of proteins which contain presented peptides (Presented proteome) and those which are only found in the cellular lysate (cellular proteome). Error bars indicate confidence intervals on the mean (Mann-Whitney ****p < 0. 0001). (E) The ratio of cellular protein abundance between stimulation with TNFα + IFNγ (T+I) and unstimulated (UT) is plotted against the ratio in intensity for the presentation of each protein (Log2 transformed ratios, Pearson correlation (r) is displayed on the graph, n = 1,239).
ACKNOWLEDGMENTS We thank  Avital Eisenberg-Lerner ,  Adi Ulman ,  Noa Hizkiahou ,  Assaf Kacen , and other members of the  Merbl lab  for fruitful discussions and critically reviewing the manuscript. We acknowledge the aid of  Shifra Ben-Dor  in primer design. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  677748 ); The  I-CORE Program of the Planning and Budgeting Committee  and  The Israel Science Foundation  (grant No. 1755/12 ). YM the incumbent of the Leonard and Carol Berall Career Development Chair.
We present the brightest cluster galaxies (BCGs) catalog for SPectroscoic IDentification of eROSITA Sources (SPIDERS) DR14 cluster program value-added catalog. We list the 416 BCGs identified as part of this process, along with their stellar mass, star formation rates, and morphological properties. We identified the BCGs based on the available spectroscopic data from SPIDERS and photometric data from SDSS. We computed stellar masses and SFRs of the BCGs on the basis of SDSS, WISE, and GALEX photometry using spectral energy distribution fitting. Morphological properties for all BCGs were derived by Sersic profile fitting using the software package SIGMA in different optical bands (g,r,i). We combined this catalog with the BCGs of galaxy groups and clusters extracted from the deeper AEGIS, CDFS, COSMOS, XMM-CFHTLS, and XMM-XXL surveys to study the stellar masshalo mass relation using the largest sample of X-ray groups and clusters known to date.
AEGIS, COSMOS, and CDFS A full description of the data available in the AEGIS, COSMO, and CDFS fields is provided in §2. 1 of  Erfanianfar et al. (2014). Briefly, all of these fields have been covered by deep Chandra and XMM-Newton observations  (Erfanianfar et al. 2014; Finoguenov et al. 2007; George et al. 2011 George et al. , 2013)). In addition to deep X-ray data, all of these surveys take advantage of deep multiwavelength imaging data extending to the radio regime and a dense (optical) spectroscopic sampling. We used clusters and groups in these fields with an assigned identification flag equal to 1 and redshifts below z = 0. 65. These criteria add an additional 45 (typically less massive) X-ray groups to our full sample.
XMM-CFHTLS The XMM-CFHTLS catalog has been constructed through a series of short XMM-Newton observations of faint RASS ( ROSAT all-sky survey ) sources in the CFHTLS wide fields  (Mirkazemi et al. 2015). We used the T0007 data release of CFHTLS and its corresponding photometric redshift catalog. The photometric redshifts were computed using the methods of  Ilbert et al. (2006)  and  Coupon et al. (2009). The spectroscopic redshifts were derived from a series of Hectospec observations with MMT  (Fabricant et al. 2005; Mirkazemi et al. 2015). We also added the spectroscopic data from DR14 SDSS to this sample. We used in total 75 X-ray clusters from XMM-CFHTLS with 0. 1< z <0. 65.
XMM-XXL The XXL Survey is the largest area (50 deg 2 ) XMM program to date  (Pierre et al. 2016)  with the aim of identifying several hundred galaxy clusters along with several tens of thousands of AGNs in the [0. 5-2] keV band. We used those clusters which overlap with the W1 field of CFHTLS to take advantage of the photometric and spectroscopic data available in this wide field. In total, this adds 25 (predominantly massive) X-ray clusters from XMM-XXL to our study. Figure  1  shows the M 200 -redshift relation for the full sample of groups and clusters used in this work.
Data base summary Our sample of clusters includes 416 from SPIDERS and 110 from the other five cluster surveys. During our analysis we detected two duplications between these surveys. There was one cluster in common between CODEX and CFHTLS and one between CODEX and XMM-XXL. The X-ray properties were consistent. However, we kept those from CFHTLS and XMM-XXL due to deeper X-ray and optical data. The locations in the massredshift plane of the 526 unique clusters in our total sample is presented in Figure  1. The clusters cover redshifts between 0. 1 and 0. 65.
Stellar mass and star formation rate We computed the SFR and stellar masses for the BCGs of CODEX-SPIDERS clusters using Le PHARE (PHotometric Analysis for Redshift Estimations;  Arnouts et al. 2001; Ilbert et al. 2006 ), a publicly available code based on a χ 2 template fitting procedure. We used Galaxy Evolution Explorer (GALEX) data for the ultraviolet (UV) part of the spectrum. The GALEX instrument provides UV broadband information: far-UV (FUV, 1344-1786Å) and near-UV (NUV, 1771-2831Å). For the op-  tical part of the spectrum we used SDSS u, g, r, i, z cModel magnitudes. We also used Wide-Field Infrared Survey Explorer (WISE) forced photometry  (Lang et al. 2016)  for the infrared part of spectrum. We followed the procedure described in  Ilbert et al. (2009 Ilbert et al. ( , 2010)). First we adjusted the photometric zero points, as explained in  Ilbert et al.
Structure parameters The BCGs in the SPIDERS sample were modeled using GAL-FIT  (Peng et al. 2010 ) through the Structural Investigation of Galaxies via Model Analysis code (SIGMA), which is the host pipeline that runs GALFIT  (Kelvin et al. 2012 ; see relevant sections in  Furnell et al. 2018 ). An R-based pipeline software previously used in the GAMA (Galaxy And Mass Assembly) survey  (Driver et al. 2011 ), SIGMA provides model light profiles for ∼10 5 GAMA galaxies in five optical (SDSS-ugriz) and four NIR (UKIRT-YJHK) passbands (see  Hill et al. 2011 ). This pipeline is capable of performing a full fit, including: object extraction through Source Extractor (SExtractor;  Bertin & Arnouts 1996) , creating a model of the field PSF (PSFEx; Bertin 2011), estimating the local sky about an object, masking external objects and, finally, fitting a two-dimensional light profile through GALFIT.