dataesr/scientific-paragraphs-categorization huggingface dataset:
Although a minimal phenotype may still yield important biological insights in large samples  [95] , discrepancies between phenotype operationalizations should be kept in mind as we draw connections across datasets. Fourth, all three data sources comprised predominantly White populations, restricting the generalizability of our findings. As African American and non-White Hispanic adolescents experience a greater burden of childhood adversity  [96, 97] , interactions between genetic risk and environmental exposures may also disproportionally contribute to susceptibility for mental health disorders in racial and ethnic minority populations and perpetuate health disparities  [98]. It should remain a priority of neuropsychiatric genetic studies to expand the recruitment and research of non-White samples  [99].
A comprehensive list of grants funding is available on the ALSPAC website (http://www. bristol. ac. uk/alspac/ external/documents/grant-acknowledgements. pdf). GWAS data were generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe, Inc. This publication is the work of the authors and all authors will serve as guarantors for the contents of this paper. JWS is an unpaid member of the Bipolar/ Depression Research Community Advisory Panel of 23andMe, Inc. , a member of the Leon Levy Foundation Neuroscience Advisory Board, and has received an honorarium for an internal seminar at Biogen, Inc. He is the Principal Investigator of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe, Inc.
ACKNOWLEDGEMENTS We thank  Oliver Hoffman, PhD , formerly of the  Harvard Chan Bioinformatics Core, Harvard TH Chan School of Public Health, Boston, MA ,  Meeta Mistry ,  Virginia Fisher , and  Kuang Zheng  for their assistance with early data analyses. We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. We also thank the research participants and employees of 23andMe,  Inc. for making this work possible.
Definition of likelihood functions The analysis of the mass function is performed through Bayesian inference, by maximizing a likelihood function. The posterior distribution is explored with a Monte Carlo Markov Chains (MCMC) approach  (Heavens 2009) , by using a python wrapper for the nested sampler PyMultiNest  (Buchner et al. 2014). The likelihood commonly adopted in the literature for number counts analyses is the Poissonian one, which takes into account only the shot-noise term. To add the sample variance contribution, the simplest way is to use a Gaussian likelihood. In this work, we considered the following likelihood functions: -Poissonian: L(x | µ) = N z α=1 N M i=1 µ x iα iα e -µ iα x iα ! , (5) where x iα and µ iα are, respectively, the observed and expected number counts in the i-th mass bin and α-th redshift bin.
Specifically, we fix the parameter values at Ω m = 0. 295, log 10 A s = -8. 685 and σ 8 = 0. 776 for the lower case and Ω m = 0. 320, log 10 A s = -8. 625 and σ 8 = 0. 884 for the higher case. We notice, also from the FoM comparison in Fig. 11 , that there is no appreciable difference between the first two cases. In contrast, when a wrong-cosmology covariance  matrix is used we can find either tighter or wider contours, meaning that the effect of shot-noise and sample variance can be either under-or over-estimated. Thus, the use of a cosmologyindependent covariance matrix in the analysis of real cluster abundance data might lead to under/overestimated parameter uncertainties at the level of statistic expected for Euclid.
Acknowledgements. We would like to thank  Laura Salvati  for useful discussions about the selection functions. SB, AS and AF acknowledge financial support from the  ERC-StG  ' ClustersxCosmo ' grant agreement  716762 , the  PRIN-MIUR 2015W7KAWC grant , the  ASI-Euclid  contract and the  INDARK grant. TC is supported by the  INFN INDARK PD51  grant and by the  PRIN-MIUR 2015W7KAWC  grant. Our analyses have been carried out at: CINECA, with the projects INA17_C5B32 and IsC82_CosmGC; the computing center of  INAF-Osservatorio Astronomico di Trieste , under the coordination of the  CHIPP  project (Bertocco et al. 2019;Taffoni et al. 2020). The Euclid Consortium acknowledges the  European Space Agency  and a number of agencies and institutes that have supported the development of Euclid, in particular the  Academy of Finland , the  Agenzia Spaziale Italiana , the  Belgian Science Policy , the  Canadian Euclid Consortium , the  Centre National d'Etudes Spatiales , the  Deutsches Zentrum für Luft-und Raumfahrt , the  Danish Space Research Institute , the  Fundação para a Ciência e a Tecnologia , the  Ministerio de Economia y Competitividad , the  National Aeronautics and Space Administration , the  Netherlandse Onderzoekschool Voor Astronomie , the  Norwegian Space Agency , the  Romanian Space Agency , the  State Secretariat for Education, Research and Innovation (SERI)  at the  Swiss Space Office (SSO) , and the  United Kingdom Space Agency.
RNAseq RNA used in RNAseq experiments was sent to the Delaware DNA Sequencing and Genotyping Center (Newark, DE) for quality control analysis, library preparation and paired end sequencing on the Illumina HiSeq 2500. Read length was set to 51 bases with the samples run on two separate lanes. Raw sequencing data les were uploaded to the public Galaxy server at usegalaxy. org  [51]. Read les from the two lanes were concatenated and read quality was determined using FastQC  [52]. The reads were processed using the Trim Galore! with the FastQC output as a guide  [53]. Retained reads had a quality score minimum of 30, and with the rst 9 bases on the 5' end and the last base on the 3' end removed. Though rRNA was depleted prior to sequencing, we discovered that depletion of rRNA was incomplete. The rRNA reads were computationally removed by alignment to the M.
chelonae rRNA operon using BowTie2 and saving the non-aligned reads  [54]. Average number of reads per sample was 2,280,954 reads for the M. chelonae (McProf) samples and 2,484,260 reads for the double lysogen (BPs+McProf) samples This gave us an average read depth of 40 reads per base for the transcriptome of both the WT (McProf) and double (BPs, McProf) lysogen samples. Processed reads were then quantitated using Salmon  [55]  by aligning in a strand-speci c orientation to the M. chelonae (CCUG 47445) transcriptome using a coding transcript fasta (GenBank). The alignment was adjusted for the high GC content of the mycobacterial genome. Mate pair 1 was speci ed as coming from the reverse strand (SR). Strand speci city was necessary because reads from two convergent genes often overlapped.
Output from Salmon quanti cation was used for pairwise comparisons of expression, using the R statistical package DESeq2 [56] and the NumReads values as described by the authors. Genes with low expression levels (reads <10) were removed. Genes were considered signi cantly regulated if Log2 fold change (Log2FC) was greater than 1. 0 and the False Discovery Rate (FDR) was less than 0. 05. Although the M. chelonae genome is sequenced and has an annotation, the gene functions are poorly characterized. We therefore generated a table of M. chelonae genes with orthologs in M. tuberculosis, M. abscessus and M. smegmatis using the OrthoDB pipeline, a series of scripts from OrthoDB  [57]. This gave us the best alignment between the three genomes and together with blastP on MycoBrowser and HHpred, helped identify numerous signi cantly upregulated M.
chelonae genes with potential virulence functions  [57] [58] [59]. The RNAseq data set was validated in two independent RNA isolation experiments using qPCR assays that quanti ed expression of upregulated (whiB7 and tap) and downregulated genes (glycerol kinase (glpK)) from the RNAseq data set (data not shown). RTqPCR cDNA was synthesized from 500 ng of total RNA in 20-µl reactions containing qScript cDNA Supermix (Quantabio, Beverly, MA) according to the manufacturer's recommendations. Reactions were incubated for 5 min at 25°C, 20 min at 42°C and heat inactivated at 85°C for 5 min. cDNA was diluted 1:6 in 10 mM Tris and stored at -20°C. Real-time PCR assays were performed using the Bio-Rad CFX96 Real-Time system (Bio-Rad Laboratories, Hercules, CA). Using Primer3 software, primer sets were designed to amplify a 100-bp sequence in the gene of interest (Table  5 ).
Acknowledgements We would like to thank  Dr. Graham Hatfull  for providing Mycobacterium phage BPs and for his advice and suggestions in the preparation of this manuscript. We thank  Erica Sewall  the discovery of McProf. We also thank  Dr. Brewster Kingham  and the  University of Delaware DNA Sequencing and Genotyping Center  for assistance with RNAseq sequencing services. We thank  Dr. Heidi Munger  at  Genome Technologies of The Jackson Laboratory (Bar Harbor, ME ) for assistance and services sequencing bacterial genomes and  Dr. Benjamin King  of the  University of Maine  for his assistance with bioinformatic analyses.
Although the McProf prophage was identi ed and characterized in M. chelonae, it is closely related to prophages found in clinical M. abscessus isolates. BlastN analysis of the McProf prophage genome in M. abscessus-speci c databases (e. g. phagesdb. org) identi ed 25 M. abscessus isolates with McProf-like prophage sequences [31]. The WXG-100 family polymorphic toxin cassette identi ed in McProf is also prevalent in M. abscessus genomes. BlastP analysis of the McProf Tde-like polymorphic toxin (gp97 toxin) results in 100 high-similarity protein alignments to mycobacterial proteins with 91% matching M. abscessus sequences. An initial random screen of 10 of the aligned sequences showed that they were all located in prophage genomes anked by a WXG-100 family gene and an immunity gene. Lysogenic gene expression pro les from the BPs and McProf prophage genomes.
This analysis was repeated 100,000 times, randomly selecting a different set of eight genes at each reiteration. Setting the threshold of significance at the same value as the one observed for the predictor (r = 0. 99, p = 5. 67E-05), the percentage of random significant correlations with PFS was 0. 994%. The observation that randomly selected sets of genes generated significant correlations with PFS suggests that (i) specificity of correlations could be increased only with a larger number of patients used as training and test datasets, and (ii) DDPP could potentially be used for identification of new genes/biomarkers, if used not in a random selection manner, but rather in a systematic selection manner. DDPP tested as a biomarker discovery method. The most significant correlation between the differential expression of a new identified gene and PFS of patients treated with everolimus was observed for UBAP1 (ubiquitin associated protein 1): r = 0.
99, p = 4. 68E-04) the percentage of significant correlations was 0. 059%. Leave one out experiments. We performed (using the same "stepin" vectorial summation methodology) five leave-one-out reiterations, discarding at each experiment one patient and building a predictor on the remaining four to address potential overfitting. We observed again an instability of the predictors and dependence on the composition of the cohorts at each reiteration. The concordance between real PFS of the patients left out, and the predicted PFS using the correlator obtained at each reiteration was lower than for the everolimus example (r = -0. 81, p = 0. 1) likely related to a lower number of patients in each reiteration. These data suggest again that performance and accuracy of the prediction of the PFS could be increased only with a higher number of patients in the training and validation datasets.
In an attempt to overcome the limitations of current oncology biomarkers, our transcriptomic approach allows (1) the exploration of the whole transcriptome (~20,000 genes), providing insight about the status of activation of almost all drug targets in the context of the network of genes or pathways; (2) the data can be obtained from a single assessment requiring very small amount of tumor and analogous normal tissues; and (3) the prediction of the duration of the PFS under a specific therapeutic regimen, as a continuous variable in contrast with binary predictions. One of the main challenges of finding such new biomarkers is the small size of the available datasets, often involving fewer than 10 patients treated with the same drug, for whom both molecular profiles (from tumor and analogous normal tissues) and PFS data are available.
The work presented here overcomes the above mentioned limitations of current biomarker strategies and identifies the genes and generates linear regression that correlate with clinical outcome. In particular, the specific contribution of the DDPP and the features that distinguish it from other existing methods such as MLR or multiple Cox regression (MCR) consists in the use of nonparametric combination of the relative expression of multiple genes. Indeed, MLR and MCR investigate in the same model not only expression data, but also apply simultaneously coefficients to better differentiate genes impacting outcomes, compared to those that do not. The learning process defining such coefficients is a major limiting factor to these methodologies when using small datasets. DDPP on the other hand, in order to overcome this limitation empirically decides (1) how many features (genes) to include in the predictors rather than stopping when the fit is no longer improved, and (2) which basic parameter-free summation method best predicts the outcome.
The final result of the DDPP model consists in defining the equations that link gene expression features to clinical outcome. However, it is important to emphasize the limitation of the linear regression equations presented here, which are based on a prototype version of the DDPP. Indeed, the DDPP tools presented have the purpose of illustrating the concept and will require validation in a larger prospectively obtained dataset to confirm whether its empirical approach and applicability in any cohort size can be confirmed. Prospective studies will indeed assess the ability of DDPP algorithm to test multiple summation methods in cohorts of different sizes. One of the pillars, and potential limitations of the DDPP methodology is the investigation of tumor and analogous organmatched normal tissue biopsies from the same patient, which is not the current practice in standard of care or in translational research and clinical trials.
This raises the legitimate question of what is the likelihood of these findings to apply to routine oncology practice. This will be possible only if the DDPP predictor will demonstrate superiority to existing/current biomarkers models and provide a direct benefit for patients. Use of matched tumor and normal biopsies is essential for accurate interpretation of the transcriptomic data, as it discards the transcriptomic genetic variability background noise in each patient, and lowers significantly the variance of transcriptomic measurements  22, 23. We were able to use such transcriptomic and PFS data from the WINTHER trial database. The WINTHER trial remains the only clinical trial that used transcriptomics in a prospective clinical setting in addition to conventional DNA sequencing to help inform the treatment decision for patients with advanced cancer.
Experimental evidence suggests association of anti-TLR-4 with anti-PDL-1 treatments could be of interest with the aim to increase the fraction of patients who could benefit from IO treatments. Validation of DDPP will require further prospective investigation, ideally using biospecimens and clinical data obtained from patients participating in prospective clinical trials  28  , in which implementation of dual tumor and normal biopsy and integration of transcriptomics are feasible  10. In conclusion, the clinical records available from the WINTHER trial, and the unique transcriptomic dataset obtained from tumor and organ-matched normal tissue biopsies were essential to enable correlations with clinical outcome under treatment, with TKI inhibitors and IO. The DDPP is potentially a new global biomarker model that can apply to any type of drug (IO or non-IO targeted drugs) alone or in combination, agnostic of tumor type, and can lead, pending further prospective validation, to a new approach to optimal treatment selection for patients with cancer.
METHODS Dataset The DDPP prototype was developed exploiting the transcriptomics data from the WINTHER trial (NCT 01856296-approved in France (ANSM), Spain (Agencia Espanola de Medicamentos y productos sanitarios), Israel (Ministry of Health), Canada (Health Canada), and USA (FDA)). The full methodology of transcriptomic assessment and patient treatment is described in the WINTHER trial published in Nature Medicine  10. Detailed clinical and biological information for each patient selected for this study is available in Table  1. Patients who had provided written consent underwent a dual biopsy from the metastasis and from the histologically matched normal tissue. The interventional radiologist selected for biopsy one of the non-necrotic and >2 cm metastatic lesions. The image-guided biopsies were performed under standard operating rules and guidelines using adequate devices, such as trucut 18 gauges needles placed at the periphery of the lesion biopsied.
Probe labeling and linear amplification were generated using Agilent Technologies reagents and T7 RNA pol that generated labeled complementary labeled RNAs (tumor labeled with Cy5 and normal with cRNAs with Cy5). After fragmentation and purification of labeled cRNA, hybridizations were performed as dual dye swapped (direct and inversed labeling) experiments with direct cohybridization of equal amounts of labeled tumor and normal probes. After washing drying, microarrays were read using Agilent 2000 scanner version C. After processing with Agilent Feature extractions software, data were used for direct comparison of intensities and editing of a report containing a data quality information, identity of mRNAs differentially expressed (overexpressed or underexpressed in tumor versus normal, and providing for each gene fold changes and intensities together with p values for each measure, type of structural abnormalities (amplifications/deletions), threshold, fold changes, and intensities).
ACKNOWLEDGEMENTS In memory of  Gaspard Bresson ,  John Mendelsohn , and  Thomas Tursz  who inspired the creation of the  WIN Consortium. The research leading to these results has received funding from the  European Union   Seventh Framework Program  ( FP7/ 2007-2013  under grant agreement no. 306125 ). Funded in part by  ARC Foundation for cancer research (France) ,  Pfizer Oncology, Lilly France SAS , and  Novartis Pharmaceuticals Corporation. Funded in part by The  Fero/J. P. Morgan Private Bank Clinical Oncology Research Grant ,  National Cancer Institute  grant  P30 P30-CA023100 (R. K. )  and  Israeli Science Foundation  grant  1188/16  (E. R. ),  Instituto Salud Carlos III-Programa Rio Hortega Contract  grant  CM15/00255  (I. B. ), and  Canadian Institutes for Health Research  (grant  MOP-142281 , G.
DATA AVAILABILITY Expanded clinical and biological information for each patient is available in the data file Patient_demographic_clinical_data. csv  29  which expands on the information presented in Table  1. The data file DPP_WINTHER_Expression_Data. csv  29  supports Figs. 1 2 3 , Supplementary Tables 1, 3-5, and Supplementary Figs. 1 2 3. The transcriptomic expression data are available from Gene Expression Omnibus: https://identifiers. org/geo:GSE168621 (ref. 30 ). The data generated and analyzed during this study are described in the following data record: https://doi. org/10. 6084/ m9. figshare. 14166731 (ref. 29 ).
3 ERBB2 A775_G776insYVMA Afatinib 136 M Lung 3 0. 4 ERBB3 amp; MET splice site 3028 + 1G > A; STK11 Q100* ATM Afatinib L2450fs*11; BRCA1 E23fs*17; CDK4 amp; CDKN2A/B loss; MDM2 amp; APC I1307K; KDM5C truncation; MAP3K1 S1475* 237 M HN 6 19. 3 CCND1 amp; FGFR2 amp; CDKN2A/B loss; FGF19 amp; FGF4 NCT01004224 BGJ398 amp; BAP1 trunc exon 3; FGF3 amp; MAGI2 Q1077*; PBRM1 E1155fs*17 247 M Esophagus 2 1. 6 FGFR2 amp; CDKN2A/B loss; TP53 W91*; ASXL1 splice site 472- NCT02052778 TAS-120 2A > G 228 M CRC 5 0. 7 FGFR1 amp; TP53 C176F; APC E1322*, R213*; SMAD4 loss; SOX9 NCT02052778 TAS-120 V163fs*21 183 M CRC 2 61. 0+ RBB3; V104M; MAP2K1; E203K; CDKN2A/B loss; FBXW7 R465C; Pembrolizumab PIK3CA E39K; PIK3R1 R348*, R639*; PTEN R233*, splice site 801 + (TMB: 74. 8) 2T > G; TP53 R158H, R273H; APC R1450*, R499*; ARID1A (MSI: +) P1115fs*46, Q1306fs*17; ATRX Q2422*; CDH1 D433N; EP300 R2263*; FAM123B R631*; FAT1 A4305V; FLCN H429fs*39; MSH6 L1330fs*12, S279fs*12 294 M HN 1 1.
Anti-PD-1/PDL-1-key genes: PDL-1, PDL-2, PD-1, CTLA-4, CD28, CD80, CD86, LAG-3, and TLR-4, together with specific markers of the presence of effector tumor-infiltrating immune cells: CD8A (cytotoxic lymphocytes T), CD16 (natural killer cells), and FOXP3 (T-regs cells). 2. Selection of the patients with available transcriptomics data and clinical outcome (PFS) under treatment with each drug available. As we observed only six instances (patients with known PFS values under treatment with everolimus), this is a crucial stage: without dramatic decrease in the number of genes, overfitting (i. e. , the development of a perfect model that is specifically tailored to the given dataset used for training), is almost guaranteed. Identification of key genes involved in drug's mechanisms of action, based on recent literature and based on the FDA US Prescribing Information (USPI).
Acknowledgements. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. Computing resources (https://doi. org/10. 5065/D6RX99HX)were provided by the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation  and other agencies. We thank the editor and referees for their insightful comments on the paper. Financial support. Charles D. Koven  has been supported by the Director,  Office of Science, Office of Biological and Environmental Research of the US Department of Energy  under contract  DE-AC02-05CH11231  through the  Early Career Research Program  and the  Regional and Global Model Analysis Program (RUBISCO SFA).
Charles D. Koven  and  Rosie A. Fisher  have been supported by the  Next Generation Ecosystem Experiment-Tropics  ( NGEE-Tropics ) project. William R. Wieder ,  David M. Lawrence , and  Rosie A. Fisher  have been supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation , under cooperative agreement 1852977. William R. Wieder  has been supported by the  US Department of Agriculture NIFA  award  2015-67003-23485  and  NASA   Interdisciplinary Science Program  award number  NNX17AK19G. Benjamin M. Sanderson  and  Rosie A. Fisher  have been supported by  H2020  programs  ESM2025  (grant agreement no. 101003536 ) and  4C  ( GA 821003 ). Chris D. Jones has been supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and the  European Union 's  Horizon 2020 research and innovation program  CRESCENDO (grant agreement no.