dataesr/scientific-paragraphs-categorization huggingface dataset:
Preferably regular and stable patients Less experienced GPs mostly took on patients (or relatives of patients) who were already in their client database. Most of these patients were stabilized, working again and had a family life. Most non-experienced GPs refused unknown new addicted patients. I knew M as a 16-year-old boy. He came back a few years ago with a request for methadone. I tried to put him off. but in the end, I did it anyway. A couple of years, or no, not so long after this, his girlfriend came to see me, and now I prescribe it for her as well. (GP with experience) Additionally, the GPs reflect on the possible advantages of OST for patients when care is provided in the general practice instead of the addiction centres, which are safety, privacy, no waiting lists, no stigma and being able to deal with the co-morbidities of the patient.
Observations and data reduction Observations of Z CMa in the Hα and [O i] lines were performed using the ZIMPOL instrument  (Thalmann et al. 2008; Schmid et al. 2012)  of SPHERE on the night of 31 March 2015. Simultaneous images of Z CMa in Hα and in the adjacent continuum were acquired with the two ZIMPOL cameras using the B Ha (λ c =655. 6 nm, ∆λ=5. 5 nm) and Cnt Ha (λ c =644. 9 nm, ∆λ=4. 1 nm) filters. Exposures were taken in field-stabilized mode using two different field rotation angles and swapping the filters in front of each camera to optimize artefact removal during the reduction. This resulted in 60 frames per filter (15 for each setup) for a total exposure time of 30 minutes. Images in the [O i] line with the OI 630 filter 1 (λ c =629. 5 nm, ∆λ= 5. 4 nm) were obtained in field-stabilized mode using two different field rotation angles for a total of 30 frames and an integration time of 30 minutes.
This also agrees with the non-detection of the jet in the SINFONI data shown by W10. As we can estimate a mean signal-to-noise ratio of about 12 on the FUor jet, we conclude that we did not observe the collimated jet from the Herbig because of sensitivity limits. Interferometric measurements of the Brγ line during the 2008 outburst  (Benisty et al. 2010 ) suggested a bipolar (non fully spherical) wind from the star on mas scales, roughly aligned in the direction of the large-scale jet. As the Brγ emission disappeared outside the outburst phase, the authors concluded that the wind emission is connected to the event of increased mass accretion driving the outburst. To understand if the emission detected in our images is related to such bipolar wind, we evaluated whether the signal may be attributed to an ejection event from the recent Herbig star bursts, which occurred in January 2008 and February 2011  (Canovas et al.
g. Antoniucci et al. 2008) , which depend on the gas temperature T and electron density n e. For the computation, we considered an oxygen abundance of 4. 6 ×10 -4  (Asplund et al. 2005)  and assumed that all oxygen is in neutral state. The emissivities were taken from the grids of the the NEBULIO database 2 (see also  Giannini et al. 2015). By adopting n e = 10 4 cm -3 and a gas temperature T=10 000 K (as typically measured in T Tauri jets; see  Bacciotti & Eislöffel 1999; Maurri et al. 2014 ) we obtain Ṁ jet 2. 7 ± 1. 5 × 10 -7 M yr -1. Allowing for a range of values be- tween 5 × 10 3 and 5 × 10 4 cm -3 for the electron density and between 8 000 K and 12 000 K for the temperature, we eventually obtain a possible range of Ṁ jet between 1×10 -8 and 1×10 -6 M yr -1. To our knowledge this is the first direct measurement of the mass loss rate from jet emission observed close to a driving FUor object.
" For the variable "partner desired pregnancy at that time," results were grouped according to the response if the participant reported that her partner started conversations with her about pregnancy. Whenever partner's verbalization of his interest in pregnancy was not confirmed, even if the participant observed partner's desire for pregnancy, the response was classified as "did not declare" and was categorized as "missing" to minimize induction bias. Precoded answers composed a database in the public domain application Epiinfo 6. 04 (US Center for Disease Control and Prevention, Atlanta, GA, USA). Statistics and inferential analysis of the data were performed using the software Statistical Package for Social Sciences, version 17. 0 (SPSS Inc. Chicago, IL 60606, USA). For the univariate data analysis, descriptive statistics were applied.
Growth chronologies Growth chronologies from Lutjanus argentimaculatus, Porites spp. and C. columellaris were obtained from earlier studies as mentioned above (see Supplementary Table  S1  for details on the type of data, length of chronology, location, and source). These were supplemented with a new growth chronology developed from otoliths of another tropical fish, the spangled emperor (Lethrinus nebulosus). For all species, we only used data for the years 1984-2003, which were common to chronologies from all taxa. The quality of the chronologies was assessed using the mean of pair-wise series correlations ( r), an estimate of fractional common variance, and expressed population signal (EPS), a measure of how well the chronology represented the theoretical population chronology  (Wigley et al.
, 1984). These were analyzed using the R package 'dplR'  (Bunn, 2008). Spangled emperor growth chronology. Archived collections of the otoliths of spangled emperor (L. nebulosus) were obtained from the Department of Fisheries (Government of Western Australia). These otoliths came from fish collected in the Gascoyne Coast region of WA (Fig. 1 ) from 2006 to 2010  (Marriott et al. , 2010). The sagittal otoliths of each fish were cleaned and one otolith was embedded in epoxy resin. Two to three thin transverse sections were made near the primordium in a direction perpendicular to the sulcus acusticus with a low speed saw containing a diamond-wafering blade, following the methods of  Marriott et al. (2010). The sections were then washed by agitating in 2% hydrochloric acid for up to 10 s (to remove calcium build-up), followed by rinsing in water.
Climatic and environmental datasets Recent studies have shown that ENSO (represented by the Niño-4 index) and SSS are important drivers of the growth of mangrove jack  (Ong et al. , 2015) , while coral growth has been correlated with decadal trends in SST  (Cooper et al. , 2012). The growth of Callitris trees in the Pilbara mainly responds to rainfall in the austral summer from December to May  (Cullen et al. , 2008; O'Donnell et al. , 2015). We compared growth patterns to the Niño-4 index (based on SST in the Western Pacific between 5°N -5°S and 160°E -150°W;  Rayner et al. , 2003) , SST (HadISST;  Rayner et al. , 2003) , SSS  (Good et al. , 2013) , and rainfall  (Jones & Harris, 2008). All environmental data were obtained from the Royal Netherlands Meteorological Institute (KNMI) Climate Explorer  (Trouet & Van Oldenborgh, 2013) , a web application for climate data (http://climexp.
Collinearity between all six environmental variables (|r| > 0. 5, P < 0. 01) was evaluated. The R package 'MUMIN'  (Barton, 2015)  was used for model selection using the second-order Akaike information criterion (AICc) based on Kullback-Leibler (K-L) information loss and accounting for small sample sizes  (Burnham & Anderson, 2004). Differences in AICc values (DAICc) were used to assess the different models. Adjusted R 2 values, F-statistic, t-statistic, and P-values were reported. Model validation was carried out to ensure that the models conformed to the assumptions of linear models and tested for auto-correlation. All statistical analyses were completed in R version 3. 1. 3 (R Development Core Team, 2008). After the model selection process, spatial correlation maps of the significant regional variables were made in the web application KNMI Climate Explorer to show the relationships at a higher spatial resolution.
Our study shows that where strong links between the growth of fishes and trees can be established, chronologies of tree growth may provide a proxy to understand the response of fish populations to climate change, both in the past and the future. In summary, we have provided the first empirical evidence for climate-driven synchrony between marine and terrestrial ecosystems in the Southern Hemisphere at annual time scales. These links occur through the influence of ENSO events on regional environmental variables that affect the annual growth of fishes, corals, and trees throughout the region. Although we lacked an overlap of all taxonomic groups across the entire region, this is a common limitation of any program that seeks to access legacy datasets where researchers had no control over the intensity and location of sampling in the past.
Acknowledgements This work was funded by the  Australian National Network in Marine Science  and the  Australian Institute of Marine Science. JJLO was supported by a scholarship from the  Australian Postgraduate Awards. JZ was supported by a  UWA/AIMS/CSIRO  fellowship and a  Curtin Senior Research Fellowship. The authors acknowledge the facilities, and the scientific and technical assistance of the  Australian Microscopy & Microanalysis Research Facility at the Center for Microscopy, Characterization & Analysis , the  University of Western Australia , a facility funded by the  University, State and Commonwealth Governments. In particular, we would like to thank  Professor Paul Rigby  and  Miss Alysia Buckley  for the help and advice they have provided.
Oncogenic Alterations in Human Tumors We built a comprehensive map of the oncogenic alterations in human tumors using data from TCGA, ICGC, and other studies (Figure  1A ; Table  S1C ). The map consisted of (1) cancer genes (CGs) for which the mutation pattern in whole-exome sequencing (WES) data is consistent with positive selection, 2) focal recurrently aberrant copy number segments (RACSs) from SNP6 array profiles, and 3) hypermethylated informative 5 0 C-phosphate-G-3 0 sites in gene promoters (iCpGs) from DNA methylation data, hereafter collectively referred to as ''Cancer functional events'' (CFEs). We identified CFEs by combining data across all tumors (pan-cancer), as well as for each cancer type (cancer specific) (Tables  S2A, S2D , and S2H). The WES dataset consisted of somatic variant calls from 48 studies of matched tumor-normal samples, comprising 6,815 samples and spanning 28 cancer types (Tables  S1A-S1D ).
CGs were detected per cancer type by combining the outputs of three algorithms: MutSigCV, OncodriveFM, and Oncodrive-CLUST  (Lawrence et al. , 2013; Rubio-Perez et al. , 2015; Tamborero et al. , 2013a). This identified 461 unique pan-cancer genes (Table  S2A ). We further added nine genes identified as putative tumor suppressors  (Wong et al. , 2014). We mined the COSMIC database to identify likely driver mutations in 358 of the 470 CGs (Table  S2B ; Supplemental Experimental Procedures). Most tumors harbored only a few driver mutations (median n = 2, range 0-64), consistent with previous reports  (Kandoth et al. , 2013; Vogelstein et al. , 2013). RACSs were identified using ADMIRE for the analysis of 8,239 copy number arrays spanning 27 cancer types  (van Dyk et al. , 2013)  (Table  S1D ; Supplemental Experimental Procedures).
A Therapeutic Landscape of Human Cancers Modeling Pharmacogenomic Interactions To investigate how CFEs detected in primary tumors impact drug response, we first mapped these on our panel of cell lines (Figure  1C ; Tables  S2C, S2G,  and S2J ). Cell lines underwent extensive drug sensitivity profiling, screening 265 drugs across 990 cancer cell lines and generating 212,774 dose response curves (median number of screened cell lines per drug = 878, range = [366, 935]; Figure  1D ). This is an expansion on previous pharmacogenomic datasets (  Barretina et al. , 2012; Basu et al. , 2013; Garnett et al. , 2012; Seashore-Ludlow et al. , 2015). The effect of each drug on cell number was used to model sensitivity as IC 50 (drug concentration that reduces viability by 50%) or AUC (area under the dose-response curve) values (Tables  S4A  and S4B ).
We used three distinct analytical frameworks to define the contribution of CFEs to the prediction of drug sensitivity (Figure  1E ). ANOVA was used to identify single CFEs as markers of drug response. Logic models identified combinations of CFEs that improve the prediction of drug response. Lastly, we used machine-learning algorithms to assess the contribution of each molecular data type (CGs, RACS, iCpGs, and gene expression) in explaining variation in drug response. For consistency, all analyses used IC 50 values. We carried out a pan-cancer, as well as a cancer-specific, analysis (for those 18 cancer types of sufficient sample size, n > 15 cell lines).
We required solutions to have specificity greater than 80%. The input features included the CGs, RACSs, gene fusions, and binarized pathway activity scores derived from the basal gene expression profiles of the cell lines (Figure  S5A ; Tables  S5A, S5B , and S5D). The latter is based on 11 transcriptional signatures of pathway activation  (Parikh et al. , 2010)  (Table  S5B ; Supplemental Experimental (B) A summary of established pharmacogenomic interactions detected in this analysis including a subset of clinically approved markers. The total number of significant and significant large-effect interactions for each cancer type is provided. Testable interactions that were validated on the CTRP datasets are also indicated. (legend continued on next page) Cell 166, 740-754, July 28, 2016 747 Procedures).
LOBICO was executed for each drug separately utilizing pan-cancer and cancer-specific molecular datasets. This led to the inference of 1,112 logic models (Table  S5E ). In the pan-cancer dataset we found that for 69% (182 of 265) of the drugs, the IC 50 s were better explained than expected by chance (p value < 0. 05 and FDR < 5%). Across the cancer-specific datasets, on average, 24% of the drugs were explained by the inferred logic models (Figure  5A ). We termed these logic models (182 from the pan-cancer dataset and 208 from the 18 cancer-specific datasets) ''predictive models''. When considered together, the pan-cancer and cancer-specific LOBICO analyses identified predictive models for 208 out of 265 (78%) drugs. Importantly, for 85% of the 390 predictive models, a multi-input model achieved better performance than did the best single-predictor model (Figure  5B ).
Although the pan-cancer dataset produced the largest number of predictive models, the CV error was consistently higher than for cancer-specific datasets (Figure  S5B ). This is in agreement with the ANOVA analysis, where larger effect sizes were observed for the cancer-specific datasets. The response to drugs that target the p53 or ERK-MAPK pathway were especially well-predicted by LOBICO (Figure  S5C ). We observed that CGs had the largest role in explaining drug response, followed by RACSs and the pathway activities derived from gene expression (Figure  S5A ; Supplemental Experimental Procedures). The small number of pathway signatures had a disproportionately large effect in the logic models, showing that basal pathway activation scores provide relevant information to predict drug response beyond the genomic CFEs  (Costello et al.
, 2014)  (Figure  S5D ). LOBICO uncovered many known, as well as novel, associations (Table  S5F ). Figure  5C  depicts a selection of particularly strong and consistent ''and/or'' combinations found for clinically approved drugs. For example, in the pan-cancer dataset, the ''or'' combination of KRAS or BRAF improved the precision and recall compared to single predictor models to explain cell line sensitivity to a number of  MEK and RAF inhibitors (e. g. , Trametinib in Figures 5C and 5D). In general, the ''or'' combinations led to models with higher recall (Figure  5C , right quadrants) as compared with the singlepredictor model. For example, HNSC cell lines that have an EGFR amplification or a SMAD4 mutation account for 45% (10 out of 22) of cell lines sensitive to the ERRB2/EGFR inhibitor Afatinib, whereas considering only the EGFR amplified cell lines accounts for only 32% (7 out of 22) of the sensitive cell lines (Figure  5E ).
Validation of Pharmacogenomic Modeling Results on Independent Datasets We sought to validate our pharmacogenomic models using independent drug sensitivity datasets from the Cancer Cell Line Encyclopedia (CCLE)  (Barretina et al. , 2012)  and the Cancer Therapeutics Response Portal (CTRP; second version)  (Seashore-Ludlow et al. , 2015). This analysis was for necessity restricted to only those compounds and cell lines shared with our own study (hereafter referred to as GDSC). The shared set consisted of 466 cell lines and 76 compounds from the CTRP study (Tables  S4I-S4K ) and 389 cell lines and 15 compounds from the CCLE study (Tables  S4E-S4G ; Supplemental Experimental Procedures). Validation was performed using IC 50 values from the GDSC and CCLE studies and AUC values from the CTRP study (where IC 50 values were not reported).
We performed ANOVA on the overlapping set of cell lines/ compounds. We validated 53% (19 of 36 on CTRP) and 86% (6 of 7 on CCLE) of the testable sensitivity associations identified in the GDSC, and 21% (6 of 29 on CTRP) and 0% (0 of 7 on CCLE) of testable resistance associations (p < 0. 05, Fisher's exact test CTRP: p = 8. 1 3 10 À9 ; CCLE: p = 0. 01; Figures  S4J  and S4K ; Tables  S4H  and S4L ; Supplemental Experimental Procedures). A significant Pearson correlation of the CFE-drug interaction significance was observed between the GDSC dataset and the other two datasets (R = 0. 86 for CTRP and R = 0. 86 for CCLE; Figures  S4J  and S4K ). Similarly, using LOBICO, we validated 44% (17 of 39) of testable models using the CTRP, including both single and multi-input models, and observed a significant Pearson correlation of the interaction significance between the two datasets (R = 0.
Contribution of Different Molecular Data Types in Predicting Drug Response To investigate the power of different combinations of molecular data to predict drug response, we built linear and non-linear models of drug sensitivity (elastic net  [EN]  regression and Random Forests  [Costello et al. , 2014] ). As input features, we used CGs, RACSs, iCpGs, and gene expression data. Here, we refer to EN models using IC 50 values (Table  S4A ), but very similar results were obtained with Random Forests (Figure  S6F ; Table  S6A ). We assessed the predictive power of (C) Volcano plot with effect size (x axis) and significance (y axis) of large-effect cancer-specific pharmacogenomic interactions. Each circle corresponds to a significant CFE-drug interaction. Circle size is proportional to the number of altered cell lines, and the color indicates cancer type.
DISCUSSION Constructing a Pharmacogenomics Resource Cancer cell lines are important tools for drug development. Here, we have extended previous efforts with the systematic expansion of the pharmacological, genomic, transcriptomic, and epigenetic characterization of 1,001 human cancer cell lines. These datasets can be investigated through the COSMIC and Genomics of Drug Sensitivity in Cancer Web portal (http:// www. cancerrxgene. org). To the best of our knowledge, this is the largest and most extensively characterized panel of cancer cell lines and should enable a broad range of studies linking genotypes with cellular phenotypes. Our analysis of >11,000 patient tumor samples and the subsequent superimposing of salient cancer features on cell lines exemplifies how large-scale cancer sequencing can be used to empower biological research and maximizes the potential clinical relevance of the pharmacological models reported.
, 2011). Our analyses suggest that clinical studies in cancer patients should be designed to enable combinations of genomic alterations to be detected, which has implications for both trial size and the statistical approaches employed. We validated our pharmacogenomic models using independent datasets from the CCLE and CTRP. Consistent with previous reports, this demonstrated good consistency in the set of markers identifiable across these studies  (Cancer Cell Line Encyclopedia Consortium, 2015)  and lends additional support to the results presented here. However, our ability to validate some pharmacogenomic associations was restricted by the limited number of overlapping cell lines and compounds between these studies. Furthermore, the consistency between datasets is not perfect, and efforts toward standardization to reduce methodological and biological differences across the different studies are likely to improve future correlation between datasets.
Using machine learning, we determined that within each specific cancer type, genomic features (either driver mutations or copy number alterations) generated the most predictive models, with the addition of methylation data further improving our models. While informative in the pan-cancer setting, baseline gene expression data was less informative in the more clinically relevant tissue-specific setting. Prioritizing the design of diagnostics that deliver driver mutations, copy number alterations, and DNA methylation profiles might be the most cost effective means in the short-term to stratify patients for cancer treatment.
Conclusions The clinical development of molecularly targeted cancer therapies remains a formidable challenge. Our current analysis is restricted by the availability of patient genomic datasets, the cell lines and compounds screened, and methodological and biological variables, as well as the inherent limitation associated with the use of in vitro cancer cell lines. Nonetheless, our results represent a comprehensive attempt to describe the landscape of clinically relevant pharmacogenomics interactions in cellular models of cancer, complementing previous efforts  (Barretina et al. , 2012; Basu et al. , 2013; Garnett et al. , 2012; Seashore-Ludlow et al. , 2015). The data resource and analyses described here should enable the matching of drug response with oncogenic alterations to provide insights into cancer biology and to accelerate the development of patient stratification strategies for clinical trial design.
EXPERIMENTAL PROCEDURES Cancer Cell Line Characterization Genomic data for a panel of 1,025 genetically unique human cell lines were assembled from the COSMIC database. 1,001 cell lines were included in this study (Table  S1E ). Variants and copy number alterations were identified as described in the Supplemental Experimental Procedures. Microsatellite instability data were assembled as detailed in the Supplemental Experimental Procedures. Gene fusions from a subset cell lines ($700) were identified by targeted PCR sequencing or split probe fluorescence in situ hybridization (FISH) analysis (Table  S2C ).
Variant Identification in Tumors Variant data from sequencing of 6,815 tumor normal sample pairs derived from 48 different sequencing studies were compiled  (Rubio-Perez et al. , 2015). To aid in the analysis, the tumor data were reannotated using a pipeline consistent with the COSMIC database (Vagrent: https://zenodo. org/record/16732#. VbeVY2RViko).
ACKNOWLEDGMENTS This work was funded by the  Wellcome Trust  ( 086375  and  102696 ). F. I. was supported by the  European Bioinformatics Institute  and  Wellcome Trust   Sanger Institute post-doctoral  ( ESPOD) program. T. A. K. was supported by the  National Cancer Institute  ( U24CA143835 ) and the  Netherlands Organization for Scientific Research. D. T. was supported by the  People Programme  ( Marie Curie Actions ) of the 7 th Framework Programme of the  European Union  (FP7/2007-2013;  600388 ) and the  Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO ´). N. L. -B. was supported by  La Fundaciola Marato  ´de TV3. M. E. was funded by the  European Research Council  ( 268626 ), the  Ministerio de Ciencia e Innovacion  ( SAF2011-22803 ), the  Institute of Health Carlos III (ISCIII)  under the  Integrated Project of Excellence  ( PIE13/00022 ), the  Spanish Cancer Research Network  ( RD12/0036/0039 ), the  Health and Science Departments of the Catalan Government Generalitat de Catalunya   2014-SGR 633 , and the  Cellex Foundation.