dataesr/scientific-paragraphs-categorization huggingface dataset:
Processing of the drug response assay datasets The Genomics of Drug Sensitivity in Cancer (GDSC) Project data: the GDSC1 dataset of drug sensitivities was downloaded from the Genomics of Drug Sensitivity in Cancer Project (https://www. cancerrxgene. org; (Yang et al, 2012) , separately for different cancers: diffuse large B-cell lymphoma (DLBCL, n=30), acute lymphocytic leukemia (ALL, n=25), acute myeloid leukemia (AML, n=24) and breast carcinoma (BRCA, n=47). As a drug sensitivity measure, the dataset includes the area under the curve (AUC), defined as the area under the sigmoid-fit dose-response curve (5-point or 9-point)  (Vis et al, 2016). The AUC values were converted into z-scores, by computing mean and standard deviation for each cell line, across the drugs. These z-scores were used as the drug sensitivity matrix Y, including sensitivity data for 66 drugs.
Cell lines with more than 24 (≙ 35%) missing values were removed, resulting in a total of 126 cell lines. The missForest imputation method from the missForest package (V 1. 4;  Stekhoven & Bühlmann, 2011)  was used to impute missing values in the drug sensitivity matrix for the remaining cell lines. The cancer cell lines were annotated with their cancer types and mutation status obtained from the GDSC project page. Additionally, Her2 status of breast cancer cell lines were annotated based on information from prior publications  (Dai et al, 2017; Jernström et al, 2017). BeatAML data: the BeatAML dataset  (Tyner et al, 2018)  contains ex-vivo drug sensitivity data of 528 tumor specimens collected from acute myeloid leukemia (AML) patients. The data were downloaded from the National Cancer Institute's CTD2 Network (BeatAML_Waves1_2).
The AUC from a 7-point dose-response curve was used as described by  Tyner et al. 2018  and the z-scores of the AUC for each cell line across all drugs were used as the response value for generating the drug sensitivity matrix Y, including sensitivity data for 61 drugs. Samples with more than 15 (≙ 24%) missing values were removed, resulting in a total number of 421 samples. Cut-off selection and missing value imputation were done as described for the GDSC1 dataset. Ex-vivo drug response assay on primary leukemic tumor samples: we performed ex-vivo drug response profiling on primary tumor samples from CLL (n=117), mantle cell lymphoma (MCL, n=7), or T-cell prolymphocytic leukemia (T-PLL, n=7) patients. We termed this drug screen dataset as the EMBL2016 dataset. The samples were annotated with available genomic patient metadata.
In the gene-level count matrix of the RNA sequencing data of the tumor samples, the 80% quantile of its counts across samples was computed for each gene, and a gene was considered not or only weakly expressed if this value was less than 10. Thus, 20 of the 377 kinases were removed from the downstream analysis for the EMBL2016 dataset, and 14 of the 380 kinases in the BeatAML analysis. The final dimensions of the input matrices X were: GDSC: 66 drugs × 118 proteins; BeatAML: 61 drugs × 112 proteins; EMBL2016: 85 drugs × 131 proteins (Figure  1A ).
k-means clustering and Rand index calculation k-means clustering analysis was performed on ß ! using the eclust function from the factoextra package, which centers and scales the columns of the matrix. Class information (five cancer types in the case of the GDSC data, IGHV mutational status for the EMBL2016 CLL data) was withheld from the clustering method, but used to set the number of clusters k. 50 different random starting configurations (nstart) were attempted within the eclust function and the best configuration was chosen. As a measure of concordance of the resulting clusters and the 'ground truth' classes, the adjusted Rand Index  (Rand, 1971)  was calculated using the cluster. stats function from the fpc package (V 2. 2-9;  (Hennig & Imports, 2015) ). k-means clustering of the drug sensitivity matrix was performed analogously.
Differential gene expression analysis To identify gene expression signatures related to IGHV mutation status, an RNA sequencing dataset from the European Genome-phenome Archive (EGA; http://www. ebi. ac. uk/ega/, accession: EGAS00001000374  (Ferreira et al, 2014) ) was analyzed. Differential expression testing was performed using the DESeq2 package (V 1. 28. 1; (Love et al, 2014) ). Genes with overall low counts (sum across samples < 10) were excluded from the analysis. The camera method was used to test for differential expression of hallmark gene sets from the MSigDB database v7. 2  (Subramanian et al, 2005; Liberzon et al, 2015) , using the implementation in the eponymous function in the limma package  (V 3. 44. 3; (Ritchie et al, 2015). To identify gene expression signatures related to B-cell stimulation, we analyzed gene expression microarray dataset of anti-IgM triggering in primary CLL samples (E-GEOD-39411)  (Vallat et al, 2013).
Data and code availability All data and code used in this study are available at (https://github. com/Huber-group-EMBL/DepInfeR_workflow). A website of rendered html reports is available at: https://www. huber. embl. de/users/jlu/depInfeR/index. html We provide an R/Bioconductor package, DepInfeR (https://github. com/Huber-group-EMBL/DepInfeR), for users to estimate cancer protein dependence coefficient using their own datasets.
Results We aimed to infer the extent and direction of the dependence of individual tumor samples on individual proteins. To perform this, we used two available data types (Fig. 1A ): 1) a drugprotein profiling matrix, which reports binding affinities of each of a set of drugs to proteins, and 2) a drug sensitivity matrix, which reports the effects on cell viability of each drug across a set of tumor samples. The effects are measured as dimensionless ratios, relative cell viability compared to DMSO (dimethyl sulfoxide) control. As the first data source, we used the kinase inhibitor profiling dataset by  Klaeger et al. , 2017. The authors immobilized 243 kinase inhibitors on kinobeads and detected presence and abundance of kinases binding after exposition of the beads to cell lysate by mass spectrometry proteomics.
This task can be posed in terms of multivariate multi-response regression involving three matrices: the known drug-protein binding matrix, a known drug-sample sensitivity matrixbelow we consider three different instances of such a matrix -and the unknown protein dependence matrix (Fig. 1A ). While no useful inference can be expected if there is only data for a single or a small number of samples, the regression becomes increasingly identifiable as the number of samples increases. S2 ). We termed our method as DepInfeR and implemented an R package with the same name to perform the data preprocessing, the multivariate regression and various downstream analyses and visualizations. As the second data source, we used three separate drug sensitivity datasets: one from cancer cell line collections and two from drug sensitivity studies of primary blood cancer samples (AML and CLL).
Inferred protein dependence profiles reflect differential inter-and intracancer type dependencies in cell lines As a first test of DepInfeR, we applied it to the cancer cell line drug sensitivity data from the GDSC database  (Yang et al, 2012). We used the subset of data for cell lines derived from diffuse large B-cell lymphoma (DLBCL), acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), and breast carcinoma (BRCA). We concatenated these data into a single matrix of relative cell line viabilities (n=126) under the effect of the different drugs (n=66). Besides the relative viabilities, the matrix did not contain explicit information on cancer type or any other properties of the cell lines. We inferred the protein dependence matrix and visualized it with a heatmap with rows and columns ordered in accordance with hierarchical clustering (Supplementary Figure  S4 ).
DepInfeR method correctly infers kinase dependencies in AML We next applied DepInfeR to a dataset of primary tumor samples, the BeatAML data, which report the ex-vivo drug sensitivities of 672 tumor specimens from 562 AML patients  (Tyner et al, 2018). The inferred protein dependence coefficients indicated significantly higher dependence on FLT3 for tumors with FLT3-ITD mutation, a known proliferative driver in AML  (Daver et al, 2019) , consistent with the findings by Tyner et al. and in line with biological expectation (Supplementary Figure  S5 ). An additional positive correlate of FLT3-ITD was the inferred dependence on the Lck kinase. This association was not described by  Tyner et al. 2018,  but supports cooperative roles of Lck activity with FLT3-ITD mutation as suggested previously  (Marhäll et al, 2017).
The protein dependence profiles of primary CLL samples recapitulate major molecular subgroups Having established the feasibility of protein dependence inference on these existing datasets, we proceeded to generate a dataset tailored to the study of lymphocytic leukemias. We selected samples from 131 patients diagnosed with three prevalent leukemia types: CLL (n=117), MCL (n=7), and T-PLL (n=7). This study design enabled us to consider entity specific properties of MCL and T-PLL, and to further resolve the considerable intra-entity heterogeneity of CLL with respect to molecular factors including the mutational status of immunoglobulin heavy chain variable region (IGHV) genes, trisomy 12 status, and TP53 mutations  (Dietrich et al, 2017; Zenz et al, 2010). We measured drug sensitivity phenotypes of these tumors towards 85 kinase inhibitors and applied DepInfeR to infer the protein dependence map (Supplementary Figure  S6 ).
Based on the kinobeads dataset, 4 out of 7 CHEK1 inhibitors (kd < 1000nM) also target BCR pathway components (BTK, SYK, LYN) (Fig. 4B ). When looking at the effect of the other three drugs, we found a significant association with the IGHV mutational status for two (Rabusertib, MK-8776) of the three CHEK1 inhibitors without any BCR pathway off-targets (Fig. 4C ). The third CHEK1 inhibitor (PF-3758309) targets 54 different kinases and binds CHEK1 only very moderately (Kd = 210. 19 nM). In combination with our DepInfeR results, this indicates that the differential effect of CHEK1 inhibitors is due to the inhibition of CHEK1 itself and not due to the BCR pathway off-target effect of these drugs. The association of U-CLL to CHEK1 pathway was further corroborated by an analysis of differential gene expression between M-CLL and U-CLL patient samples from the data of ).
Acknowledgments: J. Lu. and W. H. were supported by the  German Federal Ministry of Education and Research  ( CompLS Project MOFA  under grant agreement  031L0171A , and  SMART-CARE  under grant agreement  031L0212E ). T. Zenz was supported by the  CRPP "Next Generation Drug Response Profiling for Personalized Cancer Care " , the  Swiss Cancer Research foundation  ( KFS-4439-02-2018 ), and the  Monique-Dornonville-de-la-Cour Stiftung. J. K. was supported by postdoctoral fellowships from the  Sigrid Juselius Foundation  and the  Finnish Cultural Foundation.
F. Implementation The present discretization and solution methods have been implemented in an open-source software, Exasim  [49] , which generates DG codes to numerically solve a wide variety of partial differential equations (PDEs). Exasim combines high-level languages with lowlevel languages to allow users to define PDE models and obtain highperformance C++ codes that can run on both CPU and GPU clusters. The kernels for the discretization and solution methods are written in C++ with MPI for distributed-memory systems and CUDA/OpenMP for shared-memory processors, while the code generators and preprocessors are written in Julia, Python, and MATLAB ® to produce C++ codes that handle fluxes, source terms, boundary conditions, and initial solutions for a particular PDE system. Exasim have been thoroughly validated through a large collection of examples for a wide variety of partial differential equations including the compressible Navier-Stokes equations.
The unstructured grid is generated by using the Gmsh software  [52]  and has two separate blocks. The first unstructured block resides on top of the structured grid and extends from x∕c 0. 3 to x∕c 0. 7. It is carefully constructed to capture the shock motion of the buffet phenomenon. The second unstructured block occupies the remaining computational domain, which extends over 50c around the airfoil in the (x, y) plane; see Fig. 1. The mesh has been designed such that Δy δy∕d v < 1, where δy denotes the distance from the wall to the first high-order node along the wall-normal direction and d v ν∕u τ is the viscous distance based on the local friction velocity u τ. Δx and Δz are similarly constructed by using nodes spacings in the streamwise and § Data available online at https://github. com/exapde/Exasim [retrieved 7 September 2020].
results We collected data from 27,502 participants from 45 countries. Due to our exclusion criteria, we had to exclude 80. 6% of the sample from the main analysis (see Table  1  for the various exclusion criteria). Note that, as we registered, we conducted the analysis without excluding the data of the participants who were familiar with the trolley problem (36. 2% exclusions), and we also conducted a post hoc explorative analysis in which we applied no exclusion criteria. All participants were presented with two moral dilemmas that were equivalent in structure but different in wording: trolley dilemmas and speedboat dilemmas. The former described a situation involving a trolley and people on the tracks, while the latter described a situation with people on a speedboat and others drowning in the sea.
Our results cannot differentiate between the above-described explanations (which are not necessarily mutually exclusive). Although we found no strong evidence for the association between collectivism-individualism and the effects of personal force and intention, future research should test for other cultural variations. There are a number of interesting candidates that we did not examine, including cultural tightness  47  and social mobility  48. Our database provides opportunities to the field to examine different aspects and cultural moderators of moral judgement. This research has a number of limitations that future work will need to address. Although we call the personal force effect 'universal' , it is only universal to the cultures we tested. This puts a limit to the universality of the effects: we did not (nor did we intend to) reach small-scale hunter-gatherer societies, for example.
It is also possible that people with less working memory capacity or poor text comprehension abilities were more likely to be excluded due to the stringent attention checks. This is why we included an exploratory analysis in which we analysed data from all of our participants, without applying any exclusions. Our results on the full sample (with no exclusion criteria applied) supported our previous conclusions (drawn based on the data with exclusions) except in the cultural analysis, in which we found strong evidence for cultural variations only when no data were excluded. Thus, future work, especially replication work, should take caution when applying stringent exclusion criteria as it may be entirely unnecessary and even hurt the discovery of new effects. Another limitation of our study might come from the fact that we used a single continuous measure of deontological-utilitarian tendencies.
Conclusion With this replication study, we present empirical results about how people around the world make judgements in moral dilemmas that have long interested moral philosophers and psychologists. Empirical studies in this field have been conducted mostly on WEIRD samples, with little attention paid to cultural universality and variations. Our research allows us to avoid some important selection biases by having participants take the survey in their native language from 45 countries. The shared dataset should allow the assessment of different effects on moral dilemma judgements, such as religion or second-language effects. Overall, we found (1) the negative main effects of personal force and intention on moral dilemma judgements are universal; (2) the interaction between intention and personal force was replicated in the Southern and Western clusters, finding people are less likely to support sacrificing one person's life for the sake of saving the lives of several others, if they have both to intentionally engage in an action to do this and to use personal force; and (3) this interaction is not associated strongly with individual or country-level collectivismindividualism measures.
methods Participants. A large, culturally and demographically diverse sample of participants was recruited from collaborating laboratories through the Psychological Science Accelerator  51. The data collection team originally proposed to include 146 laboratories from 52 countries. All of these participating laboratories obtained institutional review board approval (verified before the last round of stage 1 submission). Combined, these laboratories committed to collect a minimum of 18,637 participants. More laboratories were expected to be recruited before data collection commenced. Each laboratory recruited participants for the study by sending out the survey link along with the consent form to their participant pool or online platforms (such as MTurk), or testing them in the research laboratory.
Due to some dropouts, the data collection team included 140 laboratories from 45 countries. Eligibility for participation was based on age (≥18 years) and being a native speaker of the language of the test (more details on this criterion in Controlling for possible confounds section). Data were collected either from local university participant pools or via data collection platforms (for example, MTurk). Altogether, 41,090 participants started our survey, and 27,502 finished it, whose data were analysed (17,961  We did not collect any identifiable private data during the project that can be linked to individual survey responses. Each laboratory ascertained the agreement of the local institutional ethical review board with the proposed data collection. This study was conducted in accordance with the Declaration of Helsinki.
These dilemmas are taken from studies 1b and 2b of Greene et al. and can be found in Supplementary Methods Section 1, with the exception of the dilemmas in the obstacle collide and standard footbridge conditions, which were provided by Joshua Greene during the review of the study. The order of dilemma presentation was fixed, so that the trolley version was always presented first. Study 1 was run before study 2, but participants were randomly assigned to one of the dilemmas within each study. Additional measures. Although an exploration of individual-level factors associated with moral thinking is not the aim of the present research, to enrich our database for future studies and secondary analyses, we expanded our survey with additional individual-level measures: (1) total yearly household income, (2) place of living (urban or rural area), (3) position on the four-dimensional Individualism-Collectivism Scale 38 (16 items) for disentangling cultural differences in participants' responses  52  and (4) religion (specific religion of the participant, plus one question to measure their level of religiosity: "On a scale from 1 to 10, how religious are you?").
Specifically, participants were asked to select the option which most accurately described the situation that they had been presented with. Each option described the nature of the physical action that was the key manipulation in the experiment. Because attention to the trolley and speedboat dilemmas was measured by different questions, when analysing the responses, we excluded the data for the correspondingly failed attention check question. For example, people who gave a correct response on the trolley but not on the speedboat attention check question were included when analysing the trolley dilemma but excluded when analysing the speedboat version. As moral dilemmas are becoming more and more common in psychological research and in summaries of this research in popular media and culture and teaching, it is possible that some participants may have previous knowledge of these dilemmas, which may affect their responses.
Individual-level horizontal and vertical individualism-collectivism. Triandis and Gelfand  49  defined individualistic and collectivistic cultural tendencies using four dimensions: vertical individualism, vertical collectivism, horizontal individualism and horizontal collectivism. We added these continuous measures to our Bayesian linear regression analysis. The predictive power of all four measures was assessed separately. Including familiar participants. A potentially large number of participants were excluded due to familiarity with the trolley dilemma, and there was a possibility that this exclusion criterion would affect the data from some countries or cultural clusters more than others. To avoid this potential sampling bias, we computed all the above-listed analyses on moral dilemmas (confirmatory and exploratory) on the full sample from which we did not exclude the participants familiar with moral dilemmas.
Second, we computed all analyses specifically on data coming from people who were familiar with moral dilemmas, to compare the results of familiar and unfamiliar participants. This latter analysis can be found in Supplementary Analysis Section 2. 3 and was limited to the confirmatory hypothesis tests. Pilot testing. To ascertain that the survey software operated without any technical problems, we planned to conduct a pilot test in which each participating laboratory would have been expected to collect data from ten participants. We would have only assessed the expected functioning of the survey software, without analysing the collected data. Timeline. We planned to finish data collection within 6 months from stage 1 in-principle acceptance, and we planned to submit our report within 1 month from then.
Deviations from registration. We preregistered that we would collect data from 3,000 participants for study 1 (test of personal force; hypotheses 1a and 1b), after exclusions. Unexpectedly, the exclusion criteria led to 80. 6% exclusion of our collected data. At the point when this was realized, it seemed likely that study 1 would exhaust the available sample pool, not leaving capacity for study 2. Therefore, with the agreement of the journal editor, we decided to collect participants for study 1 only until our BF evidence thresholds were reached after all exclusion criteria were applied. This modification allowed us to collect data for study 2 as well. At the time of this decision, the distribution of responses has been taken into account: We had collected data from 3,473 participants: 1,319 from the Western cluster, 1,762 from the Southern cluster and 392 from the Eastern cluster.
Exploratory analysis. During the data pre-processing, we excluded 229 participants from three US-based laboratories as they received a wrong survey link. Furthermore, 13,359 participants started but did not finish the experiment, therefore their data were also dropped from further analyses. These participants did not count towards our final sample and are not part of the data in any way. The final sample used for data analyses consisted of 27,502 participants. Further information on the demographics of our participants can be found in Supplementary Analysis Section 1. Note that we limited the use of RRs for the confirmatory hypothesis tests.