dataesr/scientific-paragraphs-categorization huggingface dataset:
(2018) and [nonNE]  is the mean expression of genes in non-neuroendocrine cells from  Zhang et al. (2018). The R script that calculates the NE score from the SCLC-Global expression data is available in the supplementary materials. t-SNE clustering of GDSC lung cell lines using gene expression SCLC and NSCLC cell line grouping was performed with the gene expression data from the GDSC microarray dataset using the t-SNE algorithm in R (v3. 5. 1). The random seed was set to 1, the Euclidean distance of genes was calculated with the dist() function with default settings. The t-SNE grouping was calculated using the Rtsne() function from the Rtsne (van der Maaten, 2014) package (v0. 15) using the calculated distance matrix, with perplexity set to 10, and 5k maximum iterations. Clustering drug data of NCI-SCLC cell lines SCLC cell line expression heatmaps for the SCLC markers, NAPY genes and MYC genes were done using the ComplexHeatmap  (Gu et al.
, 2016 ) R package (version 1. 20. 0). The NCI SCLC drug activity heatmap was generated using R. First, drugs with coefficient of variation less or equal to 0. 09 were filtered out. Then the remaining data for the selected 134 drugs (from originally 527) across the 66 SCLC lines were clustered using the hierarchical method based on Euclidean distance and complete linkage. Gene set enrichment analysis and GSEA analysis A preranked gene set enrichment analysis was run in R using the clusterProfiler  (Yu et al. , 2012)  and ReactomePA  (Yu and He, 2016)  packages. Pathways with an adjusted p value below 0. 05 were considered as significantly enriched. Single sample gene set enrichment score (APM score) was computed using the R package GSVA (version 1. 28. 0). A pre-ranked gene set enrichment analysis (GSEA version 4.
(D) Highly significant correlation between MYC copy number (NCI/DTP) and MYC expression (CCLE) for the 36 common SCLC cell lines. (E-G) Examples of drug activity across databases for the common cell lines. (H) High proliferation signature of SCLC cell lines on the basis of high PCNA and MYC expression. Snapshot shows that SCLC (green) overexpress PCNA and fall into two groups with respect to MYC.
Significantly positive and negative correlations are in red and blue, respectively. (H) Snapshot from SCLC-CellMiner illustrating the correlation between the YAP1 and IFITM3 transcripts across the 116 cell lines of SCLC-Global (FigureS6). (I) Selective expression of the DLL3 and CEACAM5 (FigureS6). (J) Potential surface biomarker targets for NE-SCLC and SCLC-P cells. (K) Potential surface biomarkers for SCLC-Y cells. Data in (A)-(E) and (I)-(K) are from the 66 cell lines from the NCI-DTP drug and genomic database.
ACKNOWLEDGMENTS C. T. , L. P. , F. E. , N. R. , S. V. , V. N. R. , R. S. , M. I. A. , W. C. R. , A. T. , and Y. P. are supported by the  Center for Cancer Research, CCR-NCI-NIH  ( Z01-BC-006150 ). P. M. is supported by the  Center for Cancer Research, CCR-NCI-NIH  ( ZIA BC 011091 ). J. K. and B. A. T. are supported by the  Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis (DCTD)-NCI-NIH  ( HHSN261200800001E ). L. G. , K. E. H. , and J. D. M. are supported by  P50 CA070907 ,  U24 CA213274 ,  U01 CA2113338 , and  Cancer Prevention and Research Institute of Texas (CPRIT)   RP110708. We acknowledge the role of the late  Adi Gazdar  in generating many of the SCLC lines.
EXPERIMENTAL MODEL AND SUBJECT DETAILS SCLC-CellMiner is a dedicated CellminerCDB version for SCLC cell lines  (Reinhold et al. , 2012 (Reinhold et al. , , 2014 (Reinhold et al. , , 2017 (Reinhold et al. , , 2019) )  https://discover. nci. nih. gov/cellminercdb/). The cell line sets included in SCLC-CellMiner Cross-Data-Base (CDB) currently are from the National Cancer Institute SCLC cell lines from the Developmental Therapeutics Program Small Cell Lung Cancer Project (SCLC NCI-DTP), Cancer Cell Line Encyclopedia RESOURCE SOURCE IDENTIFIER Deposited Data NCI-SCLC cell line methylation (850K array) This paper and (Krushkal et al. , 2020) GSE145156 UTSW cell line RNA-seq (McMillan et al. , 2018) dbDAP phs001823. v1. p1 CellMinerCDB cell line data (Rajapakse et al. , 2018) https://discover. nci.
nih. gov/cellminercdb/ Software and Algorithms ChAMP (Tian et al. , 2017) https://bioconductor. org/packages/ release/bioc/html/ChAMP. html STAR aligner (Dobin et al. , 2013) https://github. com/alexdobin/STAR Cufflinks (Trapnell et al. , 2012) http://cole-trapnell-lab. github. io/cufflinks/ Ape (Paradis et al. , 2004) https://cran. r-project. org/web/packages/ ape/index. html Relaimpo (Gromping, 2006) https://cran. r-project. org/web/packages/ relaimpo/index. html dynamicTreeCut (Langfelder et al. , 2008) https://cran. r-project. org/web/packages/ dynamicTreeCut/index. html ComplexHeatmap (Gu et al. , 2016) https://bioconductor. org/packages/ release/bioc/html/ComplexHeatmap. html Rtsne (van der Maaten, 2014) https://cran. r-project. org/web/packages/ Rtsne/index. html clusterProfiler (Yu et al.
nci. nih. Figures 2, POU2F3 is selective for SCLC; CellMinerCDB gov/cellminercdb 4, and 5 YAP1 is expressed widely beyond SCLC; ASCL1 is co-expressed with NEUROD1 select and compare subsets Figures 2H, NEUROD1 and ASCL1 are of cell lines based on tissue 5F, S3, and S6 also selectively expressed of origin or metadata: in CNS cancer cell lines NAPY, TNBC, NSCLC test phenotypic data Figures NE cell lines have low (mda): NE, APM, EMT 4H and 6 antigen-presenting machinery (APM) score tissue-or subset type- Figures 5, 6, YAP1 cell lines have specific analyses and S4-S6 lower replication and (NAPY; NE) highest APM score epigenetics: promoter ''Univariate Analyses: Plot Data:'' Figure S1 promoter methylation is methylation for any expression of a given gene versus its a driver for gene given gene methylation (X and Y ''Data Type'') within a expression (NAPY genes; given ''Cell Line Set'' or across datasets SLFN11; MGMT; (independent datasets can be tested for SMARCA1; CGAS) missing ''Data Type'' and confirmation) gene amplification ''Univariate Analyses: Plot Data:'' Figures 1, MYC genes and other and deletions for expression of a given gene versus copy 3, and S1 oncogenes are often any given gene number (X and Y ''Data Type'') within a given driven by copy ''Cell Line Set'' or across datasets number variation (CNV) (independent datasets can be tested for validation and missing ''Data Type'') integrate and complement ''Univariate Analyses: Plot Data:'' plot Figures 1, drug response data in different datasets for common cell lines number, and promoter methylation data, the numbers indicate the number of genes.
For RNA-seq data, the numbers indicate the number of transcripts. The different parameters (''Data Type for'' 2, and 6 one dataset can be mutations, copy genomic or drug response) across ''Cell correlated with genomics (C) Cell line overlap between data sources (see Table S1 for details). in one dataset bottom row shows the total number of cell lines (N = 118) integrated in SCLC-CellMiner. New data analyses are highlighted in yellow. Line Sets'' (X and Y) to counter missing data of another dataset (D) Drug overlap between data sources. genomic pathway ''Univariate Analyses: Plot Data:'' Figures 5, 6, ASCL1 and YAP1 are discovery (coregulated expression of a given gene (X or Y ''Data S2, and S3 integrated in tight genes and microRNAs) Type'') within a given dataset or across genomic networks datasets; also use the ''Compare Patterns'' connected with the tab NOTCH pathway discover determinants ''Univariate Analyses: Plot Data: Compare Figures of drug response and Patterns:'' coregulated genes for a given 6 and S6 targeted drug delivery gene (X or Y) within a given ''dataset'' (independent datasets can be tested for confirmation) Cell Reports 33, 108296, October 20, 2020 3 (B) Summary of the genomic and drug activities data in SCLC-CellMiner (https://discover.
0 ST array (Affymetrix GeneChip technology). Microarrays were scanned using an Affymetrix GeneChip Scanner 3000 7G (Affymetrix UK Ltd. , High Wycombe, UK). The GeneChip Operating v1. 4 (GCOS) and Expression Console v1. 1 softwares (Affymetrix UK Ltd. , High Wycombe, UK) allowed quantification and initial analyses. Expression levels were calculated using the following parameters: sketch quantile for normalization, median polish as in RMA for summarization. Subsequent analysis was performed on the extended set of probesets. Reference genes were eliminated. The microarray data obtained in this publication were deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE149829 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE149829). Northern blot analyses (all three muscles; N = 5/group) were performed following a previously described procedure  [99].
Specific antisense oligonucleotide probes end-labeled (5 ) with digoxigenin (Eurogentec, Seraing, Belgium) were used to target superoxide dismutase 1 (Sod1; 5 -CCGTCCTTTCCAGCAGCCACATTGCCCAGGTC-3 ; Genbank accession number: M25157), selenium-dependent glutathione peroxidase (Gpx1; 5 -GGGCTCGAACCCACCACCGGGTCGGACATACT-3 ; Genbank accession number: S41066), heme oxygenase 1 (Hmox1; 5 -GTGTGAGGACCCATCGCAGGAGCGGTGTCTGG-3 ; Genbank accession number: NM_012580), and catalase (Cat; 5 -CCTGGAGCATCTTGTCCGGGCTGGGCTCAATG-3 ; Genbank accession number: M11670). The Scion Image software v 4. 02 (http://www. scioncorp. com/) allowed quantification after correction of mRNA levels for differences in gel loading or blotting by reference to the level of 18S rRNA. Quantitative values in fasted animals were normalized to those in fed rats that were assigned an arbitrary value of one.
Membranes were then washed three times for 10 min (0. 05% Tween-20 in TBS), then incubated for 1 h at room temperature with peroxidase-conjugated secondary antibodies, either anti-rabbit immunoglobulin G (IgG, diluted 1/5000; #7071 from Cell Signaling Technology, Boston, MA, USA), donkey anti-goat IgG (diluted 1/5000; sc-2020 from Santa Cruz Biotechnology), or anti-mouse IgG (diluted 1/5000 to 1/20000; sc-2005 from Santa Cruz Biotechnology). After three 10-min washes (0. 05% Tween-20 in TBS), we added chemiluminescence reagent (ECL Western blotting substrate, ThermoScientific, Rockford, IL, USA), and hybridization signals were visualized using Amersham Hyperfilm ECL (GE Healthcare, Buckinghamshire, UK). Densitometry analysis was performed using Scion Image software v 4. 02. After correction of protein levels for differences in gel loading or blotting by reference to the level of actin, we normalized quantitative values in fasted animals to those in fed rats that were assigned an arbitrary value of 1.
Bioinformatic Analysis Hierarchical clustering of transcriptomics data (log 2 mRNA abundances) was performed using the Cluster v3. 0 software  [100]. Only significantly changed transcripts (adjusted p-value below 0. 001) were used. Parameters were set as follows: median centering and normalization of genes for adjusting data and centroid linkage clustering for both genes and arrays. Dendrograms were generated and visualized using the Treeview v1. 1. 3 program. Enrichment and functional annotation analyses of transcriptomics data were performed using the desktop version of DAVID (Ease v2. 1) and a version of GO databases downloaded in July 2019. Stringent criteria for considering significantly enriched GO terms included an Ease score lower than 0. 1, a Benjamini p-value lower than 0. 05, and a fold enrichment higher than 2.
Measures The analyses presented below stem from the International Situations Project (ISP), a large cross-cultural study assessing situational experience, daily behavior, and individual differences. Other analyses based on this large and diverse data set have been published  [28] [29] [30]  or are in progress, but all analyses reported in the present article are new and unique. For an overview of the project, including all measures and translations, see situationslab. com/theinternational-situations-project. Only measures included in the present analyses are described in this article, along with country-level variables collected previously and separately by other researchers or obtained from public databases. Independent happiness. The Western measure of happiness was the Subjective Happiness Scale (SHS:  [24] ).
The SHS is one of the most widely used measures of happiness in studies conducted in the US and Europe. The measure has 4 items to which participants respond on a 7-point scale (e. g. , "Compared with most of my peers, I consider myself. " 1 = less happy to 7 = more happy). Interdependent happiness. The Eastern measure of happiness was the Interdependent Happiness Scale  [22]. The IHS was developed in Japan and validated against samples in the United States, Germany, and South Korea. The measure has 9 items to which participants respond on a 5-point scale (e. g. , "I believe that my life is just as happy as that of others around me" 1 = strongly disagree, 5 = strongly agree). Country-level variables. The current analyses use several country-level variables obtained from publicly available databases.
These variables were also chosen to be as independent from each other as possible, as many country characteristics (e. g. , GDP & life expectancy) are highly correlated with each other and would thus produce redundant results. Human Development Index (HDI). The Human Development Index (HDI) is a composite measure of a country's development, consisting of life expectancy, educational opportunities, and standard of living  [32]. HDI scores were available for all ISP countries except Taiwan. Country HDI scores ranged from. 49 (Uganda & Senegal) to. 95 (Norway), with higher scores indicating greater economic development. Population density. Population density is the number of people per sq. km of land area (The World Bank  [33] ). Population density data was available for all ISP countries except Taiwan; however, both Hong Kong (7,040 people per sq.
km) and Singapore (7,916 people per sq. km) were excluded from analyses because their unusually high density skewed the country-level results. The remaining population density scores ranged from 3 people per sq. km (Australia) to 756 people per sq. km (Palestine). Growth rate. Population growth rate is the average annual percent change in population of a country  [34]. Growth rate data was available for all ISP countries except Palestine. Country scores ranged from -1. 08 (Latvia) to 3. 20 (Uganda), with positive scores indicating an increase in population size and negative scores indicating a decrease in population size. Suicide rate. Suicide rate is the age-standardized suicide rate per 100,000 people, averaged across sexes  [35]. Suicide rates were available for 60 ISP countries. Country scores ranged from 2.
50 (Pakistan) to 26. 10 (Lithuania), with high scores indicating a higher suicide rate. Average temperature. Average temperature is the average daily temperature throughout the entire year in Celsius  [36]. Because some larger countries have a wide range of average temperatures depending upon exact location, the average temperature used was that of the city or cities in which ISP data collection took place. For most countries, only one city was included in the average daily temperature. Data on average daily temperature was available for all 63 ISP countries and ranged from 4˚C (Russia) to 29˚C (Thailand). Subjective country level variables. Subjective country level variables were chosen to reflect the psychological or cultural characteristics of a country. WEIRDness. WEIRD country level scores are a measure of cultural distance from the United States  [37].
27, p =. 03, but not for the general factor saturation r(61) = -. 03, p =. 82. Country-level predictors of happiness measure properties. The last set of analyses attempted to find predictors of the happiness measure reliabilities and associations. If there are meaningful patterns in the data for the assessment of happiness across countries then these patterns can be predicted from other country-level variables. The first set of predictors were objective country level variables obtained from government sources and include the Human Development Index (HDI), population growth rate, population density, average suicide rate, and average temperature of a country. Full results are shown in Fig 2. Across these objective country level variables, the best predictor of happiness measure reliability was HDI, and these  The correlations for subjective country level variables included a measure of WEIRDness and Schwartz's values (see  Fig 3).
The research was supported by the  US National Science Foundation  under Grant  BCS-1528131  for the primary authors. Data gathering in the Czech Republic was supported by grant  17-14387S  by the  Czech Science Foundation  and by institutional research funding RVO: 68081740 from the  Institute of Psychology, Czech Academy of Sciences. Data gathering in Chile was partly supported by the  Centre for Social Conflict and Cohesion Studies  ( FONDAP 15130009 ) and  Center for Intercultural and Indigenous Research (CIIR)  ( FONDAP 15110006 ).
FD but Not FDP Binds to Flowering and Floral Regulator Genes To further compare the roles of FD and FDP, chromatin immunoprecipitation sequencing (ChIP-seq) was performed to compare their direct target genes. Inflorescences of 30-day-old VE-NUS:FD and VENUS:FDP plants were used because, at this developmental stage, VENUS accumulation was high in the shoot apices of both lines (Figures  S5A  and S5B ). ChIP-seq experiments for each transcription factor were performed with biological replicates, and a high reproducibility between the replicates was found as described in the STAR Methods (R = 0. 96 for VENUS:FDP and R = 0. 88 for VENUS:FDP; Pearson correlation coefficient) (Figure  S5C ). For ChIP-seq of VENUS:FD, 752 merged peaks were called (STAR Methods), which were assigned to 1,457 neighboring genes (Figure  5A ; Figure  S5E ; Data S1).
We also compiled unique target lists by using the more stringent criterion of identifying those regions bound by one factor in both replicates and not bound by the other factor in either of the two replicates. Using this criterion, 350 FD-bound regions were unique and 24 FDP-bound regions were unique. The unique target genes identified by both criteria are listed in Data S1. Afterward, we analyzed those unique genes identified by directly comparing the two original sets of target genes (132 for FDP and 1,037 for FD). Nine genes were identified as uniquely bound by FDP and related to ABA stimulus (p = 0. 00026), and among them were CALCIUM-DEPENDENT PROTEIN KINASE 1 (CPK10), MOTHER OF FT (MFT), or SNF1-RELATED PROTEIN KINASE 2. 7 (SNRK2. 7) (Data S1;  Fujii et al. , 2011; Cutler et al. , 2010; Xi et al.
, 2010). Uniquely bound by FD, were many flowering and floral regulator genes, such as SOC1, LFY, and AP1 (Figure  6B ;  Data S1), which were reduced in expression in fd mutants  (Figure 6C; Collani et al. , 2019; Searle et al. , 2006; Abe et al. , 2005; Wigge et al. , 2005). Although FDP did not bind to these floral regulators, their mRNA levels increased earlier in fdp-CRP mutants (Figures  1F, 1G , and 6C; Figures  S3A  and S3B ), suggesting that FDP indirectly regulates them. Strikingly, in an independent ChIP-seq dataset from 35S::GFP:FDP plants (Figure  2C ), in which FDP is more broadly and highly expressed at the SAM, FDP was found to bind in the vicinity of most of these flowering genes, such as AP1, SOC1, and SEP3 (Data S1; Figure  6D ). Furthermore, all except 11 of the binding sites (17 target genes) of FDP when expressed from its endogenous promoter were included in the 35S::GFP:FDP dataset (Figure  S5I ).
FD but Not FDP Directly Regulates Floral Transition To further characterize FDP function, RNA sequencing (RNAseq) was performed using apices of fdp-CRP2, fd-3, and WT grown under LDs for 17 days (Figure  S6A ), when both FDP and FD are expressed (Figures  4B  and 4C ; Figures  S5A  and S5B ). At this stage, 153 and 1,458 differentially expressed genes (DEGs) (with adj. p % 0. 05) were identified in fdp-CRP2 and fd-3 mutants, respectively (Figure  6E ; Data S2). The ChIP-seq data obtained in inflorescences were then compared with the DEGs in apices of each mutant. For FD, 109 DEGs were directly controlled by FD, a statistically significant overlap (one-sided Fisher's exact test, p = 0. 005094) (Figure  6E ; Data S2). Of these 109 genes, 61 (56%) were upregulated in fd-3 and the others were downregulated (Figure  6E ).
Therefore, in inflorescences, FD transcriptionally activates or represses its direct targets. Gene Ontology (GO) analysis of FD directly regulated genes showed enrichment in biological processes, including maintenance of meristem identity, flower development, and response to abiotic stimulus (Figure  6F ;  Maere et al. , 2005; Shannon et al. , 2003). Genes within these clusters included the floral regulator genes SEPALLATA 1 (SEP1), SEP2, and SEP3; AP1; CAULIFLOWER (CAL); LFY; and SPL8; which were all downre-gulated in fd-3, whereas the GA catabolic enzyme ARABIDOP-SIS THALIANA GIBBERELLIN 2-OXIDASE 6 (GA2ox6) as well as the negative regulator of ABA signaling ABI FIVE BINDING PROTEIN 3 (AFP3) were upregulated (Data S2). In contrast, a comparison of the DEGs in apices of fdp-CRP2 with the ChIPseq of FDP identified only four genes in common in these two datasets (Figure  6E ; Data S1), which was not a significant overlap (one-sided Fisher's exact test, p = 0.
Furthermore, 50% (19 of 38) of the genes bound by FDP and differentially expressed in fdp-CRP2 were also found in the dataset of genes directly regulated by FD in seedlings (p = 1. 36 10 À27 ) (Figure  7D ; Data S2). GO term analysis of this subset of 19 genes showed an enrichment in processes related to ABA, water deprivation, and JA (Figure  S6A ), which is similar to the GO term enrichments obtained within the DEGs in seedlings that are bound by FD or FDP (Figures  S6C  and S6D ). We identified 20 genes (p = 2. 5 3 10 À7 ) within the 321 genes bound and DE in fd-3 that were related to ABA processes and mostly upregulated in expression in fd-3 seedlings. Among them, was CYP707A2 that encodes a cytochrome P450 enzyme involved in ABA catabolism  (Kushiro et al. , 2004) , as well as ABA INSENSITIVE 1 (ABI1) and HYPERSENSITIVE TO ABA1 (HAB1), which encode phosphatases that inhibit ABA signaling  (Saez et al.
, 2010) ; and CIPK15/PKS3 (calcineurin B-like protein [CBL]-INTERACTING PROTEIN KINASE 15) and ARABI-DOPSIS THALIANA MYB DOMAIN PROTEIN 44 (MYB44) that both mediate responses to abiotic stresses  (Jaradat et al. , 2013; Guo et al. , 2002; Data S1 and S2). Also, binding of FD and FDP to the expected sites in ABI5, AFP2, and AFP4 was validated by ChIP-PCR (Figure  7F ). Although FD and FDP are implicated in the regulation of ABA-related processes in seedlings, we detected no significant alteration in FD or FDP mRNA levels by application of ABA to seedlings. Group A bZIPs, such as ABI5, are required during seedling establishment  (Lopez-Molina et al. , 2001). Hence, the sensitivity to ABA of fd and fdp mutants was determined in a cotyledon greening assay. The proportion of seedlings exhibiting green cotyledons 7 DAS was tested in the presence of several concentrations of ABA.
RT-qPCR was performed with the LightCycler 480 instrument (Roche). The RT-qPCR primers are listed in Data S3. PEX4 was used as an internal control in all RT-qPCR experiments. All RT-qPCR assays were repeated at least twice for independent biological replicates. In situ hybridization Plants were grown for 3 weeks in SDs and then transferred to LDs for 3, 5 and 7 days. The samples were mainly apices collected at ZT3. In situ hybridization was performed as previously described  (Torti et al. , 2012). To amplify the FDP, FD, FUL and AP1 RNA probes, the primers listed in Data S3 were used.