dataesr/scientific-paragraphs-categorization huggingface dataset:
As in  [1] , we observe fringe visibility shifts towards the direction predicted by the mind-matter hypothesis. However, our analysis shows that these shifts are not statistically significant, with no p-value under 0. 05. In an effort for reproducible research, the *80 Gb of raw data are publicly available on the Open Science Framework platform at the address https://osf. io/ywktp/. Moreover, the Matlab codes used in this paper (and necessary to reproduce all experiments and figures) are available on the author's website at http://www. gipsa-lab. fr/~nicolas. tremblay/files/codes_mind_ matter. zip. The outline of the paper is as follows. We briefly recall the experiment's protocol in Section 2. 1, and define the difference in fringe visibility Δν in Section 2. 2 as the main statistics we will focus our analysis on.
In 2013, the feedback was inversely proportional to a sliding 3-second span average of the fringe visibility: the higher the line, or the higher the pitch of the tone, the lower was the fringe visibility, the closer was the system to "particle-like" behaviour. In 2014, due to a coding error, the feedback was inversed: the feedback now increased when the fringe visibility increased. The participant's task was still to increase the feedback, but this time the higher the line, or the higher the pitch of the tone, the higher was the fringe visibility, the closer was the system to "wave-like" behaviour. As controls, a Linux machine connects to the server via Internet at regular intervals. The server does not know who it is dealing with: it computes and sends feedback, and records interference data just as it would do for a human participant.
Pre-analysis: From the raw data to difference in fringe visibility The camera records at 4Hz a line of 3000 pixels, an example of which is shown in  Fig 1,  where are also displayed the maximum (noted env M ) and minimum (note env m ) envelopes of the interference pattern computed with cubic spline interpolation between local extrema. Local extrema are automatically detected after a Savitzky-Golay filter of order 2 on a 29-pixel moving window that smooths the interference pattern in order to remove the pixel jitter that appears on some camera frames. We have also tried other smoothing options: same order Savitzky-Golay filters with 39 and 49-pixel window-lengths, as well as simple moving average filters with 20 and 30-pixel window-lengths, with no significant change in the overall results.
Acknowledgments We would like to thank the authors of [1] for an unlimited access to both years' data, and their patience in answering all our questions regarding technical details of the data and the experimental protocol. Also, we thank  Nikolaus von Stillfried  and  Jan Walleczek  ( Phenoscience Laboratories, Berlin ) for pointing out to us a statistical error in the original version of this manuscript. They identified the issue in the context of a research project sponsored by the  Fetzer Franklin Fund of the John E. Fetzer Memorial Trust.
1c  and Supplementary Table  16 ). (We note, however, that some brain regions identified in analyses we report below are areas where dopamine and serotonin play important roles. ) Second, we performed a MAGMA  31  gene analysis to test each of ~18,000 protein-coding genes for association with general risk tolerance (Supplementary Note). After Bonferroni correction, 285 genes were significant (Supplementary Fig. 10  and Supplementary Table  17 ). To gain insight into the functions and expression patterns of these 285 genes, we looked them up in the Gene Network 32 co-expression database. Third, to identify relevant biological pathways and identify tissues in which genes near general-risk-tolerance-associated SNPs are expressed, we applied the software tool DEPICT 33 to the SNPs with P values less than 10 -5 in our GWAS of general risk tolerance (Supplementary Note).
Phenotype definitions, GWAS, quality control, and meta-analysis For our discovery GWAS of general risk tolerance (n = 939,908), we performed a samplesize-weighted meta-analysis of results from the UK Biobank (UKB, n = 431,126) and a sample of research participants from 23andMe (n = 508,782). For our replication GWAS of general risk tolerance (n = 35,445), we performed a sample-size-weighted meta-analysis of results from ten smaller cohorts from seven studies: Army STARRS, BASE-II, NFBC 1966, RSIII, STR, UKHLS, and VIKING. The exact measures for the general risk tolerance phenotype vary across cohorts in wording and number of response categories, but all measures are similar and ask about one's tendency, preparedness, or willingness to take risks in general (Supplementary Table  4 ). For our GWAS of adventurousness, we analyzed data from a sample of research participants from 23andMe (n = 557,923).
We assigned a value of zero to participants who reported having never had sex, and we again normalized this measure separately for males and females. Our first PC phenotype is the first PC obtained from a principal component analysis (PCA) in the UKB of the four risky behaviors (Supplementary Table  23 ). All seven phenotypes were coded such that higher phenotype values are associated with higher risk tolerance or risk taking. Table  1  lists, for each GWAS, the datasets we analyzed and the GWAS sample size. The Supplementary Note and Supplementary Tables  4  and 5  provide additional details on the cohorts and phenotype definitions. All GWAS were performed at the cohort level in European-ancestry subjects according to a pre-specified and publicly archived analysis plan (see URLs). All GWAS included controls for the top 10 (or more) principal components of the genetic relatedness matrix and for sex and birth year.
Genotyping was performed using a range of commercially available genotyping arrays. We applied extensive quality-control (QC) procedures to the cohort-level summary statistics, including but not limited to the EasyQC protocol developed by the GIANT consortium  45. We used Haplotype Reference Consortium v1. 1 (HRC) data to construct our main reference panel, which we used for quality control of the GWAS summary statistics and to determine the independence of significant loci. For the 23andMe and UKB cohorts, only SNPs with minor allele frequency (MAF) greater than 0. 001 were analyzed. All meta-analyses were restricted to SNPs with a sample size greater than half of the maximum sample size across all the SNPs in the GWAS. In total, 9,284,738 SNPs were analyzed in the discovery GWAS of general risk tolerance; 9,339,358 SNPs were analyzed in the GWAS of adventurousness; and ~11,515,000 SNPS were analyzed in the GWAS of the four risky behaviors and their first PC.
For the COJO analyses, for each of the seven main GWAS we restricted the analysis to the set of SNPs that (1) pass all GWAS quality control filters, and (2) are located within the loci of the phenotype (which includes all of the lead SNPs). Supplementary Tables  3, 6 , 7, and 27 report the lead SNPs, the loci, the results of the COJO analyses, and the results of a lookup of the lead SNPs in the NHGRI-EBI GWAS Catalog database  20  for our seven main GWAS; Supplementary Fig. 12  shows the GWAS estimates of general-risk-tolerance lead SNPs in the 23andMe and UKB cohorts and in the replication GWAS, and Supplementary Data 1 shows LocusZoom plots for all the loci identified in the seven GWAS.
Replication of the general-risk-tolerance lead SNPs and maxFDR calculation To assess the credibility of the lead SNPs from our discovery GWAS of general risk tolerance, we compared those results to the estimates from our replication GWAS of general risk tolerance. (We did not attempt replication of the results of our six supplementary GWAS in independent data, because we did not have access to such data for these phenotypes. ) We first filtered out SNPs with sample size less than one-half the maximum sample size in the replication GWAS. After applying this filter, 122 of the 124 lead SNPs were directly available in the replication GWAS summary statistics, and one of the two remaining lead SNPs was well proxied by a SNP in high LD (r 2 > 0. 8) with it. For the resulting 123 SNPs, we conducted a (one-sided) binomial sign test to assess whether the directions (i.
Genetic correlations We used bivariate LD Score regression  9  to estimate genetic correlations between general risk tolerance and various phenotypes. We used the scores computed by Finucane et al. 50 , which are based on genotypic data from the European-ancestry samples in the 1000 Genomes Project and only HapMap3 SNPs. As is common in the literature, we restricted our analyses to SNPs with MAF > 0. 01. We used the summary statistics of the meta-analysis combining our discovery and replication GWAS of general risk tolerance to estimate genetic correlations with general risk tolerance, and we used the summary statistics of our GWAS of adventurousness, our four main risky behaviors, and their first PC to estimate genetic correlations with those phenotypes. For most other phenotypes, we used published GWAS results.
We obtained the summary statistics from the GWAS of lifetime cannabis use  25  and of ADHD  51  from the International Cannabis Consortium and the Psychiatric Genomics Consortium, respectively. We conducted our own GWAS using the first release of the UKB data for five phenotypes: age first had sexual intercourse (n = 98,956), teenage conception among females (n = 40,077), use of sun protection (n = 111,560), household income (n = 97,059), and Townsend deprivation index score (n = 112,192). The sex-specific GWAS of general risk tolerance used to estimate the genetic correlation between males and females were conducted in the full release of UKB data, separately for males and females, following the same methodology and QC protocol as for our other GWAS in the full release of UKB data. The Supplementary Note and Supplementary Tables 9, 31 provide additional details.
Biological annotation: additional bioinformatics analyses We conducted a series of additional bioinformatics analyses using the results of the combined meta-analysis of our discovery and replication GWAS of general risk tolerance. We conducted a gene analysis with MAGMA 31 to test each of 18,224 genes for association with general risk tolerance in a hypothesis-free manner (the 18,224 genes are the set of all genes containing at least one SNP in our combined meta-analysis results). We used our main reference panel to estimate LD. Bonferroni correction was applied to account for multiple testing, counting each gene as an independent test. We then used the Gene Network 32 coexpression database to gain insight into the functions of the significant MAGMA genes. In all panels, the x-axis is chromosomal position; the y-axis is the GWAS P value on a -log 10 scale (based on a two-tailed z-test); each lead SNP is marked by a red "×"; each conditional association is marked by a red "o"; and each SNP that is both a lead SNP and a conditional association is marked by a red "⊗".
Science Genetic Association Consortium (SSGAC), https://www. thessgac. org/data; BCFtools, https://samtools. github. io/bcftools/bcftools. html; BEAGLE, http://faculty. washington. edu/browning/beagle/b3. html; BOLT-LMM v. 2. 3. 2, https://data. broadinstitute. org/alkesgroup/BOLT-LMM/; DEPICT (Retrieved Feb 2015), https://data. broadinstitute. org/mpg/depict/;.
Science Genetic Association Consortium (SSGAC), https://www. thessgac. org/data; BCFtools, https://samtools. github. io/bcftools/bcftools. html; BEAGLE, http://faculty. washington. edu/browning/beagle/b3. html; BOLT-LMM v. 2. 3. 2, https://data. broadinstitute. org/alkesgroup/BOLT-LMM/; DEPICT (Retrieved Feb 2015), https://data. broadinstitute. org/mpg/depict/; Python v2. 7, https://www. python. org/download/releases/2. 7/; QCtool v2, http://www. well. ox. ac. uk/~gav/qctool_v2/; R, https://www. r-project. org/; REGSCAN v0. 2. 0, https://www. geenivaramu. ee/en/tools/regscan; Rstudio, https://www. rstudio. com/; ShapeIT, http://mathgen. stats. ox. ac. uk/genetics_software/shapeit/shapeit. html; SMR, https://cnsgenomics. com/software/smr/; SNPTEST, https://mathgen. stats. ox. ac. uk/genetics_software/snptest/snptest.
html; Stata v14. 2, https://www. stata. com/install-guide/windows/download/. EasyQC v9. 0, http://www. uni-regensburg. de/medizin/epidemiologie-praeventivmedizin/ genetische-epidemiologie/software/; GCTA, http://cnsgenomics. com/software/gcta; HESS, http://bogdan. bioinformatics. ucla. edu/software/hess/; IMPUTE2, http://mathgen. stats. ox. ac. uk/impute/impute_v2. html; IMPUTE4, https://jmarchini. org/impute-4/; LD Score Regression (ldsc), https://github. com/bulik/ldsc/; LDpred, https://bitbucket. org/bjarni_vilhjalmsson/ldpred; Mach2QTL, http://csg. sph. umich. edu/yli/mach/download/mach2qtl. source. V112. tgz; MAGMA, https://ctg. cncr. nl/software/magma; Minimac2, https://genome. sph. umich. edu/wiki/Minimac2; MTAG software, https://github. com/omeed-maghzian/mtag; PBWT, https://github.
Most comparative lncRNA studies were so far restricted to adult organ transcriptomes. These comparisons showed that lncRNAs are preferentially expressed in adult testes, during spermatogenesis  (Soumillon et al. 2013 ). This process is characterized by a permissive chromatin environment, which can promote nonfunctional transcription  (Soumillon et al. 2013 ). The resulting lncRNA data sets may thus be enriched in nonfunctional transcripts. Additional lines of evidence suggest that the search for functional lncRNAs should be extended beyond adult organ transcriptomes. For example, involvement in developmental phenotypes was proposed for many experimentally tested lncRNAs  (Ulitsky et al. 2011; Grote et al. 2013; Sauvageau et al. 2013) , and an enrichment for developmental transcription factor binding was reported for the promoters of conserved lncRNAs  (Necsulea et al.
Evolutionary Divergence of Individual lncRNA Expression Profiles To further quantify lncRNA expression differences between species, we measured the Euclidean distance between relative expression profiles (average TPM values across biological replicates, normalized by dividing by the sum of all values for a gene, for each species), for mouse and rat orthologs (supplementary methods, data set 7, and table 9, Supplementary Material online). The resulting expression divergence values correlate negatively with the average expression level (R 2 0. 13, t-test P value <1e À10 , fig. 9A ), as expected given that abundance estimation is less reliable for weakly expressed genes. Although the raw expression divergence values are significantly higher for lncRNAs (median 0. 18) than for proteincoding genes (median 0.
RNA-Seq Data Processing We used HISAT2  (Kim et al. 2015)  release 2. 0. 5 to align the RNA-seq data on reference genomes. The genome sequences (assembly versions mm10/GRCm38, rn6/Rnor_6. 0 and galGal5/Gallus_gallus-5. 0) were downloaded from the Ensembl database  (Cunningham et al. 2019). Genome indexes were built using only genome sequence information. To improve detection sensitivity, at the alignment step, we provided known splice junction coordinates extracted from Ensembl. We set the maximum intron length for splice junction detection at 1 Mb. The following command-line arguments were used: -rna-strandness R -known-splicesite-infile¼SpliceSites_Ensembl. txt -max-intronlen 1000000dta-cufflinks, where SpliceSites_Ensembl. txt corresponds to the exon junction coordinates extracted with hisat2_ex-tract_splice_sites.
Transcript Assembly and Filtering We assembled transcripts for each sample using StringTie  (Pertea et al. 2015) , release 1. 3. 5, based on read alignments obtained with HISAT2. We provided genome annotations from Ensembl release 94 as a guide for transcript assembly. We filtered Ensembl annotations to remove transcripts that spanned a genomic length >2. 5 Mb. For protein-coding genes, we kept only protein-coding transcripts, discarding isoforms annotated as "retained_intron" and "processed_transcript. " We set the minimum exonic length at 150 bp, the minimum anchor length for splice junctions at 8 bp and the minimum isoform fraction at 0. 05. The following StringTie command-line arguments were used: -G Ensembl_annotations. gtf -m 150 -a 8 -f 0. 05 -p 8 -rf, where Ensembl_annotations. gtf correspond to the Ensembl annotations filtered as described earlier.
We compared the resulting assembled transcripts with Ensembl annotations and we discarded read-through transcripts, overlapping with multiple multiexonic Ensembl-annotated genes. For strand-specific samples, we discarded transcripts for which the ratio of sense to antisense unique read coverage was <0. 01. We discarded multiexonic transcripts that were not supported by splice junctions with correctly assigned strands. The filtered transcripts obtained for each sample were assembled into a single data set per species using the merge option in StringTie. For increased sensitivity, we removed the minimum FPKM and TPM thresholds, but required a minimum isoform fraction of Functionally Constrained lncRNAs in Embryonic Development. doi:10. 1093/molbev/msz212 MBE 0. 05 for transcript inclusion. The following StringTie command-line arguments were used: stringtie -v -merge -G Ensembl_annotations.
gtf -m 150 -a 8 -p 8 -F 0 -T 0 -f 0. 05. We constructed a combined annotation data set, starting with Ensembl annotations, to which we added newly assembled transcripts that had no exonic overlap with Ensembl genes. We also included newly annotated isoforms for known genes if they had exonic overlap with exactly one Ensembl gene, thus discarding potential read-through transcripts or gene fusions.
Protein-Coding Potential of Assembled Transcripts To determine whether the newly assembled transcripts were protein-coding or noncoding, we mainly relied on the codon substitution frequency (CSF) score  (Lin et al. 2007). As in a previous publication  (Necsulea et al. 2014) , we scanned whole-genome alignments and computed CSF scores in 75 bp sliding windows moving with a 3-bp step. We used precomputed alignments downloaded from the UCSC Genome Browser  (Casper et al. 2018) , including the alignment between the mouse genome and 59 other vertebrates (for mouse classification), between the human genome and 99 other vertebrates (for rat and chicken classification) and between the rat genome and 19 other vertebrates (for rat classification). For each window, we computed the score in each of the six possible reading frames and extracted the maximum score for each strand.
We considered that transcripts are protein-coding if they overlapped with positive CSF scores on at least 150 bp. As positive CSF scores may also appear on the antisense strand of protein-coding regions due to the partial strand-symmetry of the genetic code, in this analysis, we considered only exonic regions that did not overlap with other genes. In addition, we searched for sequence similarity between assembled transcripts and known protein sequences from the SwissProt 2017_04 (The UniProt Consortium 2017) and Pfam 31. 0 (El-Gebali et al. 2019) databases. We kept only SwissProt entries with confidence scores 1, 2, or 3 and we used the Pfam-A curated section of Pfam. We searched for sequence similarity using the BlastX utility in the BLASTþ 2. 8. 1 package  (Altschul et al. 1990; Camacho et al.
Gene Expression Estimation We computed the number of uniquely mapping reads unambiguously attributed to each gene using the Rsubread package in R  (Liao et al. 2019) , discarding reads that overlapped with multiple genes. We also estimated read counts and TPM values per gene using Kallisto  (Bray et al. 2016). To approach absolute expression levels estimates, for better comparisons across samples, we further normalized TPM values using a scaling approach  (Brawand et al. 2011). Briefly, we ranked the genes in each sample according to their TPM values, we computed the variance of the ranks across all samples for each gene, and we identified the 100 leastvarying genes, found within the interquartile range (25-75%) in terms of average expression levels across samples. We derived normalization coefficients for each sample such that the median of the 100 least-varying genes be identical across samples.
Differential Expression Analyses We used the DESeq2  (Love et al. 2014 ) package release 1. 22. 2 in R release 3. 5. 0 (R Core Team 2018) to test for differential expression across developmental stages, separately for each organ and species. We analyzed both protein-coding genes and lncRNAs, selected according to the criteria described earlier. We first performed a global differential expression analysis, using the likelihood ratio test to contrast a model including an effect of the developmental stage against the null hypothesis of homogeneous expression across all developmental stages. This analysis was performed on all proteincoding and lncRNA genes for each species, as well as on 1to-1 orthologs for mouse and rat. In addition, we downsampled the numbers of reads assigned to protein-coding genes to obtain identical average numbers of reads for protein-coding genes and lncRNAs.
Acknowledgments We thank  Denis Duboule  and all members of the laboratory for advice and support,  Mylene Docquier ,  Brice Petit , and the  Genomics Platform of the University of Geneva  for RNA-seq data production,  Amanda Cooksey  and the  Chickspress Team at the University of Arizona  (http://geneatlas. arl. arizona. edu)for granting us access to chicken RNA-seq data,  Ioannis Xenarios , and the  Vital-IT team  for computational support. We thank  Jean-Marc Matter  ( University of Geneva ) for providing chicken eggs. This work was performed using the computing facilities of the  CC LBBE/PRABI , the  Vital-IT Center  for high-performance computing of the  SIB Swiss Institute of Bioinformatics  (http://www. vital-it. ch)and the computing center of the  French National Institute of Nuclear and Particle Physics  ( CC-IN2P3 ).
This project was funded by the  Swiss National Science Foundation  ( SNSF Ambizione  grant  PZ00P3_142636 ) and the  Agence Nationale pour la Recherche  ( ANR JCJC 2017  LncEvoSys). F. D. was supported by a  FP7 IDEAL grant  ( 259679 ). Functionally Constrained lncRNAs in Embryonic Development. doi:10. 1093/molbev/msz212MBE.
Data Availability The RNA-seq data were submitted to the NCBI Gene Expression Omnibus (GEO), under accession number GSE108348. Supplementary data sets, Supplementary Material online, containing additional processed data are available at the address: ftp://pbil. univ-lyon1. fr/pub/datasets/Darbellay_LncEvoDevo. The scripts used to analyze the data are available through a GitHub repository: https://github. com/anecsulea/LncEvoDevo. See also supplementary methods, Supplementary Material online.
The preliminary implementation of AMOEBA+ on GPU can achieve ~20 ns/day on RTX2080 for a DHFR system (23,555 atoms) using a 2-fs time step. In another implementation, Tinker-HP  89  takes advantage of the massive parallelization of MPI and provides excellent scalability of performance over a large number of CPU cores for systems of ~ 1 million atoms. Preliminary implementation of AMOEBA+ GPU code in Tinker-OpenMM has been completed and utilized in this work. Implementation of AMOEBA+ in Tinker-HP, for CPU and CPU-GPU hybrid platform, is in progress. The Tinker-CPU code is available at TinkerTools GitHub site as AMOEBA+ branch.