dataesr/scientific-paragraphs-categorization huggingface dataset:
264A second example comes from the publicly available DIC Challenge database 265[19,20]  provided by the Society for Experimental Mechanics. The tested images contain 266 uniform patterns, as shown in Figure9. 267For each trial with the original QSF method, the template window is placed at a 268 different position in the processed images, as illustrated in Figures5 and 9. Among all 269 the trials, the number of cases where the fitted quadratic surface exhibits a saddle point 270 (absence of maximum) is counted. The second type of failures, with maximum outside 271.
Movies at high temporal resolution were then acquired with a 488 nm laser at 2% (exposure time 200 ms) at the rate of 1 frame every 250 ms for 700 timeframes (1. 75 min). Analysis was performed with Fiji and Python with custom-written scripts. Briefly, the nuclei in the 488 nm channel were registered for XY translation and rotation using the StackReg plugin. For each nucleus, four lines were hand-drawn on the nuclear envelope (NE) and resliced over time to obtain 4 kymographs, one for each individual point in the NE. A custom-written Python script was used to determine the position of the NE at each timepoint of each kymograph. For each timepoint, a parabola was fitted on the pixel with the highest intensity and its 10 neighboring pixels. The maximum of the parabola was assigned as the NE position.
RNA-seq analysis RNA-seq reads mapping and quantification. RNA-seq reads were mapped to the hg19 reference human genome assembly using STAR v2. 6. 1c (37)  with GENCODE v33 (GRCh37) gene annotation  (38). Read counts per gene were obtained using the '-quantMode' parameter in STAR. Uniquely mapped reads with MAPQ > 30 were selected using SAMtools v1. 5  (39). Reads overlapping with the hg19 blacklist regions  (40)  were discarded using bedtools v2. 25. 0  (41). The bigwig files with the CPM-normalized transcription signal in 50 bp bins were generated for merged replicates using bamCoverage function from deepTools v3. 3. 0  (42). Visualization of the signal profiles was created in the UCSC Genome Browser  (43). Statistics of the RNA-seq data processing can be found in Supplementary Table  S2A.
Gene set variation analysis (GSVA) was performed using the GSVA v1. 36. 2 R package  (52). Publicly available RNA-seq datasets of normal lung (N = 48) and NSCLC (N = 61) cell lines were used as a background. The datasets were downloaded from the GEO database (GEO accession numbers: GSE63900, GSE93526, GSE85447, GSE101993, GSE97036, GSE42006, GSE152446, GSE113185, GSE80386, GSE148729, GSE109821, GSE123769, GSE141666, GSE123631, GSE113493, GSE150809, GSE74866, GSE99015, GSE79051, GSE107637, GSE86337, GSE78531, GSE78628, GSE61955 and processed as described above (section 'RNA-seq Reads Mapping and Quantification') to obtain read counts per gene. Lung-specific gene set was extracted from the Human Protein Atlas v20. 0 database  (53) , other gene sets were retrieved from the MSigDB v7. 1 database.
Ensembl gene identifiers were converted to Entrez using Biomart  (54)  with Ensembl genes (release 100). GSVA scores were estimated for log-transformed CPM gene counts for each sample (Supplementary Table  S3D ). Repetitive elements expression analysis. RepEnrich2 (55) (https://github. com/nerettilab/RepEnrich2) was used to determine the expression levels of repetitive elements from the hg19 RepeatMasker annotation. First, RNA-seq reads were mapped to the hg19 reference human genome assembly using Bowtie v2. 2. 3  (56). Second, unique and multimapped reads were separated by a MAPQ cutoff of 30. Finally, fraction counts were calculated for each repetitive element. Differential expression analysis was performed with edgeR v3. 30. 0 R package. Library size was defined as N reads processed -N reads aligned 0 times , based on the log files from Bowtie2.
Differential enrichment analysis. DiffBind v2. 16. 0 (59) was utilized to analyze the differential enrichment of the H3K9me3 and H3K27ac ChIP-seq peaks, and differentially regulated super-enhancers. To analyze heterochromatin domains and continuous enhancer clusters, peaks within 12. 5 kb from one another for H3K9me3 and H3K27ac data were stitched together  (60). Stitched ChIP-seq peaks and annotated super-enhancers were pooled together for control and A549 SETDB1 LOF conditions and used for differential enrichment analysis. DiffBind pipeline was executed with default parameters. FDR threshold of 0. 01 and absolute log 2 FC of 1 were used to define differentially regulated regions (Supplementary Table  S4B-D ). Differential enrichment of CTCF was determined based on the fold change of the ChIP-seq signal density at the pooled peaks level.
Motifs search. HOMER v4. 11  (72)  was used to identify the motifs present in the ATAC-seq peaks within the annotated super-enhancers. ATAC-seq peaks for the Downloaded from https://academic. oup. com/nar/article/50/8/4389/6574680 by UNIVERSITE DE PARIS (UDP) user on 18 November 2022 A549 cell line from the ENCODE Project Consortium were used (ENCODE accession numbers ENCSR032RGS and ENCSR220ASC). Peak coordinates were converted from hg38 to hg19 reference human genome assembly with UCSC liftOver tool. HOMER motif discovery algorithm (findMotifsGenome. pl) was executed with default parameters, and known motifs with P-values <0. 01 were used for the analysis. The obtained motifs were filtered for transcription factor (TFs) motifs only using the Human Transcription Factor Database v1. 01  (73)  and converted to the orthologous human gene names if necessary.
Annotation of chromatin compartments. A/Bcompartments were annotated using cooltools v0. 3. 2 call-compartments function for 100 kb resolution contact matrices. The orientation of the eigenvectors (PC1) was selected such that it correlates positively with GC content. Consequently, B compartment bins were assigned with negative eigenvector values, and A compartment bins were assigned with positive. To validate the compartment annotation, histone modification ChIP-seq signals from the ENCODE Project Consortium were used (ENCODE accession numbers ENCSR000AUL, ENCSR000AUN, ENCSR000AUI, ENCSR000ATP, ENCSR000AUK, ENCSR000AVI, ENCSR000ASH, ENCSR000ASV, and ENCSR000AUM). ChIP-seq signal fold change was calculated for each compartment, and was defined as the mean signal in the compartment divided by the mean signal across the whole genome.
Identification of compartment switches. Compartment switches were identified using the genome-wide eigenvector difference ( PC1) between A549 SETDB1 LOF and control conditions. Only genomic bins where both conditions had annotated A/B compartment state were considered. Genomic bins with PC1 difference between conditions >1. 645 standard deviations from the mean PC1 were annotated as compartment switches. The annotated compartment switches were then classified into Stable A, Stable B, A to B, and B to A compartments based on the sign of the eigenvector difference (Supplementary Table  S5 ). To correlate PC1 and ChIP-seq profiles or RNA-seq data, the fold changes of ChIP-seq or RNA-seq data were averaged across 100 kb bins. Saddle plots. Saddle plots were generated using cooltools v0. 3. 2 compute-saddle function using 100 kb observedover-expected cis and trans matrices, respectively.
RT-qPCR data were normalized to the PPIA mRNA expression values in control A549 cells that were set as 100% (N ≥ 3 independent experiments; mean ± SEM; *P-value < 0. 05, Mann-Whitney U-test). (F) Effect of rescued expression of SETDB1 on mRNA expression of selected mesenchymal and epithelial markers, and genes related to the WNT/␤-catenin signaling pathway in A549 SETDB1 LOF transfected with pcDNA3. 1(+)-IRES-GFP or pcDNA3. 1(+)-SETDB1 WT -IRES-GFP. RT-qPCR data were normalized to the PPIA mRNA expression values in A549 SETDB1 LOF transfected with pcDNA3. 1(+)-IRES-GFP that were set as 100% (N = 3 independent experiments; mean ± SEM; *P-value < 0. 05, Mann-Whitney U-test). (G) mRNA expression of selected mesenchymal and epithelial markers, and genes related to the WNT/␤-catenin signaling pathway in control (N = 8 mice, cyan) and SETDB1 LOF A549 xenografts (N = 7 mice, pink) (see Figure  1H ).
Together, these data suggest that SETDB1 LOF attenuates the NSCLC-like phenotype of A549 cells. To assess the overall trend of gene expression program changes in SETDB1 LOF cells, we re-analyzed public transcriptomic data for 48 normal lung and 61 NSCLC cell lines datasets and compared the expression levels of several relevant gene sets using gene set variation analysis (GSVA). The apparent feature of SETDB1 LOF cells was a correlation with normal epithelial cell lines across several gene sets, including MYC-targets, cell-cell adhesion, G2M-checkpoint, DNA replication, cell proliferation, embryonic stem cell (ESC)-specific genes, and genes associated with poor survival of NSCLC patients (Figure  2D ). We confirmed RNA-seq data by RT-qPCR and found a reduction in mRNA levels of mesenchymal genes and genes related to WNT/␤-catenin signaling (MYC, CCND1, FN1, SERPINE1) and an increase in mRNA levels of epithelial genes (CDH1, CDH3, PCDHA6, PCDHB6, PCDHGA4) (Figure  2E ).
(E) mRNA expression of selected mesenchymal and epithelial markers, and genes related to the WNT/␤-catenin signaling pathway in control (cyan) and SETDB1 LOF (pink) A549 cells. RT-qPCR data were normalized to the PPIA mRNA expression values in control A549 cells that were set as 100% (N ≥ 3 independent experiments; mean ± SEM; *P-value < 0. 05, Mann-Whitney U-test). (F) Effect of rescued expression of SETDB1 on mRNA expression of selected mesenchymal and epithelial markers, and genes related to the WNT/␤-catenin signaling pathway in A549 SETDB1 LOF transfected with pcDNA3. 1(+)-IRES-GFP or pcDNA3. 1(+)-SETDB1 WT -IRES-GFP. RT-qPCR data were normalized to the PPIA mRNA expression values in A549 SETDB1 LOF transfected with pcDNA3. 1(+)-IRES-GFP that were set as 100% (N = 3 independent experiments; mean ± SEM; *P-value < 0.
ACKNOWLEDGEMENTS We thank  Ekaterina Boyarchuk  ( UMR7216 ,  Université de Paris, Paris, France)  for technical help and  Sonja Boland  ( Institut Jacques Monod, Paris, France ) for providing the NHBE cell line, advice, and help with their culture. We thank  Natalia Klimenko  ( Institute of Gene Biology, Russian Academy of Science, Moscow, Russia ) for advice on the ChIP-seq data analysis. We also thank  Mathieu Maurin  ( U932 ,  Institute Curie, INSERM, Paris, France ) for help with image analysis. supported by the  Ministry of Science and Higher Education of the Russian Federation  and facilities of the  Center for Precision Genome Editing and Genetic Technologies for Biomedicine, IGB RAS. Funding for open access charge: FRM,  AFM-Telethon ,  ANR ,  RSF ,  INCa.
The paper proceeds as follows: Section 2 reviews literature on phylogenetic autocorrelation-the tendency of languages to show similarities due to phylogenetic relatedness-and the methodological responses to it in linguistic typology and cognate fields (comparative biology, in particular). Section 3 then introduces the concept of phylogenetic signal, the degree of phylogenetic autocorrelation that is present in a comparative dataset, and describes statistical tools for quantifying it. Section 4 addresses the topic of uncertainty in linguistic genealogies, and discusses ways in which phylogenetic comparative methods enable a nuanced, explicit examination of how inferences that are drawn from cross-linguistic data are affected by hypotheses about genealogy. In Section 5, because two of the most common types of scientific finding in typology are cross-linguistic averages of typological variables and proportions of languages that have particular properties, we describe phylogenetic methods for the calculation of averages and proportions that take genealogy into account.
Phylogenetic autocorrelation: The consequences of relatedness Phylogenetic autocorrelation is common to many comparative fields of science. It is a potential problem for comparative study, because shared phylogenetic histories limit the independence of observations in a comparative dataset. Observations from more closely related entities will tend to show less variation than more distantly related entities, because they share a longer period of common history and have had less time to diverge since the splitting up of their most recent common ancestor. If this tendency towards similarity due to shared phylogenetic history is not taken into account, it will introduce bias into the dataset and consequently affect statistical analysis. This section discusses phylogenetic autocorrelation and the history of responses to it in different fields.
Phylogenetic autocorrelation across the sciences Different fields have their own lines of literature grappling with phylogenetic autocorrelation extending back many decades. In comparative anthropology, this issue was noted as early as 1889 by Sir Francis Galton in the context of cross-cultural datasets, which lack independence due to shared histories of cultural innovation and exchange between societies  (Naroll 1961: 15). This phenomenon, known as Galton's Problem, is now more precisely understood as a form of statistical autocorrelation, i. e. , similarity between observations that correlates with their proximity, in this case, their proximity in evolutionary time. The same phenomenon has been recognised in comparative biology too. A seminal study concerning comparative studies of phenotypes,  Felsenstein (1985)  demonstrates that data from species cannot be assumed to be independently drawn from the same distribution, because species are related to one another via a branching, hierarchical phylogeny, thus, statistical methods that assume independent, identically-distributed observations will inflate the significance of the test (discussed further in Section 2.
Before turning to biology, it is worth underscoring how linguistic typology has arrived at its current mode of response to phylogenetic autocorrelation. The starting point is that many conventional statistical methods require observations that are independent, yet languages are non-independent. For four decades, the response has been to change the dataset, by means of balanced sampling, so that it better corresponds to the requirements of the statistics. Doing so requires excluding the vast majority of documented languages from the dataset and hence from the analysis, and even then, the result is still not truly independent. In the next section, we will see that biology initially followed the same path. The key breakthrough, though, was to invert the response to the original problem that phylogenetic autocorrelation posed: to change not the dataset to suit the statistics, but the statistics to suit the dataset.
In an approach with strong conceptual similarities to the area-by-area robustness checking of  Dryer (1989)  and  Nichols (1992) ,  Baker & Parker (1979: 85-86)  discussed how the causal effects of ecological areas might be addressed while constructing a sample which is genealogically balanced. To do so,  Baker & Parker (1979)  replicate their analysis within individual families as well as within different ecological areas, with the assumption that if the same associations are observed within different areas as across the dataset as a whole, then one can discount the possibility that the full analysis is simply picking up differences between different families or different ecological areas. 1 Once Clutton-Brock & In essence, both linguistics and biology face the same phenomenon of phylogenetic autocorrelation including the complication of areality, and for several decades explored strikingly similar methodological responses based on sampling.
Like balanced sampling methods, phylogenetic comparative methods are a response to phylogenetic autocorrelation, one of the central and most persistent problems of linguistic typology. Methodologists working on balanced sampling have striven to generate samples that come as close as possible to phylogenetic independence, but the goal cannot be fully attained even with the most elaborate sampling procedures, and in the meantime procedures of balanced sampling require the exclusion of the vast majority of documented languages from the dataset and hence from the analysis. As it turns out, the solution is to be found not in phylogenetically independent samples, but in phylogenetically independent contrasts (PICs). By focussing on PICs, Felsenstein unlocked a method for obtaining truly independent observations, without excluding data.
Blomberg, Garland & Ives (2003)  provide a suite of tools for quantifying phylogenetic signal, which have become somewhat of a standard in the field (cited 3780 times as of September 2021, according to Google Scholar). 3 Recent comparative studies using these tools include  Balisi, Casey & Valkenburgh (2018 ), Hutchinson, Gaiarsa & Stouffer (2018)  and Macklin-Cordes,  Bowern & Round (2021). Blomberg, Garland & Ives (2003)  present a descriptive statistic, , which is generalizable across phylogenies of different sizes and shapes. In addition, they provide a randomisation test for checking whether the degree of phylogenetic signal for a given dataset is statistically significant. can be calculated using either phylogenetically independent contrasts (PICs)  (Felsenstein 1985)  or generalised least squares (GLS)  (Grafen 1989 ) (see Section 2.
4). In a Brownian motion model, where variable values can wander up and down with equal probability through time, PIC variances are expected to be proportional to elapsed time. Among more closely related languages, where there has been less divergence time for variable values to wander, the variance of PICs is expected to be low. The randomisation test works by comparing whether observed PICs are lower than the PIC values obtained by randomly permuting the data across the tips of the tree. The process of permuting data across tree tips at random is repeated many times over. If the real variances, with data in their correct positions on the tree, are lower than 95% of the randomly permuted datasets, then the null hypothesis of no phylogenetic signal can be rejected at the conventional 95% confidence level.
Phylogenetic signal in binary variables The methods so far described concern continuously-valued data. Other methods have been proposed for quantifying phylogenetic signal in binary and categorical variables too. Abouheif (1999)  presents a simulation-based approach for testing whether discrete values along the tips of a phylogeny are distributed in a phylogenetically non-random way. Although this method is useful for testing whether the phylogenetic signal in a set of discretely-valued data is statistically significant, it does not provide a quantification of the level of phylogenetic signal which is comparable between different datasets. Although specific to binary data only,  Fritz & Purvis (2010)  present a statistic, , which quantifies the strength of phylogenetic signal for binary variables.
And similarly, it is even more unsatisfactory for Figure  3b  than for Figure  3a , though less so for Figure  3c. There already exists a substantial literature on how to obtain principled values for proportions and averages that are sensitive to genealogy. Here we present two of the methods that have been developed. Before we do, it is useful to recall that even within non-phylogenetic statistics, there are multiple ways of formulating and defining an average, including means, medians, modes, harmonic means, geometric means, and so forth. Each of these operationalises a slightly different concept of the 'representative middle value', or central tendency, of some set of observations. Different averages have different properties which may prove advantageous or not, depending on the objectives and datasets at hand.
In this section we examine continuous variables of this kind, which describe the relative predominance of pre-palatals versus dentals in nine phonotactic positions, across 76 languages that possess the contrast. Data is from a phonemicised lexical database of Australian languages, which is under development  (Round 2017) , and which extends and enhances the Chirila database  (Bowern 2016). Raw data tables and details of the primary language documentation sources are provided in Section S2 of the Supplementary Materials. Our choice of nine variables is informed by the typological literature on Australian phonology. One long-established characteristic of Australian laminals is that their relative frequencies are sensitive to the quality of neighbouring vowels  (Dixon 1970 (Dixon , 1980)). 7  Most Australian languages have three contrastive vowel qualities  (Round 2022) , with /i/ contexts favouring the laminal pre-palatal, /u/ contexts favouring the dental, and /a/ contexts somewhere in between.
A short orientation to R The code presented here runs in R. Here we quickly introduce R objects (such as variables and dataframes) in Section 2. 1, R packages in Section 2. 2 and how R works with tree objects in Section 2. 3. Readers already familiar with the basics of R may wish to skip directly to Section 2. 3. 1 Below, chunks of R code appear against a grey background. The results that R produces from each chunk is shown below it, either as lines of text preceded by ## or as a plot that R generates, or both. The code within the grey boxes can be copied and pasted into R. Within the code chunks, where a single R command runs longer than one printed line, we have indented the lines after the first; to run these in R, you will need to copy and paste the whole command.
For example, here is a string that represents a tree with four tips, A, B, C and D, which we assign to the object my_newick: my_newick <-"(((A,B),C),D);" A Newick-formatted string can then be converted to a tree object by supplying it as the text argument of the function read. tree(), from the ape package: my_tree <-read. tree(text = my_newick) We can plot the tree using the plot() function: plot(my_tree) 3 A B C D By default, trees in R are plotted horizontally following the convention in biology. The glottoTrees package provides a function plot_glotto() which plots trees in a more typical, downward-running linguistic format, as below.
Genealogically-sensitive averages and proportions We now turn to the calculation of genealogically-sensitive proportions and averages. To run the code in this and subsequent sections, ensure that the packages described in Section 2. 2 have been installed and loaded. Calculating averages and proportions will require two key components: 1. A phylo or multiPhylo object containing one or more trees. 2. A dataframe, which (i) contains the typological data to be averaged and (ii) relates that data to the tips of the trees. The phylo or multiPhylo object can be manually defined, as described in Section 2. 3, can be read from a file, 2  or it can be constructed by using and adjusting materials freely available from glottolog. com, as described below in Section 4. The dataframe can be manually defined or be read from a file.
Perhaps the easiest method is to read from a file that you have created and saved in "CSV" (comma separated value) format. CSV files can be created in commercial spreadsheet software like Excel, and then read by R using the read. csv() function like this: my_dataframe <-read. csv("my_csv_file. csv") The dataframe must contain one column named tip (note that in R, names of columns and other objects are case sensitive) plus at least one column containing numerical data. The contents of the tip column must be the same as the tip labels of the tree(s) in the phylo or multiPhylo object. The contents of the numerical columns will depend on whether a proportion or an average is desired. To calculate a proportion, fill a numerical column with 1 if the language possesses the property and 0 if it does not.