dataesr/scientific-paragraphs-categorization huggingface dataset:
Knock-down of hypoxia-regulated lncRNAs results in weak and heterogeneous condition-dependent transcriptomic modulations We then applied the SAE workflow to classify cells treated with the 6 gRNA targeting hypoxia-regulated lncRNAs and cultured in the 4 conditions. Globally, the SAE was able to classify perturbed and control cells with a good overall accuracy around 80%, except for SNHG12 and SNHG21 (Table  4 ). For the SNHG12 and SNHG21 datasets, the first round of SAE selected only around 10% of perturbed cells. Thus we could not run the SAE for the second round because of a too low number of cells for the 4 fold cross validation. For those 2 genes, we just reported the average accuracies obtained after the first round of the SAE. These initial good performances for LINC00152, MALAT1 and NEAT1 were not found to be associated with a specific transcriptomic perturbation signature, but were exclusively due to the strong inhibition of the target gene, as indicated by the high means and standard deviations of the ranks obtained for the other genes (Figure  4A-C ).
We also used the Log Fold Change (LFC) results, which is a common analysis in scRNA-seq, even if this particular metric does not produce a value of accuracy. We ranked the top differentially expressed genes according to p-values obtained with Wilcoxon Rank Sum test and ajusted with Bonferroni correction. We performed this comparison for 3 representative datasets, namely HIF2-targeted cells versus 24h hypoxia-related control cells, LUCAT1-targeted cells versus 24h hypoxia -related control cells, and SNHG12-targeted cells versus 3h hypoxia-related control cells. For the first dataset, HIF2 inhibition induced a large perturbation signature. With an accuracy of 97. 67%, the SAE outperformed both PLS and RF, for which we obtained an accuracy of 92. 5% and 72. 42% respectively (Table  5 ). Note that selecting the cells thanks to the confidence score improved significantly the accuracy of the SAE by 4.
67%. However, the top 15 selected features between all methods were highly similar, because of the strong effect of HIF2 inhibition (Figure  7A ). TMEM141 TMEM141 IGFBP3 TMEM141 IGFBP3 PGK1 IGFBP3 FTL PGK1 BNIP3 IGFBP3 FAM162A PGK1 IGFBP3 FAM162A BNIP3 BNIP3 TMEM141 BNIP3 EPAS1 EPAS1 EPAS1 BNIP3 FAM162A PGK1 FAM162A PGK1 FAM162A ENO1 FXYD2 FXYD2 TESC EPAS1 EPAS1 ATP1B1 GPI ATP1B1 FXYD2 FTL TESC SLC16A3 FXYD2 ATP1B1 FXYD2 SLC16A3 ANXA4 SLC16A3 ENO1 ATP1B1 GPI TESC LOXL2 GPI TESC ALDH3A1 FTL GPI TESC GPI ANXA4 PGAM1 ANXA4 PGD LOXL2 GAL3ST1 ATP1B1 CYP1B1 ALDH1A1 PGAM1 FTL LOXL2 DSP ALDH3A1 VIM We observed more extreme results for the LUCAT1 dataset. Indeed, with an accuracy of 85. 67%,the SAE outperformed both PLS and RF, for which we obtained an accuracy of only 62% and 54. 6% respectively (Table  6 ).
Note that selecting the cells thanks to the confidence score improved the accuracy of the SAE by 1. 67%. Furthermore, most of the obtained signatures were specific to a particular Machine Learning method, with some overlaps (Figure  7B ) such as ATP6AP1 that is also detected by PLS. NIT1, PEX1, KDMC5, PHF20 and TMEM175 were all found specific to the SAE. For the SNHG12 dataset, we stopped the workflow of SAE tests before the selection step since only 14% of perturbed cells were found perturbed. All methods performed similarly, with a poor average accuracy of 70% (Table  7 ). They all detected for instance the strong modulation of BAG1, but the other selected genes were mostly July 10, 2023 12/19 among other things, on the expression levels of the transcribed gRNA and dCas9, as well as the accessibility of the target gene locus, that may be heterogeneously regulated at the epigenomic levels in the different cells.
Finally, this unique dataset provides a global and dynamic description of the transcriptomic modulations mediated by the two main regulators of the hypoxic response in LUAD A549 cells. Surprisingly, we did not detect any relevant and stable perturbation in cells targeted for LINC00152, MALAT1, NEAT1 and SNHG21, in the four culture conditions. This result appears quite unexpected for MALAT1 and NEAT1, two of the most studied lncRNAs that are associated with various functions in cancer, including proliferation, migration, and invasion  [44, 47]. In particular, it has been shown that MALAT1 knockout in the same cellular model (A549) modulated a set of metastasis-associated genes  [48]. Although CRISPRi-mediated knock-down achieved an efficient knock-down (¿95%), it is however possible that based on the very high level of MALAT1, the remaining transcripts are sufficient to mediate the cellular function.
However, SAE has some limitations. Like all statistical method, it is highly dependent on the number of samples/cells compared. Low cell number impact classification performance and can produce inconsistent results, such as better accuracy and robustness of selected features for HIF2-targeted cells after 3h of hypoxia compared with 6h exposure. In this context, the relevance of the top selected genes list and their superiority over other compared methods can be asserted by evaluating the robustness of the ranks and the classification accuracies. The size of the perturbation signatures obtained for LUCAT1 and SNHG12 datasets prevented the utilization of functional enrichment analysis to characterize their modulated functions. Furthermore, as these small signatures were found in specific subsets of targeted cells, it seems complicated to validate them using a bulk experimental approach that will average the signal across all cells.
( CNRS), Université Côte d'Azur, Canceropôle PACA (Action Structurante CRISPR SCREEN), the French Government (National Research Agency, ANR) program "Investissements d'Avenir" UCAJEDI n°ANR-15-IDEX-01 (PERTURB-ENCODER) and ANR-22-CE17-0046-01 MIR-ASO, Plan Cancer 2018 "ARN non-codants en cancérologie : du fondamental au translationnel" (number 18CN045).
Data set We performed a cross-sectional study within the Generation R cohort. Generation R is a populationbased prospective study based in Rotterdam, the Netherlands that started in 2002 and is still ongoing. The study enrolled children and their parents from the prenatal period until adulthood with the aim of identifying early genetic and environmental causes of health and its relationship with diseases later in life. 13  We focused at the age category of 13 years(''Focus@13'') in which adolescents and their parents visited the research center and completed questionnaires.
Acne phenotyping As part of the followup measurements, threedimensional photos of the frontal and lateral aspects of the face, taken in standardized conditions were available. Two trained physicians (WW and LP) independently scored the photos using the Global Evaluation of the Acne Severity score (GEA) (Supplementary Table  I , available via Mendeley at https://data. mendeley. com/datasets/zjddtdsbjn/1). 14  In the case of unsolved discordance, an independent dermatologist (ER) decided on a final score or exclusion due to either an underlying disease or insufficient quality of the photo (Fig  1 ). Based on the distribution of the GEA scores in the population 3 ordinal categories were created: (almost) clear (GEA 0-1), mild (GEA 2), and moderate-to-severe (GEA 3-5). Visible acne prevalence was defined as GEA 2 to 5.
Statistical analysis IBM SPSS version 28. 0. 1. 0 (142) and R version 4. 2. 1 were used for all analyses. The inter-and intraobserver intraclass correlation coefficient was calculated using a 2-way mixed model to test for the absolute agreement for single and average measures. First, categorical covariates were analyzed separately in relation to the acne severity categories using complete cases and chi-square tests. Then, owing to the sex-specific Tanner definitions sex stratified analyses were conducted. Missing data in covariates were imputed assuming missingness at random. Missing values ranged from 0% to 34. 6%. Multiple imputation was done using chained equations with the Markov Chain method 21 in which 30 data sets with 50 iterations were created (Supplementary Tables  II  and III , available via Mendeley at https:// data.
Demographics study population Out of the 9749 participants who were initially enrolled in the Generation R study, 4929 visited the research center in this follow-up, of whom 4561 were eligible for analysis (Fig  1 ). Adolescents who were excluded differed compared with those included (Supplementary Table  IV , available via Mendeley at https://data. mendeley. com/datasets/ zjddtdsbjn/1). x Youth Self-Report scores are displayed as sum scores. Table  I  shows the characteristics of the adolescents (51% girls at median age of 13. 5 years) and their mothers stratified by sex. The median age, ethnicity, perceived skin color, and Cole weight status were all comparable across both sexes. Sixty-two percent of the children had a Dutch ethnicity and the 4 largest non-Dutch groups were: European, Surinamese, Moroccan, and Turkish.
mendeley. com/ datasets/zjddtdsbjn/1). Adolescents with darker skin had moderate-tosevere acne more often compared with those with skin colors I to II (24. 2% vs 9. 8%; P \. 01) (Table  II ). Darker skin remained significantly associated with acne severity in the multivariable model (adjusted ORs: 1. 90 [95% CI, 1. 17-3. 08] and 2. 43 [1. 67-3. 56]) for girls and boys (Tables  III  and IV ). Adolescents who were more advanced in puberty, had more severe acne. The Tanner (PH) had a slightly lower OR of 1. 38  Adolescents with mothers who had a high education had less severe acne (P \. 01), but this negative association disappeared in the multivariable model. Finally, the internalizing and externalizing symptoms were not significantly associated with acne severity in both sexes.
25 0. 77 e0. 18 0. 89 0. 14 0. 19 e0. 28 0. 46 e0. 02 e0. 04 e0. 14 e0. 40 their ethnicities were explored and showed a large mix of skin colors per ethnicity group. Supplementary Fig 2 (available via Mendeley at https://data. mendeley. com/datasets/zjddtdsbjn/ 1) displays a separate sensitivity analysis with the relation of Tanner (PH) stage and sex and shows a higher percentage of boys with Tanner (PH) stages 1 to 3, whereas a higher percentage of girls have The b, mendeley. com/datasets/zjddtdsbjn/1) the preva-Tanner (PH) stages 4 to 5. SE 0. 08 0. 08 0. 14 0. 19 0. 25 0. 26 0. 20 0. 16 0. 07 0. 07 0. 18 0. 20 Adjusted OR (95% CI) 1. 29 (1. 11-1. 49)* 2. 16 (1. 86-2. 51)* 0. 84 (0. 64-1. 10) 2. 43 (1. 67-3. 56)* 1. 15 (0. 70-1. 89) 1. 21 (0. 73-2. 00) 0. 76 (0. 52-1. 11) 1. 58 (1. 15-2.
Heat transfer simulation The above optical diagnostics provide insight into the temperatures at specific locations where phosphor-coated particles are placed. Using those measurement data, the internal temperature distribution within the rest of the bed can be estimated using heat transfer modeling in a data assimilation approach. Model parameters such as particle-to-particle air gap can be adjusted to match the measured data at the specific location. Transient heat transfer simulations of the packed bed were performed with ANSYS, Inc. Products 2021 R2, using the toolbox Transient Thermal, i. e. , applying a finite element method (FEM). The preprocessing steps were done with ANSYS SpaceClaim. The whole setup, including the particle and the fused silica cuvette with 2. 5 mm walls, was simulated.
Three point measurements For demonstrating multi-point temperature detection, we tested the case of three luminescent spheres, S1 (À0. 5 -0. 5 1), S2 (0 0 0) and S3 (À0. 5 -0. 5 -1), their locations as shown in the right of Fig. 8. In order to validate the temperature measurements, S2 was the luminescence sphere with an embedded thermocouple. The experiment was started under isothermal room temperature condition. At t ¼ 0 min, heating was imposed from the top sphere, which temperature was measured by a thermocouple to provide the heated source temperature and also input data for heat Fig. 13. Temperature histories measurement for the three luminescent spheres case described in Section. 5. 2, but using simulated spatial intensity distribution function S (j) (x, y) for signal separation. transfer simulation (see Section.
Acknowledgments The authors gratefully acknowledge funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) e Project -ID 422037413 e TRR 287. The authors thank  Chaturah Jinath Nammuni Arachchilage  for his help with the experiment,  Gunar Boye  for the support with the manufacturing, and  Katharina Z€  ahringer for the discussions.
The grey mouse lemur (Microcebus murinus) is a small non-human primate with a lifespan of 5-13 years. Because of its small size, rapid reproduction, and close genetic proximity to humans, the mouse lemur is an appealing model organism for studies of primate biology, behavior, aging, and disease  [9] [10] [11] [12] [13]. With the advances in single-cell RNA-sequencing (scRNAseq) techniques, we have recently created a molecular cell atlas of the mouse lemur by profiling transcripts from ~225,000 cells from 27 mouse lemur organs and tissues of 4 individuals  14  (https://tabulamicrocebus. ds. czbiohub. org/). This organism-wide dataset provides an unprecedented opportunity to study global hormone regulation at single cell resolution in a non-human primate. In this study, we present a comprehensive analysis of primate hormone signaling based on the information in the mouse lemur molecular cell atlas  14.
A comprehensive table of genes involved in the biosynthesis and sensing of human and mouse lemur hormones Although many hormones have been studied for decades, there is not a well-annotated database of hormone ligands, synthases, processing enzymes, and receptors. Therefore, we integrated multiple sources to compile a comprehensive gene table for human hormone signaling (Table  1 ). For the hormone list, we mainly referenced the Handbook of Hormones  2  , the Wikipedia List of human hormones, and the Handbook of Biologically Active Peptides  15  , and added newly discovered hormones including asprosin  16  and the endocrine fibroblast growth factors FGF21, FGF19, and FGF23  17. To collect the genes involved in hormone production, we manually assembled the ligand genes for peptide and protein hormones and the hormone synthesis genes for small molecule hormones.
Maturation or activation of many hormones requires specific enzymes such as angiotensin-converting enzyme (ACE) for angiotensin, the deiodinases DIO1/2 for thyroid hormones, and prohormone processing enzymes (e. g. , PCSK1, PCSK2, CPE) for many peptide hormones  15. In addition, hormone function may involve endocrine modulators such as IGF binding proteins (IGFBPs) for IGF1/2. Accordingly, we added these processing enzymes and modulators for the corresponding hormones. Next, we collected the hormone receptor genes that are responsible for signaling in the target cells. We searched ligand-receptor databases  18, 19  for the receptor genes of protein and peptide hormones, and manually added the receptor genes for the small molecule hormones. Most hormones have characterized ligands and receptors with a few exceptions in which the genes remain unidentified (e.
On the other hand, some receptor subtypes function independently. For example, there are nine different adrenergic receptor genes, and none of these receptors require any of the others for function. Genes like these were linked by OR gates, designated by a vertical line (|). We next looked up the mouse lemur orthologs of the human hormone genes in the most up-todate mouse lemur genome assembly (Mmur_3. 0) with both NCBI and Ensembl ortholog databases (Table  2 ). Almost all (99. 1%, 329/332) of the human hormone-related genes were annotated in the mouse lemur genome. The three genes without mouse lemur orthologs (LHB, GALP, and INSL4) may result from incomplete annotation of the mouse lemur genome. For example, LHB encodes the beta subunit of luteinizing hormone, which is thought to be expressed in the gonadotrophs of the pituitary gland across all vertebrates.
Discussion Hormonal signaling is vital to multicellular life, allowing cells to communicate with both surrounding cells and remote cells in other tissues. In this study, we systematically mapped hormone-producing and -target cells across the 739 cell types from 27 different tissues from the mouse lemur molecular cell atlas. We generated a browsable atlas that depicts gene expression of ligands, modulators, and receptors for 87 hormone classes (Fig. S1 , Fig. 1 ). This dataset complements the scale and resolution of classical endocrine studies and brings a cellular and molecular foundation for studying novel aspects of hormone regulation. The mouse lemur hormone atlas represents a primate model of hormone signaling and should be useful in future organism-wide cross-species comparisons. The hormone atlas also provides an opportunity to extract global principles of hormone signaling.
g. , pancreas). In this study, we scored positive expression of a hormone ligand only when both the ligand gene and the necessary modulators (such as prohormone processing enzymes) were present. This approach mitigated false-positive detection of highly abundant ligands. Lastly, scRNAseq has low capture efficiency for certain cells such as neurons and large sized adipocytes. Nevertheless, the mouse lemur cell atlas included several neuronal subtypes from the brain cortex, brainstem, and retina, as well as both UCP1-high and UCP1-low adipocytes from multiple tissues  14. Although not yet a complete sampling, the mouse lemur cell atlas is one of the most comprehensive and accurately-annotated organism-wide scRNAseq datasets reported to date. Other organism-wide scRNAseq atlases have been created or are being created for other species, including mouse  54, 109, 110  , ascidian 111 , cynomolgus monkey 112 , and human 113 , bringing exciting resources to the study of the evolution of hormone signaling, as well as the cellular and molecular mechanisms and interactions underlying hormone-regulated biology.
20  to first predict its chromosome location in the mouse lemur genome. In brief, the pipeline employs the approach of groHMM 114 to detect transcriptionally active regions (TARs) from aligned sequencing data and annotates these TARs as unannotated (uTARs) or annotated (aTARs) based on their overlap with the existing annotation. For the LHB_uTAR counts, we uncovered uTARs from the mouse lemur pituitary and hypothalamus dataset and used BLASTn 115,116 to find nucleotide sequence homology of all uTARs against human LHB. We found one uTAR at position NC_033681. 1:19143249-19145949 with high sequence homology to human and macaque LHB. The LHB_uTAR is highly differentially expressed in the expected cell type (gonadotroph). The LHB_uTAR is located next to gene RUVBL2, in conservation with the co-location pattern of the two genes in humans and mice  21.
Acknowledgments We are grateful to  Sandra Schmid ,  Uri Alon ,  Chaitan Khosla , the  Krasnow lab , and the  Ferrell lab  for helpful discussions and comments on the manuscript. This work is supported by  NIH  grant  R35 GM131792  to  J. E. Ferrell , funding from  Wall Center  to  M. A. Krasnow , and a fellowship from the  Wu Tsai Neurosciences Institute  to  S. Liu. M. A. Krasnow  is an investigator of the  Howard Hughes Medical Institute.
Sequence analysis and structural prediction The AapB sequence was loaded into SyntTax 81 , in order to search for homologous sequences amongst other Thermoprotei species. The top six differing results were then compared against each other using Clustal Omega 82. To visualise the predicted gene cluster surrounding AapB, the KEGG genome database was used  80  , followed by Synttax to search for homologous proteins in differing archaeal species  81. Protein structure prediction was performed with AlphaFold2, using the online ColabFold tool  83.
Acknowledgements We would like to thank  Diamond Light Source  for providing access to the cryoEM facilities at the UK national electron bio-imaging centre (eBIC), funded by the  Wellcome Trust ,  MRC  and  BBSRC. Filament preparations were initially screened  Faculty of Biology at the EM facility of the University of Freiburg , who we would like to thank for providing access to their microscopes. The TEM (Hitachi HT7800) was funded by the  DFG  grant (project number  426849454 ) and is operated by the  University of Freiburg, Faculty of Biology , as a partner unit within the Microscopy and Image Analysis Platform (MIAP) and the  Life Imaging Center (LIC) , Freiburg. BD, MG, MM and RUH were supported by an  ERC Starting  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  803894 ), awarded to BD.
Data availability The cryoEM map generated in this study has been deposited in the EM DataResource under accession code EMD-18119. The corresponding atomic coordinates have been deposited in the protein Data Bank database under accession code PDB-8Q30. The S. acidocaldarius (DSM639) genome can be accessed via the KEGG accession code T00251 (https://www. genome. jp/entry/gn:T00251) or the NCBI gene bank code CP000077 112. The transcriptomics data analysed in this study can be accessed in the Pan Genomic Database for Genomic Elements Toxic To Bacteria.
PE shows features of a biallelically active enhancer from ES to NPC and in the neonatal brain. The PE intronic region is one of the enhancers annotated by mouse transgenic experiments  (Visel et al. , 2010)  with activity in mouse brains (dataset ID: mm1679 in Vista Enhancer Browser). This observation, combined with our chromatin structure studies, supports that this intronic region could be an enhancer for both Peg13 and Kcnk9, but that the contacts already established in ES cells are not sufficient per se to induce maternal Kcnk9 expression and paternal upregulation of Peg13. We therefore performed an integrative analysis to assess whether the changes in the molecular signature at this region could account for the change in expression of Kcnk9 and Peg13 between ES and NPC. Non-allelic ATAC-seq and WGBS datasets indicated that this region is in an open chromatin configuration, with a strong ATAC-seq signal and depletion for DNA methylation in both cell types and brain tissue (Fig.
DNA methylation data mining ES and NPC WGBS data were obtained from Geo DataSets under the accession number GSM748786 and GSM748788, respectively. The reads were first processed using TrimGalore and then mapped to the mm39 genome using Bismark. Duplicated read alignments were removed with the script deduplicate_bismark. The CpG methylation levels were computed from the selected alignments using bismark_methylation_extractor ( --no_header --cutoff 4 -bedgraph) and coverage2cytosine scripts. The output was converted with the bedGraphToBigWig tools in order to be loaded on UCSC genome browser. CpG methylation levels of ESC, NPC and frontal cortex on the mm10 genome were obtained from the tracks Stadler 2011 and Lister 2013 of the DNA methylation Hub on UCSC.
genomes production for NGS data alignment of hybrid samples: The sequences of the JF1 strain have been obtained from the DDBJ database under the accession number: DRP000326 and DRP000984. Paired-end reads were filtered using the CutAdapt tool to exclude poor quality reads. (--minimum-length=101 --pair-filter=any -q 20). The remaining reads were mapped on the mm39 genome with bowtie2. Alignments were filtered for poor quality with samtools wiew (-q 20). Duplicated alignments were excluded using samtools fixmate and markdup (-r). In order to identify JF1 polymorphisms, the filtered alignments were analysed using the freebayes tool (m 20 -q 30 -C 10 -F 0. 75) and the output was normalized using the tools bcftools norm. The variants were then decomposed using the vcflib vcfallelicprimitives (-kg) tool.
Gene expression data mining Expression data of cortex at embryonic day 13. 5 from B6xJF1 and JF1xB6 genetic backgrounds were obtained from GSE58523 dataset. The RNA-seq treatment was based on the pipeline describe above adapted for single end RNA-seq. Expression data of the ES cells KO for the PRC2 components were obtained from the GSE58016 dataset and were aligned to the mm39 genome and were processed for visualisation on the UCSC genome browser.