dataesr/scientific-paragraphs-categorization huggingface dataset:
Searches The search strategy has been designed by a biomedical information specialist (WMB) from the Medical Library of Erasmus MC, University Medical Center Rotterdam following the method as described by Bramer et al. 24  We will search MEDLINE via Ovid, Embase. com, CINAHL via EBSCOhost, PsycINFO via Ovid, AMED and Google Scholar for published and peer-reviewed systematic reviews or meta-analyses. The search strategies for Embase and Medline used relevant thesaurus terms from Emtree and Medical Subject Headings, respectively. Next to that, in all databases terms will be searched in titles and abstracts of articles. The search combined terms for (1) postpartum depression or depression during pregnancy and (2) different forms of complementary medicine such as mindfulness and plant extracts, as well as terms for e-health.
No time restriction and language restriction will be set, but conference abstracts are excluded from the search in Embase. com. We will also handsearch the reference list of the included reviews/meta-analyses for additional relevant systematic reviews or meta-analyses not retrieved in the electronic database searching. The full search strategy, search terms and syntax are presented in online supplemental table 1. The search will be updated prior to publication, so that there is no more than 6 months between the last search date and the date of publication. The umbrella review will begin on 1 September 2021 with an estimated completion date is 1 July 2022. Types of studies to be included We will include systematic reviews with or without metaanalyses. To define which articles can be considered as a systematic review, we will use the criteria proposed by Krnic Martinic et al  25  : (1) specific research question;.
Open access (2) sources that were searched with a reproducible search strategy; (3) clear inclusion and exclusion criteria; (4) selection (screening) methods; (5) critical appraisal and reporting of the quality/risk of bias of the included studies and (6) for meta-analyses, information about data analysis and synthesis that allows for the reproducibility of the results. To be included, systematic reviews or meta-analyses will need to focus mostly on individual studies reporting a randomised controlled design (ie, >50% of RCT in the individual studies). Because a sufficient number of randomised controlled studies have been conducted since 2018,  14  we will exclude systematic reviews or meta-analyses focusing predominantly on non-randomised observational studies, retrospective studies, case-control studies or case studies.
We will exclude systematic reviews or meta-analyses aiming at the prevention of PPD. In case of overlapping reviews, we will include the largest (ie, including the largest underlying dataset), the most recent and those of highest quality. In case the overlap is less distinctive, we will include both reviews. Overlap between reviews will be described and quantified. 26 We will describe the consistency or inconsistency between overlapping reviews in the narrative synthesis of the qualitative findings. Meta-analysis will be performed by using the data from the individual RCT studies included in the systematic reviews or meta-analyses. Duplicate studies will be excluded. We will check if any individual study included in smaller meta-analyses was not included in the largest meta-analysis. In that case, we will rerun the meta-analysis including studies from the largest metaanalysis and any additional study from the smaller one that meets the inclusion criteria detailed below.
Acknowledgements This paper is part of the  COST Action Riseup-PPD CA18138  and was supported by  COST  under  COST Action Riseup-PPD CA18138. Contributors JD drafted the article. WMB made the search strategy and will conduct the database searching. AG-A, FP and ML-vdB will make the first screening based on titles and abstracts and the second screening based on the full text papers against the eligibility criteria to select the included studies. NA-M, AG-A and AMK will extract the data and create the extraction form. AG-A, FP and ML-vdB will make the quality assessment. AMK will conduct the statistical analysis. All authors will contribute to the interpretation of the data and critically revise the article.
Logarithmic transformation was required to standardize observations on daily fecal bile acid excretion. When significance was found, differences between weeks were identified using Tukey-Kramer adjusted, post-hoc multiple comparisons. For each dog group, the significance of difference in body weight before and after antibiotics administration was determined by paired t-test analysis. The significance of differences between initial and final body condition scores was determined with sign tests. The significance of correlations between normalized daily SAA intake and taurine concentrations in plasma, whole blood, and semimembranosus muscle were determined with Spearmenrank order correlation analyses. Statistical software was used to conduct the analyses (SAS 9. 4, SAS Institute, Cary, NC, USA).
Doxorubicin Accumulation in Cells Cells were seeded on coverslips placed in 24-well plates. Once cells reached 80% of confluence, the cells' medium was replaced with a physiological buffer (140 mM NaCl, 5 mM KCl, 1 mM CaCl 2 , 1 mM MgSO 4 , 5 mM glucose, 20 mM HEPES, pH 7. 4) containing 2 µM of doxorubicin (dxr). After different incubation time points  (15, 30, 60 , 180 and 240 min), coverslips were fixed with PFA 4% in PBS, washed with PBS and mounted using a SlowFade Gold antifade reagent containing DAPI (Invitrogen). Images of the coverslips were taken using a Zeiss Axioplan 2 fluorescence microscope coupled to a charge-coupled device camera using a 40×/1. 3 Plan NeoFluar objective and filters for Alexa 594. Dxr fluorescence was measured using ImageJ software 2015. Around 100 cells (selection of cells was performed randomly) from three different coverslips were measured for each time point and condition of the experiment.
RNA-Seq Libraries were generated from 500 ng of total RNA from parental H295R and H295R-PM-Ptc+ cells using Truseq Stranded mRNA kit (Illumina). Libraries were then quantified with Qubit dsDNA High Sensitivity Assay Kit (Invitrogen) and pooled. A measure of 4 nM of this pool was sequenced on a NextSeq 500 platform (Illumina) with 2 × 75 bp paired-end chemistry. All sequencing results were submitted in the GEO database under the super series accession number GSE189424  [28]. Reads were mapped with STAR 2. 4. 0a on human genome (build hg38). Counting was performed with featureCounts (subread-1. 4. 6-p1-Linux-x86_64, "-primary-g gene_name -p-s 1-M " options) using Ensembl GRCh38. 76 release. Statistical analysis was performed using R version 3. 3. 1. The reproducibility of the replicates for each condition was assessed by performing principal component analysis using the R package "htSeqTools" (R package version 1.
14. 0; data not shown). Functions from the Bioconductor package DESeq2 were used to normalize data for differences in their sequencing depth and assess differential expression. The Benjamini-Hochberg method was used to control the false discovery rate. Results of DESeq2 differential expression analysis are presented in Supplementary Materials Table  S1. Genes considered as differentially expressed with a p-value < 0. 05 and an absolute log2 fold change >1 are listed in Supplementary Materials Table  S2. Genes associated with a p-value < 0. 05 are listed in Supplementary Materials Table  S3. Functional enrichments for Gene Ontology terms, KEGG, WikiPathways and Reactome Pathways were retrieved using Cytoscape  [29]  and the StringApp plugin  [30]. The enrichments of differentially expressed genes reported in Supplementary Materials Table  S2  are listed in the "enrichments underexpressed", "enrichments overexpressed" and "enrichments all" worksheets in Supplementary Materials Table  S2.
AMINE (Active Module Identification through Network Embedding)  [31]  was used to identify modules of genes whose expression was triggered in the biological experiments. The interaction of different genes in the experimental conditions were then compared to a background database, indicating an involvement of these genes in the studied process. String database  [32]  was used to retrieve gene interaction data with a combined evidence score greater than 0. 7. Measurements of the activity of genes are represented by the p-values computed by DESeq2. Internally, the method merges the interaction data with the genes' associated p-values to generate an attributed gene network in which vertices represent genes, edges represent interactions between genes and each vertex is annotated with a numeric attribute reflecting its associated p-value.
The method relies on Node2vec  [33]  to learn a vector representation of the interaction network. As a result, each gene is represented by a vector in an embedded space in which the cosine distance between the vectors representing the nodes accurately reflects their proximity in the original network. The algorithm then uses a greedy approach to build increasingly large clusters of genes based on the similarity of their encoding vectors and evaluates them according to a metric considering the activity of the contained genes. The details of the method are described in  [31]. However, this whole internal workflow is transparent to users who can simply apply the method on their differential expression data from the web site  [34]. On this site, it is possible to perform the identification of the active modules by simply uploading the file generated by DESeq2 and specifying the index of the column containing the gene names, the index of the p-value column and the index of the log2 fold change.
This result confirms that the stronger resistance of H295R-PM-Ptc+ cells is correlated to the increased level of Ptch1 at the plasma membrane of these cells. the annotated intron 3. Similarly, a high coverage of reads and a missing splice connectio between exons 2 and 3 were observed when analyzing another five dog breeds (Figure  S1  suggesting a misannotation of the OCT1 gene structure or an incomplete dog genome. In contrast, when we mapped RNA-Seq datasets from dog kidney for OCT2 using th same reference genome (CanFam3. 1), no differences between the annotated dog OCT2 gen structure (CanFam3. 1) and the RNA-Seq datasets for kidney could be observed (Figure  S2  This suggests first, that the annotation of dog OCT2 in the genome is correct, and second verified the methodological bioinformatics approach used.
To measure the speed of closing, the wound area was measured for each time point. H295R-PM-Ptc+ cells showed a migration speed 1. 3 times greater than the parental H295R cell line over the first 24 h (Figure  3D ). Accordingly, H295R-PM-Ptc+ cells were able to close the wound completely after 20 days, whereas H295R cells did not. The invasiveness properties of these cells were addressed using a transwell invasion assay. After correction to take into account the proliferation differences in each experiment, the data showed that the number of cells able to pass through the Matrigel-coated filters was significantly higher with H295R-PM-Ptc+ cells compared to the parental H295R cells (Figure  3E ). Taken together, our results strongly suggest that H295R-PM-Ptc+ cells possess an increased ability to form clones, to migrate and are more invasive than the parental cells.
of closing, the wound area was measured for each time point. H295R-PM-Ptc+ cells showed a migration speed 1. 3 times greater than the parental H295R cell line over the first 24 h (Figure  3D ). Accordingly, H295R-PM-Ptc+ cells were able to close the wound completely after 20 days, whereas H295R cells did not. The invasiveness properties of these cells were addressed using a transwell invasion assay. After correction to take into account the proliferation differences in each experiment, the data showed that the number of cells able to pass through the Matrigel-coated filters was significantly higher with H295R-PM-Ptc+ cells compared to the parental H295R cells (Figure  3E ). Taken together, our results strongly suggest that H295R-PM-Ptc+ cells possess an increased ability to form clones, to migrate and are more invasive than the parental cells.
A representative image of clones stained with crystal violet is provided for each cell group. (D) H295R-PM-Ptc+ cells migrate faster than H295R cells. Migration ability of the two cell groups was evaluated using wound healing experiment. Slope of migration for 24 h, normalized to H295R, n = 5, (p = 0. 005). H295R-PM-Ptc+ cells completely reclose the wound compared to H295R cells, images taken 12 and 20 days after producing the wound (5× objective, scale = 100 µm). (E) H295R-PM-Ptc+ cells are more invasive than parental cells. More H295R-PM-Ptc+ cells were able to pass through Matrigel-coated filters compared to H295R. Invasion was normalized to H295R and proliferation differences, cells passed through the filter were counted and reported to the total cell number (n = 2). All data presented are the mean ± SEM of independent experiments.
In their absence, Gas1, an Hh-binding protein, mediates Hh signaling  [35]. The number and composition of the 21 active modules, their representative enrichment and the role of differentially expressed genes (DEGs) in cancers are summarized in Table  2. Pharmaceutics 2022, 14, x FOR PEER REVIEW 15 of 28  1. The nodes on the network correspond to genes. Node colors represent the log2 fold change values of the corresponding gene on a scale varying from blue (for the most underexpressed genes) to red (for the most overexpressed genes). Edges correspond to interactions reported in the String database with a combined evidence score ≥0. 4. Each module is identified by its number (specified in Supplementary Materials Tables  S2  and S3 ). The complete lists of the enrichments of all the modules are shown in Supplementary Materials Tables  S4.
1. The nodes on the network correspond to genes. Node colors represent the log2 fold change values of the corresponding gene on a scale varying from blue (for the most underexpressed genes) to red (for the most overexpressed genes). Edges correspond to interactions reported in the String database with a combined evidence score ≥0. 4. Each module is identified by its number (specified in Supplementary Materials Tables  S2  and S3 ). The complete lists of the enrichments of all the modules are shown in Supplementary Materials Table  S4. shows that some proteins involved in cancer stem cell maintenance are overexpressed in H295R-PM-Ptc+ cells.
3. The differential expressions of genes from almost all the modules are involved in cancer progression, cell migration or invasion, and are often associated with poor prognosis. All these characteristics suggest that these cells are cancer stem-like or persistent cells. Indeed, several differentially expressed genes are known to be involved in stem cell and/or quiescence maintenance. The differential expression of other genes has been associated to de-differentiation in different tumors. Several genes specific to adrenocortical carcinoma are downregulated. One of these genes is STAR that encodes for a steroidogenic acute regulatory protein responsible for cholesterol transport to the mitochondria which is the rate-limiting step in steroid hormone production  [105]. This transport protein is present in steroid-producing cells such as ovary theca and luteal cells, testis Leydig cells and some adrenocortical cells.
H295R-PM-Ptc+ cells completely reclose the wound compared to H295R cells, images taken 12 and 20 days after producing the wound (5× objective, scale = 100 µm). (E) H295R-PM-Ptc+ cells are more invasive than parental cells. More H295R-PM-Ptc+ cells were able to pass through Matrigel-coated filters compared to H295R. Invasion was normalized to H295R and proliferation differences, cells passed through the filter were counted and reported to the total cell number (n = 2). All data presented are the mean ± SEM of independent experiments. **: p-value < 0. 005, ***: p-value < 0. 0005.
(E) H295R-PM-Ptc+ cells are more invasive than parental cells. More H295R-PM-Ptc+ cells were able to pass through Matrigel-coated filters compared to H295R. Invasion was normalized to H295R and proliferation differences, cells passed through the filter were counted and reported to the total cell number (n = 2). All data presented are the mean ± SEM of independent experiments. **: p-value < 0. 005, ***: p-value < 0. 0005.
Funding: This work was supported by grants from  Centre National de la Recherche Scientifique (CNRS)  through the  "Soutien au transfert" program ,  Association France Cancer, Region Provence Alpes Côte d'Azur  through the  APRF program  ( 2013-17362 ),  Canceropôle PACA ,  French National Research Agency (ANR)  through the  Investments for the Future UCA JEDI  ( ANR-15-IDEX-01 ) and  LABEX SIGNALIFE program  ( ANR-11-LABX-0028-01 ). Institutional Review Board Statement: The animal study protocol was approved by the  Institutional.
005). H295R-PM-Ptc+ cells completely reclose the wound compared to H295R cells, images taken 12 and 20 days after producing the wound (5× objective, scale = 100 µm). (E) H295R-PM-Ptc+ cells are more invasive than parental cells. More H295R-PM-Ptc+ cells were able to pass through Matrigel-coated filters compared to H295R. Invasion was normalized to H295R and proliferation differences, cells passed through the filter were counted and reported to the total cell number (n = 2). All data presented are the mean ± SEM of independent experiments. **: p-value < 0. 005, ***: p-value < 0. 0005.
How the data were acquired Spectra were recorded by Fourier transform interferometry  [1] , on a Bomem DA3. 002 spectrometer operated with with proprietary software (PCDA data acquisition). Light from an external quartz halogen white light source (Osram) running on a stabilized DC power supply (Fontaine) was collimated and passed through a 10 cm sealed evacuated cell, containing a small amount of 130 Te (Ideal Vacuum Products). Interferograms were acquired at nominal apodized resolution of 0. 02 to 0. 033 cm -1. To achieve an acceptable signal-to-noise ratio, the spectrum was recorded in several pieces using combinations of optical filters, averaging between 200 (above 500 nm) and 600 scans (in the violet, where emission from the lamp was much weaker) in each piece.
The absorption spectra generated in Bomem GRAMS format were adjusted so that their wavenumber scale matched reference data from other sources  [4] [5] [6]. A wavenumber-dependent correction was necessary, attaining 0. 03 cm -1 at 23000 cm -1. The pieces were then concatenated (see Figure  2 ), averaging the regions of overlap using wave arithmetic software provided by IGOR-Pro 6  [7]. The percentile and boxcar smoothing algorithms in IGOR-Pro were used to generate the baseline contour required to generate transmittance and absorbance traces. Transmittance was calculated from Transmission/Baseline, and Absorbance as log 10 (1/Transmission). The results were exported using a cubic spline interpolation to generate Ascii files with evenly-spaced wavenumber points (a short extract is given in Table  1 ) covering the blue-green to violet region of the optical spectrum.
In this work, we used a set of 502 well-characterized Caucasian women from the SU. VI. MAX cohort that were genotyped with the Illumina HumanOmni1-Quad BeadChips leading to 795,063 SNPs after quality control. We looked for new associations at the level of biological pathways for four major outcomes of skin aging, namely, photoaging, solar lentigines, wrinkling, and sagging. For each outcome, we performed a classical GWAS using a regression adjusted for various confounding factors, and we then treated the data in the GSEA pipeline to look for pathway associations.
Covariates Used for the Statistical Analysis Several characteristics susceptible to play a role in the onset of the different outcomes analyzed were taken into account: age (in years), body mass index (BMI; in kg. m -2 ), smoking habits (never, former, and current), and hormonal status (nonmenopausal, menopausal with hormone replacement therapy, and menopausal without hormone replacement therapy). BMI was categorized as underweight or normal (BMI < 25 kg m -2 ), overweight (25 ≤ BMI < 30 kg m -2 ), or obese (BMI ≥ 30 kg m -2 ) according to the World Health Organization (WHO) recommendations (WHO, 1995) Moreover, lifetime sun exposure intensity was estimated by a score based on data collected by a self-reported questionnaire  (Ezzedine et al. , 2013). The design, validation, and description of this score have been described previously  (Guinot et al.
Quality Control The quality control steps have been described in a previous study (Le  Clerc et al. , 2013). Briefly, nine samples with a call rate (percentage of SNPs genotyped by sample) of <95% in the Illumina clusters were removed. The SNPs with a call frequency (percentage of samples genotyped by SNP) of <99% were reclustered and, after that, samples with a call rate of <98% were deleted. In total, after these quality control steps, 56,479 SNPs with a call frequency of <98% (2% of missing data) were excluded. The Hardy-Weinberg equilibrium analysis was performed for each SNP by using an exact statistical test implemented in the PLINK software  (Purcell et al. , 2007). Thus, 3,866 SNPs, which were not in the Hardy-Weinberg equilibrium (p < 1 × 10 -3 ), were rejected. Finally, we removed 191,123 SNPs with minor allele frequency <1% to avoid errors of genotyping, leaving a total of 795,063 SNPs.
Identification of Population Stratification To correct for possible population stratification, genotypes were analyzed using EIGENSTRAT utility of the EIGENSOFT package version 4. 2  (Price et al. , 2006). The two first pass with the EIGENSTRAT software pointed out 18 outliers, who were removed from further analyses. Then, a third pass without outliers was performed to determine the Eigen vectors. In the statistical analysis, we used the top two Eigen vectors as covariates to correct for population substructure in the association analyses.
Statistical Analysis We first performed GWAS on each of the four outcomes as described in a previous work (Le  Clerc et al. , 2013). Briefly, for each outcome, the association between each SNP and the outcome was measured using a linear regression with a genotypic model adjusted on the first two Eigenstrat principal components and the potential confounding factors (smoking habits, BMI, hormonal status, lifetime sun exposure intensity, and age) using the PLINK software  (Purcell et al. , 2007). Then, we looked for associations between the biological pathways and each outcome by performing the gene set enrichment analysis (GSEA) implemented in the GenGen software suite  (Wang et al. , 2007; 2010a). 5,000 p-value permutations were performed for the normalization of the enrichment score. The significance and correction for multiple tests were based on the 5,000 permutations and finally assessed using the false discovery rate (FDR).
Enrichment Analysis According to the classification of the KEGG pathways, the pathways are assigned to six categories: "environmental information processing," "cellular processes," "metabolism," "organismal system," "genetic information processing," and "human disease. " For each outcome, we looked for an enrichment of significant pathways (FDR < 0. 25) in the six categories compared to the distribution of these categories in the whole KEGG database. We used a Fisher's exact test based on hypergeometric distributions to compare the distribution of the significant pathways in each category with the distribution of the pathways of the whole KEGG database in these categories.