dataesr/scientific-paragraphs-categorization huggingface dataset:
The DLQI questionnaire is the first dermatology-specific QoL instrument designed to assess the impact of skin diseases and associated treatments on patient QoL  [32, 33]. Intended for patients aged >16 years, the DLQI represents the sum of all scores (0-30). The results can equally be expressed as percentages (0-100%). Pearson correlation was calculated to assess the validity between BoA and the other three questionnaires. The data were analysed using SAS software Version 9. 4 (SAS Institute, Cary, NC, USA) for Windows, with a significance level set at 0. 05.
Discussion Processivity mostly relies on FH2-filament interactions, with an unexpected contribution of FH1 domains Formin control of actin filament elongation at the barbed end is mediated by its homology domains FH1 and FH2, as well as its tail domain, DAD  (Gould et al. , 2011; Vizcarra et al. , 2014). We quantified formin processivity by measuring its dissociation rate k off. Our data indicate that FH2-barbed end interactions are destabilized by ions (Figure  2A ). These results confirm that salt bridges mediating FH2-actin interactions, which have been predicted from molecular dynamics simulations  (Baker et al. , 2015) , are essential determinants of the residence time of formin at the barbed end. Our data also indicate that FH2-actin interactions are destabilized by the presence of a fluorescent label on actin subunits (Figure  2C ), consistent with the notion that the lateral contacts of FH2 with actin subunits are essential to maintain the formin at the barbed end  (Otomo et al.
Microscopy and image acquisition The microfluidic setup was placed on a Nikon TiE inverted microscope, equipped with a 60x oilimmersion objective. We either used TIRF, HiLo or epifluorescence depending on the background fluorophore concentration in solution. Two different TiE microscope setups were used. The TIRF setup was controlled by Metamorph, illuminated in TIRF or epifluorescence by 100 mW tunable lasers (iLAS2, Roper Scientific), and images were acquired by an Evolve EMCCD camera (Photometrics). The other TiE setup was controlled by micromanager  (Edelstein et al. , 2014) , illuminated with a 200W Xcite lamp (Lumen dynamics) and images were acquired by an sCMOS Orca-Flash4. 0 V2 + camera (Hamamatsu). Images were analyzed using ImageJ software. The experiments were performed at room temperature, in an air-conditioned environment.
Data analysis To avoid any bias related to the selection of filaments during analysis, a rectangular region containing a few tens of filaments was randomly chosen in the microscope field of view, and all the filaments in this region were analyzed. Within this population, filaments were excluded from our analysis only in the following specific cases. We excluded filaments whose ends were difficult to locate because they overlapped with other filaments. We also excluded filaments that sometimes seemed to stick to the surface or, in the case of experiments with anchored formins, appeared to stall (see Supp. Movies). Movies were analyzed with ImageJ. The Subtract Background plugin was sometimes used to enhance the contrast, with a rolling ball radius of 50 pixels. Quantifying formin dissociation rates and their error bars, in the absence of force For each experiment, from a randomly chosen filament population, we noted the time at which each individual formin dissociated from the barbed end.
The survival fraction of each experiment was then fitted by a single exponential using the numpy/scipy numerical packages of Python. In order to quantify the statistical uncertainty in the estimation of the dissociation rate k off resulting from the exponential fits of the survival fractions S(t) (shown for example in Figures 1,2A; Figure  2C ), we performed numerical simulations of the experiment (using Python). The program simulated a large number (M = 10,000) of experiments, each consisting in N filaments randomly losing their formin with rate constant k 0. The survival fraction of each simulated experiment was fitted by a single exponential, resulting in the generation of M estimated rates k est. The distribution of these k est , centered on k 0 , allowed us to compute the width of the confidence intervals.
Acknowledgements The authors thank  David Kovar  and  Dennis Zimmermann  for the plasmids of formin mDia2 and mutant profilin-R88E. The authors acknowledge funding from the  Fondation pour la Recherche Medicale  (grant to GRL and ML), the  Human Frontier Science Program  (grant  RGY0066  to GRL), the  H2020 European Research Council  (grant  StG-679116  to AJ, grant  StG-677532  to ML) and the  Fondation ARC pour la Recherche sur le Cancer  (postdoctoral fellowship to HW).
Ultrathin sections were cut, stained with 5% uranyl acetate 5% lead citrate, and placed on electron microscopy grids coated with collodion. The sections were then observed with a Jeol 1010 transmission electron microscope (Tokyo, Japan) connected to a Gatan digital camera driven by Digital Micrograph software (Gatan, Pleasanton, CA, USA). For each biopsy specimen, at least 5 consecutive squares of the TEM grid (5000 µm 2 ) were observed, representing several hundred hepatocytes.
, 2017) , and MYOD1, the key regulator of myogenesis and direct target of BMAL/ CLOCK  (Andrews et al. , 2010)  were rhythmically expressed in human skeletal muscle, likely orchestrating the temporal muscle transcriptome (Figure  1H ). In addition to these transcription factors, genes involved in the secretion of myokines, glucose homeostasis and lipid metabolism displayed rhythmic transcription (Figure  1 -figure supplement 1A,B and C, respectively). To gain more insight into the rhythmic transcriptional regulation observed in this dataset, we performed a DNA-binding motif enrichment analysis to identify those with rhythmic activity. As shown in Figure  1I , the 16:00 peak is enriched in MEF2 and MYOD1 motifs, in phase with MYOD1 expression, and both proteins synergize to activate gene expression  (Taylor and Hughes, 2017).
Furthermore, enrichment analysis using the Reactome pathway database was performed on the down-and/or upregulated genes. Of note, overrepresentation of genes related to muscle contraction, regulation   In addition, 42 transcripts involved in lipid metabolism were affected by CLOCK disruption. These comprised genes related to glycerophospholipid and triglyceride metabolism as well as lipid storage and transport (Figure  2C ), in addition to those regulating inositol phosphate (Figure  2D ) and sphingolipid metabolic pathways (Figure  2E ). Importantly, the observed modifications in gene expression level were in an accord with significant alterations in absolute lipid metabolite levels, resulting in total phosphatidylcholine levels being downregulated, and glycosylceramide levels being upregulated in the absence of a functional myotube clock (Figure  2F , Supplementary file 1-table S1, donors 7-10 for in vitro part).
RNA-seq reveals rhythmically expressed genes in cultured hSKM synchronized in vitro We next aimed at identifying genes that exhibited rhythmic profiles in hSKM synchronized in vitro. The existing algorithms JTK_CYCLE  (Hughes et al. , 2010)  and CosinorJ  (Mannic et al. , 2013)  do not allow for a satisfactory analysis of datasets containing large differences in amplitude, observed among the two cycles in our datasets. We therefore developed a novel algorithm, adapted for the analysis of our RNA-seq datasets, comprising high-resolution analysis of two full cycles (25 time points over 48 hr) following in vitro synchronization (see Materials and methods for details). Our in vitro algorithm was validated on the temporal expression profiles of key core clock genes from two published large-scale time series  (Atger et al.
, 2015; Petrenko et al. , 2016)  and on the model dataset cycMouseLiverRNA from the MetaCycle R package. Briefly, after conversion of the raw data to log 2 RPKM values and filtering for log 2 RPKM >0, temporal patterns of the resulting 12,985 genes were grouped into 16 models (Figure  4A , Figure  4 -source datas 1 and 2), with models 1 to 15 comprising 994 rhythmic genes (7. 65%), and model 16 comprising 11,991 non-rhythmic genes (92. 35%). Because the number of rhythmic genes exhibited larger variations between the two analyzed cell cultures, established from two different human individuals than between siControl and siCLOCK (Figure  4 -figure supplement 1A), we proceeded with a deeper analysis of models 1 to 4, which group together genes that are rhythmic in the siControl situation for both donor cell cultures.
For instance, CAMKK1, classified as rhythmic in both siControl and siCLOCK conditions (model 1), exhibited a significant circadian amplitude reduction upon siCLOCK (Figure  4D , Supplementary file 1-table  S6 ). In addition, SERPINE1, a myokine whose secretion by myotube cells was reduced upon clock disruption  (Perrin et al. , 2015) , presented lower amplitude in siCLOCK-transfected cells (Supplementary File 1-table  S6 ). Panther database analysis for genes assigned to models 1-4 suggested enrichment for a number of GO term and Reactome pathways, comprising cell cycle and mitotic regulation (Figure  4 -figure supplement 3, Supplementary file 1-tables S4-5, Figure  4 -source datas 3 and 4).
Comparative analysis of diurnal rhythms of gene expression in human skeletal muscle tissue and cultured hSKM Consequently, we compared rhythmic gene expression between muscle biopsy and cultured hSKM cells (Figure  5A ). Among the 190 transcripts that were identified as rhythmic in hSKM cells (Figure  4A , models 1-4, Figure  5 -source data 1), 14 transcripts were excluded as they were representing non-protein coding sequences or pseudogenes. Additional 26 genes, associated with mitotic cell cycle functions, and further 17 genes related to cell proliferation, adhesion and differentiation, Source data 1. List of genes found in each of the 16 models identified by rhythmic analysis of the RNA-seq data. DOI: https://doi. org/10. 7554/eLife. 34114. 018 Source data 2. This dataset contains the log 2 RPKM values for all 25 time points (0 to 48 hr) and the mean of all time points per donor and per condition (siControl/siCLOCK) as well as the model where each gene is grouped.
Within this group of overlapping genes, 35 were also qualified as rhythmic at the mRNA level (q-val <0. 05) in muscle tissue (Figure  5 -source data 1). The genes rhythmic in both in vivo and in vitro datasets included the core clock components (Figure  5A ), as well as functional genes that were enriched at the 04:00 time point (Figure  5B ). Interestingly, genes implicated in glucose homeostasis (KLF11, GFPT2, NAMPT) and in muscle regeneration (PLAU, PLD1, PIM1), were rhythmic both in vivo and in vitro, suggesting an important role for the circadian clock in regulating muscle physiology (Figure  5C ).
, 2007; Miller et al. , 2007; Dyar et al. , 2014; Zhang et al. , 2014; Dyar et al. , 2015). Finally, to dissect the effects of the cell-autonomous endogenous clock from SCN signals and entrainment cues, this dataset was compared to the diurnal transcriptome of human skeletal muscle collected in form of serial muscle biopsies across 24 hr (Figures  1  and 5 ).
Moreover, we cannot exclude that discrepancies between the in vivo and in vitro circadian datasets are in part also influenced by the fiber type composition of vastus lateralis and gluteus maximus, as demonstrated by myosin isoform analysis  (Loizides-Mangold et al. , 2017). Additional differences with respect to gene rhythmicity between the two datasets may stem from potential differences due to the different algorithms employed for the data analyses. Among the 35 genes classified as rhythmic in cell culture and in skeletal muscle tissue, were genes involved in glucose metabolism and in muscle regeneration, including PLAU (LluisLluı ´s et al. , 2001) and PIM1 kinase  (Fischer et al. , 2009) , along with core clock components such as NR1D1 and NR1D2 (Figure  5A ), previously identified as the most rhythmic transcripts across all human and mouse datasets  (Laing et al.
, 2015). Phospholipase PLD1  (Teng et al. , 2015) , involved in intracellular membrane trafficking and maintenance of glucose homeostasis in human skeletal muscle  (Huang et al. , 2005)  was rhythmically expressed in vivo and in vitro. Moreover, oscillatory genes in both datasets included NAMPT, KLF11, and GFPT2, the latter controlling the flux of glucose into the hexosamine pathway, tightly linked to hyperglycemia and insulin resistance  (Coomer and Essop, 2014). The expression of NAMPT, a key regulator of NAD + synthesis and muscle maintenance  (Frederick et al. , 2016) , was previously shown to be regulated by CLOCK and BMAL1 in complex with SIRT1  (Ramsey et al. , 2009; Garten et al. , 2015). Importantly, the diurnal rhythm of secreted NAMPT is disturbed by sleep loss, and positively associates with impairment of postprandial glucose metabolism  (Benedict et al.
, 2012). The transcription factor KLF11, a glucose-inducible regulator of insulin transcription and secretion, that is a member of the Kru ¨ppel-like family of transcription factors proposed as circadian  (Yoshitane et al. , 2014) , was found to be regulated by the circadian clock in mouse kidney and epididymal fat tissue  (Guillaumond et al. , 2010) , and is possibly involved in postprandial glucose metabolism in skeletal muscle  (Neve et al. , 2005). Comparison of our in vitro dataset to the results published on U2OS cells  (Hughes et al. , 2009; Krishnaiah et al. , 2017)  revealed that among the 190 genes that were rhythmic in human skeletal myotubes, 30 were also rhythmic in U2OS. Among those were members of the core-clock machinery, and multiple genes involved in cell cycle progression and mitosis.
Moreover, the sulfatase ARSJ, involved in glycosphingolipid metabolism, EXOSC8, a regulator of mRNA stability, LRRC16A, involved in actin filament organization, and TUBA1C, encoding for a structural constituent of the cytoskeleton, as well as E2F1 were rhythmic in skeletal myotubes and in U2OS cells. Interestingly, the RING finger domain protein-encoding gene TRIM47 exhibited a rhythmic expression in human skeletal myotubes, in human muscle biopsies, and in U2OS cells. A comparison of our in vivo dataset (exonic signals) to published temporal gene expression databases of mouse skeletal muscle  (Dyar et al. , 2014; Zhang et al. , 2014)  revealed 107 common rhythmic genes between mouse and human skeletal muscle. When comparing the circadian phases of core-clock components in our database to the temporal profiles of the corresponding genes in rodents  (Dyar et al.
Given that inflammatory cytokines have been described as myokines and important regulators of muscle physiology, this could thus represent a true signature with relevant outcomes for muscle physiology. One limitation of the comparison between RNA-seq datasets obtained for in vivo and in vitro skeletal muscle and hSKM samples in the present study stems from different analyses methods applied for the two datasets. The algorithms applied here for these two datasets were chosen to optimally fit each dataset differing in the number of time points and the time span of samples collection. Indeed, cellular samples were collected every 2 hr over 48 hr resulting in 25 time points, whereas muscle tissue biopsies were collected every 4 hr over 24 hr, resulting in six time points only, due to obvious practical limitations of repetitive muscle tissue biopsy sampling from the same individuals.
Conclusions In summary, our study provides (1) a comparison between rhythmic transcriptome databases obtained from human muscle tissue and cultured primary cells derived from muscle biopsies, and (2) the identification of pathways regulated by CLOCK in skeletal muscle, involved in glucose uptake, myokine secretion, and lipid metabolism (Figure  6 ). Human primary cells cultured in vitro have been used as a model to study human disease and metabolism  (Aas et al. , 2013; Saini et al. , 2015). In combination with tissue analysis as presented here, primary cell culture constitutes a powerful model to study human metabolism, and warrants further analyses in additional metabolically active tissues in physiological conditions, and in the context of metabolic diseases. Continued on next page number was evaluated using the Cycler Correction Factor method as described previously  (Atger et al.
, 2015). Purified libraries were quantified with Picogreen (Life Technologies) and the size pattern was controlled with the DNA High Sensitivity Reagent kit on a LabChip GX (Perkin Elmer). Libraries were then pooled by 24, and each pool was clustered at a concentration of 8 pmol on 8 lanes of v4 paired-end sequencing flow cells (Illumina). Sequencing was performed for 2 Â 125 cycles on a HiSeq 2500 strictly following Illumina's recommendations. Paired-end reads were mapped to the Homo sapiens genome (GRCh38/hg38) using STAR  (Dobin et al. , 2013)  with parameters "-alignIntronMin 20 -alignIntronMax 1000000 -GTF (option -sjdbGTFfile). Mapped reads were quantified in intronic and exonic regions. For each gene, we defined a gene body by merging all the respective transcripts using BEDtools  (Quinlan and Hall, 2010).
A region was defined as exonic if it occurs in a least one of the transcripts while an intronic region had to be shared between all the transcripts. We assigned uniquely mapping paired-reads to exonic regions (exon/exon and complete exon reads) or intronic regions (intron-exon and complete intron reads) considering reads orientation. Genes with less than two intronic reads or 10 exonic reads on average were discarded. Intronic and exonic reads count were normalized using edgeR  (Robinson et al. , 2010)  by the library scaling factor from the exonic regions and the respective intronic and exonic length (rpkm()). Genes with less than À2 RPKM (log2) at the exonic level were discarded. Genes with less than À3 RPKM (log2) at the intronic level were reported as NA for the intronic quantification.
Key resources table Rhythmicity was assessed with a linear mixed-effects model using lmer() function from the lme4 R package applied on the log2 normalized reads count. A standard harmonic regression with a 24 hr period was fitted with a donor-dependent random effect on the baseline: y ID; t ~cos 2p 24 t þ sin 2p 24 t þ 1jID ð Þ where y ID,t is the log2 normalized reads count for patient ID at time t. This full model was compared to the null model (without the harmonic terms) using the likelihood ratio test function ltest() from the lmtest R package. The p-values were computed from a chi-squared distribution and were adjusted using the Benjamini-Hochberg procedure. Gene ontology analysis was performed using the TopGO R package. Enrichment analysis for GO terms derived from 'Biological Process' was performed for the genes rhythmic in the three groups (R-I.
Transcription factors enrichment analysis Predictions of transcription factor binding sites (MotEvo) and promoter regions were downloaded from http://swissregulon. unibas. ch/sr/downloads (database Homo sapiens, hg19:FANTOM5)  (Pachkov et al. , 2013). Transcription factor binding sites were assigned to their corresponding genes using the promoter regions table. Genes rhythmic at the intronic level, and with amplitude larger than 0. 5 (log2), were grouped according to their phase in 4 hr bins. All the genes expressed in the dataset were used as a background and a hypergeometric test was computed for the overrepresentation of transcription factor binding sites in the different bins. Transcription factor binding sites with -log10(p-value) >10 4 and belonging to at least five genes were reported and annotated in Figure  1I.
Triacylglyceride analysis was performed by mass spectrometry analysis on a hybrid ion trap LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific) equipped with a micro LC binary pump UFLC-XR (Shimadzu, Kyoto, Japan). Lipid identification was carried out with the Lipid Data Analyzer II (LDA v. 2. 5. 1, IGB-TUG Graz University)  (Hartler et al. , 2011). hSKM mRNA extraction and quantitative PCR analysis Differentiated myotubes were synchronized by 10 mM forskolin, collected every 2 hr during 48 hr (0-48 hr), deep-frozen in liquid nitrogen and kept at À80 ˚C. Total RNA was prepared using RNeasy Mini kit (Qiagen). 0. 5 mg of total RNA was reverse-transcribed using Superscript III reverse transcriptase (Invitrogen) and random hexamers, and PCR-amplified on a LightCycler 480 (Roche). Mean values for each sample were calculated from the technical duplicates of each RT-qPCR analysis, and normalized to the mean of two housekeeping genes (HPRT and 9S), which served as internal controls.
Primers used for this study are listed in Supplementary file 1-table  S9. hSKM RNA-sequencing and data analysis Total RNA was prepared from primary human skeletal myotubes from two donors, transfected either with siControl or siCLOCK, synchronized with a forskolin pulse and collected every 2 hr during 48 hr in duplicates, using RNeasy Mini Kit (Qiagen). Total RNA libraries were prepared from 300 ng of RNA following customary Illumina TruSeq v2 protocols for next generation sequencing. PolyAselected mRNAs were purified, size-fractioned, and subsequently converted to single-stranded cDNA by random hexamer priming. Following second-strand synthesis, double-stranded cDNAs were blunt-end fragmented and indexed using adapter ligation, after which they were amplified and sequenced according to protocol.
RNA libraries were sequenced with a HiSeq2000 sequencer producing 49pb paired-end reads. Standard quality checks for material degradation (Bioanalyzer and TapeStation, Agilent Technologies, Santa Clara, CA) and concentration (Qubit, Life Technologies) were done before and after library construction, ensuring that samples are suitable for sequencing. Paired-end reads were mapped to the human genome (version GRCh37/hg19) with GEMTools (v1. 7. 1) using GENCODE v19 as gene annotation. RNA-seq reads were subsequently filtered for correct orientation of the two ends, a minimum mapping quality score of 150 and allowing a maximum of 5 mismatches in both ends. GENCODE annotation v19 was used to assign filtered reads to their corresponding exons and genes. For each gene, we processed the exons from all transcripts, which were quantified by considering only filtered reads as above, in which both ends map to exons of the same gene.
The gene counts were incremented non-redundantly, meaning reads overlapping two exons were counted once to the total count sum per gene. The differential gene expression analysis was performed with the R package edgeR  (Robinson et al. , 2010). First, transcripts expressed lower than three counts per million (CPM) and noninformative (e. g. non-aligned) were filtered. To minimize the log-fold changes between the samples for most genes, a set of scaling factors for the library sizes were estimated with the trimmed mean of M-values (TMM) method  (Robinson and Oshlack, 2010). The dispersion was estimated with the quantile-adjusted conditional maximum likelihood (qCML) method. Once the dispersion estimates are obtained, we performed the testing procedures for determining differential expression using the exact test  (Robinson and Smyth, 2008).
The same number of genes for the three other groups were also integrated in the training dataset. This dataset was then passed to the training algorithm for random forests and gene conditions that were assigned to one of these categories with a high score (0. 9) were integrated in the training dataset. This procedure was repeated until 500 curves per group were identified. The last model was kept to classify the whole dataset. 1485 curves from 994 transcripts were identified as rhythmic among 12,985 transcripts. Altogether, transcripts were grouped into 16 models.