dataesr/scientific-paragraphs-categorization huggingface dataset:
4 accounts for the multiple detections of the same fluorescence dye. dSTORM involves antibody labeling, where secondary antibodies have a variable number of the conjugated fluorophores, which is not accounted for in the first term of Eqn. 4. The equation 4 was fit using Matlab lsqcurvefit function to recover d, and A. Average density ρ was estimated from the dataset using Voronoi diagrams, and σ using localization precision distributions, and σ was typically ~10 nm. The fit was typically performed for the distances from r =10 nm, to r = 500 nm, but was adjusted for noisy ACF at short distances if necessary. The number of molecules in the domain d can be calculated as follows:  = 2 2. The pair correlation function PCF is not influenced by the over-counting, and it can be fitted with the following model as shown before  (Sengupta, Jovanovic-Talisman and Lippincott-Schwartz, 2013) : Where C(r) is a cross-correlation of the two point spread functions corresponding to two imaging channels and is also given by the Gaussian function with the sigma   = √ 1 2 +  2 2.
QUANTIFICATION AND STATISTICAL ANALYSIS Image and statistical analysis Samples were imaged with TCS SP8 microscopes (CAIC, University of Cambridge) at 20X or 40X magnification and 1024/1024 pixel resolutions. Live imaging experiments of the wing discs were performed as described in  [54]. ImageJ software was used to analyze images and measure reporter expression area  [46]. For experiments to compare and measure the reporter expression area, samples were prepared and analyzed in parallel, with identical conditions and the same laser parameters used for image acquisition. For each confocal stack a similar Sum slices projections was generated using ImageJ software. The percentage of cells expressing the reporter was obtained by calculating (automatically) for each sample the total myoblasts area (based on myoblasts Twist or Cut markers), and the area of the GFP expression.
Tissues volume (in mm 3 ) were measured using Volocity 3D Image analysis software (PerkinElmer). The mitotic index was calculated by dividing the total number of Myoblasts (Twist +) by the number of the cells undergoing mitosis (PH3 +). Statistics were calculated with GraphPad Prism and, depending on the Gaussian distribution of the values, either the unpaired t test or the Mann-Whitney non-parametric test was used as indicated in the legends.
MS2 computational analysis Maximum projections of the videos were made using Fiji software and cells were segmented and tracked over the time with a MATLAB script (available at https://github. com/juliafs93/MS2_cells). Briefly, cells were segmented from the MCP-GFP signal using a combination of median filtering and identification of circular shapes using the ''imfindcircles'' function. Cells were tracked over the time by finding the closest neighbor in a 15-pixel radius and allowing search in the previous 5 frames. Tracks containing fewer than 360 frames (~1 hour) were removed from the analysis. Tracked cells were overlaid with the MCP-GFP signal to obtain the maximum intensity pixel for each nucleus, which was used as a proxy of the transcriptional spot fluorescence. This analysis provided values of maximum fluorescence for each tracked cell over time.
ACKNOWLEDGMENTS We thank  Hector Herranz ,  Steve Cohen ,  Ruth Lehmann ,  Eileen Furlong , the  Bloomington Stock Center , the  VDRC Stock Center , and the  Developmental Studies Hybridoma Bank  for Drosophila strains and antibodies. We thank  Julia Falo-Sanjuan  for help with data analysis,  Kat Millen ,  Gustavo Cerda-Moya  and  Ismael Morin-Poulard  for advice and technical help. We thank other members of the  S. B. lab  for valuable discussions. H. B. thanks  Fabienne Pituello ,  Michele Crozatier , and  Alain Vicent  for accommodating him in the  Centre de Biologie du D eveloppement  while revising the manuscript. This work was funded by a  MRC  programme grant ( MR/L007177/1 ), an  Isaac Newton Trust  research grant, and a  BBSRC  project grant ( BB/P006175/1 ). H. B. was supported by  EMBO Long Term  ( ALTF 325-2013 ) and AFM-T el ethon (22599) fellowships, and Z.
This result excludes an extremely high mutation rate at the position of the five causal mutations. However, the size of the MA line dataset limits our ability to detect quantitative differences in mutation rates that could be significant at evolutionary time scales. We thus used two further datasets with abundant variation: the Million Mutation Project (MMP,  Thompson et al. , 2013)  and the Caenorhabditis elegans Natural Diversity Resource (CeNDR,  Cook et al. , 2017). The MMP dataset provides enough power, but is derived from lines after chemical and/or ultraviolet mutagenesis aiming at producing deletions (2007 strains with about 400 mutations each,  Thompson et al. , 2013). None of the five nucleotide positions (breakpoints for deletions) were mutated in this dataset (Supplementary file 7).
One deletion was found in gcn-1 but breakpoints do not match the identified direct repeats. The caveat of using the MMP dataset is that the pattern of artificially induced mutations may differ from that of spontaneous mutation. Second, we explored the C. elegans natural diversity (almost 3 million genomic variations from 766 wild strains;  Cook et al. , 2017) , and none of the positions (the breakpoints for deletions) were mutated either (Supplementary file 7). The caveat of using this dataset is that selection has acted on the polymorphism pattern; note however that the gcn-1 repeats lie in intronic regions where mutations may have less functional impact (Figure 5-figure supplement 1j). We thus conclude that the five identified mutations are not in mutational hotspots. We next wondered whether the underlying genes (rather than the precise positions) -the first level of sequence to phenotype mapping -could display higher mutation rates.
Lysates were incubated for 1 hr at 37˚C after adding 10 ml of RNAse A (20 mg/ml) and proteins were precipitated with 200 ml of Protein Precipitation Solution (Qiagen Gentra Puregen Cell kit). After centrifugation, DNA was precipitated from the supernatant with 600 ml of isopropanol, washed twice with ethanol 70%, dried for 1 hr and finally resuspended in 100 ml TE buffer. This procedure typically yielded concentrations of ~500 ng/mL (range: 200 ng to 1 mg per ml) of high-quality genomic DNA. Short insert libraries (mean insert size around 500 bp) were prepared by BGI (http://www. genomics. cn/en/index) and paired-end sequenced on Illumina Hiseq2000 with 100 bp reads to obtain 2. 2 Gb (aiming at ~20 x mean coverage) of clean data per samples after manufacturer's data filtering (removing adapter sequences, contamination and low-quality reads).
, 2010) (v3. 6 or later) with default parameters (unless otherwise stated), and by adapting the authors' recommendations of best practices  (DePristo et al. , 2011; Van der Auwera et al. , 2013). Read mappings were pre-processed by tagging duplicate reads with Picard (http://broadinstitute. github. io/picard), by re-aligning reads around indels (GATK tool suite) and by one round of Base Quality Score Recalibration (GATK tool suite) with the HaplotypeCaller tool, resulting in analysis-ready bam files for each sequenced sample. To call short variants (SNPs and indels generally less than 100 bp), these bam files were separately pre-called for variants using the tool HaplotypeCaller in a gVCF mode (option '-ERC GVCF'). Finally, a joint genotyping (with GATK's tool GenotypeGVCFs) was performed using as inputs all the gVCF records of a cohort consisting of the ancestor strain and its derived MA lines.
This high similarity of variation within a cohort was used to increase the specificity of the calls for the PB306 and HK104-derived cohorts (the N2-derived cohort had few background mutations). In both cohorts, background variations were used to train a Variant Quality Score Recalibration (VQSR). In practice, shared variant sites within a cohort were split into background SNPs and indels to perform parallel recalibrations (tool VariantRecalibrator in mode SNP and INDEL, respectively). These training sets of variants were considered to be representative of true sites and were then used to train the model with a prior likelihood of Q12 (93. 69%), corresponding to options 'training = true, truth = true, prior = 12. 0'. In the case of HK104, we added another training dataset, consisting of validated SNP markers previously used to genotype recombinant progeny between the HK104 and the reference strain AF16  (Koboldt et al.
, 2010; Ross et al. , 2011)  and the 13 new polymorphisms (SNPs or small indels) that were directly validated by pyrosequencing (see below). This additional set is small (948 variants, see the list in vcf format in Supplementary file 9) but has a high degree of confidence: we fixed the prior likelihood to Q15 (96. 84%) (other parameters of VariantRecalibrator: training = true, truth = true, prior = 12. 0). Then, each type of variant was recalibrated (tool ApplyRecalibration) so that 99% of the training dataset should be contained in this quality tranche (option '-ts_filter_level 99. 0'). Finally, for each MA line, sites containing an allele passing the VQSR threshold but different from the ancestral line were selected and classified based on the number of other MA lines within the cohort that shared the same genotype.
To validate the 54,355 bp deletion on the chromosome IIIR of MA line 418 and identify the causal gene(s), we generated frameshifting indels in the seven protein-coding genes within the deleted region, using CRISPR/Cas9 editing without repair template (non-homologous end-joining) as described in  Friedland et al. , 2013; Arribere et al. , 2014. Guide RNAs were designed with the CRISPOR online program  (Haeussler et al. , 2016). To generate the pU6-target-sgRNA plasmid, we replaced the dpy-10 target site with the desired target gene site in the pJA58 plasmid  (Arribere et al. , 2014) , using the Q5 Site-Directed Mutagenesis Kit (New England BioLabs) and the online tool NEBasechanger to design the mutagenesis primers. For genome editing, young adult PB306 hermaphrodites were injected with the following injection mix: 100 ng/ml of the pU6-target-sgRNA plasmid, 50 ng/ml of Peft-3::Cas9-SV40NLS::tbb-2 3'UTR plasmid  (Friedland et al.
Genomic analysis and data visualization The GC content of DNA sequences was computed using bedtools  (Quinlan, 2014). Extraction of sequences with different annotations was performed using the R package 'GenomicFeatures'  (R Development Core Team, 2015; Lawrence et al. , 2013). Repeats were retrieved from 'masked' genome fasta files available from Wormbase (WS243 and WS234 for C. elegans; WS238 for C. briggsae). To compare mutation rates in other C. elegans datasets, the mutation found in C. briggsae sfrp-1 was transposed to the homologous base pair in C. elegans. Additional filters were applied to the published list of mutations found in the dataset of  Saxena et al. , 2019 , in order to remove most likely false positive calls: overlapping SNPs or indels at the same locus in the same line (initial calling procedure was performed separately), SNPs at 2 bp or less from an indel in the same line, identical mutations shared by related lines (likely background mutations), groups of identical mutations over large chromosomal regions found in multiple lines (possible cross-contamination during sequencing).
Since this previous study did not look for large structural variants, we systematically looked with the Tablet alignment viewer  (Milne et al. , 2013) , using bam files kindly provided by the authors, for large deletions falling in the exons of the five causal genes in all the MA lines of the dataset and tested all dubious instances by direct PCR and Sanger sequencing (see Supplementary file 7b) for the list of tested MA lines and re-sequenced genomic regions (corresponding PCR oligonucleotide sequences are listed in Supplementary file 10b). We did not detect any structural variants in the exons of the five causal genes. Functional annotations of natural polymorphisms were predicted using snpEff. How snpEff classifies the putative effect of genomic variants into high or moderate is available online (https://www.
Statistical analysis Differences between P3. p division frequencies were evaluated using pair-wise Fisher exact tests with false-discovery rate (fdr) level of 0. 05 to correct for multiple testing (R, fmsb package). The resulting pair-wise matrix of adjusted p-values was used to generate post-hoc labeling of each strain. Other statistics were computed using R, stats package (R Development Core Team, 2015) (specifically confidence intervals with prop. test, Pearson's correlation test with cor. test and X 2 -test with chisq. test). All raw sequencing data supporting the conclusions of this article have been submitted to ENA. Study and sample accession numbers corresponding to the sequencing data of the ancestor and MA lines used in this study are listed in Supplementary file 2. Custom scripts were used to pipeline the different tools during the variant analysis (bash/python scripts) or to perform statistical analysis and to plot results (R scripts).
The data set containing all mutations in the mutagenized strains of the Million Mutation Project was downloaded online (http://genome. sfu. ca/mmp/mmp_mut_strains_data_Mar14. txt). Hard-filtered Variant data of the latest release of the Caenorhabditis Natural Diversity Resource (release ID: 20180527) was downloaded online (https://www. elegansvariation. org/data/release/latest).
Acknowledgements We are very grateful to  Ayush Saxena ,  Michael Snyder  and  Charles Baer  for sharing data, strains and DNA. Some strains were provided by the  Caenorhabditis Genetics Center , which is funded by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD-010440 ). We acknowledge WormBase,  CeNDR , the  Million Mutation Project  and the  National Bioresource Project of the Mitani laboratory. We gratefully acknowledge the  PSMN (Pole Scientifique de Modelisation Numerique) computing center of ENS-Lyon  for support during the latter part of the genomic analysis. The authors declare that they have no competing interests.
Agence Nationale de la Recherche   ANR-12-BSV2-0004-01   Marie-Anne Felix Agence Nationale de la Recherche   ANR-18-CE13-0006-01   Marie-Anne Felix H2020 Marie Skłodowska-Curie Actions Training Grant 751530-EvoCellFate Joao Picao-Osorio The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
The product was then purified and enriched with 15 PCR cycles. DNA 1000 Labchip on Bioanalyzer (Agilent) was used to check the final cDNA library and KAPA qPCR kit was used for quantification. For each sequencing lane, four or six libraries were pooled in equal proportions, denatured with NaOH and diluted to 6. 5 pM. Following the manufacturer's instructions, 100 nucleotides single read sequencing was done after the clustering of reads. Image analyses and base calling were performed using the HiSeq Control Software and Real-Time Analysis component (Illumina). The quality of the data was evaluated using FastQC software from the Babraham Institute (http://www. bioinformati cs. babraham. ac. uk/projects/fastqc/) and the Illumina software Sequence Analysis Viewer. Using the Illumina algorithm 85 CASAVA (1.
Differential expression analysis between photoperiods and between time points and single enrichment analysis of DEGs For each sample, we calculated RPKM (reads per kilo base of transcript per million mapped reads) for every transcript (Table  S1  available as Supplementary Data at Tree Physiology Online). To compare the expression levels, the raw data were normalized with Bioconductor's DESeq2 v1. 14. 1 package  (Love et al. 2014 ; Table  S2  available as Supplementary Data at Tree Physiology Online). Significant differences in expression were identified by DESeq, and significant changes were identified by calculating an adjusted P-value (false discovery rate (FDR) <0. 05; Benjamini-Hochberg). For each gene, we calculated the fold-change between the same time points of both photoperiods (Table  S2  available as Supplementary Data at Tree Physiology Online).
We also calculated the fold-change between all-time points of 12 h/12 h photoperiod and all-time points of 8 h/16 h photoperiod. Gene ontology (GO) analysis with the differentially expressed genes (DEGs) was performed using the singular enrichment analysis tool in agriGO v2. 0  (Tian et al. 2017)  according to the C. canephora annotation  (Denoeud et al. 2014 ). Significant GO terms were found using the default FDR P ≤ 0. 05 cutoff value. Graphs were performed using Excel or R. Heatmap and hierarchical cluster analysis were performed in R 3. 2. 4 software using the default functions and the gplots package.
Determination of rhythmic genes The rhythmic parameters of gene expression, period and phase were determined using JTK_CYCLE algorithm implemented in MetaCycle v1. 1. 0 (Wu et al. 2016). In order to identify the transcripts showing diurnal oscillations, we analyzed the DESeq2 normalized data with JTK_CYCLE, which uses a nonparametric test  (Hughes et al. 2010). JTK_CYCLE was run using a dataset containing 8 h/16 h and 12 h/12 h photoperiods data. We regard transcripts with Benjamini-Hochberg Q values (BH. Q) <0. 05 as rhythmic transcripts. JTK-CYCLE was run with a range of periods between 21 and 27 h. We considered as rhythmic genes that were identified by JTK_CYCLE and showed at least one significant difference of expression during the diurnal cycle under the 12 h/12 h and the 8 h/16 h photoperiods (Table  S3  available as Supplementary Data at Tree Physiology Online).
Genes potentially regulated by evolutionary conserved circadian cis-elements (Gbox, EE, CBS, PBX, TBX, SBX, ME and LBS; Table  S4  available as Supplementary Data at Tree Physiology Online) were identified by an exact string pattern matching approach on the complete promoterome of C. canephora genome. This analysis was done considering the 1000 bp upstream sequences of each gene as promoters. To establish how the photoperiod influences the circadian clock and the metabolism of coffee plants, we focused on genes of the clock and of primary and secondary metabolism for which the sequences were verified against the C. arabica genome. Those were identified using BLASTP search against the C. arabica phytozome  (Goodstein et al. 2012 ; https://phyto zome. jgi. doe. gov/pz/portal. html) database with e value cutoffs of 1e -15.
Validation of RNAseq by qRT-PCR analysis Validation of RNAseq studies is frequently performed by qRT-PCR. Based on the literature, we targeted three of the circadian clock key genes from C. canephora CcLHY (Cc02_g39990), CcGI (Cc10_g15270) and CcLUX-ARRYTHMO (Cc06_g20160), two genes involved in chlorophyll biosynthesis CcPOR1A (Cc05_g12370) and CcPOR1B (Cc05_g06850) and two genes involved in starch degradation CcISA3 (Cc10_g06640) and CcGWD1 (Cc11_g15490). cDNA synthesis and PCR experiments were carried out, as previously described by  Marraccini et al. (2012). All reactions were performed in triplicate for each of the three biological replicates in qRT-PCR. Primers were designed using Primer3Plus online software (http://www. bioinformatics. nl/cgi-bin/primer3plus/primer3plu s. cgi). By analyzing the T m (dissociation) of amplified products, the specificity of the PCR products generated for each set of primers was verified.
PCR efficiency (E) was estimated using absolute fluorescence data captured during the exponential phase of amplification of each reaction with the equation (1 + E) = 10 (-1/slope)  (Ramakers et al. 2003). Expression levels were calculated by applying the formula (1 + E) -Ct , where Ct, target=Ct, targetgene-Ct, CaGAPDH and Ct = Ct, target-Ct, references sample, with the T 0 sample being used as references for each construct. The expression level of the CaGAPDH gene (GB accession number GW445811) was used as an endogenous control to normalize the expression level. The relative log expression normalized data estimated from RNAseq had a high correlation with that from the qRT-PCR (r = 0. 79).
Regulatory networks inference In order to identify the interactions between five core clock genes (CaGI, CaLHY, CaELF4, CaPRR5 and CaPRR7) and genes involved in four metabolic pathways, networks inference was performed using the R package TDCor version 0. 1-2  (Lavenus et al. 2015). The parameters used to run TDCor are described in Table  S5A  available as Supplementary Data at Tree Physiology Online. We performed four analyses corresponding to four pathways grouped in four datasets (namely, chlorophyll biosynthesis Tree Physiology Volume 00, 2020 and degradation, light phase of photosynthesis, the second phase of photosynthesis, starch synthesis and degradation), and we established inference with each core clock genes. Datasets contain only genes showing diurnal oscillations and at least one significant difference in expression (P adj < 0.
05) between both photoperiods during the diurnal time course. We inferred networks from each of the two photoperiods. The networks considered were those that (i) showed at least one existing interaction (bootstrap >79. 4%) between the circadian gene under study and at least one gene of the metabolic pathway under consideration, and (ii) this interaction was present under both the 12 h/12 h photoperiod and 8 h/16 h photoperiod (Table  S5B  available as Supplementary Data at Tree Physiology Online). Then the regulatory networks were visualized using Cytoscape v3. 6. 1. (Cline et al. 2007 ; Table  S5C  available as Supplementary Data at Tree Physiology Online).
In order to determine if these genes were also rhythmic in Arabidopsis, we mined lists of Arabidopsis rhythmic genes in datasets available for 11 diurnal and circadian conditions. Data were retrieved from the DIURNAL website (http://diurnal. mo cklerlab. org/). For more information, see legend of Figure  5. Altogether, 89% of the coffee rhythmic photo-temperaturedependent genes were also rhythmic in Arabidopsis (Figure  5 ). This percentage was similar when using lists of genes showing rhythmicity under light/dark and 22 • C/12 • C cycles or under constant light and 22 • C/12 • C temperature conditions. Consequently, temperature cycles seemed to be responsible for the rhythmicity of the large majority of the genes, as very few additional genes showed rhythmicity when changes in light/dark cycles were associated with variations in temperature.
Arabidopsis circadian genes can be found on DIURNAL website (http://diurnal. mocklerlab. org/). We consider the genes correlated with a threshold of 50%. Lists are provided in Table  S3  available as Supplementary Data at Tree Physiology Online. Figure  6. Time-dependent enrichment in core clock elements for both photoperiods: (a) radar chart that represents the number of rhythmic genes with a maximum value of the expression level through photoperiod 12 h/12 h (orange line) and 8 h/16 h (purple line). (b, c) Radar charts that represent the number of rhythmic genes containing cis-regulatory modules (EE and CBS) with the maximum value of the expression level through photoperiod 12 h/12 h (orange line) and 8 h/16 h (purple line). at the beginning of the day (ZT3 and ZT6), sucrose content was higher at 8 h/16 h than under the 12 h/12 h photoperiod (Figure  7c ).
Background: Despite multiple Harm Reduction (HR) programs worldwide, there are still an important number of people who use drugs (PWUD) who do not access those services. Their di culties to obtain HR tools are due to their inability to reach such services (remoteness and/or limited customer service hours), costs, quantitative restrictions, fear of judgment, lack of con dentiality in pharmacy and unfamiliarity with HR programs. We tested an innovative approach using the power of remote online communication and the national postal distribution network to improve HR tools access and counselling. Methods: Based on these observations, SAFE association created HaRePo in 2011, a free and con dential program designed for people who have di culties accessing HR tools and counselling. PWUD can access the program by phone and/or email.