dataesr/scientific-paragraphs-categorization huggingface dataset:
The ChIP-on-chip method identifies protein DNA occupancy genome-wide using microarray technology with probes on the array representing only the promoter regions of the genome, as whole genome probe arrays are cost inhibitive  [13]. The onset of next-generation sequencing, Solexa platform and Illumina HiSeq platform, allowed sequencing of all DNA with transcription factor occupancy across the genome without promoter bias  [13]. In this chapter, we describe optimized methods for ChIP-seq and ChIP-qPCR in quiescent and proliferating adult hippocampal neural stem cells for the transcription factor REST, histone mark H3K27Ac, acetylation at the 27 lysine residue of the histone H3, and RNA Pol II. This protocol will be generally applicable to other types of biological cells and tissue. We also provide an overview of ChIP-seq data analysis.
1. ChIP-seq run on Illumina or Solexa generates fastq read files. This section gives a brief overview of how to obtain occupancy of the protein on the DNA and other biologically relevant information from these raw reads. Alternately, ChIP-seq published data may be obtained from ENCODE https://www. encodeproject. org/_ or NCBI GEO https://www. ncbi. nlm. nih. gov/geo/ and analyzed with the same pipeline described below.
2. Running platform and software installation: Web-based open resource platform Galaxy https://usegalaxy. org/ is a good starting point. For command line-based analysis, Mac users can use Terminal and Windows (UNIX operating system) users can use cmd or Cygwin or Ubuntu on Virtual box. For intense computing installing linux ubuntu operating system or cloud computing options may be considered. Many bioinformatics core host Galaxy cluster, which can be accessed through Terminal and Putty by Mac and Windows users, respectively. Local installation of the FastQC, Bowtie, SAMtools, and HOMER is required for ChIP-seq. For RNA-seq Tophat and Cuffdiff is also required. HOMER tools are described in detail on the website http://homer. salk. edu/homer/.
4. Quality check and mapping on genome. The ChIP-seq fastq data after fastq quality check and FastqGroomer (tool to convert fastq file to fastqsanger) is mapped to the genome of the organism using Bowtie for ChIP-seq (or Tophat for RNA-seq)  [14, 15]. The fastq dataset obtained from ENCODE or NCBI GEO can also be similarly run through fastq quality check and FastqGroomer  [16] [17] [18]. ENCODE and NCBI GEO files are downloadable by ftp transfer in bam and fastq formats. The NCBI GEO fastq file is often available in the sra format, which can be converted to fastq with the command. Example: #!/bin/sh # request Bourne shell as shell for job #$ -S /bin/sh # run the fastq-dump on the specified sra file /usr/local/bin/sratoolkit/bin/fastq-dump/home/example. sra.
6. Visualization of mapped ChIP-seq and RNA-seq data on UCSC genome browser. To visualize the mapped reads on the UCSC genome browser, run HOMER tools makeTagDirectory and makeUCSCfile on bam files  [20]. The resultant bedGraph file can then be uploaded and visualized on the UCSC genome browser as a custom track (create free UCSC genome browser login to save these tracks). Once the UCSC custom track is created it can be used to visualize protein binding peaks and can be used for primer design (Fig. 6 ). This method can also be used on RNA-seq bam files to upload them on the UCSC genome browser. Example: #!/.
9. Annotation of ChIP-seq peaks and intersection with RNA-seq. To determine the gene expression changes associated with ChIP-seq protein-DNA occupancy. First, Cuffdiff analysis is performed on RNA-seq bam files generated by using Tophat (step 4)  [23, 24]. Second, the output Cuffdiff file is intersected or joined with the ChIP-seq gene annotated file from ChIP-seek above (step 9) by gene name. This join function can be performed on excel or on Galaxy using the Join two dataset function.
6. Normalize ChIP-qPCR signal on binding site primer with 5% or 1% input or IgG ChIP-qPCR using Delta Ct (δCt) method. Similarly, normalize to nonbinding site primer signals using the δCt method. Enrichment of at least twofold of antibody ChIP-ed signal over input or IgG or nonbinding site indicates real binding (see  Note 9). Calculate the δCt value using formula, Next, calculate the fold enrichment from the δCt values using the formula, fold enrichment = 2(-δCt). Flowchart of bioinformatics for ChIP-seq and RNA-seq intersection. Related to Subheading 3. 7 ChIP-seq bioinformatics data analysis, a brief overview.
Acknowledgments We thank  Stephen Johnson ,  Ralf Kittler ,  Francois Guillemot ,  Jane Johnson ,  Victor Corces ,  Sean Goetsch ,  Bradford Casey ,  Mark Borromeo ,  Derek Smith ,  Tulip Nandu ,  Xin Liu ,  Caelin Potts , and  Benjamin Nelson  for helpful advice on the ChIP-seq project. We also thank the  UT Southwestern Medical Center  next-generation sequencing core facilities (McDermott sequencing core for library preparation of samples,  Illumina HiSeq ChIP-sequencing and bioinformatics support. Genomics and Microarray core facility for Bioanalyzer,). Jose Cabrera  for graphical support. The ChIP-seq work was supported by  US National Institutes of Health  grants ( R01NS093992 ,  R01NS089770 ,  R01NS081203 , and  K02AG041815 ),  American Heart Association   15GRNT25750034 ,  Department of Defense   W81XWH-15-1-0399 , and a grant from the  Texas Institute for Brain Injury and Repair.
Background-Comprehensive geriatric assessment (CGA) is a multidimensional, interdisciplinary diagnostic process to determine the medical, psychological and functional capabilities of a frail elderly person in order to develop a co-ordinated and integrated plan for treatment and long-term follow up. Objectives-We sought to evaluate the effectiveness of CGA in hospital for older adults admitted as an emergency. Search methods-We searched the Cochrane Effective Practice and Organisation of Care (EPOC) Group Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library), the Database of Abstracts of Reviews of Effects (DARE), MEDLINE, EMBASE, CINAHL and AARP Ageline, and handsearched high-yield journals. Selection criteria-We searched for randomised controlled trials comparing CGA (whether by mobile teams or in designated wards) to usual care.
Data collection and analysis Selection of studies-Three independent review authors (GE, DR and MW) screened titles and abstracts of papers identified by the literature searches for their potential relevance. We excluded any papers that did not meet the inclusion criteria at this stage. Where there was a disagreement, we considered the paper as potentially relevant and proceeded to the next stage. We retrieved all papers of potential relevance and three independent review authors (GE, DR and MW) assessed the full text for inclusion in the review. Where there was disagreement, GE and DO'N moderated; reasons for excluding full papers are reported. Data extraction-We performed data abstraction using a pre-designed data abstraction form on all studies that fulfilled the inclusion criteria. Two review authors (DR and MW) abstracted the data independently, including data on design characteristics, the study population, the intervention, outcome measures used and length of follow up.
Risk of bias in included studies The studies identified were heterogeneous in quality (Figure  3 ). All employed some method of individual patient randomisation, however reporting of key issues such as allocation concealment varied. Outcome assessment was seldom blinded. This is less significant for hard outcomes such as death or institutionalisation, but becomes more important for outcomes such as activities of daily living or cognition. We extracted data from published information only. We did not contact trialists for additional data. Reporting of outcomes was variable. Different reporting mechanisms were used for description of some data (for example, activities of daily living, which could be reported as a categorical or variable outcome). For this reason, we did not combine some outcomes for meta-analysis.
Effects of interventions Comparison 1: Comprehensive geriatric assessment (CGA) versus usual care -The first primary outcome summarises data from 22 trials of 10,315 participants comparing CGA with usual care (e. g. on a general medical ward). We analysed this using the main subgrouping of CGA wards and CGA teams versus usual care. Subgroup results are only reported where significant subgroup interaction exists. Comparison 1. 1: living at home (up to six months): 'Living at home', as reported previously, is the inverse of death or institutionalisation and is worded in the positive as it is felt to represent an outcome vital to patients themselves. We analysed data from 14 studies (5117 participants). The odds of a patient being alive and in their own home at interim analysis (median six months, range six weeks to six months) were significant (odds ratio (OR) 1.
Comparison 1. 7: dependence: Eight studies (4128 participants) reported data on dependence. All of the studies tested CGA wards. No usable dependence data were recorded for CGA teams. There was no statistically significant difference between intervention and control groups for the outcome of dependence (OR 0. 94, 95% CI 0. 81 to 1. 10, P = 0. 44; Chi 2 = 9. 68, P = 0. 21, I 2 = 28%). Trial data from one trial  (Collard 1985)  have been included despite a significant dropout rate for this one outcome. The results must be interpreted with caution. Analysis without results for this trial does not alter the result significantly (seven studies, 3669 participants, OR 0. 89, 95% CI 0. 76 to 1. 06, P = 0. 19; Chi 2 = 7. 40, P = 0. 29, I 2 = 19%).
Comparison 1. 8: death or dependence: There was no significant difference for the outcome of death or dependence (three studies, 1212 participants, OR 0. 98, 95% CI 0. 77 to 1. 25, P = 0. 87; Chi 2 = 0. 57, P = 0. 90, I 2 = 0%). No usable dependence data was recorded for CGA teams. Comparison 1. 9: death or deterioration: Death or deterioration is a combined outcome that looks at both mortality and functional decline (measured as an increase in dependence). Analysis is combined since a reduction in mortality in one group may be offset by an increasing dependence in survivors and so the two need to be considered together. Analysis of data for the outcome of death or deterioration (five studies, 2622 participants) revealed a significant reduction in death or deterioration (OR 0. 76, 95% CI 0.
05, P = 0. 15, I 2 = 51. 1%). Comparison 1. 11: cognitive function: Data were more limited for this outcome (five studies, 3317 participants), but did reveal an overall benefit on cognitive measures (SMD 0. 08, 95% CI 0. 01 to 0. 15, P = 0. 02; Chi 2 = 7. 65, P = 0. 11, I 2 = 48%) for patients in receipt of CGA. There was no statistically significant subgroup interaction (Chi 2 0. 05, P = 0. 83, I 2 0%), although data were only available from one CGA ward study (375 participants). Comparison 1. 12: length of stay: Length of stay data were analysed for 12 studies (4034 participants); there was significant heterogeneity (Tau 2 = 12. 67, Chi 2 = 84. 50, P < 0. 00001, I 2 = 86%) and for this reason meta-analysis was not retained. For the ward subgroup length of stay ranged from a mean reduction of -9.
Ward and team by admission criteria (i. e. age alone versus age plus other criteria) • Timing of admission • Outpatient follow up versus none Search methods for identification of studies We reviewed the Cochrane Effective Practice and Organisation of Care (EPOC) Group Register and the database of studies awaiting assessment. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library) (April 2010) and checked the Database of Abstracts of Reviews of Effects (DARE) (latest issue) for relevant reviews and reviewed their reference lists. We also searched the following bibliographic databases: MEDLINE (from 1966 to April 2010); EMBASE (from 1980 to April 2010); CINAHL (from 1982 to April 2010); (ACP) and AARP Ageline (from 1978 to April 2010).
• Ward and team by admission criteria (i. e. age alone versus age plus other criteria) • Timing of admission • Outpatient follow up versus none Search methods for identification of studies We reviewed the Cochrane Effective Practice and Organisation of Care (EPOC) Group Register and the database of studies awaiting assessment. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library) (April 2010) and checked the Database of Abstracts of Reviews of Effects (DARE) (latest issue) for relevant reviews and reviewed their reference lists. We also searched the following bibliographic databases: MEDLINE (from 1966 to April 2010); EMBASE (from 1980 to April 2010); CINAHL (from 1982 to April 2010); (ACP) and AARP Ageline (from 1978 to April 2010).
Introduction More than 2000 sequence variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR; NM_000492. 3; MIM# 602421) have been reported in patients with cystic fibrosis (CF; MIM# 219700)  [1] , one of the most frequent lifeshortening autosomal recessive diseases in the Caucasian population, and in a broad spectrum of phenotypes, so called CFTR-related disorders  [2]  (Cystic Fibrosis Mutation Database (CFMDB)). Phenotype variability is mostly explained by CFTR mutational and genotype heterogeneity, interplaying with other genetic and environmental factors. Establishing a CF diagnosis is critical for patients and their families, in order to provide appropriate medical care, follow-up, and genetic counseling. Laboratory criteria include a positive sweat test (chloride value above 60 mmol/L) and/or abnormal values of electrophysiological measurements and/or the presence of two CF-causing variants, one in each CFTR gene copy  [3].
CFTR gene analysis and in silico analysis Extensive CFTR gene analysis led to identify the c. 4064G>T, p. Cys1355Phe variant in exon 25, in addition to the known CF-causing variant c. 2051_2052delinsG. No other variant or large rearrangement was detected. Analysis of the parents showed that the two variants were in trans, p. Cys1355Phe being inherited from the father (Fig. 1 ). The father was also found to carry c. 224G>A, p. Arg75Gln (R75Q) on the other chromosome. The p. Cys1355Phe variant was not found in CFTR mutation databases, CFMDB, and CFTR-France Database  [8]. Additionally, it was not reported in dbSNP or in 1000 Genomes Project. It was found only once in a non-Finnish European population (1/66732 alleles, 0. 0015%) in the Exome Aggregation Consortium. In silico analysis showed contrasted results: the variant was predicted either deleterious (Grantham, SIFT, PolyPhen2, and Mutation Taster) or neutral (GVGD, KD4v).
Phe508del (P < 0. 01) (Fig. 3B ). Immunohistochemistry was performed on transiently transfected HeLa cells to monitor CFTR subcellular localization. Cells expressing either CFTR-WT or CFTRp. Cys1355Phe presented similar patterns, although a more marked intracellular staining for CFTR-p. Cys1355Phe (Fig. 3C ), in concordance with biochemical data. We also performed a YFP-based functional assay to measure CFTR function in transiently transfected HEK293 cells. Upon addition of a PBS-NaI solution, YFP fluorescence was quenched by the flow of I À within the cell through active CFTR channels (Fig. 3D ). Quenching was not observed in cells transfected with empty plasmid (data not shown). When comparing to CFTR-WT-transfected cells, CFTR-p. Cys1355Phe transfected cells presented reduced fluorescence quenching.
Database, https://cftr. iurc. montp. inserm. f r/CFTR/index. html NCBI dbSNP build 142 (Oct. 2014) through Ensembl e80, http://www. ncbi. nlm. nih. gov/projects/SNP/ 1000 Genomes Project, http://www. 1000genomes. org/ Exome Aggregation Consortium (ExAC) database (accessed November 2014), http://exac. broadinstitute. org/.
Acknowledgments Marie-H el ene D ealbert and  Catherine Dubeau  ( Association Franc ßaise pour le D epistage et la pr evention des Handicaps de l'Enfant ) are thanked for their technical help. The French CF association  Vaincre la Mucoviscidose  is thanked for its financial support. We also thank  L. Galietta  for kindly providing the YFP plasmid.
Choice of markers The plastid markers rps4, trnL-F and psbA-trnH, classically used in phylogenetic reconstructions among bryophytes  (Quandt & Stech 2004; Preuβing et al. 2010; Carter 2012) , were selected because of their small size (< 1000 bp) allowing good amplification success and their potential informative variability at the infra-generic level  (Liu et al. 2010; Stech & Quandt 2010). In total, 291 new sequences generated from 98 samples were used in this study: 95 rps4 sequences, 98 trnL-F sequences and 98 psbA-trnH sequences. GenBank accession numbers are given in the Appendix, together with voucher details.
Molecular species delimitation Identification of Riccardia species is a challenging exercise and misidentifications are very common among herbarium materials  (Reeb & Bardat 2014). Our sampling contained multiple accessions of several morphological species, e. g. , R. chamedryfolia (With. ) Grolle (13 specimens), R. longispica (Steph. ) Pearson (8) and R. fucoidea (Sw. ) Schiffn. (6); others were represented by singletons only (R. diminuta Schiffn. , R. crenulata Schiffn. , etc. ; see Appendix). We used molecular species delimitation tools to check the congruence of morphological identifications with genetic signals and to clarify the initial dataset for the phylogenetic analyses while keeping the largest sampling of potential species. We first used a non-tree based method, ABGD (Automatic Barcode Gap Discovery;  Puillandre et al.
2012) , not requiring monophyly to propose species delineation, and a tree-based method  (Fontaneto et al. 2015) , here the Poisson Tree Processes model (PTP;  Zhang et al. 2013). We analysed the initial dataset with ABGD in order to test morphological species delimitation, especially for samples identified as the same taxon. The following parameters were selected: distance Kimura-Nei, P = 0. 0057 (psbA-trnH and trnL-F) and P = 0. 0037 (rps4). Each gene was analysed independently. We also ran PTP on the bPTP server (http://species. h-its. org/ptp/), with 500 000 MCMC generations, thinning set to 100, burn-in 0. 25 and the "remove out-groups" option selected. Input trees were RAxML trees calculated on CIPRES Science Gateway  (Miller et al. 2010 ) under default parameters. Two analyses are provided: PTP_ML (maximum likelihood solution) which gives the most likely solution among the dataset, and PTP_sh (Bayesian solution) which considers the frequency of the nodes across the sampling  (Lang et al.
Phylogenetic analyses File commands for the parsimony ratchet analysis  (Nixon 1999)  were generated by PRAP2  (Müller 2007 ) and run in PAUP. 4. 0  (Swofford 2002)  with the following parameters: 10 cycles of 200 iterations each, with 25% positions chosen randomly and overweight to 2. Gaps were coded as missing data. Branches of minimum size of 0 were automatically collapsed. The dataset was partitioned a priori on the basis of gene identity, i. e. , rps4, psbA-trnH and trnL-F. For each alignment, the best partitioning scheme and the best nucleotide substitution model were defined using Partition Finder v. 1. 1. 1 (Lanfear et al. 2012)  based on the Akaike Information Criterion. The GTR + Γ + I model of sequence evolution and the restriction site model (F81) for binary data were selected.
Description of the new genus Afroriccardia In the consensus tree Afroriccardia comosa is a strongly supported lineage sister to the genus Riccardia (Figs  1 2 ). The four samples of this taxon in the initial dataset (Appendix) were confirmed by all species delimitation analyses as a single molecular species. It resembles Lobaticcardia in the broad, pinnately branched thallus and the wide expansion of rhizoids on the ventral face  (Furuki 1991; Preußing et al. 2010) , and was therefore initially assigned to the latter genus by  Reeb & Bardat (2014). Hence Aneura comosa Steph. is not cited in the world checklist  (Nebel 2016). However, the species clearly differs from Lobatiriccardia by having long female branches (to 1 cm long) and two regular rows of gametangia. The latter two characters are shared with Riccardia but the dense clusters of rhizoids covering the archegonia clearly separate Afroriccardia comosa from Riccardia.
Discussion Differences in success of amplification for the three markers We observed a significant difference of the PCR success between the three plastid markers (Table  5 ). The matching of primers on rps4 sequences allowed us to detect variability near the 3' end of the two primers (F and R). This variability was only found in Riccardia and might affect primer annealing and thus reduce the amplification success. New primers were designed and tested in order to enhance rps4 PCR success (Tables  1 2 ). Only a part of rps4 could be amplified, for 40% of our final dataset. Improving this marker amplification for classical PCR still remains a challenge. Most of the samples were herbarium specimens of various ages, dried under unknown conditions. Since these poikilohydrous plants are quite sensitive to humidity variation in their storage area, the DNA of these herbarium specimens can easily degrade.
Species delimitation Molecular species delimitation using ABGD and PTP is sensitive to balanced sampling, especially in number of replicates per taxon  (Puillandre et al. 2012; Zhang et al. 2013; Lang et al. 2015). The initial dataset (Appendix) contained singletons and some taxa with more than ten samples (Riccardia chamedryfolia). Some specimens of the latter species, from the Atlantic islands, Africa and Guadeloupe, were not initially identified as R. chamedryfolia but their proximity was revealed by the molecular analyses. Even if psbA-trnH, trnL-F and rps4 are located in the same area of the plastid genome  (Wicke et al. 2011) , they have not necessarily evolved in the same way or at the same speed  (Preuβing et al. 2010). ABGD was initially a unilocus tool based on calculated barcode gap outside the confidence interval (at 95%) of the population mutation rate θ, given a prior P of maximum intraspecific divergence  (Puillandre et al.
2012; Fontaneto et al. 2015). We therefore analysed each gene independently. Although the results of the analysis of each of the three markers were rather similar, the dataset was most finely split with psbA-trnH. On the other hand, analysis with the concatenated markers did not separate all species. However, the latter has to be considered carefully. If the inversions that might occur at the population level (compare with  Quandt et al. 2003) , as, e. g. , observed here in the psbA-trnH intergenic spacer, are not detected, the molecular species delimitation using ABGD and PTP will return a wrong concept (data not shown). With PTP, even though the convergence of the runs was moderate to good, only singleton species were highly supported (> 0. 91). Changing of parameters did not improve the results.
updated) and GenBank accession number (* = missing data). Numbers in the ABGD columns represent the original number of the group given by ABGD for each sample. "Y" in the PTP column indicates retained PTP species. Taxon names in bold indicate samples that appear in Figs 1-2. The full dataset was used in ABGD and bPTP. ASV = Herbarium Alfons Schäfer-Verwimp. Numbered columns: 1, ABGD rps4; 2, ABGD psbA-trnH; 3, ABGD trnL-F; 4, ABGD total; 5, bPTP rps4; 6, bPTP psbA-trnH; 7, bPTP trnL-F; 8, PTP total.