dataesr/scientific-paragraphs-categorization huggingface dataset:
11. 4)  (Andrews). Alignment was performed against the Homo sapiens GRCh19 assembly. The assembly was first indexed using STAR (version 2. 5. 1b) before mapping trimmed reads, using STAR (version 2. 5. 1b) in paired-end mode with default behaviour  (Dobin and Gingeras, 2015). Duplicate reads were removed using Picard (version 2. 7. 11) (2018), before using featureCounts to generate raw read counts for each gene. Differential gene expression (DEG) analysis was performed using DESeq2  (Love et al. , 2014). Heatmaps were generated with Heatmapper  (Babicki et al. , 2016). Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) function  (Kanehisa, 2019; Kanehisa and Goto, 2000; Kanehisa et al. , 2019)  of the Database for Annotation, Visualization, and Integrated Discovery (DAVID)  (Huang et al.
For NMR, dried samples were re-suspended in 60 µL of 100 mM sodium phosphate buffer (pH 7. 0) containing 500 µM DSS and 2 mM Imidazole, 10% D20, pH 7. 0. Samples were vortexed, sonicated (5-15 min) and centrifuged briefly, before transferred to 1. 7 mm NMR tubes using an automated Gilson. One-dimensional (1D)-1 H NMR spectra and two-dimensional (2D)-1 H, 13 C Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR spectra were acquired using a 600 MHz Bruker Avance III spectrometer (Bruker Biospin) with an inverse cryogenic probe for 1. 7 mm NMR sample tubes, fitted with a z-axis pulsed field gradient, at 300 K. Spectral widths were set to 13 and 160 ppm for the 1 H and 13 C dimensions, respectively. For the indirect ( 13 C) dimension of the 2D-1 H, 13 C HSQC NMR spectra, 1228 out of 4096 (30%) data points were acquired using a non-uniform sampling scheme.
0 (Chenomx INC). 2D-1 H, 13 C HSQC NMR spectra were reconstructed using compressed sensing in the MDDNMR and NMRpipe software  (Delaglio et al. , 1995; Kazimierczuk and Orekhov, 2011; Orekhov and Jaravine, 2011). The final spectrum size was 922 real data points for the 1 H dimension and 16,384 real data points for the 13 C dimension. Analysis was performed using MetaboLab and pyGamma software was used in multiplet simulations  (Smith et al. , 1994). The methyl group of lactate was used to calibrate the chemical shift based on its assignment in the human metabolome database  (Wishart et al. , 2013).
For Diffbind analysis, data were normalised to a pooled input for each group and an IgG control. DHMRs were assigned to genes and other genomic features using the HOMER (v. 4. 8; hg19) annotatePeaks tools  (Heinz et al. , 2010). For candidate hmeDIP analysis, the concentration of each sample was extrapolated from a standard curve of arbitrary concentrations and normalised to 10% input. Regions of interest were identified from the hmeDIP-sequencing dataset. Primers were designed using the NCBI primer-BLAST software (Table  S2 ). Data are available through the Gene Expression Omnibus (GSE144955). Sliding window profiles and heatmaps were generated using deepTools  (Ramírez et al. , 2014) , using the plotProfile and plotHeatmap functions, respectively, with blacklisted regions subtracted.
Statistical analysis All statistical analyses were performed using Graph Prism Version 8. 0 for Windows or macOS, GraphPad Software, La Jolla California USA, www. graphpad. com. Normality of data distribution was measured using the Shapiro-Wilks test. Where indicated, data were analysed by unpaired Student's t-test, Mann-Whitney test, one-way analysis of variance (ANOVA) or two-way ANOVA. Data were considered to be significant where p <0. 05.
Acknowledgements: MCS was supported by a  British Heart Foundation  PhD studentship ( FS/16/54/32730 ) and by the  British Heart Foundation Centre of Research Excellence. SW-Z was funded by the  Deutsche Forschungsgemeinschaft  ( SFB960 ). This work was supported in part by the  Wellcome Trust  [grant number  208400/Z/17/Z ] and we thank  HWB-NMR  at the  University of Birmingham  for providing open access to their  Wellcome Trust -funded NMR equipment. AJD was funded by the  British Heart Foundation Centre of Research Excellence, University of Edinburgh. Our thanks go to the  Wellcome Trust Clinical Research Facility Genetics Core ,  Western General Hospital, Edinburgh, UK. DCH, JMR, BLV were supported with awards from the  MRC Doctoral Training Partnership  ( MR/K501293/1 ) and the  Chief Scientist Office  ( TCS/16/37 ).
Within the chosen areas in the SC an area of the ICS as well as all the ICLL in this selected area were semiautomatically marked using a software plug-in developed for Image J software (http://rsbweb. nih. gov) and subsequently length of ICLL per ICS was measured. In order to compare ICLL in the different samples, the ratio ICLL/ICS was normalized to an area of 1,000 nm 2. The normalized ICLLs (nICLL) were used for subsequent statistical analysis  23.
Outliers were excluded according to Extreme Studentized Deviate (ESD) outlier test (n > 15) or to the interquartile range (IQR) with factor 2. 2 (n = 1-15)  34, 35. Correlations were calculated by Pearson′s (linear) or Spearman's (non-linear) rank correlation. For correlations calculated in RHE all siRNA-treated samples (including siRNA control) but not wild type (wt) samples were used to guarantee identical influences except for Cldn-1 levels. Statistical calculations were performed by SPSS 24 (IBM, Armonk, USA). * = p < 0. 05, ** = p < 0. 01; *** = p < 0. 001 (same for symbols #, $, §).
Quantitative real-time-polymerase chain reaction (Q-PCR) certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted October 2, 2019. ; https://doi. org/10. 1101/789917 doi: bioRxiv preprint 7 RPE samples were collected at ZT23 and ZT1 and subjected to RNA extraction using Trizol (Thermo Fisher Scientific, Santa Clara, CA # 15596026). Q-PCR was performed using the CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA) using iQ SYBR Green Supermix (Bio-Rad Laboratories Hercules, CA# 1708880). All data for individual genes were normalized to 18S, according to a previously published protocol  (21).
RNA seq runs, mapping and estimation of Reads per kilobase per million (RPKM) The 12 RNA-seq libraries were then sequenced on the Illumina HiSeq2000 platform at the to produce approximately 65 million, 100 nucleotide paired-end reads per sample (Reads 1 and 2). The reads were mapped to the University of California-Santa Cruz (UCSC; Santa Cruz, CA, USA) mouse genome assembly and transcript annotation (mm10). Mapping was performed with Bowtie2 (v 2. 1. 0) using the default settings. HTSeq-count (PyCharm Community Edition certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted October 2, 2019. ; https://doi. org/10. 1101/789917 doi: bioRxiv preprint 8 2016. 3. 2) was used to generate counts of reads uniquely mapped to annotated genes using the UCSC mouse assembly mm10 gtf file.
Further, RPKM (reads per kilobase per million) were calculated manually and only the genes having an RPKM of ³ 1 were considered for further analysis  (23). Fold change was later calculated by using the RPKM values of the same gene at two different time points (ZT1 vs. ZT23). Finally, we used t-test (paired-end; £0. 05) and a cutoff of fold change ³ 1. 5 to determine the differentially expressed genes (DEGs). To evaluate the significance of the identified DEGs, we used the Protein ANalysis THrough Evolutionary Relationships (PANTHER) Classification System and analysis tools to categorize DEGs by protein class, Gene Ontology (GO), Molecular Function, and GO Biological Process and also to determine if any of these classes or GO terms were overrepresented. First, differentially expressed genes with a fold change ³2, were identified among the DEG's in each genotype and entered in the PANTHER database in increasing order of the p-value.
3 to 500 Hz. Data were recorded and analyzed by EM for Windows (ver. 8. 2. 1, LKC Technologies). In the dark-adapted (scotopic) ERG protocol, seven series of flash intensity between from 0. 03 to 6. 28 cd*s/m 2 were presented to the mouse eye. Flashes were generated by 530-nm green LEDs in a Ganzfeld illuminator (LKC certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted October 2, 2019. ; https://doi. org/10. 1101/789917 doi: bioRxiv preprint 10 Technologies), and intervals of flashes increased from 0. 612 to 30 s as the intensity of the flashes increased. Responses of 3-10 flashes were averaged to generate a waveform for each step of light intensity, and a-wave and b-wave of ERG measurement were analyzed from the trace of waveforms  (22)  To measure the light-adapted (photopic) ERG, mice were placed in a Ganzfeld illuminator and cone-associated activity was isolated by saturating rods with 63 cd*s/m 2.
Purpose The aim of this prospective study (ClinicalTrials. gov: NCT01880203) was to evaluate the diagnostic and prognostic value of a 7-panel mutation testing in the aspirates of thyroid nodules with indeterminate cytology (IC). Methods Eligible patients had a thyroid nodule ≥15 mm with IC (Bethesda III-V) for which surgery had been recommended. Detection of BRAF and RAS mutations was performed using pyrosequencing and RET/PTC and PAX8/PPARγ rearrangements using Real-Time quantitative reverse transcription-polymerase chain reaction (RT-PCR). Results Among 131 nodules with IC, 21 (16%) were malignant including 20 differentiated cancers and one thyroid lymphoma. Molecular abnormalities were identified in 15 nodules with IC corresponding to 10 malignant and 5 benign tumours. BRAF mutation was detected in 4 nodules all corresponding to classic PTC, and PAX8/PPARγ rearrangement in 2 HCC.
) were cycled in parallel with each run. To decrease the likelihood of false negatives, GAPDH was amplified in parallel for each sample. Fluorescence data were analysed by the 7500 Fast Dx software and expressed as Ct, the number of cycles needed to generate a fluorescent signal above a predefined threshold. Baseline and threshold values were set by the 7500 Fast Dx software. Samples with a delta Ct inferior to 10 have been considered as positive.
The study protocol was approved by the Local Ethics Committee (Ref. 2012-35, Comité de protection des personnes Nord-Ouest III) and the French Health Authorities (Ref 130213B-22). This trial is registered as ID-RCB 2012-A01313-40, ClinicalTrials. gov NCT01880203. It was conducted according to the provisions of the Declaration of Helsinki and the Good Clinical Practice Guidelines of the International Conference of Harmonization. Written informed consent was obtained from all patients.
Funding The study was funded by  ANR  ( ANR-126BS08-0022-01 ),  ART Viggo , the ' Prevention et Traitement des Décalcifications (PTD)' Association, Arthritis Courtin foundation (Arthritis R&D   2018-2019 ) and the  French Society of Rheumatology  ( SFR 2017-2018 ,  SFR 2018-2019 ). LC-G was financially supported by grants from  ANR  and  ART Viggo , and FR by  Paris Diderot University and ART Viggo, PTD.
0026)) and by the  European Union 's  Horizon 2020 Research and Innovation program  under grant agreement no. 675006  ( SE2B ). P. C. salary is financed by  RECYFUEL  ( ANR  project). The research is also supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  Commissariat à l'Energie Atomique (CEA).
Study Design Details of the design of this multicenter, longitudinal natural history study (NCT03146078) have been described previously. 8 Briefly, 127 participants were enrolled at 16 clinical sites in North America and Europe. The study adhered to the tenets of the Declaration of Helsinki and was approved by the institutional review boards (IRBs) or ethics boards associated with each participating site. Participants were at least 8 years old with a clinical diagnosis of rod-cone degeneration associated with at least 2 disease-causing USH2A sequence variants in trans. A committee reviewed all genetic reports to confirm the variants as pathogenic or likely pathogenic. The majority of testing was performed in the "study" eye, defined as the eye with better baseline BCVA. The primary cohort included 105 participants with a baseline ETDRS letter score of 54 or greater (20/80 or better) in the better eye, central visual field at least 10 degrees diameter, and stable fixation.
Acknowledgments David Birch is supported by an  NIH  grant  EY009076. Isabelle Audo  is a member of the  French National Rare Diseases network  of the eye and the ear, SENSGENE, and the  ERN-EYE (European Reference Network for Rare Eye Diseases ) and is supported by  RHU-Light4deaf  [ ANR-15-RHU-0001 ] and  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program. Katarina Stingl  is a member of the ERN-EYE (European Reference Network for Rare Eye Diseases). Jacque Duncan is supported by an unrestricted grant from  Research to Prevent Blindness  to UCSF and  NIH-NEI  core grant  NEI-EY002162. Abigail Fahim is supported by a  NIH   K12  grant  K12EY022299. Rachel Huckfeldt  is supported by  Foundation Fighting Blindness  ( CD-CMM-0918-0747-MEEI ).
During a second phase, we therefore generated a B. aethiopum-specific genomic dataset through high-throughput sequencing and used it for the de novo detection of potential microsatellite markers. Among these, 11 enabled polymorphism detection and were further used for analyzing genetic diversity in nine B. aethiopum populations. Our results show that genetic diversity of Beninese fan palm populations is low, with an overall average expected heterozygosity (He) of 0. 354. Moreover, the positive values of the fixation index (F) in populations from both the Central (Soudano-Guinean) and the Southern (Guinean) regions suggest limited gene flows. Our analysis show that sampled B. aethiopum populations are clustered into two groups, one spanning populations from both the Southern and most of the Central region, and the other including the Central population of Savè (which also has the highest He) and populations from the North.
De novo identification of microsatellite loci in the B. aethiopum genome, marker selection and diversity analysis One B. aethiopum leaf sample was randomly selected and genomic DNA purification was performed according to the protocol of  [57]. This DNA extract was then used for the construction of an Illumina paired-end library, as described in  [58] , before high-throughput sequencing on a MiSeq v3 Illumina platform. Demultiplexing of the raw data output was performed using the Maillol script (https://github. com/maillol/demultadapt), with a 0mistmatch threshold. Adapters were eliminated using Cutadapt v1.
Data analysis Amplification products were scored using the GeneMapper software V3. 7 and only unambiguous amplification products were considered for data analysis. Genetic parameters such as total number of alleles, allelic frequency, expected heterozygosity (He), observed heterozygosity (Ho), were calculated for each locus and each population in the GenAIEx software Version 6. 502  [60]. The F-statistics analysis assessing genetic differentiation and the Analysis of MOlecular VAriance (AMOVA) for estimation of genetic differentiation within and among populations were performed with the same software. A Principal Coordinates Analysis (PCoA) was also performed using GenAIEx software to enable the visualization of genetic variation distribution across the individuals under study. We used the STRUCTURE software version 2.
3. 4  [62]  for the determination of the most probable number of clusters for population structure (K value). Using the admixture model, eight simulations were performed for each inferred K value, with a running length composed of 300,000 burning periods and 50,000 Markov chain Monte Carlo (MCMC) iterations to allocate accessions to different populations. The output from this analysis was then used as input in the Structure HARVESTER online program to determine the exact  [62]. Based on this value, a clustering analysis of the studied populations was performed and using the genetic distance matrix obtained from previous analysis, a dendrogram was constructed with the.
Acknowledgements The Authors wish to thank  Emira Cherif ,  Carina Castillo  and  Frédérique Aberlenc  ( IRD, UMR DIADE, Montpellier, France ) for sharing their unpublished microsatellite markers data for transferability tests, and  Dr Jaume Pellicer  ( Comparative Plant & Fungal Biology, Jodrell Laboratory, Royal Botanical Gardens, Kew, UK ), for allowing us to use the palm flow cytometry data.
Funding statement The work described in this article was funded through travel grants to MJK and KA under the framework of the  MooSciTIC  project granted to EJ by  Agropolis Fondation  ( ID 1501-011 , " Investissements d'Avenir" Program -Labex Agro :  ANR-10-LABX-0001-01 ). Additional funding was provided by the  Sud Expert Plantes -Développement Durable  ( SEP2D ) programme to KA (GenPhyB project, ID  AAP3-64 ). MJK is the recipient of a PhD fellowship from the  French Embassy in Benin.
The transition state can be simulated by employing three prominent mimic groups in combination with ATP or ADP: aluminium fluoride (ADP:AlF x )  [27, 49, 50] , magnesium fluoride (ADP:MgF x )  [51] , and vanadate (ADP:Vi)  [52]. In some enzymes, ATP hydrolysis is required prior to the binding of the transition-state mimic  [53, 54]. In structural studies, aluminium fluoride is most frequently used as a mimic of the γ-phosphate in the transition state, as evidenced by analysing the number of deposited structures in the PDB database (Figure  2A ). When the analogue in the presence of ADP is complexed with the protein, ADP:AlF x is believed to mimic the transition state of an ATP molecule. Two configurations of this analogue have been observed: ADP:AlF 3 and ADP:AlF 4 -. In the ADP:AlF PDB deposited AlF x -containing structures) the AlF 4 -group is in a squared-planar geometry and forms an octahedral complex with two oxygen ligands in the apical positions.
For BmrA, and also for other ABC transporters, it was shown that the mutation of the catalytic glutamate (E504 in BmrA) does not significantly affect the conformational change occurring upon nucleotide binding  [23, 99]. In contrast, the protein cannot achieve the pre-hydrolytic conformation when the nucleotide-binding Lys residue of the Walker A motif is mutated, here K380A  [23, 99]. We incubated the mutant E504A with ATP, and then sedimented it for analysis in the solid-state NMR rotor. While E504A is not completely inactive, it displays a very low ATPase activity (but still even crystals were obtained recently, PDB accession code 6R72, and a cryo-EM based structure was reported, PDB accession code 6R81) when compared to K380A, used as a fully inactive control (Figure  5A ). After 40 h, only 50% of ATP is consumed, which allowed for the acquisition of 1D and 2D solid-state NMR experiments.
While E504A is not completely inactive, it displays a very low ATPase activity (but still even crystals were obtained recently, PDB accession code 6R72, and a cryo-EM based structure was reported, PDB accession code 6R81) when compared to K380A, used as a fully inactive control (Figure  5A ). After 40 h, only 50% of ATP is consumed, which allowed for the acquisition of 1D and 2D solid-state NMR experiments. The resulting 1D 31 P CP spectrum displays three narrow peaks corresponding to the three phosphate groups from the ATP bound to the protein (Figure  5B ). The 2D 13 C- 13 C DARR spectrum displays CSPs and peaks appearing, both induced by the conformational and dynamic changes in the protein as a consequence of ATP binding (Figure  5C )  [92]. To summarize, our data show that analysis of the pre-hydrolytic state is difficult both for DnaB and BmrA, since first the corresponding ATP mimics do not behave in a homogenous manner, i.
Maltose/maltodextrin import ATP-binding protein MalK from Escherichia coli K-12, (PDB ID 3RLF, chain A) (D), Gene 4 Ring Helicase from Escherichia phage T7 (PDB ID 1E0J, chain A) (E), Structures 1E0J and 3RLF superimposed by phosphate chain (F).
Funding: This work was supported by the  ETH Career   SEED-69 16-1  (T. W. ) and the  ETH Research Grant   ETH-43 17-2  (T. W. ), an  ERC Advanced Grant  (B. H. M. , grant number  741863 , FASTER), by the  Swiss National Science Foundation  (B. H. M. , grant number  200020_159707  and  200020-188711 ), the  French Agence Nationale de Recherche (A. B. ,  ANR-14-CE09-0024B ,  ANR-19-CE11-0023 ), the  LABEX ECOFECT (A. B. ,  ANR-11-LABX-0048 ) within the  Université de Lyon program Investissements d'Avenir (A. B. ,  ANR-11-IDEX-0007 ), the  EvoCell Program of the Osnabrueck University (M. I. K. )  and a grant from the  Russian Science Foundation  (A. Y. M. ,  17-14-01314 ).
Samples B40 and B42 correspond to the product obtained from beet raw materials (B41), following the bioethanol production process described in ref 27. As expected, the latter sample (B41) contained a higher concentration of additional products than the bioethanol obtained at the end of the process. 2. 3. Evaluation of the Quality of the Analyzed Samples. The total content of nonethanol compounds was evaluated by considering the quantitative data provided by the GC-FID instrument. The results of the total concentration of VOCs are summarized in Figure  4. Interesting conclusions were drawn from these data. First, it was observed that, for wheat samples, the total content of organic compounds different from ethanol was virtually independent of the water percentage. Also interesting was to notice the great variability in the total content of VOCs depending on the bioethanol produced from beet or sugarcane.
■ ACKNOWLEDGMENTS Authors are grateful to the  Spanish Ministry of Science, Innovation and Universities  for the financial support (Project  Ref. PGC2018-100711-B-I00 ). C. S. thanks to the  Ministry of Education, Spain  for the contract  FPU 13/01438. The authors also thank  J. P. Vidal  from  UNGDA  for providing some of the analyzed samples together with valuable information about their nature.
Growing evidence suggests that human gene annotation remains incomplete; however, it is unclear how this affects different tissues and our understanding of different disorders. Here, we detect previously unannotated transcription from Genotype-Tissue Expression RNA sequencing data across 41 human tissues. We connect this unannotated transcription to known genes, confirming that human gene annotation remains incomplete, even among well-studied genes including 63% of the Online Mendelian Inheritance in Man-morbid catalog and 317 neurodegenerationassociated genes. We find the greatest abundance of unannotated transcription in brain and genes highly expressed in brain are more likely to be reannotated. We explore examples of reannotated disease genes, such as SNCA, for which we experimentally validate a previously unidentified, brain-specific, potentially protein-coding exon.
INTRODUCTION Genetic and transcriptomic studies are fundamentally reliant on accurate and complete human gene annotation, being defined as the genetic coordinates of all transcripts of a given gene. Among other analyses, this is required for the quantification of expression or splicing from RNA sequencing (RNA-seq) experiments, interpretation of significant genome-wide association study (GWAS) signals, and variant interpretation from genetic tests. As our understanding of transcriptomic complexity improves, it is apparent that existing gene annotation principally originating from four sources (RefSeq, GENCODE, Ensembl, AceView) remains incomplete  (1) (2) (3) (4). Comparison of these different existing gene annotation databases reveals that more than 17,000 Ensembl genes fall into intronic or intergenic regions according to the AceView database, and the choice of reference annotation greatly influences the performance of variant interpretation software, such as VEP and ANNOVAR  (5, 6).