dataesr/scientific-paragraphs-categorization huggingface dataset:
Plant Material, Fungal Endophyte Isolation and Identification Posidonia oceanica shoots were collected from the shores of Banyuls-sur-Mer in France at a depth of 5-10 m. Fresh plant parts (leaves, roots, and rhizomes) were cleaned under stream water and then dipped into 70% ethanol for 3 min. Samples of all plant parts were cut into 1-cm 2 pieces and placed in a culture plate containing potato dextrose agar (PDA). Fungal tips were transferred to a new PDA culture plate as soon as they appeared and were left to grow for 30 days. Samples of the fungal cultures were sent to Bio2Mar, France (http://bio2mar. obs-banyuls. fr), who performed the amplification and sequencing of the internal transcribed spacers plus the 5. 8S (ITS). After the removal of small and large subunit ITS flanking regions, a first identification of this fungal isolate was performed, searching for similarity of that ITS sequence to those deposited in GenBank (National Center for Biotechnology Information, U.
S. )  (Benson et al. , 2018). Sequence similarity search in GenBank (BLAST; https://blast. ncbi. nlm. nih. gov/Blast. cgi) used the "blastn" (Megablast) option excluding "uncultured/environmental sample sequences. " To identify the selected Fusarium strain FEP 16 more precisely, DNA was extracted from a sample of the fungal culture placed in 500 µl of cetyl-trimethyl-ammonium bromide buffer (CTAB 1x). DNA extraction was performed according to  Hofstetter et al. (2002). Four more loci were amplified and sequenced: part of the transcription elongation factor 1-alpha (TEF1-α) using primers EF1-1F and EF1-1R  (Morehouse et al. , 2003) , part of the RNA polymerase II second largest subunit [RPB2] using primers fRPB2-5F and fRPB2-7cR for regions 5-7 and fRPB2-7cF and fRPB2-11aR for regions 7-11  (Liu et al.
General Experimental Procedures The NMR spectroscopic data were recorded at 298 K on a Bruker Avance III HD 600 MHz. An NMR spectrometer equipped with a QCI 5 mm Cryoprobe and a SampleJet automated sample changer (Bruker BioSpin, Rheinstetten, Germany) was used. Chemical shifts are measured in parts per million (δ) using the residual DMSO-d6 (δH 2. 50; δC 39. 5) as the internal standard for 1H and 13C, respectively, and coupling constants (J) are reported in Hz. Complete assignments were performed based on 2D-NMR experiments (correlation spectroscopy (COSY), rotational nuclear Overhauser effect spectroscopy (ROESY), heteronuclear single quantum correlation (HSQC), and heteronuclear multiple bond coherence (HMBC)). Optical rotations were measured using a Jasco P-2000 polarimeter (JASCO Corporation, Tokyo, Japan).
The gradient was set as follows: 34-100% of B in 16. 57 min, followed by 3 min of washing at 100% B. The flow rate was 0. 3 ml/min, and the separation was conducted at 40 °C. UHPLC-HRMS/MS analyses was performed on a Thermo Dionex Ultimate 3000 UHPLC system interfaced with a Q Exactive Plus MS (Thermo Scientific, Bremen, Germany) supplemented with a heated electrospray ionization source (HESI-II). Thermo Scientific Xcalibur 3. 1 software was used for instrument control. The HESI-II parameters were set according to the work of  (Rutz et al. , 2019). For the profile of the ethyl acetate extract, 2 µl of the extract was injected using the same column as mentioned above. The mobile phase was H 2 O (A) and MeCN (B), both containing 0. 1% FA. The gradient mode was as follows: 5-100% of B in 18 min, followed by 4 min of 100% B.
Generation of Molecular Networks Raw spectral data of the extract were analyzed using MZmine 2. 53  (Pluskal et al. , 2010). The parameters were adjusted as follows: mass detection performed as MS level 1 (noise level at 10 6 ) and MS level 2 (noise level at 0). The ADAP chromatogram builder was used with the threshold set to 4 × 10 5. Chromatogram deconvolution (algorithm ADAP) was set within its default parameters, except for the R t wavelet range, which was set as 0-0. 1. The most intense isotopes were kept through the isotope peak grouper. Peak alignment was performed using the join aligner method where absolute R t tolerance is at 0. 03 min. Adduct search was performed for Na + , K + , NH 4 + , and ACN + in the positive mode and [M-2H+Na] -and [M-2H+K] -in the negative mode  (Thomas et al.
Generation of 2D Pseudo-LC-NMR Plot For the generation of the pseudo-LC-NMR 2D plot (Figure  1F ), aligned 1 H-NMR spectra of all 135 fractions were divided into equally sized bins (0. 01 ppm) in a range from -1 to 15 ppm and then exported as an Excel file (. csv) using MNova v14  (MasterLab, Santiago De Compostela, Spain). Sample names (number of fractions) were added manually to the Excel file. The file was loaded into an R script using Rstudio V1. 2. 5042, which was written to create an interactive 2D plot that combines all binned spectra in a matrix (sample vs. ppm). The following R packages were used: "plotly," "stringr," "reshape2," "dplyr," and "readr. " The R script is freely available here: (https://github. com/oolonek/ pseudo_lcnmr_plotter/blob/main/src/plotter/NMR_data_plotter. Rmd).
Description of the New Compounds The spectral data for all new NPs are summarized below; those recorded for previously isolated compounds can be found in the supplementary materials. The 1D-and 2D-NMR spectra of all described compounds below can be found in the supplementary materials (Supplementary Figures  S5-S109 ). All NMR data produced in the article are available in the public archive Yareta (https://doi. org/10. 26037/yareta:tmpbgvbqsvfrvltepotmjuunua). 6-(1-hydroxyethyl)-3-methyl-2H-pyran-2-one (3): white amorphous solid; 1 H-NMR (DMSO-d 6 , 600 MHz) δ 1. 30 (3H, d, J 6. 6 Hz, H 3 -8), 1. 96 (3H, d, J 1. 2 Hz, H 3 -9), 4. 41 (1H, q, J 6. 6 Hz, H-7), 5. 56 (1H, d, J 4. 4 Hz, OH-7), 6. 26 (1H, dd, J 6. 7, 0. 8 Hz, H-5), 7. 36 (1H, m, H-4); 13 C-NMR (DMSO-d 6 , 151 MHz) δ 16. 2 (C-9), 21.
FUNDING This work has been supported by joint collaboration between the  Swiss National Science Foundation  and the  Agence Nationale de la Recherche  ( SNSF-ANR ) as part of the project  SECIL  (ref  SNF  N  °310030E-164289  and  ANR-15-CE21-0016 ). J-LW is thankful to the  Swiss National Science Foundation  for the support in the acquisition of the  NMR 600 MHz (SNF Research Equipment  grant  316030_164095 ). AA is thankful to the  Saudi Ministry of Education  and the  Saudi Cultural Mission in Paris  for their kind financial and logistic support to his Ph. D thesis.
All variants were classified for their pathogenicity using the American College of Medical Genetics and Genomics (ACMG) guidelines, after which they were uploaded into the Leiden Open source Variation Database (LOVD) and ClinVar database for PRPH2. In addition, we attempted to establish genotype-phenotype correlations. Several variants are reported to be associated with only one specific IRD phenotype, and these diseases can best be described as allelic disorders. However, for most variants in this study, there appears to be a high phenotypic variability between different families, as well as between members of the same family. By performing this study, we aim to shed light on how to experimentally assess the true causality of PRPH2 variants in the future, as well as to explain the observed phenotypic variability within IRD patients.
| Literature search We collected all publications up to July 2020 that report PRPH2 variants in patients with inherited retinal disease. The following PubMed search terms were used: "(retinal+degeneration+slow+OR+PRPH2+OR+peripherin)+AND+(central+areolar+choroidal+dystrophy+OR+cacd+OR+vi-sion+disorders+OR+retinal+dystrophy)+AND+(mutation+OR+variant+ OR+mutations+OR+variants)". In addition, the HGMD professional database was used to search for variants or articles that were possibly missed with our PubMed queries. Variant detection, variant combinations, patient age, patient gender and age at onset, disease phenotype, segregation analysis, and allele frequencies (gnomAD) were collected. Obvious duplicates, in some cases following contact with the corresponding authors of the respective papers, were removed from the data set.
| Subjects This study was approved by the institutional review boards of the Radboud University Medical Center (Radboudumc) and was conducted in adherence to the tenets of the Declaration of Helsinki. All Sanger sequencing and/or whole (exome) sequencing data from the Radboudumc genome diagnostic laboratory were analyzed to determine the causative genetic defects in patients with visual impairment. PRPH2 variants were also identified by using a targeted sequencing approach based on molecular inversion probes (MIPs)  (Hiatt et al. , 2013; Neveling et al. , 2017; Weisschuh et al. , 2018). Singlemolecule MIPs were synthesized to capture and sequence overlapping 110-nt segments of the three PRPH2 protein-coding exons and flanking splice sites, similarly as described previously for the ABCA4 gene  (Khan et al.
| Variant analysis The complementary DNA (cDNA) was numbered as follows: the A of the ATG translation initiation codon of the PRPH2 reference sequence (NM_000322. 5) was reported as +1 while the initiation codon was reported as codon 1. Allele frequencies of PRPH2 variants in control populations were extracted from the genome aggregation database (gnomAD: v2. 1. 1 and v3). This database contains both whole exome and whole genome sequencing data obtained from 213,158 healthy individuals from all over the world (https://gnomad. broadinstitute. org/). Next, we performed statistical analysis to compare allele frequencies in the index patient group to the normal population  (gno-mAD). This enabled us to assess whether a specific variant is enriched in patient versus control groups. For this purpose, the Fisher's Exact test was used.
| In silico predictions For PRPH2 missense variants, Polyphen-2 (benign=0. 000-0. 150; possibly damaging = 0. 150-0. 850; probably damaging = 0. 850-1. 000), Combined Annotation Dependent Depletion (CADD, tolerated = <15; damaging = >15), and SIFT scores (tolerated = 0. 050 -1. 000; damaging = 0. 000-0. 050) were obtained from http:// genetics. bwh. harvard. edu/pph2/, http://cadd. gs. washington. edu/home (Kircher et al. , 2014)  and https://sift. bii. a-star. edu. sg/, respectively. For splice site variants, software to gather splicing scores available via Alamut Visual version 2. 13 (Interactive Biosoftware) was used.
Most of these variants are considered rare, and only four of them were present in the gnomAD database. We classified a proteintruncating variant as damaging if the variant induces a premature stop codon before amino acid 331 since the p. Gln331* variant was reported to be pathogenic  (Grover et al. , 2002). Moreover, it was determined that the valine residue at position 332 is critical for targeting PRPH2 toward the OS of photoreceptor cells  (Salinas et al. , 2013). Based on this knowledge, we classified all protein-truncating variants as pathogenic or likely pathogenic (Table  S2 ). One aspect to take into account is that the annotations of p. Tyr140ins 1bp, p. Ser217_dup16bp, and p. 224ins-37bp, as described in the original articles, are not correct. We used Alamut to check whether we were able to identify the correct cDNA annotation, but this could not be deduced.
| Synonymous variants Three synonymous variants were reported, namely p. Tyr101Tyr (c. 303T>C), p. Tyr236Tyr (c. 708T>C), and p. Glu335Glu (c. 1005G>A). These variants were exclusively found in patients with macular dystrophy or pattern dystrophy. All variants were first classified as likely benign due to the fact that in silico prediction programs defined them as benign/tolerated. Moreover, no evidence for putative splice defects was predicted. However, the p. Tyr101Tyr (c. 303T>C) and p. Glu335Glu (c. 1005G>A) variants were not reported in the gnomAD database, indicating that they are not commonly found in control individuals (Table  S2 ). For this reason, we classified these variants to be of uncertain significance (Tables  S2  and S3 ). The p. Tyr236Tyr (c. 708T>C) variant was reported in gnomAD, and statistical analysis showed that the variant was significantly enriched in the healthy control group when compared to index patients.
, 2020). These findings indicate that besides RP, also cone-rod and pattern dystrophy might be caused by digenic variants (i. e. one in PRH2 and one in another gene). However, mutations in IRD associated genes are relatively frequent. Nishiguchi and Rivolta screened high-quality genome sequences of control individuals from various ancestries and estimated that ~1 in 4-5 individuals from the general population carry variants that are associated with IRD  (Nishiguchi & Rivolta, 2012). Furthermore, Hanany and colleagues created a database containing 276,921 sequence variants that were identified in 187 autosomal recessive IRD genes and found that 2. 7 billion people worldwide (36% of the population) are being healthy carriers of at least one IRD variant  (Hanany et al. , 2020). Thus, patients may carry variants in multiple IRD genes without any clinical consequences.
Another example is the c. -11A>C variant, which is located in the 5′-UTR region. This variant was classified as likely pathogenic because it was not present in single-nucleotide polymorphism databases, and was not found in 92 controls  (Boon, Klevering, et al. , 2007). However, we have classified the variant as benign since when comparing the allele frequency in gnomAD to the allele frequency in the reported index patients, the variant was found to be enriched in the control population (gnomAD). There were also some variants that were considered to be of uncertain significance in initial publications but were classified as (likely) pathogenic following our classification. For instance, the p. Gly167Ser variant  (Meunier et al. , 2011; Strom et al. , 2012)  is located in the D2 loop (mutational hotspot; PM1), was absent in controls (gnomAD; PM2), was predicted to be damaging by all three in silico prediction programs (PP3; Table  S2 ), and co-segregated with disease (PP1).
Finally, when looking at the location of likely pathogenic PRPH2 variants, it was striking that these variants seem to cluster in the D2 loop (Figure  1c ), suggesting that this loop is a mutational hot spot for amino acid changes associated with PRPH2-associated IRD. Taken together, additional genetic data from IRD patients would be of great help to determine genotype-phenotype correlations and to study the often observed intra-and inter-familiar phenotypic variability. SIFT uses datasets of functionally related protein sequences that are obtained via a protein database. The algorithm scans each position in the sequence of interest, after which the probabilities for all possible 20 amino acids at that position are calculated, resulting in one final SIFT score  (Kumar et al. , 2009). The CADD in silico prediction software is also available via a webserver.
The program integrates multiple lineages of data, including genomic features within the surrounding sequence of interest, gene model annotations, evolutionary data from multiple species, epigenetic measurements, as well as functional predictions. In contrast to many other in silico prediction programs, CADD is not limited to the number of genomic variants of which either a pathogenic or benign status is already known  (Rentzsch et al. , 2019)   (Richards et al. , 2015). Only when at least two out of three of the in silico programs used give the same prediction output, the evidence can be counted as supporting (PP3)  (Richards et al. , 2015). Based on this, the in silico predictions for the p. Lys15Arg, p. Ile32Val, p. Ala85Thr, p. Ala116Ser, p. Glu127Gly, p. Met152Val, p. Lys154Gln, p. Ile161Met, p.
| CONCLUDING REMARKS Taken together, in this study, we describe 245 reported and seven novel PRPH2 variants identified in 891 alleles in 867 index cases and uploaded these to the LOVD and ClinVar database for PRPH2. This study thereby provides an important step toward a complete overview of all PRPH2 variants in a single database. Continuous addition of genetic data from newly identified patients with PRPH2 variants is of great importance for more robust classification of pathogenic variants. Furthermore, additional data regarding phenotypes might aid the identification of genotype-phenotype correlations. Our analysis resulted in the in silico classification of all the 245 reported, as well as the seven novel identified PRPH2 variants. More importantly, our study illustrates the need for experimental assays to identify the true causality of the many PRPH2 variants that are now still assigned to be variants of uncertain significance.
ACKNOWLEDGMENTS We gratefully acknowledge  Stéphanie S. Cornelis  for the assistance with the statistical analysis and LOVD submission, as well as  Bjorn Bakker  for technical assistance. Members of the  PRPH2 study group  ( Drs. C. Ayuso ;  S. Banfi ;  T. Barakat ;  N. Bax ;  T. Ben-Yosef ,  M. Breukink ;  J. de Hoog ;  J. de Roach ;  A. Delbecq ;  J. Ferraz Sallum ;  K. Fujinami ;  M. Gorin ;  W. Hartstra ;  Y. Hettinga ;  S. Ijzer ;  J. Keunen ,  A. Kievit ;  T. Kleefstra ;  T. Lamey ;  P. Liskova ;  M. Oldak ;  J. Ossewaardevan Norel ;  M. Phan ; O. Podhajcer; P. Rump;  M. Sinnema ;  D. Smailhodzic ;  C. Stumpel ;  A. Thiadens ;  J. van de Ven ;  R. van Leeuwen ;  S. Vermeer ;  J. Verheij  and  B. Weber ) who submitted DNA samples in which PRHP2 variants were identified. This study was supported by an internal RadboudUMC PhD grant provided by the  Donders Institute for Brain, Cognition and Behavior , as well as by the  Foundation Fighting Blindness USA , grant  BR-GE-0120-0775-LUMC.
WEB RESOURCES https://gnomad. broadinstitute. org/ http://genetics. bwh. harvard. edu/pph2/ http://cadd. gs. washington. edu/home https://sift. bii. a-star. edu. sg/ https://databases. lovd. nl/shared/genes/PRPH2 https://www. ncbi. nlm. nih. gov/clinvar/?term=PRPH2[all] DATA AVAILABILITY STATEMENT All variant data described in this study are freely available via the LOVD and ClinVar database for PRPH2 (https://databases. lovd. nl/ shared/variants/PRPH2; PRPH2[all] -ClinVar -NCBI (nih. gov)). Other data that support the findings detailed in this study are available from the corresponding author, upon reasonable request.
Ruminant livestock enteric fermentation contributes approximately one-third of the global anthropogenic methane (CH4) emissions and is projected to increase significantly to meet the increasing demand for animal-sourced protein. Methane, a short-lived greenhouse gas, needs to be reduced -24 to -47% by 2050 relative to 2010 to meet the 2. 0°C target. This study describes the results of a comprehensive meta-analysis to determine effective mitigation strategies. The database included findings from 425 peerreviewed studies (1963 to 2018). Mitigation strategies were classified into three main categories [animal and feed management, diet formulation, and rumen manipulation (additives and methods used to modify the rumen)] and up to five subcategories (98 total mitigation strategy combinations). A random-effects meta-analysis weighted by inverse variance was carried out (Comprehensive Meta-Analysis, V3.
Literature Search and Classification of Mitigation Strategies. The database for this meta-analysis was compiled using data obtained by searching the databases of the Commonwealth Agricultural Bureau International (CABI), the EBSCO Discovery Service, and the Web of Science from 1964 to 2018. Publications from 1964 through 2016 were searched using CABI and EBSCO Discovery Service with the search terms 'rumen' AND 'methane' and an additional four searches were completed in the EBSCO Discovery Service using the term 'rumen' in combination with 'methane', 'energy partitioning', 'energy metabolism', or 'energy balance'. Publications from 2017 through 2018 were searched using CABI and Web of Science databases. Seven searches were conducted with the search term 'methane' in combination with 'beef', 'cattle', 'dairy', 'goat', 'sheep', 'rumen', or 'ruminant' and three searches with the search term 'rumen' in combination with 'energy balance', 'energy metabolism', or 'energy partitioning'.
Publications listed in an independently developed database supported by the AnimalChange project, MitiGate 13 , were merged with the database created in the current analysis. The abstracts of the publications found in the search were reviewed and, based on abstract content, publications were selected for further consideration if they included in vivo measurement of enteric CH4 emissions, a clearly defined treatment and control, and multiple replications (i. e. , at least four or more animals in continuous design experiments, crossover design experiments, etc. ). Publications were excluded if they were not from peer-reviewed literature or if they were not in English, French, German, or Portuguese. Furthermore, publications were excluded if they were based on inappropriate study design (i. e.
, experimental period ≤ 10 days) or measurement technique (e. g. the 'sniffer technique' that is based on the CH4-to-carbon dioxide ratio of exhaled breath  56, 57  ). The completed database consisted of 650 publications. From these, only publications that had a treatment that could be assigned to one of three main mitigation categories, as described below, and reported statistical variance for at least one of the CH4 emissions emission metrics (e. g. LSD, RSD, or SE of the mean) were included in the final analysis. WebPlotDigitizer (https://automeris. io/WebPlotDigitizer/; accessed 30 October 2019) was used to determine absolute values for a total of nine metrics in seven publications where data were reported as figures. Data were classified into three main mitigation categories: (1) animal and feed management, (2) diet formulation, and (3) rumen manipulation, each of which was then further classified into up to five subcategories (Supplementary Table  2 ).
Digestibility (of fiber) metrics included only apparent digestibility of neutral detergent fiber. Where multiple treatments of a common treatment type were present within an experiment, those treatment means were averaged, and their respective errors pooled, such that each experiment produced a single "Treatment" and "Control" pair of response means and SDs. The final meta-analysis included data from 425 peer-reviewed publications, of which 66% were with cattle, 31% with small ruminants (sheep and goats) and 3% with other ruminant species (buffalo, deer, and yak). The complete list of references used in the current analysis is given in Supplementary Table  1. The majority of publications reported daily CH4 emissions (92%), feed intake (84%), and CH4Y (71%), whereas less than half of the publications reported weight gain for all animal types (growing, lactating, and other adult animals) (49%), Ym (48%), fiber digestibility (41%), milk yield (29%), CH4IM (21%), or CH4IG (7%) (Supplemental Fig.
2 ). The final analysis only included weight gain data for growing animals (106 publications), which led to the exclusion of the weight gain data of half of the publications (104) that reported weight gain data for lactating and other adult animals. Statistical Analysis. A mixed model meta-analysis weighted by inverse variance was carried out considering treatment mean comparisons within publication as a random effect. Analyses were run across all ruminant species (cattle, buffalo, deer, goat, sheep, and yak) and included main mitigation strategies and their respective subcategories as potential moderator fixed effects. Analyses were conducted separately for each of the nine response variables (daily CH4, CH4Y, Ym, CH4IG, CH4IM, feed intake, weight gain for growing animals, milk yield, and fiber digestibility) using a log ratio of means, namely log(Treatment/Control), in order to standardize treatment effects across multiple measures, species, and outcomes, as well as to allow the expression of treatment differences as relative percentages  58, 59.