dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, if the reference database consists of sequences more dissimilar to the one retrieved from the environment, the NBC method might be more advisable to obtain taxonomic information, but the accuracy of this taxonomy might be low. ASVs have substantial merits for the analysis of functional genes for which OTU similarity values are unknown, and in general, the field is shifting to ASVbased resolution of amplicons (e. g. , see reference 13). Yet the reference databases used to taxonomically place the amplicons are still based on traditional threshold-based clustering and should improve as more and more ASV-based studies become available in the future. Currently, there is a shift in microbial ecology from OTUs to ASVs, and this approach has now been widely adopted for 16S rRNA gene studies  (33, 36, 54).
The choice of software was a result of our recent work  (61, 62)  where it was shown that the above-described strategy of read trimming followed by error correction and overlapping reads reduces the substitution rates significantly. After having obtained the consensus sequences from each sample, the VSEARCH (v2. 3. 4) pipeline (all of these steps are documented at https://github. com/torognes/vsearch/wiki/VSEARCH-pipeline) was used for OTU construction. Reads were pooled from different samples, and barcodes were added to keep an account of the samples that the reads originated from. Reads were then dereplicated and sorted by decreasing abundance, and singletons were discarded. In the next step, the reads were clustered based on different similarity thresholds (97%, 95%, 90%, and 85% for AOA and AOB amoA; 97%, 95%, 90%, and 83% or 82% for nirK [83%] and nirS [82%]; and 97% for nxrB and nrfA), followed by the removal of clusters that have chimeric models built from more abundant reads (-uchime_denovo option in VSEARCH).
A few chimeras may be missed, especially if they have parents that are absent from the reads or are present at a very low abundance. Therefore, in the next step, we used a reference-based chimera-filtering step (-uchime_ref option in VSEARCH) using the reference databases created as described above. The quality-filtered barcoded reads were matched against clean OTUs with different similarity thresholds to generate OTU tables. Amplicon sequence variants (ASVs) were constructed in R using the DADA2 package, according to the tutorial at https://benjjneb. github. io/dada2/tutorial. html. First, quality trimming was done using filterAndTrim(). The trimRight and trimLeft parameters, used to trim the 39 ends of reads and primer sequences, respectively, were adjusted for each target gene, as listed in Table  S1.
The R script used for ASV construction is available in Text S1. Taxonomic assignment by NBC and BLCA. For each nitrogen cycle gene, reference sequences (nucleotides) were downloaded from Fungene (http://fungene. cme. msu. edu/); for AOA and AOB amoA, a second database was constructed by downloading the sequences corresponding to the different clusters defined previously by Zhang et al. (8). Subsequently, the R rentrez package  (65)  was used to obtain taxonomic information at different levels, generating a taxonomy file. The FASTA file and the corresponding taxonomy file were formatted to work with Qiime  (66). To assign taxonomy to the representative ASVs, two different approaches were used: representative ASVs were classified using a naive Bayesian classifier (NBC) k-mer classifier (Qiime feature classifier classify-sklearn) or the Bayesian lowest common ancestor (BLCA)  (39)  against the reference databases, using default parameters.
ACKNOWLEDGMENTS We acknowledge the cytometry and imaging facilities and the molecular core facilities of the  LIENSs laboratory, University of La Rochelle. We acknowledge the following funders:  Thomas Crawford Hayes NUIG  (awarded to C. J. S. and A. L. ), a  Harry Smith vacation studentship  (awarded to A. L. ), a mobility grant from the  French Embassy in Ireland  (awarded to H. A. and C. J. S. ), a research visit grant, a  Microbiology Society  and  Research Student Mobility grant , the  University of Glasgow  (awarded to F. C. ),  La Rochelle University , and the  Environmental Protection Agency   STRIVE Doctoral Scholarship Scheme  ( 2012-W-PhD-6 ) (awarded to C. J. S. ). F. C. is supported by a  University of Glasgow College of Science  and Engineering doctoral scholarship, U. Z. I. was funded by  NERC IRF   NE/L011956/1 , and C.
Transcriptome Analysis Total mRNAs were isolated from three replicates of 100 embryos and 50 endosperms and hybridizations on the Affymetrix GeneChip R Rice Genome Array (Affymetrix, Santa Clara, CA, USA) were performed as previously described  (Galland et al. , 2014 (Galland et al. , , 2017)). To obtain presence/absence calls for each probe, the CEL files were normalized by the MAS5 algorithm (Affymetrix). The CEL files were then normalized with the GC-RMA algorithm using the "gcrma" library available from the R Bioconductor suite of open-source software  (Huber et al. , 2015). Differentially expressed genes in the embryo and endosperm transcriptomes were detected by using a two-group t-test, followed by the Bonferroni method (adjusted P-value is <0. 01). All raw CEL files are available from the Gene Expression Omnibus under the accession GSE43780.
LC-MS/MS Analysis Peptide separation by NanoLC was performed as described previously  (Bonhomme et al. , 2012). Eluted peptides were analyzed on-line with a Q-Exactive mass spectrometer (Thermo Electron) using a nano-electrospray interface. Peptide ions were analyzed using Xcalibur 2. 1 with the following data-dependent acquisition parameters: a full MS scan covering 300-1,400 range of mass-to-charge ratio (m/z) with a resolution of 70,000 and an MS/MS step (normalized collision energy: 30%; resolution: 17,500). MS/MS Step was reiterated for the 8 major ions detected during the full MS scan. Dynamic exclusion was set to 45 s. For database searching, X!Tandem  (Langella et al. , 2017)  was set up to search the 7th annotation of the Rice Genome Annotation Project database  (Kawahara et al. , 2013; Sakai et al.
, 2013)  and a contaminant database (trypsin, keratins). Enzymatic cleavage was declared as a trypsin with two possible misscleavage. Cys carboxyamidomethylation was set to static modifications. Met oxydation was set as possible modifications. Precursor mass and fragment mass tolerance were 10 ppm and 0. 02 Th, respectively. Only peptides with an E-value smaller than 0. 1 were reported. Peptide quantification was performed by extracted ion current (XIC) using MassChroQ software  (Valot et al. , 2011). A 5 ppm precision windows was set for XIC extraction. The peptide ions not specific of a single protein were eliminated and reliably detectable ions that were detected at least twice out of the three biological replicates were used for the quantification. The Total Ionic Current (TIC) area under peak corresponding to the same ions was obtained for each peptide and then summed to get a protein abundance.
GO Enrichment Analysis for Differentially Accumulated Proteins Differentially accumulated proteins during germination were determined by Student's t-test (p < 0. 05) in embryos and endosperms, respectively. GO enrichment analysis for the up or down accumulated proteins was performed using the Functional Annotation tool in the CARMO (http://bioinfo. sibs. ac. cn/carmo/) with default parameters for thresholds (p < 0. 05, fold enrichment > 2), followed by the Benjamini-Hochberg Procedure (adjusted p < 0. 05). The values for -log10(adjusted p-values) and fold change of obtained GO terms in Biological Process were drawn using the ggplot2 package in R.
Metabolome Analysis by Gas Chromatography Coupled to Mass Spectrometry (GC-MS) Metabolite samples were obtained starting from three replicates of 100 rice seeds manually dissected in embryo and endosperm. Embryos and endosperms were ground with a mortar and pestle under liquid nitrogen and with a Cyclotec TM 1093 Sample Mill (FOSS, Hillerød, Danemark), respectively. All samples were lyophilized and around 20 mg dry weight (DW) of each sample were placed in 2 ml Safelock Eppendorf tubes (Eppendorf AG, Hamburg, Germany). All analysis steps, including extraction, derivatization, analysis, and data processing, were adapted from the original protocol described by  Fiehn (2008)  and following the procedure described by  Avila-Ospina et al. (2017). The extraction solvent was prepared by mixing water:acetonitrile:isopropanol at the volume ratio 2:3:3 allowing to extract metabolites with a broad range of polarities.
For derivatization step, N-methyl-Ntrimethylsilyl-trifluoroacetamide (MSTFA; Sigma-Aldrich) was used in silylation procedure of metabolites. Samples were analyzed on an Agilent 7890A gas chromatograph coupled to an Agilent 5975C mass spectrometer. Raw Agilent datafiles were converted in NetCDF format and analyzed with AMDIS (Automated Mass Deconvolution and Identification System; http://chemdata. nist. gov/mass-spc/amdis/). A home retention indices/mass spectra library built from the NIST, Golm, and Fiehn databases and standard compounds was used for metabolites identification. Peak areas were then determined using the QuanLynx software (Waters, Milford, USA) after conversion of the NetCDF file in MassLynx format. specifically detected in the embryo or "FALSE" if detected in both tissues (embryo and endosperm), E X _R Y : GCRMA normalized probe intensity across the germination time course (X: hours after imbibition, Y: replicate number).
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Transcriptomic data are available on the National Center for Biotechnology Information (NCBI) BioProject database under accession number GSE43780; Proteomic data are available on ProticDB, http://proteus. moulon. inra. fr/w2dpage/proticdb/ angular/#/projects/163.
The United Nations Decade on Ecosystem Restoration underlines the importance of understanding how different taxa are affected by human induced, global changes in ecosystems. Here, we investigate if this impact can be quantified for the globally distributed tropical plant group Annonaceae (Soursop family) using distributional data and International Union for Conservation of Nature (IUCN) Red List assessments. We find that even for a taxonomically well-studied tropical plant family such as Annonaceae, little is known about the true distribution and ecological requirements of, and threats to, species in this group. We discuss several improvements in data collection that should enable more in-depth analyses in the future.
| Taxonomic data sources Because Annonaceae are taxonomically well studied (see for instance for the Neotropics:  Erkens et al. , 2017; for Africa: Hoekstra et al. , 2021; and for Asia: Meade & Parnell, 2018) , many herbarium specimens have become available over the years. A well worked-out taxonomy is important for making inferences within a family, because without it, analysis can result in faulty conclusions, mismatched records and inflated species numbers  (Boyle et al. , 2013). Data were therefore primarily collected from recent monographs and revisions, from examining herbarium specimens during ongoing herbarium work for flora projects (e. g. , Flora Neotropica, Flora of the Guianas, Flora do Brazil and Flora de Gabon) and from the databases BIEN (for the Neotropics; https://bien. nceas.
ucsb. edu/bien/; Maitner et al. , 2018)  and RAINBIO (for Africa; https://gdauby. github. io/rainbio/index. html; Dauby et al. , 2016). GBIF data (Global Biodiversity Information Facility [GBIF]; https://www. gbif. org/) were only used in instances when insufficient specimen data were available based on the previous data sources. GBIF data were restricted to accessions with a coordinate uncertainty of less than 1000 m. The resulting data set consisted of 108 out of 110 Annonaceae genera (Table  S1 ) and 67,966 specimens. Because the highest likelihood of taxonomic mistakes lies at the species level, all analyses were carried out at the genus level, a socalled higher-taxon approach  (Gaston & Williams, 1993). This allowed us to aggregate occurrence data of a large number of species (and specimens) at large spatial scales and should result in less error due to missing or unreliable identifications at the species level  (ter Steege et al.
science. kew. org/) or occurred in water were removed when these misplacements were not due to correctable georeferencing mistakes. When it was unclear how misplacements should be corrected, they were omitted. Duplicates of occurrences on the same locality were not removed at this stage, as they usually represented multiple occurrences that were found in close proximity to each other. Serious effort has been made to manually remove duplicates of the same specimen represented by accessions from multiple herbaria. In ArcGIS Pro, a spatial join was performed to assign each specimen to a country, biome type, biome or anthrome. Polygon layers were matched to the specimens using spatial-join analyses, and the raster layer using an extract-values-to-points analysis. Specimens that had no data assigned because they were adjacent to the polygon or raster had their features manually matched to the closest feature.
| IUCN Red List assessments Data on the IUCN Red List status of Annonaceae were downloaded from the IUCN Red List website (www. iucnredlist. org) when published or were obtained as draft from the Global Tree Assessment (GTA) project. The draft assessments from the GTA project only contain Annonaceae species with a tree habit, not lianas. See  Verspagen and Erkens (2022)  elsewhere in this Special Issue for a discussion on the methodology used for the assessments made in the context of the GTA project.
, 2008). Next to a tropical distribution, the data also show the occurrence of Annonaceae in dryer and lower temperature conditions. Nearly 5% of the specimens were collected in the temperate vegetation with trees biome type (temperate forests, savannas and shrublands). Of these, the large majority are from the mainly subtropical genus Asimina  (Li et al. , 2017). This genus is very well collected in the United States (mainly in Georgia and Florida states) with one species (Asimina triloba) naturally extending far into the temperate zone (including the southern part of Ontario, Canada; van Heusden, 1992; Table  S2 ). It must be noted, however, that part of the 3235 specimens (Figure  1 ) included in this study will be of cultivated origin, because A. triloba (also known as the 'poor man's banana') is widely planted  (Layne, 1996).
, 2004)  and tend to experience high temperature variations due to their lack of vegetation  (Guo et al. , 2020). This biome is not regularly associated with Annonaceae. However, this biome includes xeric habitats, and 16 genera of Annonaceae have data points in such dry conditions (Tables  3  and S4 ). For the large majority of the genera, the proportion of the specimens assigned to this biome is around 0. 6%, with a few between 1 and 4% (Artabotrys and Popowia: 1% and Fenerivia: 3. 5%). We cannot assess if this low percentage is due to georeferencing errors or GIS projection issues (see below). However, the Annona specimens represent more than half of the total specimens located in the desert biome including xeric shrublands. One possibility is that this is due to the large number of cultivated specimens.
| Threat assessments of Annonaceae Next to spatial analyses, another way to assess the level of threat to a taxon is to look at the IUCN Red List threat assessments. For Annonaceae, 1225 species have published or draft assessments available (Table  5 ), most of these being trees. Approximately 20% of the assessed species fell in one of the three threat categories (Endangered, Vulnerable and Critically Endangered). This is about the same level as for plants in general  (Brummitt et al. , 2015). It seems therefore that Annonaceae do not stand out in terms of the currently assessed threat levels. However, the number of species assessed as Data Deficient represents just under half of the species assessed to date (Table  5 ). This difference is partly an artefact of data accessibility. It was noted before that although many organisations collect biodiversity data, these valuable data can often not be fully (re)used  (Michener, 2015; Roche et al.
, 2019) , it is very difficult to know the level of certainty one can attach to the data. Furthermore, for the Asian draft assessments, mainly GBIF data were used that contained only few collections for many species. In combination with the fact that verifiable spatial data were often lacking, not enough specimens were available to make proper Red List assessments, leading to a Data Deficient status. Digitising more specimen data and presenting them in a FAIR way also enables the mass assessment of threat levels of species for the IUCN Red List at, for example, continental levels  (Stévart et al. , 2019). For many of the assessments made in the context of the GTA project, assessment information on species was mass uploaded via the IUCN SISConnect portal. Although some depth is lost in the assessment with this method, it does not differ that much in terms of outcomes from the individual approach  (Bachman et al.
ACKNOWLEDGEMENTS The authors thank the BIEN and RAINBO networks for sharing data on which part of this research is based. Paul Maas  is acknowledged for contributing verified herbarium specimen data from his ongoing taxonomic work in Annonaceae. We also thank  Botanic Gardens Conservation International (BGCI)  for providing the funding for this project and making available the Annonaceae draft assessments of the  Global Tree Assessment project. Sara Oldfield  has provided feedback on an early draft of the manuscript. This study was partially funded the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 865787 ). Finally, we thank  Domingos Cardoso  and two anonymous reviewers for their constructive comments that improved the manuscript.
The suspension was kept under sonication for 1 h in an ice bath under N 2 for delamination, followed by a vacuum-filtering process for preparing the freestanding HF-Ti 3 C 2 T x electrode. Physical Characterizations: X-ray diffraction (XRD) data were collected by a D4 X-ray diffractometer (Bruker, Germany) equipped with Cu K-α radiation (λ = 0. 154 nm). The morphology of the MXenes was observed with a Scanning Electron Microscope (SEM) JSM 7100F (JEOL, Japan) with energy-dispersive X-ray spectroscopy (EDX) capabilities (Bruker, XFlash 6130). In situ high-temperature XRD was performed under an inert atmosphere (N2, 100 mL min -1 ) with D8 X-ray diffractometer (Bruker, Germany) equipped with Cu K-α radiation (λ = 0. 154 nm). The sample was placed in a ceramic cell and heat-treated up to 700 °C at a rate of 10 °C min -1.
Samples were dispersed in ethanol and sonicated for ≈10 min. Dispersions were then drop-casted on lacey carbon Ni grids (MicroToNano) and dried prior insertion to the TEM. The TEM was operated at 200 kV and a probe convergence angle of 25 mrad was used for scanning (S)TEM operation. High-angle annular dark-field (HAADF-) STEM images were collected with an angular collection semi-angle range from 50 to 180 mrad. A dispersion of 0. 25 eV per channel was used for electron energy loss spectroscopy. Data were treated using Gatan's Digital Micrograph (GMS3) version 3. 4. In particular, HR-STEM images were filtered by applying a combined (default) soft rectangle and band pass filter. The Brunauer-Emmett-Teller (BET) equation was used to calculate the specific surface area (SSA), from the linear plot in the relative pressure range (P/P0).
Acknowledgements L. L. was supported by  ERC  Synergy Grant MoMa-Stor# 951513. P. S. and P. L. T. acknowledge the support from  Agence Nationale de la Recherche  ( Labex Store-ex ) and  ERC  Synergy Grant MoMa-Stor # 951513. The authors would like to thank  Antje Völkel  and  Christelle Chiron  for help with elemental analysis. Open access funding enabled and organized by Projekt DEAL.
| Inclusion of genes Gene inclusion criteria was based on genes associated with iMD and/ or AMD listed on the Retinal Information Network online resource (https://sph. uth. edu/retnet/: Accessed August 7, 2020) and those reported in literature. The Human Gene Mutation Database (HGMD) (http://www. hgmd. cf. ac. uk/ac/index. php: Accessed August 7, 2020) was also used to highlight genes associated with reported iMD and AMD phenotypes. Specifically, iMD-associated genes comprised genes involved in MDs and cone-prominent IRDs.
| Generation of the MD-smMIPs panel We sought to assemble a panel of genomic regions associated with iMD and AMD phenotypes to target using smMIPs. The 5′ untranslated regions (UTRs), protein-coding exons and alternate protein-coding exons of 105 genes/loci associated with iMD and AMD were selected as targets for our new panel. Transcript numbers were selected from Alamut Visual software version 2. 13 (Interactive Biosoftware), selecting the protein-coding transcript or the longest transcript, and checked using the UCSC Table Browser  (Karolchik et al. , 2004). Genomic coordinates were extracted from UCSC Genome Browser, hg19 (GRCh37)  (Haeussler et al. , 2019). All transcripts were also evaluated for the presence of alternative exons and alternative 5′ UTRs using the Ensembl Genome Browser (GRCh37; Ensembl release 101)  (Yates et al.
| Average coverages per nucleotide To determine the number of all reads covering each nucleotide of nuclear targets (428,562 nt) and mtDNA targets (16,569 nt) in MD run 01, the base calls of aligned reads to a reference sequence were counted in individual sample BAM files using the "pileups" function of SAMtools  (Li et al. , 2009). The following parameters were used for the generation of pileups data: minimum mapping quality = 0; minimum base quality = 12; anomalous read pairs were discarded; overlapping base pairs from a single paired read as a depth of 1 were counted. An average coverage per nucleotide was generated for each nucleotide position across all samples sequenced in MD run 01, followed by an average coverage for all genes/loci targeted in the MD-smMIPs panel. The average coverage per nucleotide for RPGR was calculated excluding exon 15 of the RPGR-ORF15 transcript, and the coverage of exon 15 of the RPGR-ORF15 transcript was determined independently.
| Variant prioritization First, using an Excel script as previously described  (Khan et al. , 2020) , CNV analysis was conducted for all samples. The presence of six consecutive smMIPs with a normalized coverage across all samples included in the sequencing run of ≤0. 65 assumed a deletion, whereas those with a normalized coverage ≥ 1. 20 suspected a duplication. The second phase of analysis focused on SNVs and indels that were present in ABCA4, compared to a list of previously identified variants, as listed in the Leiden Open (source) Variation Database (LOVD; https://databases. lovd. nl/shared/genes/ABCA4), to highlight frequent ABCA4 variants, for example, c. 2588G > C p. [Gly863Ala, Gly863del] and c. 5603A > T p. (As-n1868Ile)  (Cornelis et al. , 2017; Maugeri et al. , 1999; Zernant et al.
, 2017) , and/or previously published causative variants in ABCA4. In addition, the presence of known pathogenic DIVs targeted by the MD-smMIPs panel were highlighted. Next, all homozygous and compound heterozygous SNVs and indels with a minor allele frequency ≤ 0. 5% and all heterozygous variants in genes associated with autosomal dominant retinal dystrophies with a minor allele frequency of ≤0. 1% in an in-house cohort, containing 24,488 individuals with numerous phenotypes, and the Genome Aggregation Databases (gnomAD) for control exome and genome populations were assessed. Variants with an allele frequency variation between 35% and 80% were considered as heterozygous variants, and multiple variants present in a given gene were considered compound heterozygous candidates. Variants with an allele frequency variation ≥ 80% were classified as homozygous variants.