dataesr/scientific-paragraphs-categorization huggingface dataset:
Investigating the effect of random selection of the negative examples on predictions While positive examples are always the same in different sample sets, the negative examples are randomly picked out of the genome. The performance of the model in different regions of chromosome 21 can thus vary for different training sets  (Wesolowska-Andersen et al. , 2020). To investigate this variation, we set up 30 balanced 1 * datasets and train 30 CNNs separately. The 30 models are then applied over human chromosome 21 to study the fluctuations of the predictions. The variation of 30 predictions is depicted in Fig. 2B. The first observation is that almost all predictions present a peak over the DIP2A GSS. However, the large gap between the minimum and maximum predictions underlines the variability of predictions obtained with different training datasets.
CONCLUSIONS With the surge of DNA sequencing technologies, over a million genome datasets are now available and petabases of transcripts are sequenced every year to annotate these datasets with functional marks  (Wainberg et al. , 2018). It has not escaped the notice of many computational biologists that deep neural networks are a key tool to deal with this exponentially increasing amount of data  (Wainberg et al. , 2018). One possible application is to leverage datasets with good annotations in order to train neural networks and to predict annotations on other datasets. One of the practical issues when applying neural networks on genomic sequences is unbalanced data, a well-known issue in the machine learning literature  (He & Garcia, 2009; Chawla, Japkowicz & Kotcz, 2004; Batista, Prati & Monard, 2004 ).
In the present paper, we address this problem using GSS as a case study. Indeed, GSS occupy only a few locations on the genome (31,037 GSS for human) leading to extreme unbalances in datasets (i. e. , the ratio of GSS-containing 299 bp windows to non-GSS in the human genome is 1/400). In this case, the lack of examples of the minority class (i. e. , true GSS) impacts the learning process as conventional machine learning algorithms usually measure the model performance on the majority class (i. e. , non-GSS) leading to biased or inaccurate prediction of the minority class. To deal with this disparity, we adopt a weighting strategy to decrease the importance of the majority class samples (non-GSS) during the learning process thereby improving identification of the rare minority class samples (GSS).
Using this approach, which we call ''limited unbalanced datasets'', we show that learning on imbalanced datasets can be performed effectively, and that for GSS recognition, a ratio of 1 to 100 positive over negative examples is usually sufficient to achieve a good signal to noise ratio in the prediction. This approach can be easily extended to identify other functional regions in any annotated genome. We also show that our method can be efficiently used across genomes of different species, i. e. , training the model on one genome and applying it to another genome. We use the X chromosomes of human and mouse GSS as a case study, and apply models trained on each one's other chromosomes to its own and the other one's X chromosome. While the sequence of this chromosome has evolved differently in both species, many genes are homologous  (Sinha & Meller, 2007).
In this context, studying speciation events through time in endemic ARF clades spanning both separated regions will provide complementary data to test these hypotheses. In order to test these hypotheses and infer vicariance ages, we can use dated molecular phylogenies of clades spanning east and west/central Africa, with endemic species in both regions  [9, 36]. Annonaceae are a pantropical family of trees, shrubs and lianas that are the most species-rich family of Magnoliales  [37, 38]  and represent an important component of ARF in general [5] and locally  [39]. Like numerous clades, Annonaceae are less diverse in Africa compared to the other main tropical rainforests regions, with 1100 species in South East Asia, 930 in the Neotropics and only 400 in Africa. The phylogeny of this family was studied for the first time more than 20 years ago  [40] , and since then, phylogenetic reconstructions have mainly been based on chloroplast markers  [37, 38, 41, 42].
This kit targets a total of 469 exons Annonaceae wide. Details on all 53 species (Table  S2 ) used in this study, such as field sampling, DNA extraction and DNA sequencing are presented in  Couvreur et al. (2019)   [43]. We therefore retrieved fastq files from the AFRODYN bioproject (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA508895), which contains demultiplexed, trimmed and sorted DNA paired reads (R1 forward + R2 reverse, 150 bp per read) of more than 120 Annonaceae species. Couvreur et al. (2019)   [42]  also provided the reference file for the baited regions, a fasta file comprising 469 targeted exons.
Contig Assembly, Alignment and Paralog Identification Raw reads from 53 individuals were cleaned as in  Couvreur et al. (2019)   [43]. We processed our data with HybPiper (v. 1. 2)  [50]  taking, as inputs, the clean fastq reads and the reference file containing the targeted exon sequence data. We retrieved contigs of exons and flanking introns and we aligned them separately with MAFFT (v. 7. 305)  [51]. Poorly aligned regions were cleaned using Gblocks (v. 0. 91b, gaps allowed)  [52]. HybPiper flags potential paralogs, which we assessed and removed from downstream analyses as follows. If several contigs covered more than 85% of the length of the same target gene, they were set aside for verification. Phylogenetic trees were created for each group of potential paralogs using RAxML  [53]. It might be expected that alternative sequences were grouped by individual (e.
Exons and Introns Selection for Molecular Dating To minimize missing data for our phylogenetic inference, we selected exons and introns that were present in more than 75% of individuals and for which we recovered at least 75% of the exon's length (referred to as the 75/75 dataset), following  Couvreur et al. (2019)   [43]. Firstly, a phylogenetic tree was inferred with RAxML  [53]  for each of the exons and introns in our 75/75 dataset. These trees were then rooted one by one with the most phylogenetically distant outgroup available. We used TreeShrink  [54]  with default parameters, to automatically identify and remove outliers in our trees The number and length of loci in our relatively large dataset made it computationally intractable to use all of the data for divergence time estimation.
Diversification Analyses Diversification analyses were conducted on the Piptostigmateae tribe only, because Annickieae is sister to all Malmeoideae and the sampling within the rest of the tribe (besides Piptostigmateae) is highly incomplete. The 21 outgroups as well as the seven Annickia were thus pruned from the MCC tree using the "drop. tip" function of the package ape (v. 5. 3)  [62, 63]. For each analysis, the number of missing taxa in the phylogeny was specified for each of the following three methods, as our data contained 74% of known Piptostigmatae species. In order to cross validate our results, we undertook three different diversification analyses under maximum likelihood and Bayesian approaches. We first used RPANDA (v. 1. 5)  [64] , to fit different diversification models to the dated phylogenetic tree under a maximum likelihood framework and estimate speciation and extinction rates through time.
Secondly, in order to conduct a comparison with RPANDA results, we used a Bayesian approach as implemented in TESS (v. 2. 1. 0) [66]  to fit the same six diversification models as above and compute their marginal likelihoods. We set an exponential prior distribution with rate = 10 for the speciation and extinction rates for all models, and ran Markov chain Monte Carlo (MCMC) analyses (100,000 iterations, 10,000 burn-in). We then used steppingstone simulations to estimate the marginal likelihood for each of our models (100 stepping-stone iterations, 10 burnin). MCMC convergence was verified with the coda package to ensure that ESS values were at least above 100 (we also verified mixing with a visualization of trace and density of the MCMC), and models were compared using Bayes Factors (BF).
Thirdly, we used BAMM (v. 2. 5. 0) [67, 68]  to detect possible diversification rate shifts along the branches of the tree. This Bayesian inference method uses a reversible jump Markov chain Monte Carlo (rjMCMC) approach to explore various models, detect speciation rate shifts over time and estimate diversification parameters (i. e. , speciation and extinction). Initial parameters were estimated with the R package BAMMtools (v. 2. 1. 6) [67]  (expected number of shifts = 1), and the other priors were set to their default values. We ran a rjMCMC for 10 million generations (sampling every 1000 generations, 10% burnin) in order to ensure the convergence on four Metropolis-coupled MCMC chains (default values). Effective sample sizes for the likelihood and number of shifts were verified (ESS > 200) using the package coda  [69].
Phylogenomics We recovered all the 469 exons present in the baiting kit with their corresponding introns. Our 75/75 dataset contained 330 exons and 329 introns. A total of 17 exons and 17 introns were flagged by HybPiper as potential paralogs, and of these, 32 were part of the 75/75 dataset. These were discarded, leaving a total of 298 exons and 297 introns for downstream phylogenetic analyses. After using our "gene shopping" approach, the 31 exons and one intron selected based on root-to-tip variance had a total length of 22,001 bp. The appropriate nucleotide substitution models selected for each exon and intron can be found in Table  S3. Our three MCMC chains converged to similar values and the combined ESS values was above of 200 for all parameters and greater than 600 for posterior and likelihood values.
This can be explained by the use of more sequence data (32 exons/intron versus 10 plastid markers), which might also be more informative. Our larger dataset allowed us to select the markers most appropriate for divergence time estimation, making our estimates the most accurate and reliable for the tribe to date. Our study provides two different time estimations of speciation events between species of ARFs endemic to the GC and EA regions, all occurring during the Neogene (Miocene/Pliocene). The oldest inferred split is between the two genera Mwasumbia (endemic to EA) and Sirdavidia (endemic to GC) dated to the Late Early Miocene (mean: 16. 13 Myr). This date falls into the Middle Miocene Climatic Optimum (MMCO) (17-14. 7 Myr)  [77]  and coincides with the stabilization of the East Antarctic Ice Sheet  [78].
Acknowledgments: We thank  Raoul Niangadouma  and  Narcisse Kamdem  for help in the field. The authors acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources. This study was supported by the  Agence Nationale de la Recherche  (grant number  ANR-15-CE02-0002-01  to TLPC). We are grateful to the  Centre National de la Recherche Scientifique et Technique (CENAREST) , the  Agence National des Parques Nationaux (ANPN)  and  Prof. Bourobou Bourobou  for research permits ( AR0020/16 ;  AR0036/15  (CENAREST) and  AE16014 (ANPN) ). Fieldwork in Cameroon was undertaken under the "accord cadre de cooperation" between the  IRD and Ministère de la Recherche Scientifique et Technique (MINRESI). Finally, we thank three anonymous reviewers for providing detailed comments on a previous version of this article.
Discussion In this study, we have explored the impact of NMD on SOD1 and shown that the activity of the NMD pathway is of broad importance for ALS caused by PTC in SOD1. Through a large-scale analysis of human cancer exomes and transcriptomes we were able to confirm that SOD1 standardly obeys to the NMD pathway and its rules. Our conclusion is supported by several arguments from the literature: (1) The presence of PTC in a region that escape NMD has been detected in mRNA extracted from immortalized lymphoblast cell lines from two patients harboring the c. 358-304C > G mutation, thus confirming the impact on the protein level p. Val120Glnfs*8  19  ; (2) Conformational changes of truncated proteins have been well characterized for PTC located in the region that escape NMD 20 ; (3) Heterozygous PTCs located in the region that trigger NMD seem to be more frequent in individuals from general population: in gnomAD database, for example, we found 6 individuals with a PTC theoretically triggering NMD (p.
In the first clinical trial of ASO treatment in human beings, only ASO targeting missense mutations were developed. This trial had favorable safety outcomes, and a trial to assess the safety, tolerability and pharmacokinetics of a second generation SOD1 ASO is currently in progress (ClinicalTrials. gov, NTC02623699)  26. To our knowledge, no ASO targeting a PTC has been investigated so far. This could be explained by the low proportion of patients carrying such mutations. For example, PTCs in SOD1 are absent from MinE Database which includes 4366 whole genomes from ALS patients and 1832 whole genomes from controls, from different European ancestry  27. Recently, complete loss of function of SOD1 in human has been reported in a 2 years old girl with a homozygous truncating mutation and an absence of SOD1 activity.
Thus, while caution might be exercised regarding the use of gene therapies that may markedly depresses SOD1 activity, reduction of SOD1 appears to be well tolerated, as outlined by the favorable clinical trial safety outcomes. Animal models, particularly SOD1 rodent model  30  , initially developed to investigate the complex processes occurring in ALS, had played a major role in performance evaluation of these silencing approaches  22. More recently, other models like zebrafish  31  , Drosophila  32  or patient-derived induced pluripotent stem cell  33  have also Table  1. List of truncating mutations in SOD1 associated with ALS. a Human genome variation society (HGVS) nomenclature V2. 0 according to mRNA reference sequence GenBank: NM_000454. 4. Nucleotide numbering uses + 1 as the A of the ATG translation initiation codon in the reference sequence, with the initiation codon as codon 1.
Methods The PTCs of the human SOD1 gene (NM_000454. 4) resulting from nonsense, frameshift and splicing mutations that are associated with ALS were obtained from the Human gene mutation database (HGMD)  34  , which provides systematic and in-depth qualitative and quantitative overviews of genetic research in both familial and sporadic ALS. Intronic mutations located outside the canonical sites and not confirmed by transcript analysis were excluded from this study. The cBio Cancer Genomics Portal (http://cbiop ortal. org)  17, 18 , an open platform for exploring multidimensional cancer genomics data, was used to select tumor samples with PTC in SOD1 for which whole exome sequencing (WES) and RNA-sequencing (RNA-seq) experimental data were performed. We generated plot from cBio Cancer Genomics Portal  17, 18  to analyze SOD1 mRNA expression correlation with SOD1 mutations.
Raw data (Bam files) from sample of interest were downloaded, when available, from NCBI Sequence Read Archive (SRA) and visualized with the Integrative Genome Viewer software (version 2. 8. 4, http://softw are. broad insti tute. org/softw are/igv/)  35. of WES and RNA-Seq alignments of 3 samples from the Cancer Cell Line Encyclopedia  52. The missense mutation p. Ile150Val (I150V, sample 3) is used here as a control as it has no effect on NMD activity and is thus found heterozygous in both WES and RNA-Seq alignments. The non-sense mutation p. Glu79* located in exon 3 of SOD1, (E79*, sample 1) was detected on 9% of RNA-Seq reads versus 46% of WES reads, showing the degradation of the SOD1 mutated transcript by NMD. In contrast, heterozygous frameshift mutation p. Lys137Aspfs*26 (K137Dfs*26, sample 2), located in the last exon of SOD1 was detected in 32% of RNA-Seq reads versus 56% of WES reads, highlighting NMD escape.
These patterns correspond with documented patterns of plant diversity knowledge  (Meyer, Weigelt, & Kreft, 2016) , suggesting that the documented geographic pattern of global plant extinction is, at least in part, driven by unequal levels of knowledge among regions  (Daru et al. , 2018; Meyer, Jetz, Guralnick, Fritz, & Kreft, 2016). For vertebrates, socio-economic factors have been shown to correlate with levels of occurrence data coverage  (Meyer, Jetz, et al. , 2016) , while a positive correlation exists between digital inventory completeness and per capita gross domestic product  (Meyer, Kreft, Guralnick, & Jetz, 2015). Here, we use the grasses (Poaceae) of Madagascar and compare them to those of the British Isles (Britain and Ireland) to explore data inequality as a driver of geographic unevenness in records of plant extinction.
The flora of Britain and Ireland could be the best documented flora in the world, while that of Madagascar is less well documented than average. This example is also based on the first author's research experience with Malagasy grasses and is intended to provide a window into the nature and possible consequences of data inequality, including how it may impact assessments of extinction. It is not intended to be representative of all plants or geographic regions. Grasses are essential sources of food for humans, ecologically critical successful colonizers  (Linder, Lehmann, Archibald, Osborne, & Richardson, 2018) , and one of the best-known plant families, acknowledged as a model for the study of speciose plant groups  (Hodkinson, 2018). Poaceae are the fifth largest plant family, with 11,369  (Clayton, Vorontsova, Harman, & Williamson, 2019) to 11,454 (World Checklist of Selected Plant Families; WCSP, 2018)  currently accepted species.
| Spatial bias, accessibility, and the sociopolitical situation Unlike the British Isles, many parts of Madagascar are very difficult to access due to the combination of its mountainous, dissected topography, and poorly developed road network. Madagascar has one of the lowest densities of roads per capita in the world  (Carret, 2013) : two-thirds of secondary and tertiary roads are estimated to be in poor condition, and most non-primary roads need to be rehabilitated to bring them to maintainable standard  (Fall, 2018). Field biologists TA B L E 1 Areas where more than 1% of the native plants are thought to be extinct, according to data from  Humphreys et al. (2019)   must often walk for several days to reach localities of interest (e. g. ,  Vorontsova, 2018a ). Madagascar's terrestrial protected area system has recently expanded to cover 6.
| Impediments to building plant diversity knowledge in modern Madagascar Madagascar's two largest herbaria contain material of an estimated 50%-60% of the c. 300,000 collections made in Madagascar  (Lowry et al. , 2018)   Global distribution of all species in Madagascar (dark blue) and the British Isles (pink, see also Figure  S2 )  (Johnson, 2006). Both students and professionals spend significant effort on translations and language learning, a time sink largely absent from British science. One of the co-authors, Rasolohery Andriambolantsoa, is a botanist with recent personal experience working in both Madagascar and the UK. He confirms a tangible difference in data access TA B L E 3 Endemic Malagasy Poaceae considered missing since at least 2011, the start of the Madagascar grass project and the associated field trips.
Single collection made in 1909 Andrafiamena in north Madagascar Infrequent flowering  (Dransfield, 1998)  infrastructure, including access to internet, online datasets, and sci- (http://www. resea rch4l ife. org/), HINARI, and AGORA seek to improve equality in access to online journals, but their reach is still limited. Opportunities to publish are also unequal: journals with higher impact factors have higher publication costs  (Gray, 2020) , and the financial model of Golden Open Access in mega-journals favors western researchers  (Ellers, Crowther, & Harvey, 2017). Wellintentioned free access provided to national institutions often fails in Madagascar because most are unable to provide web access, logins, or email addresses to staff and students. Researchers must use personal email addresses and web connections, which are not eligible for free access.
| G LOBAL PAT TERN S OF ENDEMIS M , S PECIE S DE SCRIP TI ON , AND E X TIN C TI ON RIS K FOR G R A SS E S The flora of Britain and Ireland could be the best documented flora in the world, while Madagascar's is less well documented. In addition to being both diverse and well known, the grass family has been the subject of expert-led modern global syntheses of data on species diversity and distribution  (Clayton et al. , 2019; Kellogg, 2015)  providing a reliable data platform for comparisons of diversity, the history of our knowledge, and extinction risk.
| CON CLUD ING REMARK S AND RECOMMENDATI ON S FOR THE FUTURE The grass floras of Madagascar and the British Isles differ in many striking respects, as do the histories of their discovery and description. The comparative analysis presented here offers valuable insights into the constraints that have impacted knowledge production for Malagasy Poaceae and very likely for the entire flora of most biodiversity-rich yet resource-poor areas. It is considerably more likely that unknown extinctions have taken place among Malagasy grasses than in Britain and Ireland, and that projections of future extinctions in Madagascar will be less accurate. The case study presented here highlights some of the underlying causes of differences in data from different parts of the world and suggests some of the consequences with regard to projecting future extinction.
cords. The Botanical Society of Britain and Ireland Maps Scheme is one of the world's longest-running natural history distribution mapping projects in which volunteers, county recorders and referees search for plants in each 1 km × 1 km grid square, irrespective of its history and current land use (BSBI, 2019). By contrast, botany F I G U R E 2 (a) Change in the Human Development Index placements across global regions 2000-2018, and for the United Kingdom and Madagascar (data from Sustainable Development Solutions Network, 2019). (b) Comparative timeline of publications on the biodiversity and biogeography of Malagasy vertebrates published 1959-2019 and recorded in Noe4D biodiversity database, by first authors based in a Malagasy versus foreign institution, 8,720 publications updated and modified from Waeber et al.
Assembled scaffolds of Ն2,500 bp were binned separately using MetaBAT v0. 26. 1  (12)  and MyCC  (13) , and the high-scoring, nonredundant set of bins were dereplicated and selected using DASTool  (14). QUAST v4. 1  (15)  was used to assess the quality of acquired genome sequences, and their completeness and contamination were estimated using CheckM (16) (taxonomy_wf domain Bacteria). Coverage information was determined using Bowtie 2 v2. 3. 4. 3  (17)  and SAMtools v1. 6  (18). Genome sequences were annotated using the Prokaryotic Genome Annotation Pipeline (PGAP)  (19). Putative magnetosome genes were checked using NCBI PSI-BLAST  (20). Average amino acid identity (AAI) values were calculated using enveomics  (21). Unless otherwise specified, default parameters were used for all software.