dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, HbVar can constitute a focal point for genotype and phenotype data collection from a very large number of hemoglobinopathy patients in registries and clinics world-D1196 Nucleic Acids Research, 2021, Vol. 49, Database issue wide. Similar to the CFTR2 project (www. cftr2. org; (15) , such long-term effort would entail a thorough genotype and clinical phenotype data contribution, based on the already well-documented microattribution approach  (16, 17) , allowing the identification of rare variants associated with disease. In these individuals, 159 CFTR gene variants had an allele frequency of 0. 01%. These variants were evaluated for both clinical severity and functional consequence, with 127 (80%) meeting both clinical and functional criteria consistent with disease. Assessment of disease penetrance in 2,188 fathers of individuals with cystic fibrosis enabled assignment of 12 of the remaining 32 variants as neutral, whereas the other 20 variants remained of indeterminate effect.
Within cognitive functions, we focused on working memory, executive functions, and procedural learning. We chose these domains because 1) the relationship between working memory, executive functions and subjective sleep quality has remained inconclusive, and 2) the relationship between procedural learning/memory and subjective sleep quality has largely been neglected in previous studies. Therefore, in the latter case, we explored several measures of procedural learning in order to obtain a more detailed picture of the potential associations with subjective sleep quality. To increase the robustness of our analyses, we created a database of 235 participants' data by pooling three separate datasets from our lab. We assessed subjective sleep quality by PSQI and AIS (Study 1-3), Groningen Sleep Quality Scale (GSQS, Study 2), and a sleep diary (Study 2).
Methods participants. Participants were selected from a large pool of undergraduate students from Eötvös Loránd University. The selection procedure was based on the completion of an online questionnaire assessing mental and physical health status. Respondents reporting current or prior chronic somatic, psychiatric or neurological disorders, or the regular consumption of drugs other than contraceptives were excluded. In addition, individuals reporting the occurrence of any kind of extreme life event (e. g. , accident) during the last three months that might have had an impact on their mood or daily rhythms were also excluded from the study. The data was obtained from three different studies, each with a slightly different focus. Importantly, the analyses presented in the current paper are completely novel, none of the separate studies focused on the relationship between subjective sleep quality and cognitive performance.
In addition to these learning indices, we measured the average reaction times (RTs) and accuracy (ACC), which reflect the average general performance of the participants across the task, and the changes in RT and ACC from the beginning to the end of the task, which indicate general skill learning that occurs due to more efficient visuomotor and motor-motor coordination as the task progresses  45. Data analysis. Statistical analyses were conducted in R 3. 6. 1  46  using the lme4 package  47. Bootstrapped confidence intervals and p-values were calculated using the boot package  48, 49. The data and analysis code can be found on the following link: https://github. com/nthun/performance_sleep_quality/ Analysis of the relationship between subjective sleep quality and cognitive performance. Subjective sleep quality scales (PSQI and AIS) were combined into a single metric, using principal component analysis.
88 (SD = 40. 94) for average RT, and 31. 13 (SD = 30. 15) for RT general skill learning. Accuracy scores were as follows: 0. 04 (SD = 0. 03) for ACC Triplet learning, 0. 02 (SD = 0. 03) for ACC Higherorder sequence learning, 0. 03 (SD = 0. 03) for ACC Statistical learning, 0. 90 (SD = 0. 10) for average ACC, -0. 02 (SD = 0. 09) for ACC general skill learning, in all three studies. Note that for accuracy, these values represent proportions (e. g. , the average ACC was 90%, hence 0. 90), and the learning scores are difference scores (e. g. , the ACC Triplet learning score shows that participants were on average 4% more accurate on high-frequency triplets compared to the low-frequency ones). All presented RT and ACC scores represent typical values in ASRT studies with healthy young adults. We also provide descriptive data for Study 2 separately, as additional analyses were run on cognitive performance from this dataset and GSQS and sleep diary scores.
Discussion Our aim was to investigate the relationship between subjective sleep quality and cognitive performance in healthy young adults. Cognitive performance was tested in the domains of working memory, executive functions, and procedural learning. To provide more reliable results, we pooled data from three different studies, controlled for possible confounders, such as age, gender, and chronotype, and performed robust frequentists as well as Bayesian statistical analyses. We did not find associations between subjective sleep quality and cognitive performance measures using the robust frequentist statistical analyses. Moreover, the Bayes factors provided substantial evidence for no association between subjective sleep quality and measures of working memory, executive functions, and procedural learning.
Acknowledgements This research was supported by the  Research and Technology Innovation Fund ,  Hungarian Brain Research Program  ( National Brain Research Program , project  2017-1. 2. 1-NKP-2017-00002 );  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d' Avenir  ( ANR-16-IDEX-0005 );  Hungarian Scientific Research Fund  ( NKFIH-OTKA PD 124148 , PI: KJ;  NKFIH-OTKA K 128016 , to DN); and  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (to KJ). The authors are thankful to  Csenge Török ,  Kata Horváth ,  Eszter Tóth-Fáber ,  Orsolya Pesthy ,  Noémi Éltető ,  Andrea Kóbor , and  Ádám Takács  for their help in data collection, to  Kate Schipper  for proofreading the manuscript, and to the reviewers for their helpful comments and suggestions to improve the paper.
We also consider emerging trends in extinction risk, with new data for megadiverse Brazil and Madagascar, and an overview of some new metrics and technologies with potential to transform this field in the coming decade. Although plant and fungal diversity include the taxonomic, (phylo) genetic, and functional diversity within species, between species, and of the ecosystems of which they form part, we focus our quantitative overview primarily on taxonomic diversity at the species level because species represent the most fundamental unit of biodiversity, in biology and conservation science. Species represent the taxonomic level at which plant and fungal diversity is most consistently recorded and analyzed for the purposes of inventory, monitoring, or monography as well as for conservation assessment, analysis, action, and legislation.
| Progress toward Target 2 of the global strategy for plant conservation The rapid growth in plant coverage on the Red List over recent years is due in part to the Global Strategy for Plant Conservation, through which parties to the Convention on Biological Diversity (CBD) called for "assessment of the conservation status of all known plant species" by 2020  (CBD, 2012; Paton & Nic Lughadha, 2011). Responses to this target at national and international level generated global assessments for many thousands of plant species (including assessments of single-country endemics). These were published in diverse formats (e. g. , National Red Lists, online resources, and academic journals), and over 60% of the species they cover lack an assessment on the global Red List (Methods S1). The ThreatSearch database collates digitally available, evidence-based plant assessments, and is regularly updated by Botanic Gardens Conservation International (BGCI, 2020).
Bachman, Nic Lughadha, and Rivers (2018)   We repeated the above analysis using ThreatSearch datasets from November 2016 to January 2020 (Methods S1). Our methods differed only in reconciling names, including basionyms, to the World Checklist of Vascular Plants  (WCVP, 2020). Results suggest that Bachman et al. underestimated both numbers of global assessments and species assessed as threatened, probably due to the many unresolved names in TPL now resolved in WCVP. Our new analysis (Figure  1 ), with the new (higher) 2016 baseline, shows marked growth in total assessments (+37. 2%), in global assessments matched to accepted names (+23. 1%), which now equate to 28. 3% of vascular plant species, and in global threatened assessments matched to accepted names (+16. 1%). The proportion of assessments reporting globally threatened species decreased slightly from 36% to 34%.
| Biases and gaps in plant and fungal coverage on the Red List The non-random sample of plants and fungi on the Red List results from selecting species for assessment influenced by data availability; human interest in useful, attractive, or unusual species; national assessment initiatives; and expectations that certain species or groups are exceptionally threatened  (Bachman et al. , 2019). These differing priorities have left biases and gaps in current assessment coverage. We used comprehensive data from the WCVP (2020) to quantify biases in Red List coverage of vascular plants, using only assessments published since 2001 to maximize consistency (Methods S2). We considered attributes likely to have influenced assessment priorities: the life-form of the species, the family to which it belongs, on what continent the species occurs, whether the species is endemic to a single botanical country, and if the species had a recorded human use  (Diazgranados et al.
F I G U R E 1 Comparison of the total number of assessments and coverage of accepted names by assessments in the ThreatSearch database for the dataset from the end of 2016 and the start of 2020. The total number of assessments includes multiple assessments at different scales for many species. After matching assessments to an accepted name, we count at most one assessment per threat level for each accepted name. Assessments were matched to species names in the World Checklist of Vascular Plants (WCVP, 2020) database Geographical biases in assessments were evident, with particular effort focused on African plants (Figure  2a ), and a big gap in assessments for plants from both temperate and tropical Asia. Woody perennials were vastly overrepresented by assessments on the Red List, a bias likely to increase with the rapid progress of the Global Tree Assessment  (Newton et al.
, 2015; Rivers, 2017). Species having recorded human use were also overrepresented, their societal importance having prompted assessment (e. g. ,  Davis et al. , 2019; Howes et al. , 2020). The impacts of targeted assessment programs on the taxonomic coverage of the Red List were evident. The most overrepresented families include those targeted by the assessment programs, such as Cactaceae  (Goettsch et al. , 2015)  and Myrtaceae  (Nic Lughadha et al. , 2019). The most underrepresented families include some of the most species-rich, including Asteraceae, Orchidaceae, Poaceae, and Lamiaceae (which together comprise almost 25% of all vascular plants; Dataset S1). Of the 285 fungi assessed for the global Red List, 234 are species of Basidiomycota, one of seven phyla of Kingdom Fungi  (Mueller, Dahlberg, Scott, & Westrip, 2020).
| Most and least threatened plant groups Addressing assessment coverage gaps is paramount for a realistic picture of extinction risk to plants. Varying threat levels reported for the groups of species assessed interact with these gaps and biases to distort our current picture. We applied a statistical modeling method, used successfully in recent years for predicting the outcomes of political elections from unrepresentative polls  (Wang, Rothschild, Goel, & Gelman, 2015) , to estimate the different levels of threat for groups defined by the attributes identified above. We trained our model on vascular plant species assessed for the Red List since 2001 for which we had both geographic and life-form data from WCVP (28,479 of 33,029 assessed species). We then applied our model to all known vascular plant species to infer their extinction risk while accounting for quantifiable biases and gaps in assessment coverage (Methods  S3 ).
These included families already recognized as having high threat levels, such as the Zamiaceae (cycads), as well as some larger families, for example Myrtaceae and Orchidaceae (Methods S3, Table  S10 , Dataset S2). Predicted levels of threat for all known vascular plants from our model reveal where the Red List may currently over-or underestimate extinction risk. Comparison of predictions and Red List assessment suggests that sample bias has caused levels of threat to be underestimated in Southern America, Northern America, and tropical Asia. Myrtaceae, Lauraceae, Fagaceae, and Cyperaceae are among the families with the most underestimated level of threat (Table  S11 ), while Arecaceae (palms) and Ebenaceae (ebony and relatives) are among those families most overestimated (Table  S12 ). At 39.
These relatively low rates are likely underestimates due to underreporting (despite the rediscovery rate) and long extinction lag times (see 3. 2). Our further analysis of the plant extinction database  (Humphreys et al. , 2019; Methods S6)  showed that of the 10 areas with most disproportionately high numbers of recorded extinctions, nine are islands (the exception being India). Hawai'i and St. Helena have the largest proportion of their flora recorded as extinct (7. 3%). Elevated extinction levels on islands mirror higher levels of species assessed as threatened for the Red List. Many of the islands most overrepresented in extinctions are also overrepresented in threatened species, with Hawai'i and St. Helena again most overrepresented (Figure  2b ). In contrast, megadiverse Madagascar and Ecuador are overrepresented in threatened species but not recorded extinctions.
When paralogs from a given lineage have evolved by gene duplication that happened after a speciation event, they can be referred to as in-paralogs. On the opposite, paralogous genes which have evolved by duplication events happening before a speciation event, can be referred to as out-paralogs. Many other terms, although less used, have been proposed to take into account chromosomal position retention, the combination of vertical and horizontal transmissions or to highlight paralogous genes appearing to be orthologs due to differential gene loss  [9]. In a genome, duplicated genes can thus be formed by various mechanisms and may have different ages and fates. This makes their bioinformatic identification all the more difficult since according to the methods used, different duplicated gene datasets will be identified inside the same organism.
In the case of homology, the alignment is generally performed on protein sequence instead of the gene. This allows a greater sensitivity since amino acid substitutions occur less frequently than nucleotide substitutions allowing silent mutations and because introns generate a lot of noise  [121]. With these methods, the homology is tested against a cutoff on three different metrics: E-value, bit-score, or percent identity. The e-value is a statistic representing the expected number of times a given alignment score would occur by chance given the length and number of sequences being aligned. It is the most widely used metric as a first step to assess homology. Since the e-value is dependent on the database size, a potential caveat when setting a cutoff is to apprehend how the results might change for different databases.
Since a gold standard cutoff is impossible to determine, a variety of values are used, sometimes combining different metrics leading to different results (Table  1 ). All-against-all protein sequence similarity search using BLASTP (e-value cutoff of 1. 0). For each pair of genes, blast-hits were merged to compute the total length and the global similarity of the aligned regions. Two datasets were constructed with respectively 30 and 50% sequence identity over respectively 70 and 90% protein length. Gene families were built through single-linkage clustering. Not specified, genes families were all obtained (gene families)  [73]  22,810 21,622 94. 8 * All-against-all protein sequence similarity search using BLASTP (top five non-self protein matches with e-value of 10e-10 were considered). Genes without hits that met a threshold of e-value 10e-10 were deemed singletons.
0). For each pair of genes, blast-hits were merged to compute the total length and the global similarity of the aligned regions. Two datasets were constructed with respectively 30 and 50% sequence identity over respectively 70 and 90% protein length. Gene families were built through single-linkage clustering. Not specified, genes families were all obtained (gene families)  [73]  27,910 21,461 76. 9 * All-against-all protein sequence similarity search using BLASTP (top five non-self protein matches with e-value of 10e-10 were considered). Genes without hits that met a threshold of e-value 10e-10 were deemed singletons. Pairs of WGD duplicates were downloaded from published lists. Single gene duplications were derived by excluding pairs of WGD duplicates from the population of gene duplications.
When not working on a genome-scale but on specific sequences, homology imputation can be reinforced by looking at the gene structure. Domains shared by proteins are strong indicators of homology. Conserved domains can be found in databases such as Pfam  [132]  or InterPro  [133]  and searched against sequences of interest. This method is also a great tool to unravel complex evolution such as gene splitting and fusion for multi-domain proteins but require a time consuming manual expertise. When searching for duplicated genes within a genome, assessing homology inside this genome is enough. However, when comparing multiple genomes, a link needs to be made between homologs of the different genomes. This raises the issue of resolving ortholog and paralog relationships. For this, a different kind of method needs to be applied.
Detection of Syntenic Blocks (WGD-Segmental Duplications) A syntenic block can be defined as a region of the genome spanning a number of genes that are orthologous and co-arranged compared to another genome  [154]. Two regions of a genome with a number of homologous genes co-arranged with each other can also be defined as a syntenic block. Here, we focus on this second definition because pairs of homologous genes between these pairs of regions correspond to duplicated genes. It can be interesting to access different databases storing pre-calculated syntenic blocks shared between different species. This makes it possible for an easy and direct access to reliable information without any computation. Nevertheless, these databases cannot include every contemporary species nor information about recently released genomes.
SyMap SyMap  [163]  is a tool based on DAGChainer  [165]. The advantage of this software is that its interface via a web application allows the user to be free from any configuration and data preparation via the code. In addition, this tool allows retrieving various additional information and in particular the Ka/Ks ratio using PAML  [182]. The intermediate results can be retrieved and the final results can be visualized in an interactive dot-plot. Once the genomes have been added to the database and the parameters have been defined, the computations are launched. The SyMap algorithm works as follows. First, the genomes are aligned using an alignment software. Different tools can be used for this step, including BLAST. Then, different filters are applied and in particular the condensation of tandemly duplicated genes into a single occurrence and filtering out of repeated sequences.
Detection of Tandemly Arrayed Genes (TAGs) Specific methods have been developed to handle specifically tandem duplication detection. TAGs are gene family members that are tightly clustered on a chromosome  [73]. The vast majority of the methods are home-made pipelines available from the authors and may require programming skills. A few tools, particularly those related to the identification of syntenic blocks, are able to help in the identification of TAGs because they are generally summarized in a single occurrence of the dataset to lower the statistical noise. In general, they are not dedicated methods but more trivial algorithms. However, they have the advantage of being simpler to use. Most of these algorithms rely on protein comparisons, making them dependent on genome annotation. However, there exists very few methods that can deal with genomic sequences to search for long DNA tandem repeats.
Detection at Protein Level These methods begin with the identification of homologous gene pairs. This can be done using different algorithms, in most cases an all-by-all BLASTP comparison of the proteome against itself or between the proteomes of two species, followed by a filtering using a threshold to retain only homologous pairs. The difference between these approaches lies in the homology assessment and the degree of sophistication to filter out false positives. The most straightforward, but trivial, way is used in the first step of WGD detection algorithms such as MCScanX or i-ADHoRe  [157, 158]. These algorithms take as input homologous gene pairs, the preferred format being the BLASTP output. Then, the program classifies homologous pairs according to their rank along the chromosome.
Detection at DNA Level The vast majority of Tandem Repeat detection methods at DNA level deal with the identification of short highly repeated sequences. They are used to mask sequences corresponding to TEs or/and segments of low complexity before genome annotation or to explore the amplification of short duplications associated with human diseases for example, or copy number variation (CNV) between genotypes. These types of DNA duplication are not the focus of this review and will not be treated in detail. Here, we give a list of some famous short DNA Tandem Repeat detection tools able to deal with large datasets: DUSTMASKER  [188] , SEGMASKER  [189] , Tandem Repeat Finder (TRF)  [190] , TANTAN  [191]  and more recently ULTRA  [192] , TARDIS  [193] , and dot2dot  [194]. We will now focus on long tandem duplication detection because all studies on TAGs based on protein similarity are biased by the quality of the available genome annotation.
Syntenic Information These databases have the advantage to not require computations and therefore no programming skills for the user. Some of them also offer visualizations and search tools. The main disadvantage is that they do not contain information from all organisms. Each of these databases provide particular features but some elements are common. In some cases, it is possible to access all the syntenic blocks between two organisms. The list of organisms is more or less extended depending on the database. Some of them propose to visualize these blocks using various representations such as circular visualizations, chromosome painting, or dot-plots. Some databases allow manually importing genomes to identify blocks of synteny. In this case, different tools may be implemented for the identification and are more or less easy to configure.