dataesr/scientific-paragraphs-categorization huggingface dataset:
The contribution of FOXI3 mutations to CFM The frequency of the FOXI3 causative variants in CFM patients of European ancestry is 5 out of 124 (4. 0%); the case of EUR06 was not counted since it has been described in the literature and the total number of CFM patients studied was not mentioned. In the 48 unrelated CFM families of Chinese ancestry, 5 had FOXI3 pathogenic variants (10. 4%). In the 498 additional Chinese CFM cases, there were ten patients with pathogenic FOXI3 variants (2. 0%). Thus, FOXI3 pathogenic variants account for 3. 1% of CFM cases of European and Chinese ancestry combined (21 of 670 cases). The frequency of all FOXI3 variants with a CADD score of >20 in the BRAVO database  23  (https://bravo. sph. umich. edu/freeze8/hg38) is 0. 34% (456 heterozygous in 132,345 samples) which is 10 times less frequent than in CFM (χ 2 test P value 10 -5 ).
In silico analysis of FOXI3 deleterious variants Figure  2A  depicts the mapping of the 18 identified likely pathogenic variants including the 14 non-synonymous variants in FOXI3; 6 map within the FHD, further 6 within the NLS, and 2 within the IDR domains. These variants were predicted to be damaging to FOXI3 according to the scores of SIFT (Sorting Intolerant From Tolerant), Polyphen (Polymorphism Phenotyping v2), CADD (Combined Annotation Dependent Depletion), or SNAP2 (Fig. 2A ). We performed 3D modeling based on the structures of the related human FOXC2 (PDB ID: 6AKO)  24  or Foxk1A (PDB ID: 2C6Y)  25  (Supplementary Fig. 5A ). The modeled FOXI3:DNA complex covers the FHD and NLS which harbor 12 variants distributed at 9 sites (Supplementary Fig. 5B, C ). The p. (Pro147Thr), p. (Cys225Arg), and p.
Compared to the wild type, the mutant FOXI3-EGFP fusion protein is significantly trapped in the cytoplasm and the amount of mutant FOXI3-EGFP is significantly reduced in the nucleus. Significant differences between two groups (wild type and each mutant) were determined by unpaired Student's t-test (two-tailed). Data are presented as the means ± s. d. Source data are provided as a Source Data file. trans with the pathogenic allele. We propose the following hypothesis (Fig. 4A ): (i) homozygotes for a pathogenic variant allele show microtia (pedigree F252). We term this genotype combination FOXI3 Mut/FOXI3 Mut; (ii) heterozygotes for a pathogenic variant and a modifier trans allele also show microtia (pedigree EUR01). This genotype combination is FOXI3 Mut / FOXI3 modifier; (iii) heterozygotes for a pathogenic variant and a non-modifier trans allele: there is no microtia in these cases; instead, the external ear could be normal or mildly dysmorphic.
Dataset processing and family-based burden test Burrows-Wheeler aligner tool (v0. 7. 17)  66  , SAMtools (v1. 9)  66  , PICARD (v2. 0. 1), and the Genome Analysis Toolkit (v4)  67  were used to analyze the exome/target-capture sequencing data. Sequenced reads were aligned to the GRCh38/hg38 reference human genome, and the filtering of variants was performed as described in previous studies  [68] [69] [70]. To prioritize the potential causal genes, family-based gene burden tests were performed on the rare loss-of-function variants of the 48 CFM families, and the promising variants were screened in the prioritized genes of each family according to inheritance patterns. The detailed steps were as follows: Firstly, the 273,164 high-quality variants were annotated by ANNOVAR software (Version: 2018Apr16)  71  , and the loss-of-function variants including splicing-site variants, missense variants, stop-gain-and stop-loss-variants, and frameshift variants were retained.
Secondly, the variants with minor allele frequency less than 0. 001 in the Genome Aggregation Database (gnomAD v3. 1. 2) were kept considering CFM is a rare congenital disorder. Thirdly, family-based burden tests were performed on the rare loss-of-function variants of the enrolled 48 families using Pedgene software (v3. 6)  72. The genes with burden P-values less than 0. 001 were selected as candidates for subsequent analysis, and the detailed burden test statistics were shown in Supplementary Table  2. Fourthly, the variants within candidate genes were screened according to the inheritance pattern including autosomal dominant, autosomal recessive, de novo, or compound heterozygous. Fifthly, the effect of each variant identified from the above step was evaluated by SIFT (http://provean. jcvi.
org/ index. php), Polyphen-2 (http://genetics. bwh. harvard. edu/pph2/), and CADD (https://cadd. gs. washington. edu/) software. The variants predicted as "deleterious" or "probably damaging" or with CADD score larger than 10 were considered pathogenic ones. In addition, the functional effects on protein of the remained candidate single amino acid substitutions were predicted by SNAP2 software (v3. 0, https:// rostlab. org/services/snap2web/).
In silico analysis To assess the conservation of the mutated sites, multiple alignments were performed on sequences of FOXI3 among different species. Then the nuclear localization sequence (NLS) of FOXI3 was predicted by NucPred software (https://nucpred. bioinfo. se/cgi-bin/single. cgi) and the effect of the variants within NLS was evaluated by NLStradamus software (version: r. 9, http://www. moseslab. csb. utoronto. ca/ NLStradamus/) with default parameters. To further predict the effect of the missense variants located in the conserved FHD or NLS on the three-dimensional (3D) structure, the sequence from 135 to 243 amino acid residues of human FOXI3 was primarily mapped to the other members of FOX family with known crystal structure referred to PDB database (https://www. rcsb. org/), including HNF-3 (1VTN), FOXA2 (5×07), FOXC2 (6AKO), Foxk1A (2C6Y), and FOXO1 (4LG0); and a phylogenetic tree of these proteins was then constructed by MEGA-X software (Version:10.
0. 5) (Supplementary Fig. 5A ). Based on the phylogenetic tree and homologous evaluation, the crystal structures of 6AKO and 2C6Y were used to model the homologous sequence of FOXI3 by Modeller software (Version:9. 25) (Supplementary Fig. 5B, 5C ). The modeled 3Dstructure of mutant proteins was presented through Mutagenesis function in UCSF Chimera software (Version:1. 16) (Supplementary Fig. 5E ). In addition, the crystal structures of the monomer and protein-DNA complex of 6AKO were used to mimic the effect of variants on DNA-binding for FOXI3 by FoldX software (Version:5. 0). The prediction of free energy changes of each variant on monomer and protein-DNA complex was replicated 10 times with default parameters. The consequence of the variant on the protein was classified as "destabilize fold" when the difference of free energy (ΔΔG) > 1.
The band density was quantified by ImageJ software v1. 8. 0. 345 (National Institutes of Health, Bethesda, Maryland, USA). The uncropped and unprocessed scans of the western blots were provided in the Source Data file. Constructing Foxi3 variant mouse models using CRISPR Generation of triple variant mice. C57BL/6 mice carrying the three variants p. (Arg220Trp), p. (Arg222Gln), and p. (Arg224His), orthologous to FOXI3:p. (Arg236Trp), p. (Arg238Gln) and p. (Arg240His) in one allele were generated by Cas9/CRISPR-mediated genome editing (Cyagen Biosciences, Guangzhou, China). Briefly, a gRNA (TCGGAAGCGAAGGCGCCGCG-CGG) to the mouse Foxi3 gene, a donor oligo with a targeting sequence containing p. (Arg220Trp) (codon from CGG to TGG), p. (Arg222Gln) (codon from CGA to CAA) and p. (Arg224His) (codon from CGC to CAC) mutations sites, and Cas9 protein were co-injected into fertilized mouse eggs to generate targeted knock-in offspring.
Micro-computed tomography (Micro-CT) analysis of skull tissue Newborn mice were euthanized, fixed in 4% PFA and scanned with a photon counting detector-based spectral Micro-CT developed by the Institute of High Energy Physics, Chinese Academy of Sciences. The parameters are used as follows: 20 μm resolution, 50kVp, 144mAs without a filter. Raw images from all scans were reconstructed using the FDK algorithm. Reconstructed scan data were imported into VG Studio software version 3. 1(Volume Graphics, Inc. , NC, USA) and.
Data availability The likely pathogenic FOXI3 variants described in this study have been submitted to the ClinVar database (https://www. ncbi. nlm. nih. gov/ clinvar/) and their accession numbers are SCV003803092-SCV003803100 and SCV003806728-SCV003806732. The raw datasets (FASTQ files) supporting this study have not been deposited in a public repository because of confidentiality of medical tests; however, VCF files for selected genes are available under restricted access from S. E. A. (stylianos. antonarakis@unige. ch) or Y. -B. Z. (zhangyongbiao@buaa. edu. cn) upon request. The access to these data requires a short description of the project, and signing a data-use agreement that includes restrictions on downstream data, and use of the data only by the requesting investigator. There is no authorship requirement for a reanalysis of the data unless there is an extensive follow-up collaboration and input from the authors of this paper.
Neuropsychological data show that patients suffering from Alzheimer's Disease seem capable of adjusting their metacognitive evaluations for image span tasks and are as accurate as healthy older adults  40. These outcomes suggest an updating of metacognitive evaluations following the encoding experience. However, the problem with the use of images (of objects) and (real) words in these studies is that they encourage strategies that broadly involve LTM knowledge. It is worth noting that these previous studies used either only global judgments or a variation of them. Global judgments are used to study the ability to evaluate the general performance for a task, unlike local judgments which are made on each item and allow a more fine-grained analysis of monitoring  41, 42. The problem is that global predictions are limited and allow the measurement of only metacognitive bias while neglecting sensitivity  43, 44.
Results Data are available on https:// osf. io/ hsb9e/. We tested thirty-four participants. Each participant performed 16 sixletter trials resulting in 544 trials in total (272 per CL condition). Based on typical TBRS analyses, we discarded the trials that did not achieve the criterion of 80% correct key press responses of the processing task to ensure that participants actually manage to perform the dual task. This procedure resulted in 4. 6% of discarded trials, but all participants contributed data to the final analysis. In total, we analyzed 254 trials in high CL condition and 265 trials in the low CL condition. A first comparison between response times of the parity and location judgments provided a manipulation check of cognitive load. A Wilcoxon signed rank test confirmed the parity judgment effectively elicited longer RT with a median of 618 ms (IQR = 600-648) than the location judgment task with a median 445 ms (IQR = 423-488) (z = -5.
012, p ≤ 0. 001). Participants reported confidence for each of six positions in each trial, which makes a total of 1524 and 1590 performance-confidence observations for high and low CL conditions respectively. We computed two WM scores: item recall and strict recall as described above and participants provided metacognitive evaluations corresponding to these scoring methods: retrospective confidence of item recall and retrospective confidence of strict recall. Data were analyzed by using R (version 4. 2. 0)  57. For WM scores, we used the lme4 package (version 1. 1-30) 58 and the function glmer to perform two logistic regressions through generalized linear mixed models (GLMM) that allow for analyzing binary responses. Mixed effects analyses maximize the generalizability of our results to other participants  59.
Moreover, since CL can vary across participants, mixed models allow us to control the variability that could be accounted for individual differences. We used the method proposed by Bates et al. 60  to construct parsimonious mixed models preventing convergence problems. For metacognitive evaluations, we also applied two ordinal logistic regressions since we used ordinal confidence scales. Thus, we used Ordinal package (version 2019. 12-10)  61, 62. Ordinal logistic regression models aimed to evaluate whether or not magnitude of confidence for both strict recall and item recall were affected by CL and serial position. The analysis script is available on https:// osf. io/ pbktn/. Finally, in keeping with other studies in the literature, we performed gamma correlations to calculate metacognitive sensitivity.
Participants were more confident for items recalled in the first positions than for items in other positions. Likewise, they were more confident for items recalled in the sixth position than for items in the fourth and fifth positions. These analogous patterns between retrospective confidence and WM scores suggest an association between metacognitive evaluations and recall. This association was outlined by high levels of metacognitive sensitivity as measured by Gamma, meaning that participants accurately distinguished between correctly and incorrectly recalled items in their confidence judgments. The logistic regression models also showed that confidence is a reliable predictor for strict recall and item recall, endorsing a strong association between first-and secondorder performance. Altogether, our data show that participants were able to adjust their confidence according to the likelihood of their recall being correct.
We describe a strategy to accelerate the rate of ocean species discovery by: 1) employing consistent standards for digitisation of species data to broaden access to biodiversity knowledge and enabling cybertaxonomy; 2) establishing new working practices and adopting advanced technologies to accelerate taxonomy; 3) building the capacity of stakeholders to undertake taxonomic and biodiversity research and capacity development, especially targeted at low-and middleincome countries (LMICs) so they can better assess and manage life in their waters and contribute to global biodiversity knowledge; and 4) increasing observational coverage on dedicated expeditions. Ocean Census, is conceived as a global open network of scientists anchored by Biodiversity Centres in developed countries and LMICs. Through a collaborative approach, including co-production of science with LMICs, and by working with funding partners, Ocean Census will focus and grow current efforts to discover ocean life globally, and permanently transform our ability to document, describe and safeguard marine species.
Data from the WORMs Register of Marine Species (https:// www. marinespecies. org/index. php), accessed 20/4/2023. Data for Onycophora come from  Baker et al. (2021). contributions to society is transforming our understanding of the diversity and distribution of life in the ocean. This transformation is not currently proceeding at sufficient speed because taxonomy has largely been reliant on methods first developed in the 18 th and 19 th Centuries  (Rogers et al. , 2022a). Even though this process is now assisted by DNA sequencing, it is still very slow and often requires examination of the original samples on which previous species descriptions were based (type specimens). Limited capacity in terms of numbers of taxonomists across all groups of marine organisms is also a key challenge  (Engel et al.
Data from World Register of Marine Species (https://www. marinespecies. org/), downloaded 14 th April, 2023. and a Feasibility Report  (Rogers et al. , 2022b). These reports were reviewed and commented upon by a team of Senior Experts (see Acknowledgements) and finalised following revision. The present paper summarises the findings of the Interim Reports and Feasibility Report prepared by the Expert Working Groups and provides a holistic view of how to accelerate species discovery in the ocean, how to translate biodiversity data to knowledge that can be applied in decision making with respect to human activities in the ocean, and how to expedite future biodiversity monitoring in the ocean.
As well as the specimen information, a rigorous standardised approach to the digitisation of all information related to the collection of a specimen in the field is adopted (see below). This will include use of the Taxonomic Databases Working Group (TDWG) standards and A summary of Ocean Census approaches to accelerating species discovery and description. We note that many of these approaches benefit studies of both evolutionary and ecological taxonomy although some are more relevant to the latter (e. g. New Ways of Working and Standardisation). Together these represent the Ocean Census integrated taxonomic strategy. vocabularies embodied in the Access to Biological Collection Data (ABCD) and Darwin Core (DwC) aimed to support the interoperability of collection and observation data from physical to digital  (Berendsohn et al.
, 2011; Wieczorek et al. , 2012). Usage of these community-led, globally accepted data standards is critical to making data Findable, Accessible, Interoperable and Reusable (FAIR;  Wilkinson et al. , 2016b) , and therefore to incorporation into environmental policy and widespread public use downstream (see  Rabone et al. , 2023 for discussion). Further digital information will be acquired whilst a specimen is processed from a collection in a museum or other institution. Data should include (see Figure  5 ): • A Globally Unique Identifier (GUID) for the specimen or sample (e. g. Rabone et al. , 2019; Horton et al. , 2021   Pereira et al. , 2013; Constable et al. , 2016; Muller-Karger et al. , 2018; Sloyan et al. , 2018; Woodall et al. , 2018; Centurioni et al. , 2019; Jetz et al. , 2019; Levin et al.
In this respect, this concept bears some similarities to the idea of quantum taxonomy  (Maddison et al. , 2012)  and to the Digital Extended Specimen concept described by  Hardisty et al. (2022). Data collected with specimens are uploaded to appropriate global databases as soon as possible following collection (e. g. species occurrence records to the Ocean Biodiversity Information System -OBIS https://obis. org/; genetic data to the International Nucleotide Sequence Database Collaboration -INSDC https:// www. insdc. org/; taxonomic data to the World Register of Marine Species -WoRMS https://www. marinespecies. org/; genomic sample availability to the Global Genome Biodiversity Network -GGBN https://www. ggbn. org/ggbn_portal/). Where such databases are not available (e. g. for image data), data incubators will be developed to hold and allow access to data while the community of scientists who specialise in the data type can develop standards for data acquisition, metadata, storage and access.
This should build towards obtaining the funds for a new global database. Our suggested approach to marine species discovery is an example of integrated taxonomy which introduces an intermediate step between the sampling process and final species description. The introduction of the DLF enables ecological and other studies to be undertaken on consistently identified organisms prior to formal description. A proof of concept of this approach has been demonstrated through the Moorea Biocode Project (see Supplementary Material 2. 0), whereby an effort was made to produce both genetic (DNA) and morphological barcodes (digital keys), dynamically linked to images and other data to create a model system for biodiversity science  (Meyer et al. , 2006). Using these data, ecological studies, such as on food webs, have been possible in the absence of described species but by using consistent identification based on DNA barcodes (e.
g. Leray et al. , 2012; Leray et al. , 2015). A similar initiative for wholly image-based workflows is the SMarTaR-ID marine taxon reference image database  (Howell et al. , 2019). DLFs will be useful for short-term and finer-scale ecological studies, but crucially we see the digitisation and accessibility of species data as a tool to assist taxonomists in the ultimate aim of naming and describing species. Naming species with reference to related species will reconcile provisional species discoveries with the knowledge built over the last 250 years using morphological characters. This final step is important to provide knowledge of biodiversity over large spatial and temporal scales. Since Linnaeus first devised a system and language to describe biodiversity, taxonomists have named, defined and refined an inventory of life on Earth, including more than 240,000 marine species (WoRMS, 2023) and counting.
, 2015). Both are important as they provide insight into community change and their drivers across different spatial and temporal scales  (Pavoine and Bonsall, 2011). Fundamental to our digitisation concept is that data are, where feasible, interoperable across institutional, national, regional and global databases, and that they are quality assured and traceable. This is essential to ensure that they are viewed by all stakeholders as trustworthy for application to science, ocean conservation and management. Accessibility of taxonomic and biodiversity data to end users is key to resolving the current taxonomy crisis and to ensuring long-term security for taxonomists and their institutions  (Ebach et al. , 2011; Saunders, 2020). It is also important that digitisation is aligned with international principles such as the FAIR (Findability, Accessibility, Interoperability, and Reusability;  Wilkinson et al.
The cyber-biodiversity system Ocean Census will develop a Cyber-Biodiversity System providing the technological infrastructure, including the workflows and tools, required to ensure that all Ocean Census data are transparently and efficiently managed. The system will utilise existing data workflows and technologies (e. g WoRMS, OBIS, GBIF and DarwinCore vocabulary) and integrate them. The Ocean Census Cyber-Biodiversity System will be cloud-based, providing on-demand access to data and digital services for digitising, processing, storing and sharing marine biodiversity data, information and knowledge using standard operating procedures. This system will be built in close consultation with Ocean Census partners and will support delivery of the discovery and capacity development objectives of the programme by co-designing the workflows and tools necessary to enable more effective collection, curation, and translation of data on ocean life for scientists and other users from all countries.
A modular approach will be used to develop the Cyber-Biodiversity System (Figure  5 ). A Digitisation and Data Staging Module will provide a secure and high-performance space to gather, clean, quality control, and standardise all Ocean Census data and information before it is distributed for processing or archiving. The digital output of each Ocean Census expedition will be formidable: images, acoustics, video recordings, gene and genome sequences, sensor readings, and written observations are just a handful of the (meta)data types that Ocean Census will generate and synthesise. Rapidly 'staging' or hosting, cleaning, and organising data so they can be efficiently processed, combined with other data, and delivered to users or long-term archives will be essential to the efficient discovery of new biodiversity as well as the digital legacy of the Ocean Census programme.