dataesr/scientific-paragraphs-categorization huggingface dataset:
As computational capabilities that are available to GNSS receivers continue to improve, more possibilities can be found with signal processing in the receiver. New techniques like Three-Dimensional (3D) mapping and neural networks  (Suzuki and   Kubo  1 3  2013; Adjrad and Groves 2018), or additional information from other datasets, e. g. , aiding sensors, are also expected to help mitigate the multipath effects  (McGraw et al. 2020).
There are several methods to do so, like VTEC modelling  (Psychas et al. 2019 ), ionospheric tomography (Olivares-Pulido et al. 2019) , best linear unbiased predictor (e. g. , Kriging)  (Teunissen et al. 2010; Zhang et al. 2011) , distance-based interpolation  (Nadarajah et al. 2018; Zha et al. 2021) , etc. Some of them have achieved a millimeter-level positioning accuracy in the horizontal and single-epoch instantaneous PPP-AR, but they were restricted in a small local/regional network (dozens of kilometers) by the precision of the derived ionosphere information. Similarly, the tropospheric delays can be introduced in the same way as implemented in the ionosphere-corrected model. The troposphere model derived from meteorological datasets  (Hadas et al. 2013)  or estimated based on a GNSS network over a small/large region  (Li et al.
Therefore, satellite metadata is always preferred. On the one hand, the orbit model and the satellite antenna model can be refined, while on the other hand, hidden potencies of GNSS PPP are expected to be unveiled. Moreover, the multi-GNSS with multi-frequency have been demonstrated to be able to shorten the convergence time and speed up the PPP-AR with PPP-RTK. It is also worth noting that possibilities have been revealed by combining GNSS PPP with other techniques, e. g. , SLR, VLBI and Interferometric Synthetic Aperture Radar (InSAR). No observation system stands alone, even for GNSS, which indicates they may suffer from common error sources to different extents, especially for those ground-based measurements. In this case, an accurate error model can be established and shared with other techniques.
Examples are found in SLR and LEO, which can help improve the GNSS orbit determination and calibrate the absolute satellite antenna PCC model of GNSS, respectively. Consequently, more accurate measurements will be obtained so that the error model can be further improved and vice versa. Though acting as a positive feedback system, better cooperation between systems is required. Unfortunately, there are often systematic technique-specific errors in most cases, preventing from doing so. It is thereby still challenging to find a way with optimal and holistic use of these techniques. In the long run, considering the enhancement of error models, data or information that was buried in the noise of these error models will show up. With most unmodelled systematic techniquespecific errors being corrected, observations at low elevations may be fully exploited to probe the atmosphere, and combinations of GNSS with other space geodetic techniques will become easier.
2021 ) since they take longer trajectories than those of GNSS when passing through ground stations. However, all these opportunities are accompanied, at the same time, by challenges, including the design and maintenance of constellation, computational burden, etc. (Zhang and Ma 2019; Ge et al. 2022) , and most of the above results are based on simulations or real datasets from a few LEO satellites today. Nevertheless, they have revealed the potencies of LEO-augmented GNSS, in terms of almost all error sources in GNSS PPP. To date, over 20 LEO satellite launch plans have been announced by famous companies or institutes  (Ren et al. 2020). Thousands of LEO satellites will be fully operational once all the plans are finished. Until then, a new era would be expected to open not only for GNSS-based space geodesy but also for the GGOS.
g. , only one coefficient a is estimated or calculated with fixed empirical b and c from ray-traced mapping factors at several specific outgoing elevation angles based on meteorological datasets like radiosonde. These methods are more simple and more efficient and have been applied in the most commonly used mapping function today, such as VMF1/VMF3 (Boehm and Schuh 2004; Landskron and Böhm 2018) , but with mixed results. By combining the rigorous method (estimate a, b and c altogether) with the simplified methods (e. g. , estimate a or a/b with the rest fixed), an improved mapping function modelling method was also introduced by Zhou et al. (2021).
Data processing For each trial, the Cartesian coordinates of the points of all the drawn traces were collected. Some typical examples of traces are shown in Figure  4. We then computed the perceived angular position in azimuth h p and elevation / p , the distance R p and the apparent source width as follows. Each plot is firstly fitted by an ellipse in the azimuthelevation plane, using linear least squares ellipse fitting from Matlab fitellipse function by Richard Brown  [48]. The angular position is given by the polar coordinates of the center of the ellipse. The apparent width of the ellipse is characterized by an equivalent solid angle S eq given by the following formula: S eq ¼ 4p Â sin A 2 Â sin B 2 ; with A and B, respectively the small and large radii of the ellipse, expressed in radian.
A repeated measures ANOVA was performed to measure the significance of the fixed effects. The magnitude of the fixed effects was estimated using g 2 , which measures the amount of explained variance. Post-hoc analyses were performed with a Tukey adjustment. Outliers were defined as responses with an absolute error in azimuth greater than 45°or absolute errors in elevation greater than 60°and were removed from the analysis of angular errors data. Thus, 1. 2% of responses were excluded from the azimuthal data analysis, and 3. 6% were excluded from the elevation data analysis. These analyses were carried out using the "LmerTest" library in the R software  [49]. The use of this analysis methodology was inspired by a statistical analysis conducted by Ahrens et al. , on sound source localization data under different audio-visual conditions  [50].
Data availability statement The data that support the findings of this study are available from the corresponding author upon request and at https://www. prism. cnrs. fr/publications-media/ ACTAACUS23_Fargeot  [63]. In addition, a demo version of the proposed VR interface for reporting spatial attributes of sound sources (see Sect. 1. 3), is available at https://gitlab. prism. cnrs. fr/fargeot. prism. cnrs. fr/VRLoc-Toolkit/  [64].
Plant material and greenhouse conditions Triticum turgidum 'Kronos' and Triticum aestivum 'Cadenza' were used as wild-type controls for experiments involving TILLING mutant lines received from www. SeedStor. ac. uk. The Ensembl Plants database (http://plants. ensembl. org) was used to identify ASY1 genes: TtASY1-5A, TRITD5Av1G167820; TtASY1-5B, TRITD5Bv1G159710; TaASY1-5A, TraesCS5A02G286500; TaAS Y1-5B, TraesC S5B02G28 580 0; and TaASY1-5D, TraesCS5D02G294100. TILLING mutants were screened by BLAST search on the Wheat TILLING database (http:// www. wheat-tilling. com/): Ttasy1a, K0706; Ttasy1b-1, K0157; Ttasy1b-2, K2071  (Krasileva et al. , 2017) ; and Taasy1b, C0971  (Appels et al. , 2018). To create hypomorphic mutants, homozygous lines were crossed (K0706 Ttasy1a × K0157 Ttasy1b-1 and K0706 Ttasy1a × K2071 Ttasy1b-2), while heterozygous individuals from the F1's (AaBb) were self-pollinated to create F2's.
Cytological procedures Anther sizes were measured with a Nikon SMZ 745 dissecting microscope and 10 mm/0. 1 mm graticule. Chromosome spreads were stained with DAPI and examined by light microscopy as previously described  (Higgins, 2013; Desjardins S. et al. , 2020). Nikon Ni-E and Eclipse Ci fluorescence microscopes equipped with NIS elements software were used to image chromosomes. The following primary antibodies were used for immunolocalization: anti-TaASY1 guinea pig, 1:500  (Desjardins S. D. et al. , 2020) ; and anti-AtZYP1 rabbit 1:500  (Osman et al. , 2018). Secondary antibodies: goat anti-guinea pig Alexa Fluor 488 (https:// www. abcam. com/) and goat anti-rabbit DyLight 594 (https:// www. 2bscientific. com/) were used at 1:200. Chiasma counts were performed using NIS software, and significance (p adj < 0.
We further recovered six hits for cNRE-like sequences in HSV1, four in terminal repeat regions and two in unique sequence regions of the virus (Table  1 ). cNRE signatures are thus present in the sequences of Alphaherpesvirinae, suggesting that cNREs may be ancestral sequences from a family of viruses that infects and integrates into the genomes of vertebrates as diverse as avians and mammals. Since galliform birds are susceptible to infection by a host of other viruses besides Alphaherpesvirus, we screened for cNRE-like sequences in the avian viral database. In addition to herpesviruses, we found statistically signi cant cNRE-like matches in the genomes of papillomaviruses, paramyxoviruses, and retroviruses (Fig. 7 ). Of these, only paramyxovirus seem to lack the ability to integrate into avian genomes  (Gogoi et al.
, 2003). Each complex (AB) was split into two parts: the nuclear receptor structure (A) and the cNRE structure (B), and the energy was calculated for the whole complex (AB) as well as for each part, (A) and (B). Binding energy differences were obtained according to: ΔΔG = ΔG(AB) -(ΔG(A) + ΔG(B)). Binding free energies for all cNRE hexads were plotted with reference to its rst nucleotide. Data were pooled and analyzed as a box plot to identify values below the 10th percentile. These values were used to identify all potential hexads within the cNRE. To quantify the potential for nuclear receptor binding within the cNRE, we scored the number of times a given cNRE nucleotide was part of a hexad.
Phylogenetic analyses Full-length amino acid sequences of myosin heavy chain (MYH) 6, 7, and 7B of representative Archosaurian species were recovered from publicly available sequence databases. Accession numbers are provided in Supplementary Table  S1. An initial amino acid alignment was performed using MAFFT  (Katoh & Standley, 2013) , which was followed by automated re nement using  (Criscuolo & Gribaldo, 2010) , and nal manual curation. The phylogenetic tree was calculated based on the alignment of 39 sequences using 1935 amino acid positions and allowing gaps (Supplementary File S1). Phylogenetic relationships were assessed using the Maximum Likelihood (ML) method as implemented in RAxML v8. 0. 0  (Stamatakis, 2014)  and Bayesian Inference (BI) using Mr. Bayes v3. 2. 7  (Huelsenbeck & Ronquist, 2001 ).
Searching vertebrate genomes for cNRE-like sequences To search different animal genomes cNRE-like sequences, the command-line version of BLAST (BLAST+, version 2. 5. 0) was used. Global analyses of vertebrate genomes included all the taxa in the RefSeq Representative Genomes database, excluding the Galliformes (taxid:8976). For the cNRE searches of Archosauria genomes, we used all genomes available in the RefSeq Representative Genomes database as well as in the Whole-Genome Shotgun Contigs database. The searches were conducted using blastn-short, as this allowed for a search tailored to a query sequence as short as the cNRE. All parameters were set to default, except for word size (which was always set to 7 to maximize search accuracy) and eval (which was de ned as 1). We retained, for further analysis, cNRE-like sequences that spanned all 3 hexads (i.
e. , position 3 to 29) and that were characterized by a maximum of 1 mismatch per hexad (multiple mismatches were allowed outside the hexads) when compared to the Coturnix coturnix 5' cNRE. Hit sequences against animal genomes were individually extracted from the databases, since BLAST + is a local alignment algorithm that only yields parts of hit regions within a given genome. Each animal genome was thus loaded into R to obtain the exact position of the cNRE hit from the BLAST + alignment. If the best hit for the cNRE was on the minus strand, the genome sequence was reverse complemented. Each sequence was subsequently extracted from the genome based on the genomic location of the initial BLAST + hit and manually re ned to allow a full alignment of the cNRE within a given genome sequence. In parallel, for each genome containing a cNRE-like sequence, the location of the best Gallus gallus MYH7-like/SMyHC III hit was determined by tblastn, using default parameters.
Acknowledgments We thank  Dr. Koichi Kawakami  for providing the plasmid used to produce transgenic zebra sh. We thank the members of the  Ramialison group  and  Akriti Varshney ,  Gulrez Chahal , and  Julian Stolper  for their feedback and support and  Jeannette Hallab ,  Jeanette Rientjes , and  Ekaterina Salimova  for their assistance with experimental troubleshooting and design. We thank the members of the  Monash Bioinformatics Platform  for invaluable advice, especially  Stuart Archer ,  Adele Barugahare ,  Paul Harrison ,  David Powell ,  Michael See , and  Nick Wong. We thank the  ARMI FishCore  staff and  Melbourne University Fish Facility. We also thank  Lucas Buscaratti  for his drawing created with BioRender. com that helped us to illustrate the different mutations on the SMyHC III promoter comparing mice and zebra sh.
Funding This was supported in part by  FAPESP  grants  00/04082-1 ,  03/06555-2 ,  15/12549-2 , and  18/09839-7  and by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES)  -Finance Code 001, by  CNPq  grant  481983/2013-9 , and by the  Ceara State Scientist- in-chief program of  Fundação Cearense de Apoio ao Desenvolvimento Cientí co e Tecnológico  ( FUNCAP 08908197/2019 ) to JXN. MR is supported by Grants from the  Australian Research Council  and the  NHMRC. The  Australian Regenerative Medicine Institute  is supported by grants from the  State Government of Victoria  and the  Australian Government. The  Novo Nordisk Foundation Center for Stem Cell Medicine  is supported by a  Novo Foundation   Grant  ( NNF21CC0073729 ). MS is funded by the  CNRS  and the  ANR  ( ANR-21-CE34-0006-02 ).
7) , using Dfam curated threshold to estimate the signi cance of the hit (Storer et al. , 2021). The results were then compiled into individual gures to assess the distribution of transposable element family signatures within the genome sequences in relation to the cNRE-like hit. Additionally, RepeatMasker (http://www. repeatmasker. org) was used to screen for transposable elements and repetitive regions within the SMyHC III genome loci of Numida melleagris (NumMel1. 0 NC_034426. 1 -Chromosome 18: 22500-44500) and Gallus gallus (GalGal5. 0 NC_006106. 4 -Chromosome 19: 15000-37000) as well as within the available genomic sequence of the Coturnix coturnix SMyHC III locus (GenBank reference U53861). Results of the RMBlast were then loaded and visualized using Gviz (Hahne & Ivanek, 2016)  along with the SMyHC III coding sequences and the cNRE elements.
Results and Discussion Comments on general scope I collected alignments of orthologous sequences that were conserved in Bilateria (which I define as presence in both protostomes and deuterostomes -see methods). When cnidarian sequences were orthologous, I included these. All sequences and orthology group assignments are available at https://github. com/rcply/antp/. I have not included 'orphan' sequences, that is, isolated or likely misplaced long-branched sequences that did not show clear orthology with other bilaterians or cnidarians. Throughout I use a standard homeobox numbering of scheme of 60 amino acids where residue 50 is the Q of WFQN in the DNA recognition helix  [4]. Most gene names are taken from their human orthologs (without the numeric distinction between paralogs e. g. EMX rather than EMX1).
In addition to the CRE analysis, I examined evolutionary couplings, that is, the correlations between residues in this alignment of ANTP-class sequences using the program 'plmc'  [37]. The highest scoring pair of residues  (19, 30) , involved in a salt bridge in some 3D structures, have previously been identified as highly correlated  [38, 39]. That the presence and polarity of these residue pairs has phylogenetic significance, however, has not been a focus of previous reports. In PRD-class genes, both residues are neutral. In HOX-like genes residue 19 is negative and residue 30 positive, whereas in NK-like genes residue 30 is always negative. In the majority of NK-genes residue 19 is positive, leading to the possibility of a salt bridge with inverted polarity relative to the HOX-like genes, although in several NK-like crystal structures residue 30 instead interacts with residue 23 (Figure  3 ).
Similarly, EMX, VAX and NOTO consistently form clade under a variety of models, along with sponge representatives. Both EMX and HMX like genes encode EH1 and hexapeptide-like motifs in bilaterians (Figure  4 ). In the scenario outlined in Figure  7  the GBX-lke state is a crucial intermediate in the evolution of HOX-like genes. GBX itself lacks an EH2 or hexapeptide motif, so taken at face value, this implies that the GBX/HOX lineage must have diversified rapidly, before the ur-GBX had an opportunity to lose its hexapeptide motif. Similar arguments may account for the loss of EH1 motifs at the N-terminal end of anterior HOX genes, but not, for instance, the parahox gene GSX. The questions arise as to whether there is any biological significance to the loss of proline as a defining feature of ANTP-class homeodomains, and the significance of the salt bridge inversion between HOX and NK-like homeodomains -that is, do Hox genes only work as Hox genes at the organismal level because their salt bridge has this polarity? In the case of loss of proline, Fersht and co-workers have argued that the conformational flexibility of the network of residue interactions centred around L26 is important for induced fit interactions with DNA  [56].
Identification of homeobox sequences, master alignment and phylogeny I used hmmer to search a representative set of metazoan proteins derived from complete genome sequences with the Pfam Homeobox (PF00046. 28) Hidden Markov Model, selecting hits based on the 'gathering' threshold ('--cut_ga')  [58]. Sequences were filtered and aligned as described in  [41]. I inferred a phylogenetic tree using iqtree with the LG+C20 model. Homoscleromorph sponge transcriptome assembly was described previously  [41].
e. those reported with cigar string '62M') extracted to make an ungapped multiple sequence alignment. The alignments were merged to form a single alignment with each sequence coming from a known orthology group. Protostome/Deuterostome orthology of central and posterior Hox genes is hard to establish, so I treated orthology groups 5-8 as a single central group (HOX-c), and 9-13 as a single posterior group (HOX-p). Alignments are available at: https://github. com/rcply/antp.
Sub-type specific amino acids Aspects of the methodology of PROUST (Prediction of Unknown Subtypes) to identify specificity determining residues  [29]  were reimplemented in python, and using Hidden Markov Models of the HMMER3 package  [58]. Orthology or higher level evolutionary groupings (e. g. ANTP vs PRD) were used to define the groups for which the most likely specificity determining residues were to be found, and over which cumulative relative entropy was calculated. Code is available at: https://github. com/rcply/antp.
Study Design, Sample, and Data Preparation This is a cross-sectional study, using secondary data available and downloaded from the official Ironman ® website (www. ironman. com, accessed on 22 June 2022) using a Python script. We analyzed elite (PRO) finishers race data of all Ironman ® 70. 3 races recorded in the Ironman website between 2004 and 2020. To register as an elite triathlete, an athlete must apply in the Ironman ® 70. 3 community to become a "PRO" member with a copy of the athlete's license status as a professional athlete from the respective national triathlon federation, and must also be affiliated with the International Triathlon Union (www. triathlon. org, accessed on 22 June 2022). Data obtained include the athletes' sex and country of origin, the event location and year, and the race finish time.
Since the outcome (or predicted variable) was the finish time in seconds, the fastest courses were defined as the race locations where the average finish time was the lowest. The triathletes' gender and country of origin, as well as the event location (race course) were used as predictors. As three of them were categorical variables, they were encoded into numerical values. Gender was encoded as 0 = female, 1 = male, whilst for the country and event location, a rank-encoding scheme based on the frequency was used. Data processing and analysis were performed using Python and associated ML libraries in a Jupyter notebook (Google Colab).
According to the decision tree then, the Ironman ® 70. 3 World Championship is the fastest race course. Figure  4  represents the decision tree algorithm implemented as model 4. As already explained, all four predictive models are ensemble variations in the basic decision tree; hence, interpreting the latter is relevant to understanding all of them. Starting at node 0 (with gender encoded as 0 for women and 1 for men), the decision node Gender_ID <= 0. 5 leads to the women's results on the top branch (node #1) and men s at the bottom (node #16). Note the color coding in which the darker colors represent longer finish times (represented by the "value" in seconds). As we are searching for the best times, we continue to the men's branch, in which decision node #16 Country <= 11. 5 splits the dataset into the first athletes' countries by the number of records (i.
Methods: This scoping review will follow the methodology outlined in the Joanna Briggs Institute Reviewer's Manual and adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews) guidelines for reporting. Peer-reviewed articles will be searched in databases like Ovid MEDLINE, OVID Embase, PEDro, OVID PsycINFO, EBSCO CINAHL, the Cochrane Library, Scopus, IEEE Xplore, Web of Science, and Global Index Medicus. The research team will create a data extraction form covering study characteristics, participant characteristics, underlying models and frameworks, research findings, implications for technology coadaptation, and any identified study limitations. A directed content analysis approach will be used, incorporating the Selection, Optimization, and Compensation framework and Sex-and Gender-Based Analysis Plus theoretical framework.
These terms will be combined using Boolean operators (such as AND and OR) to construct search queries tailored to specific databases or search engines. An initial search will be conducted in Ovid MEDLINE, using text words in article titles and abstracts, as well as index terms, to identify relevant articles. This strategy will undergo press peer review to minimize biases and ensure comprehension prior to being finalized  [64]. Once the strategy is finalized in Ovid MEDLINE by the research team, it will be translated and adapted for other databases including OVID Embase, PEDro (Physiotherapy Evidence Database), OVID PsycINFO, EBSCO CINAHL, IEEE Xplore, Web of Science, the Cochrane Library, Scopus, and Global Index Medicus by the information specialist. The aim is to ensure comprehensive coverage of relevant literature by searching multiple databases.