dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression Data Expression data were obtained from a previous transcriptome study of skeletal muscle in pig during development using microarrays  (Voillet et al. , 2014). The dataset consists of 44,368 probe measurements for 17 samples (LW animals) at two different gestational stages: eight samples at 90 days and nine samples at 110 days. A precise description of the experimental design and data collection can be found in  Voillet et al. (2014). Normalized expression data (log 2 transformed) and sample information are available in NCBI (GEO accession number GSE56301). Log 2 transformed expressions and log fold change (logFC) of these expression values at 90 vs 110 days (reference time point: 90 days) were used in our integrative analyses. Since the microarray was originally designed on a former version of the pig genome, probes were remapped on the Sscrofa11.
The lack of available data is another obstacle to correctly assess TAD variability, even among samples from the same tissue or cell line. Indeed, due to their high experimental cost compared with other assays like RNA-seq for instance, Hi-C experiments are usually not performed on a large number of replicates. Consequently, apart from some widely used human or mouse cell lines, most of the currently available datasets only propose biological duplicates, in particular for tissue samples. Obviously, the lack of a proper and commonly accepted definition of TADs TABLE 1 | Numbers and proportions of nuclei with an observed association between telomeres. Proportions of nuclei harboring the probed telomeric associations in muscle cells at 90 and 110 days of gestation: SSC2pter -SSC9qter, SSC13qter -SSC9qter and SSC15qter -SSC9qter.
ACKNOWLEDGMENTS We would like to thank the  GeT-PlaGe facility (INRAE, France) , the T. R. I. Genotoul imaging core facility (Toulouse, France), and the  Genotoul bioinformatics platform (Toulouse, France)  for providing assistance and technical support. We also thank  L. Liaubet 's team ( INRAE, France ) for providing samples and assisting with the experiments,  H. Acloque  ( INRAE, France ) for pioneering the Hi-C in our institute, and both the Chrocogen working group and the  FAANG community  for their support.
FUNDING This work was supported by a grant to MM-M from the  French "Ministère de l'Education Nationale, de la Recherche et de la Technologie ". Tissue sampling and Hi-C library sequencing were funded by the 2014  Animal Genetics Division AAP grant  ( INRA, France ), and by the " Osez l'interdisciplinarité ! " grant of P. Neuvial's  SCALES  project (CNRS, France). Animals were raised in the experimental farm  GenESI (INRAE, France). Further support was provided to MM-M with a training program founded by the " Soutien à la mobilité internationale " scholarship from the  INP (Toulouse, France)  and hosted by  M. Groenen 's group ( WUR, Netherlands ).
DATA AVAILABILITY STATEMENT Animals and samples metadata have been registered at BioSamples (https://www. ebi. ac. uk/biosamples) and are available using the accession ID SAMEA7390788. Experimental protocols have been deposited at the FAANG DCC: https://data. faang. org/api/ fire_api/samples/INRAE_SOP_pig_muscle_tissue_sampling_ 20200812. pdf (sampling) and https://data. faang. org/api/fire_api/ assays/INRAE_SOP_Hi-C_pig_muscle_tissue_20200812. pdf (Hi-C libraries). Raw sequencing data from Hi-C experiments have been uploaded to the ENA under the accession PRJEB40576 (ERP124229) and to the FAANG DCC along with the associated metadata, as per the FAANG consortium agreement (https://www. faang. org/data-share-principle). Hi-C matrices, TADs and A/B compartments generated in this study have been made available at: https://doi.
Background: Recently, the successor of the Conventional Gait Model, the CGM2 was introduced. Even though achievable reliability of gait kinematics is a well-assessed topic in gait analysis for several models, information about reliability in difficult study samples with high amount of subcutaneous fat is scarce and to date, not available for the CGM2. Therefore, this study evaluated the test-retest reliability of the CGM2 model for difficult data with high amount of soft tissue artifacts. Research question: What is the test-retest reliability of the CGM2 during level walking and stair climbing in a young obese population? Is there a clinically relevant difference in reliability between a standard direct kinematic model and the CGM2? Methods: A retrospective test-retest dataset from eight male and two female volunteers was used.
It comprised standard 3D gait analysis data of three walking conditions: level walking, stair ascent and descent. To quantify test-retest reliability the Standard Error of Measurement (SEM) was calculated for each kinematic waveform for a direct kinematic model (Cleveland clinic marker set) and the CGM2. Results: Both models showed an acceptable level of test-retest reliability in all three walking conditions. However, SEM ranged between two and five degrees ( • ) for both models and, thus, needs consideration during interpretation. The choice of model did not affect reliability considerably. Differences in SEM between stair climbing and level walking were small and not clinically relevant (<1 • ). Significance: Results showed an acceptable level of reliability and only small differences between the models.
Introduction The Conventional Gait Model (CGM) is a wide-spread model used in clinical gait analysis  [1]. It was developed in the 1980s, and besides several strengths, faces some well-recognized limitations. Recently, the CGM2 was introduced as an evolution of the CGM  [2]. This gait model was designed to be backward compatible with the original CGM while including technical advancements such as optimized hip joint center estimation  [3, 4] , an inverse kinematic approach  [5] , the use of marker-cluster within an optimized marker set  [6] , upon some other changes. The CGM2 is implemented as an open-source python package (pyCGM2: https://pycgm2. github. io) and is freely available. Motion-capture techniques that are based on skin-mounted markers are prone to errors introduced by inaccurate and/or inconsistent marker placement and soft tissue artifacts (STA)  [7].
Methods The retrospectively utilized dataset consisted of 3D gait analysis data (3DGA) of level walking from an earlier study at self-selected speed  [11]  and new data from metronome-assisted stair ascent/descent at ∼110 steps per minute  [13]. See Figure  S1  in the supplementary material for details to the stair case. The dataset comprised eight male and two female volunteers (mean (standard deviation, SD); age: 15 (2. 3) yrs, height: 171. 5 (8. 7) cm, mass: 100. 8 (18. 8) kg; BMI: 34. 1 (4. 1) kg/m 2 ) assigned to two 3DGA sessions which were separated by a minimum of one day and on average 3. 4 (SD 2. 0) days. Data were assessed by the same person, who had several years of experience. All participants were above the 95th percentile of the central Europe specific sex-and age-based body-mass-index (BMI) and considered as obese  [14].
8  [23]  and is available on GitLab (https://gitlab. com/ EliseVerrier1/abm-pollinators-scent-mark). A detailed version of the model description based on the ODD (Overview, Design concepts, Details) protocol  [24, 25]  is available in S1 File along with a detailed description of the model parameters, variables, and their values (Tables S1. 1-S1. 4 in S1 File).
Sensitivity analyses The impact of each parameter on the quantity of collected nectar was first assessed using sensitivity analysis by simulation that explored a range of parameters (Table  1 ). We ran 10,000 simulations, and for each simulation, parameters were assigned a value that was randomly taken from the variation interval. Their impact was studied by performing a random forest analysis in R version 4. 0. 3 [26]  using the randomForest package  [27]. We built 2,000 regression trees and examined three variables at each split. The sensitivity analysis explained more than 95% of the variance. It is presented in detail in S2 File.
5% of the simulation) to focus on the consequences of competition on information use. The intensity of competition experienced by pollinators depends on the ratio of pollinators to flowers, while the time spent locating the flowers depends on the relation between the number of flowers and the size of the simulated meadow. These parameters increase the mean standard error of the model by almost 50% (Fig S2. 1 in S2 File). We chose to fix the number of flowers in relation to the meadow size and examine the impact of varying the number of competing pollinators. We set the maximal number of pollinators to 500, with the density of flowers and the size of the map being chosen in consequence. The analyses were performed with R 4. 0. 3 [26]  using the Rmisc  [28]  and plyr  [29]  packages. The graphs were created with the ggplot2 package  [30].
E. V. is funded by the  French Ministry of Research  and the  SEVE Doctoral School. This work is part of the " Investments d'Avenir" Programme overseen  by the  French National Research Agency (ANR)  ( LabEx BASC ;  ANR-11-LABX-0034 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Preliminary Analysis for the Presence of ASPV Variants by RFLP RFLP analysis was used to identify diverse ASPV variants using the MspI, RsaI an Sau3AI enzymes. The amplicons from a total of 146 ASPV isolates from apple (89), pea  (32)  and quince  (25)  were used in the RFLP analysis. The results showed the existence o different digestion patterns (data not shown). In apple samples 15 such patterns wer identified, whereas 9 and 12 patterns were identified in pear and quince samples, respec tively. Considering the RFLP profile of the isolates, the host and the district of origin, 1 ASPV isolates (five from apple, four from pear and five from quince) were selected fo further analysis (Figure  1 ). In the case of quince, one of the five chosen samples was co lected from the asymptomatic one. The names of the selected isolates are provided in Ta ble 1.
The overall results of the complete CP sequences comparison between the 484 selected ASPV sequences (GenBank plus new sequences reported here) showed a very large divergence range of 6-37% nt identity and of 7-33% aa identity in the encoded CP. All the The multiple alignments of 484 ASPV CP gene sequences (429 from the GenBank, 14 from Greek isolates, 24 from botanical Pyrus and 17 from Brazilian apples) showed that the Greek, botanical and Brazilian isolates shared a similar range of CP lengths (Table  1 ), with most ASPV isolates CP genes ranging from 1104 to 1245 nt (extremes 1092 and 1248 nt). Taking as reference 1191 nt most frequent length (184 out of 484 isolates), the analysis showed the presence of a high number of indel polymorphisms (30 additional different indels). The indels observed in Greek isolates were identified again in isolates of the same CP length: IND1ins was also observed in 38 isolates (35 pears, one apple, two botanical: P.
ussuriensis, P. pyrifolia) (Figure  S1 ), IND4del in 17 pear isolates (16 cultivated, one botanical: P. pyrifolia), IND3del in 11 pear isolates, and IND2del in 34 isolates (29 pears, two apples, one Brazilian apple, 2 botanical: P. ussuriensis, P. pyrifolia) (Table  1 ), suggesting that there is no or only limited correlation between the presence of particular indels and either the country of origin or the isolation host. Many unique point mutations were also identified among all isolates (19 in Greek, 13 in botanical, 9 in Brazilian). Interestingly, in the AUM109 apple isolate, two point mutations (unique insertion of a C 444 nucleotide and an nt 478 deletion) cause creating a unique motif of 11 aa (ATWCQSLNS-AS) compared to all other isolates. The sequences used in this study were deposited in GenBank under accession numbers MW810244-MW810257 (Greek) and MW842988-MW843004 (Brazilian).
The overall results of the complete CP sequences comparison between the 484 selected ASPV sequences (GenBank plus new sequences reported here) showed a very large divergence range of 6-37% nt identity and of 7-33% aa identity in the encoded CP. All the Greek isolates shared extreme nt divergence values of 34-37% with the following five isolates: FJ12 (pear, USA), Brae2 (apple, Brazil), N (apple, India), LYC (pear, China) and XJ (hawthorn, China) with the highest divergence of 37% observed between QAT4 and the Chinese XJ isolate from hawthorn. Apart from the extremely divergent isolates mentioned above, the Greek isolates also shared high nt divergence levels of 30-32% with isolates from cultivated species (pear: KY176819, JX673822, JX673821; apple: KY081215, KY081220) originating from Poland and China, with two new Brazilian isolates (Royal Gala ct5130 and ct1102), and with the isolates from botanical species P.
Recombination Analysis Changes in the phylogenetic relationships of a virus, when considering different genome regions, are often indicative of recombination events  [9]. The existence of recombination events in the CP gene of ASPV has been reported previously  [20, 24, 28]. We analyzed the potential recombination events in the CP gene using the dataset of 124 selected isolates described above, utilizing the RDP4 program. Only those events indicated by five or more programs incorporated in RDP4 were considered significant. The analysis revealed several potential recombination events listed in Table  2. In particular, potential recombination events were detected in three Greek isolates from quince and pear, whereas no recombination events were detected in the CP gene of apple isolates. These events were identified in QUL10 with KY176823 (apple) and KY243369 (pear) isolates as potential parents, and in PCK74 (same potential recombination event also present in pear isolate PHL193) with KY176815 (pear) and KY429181 (pear) as potential parents.
Analysis of Selection Pressure in the ASPV CP Gene The CP genes of plant viruses are in general under negative (purifying) selection constraints, although the strength of this purifying selection has been proposed to be modulated by the mode of transmission  [31]. On the other hand, while most codons are under purifying selection, a limited number of codons under diversifying selection pressure have been identified (see, for example,  [32, 33] ). This possibility was evaluated in the case of the CP gene of ASPV using a dataset of 112 sequences obtained by removing all recombinant isolates identified by an RDP4 analysis from the 124 selected sequences dataset since the presence of such recombinant isolates is known to increase the probability of false-positive detection. Several programs of the HyPhy package implemented on the Datamonkey server (http://www.
datamonkey. org/ accessed on 10-14 December 2020) and involving either ML (FEL, SLAC, MEME) or Bayesian (FUBAR) approaches were used. No statistically significant evidence for positive selection was obtained using SLAC, MEME and FUBAR, while FEL identified codon 38 as possibly under diversifying selection (p-value 0. 079). However, all three programs assume a constant selection pressure on individual codons along the entire phylogeny and, therefore, cannot account for the possibility that isolates belonging to different clusters may be under different selection pressures. This may explain why additional evidence for selection at the codon level was observed by Komorowska et al. [24]  using a more restricted dataset.
Protein Co-Evolution Networks of ASPV CP Protein evolution depends on intramolecular co-evolutionary networks whose complexity is correlated with functional and structural interactions among particular sites  [34]. One of the key forces shaping proteins is the co-evolution of amino acid residues. Knowing which residues coevolve in a particular protein is essential to understand protein evolution, structure, and function and helps to identify substitutions that may affect its properties  [35]. The presence of coevolving amino acids in ASPV CP was analyzed using the protein sequences derived from the CP gene dataset. CAPS2 analyses revealed 15 groups of coevolving amino acids (with a total of 25 aa involved). The coevolving groups, together with overlapping groups of coevolving amino acids, are presented in Figure  4.
Larger nodes correspond to greater numbers of connected sites. FUBAR, while FEL identified codon 38 as possibly under diversifying selection (p-value 0. 079). However, all three programs assume a constant selection pressure on individual codons along the entire phylogeny and, therefore, cannot account for the possibility that isolates belonging to different clusters may be under different selection pressures. This may explain why additional evidence for selection at the codon level was observed by Komorowska et al. [24]  using a more restricted dataset.
Recently, it was also shown that ASPV CP functions as a symptom determinant and differences in aa in the CP N-terminus might affect the ability of variant CP subunits to aggregate in Nicotiana benthamiana  [40]. The addition of other hosts (botanical pear species, quince) enriched the analysis of the average divergence values of intra-and inter-groups. This showed that contrary to a previous report  [24] , the highest contribution to the observed variability is due to differences between isolates from different hosts and, second, to differences between isolates from the same host, providing a new understanding of the forces shaping ASPV populations' structure. Compared to the study of Komorowska et al. [24] , in which only Polish isolates were analyzed, using a larger dataset with sequences from additional hosts and from different countries also suggests the country of origin as another parameter structuring the observed ASPV variability.
Identification of Additional ASPV CP Gene Sequences from High-Throughput Sequence Data Additional ASPV CP gene sequences were obtained from two sources. The first is Illumina HiSeq X RNAseq data obtained from three Brazilian apple trees that showed multiple infections by ASPV isolates. While the presence of ASPV and of Buyna-like viruses in these trees has been previously described  [42] , a more detailed analysis of the contigs from each tree allowed in each case the recovery of several complete CP gene sequences. In parallel, publicly available transcriptomic RNAseq data for botanical Malus and Pyrus available as sequence reads archive (SRA) in GenBank were screened by Blast analysis. Those SRAs with significant ASPV presence (only observed in Pyrus SRAs) downloaded for further analysis.
Molecular Variability and Recombination Analysis To estimate the divergence levels, multiple alignments of nucleotide nt or translated aa sequences were performed using the ClustalX  [43]  and/or Muscle  [44]  software implemented in MEGA X  [43]. The multiple sequences (nt) were aligned by codons for the phylogenetic analysis after removing the gap positions using the Muscle software as implemented in MEGA X  [44, 45]. A preliminary evaluation of the 429 available ASPV CP sequences in the NCBI (from apple, pear, hawthorn and from 10 countries: China, Poland, Germany, Brazil, India, Japan, South Korea, UK, USA, Czech Republic) was conducted in which the isolates with a non-functional CP gene were removed. In a second step, isolates with more than 90% nucleotide identity were excluded along with the isolates of the same origin and host, which were placed in the same phylogenetic clusters so that in each case, a single representative isolate was retained.
The same was applied to the new sequences from Brazilian apples and from botanical pear species SRAs. This yielded a final dataset composed of 124 sequences corresponding to the 14 Greek isolates, 69 ASPV sequences (of different CP-length, host and origin) from GenBank, 24 sequences from wild and ornamental Pyrus species SRAs and 17 sequences from Brazilian apple samples. In addition, this nucleotide sequence dataset was used to calculate average values of intraand inter-groups divergence using host-based (four types: apple, pear, quince, botanical species) or country of origin-based groups (five origins: Brazil, China, Greece, Poland and Rest of the world) using MEGA X  [45]. Before phylogenetic analyses, the occurrence of potential recombination events was evaluated using various methods (RDP, GENECONV, Chimaera, MaxChi, BootScan, SiScan and 3Seq) implemented in RDP4  [46].
Phylogenetic Analysis and Evolutionary Networks Sequence alignments were used for inferring phylogenetic relationships and the construction of NJ and ML trees using MEGA X. The best nucleotide substitution model was calculated using the ModelTest program implemented in MEGA X. Specifically, the general time-reversible (GTR) model  [48]  with a gamma distribution (parameter α = 0. 8273) of variable and invariant sites (G+I) (I = 0. 1436) was used to compute the ML tree of the 124 ASPV CP gene nt sequences, whereas the JTT and amino acid frequencies (F) model  [49]  with G+I (parameter α = 0. 4851; I = 0. 1438) was used to compute the ML tree of the 124 ASPV CP aa sequences. Confidence in the branch points was assessed by the bootstrap method with 1000 pseudorandom replicates  [50]. Evolutionary networks were studied by parsimony phylogenetic networks, constructed with SplitsTree4  [47]  using the same alignment of the 124 ASPV nt sequences used in the recombination and phylogenetic analyses.
Inference of Selection Pressure in the ASPV CP Gene and Co-Evolution of Amino Acids To try to identify codons under positive selection (i. e. , with a dN/dS ratio significantly higher than 1) in the ASPV CP gene, ML or Bayesian approaches implemented in the HyPhy package on the Datamonkey server (http://www. datamonkey. org/, accessed on 3 March 2021) were used. As the presence of recombinant isolates leads to an increase in false positives detection, all recombinant isolates identified using RDP4  [46]  were removed from the dataset before selection analysis. Multiple alignments of the remaining sequences were then obtained using Muscle software as implemented in MEGA 7. In HyPhy, the single-likelihood ancestor counting (SLAC)  [51] , fixed effects likelihood (FEL)  [51] , mixedeffects model of evolution (MEME)  [52]  and fast unconstrained Bayesian approximation (FUBAR)  [53]  methods were used.
The scale bars represent a genetic distance of 0. 05; Figure  S4. ML phylogenetic tree (classical shape) conducted in MEGA X using full-length ASPV CP nucleotide sequences. The analysis involved 124 ASPV isolates, with a total of 1316 positions in the final dataset. All GenBank isolates are reported with their accession numbers, isolate name, host type and country of origin. The isolates from this study are indicated by their isolate name, host and country of origin. In parenthesis is given the size of the CP gene. isolates, which possess indels, are indicated last by indels code name. Indels are also color-coded: green: IND1ins, pink: IND2del, orange: IND3del, blue: IND4del, brick red: IND5ins, light blue: IND8del, purple: IND9del, gray: IND10del. By red and brown color are indicated, respectively isolates of different CP-size with deletions of 69 and 87 nt.
I-III and a-h represent, respectively, the clusters and subclusters. Branches with <70% bootstraps were collapsed. The scale bars represent a genetic distance of 0. 05; Figure  S5 : The ML phylogenetic tree (radical shape) of the CP nucleotide sequences, conducted in MEGA X. The identified clusters (I to III) and subclusters (a to h) are easily visible in this tree shape. Greek isolates are reported by their isolate name and a black dot. The scale bars represent a genetic distance of 0. 2; Figure  S6 : The ML phylogenetic tree (radical shape) of the CP nucleotide sequences, conducted in MEGA X. The isolates are indicated by their accession number or isolate name, host and country of origin. The sizes of the CP gene are given in a parenthesis and also are color-coded: green: 1233, pink: 1185, orange: 1131, blue: 1125, dark green: 1221, light blue: 1245, purple: 1218, gray: 1194, fuchsia: 1212, red: 1191, yellow: 1200 and brown: 1197.