dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  Carla Ramina  and  Carmine Nicoletti  for technical assistance and  Eric N. Olson  and  Rhonda Bassel-Duby  for providing HDAC4 conditional mutant mice. We thank the  Myobank-AFM of the Institute of Myology (Paris, France)  for human DMD samples. We thank  Anna L. Colin Mazzotti  for the English editing of the manuscript. All authors of this manuscript comply with the guidelines of ethical authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. 44.
Selection of Cases All the cases of bites by a reptile animal belonging to the snakes, anguimorphs (varanids), and iguanids groups, not native to mainland France and reported to the French PCCs, were included. The following data were analyzed: socio-demographic aspects (age, sex, geographical region of the case, place, and time of the bite); the species of reptile involved; the location of the bite; clinical manifestations following the bite; and the severity of the bite. Only the proteoglyphic and solenoglyphic snakes (Elapidae and Viperidae), and the lizard genus Heloderma, were considered venomous in the analysis of the cases. Severity is calculated from the symptoms, the results of paraclinical examinations, and some recorded medical management. We used the poisoning severity score (PSS)  [4].
e. population source, number of independent introductions), as shown in previous studies  (Guillemaud et al. 2010; Cristescu 2015). Luckily, approximate Bayesian computation (ABC) is an effective method for dealing with complex scenarios. It is a powerful tool that combines the use of simulated datasets and summary statistics  (Beaumont et al. 2002; Csilléry et al. 2010; Bertorelle et al. 2010; Beaumont 2010) , and it is a particularly good analytical fit for research on non-native species (e. g. ,  Lombaert et al. 2010; van Boheemen et al. 2017; Fraimout et al. 2017; Lippens et al. 2017; Lesieur et al. 2019). Indeed, in the case of recently established invasive populations, geographical and historical information can be used to define and assess hypothetical invasion scenarios in order to infer introduction route(s)  (Estoup and Guillemaud, 2010).
Furthermore, ABC can be used to analyze large datasets and to estimate demographic, historical, and genetic parameters  (Guillemaud et al. 2010). This method was used to investigate the possible invasion routes of certain non-native insects (e. g. ,  Ryan et al. 2019; Mutitu et al. 2020) , including species that were observed spreading quickly across Europe  (Roques et al. 2016 ) like the Harlequin ladybird, Harmonia axyridis  (Lombaert et al. 2010 (Lombaert et al. , 2014a, b), b) , the spotted wing drosophila, Drosophila suzukii  (Fraimout et al. 2017) , and the western conifer seed bug, Leptoglossus occidentalis  (Lesieur et al. 2019). In these three cases, researchers observed that invasion dynamics and dispersal processes were complex, involving multiple introductions, admixture events, and bridgehead effects.
Genetic analyses: genetic diversity and population structure In a first step, we assessed the genetic diversity and the relationship between populations both from the native and invaded ranges. The software Arlequin v. 3. 5  (Excoffier and Lischer 2010)  was used to calculate mean allele number (An), observed heterozygosity (Ho), expected heterozygosity (He), and to test for significant deviation from Hardy-Weinberg proportions (HW), and linkage disequilibrium (LD). To deal with multiple pairwise comparisons between loci, a sequential Bonferroni correction (Rice 1989) was applied to the results for both HW and LD. Null alleles at each locus were estimated using FreeNA  (Chapuis and Estoup 2007). Allelic richness (AR) for each population was calculated based on the minimum sample size of 14 individuals using FSTAT v.
2. 9. 3  (Goudet 1995). FreeNA software was also used to calculate pairwise F ST values using the excluding null allele correction method. We reconstructed a population-level neighbor-joining tree using  Population v. 1. 2. 32 (http:// bioin forma tics. org/ ~tryph on/ popul ations/) and Cavalli-Sforza and Edwards chord distances. A discriminant analysis of principal components (DACP)  (Jombart et al. 2010 ) was employed to preliminarily explore the genetic structure of the dataset; the adegenet package  (Jombart 2008)  implemented in R software (R Development Core Team 2008) was used. To further explore the dataset's genetic structure and to identify its potential number of genetic clusters, we used a Bayesian clustering approach implemented in STRU CTU RE  (Pritchard et al. 2000). Three independent STRU CTU RE analyses were performed using (i) the data from all the specimens; (ii) the data from the native populations (eastern Asia); and (iii) the data from the invaded populations (Europe, Eastern Black Sea region).
Parameter values were estimated using an admixture model where allele frequencies were correlated among populations. For each analysis, 15 replicate runs were performed for each value of cluster number (K). Every run comprised a burn-in period of 200,000 MCMC iterations that was followed by 1,000,000 iterations. For the full dataset and the dataset for the invasive populations, K was set to range between 1 and 15. For the dataset for the native populations, K was set to range between 1 and 10. The uppermost level of population structure was estimated for each analysis using the ΔK method  (Evanno et al. 2005) , which was implemented in STRU CTU RE Harvester  (Earl and vonHoldt 2011). For each K, the mode among runs was checked using the Greedy algorithm in Clumpp  (Jakobsson and Rosenberg 2007 ) via the online pipeline CLUMPAK  (Kopelman et al.
Because our sampling efforts were unsuccessful in several parts of BTM's native range (e. g. , Japan, central China), we included a fourth, unsampled population (N-US) in the ABC analyses. No alternative datasets were available because we only obtained one population for South Korea and one population for southwestern China. We selected six populations for the STRU CTU RE analysis of the invaded range. In making our choices, we took into account geographical position and historical information (e. g. , year of first introduction, country-specific data on the ornamental plant trade). First, we selected Weil Am Rhein (I-WAR), Kehl (I-KE), and Giessen (I-GI) (Van der Straten and Muus 2010) because they are where BTM was first observed in Europe. To obtain a better coverage of the invaded range, we also selected one population to represent each of the following sampling regions: western Europe, eastern Europe, and Eastern Black Sea region.
Our choices were Lucca (I-LU), Budapest (I-BU), and Solokhaul (I-SO), respectively. Small sample sizes can negatively affect the rate at which the most probable scenario is identified  (Guillemaud et al. 2010). For Solokhaul (I-SO) for which we were only able to genotype 15 individuals, we thus adopted the methodology described in  Lombaert et al. (2014a, b)  and employed an alternative dataset, that associated with the next closest population, Bzyb Valley (I-BV). The ABC analyses in themselves involved two steps. In the first step, we examined the oldest invasive populations recorded in Europe (Weil Am Rhein, I-WAR; Kehl, I-KE; and Giessen, I-GE) to better understand the pathways followed by BTM from eastern Asia. The goal was to (i) verify that eastern China was the source and (ii) determine whether the three first invasive populations in Europe, which were established in 2007 at distant locations from one another, resulted from a single or multiple introduction events (Table  2 ).
This method has the capacity to discriminate among scenarios more efficiently than traditional ABC methods, especially when complex invasion pathways are involved (see  Fraimout et al. 2017 ). The ABC-RF method is a nonparametric, machinelearning tool which carries out the classification of hundreds of bootstrapped decision trees using summary statistics as predictor variables. It extracts the maximum of information from the entire set of summary statistics, avoiding choosing arbitrary a subset as required in other classical ABC methods. A "prior error rate" (PER) can also be estimated using the simulations that were not employed to build the trees. PER provides a direct means of cross validation. With DIYABC, we generated 10,000 microsatellite datasets per scenario, which we then used to grow a classification forest of 1000 trees.
The scenario with the greatest number of votes was defined as being the most likely. When the number of votes was similar among several scenarios (i. e. , scenarios differed by fewer than 50 votes), the two scenarios with the greatest numbers of votes were repeated using the ABC-RF method to verify the accuracy of scenario selection. We used the abcrf R package  (Pudlo et al. 2016)  to perform all the ABC-RF analyses. Finally, we inferred posterior probabilities (PPs) for the final scenario in the final analysis by performing Random Forest regression  (Raynal et al. 2019 ) on classification forests of 1000 trees. Simultaneously, model checking was run with DIYABC to evaluate the fit between the most likely scenario and the observed dataset. Table  2  Description of the scenario selection process used to infer the invasion routes of the box tree moth (Cydalima perspectalis) and results of each ABC analysis For each ABC analysis, we have indicated the number of scenarios compared; the invasive population under consideration; the potential source populations tested; signs of admixture events; the scenario selected based on the Random Forest (RF) method; the number of summary statistics used (NSS); the prior error rate (PER); the posterior probability (PP) estimated by the RF method; and the number of RF votes for 1000 simulated trees.
Genetic diversity of the box tree moth Overall, we genotyped 896 individuals from 37 sampling sites. Genotyping success was high (98. 85%), and there was a mean of 8. 07 alleles per locus. In this full dataset, six cases of linkage disequilibrium were found after Bonferroni correction. A given pair of loci never displayed significant linkage disequilibrium in more than one population. The fifteen microsatellite markers were thus considered to be independent. After Bonferroni correction, three populations in the native range (N-BE, N-XI, N-HU; Table  1 ) and 12 populations in the invaded range (I-KE, I-GI, I-RH, I-LU, I-LE, I-BR, I-KA, I-NI, I-PA, I-LR, I-SA and I-CV; Table  1 ) had at least one locus that deviated significantly from Hardy-Weinberg proportions. When looking at Hardy-Weinberg proportions for each locus, only the locus BTM22 departed significantly from Hardy-Weinberg equilibrium in 11 populations (1 native population and 10 invasive populations).
It also had a high frequency of null alleles (21%). For the other loci, mean frequencies of null alleles were less than or equal to 10%. We thus performed the analyses with and without BTM22 to determine whether inclusion of the locus introduced bias. Since the results were similar in both cases, we have reported the findings obtained with the full, fifteen-microsatellite dataset. Mean allelic richness was higher in the native populations (4. 23) than in the invasive populations (2. 96) (Table  1 ). For the native populations, the ranges of observed heterozygosity (Ho) and expected heterozygosity (He) were 0. 43-0. 56 and 0. 50-0. 57, respectively. Ho and He were lower in the invasive populations, and their ranges were 0. 33-0. 48 and 0. 28-0. 49, respectively. For the full dataset, the range for pairwise F ST was 0.
The second set of ABC analyses focused on the invasive populations established after 2007, those in Lucca, Italy (I-LU), Budapest, Hungary (I-BU), Solokhaul, Russia (I-SO), and the Bzyb Valley, The fast invasion of Europe by the box tree moth: an additional example coupling multiple… 1 3 Vol. : (0123456789) Georgia (I-BV) (Fig. 2B ). The best scenario for Lucca (I-LU) was that population arose from admixture between the Weil Am Rhein (I-WAR) and Giessen (I-GI) populations (PER: 29. 46% and PP: 0. 47; Table  2 ). That said, there was a difference of less than 50 votes between this scenario and a scenario in which only the Giessen (I-GI) population served as its source. We therefore carried out an ABC-RF analysis using just these two scenarios, and the results supported the admixture event between Results for the full dataset, native range dataset, and invaded range dataset respectively are represented.
The population codes appear below the plots (see Table  1 ). Each individual is represented by a vertical line, and each color represents a particular genetic cluster. In bold is the best K for each STRU CTU RE analysis (full dataset, native range alone and invaded range alone, respectively), as defined by STRU CTU RE Harvester Vol:. (  1234567890 ) Weil Am Rhein (I-WAR) and Giessen (I-GI) populations as the most likely scenario (PER: 9. 13% and PP: 0. 58). Consequently, this scenario was used in subsequent analyses. For Budapest (I-BU), the most likely scenario was that the population had arisen also from admixture between Weil Am Rhein (I-WAR) population and the Giessen (I-GI) population (PER: 33. 56% and PP: 0. 52). However, there was a difference of less than 50 votes between this latter scenario and two other scenarios: admixture occurred between the Shanghai (N-SHA) and Giessen (I-GI) populations or admixture occurred between the Shanghai (N-SHA) and Lucca (I-LU) populations.
We investigate how properties of the Alfvénic turbulence evolve radially and how SIRs and HCS modify its evolution. A superposed-epoch analysis of HCS crossings at 1 AU is carried out using the OMNI dataset and the turbulence properties near the HCS are examined. The paper is organized as follows. In Section 2 we present the setup of the MHD simulations and the numerical results. In Section 3 we present the superposed-epoch analysis of the HCS crossings observed at 1 AU. We then discuss our results in Section 4 and conclude in Section 5.
Selection and structure of HCS For the current study, we use the OMNI dataset, which contains magnetic field and plasma data from multiple spacecraft, including ACE and WIND  (King & Papitashvili 2005). Time resolution of the data is 1 minute, enough for study of MHD turbulence. We analyze data during two 4-year periods: 2000-2003 which is around solar maximum of solar cycle 23, and 2007-2010 which is around solar minimum between solar cycles 23 & 24, as shown by the shaded regions in Figure  6 , which plots monthly sunspot number. The procedure to select HCS crossings is stated as follows. We first calculate the one-day average of B x,GSE , which is equivalently the opposite of radial component of the solar wind magnetic field. Then we find days when its polarity changes and we require that the polarities before and after each polarity-reversal day maintain at least 4 days.
ACKNOWLEDGMENTS The OMNI data were obtained from the  GSFC/SPDF OMNIWeb  interface at https://omniweb. gsfc. nasa. gov. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) EXPANSE at SDSC  through allocation No. TG-AST200031 , which is supported by  National Science Foundation  grant number  ACI-1548562  (Towns et al. 2014). The work was supported by  NASA HERMES DRIVE Science Center  grant No. 80NSSC20K0604.
9, 10 2] [13]  TKV growth rate is accepted as a reasonably likely surrogate endpoint for ADPKD progression trials by the Food and Drug Administration and may provide evidence for accelerated drug approval, though subsequent determination of the intervention's effect on a clinical endpoint, such as eGFR, is necessary if an effect on the reasonably likely surrogate endpoint is demonstrated. 8, 14, 15 he STAGED-PKD trial (Study To Assess Glucosylceramide synthase inhibitor Efficacy in ADPKD; NCT03523728) is an international, multicenter, randomized, double-blind, placebo-controlled study designed to characterize the efficacy, safety, tolerability, and pharmacokinetics of venglustat in patients with rapidly progressing ADPKD. This paper describes the adaptive 2-stage design of STAGED-PKD that used (1) a patient enrichment strategy that identified a rapid progressor population for inclusion in the trial and (2) first-time use of modeling exploring the relationship between TKV and eGFR.
METHODS Enrichment of Patients at Risk of Rapidly Progressing ADPKD Prognostic enrichment was used for the primary analysis population to select patients at risk for rapidly progressing ADPKD based on age (18-50 years), chronic kidney disease stage (G2-3A), and Mayo Imaging Classification (1C-1E). Using prognostic enrichment to define eligibility criteria enables the detection of treatment benefit on TKV and eGFR in the same population, avoiding the need to perform separate trials in patients with early-and late-stage ADPKD. 7] [18]  Patients included in CRISP and HALT-PKD A were used (Table  S1 ) because databases from both were available in the National Institute of Diabetes and Digestive and Kidney Diseases central repositories, and both studies included patient populations similar to the STAGED-PKD target population.
Although measured glomerular filtration rate is the gold standard for measuring kidney function, longitudinal changes in measured glomerular filtration rate and eGFR have been found to be similarly associated with chronic kidney disease-relevant outcomes. 22 Stage 2 secondary endpoints include the annualized rate of change in TKV based on magnetic resonance imaging from baseline to 18 months. Reversible, negative acute treatment effects on eGFR have been observed in ADPKD clinical trials, and total eGFR slope calculated from prerandomization baselines can be misleading as a result. 17, 23 The primary analysis in STAGED-PKD assumes no acute effect, and the slope is based on all data from baseline to month 24; a secondary analysis includes only data after 1 month. Our preclinical data and clinical trials in other indications do not support any hemodynamic effect on eGFR.
The interim analysis will focus on the primary endpoint in stage 1 (annualized rate of change in TKV) and stopping rules will be based on this primary endpoint. Stage 1 data will be analyzed when all patients from stage 1 have been randomized and all data are available up to month 18. Stage 2 analysis will include combined stage 1 and stage 2 data available from baseline to the end of the 24-month treatment period. The primary analysis of stage 1 data will be conducted on all randomized patients in stage 1 (intent-to-treat population). The primary analysis of combined stage 1 and stage 2 data will include all patients with an eGFR ≥45-<90 mL/min/1. 73 m 2 at screening randomized in stage 1 and stage 2 (intent-totreat population). For analysis of the primary endpoint in stage 1 (annualized rate of change in TKV), a linear mixed-effect model will be fitted to the log 10 -transformed TKV, including fixed effects of treatment (venglustat 8 mg, venglustat 15 mg, or placebo), time (as a continuous variable), and treatment × time interaction (Item S12).
Acknowledgements: The datasets from  CRISP  and HALT-PKD A were obtained from the  NIDDK central repository. Editorial support was provided by  Jane Bryant, PhD , and  Zeeba Kabir, PhD  of  LiNK Medical , with funding from  Sanofi. Peer Review: Received February 1, 2022 as a submission to the expedited consideration track with 3 external peer reviews. Direct editorial input from an  Associate Editor , who served as Acting Editor-in-Chief. Accepted in revised form July 21, 2022. The involvement of an Acting Editor-in-Chief was to comply with Kidney Medicine's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
These two regions harbour the most striking sequence differences between drosophila and vertebrates with 6% identical residues only. The N-terminal domain has a 7-stranded twisted beta sheet followed by a 3-helix bundle, and spans residues 30-263 (as numbered in hSuFu). The C-terminal domain has two beta sheets with 6 and 4 strands respectively and two helices; it consists of residues 267-280 and 356-457. The structure of free hSuFu (pdb code 4KM9/4BL8) is extended and the two domains become close to each other when a peptide from Gli is bound to hSuFu (pdb code 4KMD/4BLB), showing a potential tendency of the two domains to move with respect to each other. The drosophila SuFu crystal structure pdb code 4KMA spans residues 13-259 for the N-terminal domain and 265-454 for the C-terminal domain.
3 µm. The cuvette was placed in an airtight, metal sample holder that was positioned in the beamline Peltier temperaturecontrolled chamber, allowing for quick temperature changes with very little evaporation. Spectra were measured between 280 and 170 nm, each final spectrum being averaged from three repeated measurements. Thermal unfolding measurements were performed by averaging three spectra collected in steps of 5 • C between 10 and 90 • C. Buffer spectra were taken in the same conditions for subtraction from the protein spectra. Spectra were then processed for buffer subtraction and scaling using the CDTools program  (Lees et al. , 2004). The melting temperature was evaluated by fitting the curve displaying the CD at 208 nm with a sigmoidal function in Kaleidagraph (Synergy Software), as a function of temperature.
The sample to detector distance is listed in Table  2. The images were processed using the FOXTROT software (https ://www. synchrotron-soleil. fr/en/beamlines/swing) developed at the SOLEIL synchrotron. The curves derived from buffer measurement were averaged and subtracted from the sample curves. Then the Guinier qrange was estimated on the most intense curve and a radius of gyration (Rg) was calculated using a Guinier plot for all subtracted scattering curves, thus producing a plot of Rg and scattered intensity, I 0 , for all frames. Curves from hSuFu and zSuFu were processed with US-SOMO  (Brookes et al. , 2016)  to deconvolute the contribution from oligomers. The curves obtained from dSuFu did not indicate higher order species and consequently did not need deconvolution. The subtracted frames, corresponding to the deconvoluted peak with a stable Rg, were selected to produce the protein scattering curve of scattered intensity as a function of the momentum transfer q = 4πsin(θ)/λ, where 2θ is the diffusion angle and λ is the wavelength.
Molecular modelling of dSuFu The dSuFu crystal structure 4KMA was modified into an open conformation using Rosetta,  (Kaufmann et al. , 2010)  based on the hSuFu structure (pdb ID 4KM9), as a template for relative domain orientation. This model structure, and the crystal structure 4KMA for dSuFu, were used as the initial and final frame, respectively, to guide a linear morphing obtained through the program Chimera  (Pettersen et al. , 2004). Each of the 20 morphing frames has been minimised without any constraint using the AMBER force field as implemented in Chimera  (Pettersen et al. , 2004). The 20 frames have then been analysed through the program Foxs  (Schneidman-Duhovny et al. , 2013)  in order to calculate the corresponding scattering curve and its agreement with experimental data. The frame with the curve having the lowest chi 2 value with respect to the experimental data was retained as the experimental model.
Molecular modelling of hSuFu missing regions hSuFu residue numbering is according to Uniprot entry Q9VG38. Our SAXS data was measured on full-length hSuFu. The crystal structure of hSuFu PDB code 4KM9 was used as a starting point. It shows two ordered domains, but the first 20 aminoacids (IDR1) and 76 residues (IDR2) in the C-terminal domain are disordered. Modeller  (Fiser et al. , 2000; Šali and Blundell, 1993)  was used to generate 20 models with missing residues, amongst which five were chosen to have IDR2 structures with different shapes. The chi 2 of the calculated scattering curve with respect to the experimental curve ranged between 1. 58 and 6. 32 with an average of 3. 8. The five models were then submitted to Dadimodo server https://dadimodo. synchrotron-soleil. fr  (Rudenko et al.