dataesr/scientific-paragraphs-categorization huggingface dataset:
A SWS normally corridor was calculated as the range [5 th -95 th percentile] for each ligament. The determination of the sample size indicates that 38 samples are required to have a 90% chance of correctly rejecting the null hypothesis of r = 0 at the 0. 05 significance level assuming a linear correlation of 0. 5 (moderate correlation) between spring ligament and posterior tibial tendon. Statistical analysis was performed using the R Language and Environment for Statistical Computing version 3. 6. 3 (R Core Team, version 2020, Vienna, Austria).
Impact of demographic characteristics No demographic data (age, sex, dominant side, height) had an impact on spring ligament stiffness as assessed by SWE. PTT SWS decreased with age (ρ = -0. 34; p = 0. 03) and with male gender (p = 0. 03). The impact of demographics on spring ligament complex stiffness is reported in Table  2. Flat-foot morphology was associated with a significant increase in spring ligament stiffness in the 20° valgus position (p = 0. 01), but had no impact on PTT. Hindfoot type had no impact on variation in PTT and spring ligament stiffness. The impact of foot morphological data on ligament complex stiffness is presented in Table  3. Finally, there was a statistical correlation in the stiffness of the spring ligament and the PTT in varus 30° (ρ = 0. 43; p = 0. 006). There was no correlation in stiffness between the two structures in other positions (Table  4 ).
In this paper, we use the publicly available Monte-Carlo code CRPropa  (Batista et al. 2016; Merten et al. 2017; Alves Batista et al. 2022)  to describe the 3D transport in M 51. While originally written to describe the extragalactic transport of hadronic cosmic rays via the solution of the equation of motion, CRPropa has been extended to a second propagation method, that is solving the transport equation via the approach of stochastic differential equations (SDEs). The conversion of a Fokker-Planck equation into an SDE is useful here, as the particle densities are derived from the pseudo-particle trajectories. This way, the equation of motion approach and the transport equation can be treated in one framework since both work with individual particle trajectories   (Merten et al. 2017). This approach also allows for both continuous and catastrophic losses for the production of full particle showers in the interactions that can be followed up on, etc.
Acknowledgements. J. D. , P. R. , and J. B. T. acknowledge the support from the  Deutsche Forschungsgemeinschaft, DFG  via the Collaborative Research Center SFB1491 Cosmic Interacting Matters -From Source to Signal. Part of this work was supported by the  DFG  project number  Ts 17/2-1. This work was made possible by the following software packages: CRPropa (Batista et al. 2016;Merten et al. 2017; Alves Batista  et al. 2022), dask (Rocklin 2015), ipython (Pérez & Granger 2007), matplotlib (Hunter 2007), numpy (Harris et al. 2020) and pandas (Wes McKinney 2010). A111, page 6 of 8  J. Dörner  et al. : Cosmic-ray electron transport in the galaxy M 51.
2003). In New Zealand, fieldwork was carried out in 2015 on South East Island (Hokorereoro/Rangatira: 44° 20′ S, 176° 10′ W). We collected samples from GBSP and Whitefaced Storm-petrels (WFSP) Pelagodroma marina from 25 November to 6 December 2015 (late incubation/hatching period). These data have previously been compared to other sympatric petrel species  (Thébault et al. 2021 ). In the Pacific Ocean, we sampled Black Storm-petrels (BLSP) Hydrobates melania, Leach's Storm-petrels (LESP) Hydrobates leucorhous and Ashy Storm-petrels (ASSP) Hydrobates homochroa on Coronado Island (32°27′ N, 117°18′ W) from 11 to 15 July 2018 and additionally, Ashy Storm-petrels at Todos Santos Sur Island (31°48′ N, 116°47′ W) from 16 to 18 July 2018 (during the chick-rearing season).
Compound-Specific Isotope Analyses of Amino Acids (CSIA-AA) CSIA-AA can be used to provide a baseline-independent estimate of the trophic position of marine organisms from temporally and spatially variable environments. Therefore, it is especially suitable for this dataset spanning samples from different ocean basins and latitudes and thus, with different bulk stable isotope baselines. CSIA-AA were performed at the UC Davis Stable Isotope facility (USA), as described previously  (Quillfeldt & Masello 2020). Trophic positions (TP) were calculated from nitrogen stable isotope values of glutamic acid (Glx) and phenylalanine (Phe), using a stepwise trophic discrimination factor (multi-TDF Glx-Phe , with the equations: 13 C or 15 N = R sample R standard -1 × 10 3 For a detailed discussion, see  Quillfeldt and Masello (2020).
Distribution Data Distribution data were downloaded from GBIF. org on 08 October 2021, and plots for Fig. 1  from OBIS-SEAMAP (https:// mapper. obis. org/,  Halpin et al. 2009). GBIF data were summarized monthly (Fig. 2 ) and seasonally: the winter season was defined as June to August for southern hemisphere species, and December to February for northern hemisphere species. For WFSP, only southern hemisphere records were counted to exclude the northern hemisphere populations of this species.
Data Analyses Data were analysed and visualized in R version 4. 1. 0. We used Shapiro tests and qq plots to test normality. Stable isotope and Hg values differed significantly from normal distribution. Therefore, univariate statistics on these were carried out with nonparametric tests, and the data were transformed using transform Tukey in the R package rcompanion before carrying out multivariate statistics such as linear models. A comparison of the model outputs did not show any major differences between models using transformed and untransformed data. To enhance readability, effect plots are thus given from models of untransformed data, i. e. showing the actual scale of the data. Means are given with standard deviations. To measure effect size, we included eta squared values (η 2 ), obtained with the EtaSq function in the R package DescTools.
The present data from three storm-petrel species from the Eastern Pacific Ocean show a comparable level of Hg contamination to species in the Northern Atlantic  (Pollet et al. 2022, Table   S1a ). In feathers, elevated Hg concentrations were only observed in the BBSP population from King George Island/25 de Mayo Island (Antarctica), suggesting that they accumulate Hg during their yearly cycle, which includes winter months in tropical waters of the Atlantic or Pacific Ocean. In comparison, BBSP from the Kerguelen Islands had lower Hg values in feathers, suggesting that they had not accumulated such high Hg stores throughout their annual migration, which is probably restricted to the Indian Ocean. A comparison of Hg concentrations of individual feathers (subsamples of N = 6 per colony) suggested that feathers sampled from individual BBSP were relatively similar to each other (repeatability 81%) in the King George Island/25 de Mayo Island population.
Acknowledgements We thank  Justine Thébault  for collecting samples in the field and preparing samples for analyses, and  Yvonne Schumm ,  Phillip Krämer  and  Miguel Corrales Sauceda  for help with fieldwork. We appreciate the support of  C. Churlaud  and  M. Brault-Favrou  from the  Plateforme Analyses Elémentaires of the LIENSs laboratory during Hg analyses , and of  G. Guillou  from the  Plateforme Analyses Isotopiques of the LIENSs laboratory  for running stable isotope analyses. We gratefully acknowledge helpful comments from two anonymous reviewers. A poster with some preliminary results (restricted to Wilson's Storm-petrel and Black-bellied Storm-petrels) was presented at the meeting of the German Society for Polar Research in Potsdam,  01-05 May 2022  (https:// epic. awi. de/ id/ eprint/ 55943/1/ BzPM_  0762_ 2022.
The road network is assumed to impede brown bear movements, like it has been shown for other European populations (e. g. Slovakia:  Skuban et al. , 2017 , Cantabrian: Mateo-Sánchez et al. , 2014). However, the degree of avoidance to roads and the population-level consequences have never been quantified. We used transnational non-invasive genetic sampling data across France and Spain to draw population-level inferences about connectivity. To do so, we accounted for imperfect and heterogeneous detections by building a SCR model with non-invasive genetic sampling data obtained from the structured and opportunistic monitoring of the population.
Opportunistic monitoring We also used opportunistic detection data consisting in all the genetic samples collected following a depredation event on livestock or beehives. We did not consider other opportunistic signs of presence, because we were not able to define a corresponding search effort. In order to match the study period defined for the structured monitoring, we only considered those collected from May to November. The trap array can be assimilated to the distribution of livestock in the Pyrenees. It is recommended that aggregated traps be spaced 1. 5 σ apart (σ is the scale parameter estimated in spatial capture-recapture model), or approximately 5km for bears in the Pyrenees  (Milleret et al. , 2018). To match opportunistic detection data to a flock, we defined a 5x5 km grid and consider a cell to be active if at least one depredation event occurred between 2010 and 2020.
Statistical modelling: Spatial capture-recapture model We estimated bear density and landscape connectivity simultaneously with spatial capture-recapture (SCR) models. These models combine 1) a detection process to account for the imperfect detection of individuals from individual encounter history and 2) a spatial point process to model the distribution of individuals in space through the estimation of their activity center's location  (Efford, 2004; Royle et al. , 2013). Here, we used a multi-session sex-structured SCR model  (Sutherland et al. , 2019). This model provides a framework to account for variation in densities between males and females and between different monitoring sessions. First, we fitted the SCR model to the structured survey data and accounted for heterogeneity in detection.
Second, we integrated the opportunistic data, and evaluated the added value of data integration to improve inference about spatial heterogeneity in density, detection and resistance. Finally, we quantified the impact of roads on the shape of brown bear home range, using non-Euclidean distance SCR models  (Sutherland et al. , 2015). Parameters were estimated by maximum likelihood implemented in R using the package oSCR  (Sutherland et al. , 2019).
We distinguished cell use (g) from imperfect detection (y)  (Sutherland et al. 2015). Given an activity center location, we computed the 95% kernel of the utilization distribution to estimate the home range size in a particular landscape context. The potential connectivity, denoted PC, represents the total utilization assuming that one individual activity center is located in each cell of the habitat. In other word, PC illustrates the number of individuals reaching a cell given that individuals are evenly distributed in the landscape. In the oSCR package, the matrix recording the least-cost path between all cells ν, is computed by the Dijkstra algorithm implemented in the gdistance package  (van Etten, 2012). Realized densities and potential connectivity can be combined to obtain a realized map of connectivity called density weighted connectivity (DWC) for males and females since their home range size are estimated separately  (Morin et al.
Spatial variation in density When considering only detection data from the structured monitoring, none of the models that accommodated spatial covariates on density performed better than the null model (Table  A4. A). In contrast, after integrating the opportunistic detection data, the model with an additive effect of ruggedness and human density was considered as the best model (Table  A4. B). Bear density was negatively correlated with human density (Figure  2D ), and positively correlated with ruggedness (Figure  2C ). When there was on average one inhabitant/km 2 over the 200km 2 surrounding, brown bears density was estimated maximum at 0. 015 (CI95 = [0. 007,0. 029]) and 0. 009 (CI95 = [0. 005,0. 018]) respectively for females and males (Figure  A5 ).
Using Machine Learning to Provide a New Empirical Perspective on Data about Eaters Profiles Advances in data analyses using Machine Learning (ML) offer exciting potential for analyzing body image disturbances datasets  [60]. ML can be more powerful than traditional regression methods to unravel and clarify relationships between interdependent variables such as eating attitudes and behaviors  [61, 62]. As shown in the literature review from Fardouly et al. [60] , there are less than a dozen publications using ML to analyze self-reported data on disordered eating and eating disorders. ML offers robust solutions for investigating complex multivariate relationships using innovative mathematical solutions. Haynos et al. [61]  found that ML approaches were superior to regressions in predicting the outcome of eating disorders in longitudinal studies.
However, their approach is very sensitive to the nature of the data, with small changes in the data having a large impact on the results. Their ML method could be improved by pruning (removing) variables from the algorithm, one at a time, to achieve a stable solution where participants remain in their respective clusters despite changes in the dataset, as suggested by Faghihi et al. 's  [63]  Causal Reasoning work. Such innovative analytical techniques of an iterative ML approach with pruning of each measure to validate cluster models built with more computationally intensive methods than traditional linear regressionbased techniques could provide an opportunity to improve the results from Monthuy-Blanc et al. [29]  and refine the clusters found using a more traditional analytical approach.
Sample This study used the same initial sample and inclusion criteria as the study by Monthuy-Blanc et al. [29]. A total of 468 participants gave free and informed consent to participate and to have their data used anonymously. Only participants without missing data on the total score once computed according to recommendations were retained for analyses for variables used to identify clusters, resulting in a final sample of 317 participants. Missing data can lead to a lack of precision in the statistical analysis and result in building a biased ML model, leading to incorrect results if the missing values are not handled properly. Although K-nearest and Naïve Bayes can handle data with missing values, most ML algorithms fail when the dataset contains missing values  [64]. The final sample retained for ML analysis consisted of 88.
In our sample, Cronbach's alpha was 0. 93, indicating excellent internal consistency. The restraint subscale of the Eating Disorder Examination Questionnaire (EDE-Q)  [69, 70]  was used to assess attitudes and behaviors related to restraint. Items are scored on a 7-point scale and a score of ≥4 has been identified as a clinical cut-off  [69]. In the current study, Cronbach's alpha for the restraint subscale was good at 0. 84. In order to interpret the characteristics of the eaters profiles identified in the present study, the average scores of body perceptions and eating attitudes and behaviors measures were interpreted in the discussion by referring to information reported in the validation studies of the aforementioned questionnaires. The normative data for body image perceptions and eating attitudes and behaviors measures are reported hereafter.
Procedure This study was conducted on the same dataset as the Monthuy-Blanc et al. 's  [29]  study, so the same procedure and choice of measures were used. This cross-sectional study took place from 29 May to 1 September 2020. In Canada, there was a slight reduction in COVID-19-related regulations during the summer of 2020. However, in August 2020, Canada experienced a second wave of the pandemic, and in early October 2020, most regions entered the maximum alert phase, resulting in the closure of non-essential businesses, travel restrictions, bans on gathering, evening curfews, mandatory remote work, and mandatory online schooling  [71]. The online survey was distributed through various platforms, including websites, social media, and community listservs for students and professionals. This study was approved by the Ethical Committee of the University of Quebec in Trois-Rivières (CER-20-266-10.
Statistical Analyses In the current study, a ML approach to cluster analysis was applied. The analyses were performed in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profiles using a procedure inspired by Faghihi et al. 's  [63]  Causal Reasoning approach, where the validation of the final solution is based on the behavior of the system in the presence and absence of each variable. The method is consistent with Ren et al. [62]  approach of first clustering the dataset using ML, then pruning (removing) each variable one at a time, and reclustering the dataset to log and compare participant movements between clusters. It adds to Ren et al. [62]  by testing the stability of the clustering model and by applying both linear and nonlinear mathematical models.
Validation of the 7-Cluter Model The model was validated using correlations between measures assessed using a clustermap from Python's Seaborn library. A clustermap uses hierarchical clustering algorithms to cluster data by similarity and plots a heatmap matrix. Figure  1  shows the similarity of behavior between variables. For example, the variables "bulimia" (i. e. , EDI-B) and "restraint" (i. e. , EDE-Q-R) shared more similarities and were more similar to the variable "body dissatisfaction" (i. e. , EDI-BD). Figure  1  also shows that the variable "reliance on hunger and satiety cues" (i. e. , IES-HSC) shared strong similarities with "eating for physical reasons rather than emotional reasons" (i. e. , IES-EPR) and both were similar to "unconditional permission to eat" (i. e. , IES-UPE).
Acknowledgments: Special thanks go to the foundations of the  University Institute of Mental Health of Montreal ,  RBC Royal Bank , and the Lemaire family, and the  Social Sciences and Humanities Research Council of Canada  and  Takeda , whose patronage helped finance the completion of this study. Special thanks to participants during the pandemic context of COVID-19 for their time and commitment, and to  Marlene Menzies  for comments and revisions on the text.
Whether other SARS-CoV-2 host entry factors, such as the cell coreceptor neuropilin-1  56  , which is internalized via dynamin-independent endocytosis  62, 63  , could contribute to dynamin-independent virus entry will require further studies. Currently, the dominant SARS-CoV-2 variant is omicron. Recent evidence has shown that this variant favours endosomal entry rather than direct fusion at the PM  16. The implication of these findings is that variants such as omicron, which still causes thousands of deaths worldwide every month, would strongly relay on endocytosis to access the endo/lysosomal compartment. Based on our results, we propose that dynamin inhibitors could have beneficial therapeutic potential in combination with other antivirals. (GNT1155794) to F. A. M. P. Y. L is supported by the Agence Nationale de la Recherche (ANR) funding (grant numbers ANR-21-CE11-0012 and ANR-22-CE15-0034).
Work in the Greber lab was supported by grants from Schweizerischer Nationalfonds (Swiss National Science Foundation 31003A_179256 and 310030_212802). We are grateful to the Jane and Aatos Erkko Foundation for support to M. V. R. and the Academy of Finland under award numbers 330896 to M. V. R. ) and 332615 to E. M. J. M. was supported by core funding to MRC Laboratory for Molecular Cell Biology at University College London (MC_UU_00012/7). High-throughput imaging was performed at the Light Microscopy Unit of the University of Helsinki. The funders had no role in the study design, data collection, or interpretation. We declare no competing interests.
Confocal imaging and colocalization analysis Samples on coverslips (i. e. internalization of SARS-CoV-2 soluble trimeric spike) were imaged with a W1 Yokogawa Spinning Disk confocal microscope using a 60x oil immersion objective. Images were acquired with a Hamamatsu ORCA-Flash4. 0 V2 sCMOS (photon conversion factor 0. 46) camera. Colocalization was performed with Imaris software to quantify the fraction of internalized spike particles colocalizing with the endocytic markers Rab5-EGFP and Tf-A647. For each three-dimensional confocal stack, spike particles and endocytic vesicles were automatically detected using the Imaris spot colocalization tool as described  26.
no. H3569) and secondary antibodies conjugated to a different fluorophore, Alexa-647 (1:500 dilution of goat anti rabbit Alexa 647; Thermo Fisher Scientific, cat. no. A32733), for 2 h at room temperature. After 3 washes in PBS, cells were imaged by spinning disk confocal microscopy using a followed by high content spinning disc confocal imaging using a Yokogawa CQ1 microscope equipped with a 40x air objective. Automated image analysis to detect particles stained by both fluorophores (i. e. particles at the cell surface) and particles stained only by the second fluorophore (i. e. particles inside the cell) was performed with the Yokogawa Cell Path Finder software. Tracks shorter than eight frames were excluded from the analysis to minimize nonspecific background. The Log10D immobile and mobile fraction distributions were calculated as previously described  72  , setting the displacement threshold to 0.
Acknowledgments We thank the  University of Helsinki Graduate Program in Microbiology and Biotechnology  for supporting R. O. The  Academy of Finland  Research Grants  335527  (G. B), the  Jane and Aatos Erkko Foundation  (O. P. V. )  Helsinki University Hospital  funds  TYH2021343  (O. P. V. ),  European Union 's  Horizon Europe Research and Innovation Program  grant  101057553  (G. B. , O. P. V. ),  The University of Queensland Amplify Fellowship  (M. J. ), the  Australian National Health and Medical Research council  (grant  2010917  G. B. and F. A. M. ). This work was also supported by  Australian Research Council  ( ARC)   Discovery Early Career Researcher Award  ( DE190100565 ) and  The University of Queensland Amplify  fellowship to M. J. The work was supported by an  ARC   Linkage Infrastructure Equipment and Facilities grant  ( LE130100078 ) and an  National Health and Medical Research Council (NHMRC)   Senior Research Fellowship.
We present PureCake, a mechanically-verified compiler for PureLang, a lazy, purely functional programming language with monadic effects. PureLang syntax is Haskell-like and indentation-sensitive, and its constraintbased Hindley-Milner type system guarantees safe execution. We derive sound equational reasoning principles over its operational semantics, dramatically simplifying some proofs. We prove end-to-end correctness for the compilation of PureLang down to machine code-the first such result for any lazy language-by targeting CakeML and composing with its verified compiler. Multiple optimisation passes are necessary to handle realistic lazy idioms effectively. We develop PureCake entirely within the HOL4 interactive theorem prover.
PURELANG 3. 1 Features Figure  1  showcases a small PureLang program accepting an integer on the command line and printing the first numbers of the factorial sequence. PureLang syntax is indentation-sensitive and inspired by Haskell; GHC accepts this program with minimal tweaks. Features common to functional languages are supported: first-class functions (map on line 12), general recursion (factA on lines 6-9), algebraic data types and pattern matching ([]/h:t on lines  [16] [17]. PureLang borrows several other features from Haskell. Top-level declarations are mutually recursive and can be reordered freely. Evaluation is call-by-need: expressions are computed only as deeply as they are inspected and never recomputed, so infinite data structures can be constructed (e. g. , numbers on lines 1-4).