dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1996)  and both males and females can be harvested. Nationwide, there are almost 50,000 black grouse hunters, and in Central Finland, the number of grouse hunters averaged around 7500 for the period 2010-2020 (National Resources Institute Finland, 2020 1 ). Typically, within the national and regional limits set each year, a member of a local hunting society is licensed between zero and three black grouse per autumn in an area rented from private or public landowners. In addition, societies may voluntarily agree on other local restrictions based on the census count in the corresponding area, such as no-take sites or years. At state-owned hunting grounds, any citizen approved for hunting can purchase a license that is restricted to a certain number of days. Here, we generated and analysed a large microsatellite dataset comprising 3248 black grouse individuals from 12 sites in Central Finland.
| Field methods and sample collection Blood samples were collected between 2001 and 2007 from a total of 1878 adult black grouse, of which 1065 were males and 813 were females (mean = 157 samples per site, range = 40-308, Tables  S1  and S2 ). This dataset expands on previously published data in  (Lebigre et al. , 2008) , which only included adult males and females captured in 2006. The birds were captured in walk-in traps baited with oats, aged as yearlings or older according to the shape of the outmost primaries  (Helminen, 1963)  and marked with metal and colour rings for future identification. Blood (1-2 ml) was taken with a heparinized syringe from the brachial vein. After centrifugation, the red blood cells were stored in 70% ethanol at 4°C for subsequent DNA analysis. While the vast majority of adult birds were faithful to a particular site, 22 individuals (16 males, four females) were observed and/or captured at two different sites across different years.
The locations where these individuals were first observed were therefore used as their sampling sites for all analyses. In addition to the adults, 1370 chicks were sampled (370 fe-   S1  and S2 ). Nesting sites were located after the lekking season and the hatching date of the chicks was estimated by floating the eggs in warm water as described by  (Lebigre et al. , 2007). The chicks were then captured on the approximate day of hatching (in May/ June) for blood sample collection where possible, and if not, egg shells were collected instead. As multiple chicks were sampled per brood, we sought to minimise any potential biases caused by the inclusion of related individuals or differences in brood size by selecting a single chick at random from each brood for all of our data analyses. We refer to the resulting subset of chicks as 'unrelated chicks' (N = 200).
| Genetic differentiation We investigated overall patterns of genetic differentiation for the combined dataset of adults and unrelated chicks. Pairwise F st values and their statistical significance were calculated based on 1000 bootstraps using the package hierFstat 0. 5-7  (Goudet, 2005). We then used STRUCTURE  (Pritchard et al. , 2000)  to perform Bayesian clustering analysis, which infers the most likely number of genetic clusters (K) from a genetic dataset. We ran STRUCTURE on multiple cores using Parallel Structure 1. 0 in R (Besnier & Glover, 2013). Ten independent runs were implemented for K = 1-12 (equivalent to the number of sites). We used the admixture ancestry model without prior population information and specified an initial burn-in of 10,000 iterations and 10,000 Markov chain Monte Carlo repetitions per run.
| Spatial autocorrelation analysis We used GenAlEx 6. 5  (Peakall & Smouse, 2012)  to test for signatures of sex-specific spatial autocorrelation in the data  (Peakall & Smouse, 2012). This software calculates the spatial autocorrelation coefficient r using two pairwise distance matrices, one containing geographic distances and the other containing squared genetic distances  (Smouse & Peakall, 1999). The autocorrelation coefficient is calculated for a specified number of distance classes and provides a measure of the genetic similarity between pairs of individuals falling within each class. We selected 12 even distance classes of 5 km each. Tests for statistical significance were performed using random permutation and bootstrapping as described by  (Peakall et al. , 2003) , with the number of permutations set to 999 and the number of bootstraps set to 1000.
| Effect of hunting on migration rates Migration rates and directions among the 12 sampling sites were quantified using the program BayesAss edition 3 (BA3)  (Wilson & Rannala, 2003). BA3 uses multilocus genotypes for Bayesian inference and estimates contemporary migration rates between pairs of sites, as well as allele frequencies and inbreeding coefficients. We explored various proposal step lengths for the mixing parameters to ensure an optimal acceptance rate of between 20% and 60%  (Wilson & Rannala, 2003). Subsequently, we analysed migration using 5 × 10 7 interactions, a burn-in of 1 × 10 6 iterations, and an interval between MCMC samples of 500. The mixing parameters for migration rate, allele frequency and inbreeding coefficient were set to 0. 1, 0. 3, and 0. 4 respectively. As recommended by the authors of BA3, we executed five independent runs that were initiated with different seeds, and we compared the posterior mean parameter estimates to ensure concordance.
Chain mixing and convergence were analysed using Tracer 1. 7. 1  (Rambaut et al. , 2018). Next, we used the resulting migration rate estimates to investigate the relationship between hunting status and dispersal. Specifically, we constructed two linear mixed effect models to test for the effects of hunting status on emigration and immigration rates respectively using the R package glmmTMB 1. 1. 2. 3 (Brooks Mollie et al. , 2017). We used the package glmmTMB as the resulting model outperformed identical models constructed with lme4, as evaluated by the 'compare performance' function within the performance 0. 8. 0 package  (Lüdecke et al. , 2021). This function scores model performance based on a number of commonly used information criteria (Akaike's, Bayesian, Watanabe-Akaike and leave-one-out cross-validation) and by taking the mean value of the normalized indices for each model  (Lüdecke et al.
Model homogeneity and the normality of residuals were investigated using the DHARMa package  (Hartig, 2022). The residuals of models were uniform (KS-test: emigration D = 0. 15, p = 0. 22; immigration D = 0. 16, p = 0. 15), no significant under-or overdispersion was detected (DHARMa nonparametric dispersion test: emigration model, dispersion = 0. 71, p = 0. 99; immigration model, dispersion = 0. 86, p = 0. 84), and we did not find any evidence for outliers (DHARMa outlier test: emigration model, p = 1. 00; immigration model, p = 0. 33). The conditional coefficient of determination (R 2 ), i. e. the proportion of variance explained by the entire model  (Barton, 2009; Nakagawa et al. , 2017) , was calculated using the trigamma function from the MuMIn 1. 46. 0 package  (Barton, 2009). Additionally, the intraclass correlation coefficient (ICC), i.
, 2014). Hence, the direct benefits for otherwise non-reproductive males When we modelled the effect of hunting status on migration rates, we took a conservative approach by excluding site-pair combinations with an effective sampling size of under 200. As this strict filtering step reduced the sample size of estimated migration rates considerably, we additionally investigated whether our results uphold when including the unfiltered migration rates that include relatively poor estimates. We found that hunted sites also have significantly higher immigration rates when using the unfiltered migration rates, which provides additional confidence the results described above. Moreover, using the unfiltered migration rates, we also observed lower emigration rates out of hunted sites. This pattern is not supported when filtering the dataset, but can be explained by potential behavioural changes in response to a higher perceived predation risk.
) and a peak in ∆K at K = 2. Membership coefficients for the inferred genetic clusters based on the highest log likelihood values are summarized in Figure S2, where each vertical bar represents a different individual, and the relative proportions of the different colours indicate F I G U R E 2 Patterns of genetic differentiation in black grouse, where (a) shows heatmaps of pairwise F st values based on the combined dataset, with 'NS' denoting non-significant comparisons and hunted sites highlighted in bold. Panels (b) -(d) show correlogram plots of the genetic correlation coefficient (r) as a function of geographic distance for adult males, adult females and unrelated chicks, respectively. The blue shaded areas show permuted 95% confidence intervals and the error bars show bootstrapped 95% confidence intervals.
2 D ). Together, these results clearly highlight the different binding behaviors of the two VASH-SVBP enzyme complexes and the VASH2-SVBP (V2_FL and deadV2_FL) bound to microtubules in the same experimental conditions as in A-C. (D) Representative kymographs. Scale bars: horizontal, 5 µm; vertical, 5 s. Examples of TIRF movies for active VASH1-SVBP and VASH2-SVBP from which kymographs were extracted are presented in supplemental data (Videos 1 and 2). (E-H) Analysis of the binding characteristics. Results for VASH1-SVBP are in blue and for VASH2-SVBP in orange, with plain circles for active enzymes (V1_FL and V2_FL) and empty circles for their catalytically dead versions (deadV1_FL and deadV2_FL). (E) Representation of the microtubule surface covered with VASH-SVBP molecules (white) or not covered (black) during the 45 s of a TIRF movie.
Each horizontal line represents a microtubule (at least 19 microtubules were analyzed). The red value corresponds to the mean covering (in %). (F) Cumulative frequency of the residence times measured in TIRF movies taken during the 30 min following addition of enzyme complexes to microtubules. The mean residence time (τ) is obtained by fitting the curve with a mono-exponential function. (G) Analysis of binding frequency. Each point represents a microtubule. (H) Analysis of diffusion. Each point represents a single molecule, the molecules moving on at least 15 microtubules were analyzed. Data are represented as the median with the interquartile range. Statistical significance was determined using Kruskal-Wallis test. *P < 0. 01, ***P < 0. 005, ****P < 0. 0001. Binding characteristics are also summarized in Table  1.
Comparison of the microtubule-binding mode of VASH1-SVBP and VASH2-SVBP A cryo-EM-derived structure of microtubule-bound VASH1-SVBP was recently solved based on a truncated, catalytically inactive version of VASH1 (Cys169 mutated to serine) and recombinant human tubulin  (Li et al. , 2020 ; PDB ID 6WSL). Superimposition of the microtubule-bound structures of VASH2-SVBP and VASH1-SVBP revealed minimal structural differences between the two enzyme complexes (root-meansquare deviation over 244 Cα atoms of 1. 2 Å). As shown in Fig. 4  A, comparison of the microtubule-binding modes revealed that both enzyme complexes contact two adjacent α-tubulins across neighboring protofilaments simultaneously and position their catalytic site directly above the H12 helix of one of the two α-tubulin subunits.
TIRF in vitro assay Images were recorded within the first 30 min following addition of the assay-mix solution (see above) on an inverted microscope (Eclipse Ti, Nikon) equipped with a Perfect Focused System, a CFI Apochromat TIRF 100×/1. 49 N. A oil immersion objective (Nikon), a warm stage controller (Linkam Scientific) and a Technicoplast chamber to maintain the temperature, an objective heater (OkoLab), an iLas2 TIRF system (Roper Scientific), and a sCMOS camera (Prime95B, Photometrics) controlled by MetaMorph software  (version 7. 10. 3, Molecular Devices). For dual view imaging, an OptoSplit II bypass system (Cairn Research) was used as image splitter and illumination was provided by 488-and 561-nm lasers (150 and 50 mW, respectively). Temperature was maintained at 35°C for all imaging purposes.
, 2016)  and secondary antibodies were anti-rat coupled to Alexa Fluor 488  (Jackson ImmunoResearch,  and antirabbit coupled to Cyanine 3  Jackson ImmunoResearch) , both diluted to 1:500. Images were obtained using a LEICA DMI600/ROPER microscope controlled by Metamorph Video software using the same illumination conditions. For each condition at least three independent experiments with different protein preparations were done.
Cryo-EM data collection and image processing Movies were recorded on a Titan Krios at 300 keV (Thermo Fisher Scientific), with a GIF Quantum LS Imaging filter (20 eV slit width) and a K2 Summit electron counting direct detection camera (Gatan) at a magnification of 47,260 ×, resulting in a pixel size of 1. 058 Å, using SerialEM  (Mastronarde, 2005). The defocus varied between -0. 6 and -2. 2 µm. 3,345 movies were recorded with a total dose of 65 e-/ Å2 per movie (9 s exposure in total, 0. 2 s per frame, 45 frames in total). The dose rate was ∼7. 2 e-/ Å2 per second (∼1. 4 e-/ Å2 per frame). For image processing, the software Relion 3. 0. 8 (Scheres, 2012)  was used. The protocols described in  Cook et al. (2020)  and  Debs et al. (2020)  were followed with some adaptations. In brief, the movies were driftcorrected and dose-weighted using MotionCor2  (Zheng et al.
, 2017)  and the contrast transfer function parameters were estimated from the motion-corrected electron micrographs using Gctf  (Zhang, 2016). Afterwards, microtubule sections were manually picked from the HeLa tubulin data set. Boxes with a size of 600 pixels, re-scaled to 150 pixels with inter-box distance of 82 Å, were extracted. Superparticles were created and processed as previously described  (Cook et al. , 2020). As the microtubule decoration by VASH2-SVBP was incomplete, we classified the decorated and undecorated protofilaments to improve the signal-to-noise ratio of the VASH2-SVBP density. For this, we expanded the symmetry of the particles depending on the number of protofilaments. We focused on one VASH2-SVBP particle and created a mask including regions of the protofilament and VASH2-SVBP.
Model building and refinement An initial structural model of the microtubule bound VASH2-SVBP complex was generated in Chimera by combining the GMPCPP-stabilized microtubule  (Zhang, 2016 ; PDB ID 3JAL) with the crystal structure of the human VASH2-SVBP complex (PDB ID 6QBY). The models were imported into Chimera  (Pettersen et al. , 2004) , and the Fit-in-Map function was used to perform a rigid-body fit of one VASH2-SVBP complex structure and two laterally aligned αβ-tubulin heterodimers into the final cryo-EM map. The models were then combined and refined in Coot  (Emsley and Cowtan, 2004)  using "Set Geman-McClure alpha 0. 01" while all molecule self-restraints were set to 5. 0 in the ProSMART module  (Nicholls et al. , 2014). The model was then further refined in Coot using the "Chain Refine" command.
Data analysis of immunofluorescence For measurement of Tyr and deTyr tubulin intensities, FIJI software was used. A mask was created by addition of the two channels, and each object in the image was saved as a region of interest. The mean intensity of regions of interest was measured on each channel after background subtraction. In each experiment, the values were normalized to the median of the control without enzymes. Data analysis was then performed with GraphPad Prism 7. Statistical analyses were obtained using nonparametric one-way ANOVA assuming non-paired and non-Gaussian distribution (Kruskal-Wallis test). Quantification of the colocalization of VASH2 proteins with microtubules in cells was performed as in  Wang et al. (2019). Briefly, for each cell a cytoplasmic region excluding the perinuclear region was selected.
Each point represents a microtubule (at least 150 microtubules were analyzed). Data are represented as the median with the interquartile range. (E-H) Effect of decreasing tyrosinated tubulin. TIRF microscopy study of single molecules of sfGFP-tagged VASH1-SVBP (V1_FL and deadV1_FL, 50 pM) and VASH2-SVBP (V2_FL and deadV2_FL, 50 pM) bound to Taxol-stabilized Tyr-MTs or deTyr-MTs. Binding characteristics are summarized in Table 1. (E and G) Representative kymographs. Scale bars: horizontal, 5 µm; vertical, 5 s. (F and H) Analysis of binding frequency. Each point represents a microtubule. At least 15 microtubules were analyzed. Data are represented as the median with the interquartile range. Statistical significance was determined using Kruskal-Wallis test. **P < 0. 009, ****P < 0. 0001.
Acknowledgments We would like to thank  Isabelle Jacquier  and  Lisa De Macedo  of  Grenoble Institute Neuroscience (GIN ) for technical help in preparation of proteins,  Yasmina Saoudi  of  Photonic Imaging Center of GIN  for helping us with TIRF microscopy, and  Benoît Decherf  for the design of the FIJI software plugin for detection of single-molecule tracks in the kymographs of TIRF experiments. We thank  Yves Goldberg  for the valuable discussion on statistics. Modified plasmid pETDUet-1 was a generous gift from  Sandra Jeudy  ( IGS, Marseille ). We thank the  Electron Microscopy Facility  of the  Paul Scherrer Institute  and the  BioEM Lab of the Biozentrum, University of Basel , for excellent access to cryoelectron microscopes. We are indebted to  Carolyn Moores  for initial help with the image analysis procedure.
This work was supported by the  Leducq Foundation , research grant  n°20CVD01  (to M. -J. Moutin), by  Agence National de la Recherche (ANR) , grant  SPEED-Y n°ANR-20-CE16-0021  (to  M. -J. Moutin ) and grant n°ANR- 20-CE13-0011  (to C. Janke), by Appel à projets 2019  CNRS/INSERM Défi Santé Numérique  (to V. Stoppin-Mellet), by  France Alzheimer  grant  2023  (to M. M. Magiera), by  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS) , and  University Grenoble Alpes , and by the  Swiss National Science Foundation , grant  n°310030_192566  (to M. O. Steinmetz). Part of the salaries of  C. Sanyal  and  S. Ramirez-Rios  were from a collaborative program between Servier laboratories and  M. -J. Moutin  team. This work was supported by the  Photonic Imaging Center of GIN (University Grenoble Alpes -INSERM   U1216 ), which is part of the  Imagerie des Sciences du Vivant core facility  and certified by the  Infrastructures en Biologie Santé et Agronomie  label.
One of the main challenges lies in extracting the 3D surfaces from CT data, which are prone to artefacts such as beam-hardening, scatter radiation and partial volume effect  [37]. The forward model in analytic X-ray CT reconstruction is usually based on the Radon transform, which amounts to assuming a monochromatic Beer-Lambert attenuation law and therefore translates into discrepancies in the reconstructed CT data. Sophisticated image processing techniques are therefore necessary to accurately compute the surface  [12] , and many steps are usually required to extract CAD models from 3D data. When the input data is 3D point clouds of object surfaces, up to 11 phases are needed  [18] , including segmentation, region classification and surface fitting, and the CAD surfaces to be modelled are often engineering-based like planes and quadrics.
Analytic methods based on ray-tracing provide a fast alternative as they rely on a simple forward model  [7]. They are particularly suited for GPU implementations  [30]  but sometimes lack realism to reproduce observed CT artefacts. We have developed and validated an open source library (gVirtualXRay) to perform the attenuation calculations on GPU using OpenGL  [29, 32, 33]. gVirtualXRay is written in C++ and GLSL. Wrappers to other popular languages such as Python, R, Java, are provided. We used Python in this study. Although this choice reduces the speed of the framework, it allows us to prototype it as a proof-of-concept. The beam polychromaticity, the detector response, the phase fringes and the noise have been added to the forward model to simulate realistic projections. Both the geometrical and physical properties of the sample are considered as unknown parameters to tune, which is performed as an optimisation problem.
Our code is open source and we produced a Jupyter notebook as a step-by-step tutorial. We show that realistic X-ray simulation is crucial to get an accurate estimation of the CAD model and that manual segmentation of CT data may be prone to bias. We also demonstrate that the choice of objective function is important to produce high fidelity results. The data is analysed using interactive information visualisation (InfoVis). Our visualisations are also available online. The paper is organised as follows. Section 2 provides some context about artefacts in computed tomography. Section 3 describes the realistic forward model and our registration pipeline. We quantitatively and visually assess our results in Section 4, and we also compare the performance of the framework on nodes with GPU on a supercomputer and a desktop computer.
Materials & Methods The research presented here is available for download  [32] : (i) some of the sinograms acquired at ESRF are provided, (ii) the code of the X-ray simulation package is open source  [29] , (iii) our registration code is provided on GitHub  [31] , and as supplementary material with this paper, (iv) the code of our visualisations is provided in the same locations and the corresponding HTML pages can be consulted on gVirtualXRay's website  [29] , and (v) a tutorial is provided as a Jupyter Notebook as supplementary material. The steps in the sub-sections below refer to the step numbers in the notebook. We first present the issues to be addressed, namely (i) how to enhance the Beer-Lambert law to simulate realistic X-ray projections including phase contrast, Poisson noise, impulse response and beam hardening, and (ii) how to select the optimisation algorithm and the objective function.
These test registrations will be performed on GPU nodes of a supercomputer such as Supercomputing Wales (SCW, the supercomputer accessible by Welsh universities, https://www. supercomputing. wales/). For the best performance, we use a single GPU node per registration. Otherwise hardware resources might be shared with other users. A statistical analysis and interactive visualisations will be used to explore the large amount of data that will be generated. The aims are (i) to identify which objective function(s) is significantly better than others and (ii) to assess, again, variability of the results. Conducting 350 registrations on a desktop computer may not be feasible. However, we will perform 25 registrations with the objective functions that are identified as the 'best' ones. This way we will compare the relative computational performance between a GPU node on the supercomputer and a regular desktop computer, which is just the office PC of one of the co-authors.
A user study will be conducted. Suitable volunteers will be recruited online. To contribute to the user study, volunteers must be familiar with ImageJ or another image analysis software that is equivalent. The reference image is provided in full resolution and with single-precision floating point accuracy. We prepared an online form with instructions and a questionnaire (see supplementary materials). The volunteers are asked: 1. To make sure they record the time when they started the experiment. 2. To find the structure that corresponds to the Ti90Al6V4 matrix in the image and measure or estimate its: (a) Width, (b) Height, (c) Centre of this structure, (d) Rotation angle. In addition they are asked to evaluate the difficulty of these tasks using a Likert scale. 3. Find in the image the structures that corresponds to three pre-selected fibres.