dataesr/scientific-paragraphs-categorization huggingface dataset:
This means that estimating the dynamical system metrics means retrieving information on the predictability of the system (an explicit connection is provided in Faranda and Vaienti  [47] ). Numerical codes to evaluate the dynamical system metrics can be found at https://it. mathworks. com/matlabcentral/fileexchange/95768attractor-local-dimension-and-local-persistence-computation (Matlab version, accessed on 12 January 2022), https://github. com/yrobink/CDSK (Python version, accessed on 12 January 2022), and https://github. com/thaos/dtheta (R version, accessed on 12 January 2022). For the sake of clarity, we define (D AL , θ AL ) and (D SYM-H , θ SYM-H ) as the univariate metrics (i. e. , the dynamical systems metrics derived from the univariate time series), while we define (D AL,SYM-H , θ AL,SYM-H ) as the metrics derived from the bivariate time series consisting of both geomagnetic indices.
Results In the following, we investigate whether and how macaques associate visual and auditory stimuli based on their semantic content, and we characterize the neuronal bases underlying this audio-visual integration. We obtained neural and autonomic data from two macaques using functional magnetic resonance brain imaging and video-based heart rate tracking. We designed six variants of a unique task in which we systematically manipulated the general semantics or meaning of the context as specified by visual information and presented as independent runs in the sessions. Each context, and so each independent run, combined visual stimuli of identical social content with either semantically congruent or incongruent monkey vocalisations presented together with the visual stimuli or not. The semantic context was set by the social content of the visual stimuli presented within a given variant of the task.
Data analysis Data were pre-processed and analysed using AFNI (Cox, 1996), FSL (Jenkinson et al. , 2012; Smith et al. , 2013), SPM software (version SPM12, Wellcome Department of Cognitive Neurology, London, UK, https://www. fil. ion. ucl. ac. uk/spm/software/), JIP analysis toolkit (http://www. nitrc. org/projects/jip) and Workbench (https://www. humanconnectome. org/software/get-connectome-workbench). The T1-weighted and T2-weighted anatomical images were processed according to the HCP pipeline  121, 122  and were normalized into the MY19 Atlas  123. Functional volumes were corrected for head motion and slice time and skull-stripped. They were then linearly realigned on the T2weighted anatomical image with flirt from FSL, the image distortions were corrected using nonlinear warping with JIP.
Behaviour and heart rate During each run of acquisition, videos of the faces of monkeys S and T were recorded in order to track heart rate variations (HRV) as a function of contexts and blocks  45. We focus on heart rate variations between auditory congruent and incongruent stimuli. For each task, we extracted HRV during AC and AI blocs. As changes in cardiac rhythm are slow, analyses were performed over the second half (8 s of each block). This has been done for each run of each task, both monkeys. Because the data were not normally distributed (Kolmogorov-Smirnov Test of Normality), we carried out Friedman tests and non-parametric post hoc tests.
Acknowledgements S. B. H. were funded by the  French National Research Agency (ANR) ANR-16-CE37-0009-01  grant and the  LABEX CORTEX funding  ( ANR-11-LABX-0042 ) from the  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We thank  Fidji Francioly  and  Laurence Boes  for animal care,  Julian Amengual  and  Justine Cléry  for their rich scientific exchanges during data collection and analyses,  Franck Lamberton  and  Danièle Ibarrola  for their MRI methodological support and  Holly Rayson  for her help on visual stimuli collection. We thank  Serge Pinède  for technical assistance on the project.
AIS patient sample All MRI-Genetics Interface Exploration (MRI-GENIE)  5  AIS patients with available high-quality diffusion-weighted imaging (DWI)-derived lesion segmentations  6  and 3 months (60-190 days) modified Rankin Scale (mRS) data were included in this complete case study (c. f. , Supplementary materials for a sample size calculation). MRI-GENIE is a large international collaboration, built upon the infrastructure of the Stroke Generics Network (SiGN). 7 It assembled sociodemographic, clinical, neuroimaging and genetic data of 3300 AIS patients, with the primary aim to facilitate genetic analyses of neuroimaging phenotypes as derived from clinical scans. 5 While MRI-GENIE merged data from 12 international sites overall, analyses here primarily relied on five individual studies that could additionally share functional outcomes (c.
Consequently, we obtained two LNM exemplars, a male-and a female-specific one, for each lesion pattern (conventionally: one LNM exemplar based on connectomes of n = 1000 healthy male and female participants). 14 The preprocessing of sex-specific resting-state fMRI data itself was performed as previously described. Global signal regression was included. After estimating the whole-brain voxel-wise lesion connectivity and Fisher's z-transformation, we summarized the positive and negative t-values, i. e. the 'intensity' of lesion connectivity, within Yeo/Schaefer-defined cortical networks  17, 18  (7 per hemisphere, therefore 14 in total; visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, default mode network). The Yeo-Schaefer atlas was here chosen to match the functional data at hand, as it was derived from functional data, in contrast to the Harvard-Oxford (HO)  8  or JHU atlases,  9  which are based on structural data.
Code and data availability The authors agree to make data available to researchers for the explicit purposes of reproducing the here stated results, pending the permission for data sharing by the Massachusetts General Hospital's institutional review board. The HO and JHU DTI-based white matter atlases are openly available at https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/ Atlases. Analyses were implemented in the Python 3. 7 (mainly employing the packages nilearn  19  and pymc3  20  ). Exemplary code is available under: https://github. com/ AnnaBonkhoff/BMH_functional_outcomes_sex_differences.
Furthermore, the here-derived low-dimensional lesion representations have been shown to be highly reproducible in independent datasets in previous work. 4 The mRS captures a patient's functional status on a very general level only, but is frequently used as a primary outcome measure in large clinical stroke trials. 28 It may thus represent a natural first choice; especially, as ascertaining sex differences in lesion topographies for such a global outcome automatically suggests a meaningful impact on a broad stroke population basis. Future studies are, however, needed to pinpoint the exact brain correlates and brain functions of the detected female-specific effects and to furthermore disentangle the effects of pre-stroke functional states and acute recanalization therapies. For example, a concrete first step could be to conduct prospective stroke trials to systematically evaluate sex-specific treatment decisions, such as more aggressive endovascular reperfusion therapies of the posterior cerebral artery (PCA) occlusions in women.
To cope with these issues, the BSRD database, which compiles bacterial sRNA sequences and their annotations, was created in 2013, which was soon followed by the specialized Staphylococcus regulatory RNA database (SRD) (http://srd. genouest. org/, accessed on 27 June 2022)  [14, 27]. From a list of 894 theoretical sRNA sequences, and after meticulous refining, the SRD authors described 575 sRNA sequences devoid of any redundancies  [14]. In 2016, a transcriptome analysis in a USA300 strain enabled the annotation of 303 sRNAs, whose expressions were subsequently analyzed  [3, 24]. Although the number of sRNAs is steadily increasing, the community agrees that it is difficult to assess whether the identified sequences actually correspond to sRNAs, and especially when no validation is performed downstream of RNA-Seq.
The results were surprising for several reasons: (i) RNAIII was poorly expressed in vivo, (ii) there was no correlation between the RNAIII expression and the bacterial burden, and (iii) there were discordances in the RNAIII target expression. This was the first description of the significance of the host signals in sRNA expression. A new term called the complex regulatory network therefore emerged because a virulence factor could be regulated by other regulators. Since then, in vivo sRNA-expression patterns have been measured with different approaches, such as RT-qPCR and, more recently, RNA-Seq. Because the concept of sRNAs is still recent, there is limited data on the expressions of S. aureus sRNAs in vivo. Overall, RNAIII is the most studied sRNA. A key step in monitoring sRNA expression is to link the expression and severity of the infection.
A large transcriptomic study accompanied by computational analyses provided an extensive repository of S. aureus genes (coding and noncoding) expressed in the strain HG001  [25]. Among all the genes differentially expressed under conditions that mimic the laboratory or infection, 145 putative SigB-dependent genes (86 coding genes and 59 srna genes) were identified with a SigB consensus upstream of their transcription start sites. Conversely, 1269 genes were predicted as SigA-dependent, including 464 putative srna genes identified by the authors, with some of them being described in the SRD database  [14] , or in the bona fide sRNA list proposed by Liu and coauthors  [28]. An analysis and comparison with sRNAs detected by RNA-Seq in the SRD database suggest that only three srna genes contain a SigB binding site, including the bona fide SigB-dependent sRNA RsaA  [18, 28].
MRI pre-processing CEST images were first processed pixel-by-pixel and analyzed using in-house programs developed on MATLAB software (MathWorks Inc. , Natick, MA) used to generate Z-spectra by plotting the longitudinal magnetization as a function of saturation frequency. The gluCEST contrast was isolated using Asymmetrical Magnetization Transfer Ratio (MTRasym)  ( Liu et al. , 2010 )  and was calculated as follows: MTRasym( Δ ) = 100 × (Msat( -Δ ) -Msat( +Δ )) / Msat( -5 ppm), Msat( ±Δ ) being the magnetization acquired with saturation pulse applied at + or -Δ ppm. GluCEST images were calculated with Δ centered at ± 3 ppm. GluCEST image was converted into NIfTI-1 format. Anatomical images were exported as DICOM files then converted into NIfTI-1 format. Then spatial pre-processing was performed using the python module sammba-mri (SmAll MaMmals BrAin MRI; http://sammba-mri.
github. io )  ( Celestine et al. , 2020 )  which, using nipype for pipelining  ( Gorgolewski et al. , 2011 ) , leverages AFNI  ( Cox, 1996 )  for most steps and RATS  ( Oguz et al. , 2014 )  for brain extraction. Anatomical images were mutually registered to create a study template, which was further registered to a high-resolution anatomical mouse lemur template of the functional atlas  ( Garin et al. , 2021 ). Then, gluCEST images were brought into the same space of the mouse lemur template by applying transformation from individual anatomical space to the study template.
Data availability The template and atlas used in this study are available for download in NIfTI-1 format at https://www. nitrc. org/projects/mouselemuratlas. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; https://sammba-mri. github. io ). MR images used in this study can be made available upon request after signing a data sharing agreement required by authors' institution.
Acknowledgments We thank the  France-Alzheimer Association ,  Plan Alzheimer Foundation  and the  French Public Investment Bank "ROMANE " program  for funding this study. The 11. 7T MRI scanner was funded by a grant from  NeurATRIS: A Translational Research Infrastructure for Biotherapies in Neurosciences ( "Investissements d'Avenir  ",  ANR-11-INBS-0011  ). C. G. was financed by the  French Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.
Experiment 2 (Host variation) For each of the ten fly lines, we set up single-sex groups of flies, divided into infected and control, with each fly line-sex-treatment was replicated 11-12 times, for a total of 466 social aggregation assays. Each group consisted of 12 flies systemically infected with P. entomophila (or sterile LB medium for uninfected control groups) using a stainless pin. Following infection, flies were lightly anaesthetised with CO2 and transferred to 55 mm Petri dishes containing agar. After a 30-minute habituation period, photographs were taken. Here we used the median nearest neighbour distance (NND) of each group as a measure of social aggregation  [25, 44]. Individual fly positions in each image were marked in the middle of the fly thorax using Fiji (Fiji Is Just ImageJ), and the nearest neighbour distances between each pair of flies was calculated using the 'NND' plugin within the software Fiji (Schindelin et al, 2012).
Statistical analysis All raw data and analysis R code are available at https://doi. org/10. 5281/zenodo. 6554320. Data from experiment 1 was analysed using linear mixed effects models, separately for each social class (i. e. , within infected, within susceptible, between infected and susceptible). We used the mean pairwise distance as the response factor, pathogen, dose and time as predictor variables, and day of assay as a random effect. For experiment 2, we used a linear mixed effects model with the log10 of median NND as the response variable, line, sex and infection status as predictors, and day of the assay as a random effect. All possible interactions between line, sex and infection status were included. A more detailed description of the analysis can be found in Supplementary material.
Statistical analysis All raw data and analysis code are available at https://doi. org/10. 5281/zenodo. 6554320. All statistical analyses and graphics were carried out and produced in R 4. 0. 5 using the ggplot2  [47] , car  [48] , dplyr  [49] , lme4  [50] , lmerTest  [51] , emmeans  [52]  and DescTools (x) packages. Data from experiment 1 was analysed using linear mixed effects models, one per each class distance (i. e. , within infected, within susceptible, between infected and susceptible). We used the mean pairwise distance as the response factor, pathogen, dose and time as predictor variables, and day of assay as a random effect. All possible interactions between pathogen, dose and time were included. For experiment 2, median NND was log-transformed to achieve a normal distribution of residuals.
Parameter estimation of the gravity model is usually feasible with enough observed data  [44]. That is, the parameters µ, ν, γ, and κ might be estimated in equation 1 if enough pairwise flows (left-hand side of the equation) are known, given the corresponding sizes and distances. A frequent technique (although problematic with too many zero flows) is to take the logarithms on both sides of equation 1 and estimate the parameters and intervals through a regression. However, other estimation techniques, based, for example, on a Poisson regression or other statistical methods, are also possible  [40, 45]. Here, however, we have the opposite situation. The size and location of each metropolitan area are available, and the commuting times between each pair of cities is estimated. Yet, limited information is known concerning the actual number of journeys (or trade, or interactions or financial transactions) for different pairs of cities.
Data Primary roads, highways and trunks were obtained from OpenStreetMap. The latest Africa OpenStreetMap data was downloaded on March 17, 2021 from http://download. geofabrik. de/. The data gives the x, y coordinates as a sequence of vertices of each road segment. Other road types, including paths, secondary and tertiary roads, are not considered since most are part of urban polygons and are not frequently used for city to city mobility. The raw data contains 5. 4 million coordinates that form 221,378 road segments, with 415,231km of roads in the continent. For straight roads, fewer coordinates are needed, but curves are composed with a sequence of multiple vertices. We assume that a straight road connects them for every two consecutive vertices, and we keep the physical distance between the two vertices as the length of that segment, using  [50, 51].
Edges in the urban network The connected and simplified continental network of Africa obtain with our procedure can be described with 7,361 nodes (2,162 are cities and 5,199 are transport nodes, expressing road crossings and bifurcations) and with 9,159 edges. We consider pairs of cities at distances smaller than 24km and compare the network and spatial distances. For cities with a ratio greater than 10 we added that edge. The reasoning behind this is that such small distances can be crossed by alternative methods even if OpenStreetMap data did not identify them. Likely, these roads correspond to secondary roads or other types of infrastructure beyond highways. Although there might be some errors here, where connections that do not exist are introduced, it reduces a more severe issue: seemingly disconnected patches of nearby nodes.
Hardware The proposed SPI setup consists of two Triton TRI050S-QC 5. 0 MP color polarization cameras, manufactured by Lucid Vision Lab. These cameras embed the SONY IMX250 MYR sensor,  33  the properties of which are shown in Fig. 1(a). This is a division-of-focal-plane sensor, in which each pixel senses one spectral channel and one polarization channel. The filter arrays are composed of three spectral channels (c ∈ R; G; B) spatially organized in a Quad-Bayer arrangement  34  and four polarization channels (p ∈ f0;45;90;135g deg) arranged in a Chun pattern  35  [see Fig. 1(b) ]. As shown in Fig. 1 (d), the two cameras are in a stereo configuration. In front of each camera, a lens with a fixed focal length of 12 mm is added. They are configured not to perform any analog/digital gain or white balance corrections, to ensure that the raw data are linear and not corrupted by any preprocessing.
Spectral reconstruction method The spectral properties of surfaces are approximated by a few basis vectors and described by a low-dimensional linear model. 45 Here, we describe the spectral calibration to estimate the reflectance data r of a scene from the vector ρ containing the digital values ρ j. We use the method based on linear regression that links the spectral reflectance r to the camera responses I directly  46  as follows: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 2 ; 1 1 6 ; 1 5 0 r ¼ MI ; ( ) 2 where the matrix M is a reconstruction operator and I is a vector containing the measured intensities by the spectral system. In our case, I is of size ½1 × 6 and contains the camera intensities I s , where s ∈ R bg ; R y ; G bg ; G y ; B bg ; B y. This is equivalent to calculating the first Stokes component  47  from ρ j as follows: I s ¼ ρ s;0 þ ρ s;45 þ ρ s;90 þ ρ s;135 2 : (3) Here, M is estimated using a set of reference spectra, which are previously measured by a spectrometer.
Database We provide a database of 28 scenes with different materials, including plastic, wood, and metal. The scene called "chart" is custom plastic patches manufactured with a 3D printer, with a varying layer height parameter. Figure  8  shows the database in sRGB representation, after the color transform from Sec. 4. The brand logos are removed from the database images. The images are cropped to select the relevant content of the objects. The code and database folder is organized as follows: 1. Data folder: a. Pairs of preprocessed images for each scene, after demosaicking, flat-field correction, and geometrical correction. They are multipage. tif files (one page per polarization angle).
Discussion Our stereo setup offers the possibility to perform reflectometry by fusing observations of the polarization state of light reflected off the surface from two different views. 51 In a binocular stereo configuration, the specular intensity can be different from the two sensors, depending on the shape of the object to be considered and the lightning configuration. As an example, our stereo setup can be configured to optimize the roughness measurement by modifying the vergence, the working distance, or the focal length of the camera lenses. In the database, this is unconstrained because surfaces in the scene are multiple and unknown. But in the case of measurement on samples with a priori knowledge (roughness range, convexity shape, and index of refraction), the setup can be slightly adapted.
Conclusion We developed a snapshot spectropolarimetric acquisition system. It has the advantages of a high spatial resolution and a relatively good spectral reconstruction precision. We designed a full processing pipeline that consists of preprocessing, geometric calibration, spectral calibration, and data transform. We also provide a method to better estimate the reflectance of specular surfaces using a filtering process based on the Stokes components. A database of 28 high-resolution spectropolarimetric images is made available online, along with the code to transform the data. The configuration of the proposed system makes it possible to reconstruct the 3D spectral information using two complementary strategies: (1) the polarization information (the so-called "shape from polarization" method) or (2) the stereo depth computation.
3E ). Their size ranges from less than 100 nm to no more than 1 µm with a mean size of 0. 34 µm two-dimensional equivalent diameter. The mode and standard deviation have been also estimated as respectively equal to 0. 26 and 0. 44 µm using a log-normal best-fit distribution of the size dataset (Fig. 3F ). To better characterize the distribution of these pores with respect to plastic features of quartz grains, we acquired EBSD maps with a very fine step size (1/4 µm) over two areas in grade 4 where numerous micro-pores have been observed, including some of them as small as a few tens of nanometers (Fig. 4A-C ). Most of the micropores align with high-angle (grain) boundaries, but a significant part of them decorates intra-grain boundaries, such as low-angle (sub-grain and inner) boundaries and deformation bands, i.
34/M = 0. 21 below 10 µm to J = 5. 11/M = 0. 46 above 20 µm (Fig. 7B ). We also made sure that a significant number of grains was selected. Indeed, because of density overestimations through calculation of J and M, both indices may be artificially dependent on the number of discrete data  32. Based on olivine datasets, a minimum of 150 grains has been proposed to avoid this artefact and calculate a relevant fabric strength  32  , but this number is questionable  33  and may change depending on the crystal symmetry and intensity of LPO. Hence, we developed a new method to detect the threshold number above which density overestimations are not effective anymore to calculate texture strength. This implies using a map dataset to calculate the J index (or M index ) for several classes of grain size, but for many more grain size categories by considering a cumulative number of grains sorted by ascending grain size (Fig.