dataesr/scientific-paragraphs-categorization huggingface dataset:
The last step involves storing the average Λ : , / : of the learning dataset. The test phase is where the decision whether the clusters, taken at a certain threshold t, are considered foreign to the GRF, is made, according to a given pval. For all the images A 6 in the test set: • Gaussianize A 6 so that it can be approximated by a realization of a GRF. The result is a new image A ; , whose histogram is Gaussian with identical variance to that of A 6. • For a given list of thresholds Q = (R < , R * , … , R = ): § Binarize A ; according to the threshold R > § Find the list of connected components in the binary image resulted from thresholding and for every (labeled ) connected component )U < , U * , … , U 2 -: o Compute ( $ (Eq. 1), using the learnt model parameter / :. If its value is lower than a chosen pval, the cluster is considered a foreign element to a GRF.
27 9:/pixel; 70 images/variant for normal FN (N) and 65 images/variant for tumor-like FN networks) as follows. (1) For comparison of (N) vs (T) FN networks, we considered 50 (N) samples as the learning dataset, 20 (N) as test set for normal, and 65 (T) as test set for tumor-like networks. (2) Upon comparing (N) FN variant networks amongst each other, e. g. Ni vs Nj , where i,j ∈ {B-A, B-A+, B+A-, B+A+}: 50 samples out of Ni were taken as the learning dataset, 20 out of Ni as test set, while 70 out of Nj comprised the test set. (3) Finally, we compared (T) FN variant meshworks amongst themselves, e. g. Ti vs Tj , 50 samples out of Ti were taken as the learning dataset, 15 out of Ti as test set, while 65 out of Tj comprised the test set. In all scenarios, a cluster is detected as foreign to GRF if either ( , or ( $ are less or equal than pval, pval ≤ 0.
In Silico Analysis After data quality validation, the S. aureus genomes were de novo assembled using Spades software (version 3. 15. 4) and blasted against the NCBI GenBank database, and also analysed on Type-Strain Genomes Server (https://tygs. dsmz. de/user_requests/new) online platform for more precision in bacteria identification. The first analysis of wholegenome MultiLocus Sequence Typing (wgMLST) was provided using EPISEQ ® CS V1-2 software (BioMerieux), a fully integrated web-based software application for genome assemblies and MLST. This software determined the percentage of sequence similarities between two genomes. The finalization of genome annotation was performed using CLC Genomics Workbench software (https://digitalinsights. qiagen. com/products-overview/discovery-i nsights-portfolio/analysis-and-visualization/qiagen-clc-workbench-premium/?cmpid=Q DI_GA_DISC_CLC&gad_source=1&gclid=Cj0KCQiApOyqBhDlARIsAGfnyMqxFp3a-BEx OSD0QM3ZwZXsO2JDw4EI5MinfGEm56EPCBijKUwYB_AaArP7EALw_wcB, accessed on 10 March 2023) (Qiagen, Germantown, MA, USA).
Pangenome analysis was performed by comparison of the annotated S. aureus genomes using Roary tools (Version 3. 13. 0) available on Galaxy online software (https://www. usegalaxy. org. au/, accessed on 10 March 2023), then visualized on Phandango online tools. ResFinder 4. 1, VirulenceFinder 2. 0 and Plas-midFinder 2. 1 were used for sequence analysis  [39] [40] [41] [42]. Antimicrobial resistance encoding genes, virulence, pathogenicity, and plasmids were in silico predicted using CGE online platform (http://www. genomicepidemiology. org/services/, accessed on 10 March 2023). Direct comparison between the strains isolated from the same patient was generated using BLAST Ring Image Generator (BRIG)  [43]. Single-nucleotide polymorphisms (SNPs) analysis based on whole genome alignment was performed using Snippy  [44].
Controls. We compared MK to two groups of healthy typical controls, whose data were collected in the context of other protocols unrelated to synesthesia. One group (G1) included 22 participants, aged 20-36 years (median 28 years, 10 men). The other group (G2) included 14 older controls, aged 51-75 years (median 64 years, 5 men). All participants were native French speakers, right-handed according to the Edinburgh Inventory  23  , had no history of neurological or psychiatric disorders. The research was approved by the institutional review board of the Institut national de la santé et de la recherche médicale (protocols C10-50 and C13-41), and all participants provided informed written consent. All studies were carried out in accordance with the Declaration of Helsinki. Experiment 1: normal and reversed speech perception.
3 s; TE 2. 76 ms; flip angle 9°; voxel size 1 × 1 × 1 mm). For G2, single-echo sequences were acquired on a Siemens 3 T MAGNETOM Verio scanner, with a 20-channel receive-only head coil: TR 3 s; TE 25 ms; flip angle 90°; voxel size 2 × 2 × 2. 5 mm; 49 slices. An anatomical T1-weigthed image was acquired: TR 2. 3 s; TE 4. 18 ms; flip angle 9°; voxel size 1 × 1 × 1 mm). Preprocessing. For MK and G1, using afni_proc. py, all echo timeseries were slice-time corrected, motion correction was computed on the first echo and applied to all echos, and echo images were merged using t2smap. py from the meica. py toolbox provided with AFNI: http:// afni. nimh. nih. gov/ afni/. Then, using SPM12 (Wellcome Institute of Imaging Neuroscience, London, UK) implemented in Matlab, functional time-series were co-registered to the anatomical volume, normalized, resampled to 3 mm cubic voxels, and spatially smoothed (6 mm FWHM).
Subjects had to detect the occasional target pseudoword "tatatata", pronounced by the same voice as stimuli. MRI acquisition, preprocessing, and statistical analysis. Acquisition and preprocessing were the same as for multi-echo data in Experiment 1. Univariate analyses and thresholding followed the same method as in Experiment 1, apart from the definition of regressors of interest: here we defined regressors for low-frequency words, high-frequency words, pseudowords, targets and responses to targets. As controls were all younger than MK, an age regressor would have absorbed any difference between the two. Hence no age regressor was included in second-level analyses. Moreover, multivariate pattern analyses (MVPA) were conducted using The Decoding Toolbox  27  , in order to decode words from pseudowords.
We assessed this prediction using DCM, a method whose aim is to estimate the functional coupling among brain regions, and the changes in this coupling that are associated with different experimental conditions. Alternative models of how observed time series were generated in the brain are compared using a Bayesian approach (see Methods)  42. We modeled the causal links among the core regions involved in TTS generation, based on the hypothesis that speech perception triggers interacting phonological and lexical processes, eventually generating a vivid orthographic image. This is equivalent to assuming, as proposed before, that TTS results from the top-down operation of a two-route reading system. We re-analyzed MK's data from Experiments 1 and 3 using dynamical causal modeling (DCM), in order to identify the best model among alternative candidates.
In addition, at each step, we 1 György Inzelt (2022). ARFIMA(p,d,q) estimator (https://www. mathworks. com/matlabcentral/fileexchange/30238-arfima-p-d-q-estimator), MATLAB Central File Exchange. Retrieved July 12, 2022. describe the formal aspects underlying the construction of the algorithm. We also propose to the reader a single-file and standalone algorithm to facilitate its use and diffusion. Then, to facilitate reading, we have used a different font for the variables, parameters, and commands used in MATLAB. We suggest the reader to download the code files and datasets deposited in a GitHub repository at https://github. com/clementroume/Whittle_maximum_ likelihood_estimator_tutorial and add the downloaded folder to the MATLAB path to follow this tutorial. The remainder of this article is organized as follows: Section 2, Whittle's maximum likelihood estimator in MATLAB, gives the step-by-step construction method of the whittle.
Section 2. 4, Whittle's log-likelihood function, describes the computation of Equation 1. Section 2. 5, Resolving the optimization problem, introduces the method to find the maximum of Whittle's log-likelihood function. Section 2. 6, The case of non-stationary signals, proposes a method to detect non-stationary signals (i. e. , with H > 1) and estimate their fractal exponent. Finally, Section 2. 7, The construction of whittle. m algorithm, presents the order in which the various code sections must be arranged to obtain the whittle. m all-in-one algorithm. Each step will be represented first in the mathematical formalization and then as a MATLAB code. Finally, we would like to clarify how the different exponents H, d, and α are used in the seven sections of this tutorial. The first four sections consist of the construction of Whittle's function, on the one hand, with the fBm/fGn model, and, on the other hand, with the ARFIMA (0,d,0) model, so these parts are presented around the two exponents H and d.
From Section 2. 5 onwards, we introduce the use of the α exponent, which allows the algorithm to analyze stationary and nonstationary processes without prior distinction. However, to maintain readability between the two models, we have named the two output variables Afgn for the value of α estimated from the fGn/fBm model and Aarf for the value of α estimated from the ARFIMA (0,d,0) model. Before beginning this guide, the reader can type load fractalsignals. mat in the MATLAB command window to load the time series: choleskyfgn, arfima0d0, whitenoise, and empirical. These signals will be used all along this guide to illustrate each step of the construction of the algorithm. choleskyfgn is a simulated exact fGn signal with an H exponent equal to 0. 8, and it was generated via the Cholesky decomposition method.
arfima0d0 is a simulated ARFIMA (0,d,0) process with a d exponent equal to 0. 3, which is equivalent to a fGn with H 0. 8. whitenoise is a normally distributed random noise signal, which was generated with the following MATLAB command: whitenoise = normrnd (0. 1, [1024. 1]). empirical is a signal retrieved from the study by  Almurad et al. (2018) , and it corresponds to step-to-step timing in an arm-in-arm synchronized walking task. Please consider that all the lines of code presented in this tutorial have been written and tested under MATLAB version 2021b. Although most of the codes work regardless of the version of the software application, some recent functions like nexttile, introduced with MATLAB 2019b, could cause compatibility problems and should be replaced by the subplot function.
Finally, note that the value of the periodogram is doubled. This is a method described at the end of the help paragraph The first line standardizes the observation vector x by setting its mean to 0 and its standard deviation to 1. This operation is relatively common in the field and is essential for the rest of this tutorial because Equations 3, 4 in the following sections are derived from the autocorrelation function (and not from the autocovariance function) and therefore assume a variance equal to 1. This operation also normalizes the total power of the spectrum P, but it does not change the shape of the spectrum, so it does not alter the information of the fractal exponents. The second line returns the length N of the input signal x, and the third computes the upper bound m. In the fourth line, we use the Signal Processing Toolbox command periodogram() to estimate the power spectral density of the input signal x, and an alternative code using the fft() command is given if the periodogram() command is not included in your MATLAB version.
Theoretical power spectral density of the model process In this sub-step, we present the equations and their computation for the two theoretical spectral densities derived from the fGn/fBm and ARFIMA (0,d,0) models, respectively. The theoretical fGn spectral density was given by  Li and Lim (2006)  as follows: T fGn ′ ω j ; H sin Hπ ( )Γ 2H + 1 ( )ω j 1-2H , (3) where Γ is the gamma function, and the other variables used in Equation 3 are the same as those described in Equation  1. The following MATLAB code is the computation of Equation 3. T ARFIMA ′ ω j ; d 1 2π 2 sin ω j 2 -2d , ( 4 ) where d is the integration parameter belonging to ] -0. 5, 0. 1. Type Fig2_TPSD in the command window to access Figure  2.
Whittle's log-likelihood function In this second step, we construct Whittle's log-likelihood function by injecting the two previous sub-steps into Equation 1, which is the function that we want to maximize. However, the optimization toolbox of MATLAB only allows minimizing functions, so we will have to minimize the inverse of Equation  1. This inverse function is written in the MATLAB code as follows: where lwH is the inverse of Whittle's likelihood function, N is the length of the observation vector, T is the scaled theoretical periodogram computed via MATLAB codes 2 or 3 and then MATLAB code 4, and P is the estimated periodogram of the observation vector computed via MATLAB code 1. MATLAB codes 6 and 7 are given in the following sections, which are the functions declared in MATLAB and which we will have to minimize.
These codes correspond to the combination of MATLAB codes 2-5. MATLAB code 6 gives the function based on the theoretical spectrum of fGn, while MATLAB code 7 gives the function based on the theoretical spectrum of ARFIMA (0,d,0). These functions will have to be placed either at the end of the mother code whittle. m (as is the case with the provided code) or in two separate files that can be named WLLFfgn. m and WLLFarf. m. In the first line, the function is used to declare the functions WLLFfgn and WLLFarf. The outputs are lwHfgn for code 6 and lwHarf for code 7 and correspond to  W (H) in Equation  1. The inputs are as follows: H is the Hurst exponent, w is the Fourier frequencies, P is the estimated periodogram of the observation vector, and N is the length of the observation vector. In Figure  4 , we present the plots corresponding to these two functions for our four test signals: choleskyfgn, arfima0d0, whitenoise, and empirical, with H values ranging from 0.
In order to implement the operation of Equation  6 , we use the MATLAB function fminbnd(), which is a minimizer based on golden section search and parabolic interpolation. The MATLAB codes to find the minimum of the WLfgn and Wlarf functions are as follows: the efficiency of these analysis methods, one must account for both their bias and variance. We therefore calculated Mean Squared Error (MSE), which is defined as the average of the squared error values. These analysis methods, being estimators in the sense of statistics, MSE can also be written as the sum of the variance and the squared bias of the values of α, allowing us to directly compare their quality. Thus, when comparing two estimators, the estimator characterized by the smallest MSE value can be considered better. First, for each estimator, we calculated the 5,040 (42 α-values × 120 signals) squared error values, which were then averaged.
Acknowledgments The author thanks  Pr. Olivier Haeberlé  and  Dr. Ali Moukadem  ( University of Haute-Alsace ) for their comments and suggestions that greatly improved the manuscript. The author would also like to show his gratitude to  Pr. Didier Delignières , for the whole of their discussions on this subject, but also for his benevolence and expertise which allowed the author to propose this paper. Merci pour tout  Didier.
A social-ecological model integrating the impact of information-based instruments We extend an agent-based model of a social-ecological system (SES) from a previous study  (Bourceret et al. , 2022). This model, representing a water catchment area, uses the Theory of Planned Behaviour (TPB). This theory has been frequently applied for predicting human behaviour, more particularly to understand farmers' behaviour in a voluntary policy context  (Sok et al. 2020). First, we present the core elements of the model previously developed (Section 3. 1). Second, we present the adaptations made to the model to analyse the effect of an information policy on farming practices (Section 3. 2). See Appendix 1 for the Overview Design concepts and Details (ODD) protocol description. The code is available here : https://github.
; https://doi. org/10. 1101/2022. 12. 06. 519416 doi: bioRxiv preprint distributions (or ecological niche) of all species of these genera based on georeferenced specimens catalogued in museum collection databases and ENM methods; (iii) we reconstruct the ecological niche of MPXV using reliable data on human index cases and MPXV sequences available for georeferenced wild animals; and (iv) we make statistical overlap comparisons between the ecological niches of mammals and MPXV in order to identify the most probable mammalian reservoir(s).
Occurrence records of selected mammal species To reconstruct the ecological niche of mammal species, we obtained species occurrence data from the Global Biodiversity Information Facility (GBIF), Integrated Digitized Biocollections (IdIgBio), VertNet and Inaturalist using the R package spocc  [53]. All these databases carry occurrence records for mammal species, but all have their specificity: GBIF is the largest database of species occurrence records; IdIgBio is the United States digitised collection of specimens  [54] ; VertNet is a publicly accessible database specialised in vertebrate specimen records from natural history collections around the world  [55] ; finally, iNaturalist is a citizenscience database with research-grade observations of multiple taxa. Both IdIgBio, VertNet, and Inaturalist communicate part of their data to GBIF, and some records can be found in multiple databases.
e. , Africa (including Madagascar and other islands). According to van Proodij  [87] , the minimum number of occurrence records required to build an ecological niche in Africa is 14 for narrow-ranged (with a species prevalence below 0. 2) and 25 for widespread species (with a species prevalence above 0. 2). The ecological niches were reconstructed using occurrence records and variables available in the WorldClim 2. 1  [88, 89]  and ENVIREM  [90]  datasets. The WorldClim dataset includes 19 bioclimatic variables derived from monthly temperatures and rainfall values; it is the most employed dataset for ENM studies. The ENVIREM (ENVIronmental Rasters for Ecological Modeling) dataset consists of 18 biologically relevant climatic and topographic variables derived from monthly temperature and precipitation data and monthly extraterrestrial solar radiation, which are intended to supplement the 19 bioclimatic WorldClim variables  [90].
Since one ENVIREM variable (monthCountByTemp10) is categorical, it was not included in the analyses. We added the elevation to these 36 variables and studied them for an area corresponding to a 300 km radius. CC-BY 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 7, 2022. ; https://doi. org/10. 1101/2022. 12. 06. 519416 doi: bioRxiv preprint around the selected points and the caret R package  [91]  was used to determine and select the least correlated variables (|r|< 0. 7)  [92]. For MPXV and each mammal species, ecological niche modelling was performed with the MaxEnt (Maximum Entropy) algorithm, a machine learning method, with ENMTools in R  [93]  using 70% of the dataset points as training.
To account for the slight differences of results due to the use of a machine learning algorithm  [94] , the ecological niche modelling was repeated 10 times. The MaxEnt approach was chosen over presenceabsence models (Generalized Linear Models (GLM) or BIOCLIM) because it is based on presence-only data and can produce reliable results even with a limited number of GPS records  [95]. The area under the curve (AUC) was used as the measure of model accuracy, a value of 0. 5 indicating model accuracy not better than random, and a value of 1 indicating perfect model fit  [96]. To determine if the ecological niche of each mammal species fits well with that of MPXV, niche overlap comparisons were performed with ENMTools in R [93] using Schoener's D  [97]  and Hellinger's I  [98]  metrics. The D and I metrics were obtained by comparing the estimated habitat suitability for each grid cell of the ecological niche models.
Ecological niches of mammal species and overlap with the MPXV niche As indicated in Table  1 , the 14 genera currently detected as MPXV hosts belong to four mammalian orders and represent 11 families: Eulipotyphla (Erinaceidae and Soricidae), Macroscelidea (Macroscelididae), Primates (Cercopithecidae, Hominidae, and Lorisidae), and Rodentia (Dipodidae, Gliridae, Muridae, Nesomyidae, and Sciuridae). In Africa, the 14 genera are represented by 213 species (S1 Table ) for which we gathered occurrence records. In the international databases, we found less than 14 occurrence records for 112 species (52. 6%) classified as narrow-ranged, and only 17 occurrence records for one species classified as widespread, i. e. , Crocidura viara. It was therefore impossible to predict the ecological niche of these 113 species.
Ecological niche of MPXV fragmented into three rainforests The MPXV niche was previously reconstructed in four studies  [27, [39] [40] [41]  based on different criteria for the selection of occurrence records: Levine et al. [40]  used 156 georeferenced villages in which at least one human case was detected; Lash et al. [41]  used 231 occurrence points corresponding to the 404 cases identified by the World Health Organization between 1970 and 1986; Nakazawa et al. [27]  completed the dataset with four occurrences from the South Sudan outbreak; and Nakazawa et al. [39]  divided the same dataset into 25 subsets of about 41 to 43 localities at least 50 km apart (to account for the higher number of occurrences in DRC). While recent MPX cases were added to our dataset, only 96 occurrence points were selected to infer our niche, as we chose to retain only localities of animal cases  (6)  and human index cases  (90).
This approach was adopted to focus on human cases with the highest probability of MPXV infection from an animal and to avoid any bias due to human-to-human transmission. Although based on a different occurrence dataset, our results are quite similar to previous ones, since the five MPXV niches cover more or less all rainforests of West and Central Africa. However, it is important to note that our MPXV niche has a fragmented distribution, with at least three (and possibly four) separate rainforest regions (Figure  1B ), including in West Africa (i) the Upper Guinean forests (UGF; from Guinea and Sierra Leone through Liberia, Côte d'Ivoire and Ghana to western Togo) and (ii) Lower Guinean forests (LGF; from southeastern Benin through Nigeria to western Cameroon), and in Central Africa (iii) the northern part of Atlantic Equatorial coastal forests (AECF) (from the Sanaga River in southwestern Cameroon to Equatorial Guinea), possibly separated (large area with p < 0.
The participants involved in The Rotterdam Study (RS), The TwinsUK Study (TwinsUK), The Taizhou Longitudinal Study (TZL), The National Survey of Physical Traits Study (NSPT) and The Consortium for the Analysis of the Diversity and Evolution of Latin America (CANDELA) Study datasets were not collected with broad data sharing consent. Given the identifiable nature of both ear and genomic information and unresolved issues regarding risks to participants of inherent reidentification, participants were not consented for inclusion in public repositories or the posting of individual data, meaning that the raw ear images and/or individual-level genomic data cannot be made publicly available due to restrictions imposed by the ethics approval. The same phenotype acquisition pipeline used by different cohorts is available at Github (https://github.