dataesr/scientific-paragraphs-categorization huggingface dataset:
The quality of the experimental data was determined by means of the average localization precision. Single-particle tracking. The localizations of individual MET-ligand complexes in consecutive frames were connected to trajectories using the swift tracking software  (Turkowyd et al. , 2020; Version 0. 4. 2, used  in this manuscript, and all subsequent versions of the swift software, as well as documentation and test data sets, can be obtained on the swift beta-testing repository http://bit. ly/swifttracking and upon request to the authors). Parameters for swift were estimated using the SPTAnalyser software (https://github. com/Johan-naRahm/SPTAnalyser). The following parameters were set globally for all cells: "diffraction_limit" = 14 nm, "exp_displacement" = 85 nm (Fab) / 75 nm (InlB), "p_bleach" = 0.
HMM parameter Hidden Markov modeling of single-particle tracking data Hidden Markov modeling was performed using the ermine (estimate reaction rates by Markov-based investigation of nanoscopy experiments) package for python (https://github. com/SMLMS/py-Ermine). HMMs were used to map the diffusion behavior of membrane-bound MET receptors measured at discrete time points equally separated by Δt onto a hidden transition network. This transition network is defined by a set of N states S = {s 1 , …, s N }. The starting probability for each state is given by , , N 1 { } π = π … π with 1 i i N 1 ∑ π = =. The probability of interstate transitions between consecutive discrete time steps is given by an N × N-state transition probability matrix A = {a i,j }. Given a discrete time sequence H = (h Δt ,…, h MΔt ) of M hidden states drawn from S, a state at time t is denoted by h t = s i.
, 2021). Initial model parameter determination. Initial diffusion models B associated with the hidden states S were determined by optimizing the probability of observing the distribution of jump distances given a diffusion mixture model B M ax P r B Pr t r , a rg | , , | i i i N init init 2 1 ∑ ( ) ( ) ( ) ω = ω = ω ∆ = (Supplemental Material: Appendix A, Equations 1-7). The optimization process was performed using the expectation-maximization algorithm  (Dempster et al. , 1977)  implemented in the "JumpDistance-MixtureModel" class of the ermine package with B and ω as free fit parameters. The initial starting probability of the HMM π init was set to ω init. Further, the states were assumed to be relatively stable. Therefore, the entries of the diagonal of the state transition matrix A init were initially set to ( ) = = = +∆ = P h s h s | 0.
9 t t i t j i while the remaining entries of the state transition matrix were filled with a uniform distribution ( )( ) ( )   Rabiner, 1989; Li et al. , 2000). The optimization process was performed using the "Ermine-HMM" class of the "ermine" package, which uses a diffusion mixture HMM that is designed using the _BaseHMM class of the hmmlearn package (https://github. com/hmmlearn/hmmlearn) with a diffusion mixture model as custom emission probability. = = = - - +∆ ≠ P h s h s N | 1 0. Reaction state sequence estimation. For the optimal HMM, the most likely sequence of hidden states H opt underlying the observation sequence of jump distances O was postulated using the Viterbi algorithm  (Viterbi, 1967). Reaction state sequence estimation was performed using the "predict" function of the "ErmineHMM" class of the ermine package.
The reaction rate constant k s i j 1     → - was calculated from Δt and P(j|i) (Supplemental Material: Appendix C). With the reaction rate constants k s withi j ( [ ] ) i j ( ) ( 1) ≠ → at hand, the system is then fully described by the chemical master equation (CME) ∑ ( ) ( )[ ] = →     - → ≠ dx dt j i x k i j x i j i j i. Stochastic simulation of the chemical master equation. The CME was transformed into a stochastic PN  (Bause and Kritzinger, 1999)  and Monte Carlo simulations of the state trajectories were simulated using the Stochastic Simulation Algorithm (SSA;  Gillespie, 1977)  as implemented in the fossa (free objective-c stochastic simulation algorithm) software (https://github. com/SMLMS/fossa). For first-order chemical reactions the reaction rate constant equals the stochastic rate constant k s c s ( [ ] [ ] i j i j ( ) ( 1) ( ) ( 1) = → -→ with i ≠ j;  Gillespie, 2007).
Machine learning associated model interpretation Data set definition. A labeled data set was created from the extracted physiologically interpretable parameters obtained by HMM and diffusion mixture model training with optimal mobility mode number. From these optimized parameters (ω opt , A opt , D opt ), the mode occupation probability (ω opt ), the transition probabilities of a molecule switching between mobility modes (P(j|i) with i≠ j), and the diffusion coefficients that characterize the mobility modes ( ) ≠ D i , immobile i opt were defined as features and the measured cells as instances. Therefore, the resulting data set of an N-modal HMM comprises n = N + (N 2 -N) + (N -1) extracted physiologically interpretable parameters. Model training. The data set was split in a two to one ratio into a training set and a validation set.
The feature importance was given by the mean absolute SHAP value of a feature. The higher the mean absolute SHAP value of a feature, the more the feature contributes to the decision of the binary classifier. Based on the SHAP value distribution, the feature set yielding the optimal amount of information about the data was identified by ABC analysis, which identifies the features that achieve the maximum effect with the lowest possible effort. This analysis is a generalization of the Pareto 80/20 rule, which claims that 80% of the results are achieved with 20% of the total effort. To verify the model interpretation, the classification procedure was repeated with a reduced data set that only contained the HMM parameters rated as "A" by the ABC analysis. The classification performance was evaluated using the balanced accuracy, ROC-AUC score, F1 score, precision score, and recall score.
Package implementation and data availability Ermine. The software for estimation of reaction rates by Markovbased investigation of nanoscopy experiments (ermine) is implemented as a python library and thus requires the python software package (https://pypi. org/project/pyErmine/). The methods included in the ermine library can be used in python scripts to produce individual problem-specific workflows or in interactive jupyter notebooks that follow a specific preprocessing workflow. The ermine package is free and open source software. The source code is freely available at GitHub (https://github. com/ SMLMS/pyErmine) and can be redistributed and/or modified under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 (GPLv3) of the License, or any later version.
The programming work was performed in the python language and is based upon the library hmmlearn (v. 0. 2. 4). Tutorials on the usage of the ermine package can be obtained from our github site (https://github. com/SMLMS/ ermine-tutorial). fossa. The free open-source stochastic simulation algorithm (fossa) was written in objective-c for the purpose of simulation execution speed  (Blankenbecler, 1990). It uses the open source standard library framework "gnustep"  (v. 1. 25. 1; Botto et al. , 2001)  and was compiled using gnustep-make (v. 2. 7. 0) and gcc (v. 7. 5. 0). The "fossa" package is free and open-source software. The source code is freely available at GitHub (https://github. com/SMLMS/fossa) and can be redistributed and/or modified under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 (GPLv3) of the License, or any later version.
ACKNOWLEDGMENTS We thank  Ulrike Endesfelder  for providing the swift software. We thank  Jakob T. Bullerjahn  for fruitful discussions about single particle-tracking theory. We thank  Jörg Ackermann  for the kind introduction in stochastic PNs. S. M. acknowledges funding by the  Federal Ministry of Education and Research (BMBF)  in the form of a " Forschungspraktikum für Systembiologen " (Grant e:Bio). M. H. , J. V. R. , and M. S. D. acknowledge funding by the  German Science Foundation  (Grant iMol,  RTG 2566 ). J. B. S. acknowledges funding by the  National Infrastructure France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). J. L. acknowledges funding by the  German Science Foundation  (Grant  DFG LO 612/16-1 ).
from the dataset because they would not contribute to classification. The result was two balanced datasets with n = 11 features (n = N + (N 2 -N) + (N -1)). The training data consisted of 72 instances, of which 36 represented untreated and 36 represented InlB-treated cells. The validation data consisted of 36 instances, of which 18 represented untreated and 18 represented InlB-treated cells.
MRI acquisition MRI data were acquired using a whole-body 7 T magnet (Siemens Healthineers, Erlangen, Germany) equipped with a 32-channel phased-array head coil (Nova Medical, Wilmington, MA, USA). High-resolution (0. 7 mm isotropic nominal voxel size) whole-brain quantitative R 1 and B 1 + maps (2 mm isotropic nominal voxel size) were obtained using the 3D MP2RAGE  31  and 3D Sa2RAGE 32 sequences, respectively. R 1 is an intrinsic property (i. e. longitudinal relaxation rate) of brain tissue that can be quantified using MRI and relates to tissue integrity (e. g. it decreases with demyelination). 33 In addition to the anatomical scans, whole-brain resting-state functional MRI (rs-fMRI) data with an 1. 4 mm isotropic nominal voxel size were acquired using a 2D Multi-Band Echo Planar Imaging (2D MB-EPI) sequence to probe functional connectivity between cerebellar and cortical areas.
Anatomical data preprocessing MP2RAGE anatomical data were preprocessed as described previously, including the removal of non-brain tissue,  35  correction for image inhomogeneities  36, 37  and cortical surface reconstruction and parcellation using the FreeSurfer (v6. 0). 38 ative-resolution surface meshes ( 164 k vertices) were downsampled to the 'fsLR' surface space ( 32 k vertices) using the instructions and transforms provided by the Human Connectome Project (https://github. com/Washington-University/HCPpipelines). 39.
51 Finally, each slice-timing-corrected rs-fMRI EPI volume was resampled and resliced into the MNI template space using a one-step procedure that included: (i) motion correction, (ii) gradient non-linearity, (iii) readout distortion and (iv) the MNI-space transformation. Within the CONN functional connectivity toolbox (https:// web. conn-toolbox. org/),  52 resampled rs-fMRI data were then denoised using the aCompCor [WM and CSF region of interests (ROIs), five components each],  53  scrubbing (number of identified invalid scans), motion regression (12 regressors: six motion parameters + six first-order temporal derivatives), temporal band-pass filtering (0. 08-0. 8 Hz), detrended and demeaned. In parallel, left and right hemisphere cortical (i. e. fsLR) surfaces were transformed to MNI space using the obtained inverse EPI-to-anatomical transformation matrices and Connectome Workbench's 'surface-apply-warpfield' command for projection of the denoised data onto the surface.
Summed effect sizes ranged from 4. 31 in parietal regions up to 16. 99 in frontal regions (Supplementary Fig. 7B ). Comparison of the corresponding spatial pattern to the results extracted from 14 371 studies in the Neurosynth database revealed a strong correlation with broad terms such as 'visual' ('correlation weight' = 10,097. 54, Supplementary Fig. 7C ), 'motor' (5161. 05) and 'attention' (3906. 84) where the term's font size scales with its corresponding weight.
e. executive functioning). 95 Moreover, focal frontal and parietal lobe lesions resulted in increased errors and slowness in response speed during the Stroop test. 96, 97 imilarly, the frontal-parietal cortical network appears to be strongly engaged during the LDST task. 98 In line with these previous studies, our correlational analyses between functional and cognitive profiles show that cerebellocortical connections characterized by a significant group effect, are weaker in patients with lower LDST and Stroop performances. Additionally, the left frontal lobe is considered the anterior convergence zone of the dorsal (i. e. phonology) and ventral (i. e. semantics) language streams,  99  thus playing an essential role in this dual-stream model. The central role of the frontal lobe in this model of language processing explains the appearance of terms like 'language', 'words' and 'semantic' when comparing our statistical maps to those included in the NeuroSynth database  62  and could provide novel insights into the cognitive deficits related to the m.
Material and methods To learn more about the contact situation between these groups, we adopt a multidisciplinary approach involving linguistic, royalsocietypublishing. org/journal/rsfs Interface Focus 13: 20220056 sociocultural and genetic databases. Each of these types of data informs us about different sociocultural, demographic and historical aspects of the human societies of Northwestern Amazonia. Here we describe the kinds of data and the analytical approaches used for each type of evidence.
The first one uses allele-frequency-based methods and includes PCA (as implemented in the Eigensoft package  [64] ), model-based ancestry estimation with ADMIXTURE  [65] , and fstatistics  [16, 66]. For PCA and ADMIXTURE we merged the NWA dataset with data from other modern Native American populations previously described  [16, [67] [68] [69] , using the program mergeit implemented in EIGENSOFT software package v. 7. 2. 0 with default settings  [64]. For the ADMIXTURE analysis, we pruned the merged dataset for linkage disequilibrium with PLINK v. 1. 90b5. 2, using the command -indep-pairwise 100 20 0. 5 leaving a total of 87 297 SNPs. ADMIXTURE clusters together genetically similar individuals, based on the estimation of allele frequencies, and assigns ancestry components from an a prioridefined number of 'K' ancestral source populations  [65].
We ran ADMIXTURE from K = 2 to K = 14 with 20 replicates for each K and used the cross-validation error procedure to find the 'K' that best describes the genotype data. We visualized ADMIXTURE results with the software pong  [70]. We used ADMIXTOOLS  [16]  as implemented in the R package admixr  [71]  and the R package ADMIXTOOLS2  [72]  to estimate pairwise f 2 -statistics, outgroup-f 3 -statistics, f 4 -statistics and f4-ratios (see  [66]  for discussion). These statistics are useful to infer genetic relationships and to test hypotheses about genetic admixture and common ancestry among populations. The second approach uses haplotype-based methods, that is, long and uninterrupted blocks of DNA inherited from a set of common ancestors  [73]. We performed statistical phasing of the genotype data with the software SHAPEIT v.
2. r904  [74] , using the recombination map and a reference panel of American populations from the 1000 Genomes Project Phase 3 (1000 Genomes Project Consortium et al. 2015). We ran SHAPEIT with optionsburn 10, -prune 10 and -main 30 for iteration number with 500 conditioning states, leaving other parameters as default as previously reported  [20]. We used the phased output to run the software ChromoPainterv2  [75] , which reconstructs the haplotype(s) of a 'recipient' individual using the haplotypes from all other individuals in the sample as potential donors. This process is repeated for every haplotype in turn, so every individual's genome is ultimately reconstructed in terms of all the other individual genomes  [75]. In addition, we used the software RefinedIBD to detect royalsocietypublishing.
org/journal/rsfs Interface Focus 13: 20220056 identity-by-descent (IBD) and homozygous-by-descent (HBD) blocks, IBD and HBD were merged and split by length category into three datasets as follows: 1-5 centimorgans (cM), 5-10 cM and over 10 cM, as previously described  [19, 20]. The plot of sharing of IBD was adapted using scripts described here: https://github. com/dangliu/Massim_project. With the uniparental data we focused on reconstructing haplotype networks, implemented in the R-package pegas v. 1. 1  [76] , using the minimum spanning network method. Haplotype network visualizations are useful to infer phylogenetic relationships among individuals' haplotypes and to identify the sharing of identical haplotypes among groups, which are informative about recent common ancestry or sex-specific genetic admixture.
Sociocultural data We started by compiling a large set of sociocultural data from ethnolinguistic groups in Western South America (see electronic supplementary material, data availability section), designed to address a broad range of questions regarding the population history of this part of the continent. We subset this database to match the ethnolinguistic groups described in table 1, leaving us 117 variables from the 18 societies. The dataset covers the most basic types of social and cultural information available in the ethnographic literature, including subsistence practices, settlements and architecture, kinship and marriage, social and political organization, material culture, body modification, cosmology and ritual. The data come from monographs, articles and book chapters, travel reports, pre-existing datasets, personal communication with experts and a range of other ethnographic materials.
A detailed list of sources and a description of our strategy for coding variables can be found in the electronic supplementary material. A major limitation in developing a dataset like this is the patchy and inconsistent ethnographic record of Western Amazonia, which inevitably leads to a fair amount of missing data. We followed the best practices in handling missing data  [77]. For this paper, given the importance of gender-specific sociocultural practices in NWA, we chose to separately analyse practices generally transmitted from fathers to sons, and those generally transmitted from mothers to daughters (table  2 ). (This means that these gender categories reflect the particular gender dynamics of this region of South America. ) However, after subsetting the data, we ended up with several variables with no variation, and societies with a high degree of missing data.
For this reason, prior to calculating distances between societies, we dropped all variables with less than two levels (that is, one level, or one level and N/A). Subsequently, we created a distance matrix based on the full dataset and used backward elimination to remove those societies with the highest number of non-calculable distances. We repeated this process until we were left with a distance matrix that contained pairwise distances between all remaining societies. For the male-specific subset, this resulted in distances being calculated based on 74 variables for 17 groups. For the female-specific dataset, distances were calculated based on 15 variables for 13 societies.
Linguistic databases We consulted written sources to collect data on the grammars of 18 NWA languages belonging to the Tukanoan and Arawakan language families (table  1 ). We collected data that give us a broad cross-section of language structure, divided into the following topics (for a more detailed description, see the electronic supplementary material): 1. Phonemes and allophones: phonemes and their realizations in different contexts, comparing both the realizations and the characterizations of the contexts. 2. Person: the form and meaning of elements whose interpretation includes grammatical person. 3. Noun classification: the form, (generalized) meaning, and grammatical contexts of noun classification marking strategies. 4. Case marking: the form and meaning of case markers. 5. Tense, aspect, modality, evidentiality marking: the form and meaning of markers that code temporal, aspectual, modal or evidential information.
Analysis of sociocultural and linguistic data After we standardized the databases described in § § 2. 2 and 2. 3 using the glottospace R package  [78] , we calculated the degree of (dis)similarity between the ethnolinguistic groups using Gower's general coefficient of similarity  [79]. The resulting distance matrices were used as input to perform Nonmetric Multidimensional Scaling (NMDS)  [80]. NMDS results were subsequently plotted in two and three dimensions to explore dissimilarities between ethnolinguistic groups. To assess whether pre-defined sets of ethnolinguistic groups are different from each other to a degree that would be considered statistically significant, we performed overall and pairwise permanova on the raw distance matrices  [81]. Data preparation, analysis and visualization were conducted through the workflows implemented in the glottospace R package, which provides wrappers to functions of several other packages, including the cluster package for calculating distances  [82] , the vegan package  [83]  for performing NMDS and permanova, and ggplot2  [84]  and plotly  [85]  for visualizing NMDS results.
These broad patterns of genetic similarities are also observed in the ADMIXTURE analysis. Figure  1b  shows the results for the K ancestral populations with the lowest cross-validation error (electronic supplementary material, figure  S2 ). We provide the full result (i. e. K = 2-14) in the supplementary materials (electronic supplementary material, figure  S3 ). In figure  1b  we observe that Yukuna, Matapi and Tanimuka are assigned a similar ancestry profile. By contrast, other ET-speaking groups, the language family to which Tanimuka's language belongs, are assigned a mixture of ancestries. However, the ancestry component that is maximized in Yukuna, Matapi, and Tanimuka (blue ancestry) represents on average 46% of the ancestral components assigned to ETs (figure  1b ). We used f-statistics to determine genetic affinities between Yukuna, Tanimuka, Matapi and other groups in NWA; for these we excluded individuals that showed more than 10% European-and African-related ancestry ( pink and brown components in figure  1b ).
This pattern might be the result of parallel structures between the families, which may be due to coincidence or contact-induced convergence or shift at the protolanguage level. The opposite pattern (e), with 'yes' for all three questions, would be a situation in which Tanimuka and royalsocietypublishing. org/journal/rsfs Interface Focus 13: 20220056 Yukuna have both developed away from their respective family profiles, but also from each other. The historical scenarios that we focus on in this paper are convergence and language shift. These two scenarios would be compatible with patterns d and f, respectively. Scenario d (symmetric diffusion) can be identified if the Arawakan and Tukanoan profiles are significantly different from each other (Q1 = Y), Tanimuka and Yukuna are different from their respective family profiles (Q2 = Y for both languages), 2  and Tanimuka and Yukuna themselves are not significantly different from each other (Q3 = N).
At the same time, Yukuna is not significantly different from the Arawakan profile (row 6), whereas Tanimuka is distinct from the Tukanoan profile (row 1). This spells out an asymmetrical pattern, in which Tanimuka has been influenced by Yukuna, but not vice versa. The other two areas of grammar where we find asymmetrical patterns in which Tanimuka has become Arawakanized, but Yukuna has not become Tukanoanized (or at least much less so) are classifiers and person markers. Nevertheless, the patterns are slightly different for these two datasets (electronic supplementary material, figures S8 and S9 and electronic supplementary material, tables  S2  and S3 ). The results for person markers are similar to those of TAME, except that the difference between Tanimuka and Arawakan is also non-significant.