dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we recorded the activity of control and emotion-linked muscles (i. e. , COR and FRO) that were positioned on the non-dominant side of the face (i. e. , the left side for righthanded participants), while sensors recording the activity of the speech muscles (i. e. , ZYG and OOI) were positioned on the dominant side of the face. The experiment was video-monitored using a Sony HDR-CX240E video camera to track any visible facial movements. A microphone was placed 20-30 cm away from the participant's lips to record any faint vocal production during the inner speech and listening conditions. Stimuli were displayed using the OpenSesame software  [68]  on a 19-inch colour monitor. Linguistic material. We selected ten rounded and ten spread bi-syllabic nonwords (cf. Table  1 ). Each class of nonwords was specifically designed to either induce a greater activation of the lip muscle (rounded items) or a greater activation of the zygomaticus muscle (spread items).
EMG signal processing EMG signal pre-processing was carried out using Matlab r2014a (Version 8. 3. 0. 532, www. mathworks. fr). We first applied a 50Hz frequency comb filter to eliminate power noise. Then, we applied a 20 Hz-450 Hz bandpass filter to the EMG signals, in order to focus on the 20-450 Hz frequency band, following current recommendations for facial EMG studies  [71, 72]. Although participants were explicitly asked to remain still during inner speech production or listening, small facial movements (such as swallowing movements) sometimes occurred. Such periods were excluded from the final sample of EMG signals. To remove these signals, we first divided the portions of signals of interest into periods of 1 second. The baseline condition was therefore composed of 60 trials of 1 second.
Data analysis Statistical analyses were conducted using R version 3. 5. 3 [73] , and are reported with the papaja  [74]  and knitr  [75]  packages. To assess the effects of the condition and the class of nonwords on the standardised EMG amplitude, we analysed these data using Condition (3 modalities: speech, inner speech, and listening) and Class of nonwords (2 modalities, rounded and spread, contrast-coded) as within-subject categorical predictors, and the standardised EMG amplitude as a dependent variable in a multivariate (i. e. , with multiple outcomes) Bayesian multilevel linear model (BMLM). An introduction to Bayesian statistics is outside the scope of this paper. However, the interested reader is referred to  [76]  for an introduction to Bayesian multilevel modelling using the brms package.
In order to take into account the dependencies between repeated observations by participant, we also included in this model a varying intercept by participant. Contrary to what we pre-registered, we used a multivariate model (instead of separate models by muscle). This allowed us to estimate the correlation between each pair of muscles. Models were fitted with the brms package  [77]  and using weakly informative priors (see the S1 Text for code details). Two Markov Chain Monte-Carlo (MCMC) were run for each model to approximate the posterior distribution, including each 5. 000 iterations and a warmup of 2. 000 iterations. Posterior convergence was assessed examining trace plots as well as the Gelman-Rubin statistic R. Constant effect estimates were summarised via their posterior mean and 95% credible interval (CrI), where a credible interval can be considered as the Bayesian analogue of a classical confidence interval.
Contrary to theoretical expectations based on phonetics and speech production theory  [78] [79] [80] [81] , the activity of both muscles was of higher amplitude for the pronunciation of rounded nonwords (as compared to spread nonwords) during overt speech production. Additionally, the EMG amplitude on both muscles of interest was similar during the inner production (or listening) of the two classes of nonwords. However, in the exploratory analyses section, we report results from supervised machine learning algorithms (classification using random forests), showing a reasonable accuracy to classify EMG signals according to the class of nonwords during overt speech production. This strategy was however unsuccessful for the inner speech and the listening conditions. Before moving to the statistical results, we represent the distribution of the whole dataset, by class, by condition and by muscle for the two main muscles of interest (OOI and ZYG) in Fig 1.
More precisely, the first row of this figure represents the distribution of the standardised EMG scores in the inner speech condition, the second row depicts the distribution of these scores in the listening condition, whereas the third row depicts the distribution of the standardised EMG scores in the overt speech condition. The first column depicts the distribution of the standardised EMG scores recorded over the OOI muscle whereas the second one represents the distribution of the standardised EMG scores recorded over the ZYG muscle. Each individual data point is represented as a vertical bar along the x-axis of each panel whereas the vertical coloured line represents the class-specific median. Additionally, a vertical dashed line is plotted at zero, which represents the baseline level. Thus, a positive value on the x-axis represents EMG standardised scores that are higher than baseline.
In Table  2 , we report the mean standardised EMG amplitude of all recorded muscles in each condition. Given the skewness of the distribution of these scores, the mean and the standard deviation (SD) are not the best indicators of the central tendency and dispersion of these distributions. Therefore, we also report the median, the median absolute deviation (MAD), and the inter-quartile range (IQR). We also created a shiny application  [82]  allowing for further visual exploration of the data by muscle, by condition, and by participant, in the 3D space formed by three (to be chosen) muscles. This application is available online (at https://barelysignificant. shinyapps. io/3d_ plotly/) and the associated code is available in the OSF repository (https://osf. io/czer4).
A useful diagnostic of the model's predictive abilities is known as posterior predictive checking (PPC) and consists in comparing observed data to data simulated from the posterior distribution  [83]. The idea behind PPC is that a good model should be able to generate data that resemble the observed data  [84]. In this vein, Fig  3  represents the distribution of the whole dataset (across all participants and conditions) by muscle (the dark blue line) along with the distribution of hypothetical datasets generated from the posterior distribution of the model (the light blue lines). As can be seen from this figure, the distributions of the data generated from the model differ considerably from the distribution of the observed data. Therefore, in the next section, we turn to a more appropriate model for these data.
Thus, we used a supervised machine learning algorithm aiming to classify speech signals according to the class of nonwords. Broadly, the machine learning approach seeks to find a relationship between an input X (e. g. , EMG recordings over the four facial muscles) and an output Y (e. g. , the class of nonwords). Once trained, it allows predicting a value of the output based on some input values, whose prediction can be evaluated against new observations. We used a random forest algorithm, as implemented in the caret package  [86]. Random forests (RFs) represent an ensemble of many decision trees (a forest), which allow predictions to be made based on a series of decision rules (e. g. , is the score on predictor x 1 higher or lower than z? If yes, then. , if not, then. ). The specificity of RFs is to combine a large number of trees (usually above 100 trees), and to base the final conclusion on the average of these trees, thus preventing overfitting.
We used three separate RFs to classify EMG signals in each condition (Overt Speech, Inner Speech, and Listening). To evaluate the performance of this approach, we report the raw accuracy (along with its resampling-based 95% confidence interval), or the proportion of data points in the test dataset for which the RF algorithm predicted the correct class of nonwords. First, we randomly split the entire dataset into a training (80%) and a test set (20%). The training set was used for the learning whereas the test set was used to evaluate the predictions of the algorithm. To prevent overfitting, we used repeated 10-fold cross-validation during the learning phase. Predicting the class of nonwords during overt speech production. We first tried to predict the class of nonwords produced in overt speech, based on the activity of the four facial muscles (OOI, ZYG, COR, FRO).
Acknowledgements We thank  M. Hetherington-Rauth  for discussions that motivated this work. A. Dao ,  B. Cohan ,  M. Escobar ,  J. Viliunas ,  L. Miles , and  S. Innes  provided valuable assistance in data collection. We thank  M. Kalich  and  B. Pitton  for assistance and space in the greenhouse. Comments from the  EvoEco lab at UofT Mississauga  and  D. Schluter 's group at  UBC  greatly improved the manuscript. Authors received funding from  NSERC  ( NSERC PGS-D  to JSS and  NSERC  CGS-D to LRR and KAT) and the  Izaak Walton Killam Foundation (KAT). An  NSERC   Discovery Grant  to  Marc T. J. Johnson  provided the supplies, equipment and greenhouse space rental used to carry out this project.
Figures The Oncoprint figure was created using R 3. 6. 0, RStudio 1. 2. 1335, and the Oncoprint function of the Complex-Heatmap 2. 2. 0 package. The Circos plot was generated using the online Circos Table Viewer (http://mkweb. bcgsc. ca/tableviewer). All other figures were created using GraphPad Prism (v. 8) software.
Caris Life Sciences performs exome sequencing on 592 genes for mutational analysis, evaluates a proportion of these genes for copy number alterations, and assesses for fusions involving targeted genes with RNA-based anchored multiplex PCR (https://www. carismolecularintelligence. com/ profiling-menu/mi-profile-usa-excluding-new-york/). GlioSeq uses amplification-based DNA and RNA sequencing to evaluate for mutations, copy number alterations, and structural rearrangements involving genes relevant to primary central nervous system (CNS) tumors. A list of genes included in the GlioSeq panel can be accessed at https://mgp. upmc. com/Home/Test/Glio-Seq_details. Copy number data was determined from DNA-based NGS and methylation profile plots.
NTRK cohort genome-wide methylation profiling and analysis To analyze the NTRK cohort in our study, the raw idats generated from iScan were processed and analyzed using Bioconductor R package Minfi. All the Illumina EPIC array probes were normalized using quintile normalization and corrected for background signal. Samples were then checked for their quality using mean detection p-values (p-value < 0. 05). Unsupervised principal component analysis (PCA) was performed to check for biological variation within the cases. To identify the differentially methylated CpG probes, the samples were grouped based on NTRK gene involved, histologic grade, and age. Beta values were generated and probes with FDR cutoff (q < 0. 05) were considered the most significantly variable probes. Heatmaps were generated in a semi-supervised manner, showing the hierarchical clustering pattern of the top 10,000 significant differentially methylated genes/probes by NTRK gene involved.
Acknowledgements We would like to acknowledge the staff members of the pathology, clinical cytogenetics, and molecular laboratories at  Brigham and Women's Hospital, Boston Children's Hospital, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia ,  NYU Langone Health , and  Foundation Medicine  for their contributions and support. MT currently receives salary support from an  NIH   Institutional Training Grant  ( T32 HL007627 ). NYU DNA methylation profiling was in part supported by a grant from the  Friedberg Charitable Foundation  (to MSn).
other underlying health conditions are higher in SSA (Table  S2 ), and their interactions with SARS-CoV-2 are, as of yet, poorly understood. The highly variable social and health contexts of countries in SSA will drive location-specific variation in the magnitude of the burden, the time-course of the outbreak, and options for mitigation. Here, we synthesize the range of factors hypothesized to modulate the potential outcomes of SARS-CoV-2 outbreaks in SSA settings by leveraging existing data sources and integrating novel SARS-CoV-2 relevant mobility and climate-transmission models. Data on direct measures and indirect indicators of risk factors were sourced from publicly available databases including from the WHO, World Bank, UNPOP, DHS, GBD, and WorldPop, and newly generated data sets (see Table  S3  for details).
Many possible sources of bias complicate interpretation of these associations  43  , and while they provide a useful baseline, inference is also likely to change as the pandemic advances. To reflect this, our analysis combines a number of high level variables likely to broadly encompass these putative risk factors (e. g. , non-communicable disease (NCD) related mortality and health life expectancy) with more specific measures encompassed in evidence to date (e. g. , prevalence of diabetes, obesity, and respiratory illness such as Chronic Obstructive Pulmonary Disease (COPD)). We also include measures relating to infectious diseases, undernourishment, and anemia given their interaction and effects in determining health status in these settings  44. Data on the identified indicators were sourced in May 2020 from the World Health Organization (WHO) Global Health Observatory (GHO) database (https://www.
To assess potential differences in data quality between geographic areas we compared the year of most recent data for variables (Figure  S2 ). The mean (range varied from 2014. 624 to 2014. 928 by region) and median year (2016 for all regions) of the most recent data varied little between regions. To account for uncertainty associated in the estimates available for a single variable, we also include multiple variables per category (e. g. , demographic and socio-economic factors, comorbidities, access to care) to avoid reliance on a single metric. This allows exploring variation between countries across a broad suite of variables likely to be indicative of the different dimensions of risk. Rice et al | 2020 07 28 | Page 19 Although including multiple variables that are likely to be correlated (see PCA methods below for further discussion) would bias inference of cumulative risk in a statistical framework, we do not attempt to quantitatively combine risk across variables for a country, nor project risk based on the variables included here.
Variable selection and data sources for variables hypothesized to modulate the rate of viral spread In addition to characterizing variation among factors likely to modulate burden, we also synthesize data sources relevant to the rate of viral spread, or pace, for the SARS-CoV-2 pandemic in SSA. Factors hypothesized to modulate viral transmission and geographic spread include climatic factors (e. g. , specific humidity), access to prevention measures (e. g. , handwashing), and human mobility (e. g. , international and domestic travel). Table  S2  outlines the dimensions of risk selected and references the previous studies relevant to the selection of these factors. Climate data was sourced from the global, gridded ERA5 dataset  47  where model data is combined with global observation data (see Section A7 for details).
International flight data was obtained from a custom report from OAG Aviation Worldwide (UK) and included the departure location, airport of arrival, date of travel, and number of passenger seats for flights arriving to 113 international airports in SSA (see Section A5). As an estimate of connectivity within subregions of countries, the population weighted mean travel time to the nearest city with a population greater than 50,000 was determined; details are provided in Section A6. To obtain a set of measures that broadly represent connectivity within different countries in the region, friction surfaces from ref  24  were used to obtain estimates of the connectivity between different administrative level 2 units within each country. Details of this, alongside the metapopulation model framework used to simulate viral spread with variation in connectivity are in Section A6.
Principal Component Analysis The PCA was conducted on each of the three subsets described above, using the scikitlearn library  64. In order to avoid biasing the PCA due to large differences in magnitude and scale, each feature was centered around the mean, and scaled to unit variance prior to the analysis. Briefly, PCA applies a linear transformation to a set of n features to output a set of n orthogonal principal components which are uncorrelated and each explain a percentage of the total variance in the dataset  65. A link to the code for this analysis is available online at the SSA-SARS-CoV-2-tool (https://labmetcalf. shinyapps. io/covid19-burden-africa/). The principal components were then analyzed for the percentage of variance explained, and compared to: (i) the number of COVID-19 tests per 100,000 population as of the end of June, 2020 (Table  S1 ), (ii) the per capita GDP, and (iii) the GINI index of wealth inequality.
PCA Results The first two principal components from the analysis of 29 variables explain 32. 6%, and 13. 1% the total variance, respectively, in the dataset. Countries with higher numbers of completed SARS-CoV-2 tests reported tended to associate with an increase in principal component 1 (Pearson correlation coefficient, r = 0. 67, p = 1. 1e-7, Figure  S5A ). Similarly, high GDP countries seem to associate with an increase in principal component 1 (Pearson correlation coefficient, r = 0. 80, p = 6. 02e-12), Figure  S5B ). In contrast, countries with greater wealth inequality (as measured by the GINI index) are associated with a decrease in principal component 2 (Pearson correlation coefficient, r = -0. 42, p =. 0042, Figure  S5C ). Despite these correlations, a relatively low percentage of variance is explained by each principal component: for the 29 variables, 13 of the 29 principal components are required to explain 90% of the variance (Figure  S5D ).
PCA Discussion These data suggest that inter-country variation in this dataset is not easily explained by a small number of variables. Moreover, though correlations exist between principal components and high-level explanatory variables (testing capacity, wealth), their magnitude is modest. These results highlight that dimensionality reduction is unlikely to be an effective analysis strategy for the variables considered in this study. Despite this overall finding, the PCA on the access to care subset of variables highlights that the variance in these variables is more easily explained by a small number of principal components, and hence may be more amenable to dimensionality reduction. This finding is unsurprising as, for example, the number of hospital beds per 100,000 population is likely to be directly related to the number of hospitals per 100,000 population (indeed r = 0.
Metapopulation model methods Once SARS-CoV-2 has been introduced into a country, the degree of spread of the infection within the country will be governed by subnational mobility: the pathogen is more likely to be introduced into a location where individuals arrive more frequently than one where incoming travellers are less frequent. Large-scale consistent measures of mobility remain rare. However, recently, estimates of accessibility have been produced at a global scale  24. Although this is unlikely to perfectly reflect mobility within countries, especially as interventions and travel restrictions are put in place, it provides a starting point for evaluating the role of human mobility in shaping the outbreak pace across SSA. We use the inverse of a measure of the cost of travel between the centroids of administrative level 2 spatial units to describe mobility between locations (estimated by applying the costDistance function in the gdistance package in R to the friction surfaces supplied in ref  24  ).
5 to approximately represent the R0 of SARS-CoV-2); the exponent , = 0. 97 is used to capture the effects of discretization 71 , ) !,# captures the introduction of new infections into site j at time t, and + reflects the introduction of new susceptible individuals resulting from the birth rate, set to reflect the most recent estimates for that country from the World Bank Data Bank (https://data. worldbank. org/indicator/SP. DYN. CBRT. IN). We make the simplifying assumption that mobility linking locations i and j, denoted 1 ',! ,scales with the inverse of the cost of travel between sites i and j evaluated according to the friction surface provided in  24. The introduction of an infected individual into location j is then defined by a draw from a Bernouilli distribution following: ) !,# ∼ 3456(1 -49:(-; ( % 1 ',! " ',# /! ' )) where L is the total number of administrative 2 units in that country, and the rate of introduction is the product of connectivity between the focal location and each other location multiplied by the proportion of population in each other location that is infected.
Acknowledgements REB is supported by the  Cooperative Institute for Modeling the Earth System (CIMES). AA acknowledges support from the  NIH   Medical Scientist Training Program   1T32GM136577. AJT is funded by the  BMGF  ( OPP1182425 ,  OPP1134076  and  INV-002697 ). MB is funded by  NWO Rubicon  grant  019. 192EN. 017.
M e t h o d o l o g y In order to understand and explain the reasons behind this engagement a survey was conducted from 23/10/2017 to 26/02/2018. The mixed-methods analysis was based on a semi-structured questionnaire, whose qualitative open-ended questions were optional except for one open-ended question included in the demographics. Open-ended questions were optional in order to further understand the feelings, the attitudes and the opinions of the interviewees without obliging them to answer so as to minimize the chances of completely missing values. The proportional spread of the 708 respondents throughout Greece gives a representative sample to this study. The type of administrative method applied to this survey was web-based and was posted on the online survey software Survey Monkey.
Data originating from the in-line blood sampler were corrected for dispersion caused by the length of the arterial sampling line, according to Munk et al. 24  Composite arterial input function was built from these measurements using AniMate software. 25 LP-1r binding potentials were obtained for the pancreas, the duodenum, and the abdominal fat using compartmental kinetic analysis of the radioactivity in the abdominal organ's volume of interest (VOI) with the arterial input function after corrections for attenuation, scatter, dead time, and partial volume effect. Briefly, PET images were reconstructed using an iterative OSEM algorithm (3D iterative, 4 mm Hann filter, two iterations/21 subsets) into a 128×128 matrix with pixel size 2. 34×2. 34×3. 27 mm. Once reconstructed and timeaveraged, these were used, together with CT images, to build semi-automatically VOI of the abdominal organs using ITK-Snap software.
68 Ga-DOTA (1. 2 MBq/kg) was administered intravenously over 10 s using an automated injector and animals were examined with a dynamic PET protocol for 10 min (46 consecutive frames-30×5 s, 6×10 s, 3×20 s, 4×30 s, and 3×60 s). Image reconstruction was performed as described earlier including partial volume correction irrespective of the size of the VOI. K1, the unidirectional transfer rate from blood to tissue obtained from the iterative fitting of PET and arterial input data to a one-tissue compartment model (Pmod), was used to quantify visceral blood flow. 32.