dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection and preprocessing The English and Chinese brain imaging data were acquired with a 3T MRI GE Discovery MR750 scanner with a 32-channel head coil. Anatomical scans were acquired using a T1-weighted volumetric magnetization prepared rapid gradient-echo pulse sequence. Blood-oxygen-level-dependent (BOLD) functional scans were acquired using a multi-echo planar imaging sequence with online re- The English and Chinese fMRI data were preprocessed using AFNI version 16  (Cox, 1996). The first 4 volumes in each run were excluded from analyses to allow for T1-equilibration effects. Multi-echo independent components analysis (ME-ICA,  Kundu et al. , 2012)  was used to denoise data for motion, physiology, and scanner artifacts. Images were then spatially normalized to the standard space of the Montreal Neurological Institute (MNI) atlas, yielding a volumetric time series resampled at 2 mm cubic voxels.
The French brain imaging data was collected with a Siemens Prisma Fit 3T scanner. T1-weighted anatomical images were acquired with a 1 mm isotropic resolution. The EPI functional images were acquired with a resolution of 3. 75 x 3. 75 x 3. 8 mm (34 axial slices with an interleaved acquisition scheme). The 3 echo times were 10 ms, 25 ms, and 38 ms. Preprocessing was performed with ME-ICA  (Kundu et al. , 2012)  using the default parameters and spatial normalisation was done in MNI space. The final volumetric time series consists of 3. 15 mm cubic voxels.
Because of this, we have two native Chinese speakers annotate the Chinese nouns with singular/plural judgments. Calculating Cohen's kappa coefficient  (Cohen, 1960) , a measure of inter-rater reliability, over the Chinese annotations results in a kappa = 0. 96, a high degree of inter-rater reliability. We do not use any nouns in the analysis for which the two annotators disagreed in their number judgements. The time resolution for all three of the fMRI data sets is 2. 0 seconds, much slower than a natural speech rate. Because of this, we remove observations where nouns of different number would occur together within the same volume. That is, if more than one singular noun occur in the same volume or if more than 10 one plural noun occur in the same volume, they are retained. If a singular and plural noun occur in the same volume, however, they are not kept for analysis.
speakers, and had no history of psychiatric, neurological or other medical illness that could compromise cognitive functions. All participants were paid and gave written informed consent prior to participation, in accordance with the guidelines of the Human Research Participant Protection Program at Cornell University. Saint-Exupéry, 1946)ncludes 30 healthy, right-handed adults (age range = 20-40). They self-identified as native French speakers and had no history of psychiatric, neurological, or other medical illness that could compromise cognitive functions. All participants gave written informed consent prior to participation, in accordance with the guidelines of the Regional Committee for the Protection of Persons involved in Biomedical Research. The Chinese dataset includes 35 healthy, right-handed, young adults (15 female, mean age = 19.
5 This led GLMs to be replaced by linear mixed models (LMMs)  [6] [7] [8]  that co-vary out all features by fitting them as random effects. LMMs have been shown to better control the inflation of false positive associations arising from LD or correlation between probes and to minimize the occurrence of false positives in both GWAS and MWAS. 6, 7, 9 MMs are commonly used in neuroimaging to model longitudinal data. 10 Instead, we rely here on a formulation that allows fitting the high-dimensional brain image as a single randomeffect. Such LMMs allow estimation of the overall degree of association between a trait and a high-dimensional brain image, coined "morphometricity" in the context of structural brain measurements. 11, 12 Recently, we have shown that a single LMM framework was suited to estimate morphometricity in large datasets, to draw links between traits through their associations with similar brain structure (gray-matter correlation), and to build brain-based predictors.
MRI Image Processing MRI images were mostly collected in Cheadle (for 96% of the sample) and Newcastle using a 3T Siemens Skyra machine (software platform VD13) and a 32-channel head coil  13  (see Supplementary 1 in the Supplemental Material, for MRI sequence details). We processed the T1w and T2 FLAIR images together to enhance the tissue segmentation in FreeSurfer 6. 0,  17  which should result in a more precise skull stripping and pial surfaces definition. When the T2 FLAIR was not acquired or not usable, we processed the T1w image alone, though a recent report showed this results in systematic differences in cortical thickness. 18 This may represent a source of noise in the data, albeit it was limited in term of number of individuals (see quality control, Supplementary 1 in the Supplemental Material 1).
We extracted vertex-wise data mapping cortical surface area and thickness ("recon-all" processing in FreeSurfer) and used the maximal resolution allowed by the software (fsaverage atlas -unsmoothed). In short, FreeSurfer segments the gray/white and gray/cerebrospinal fluid borders, which delimits the gray-matter. Surfaces are mapped onto a spherical atlas to align the cortical folding patterns of the individuals, and a tessellation is applied. Cortical thickness is calculated as the closest distance from the two gray-matter boundaries, for each vertex on the tessellated surface. 19 urface area is measured as the mean area of all faces that meet at a particular vertex, on the gray/ white matter surface. 20 We previously showed that this cortical processing maximized the morphometricity for a wide range of phenotypes.
15 In other words, this cortical processing maximized the information retained by the processed MRI images. In addition, we applied the ENIGMA-shape processing,  21, 22  where subcortical structures segmented in FreeSurfer are projected onto spherical atlases to quantify vertex-wise radial thickness and log Jacobian determinant,  21, 22  which is analogous to a surface area. 23 This yielded a vertex-wise characterization of the hippocampus, putamen, amygdala, thalamus, caudate, pallidum, and accumbens. Overall, the imaging data used in the analyses comprised 652,283 vertex measurements per individual: 299,009 for cortical thickness, another 299,034 for cortical surface area, 27,120 for subcortical thickness and 27,120 for subcortical surface area. In a post-hoc analysis, we also utilized smoothed cortical data (surface based kernel with FWHM = 20 mm), in order to evaluate the robustness of our results to variation in the MRI processing.
Independent Samples for Prediction and Replication Our first independent sample included an additional 4942 participants of the UKB with a T1w image (downloaded in May 2018, most participants also had an exploitable T2w). The final sample (N ¼ 4160 after processing and QC) was on average 63. 1 years old (SD ¼ 7. 46, range 46. 1 to 80. 3) with 52. 1% of females. In addition, we used the OASIS3 (Open Access Series of Imaging Studies) sample  24  to evaluate the generalizability of the prediction. The OASIS3 dataset gathers several longitudinal MRI studies conducted in the Washington University Knight Alzheimer Disease Research Center over the past 15 years. Our final sample included 1006 unique participants after processing based on T1w images and QC. When several visits were available for a participant, we selected the one with the most phenotypic information.
g. , the same confounders are more likely to be shared in the same cohort than across different cohorts). In contrast, the prediction accuracy from LMMs was comparable between the UKB and OASIS3 samples, pointing toward a better generalizability of the prediction. This suggests that the higher prediction accuracy in the UKB replication sample for GLM is likely to be driven by confounding factors shared between UKB data sets. The comparable performance of GLM and LMM seen on OASIS3 for BMI aligns with our simulations. For age and sex prediction, prediction accuracy of LMMs was sometimes inferior to that achieved from GLMs, in particular those from the simplest models ("no covariates" and "age, sex, ICV"). Overall, prediction based on LMMs generalized well (comparable accuracy in the UKB and OASIS3), while the GLMs often displayed heterogeneous performances across the test samples (in particular for the GLMs with PCs, which may suffer from PCs being different between samples).
Description of Associated Regions We listed the significant associations identified using LMM (global BRM) in Table  S3  in the Supplemental Material (Figs. S9-S11 in the Supplemental Material for Manhattan plots, and Fig. 4  Number of significant clusters and prediction accuracy for the real UKB phenotypes. Bars represent the 95% confidence intervals of the prediction accuracy (correlations). Dots indicate prediction accuracy in the UKB replication sample, while stars correspond to the prediction achieved in the OASIS3 sample. Prediction accuracy is reported controlling for age, sex (when pertinent), ICM, and site/machine. In the OASIS3 dataset, we further controlled for clinical status. The dashed lines correspond to the estimated morphometricity, which corresponds to the theoretical maximum prediction accuracy achievable from a linear predictor.
Figs. S12-S16 for brain plots in the Supplemental Material). The significant associations were in the range of R 2 ¼ 0. 5% to 1%. Most associations were observed with subcortical volumes though the top cluster for sex was spatially located at the border of the lateral-orbitofrontal and medial orbitofrontal gyri (based on the Desikan atlas  26  ). Out of the 85 vertices associated with age, sex, and BMI, 68 replicated in an independent UKB sample (p < 0. 05∕85, Table  S2  in the Supplemental Material). In particular, 4/11 associations replicated for BMI, 43/47 for age, and 21/27 for sex. The replication rate was slightly lower in the OASIS3 dataset, where none of the vertices reached significance for BMI, 15/47 associations were replicated for age, and 12/27 for sex. Overall, the sign of the associations was consistent across the three datasets (Table  S3  in the Supplemental Material).
As in the simulations, the LMMs identified fewer vertices and clusters than the GLMs (Fig. 4 , Table  S2  in the Supplemental Material). The LMM with multiple random-effect components was the most stringent (a single cluster of association), consistent with simulations which showed it had the lowest FWER and statistical power. In contrast, the LMM with a single random-effect component identified several cortical and subcortical associations with BMI, age and sex (Table  S3  in the Supplemental Material). Most (12/19) of the top vertices in the associated cluster replicated in the UKB left out sample, and six replicated in the OASIS3 sample (Table  S3  in the Supplemental Material). The lower replication rate in OASIS3 may be due to a lower power even though we cannot rule out that the same confounders might act similarly on the two UKB data sets.
Finally, unlike in our simulations (Fig. 3 ), LMMs often resulted in lower prediction accuracy than GLMs in the UKB left out sample (Fig. 4 ). Nonetheless, prediction from LMMs generalized better in the OASIS3 dataset (Fig. 4 ). 24 This suggests that LMMs result in a more robust and parsimonious predictor, less sensitive to sample specific vertexwise patterns and confounders. In the past years, many studies have been published on the association between gray-matter structure and our phenotypes of interest (see Tables S4-S8 in the Supplemental Material for a selective review of publications). Our simulation and empirical results suggest that some of these studies could report a substantial number of false positive or redundant associations. Nevertheless, due to the limitations outlined below, it is unclear which of these studies suffer from this issue and to which extent.
Code and data availability. The dH map is available upon request and will be available on a public repository, pending publication. ,The Pléiades DEM is available at https://doi. org/10. 5281/zenodo. 6979691 (Berthier, 2022). License restrictions apply to the Pléiades ortho images, that are available upon request to E. B. , pending the signature of license agreement with CNES. VENµS data are available at: https://doi. org/10. 5281/zenodo. 6685515 (Brun, 2022)  The wind erosion code is available at: https://github. com/antonplanchot/wind-snow-scg (Planchot and Amory, 2022)      Mass balance simulations presented in this study are available at https://doi. org/10. 5281/zenodo. 7006744, as well as forcing and initialisation information to reproduce Crocus simulations. SURFEX/ISBA-Crocus model based on version v8.
In addition, clusters in the visual word form area and the left visual cortex were negatively associated with reading speed  (Skeide et al. , 2016). Thus, crucial regions of the dorsal and ventral stream predicted reading with high precision. Choosing a similar approach  (Cui et al. , 2018) , used large datasets of the Human Connectome Project to predict individual reading comprehension and decoding skills in young adults. More specifically, they performed an elastic net penalized linear regression to predict individual literacy scores based on whole-brain cortical volume. The most critical predictive clusters were located in frontal and subcortical regions. The generalizability of the prediction model was then tested in an independent sample of Chinese children (8-to 13-years-old) with mixed results.
, 2018, for promises and pitfalls). Secondly, we included several macrostructural features and compared their relative importance. Thirdly, we used both structural and behavioral information (RAN and PA tested at the end of kindergarten) in the CV models to investigate whether or not, and if so, to what extent adding neuroanatomic data would improve the prediction of future literacy over and above cognitive-linguistic preliterate skills. For that, we obtained cortical volume, surface area, and local gyrification (lGI) from structural scans of preliterate children at the end of kindergarten. The reasoning behind our choice was the following: Firstly, we included cortical volume as one of the most widely tested cortical measures to show the validity of our data and see if we could replicate the well-established pattern using a CV-algorithm.
T1-weighted imaging analysis First, the T1-weighted images were visually inspected by two independent raters using Freeview 3. 0, FreeSurfer's visualization tool  (Fischl, 2012) , and MANGO 4. 1, a multiimage analysis graphical user interface  (Lancaster and Martinez, 2006). Additionally, image quality was assessed using the Computational Anatomical Toolbox (CAT) 12, an extension to the statistical parametric mapping (SPM) 12 software (Welcome Department of Cognitive Neurology;  Ashburner et al. , 2021). Fourteen participants were excluded due to severe ringing and blurring artifacts in the MRI scans caused by head motion. Children with moderate rigid body movement were marked and treated with special care in the subsequent visual inspection step, i. e. , after reconstructing the surfaces during preprocessing.
The cortices of eleven children were insufficiently reconstructed and excluded from subsequent analyses. A fully automated pipeline of the FreeSurfer 7. 1. 1 software package  (Fischl, 2012)  was utilized to preprocess the T1weighted MRI scans, which included removal of nonbrain tissues, transformation, and intensity normalization, segmentation of white and deep gray matter, correction of topological errors, and reconstruction of the cortical surface. Surface area and cortical volume were extracted from the T1weighted image. In FreeSurfer, surface area is quantified as the sum of the areas of adjacent triangle faces on the surface mesh, computed in each participant's native space, allowing for individual variations in the area of each triangle. Cortical volume is defined as the amount of gray matter between the gray/white and pial boundary.
, 2006). This automatic labeling has been discussed as having higher accuracy than manual parcelation  (Desikan et al. , 2006). Next, the mean metrics for all ROIs were extracted from the FreeSurfer output and imported into MATLAB. Although the FreeSurfer average participant is adult-based, it is frequently used in pediatric samples, and surface-based registration has been validated in children ages 1-11 with good alignment of cortical landmarks  (Ghosh et al. , 2010). No smoothing was applied to the lGI data as it is already intrinsically smoothed on the individual level as defined by the algorithms employed during the lGI procedure (averaging across a 25-diameter circle). Surface area and cortical thickness metrics were smoothed at full width half maximum (FWHM) of 20 mm to approximate the intrinsic smoothing of the lGI algorithms and increase the signal-to-noise ratio (mri_surf2surf).
Statistical analyses Demographic and psychometric data were assessed in MATLAB 2020b. Literacy ability was calculated as the mean of reading fluency of words and pseudowords (SLRT-II), reading comprehension of words, sentences, and text (ELFE 1-6), and spelling accuracy scores from dictation (DERET 1-2+) to match other attempts of reading predictions in German-speaking samples (e. g. ,  Kuhl et al. , 2020). Instead of age-normed standard scores, raw scores for all psychometric measures were utilized as we were interested in the within-subject association between measures at TP1 and TP2 and not in comparisons between peers. A CV elastic net linear regression  (Zou and Hastie, 2005 ) was used to perform a variable selection of the best preliterate brain and behavioral predictors of literacy ability, as introduced above.
Here, both α and λ were estimated within the inner loop of a nested leave-one-out cross-validation (LOOCV). LOOCV is the most extreme form of k-fold CV  (Allen, 1974; Stone, 1974; Geisser, 1975). When using LOOCV, the number of folds equals the number of observations (k = n), which is especially valuable when the sample size is small  (Allen, 1974; Stone, 1974). A nested LOOCV method was chosen to avoid a biased, overly optimistic estimate of the true generalization error, which may be the case if observations are part of both the training and test dataset  (Varma and Simon, 2006). This framework is visualized in Figure  1. The best model in the inner loop was selected based on the lowest prediction error, quantified by the root mean squared error (RMSE). The resulting model was then utilized in the outer loop to predict literacy ability.
Funding This work was supported by the  German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF)  with grants  FKZ 01GJ1010A  and  01GJ1404. JZ was supported by the  Alexander-von-Humboldt Stiftung  and the  Institute of Language, Communication  and the  Brain  ( ANR-16-CONV-0002 ).
Simple Summary: Recent advances in technology allow for the study of animal behaviours through indirect observations. This facilitates research on cryptic animals for which direct observations may miss a considerable portion of their activity. The validity of accelerometers in obtaining accurate animal behaviours, however, needs to be tested before collecting data in the wild. Modern zoos offer excellent opportunities for researchers to test field techniques in a safe setting. Here, we describe a non-invasive training program to attach an accelerometer to an individual Bengal slow loris at the Shaldon Wildlife Trust. This training took 39 15-min sessions and allowed for the attachment of the accelerometer for validation with reduced stress for the animal. We also collected videos to associate to accelerometer data to estimate the accuracy of accelerometers in identifying the behaviours of Bengal slow loris.
bengalensis) confiscated from the illegal wildlife trade. As the closest relative to Javan slow lorises, which do not occur in captivity and have a similar body size, the zoo management agreed to collaborate in undertaking a 3-axis accelerometer study with two objectives. First, we investigated whether target training was an option to train a slow loris to wear a collar fitted with an accelerometer. Second, we sought to use data gained from the accelerometer to validate behavioural data of Nycticebus to provide a baseline to apply to the wild. We discuss the results in relation to the welfare and ecological research of cryptic species and how zoological collections and researchers can work together to achieve these ends.
Data Collection We conducted the research at the Shaldon Wildlife Trust, Devon, United Kingdom, in the zoo's nocturnal house. Animals are kept on a reverse light cycle, with twelve hours of nocturnal illumination provided by a combination of red and blue LED lights from 600 h to 1800 h. The Bengal slow loris enclosure is approximately 205 cm long, 61 cm wide and 91 cm high, and is enriched with a mix of fixed and dynamic branches and logs of various sizes providing platforms for continuous movement. The enclosure was thoroughly checked for potential hazards prior to beginning the experiment. Food is provided on wooden platforms, with gum provided in logs with pre-drilled holes. The temperature of the enclosure is approximately 24 • C. The subject of the study was a single adult female Bengal slow loris (ZIMS ID 1190; call name Tina), weighing ~1500 g at the time of study.
We chose a snap-on safety cat collar for the training as it was easy to take on and off quickly and had a quick release mechanism if for any reason it became stuck on the cage furniture. After training, the cat collar was equipped with a three-axis accelerometer (Technosmart, Axy 5 S) with dimensions of 22 mm × 13 mm × 10 mm, weighing 4. 5 g (thus well below the 5% of body weight threshold suggested when collaring animals). The accelerometer was set to collect data at a frequency of 25 Hz. We collected data between January-June 2020 and January-February 2021, totalling 42 h of videos, from which we collected the data for validation. Two separate night vision video cameras were positioned to ensure the best possible view of the behaviours of the animal, but the animal remained still out of sight for most of the time or the behaviours were not clear, so we managed to associate 54 min of video to the accelerometer data.
Data Analysis From the raw data collected by the accelerometer, we calculated the static and dynamic acceleration, amplitude (i. e. , standard deviation of the dynamic acceleration), ODBA (overall dynamic body acceleration) and pitch by using the package "plotrix" over a smoothing factor of 2 s to reduce noise  [19]. The full dataset with associated behaviours used for the analysis contained 81,051 datapoints. We first created a new dataset with an even number of datapoints per each category (i. e. , we reduced, using a random selection, the number of datapoints for each behaviour to match the number of datapoints of the less frequent behaviour). This was to avoid unbalanced categories that might have biased the classification. We created two random datasets: one training and one validation set containing 70% and 30% of the data, respectively.
We performed the random forest on the training set with the function "randomForest" from the package "randomForest"  [20]. The behaviours were set as the response variable and the variables obtained from the accelerometers were the predictors in the random forest classifier. We calculated the optimal mtry (i. e. , number of features used in the construction of each tree) via the function "tuneRF". We obtained the importance of each variable in the random forest classifier via the function "importance" and plotted with the function "varImpPlot". We then used the random forest to predict the validation set via the function "predict" and reported the accuracy of predictions. A detailed description of the use of random forest models for validation can be found in Dickinson et al. [21]. The analyses were performed via R v 4.