dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study has been conducted in the framework of the project  MUSICA  which is funded by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement number  256961. E. Sepúlveda is supported by the  Ministerio de Economía and Competitividad of Spain  for the project  NOVIA  ( CGL2012-37505 ). The aircraft campaign has been co-funded by the project  MU-SICA  and the  Spanish national project AMISOC  ( CGL2011-24891 ). We are grateful to  INTA Aerial Platforms , a branch of the  Spanish ICTS program , and the  Spanish Air Force  for their efforts in maintaining and operating the aircraft. The AERONET sun photometer at Izaña (principal investigator:  Emilio Cuevas ) has been calibrated within AERONET EUROPE TNA supported by the  European Community Research Infrastructure Action  under the  FP7 Capacities program  for Integrating Activities, ACTRIS grant agreement number  262254.
The expression of STK32B was increased in the cerebellar cortex of patients and expression quantitative trait loci database mining showed association between the protective minor allele of rs10937625 and reduced expression in cerebellar cortex. We found no expression differences related to disease status or marker genotype for the other two genes. Replication of two lead single nucleotide polymorphisms of previous small genome-wide association studies (rs3794087 in SLC1A2, rs9652490 in LINGO1) did not confirm the association with essential tremor.
Imputation The pruned dataset was pre-phased using SHAPEIT (version 2. 12). We used IMPUTE2 (version 2. 3. 0) for imputation. In both steps 1000 Genomes Phase I integrated variant set release (v3) in NCBI b37 was used as reference panel. Only imputed variants with a minor allele frequency 4 0. 01 and an imputation quality score (INFO) above 0. 3 were analysed further.
Replication The most promising SNPs (n = 59) in the finding stage were studied further. We selected SNPs in narrow regional bins in linkage disequilibrium (LD) and controlled their cluster plots. LD bins were defined as SNPs being located in a 500 kb window and with r 2 4 0. 5. We used two Sequenom Õ panels, processed all samples in one centre and tested for association using logistic models implemented in PLINK. Due to incomplete phenotype data, additional covariates could not be included without severely compromising sample size. We investigated follow-up SNPs in the combined discovery and replication data using logistic regression (PLINK).
STK32B marker rs10937625 associated with eQTL in public database In the Braineac eQTL database, the expression of probeset 2716778 (Affymetrix Probeset ID, chr4:5532972-5533090) located in the STK32B coding region is highly associated with the genotype of SNP rs1093762 (P 5 0. 0008) in cerebellar cortex but not in other brain regions  (Trabzuni et al. , 2011)  (Supplementary Fig. 18 ). We observed an additive, negative effect of the minor, protective allele on expression of the STK32B probe set. Expression of PPARGC1A and CTNNA3 were not associated with the genotypes of the corresponding essential tremor-associated SNPs in the Braineac data.
However, it has to be noted that these SNPs failed replication and carry an increased risk of a type 1 error. The biological function of the serine/threonine kinase STK32B is unknown. The gene is located in a 520 kb deletion causing the rare skeletal dysplasia Ellis-Van-Creveld syndrome  (Temtamy et al. , 2008). The association of rs10937625 in the STK32B gene with essential tremor, the increased expression in the cerebellar cortex of patients with essential tremor and the association of the protective minor allele with reduced STK32B expression in cerebellar cortex in the Braineac eQTL database support a role of STK32B in the pathogenesis of essential tremor. We found no association between the rs10937625 genotype and STK32B expression in our samples which might be a related to the small sample size.
Funding This was made possible by funding from the  CIHR ,  Canada Research Chair  in genetics of the nervous system and from the  DFG  ( KU1194/9-1 ) and from the  National Institutes of Health  ( NIH R01 NS073872 ). S. L. G. is grateful to  FRQS  for financial support. G. A. R. is grateful to  CIHR  and to the  Canada Research Chair  in genetics of the nervous system. G. K. , G. D. and F. H. are grateful to the  DFG  for financial support ( KU1194/9-1 ). E. D. L. and L. N. C. were supported by  NIH   R01 NS073872. KORA research was supported within the  Munich Center of Health Sciences (MC-Health ), Ludwig-Maximilians-Universita ¨t, as part of LMUinnovativ. The  Mayo Clinic Florida is a Morris K. Udall Parkinson's Disease Research Center of Excellence  ( NINDS P50  # NS072187 ), and this work was supported by the  Mayo Clinic Florida Research Committee.
Instead, they constitute the signature of a new, tactile script representation emerging in the visual cortex. Cortical reorganization that crosses the sensory boundaries is prominent in blind and deaf humans  (Hirsch et al. , 2015; Pavani and Roder, 2012; Sadato et al. , 2002)  and in congenitally deaf cats  (Lomber et al. , 2011). In the latter, the deprived auditory areas play a vital role in the deaf cats' superior visual perception. The above-mentioned studies used the same paradigms in non-deprived humans and cats, but did not find any signs of cross-modal reorganization (e. g. Figure  7  in    et al. , 2002). One could have thus expected that learning to read by touch would lead to the emergence of a tactile reading area in the somatosensory cortex. Yet, our study produced a different result.
Subjects Thirty-six subjects took part in the first fMRI study (32 females, 4 males; mean age = 29. 17). All were right-handed, fluent in Polish and had normal or corrected-to-normal vision. They were either Braille teachers/professionals (14 subjects), special education students specializing in blindness and related disabilities (11 subjects), or close relatives of blind people (4 subjects). All subjects had or was close to obtain higher education. All subjects showed high motivation to pursue the course. All but three subjects were familiar with Braille visually (see Appendix 1. 2). However, all were naive in tactile Braille reading, which was verified by the baseline testing session (see Appendix 1. 1). To ensure appropriate statistical power, prior to data collection we decided to recruit at least 30 subjects.
The research described in this article was approved by the Commitee for Research Ethics of the Institute of Psychology of the Jagiellonian University (decisions 28/06/2012 and 12/03/2014). Informed consent and consent to publish were obtained from each of the participants in accord with best-practice guidelines for MRI and TMS research. During the first fMRI examination, one subject was eliminated from the study due to a medical condition discovered by the neurologist supervising the study. Right after the first session, two subjects resigned for personal reasons. Another four subjects resigned during the tactile Braille reading course. Thus, 29 subjects completed the tactile Braille course and were included in the data analysis. At the end of the course, nine of the subjects mentioned above who achieved tactile reading speeds between 3 to 17 WPM participated in the TMS experiment.
fMRI data acquisition All fMRI data were acquired on the same Siemens MAGNETOM Tim Trio 3T scanner (Siemens, Mu ¨nchen, Germany). The data from the fMRI reading experiment were collected using a 12-channel head coil. Resting-state fMRI data were acquired with a 32-channel head coil. All data were collected using the same scanning parameters. We used a gradient-echo planar imaging sequence sensitive to blood oxygen level-dependent (BOLD) contrast (33 contiguous axial slices, phase encoding direction=posterior-anterior, 3. 6 mm thickness, TR=2190 ms, angle=90 ˚, TE=30 ms, base resolution=64, phase resolution=200, matrix=64x64, no iPAT). The fMRI reading experiment was divided into four runs: the first two composed the main reading experiment (282 functional images for each run); the latter two composed the control imagery experiment (346 images in the first run, and 353 images in the second run).
The resting-state data were collected in a separate scanning session, in a single run (282 volumes). In each scanning session, T1-weighted images were also acquired for anatomical localization. To make acquisition conditions as similar as possible between the before-and after-training scans, in the before-course scan we measured each subject's head position relative to the head coil. Then, in the after-course scan, we reproduced the previously measured position. In addition, we used a standard Siemens Automatic Alignment scout MRI sequence before the two scans.
Main experiment In the main experiment, subjects were presented with Polish words and control stimuli. We used seventy-two 3-to 6-letter-long Polish words with a frequency higher than 1 occurrence per million according to SUBTLEX-PL;  Mandera et al. , 2014). All had low imageability scores (100-400 according to the MRC database;  Wilson, 1988). There were six experimental conditions, each repeated four times. The conditions were: visually displayed Braille words (VB), visually displayed regular visual words (VW), words in tactile Braille (TB) and suitable control conditions. The words were counterbalanced between the conditions. The control conditions were: strings of 4-6 meaningless Braille signs composed of all 6 Braille dots presented either tactually or visually and strings of 4-6 hash signs (#) presented visually.
fMRI data analysis The data from the fMRI reading experiment were analyzed using SPM8 software (www. fil. ion. ucl. ac. uk/spm/software/spm8/). Data preprocessing included: 1) slice timing, 2) realignment of all EPI images from the before-course and the after-course scans together, 3) coregistration of the anatomical image from the first time point to the mean EPI image, 4) segmentation of the coregistered anatomical image, 5) normalization of all images to MNI space and 5) FWHM spatial smoothing (5 mm). The signal time course for each subject was modelled within a general linear model  (Friston et al. , 1995)  derived by convolving a canonical hemodynamic response function with the time series of the experimental stimulus categories and estimated movement parameters as regressors. Statistical parametric maps of the t statistic resulting from linear contrasts of each stimulus type minus baseline were generated and stored as separate images for each subject.
control) and tactile Braille reading (tactile Braille vs. control), all after the course. Thus, for each voxel in the ROI, we extracted beta values for each reading condition. Then, those beta values were correlated pairwise (visual words x visual Braille, visual Braille x tactile Braille and tactile Braille x visual Braille). As a result, for each participant, we obtained a neural similarity matrix describing how representationally similar the activity patterns associated with the three conditions were. The resulting correlation coefficients for each condition pair were Fisher r-to-z transformed (z(r)) and compared on a group level in one-way repeated measures ANOVA with a factor of script pairs (3 levels: tactile Braille and visual Braille, tactile Braille and visual words, and visual Braille and visual words).
Resting-state fMRI Data Processing Assistant for Resting-State fMRI (DPARSF; Chao-Gan and Yu-Feng, 2010) and SPM8 (www. fil. ion. ucl. ac. uk/spm/software/spm8/) were used to process the data. The first 10 volumes of each subject's scan were discarded for signal stabilization and for subjects' adaptation to scanner noise. Then, slice-timing correction and head-motion correction were applied. The magnitude of participant head motion was quantified by computing mean relative displacement  (Van Dijk et al. , 2012)  and mean frame displacement  (Power et al. , 2012)  measures. Both measures showed no difference in the magnitude of head motion between the first and second scanning session. T1 images were segmented, and both anatomical and functional images were normalized to MNI space. Two steps specific to the functional connectivity analysis -regression of nuisance covariates and bandpass filtering -were performed to reduce spurious variance unlikely to reflect neuronal activity.
g. Lewis et al. , 2009; Urner et al. , 2013; Voss et al. , 2012). We thus expected that final resting-state functional connectivity reflects subjects' intensity of training in last days of the Braille course. This can be quantified as a change in Braille reading speed over last month of the course. This behavioral measure was thus used for correlation. Given our apriori hypothesis, uncorrected significance value is reported. However, the same result was obtained even when correction for multiple comparisons was applied to account for multiple behavioral sessions. Whole-brain analysis was thresholded at p=0. 001 voxel-wise and p=0. 05 cluster-wise. The Brain-Net Viewer toolbox was used for data visualization  (Xia et al. , 2013).
The TMS protocol used in this experiment is in agreement with established safety limits  (Rossi et al. , 2009). Similar protocols have been widely used to interfere with processing in the VWFA  (Duncan et al. , 2010)  as well as in other brain regions  (Go ¨bel et al. , 2001; Gough et al. , 2005; Sandrini et al. , 2008). A MagPro X100 stimulator (MagVenture, Hu ¨ckelhoven, Germany) with a 70 mm figure-eight coil was used to apply the TMS. Moreover, a frameless neuronavigation system (Brainsight software, Rogue Research, Montreal, Canada) was used with a Polaris Vicra infrared camera (Northern Digital, Waterloo, Ontario, Canada) to guide stimulation. and reaction time analysis due to missing data from the second testing session. Thus, the accuracy analysis was performed on 28 subjects and the reaction time analysis, on 25 subjects.
Acknowledgements Supported by a  National Science Centre Poland  grant ( 2012/05/E/HS6/03538 ), a  Marie Curie Career Integration grant  ( 618347 ) and funds from the  Polish Ministry of Science and Higher Education  for co-financing of international projects, years 2013-2017, awarded to MS. AM and KJ were supported by a grant from the  National Science Center Poland  ( 2014/14/M/HS6/00918 ). This project was realized with the aid of CePT research infrastructure purchased with funds from the  European Regional Development Fund  as part of the  Innovative Economy Operational Programme , 2007-2013. We gratefully acknowledge  Boris Gutkin ,  Christophe Pallier ,  Antonio Moreno ,  Valentina Borghesani ,  Karim N'dyaie ,  Małgorzata Kossut ,  Weronika De ˛bowska ,  Adam Ryba ,  Karolina Dukała ,  Avital Hahamy ,  Paweł Hanczur , the subjects, and the  Polish blind community.
Exome sequencing is becoming a standard tool for gene mapping of genetic diseases. Given the vast amount of data generated by Next Generation Sequencing techniques, identification of disease causal variants is like finding a needle in a haystack. The impact assessment and the prioritization of potential pathogenic variants are expected to reduce work in biological validation, which is long and costly. One of the possible approaches to determine the most probable deleterious variants in individual exomes is to use protein function alteration prediction. We propose in this paper to use a machine learning approach, the random forest to build a new meta-score based on five previously described scores (SIFT, Polyphen2, LRT, PhyloP and MutationTaster) and compiled in the dbNSFP database. The functional meta-score was trained on a dataset of 61 500 non-synonymous Single Nucleotide Polymorphisms (SNPs).
Introduction Exome sequencing is a recent and important innovation for the exploration of patients affected by a genetic disease, in particular in situations when the other approaches have failed. The difficulty is often to find the disease causal variant(s), and it may be useful to focus on computational approaches like functional alteration predictors, to synthesize all available a priori information for variants prioritization before further functional studies. Many different methods have been developed and published over the past fifteen years, each of these has distinct advantages and disadvantages, but none can be considered as the gold standard  123. The prediction scores of some of these methods have been compiled in the dbNSFP database for all known protein coding genome positions  4.
Data collection for model building First, we constructed a Single Nucleotide Variant (SNV) dataset with a status variable, taking values "neutral" or "deleterious", in order to build the prediction model. For the deleterious variants, we used the OMIM database (Online Mendelian Inheritance in Man)  7  -23/09/2001 version-available at https://main. g2. bx. psu. edu/library, which inventories variants and phenotypes associated with Mendelian diseases. It contains 9130 human genome variants using the hg19 map. These variants will be considered as deleterious. To build an assumed neutral variant set, we started with the 1000 genome database available via ANNOVAR 8 http://www. openbioinformatics. org/annovar/ (version from November 2010 updated in June 2011), which inventories the genetic data of supposed healthy subjects.
Among these data, we selected the missense variants with the already existing hg19_avsift filter of ANNOVAR, and those with an allele frequency < 1% in the population (rare variants); their neutral nature is not obvious and corresponds to the reality with which researchers are faced. For each of these variants, a score has been attributed with the five following methods: SIFT (released August 2011) 9 , Polyphen2 (HumDiv classifier model v2. 1. 0)  10 , Mutation Taster (released March 2010)  11  , LRT (released November 2009)  12  and PhyloP 13 thanks to the dbNSFP public database https://sites. google. com/site/jpopgen/dbNSFP. 4. This database contains all possible SNPs within human genome coding regions, which have been determined by the CCDS project  14  , and for each of the 87 million SNPs, the scores of the five predictors have been pre-calculated and made available.
Data Collection for external validation In order to evaluate the prediction model on independent datasets, we have used 2 general and 2 more precise datasets:  A published variant dataset -EXOVAR -already used to evaluate similar methods (available at http://statgenpro. psychiatry. hku. hk/limx/kggseq/download/ExoVar. xls) including both 4752 neutral (from 1000 Genomes Project  15  , with a derived allele frequency <1%) and 5340 disease causing variations (with known effects on the molecular function causing human Mendelian diseases from the UniProt database)  16  , but only 1740 neutral and 3601 disease causing variants not included in our learning dataset with all necessary scores available.  A validation dataset composed of 1100 pathogenic non-synonymous variations coming from ClinVar database (annotated as "Pathogenic") and of 5412 variations coming from 1000 Genomes Project  15  with a minor allele frequency between 5% and 20% , considered as non-deleterious and for which the 5 predictors give a score.
None of these variations are part of our learning dataset. Two more specific missense genetic variant datasets coming from two genes having many deleterious or polymorphic known variants:  The first one is the COL4A5 gene on the X chromosome which is composed of 51 exons making up a total length of 257kb. Many variants in this gene are implied in the Alport syndrome [MIM 301050], but SNPs without known disease association are also reported in public databases. After having discarded the SNPs included in our learning dataset, this first validation dataset contains: o 34 neutral variants coming from the dbSNP database -build 137  17  , with the "clinical significance" annotation in the database different from "probable pathogenic" o 168 deleterious variants coming from dbSNP database -build 137 also, annotated "probable pathogenic", and from HGMD database (public version)  18.
 The second analyzed gene is the COL7A1 gene on the chromosome 3 which is composed of 118 exons making up a total length of 32kb. Some variants in this gene are associated in the dystrophic epidermolysis bullosa [MIM 131750]. Our validation dataset contains: o 325 neutral variants coming from the dbSNP database -build 137, with the "clinical significance" annotation in the database different from "probable pathogenic" o 162 deleterious variants coming from dbSNP database -build 137 annotated "probable pathogenic" and the col7info database  19.
Statistical method Relationships between the five scores on the training dataset were analyzed by computing the Spearman's rank correlation matrix. The meta-score rfPred was derived from the five individual scores using the predictions of a random forest model  20. Briefly a random forest is based on a set of classification trees trained on a random subset of observations and variables of the complete dataset. Each tree votes for a given class, here either neutral or deleterious variant, and the final classification is based on the trees vote's majority. In fact, more precise information is provided by the proportion of tree votes in favor of the deleterious class and it can be used as a credibility index of the classification. So we used this vote proportion as a predictive meta-score of functional alteration.
Statistical model evaluation To compare the predictors' performances with the rfPred one, we have used the individual prediction scores (Polyphen2, SIFT, LRT, PhyloP and MutationTaster) coming from dbNSFPv2. 0  23 and CADD scores computed via its web-interface 6. We have then computed the ROC curves and the areas under the curves of the different classifiers thanks to the R package "Hmisc"  24  on the learning dataset and on the validation datasets. In order to have a more useful comparison between the classifiers, we have considered also two particular scenarii corresponding to the reality faced by molecular biologists: In the one hand, the classification model is used on a very long variants list coming directly from the exome alignment on the reference genome in order to establish a list a potentially pathogen variants.
Missing data handling For some exome positions, one or several pre-calculated scores were missing in dbNSFP 2. 0  23. Because we wanted to be able to use rfPred even if one of the score is missing for a variant position, we have imputed the missing score value by a random forest approach implemented in the R package "yaImpute"  25  , based on a k-NN algorithm. We have used k=1 in our procedure.
Results Spearman's correlation matrix for the individual predictors is given in Table  1. It shows low to moderate correlation between scores (0. 18 for the minimal and 0. 66 for the maximal correlation) on the learning dataset, indicating that the information contained in the five prediction scores should not be completely redundant and that a combination of them could therefore be pertinent. Table  1 : Spearman correlation coefficients matrices on learning dataset with OMIM deleterious variants and neutral variants with allele frequençy < 1%.
Model prediction performance The random forest prediction model (rfPred) has an AUC of 0,849 on the "Out of bag" learning dataset (unbiased AUC), whereas the best individual predictor on the same dataset, MutationTaster, obtains only 0,804 of AUC. The simple logistic model meanwhile obtains a maximal AUC of 0,827, even if the AUC is computed on the totality of the sample used to build the model. About CADD, it reaches the AUC of 0,770. We can note that rfPred ROC curve lies above all the others on the whole curve. (Figure  1 ) The variable importance is measured in the rfPred model by the mean decrease in Gini coefficient. It is a measure of how each variable contributes to the homogeneity of the nodes and leaves in the resulting random forest. Each time a particular variable is used to split a node, the Gini coefficient for the child nodes are calculated and compared to that of the original node.