dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondary participants completed MRI scanning as part of a whole-day assessment at one of two sites (Cambridge and London, UK), on at least two occasions. Cohort retention for the MRI follow-up was 74%  (Kiddle et al. , 2018). Further inclusion criteria for the present study were availability of T1w and MT data available at two timepoints, resulting in 234 healthy adolescents. Following additional quality control on raw images, surface reconstructions and MT profiles, detailed below, the sample included 180 individuals (age stratification at baseline, n = 39/41/39/33/26, inter-scan interval = 15. 4 ± 3. 5 months, 86 females). T1w and MT imaging were acquired as part of the quantitative multiparameter mapping (MPM) sequence  (Weiskopf et al. , 2013)  on three identical TIM Trio 3T scanners; two located in Cambridge and one located in London.
Given 1 mm isotropic voxels and 1. 4-4. 2 mm cortical thickness in present dataset, we estimate that each vertex-wise depth profile contains approximately 2-5 voxels. Using trilinear volume interpolation to each intracortical surface allowed for greater nuance in the intensity estimates along the depth profile. In total, each parcel-wise profile contains 71 ± 5. 4 voxels. The parcellation scheme was mapped from a standard space (fsaverage) to native space for each subject using surface-based registration  (Hong et al. , 2017). We corrected for depth-specific partial volume effects (PVE) of cerebrospinal fluid using a mixed tissue class model  (Kim et al. , 2005)  to reduce potential bias of averaging MT values in voxels with CSF  (Mossahebi et al. , 2015). Specifically, we fitted a linear model at each node (n) and each surface (s) of the form MTðn; sÞ ~b0 þ b 1 CSFðn; sÞ where MT(n,s) and CSF(n,s) represent node-specific, surface-specific MT value and CSF partial volume effect estimates.
We selected hyper-stained myelin pictures from classical literature  (Vogt and Vogt, 1919; Vogt, 1911)  that represent different levels of cytoarchitectural complexity and have been well characterised in recent work  (Palomero-Gallagher and Zilles, 2019). We extracted a rectangle section of each image (spanning from the pial layer to the white matter boundary; Figure  1A ), inverted the colour to align with MT imaging (i. e. ,: myelin is more white), obtained intensity values per pixel by reading the image into MATLAB (v2017b), then generated a region-specific intensity profile by averaging values row-wise. Finally, we calculated the MT moments for each region and contrasted these values with baseline group-average MT moments for matched regions. Additionally, we contrasted baseline group-average MT moments across levels of laminar differentiation  (Mesulam, 2002; Paquola et al.
Longitudinal assessment of age-related changes in MT moments Age-related changes in MT moments were estimated at each node within four linear mixed effect models (LME) using SurfStat  (Worsley et al. , 2009)  for Matlab, accounting for the non-independence of subjects as well as sex, using the following model: momentðnÞ ~b0 þ b 1 age þ b 2 sex þ ð1jsubject þ "Þ where n represents the node. The addition of person-specific random intercepts significantly improves model fit in accelerated longitudinal designs. LMEs were performed on each node and t-statistics were projected onto the cortical surface. Additionally, we examined the relationship between level of laminar differentiation and adolescent development of MT moments. To assess the relative over-or under-representation of significant regions per laminar class we generated 10,000 spin permutations of the t-maps, which controls for spatial contiguity and hemispheric symmetry across permutations  (Alexander-Bloch et al.
Gene expression analyses We measured the spatial overlap between our baseline maps of MT moments (e. g. , one scan per subject; Figure  1E ) and t-statistics (Figure  2A ) and maps of post mortem gene expression from the Allen Institute for Brain Sciences (AIBS). We used Neurovault gene decoding of the AIBS dataset to identify the significant associations of spatial gene expression with t-maps  (Hawrylycz et al. , 2012; Gorgolewski et al. , 2015). Neurovault implements a non-linear co-registration of each AIBS donor brain to a standard MNI template. Visual inspection revealed that one donor required additional manual correction due to a cerebellar deformations. Then, 4 mm spheres were drawn around each probe coordinate and values from the input t-maps, already in MNI space, were averaged within each sphere.
Mixed effect analysis modelled the associations between each individual gene across the six donors and each input t-map. Meta-data from the gene probes (i. e. , their EntrezID's) were downloaded within the same pipeline and thus always had the latest information available from AIBS. Genes without EntrezID were excluded from subsequent analysis. Filtering was omitted on the genes to ensure a fully data-driven approach. Resultant gene associations p-values were corrected at an FDR level of 0. 05 across all included genes and only genes passing correction were included in subsequent analyses. Enrichment analyses were conducted using Enrichr  (Chen et al. , 2013; Kuleshov et al. , 2016) , using a Z-score modification of Fisher's exact test and FDR correction. It should be noted that the AIBS data are based on adult post-mortem and that the developmental associations are indirect associations between the enriched gene-set from spatial overlap analysis and a different developmental gene-expression dataset.
Specifically, the t-statistic maps of agerelated changes in the moments were spatially correlated with gene expression maps from the AIBS dataset using Neurovault (https://github. com/NeuroVault/NeuroVault/tree/master/ahba_docker). The genes derived from this spatial analysis were subsequently compared against developmental expression profiles from the BrainSpan dataset (http://www. brainspan. org/) using the CSEA developmental expression tool developed by the Dougherty lab (http://genetics. wustl. edu/jdlab/cseatool-2/).
Data and code availability Preprocessed microstructure profiles are available via GitHub: https://github. com/MICA-MNI/ micaopen/tree/master/a_moment_of_change  (Paquola, 2019 ; copy archived at https://github. com/ elifesciences-publications/micaopen/tree/master/a_moment_of_change). The repository also includes Matlab and R scripts to reproduce the primary analyses and figures. The following datasets were generated:.
Acknowledgements CP is supported by the  Fonds de la Recherche du Quebec -Sante(FRQS) , RAIB is supported by a  British Academy Postdoctoral  fellowship and Autism Research Trust. CP, RAIB, GW, BB are supported by a  Cambridge-MNI collaborative research grant. RRG was funded by the  NSPN  and the  Guarantors of Brain. JS was supported by the  NIH Oxford-Cambridge Scholars ' Program. KW was supported by the  Health Brain Healthy Lives (HBHL) Initiative. KJW was funded by a  The Alan Turing Institute  under the  EPSRC grant   EP/N510129/1. PEV was supported by the  MRC  ( MR/K020706/1 ), is a Fellow of MQ:  Transforming Mental Health  ( MQF17/24 ) and is a Fellow of the  Alan Turing Institute  funded under  EPSRC  ( EP/N510129/1 ). BB is supported by  National Science and Engineering Research Council of Canada (NSERC ,  Discovery-1304413 ), the  Canadian Institutes of Health Research (CIHR ,  FDN-154298 ), the  Azrieli Center for Autism Research of the Montreal Neurological Institute (ACAR) ,  SickKids Foundation  ( NI17-039 ), and received salary support from  FRQS  ( Chercheur Boursier Junior  1).
EB is supported by a  NIHR Senior Investigator award. Data were curated and analysed using a computational facility funded by an  MRC research infrastructure award  ( MR/M009041/1 ) and supported by the  NIHR Cambridge BRC. This work was supported by the  Neuroscience in Psychiatry Network (NSPN)  Consortium, a strategic award from the  Wellcome Trust  to the University of Cambridge and University College London ( 095844/Z/11/Z ); by the  Cambridge NIHR Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the  Department of Health and Social Care.
Data The dataset represents the raw data (supplementary Table ), frequencies of alleles, genotypes and haplotypes for single nucleotide polymorphisms (SNPs) rs4946651, rs7759938, rs314280 and rs314276 of the LIN28B gene associated with age at menarche in previously published genome-wide and candidate gene association studies https://www. ncbi. nlm. nih. gov/pmc/articles/PMC5738205/, [1e7]. The data were divided into three groups according to the age at menarche (AAM) of the participants: the whole sample, the early age at menarche (<12 years), and the average age at menarche (12e14 years). The frequencies of the alleles, genotypes and haplotypes are presented in https://www. ncbi. nlm. nih. gov/pmc/articles/PMC5738205/table/t0005/, Table  1  and https://www. ncbi. nlm. nih. gov/ pmc/articles/PMC5738205/table/t0010/, Table  2  respectively.
Statistical analysis The correspondence of the SNPs to the Hardy-Weinberg equilibrium was checked using the chisquare test. No significant differences in allele frequencies between the group with the early age at menarche (<12 years) and group with the average age at menarche (12e14 years) (p > 0. 05) were revealed. The Haploview version 4. 2 software (https://www. broadinstitute. org/haploview/haploview) was used to quantify the linkage disequilibrium (LD) between rs4946651, rs7759938, rs314280 and rs314276 in LIN28B gene. Haplotype frequencies were determined using the EM algorithm. The LD block structure was defined using the Solid Spine of the LD algorithm  [11]  provided by the Haploview 4. 2. The degree of genetic linkage between the 4 SNPs in each groups was estimated as Lewontin's coefficient D 0 , where no color (D 0 ¼ 0) indicates that LD is weak or nonexistent and the dark red (D 0 ¼ 1) indicates that there exists strong pairwise LD between SNPs (Fig.
1 ). Rayyan software (https://rayyan. qcri. org) was used as a screening tool to facilitate blind screening within the teams. 13 Every citation was reviewed by two members of the research team using the same inclusion criteria. Any conflicts in including or excluding articles were resolved through a discussion within the research teams. Eligible full-text articles were obtained and categorised according to sub-questions developed for each topic. They were summarised and evaluated according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system,  14  which systematically evaluates the available literature and focuses on the level of evidence based upon the types of studies included. Each research team worked with one of the experts to formulate recommendations for their sub-questions based on the available literature and the input of their assigned expert.
We argue in line with the hypothesis that some of the problems associated with the training of GANs are in part due to lack of control of the discriminator. In light of this, we propose a simple yet powerful diversity regularizer for training GANs that encourages the discriminator to extract near-orthogonal filters. The problem abstraction is that in addition to the gradient information from the adversarial loss made available by the discriminator, we also want the GAN system to benefit from extracting diverse features in the discriminator. Experimental results consistently show that, when correctly applied, the proposed regularization enforces diverse features in the discriminator and better stabilize the GAN training with mostly positive effects on the generated samples. The contribution of this work is two-fold: (i) we propose a new method to regularize adversarial learning by inhibiting the learning of redundant features and availing a stable gradient for weights updates during training and (ii) we show that the proposed method stabilizes the adversarial training and enhances the performance of many state-of-the-art methods across many benchmark datasets.
Related Work As originally introduced in  [5] , GANs consist of generator and the discriminator that are parameterized by deep neural networks and are capable of synthesizing interesting local structure on select datasets. The representation capacity of original GAN was extended in conditional GANs  [13]  by incorporating an additional vector that enables the generator to synthesize samples conditioned on some useful information. This extension has motivated several conditional variants of GAN in diverse applications such as edge map  [14, 15] , image synthesis from text  [16] , super-resolution  [17] , style transfer  [18] , just to mention a few. Learning useful representation with GANs has shown to heavily rely on hyperparameter-tuning due to various instability issues during training  [8, 12, 19].
Experiments All experiments were performed on Intel(r) Core(TM) i7-6700 CPU @ 3. 40Ghz and a 64GB of RAM running a 64-bit Ubuntu 16. 04 edition. The software implementation has been in PyTorch library 1  on two Titan X 12GB GPUs. Implementation of DiReAL is available at https://github. com/keishinkickback/DiReAL. Diversity regularized adversarial learning (DiReAL) was evaluated on MNIST dataset of handwritten digits  [37] , CIFAR-10  [38] , STL-10  [39] , and Celeb-A  [40]  databases. In the first set of experiments, an ubiquitous deep convolutional GAN (DCGAN) in  [6]  was trained using MNIST digits. The standard MNIST dataset has 60000 training and 10000 testing examples. Each example is a grayscale image of an handwritten digit scaled and centered in a 28 × 28 pixel box. Both the discriminator and generator networks contain 5 layers of convolutional block.
Fig. 3b  shows the handwritten digit samples synthesized with and without DiReAL and it can be observed that diversification of features is beneficial for stabilizing adversarial learning and ultimately improving the samples' quality. Another observation is that DiReAL also prevents learned weights from collapsing to an undesirable manifold thus highlighting some of the benefits of pushing weights near the orthogonal manifold. In the second large-scale experiments, CIFAR-10 dataset was used to train GAN   using DiReAL and the results compared to the unregularized training. The dataset is split into 50000 and 10000 training and testing sets, respectively. Similar to experiments with MNIST, Fig. 4b  shows the diversity loss of the discriminator with and without DiReAL trained on CIFAR-10 database.
It must be remarked that the calculation of Inception Scores is library dependent and that is why the scores reported in Table  1  is different for those reported by Miyato et al. [24]. While our implementation was in PyTorch,  [24]  was in Chainer 2. In the next set of large-scale experiments, STL-10 dataset was used to train generator under diversity regularization and compared with other state-of-the-art regularization techniques. As can be observed in Fig. 6 , images synthesized by generator trained with DiReAL was able to generate images with competitive quality in comparison with other regularization methods considered. Performance of DiReAL was also observed to be competitive to regularization methods such as WGAN-GP and spectral normalization. In Fig. 7  we show the images produced by the generators trained with DiReAL using Celeb-A dataset.
Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20-40 years) while performing a visual working memory task with a T set of 150 Independent Component Analysis (ICA) decompositions by Extended Infomax using RELICA, each on a bootstrap resampling of the data. These data are linked to the paper "Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition"  [1]. Independent components (ICs) are clustered within subject and thereby associated with a quality index (QIc) measure of their stability to data resampling. Sets of single ICA decompositions obtained after applying Principal Component Analysis (PCA) to the data to perform dimension reduction retaining (85%, 95%, 99%) of data variance are also included, as are the positions of the best fitting equivalent dipoles for ICs whose scalp projections are compatible.
Subject area Neuroscience The data can be used to compare the effects that dimensionality reduction by PCA has on sub- sequent decomposition by ICA  [1]  with that on other BSS algorithms. To this aim, the dataset includes Extended Infomax ICA decompositions obtained after applying Principal Component Analysis (PCA) with various values of retained variance (85%, 95%, 99%). The data include a full PCA decomposition (PCA-Only) that can be used to replicate the results in  [1]  with different amounts of retained variance or using different methods for dimensionality reduction (e. g. , linear discriminant analysis -LDA, canonical correlation analysis -CCA) The data can also be useful to test the effects that sampling frequency (e. g. , after downsampling to 50 Hz or oversampling to 1000 Hz) or channel number (e.
Data The data are a collection of 14 electroencephalographic (EEG) datasets recorded from 14 subjects (7 males, 7 females, aged 20-40 years) performing a visual working memory task. Subjects first memorized a set of letters, then a set of probe letters was presented and participants had to press a button to indicate whether the letter was present in the memorized sequence or not. An audio cue gave them feedback on the correctness of their answer. Each subject performed 100 -150 trials. Raw data are available at http://sccn. ucsd. edu/eeglab/BSSComparison/ with an analysis script in Matlab to compare results of linear decomposition with 18 other blind source separation methods  [2]. Here the data are reformatted to include: PCA applied directly on the data (PCA-Only); Extended Infomax ICA applied after PCA with 85%, 95%, 99% retained variance respectively (PCA 85 ICA, PCA 95 ICA, PCA 99 ICA); Extended Infomax ICA applied directly to the data (ICA-Only); Extended Infomax ICA applied 150 times to bootstrapped versions of the data -i.
e. , the same dataset resampled with replacementsee  [3, 4]. For each IC an EEGLAB dipfit structure  [5]  is provided, containing information on the equivalent best fitting dipole. See Figs. 1-3 and data at https://data. mendeley. com/datasets/gvt64rndnd/1 doi:10. 17632/ gvt64rndnd. 1.
This signals the start of a new trial. Each participant performed 100-150 trials. EEG data were collected from 71 channels (69 scalp and 2 periocular electrodes), referred to the right mastoid, at a sampling rate of 250 Hz with an analog pass band of 0. 01 to 100 Hz (SA Instrumentation, San Diego) and with input impedances under 5 kΩ after careful scalp preparation. Continuous data were high-pass filtered (0. 5 Hz), epochs ([ À700 700] ms time locked to each letter presentation) were extracted and whole-epoch mean channel ("baseline") value removed  [7]. Data are presented in the form of epoched EEGLAB datasets (. set and. fdt files). To load the data it is necessary to download and start EEGLAB (http://sccn. ucsd. edu/eeglab/currentversion/eeglab_ current. zip). Matlab is required unless a compiled version of EEGLAB is usedmore info at https://sccn.
Opening and reviewing the data To open the data it is necessary to run the EEGLAB toolbox first by typing 4 4eeglab in the matlab "Command Window" (after setting the current path to the EEGLAB main folder). Each ". set" file (Sub1. set, Sub2. set,…, Sub14. set) can be opened via the EEGLAB GUI (File -4 Load existing dataset) by navigating to the folder where the datasets are. Alternatively, it is possible to type: 4 4EEG ¼ pop_loadset('filename','Sub2. set','filepath', 'PATH_SET'); Where Sub2. set is the filename of the dataset to load and PATH_SET the path to the folder containing it (e. g. , C:\my_data). The data can then be perused at will by using the extensive set of functions available in the EEGLAB environment. Data are stored in the Matlab structure EEG. To plot the raw data it is possible to type (results in Fig.
Acknowledgements Dr. Artoni 's contributions were supported by the  European Union 's  Horizon 2020 research and innovation programme   under Marie Sklodowska Curie  grant agreement No. 750947  (project  BIREHAB ). Drs. Makeig  and Delorme's contributions were supported by a grant ( R01 NS047293  Title: EEGLAB: Software for  Analysis of Human Brain Dynamics)  from the  U. S. National Institutes of Health  and by a gift to the  Swartz Center, UCSD  from  The Swartz Foundation  (Old Field NY). We acknowledge  Dr. Makoto Miyakoshi  for his support and helpful discussions.
, 2018). Thus, our genetic approach selected mutations that suppress toxicity allowing the generation of recombinant strains lacking antitoxin expression. To explore the complete landscape of suppressors, we next scaled-up the transformation assay using the WT or pIsoA3* PCR products as DNA substrates. This approach, called FASTBAC-Seq, has been described recently  (Masachis et al. , 2018). Consistent with the above-mentioned first transformation assay, deep-sequencing data analysis showed that 97. 7% of the pIsoA3* transformants contained mutations (Figure  3C ). A strong mutation rate (51. 2%) was also observed with the WT PCR product (Figure  3C ), which results from an unanticipated technical artifact linked to PCR amplification (see Material and methods section). Analysis of the number of mutations per read in the complete sequencing dataset (all replicates combined) showed that more than half of the pIsoA3* transformed clones (51.
Importantly, these suppressor mutations do not interfere with the folding pathway of the full-length mRNA, neither affecting its transcription, stability, nor its 3'-end maturation, indicating that they exclusively act on the active, truncated, aapA3 mRNA form (Figure  7 ). Interestingly, by using the KineFold software, these local hairpins were previously predicted to form during the co-transcriptional folding pathway of several aapA mRNAs  (Arnion et al. , 2017; Xayaphoummine et al. , 2005). Now, our FASTBAC-seq approach reveals that these structures are functional, that is they transiently form during transcription to prevent toxin translation before the full-length transcript is completed. Indeed, stabilizing these hairpins inhibits translation of the aapA3-Tr form. This temporal control of gene expression is achieved through the sequential formation of two RNA hairpin structures that mask the SD sequence via CU-rich elements.
Determining H. pylori transformation efficiency For the transformation assays aiming at the determination of the transformation efficiency as an indirect proof of the toxicity of the expression of a PCR construct, transformation patches (after 16 hr growth upon DNA addition) were recovered and resuspended in 1 mL BHI. Ten-fold serial dilutions adapted to each transformation case (10 7 , 10 6 and 10 5 for non-selective media; and 10 4 , 10 3 , 10 2 for selective media upon transformation with water or a toxic construct; and 10 5 , 10 4 , 10 3 for selective media upon transformation with non-toxic constructs) were performed. Allelic replacement events were selected by the use of streptomycin-containing plates (selection of loss of the rpsL Cj -erm cassette, Str R ). The number of Str R CFU/total CFU was calculated, plotted and statistically analyzed by unpaired t (student)-test (GraphPad Prism software version 7).