dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this paper we first presented an articulatory-based speech synthesizer built from articulatory-acoustic data from one reference speaker using deep neural networks, and then showed that this synthesizer could be controlled in real-time closed-loop situation by several speakers using motion capture data (electromagnetic articulography) as input parameters. Experiments included the same reference speaker in a different session, as well as other speakers. All speakers were silently articulating and were given the synthesized acoustic feedback through headphones. A calibration method was used to take into account articulatory differences across speakers (and across sessions for the reference speaker), such as sensor positioning and ways of articulating the different sounds. Subjective listening tests were conducted to assess the quality of the synthesizer and in particular its performance during real-time closed-loop control by new speakers.
e. , mid/ late October), when dry season survival strategies might be set up in sensitive stages. To control for potential cycle failure, temperature and relative humidity were recorded inside the chamber at a 10 min pace using MSR145 Data Loggers (MSR145B4HL, MSR electronics GmBH, Switzerland). We also recorded the temperature cycles to which larvae were exposed using waterproof MSR145B4T2L monitors. The daily air temperature and humidity fluctuations recorded in the climatic chamber, identical to  [18, 34] , are represented in Additional file 1: Figure  S1B  and D and water temperature variation is shown in Additional file 2: Figure  S2. For each mosquito colony, eggs were collected from three independent batches, each produced by >50 caged females. Eggs were transferred into transparent plastic trays (21.
Exome sequencing analysis Exome sequencing of 23 familial EOAD patients was performed as previously described  (Guerreiro, et al. , 2013). Exomes of the 22 sporadic EOAD patients were captured using the Agilent SureSelect Human All Exon V5 plus UTR kit. Sequencing was performed on an Illumina Genome HiSeq 2000 by 101-base paired-end reads. Alignment of sequence reads was performed against the Human Reference Genome build GRCh37. Variants were called using default settings from the GATK tool and following the best practices  (McKenna, et al. , 2010). Low-quality variants that did not pass the Variant Quality Score Recalibration (VQSR) filter  (DePristo, et al. , 2011)  were removed. Genetic data from the two pilot studies were combined and analyzed using two independent pipelines in order to maximize the chance of detecting potentially pathogenic variants.
Results were analyzed using SDS software version 2. 2 (Life Technologies, Carlsbad, CA, USA) and quantifications of TYROBP and TREM2 mRNA were first normalized to GAPDH levels and were determined relative to TYROBP WT transfected cells (Supplementary Material). For western blotting, cells were harvested using radioimmunoprecipitation assay buffer (RIPA) (Boston Bioproducts, Ashland, MA). Equal volumes of cell lysate were run on 10-20% acrylamide tris-glycine gels (Invitrogen, Carlsbad, CA, USA). After transfer, total TYROBP levels were detected using an eGFP antibody (MAB3580; Millipore, Darmstadt, Germany). TREM2 levels were assessed using a cMyc antibody (9E10; Roche, Penzberg, Germany). A GAPDH antibody was used as a protein loading control to visualize protein loading (Meridian Life Science, Memphis, ME, USA).
Statistical analyses Statistical analyses were performed using the Combined and Multivariate Collapsing test (CMC) using 100,000 permutation and the R software version 3. 1. 2 (2014-10-3, Vienna, Austria)  (Li and Leal, 2008). Rare variants (minor allele frequency < 1%) affecting the TYROBP protein sequence were considered. CMC test collapses all rare variants and compares the proportions of variant carriers among cases and controls. No adjustment for age and gender were performed. Statistical analyses for the western blot quantifications and the mRNA experiments were performed using a one-way ANOVA followed by Student t-tests for TREM2 and TYROBP levels using GraphPad Prism software (version 6. 07 for Windows, GraphPad Software, La Jolla, CA, USA).
Identification of potential candidate genes by exome sequencing We performed two independent exome sequencing pilot studies on 45 white non-Hispanic EOAD patients selected from the overall Mayo Clinic cohort negative for mutations in the known genes: 23 patients with at least one affected relative with AD (mean age at onset: 56. 7 years), and 22 patients with extreme young onset of EOAD (age at onset at or before 55, mean age at onset: 52. 0 years) with no reported family history. Three different bioinformatic filtering strategies were subsequently employed to select candidate EOAD genes from our pilot sequencing dataset of 45 exomes (Table  2 ). First, we assessed our entire patient cohort (n=45) and selected heterozygous rare coding variants with a minor allele frequency (MAF) <0. 1% in the NHLBI GO Exome Sequencing Project (ESP), Seattle, WA (URL: http://evs.
gs. washington. edu/EVS/) based on the European cohort. Only variants affecting the protein sequence and shared between at least two individuals were selected (autosomal dominant model). Additionally, variants could not be present in more than 2 families in the GEM. app database. Using this strategy we were able to detect and validate 15 variants in 15 candidate genes (Table  2 ; Supplementary Table  2 ). Second, we focused on sporadic EOAD patients (n=22) and a potential recessive mode of inheritance. We selected all homozygous variants with a less stringent MAF of less than 5% in ESP European cohort that affected protein sequence, which resulted in the identification of 13 variants in 13 candidate genes (Table  2 ; Supplementary Table 2, homozygous model). Finally, in the same cohort of sporadic EOAD patients (n=22), we searched for compound heterozygous variants with a MAF<5% in the ESP European cohort that affected the protein sequences and were present in at least two individuals.
17% of 509 EOAD patients) and healthy subjects (n=1036; no mutation detected), we identified a significant enrichment in EOAD patients (p-value=0. 002; CMC test). To further substantiate this finding we examined two additional independent cohorts for the presence of rare coding variants in TYROBP. We studied 111 EOAD patients without mutations in the known AD genes and 292 healthy individuals from the Knight ADRC at Washington University by Sanger sequencing and we assessed exome sequencing data available on 484 French EOAD patients and 498 French controls without mutation in the known AD genes. Interestingly, we identified and confirmed by Sanger sequencing two rare coding variants in three unrelated French patients  (Nicolas, et al. , 2015) , whereas no rare coding variants were detected in controls (n=595 EOAD patients and n=790 healthy subjects, Table  3 , Supplementary Table  3 ).
Acknowledgments This work was funded by the  US National Institutes of Health  ( P50 AG016574 ,  R01 NS080882 ,  P50 AG016574 ,  R01-AG032990 ,  R01-NS080820 ,  U01 AG046139 ,  R21 AG048101 , and  RF1 AG051504 ), and the  Florida State Ed and Ethel Moore Alzheimer 's  Disease Research Program. J. T. B. and R. G. are supported by  Research Fellowships from Alzheimer's Society. D. C, G. N. and A. -C. R. were supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contrat  ANR-10-INBS-09 ), by the  JPND " Perades Program " and by the  Labex   GENMED ANR-10-LABX-0013.
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registration steps at testing time. We further improve segmentation results by interpreting the CNN output as potentials of a Markov Random Field (MRF), whose topology corresponds to a volumetric grid. Alpha-expansion is used to perform approximate inference imposing spatial volumetric homogeneity to the CNN priors. We compare the performance of the proposed pipeline with a similar system using Random Forest-based priors, as well as state-of-art segmentation algorithms, and show promising results on two different brain MRI datasets.
CNNs were popularized by Lecun, after delivering state-of-art results on hand-written digit recognition  [2]. However, they fell out of favor in the following years, mostly due to hardware and training data limitations. Nowadays, the availability of large-scale datasets (e. g. Im-ageNet), powerful GPUs and appropriate software libraries, have rekindled the interest in deep learning and have made it possible to harness their power. Krizhevsky et al. [3]  published results demonstrating clear superiority of deep architectures over hand-crafted features or shallow networks, for the task of image classification. Since then, CNNs have helped set new performance records for many other tasks; object detection, texture recognition and object semantic segmentation just to name a few. Our work is similar in spirit to  [4] , but with some notable differences.
Medical image datasets tend to be smaller than natural image datasets, and segmentation annotations are generally hard to obtain. In our case, we only have a few 3D scans at our disposal, which increases the risk of overfitting. In addition, the repeated pooling and sub-sampling steps that are applied in the input images as it flows through a CNN network, decrease the output resolution, making it difficult to detect and segment finer structures in the human brain. To address these challenges, we make a series of design choices for our network: first, we opt for a shallower network, composed of five pairs of convolutional/max pooling layers. We subsample the input only for the first two max-pooling layers, and keep a stride of 1 for the remaining layers, introducing holes, as in  [7]. This allows us to keep increasing the effective receptive field of filters, without further reducing the resolution of the output response maps.
EXPERIMENTS AND DISCUSSION We used the proposed method to segment a group of subcortical structures located at the frontostriatal network, including thalamus, caudate, putamen and pallidum. We evaluated our approach on two brain MRI datasets. The first one is a publicly available dataset provided by the Internet Brain Segmentation Repository (IBSR)  [8]. It contains 18 labeled 3D T1-weighted MR scans with slice thickness of around 1. 3 mm. In this work we use the subset of 8 primarily subcortical labels, including left and right thalamus, caudate, putamen, and pallidum. The second dataset is obtained from a Rolandic Epilepsy (RE) study, including 17 children with epilepsy and 18 matched healthy individuals. For each participant, T1-weighted magnetic resonance images (MRI) were acquired with a 3 T scanner (Philips Acheiva) with an in-plane resolution of 256 × 256 and slice thickness of 1 mm.
In the second experiment, we compare the accuracy of our proposed method with two publicly available state-of-the-art automatic segmentation toolboxes, Freesurfer  [10] , and FSL-FIRST  [11]. In Table  2  we report the average Dice coefficient for the left and right structures; these results show that our method provides better segmentations compared to the stateof-the-art for three sub-cortical structures in both IBSR and RE dataset. However, Freesurfer results in better segmentation for caudate in the IBSR dataset which could be attributed to the limitation of CNN in capturing thin tail areas of the caudate structures. In Figure  3  we show qualitative results.
CNN Training and Evaluation Details The input to our network is a single 2D slice from a 3D MRI scan, along with the corresponding label map. We apply data augmentation to avoid overfitting: we use horizontally flipped and translated versions of the input images by 5, 10, 15, 20 pixels, across the x/y axes. Other transformations, such as rotation, could be considered as well. The MR image data are centered and the background always takes zero values, so we do not perform mean image subtraction as is usually the case. In the case of IBSR, we split the available data into three sets. Each time, we use two of the sets as training data (approximately 100K training samples) and the third set as test data. One of the training data volumes is left out and used as validation data. Similarly, we split RE into two subsets of equal size, using one for training and one for testing, each time.
We train on both datasets for 35 epochs starting with a learning rate of 0. 01 and dropping it at a logarithmic rate until 0. 0001. For training, we use standard SGD with a momentum of 0. 9 and a softmax loss. For all our experiments we used MATLAB and the deep learning library MatCon-vNet  [12]. Code, computed probability maps, and more results can be found at https://github. com/tsogkas/ brainseg. We also experimented with CNNs trained on 2D slices from the other two views (sagittal and coronal) but the resulting models performed poorly. The problem is rooted in the inherent symmetry of some brain structures and the fact that the CNN is evaluated on individual slices, ignoring 3D structure. For instance, when processing slices across sagittal view, the and left putamen appear at roughly the same positions in the image.
Procedure Pictures were presented to participants via a PC placed outside the magnet room equipped with Presentation software (http://www. neurobs. com/menu_presentation). A projector delivered stimuli on a translucent screen placed at the foot of the magnet bore. Participants viewed the screen through a mirror system attached to the top of the head coil. All participants underwent a training session on a different set of pictures in order to familiarize them with the task and the timing of each experimental session. After positioning in the scanner, participants were asked to speak into a plastic tube attached to the head coil and were trained to minimize jaw and tongue movements in order to reduce movement artifacts while naming. A small microphone connected to the PC was used to record naming responses.
Image Preprocessing Data were preprocessed using SPM8 v4290 (Statistical Parametric Mapping; Wellcome Department of Cognitive Neurology, London, UK), running on Matlab R2008 a (Mathworks, Natick, MA). Time series diagnostics were ran using tsdiffana (Matthew Brett, MRC CBU, http://imaging. mrc-cbu. cam. ac. uk/ imaging/DataDiagnostics) to inspect quality of the functional data. Variance of corresponding voxels from image to image, from slice to slice and mean number of voxels per image were analyzed relatively to their own mean values. No data were discarded due to excessive motion Patterns of activation for pictures (light blue) and fillers (yellow) and common patterns between these two different experimental conditions (green) superimposed on the mid axial slice of mean anatomical image of all subjects (n 5 24 subjects).
Accuracy Analysis Naming accuracy and agreement were assessed in the whole sample (n 5 24 subjects). Overall naming accuracy was 91. 63% (i. e. , 90. 1% for experimental pictures and 93. 2% for fillers). Agreement values for all items closely overlapped with those reported by normative data on the same set of pictures in Italian  (Nisi et al. , 2000 ) (Pearson's r 5 0. 70, P < 0. 001 for pictures and r 5 0. 820, P < 0. 001 for fillers). In order to assess cumulative SI (CSI) effects in terms of possible accuracy differences between successive category repetitions, a within-subjects ANOVA on accuracy scores for each category repetition has been conducted. The analysis revealed a main effect of repetition (F 5 2. 66; P 5 0. 043), and post hoc paired t-tests revealed that the third category repetition (i.
Seed-to-Voxel Connectivity Analysis on the Category "Differential" Repetition Effect A seed-to-voxel analysis task-based functional connectivity analysis was implemented in MATLAB using the CONN toolbox (http://www. nitrc. org/projects/conn; Whitfield-Gabrieli and Nieto-Castanon, 2012) to explore "categoryrepetition" connectivity differences for the first (rep1), second (rep2), third (rep3), and fourth (rep4) repetition with respect to the first presentation of a category exemplar (rep0) between LIFG and other brain regions. For each participant, the CompCor method  (Behzadi et al. , 2007)  in CONN was implemented to identify principal components associated with segmented white matter (WM) and cerebrospinal fluid. Individual participants' motion parameters and main effects of task condition (i.
, 2010). In controls, concomitant fMRI showed increased activity of the stimulated S1, bilateral PMC and basal ganglia, whereas, fMRI showed similar cortical effects to controls except for no effects in basal ganglia of dystonia patients. These data suggest a possible defective connection between both sites in the dystonic patients of that study  (Schneider et al. , 2010). In order to modulate this aberrant functioning of the SSC, a cross-over single-blind four-week study of a 30 min 1 Hz rTMS session applied over the SSC during five consecutive days each week in 15 right handed writer's cramp patients was evaluated versus sham stimulation (coil angled at 90 • ) at the end of each week (day 5, 14, and 28), the coil being maintained with a gripping arm. The SSC was localized with fMRI and the coil position was determined as the higher activation spot on the contralateral primary SSC of the hand (S1) after a passive simple movement of the hand.
He Cr reference Cr is the homologue of Yb in He (Fig  1 ). An existing reference for this region was available in 3 pieces (467,734bp, 114,741bp and 161,149bp, GenBank: KC469893. 1)31. We screened the same BAC library used previously11,31 using described procedures11 with probes designed to the ends of the existing BAC sequences and the HmYb BAC reference sequence. Two BACs (04B01 and 10B14) were identified as spanning one of the gaps and sequenced using Illumina 2x250 bp paired-end reads collected on the Illumina MiSeq. The raw reads were screened to remove vector and E. coli bases. The first 50k read pairs were taken for each BAC and assembled individually with the Phrap32 software and manually edited with consed33. Contigs with discordant read pairs were manually broken and properly merged using concordant read data.
Gaps between contig ends were filled using an in-house finishing technique where the terminal 200bp of the contig ends were extracted and queried against the unused read data for spanning pairs, which were added using the addSolexaReads. perl script in the consed package. Finally, a single reference contig was generated by identifying and merging overlapping regions of the two consensus BAC sequences. In order to fill the remaining gap (between positions 800,387 and 848,446) we used the overhanging ends to search the scaffolds from a preliminary He genome assembly of five Illumina paired end libraries with different insert sizes (250, 500, 800, 4300 and 6500bp) from two related He demophoon individuals. We identified two scaffolds (scf1869 and scf1510) that overlapped and spanned the gap (using 12,257bp of the first scaffold and 35,803bp of the second).
The final contig was 1,009,595bp in length of which 2,281bp were unknown (N's). The HeCr assembly was verified by aligning to the HmYb genome scaffold (HE667780) with mummer and blast. The HeCr contig was annotated as described previously32, with some minor modifications. Briefly this involved first generating a reference based transcriptome assembly with existing H. erato RNA-seq wing tissue (GenBank accession SRA060220). We used Trimmomatic34 (v0. 22), and FLASh35 (v1. 2. 2) to prepare the raw sequencing reads, checking the quality with FastQC36 (v0. 10. 0). We then used the Bowtie/TopHat/ Cufflinks37-39 pipeline to generate transcripts for the unmasked reference sequence. We generated gene predictions with the MAKER pipeline40 (v2. 31). Homology and synteny in gene content with the Hm Yb reference were identified by aligning the Hm coding sequences to the He reference with BLAST.
Genotype-by-phenotype association analyses Information on the individuals used and ENA accessions for sequence data are given in Supplementary Table  1. We used shotgun Illumina sequence reads from 45 He individuals from 7 races that were generated as part of a previous study31 (Supplementary Information). Reads were aligned to an He reference containing the Cr contig and other sequenced He BACs11,31 with BWA42 , which has previously been found to work better than Stampy43 (which was used for the alignments in the other species) with an incomplete reference sequence31. The parameters used were as follows: Maximum edit distance (n), 8; maximum number of gap opens (o), 2; maximum number of gap extensions (e), 3; seed (l), 35; maximum edit distance in seed (k), 2. We then used Picard tools to remove PCR and optical duplicate sequence reads and GATK44 to re-align indels and call SNPs using all individuals as a single population.
Expected heterozygosity was set to 0. 2 in GATK. 132,397 SNPs were present across Cr. A further 52,698 SNPs not linked to colour pattern loci were used to establish background association levels. For the Hm / Hn clade we used previously published sequence data from 19 individuals from enrichment sequencing targeting of the Yb region, the unlinked HmB/D region that controls the presence/absence of red colour pattern elements, and ~1. 8Mb of non-colour pattern genomic regions45, as well as 9 whole genome shotgun sequenced individuals18,46. We added targeted sequencing and shotgun whole genome sequencing of an additional 47 individuals (Supplementary Information). Alignments were performed using Stampy43 with default parameters except for substitution rate which was set to 0. 01. We again removed duplicates and used GATK to re-align indels and call SNPs with expected heterozygosity set to 0.
1. The analysis of the Hm/timareta/silvaniform included 49 individuals, which were aligned to v1. 1 of the Hm reference genome with the scaffolds containing Yb and HmB/D swapped with reference BAC sequences18, which contained fewer gaps of unknown sequence than the genome scaffolds. 232,631 SNPs were present in the Yb region and a further 370,079 SNPs were used to establish background association levels. The Hn analysis included 26 individuals aligned to unaltered v1. 1 of the Hm reference genome, because the genome scaffold containing Yb is longer than the BAC reference making it easier to compare the inverted and non-inverted regions present in this species. We tested for associations at 262,137 SNPs on the Yb scaffold with the Hn bicoloratus morph, which had a sample of 5 individuals. We measured associations between genotype and phenotype using a score test (qtscore) in the GenABEL package in R47.
This was corrected for background population structure using a test specific inflation factor, λ, calculated from the SNPs unlinked to the major colour pattern controlling loci (described above), as the colour pattern loci are known to have different population structure to the rest of the genome14,15,18. We used a custom perl script to convert GATK vcf files to Illumina SNP format for input to genABEL47. genABEL does not accept multiallelic sites, so the script also converted the genotype of any individuals for which a third (or fourth) allele was present to a missing genotype (with these defined as the lowest frequency alleles). Custom R scripts were used to identify sites showing perfect associations with calls for >75% of individuals.