dataesr/scientific-paragraphs-categorization huggingface dataset:
3T and SnpSift using the GRCh37. 75 database (including Nextprot and Motif), dbSNFP v2. 9, dbSNP b150 GRCh37p13 and ClinVar GRCh37 v2. 0 To elucidate the gene or genes genes driving a signal in the geneset, a Manhattan plot indicates the genic burden results for each of the genes included in the geneset. Hovering over individual genes will reveal more information about that gene. D Gene expression profiles extracted from GTEx. E Variant table. By default, a subset of variant information is shown; columns of interest can be selected from the dropdown menu. Minor allele frequency is based on all unrelated and QC passing samples in the Project MinE dataset (6,198 genomes). Frequency information is also stratified by phenotypic status and compared to public exome and whole genome data. For comparison, we have indicated the allele frequency on a log scale with orange bars; the longer the bar, the higher the allele frequency.
3429-  33. We obtained population frequency estimates from gnomAD7. To visua e the variant-level coverage from Project MinE and external sources, we included coverage information from Project MinE samples, gnomAD database (123,136 exome sequences plus 15,496 genome sequences). We further integrated tissue-specific gene expression profiles for 53 tissues from the GTEx resource (https://gtexportal. org/home/datasets)34. Finally, the available literature on each gene is presented through an iframe linking to either PubMed, UCSC, GeneCards, Ensembl, WikiGenes, GTEx or the GWAScatalog. A l participants gave written informed consent and the relevant institutional review boards approved this study. The informed consent clearly indicates that there is no duty to hunt for clinically actionable results and that participants will not be recontacted for genotyping results.
Barcelona, Spain 55. Hospital San Rafael, Madrid, Spain 56. Academic Unit of Neurology, Trinity College Dublin, Trinity Biomedical Sciences Institute, Dublin, Republic of Ireland 57. Department of Neurology, Beaumont Hospital, Dublin, Republic of Ireland 58. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA # shared first, § shared last, *Corresponding author: Jan H. Veldink, Department of Neurology and Neurosurgery, University Medical Centre Utrecht, Department of Neurology G03. 228,P. O. Box 85500, 3508 GA Utrecht,The Netherlands, J. H. Veldink@umcutrecht. nl Existing ALS datasets. The browser also includes freely-available summary-level data for the 2016 ALS GWAS 1 for download. Additionally, downloadable SKAT and SKAT-O burden testing results from 610 ALS cases and 460 controls with Chinese ancestry (Gratten et al, 2017) are available.
Language. The data browser can be accessed at http://databrowser. projectmine. com/. The interface is based on the statistical programming language R (v3. 4. 1, https://www. r- project. org/) together with the interactive web application framework Shiny (v1. 0. 5, https://shiny. rstudio. com/). Interactive visualisations have been created using base R and the Plotly library (v4. 7. 1,https://plot. ly/r/). The code is open-source and can be downloaded from https://bitbucket. org/ProjectMinE/databrowser. Informed consent.
e. they result of the integration of visual and auditory cues on moving objects. In this work we explored the structure of novel onomatopoeias created to describe events in multisensory scenarios. We used a group of images of different shapes and sizes performing three basic noisy physical events involving solid objects, which are acoustically correlated to the principal classes of phonemes  [21]. We hypothesized that 1. different sensory modalities present specific phonological rules, and 2. within each sensory modality, a competition is established to communicate the features of a stimulus (i. e. the shape of the object and its movement type) that can be extracted from the phonological properties of the onomatopoeias. To explore these hypotheses, we created and analyzed a database of novel onomatopoeias using audio-visual stimuli of different interacting objects.
Stimuli The stimuli were constructed using two basic objects (rounded and spiky) of two sizes (big and small) and three basic movements (slide of one object on a horizontal surface, hit between objects coming from opposite directions and a ringing object). The stimuli were generated and presented using Psychtoolbox under MATLAB  [32] , (S1 File). The experiment was divided in three blocks of different sensory modalities: Visual (V). We generated 12 videos = 2 shapes × 2 sizes × 3 movements. Videos were 4s length. Audio (A). Sound files corresponding to a slide, a hit and a ring were downloaded from www. audiomicro. com. The audio files were manipulated using Praat  [33]  to obtain a low-frequency (LF) and a high-frequency (HF) version of each sound. To do so, the spectral center of each sound was moved to 1.
1 kHz for LF and 3. 0 kHz for HF using the Praat function Modifyoverride. The original sound length was recovered using the function Convert-Lenghten. This procedure yielded a total of 6 audio files = 3 movements × 2 sounds. Audiovisual (AV). Stimuli consisted in every combination of visual and auditory stimuli (sounds were congruent with movement types). This design yielded a total of 24 videos = 2 shapes × 2 sizes × 3 movements × 2 sounds. The final database consisted of 798 recorded onomatopoeias = 19 subjects × (6A + 12V + 24AV). Participants sat in a silent room, 0. 5m away from a 23-inch led monitor. Visual stimuli were presented in black over a white background. The angle subtended by the big and small figures was 6˚and 1˚respectively. The sounds were sampled at 22. 05 kHz, were set to the same overall energy (RMS) and presented through headphones.
We then used machine learning with a linear discriminant analysis LDA (coded in MATLAB available in S7 File) as a classifier to calculate the performance of the onomatopoeias at predicting the movement types (hit, slide, ring), shapes (rounded and spiky) and frequency (high and low). A 10-fold cross-validation, repeated 1000 times, was used to estimate the performance of the classifier. Ultimately, this procedure generated 1000 repetitions of a matrix containing the percentage of times a given class (i. e. hit) was predicted to a given class (i. e. slide). These matrices where reduced to only one by computing the median and inter-quartile-range (IQR) across repetitions (S1 Table ). This procedure was repeated using either all features (n = 13), only the consonant features (n = 8) or only the vowels features (n = 5) and it was also repeated for the three types of modalities (A, V and AV).
Acknowledgments The research reported in this work was partially funded by the  Concejo Nacional de Investigaciones Cientı ´ficas y Tecnicas (CONICET) ; The  University of Buenos Aires (UBA) ;  ECOS A11S01 scientific collaboration program  ( France-Argentina );  NIH  through  R01-DC-012859 , and the program " Investissements d'avenir"  ( ANR-10-IAIHU-06 ).
The research reported in this work was partially funded by the  Concejo Nacional de Investigaciones Cientı ´ficas y Tecnicas (CONICET)  to AT and MAT; The  University of Buenos Aires (UBA) ;  ECOS A11S01 scientific collaboration program  ( France-Argentina ) to MFA, LC,  JDS , and MAT;  NIH  through  R01-DC-012859  to  MAT , and the program " Investissements d'avenir"  ( ANR-10-IAIHU-06 ) to JS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
OCT Angiography System Setup The OCT-A was performed for each enrolled subject by using an 85-kHz spectral-domain OCT (Spectralis OCT2; Heidelberg Engineering, Heidelberg, Germany). The OCT-A scanning pattern was a high-resolution 158 3 108 volume scan centered in the central macula. Each of the 261 cross-sectional scans composing the volume (11-lm distance between two consecutive cross-sectional scans) was the result of an averaging process of 35 frames. The in-built software (Heyex Software Version 1. 9. 201. 0; Heidelberg Engineering) provides en face or cross-sectional angiograms. An automated segmentation algorithm based on the capability to distinguish different levels of reflectivity between the inner retinal layers allows en face images to be obtained. In the current study the retinal capillary network was simplified into two vascular structures: the superficial capillary plexus (SCP) and the deep capillary plexus (DCP).
The magnification associated with axial length variation was considered and data were corrected accordingly. 24 Quantitative data were achieved on a 108 3 108 area corresponding to 3 3 3 mm (original image 158 3 108 area corresponding to 4. 5 3 3 mm) in order to minimize the effects of artifacts at the edges of the en face OCT angiogram. We assumed that changes in the number of decorrelated pixels on HGT OCT-angiograms could reflect changes in vascular perfusion with respect to baseline. A qualitative assessment of the SCP and DCP on baseline and HGT OCT-angiograms was also performed. The sites of potential retinal capillary perfusion changes occurring during the stress test were evidenced by using the subtraction protocol in the image calculator tool of a free imaging software (FIJI, an expanded version of ImageJ, version 1.
1. 8. 4) of the GenomeStudio Data Analysis software. We applied the following criteria for QC: (1) individuals with call rate <90% were excluded, (2) markers with call rates 90% were excluded, and (3) markers with allele distributions deviating from Hardy-Weinberg equilibrium (HWE) in controls (p < 1× 10 -5 ) were also excluded. A total 126,270 markers (101,692 of them with a MAF > 0. 1%) passed QC and were included in the analysis. The same ImmunoChip platform was used for White and African American populations. The genotyping rate was 0. 988 in the African American sample while the genotyping rate was 0. 998 among Whites. GWAS analysis: We also performed a genome-wide genotyping of both African Americans and Whites. African Americans were genotyped on the Illumina Omni2. 5 BeadChip that features ~2.
Finally, we excluded duplicate and closely related samples (PIHAT ! 0. 5). The genome wide genotyping of the White populations has been described previously in Radstake et al (2010)  [3]. In brief, Hap550K-BeadChip was used for US Whites and Illumina HumanCNV370K BeadChip in Spanish Whites. For the SNP-level analysis the association statistics was computed via logistic regression including sex as a covariate for each dataset. For the White samples, meta-analysis combining odds ratios (OR) and standard errors (SE) of individual datasets (US and Spanish, so that the controls in each set were from the same country as cases) was performed by means of the inverse-variance method under the assumption of a fixed effect as implemented in PLINK v. 1. 07. [13]. For the gene-level analysis we used Versatile Gene-based Association Study (VEGAS)  [14].
We used VEGAS because it outperforms similar methods by sensitivity and specificity from simulation studies  [15]. VEGAS can be applied to the data generated by any GWAS designs, including family-based GWAS, meta-analyses of GWAS and DNA-pooling-based GWAS. The test uses information from the complete set of markers within a gene. To account for linkage disequilibrium between markers VEGAS uses simulations from the multivariate normal distribution. VEGAS assigns SNPs to autosomal genes according to positions on the UCSC Genome Browser hg18 assembly. In order to capture regulatory regions and SNPs in LD, the gene boundaries are defined as ±50 kb of 5' and 3' UTRs. VEGAS assigned SNPs genotyped by ImmunoChip to 11,501 genes. Assuming independence of the gene level tests, the threshold for statistical significance in the analysis of ImmunoChip in Whites was set to be 4.
Funding was provided to MDM by the  National Institutes of Health (NIH ) the  National Institute of Arthritis, Musculoskeletal  and  Skin Diseases  (NIAMS https://www. niams. nih. gov/)Centers of Research Translation (CORT)  P50-AR054144 ,  NIH  grant  N01-AR-02251  and  R01-AR-055258 , and the  Department of Defense (DD) Congressionally Directed Medical Research Program  (http://cdmrp. army. mil/) W81XWH-07-1-011  and  WX81XWH-13-1-0452  for the collection, analysis and interpretation of the data. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
064 mg mL À1 ) in a solution (10 mM sodium acetate buffer with 5 mM calcium acetate, pH 7. 5). The proteolytic assay proceeded as follows: one unit (U) of protease activity was equivalent to 1 mmol of L-tyrosine (JT Baker Mexico) liberated by the amount of enzyme per minute. Data is the result of three replicates and compared to a control without CF treatment. The U for the subtilisin Carlsberg used in this work was 8. 39 AE 0. 5 mmol min À1 mg À1. CEI de Mexico SA de CV (Mexico) supplied 1,1,1,2-tetrauoroethane Dupont Suva-R134a 60 kg cylinder. Experimental assessment of the solubility of monomers in liquid 1,1,1,2-tetrauoroethane and compressed carbon dioxide L-LeuOEt and L-PheOEt substrates, individually (6 mmol) or as an equimolar mixture of both (3 mmol) were placed in a st-316 cylindrical view cell (40 mL) equipped with three sapphire windows, magnetic stirrer, and an external ceramic heating jacket.
Computational modeling of solvent-product interactions MD calculated density, solubility parameters and Cohesive Density Energy (CED) for the compressed 1,1,1,2-tetrauoroethane, poly(L-PheOEt), poly(L-LeuOEt) and the copolypeptides at different temperatures and pressures. For these calculations, a box containing liquid 1,1,1,2-tetrauoroethane and the polymers were simulated at a xed temperature and pressure in the NPT ensemble. The degree of polymerization (DP) during molecular modeling was set to the experimental value for 24 h of reaction. Geometries for all molecules under study were initially optimized at the B3LYP/DNP level of theory. The hybrid density functional (B3LYP) and the double numerical with dynamic nuclear polarization (DNP) basis set within the DFT was carried out by the soware package DMol3 in the Materials Studio suite from Biovia.
Next, the cohesive energy density (CED, eqn (1)) was determined by sampling the system and collecting data from the last 400 ps from the production runs. E CED ¼ U vdw þ U Q vM ( 1 ) where U vdW and U Q are the van der Waals and electrostatic energy, respectively. The solubility parameter of each component (d) can be expressed by eqn (2) d ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi d vdW 2 þ d Q 2 q (2) where d vdW and d Q represent the contributions from van der Waals forces and electrostatic interactions, respectively, and calculated from eqn (3) and (  4 ) d Q ¼ ffiffiffiffiffiffi E Q p ¼ ffiffiffiffiffiffiffi ffi U Q vM r ( 3 ) d vdW ¼ ffiffiffiffiffiffiffiffiffiffi E vdW p ¼ ffiffiffiffiffiffiffiffiffiffi ffi U vdW vM r (4) where E Q is the electrostatic energy density and E vdW is the van der Waals energy density; the solubility parameter has dispersion and electrostatic components, which together with hydrogen bonding are frequently used to study and explain solvency phenomena.
, 2007; Griffanti et al. , 2014)  have then been applied, first to resting state and then to task-related data, trying to remove volume-wise structured noise that remains after motion correction. However transient slice-specific artifacts are already non-linearly propagated to neighboring slices through interpolation, hence complicating their removal. Thus, as S2V improves registration, EPI-based fMRI would also benefit from slice-specific signal corrections in acquisition space prior to any volumetric processing. Therefore, there is a need for fMRI correction strategies that better take into account the MRI acquisition specificities and adapt to novel high-resolution imaging techniques such as SMS, which generate larger datasets and thus require more efficient preprocessing, analysis and data storage  (Glasser et al.
Datasets The choice of the datasets was motivated by the fact that we needed to test the method on a diversity of protocols including different experimental paradigms and type of sequences. Thus we used: The Healthy Brain Network Serial Scanning Initiative (HBN-SSI) dataset  (O'Connor et al. , 2017) , in which subjects were scanned in a series of 14 sessions aiming at test-retest reliability assessments. This dataset, acquired on a Siemens 1. 5T Avanto scanner with 32-channel coil, includes resting-state and tasks related acquisitions with a multiband sequence (TR = 1,450 ms, TE = 40 ms, FA = 55, MB_factor = 3 × 18 = 54 slices interleaved ascending, res = 2. 46 × 2. 46 × 2. 5mm), a dual-echo fieldmap, as well as an T1 MEMPRAGE (1 mm iso, FA = 7, TR = 2,730 ms, TE = 1. 64 ms, GRAPPA = 2), a root-mean-square (RMS) was computed across echoes, the readers can report to the published data note  (O'Connor et al.
, 2017)  for the full description of this dataset. A local protocol, acquired on a Siemens 3T Tim Trio B17, that included a motor sequence execution task designed for MVPA, which was performed in two separated scans for each of the 25 subjects, in a single-band acquisition (using 12-channel coil, TR = 2,160 ms, TE = 30 ms, FA = 90, 40 slices sequential ascending, res = 3. 44 × 3. 44 × 3. 3 mm, GRAPPA = 2), to test the algorithm with both single-band scan sequence and pattern analysis. After the task was completed, a short acquisition with similar parameters but with reversed phase was acquired, for estimation of inhomogeneity induced distortions. In another session, a T1 MEMPRAGE (1 mm iso, TR = 2,530 ms, TE = 1. 64, 3. 6, 5. 36, 7. 22 ms, FA = 7, GRAPPA = 2) was acquired using a 32-channel, and the echoes were combined by computing the RMS.
The exact procedure for movement generation is provided in python code in appendix A. 2 (Supplementary Material). We further introduced a smooth multiplicative spatial bias generated from a polynomial basis of degree 3 with random coefficients and we finally added noise drawn from a Rician distribution  (Henkelman, 1985). The simulations were based on the local protocol dataset, the first volume of the 2 scans of each of the 25 subjects set as input, yielding 50 simulations with different motion and bias field parameters.
Comparison to Conventional Pipeline To compare with more conventional pipeline, we processed the same datasets using MCFLIRT (FSL) motion correction  (Jenkinson et al. , 2002) , Topup  (Andersson et al. , 2003)  or fieldmap based distortion correction  (Hutton et al. , 2002)  depending on fieldmap or inverted-phase EPI availability. Then we extracted signals of interest in gray matter using volume to surface ribbon-constrained interpolation of HCP workbench tools  (Glasser et al. , 2013).
A. Data sets description The first two hyperspectral data sets used in our experiments were provided by the Applied Physics Laboratory at Johns Hopkins University. They consist of video sequences recorded during the release of chemical plumes at the Dugway Proving Ground. The videos were captured by three different long wave infrared spectrometers placed at different locations to track the release of known chemicals. The cameras were about 1. 25 miles away from each release at an elevation of about 1300 feet. The sensors captured one data cube every five seconds consisting of measurements at wavelengths in the long wave infrared (LWIR) portion of the electromagnetic spectrum. Fig. 4  features 12 consecutive frames of a hyperspectral sequence, provided by the Applied Physics Laboratory at Johns Hopkins University 1.
Then, we apply our proposed method on the third data set. The results are shown in Fig. 9. In this video, the moving object has fixed shape and size in all frames and the differences between moving object and background is clearer than the gas plume videos. Thus the detection task is easier in this hyperspectral video sequence. From Fig. 9 , it can be seen that all methods can detect the moving box and give a complete outline. This is because the scene is simpler and the original RPCA model can detect the moving object very well. TABLE V and VI shows the AUC and F-measure statistics for HVS-MB. GPD-DECOLOR gained the best performance as DECOLOR had been proven to be effective in object detection where the object keeps the same outline and has obvious differences with the background. Besides, in HVS-MB dataset, there is less noise in the scene due to the high imaging quality in 430nm-710nm wavelength.
It is reasonable that GPD-DECOLOR achieved the best results in HVS-MB. However, we can still see that the result of GPD-LRSTV is contiguous in spatial direction and has less background part being detected as moving object. We concluded that our proposed GPD-LRSTV method achieves very competitive detection results on all the tested HVS datasets. Moreover, the TV regularization can eliminate the noise errors, thus leading to the best detection accuracy while processing the HVS with low signal to noise ratio.
D. Convergence analysis In this section, we evaluate the convergence of LRSTV, as it is an iterative algorithm. LRSTV is applied on the first principal component of HVS-aa13 data set. The residual and objective function value at each iteration are computed. The curves of residual and objective function value versus the number of iterations are plotted in Fig. 12 , where we can clearly see that LRSTV convergences quickly.
E. Computational Complexity The computation costs of the gas plume detection methods have also been compared. Detailed results are presented in Table  VII. The algorithms are tested on a computer with a 64-b quad-core Intel Xeon CPU 3. 33-GHz processor under Windows 7. GPD-IALM, GPD-NRA and the proposed method are implemented in MATLAB while the core part of GPD-DECOLOR is implemented in C++. So we can infer that the methods with temporal-spatial regularization (GPD-DECOLOR, GPD-LRSTV) cost more execution time than original RPCA methods (GPD-IALM, GPD-NRA). In the future, we will explore parallel implementations of our proposed method on GPUs to accelerate the execution of the algorithm, which is important for the application of our proposed method.
This work was supported in part by the  National Natural Science Foundation of China  under Grant nos. 61471199 ,  61772274 ,  61701238 ,  91538108 ,  11431015 , the  ANR ASTRID program  under Grant no. ANR-16-ASTR-0027-01 - APHYPIS  project,  CNRS  under Grant no. PICS-USA 263484 , the  Jiangsu Provincial Natural Science Foundation of China  under Grant no. BK20170858 , the  Fundamental Research Funds for the Central Universities , under Grant no. 30917015104 , the  Research Fund of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks  under Grant no. WSNLBKF201507 , the  National Science Foundation  under grant no. DMS-1118971 ,  NSF  grant  DMS-1417674  and  ONR  grant  N00014-16-1-2119. Y. Xu  is with the School of.
STM imaging was carried out in constant height mode. Monolayer pattern formation was achieved by applying onto freshly cleaved HOPG 4µL of a solution. The STM images were recorded at room temperature once achieving a negligible thermal drift. Solutions of all salophen molecules were prepared by dissolving them in CHCl3 and diluting with 1-phenyloctane to give 1. 0 ± 0. 1 mM solution (solvent composition 90 % 1-phenyloctane + 10 % CHCl3). All the molecular models were minimized with MMFF and processed with QuteMol visualization software.
Acknowledgements Bologna University,  Fondazione Del Monte ,  SLAMM  project are acknowledged for financial support to  Andrea Gualandi  and  Luca Mengozzi. This work was supported by the  European Community  through the project  EC FP7 ICT-MOLARNET (318516 ) and the  European Research Council  project  SUPRAFUNCTION  ( GA-257305 ) the  Agence Nationale de la Recherche  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ) and the  International Center for Frontier Research in Chemistry. Keywords:  Salophen• HOPG• Self -assembly• Cyclic voltammetry• STM microscopy.