dataesr/scientific-paragraphs-categorization huggingface dataset:
Samtools  (Li et al. , 2009)  was then used to remove low quality mapping and sort alignments by position, and PCR duplicates were marked using MarkDuplicates from PicardTools v. 2. 25  (Picard Toolkit, 2019). Summary statistics of the mapping step were generated using samtools stats. Variants were called using freebayes v1. 3. 2-dirty  (Garrison & Marth, 2012)  and sites marked as MNP/complex were decomposed and normalized using the script 'vcfallelicprimitives' from vcflib  (Garrison, 2012). Next, AN/AC field was calculated using bcftools v. 1. 7 (Li, 2011)  and variant calls were filtered using vcflib  (Garrison & Marth, 2012)  and bcftools. Given that freebayes does not perform automatic variant filtering steps, we identified sets of parameters that generate reliable final SNP sets, based on two independent criteria: the highest transition/transversion ratios as reported by SnpSift (SnpEff suite,  Cingolani et al.
Bait specificity and efficiency To explore bait specificity, we calculated the percentage of high quality trimmed reads ontarget using samtools stats and bedtools intersect v2. 25. 0  (Quinlan & Hall, 2010)  using the script cal-culat_bait_target_specifity. sh (available from https://github. com/giyany/Bromeliad1776). Targets for Bromeliad1776 were defined as the bait sequences plus their 500 bp flanking regions. Targets for Angiosperms353 were defined using orthogroups to A. comosus: gene annotations from the bait set were used to assign genes to orthogroups using OrthoFinder  (Emms & Kelly, 2019). When several orthogroups were found for a single Angiosperms353 gene, we included all, resulting in 559 A. comosus genes assigned to orthogroups. Within the orthgroups, targets were again defined as exonic regions plus their 500 bp flanking regions.
Population structure and nucleotide diversity estimates To explore the genetic structure within the Tillandsia species complex, we focused on five species from 15 localities (Supporting information Table  S3  and Supporting information Figure  S1 ). We first used plink v. 1. 9  (Chang et al. , 2015)  to filter out SNPs in linkage disequilibrium. Population structure was further explored through individual ancestry analysis, with identity-bydescent matrix calculated by plink and inference of population structure using ADMIXTURE v. 1. 3. with K values ranging from one to ten, and 30 replicates for each K, using a block optimization method  (Alexander & Lange, 2011). A summary of the ADMIXTURE results was obtained and presented using pong  (Behr, Liu, Liu-Fang, Nakka, & Ramachandran, 2016). The set of LD-pruned biallelic SNPs was further filtered to allow a maximum of 10% missing data and used to perform a principal components analysis (PCA) with SNPRelate v.
Both kits provided a large number of SNPs After variant calling and filtering, we identified 47,390 and 209,186 high-quality SNPs for the Angiosperms353 and the Bromeliad1776 bait sets, respectively. On average, missing data represented 23. 7% of genotype calls per individual in Angiosperms353, but only 6. 3% for the Bromeliad1776 kit. The differences in amount of missing data are likely associated with the higher mean depth per site across the Bromeliad1776 kit (6,602), as compared to Angiosperms353 (3,437). Focusing on the subgenus Tillandsia, we identified 15,622 SNPs for Angiosperms353 (including a total of 18. 9% missing data) compared to 65,473 polymorphic sites (2. 9% missing data) for Bromeliad1776. In both full data sets and the subset including only Tillandsia samples, Bromeliad1776 recovered more variants in intronic regions compared with Angiosperms353.
Acknowledgments This paper is dedicated to Christian Lexer, a wonderful mentor, friend and colleague. The research was supported with funding from the Christian Lexer professorship startup BE772002. The analyses benefited from the Vienna Scientific Cluster (VSC) and the Montpellier Bioinformatics Biodiversity (MBB) platform services. We thank Huiying Shang and Aram Drevekenin for help 10 Supporting information 10. 1 Tables  Table S1  Genes included in the Bromeliad1776 bait design, with identifiers as annotated in Ananas comosus genome v. 3  (Ming et al. , 2015). The table includes details about exon composition, copy number and putatively associated pathways.
The parameters of the network W s and W c were initialized as random noise and were normalized during training such that the energy (Euclidean norm) of each kernel was set to 1. All the networks were trained using grayscale natural images from the STL-10 dataset (96 × 96 pixels per image)  [37]  for 9 epochs (28125 iterations with mini-batches of 32 images). The input images were pre-processed with a whitening filter similar to the one used in  [16]  to model retinal processing. Each image was then bounded between the values -1 and 1. The synaptic weight W s and W c were updated using stochastic gradient descent with a learning rate, ω, of 0. 01 and a momentum, β, of 0. 9. Additionally, the sparsity parameters λ s and λ c were gradually incremented during training up to a value of 0. 1. The full model is implemented in PyTorch and available at https://github.
Previous literature about the structural characterization of the human cerebellum is related to the context of a specific pathology or focused in a restricted age range. In fact, studies about the cerebellum maturation across the lifespan are scarce and most of them considered the cerebellum as a whole without investigating each lobule. This lack of study can be explained by the lack of both accurate segmentation methods and data availability. Fortunately, during the last years, several cerebellum segmentation methods have been developed and many databases comprising subjects of different ages have been made publically available. This fact opens an opportunity window to obtain a more extensive analysis of the cerebellum maturation and aging. In this study, we have used a recent state of the art cerebellum segmentation method called CERES and a large dataset (N=2831 images) from healthy controls covering the entire lifespan to provide a model for 12 cerebellum structures (i.
We have addressed this in two ways. First, we took advantage of the new Big Data sharing paradigm in neuroimaging  (Poldrack and Gorgolewski, 2014)  which gave us access to a large number of subjects (N = 2,831 before our quality control) comprising ages from 9 months to 94 years. And second, the images (gathered from different freely available datasets) have been processed using the same cerebellum segmentation pipeline  (Romero et al. , 2017). These images and their segmentation results were examined through a demanding multi-stage quality control to avoid poor quality acquisitions and segmentation errors that could affect the trajectories. To analyze the results and produce new knowledge, we modeled every structure growth with the best model between several options. We compared six models from simplest (linear) to most complex (hybrid) model as done in  (Coupé at al.
The details of this step are described in the original paper  (Romero et al. , 2017). 6) Intensity normalization based on ROIs as done in  (Asman and Landman, 2012)  to achieve that all the cerebellum tissues have the same intensity across the dataset. The segmentation step consists of a patch-based algorithm called OPAL  (Ta et al. , 2014 , Giraud et al. , 2015)  which uses an optimized version of the non-local label fusion  (Coupé et al. , 2011)  using Patch Match  (Barnes et al. , 2009). A dataset of 10 (5 left-right flipped) T1-weighted high-resolution (0. 5 x 0. 5 x 0. 5 mm after preprocessing) images manually segmented by experts into 26 structures (right and left lobules I-II, III, IV, VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX, X and white matter) was used to construct the template library.
Quality control It has been demonstrated that quality control (QC) has a significant impact on trajectory results  (Ducharme et al. , 2016). In this study we used a three stage QC to select adequate subjects consisting in the following steps. First, we removed 219 images (7. 7 % from the total) by visual inspection based on its image quality (this selection is shared with  (Coupé at al. , 2017)  as we used the same dataset). The second step was a visual inspection of the processing results of every subject done using the CERES PDF reports generated by volBrain platform. At this stage we validated preprocessing (denoising, intensity normalization, inhomogeneity correction, etc. ), MNI registration, intracranial cavity extraction, cerebellum tissue classification and lobule segmentation. In this step 492 images (15.
1 % from the total) were removed. Finally, we defined as possible outliers every case with a volume higher or lower than 2 standard deviations of the estimated model for the total cerebellum volume model. After carefully checking this potential outlier cases by displaying its segmentation map using ITK Snap software  (Yushkevich et al. , 2006)  we removed 70 more subjects (2. 5 % from the total). After the full QC process 2515 remained from the 2831 initial subjects (88. 8 % from the total).
However, Fan's study did not show any significant differences for lobules IV, VI and VIIA while reported significant differences for Crus II in contrast with our results. These inconsistencies could be explained by the use of a different segmentation protocol, the age range limited to 18 and 33 years old and the size of the dataset of 112 images. The trajectories we generated showed different evolution patterns. Lobules III, IV, V, VI, Crus I, VIIB, VIIIA, VIIIB and IX showed a fast growth in the early ages represented by an exponential cumulative distribution followed by a slow decrease that adjusted to a linear function or slightly curved second order model. Crus II described a curve instead of an inverted U-shape that fitted a quadratic model after the fast growth. Finally, lobule X followed an inverted U-shape represented by a cubic function without the presence of a fast growth stage.
Also, gathering several datasets can be a limitation due to the introduction of artificial differences. However, we used at least two overlapping datasets for each 5 periods and CERES preprocessing pipeline is designed to mitigate the negative impact of the use of different acquisition protocols. Finally, as occurred in  (Coupé et al. , 2017) , after the quality control we were left with no subjects younger than 9 months and the results over this period might be less accurate. As Coupé et al. explained in their work, very few studies are performed focusing on newborn since the acquisition is difficult and the low contrast before 6 months and the fast myelination progress during the first 2 years makes the analysis challenging.
C-MIND (N = 266, after QC N = 182): The images from the C-MIND dataset (https://research. cchmc. org/c-mind/) used in this study consist of 266 control subjects. All the images were acquired at the same site on a 3T scanner. The MRI are 3D T1-weighted MPRAGE highresolution anatomical scan of the entire brain with spatial resolution of 1 mm 3 acquired using a channel SENSE head-coil. //adni. loni. ucla. edu/research/protocols/mri-protocols) and a custom MPRAGE sequence was used on the 1. 5T scanner. Dataset summary.
DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS/INSERM for the DeepMultiBrain project. Also, we want to thank the investigators that collected and made available all the data in the datasets we used: The C-MIND Data Repository created by the C-MIND study of Normal Brain Development. Conducted by Cincinnati Children's Hospital Medical Center and UCLA. https://research. cchmc. org/c-mind. The NDAR data were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism.
Conducted by the Brain Development Cooperative Group. http://pediatricmri. nih. gov/nihpd/info/participating_centers. html. The ADNI data used were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co. , Medpace, Inc. , Merck and Co. , Inc. , Novartis AG, Pfizer Inc. , F. Hoffmann-La Roche, Schering-Plough, Synarc Inc. , as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.
S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www. fnih. org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. The OASIS1 data used were obtained from the OASIS1 project (http://www. oasis-brains. org/). The AIBL data used were obtained from the AIBL study of ageing (www. aibl. csiro. au). The ICBM data used and the IXI data used were supported by http://www. brain-development. org/. The ABIDE data used were supported by ABIDE funding resources listed at http://fcon_1000.
projects. nitrc. org/indi/abide/. ABIDE primary support for the work by Adriana Di Martino. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute. http://fcon_1000. projects. nitrc. org/indi/abide/. This manuscript reflects the views of the authors and may not reflect the opinions or views of the database providers.
For the NIHPD, T1-weighted images were acquired at six different sites with 1. 5 Tesla systems by General Electric (GE) and Siemens Medical Systems. The MRI are 3D T1-weighted spoiled gradient recalled (SPGR) echo sequence with following parameters: TR = 22-25 ms, TE = 10-11 ms, flip angle = 30°, FoV = 256 mm IS × 256 mm AP, matrix size = 256 × 256: 1 × 1 × 1 mm 3 voxels, 160-180 slices of sagittal orientation. The participants chosen from the Lab Study 19 of National Database for Autism Research (NDAR) were scanned using a 3T Siemens Tim Trio scanner at each site. The MRI are 3D MPRAGE sequence (voxel dimensions: 1. 0 × 1. 0 × 1. 0 mm 3 ; image dimensions: 160 × 224 × 256, TE = 3. 16 ms, TR = 2,400 ms). ABIDE (N = 528, after QC N = 450): The images from the Autism Brain Imaging Data Exchange (ABIDE) dataset (http://fcon_1000.
projects. nitrc. org/indi/abide/) used in this study consist of 528 control subjects acquired at 20 different sites on 3T scanner. The MRI are T1-weight MPRAGE image and the details of acquisition, informed consent, and site-specific protocols are available on the website. ICBM (N = 308, after QC N = 289): The images from the International Consortium for Brain Mapping (ICBM) dataset (http://www. loni. usc. edu/ICBM/) used in this study consist of 308 normal subjects obtained through the LONI website. The MRI are T1-weighted MPRAGE (fast field echo, TR = 17 ms, TE = 10 ms, flip angle = 30°, 256 × 256 matrix, 1 mm 2 in plane resolution, 1 mm thick slices) acquired on a 1. 5T Philips GyroScan imaging system (Philips Medical Systems, Best, The Netherlands). OASIS1 (N = 315, after QC N = 290): The images from the Open Access Series of Imaging Studies (OASIS1) database (http://www.
oasis-brains. org) used in this study consist of 315 control subjects. The MRI are T1-weighted MPRAGE image (TR = 9. 7 ms, TE = 4 ms, TI = 20 ms, flip angle = 10 degrees, slice thickness = 1. 25 mm, matrix size = 256 × 256, voxel dimensions = 1 × 1 × 1. 25 mm 3 resliced to mm 3 , averages = 1) acquired on a 1. 5-T Vision scanner (Siemens, Erlangen, Germany). IXI (N = 588, after QC N = 519): The images from the Information eXtraction from Images (IXI) database (http://brain-development. org/ixi-dataset/) used in this study consist of 588 normal subjects. The MRI are T1weighted images collected at 3 sites with 1. 5 and 3T scanners (FoV = 256 mm × 256 mm, matrix size = 0. 9375 × 0. 9375 × 1. 2 mm 3 ). ADNI1 (N = 228, after QC N = 218): The images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.
The model parameters can then be estimated by the expectation-maximization (EM) algorithm. Results: Experiments on phantom MEG datasets show that SMICA can recover dipole locations more precisely than usual ICA algorithms or Maxwell filtering when the dipole amplitude is low. Experiments on EEG datasets show that SMICA identifies a source subspace which contains sources that have less pairwise mutual information, and are better explained by the projection of a single dipole on the scalp. Comparison with existing methods: Noiseless ICA models lead to degenerate likelihood when there are fewer sources than sensors, while 1.
Thanks to its noise model, SMICA can estimate fewer sources than sensors. Therefore, no preprocessing for dimension reduction is required. The sources can be estimated by Wiener filtering, which takes the noise estimation into account and denoises the sources. The article is organized as follows. In section 2, the SMICA statistical model is introduced and the estimation strategy based on an Expectation-Maximisation (EM) algorithm is described. In section 3, the usefulness of SMICA is demonstrated on various MEG and EEG datasets. Notation The trace of a matrix M ∈ R p×p is Tr(M ), and its determinant is |M |. A matrix is invertible when |M | = 0, and we write M ∈ GL p. Given a vector u ∈ R p , the matrix diag(u) ∈ R p×p is the matrix containing the elements of u on its diagonal, and 0 elsewhere.
The sources in S 1 are maximally independent with respect to the separation criterion of SMICA, but might not be maximally independent with respect to another criterion. Applying another ICA algorithm on S 1 yields a square mixing matrix A 2 ∈ R q×q , and a new source matrix S 2 = A -1 2 S 1. The overall mixing matrix linking the sources S 2 and the original dataset X is the matrix product A T otal = A 1 A 2. For instance, using a non-Gaussian ICA algorithm like Infomax or FastICA on the sources found by SMICA may disentangle sources that share similar power spectrum. The practical benefits of such approach are demonstrated in the experiments presented in section 3. Complexity of SMICA SMICA only needs the spectral covariances Ĉ1 ,. , ĈB to infer the parameters of the model. Therefore, the complexity of the EM algorithm does not depend on the number of samples T ; only the computation of the covariances does.
This differs from non-Gaussian ICA algorithms, for which the estimation time is proportional to the length of the recordings. In practice, we found that fitting SMICA on a 102 sensors MEG dataset with 40 frequency bins and 100 sources takes about 15 minutes using one CPU of a recent laptop. Frequency selection SMICA exploits spectral information, but not necesarily over the whole frequency range. Typically, one may exclude the highest frequencies if they are dominated by noise or cut off by a sampling filter. One may also ignore the very lowest frequencies if they are dominated by slow drift artifacts. In general, there is no counter indication to restricting the sum (8) to the high SNR part of the frequency range.
Experiments We report some experiments comparing our approach to other (noiseless) ICA algorithms: these algorithms all model the dataset X as X = AS, where A ∈ R p×p is the square mixing matrix, and S is the source matrix. However, they estimate the independent components based on different independence criterion. We start by briefly describing the ICA algorithms which are compared to SMICA. Non-Gaussian ICA Non-Gaussian ICA algorithms model the source timeseries as independent and identically distributed, with non-Gaussian probability density functions. Some of the most popular ICA algorithms fall in this category: FastICA Hyvärinen (1999), Infomax  Bell and Sejnowski (1995) , its extended version  Lee et al. (1999) ,  JADE Cardoso and Souloumiac (1993)  and more recently  AMICA Palmer et al.
Numerical setup In our experiments, we use Infomax as the reference non-Gaussian ICA algorithm with the fast and robust optimization algorithm Picard  Ablin et al. (2018b,a)  and tanh as non-linearity. The joint-diagonalization algorithm for SOBI is a combination of  Ziehe et al. (2004)  with a backtracking line-search. The joint-diagonalization algorithm for JDIAG uses the fast implementation described in  Ablin et al. (2019). In all experiments we set the frequency bins F b of SMICA and JDIAG as uniform in the range 1 -70 Hz, with 40 bins. The M/EEG analysis is carried using the Python package  MNE Gramfort et al. (2014 , 2013). Figures are made using  Matplotlib Hunter (2007). The python code for SMICA is available online at https://github. com/ pierreablin/smica.
Comparisons on a MEG dataset We start by showing the decomposition found by SMICA, JDIAG, SOBI and Infomax on a MEG dataset, where the subject was presented checkerboard patterns into the left and right visual fields, as well as monaural auditory tones to the left or right ears. Stimuli occurred every 750 ms (See  Gramfort et al. (2014)  for a description of the dataset). MEG is acquired with 102 magnetometers and 204 gradiometers. For this experiment, we only consider the 102 magnetometer channels. Each ICA algorithm returns 40 sources (after PCA for JDIAG, SOBI and Infomax). We hand pick 10 sources to display in Figure  2 , which shows their time-course, power spectrum and topography. SMICA isolates heartbeats (source 1), eye blinks (source 7) and brain (sources 8-10) from line noise: only source 9 shows a very small peak at 60 Hz.
Comparisons on an EEG dataset We run SMICA and JDIAG on a 69-channel EEG data coming from the dataset described in  Delorme et al. (2012). Both algorithms return 20 sources, which are displayed in Figure  3. Differences between SMICA and JDIAG are now more striking, probably due to the greater noise level compared to the MEG recording. Although decompositions differ in many aspects, we want to focus on source 2 recovered by SMICA, which is not found by JDIAG. There is a sharp peak at 60 Hz, indicating that it likely corresponds to line noise. The second peak at 50 Hz may seem spurious but is most probably due to spectral aliasing. Indeed the fifth harmonic of a line at 60 Hz when sampled at 250 Hz appears at frequency 5 * 60 -250 = 50 Hz. To test whether it is a plausible line source, we resort to a separate experiment: we determine the spatial filter w ∈ R 59 such that the time series wX is of power 1 with a maximal power at 60 Hz.
Experiment on MEG Phantom data In this section, we experiment with MEG Phantom data. The recording comes from a fake plastic head with electric dipoles. Dipoles emit sinusoidal pulses at a fixed frequency 20 Hz for 0. 5 second (10 periods) and are then turned off for 1 second. This is repeated for 150 seconds. We have 24 datasets. Each dataset corresponds to one dipole location among 8 and one amplitude (either 1000, 200 or 20 nAm). The amplitude corresponds to the peak-to-peak difference. We cut each dataset in half to obtain twice as many datasets. As the true locations of the sources in the phantom are known one can map the origin of an ICA source by fitting an equivalent current dipole (ECD) to the source topographies and evaluate the localization errors. On each dataset, we apply SMICA, Infomax and JDIAG to obtain 40 sources.