dataesr/scientific-paragraphs-categorization huggingface dataset:
2018 ), includes 266,590 quasars 2 in the redshift range 1. 77 < z q < 3. 5. The distribution on the sky of these quasars is shown in Fig. 1  and the redshift distribution in Fig. 2. All spectra used for this analysis were obtained using the BOSS spectrograph  (Smee et al. 2013 ) on the 2. 5 m SDSS telescope  (Gunn et al. 2006 ) at Apache Point Observatory (APO). The spectrograph covers observed wavelengths 360. 0 λ 1040. 0 nm, with a resolving power R ≡ λ/∆λ FWHM increasing from ∼ 1300 to ∼ 2600 across the wavelength range. The data were processed by the eBOSS pipeline, the same (but a marginally updated version) as that used for the cross-correlation measurement of dMdB17. The pipeline performs wavelength calibration, flux calibration and sky subtraction of the spectra. The individual exposures (typically four of 15 minutes) of a given object are combined into a coadded spectrum that is rebinned onto pixels on a uniform grid with ∆ log 10 (λ) = 10 -4 (velocity width ∆v ≈ 69 km s -1 ).
Such contaminations would be expected to add noise (but not signal) to the cross-correlation. The summary of the Lyα forest data covering the Lyα or Lyβ region of the quasar spectrum is given in Table  1. Both samples exclude SDSS DR7 quasars and BAL quasars. The Lyα sample is derived from a super set consisting of 194,166 quasars in the redshift range 2. 05 < z q < 3. 5, whereas the Lyβ sample is taken from a super set containing 76,650 quasars with 2. 55 < z q < 3. 5. The lower redshift limits are a consequence of the forests exiting the wavelength coverage of the spectrograph for quasars with z q < 2 and z q < 2. 53, respectively. Spectra with the same object identification THING_ID (re-observed quasars) are coadded using inverse-variance weighting. For the selected forest samples, 17% of the quasars have duplicate spectra (less than 2% have more than one reobservation) taken with the BOSS spectrograph.
Cross-correlation We estimate the cross-correlation at a separation bin A, ξ A , as the weighted mean of the delta-field in pairs of pixel i and quasar k at a separation within the bin A (Font-Ribera et al. 2012): ξA = (i,k)∈A w i δ i (i,k)∈A w i. ( 8 ) The weights w i are defined as the inverse of the total pixel variance (see equation 3), multiplied by redshift evolution factors for the forest and quasar, so as to approximately minimize the relative error on ξA  (Busca et al. 2013) : w i = σ -2 i 1 + z i 3. 25 γ α -1 1 + z k 3. 25 γ q -1 , (9) where γ α = 2. 9  (McDonald et al. 2006 ) and γ q = 1. 44 (du Mas des  Bourboux et al. 2019). The validity of the correlation estimator, as well as the accuracy of the distortion matrix (section 4. 2) and covariance matrix estimation (section 4. 3) were tested and confirmed on simulated data in dMdB17.
Acknowledgements. We thank  Pasquier Noterdaeme  for providing the DLA catalog for eBOSS DR14 quasars. This work was supported by the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR) , and by  ANR  under contract  ANR-14-ACHN-0021. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is http: //www. sdss. org/. SDSS is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatório Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
Specifically, the number of new ATCOPIA93 copies was expected to plateau at around 50 copies per diploid genome after 6-14 generations (Fig. 2a ), which would be consistent with previous experimental data showing that for this TE, siRNA-mediated epigenetic silencing is initiated beyond a threshold of about 40 new copies  20. To test this aspect of the modeling, we analyzed the genome sequence obtained for the ten epiRILs advanced to generations F16. ATCOPIA93 insertions continued to accumulate between F8 and F16 in the five epiRILs that showed ATCOPIA93 mobilization at F8 but not in the others. All five epiRILs with mobile ATCOPIA93 harbored around 50 copies at F16 and in four of these epiRILs, all copies were methylated and silenced (Supplementary Fig. 3b ). Furthermore, finer grain analysis for one epiRIL (epiRIL394) indicates that all ATCOPIA93 copies underwent within one generation an 'all-or-nothing' gain of DNA methylation, thus resulting in their concomitant silencing (Supplementary Fig.
TE insertions were detected by implementing TE-Tracker software (available at http://www. genoscope. cns. fr/TE-Tracker)  51. WGS did not produce enough coverage (<10×) for 16 of 123 epiRILs analyzed at generation F8, and these 16 epiRILs were not considered further. TE-Tracker is a computational method that we have previously developed for accurately detecting both the identity and destination of newly mobilized TEs in genomes re-sequenced mate-pair libraries  51. Importantly, TE-Tracker does not rely on yet is able to integrate it, making the results easily interpretable. Briefly, TE-Tracker uses paired reads mapping information to identify discordant reads that mapped partially over TE-sequences to detect the position of TE insertions. Insertion site positions were refined at the single nucleotide resolution by exploiting sequence information contained in split-reads.
To this end, we implemented the software SPLITREADER  12  (available at https:// github. com/LeanQ/SPLITREADER). Homozygous and heterozygous insertions were defined based on the normalized number of reads supporting each insertion event. In addition, this approach enabled us also to identify insertions that were likely present in only one of the 10-20 seedlings used to extract DNA and which reflect transposition during the reproductive phase of the parent. These insertions were also called heterozygous, as this was likely the case and to reflect their very recent ancestry. Conversely, our approach was designed to exclude poorly supported insertions, which could reflect either mapping artifacts or rare somatic events. Finally, visual inspection was carried out for a random sample of over 200 insertion events and their homozygous or heterozygous status was confirmed in each case.
1 µg of multiplexed libraries were then subjected to TE-sequence capture  12. Enrichment for captured TE sequences was confirmed by qPCR and estimated to be higher than 1000 fold. Pair-end sequencing was performed using one lane of Illumina NextSeq500 and 75 bp reads. About 42 million pairs were sequenced per library and mapped to the TAIR10 reference genome using Bowtie2 v2. 3. 2  53  with the arguments -mp 13 -rdg 8,5 -rfg 8,5 -very-sensitive. An improved version of SPLITREADER (available at https:// github. com/LeanQ/SPLITREADER) was used to detect new TE insertions. Splitreads, as well as discordant reads mapping partially on the reference sequence of ATCOPIA93 and ATCOPIA78 (obtained from RepBase update) were identified, soft clipped and remapped to the TAIR10 reference genome using Bowtie2  53.
Simulations were run 2500 times using a wide space of parameter values (K = {0,0. 1,…,10}, E = {0,0. 2,…,1} and l = {20,21,…,100}. Distribution of homozygous and heterozygous insertions between simulated and observed data at F8 was evaluated using a two-dimensional Kolmogorov-Smirnov test (Peacock test), which was specifically designed to assess the goodness-of-fit of mathematical models to observed data  62  and is implemented in the package Peacock. test in R. Transcriptome analysis. RNA from wild-type, ddm1 epiRIL394 (F8) and epiRIL394 (F16) was isolated using Rneasy Plant Minikit (Qiagen) according to the supplier's instructions. Contaminating DNA was removed using RQ1 DNase (Promega). 1μg of total RNA was processed using TruSeq Stranded Total RNA kit (Illumina) according to the supplier's instructions.
About 20 M 76nt-long singleend reads were obtained per sample on the Illumina HiSeq2000. Expression level was calculated by mapping reads using STAR v2. 5. 3a 63 on the A. thaliana reference genome (TAIR10) with the following arguments -outFilterMultimapNmax 50 --outFilterMatchNmin 30 --alignSJoverhangMin 3 --alignIntronMax 10000. Duplicated pairs were removed using picard MarkDuplicates. Counts were normalized and annotations were declared differentially expressed between samples using DESeq2 64. Quantification of expression, copy number, and DNA methylation. Total RNA was extracted using the RNeasy plant mini kit (Qiagen) from plants grown under normal conditions or subjected to heat stress treatment  37  and cDNA was synthetized using oligo(dT) primers and SuperScript IV reverse transcriptase (Ther-moFisher).
Acknowledgements We thank members of the  Colot group  and especially  Pierre Baduel  for discussions and critical reading of the paper. We also thank  Edith Heard  for suggestions for improvement and  Guy Riddihough  ( Life Science Editors ) for editing services. We thank the  Genomics and Informatics facilities at IBENS  for their help. Finacial Support was received from the  Agence Nationale de la Recherche  ( ANR-09-BLAN-0237  to V. C. and P. W. ), the  Investissements d'Avenir   ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02   PSL* Research University  (to V. C. ), the  European Union Seventh   Framework Programme Network of Excellence EpiGeneSys  (Award  257082 , to V. C. ), and the  Center National de la Recherche Scientifique (MOMENTUM program , to L. Q. ). M. E. was supported by a Ph.
Supervised synaptome maps were generated by registering the data to the Allen Reference Atlas(25) delineated into 109 anatomical subregions within 12 overarching regions, including isocortex, olfactory areas (OLF), hippocampal formation (HPF), cortical subplate (CTXsp), striatum (STR), pallidum (PAL), thalamus (TH), hypothalamus (HY), midbrain (MB), pons (P), medulla (MY) and cerebellum (CB). All data and analysis tools are available in the Mouse Lifespan Synaptome Atlas (www. brainsynaptome. org). Synaptome Explorer enables in-depth exploration of raw and processed image data in single sections at individual punctum resolution, and the Synaptome Homology Viewer enables comparison of brain regions within and between mice of different ages.
SYNNOVATE). Author Contributions: MC, design of animal cohort; collection, preparation and imaging of brain samples; delineation of brain regions/subregions; calibration of synapse detection; analysis of synapse parameters; analysis of hippocampal gradients; data interpretation; ZQ, methodology development and optimization of lifespan SYNMAP pipeline; data analysis of the whole lifespan mouse cohort: image segmentation and puncta quantification, classification, unsupervised and supervised mapping of synapse parameters, types and subtypes, diversity and network topology analysis; EF, statistical analysis and computational modelling of synaptome physiology; BK, construction of Synaptome Explorer and Synaptome Homology Viewer; RG, construction of website; NHK, advice and supervision; SGNG, conception, supervision and writing.
Competing interests: The authors declare no competing interests. Data and materials availability: all data are available at the Mouse Synaptome Atlas (www. brain-synaptome. org). C. PSD95 (left) and SAP102 (right) density, intensity and size normalized from 0 to 1 for each brain subregion (see Table  S1 ) across the lifespan. D. Age of the first peak value for the density parameter. PSD95-eGFP (green) and SAP102-mKO2 (magenta). Regions: isocortex, olfactory areas (OLF), hippocampal formation (HPF), cortical subplate (CTXsp), striatum (STR), pallidum (PAL), thalamus (TH), hypothalamus (HY), midbrain (MB), pons (P), medulla (MY), cerebellum (CB). E. Differences (Cohen's d) in synapse parameters between 3M and 18M in brain subregions (see Table  S1 ). Statistical significance calculated using Bayesian analysis  (18)  with Benjamini-Hochberg multiple comparison correction: corrected *P < 0.
Figures S1-S18 F. Lifespan trajectories of synapse subtype 2, 4 and 31 (normalized) density in each of 109 subregions (rows) (see Table  S1 ). Density in each subregion was normalized (0-1) to its maximal density across the lifespan (columns). (Middle) The lifespan trajectory of intellectual abilities (adapted from(4)) in humans with ages scaled to mouse in accordance with transcriptome data  (30). The differentiation, peak age and dedifferentiation of intellectual abilities correlate with the trajectory of synaptome architecture. (Bottom) The age windows of onset of autism, schizophrenia, depression and dementia in humans  (38). We propose that the programmed changes in brain synapse composition that characterize the LSA render the brain vulnerable to the genetic and pathological mechanisms causing psychiatric and neurodegenerative disorders at different ages.
Supplementary Text Figures S1-S18 Caption for Table  S1  Other supplementary Materials for this manuscript include the following: Mouse Lifespan Synaptome Atlas (www. brain-synaptome. org) Synaptome Explorer V2 (www. brain-synaptome. org) Synaptome Homology Viewer (www. brain-synaptome. org) External Database: https://doi. org/10. 7488/ds/2711.
Detection of synaptic puncta Synaptic puncta were detected using the developed machine learning-based Ensemble detection method  (18). A training set of the 552 images (10. 8 × 10. 8 µm) for PSD95 and SAP102 (1104 images in total), respectively, across various mouse brain regions was randomly sampled using bootstrapping  (40)  from a data cohort of different age groups. Synaptic puncta were manually annotated by their centroid location in the images by four experts independently. A weighting factor (0-1) was then given as the measurement of the annotation quality for each human expert. The ground truth was finally generated by aggregating the weighting factors of the four annotations: puncta annotated with an average weight greater than 0. 7 would be considered as true puncta. To avoid any overfitting problem, a K-fold cross-validation strategy was adopted to train the puncta detector using ensemble learning as described previously  (18) : half of the training set is randomly selected to train the detector and the other half to validate and test the performance of the detector.
Computational modelling of synaptic responses Computational modeling of synaptic responses was based on our previously described model  (18)  representing physiology at 3M of age. To model synaptic physiology corresponding to 1W and 18M, differences in synaptome gradients between 3M and 1W and 18M, respectively, were estimated from PSD95 and SAP102 size data obtained in this study. More specifically, for each protein a size gradient was estimated in the tangential direction from the four regions CA1, CA2, CA3 and dentate gyrus and in the radial direction from the layers stratum oriens, pyramidale, radiatum and lacunosum moleculare of CA1. Differences in gradients were used to scale the amplitude of short-term depression and facilitation (PSD95 and SAP102 tangential gradient decrement factor, respectively), as well as time constant of short-term depression and facilitation (PSD95 and SAP102 radial gradient decrement factor, respectively).
Lifespan Synaptome Explorer and Homology Viewer To visualize the generated datasets interactively, we developed the tools Synaptome Explorer v2 and Homology Viewer. Synaptome Explorer v2 is an improved version of existing software  (18)  that is used for in-depth exploration of the synaptome of a single brain section. It enables interactive visualization of the brain region in full resolution, as captured by the microscope, and uses overlays to display the synaptic puncta and all their parameters, providing users with extensive parameter range filters to display subsets of puncta accordingly. The granularity of the data visualization is at the level of individual puncta, as users can click on a single punctum and see its parameters. An additional feature is region-based and tile-based filtered puncta statistics, where users can set up parameter range filters, select a combination of regions or subregions and interactively calculate statistics, such as mean intensity, mean size and density, visualized over the corresponding regions or tiles, respectively.
Introduction Large-scale genomics projects, including The Cancer Genome Atlas (TCGA, https://www. cancer. gov/tcga), are currently providing an overwhelming amount of omics data. The available data offer the opportunity to better understand biological systems and cancer in particular, but their high dimensionality poses considerable challenges typical of "Big Data"  [1]. A powerful approach to analyze and interpret large datasets is represented by matrix factorization (MF), a class of unsupervised methods that reduces high-dimensional data into low dimensional subspaces, while preserving as much information as possible  [2] [3] [4]. Given a data matrix X, representing measures of the expression of genes across a set of samples, MF learns two sets of low-dimensional representations: "metagenes", encoding molecular relationships, and "metasamples", encoding sample-level relationships.
Up until now, MF has been successfully used in a broad spectrum of applications: unsupervised clustering, especially in the context of cancer subtyping  [5, 6] , molecular pattern discovery  [7, 8] , mutational signatures definition  [9, 10]  and tumor sample immune infiltration quantification  [11]. Such results have been obtained by mining single large-scale datasets with MF, such as the transcriptome or methylome. Recently, we designed a metric to infer univocal correspondences between the metagenes obtained by an MF algorithm on multiple independent datasets profiled from the same biological condition (e. g. , the same cancer tissue), and used this metric to design a methodological framework that revealed relevant pathways characteristic of colorectal cancer  [12]. Here, we are interested in investigating the molecular bases of previously documented disease-disease (DD) relationships.
Despite the great potential of MF in this context, applying this methodology to the study of the molecular bases of DD relationships requires some specific modifications. We thus extend our previously defined MF framework for the particular study of DD relationships  [12]. Moreover, given the existence of positive and negative DD connections, we also adapt the framework to distinguish molecular relationships concordantly and discordantly altered in datasets coming from different diseases. Considering the inverse comorbidity between Alzheimer's disease (AD) and lung cancer (LC) as a case study  [22] [23] [24] , we applied our MF framework to 17 transcriptomic datasets, including both LC and AD samples (a total of 1367 samples), and we highlighted multiple molecular mechanisms possibly underlying the inverse comorbidity pattern.
A New MF Framework to Study Disease-Disease Relationships We previously defined the reciprocal best hit (RBH), a metric to infer univocal correspondences between the MF metagenes obtained on independent transcriptomic datasets measured from the same biological condition (e. g. , same cancer tissue)  [12]. Based on this metric we designed an RBH-based framework, which we have separately applied to three large collections of transcriptomic datasets covering a total of 10,490 patients. The method involves three sequential steps: (1) each transcriptomic dataset is independently decomposed into metagenes and metasamples with MF; (2) using the RBH metric, links between metagenes are inferred and a network is constructed; (3) communities of metagenes are detected in the RBH network. These communities of metagenes are then analyzed for functional relatedness and provide a biological interpretation of the principal factors that shape the transcriptomes.
Here, we adapted the RBH-based framework to the study of the molecular mechanisms underlying DD relationships in order to infer univocal positive/negative correspondences between MF metagenes independently obtained on datasets measured from different diseases. The improved RBH-based framework proposed here maintains the three sequential steps described above. At the same time, the following methodological novelties are introduced in each of the steps defined above: (i) we propose two methodologies for the orientation of the metagenes (i. e. , we assign a sign to the metagenes in order to express either direct or inverse similarity between them); (ii) we derive a novel definition of the reciprocal best hit (RBH) network taking into account the orientation of the metagenes; and (iii) we restrict the community detection phase to the subnetwork of interest (e.
g. , subnetwork of negative links connecting metagenes of LC and AD in our case). The structure of the framework, together with the changes here introduced are summarized in Figure  1. The code to run the framework is available at https://github. com/agreco92/inverse_comorbidity_MF. Each transcriptomic dataset is separately decomposed using MF. The framework proposed here can be combined with any MF algorithm of interest. In this work, we chose stabilized independent component analysis (sICA)  [12, 27, 28] , a stabilized version of ICA  [28] [29] [30]. sICA was indeed previously shown to outperform alternative MFs in the extraction of relevant biological knowledge from collections of transcriptomic datasets derived from the same biological condition (e. g. , the same cancer type)  [12]. Moreover, the ability of sICA to separate the various overlapping biological factors present in transcriptomic data, such as those linked to tumor cells, the tumor microenvironment, nonbiological factors, sample processing or data generation, makes this approach particularly promising for extracting relevant molecular factors from the numerous confounding factors involved in DD relationships.
By applying sICA to a transcriptomic matrix X (n x m), with n genes in the rows and m samples in the columns, we reduce it to the product of an unknown mixing matrix A (n x k), whose columns are here denoted as "metagenes" and an unknown matrix of source signals S (k x m), whose rows are here denoted as "metasamples". The metagene/metasample associated to component i will thus provide the contribution of each gene/sample present in matrix X to component i. Metasamples and metagenes are learned based upon the assumption that the number k of components occurring in the input matrix X is smaller than either its rows or columns. Here, we selected the number k of components to be 100 for those datasets with more than 100 samples and equal to half of the samples for smaller datasets. These chosen values of k are higher than the estimation of the optimal transcriptomic dimension (optimal k value defined in  [28] ), due to the fact that overdecomposition in sICA was proven not to be detrimental for the interpretability of the resulting components  [28].