dataesr/scientific-paragraphs-categorization huggingface dataset:
LOD and LLOQ values were calculated using QuaSAR  (37) , which was implemented through the Skyline interface (see supplemental Data S5). Targeted MS measurements were highly multiplexed, and used to quantify proteasome subunits across cell lysates, tissues and primary cells of human origin. The assay developed used internal standards for each analyte, to confidently detect and precisely quantify the proteins of interest. Thus, the analyses meet the expectations of Tier 2 level. Determining the Stoichiometry of the Six Major 20S Proteasome Subtypes-The concentrations and stoichiometries of all proteasome subtypes were determined as explained hereafter. Total proteasome absolute quantity was calculated by averaging the quantities of all 20S noncatalytic subunits (␣1-7; ␤3, ␤4, ␤6, ␤7) (mean (␣1,2,3,5,6,7; ␤3,4,6,7) 3 Total P20S).
For each biological replicate, results from at least two injection replicates were averaged. Probability values (p) were determined by one-way and two-tailed analysis of variances (ANOVA). Differences were statistically significant at confidence levels of 95% (*), 99% (**), or 99. 9% (***). Coefficients of variation were calculated as ratios of the standard deviation over the mean of the values and are expressed as a percentage. Accuracy was determined as the ratio of the difference of the experimental value and the reference value over the reference value and is expressed as a percentage. Label-free MS Analyses (Fig. 3B  and supplemental Fig. 11 )-The "human tissue extract" data set contains mass spectrometry results from the analysis of 11 different human tissues analyzed in triplicate, corresponding to 33 raw files.
The "ADSC" data set contains mass spectrometry results from the analysis of three different patients under two different oxygen percentages used for cell culture, samples were analyzed in triplicates, and thus produced 18 raw files. Quantitative proteomic analysis was performed on the normalized LFQ intensities from the "proteinGroups" Table in the MaxQuant output. The data sets corresponding to the mass spectrometry analyses presented in this study have been deposited in the following repositories: PRIDE (Project accession: PXD011894) for label-free MS analyses, and PeptideAtlas (Dataset identifier: PASS01219) for targeted MS analyses. The detailed descriptions of all analyses (raw and processed file names, sample name, biological replicate number, MS technical replicate number, corresponding figure) are summarized in supplemental Data S8.
With this method, absolute quantification of total 20S proteasome was achieved in protein lysates of eight different human cell lines of diverse origins (HEK 293T, HCT116, RKO, U937, HeLa S3, NB4, KG1a, and MRC5). 20S proteasome concentration determined by the SRM method was compared with those obtained with the reference ELISA method (Fig. 2A ). A high correlation was found between the two methods (coefficient of determination ϭ 0. 98; slope ϭ 1. 01). Altogether, these data demonstrate that the newly-developed method is suitable for absolute quantification of 20S proteasome in human cell lysates. Next, we implemented the method to detect the six standard and immune-specific catalytic subunits of the 20S proteasome (␤1, ␤2, ␤5, ␤1i, ␤2i, ␤5i), as the absolute quantification of these subunits is required to determine the stoichiometry of the different 20S proteasome subtypes present in biological samples (Fig.
Details on stoichiometry determination of 20S proteasome subtypes are provided under Experimental Procedures. confirms the accuracy and precision of the method developed while also emphasizing the need for absolute quantification to detect perturbations in proteasome subunit expression or incorporation of subunits into mature proteasomes. It also indicates that this broad panel of normal and tumor human cells do not contain the noncanonical alternative ␣4-␣4 20S proteasome complex in which a second copy of ␣4 occupies the position normally held by ␣3  (38, 39). The abundance of some proteasome subunits measured using our optimized MRM assay was compared with mRNA expression data retrieved from the human protein atlas database  (40)  in corresponding tissues (supplemental Fig. S9 ). Although positive correlations were observed between some proteins and mRNA molecules abundances, these results suggest that measuring mRNA levels are not suitable when seeking to predict protein expression levels for 20S proteasome subunits in human tissues.
In comparison, the TOP3 label-free quantification approach barely achieves 78% accuracy after discarding outliers arising from unrepresentative peptides. Converting peptide abundance into protein concentration requires careful selection of peptide sequences and optimization of SRM transitions  (19)  or, alternatively, the removal of incoherent peptides from global MS1-based quantification datasets, for instance using covariation of peptide abundances  (59). Thus, even if label-free MS1-based quantification methods can be used to obtain a rough estimate of protein complex stoichiometry, as previously reported (60 -62), these approaches are most appropriate for the high-throughput determination of changes in the relative abundances of protein complex subunits  (34, 35)  or estimation of protein copynumber without requiring spike-in standards  (63).
DATA AVAILABILITY The data sets corresponding to the mass spectrometry analyses presented in this study have been deposited in the following repositories: PRIDE, (https://www. ebi. ac. uk/pride/archive/, Project accession: PXD011894) for label-free MS analyses, and PeptideAtlas (http://www. peptideatlas. org/, Dataset identifier: PASS01219) for targeted MS analyses. Skyline files of all targeted experiments are available on Panorama Public (https:// panoramaweb. org/project/Panorama%20Public/2018/IPBS-CNRS%20-%20SRM_Proteasome_2018/begin. view?). The detailed descriptions of all analyses (raw and processed file names, sample name, biological replicate number, MS technical replicate number, corresponding figure) are summarized in Supplementary Data 8.
thank the  Region Midi-Pyre  ´ne foundation for PhD fellowships to TM and BF, respectively. This project was supported in part by the  Region Midi-Pyrenees, European funds (Fonds Europeens de Developpement Regional, FEDER) ,  Toulouse Me  ´tropole, by the  Agence Nationale de la Recherche  ( ANR-11-RPIB-0012 :  SAFE  Project), and by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santeprogram (ProFI ,  Proteomics French Infrastructure  project,  ANR-10-INBS-08 ). □ S This article contains supplemental Data and Figures.
Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.
Introduction Major depressive disorder (MDD) is a debilitating and highly prevalent psychiatric disorder, characterized by depressed mood and loss of interest in daily activities  [1]. Although MDD is one of the leading causes of disability worldwide  [2] , our understanding of the pathophysiological basis of the disorder remains incomplete. In recent years, neuroimaging analyses have helped to characterize the neuroanatomical basis of MDD; however, consistent patterns of brain alterations have been difficult to identify due to limited power in previous studies and heterogeneity in data analysis. To address this issue, the MDD working group within the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium (http:// enigma. usc. edu/) initiated the largest coordinated metaanalyses of brain structure in MDD to date to investigate the robustness or consistency of neuroimaging findings across many samples worldwide.
UK Biobank To examine whether our findings generalize to a population study, we analyzed a large population cohort of adults (age range: 43-78 years) from the UK Biobank (1st and 2nd release) a large-scale population health study of adults in the United Kingdom. 2096 individuals were identified to have 'probable lifetime MDD', as previously described  [24] , based on hospital admissions data and self-report of depressive symptoms. DTI measures from patients were compared to DTI measures from 3275 controls without mental disorders. Image acquisition and processing of UK Biobank data is described in Supplemental Note 3. In order to examine overlap between the UK Biobank sample and the meta-analysis, the case-control analyses in the meta-analysis were also performed including only subjects within the UK Biobank age range (43-78 years).
Discussion A coordinated approach was used to perform the largest multi-site DTI study in MDD to date. Differences in FA and diffusivity measures (AD, RD, and MD) were examined between depressed patients and healthy controls, as well as associations with clinical characteristics of the disorder. We included DTI data from 20 cohorts, including a total of 1305 patients with MDD and 1602 healthy controls. Separate analyses were performed for adolescent and adult MDD, and we found evidence for globally lower FA and higher RD across the full WM skeleton in adult MDD patients. These effects were mostly driven by patients with recurrent episodes, patients with an adult (older than 21 years) age of onset of depression, and patients not taking antidepressants at the time of scanning. In adult patients with MDD, we found evidence of subtle, but widespread lower FA in 16 out of 25 tract-based ROIs including the corpus callosum, internal capsule, inferior fronto-occipital fasciculus, corona radiata, cingulum, fornix, superior fronto-occipital fasciculus (SFO), and sagittal stratum.
Our findings are in line with a study by Zeng et al. [49] , which is to our knowledge the only study to date that has examined alterations in WM between medication-naive MDD patients and MDD patients using antidepressants. Although their sample was small, the authors conclude that antidepressant use normalizes WM deficits in MDD. Ultimately, to fully elucidate the direct effect of antidepressant use on WM microstructure, DTI measures should be incorporated in randomized longitudinal clinical trials with antidepressant medication in medication-naive MDD patients. In secondary analyses, we ran the same analyses on a large dataset from UK Biobank to examine how our findings generalize to an adult population sample. Effect sizes were generally lower and most significant findings in the meta-analysis were not significant in the UK Biobank sample.
National Institute of Child Health & Human Development (RO1 HD050735); National Institute of Biomedical Imaging and Bioengineering (Award 1U54EB020403-01, Subaward 56929223); National Health and Medical Research Council, Australia (Project Grants 496682, 1009064). NIH ENIGMA-BD2K U54 EB020403 (Thompson); R01 MH117601 (Jahanshad/Schmaal). Magdeburg: M. L. and M. W. are funded by SFB 779. Bipolar Family Study: This study has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013). This paper reflects only the author's views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award (104036/Z/14/Z). Minnesota Adolescent Depression Study: The study was funded by the National Institute of Mental Health (K23MH090421), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, the Minnesota Medical Foundation, and the Biotechnology Research Center (P41 RR008079 to the Center for Magnetic Resonance Research), University of Minnesota, and the Deborah E.
Acknowledgements The ENIGMA-Major Depressive Disorder working group gratefully acknowledges support from the  NIH Big Data  to Knowledge ( BD2K ) award ( U54 EB020403  to PMT) and  NIH  grant  R01 MH116147  (PMT). LS is supported by an  NHMRC   MRFF Career Development Fellowship  ( APP1140764 ). We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank  Rosa Schirmer ,  Elke Schreiter ,  Reinhold Borschke  and  Ines Eidner  for image acquisition and data preparation, and  Anna Oliynyk  for quality checks. We thank  Dorothee P. Auer  and  F. Holsboer  for initiation of the RUD study. We wish to acknowledge the patients and control subjects that have particiaped int the study. We thank  Rosa Schirmer ,  Elke Schreiter ,  Reinhold Borschke  and  Ines Eidner  for image acquisition and data preparation, and  Anna Oliynyk  for quality checks.
Stanford: This work was supported by  NIMH  Grants  R01MH59259  and  R37101495  to IHG. MS is partially supported by an award funded by the  Phyllis and Jerome Lyle Rappaport Foundation. Muenster: This work was funded by the  German Research Foundation  ( SFB-TRR58 , Projects  C09  and  Z02  to UD) and the  Interdisciplinary Center for Clinical Research (IZKF)  of the medical faculty of Münster (grant  Dan3/012/17  to UD). Marburg: This work was funded by the  German Research Foundation  (DFG, grant  FOR2107 DA1151/5-1  and  DA1151/5-2  to UD;  KI 588/ 14-1 ,  KI 588/14-2  to TK;  KR 3822/7-1 ,  KR 3822/7-2  to AK;  JA 1890/ 7-1 ,  JA 1890/7-2  to AJ). IMH-MDD: This work was supported by the  National Healthcare   Group Research Grant  ( SIG/15012 ) awarded to KS. Barcelona: This study was funded by two grants of the  Fondo de Investigación Sanitaria  (FIS:  PI 10/00372 ;  FIS: 13/1057 )from the  Instituto de Salud Carlos III , by the  Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM).
the National Institute of Child Health & Human Development (RO1 HD050735); National Institute of Biomedical Imaging and Bioengineering (Award 1U54EB020403-01, Subaward 56929223); National Health and Medical Research Council, Australia (Project Grants 496682, 1009064). NIH ENIGMA-BD2K U54 EB020403 (Thompson); R01 MH117601 (Jahanshad/Schmaal). Magdeburg: M. L. and M. W. are funded by SFB 779. Bipolar Family Study: This study has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013). This paper reflects only the author's views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award (104036/Z/14/Z). Minnesota Adolescent Depression Study: The study was funded by the National Institute of Mental Health (K23MH090421), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, the Minnesota Medical Foundation, and the Biotechnology Research Center (P41 RR008079 to the Center for Magnetic Resonance Research), University of Minnesota, and the Deborah E.
Results Using a Labcyte Echo 525 instrument, we assembled 500 nL reverse-transcription reactions in 384well plates, by dispensing droplets of 25 nL from a source plate containing the reagent stocks to the destination plates. After incubating the reactions, we assessed their yield and quality by quantitative DNA sequencing using the nanoCAGE method. We multiplexed two pools of reactions (2 × 1,536) using a selected set of 64 different template-switching oligonucleotides carrying sample identifiers ("barcodes"), combined with 24 sequencing indexes (Figure  1 ). As we focused on quality controls, the sequencing was kept at a small scale: ~5,000 sequence read pairs per reaction. We explored one categorical and three continuous parameters spanning multiple orders of magnitude with data points regularly spaced on a logarithmic scale.
Methods Nanoliter-scale liquid transfers were performed on an Echo 525 instrument (Labcyte). The transfer sheets were generated from event logs produced by simulations of instrument runs in the R programming language using the layout of source and destination plates as input. All the scripts are available on https://gitlab. com/charles-plessy/labcyte-rt-optimisation. We used a total RNA prepared from mouse liver. An RNA Integrity Number  (RIN, Schroeder et al. , 2006)  of 9 was measured on an Agilent Bioanalyzer with an RNA 6000 pico kit. As molarity measurement were not possible with this kit, RNA amounts are measured as a mass in this work. The template-switching oligonucleotides were synthesized by Integrated DNA Technologies, Inc. (IDT), at a scale of 10 nmoles each in a 96-well plate format and resuspended at a stock concentration of 1 mM upon reception.
To assess the quality of synthesis, we ran a control experiment in which the parameters of the reverse-transcription reactions were kept constant (see supplemental experiment six on https://gitlab. com/charles-plessy/labcyte-rt-optimisation/blob/master/Labcyte-RT_Data_Analysis_6. md for details). For the main experiment, we selected 64 oligonucleotides among those who gave yields closest to the mean. RT reaction master mixes (containing 0. 0528 M sorbitol, 0. 264 M trehalose, 0. 75 M betain, 0. 01 M DTT, 0. 625 mM dNTPs, 20 U/µL SuperScript enzyme III or IV and 1× SuperScript buffer III or IV), template-switching oligonucleotides, reverse-transcription primers, RNA and ultrapure water were transferred from independent wells of a source plate (Greiner Bio-One) to the different wells of the destination plates (Applied Biosystems) using specific liquid transfer patterns for each plate.
Data availability nanoCAGE sequence data: Zenodo 1680999 (https://doi. org/10. 5281/zenodo. 1680999) nanoCAGE sequence alignments: Zenodo 1683162 (https://doi. org/10. 5281/zenodo. 1683162) Single-cell sequence data: Zenodo 250156 (https://doi. org/10. 5281/zenodo. 250156) Single-cell sequence alignments: Zenodo 3340196 (https://doi. org/1 0. 5281/zenodo. 334019 6 ).
Higra -Hierarchical Graph Analysis is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods capable of handling large amount of data. The main aspects of hierarchical graph analysis addressed in Higra are the construction of hierarchical representations (agglomerative clustering, mathematical morphology hierarchies, etc. ), the analysis and processing of such representations (filtering, clustering, characterization, etc. ), and their assessment. Higra targets a large audience, from students and practitioners wanting an accessible library for quickly experimenting, to researchers developing new methods for hierarchical analysis of graph data. Higra is a generic toolbox for graph analysis and can be utilized in a large variety of application fields like machine learning, data science, pattern analysis and computer vision.
Motivation and significance Graphs are getting increasingly popular in machine learning, data science, pattern analysis and computer vision, as they provide a natural representation for structured or network data. Among graph analysis methods, hierarchical approaches try to capture the structure of a graph at various scales in a consistent manner by sequentially merging graph vertices into increasingly larger clusters until a single cluster remains. Such methods have proven to be useful in a wide variety of application fields in which data can be modelled as graphs such as image analysis, community detection, mesh analysis, philogenetic tree construction, and so on. * Corresponding author. E-mail address: benjamin. perret@esiee. fr (B. Perret). Higra -Hierarchical Graph Analysis is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods.
It aims at providing standard and state-of-the-art algorithms for hierarchical graph analysis capable of handling large amount of data (currently up to dozen of millions of vertices on a classical desktop computer). It can handle both hierarchical clustering and component trees, where each level of a hierarchy represents a partial clustering of the space. Furthermore, the design of Higra enables the user to easily switch between the dual representations of hierarchical clustering: trees (dendrograms) and saliency maps (ultrametric distances). Higra is a generic toolbox for hierarchical graph representation construction, processing and assessment: it is thus not focused toward a particular application or domain and its fundamental functions can be used in a variety of situations. Higra provides a Python API to ease its usage and to benefit from the synergies created by the large amount of scientific libraries available in the Python ecosystem.
This API is thought to be useable by students and expert researchers willing to quickly develop new applications, experiment new methods, or develop proofs of concepts. Vectorized operations on hierarchical representations enable writing various algorithms working on hierarchical representations efficiently in Python. It is also thought for seamless integration with classical Python data analysis pipelines such as Scikit-Learn and modern optimization framework such as PyTorch and TensorFlow. The Python interface is backed-up by a C++ module where core algorithms are implemented to ensure high performances. The C++ module is also useable as a standalone library since it does not have any dependency to the Python runtime. Graphs are a common representation for data and many libraries exist to analyse them.
Regarding generic toolbox for graph processing with a Python interface, the main publicly available libraries are graph-tool  [1] , NetworkX  [2] , and igraph  [3]. While they propose numerous algorithms for graph analysis, they offer little or no support for hierarchical analysis. Among major Python data libraries, Scipy  [4]  has a module dedicated to hierarchical clustering but it is limited to complete graphs. On the contrary, Scikit-Learn  [5]  has a module dedicated to agglomerative clustering which can handle sparse graph. While it enables creating flat clustering, also called partitioning, from a sparse graph using classical agglomerative clustering algorithms, hierarchies cannot be easily manipulated by users. The library mlpack  [6] , which is a C++ machine-learning library with Python bindings, also provides some hierarchical methods for point classification.
It also possesses numerous algorithms using hierarchical representation of metric spaces and focused around the problem of efficient distance computation; these algorithms and the associated data structures are however not exposed in the user API. Hierarchical clustering methods have also become popular in image analysis and several libraries implement algorithms dedicated to application domains and, sometimes proposing Python bindings, like Pink  [7] , Olena  [8] , Vigra  [9] , or the more specialized iamxt  [10]. Up to our knowledge, Higra is thus the first C++/Python library providing a large variety of algorithms dedicated to hierarchical graph analysis and not targeting a particular application. This article is organized as follows. Section 2 presents the main aspects of the library architecture and gives a summary of its major functionalities.