dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental validation of PRIME-predicted synergistic epistasis between CcpA and CodY Gene deletions that reduce C. difficile growth by more than 65% (i. e. , log 10 (relative growth) < À0. 456) were labeled as essential. c T test p values R 0. 05 are indicated with the ''#'' symbol and were considered not significant. We acknowledge the detailed studies from multiple groups over prior decades that provided a critical mass of information on C. difficile's nutrient and gene-level responses to support development of an EGRIN model for a gut anaerobe and toxigenic species. We emphasize that this information, the most for any obligate anaerobe, still represents a small fraction of that normally used to develop thorough EGRIN models. Despite this caveat, the EGRIN model was effective in uncovering functional and mechanistic insights into regulatory and metabolic responses of the pathogen in contexts not covered by the training transcriptome dataset-both in vitro and in vivo (i.
Data and code availability d This paper analyzes, publicly available existing transcriptional data. These accession numbers for datasets are listed in the key resources table. The compiled transcriptional compendium is available in the interactive and open access C. difficile Web Portal (http://networks. systemsbiology. net/cdiff-portal/). d All models and supporting resources reported in this study are available in C. difficile Web Portal (http://networks. systemsbiology. net/cdiff-portal/). d Original code is publicly available at the C. difficile Web Portal (http://networks. systemsbiology. net/cdiff-portal/). The code can also be found at https://github. com/marioluisao/Predictive-regulatory-network-models-for-systems-analysis-of-C. -difficile. d Any additional information required to reanalyze data reported in this paper is available from the lead upon request.
ACKNOWLEDGMENTS We thank members of the  Baliga lab  for critical discussions and feedback. Funding was provided by  National Institute of Allergy and Infectious Diseases  ( R01AI128215 ,  R01AI141953 , and  U19AI135976 ), and the  National Science Foundation  ( DBI-1565166  and  IIBR-2042948 ) to N. S. B. , and the  BWH Precision Medicine Institute ,  R01AI153605  from the  National Institute of Allergy and Infectious Diseases (NIAID)  and  P30DK034854  from the  National Institute of Diabetes, Digestive and Kidney Diseases  to L. B. J. N. W. was supported by the  Intramural Research Program of the National Library of Medicine, National Institutes of Health. Funding was also provided by the  Institut Pasteur and Universitede Paris  to B. D. and J. P. , and the  Institut Universitaire de France  to O.
We will compare our proposed tests to these existing approaches both conceptually and empirically, and we will show that our tests are more flexible and powerful than these existing tests. Furthermore, when individual-level data are available, the proposed tests can produce a more narrow list of associated genes at a locus by reducing the confounding effect of linkage disequilibrium (LD), a unique aspect of our gene-based test. A related and popular gene-based strategy is the transcriptomewide association studies (TWASs)  (13, 14)  that use GWAS data for a specific trait combined with genetic-variation geneexpression repositories, such as GTEx  (15) , to perform genebased association tests. However, TWASs are limited to expression quantitative trait loci (eQTLs) being present in the reference datasets, and the majority of genetic associations Significance Gene-based tests are important tools for elucidating the genetic basis of complex traits.
In particular, we use chromatin immunoprecipitation sequencing (ChIP-seq) peak data extracted from the ChIP-Atlas database to identify promoter regions and data from the GeneHancer database to link enhancers to their target genes  (28). We also use the activityby-contact (ABC) model to predict functional enhancer-gene connections for five cell types and tissues  (29). This is the GeneS-can3D test. Finally, when individual-level data are available, we implement the knockoff framework for a more powerful genediscovery and fine-mapping approach and refer to this test as GeneScan3DKnock. We take advantage of recent advances in region-based tests for sequencing data  (4, 22, 24)  to perform computationally efficient and comprehensive tests with genetic variation in a gene (including variants located in proximal and distal regulatory elements), while scanning the gene with a range of window sizes for improved power.
The data include 3,085 whole genomes from the ADSP Discovery Extension Study and 809 whole genomes from the Alzheimer's Disease Neuroimaging Initiative, for a total of 3,894 whole genomes (more details are available in SI Appendix). We adjusted for age, age 2 , gender, ethnic group, sequencing center, and the leading 10 principal components of ancestry. Seven tissue/cell-type specific GenoNet functional scores (31) related to brain were incorporated, including E071 (brain hippocampus [HIP] middle), E074 (brain substantia nigra), E073 (brain dorsolateral prefrontal cortex [DLPFC]), E068 (brain anterior caudate), E067 (brain angular gyrus), E069 (brain cingulate gyrus), and E072 (brain inferior temporal lobe). We show results for several tests, including GeneScan1D and GeneScan3D tests; the proposed knockoff-based approach, GeneScan3DKnock, based on five random knockoffs; as well as existing tests, including MAGMA/H-MAGMA, STAAR-O, and TWAS.
Lung function (FEV1). The COPDGene study includes chronic obstructive pulmonary disease (COPD) cases, controls, and additional smokers with varied lung function. In addition to COPD case/control status, lung-function measurements are also available, including forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC). We analyzed whole-genome sequencing data from the TOPMed freeze5b dataset, which includes a subset of 5,593 Non-Hispanic White individuals for continuous traits and 4,450 individuals for COPD case/control binary trait. We present results from the application to FEV1, adjusting for sequencing center, 10 principal components of ancestry, age, age 2 , gender, height, height 2 , smoking pack-years, and current smoking. We incorporated five tissue/cell-type specific GenoNet functional scores (31) related to lung, namely: E017 (IMR90), E088 (fetal lung), E096 (lung), E114 (A549), and E128 (normal human lung fibroblast).
05/40 = 1. 25 × 10 -3 ). Applications to GWAS Summary Statistics. GWAS for neuropsychiatric and neurodegenerative diseases. We applied the different gene-based tests to summary statistics from nine GWAS studies of brain disorders, including five neuropsychiatric traits: attention-deficit/hyperactivity disorder (ADHD)  (49) , autism spectrum disorder (ASD)  (50) , bipolar disorder  (51) , schizophrenia  (52) , and major depressive disorder  (53) ; and four neurodegenerative traits: AD (36), Parkinson's disease  (54) , amyotrophic lateral sclerosis (ALS)  (55) , and multiple sclerosis (MS)  (56). We do not include STAAR-O here since the current implementation is not applicable to summary statistics. Since these applications focus on brain disorders, we leveraged two existing Hi-C human brain datasets for the DLPFC in adult brain  (57)  and for the germinal zone (GZ) and cortical and subcortical plate (CP) in fetal brain  (58)  and used the Fit-Hi-C method to identify statistically significant promoter-enhancer interactions from these data (59) (SI Appendix).
Similarly, we applied MAGMA/H-MAGMA  (11)  to the same datasets using the same significant Hi-C interactions. We also applied TWAS/FUSION  (14)  based on 13 brain regions from GTEx version 7 (amygdala [AMY], anterior cingulate cortex, caudate, cerebellar hemisphere, cerebellum, cortex, frontal cortex, HIP, hypothalamus, nucleus accumbens, putamen, spinal cord, and substantia nigra). The Cauchy P value combination method was used to combine TWAS P values from different brain regions. We used a liberal significance threshold (10 -3 ) to select genes from these analyses (because some of the GWAS studies, e. g. , AD, ADHD, and MS, are underpowered) and investigated their expression patterns using spatiotemporal and single-cell transcriptomics data, as described below. The number of significant genes at this threshold and the overlap across the different tests are shown in SI Appendix, Table  S7  and Fig.
S11. Compared with 1D analyses (GeneScan1D and MAGMA), GeneScan3D and H-MAGMA detected a much larger number of diseaseassociated genes, as expected, given that they incorporate signals from distal regulatory elements. GeneScan3D and H-MAGMA also detected a substantially higher number of significant genes relative to TWAS, a possible reflection of the limitation of eQTL-based approaches to discover significant associations that cannot be explained by eQTLs in the reference datasets. Developmental and single-cell expression profiles. We used spatiotemporal transcriptomic data from embryonic and adult brains measured at 15 time periods, ranging from four postconceptional weeks to age ≥ 60 y (60). The gene-expression data are available for six brain regions: neocortex (NCX), mediodorsal nucleus of the thalamus, cerebellar cortex, HIP, AMY, and striatum.
Browser for results on UK/Japan Biobank data. UK Biobank. We applied GeneScan3D to 1,403 UK Biobank binary phecodes and 827 continuous phenotypes using summary statistics on 28 million imputed variants. We have created a browser that displays phenome-wide results for a given gene and genome-wide gene-based results for a given trait and provides summary tables for significant genes. These gene-based results for the UK Biobank traits complement existing databases for single-variant tests  (63)  and rare variant-focused tests, such as SAIGE-GENE, a scalable generalized mixed-model regionbased association test  (6). BioBank Japan. For non-European populations, we applied GeneScan3D to BioBank Japan binary phenotypes using available case-control GWAS summary statistics on 8,712,794 autosomal variants and 207,198 × chromosome variants with 212,453 Japanese individuals across 42 diseases  (64).
Our gene-based tests can be seen as complementary to the TWAS approach. Like TWAS, they attempt to incorporate the effect of distal regulatory elements into the test. TWAS, however, is limited to common eQTLs detectable in reference datasets, which appear to account for a minority of GWAS signal  (16, 17). In contrast, our approach has the ability to assess the effects of coding, noncoding, rare, and common variants, including those with no detectable effects on gene expression, and can scan the gene with varied window sizes. Furthermore, the knockoff framework can attenuate the confounding effect of LD and is able to produce a narrower list of possible causal genes, likely removing some of the false gene discoveries. In this paper, we have focused on using existing external data on gene-enhancer links, and we recognize the limitations of these databases, both in terms of the accuracy of these links and the number of cell types with available data.
We additionally computed the corresponding q value for a gene, q G. The q value already incorporates correction for multiple testing and is defined as the minimum FDR that can be attained when all tests showing evidence against the null hypothesis at least as strong as the current one are declared as significant. In particular, we define the q value for a gene G with feature statistic W G > 0 as q G = min t≤W G 1 + #{G : W G ≤ -t} #{G : W G ≥ t} , where 1+#{G :W G ≤-t} #{G :W G ≥t} is an estimate of the proportion of false discoveries if we were to select all genes with feature statistic > t (with t > 0). For genes with feature statistic W G ≤ 0, we set q G = 1. Multiple Knockoffs. To improve the power and stability of the knockoff procedure, we implemented a multiple-knockoff procedure  (8, 69) , where the inference is based on generating multiple, independent knockoff datasets.
Gimenez and Zou (69)  proposed an extension of the sequential model for knockoff generation to multiple knockoffs and showed the validity of the multiple-knockoff-generation procedure in controlling the FDR. We implemented this procedure here to generate multiple independent knockoff datasets. Briefly, we sequentially sample for each variant j: G1 j ,. , GM j from L(G j | G -j , G1 1 M + 1 M #{G : κ G ≥ 1, τ G ≥ t} #{G : κ G = 0, τ G ≥ t} ≤ q , where κ G = argmax 0≤m≤M T m G (note that T 0 G = T G ) and τ G = T G -medianT m G. We selected genes with W G ≥ τ , i. e. , those genes that have importance scores greater than any of those corresponding to the M knockoffs (κ G = 0) and for which the difference from the median importance score is above some threshold (τ G ≥ τ ). A q value for a gene G can be computed for the multiple-knockoff scenario, similar to the singleknockoff case.
The multiple-knockoff procedure helps improve power because at a target FDR of q, the single-knockoff approach needs to make a minimum of 1/q discoveries, while a multiple-knockoff approach with M knockoffs decreases this detection threshold to 1/Mq. Therefore, in situations where the signal is sparse and the target FDR level q is low, a single-knockoff procedure will have very low power. In such cases, the multiple-knockoff procedure will tend to improve power. Furthermore, the multiple-knockoff procedure also helps with improving the stability of the selected genes, given that each knockoff generation is random, and, therefore, the results from a single knockoff can be unstable. Data Availability. We used data from existing studies from COPDGene (TopMED; Database of Genotypes and Phenotypes [dbGaP] accession no.
phs000951. v4. p4) and the ADSP (dbGaP accession no. phs000572. v8. p4) and summary-level GWAS results on neuropsychiatric and neurodegenerative traits are available from refs. 36 and 49-56. Details of web-based resources are in SI Appendix. All study data are included in the article and/or supporting information. We have implemented GeneScan3DKnock in a computationally efficient R package, available on GitHub (https://github. com/Iuliana-Ionita-Laza/GeneScan3DKnock), that can be applied generally to the analysis of other whole-genome sequencing or GWAS studies. Details of software implementation are in SI Appendix.
ACKNOWLEDGMENTS. This research was supported by  NIH/National Institute of Mental Health  Awards  MH106910  and  MH095797  (to I. I. -L. ); and  NIH/National Institute on Aging  Award  AG066206  (to Z. H. ). We gratefully acknowledge the studies and participants who provided biological samples and data for the ADSP and TOPMed projects. The full study-specific acknowledgments are detailed in SI Appendix.
Moreover, in the long term, it would be interesting to have tools to investigate which proofs can be adapted from more conventional logics (HOL, set theory) to HoTT and conversely. The paper is organized as follows. We first give an introduction on HoTT, Dedukti and encodings of Type Theory in Dedukti. This will motivate the introduction of Two Level Type Theories (2LTT) as a more flexible way to deal with type theories having a definitional equality two complex to be encoded as rewrite rules. In the second section, we will introduce 2LTTs and their encoding as a Dedukti theory. In the third section, we will give a partial encoding of Cubical as a 2LTT, and the corresponding piece of Dedukti theory. Both encodings have been implemented and can be found here: https://github. com/valent20000/CTTDedukti.
We implemented all the above axioms in Dedukti. Interestingly, there was in the list of axioms that couldn't be implemented an axiom called (A5), which requires that external equality validates the reflection rule (that is, externally equal types are considered definitionally equal). This could not be implemented in Dedukti. In it was possible, then we would be able to encode Cubical in Dedukti without resorting to 2LTTs. We also tested the usability of this encoding by formulating the axiom of univalence. Here, for the sake of brevity, we only give the weaker form which states that the type A = B (where A and B are types of level l) is weakly equivalent to A ≈ B, the type of weak equivalence between A and B: W e a k U n i v a l e n c e : l : Lev -> A : T l -> B : T l -> eps ( lsuc l ) ( Equiv ( lsuc l ) ( TEq l A B ) ( lUp l ( Equiv l A B ))).
Here we present a new software-tool for visualizing fragment ions and sequence coverage of intact proteins in top-down mass spectrometry. TDFragMapper combines the data arising from multiple and diverse tandem mass spectrometry experiments of intact proteins. Our tool maps fragment ions onto the protein backbone sequence and allows for a rapid comparison of the results obtained when varying experimental parameters in tandem mass spectrometry (MS/MS) experiments of intact proteins: fragmentation method, selected precursor charge state, activation level, technical replicate. In summary, TDFragMapper is a unique software-tool that allows an easy selection of the best experimental conditions leading to the highest con dence in intact protein sequence identi cation.
g. , ProSight Lite  [3] ), there is currently no computational tool allowing to visualize fragments arising from diverse MS/MS experiments on a unique fragmentation map without losing information on the contribution and the speci city of each experiment. Current tools provide a unique fragmentation map per MS/MS experiment, and thus their comparison, in particular when multiple parameters are assessed, is both di cult and time-consuming. Moreover, although the intensity of fragment ions can represent a precious source of information when dealing with MS/MS data, it is often not considered when matching fragments onto a protein sequence. To circumvent these limitations, here we introduce TDFragMapper, a novel software-tool that can display and combine pre-assigned fragment ions achieved from various MS/MS experiments on a unique protein sequence, keeping an easy access to the individual contribution of each experiment and to the intensity of deconvoluted fragment ions.
Data les •TDFragMapper v1. 0 is compatible with output data les from ProSight Lite  [3]  and Thermo® FreeStyle™ software  [4]. •TDFragMapper saves results in its own format (i. e. , *. tdfm) and exports the analysis to an image in TIFF, PNG or JPG format. Procedure 1. Software installation: Download TDFragMapper by clicking on the Download button at https://msbio. pasteur. fr/tdfragmapper.
Work ow The following work ow demonstrates how to visualize and compare fragments obtained from various tandem mass spectrometry (MS/MS) experiments on intact proteins using TDFragMapper. 2. 1. Execute the TDFragMapper tool (Figure  1 ) Figure  1 : Graphical User Interface of the main window of TDFragMapper. 2 ). Specify a Deconvoluted Spectra le that was generated by Thermo® FreeStyle™ software (Xtract algorithm)  [4]  if an evaluation of fragment ions intensity is desired.
Introduction Optically stimulated luminescence (OSL, called optical dating in  Huntley et al. , 1985)  allows the dating of the last exposure of quartz grains to sunlight. The single-aliquot regenerative (SAR) dose protocol consists of comparing the natural luminescence signal to laboratory-generated signals induced by artificial irradiation  (Murray and Wintle, 2000; Wintle and Murray, 2006). The corresponding measurements, in particular at the single-grain scale, result in large datasets characterised by significant scatter, owing to a number of dispersion factors (see for example  Thomsen et al. , 2005). An OSL age Published by Copernicus Publications on behalf of the European Geosciences Union. is then obtained by dividing the equivalent dose (i. e. in the case of coarse quartz grains, the dose absorbed by the mineral) by the dose rate to which quartz grains were exposed since the last exposure to light.
, 2015; Combès and Philippe, 2017, and references therein). To implement the Bayesian models of  Combès et al. (2015)  and  Combès and Philippe (2017)  in practice, and provide easy access to the community, an R package (R Core Team, 2020) named "BayLum"  (Christophe et al. , 2020 ; version 0. 2. 0) has been developed and released on the Comprehensive R Archive Network (CRAN; see also  Mercier et al. , 2016 , for a first implementation of the central-dose model developed by  Combès et al. , 2015). The first features of this BayLum package were presented by  Philippe et al. (2019) , and its performance, when one is confronted either with large dose values or with dose variability issues, was tested in laboratory-controlled experiments  (Heydari and Guérin, 2018)  and later applied to various case studies  (Lahaye et al.
Herein we present a Bayesian modelling case study. (1) We start with how data should be pre-treated prior to using the BayLum package; a simple example of chronological modelling (samples considered independent, i. e. without stratigraphic constraints and shared errors) is first presented, yielding an output from the BayLum package to serve as a reference for the following, more elaborate models. (2) In the next step, we detail how the user can integrate stratigraphic constraints and the effect on the chronological inference. It should be noted that we take here the stratigraphic information for granted, but we warn the user against treating such information lightly, as it bears great consequences on the age calculation (see discussions in  Heydari et al. , 2020 Heydari et al. , , 2021)). (3) Then, most importantly, we explain how to build a dose covariance matrix in practice to take into account systematic errors (for the definition of this matrix, the reader is referred to  Combès and Philippe, 2017)  and what effect it has on a series of ages.
(4) For this purpose, we base our approach on dose rate measurements as performed by  Guérin et al. (2015b)  at the IRAMAT-CRP2A laboratory. The effect of integrating independent data such as radiocarbon ages, which usually do not share systematic errors affecting OSL data, is then illustrated. (5) Finally, we discuss different ways to measure dose rates and various assumptions that can be made regarding the nature (systematic or random) of additional sources of errors in OSL dating (see also  Rhodes et al. , 2003 , for a similar discussion). To help the reader, we provide in the Supplement an R markdown document with commented lines of code and ex-ample datasets, so that everything presented here may be reproduced.
Case study To illustrate how to model OSL ages, both in stratigraphic constraints and sharing systematic errors, using the R Bay-Lum package, we use the data from two sediment samples (FER 1 and FER 3) already dated by quartz OSL  (Guérin et al. , 2015b). These samples were taken from the archaeological site of La Ferrassie (France) and prepared following standard chemical preparation procedures applied to luminescence-dating samples. While modelling with Bay-Lum may be applied to both multi-grain and single-grain OSL datasets, in the following we only focus on single-grain data, as this is probably where the need for appropriate statistical models is most acute (the reliability of multi-grain OSL has been demonstrated when using a plain average (mean) for palaeodose estimation; see for example  Murray and Olley, 2002;  for theoretical justification, see  Guérin et al.
Data pre-treatment The Bayesian modelling implemented in BayLum requires information of different natures: (i) raw OSL data in the form of BIN/BINX file(s), (ii) list(s) of grains to be included in the modelling (based on pre-defined selection criteria, e. g. on recycling and/or recuperation ratios), (iii) file(s) indicating how the data should be processed (signal integration channels, reproducibility of the instrument(s), etc. ), and (iv) both natural (in Gy ka -1 ) and laboratory (in Gy s -1 ) dose rates. Based on these data, the calculations are performed all at once using Markov chain-Monte Carlo (MCMC) computations; as a result, unlike in standard frequentist data processing, there is no succession of steps in data analysis (for example, individual equivalent dose estimates are not parameterised, unlike when the CDM is used).