dataesr/scientific-paragraphs-categorization huggingface dataset:
These socalled targeted-antibacterial-plasmids (TAPs) use the conjugation machinery (tra genes) encoded by a F plasmid to be efficiently transferred to E. coli strains and to closely related Gram-negative Enterobacteriaceae. TAPs were designed to produce the CRISPR Cas system in a constitutive manner and can easily be redirected against any bacterial species of interest by changing gRNA sequence in one-step cloning. The gRNA sequence carried by the TAP determines the targeting of the antibacterial activity towards specific recipient strains only. To identify strain-specific gRNA, we have developed a bio-informatic program CSTB (CRISPR Search Tool for Bacteria) that allows the rapid and robust identification of ∼16-20 nt sequences on the basis of their presence or absence in the genome of bacterial strains selected on a phylogenetic tree.
Acquisition were performed using 50% power of a Fluo LED Spectra X light source at 488 and 560 nm excitation wavelengths. Exposure settings were 50 ms for sfGFP and 50 ms for mCherry produced from the TAPs; 100 ms for RecA-GFP, HU-mCherry and DnaN-mCherry. Image analysis. Quantitative image analysis was done using Fiji software with MicrobeJ plugin  (14). The Manualediting interface of MicrobeJ was used to optimize cell detection and the Mean intensity fluorescence, skewness and cell length parameters were automatically extracted and plotted. We defined the timing of TAP acquisition (time t = 0) by analyzing the increase of the fluorescence signal conferred by the TAPs (sfGFP or mCh). Plasmid acquisition was validated when a 15% sfGFP or a 30% mCherry fluorescence increase was observed in the transconjugant cells.
Flow cytometry Conjugation was done as described in the conjugation assay section in 0. 1 m filtered LB. At time 90 min and 180 min, conjugation mix were diluted to an A 600 of 0. 03 in 0. 1 m filtered LB and analysed into an Attune N×T acoustic focusing cytometer at a 25 l/min flow rate. Forward scattered (FSC), Side scattered (SSC) as well as fluorescence signal BL1 (sfGFP) and YL2 (mCherry) were acquired with the appropriate PMT setting and represented with the At-tune™ N×T analysis software. To verify the absence of toxicity of the Cas9 or dCas9 constitutive expression from the TAPs, we compared the growth of E. coli MS388/TAP with the cas9 or dcas9 or without any cas9 gene. Those strains were grown overnight in 0. 1 m filtered LB and diluted to an A 600 of 0. 05 in 0. 1 m filtered LB.
They were grown during 8 h and the A 600 and CFU/ml were estimated by plating assays at 0, 2, 4, 6 and 8 h. In parallel, at 1 h, 2 h and 5h30 the strains were analysed into the Attune NxT acoustic Focusing cytometer at a 25 l/min flow rate. Forward scattered (FSC) was acquired and represented with the Attune™ NxT software.
CSTB algorithm The CSTB web site can be freely accessed at https://cstb. ibcp. fr. The CSTB web service enables the comparative analysis of CRISPR motifs across a wide range of bacterial genomes and plasmids. Currently considered motifs are NGG-anchored sequences of 18-23 bp long. The CSTB back-end database indexes all occurrences of these CRISPR motifs present in 2914 complete genomes labeled as representative or reference in the release 99 of RefSeq (03/12/20). In addition, seve bacterial genomes and five plasmids of interest were added. The mean number of distinct motifs among bacterial genomes is 55 923 (5719 and 2 729 570 as respective minimum and maximum). Genomes are classified according to the NCBI taxonomy (07/22/20). Each genome is inserted in the database of motifs by processing the corresponding complete fasta using the following procedure.
Firstly, all words satisfying the CRISPR motif regular expression are detected and their chromosomal coordinates stored in a database of motifs. Secondly, all unique words are converted into an integer representation using a 2 bits per base encoding software we developed (https://github. com/glaunay/crispr-set). These integers are then sorted in a unique flat file per genome. The indexing of CRISPR motifs as integers enables computationally efficient comparison of the sets of motifs across several organisms. Finally, the original fasta file is added to a blast database. All related software can be freely accessed at https://github. com/ MMSB-MOBI/CSTB database manager. The CSTB input interface displays the 2914 genomes available for searching as two taxonomic trees. The left-hand tree allows for the selection of species whose genomes have to feature identical/similar CRISPR motifs.
CSTB software: targeting specific strains within multispecies bacterial population Numerous bioinformatic tools have been developed to identify and design gRNA spacers specific of one species  (34). However, designing TAPs that perform antibacterial activity against specific bacterial species, without affecting other bacterial strains, requires the robust identification of spacer sequences that are present in the genome of the targeted organism(s), but not in the genomes of other non-targeted strains. Since no such tools existed to achieve this task, we developed a 'Crispr Search Tool for Bacteria' CSTB algorithm (https://cstb. ibcp. fr. ) that enables the comparative analysis of ∼18-23 nt long spacer sequences across a wide range of bacterial genomes and plasmids. The CSTB backend database indexes all occurrences of these motifs present in 2919 complete genomes classified according to the NCBI taxonomy.
CSTB allows identifying appropriate spacer sequences to reprogram TAPs against unique or multiple sites in the targeted chromosome or plasmid DNA. We asked the CSTB algorithm to generate spacer sequences that target the attachment/effacement (A/E) pathogen Citrobacter rodentium strain ICC168 (Cr spacers), or the enteropathogenic E. coli EPEC strain E2348/69 (EPEC spacer), or the nosocomial pathogen Enterobacter cloacae (Ecl spacer), or the three of them (EEC spacer), without targeting other bacterial genome present in the database. TAPs directed against C. rodentium carry a Cr1 spacer that target a unique locus, or a Cr22 that targets 22 loci distributed throughout the genome (Supplementary Figure  S5a ). Transfer of TAP-Cas9-Cr1 from an E. coli donor reduces by 4-log the viability of C. rodentium transconjugant cells (Supplementary Figure  S5b ).
Software and source codes: The CSTB web service can be freely accessed at https://cstb. ibcp. fr. The software we developed to convert all unique words (spacers) into an integer representation using a 2-bits per base encoding is also available at https://github. com/glaunay/crispr-set. All additional related software can be freely accessed at https: //github. com/MMSB-MOBI/CSTB database manager.
FUNDING French National Research Agency  [ ANR-19-ARMB-0006-01 ];  Schlumberger Foundation for Education and Research  [FSER2019]; Foundation for Innovation in Infectiology [FINOVI-2014];  Foundation pour la Recherche Médicale  [ FRM-ECO201806006855  to A. R. ]. Funding for open access charge:  French National Research Agency  [ ANR-19-ARMB-0006-01 ].
Acceptance, efficiency and pile-up corrections The STARlight 2. 2. 0 MC generator  [27]  was used to generate samples of coherent and incoherent events for the production of J/ψ →µ + µ -and ψ ′ →µ + µ -+ π + π -(π 0 π 0 ). GEANT 3. 21  [28]  was used to reproduce the response of the detector. The simulated data were reconstructed with the same software as the real ones, accounting for actual data-taking conditions. Values of the acceptance and efficiency, (Acc × ε) coh J/ψ , are shown in Table  1  for the different p 2 T intervals used in this analysis. AD and V0 were used to veto activity at forward rapidity. These detectors were sensitive to signals coming from independent interactions (pile-up), which resulted in the rejection of potentially interesting events. The correction factor for this effect was obtained using a control sample of events collected with an unbiased trigger.
Unfolding of the p 2 T distribution Cross sections were measured in different p 2 T intervals. In order to account for the migration of about 45% of the events across p 2 T intervals due to the finite resolution of the detector, an unfolding procedure was used. The effect of migrations are much more important than the small difference between the data and MC p 2 T spectra, so no re-weighting has been performed previous to unfolding. Amongst many available methods, unfolding based on Bayes' theorem  [30]  was chosen to perform the unfolding, while the singular-value decomposition (SVD) method  [31]  served to study potential systematic effects. The implementations of these methods as provided by RooUnfold  [32]  were used in this analysis. Bayesian unfolding is an iterative method, therefore the result depends on the number of iterations.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences, Austrian Science Fund (FWF) : [M  2467-N36 ] and  Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications.
Note S5: 3D finite elements method simulations 3D finite elements method (FEM) simulations are performed based on the two-current driftdiffusion model mostly using the formalisms for LSVs (see Ref. 3). Figure  S7a  shows the geometry of the simulated device and the mesh of the finite elements. The geometry construction and 3Dmesh were elaborated using the free software GMSH  4  with the associated solver GETDP  5  for calculations, post-processing and data flow controlling. To simulate the spin absorption and the SC conversion at the Au/Cu interface, a thin layer (t  ) is included between the Au and Cu layers in the vertical channel, as shown in the inset of Fig. S7a. The spin accumulation profile is illustrated in Fig. S7b  when applying a charge current from the electrode F2 to the Cu channel, which shows the spin current is absorbed by the effective interface and the Au wire.
Acknowledgments The authors would like to thank  Dr. Laurent Vila ,  Dr. Juan Borge ,  Dr. Ka Shen , and  Prof. Ke Xia  for fruitful discussions. This work is supported by  Intel Corporation  through the  Semiconductor Research Corporation  under  MSR-INTEL TASK 2017-IN-2744  and the 'FEINMAN'  Intel Science Technology Center , and by the  Spanish MICINN  under the  Maria de Maeztu Units of Excellence Programme  ( MDM-2016-0618 ) and under project numbers  RTI2018-094861-B-100  and  FIS2017-82804-P. I. V. T. acknowledges support by  Grupos Consolidados UPV/EHU del Gobierno Vasco  (Grant No. IT1249-19 ). V. T. P. acknowledges postdoctoral fellowship support from ' Juan de la Cierva-Formación ' programme by the  Spanish MICINN  (grant number  FJCI-2017-34494 ).
12 [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  We focused on studies that measured plasma protein levels using the SOMAscan affinity proteomics platform (SomaLogic Inc. ), as this matches the protocol used in our study, Generation Scotland. We identified 282 proteins that were also measured in our sample (n ≤ 1064). Our first aim was to conduct GWAS and EWAS on plasma levels of 282 AD-associated proteins. Using Bayesian penalized regression, we estimated the proportion of inter-individual variability in plasma protein levels that can be accounted for by variation in genetic and DNAm factors. BayesR+ implicitly adjusts for probe intercorrelations and data structure, including relatedness. 28 For our second aim, we used Mendelian randomization (MR) and co-localization analyses to test for relationships between plasma protein levels and AD phenotypes.
GWAS Generation Scotland samples were genotyped using the Illumina Human OmniExpressExome-8v1. 0 Bead Chip and processed using the Illumina Genome Studio software v2011 (Illumina, San Diego, CA, USA). Quality control steps are outlined under supplementary information. After quality control, 561125 single nucleotide polymorphisms (SNPs) remained for 1064 individuals. In total, 1064 individuals had both genotype and proteomic data available for analyses. Bayesian penalized regression GWAS were performed using BayesR+ software in C++. 28 BayesR+ utilizes a mixture of prior Gaussian distributions to allow for markers with effect sizes of different magnitudes. It also includes a discrete spike at zero that enables the exclusion of markers with non-identifiable effects on the trait of interest. Guided by data from our previous studies, mixture variances for the stand-alone GWAS were set to 0.
EWAS Blood DNAm in Generation Scotland participants was quantified using the Illumina HumanMethylationEPIC BeadChip Array. Blood DNAm was assessed in two separate sets. After quality control, 793706 and 773860 CpG remained in sets 1 and 2, respectively. In total, 772619 CpG sites were shared across sets. Each set was truncated to these overlapping probes. In the stand-alone EWAS and combined GWAS/EWAS, mixture variances were set to 0. 001, 0. 01, and 0. 1 (n = 778). Missing DNAm data were mean imputed separately within each set as BayesR+ cannot accept missing values. Both sets were combined and adjusted for DNAm batch, set, age, and sex. Each CpG site was scaled to mean zero and unit variance. Houseman-estimated white blood cell proportions were included as fixed-effect covariates. 31 CpG sites that had a PIP ≥95% were deemed significant.
Co-localization analyses Formal Bayesian tests of co-localization were used to determine whether a shared causal variant likely underpinned two traits of interest. 33 A 200 kilobase (kb) region (upstream and downstream, recommended default setting) surrounding the variant was extracted from our GWAS summary statistics. Expression quantitative trait loci (eQTL) data were extracted from eQTLGen summary statistics. Methylation QTL (mQTL) summary statistics were extracted from phenoscanner, GoDMC, or our own mQTL analyses. Methylation QTL analyses were performed using additive linear regression models and by regressing CpG sites (beta values) on SNPs (0, 1, 2) while adjusting for age, sex, DNAm batch, set, Houseman-estimated white blood cell proportions, and 20 genetic PCs (n = 778). 5] [36]  For AD-related traits, summary statistics were extracted from the relevant GWAS.
Identification of plasma proteins associated with AD Twelve studies were identified that reported associations between SOMAscan plasma proteins and AD or related traits (Figure  1 ). Three hundred fifty-nine unique proteins were identified and 22 (6. 1%) were reported in more than one study (Table  S2-S4 ). In the Generation Scotland dataset, there were 308 SOMAmers (Slow Off-rate Modified Aptamers) that targeted 282 of 359 proteins of interest (Table  S5  and Figure  S1 ). The 282 unique proteins were brought forward for analyses (UniProt IDs and Seq-ids are shown in Table  S6 ).
GWAS on AD-associated proteins There were 1064 individuals with genotype and proteomic data in Generation Scotland. The mean age of the sample was 59. 9 years (standard deviation [SD] = 5. 9) and 59. 1% of the sample was female. In the BayesR+ GWAS, 64 independent variants (or protein quantitative trait loci, pQTLs) were associated with 41 SOMAmers that mapped to 39 unique protein targets (PIP≥ 95%; Table  S7 ). The phenotypic correlation structure of these 41 SOMAmers is presented in Figure  S2. The median correlation coefficient between SOMAmer levels was 0. 18. Thirty-six pQTLs represented cis associations (pQTLs within 10 megabases [Mb] of transcription start site [TSS] for a given gene) and 28 pQTLs were trans-chromosomal effects (Figure  2 ). The majority of variants were located in intronic regions using annotations from the ENSEMBL variant effect predictor (46.
Co-localization of protein QTLs with expression QTLs The 36 cis pQTLs identified in BayesR+ were annotated to 23 unique proteins. For 12 of 23 proteins, at least one pQTL was previously F I G U R E 1 Structured literature review of SOMAscan plasma proteins that were associated with AD in the literature, and assessment of their molecular architectures and relationships with AD in the present study. The MEDLINE, Embase, Web of Science databases, and preprint servers were queried to identify studies that reported associations between SOMAscan-measured plasma proteins and AD. GWAS, EWAS, and causal inference analyses were performed to identify molecular correlates of 282 AD-associated plasma protein levels and to probe their associations with AD and related traits. AD, Alzheimer's disease; EWAS, epigenome-wide association studies; GWAS, genome-wide association studies.
EWAS on AD-associated proteins There were 778 individuals with DNAm and proteomic data in the Generation Scotland sample. The mean age of the sample was 60. 2 (SD = 8. 8) years and 56. 4% of the sample were female. Twenty-six CpGs were associated with the levels of 20 unique proteins (PIP >95%, Table  S13  and Figure  S4 ). The median correlation coefficient between measured protein levels was 0. 16. The associations consisted of 10 cis CpG sites and 16 trans CpG loci (Figure  3 ). The cg07839457 probe in the NLRC5 locus was associated with IL18BP and CSF1R levels, and the smoking-associated probe cg05575921 in AHRR was associated with GHR, PIGR, and WFDC2 levels. We used linear mixed-effects models that accounted for relatedness to perform sensitivity analyses for the 26 CpG associations identified in BayesR+ (Table  S14 ).
Co-localization protein QTLs with QTLs Fourteen proteins had both genome-wide significant pQTL and CpG associations in our study. There were 39 possible SNP-CpG pairs across these proteins. For each pair, we used linear regression to test if the SNP was associated with CpG methylation at P < 5 × 10 -8 , thereby representing an mQTL effect (Table  S17 ). We also performed look-up analyses of mQTL databases including GoDMC and phenoscanner. 35, 36  instances where an mQTL effect was identified in more than one database, coefficients from the study with the largest sample size were brought forward for co-localization analyses. In addition, in instances where two or more mQTLs were associated with the same CpG site in a given locus, only the most significant mQTL was brought forward for co-localization analyses (n = 19 mQTLs, 13 proteins; Table  S18 ).
DISCUSSION We identified seven novel protein QTLs and 19 novel CpG sites that associated with plasma levels of 18 AD-related proteins. Using BayesR+, we provided estimates for associations between common genetic and DNAm variation and inter-individual differences in plasma levels of 282 AD-related proteins. We integrated our data with publicly available gene expression and methylation QTL databases and highlighted molecular mechanisms that might link pQTLs to differential levels of six proteins. We observed strong associations between plasma levels of TREM2 or TBCA and AD risk. These associations were driven by trans pQTLs in MS4A4A and APOE, respectively. We show that a trans pQTL (rs1530914) in the MS4A4A locus associates with higher plasma TREM2 levels. It is in strong LD (r 2 ∼ 0. 9) with the variant rs1582763, which has been associated with higher CSF TREM2 levels and lower AD risk.
3, 43 It is also in moderate LD (r 2 = 0. 6) with a variant in the 3′UTR region of MS4A6A (rs610932), which was associated with plasma TREM2 levels in a sample of 35,559 Icelanders. 11 Polymorphisms in MS4A4A were shown to alter MS4A4A expression and subsequently modulate TREM2 concentration in human macrophages. 44 We supplement existing data by identifying a novel blood CpG correlate of plasma TREM2 levels (cg02521229) located near MS4A4A that previously associated with dementia risk in Generation Scotland participants. 45 Our data suggest that risk mechanisms arising from MS4A4A polymorphisms and TREM2 levels can be captured in plasma assays and that these mechanisms involve differential methylation in the MS4A4A locus. We observed associations between plasma levels of three proteins Lines indicate an association between a CpG site and SOMAmer.
Marioni is supported by Alzheimer's  Society major project  grant  AS-PG-19b-010. Proteomic analyses in STRADL were supported by  Dementias Platform UK (DPUK). DPUK funded this work through core grant support from the  Medical Research Council  [ MR/L023784/2 ]. Kathryn L. Evans was supported by a grant from  Alzheimer's Research UK , paid to the  University of Edinburgh. Alejo J. Nevado-Holgado  was funded by a  Horizon 2020 Virtual Brain Cloud project  ( H2020-SC1-DTH-2018-1 ), in addition to funding from the  MRC, UK Rosetrees , and  King Abdullah University of Science and Technology, Saudi Arabia. Caroline Hayward  is supported by an  MRC University Unit Programme  Grant  MC_UU_00007/10  (QTL in Health and Disease). Liu Shi  is funded by  DPUK  through  MRC  [ MR/L023784/2 ] and the  UK Medical Research Council Award  to the  University of Oxford  [MC_PC_17215].
It allows selecting about 1% of the total accessions from the entire collection to represent maximum diversity. The Generation Challenge Program (GCP) (https://www. generationcp. org) recommended the development of a reference set representing 10% of the core collection to represent maximum diversity using molecular markers. From ICARDA barley in-trust collection totaling more than 32,000 accessions, the barley core collection (composite set) of 3000 accessions of both cultivated and wild progenitor species (H. spontaneum) was selected based on climatological data of the collection sites; from which the Generation Challenge Program (GCP) developed a reference subset of 300 accessions based on the diversity of EST-derived, and genomic SSR markers (https://www. croptrust. org/wp/ wp-content/uploads/2014/12/Barley_Strategy_ FINAL_27Oct08.
Modeling of the reaction to leaf rust disease The second pathway of FIGS using machine learning was investigated using the available reactions of the accessions of FIGS_LR and GCP subsets to find a function that links adaptive traits, environments (and associated selection pressures) with genebank accessions. We used environmental data from WorldClim1 databases as predictors. The WorldClim is an open access database providing global climatic layers describing past climatic profiles of collection sites intended for spatial modeling or mapping. It includes averages of monthly minimum and maximum temperatures, precipitation and bioclimatic variables  (Fick and Hijmans 2017). The following machine learning algorithms were used: K-nearest neighbors KNN (Kotsiantis 2007), Support Vector Machine SVM  (Hsu et al.
2010) , Random Forest RF  (Breiman, 2001) , Neural networks NNET  (Venables and Ripley 2002) , and Bagged Carts BCART  (Kołcz 2000). Each machine learning model was tuned to select the best tuning parameters using a training set (70% of the total set), and then the best model was selected between different machine learning models based on several metrics including accuracy, specificity, and Kappa. The modeling metrics were computed on the test set (30% of the total set). In this study, R language and caret library were used for machine learning analysis  (Kuhn 2008). Models were tuned for parameter's optimization and trained on 70% of the data and tested with 10 cross validation folds and 100 replications. In addition, modeling was done for the two isolates for the seedling stage. For the APR, modeling was done for the entire multi-locations data sets and for each location separately.