dataesr/scientific-paragraphs-categorization huggingface dataset:
To provide a more automated process of data curation before genetic analysis, we have developed stuart, an R package that implements a pipeline for automatic filtering and curation of SNP genotyping data from 2-generation crosses based on simple rules. This package formats raw SNP allele calls from Illumina files into genotypes ready for importation in R/qtl. Using 3 intercross datasets, we illustrate the consequences of inconsistent genotypes on the estimated marker map and QTL mapping, and how the curation achieved by each function in stuart leads to trustable results.
Materials and methods stuart is a tidyverse  (Wickham et al. 2019 ) based R package requiring R version 3. 5. 0 or later. Its open source is available on Institut Pasteur's GitLab: https://gitlab. pasteur. fr/mouselab/stuart/ and can be installed with devtools  (Wickham et al. 2021 ). stuart's vignette provides detailed descriptions of data import and of each function. stuart imports SNP allele calls from MUGA Illumina platform or other sources using the same file format. The central object of stuart is the marker table which summarizes for each marker, the alleles found in the population, the number of individuals of each genotype and the exclusion status resulting from the curation steps. stuart exports curated data to an R/qtl compatible format. The SNP annotation file used was downloaded from https://raw.
githubusercontent. com/kbroman/MUGAarrays/ master/UWisc/mini_uwisc_v2. csv (last accessed August 29, 2022). Three datasets were used to test the package. This article presents the results from 176 (CC001/Unc X C57BL/6J-Ifnar1 KO) F2 mice (dataset 1). The analysis of 2 other data sets, 94 (C57BL/ 6J-Ifnar1 KO X 129S2/SvPas-Ifnar1 KO) F2 mice (dataset 2) and 89 (C57BL/6NCrl X CC021/Unc) F2 mice (dataset 3) is presented as Supplementary data. Quantitative traits were studied in the 3 F2s. Phenotype distributions are presented in Supplementary Fig. 1. Genotyping was performed by Neogen (Auchincruive, Scotland) with MiniMUGA on DNA prepared from tail biopsies using standard phenol-chloroform extraction. Genotype call rate was 0. 927, 0. 931, and 0. 948 for dataset 1, dataset 2, and dataset 3, respectively.
QTL mapping was performed using R/qtl. Statistical significance of phenotype-genotype association was computed by data permutation  (Doerge and Churchill 1996) , which provides genome-wide thresholds accounting for multiple testing. The following thresholds were used, as commonly accepted (Members of the Complex Trait Consortium 2003): P ¼ 0. 05 for significant association, P ¼ 0. 1 and P ¼ 0. 63 for the suggestive association. All figures were designed with ggplot2  (Wickham 2016)  or R/qtl.
With minimally curated genotypes (exclusion of nonpolymorphic markers and markers with over 50% missing genotypes), large RF was found in many instances between closely linked markers, resulting in fan-like patterns. To further describe these distortions, we computed the distribution of the ratio between the calculated and the known genetic distances between adjacent markers (Fig. 1b  and Supplementary Fig. 2, c  and d ; to avoid exaggerated ratios, we considered only markers with a known distance of 1 cM or more). This analysis revealed 2 groups of markers. In dataset 1, for 43% % of them, the ratio was below 5 and followed a Gaussian distribution with mean ¼ 1. 31 and SD ¼ 0. 77. The other markers (57%) showed a ratio between 5 and 981. 87 (Fig. 1b ) which necessarily results from incorrect genotypes, as only a few individuals should show recombination between adjacent markers.
On chromosome 1, while the known marker positions spanned 100 cM, the cumulated genetic distance estimated from observed RF was 40,000 cM. As QTL mapping relies on coherent genotypes at a series of markers encompassing a genetic interval, problematic genotypes at a given marker will perturb the analysis and, in some cases, may result in peaks of the LOD score curve in the absence of true association  (Cheung et al. 2014). Such false positives increase significance thresholds calculated by data permutation. These 2 consequences of genotyping inconsistencies are illustrated in Fig. 1c  and Supplementary Fig. 3 , a and b which were obtained using the R/qtl scanone() function on a quantitative trait from the uncurated F2 datasets. For dataset 1, the P ¼ 0. 05 significance threshold was estimated at 19.
4 (Fig. 1c ), while it usually ranges between 3. 3 and 4. 3 depending on the inheritance model for crosses of this type and size  (Lander and Kruglyak 1995). Several peaks were detected although none reached P ¼ 0. 05 significance. Moreover, their narrow profile was highly unexpected in F2 crosses. Indeed, these peaks involved only 1-3 markers, and the LOD score curve felt abruptly between these and adjacent markers on both sides (Fig. 1d ), while genetic linkage between closely linked markers should result in progressive decrease of the LOD score curve on both sides of a peak (Guenet et al. 2015). Among the 3 datasets, we identified 4 narrow peaks reaching suggestive significance level (P < 0. 63): 2 were located at a marker with non-Mendelian allelic proportions and 2 were located at 1-3 pseudomarkers adjacent to a marker with non-Mendelian proportions (Supplementary Fig.
3, c, d  and e, f , respectively). We identified 5 other narrow peaks (LOD score between 6. 72 and 10. 03) out of which 4 resulted from the same situations as above and one was located on a pseudomaker and a marker with non-Mendelian proportions. Inconsistent marker maps may also originate from the wrong assignment of markers to their chromosome and position provided to the mapping program. Indeed, R/qtl developer K. Broman identified errors in MUGA arrays annotation files affecting marker positions, probe sequences mapping to several locations, and unmappable markers. We recommend using K. Broman's corrected annotation files available on GitHub. The conversion of SNP alleles (A, C, T, G) observed in secondgeneration individuals (SGIs) to genotypes encoded according to the parental alleles may also create genotype errors.
Data importation Genetic mapping requires both genotype and phenotype data. Required formats and instructions are detailed in the vignette (see example of phenotype data in Supplementary Table  1 ). Parental strains' genotyping data can be loaded from the same genotyping results as the SGI, from a previous genotyping file or from a reference file. Annotation data from K. Broman can be imported directly from GitHub. The geno_strains() function formats parental genotypes from a 2-allele encoding in Illumina format into a single letter encoding, and merges these data with the annotation table into a table with parental allele and marker positions.
However, this imputation is not error-free since we have observed, in rare occasions, markers which alleles were identical in the parental strains but were polymorphic in the SGI (Table  2  for such SNPs in dataset 1). This situation may occur when the parental strains used in the cross have diverged from those of the reference panel, or if one parent is heterozygous. Such markers will be excluded by the mark_allele() function but they could escape detection if allele information was missing in one parent. the ratio between estimated and known distances between adjacent markers. Markers with known and calculated distances below 1 cM were removed as they may lead to extremely small or large ratios. The expansion of the estimated map leads to a distribution tail of high ratios. The y-axis is in logarithmic scale.
Fifty-seven percent of markers have a ratio above 5 (dashed line). c) Output of the scanone function of R/qtl showing the identification of narrow LOD score peaks. Genome-wide significance thresholds computed by data permutation are shown as plain (P ¼ 0. 05), dotted (P ¼ 0. 1), and dashed (P ¼ 0. 63) lines. d) Magnification of the scanone plot restricted to chromosome 13 (peak p2). The LOD score peak is located on one marker (red tick) distant by 1. 728 and 1. 24 cM from the proximal and distal markers, respectively, on the known marker map, but by 1,001. 582 and 1,001. 506 cM based on calculated RF. Adding the parNH ¼ FALSE argument to the mark_allele() function will exclude markers missing one allele or for which one parent is heterozygous. However, while preventing rare errors, this option will also exclude a number of truly informative markers.
Filtering report and impact on QTL mapping results At every step, the markers filtered out are annotated in a marker table which can be exported for further inspection. The last column of Table  1  shows the number of markers retained after each step in the example dataset 1. Most of the starting markers (7,180/11,125 ¼ 65%) which were eventually removed by stuart's functions were removed by mark_poly() as nonpolymorphic, a ratio expected for crosses between 2 standard mouse inbred strains  (Frazer et al. 2007). mark_allele() rejected 750 markers, mark_na() 457 and mark_prop() 484. Across the 3 datasets, we found 1,546 markers with either non-Mendelian proportions or allele inconsistencies between parental strains and SGIs. Overall, 619 of them were retained by stuart's filtering in at least one of the C C T C S6J113080150 G G A G gJAX00038569 C C T C mUNC21540855 C C A C gUNC21555204 T T T C gUNC21596600 A A A G other crosses, ruling out their misassignment to the genetic map.
Out of the residual markers, were removed from all datasets for another criterion than absence of polymorphism and were therefore considered as unreliable. At this step, the dataset may still contain markers showing high RFs with adjacent markers either for a reason not tested by the current version of stuart or due to the parameters used in mark_na() and mark_prop() functions. These markers can be identified by calculating the estimated map using R/qtl est. map() and using stuart's mark_estmap() function which excludes markers presenting high RFs with adjacent markers. Over the 3 datasets, 9 markers were removed by mark_estmap(). Five of them were retained in at least one other dataset, indicating the problem was dataset specific. Finally, for dataset 1, 2,251 markers passed all steps resulting in an average genetic interval between adjacent markers lower than 2 cM, which is largely sufficient to perform QTL mapping  (Darvasi et al.
1993). After curation, phenotype and genotype data are combined and exported in the R/qtl format using the write_rqtl() function. The qtl2convert package (Broman 2021) converts this output into the adequate format required by the more recent R/qtl2 package. Figure  3a  and Supplementary Fig. 4 , a and b show the marker maps calculated after data curation with stuart. The known marker map and the estimated genetic map are consistent, with minimal expansions or contractions. Large ratios between the calculated and the known genetic distances between adjacent markers have been eliminated (Fig. 3b , Supplementary Fig. 4, c  and d ). QTL mapping analysis on curated dataset 1 is shown on Fig. 3c  (to be compared with Fig. 1c ; see Supplementary Fig. 5  for datasets 2 and 3). LOD thresholds are in the expected range for an F2, and the LOD score curve reveals broader peaks than in Fig.
1b , with progressive LOD score decrease on both sides of the peak marker. One significant and 3 suggestive QTLs were identified on chromosomes 12 (P-value ¼ 0. 037, Fig. 3d ), 5 (P-value ¼ 0. 460), 10 (P-value ¼ 0. 157), and 15 (P-value ¼ 0. 244) which were not visible using noncurated data due to very high LOD score thresholds. Being very simple to use and efficient at curating genotyping errors, stuart will facilitate the use of genotyping arrays for genetic mapping purposes in 2-generation crosses, bridging the gap between raw allele data produced by SNP platforms and genetic analysis software. Moreover, its functions can be used independently to analyze inbred strains genotypes. For example, geno_strain() creates a genotype consensus between 2 or more individuals of the same strain suitable for further inspection, which can be useful when genotyping or regenotyping a strain of interest.
Data availability All datasets used as examples in this article are available from https://gitlab. pasteur. fr/mouselab/stuart/. Dataset 1 is included in the package and can be loaded once the package is loaded (see the vignette for details). The 2 other datasets are available from GitLab in the "article" directory in separate folders (i. e. "data2" and "data3"). Each folder contains the genotypes of the SGIs in file "geno_dataX. csv," the phenotypes of the SGIs in file "pheno_dataX. csv," the parental strains' genotypes in file "parents_dataX. csv" and the reference genotypes for the parental strains in file "ref_geno_dataX. csv. " Analysis of each cross is in each folder in an R markdown file ("dataX. Rmd"). Supplemental material is available at G3 online.
Efficiency and Electricity Consumption for the Upgraded Medium and High Beta Klystrons Scenario 1 As already mentioned, three different types of klystrons are being used for the ESS linac. They have been procured from three different manufacturers, Thales, Canon and CPI. The klystrons have similar specifications, with efficiencies between 63% and 66%, in worst and best cases. These data have not been fully confirmed in site acceptance tests yet, but have been provided by the manufacturers. The calculations below are based on the data provided by Canon  [107] , as Canon klystrons are used in both the medium and high beta linac. Similar performance values are expected from the other klystrons. It is possible to increase the klystron efficiency in operation at beam voltages lower than the nominal by introducing a mismatch on the output of the klystron.
Dose Equivalent Rate Calculations The prompt dose equivalent rates were calculated from the convolution of the fluence of neutrons, protons, charged pions, and muons with their respective energy-dependent fluence-to-effective dose conversion coefficients. The residual dose equivalent rates were calculated by the convolution of the fluence of photons, electrons, and positrons from γand β-decays with the respective energy-dependent fluence-to-effective dose conversion coefficients. The dose conversion coefficient EWT74 provided by the FLUKA database was used. Various cooling times: 1 s, 1 h, 1 day, 10 days, 1 and 3 months after turning off the beam were assumed for the residual dose equivalent rates calculations. The radiation categorisation of the specific zones of the target station complex relies on the Radiation Protection Ordinance (RPO) of ESS.
inlib is strongly inspired by the OpenInventor developed by Silicon Graphics Inc. in the 1980's  [237]. The web site for EsbRootView is at https://gbarrand. github. io -the "softinex portalle"  [238]  -under the section "EsbRootView" at left. The source code of stable releases is on github at https://github. com/gbarrand/ EsbRootView. git. The license is a customisation of the FreeType license. For each release binaries are provided at least for Linux, macOS and Windows. A WebAssembly version is available through the web pages. It should be noted that inlib/exlib classes are delivered under the namespace "tools" in Geant4  [239]  as a baselayer to the analysis "category"; these facilitate the creation of histograms and ntuples, I/O at various formats (ROOT, csv, HDF5), and offscreen plotting with our scene graph logic (see  [240]  and  [241] ).
Finally, more general bin-to-bin uncorrelated uncertainties will be implemented (one independent nuisance parameter per bin) which may encode more complex situations such as the unknown shape of the neutrino cross sections at the energies of interest. These sets of nuisance parameters are meant to capture all sources of uncorrelated uncertainties affecting the far detector expectation after the measurements at the near detector and beammonitoring information have been taken into account. Thus, the near-detector events are not simulated explicitly, unlike some previous works such as  [272] , to avoid double counting its impact. For the estimation of the sensitivity, the Poisson log-likelihood will be used: χ 2 stat = 2 n i=1 N test i -N true i -N true i log N test i N true i , (8. 3) where N test is the number of events expected for the values of the oscillation parameters tested for, N true is the number of events expected for the parameter values assumed to be realised in nature (Asimov dataset) and i is the number of energy bins.
Acknowledgments This project has been supported by the COST Action EuroNuNet Combining forces for a novel European facility for neutrino-antineutrino symmetry-violation discovery. It has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 777419. We acknowledge further support provided by the following research funding agencies: Centre National de la Recherche Scientifique and Institut National de Physique Nucléaire et de Physique des Particules, France, Deutsche Forschungsgemeinschaft, Germany, Projektnummer 423761110,.
Agencia Estatal de Investigacion  through the grants  IFT Centro de Excelencia Severo Ochoa, Spain , contract No. CEX2020-001007-S  and  PID2019-108892RB  funded by  MCIN/AEI/10. 13039/501100011033 , Polish Ministry of Science and Higher Education , grant No. W129/H2020/2018 , with the science resources for the years 2018-2021 for the realisation of a co-funded project. Ministry of Science and Education of Republic of Croatia  grant No. KK. 01. 1. 1. 01. 0001 , Ramanujan Fellowship of SERB, Govt. of India , through grant no:  RJF/2020/000082 , as well as support provided by the universities and laboratories to which the authors of this report are affiliated, see the author list on the first page. The authors wish to express their deep gratitude to the members of the  ESSνSB International Advisor Panel Chris Densham (RAL) ,  Eligio Lisi  (INFN) -IAP Chair,  Shinji Machida  (RAL),  Alexander Olshevsky  (JINR) and  Jingyu Tang  ( IHEP ) for all the very constructive and helpful advice they have provided throughout the four years of the design study.
Instead, we introduce a new optimisation problem that penalises the 1 -norm of the leading left singular vector of a rank one approximation of the Miss-CUSUM transformation. This methodological proposal, which we call MissInspect, leads to considerably improved performance. Implementation code for our method is available in the GitHub repository https://github. com/wangtengyao/MissInspect. A further benefit of the MissInspect methodology is that it is amenable to theoretical analysis. In particular, we study a Missing Completely At Random model with row homogeneous missingness; in other words, the observation probability remains constant in each row, but may vary arbitrarily across rows. In Proposition 1 in Section 3, we provide a high-probability bound on the angle between the estimated and oracle projection directions.
Here, with a suitable choice of λ, the 1 penalty on ṽ in (2) exploits the sparsity of the oracle projection direction to allow for consistent estimation of (θ • √ q)/ θ • √ q 2 , even when the dimension p is large, as will be shown in Proposition 1 in Section 3. A further advantage of (2) over the semi-definite relaxation approach is that it directly exploits the row sparsity pattern of the rank one matrix (θ • √ q)γ , as opposed to just the overall entrywise sparsity of this matrix. Using the estimated oracle projection direction v, we can project the MissCUSUM transformation T Ω of (X • Ω, Ω), and estimate the changepoint by the location of the maximum absolute value in the univariate projected series. Pseudocode for the MissInspect algorithm is given in Algorithm 1. Algorithm 1: Pseudocode of the MissInspect algorithm Input: X Ω = X • Ω ∈ R p×n , Ω ∈ {0, 1} p×n , λ > 0 1 T Ω ← T Miss (X Ω , Ω); 2 Find (v, ŵ) ∈ argmax ṽ∈B p-1 , w∈B n-2 T Ω , ṽ w -λ ṽ 1 ; 3 ẑ ← median argmax t∈[n-1] (v T Ω ) t ; Output: ẑ The optimisation problem in Step 2 of Algorithm 1 is bi-convex in (ṽ, w); i.
From a theoretical point of view, the fact that v is estimated using the entire available data set X Ω makes it difficult to analyse the post-projection noise structure. For this reason, in the analysis below, we work with a sample-splitting variant of Algorithm 1, as given in Algorithm 3. Here, the projection direction v is estimated using only the observed data at odd-numbered time points, and the MissCUSUM transformation of the observed data at even-numbered time points is then projected along v to obtain the final estimate of the changepoint location. Algorithm 3: Pseudo-code for the sample-splitting variant of the MissInspect algorithm. Input: X Ω = X • Ω ∈ R p×n , Ω ∈ {0, 1} p×n , λ > 0 1 n 1 ← n/2 ; 2 Let Ω (1) ∈ {0, 1} p×n 1 and Ω (2) ∈ {0, 1} p×n 1 denote the matrices formed from the first n 1 odd and the n 1 even numbered columns of Ω respectively; 3 Let X (1) Ω ∈ R p×n 1 and X (2) Ω ∈ R p×n 1 denote the matrices formed from the first n 1 odd and the n 1 even numbered columns of X Ω respectively; 4 T (1) Ω ← T Miss (X (1) Ω , Ω (1) ) ∈ R p×(n 1 -1) ; 5 T (2) Ω ← T Miss (X (2) Ω , Ω (2) ) ∈ R p×(n 1 -1) ; 6 (v, ŵ) ← argmax ṽ∈B p-1 , w∈B n-2 T (1) Ω , ṽ w -λ ṽ 1 ; 7 ẑ ← 2 median argmax t∈[n 1 -1] (v T (2) Ω ) t ; Output: ẑ Theorem 1 is our first main result on the performance of Algorithm 3 in contexts where our data are generated from a single changepoint, row-homogeneous model P n,p,z,θ,σ,q.
The depth of the foraminifera within the core is used as a proxy for the geological age of the fossil, measured in millions of years (Ma). Our data, which are available in a GitHub repository 2 , and were previously analysed by  Samworth and Poore (2005)  and  Poore et al. (2006) , consist of measurements from 16 cores extracted from the North Atlantic, Pacific and Southern Oceans and are displayed in Figure  4. In total, there are 7369 observations at 6295 distinct time points, but Figure  4  makes clear that the heterogeneous nature of the data collection process means that it is appropriate to think of the data as containing missingness. The figure also indicates the 10 most prominent changepoints identified by applying the MissInspect algorithm in combination with binary segmentation, as discussed in Section 5 below.
ACE2 Protein Sequence. The protein sequences from 23 different species were collected from Genbank. These sequences are the following: Homo sapiens (BAB40370), Macaca mulatta (NP_001129168), Felis catus (AAX59005), Panthera tigris (XP_007090142), N. vison (QPL12211), Mustela nigripes (QNC68914), M. putorius furo (NP_001297119), M. lutreola (QNC68911), Mustela erminae (XP_032187679), Paguma larvata (AAX63775), Sus scrofa domestic (ASK12083), Sus scrofa (NP_001116542), Rhinolophus affinis (MT394227), Rhinolophus affinis (MT394223), Rhinolophus macrotis (ADN93471), Rhinolophus sinicus (AGZ48803), Rhinolophus pearsonii (ABU54053), Manis javanica (XP_017505752), Rattus rattus (XP_032746145), Mus musculus (NP_081562), Gallus gallus (QEQ50331), Pelodiscus sinensis (XP_006122891), Xenopus tropicalis (XP_002938293), and Ophiophagus hannah (ETE61880).
ACE2 polymorphisms were also modulated from this source file with a similar approach. Docked complexes were subsequently submitted to iterative cycles of energy minimization under the CHARMM molecular force field with the convergence condition of root mean square (RMS) gradient less than 0. 01 kcal. Å -1. mol -   [38]. Interaction energies were calculated from stable complexes using the Ligand Energy Inspector function of Molegro. The electrostatic potential measured and illustrated by Molegro Molecular viewer is the sum of the Coulomb potentials for each atom of the considered molecule, with a distance-dependent dielectric constant. Colour intensities of the electrostatic surface potential were quantified with the ImageJ software  [36]. Our in silico approaches have been previously validated with structural studies of several SARS-CoV-2 variants including Omicron BA.