dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank all the members of the GenEPII sequencing platform who contributed to this investigation. We also thank all the laboratories, clinicians, and patients involved in this work. We would like to especially thank  Dr. Claire Bardel ,  Dr. Romain Hernu ,  Dr. Bénédicte Clément  and  Théophile Boyer  for their input during the revisions. This work was carried out within the framework of the French consortium on surveillance and research on infections with emerging pathogens via microbial genomics (consortium relatif à la surveillance et à la recherche sur les infections à pathogènes EMERgents via la GENomique microbienne EMERGEN; https://www. santepubliquefrance. fr/dossiers/coronavirus-covid-19/consortiumemergen). We acknowledge the authors, originating and submitting laboratories of the sequences from GISAID's EpiCov Database.
Data availability The GISAID accession numbers of the Delta and Omicron virus isolates used for experimental mixes are EPI_ISL_11171170 and EPI_ISL_11171169, respectively. Sequencing data of the Delta: Omicron mixes were deposited on the SRA database under accession PRJNA817870 and PRJNA853723, and dehosted sequencing data of NPS with co-infections were deposited under accession PRJNA817806.
Code availability The seqmet bioinformatic pipeline is publicly available at https:// github. com/genepii/seqmet  29. The software versions are provided in Table  S1. The '58a2c4d28288b54cba425225bdaa9a0d642048ca' commit of seqmet should be used to reproduce the results presented herein. In addition, all data and R scripts used to generate the results are publicly available at https://github. com/genepii/seqmet/tree/ main/script/article.
INTRODUCTION E XPLORATION of 3D data is essential for all physical sciences, from the smallest phenomena in particle physics to the largest known structures in astrophysics. In many of the sciences in this spectrum (including biology) and applied fields such as medicine and engineering, it is not only important to understand data at a single spatial scale. Instead, many phenomena require a multiscale data analysis. Within visualization, researchers have contributed to this analysis by providing several means for the exploration of multiscale data (e. g. ,  [5] ,  [9] ,  [21] ,  [25] ,  [33] ,  [34] ,  [52] ,  [55] ,  [59] ), for many of the mentioned application domains. To date, most of the proposed data exploration strategies follow a temporal control in the data navigation. At any given time, the visual representation shows a subset of the 3D dataset at a single spatial scale factor, and this scale factor can be changed interactively, i.
2 ). Miao et al. [46]  proposed an abstraction space, where DNA origami strands can be continuously rearranged in space, going from a 3D to a 2D to a 1D representation. The user can adjust the abstraction level, but again only one scale is visible at a time. A steerable abstraction space  [46] ,  [52] ,  [65]  enables users to change the scale of a representation depending on interactions with a control panel. Connectomics datasets comprise structures of the nervous system and share similar properties with DNA, due to the tubular shape, large size, and given connections. In this domain, Mohammed et al. [52]  continuously abstract the representation of astrocytes and neurites through navigating a specific abstraction space. In general, past visualization techniques for molecular structures and genome data do not offer means to spatially and simultaneously unfold the various scale representations.
Elmqvist et al. [18]  proposes a space-folding technique for the same set of problems. Their distortion-based method folds the intermediary space to guarantee visibility for multiple focus regions. McCrae et al. [45]  suggest an image-based environment representation to allow users a consistent navigation in multiscale datasets, such as the Earth. There are very different approaches to deal with multiscale phenomena and the data require specific interaction techniques. As our Multiscale Unfolding technique creates a multiscale linearized representation, we have to also provide appropriate interactions for it.
SPATIALLY-CONTROLLED MULTISCALE MAPPING We base our approach on Nowotny et al. 's  [53]  reconstructed genome data, computationally derived from chromosome capture data such as Hi-C: realistic 3D locations for chromosomes, loci, fibers, and nucleosomes as well as the nucleotide positions for one nucleosome. This dataset is in an interphase configuration, not the often depicted mitotic or "H" chromosome. In this data, each chromosome contains 50-100 loci positions, a locus contains 100 fiber positions, a fiber has 100 nucleosome positions, and each nucleosome consists of 146 base pairs (bp). We derive the base pair locations from the 292 nucleotide positions in the nucleosome. We construct the linkers between two successive nucleosomes based on their orientation, with an average of 46 bp per linker.
DISCUSSION, FEEDBACK, LIMITATIONS Initially, we set out to solve an illustrative visualization problem: how to show all scales of a highly multiscale dataset in one view, without completely separating the scales. As our Multiscale Unfolding results (e. g. , Fig. 1 ) show, we achieved this goal. Furthermore, the new visual representation also leads to several new insights. Below we review our results, report on feedback from illustrators and domain experts, and discuss limitations.
2(d) , contrary to our current zig-zag structure. Moreover, astronomy data is characterized by huge empty spaces between elements and between different scales  [25]. We could address this problem and the issue of the three-dimensional character by employing a spatial straightening to 3D astronomy data, similar to our aligning of nonconnected chromosomes. Planets could be aligned on a path like pearls on a necklace. Further scales would align stars, galaxies, and so on. A remaining problem of domains like astronomy concerns the uneven density of available data  [25]. We know much more about regions close to Earth than we know about the far ends of the observable universe. An interactive exploration of the entire space, as we can facilitate it, is thus difficult in these domains.
We do not see this as a problem as we are able to introduce perspective at will. Yet it has to be used with care: sufficient perspective to convey correct scale differences may imply that only some of the scales can be shown-Fig. 2 (b) shows only two or three scales between atoms and chromosomes with perspective transitions. With respect to the application of our approach, they saw potential both for communicating the multiscale aspect of the DNA to laypeople or even biologists and for supporting expert tasks as long as additional data is overlayed on top of the existing visualization. We also conducted a semi-structured interview with a certified illustrator with three years experience and a PhD in Bioengineering. Considering that she had seen ScaleTrotter before, she preferred the clarity and simplicity of Multiscale Unfolding because, without the 3D structure, she could see all the scales in one view.
ACKNOWLEDGMENTS We thank all experts who provided their feedback and were available for interviews. Part of this work was funded under the  ILLUSTRARE grant  by both the  Austrian Science Fund (FWF) :  I 2953-N31  and the  French National Research Agency (ANR) :  ANR-16-CE91-0011-01. The research was further supported by funding from  King Abdullah University of Science and Technology (KAUST) , under award number  BAS/1/1680-01-01  and by funding from the ILLVISATION grant by  WWTF  ( VRG11-010 ). The authors also thank  Nanographics GmbH  (nanographics. at) for providing the Marion Software Framework. This paper was partly written in collaboration with the  VRVis Competence Center. VRVis is funded by  BMVIT ,  BMWFW ,  Styria ,  SFG and Vienna Business Agency in the scope of COMET -Competence Centers for Excellent Technologies  ( 854174 ), which is managed by  FFG.
Thermal Imaging Data Analysis The mean value of the pixels of each ROI was extracted for each frame (TROI) in order to obtain the temperature dynamic for the duration of the experiment. To this aim, a tracking algorithm was used to track each ROI across the images of the video to properly consider the temperature from each thermogram. The tracking software has been developed and validated in Perpetuini et al. (2021)  [31]. Both time-domain and frequency-domain data analyses were performed on the temperature time courses. Concerning the time-domain analysis, the average temperature variation (∆TROI) between the last recovery and the initial baseline temperature was considered for each ROI. Specifically, TROI was averaged over a temporal window of 10 s (i. e. , from 20 s to 10 s before the first repetition and from 10 s before the end of the experiment).
Thermal Imaging Data Analysis The mean value of the pixels of each ROI was extracted for each frame (T ROI ) in order to obtain the temperature dynamic for the duration of the experiment. To this aim, a tracking algorithm was used to track each ROI across the images of the video to properly consider the temperature from each thermogram. The tracking software has been developed and validated in Perpetuini et al. (2021)  [31]. Both time-domain and frequency-domain data analyses were performed on the temperature time courses. Concerning the time-domain analysis, the average temperature variation (∆T ROI ) between the last recovery and the initial baseline temperature was considered for each ROI. Specifically, T ROI was averaged over a temporal window of 10 s (i. e. , from 20 s to 10 s before the first repetition and from 10 s before the end of the experiment).
Statistical Analysis Data were expressed as mean ± standard deviation (SD). The normality of the data distribution was checked by the Shapiro-Wilk's normality test. All the data met the assumption of normality. A one-way analysis of variance with repeated measures (ANOVA RM) was used to compare the effect of the exercise on the thermal features between the ROIs  [33]. Least significant difference (LSD) post hoc analyses were used to compare pairs of means. A correlation analysis between RPE and the thermal features obtained by both time-and frequency-domain analysis was performed using the r Pearson's correlation coefficient. The statistical analysis was performed using Graphpad Prism software (version 7. 0, Graphpad, San Diego, CA, USA). A p-value lower than 0. 05 was considered statistically significant.
Results The temperature time course of the different ROIs of a representative participant is shown in Figure  2b. The ANOVA RM revealed a significant difference in ∆T ROI between the different ROIs (F (1. 41,9. 81) = 15. 14; p = 0. 0018). Figure  3  reports the boxplots of ∆T ROI of each ROI, together with the post hoc results. Biology 2022, 11, x FOR PEER REVIEW 6 of 11 time-and frequency-domain analysis was performed using the r Pearson's correlation coefficient. The statistical analysis was performed using Graphpad Prism software (version 7. 0, Graphpad, San Diego, CA, USA). A p-value lower than 0. 05 was considered statistically significant.
Discussion The aim of the present study was to investigate the capability of IRT to assess the central and peripheral physiological effects of exercise. Specifically, a unilateral resistance exercise was used in order to investigate the effect of the exercise on the facial (i. e. , Nose Tip and Corrugator) and muscles (i. e. , Exercised and Nonexercised Legs) skin temperature time courses. To the best of the authors' knowledge, this is the first study combining a time-and a frequency-domain analysis on thermographic data to assess the central and peripheral effects of fatiguing exercise. Time-domain analysis showed an increment in the skin temperature of the Exercised Leg and Nose Tip, whereas a slight decrement in the other ROIs (i. e. , Nonexercised Leg and Corrugator) was found (Figure  3 ).
Note that, in this paper, we use ''robust'' to mean that we obtain predictive performance similar to that when there is no misspecification. We also reimplement and use a new version of lassosum, called lassosum2, in which we better handle when per-variant sample sizes are different. We focus our investigations on LDpred2 and lassosum2 for two reasons. ] [17] [18]  Second, lassosum2 now uses the exact same input parameters as LDpred2, which makes it easy for us to test the different QCs and adjustments presented here. We additionally investigate PRS-CS and SBayesR, two competitive PGS methods,  19, 20  for which we develop some code to convert between the different formats required for the LD matrices and input GWAS summary statistics.
g. , to run the GWAS), 10,000 individuals to form a validation set (to tune hyper-parameters), and use the remaining 52,307 individuals to form a test set (to evaluate final predictive models). Among genetic variants on chromosome 22 and with a minor allele frequency larger than 0. 01 and an imputation INFO score larger than 0. 4 (as reported by the UK Biobank), we sample 40,000 of them according to the inverse of the INFO score density so that they have varying levels of imputation accuracy (Figure  S1 ). We read the UK Biobank data into two different datasets using function snp_readBGEN from R package bigsnpr,  23  one by reading the BGEN data as hard-called genotypes by randomly sampling according to the imputation probabilities (of being 0, 1, or 2), and another one by reading it as dosages (i.
Data for real analyses We also use the UK Biobank data for validation/testing in real-data analyses, and use the same individuals of Northwestern European ancestry as described in the previous section. We sample 10,000 individuals to form a validation set and use the remaining 352,307 individuals as a test set. We restrict to the 1,054,315 HapMap3 variants used in the LD reference provided in Prive ét al. 15  To define phenotypes in the UK Biobank, we first map ICD10 and ICD9 codes (UK Biobank fields 40001, 40002, 40006, 40013, 41202, 41270, and 41271) to phecodes using R package Phe-WAS. 25, 26 We also use some continuous phenotypes, namely vitamin D (data-field 30890), height (50), body mass index (BMI) (21001), systolic blood pressure (4080), and high-density lipoprotein (HDL) cholesterol (30760).
Misspecification of per-variant GWAS sample sizes We design GWAS simulations where variants have different sample sizes, which is often the case when meta-analyzing GWAS summary statistics from multiple cohorts with different sets of variants. 12 Using 40,000 variants from chromosome 22 (see material and methods), we simulate quantitative phenotypes by picking 2,000 causal variants at random, sampling their genetic effects (after scaling of the genotypes) from a normal distribution, and adding some Gaussian noise to the genetic component of the phenotype so that it has a heritability of 20%. This is implemented in function snp_simuPheno of R package bigsnpr. 23 We then randomly divide the 40,000 variants into three groups: for half of the variants, we use 100% of 300,000 individuals for GWAS, but use only 80% for one quarter of the variants and 60% for the remaining quarter.
We use a simple linear regression for GWAS, as implemented in function big_univLinReg of R package bigstatsr. 23 We then run CþT, lassosum, lassosum2 (section ''new implementation of lassosum in bigsnpr''), LDpred2inf, LDpred2(-grid), LDpred2-auto, SBayesR (GCTB v2. 03 with the automatic robust option), and PRS-CSauto  15, 16, 19, 20, 24  by using either the true per-variant GWAS sample sizes, the total sample size for all variants, or per-variant sample sizes imputed using Equation (4). When providing true per-variant GWAS sample sizes, squared correlations between the polygenic scores and the simulated phenotypes are of 0. 123 for CþT, 0. 160 for lassosum, 0. 169 for lassosum2, 0. 159 for LDpred2(-grid), 0. 140 for LDpred2-auto, 0. 141 for LDpred2-inf, 0. 138 for SBayesR, and 0. 159 for PRS-CS-auto (Figure  1 , averaging over ten simulations).
Equation  4 ), or ''any'' when the method does not use this information (the case for CþT). Red bars correspond to using the LD with independent blocks (section ''new LD reference''), which is a requirement for lassosum and PRS-CS. INFO j z varðG j Þ 2 b q j ð1 À b q j Þ. In simulations''), we verify that sdðG  S8 ). As a direct consequence of the lower standard deviation of dosages, we also show that GWAS effect sizes b g computed from imputed dosages are overestimated:  S9  and S10 ). This is the first correction of summary statistics we consider in the simulations below. As a second option, instead of using dosages to compute the GWAS summary statistics, it has been argued that using multiple imputation (MI) would be more appropriate. 41 Here MI consists in forming multiple (e. g. , 20) datasets of hardcalled genotypes by sampling them according to the imputation probabilities stored in the BGEN files, performing a GWAS on each of these datasets, and pooling the GWAS estimates.
To compare these three corrections, we conduct a simulation using the same 40,000 variants from chromosome 22 as before, whereby we simulate quantitative phenotypes assuming a heritability of 20% and 2,000 causal variants using the ''true'' dataset (cf. section ''data for simulations''). We compute GWAS summary statistics from the dosage dataset and use these summary statistics to run LDpred2, las-sosum2 and PRS-CS with either no correction of the summary statistics or with one of the three corrections described above. The LD references are computed from the validation set using the dataset with the ''true'' genotypes (i. e. , same as before). For lassosum2 and LDpred2 (-grid), which tune parameters using the validation set, correcting for imputation quality slightly improves predictive performance in these simulations (Figure  2 ).
First, it allows for correcting for a reduced standard deviation when using imputed data in the QC step we propose to better uncover possible problems with the GWAS summary statistics. Second, using one of the proposed corrections based on INFO scores may lead to an improved prediction when deriving polygenic scores. We apply these corrections to the two breast cancer summary statistics. In the comparison, we first use the QC proposed in Priveet al. 15  (which ends up filtering on MAF here, which we call ''qc1''). We then also filter out the two long-range LD regions of chromosomes 6 and 8 for the OncoArray summary statistics and remove around 500 variants when filtering on differences of allele frequencies (>0. 1) between summary statistics and the validation dataset (''qc2''). As for the correction using INFO scores, we use the first correction, ''sqrt_info,'' which is simple because it is equivalent to post-processing PGS effects, by multiplying them by ffiffiffiffiffiffiffiffiffiffiffiffi INFO p.
We then compare three different LD reference panels to use with these GWAS summary statistics (section ''alternative LD reference for FinnGen and Biobank Japan''). Interestingly, using the small Finnish LD reference panel we have defined here, composed of only 503 individuals, seems to almost always provide more predictive polygenic scores than using the large UK panel composed of 10,000 individuals or the widely used European subset of the 1000G data (Figure  7 ). For PRS-CSauto, none of the three LD references seems to consistently outperform the other two across all phenotypes considered. Note that the PGS here are validated in people of UK-like ancestry in the UK Biobank to obtain sufficient sample sizes. We also use GWAS summary statistics for four continuous outcomes from Biobank Japan.
34 These were derived using BOLT-LMM-inf. 8 First, for each phenotype, we estimate a global power improvement provided by BOLT-LMM by comparing c 2 statistics (the boost of BOLT-LMM-inf versus linear regression) across genomewide significant variants, as recommended in Loh et al. 8  This estimated power ratio, which corresponds to the increase in effective sample size, is 1. 143 for height, 1. 039 for HDL cholesterol, 1. 025 for BMI, and 1. 013 for systolic blood pressure. We then compare three different LD reference panels to use with these GWAS summary statistics (section ''alternative LD reference for FinnGen and Biobank Japan''). Interestingly, when looking at height and HDL cholesterol, where we could get the best predictive performance, using the smaller Japanese LD reference we have defined here seems to provide more predictive PGS than using the one using a larger set of East Asian individuals from the UK Biobank or the widely used East Asian subset of the 1000G data (Figure  S37 ), except when using PRS-CS-auto to predict height.
Fifth, if no validation/tuning dataset is available, we recommend using LDpred2-auto with the two new parameters introduced here, or PRS-CS-auto if there are strong misspecifications and the phenotype of interest is known to have only small effects. Sixth, we recommend to form independent LD blocks in the LD matrix, as it proved to make the methods more robust (and slightly faster). Most of these new recommendations are included in the LDpred2 tutorial. instead from the sum of the effective sample sizes of each study. 7 Indeed, take the extreme example of metaanalyzing two studies, one with 1,000 cases and 0 controls and another one with 0 cases and 1,000 controls: the effective sample size of the meta-analysis is then 0, not 2,000. Misspecifying the GWAS sample size can lead to serious issues such as misestimating the SNP heritability.
16, 19 Imposing independent blocks on the LD matrix could result in further misspecifications,  14  but here we have shown that welldefined blocks can actually make PGS methods more robust. PRS-CS is currently one of the most robust PGS methods; for example, it can use the (small) 1000G dataset as LD reference,  19  and can even use a European LD reference panel with multi-ancestry GWAS summary statistics, 12  also shown here. We believe this is made possible by the use of a strong regularization in PRS-CS (4 À 1 j À 1 j R 1, which would approximately correspond to using s ¼ 0:5 in lassosum, d ¼ 1 in lassosum2, and the new parameter shrink corr ¼ 0:5 in LDpred2-auto). Using enough regularization is good for robustness, but note that using too much regularization can also damage predictive performance.
Acknowledgments The authors thank  Timothy Shin Heng Mak ,  Shing Wan Choi , and  Matthew Stephens  for helpful discussions. The authors also thank GenomeDK and  Aarhus University  for providing computational resources and support that contributed to these research results. This research has been conducted using the UK Biobank Resource under Application Number  58024. F. P. and B. J. V. are supported by the  Danish National Research Foundation  ( Niels Bohr Professorship  to  Prof. John McGrath ) and by a  Lundbeck Foundation  Fellowship ( R335-2019-2339  to B. J. V. ).
Data and code availability The UK Biobank data is available through a procedure described at https://www. ukbiobank. ac. uk/using-the-resource/. All code used for this paper is available at https://github. com/privefl/ paper-misspec/tree/master/code. We have extensively used R packages bigstatsr and bigsnpr  23  for analyzing large genetic data, packages from the future framework  48  for easy scheduling and parallelization of analyses on the HPC cluster, and packages from the tidyverse suite  49  for shaping and visualizing results. The latest version of R package bigsnpr can be installed from GitHub, and a recent version can be installed from CRAN.
Web resources A tutorial on running LDpred2 and lassosum2 using R package bigsnpr is available at https://privefl. github. io/ bigsnpr/articles/LDpred2. html. We have recomputed the allele frequencies and imputation INFO scores within the European subset used here and across all imputed variants in the UK Biobank, and have made them available at https://doi. org/10. 6084/m9. figshare. 16635388. We have formed independent LD blocks within the Northwestern European LD references provided in Prive ét al. ,  15  and have made these updated versions available at https://doi. org/10. 6084/m9. figshare. 19213299.