dataesr/scientific-paragraphs-categorization huggingface dataset:
Another limitation related to relatively low number of nodules we analyzed. While the total number of lung nodules is enough to confirm a good concordance in radiomics analysis between FD CT and ULD CT examinations, the low number of confirmed lung nodules (27 benign and 7 malignant) and the retrospective nature of the study makes it impossible to draw any significant conclusions on the actual diagnostic performances of the radiomics software. In conclusion, a good agreement in malignancy similarity index can be obtained between ULD chest CT and FD chest CT using radiomics software. However, further studies must be done with more case material to confirm our results and elucidate the diagnostic capabilities of radiomics software using ULD chest CT for lung nodule characterization by comparison by FD chest CT.
Data recording and processing During all the postural tasks, CoP trajectories were recorded at a sampling rate of 1000 Hz. Raw data were filtered with a low-pass Butterworth filter (order: 4; cut-off: 12 Hz). Next, the following data were averaged over three trials: (i) the CoP area (mm²), computed from the 95% confidence ellipse of the CoP displacement; (ii) the CoP mean velocity (mm. s -1 ); and (iii) the root mean square (RMS) of the CoP displacements (mm). All the data processing steps were performed using MATLAB R2017 software (MathWorks, Natick, MA, USA).
Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis. Such a decomposition is commonly obtained using fixed transforms (e. g. , Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In this work, we investigate in detail a new approach of constructing such a signal or image-dependent bases inspired by quantum mechanics tools, i. e. , by considering the signal or image as a potential in the discretized Schroedinger equation. To illustrate the potential of the proposed decomposition, denoising results are reported in the case of Gaussian, Poisson, and speckle noise and compared to the state of the art algorithms based on wavelet shrinkage, total variation regularization or patch-wise sparse coding in learned dictionaries, non-local means image denoising, and graph signal processing.
The latter has been shown to be of particular interest in image denoising  [4]. In this paper, we investigate a novel signal and image representation, through a dedicated basis extracted from the signal or image itself, using concepts from quantum mechanics. First preliminary results were published in  [5]. Compared to fixed basis such as Fourier, discrete cosinus, wavelets, curvelets, etc, or dictionary learning that generally needs a training database, the proposed approach has the advantage of computing a transform adapted to the signal or image of interest. Several attempts of translating quantum principles in image or signal processing applications have been proposed in the literature. One may note the seminal work in  [6] , or, more recently, the interest of quantum mechanics in image segmentation  [7] ,  [8]  or in pulse-shaped signal analysis  [9] ,  [10].
D. Algorithm description Denoising a signal or an image using the proposed method requires the computation of eigenvalues and eigenvectors of the discretized Hamiltonian matrix (4) for appropriate values of the parameters 2 /2m and σ, project the signal or image on this basis, threshold the coefficients by an appropriate threshold in energy, and reconstruct from this a denoised signal or image. These steps are summarized in Algorithm 1 and Fig. 7 1. Algorithm 1: Denoising algorithm using the proposed adaptive transform. For very large signals and images, where the size of the matrix (4) becomes too large for practical simulations, we implement a modified version of the algorithm where the matrix (4) is diagonalized for subparts of the signal or 1 The Matlab code of the proposed denoising algorithm is available at github.
Note that for all the methods and for all  the simulation scenarios, their hyperparameters were manually tuned to obtain optimal denoising results in the sense of the quantitative measurements employed. We used the Matlab implementations available in the Numerical tours website  [30]. Three quantitative measurements were used to evaluate the denoised images: the signal to noise ratio (SNR), the peak signal to noise ratios (PSNR) and the structure similarity (SSIM)  [31]. All the quantitative results are regrouped in Table  II  where the best and the second best values have been highlighted by red and blue colors respectively for each dataset. Note that VST is only used for data-dependent noise, whereas GSP and NLM is used only for Gaussian noise. Moreover, VST, GSP, NLM, and DL were only tested for images, as initially suggested by the seminal papers.
For such images or signals, the proposed method is very efficient. On the contrary, TV and DL, for example, fail in these cases because of the non piece-wise constant nature of the synthetic data. Finally, we remark that the proposed denoising algorithm provides competitive results compared to DL that learns the redundant dictionary from a database of clean images. Of course the proposed method does not need such a database. In summary, the results show that while our method is clearly the best for some specific types of signals or images for which it is well-adapted, it is also competitive for general types of images, being in almost all cases one of the two best methods. This indicates that the algorithm we propose can be used reliably for denoising applications in a variety of contexts.
The low portability of polygenic scores (PGS) across global populations is a major concern that must be addressed before PGS can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGS are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts.
Introduction Ever larger genetic data is becoming more readily available. This enables researchers to derive polygenic scores (PGS), which summarize an individual's genetic component for a particular trait or disease by aggregating information from many genetic variants into a single score. In human genetics, polygenic scores are usually derived from summary statistics from a large meta-analysis of multiple Genome-Wide Association Studies (GWAS) and an ancestry-matched Linkage Disequilibrium (LD) reference panel  (Choi et al. 2020). Polygenic scores can also be derived directly from individual-level data when available, i. e. from the genetic and phenotypic information of many individuals  (De Los Campos et al. 2010). When using a single individual-level dataset with only moderate sample size, deriving polygenic scores usually results in poor prediction for most phenotypes, expect e.
g. for autoimmune diseases with moderately large effects  (Abraham et al. 2014; Privé et al. 2019). Fortunately, biobank datasets such as the UK Biobank now links genetic data for half a million individuals with phenotypic data for hundreds of traits and diseases  (Bycroft et al. 2018). Thanks to the availability of these large datasets and to efficient methods recently developed to handle such data  (Loh et al. 2018; Privé et al. 2019; Qian et al. 2020) , individual-level data may be used to derive competitive PGS for hundreds of phenotypes. A major concern about PGS is that they usually transfer poorly to other ancestries, e. g. a PGS derived from individuals of European ancestry is not likely to predict as well in individuals of African ancestry. Prediction in another ancestry has been shown to decay with genetic distance to the training population  (Scutari et al.
2018). Of particular interest, those individuals of diverse ancestries all live in the UK and had their genetic and phenotypic information derived in the same way as people of UK ancestry. This allows to circumvent potential confounding bias that might arise in comparative analyses from independent studies, and makes the UK Biobank data very well suited for comparing and evaluating predictive performance of derived PGS in diverse ancestries and across multiple phenotypes. To investigate portability of PGS to other ancestries, we must first define groups of different ancestries from the data. Principal Component Analysis (PCA) has been widely used to correct for population structure in association studies and has been shown to mirror geography in Europe  (Price et al. 2006; Novembre et al. 2008).
Due to its popularity, many methods have been developed for efficiently performing PCA  (Abraham et al. 2017; Privé et al. 2018 Privé et al. , 2020a) )  as well as appropriately projecting samples onto a reference PCA space  (Zhang et al. 2020a; Privé et al. 2020a) , making it possible to perform these analyses for ever increasing datasets. Naturally, PCA has also been used for ancestry inference  (Chen et al. 2013; Byun et al. 2017; Zhang et al. 2020a ). However, among all studies where we have seen PCA used for ancestry inference, there does not seem to be a consensus on what is the most appropriate method for inferring ancestry using PCA. For example, there are divergences on which distance metric to use and the number of PCs to use to compute these distances. The ancestry of an individual can also be inferred based on other approaches, including the ADMIXTURE model, its various extensions, and haplotype-based methods  (Alexander et al.
2009; Lawson et al. 2012; Raj et al. 2014; Frichot et al. 2014; Haller et al. 2017; Cheng et al. 2017; Jin et al. 2019; Cabreros and Storey 2019). However, we focus on PCA here because it is very fast. In this study, we examine the extent to which PGS are transferable between ancestries by deriving 245 polygenic scores from the UK biobank data and applying them in nine ancestry groups from the same cohort. We first propose simple, robust and effective methods for global ancestry inference and grouping from PCA of genetic data, and use them to define nine ancestry groups in the UK Biobank data. We then apply a computationally efficient implementation of penalized regression  (Privé et al. 2019)  to derive PGS for 245 traits using the UK Biobank genetic and phenotypic data only. As an alternative method, we also run LDpred2-auto  (Privé et al.
Ancestry grouping We investigate various approaches to classify individuals in ancestry groups based on Principal Component Analysis (PCA) of genome-wide genotype data. Detailed results can be found in the corresponding Supplementary Note; we recall main results here. First, we show that (squared) Euclidean distances in the PCA space of genetic data are approximately proportional to F ST between populations, and we therefore recommend using this simple distance. At the same time, we provide evidence that using only 2 PCs, or even 4 PCs, is not enough to distinguish between some less-distant populations, and recommend using all PCs visually capturing some population structure. Then, we use this PCA-based distance to infer ancestry in the UK Biobank and the POPRES datasets. We propose two solutions to do so, either relying on projection of PCs to reference populations such as the 1000 Genomes Project, or by directly using internal data only.
We show that these solutions are simple, robust and effective methods for inferring global ancestry and for grouping genetically homogeneous individuals. Here, we first use the second solution presented in the Supplementary Note, relying on PCs computed within the UK Biobank and individual information on the countries of birth, for inferring the first eight ancestry groups presented in table 1. Then, for inferring the "Ashkenazi Jewish" ancestry group, we use the first solution, projecting UKBB individuals onto the PCA space of a reference dataset composed of many Jewish and non-Jewish individuals  (Behar et al. 2013). We identify a ninth group of 1709 unrelated individuals, which is entirely disjunct from the other eight groups previously defined (Methods). The nine ancestry groups inferred here are represented in the UKBB PCA space in figure  1.
Data We derive polygenic scores for 245 phenotypes using the UK Biobank (UKBB) data only  (Bycroft et al. 2018). We read dosages data from UKBB BGEN files using function snp_readBGEN() of R package bigsnpr  (Privé et al. 2018). We divide the UKBB data in eight ancestry groups (Supplementary Note), and restrict to 437,669 individuals without second-degree relatives (KING kinship < 2 -3. 5 ). We also define a ninth ancestry group composed of 1709 unrelated Ashkenazi (see Methods below). For the variants, we use 1,040,096 HapMap3 variants used in the LD reference provided in  Privé et al. (2020b)  and that were also present in the iPSYCH2015 data (Bybjerg-Grauholm et al. 2020) with imputation INFO score larger than 0. 6. Even though the iPSYCH data is not used in this study, we plan to use the PGS derived here for iPSYCH in the future.
To define phenotypes, we first map ICD10 and ICD9 codes (UKBB fields  40001, 40002, 40006, 40013, 41202, 41270 and 41271)  to phecodes using R package PheWAS  (Carroll et al. 2014; Wu et al. 2019). We filter down to 142 phecodes of interest that showed potential genetic signals in the PheWeb results from the SAIGE UKBB GWAS  (Zhou et al. 2018; Taliun et al. 2020). There are 106 phecodes with sufficient power for penalized regression to include at least a few variants in the predictive models. We then look closely at all 2408 UKBB fields that we have access to and filter down to defining 111 continuous and 28 binary phenotypes based on manual curation. Description of the 245 phenotypes used in this study can be downloaded at https://github. com/privefl/UKBB-PGS/blob/main/phenotype-description.
Additional data: genotyped data For the genotyped data used in some follow-up analyses, we restrict to variants that have been genotyped on both chips used by the UK Biobank, that pass quality control (QC) for all batches (cf. https:// biobank. ctsu. ox. ac. uk/crystal/crystal/auxdata/ukb_snp_qc. txt) and QC for possible mismappings (Kunert-Graf et al. 2020), with a minor allele frequency (MAF) larger than 0. 01 and imputation INFO score of 1. There are 586,534 such high-quality variants, which we read from the BGEN imputed data so that there is no missing value.
Additional data: 8M+ variants We also design a larger set of imputed variants to compare against using only HapMap3 variants for prediction. We first restrict to UKBB variants with MAF > 0. 01 and INFO > 0. 6. We then compile frequencies and imputation INFO scores from other datasets, iPSYCH and summary statistics for breast cancer, prostate cancer, coronary artery disease and type-1 diabetes  (Bybjerg-Grauholm et al. 2020; Michailidou et al. 2017; Schumacher et al. 2018; Nikpay et al. 2015; Censin et al. 2017). We restrict to variants with mean INFO > 0. 5 in these other datasets, and also compute the median frequency. To exclude potential mismappings in the genotyped data  (Kunert-Graf et al. 2020 ) that might have propagated to the imputed data, we compare median frequencies in the external data to the ones in UKBB (Figure  S18 ).
Ashkenazi Jewish ancestry group First, we refer the reader to the Supplementary Note on ancestry grouping for the details on how we define the other eight ancestry groups, and also to better understand how we infer the "Ashkenazi Jewish" ancestry group. Briefly, we project the UKBB data onto the PCA space of a reference dataset composed many Jewish and non-Jewish individuals  (Behar et al. 2013). We then compute the robust center (geometric median) of the Ashkenazi Jewish reference individuals, and compute the PC distance to this center for all projected UKBB individuals. Based on visual inspection of the histogram of these distances and on the fact that the closest non-Ashkenazi Jewish reference individual, an Italian Jew (Figure  S20 ), is at distance 12. 7, we use a threshold of 12. 5 under which to assign to the "Ashkenazi Jewish" ancestry group.
Penalized regression To derive polygenic scores based on individual-level data from the UKBB, we use the fast implementation of penalized linear and logistic regressions from R package bigstatsr  (Privé et al. 2019). We have also considered the recently developed R package snpnet for fitting penalized regressions on large genetic data; however, we provide theoretical and empirical evidence that bigstatsr is much faster than snpnet (Supplementary Note). Our implementation allows for lasso and elastic-net penalizations; yet, for the sake of simplicity and because the UKBB data is very large, we have decided to only use the lasso penalty  (Privé et al. 2019). We recall that fitting a penalized linear regression with lasso penalty corresponds to finding the vector of effects β (also µ and γ) that minimizes L(λ) = ||y -(µ + Gβ + Xγ) || 2 2 Loss function + λ β 1 Penalisation , where µ is an intercept, G is the genotype matrix, X is the matrix of covariates, y is the (quantitative) We have extended our implementation in two ways by allowing for using different penalties for the variants (i.
e. having j λ j |β j | instead of λ β 1 ). First, this enables us to use a different scaling for which now gives (n -K -1) se(γ j ) 2 = yT y -γ2 j Gj T Gj Gj T Gj = yT y Gj T Gj -γ2 j ≈ var(y) var(G j ) -γ2 j , finally giving (note the added term γ2 j ) sd(G j ) ≈ sd(y) n se(γ j ) 2 + γ2 j. (1) Figure  S21  shows that the updated formula (1) is better; we now use it in the code of LDpred2, and also recommend using it for the QC procedure proposed in  Privé et al. (2020b).
Performance metric Here we use the partial correlation as the performance metric, which is the correlation between the PGS and the phenotype after they have been both residualized using the covariates used in this paper, i. e. sex, age, birth date, deprivation index and 16 PCs. To derive 95% confidence intervals for these correlations, we use Fisher's Z-transformation. We implement this in function pcor of R package bigstatsr and use it here. on the HPC cluster, and packages from the tidyverse suite  (Wickham et al. 2019).
for shaping and visualizing results. We have also used R package deming for fitting Deming regressions. R packages bigstatsr and bigsnpr can be installed from GitHub and CRAN. A tutorial on fitting penalized regressions with R package bigstatsr is available at https://privefl. github. io/bigstatsr/articles/penalized-regressions. html. A tutorial on running LDpred2 with R package bigsnpr is available at https://privefl. github. io/bigsnpr/articles/LDpred2. html. PC centers of the individuals from the nine ancestry groups derived here can be downloaded at https: //github. com/privefl/UKBB-PGS/blob/main/pop_centers. csv. Description of the 245 phenotypes used in this study can be downloaded at https://github. com/privefl/UKBB-PGS/ blob/main/phenotype-description. xlsx. Effect sizes for 215 polygenic scores derived in this study can be downloaded at https://figshare.
Code and results availability All code used for this paper is available at https://github. com/privefl/UKBB-PGS/tree/ master/code. Links to the code used for the two supplementary notes are provided there. We have extensively used R packages bigstatsr and bigsnpr  (Privé et al. 2018)  for analyzing large genetic data, packages from the future framework  (Bengtsson 2020)  for easy scheduling and parallelization of analyses.
Fitting of penalized models is relatively fast when using 1M HapMap3 variants. We have also tried fitting penalized regression using 8M variants (>3TB of data); this was possible but took several days for the phenotypes we tried, so we have not investigated this further. To the best of our knowledge, the implementation we use is the most efficient penalized regression implementation currently available. Recently,  Qian et al. (2020)  proposed snpnet, a new R package for fitting penalized regressions on large individual-level genetic data, but we have found it to be much less efficient than R package bigstatsr on UKBB data (Supplementary Note). As for LDpred2, it currently cannot be run using 8M variants, but we show how to use a subset of 1M prioritized variants out of these 8M. Using this new set of variants provides a large improvement in predicting lipoprotein(a) concentration (lipoA), but not for the other seven phenotypes studied in this analysis.
This improvement for lipoA is not surprising given that the top HapMap3 variant explains 5% of phenotypic variance compared to 29% for the (non-HapMap3) top hit (Figure  4 ). Here we only use the UK Biobank data to fit polygenic scores. We do not use external information such as functional annotations; those could be used to improve the heritability model assumed by predictive methods in order to improve predictive performance  (Zhang et al. 2020b). Moreover, we do not use external summary statistics. Nevertheless,  Albiñana et al. (2020)  have shown that an efficient strategy to improve predictive ability of polygenic scores consists in combining two different polygenic scores, one derived using external summary statistics, and another one derived using internal individual-level data. Therefore, the polygenic scores derived here could be combined with polygenic scores derived using external summary statistics; we will release these PGS publicly and share them in databases such as the PGS Catalog and the Cancer-PRSweb  (Fritsche et al.
We assessed the efficacy of FIL in biologic (bio)-naïve and bio-experienced patients with UC, and in bioexperienced patients with failure of 1 or 2 biologics or 1 or 2 mechanisms of action (MoAs). Methods SELECTION (NCT02914522) was a phase 2b/3 double-blind, randomised, placebo-controlled trial comprising two induction studies and a maintenance study. Adults (18-75 years) with moderately to severely active UC were randomised 2:2:1 to FIL 200 mg, FIL 100 mg or placebo (PBO) once daily for 11 weeks in Induction Study A (bio-naïve) and B (bio-experienced). Patients in either clinical remission or Mayo Clinic Score (MCS) response at week 10 (responders) could enter the Maintenance Study. Responders who received induction FIL were re-randomised 2:1 to continue their induction regimen or PBO through week 58.
05). Higher proportions of responders treated with maintenance FIL 200 mg than PBO achieved MCS response at week 58. Conclusion FIL 200 mg was effective in inducing and maintaining clinical remission in bio-naïve and -experienced patients. Induction results suggest FIL 200 mg is most effective in bio-naïve patients, and those who switch after failure of 1 biologic or MoA. Introduction Filgotinib (FIL) is an oral preferential Janus kinase (JAK) 1 inhibitor. FIL for the treatment of moderately to severely active ulcerative colitis (UC) was evaluated in the phase 2b/3, double-blind, placebo (PBO)-controlled SELECTION study (NCT02914522) and its long-term extension (LTE) study (NCT02914535). Here we report safety results. Methods Patients received FIL 100 mg, FIL 200 mg or PBO (2:2:1) once daily orally for up to 11 weeks for induction (cohort 1).