dataesr/scientific-paragraphs-categorization huggingface dataset:
We read the UKBB BGEN files using function snp_readBGEN from package bigsnpr. 8 For simulating phenotypes and computing summary statistics, we read UKBB data as hard calls by randomly sampling hard calls according to reported imputation probabilities. For the training and test sets, we read these probabilities as dosages (expected values). This procedure enables us to simulate phenotypes using hard calls and then to use the INFO score (imputation accuracies) reported by the UK Biobank to assess the quality of the imputed data used for the training and test sets. We simulate binary phenotypes with a heritability h 2 ¼ 0. 5 using a liability threshold model (LTM) with a prevalence of 10%. 13 We vary the number of causal variants (100, 10K, or 1M) to match a range of genetic architectures from low to high polygenicity.
Real Summary Statistics We also investigate predictive performance of CþT, SCT, lassosum, and LDpred using the UK Biobank. We first pick existing external summary statistics from published GWAS of real diseases. 20 We then divide the UK Biobank dataset into one training set and one test set. The training set is used to choose optimal hyper-parameters in CþT, LDpred, and lassosum, and to learn stacking weights in SCT. The test set is used to evaluate the final model. Training SCT and choosing optimal hyper-parameters for CþT (stdCT and maxCT) and lassosum use 63%-90% of the UK Biobank individuals reported in Table  1. Therefore, the training set can contain as many as 250K individuals. LDpred is excluded from this analysis due to computational infeasibility. To assess how sample size affects predictive performance of methods, we also compare these methods (including LDpred) using a much smaller training set of 500 case subjects and 2,000 control subjects only.
We keep all variants with a GWAS p value lower than 0. 1, except for prostate cancer (0. 05) and asthma (0. 5). This way, we keep around 1M variants for each phenotype, deriving all CþT scores and stacking them in SCT in less than 1 day for each phenotype, even when using 300K individuals in the training set. To match variants from summary statistics with data from the UK Biobank, we first remove ambiguous alleles [A/T] and [C/G]. We then augment the summary statistics twice: first by duplicating each variant with the complementary alleles, then by duplicating variants with reverse alleles and effects. Finally, we include only variants that we match with UKBB based on the combination of chromosome, position, and the two alleles. Note that, when no or very few alleles are flipped, we disable the strand flipping option and therefore do not remove ambiguous alleles; this is the case for all phenotypes analyzed here.
Discussion Predictive Performance Improvement of CþT CþT is an intuitive and easily applicable method for obtaining polygenic scores trained on GWAS summary statistics. Two popular software packages that implement CþT, namely PLINK and PRSice, have further contributed to the prevalence of CþT. 5, 7, 31, 32 Usually, CþT scores for different p value thresholds are derived, using some default values for the other three hyper-parameters. In R package bigsnpr, we extend CþT to efficiently consider more hyper-parameters (four by default) and enable the user to define their own qualitative variant annotations to filter on. Using simulated and real data, we show that choosing optimized hyper-parameter values rather than default ones can substantially improve the performance of CþT, making CþT a very competitive method.
9 ombining multiple PRSs is not a new idea,  36, 37  but we push this idea to the limit by combining 123,200 polygenic scores. This makes SCT more flexible than any CþT model, but it of course also requires a larger training dataset with individual-level genotypes and phenotypes to learn the weights in stacking. Normally, cross-validation should be used to prevent overfitting when using stacking. 9 However, cross-validation is not necessary here since building CþT scores does not make use of the phenotype of the training set that is later used for stacking. Moreover, although it has been suggested to use positivity constraints in stacking,  9  we allow CþT scores to have negative weights in the final model for three reasons. First, because CþT scores are overlapping in the variants they use, using some negative weights allows us to weight groups of variants that correspond to the difference of two sets of variants.
Limitations of the Study In this study, we limited the analysis to eight common diseases and disorders, as these all had substantial number of case subjects and publicly available GWAS summary statistics based on substantial sample sizes. For example, for psychiatric disease, we include only depression (MDD) because diseases such as schizophrenia and bipolar disorder have very few case subjects in the UK Biobank; dedicated datasets should be used to assess effectiveness of maxCT and SCT for such diseases. We also do not analyze many automimmune diseases because summary statistics are often outdated (2010-2011 1  ) and because there are usually large effects in regions of chromosome 6 with high LD, methods that use individual-level data instead of summary statistics are likely to provide better predictive models.
This lack of robustness of lassosum may be explained by the presence of large effects in the latter diseases, which can be an issue for methods that model LD. This may also be due to the initial filtering on p value that we use for real data to keep disk usage and computation time manageable when analyzing the UK Biobank dataset. We also include LDpred in some of the comparisons as it is a widely used method. A full comparison of methods (including individual-level data methods), including binary and continuous traits with different architectures, using different sizes of summary statistics and individual-level data for training, and in possibly different populations would be of great interest, but is out of scope for this paper. Indeed, we believe that different methods may perform very differently in different settings and that understanding what method is appropriate for each case is of paramount interest if the aim is to maximize prediction accuracy to make PRS clinically useful.
our efficient implementation of package bigstatsr, but not Cox regression. This is an area of future development; for now, if sample size is not too large, one could use R package glmnet to implement stacking based on Cox model. 44 ne might also want to use other information such as sex or ancestry (using principal components). Indeed, it is easy to add covariates in the stacking step as (possibly unpenalized) variables in the penalized regression. Yet, adding covariates should be done with caution (see the example of CAD at the end of Supplemental Data). Finally, note that there is an extra parameter in the SCT pipeline that makes possible for a user to define their own groups of variants. This allows to extend the grid of computed CþT scores and opens many possibilities for SCT. For example, we could derive and stack CþT scores for two (or more) different GWAS summary statistics, e.
Acknowledgments We thank  Shing Wan Choi  for helpful discussions about lassosum. We are grateful to the two anonymous reviewers who contributed to improving this paper. F. P. and M. G. B. B. acknowledge  LabEx PERSYVAL-Lab  ( ANR-11-LABX-0025-01 ) and  ANR  project  FROGH  ( ANR-16-CE12-0033 ). F. P. and M. G. B. B. also acknowledge the Grenoble Alpes Data Institute that is supported by the  French National Research Agency  under the '' Investissements d'avenir'' program  ( ANR-15-IDEX-02 ). F. P. and B. J. V. acknowledge  Niels Bohr Professorship  from the  Danish National Research Foundation  to  Prof. John J. McGrath , and the  Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH  ( R248-2017-2003 ). This research has been conducted using the UK Biobank Resource under Application Number  25589.
Study design The design of this study (NCT01014390 and CTRI/ 2012/12/003166) has been described previously  [14]. The trial was approved by the Ethics Committee at each participating center and all patients provided written informed consent. Adverse events were reported by the investigators and categorized as related or unrelated to the study stent, the stenting procedure, or the study stent removal procedure. An independent medical reviewer (DCL) adjudicated the reported relatedness for all deaths and serious adverse events (SAEs). This independent review was carried out blinded to the study site.
Of this number, 55. 2% were under 25 years old, 36. 5% were between 26 and 45 years old, and 8. 3% were over 45 years old. The same group of consumers examined the quality of apple juice samples in different exposure conditions. To prevent communications between the members of the consumer panel, they were informed about the rules of the experiment, each person occupied a separate cubicle, and the sessions were supervised. Two sessions were held over two consecutive weeks. Three types of data were gathered with the same group of consumers: (1) overall liking of the samples in blind-testing conditions, (2) expected liking of juices in packages, and (3) overall liking of the samples in an informed testing conditions; all samples were evaluated on 9-point hedonic scales (1 = dislike extremely, 5 = neither like nor dislike, and 9 = like extremely).
Consumers were asked to check all the terms they considered appropriate to describe each of the products. Experiment 2. A group of 171 consumers (68. 8% female and 31. 2% male) participated in the online study using Attensee software (https://www. attensee. com). Of these, 72. 9% were under 25 years old, 23. 0% were between 26 and 45 years old, and 4. 1% were over 45 years old. Attensee is an application used for the study of visual attention, that mimics an eye-tracker  [29] ; it provides an interface for computers and tablets and does not require any special hardware. This innovative approach to record human information lets one see what consumers look at on labels, packaging, or pictures. To get the required information, the reference areas (RA) on packaging and labels should be marked, and the percentage of consumers focusing their attention on these RA are recorded  [30].
After receiving instructions and doing some trial runs, participants performed the actual test. First, an image of all of the analyzed products on the shop shelf was presented on a computer screen for 45 s, and then the images of individual juice labels were presented, each for 30 s. The sequence of showing the individual labels was proportional and randomized across participants to eliminate the order effects. Each of the participants examined an image with all of the products (shelf picture), and randomly selected four of the eight individual product images. The photographic images of juice packages (with 4592 × 3056 pixels resolution) were captured using a digital camera (Sony A3056, Tokyo, Japan). The Attensee software can also produce different types of questionnaires. Thus, the participants were asked to answer a check-all-that-apply questionnaire with the terms describing the product, the same as in Experiment 1.
Data Analysis Consumer liking data were analyzed by multivariate analysis of variance (MANOVA). When a significant difference (p < 0. 05) was detected, a t-test with Holm correction was applied to evaluate the difference between the samples. Pearson coefficients were calculated to evaluate the correlations between the consumer liking scores gained in different evaluation conditions. Principal component analysis (PCA) was performed on the individual consumer scores in three different conditions using the correlation matrix. PCA models were validated using the full cross-validation procedure. The correspondence analysis (CA) was performed on the frequency table containing responses to the check-all-that-apply questions. Statistical analyses were performed using the R-language and XLSTAT software (Addinsoft, Paris, France) packages.
Consumer liking data were analyzed using principal component analysis (PCA), performed on the individual consumer scores obtained in three different conditions. Internal preference maps were generated, illustrating the main preference directions  [34] , presented as biplots in Figure  1. The distribution of individual consumer scores varied depending on the testing conditions. Regardless of the testing conditions, the two juices in each of the pairs belonging to a certain product category were rated similarly by consumers; thus, the points corresponding to these juices are located close together in the two-dimensional space. Most of the consumers preferred cloudy FC and freshly squeezed juices in the blind test (Figure  1a ). The first PC1 component distinctly differentiated the juices according to sensory preferences (direct vs.
Perception of Apple Juices Based on External Attributes In order to gather supplementary data on the consumer perception of the juices studied, a check-all-that-apply (CATA) questionnaire with terms related to characteristics of the product and the brand image was used. CATA questions are increasingly more often used in consumer research to investigate the perceptions of a variety of attributes perceived by consumers and to obtain a rapid product profile from consumers  [36] [37] [38]. Consumers used between one and seven terms to describe the apple juices in both laboratory and online studies. The most frequently used terms were natural and tasty; the least used term was tasteless. Most consumers indicated both cloudy NFC (E, F) and fresh (G, H) juices as natural. These juices were rated also as very healthy and expensive.
A two-dimensional representation of the data was obtained, describing respectively 92. 05% and 92. 31% of the variability of laboratory and online consumer data. The results of the correspondence analysis showed that the external attributes differentiated the studied juices. The first direction differentiating the products was related to the perceived naturalness. The second dimension was related to the expected taste of the juice and was positively correlated to tasteless and negatively to tasty. As shown in Figure  2 , the first and the second dimensions sorted the samples into the two main groups, according to consumer expectations: the first group contained FC juices (A, B, C, D) and the second group direct juices (E, F, G, H). The sample distribution was distinctly different from that obtained for blind and informed liking scores, while similar to the distribution of expected liking scores (Figure  2 ).
Visual Attention to Product Labels To examine the labels' zones that attract consumer attention, an online study was conducted using the innovative Attensee. com application. Based on the defined reference areas (RA), the application generated three types of data which are similar to the data obtained by the eye-tracking tool: (1) path of engagement specifying locations on packaging which attracted the attention of the respondents; (2) heatmap for time, showing the average time that respondents focused on an area; and (3) heatmap for attention, specifying the number of people who focused on a particular area of an image. Firstly, the image of all the analyzed products was presented to consumers. The purpose of this presentation was to investigate which products attract the most consumer attention during the decision process on the store shelf.
One notices that the popular NP-hard 0-1 knapsack problem (KP)  [17]  is a special case of the SUKP. Indeed, the SUKP reduces to the KP when we set m = n and V = U. The SUKP also generalizes the NP-hard densest k-subhypergraph problem (DkSH) that aims to determine a set of k nodes of a hypergraph to maximize the number of hyperedges of the subhypergraph induced by the set of the selected nodes  [4]. In fact, the SUKP reduces to the DkSH when we consider the elements and items as the nodes and hyperedges of a hypergraph respectively, with unit weights and unit profits as well as a capacity of k. As indicated in  [13, 14] , the SUKP has a number of relevant applications, such as financial decision making, flexible manufacturing, building public key prototype, database partitioning etc. However, as a generalization of the NP-hard KP and DkSH problems, the SUKP is computationally challenging.
Experimental Setting and reference algorithms The proposed algorithm was implemented in C++ and compiled using the g++ compiler with the -O3 option. The experiments were carried on an Intel Xeon E5-2670 processor with 2. 5 GHz and 2 GB RAM under the Linux operating system. Table  1  shows the setting of parameters used in our algorithm, whose values were discussed in Section 4. 1. Given the stochastic nature of the algorithm, we ran 100 times (like in  [14, 22] ) with different random seeds to solve each instance, with a cut-off time of 500 seconds per run. For the comparative studies, we use as reference algorithms the following three very recent algorithms: BABC (binary artificial bee colony algorithm) (2018), which is the best performing among five population-based algorithms tested in  [14]  (2018), gPSO (binary particle swarm optimization algorithm) (2019)  [22]  and MA (discrete moth search algorithm)  [9].
Patients Data on patients were obtained from two international transplant registries; the CIBTMR and the EBMT. Both registries collect data on consecutive transplantations performed at participating sites and patients are followed longitudinally until death or lost to follow up. Transplantations occurred between 2005 and 2016 in Europe or North America. Ethical approval for studying transplantations reported to the EBMT was granted by the EBMT SAA Working Party. The Institutional Review Board of the National Marrow Donor Program approved studying transplantations reported to the CIBMTR and this study.
Subgroup differences were assessed by Gray's test. Multivariable Fine & Gray regression was used to study risk factors associated with acute and chronic GVHD, providing sub-distribution HRs (sHR) with 95% confidence intervals. All p-values were two-sided and p≤0. 05 was considered significant. All analyses were performed in SPSS 23 (Armonk, NY, USA) and in R 3. 3. 2 (R Development Core Team, Vienna, Austria) using packages "survival", "prodlim" and "cmprsk".
Acknowledgments Funding Source: The CIBMTR is supported primarily by  Public Health Service Grant/Cooperative Agreement   5U24-CA076518  from the  National Cancer Institute (NCI) , the  National Heart, Lung and Blood Institute (NHLBI)  and the  National Institute of Allergy and Infectious Diseases (NIAID) ;  5U10HL069294  from  NHLBI  and  NCI ; a contract  HHSH250201200016C  with  Health Resources and Services Administration (HRSA/DHHS ); grants  N00014-15-1-0848  and  N00014-16-1-2020  from the  Office of Naval Research. The views expressed in this article do not reflect the official policy or position of the  National Institute of Health , the  Department of the Navy , the  Department of Defense, Health Resources and Services Administration (HRSA)  or any other agency of the  U. S. Government.
Large and small systems: recent results on low momentum direct photon p T spectra Recently PHENIX accomplished low momentum direct photon measurements for large systems: Cu+Cu at √ s NN = 200 GeV through their internal conversions, and Au+Au at √ s NN = 62. 4 GeV and 39 GeV with the external conversion method. In Fig. 1  one can see the results for minimum bias data samples. In the external conversion method the photons are measured through their conversions to electron-positron pairs at the HBD (or VTX) in the PHENIX detector system, and the fraction of direct photons is determined after tagging photons from neutral pion decays. Comparing these data to T AA scaled p+p fit or pQCD calculations one finds a significant excess over the scaled p+p yield of low p T direct photons in all three systems.
We used Hochberg's adjustment to correct for multiple comparisons  (Hochberg, 1988). For these analyses, we used the glm. nb function in the MASS R package  (version 7. 3-48; Venables & Ripley, 2002)  and the glm function in the stats package in R. We calculated least-squares means and 95% asymptotic confidence intervals using the lsmeans function in the lsmeans R package  (version 2. 27-61; Lenth, 2016). For some aphid species, the confidence intervals were sometimes asymmetrical; in these cases, we report means and asymptotic 95% confidence levels in the following format: mean [lower confidence level -upper confidence level]. Replicates in which females were not recovered (or died before the end of the exposure period) were not used, and after removing these replicates (7% of the experimental units), we had data for 6-14 females (mean of 10 females) from 16 parasitoid populations on each of the seven species of aphids for a total of 1,163 females.
| Geographical distances and differences in parasitism We tested the relationship between geographical distance and differences in parasitism among the Asian populations of A. certus. Because the population of A. certus in North America has only very recently established and thus is unlikely to have differentiated from Asian populations, we excluded it from this analysis. We calculated great-circle distances between collection sites using latitudes and longitudes for each site (Table  S1 ) and the distm function with the haversine method in the geosphere R package (version 1. 5-7;  Hijmans, 2016). We calculated Mahalanobis distances among parasitism means for the aphid species using the dist. quant function in the ade4 R package (version 1. 7-10;  Dray & Dufour, 2007). Because Diuraphis noxia was rarely parasitized by A.
certus females from any population, we excluded it from these calculations. Using Mantel's permutation test, we compared the geographical and host use distance matrices with the mantel. randtest function in the ade4 R package with 10,000 permutations. We also graphed the similarity in parasitism among parasitoid populations, using the qgraph R package  (version 1. 4. 4; Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom, 2012). In this network graph, the widths of the lines joining the populations are proportional to the similarity in parasitism, which was calculated as the maximum Mahalanobis distance for all populations minus the observed distance between each pair of populations. We also clustered the populations using the Mahalanobis distances in the hclust function in the base R package and tested whether clustering was associated with region (China-Inner Mongolia, China-Northeast, China-Hebei, Japan, Korea) using the adonis function in the vegan R package  (version 2.
Sequence data were processed with a reduced-representation computational pipeline called RedRep (described in  Manching et al. 2017 ; the scripts and documentation for the pipeline are available under an open source MIT license at https://github. com/UD-CBCB/RedRep. Briefly, sequences were deconvoluted by barcode using custom scripts and the FASTX-Toolkit (version 0. 0. 14; http:// hannonlab. cshl. edu/fastx_toolkit). Custom scripts and CutAdapt (version 1. 14;  Martin, 2011)  were then used to remove adapters, trim low quality read ends, and filter out sequences that did not meet minimum length/quality standards or did not meet expectations for the restriction-site sequences. High-quality reads were mapped to the draft genome of A. certus using BWA-MEM program (version 0. 7. 16a; Li, 2013).