Benchmarking CPUs and GPUs on embedded platforms for software receiver usage

Smartphones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring supercomputing technology into your pocket (or into our embedded devices). This can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light onto recent embedded processor developments, benchmarks Fast Fourier Transform (FFT) and correlation algorithms on representative embedded platforms and relates the results to the use in GNSS software radios. The use of embedded CPUs for signal tracking seems to be straight forward, but more research is required to fully achieve the nominal peak performance of an embedded GPU for FFT computation. Also the electrical power consumption is measured in certain load levels.Peer ReviewedPostprint (published version

Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology.

BACKGROUND: Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine. METHODS: This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired. RESULTS: We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images. CONCLUSIONS: The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging. AVAILABILITY: The open-source application is publicly available at , with a short video demonstration at

Uncertainty Exchange Through Multiple Quadrature Kalman Filtering

One of the major challenges in Bayesian filtering is the curse of dimensionality. The quadrature Kalman filter (QKF) is the method of choice in many real-life Gaussian problems, but its computational complexity increases exponentially with the dimension of the state. As a promising solution to overcome the filter limitations in such scenarios, we further explore the multiple state-partitioning approach, which considers the partition of the original space into several subspaces, with the goal to apply a low-dimensional filter at each partition. In this contribution, the key idea is to take advantage of the estimation uncertainty provided by the QKF to improve the interaction among filters and avoid the point estimate approximation performed in the original Multiple QKF (MQKF). The new filter formulation, named Improved MQKF, considers Gauss-Hermite quadrature rules to propagate the subspaces of interest, together with cubature rules for marginalization purposes. The nested quadrature-cubature approximation provides robustness and improves the filter performance. Simulation results for a multiple target tracking scenario are provided to support the discussion

Large-Scale Evaluation of Candidate Genes Identifies Associations between VEGF Polymorphisms and Bladder Cancer Risk

Common genetic variation could alter the risk for developing bladder cancer. We conducted a large-scale evaluation of single nucleotide polymorphisms (SNPs) in candidate genes for cancer to identify common variants that influence bladder cancer risk. An Illumina GoldenGate assay was used to genotype 1,433 SNPs within or near 386 genes in 1,086 cases and 1,033 controls in Spain. The most significant finding was in the 5′ UTR of VEGF (rs25648, p for likelihood ratio test, 2 degrees of freedom = 1 × 10(−5)). To further investigate the region, we analyzed 29 additional SNPs in VEGF, selected to saturate the promoter and 5′ UTR and to tag common genetic variation in this gene. Three additional SNPs in the promoter region (rs833052, rs1109324, and rs1547651) were associated with increased risk for bladder cancer: odds ratio (95% confidence interval): 2.52 (1.06–5.97), 2.74 (1.26–5.98), and 3.02 (1.36–6.63), respectively; and a polymorphism in intron 2 (rs3024994) was associated with reduced risk: 0.65 (0.46–0.91). Two of the promoter SNPs and the intron 2 SNP showed linkage disequilibrium with rs25648. Haplotype analyses revealed three blocks of linkage disequilibrium with significant associations for two blocks including the promoter and 5′ UTR (global p = 0.02 and 0.009, respectively). These findings are biologically plausible since VEGF is critical in angiogenesis, which is important for tumor growth, its elevated expression in bladder tumors correlates with tumor progression, and specific 5′ UTR haplotypes have been shown to influence promoter activity. Associations between bladder cancer risk and other genes in this report were not robust based on false discovery rate calculations. In conclusion, this large-scale evaluation of candidate cancer genes has identified common genetic variants in the regulatory regions of VEGF that could be associated with bladder cancer risk

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models

Objective: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, such as the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face serious limitations in portability and privacy due to their need for circulating user data in remote servers for operation. Our objective was to overcome these limitations. Materials and Methods: We refactored R-iCARE into a Python package (Py-iCARE) then compiled it to WebAssembly (Wasm-iCARE): a portable web module, which operates entirely within the privacy of the user's device. Results: We showcase the portability and privacy of Wasm-iCARE through two applications: for researchers to statistically validate risk models, and to deliver them to end-users. Both applications run entirely on the client-side, requiring no downloads or installations, and keeps user data on-device during risk calculation. Conclusions: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.Comment: 10 pages, 2 figure

Genetic and Non-genetic Predictors of LINE-1 Methylation in Leukocyte DNA.

Background: Altered DNA methylation has been associated with various diseases. Objective: We evaluated the association between levels of methylation in leukocyte DNA at long interspersed nuclear element 1 (LINE-1) and genetic and non-genetic characteristics of 892 control participants from the Spanish Bladder Cancer/EPICURO study. Methods: We determined LINE-1 methylation levels by pyrosequencing. Individual data included demographics, smoking status, nutrient intake, toenail concentrations of 12 trace elements, xenobiotic metabolism gene variants, and 515 polymorphisms among 24 genes in the one-carbon metabolism pathway. To assess the association between LINE-1 methylation levels (percentage of methylated cytosines) and potential determinants, we estimated beta coefficients (βs) by robust linear regression. Results: Women had lower levels of LINE-1 methylation than men (β = –0.7, p = 0.02). Persons who smoked blond tobacco showed lower methylation than nonsmokers (β = –0.7, p = 0.03). Arsenic toenail concentration was inversely associated with LINE-1 methylation (β = –3.6, p = 0.003). By contrast, iron (β = 0.002, p = 0.009) and nickel (β = 0.02, p = 0.004) were positively associated with LINE-1 methylation. Single nucleotide polymorphisms (SNPs) in DNMT3A (rs7581217-per allele, β = 0.3, p = 0.002), TCN2 (rs9606756-GG, β = 1.9, p = 0.008; rs4820887-AA, β = 4.0, p = 4.8 × 10–7; rs9621049-TT, β = 4.2, p = 4.7 × 10–9), AS3MT (rs7085104-GG, β = 0.7, p = 0.001), SLC19A1 (rs914238, TC vs. TT: β = 0.5 and CC vs. TT: β = –0.3, global p = 0.0007) and MTHFS (rs1380642, CT vs. CC: β = 0.3 and TT vs. CC; β = –0.8, global p = 0.05) were associated with LINE-1 methylation. Conclusions: We identified several characteristics, environmental factors, and common genetic variants that predicted DNA methylation among study participants.This work was partially supported by the Association for International Cancer Research (AICR; grant 09-0780, and a doctoral scholarship awarded to S.M.T.); Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, MINECO, Spain (grants 00/0745, PI051436, PI061614, PI09-02102, and G03/174); Red Temática de Investigación Cooperativa en Cáncer (grant RD06/0020-RTICC); the U.S. National Institutes of Health (grant RO1-CA089715); a postdoctoral fellowship awarded to A.F.S.A. from the Fundación Científica de la AECC; Fundació Marató TV3; and The Johns Hopkins University Vredenburg Scholarship awarded to A.L.C

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations

Cancer incidence in British vegetarians

Background: Few prospective studies have examined cancer incidence among vegetarians. Methods: We studied 61 566 British men and women, comprising 32 403 meat eaters, 8562 non-meat eaters who did eat fish ('fish eaters') and 20 601 vegetarians. After an average follow-up of 12.2 years, there were 3350 incident cancers of which 2204 were among meat eaters, 317 among fish eaters and 829 among vegetarians. Relative risks (RRs) were estimated by Cox regression, stratified by sex and recruitment protocol and adjusted for age, smoking, alcohol, body mass index, physical activity level and, for women only, parity and oral contraceptive use. Results: There was significant heterogeneity in cancer risk between groups for the following four cancer sites: stomach cancer, RRs (compared with meat eaters) of 0.29 (95% CI: 0.07–1.20) in fish eaters and 0.36 (0.16–0.78) in vegetarians, P for heterogeneity=0.007; ovarian cancer, RRs of 0.37 (0.18–0.77) in fish eaters and 0.69 (0.45–1.07) in vegetarians, P for heterogeneity=0.007; bladder cancer, RRs of 0.81 (0.36–1.81) in fish eaters and 0.47 (0.25–0.89) in vegetarians, P for heterogeneity=0.05; and cancers of the lymphatic and haematopoietic tissues, RRs of 0.85 (0.56–1.29) in fish eaters and 0.55 (0.39–0.78) in vegetarians, P for heterogeneity=0.002. The RRs for all malignant neoplasms were 0.82 (0.73–0.93) in fish eaters and 0.88 (0.81–0.96) in vegetarians (P for heterogeneity=0.001). Conclusion: The incidence of some cancers may be lower in fish eaters and vegetarians than in meat eaters

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReXprioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings