Correlation-informed ordered dictionary learning for imaging in complex media.

We propose a method for imaging in scattering media when large and diverse datasets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using the multidimensional scaling algorithm with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together, we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain connectivity needed in the second step. Through simulation experiments, we show that the proposed method is able to provide images in complex media whose resolution is that of a homogeneous medium.

The functional impact of rare variation across the regulatory cascade.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Multi-ancestry epigenome-wide analyses identify methylated sites associated with aortic augmentation index in TOPMed MESA.

Despite the prognostic value of arterial stiffness (AS) and pulsatile hemodynamics (PH) for cardiovascular morbidity and mortality, epigenetic modifications that contribute to AS/PH remain unknown. To gain a better understanding of the link between epigenetics (DNA methylation) and AS/PH, we examined the relationship of eight measures of AS/PH with CpG sites and co-methylated regions using multi-ancestry participants from Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA) with sample sizes ranging from 438 to 874. Epigenome-wide association analysis identified one genome-wide significant CpG (cg20711926-CYP1B1) associated with aortic augmentation index (AIx). Follow-up analyses, including gene set enrichment analysis, expression quantitative trait methylation analysis, and functional enrichment analysis on differentially methylated positions and regions, further prioritized three CpGs and their annotated genes (cg23800023-ETS1, cg08426368-TGFB3, and cg17350632-HLA-DPB1) for AIx. Among these, ETS1 and TGFB3 have been previously prioritized as candidate genes. Furthermore, both ETS1 and HLA-DPB1 have significant tissue correlations between Whole Blood and Aorta in GTEx, which suggests ETS1 and HLA-DPB1 could be potential biomarkers in understanding pathophysiology of AS/PH. Overall, our findings support the possible role of epigenetic regulation via DNA methylation of specific genes associated with AIx as well as identifying potential targets for regulation of AS/PH.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Multi-ancestry epigenome-wide analyses identify methylated sites associated with aortic augmentation index in TOPMed MESA.

Despite the prognostic value of arterial stiffness (AS) and pulsatile hemodynamics (PH) for cardiovascular morbidity and mortality, epigenetic modifications that contribute to AS/PH remain unknown. To gain a better understanding of the link between epigenetics (DNA methylation) and AS/PH, we examined the relationship of eight measures of AS/PH with CpG sites and co-methylated regions using multi-ancestry participants from Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA) with sample sizes ranging from 438 to 874. Epigenome-wide association analysis identified one genome-wide significant CpG (cg20711926-CYP1B1) associated with aortic augmentation index (AIx). Follow-up analyses, including gene set enrichment analysis, expression quantitative trait methylation analysis, and functional enrichment analysis on differentially methylated positions and regions, further prioritized three CpGs and their annotated genes (cg23800023-ETS1, cg08426368-TGFB3, and cg17350632-HLA-DPB1) for AIx. Among these, ETS1 and TGFB3 have been previously prioritized as candidate genes. Furthermore, both ETS1 and HLA-DPB1 have significant tissue correlations between Whole Blood and Aorta in GTEx, which suggests ETS1 and HLA-DPB1 could be potential biomarkers in understanding pathophysiology of AS/PH. Overall, our findings support the possible role of epigenetic regulation via DNA methylation of specific genes associated with AIx as well as identifying potential targets for regulation of AS/PH.

Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts.

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery ß = 0.0561, Q = 4.05 × 10-10; ß = 0.0421, Q = 1.12 × 10-3; and ß = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q < 0.05), including elafin leukocyte elastase inhibitor and mucin-associated TFF2 (trefoil factor 2; ß = -4.3 ml/yr, Q = 0.049; ß = -6.1 ml/yr, Q = 0.032, respectively). Pathways and processes highlighted by our study include aberrant extracellular matrix remodeling, enhanced innate immune response, dysregulation of angiogenesis, and coagulation. Conclusions: In this study, we identify and validate novel biomarkers and pathways associated with lung function traits in a racially diverse population. In addition, we identify novel protein markers associated with FEV1 decline. Several protein findings are supported by previously reported genetic signals, highlighting the plausibility of certain biologic pathways. These novel proteins might represent markers for risk stratification, as well as novel molecular targets for treatment of COPD.

Application of an Eco-Friendly Adhesive and Electrochemical Nanostructuring for Joining of Aluminum A1050 Plates.

In adhesive joints used in several industrial applications, the adherends' bonding is made using an adhesive, which is usually an epoxy resin. However, since these adhesives are derived from petroleum fractions, they are harmful to the environment, due to the pollutants produced both during their manufacture and subsequent use. Thus, in recent years, effective steps have been made to replace these adhesives with ecological (green) ones. The present work focuses on the study of aluminum A1050 joints bonded with a green adhesive; the study also involves the electrochemical anodization method applied to adherends for nano-functionalization. The nanostructured aluminum adherends allow the formation of an expanded surface area for adhesion, compared to the non-anodized adherends. For comparison reasons, two different adhesives (Araldite LY1564 and Green Super Sap) were used. In addition, for the same reasons, both anodized and non-anodized aluminum adherends were joined with both types of adhesives. The lap joints were subsequently tested under both shear-tension and three-point bending conditions. The major findings were that aluminum A1050 anodization in all cases resulted in shear strength enhancement of the joints, while joints with both aluminum anodized and non-anodized adherends and bonded with the eco-friendly adhesive showed a superior shear behavior as compared to the respective joints bonded with Araldite adhesive.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.

Scientific opportunities in resilience research for cardiovascular health and wellness. Report from a National Heart, Lung, and Blood Institute workshop.

Exposure of biological systems to acute or chronic insults triggers a host of molecular and physiological responses to either tolerate, adapt, or fully restore homeostasis; these responses constitute the hallmarks of resilience. Given the many facets, dimensions, and discipline-specific focus, gaining a shared understanding of "resilience" has been identified as a priority for supporting advances in cardiovascular health. This report is based on the working definition: "Resilience is the ability of living systems to successfully maintain or return to homeostasis in response to physical, molecular, individual, social, societal, or environmental stressors or challenges," developed after considering many factors contributing to cardiovascular resilience through deliberations of multidisciplinary experts convened by the National Heart, Lung, and Blood Institute during a workshop entitled: "Enhancing Resilience for Cardiovascular Health and Wellness." Some of the main emerging themes that support the possibility of enhancing resilience for cardiovascular health include optimal energy management and substrate diversity, a robust immune system that safeguards tissue homeostasis, and social and community support. The report also highlights existing research challenges, along with immediate and long-term opportunities for resilience research. Certain immediate opportunities identified are based on leveraging existing high-dimensional data from longitudinal clinical studies to identify vascular resilience measures, create a 'resilience index,' and adopt a life-course approach. Long-term opportunities include developing quantitative cell/organ/system/community models to identify resilience factors and mechanisms at these various levels, designing experimental and clinical interventions that specifically assess resilience, adopting global sharing of resilience-related data, and cross-domain training of next-generation researchers in this field.

Lymphocyte activation gene-3-associated protein networks are associated with HDL-cholesterol and mortality in the Trans-omics for Precision Medicine program.

Deficiency of the immune checkpoint lymphocyte activation gene-3 (LAG3) protein is significantly associated with both elevated HDL-cholesterol (HDL-C) and myocardial infarction risk. We determined the association of genetic variants within ±500 kb of LAG3 with plasma LAG3 and defined LAG3-associated plasma proteins with HDL-C and clinical outcomes. Whole genome sequencing and plasma proteomics were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Framingham Heart Study (FHS) cohorts as part of the Trans-Omics for Precision Medicine program. In situ Hi-C chromatin capture was performed in EBV-transformed cell lines isolated from four MESA participants. Genetic association analyses were performed in MESA using multivariate regression models, with validation in FHS. A LAG3-associated protein network was tested for association with HDL-C, coronary heart disease, and all-cause mortality. We identify an association between the LAG3 rs3782735 variant and plasma LAG3 protein. Proteomics analysis reveals 183 proteins significantly associated with LAG3 with four proteins associated with HDL-C. Four proteins discovered for association with all-cause mortality in FHS shows nominal associations in MESA. Chromatin capture analysis reveals significant cis interactions between LAG3 and C1S, LRIG3, TNFRSF1A, and trans interactions between LAG3 and B2M. A LAG3-associated protein network has significant associations with HDL-C and mortality.

Protein prediction for trait mapping in diverse populations.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Mechanical Behavior Modelling and Filler Geometry Effect of Glass Filler Reinforced Starch-Epoxy Hybrid Matrix Composites.

The aim of the present study is to investigate the inclusion geometry and concentration effect on the quasi-static properties of a starch-epoxy hybrid matrix composite. The composites investigated consisted of a starch-epoxy hybrid matrix reinforced with four different glass inclusions such as 3 mm long chopped strands, 0.2 mm long short glass fibers, glass beads (120 µm in diameter) and glass bubbles (65 µm in diameter) at different concentrations. The flexural modulus and the strength of all materials tested were determined using three-point bending tests. The Property Prediction Model (PPM) was applied to predict the experimental findings. The model predicted remarkably well the mechanical behavior of all the materials manufactured and tested. The maximum value of the flexural modulus in the case of the 3 mm long chopped strands was found to be 75% greater than the modulus of the hybrid matrix. Furthermore, adding glass beads in the hybrid matrix led to a simultaneous increase in both the flexural modulus and the strength.

Efficient Use of Carbon Fibers as Heating Elements for Curing of Epoxy Matrix Composites.

The aim of this study is to achieve a fully cured thermoset matrix that is heated by a direct electric current passing through the reinforcement fibers i.e., the Joule heating effect. Two types of fibers were used as heating elements for curing the epoxy resins. Kanthal resistance fibers were used as reference heating elements and subsequently, they were replaced by a Torayca Carbon Tow of the same radius. The specimens were cured by the heat produced by a direct electric current passing through the fibers and achieving temperatures of 50 °C and 70 °C. Specimens cured in a conventional oven were also manufactured, to compare the resistance heating method to the conventional one. Next, all specimens were mechanically characterized in a quasi-static three-point bending mode of loading and experimental results were compared to derive useful conclusions concerning the applicability of the technique to polymer/composite materials mass production. Finally, a preliminary economical study concerning power consumption needed for the application of both the traditional oven curing and the carbon fibers heating elements use for the manufacturing of the same amounts of materials is presented, showing a maximum financial benefit that can be achieved, on the order of 68%.

Clonal hematopoiesis associated with epigenetic aging and clinical outcomes.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA-methylation modifying enzymes DNMT3A or TET2. We used DNA-methylation array and whole-genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p < 8.6 × 10-7 ) to 3.08 years (EEAA, p < 3.7 × 10-18 ). Mutations in most CHIP genes except DNA-damage response genes were associated with increases in several measures of age acceleration. CHIP carriers with mutations in multiple genes had the largest increases in age acceleration and decrease in estimated telomere length. Finally, we found that ~40% of CHIP carriers had acceleration >0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all-cause mortality (hazard ratio 2.90, p < 4.1 × 10-8 ) and coronary heart disease (CHD) (hazard ratio 3.24, p < 9.3 × 10-6 ) compared to those who were CHIP-/AgeAccelHG-. In contrast, the other ~60% of CHIP carriers who were AgeAccelHG- were not at increased risk of these outcomes. In summary, CHIP is strongly linked to age acceleration in multiple clocks, and the combination of CHIP and epigenetic aging may be used to identify a population at high risk for adverse outcomes and who may be a target for clinical interventions.

Interrelation between Fiber-Matrix Interphasial Phenomena and Flexural Stress Relaxation Behavior of a Glass Fiber-Polymer Composite.

The response of fiber-reinforced polymer composites to an externally applied mechanical excitation is closely related to the microscopic stress transfer mechanisms taking place in the fiber-matrix interphasial region. In particular, in the case of viscoelastic responses, these mechanisms are time dependent. Defining the interphase thickness as the maximum radial distance from the fiber surface where a specific matrix property is affected by the fiber presence, it is important to study its variation with time. In the present investigation, the stress relaxation behavior of a glass fiber-reinforced polymer (GFRP) under flexural conditions was studied. Next, applying the hybrid viscoelastic interphase model (HVIM), developed by the first author, the interphase modulus and interphase thickness were both evaluated, and their variation with time during the stress relaxation test was plotted. It was found that the interphase modulus decreases with the radial distance, being always higher than the bulk matrix modulus. In addition, the interphase thickness increases with time, showing that during stress relaxation, fiber-matrix debonding takes place. Finally, the effect of fiber interaction on the interphase modulus was found. It is observed that fiber interaction depends on both the fiber-matrix degree of adhesion as well as the fiber volume fraction and the time-dependent interphase modulus.

Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.

BACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10-11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.

Precision Health Analytics With Predictive Analytics and Implementation Research: JACC State-of-the-Art Review.

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.

The Noise Collector for sparse recovery in high dimensions.

The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. It is well known that a sparse solution of the linear system [Formula: see text] can be found efficiently with an [Formula: see text]-norm minimization approach if the data are noiseless. However, detection of the signal from data corrupted by noise is still a challenging problem as the solution depends, in general, on a regularization parameter with optimal value that is not easy to choose. We propose an efficient approach that does not require any parameter estimation. We introduce a no-phantom weight τ and the Noise Collector matrix C and solve an augmented system [Formula: see text], where e is the noise. We show that the [Formula: see text]-norm minimal solution of this system has zero false discovery rate for any level of noise, with probability that tends to one as the dimension of [Formula: see text] increases to infinity. We obtain exact support recovery if the noise is not too large and develop a fast Noise Collector algorithm, which makes the computational cost of solving the augmented system comparable with that of the original one. We demonstrate the effectiveness of the method in applications to passive array imaging.

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies.

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.