Spectral gene set enrichment (SGSE)

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. Results: We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracey-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Principal component gene set enrichment (PCGSE)

Motivation: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with few non-zero loadings. Although useful when just a few variables dominate the population PCs, these methods are often inadequate for characterizing the PCs of high-dimensional genomic data. For genomic data, reproducible and biologically meaningful PC interpretation requires methods based on the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for computing the statistical association between gene sets and the PCs of genomic data. The PCGSE method performs a two-stage competitive gene set test using the correlation between each gene and each PC as the gene-level test statistic with flexible choice of both the gene set test statistic and the method used to compute the null distribution of the gene set statistic. Using simulated data with simulated gene sets and real gene expression data with curated gene sets, we demonstrate that biologically meaningful and computationally efficient results can be obtained from a simple parametric version of the PCGSE method that performs a correlation-adjusted two-sample t-test between the gene-level test statistics for gene set members and genes not in the set. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Principal Component Gene Set Enrichment (Pcgse)

Background: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing interpretation methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with mainly zero loadings. Although useful when just a few variables dominate the population PCs, these methods can perform poorly on genomic data, where interesting biological features are frequently represented by the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for unsupervised gene set testing relative to the sample PCs of genomic data. The PCGSE method computes the statistical association between gene sets and individual PCs using a two-stage competitive gene set test. To demonstrate the efficacy of the PCGSE method, we use simulated and real gene expression data to evaluate the performance of various gene set test statistics and significance tests. Conclusions: Gene set testing is an effective approach for interpreting the PCs of high-dimensional genomic data. As shown using both simulated and real datasets, the PCGSE method can generate biologically meaningful and computationally efficient results via a two-stage, competitive parametric test that correctly accounts for inter-gene correlation

Optimization of electric vehicle charging in a fully (nearly) electric campus energy system

The goal of this work is to build a set of computational tools to aid decision making for the modelling and operations of integrated urban energy systems that actively interact with the power grid of the future. District heating and cooling networks incorporating heat recovery and large-scale thermal storage, such as the Stanford campus system, dramatically reduce energy waste and greenhouse gas emissions. They have historically played a small, but important role at a local level. Here we explore the potential for other co-benefits, including the provision of load following services to the electrical grid, carbon emissions reductions or demand charge management. We formulate and solve the problem of optimally scheduling daily operations for different energy assets under a demand-charge-based tariff, given available historical data. We also explore the interaction and interdependence of an electrified thermal energy network with actively managed power sources and sinks that concurrently draw from the same electrical distribution feeder. At Stanford University, large-scale electric vehicle charging, on-site photovoltaic generation and controllable building loads could each separately represent up to 5 MW, or 15% of the aggregate annual peak power consumption in the very near future. We cooptimize financial savings from peak power reductions and shifting consumption to lower price periods and assess the flexibility of both the different components and the integrated energy system as a whole. We find that thermal storage, especially complemented with electric vehicle charging, can play the role that is often proposed for electrochemical storage for demand charge management applications and quantitatively evaluate potential revenue generators for an integrated urban energy system. Although there is little value to smart charging strategies for low penetrations of electric vehicles, they are needed to avoid significant increases in costs once penetration reaches a certain threshold – in the Stanford case, 750-1,000 vehicles, or 25% of the vehicle commuter population

A Dietary-Wide Association Study (DWAS) of Environmental Metal Exposure in US Children and Adults

Background: A growing body of evidence suggests that exposure to toxic metals occurs through diet but few studies have comprehensively examined dietary sources of exposure in US populations. Purpose: Our goal was to perform a novel dietary-wide association study (DWAS) to identify specific dietary sources of lead, cadmium, mercury, and arsenic exposure in US children and adults. Methods: We combined data from the National Health and Nutrition Examination Survey with data from the US Department of Agriculture’s Food Intakes Converted to Retail Commodities Database to examine associations between 49 different foods and environmental metal exposure. Using blood and urinary biomarkers for lead, cadmium, mercury, and arsenic, we compared sources of dietary exposure among children to that of adults. Results: Diet accounted for more of the variation in mercury and arsenic than lead and cadmium. For instance we estimate 4.5% of the variation of mercury among children and 10.5% among adults is explained by diet. We identified a previously unrecognized association between rice consumption and mercury in a US study population – adjusted for other dietary sources such as seafood, an increase of 10 g/day of rice consumption was associated with a 4.8% (95% CI: 3.6, 5.2) increase in blood mercury concentration. Associations between diet and metal exposure were similar among children and adults, and we recapitulated other known dietary sources of exposure. Conclusion: Utilizing this combination of data sources, this approach has the potential to identify and monitor dietary sources of metal exposure in the US population

Gene Ontology Analysis of Pairwise Genetic Associations in Two Genome-Wide Studies of Sporadic ALS

It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO)

Effect of Microthreads and Platform Switching on Crestal Bone Stress Levels: A Finite Element Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142288/1/jper2166.pd

Naturopathic Care for Chronic Low Back Pain: A Randomized Trial

OBJECTIVE: Chronic low back pain represents a substantial cost to employers through benefits coverage and days missed due to incapacity. We sought to explore the effectiveness of Naturopathic care on chronic low back pain. METHODS: This study was a randomized clinical trial. We randomized 75 postal employees with low back pain of longer than six weeks duration to receive Naturopathic care (n = 39) or standardized physiotherapy (n = 36) over a period of 12 weeks. The study was conducted in clinics on-site in postal outlets. Participants in the Naturopathic care group received dietary counseling, deep breathing relaxation techniques and acupuncture. The control intervention received education and instruction on physiotherapy exercises using an approved education booklet. We measured low back pain using the Oswestry disability questionnaire as the primary outcome measure, and quality of life using the SF-36 in addition to low back range of motion, weight loss, and Body Mass Index as secondary outcomes. RESULTS: Sixty-nine participants (92%) completed eight weeks or greater of the trial. Participants in the Naturopathic care group reported significantly lower back pain (-6.89, 95% CI. -9.23 to -3.54, p = <0.0001) as measured by the Oswestry questionnaire. Quality of life was also significantly improved in the group receiving Naturopathic care in all domains except for vitality. Differences for the aggregate physical component of the SF-36 was 8.47 (95% CI, 5.05 to 11.87, p = <0.0001) and for the aggregate mental component was 7.0 (95% CI, 2.25 to 11.75, p = 0.0045). All secondary outcomes were also significantly improved in the group receiving Naturopathic care: spinal flexion (p<0.0001), weight-loss (p = 0.0052) and Body Mass Index (-0.52, 95% CI, -0.96 to -0.08, p = 0.01). CONCLUSIONS: Naturopathic care provided significantly greater improvement than physiotherapy advice for patients with chronic low back pain. TRIAL REGISTRATION: Controlled-Trials.com ISRCTN41920953

Non-verbal sound processing in the primary progressive aphasias

Little is known about the processing of non-verbal sounds in the primary progressive aphasias. Here, we investigated the processing of complex non-verbal sounds in detail, in a consecutive series of 20 patients with primary progressive aphasia [12 with progressive non-fluent aphasia; eight with semantic dementia]. We designed a novel experimental neuropsychological battery to probe complex sound processing at early perceptual, apperceptive and semantic levels, using within-modality response procedures that minimized other cognitive demands and matching tests in the visual modality. Patients with primary progressive aphasia had deficits of non-verbal sound analysis compared with healthy age-matched individuals. Deficits of auditory early perceptual analysis were more common in progressive non-fluent aphasia, deficits of apperceptive processing occurred in both progressive non-fluent aphasia and semantic dementia, and deficits of semantic processing also occurred in both syndromes, but were relatively modality specific in progressive non-fluent aphasia and part of a more severe generic semantic deficit in semantic dementia. Patients with progressive non-fluent aphasia were more likely to show severe auditory than visual deficits as compared to patients with semantic dementia. These findings argue for the existence of core disorders of complex non-verbal sound perception and recognition in primary progressive aphasia and specific disorders at perceptual and semantic levels of cortical auditory processing in progressive non-fluent aphasia and semantic dementia, respectively

Developmental Programming Mediated by Complementary Roles of Imprinted Grb10 in Mother and Pup

Developmental programming links growth in early life with health status in adulthood. Although environmental factors such as maternal diet can influence the growth and adult health status of offspring, the genetic influences on this process are poorly understood. Using the mouse as a model, we identify the imprinted gene Grb10 as a mediator of nutrient supply and demand in the postnatal period. The combined actions of Grb10 expressed in the mother, controlling supply, and Grb10 expressed in the offspring, controlling demand, jointly regulate offspring growth. Furthermore, Grb10 determines the proportions of lean and fat tissue during development, thereby influencing energy homeostasis in the adult. Most strikingly, we show that the development of normal lean/fat proportions depends on the combined effects of Grb10 expressed in the mother, which has the greater effect on offspring adiposity, and Grb10 expressed in the offspring, which influences lean mass. These distinct functions of Grb10 in mother and pup act complementarily, which is consistent with a coadaptation model of imprinting evolution, a model predicted but for which there is limited experimental evidence. In addition, our findings identify Grb10 as a key genetic component of developmental programming, and highlight the need for a better understanding of mother-offspring interactions at the genetic level in predicting adult disease risk