A sedentary lifestyle is becoming common for many individuals throughout the United States; however, this comes with a health cost of various preventable diseases such as cardiovascular disease, colon cancer, metabolic syndrome, and diabetes. Many times, individuals are completely unaware of how his or her health has deteriorated because of the slow progression or the demands of a job. We seek to bring attention to these problems by identifying specific sedentary activities and propose that just-in-time interventions could be used to help individuals overcome some of these problems. Our solution involves wearable sensors and utilizes a kinematic-based activity recognition systems to identify sedentary and light-intensity activities. Our system is evaluated with a series of laboratory experiments that include data from 34 individuals and a total of over 1400 minutes of activity. Results indicate that our system has a classification accuracy of up to 95.4 percent across all activities.
BackgroundGene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of ‘ground truth’ to evaluate the quality of probes and microarray datasets.ResultsWe examined Sirtuin-3 (Sirt3) co-expressed gene sets extracted from either liver or brain tissues of BXD recombinant inbred mice in the GeneNetwork database. Depending on the microarray platform, there were as many as 26 probes that targeted different regions of Sirt3 primary transcript. Co-expressed gene sets (ranging from 100–1000 genes) associated with each Sirt3 probe were evaluated using the previously developed literature-derived cohesion p-value (LPv) and benchmarked against ‘gold standards’ derived from proteomic studies or Gene Ontology classifications. We found that the maximal F-measure was obtained at an average window size of 535 genes. Using set size of 500 genes, the Pearson correlations between LPv and F-measure as well as between LPv and mitochondrial gene enrichment p-values were 0.90 and 0.93, respectively. Importantly, we found that the LPv approach can distinguish high quality Sirt3 probes. Analysis of the most functionally cohesive Sirt3 co-expressed gene set revealed core metabolic pathways that were shared between hippocampus and liver as well as distinct pathways which were unique to each tissue. These results are consistent with other studies that suggest Sirt3 is a key metabolic regulator and has distinct functions in energy-producing vs. energy-demanding tissues.ConclusionsOur results provide proof-of-concept that literature cohesion analysis is useful for evaluating the quality of probes and microarray datasets, particularly when experimentally derived gold standards are unavailable. Our approach would enable researchers to rapidly identify biologically meaningful co-expressed gene sets and facilitate discovery from high throughput genomic data.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2621-z) contains supplementary material, which is available to authorized users.
Purpose. Considering the difficulty of accurately estimating energy expenditure (EE) in habitual physical activity (PA), efforts to improve estimation accuracy are well warranted. The aim of the study was, first, to validate the K-Sense EE estimation system to improve EE estimation accuracy of human low-intensity activities, and, second, to compare K-Sense EE estimation values against ActiGraph (GT3X+) accelerometer-derived EE estimates. Methods. A comparative analysis investigated the K-Sense EE estimation values against indirect calorimetry and ActiGraph (GT3X+) EE estimates. A sample of 18 participants (age: 24.0 ± 5.2 years) performed eight sedentary/low intensity lifestyle activities, each wearing K-Sense with sensors attached to right wrist and ankle. Results. The K-Sense estimation accuracy ranged from 89.4% to 99.9%, outperforming ActiGraph equations, which were found to overestimate EE of these low-intensity activities, achieving 70.3% estimation accuracy at best. T-tests showed no statistically significant differences between K-Sense and indirect calorimetry values. bland-Altman plots, however, illustrated an EE estimation error ranging from-9 to 7 kcal (with 95% confidence limits of agreement) among individuals. Conclusions. EE evaluation with low-cost inertial measurement units, such as those found in K-Sense, is a valid method in comparison with indirect calorimetry and ActiGraph accelerometry.
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