Improving the Expected Performance of Self-Organization in a Collective Adaptive System of Drones using Stochastic Multiplayer Games

The Internet-of-Things (IoT) domain will be one of the most important domains of research in the coming decades. Paradigms continue to emerge that can employ self-organization to capitalize on the sheer number and variety of devices in the market. In this paper, we combine the use of stochastic multiplayer games (SMGs) and negotiation within two collective adaptive systems of drones tasked with locating and surveilling intelligence caches. We assess the use of an ordinary least squares (OLS) regression model that is trained on the SMG’s output. The SMG is augmented to incorporate the OLS model to evaluate integration configurations during negotiation. The augmented SMG is compared to the base SMG where drones always integrate. Our results show that the incorporation of the OLS model improves the expected performance of the drones while significantly reducing the number of failed surveillance tasks which result in the loss of drones

Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis

Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at http://sites.google.com/site/McKinneyLab/software

Forever-Fit Summer Camp: The Impact of a 6-Week Summer Healthy Lifestyle Day Camp on Anthropometric, Cardiovascular, and Physical Fitness Measures in Youth With Obesity

Pediatric obesity is a public health concern with lifestyle intervention as the first-line treatment. Forever-Fit Summer Camp (FFSC) is a 6-week summer day program offering physical activity, nutrition education, and well-balanced meals to youth at low cost. The aim of the study was to assess the efficacy of this program that does not emphasize weight loss rather emphasizes healthy behaviors on body mass index, cardiovascular and physical fitness. Methods: The inclusion criteria were adolescents between 8 and 12 years and body mass index (BMI) ≥85th percentile. The data were collected at baseline and week 6 (wk-6) and was analyzed for 2013-2018 using paired-sample t tests. Results: The participants' (N = 179) average age was 10.6 ± 1.6 years with a majority of females (71%) and black race/ethnicity (70%). At wk-6, BMI and waist circumference decreased by 0.8 ± 0.7 kg/m2 and 1.0 ± 1.3 in, respectively. Resting heart rate, diastolic and systolic blood pressure decreased by 8.5 ± 11.0 bpm, 6.3 ± 8.8 mmHg, and 6.4 ± 10.1 mmHg, respectively. The number of pushups, curl-ups, and chair squats were higher by 5.8 ± 7.5, 6.7 ± 9.1, and 7.7 ± 8.5, respectively. Conclusion: The FFSC is efficacious for improving BMI, cardiovascular, and physical fitness in the short term. The effect of similar episodic efforts that implement healthy lifestyle modifications throughout the school year should be investigated

A Computational Model of Quantitative Chromatin Immunoprecipitation (ChIP) Analysis

Chromatin immunoprecipitation (ChIP) analysis is widely used to identify the locations in genomes occupied by transcription factors (TFs). The approach involves chemical cross-linking of DNA with associated proteins, fragmentation of chromatin by sonication or enzymatic digestion, immunoprecipitation of the fragments containing the protein of interest, and then PCR or hybridization analysis to characterize and quantify the genomic sequences enriched. We developed a computational model of quantitative ChIP analysis to elucidate the factors contributing to the method’s resolution. The most important variables identified by the model were, in order of importance, the spacing of the PCR primers, the mean length of the chromatin fragments, and, unexpectedly, the type of fragment width distribution, with very small DNA fragments and smaller amplicons providing the best resolution of TF binding. One of the major predictions of the model was also validated experimentally

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.</p> <p>Results</p> <p>In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.</p> <p>Conclusion</p> <p>Of the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.</p

Seasonal Occurrence, Horizontal Movements, and Habitat Use Patterns of Whale Sharks (\u3ci\u3eRhincodon typus\u3c/i\u3e) in the Gulf of Mexico

In the northern Gulf of Mexico (GOM), whale sharks (Rhincodon typus) form large aggregations at continental shelf-edge banks during summer; however, knowledge of movements once they leave aggregation sites is limited. Here we report on the seasonal occurrence of whale sharks in the northern GOM based on over 800 whale shark sightings from 1989 to 2016, as well as the movements of 42 whale sharks tagged with satellite-linked and popup satellite archival transmitting tags from 2008 to 2015. Sightings data were most numerous during summer and fall often with aggregations of individuals reported along the continental shelf break. Most sharks (66%) were tagged during this time at Ewing Bank, a known aggregation site off the coast of Louisiana. Whale shark track duration ranged from three to 366 days and all tagged individuals, which ranged from 4.5 to 12.0 m in total length, remained within the GOM. Sightings data revealed that whale sharks occurred primarily in continental shelf and shelf-edge waters (81%) whereas tag data revealed the sharks primarily inhabited continental slope and open ocean waters (91%) of the GOM. Much of their time spent in open ocean waters was associated with the edge of the Loop Current and associated mesoscale eddies. During cooler months, there was a net movement southward, corresponding with the time of reduced sighting reports. Several sharks migrated to the southwest GOM during fall and winter, suggesting this region could be important overwintering habitat and possibly represents another seasonal aggregation site. The three long-term tracked whale sharks exhibited interannual site fidelity, returning one year later to the vicinity where they were originally tagged. The increased habitat use of north central GOM waters by whale sharks as summer foraging grounds and potential interannual site fidelity to Ewing Bank demonstrate the importance of this region for this species

Intervention Delivery Matters: What Mothers at High Risk for Type 2 Diabetes Want in a Diabetes Prevention Program—Results from a Comparative Effectiveness Trial

Participants in the ENCOURAGE Healthy Families Study, a family-focused, modified Diabetes Prevention Program, reported challenges to and preferences for engaging in a diabetes prevention program. Challenges with flexible intervention delivery, accessibility, the traditional group-based format, and Coronavirus Disease 2019 (COVID-19) exposure risk can be mitigated by participant preferences for one-on-one, virtual/online intervention delivery.This work was supported by the JPB Foundation, New York, NY and the IUPUI Signature Center Initiative Fund. Sponsors did not contribute to the writing of this report or in the decision to submit the article for publication. The Journal’s Rapid Service Fee is funded by the University of Arizona Health Sciences Center for Border Health Disparities