Selection of important variables by statistical learning in genome-wide association analysis

Genetic analysis of complex diseases demands novel analytical methods to interpret data collected on thousands of variables by genome-wide association studies. The complexity of such analysis is multiplied when one has to consider interaction effects, be they among the genetic variations (G × G) or with environment risk factors (G × E). Several statistical learning methods seem quite promising in this context. Herein we consider applications of two such methods, random forest and Bayesian networks, to the simulated dataset for Genetic Analysis Workshop 16 Problem 3. Our evaluation study showed that an iterative search based on the random forest approach has the potential in selecting important variables, while Bayesian networks can capture some of the underlying causal relationships

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare clumping-and-thresholding (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals

Physiochemical characteristic of Harvested Rain water from Bodo Community in Rivers State, Nigeria

This study was carried out to determine the quality of rainwater from three different roofing materials (asbestos, aluminum and zinc) in Bodo Community of Rivers State, Nigeria. Samples were taken to the laboratory and analyzed using standard analytical procedures. The average result obtained ranged from 6.0-7.0, 40-66 μsi/cm, 18-48 mg/l, 32.52-86.67 mg/L, 0.06-3.18 mg/L, 5.02-13.76 mg/L, 2.79-5.02 mg/L, 0.16-0.77 mg/L, 0.27-5.79 mg/L, 0.24 0.62 mg/L and 0.03-4.08 mg/L for pH, conductivity, chloride, salinity, nitrate, sulphate, sodium, magnesium, calcium, iron and zinc respectively. Of interest were rainwater samples from asbestos roofing sheets which gave high calcium and magnesium content. Also, zinc roofing sheet gave high zinc and iron content compared to other roofing sheets. Physico-chemical properties and metal content of the harvested water from the roofing sheets were considerably different from those of control samples (water collected directly from raindrop). This could mean that the impinging of rain drop on the roof gradually erodes the roof material or could be as a result of anthropogenic inputs (deposits of pollutants in contact with roofing sheet) or geographical location. Considering the result of the analysis the harvested water could be put to other domestic use, as they can not be consumed directly.Keywords: rainwater, roof materials, water qualit

Assistive VR Gym: Interactions with Real People to Improve Virtual Assistive Robots

Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. Virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. We present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (Original Policies) solely in simulation for four robotic caregiving tasks (robot-assisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed Revised Policies based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly disagreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.Comment: IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2020), 8 pages, 8 figures, 2 table

High-Speed Fluorescence Imaging and Intensity Profiling of Femtosecond-Induced Calcium Transients

We have demonstrated a combined imaging system, where the physiology of biological specimens can be imaged and profiled at 10–20 frames per second whilst undergoing femtosecond laser irradiation. Individual GH3 cells labeled with the calcium fluorophore Fluo-3 were stimulated using a counter-propagating focused femtosecond beam with respect to the imaging system. As a result of the stimulation, calcium waves can be generated in COS cells, and laser-induced calcium oscillations are initiated in the GH3 cells. Single-photon fluorescence images and intensity profiles of the targeted specimens are sampled in real-time using a modified PerkinElmer UltraView LCI microscope

Calorimetric Measurements of Magnetic-Field-Induced Inhomogeneous Superconductivity Above The Paramagnetic Limit

We report the first magneto-caloric and calorimetric observations of a magnetic-field-induced phase transition within a superconducting state to the long-sought exotic "FFLO" superconducting state first predicted over 50 years ago. Through the combination of bulk thermodynamic calorimetric and magnetocaloric measurements in the organic superconductor $\kappa$ - (BEDT-TTF)$_2$Cu(NCS)$_2$, as a function of temperature, magnetic field strength, and magnetic field orientation, we establish for the first time that this field-induced first-order phase transition at the paramagnetic limit $H_p$ for traditional superconductivity is to a higher entropy superconducting phase uniquely characteristic of the FFLO state. We also establish that this high-field superconducting state displays the bulk paramagnetic ordering of spin domains required of the FFLO state. These results rule out the alternate possibility of spin-density wave (SDW) ordering in the high field superconducting phase. The phase diagram determined from our measurements --- including the observation of a phase transition into the FFLO phase at $H_p$ --- is in good agreement with recent NMR results and our own earlier tunnel-diode magnetic penetration depth experiments, but is in disagreement with the only previous calorimetric report.Comment: 5 pages, 5 figure

Implicit Decomposition for Write-Efficient Connectivity Algorithms

The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an $o(n)$-sized "implicit decomposition" of a bounded-degree graph $G$ on $n$ nodes, which combined with read-only access to $G$ enables fast answers to connectivity and biconnectivity queries on $G$. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on $m$ edges, we also provide the first $o(m)$ writes and $O(m)$ operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry