Computational approaches for discovery of mutational signatures in cancer.

The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field

Developing Empirical Decision Points to Improve the Timing of Adaptive Digital Health Physical Activity Interventions in Youth: Survival Analysis

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Background: Current digital health interventions primarily use interventionist-defined rules to guide the timing of intervention delivery. As new temporally dense data sets become available, it is possible to make decisions about the intervention timing empirically. Objective: This study aimed to explore the timing of physical activity among youth to inform decision points (eg, timing of support) for future digital physical activity interventions. Methods: This study comprised 113 adolescents aged between 13 and 18 years (mean age 14.64, SD 1.48 years) who wore an accelerometer for 20 days. Multilevel survival analyses were used to estimate the most likely time of day (via odds ratios and hazard probabilities) when adolescents accumulated their average physical activity. The interacting effects of physical activity timing and moderating variables were calculated by entering predictors, such as gender, sports participation, and school day, into the model as main effects and tested for interactions with the time of day to determine conditional main effects of these predictors. Results: On average, the likelihood that a participant would accumulate a typical amount of moderate-to-vigorous physical activity increased and peaked between 6 PM and 8 PM before decreasing sharply after 9 PM. Hazard and survival probabilities suggest that optimal decision points for digital physical activity programs could occur between 5 PM and 8 PM. Conclusions: Overall, the findings of this study support the idea that the timing of physical activity can be empirically identified and that these markers may be useful as intervention triggers.Society of Pediatric Psycholog

Testing quantum computers with the protocol of quantum state matching

The presence of noise in quantum computers hinders their effective operation. Even though quantum error correction can theoretically remedy this problem, its practical realization is still a challenge. Testing and benchmarking noisy, intermediate-scale quantum (NISC) computers is therefore of high importance. Here, we suggest the application of the so-called quantum state matching protocol for testing purposes. This protocol was originally proposed to determine if an unknown quantum state falls in a prescribed neighborhood of a reference state. We decompose the unitary specific to the protocol and construct the quantum circuit implementing one step of the dynamics for different characteristic parameters of the scheme and present test results for two different IBM quantum computers. By comparing the experimentally obtained relative frequencies of success to the ideal success probability with a maximum statistical tolerance, we discriminate statistical errors from device specific ones. For the characterization of noise, we also use the fact that while the output of the ideal protocol is insensitive to the internal phase of the input state, the actual implementation may lead to deviations. For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume, while for random inputs they show a more similar performance

On the energy density in quantum mechanics

There are several definitions of energy density in quantum mechanics. These yield expressions that differ locally, but all satisfy a continuity equation and integrate to the value of the expected energy of the system under consideration. Thus, the question of whether there are physical grounds to choose one definition over another arises naturally. In this work, we propose a way to probe a system by varying the size of a well containing a quantum particle. We show that the mean work done by moving the wall is closely related to one of the definitions for energy density. Specifically, the appropriate energy density, evaluated at the wall corresponds to the force exerted by the particle locally, against which the work is done. We show that this identification extends to two and three dimensional systems

Orders of chaoticity of unitaries

We introduce the concept of K-th order chaoticity of unitaries, and analyze it for the case of two-level quantum systems. This property is relevant in a certain quantum random number generation scheme. We show that no unitaries exist with an arbitrary order of chaoticity

Developing Empirical Decision Points to Improve the Timing of Adaptive mHealth Physical Activity Interventions in Youth

Current digital health interventions primarily utilize interventionist-defined rules to guide the timing of intervention delivery. As new temporally dense datasets become available, it is possible to make decisions about intervention delivery and timing empirically. The purpose of this study was to explore the timing of physical activity in youth to inform decision points (e.g., timing of support) for future digital physical activity interventions. This study was comprised of 113 adolescents between the ages of 13-18 (M = 14.64, SD = 1.48) who wore an accelerometer for 20 days. Using a special case of logistic regression, multilevel survival analyses were used to estimate the most likely time of day (via odds ratios and hazard probabilities) when adolescents accumulated their average physical activity. Additionally, odds ratios for the interacting effects of physical activity timing and moderating variables were calculated by entering predictors, such as gender, Body Mass Index (BMI), sports participation, school day, self-efficacy, social support for exercise, and motivation, into the model as main effects and tested for interactions with time of day to determine conditional main effects of these predictors. On average, the likelihood that a participant would accumulate their own average MVPA increased and peaked between the hours of 6pm-8pm before decreasing sharply after 9pm. There were differences in the timing of exercise for boys, adolescents involved in sports, on non-school days, individuals with lower physical activity self-efficacy, and participants with lower autonomous motivation. Hazard and survival probabilities suggest that optimal decision points for digital physical activity programs should occur between 5pm and 8pm. Overall, findings from this study support the idea that the timing of physical activity can be empirically-identified to determine when users are receptive to exercise and potentially used as markers to signal intervention delivery for JITAIs