AdductHunter: identifying protein-metal complex adducts in mass spectra.

Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that  automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.

Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super-resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse AR hemodynamics in a non-invasive manner.

Genetics of yellow-orange color variation in a pair of sympatric sulphur butterflies.

Continuous color polymorphisms can serve as a tractable model for the genetic and developmental architecture of traits. Here we investigated continuous color variation in Colias eurytheme and Colias philodice, two species of sulphur butterflies that hybridize in sympatry. Using quantitative trait locus (QTL) analysis and high-throughput color quantification, we found two interacting large-effect loci affecting orange-to-yellow chromaticity. Knockouts of red Malpighian tubules (red), likely involved in endosomal maturation, result in depigmented wing scales. Additionally, the transcription factor bric-a-brac can act as a modulator of orange pigmentation. We also describe the QTL architecture of other continuously varying traits, together supporting a large-X effect model where the genetic control of species-defining traits is enriched on sex chromosomes. This study sheds light on the range of possible genetic architectures that can underpin a continuously varying trait and illustrates the power of using automated measurement to score phenotypes that are not always conspicuous to the human eye.

A Prototype Exercise-Empowerment Mobile Video Game for Children With Cancer, and Its Usability Assessment: Developing Digital Empowerment Interventions for Pediatric Diseases.

BACKGROUND: Medical advances continue to improve morbidity and mortality of serious pediatric diseases, including cancer, driving research addressing diminished physical and psychological quality of life in children with these chronic conditions. Empowerment enhances resilience and positively influences health, disease, and therapy understanding. We describe the development and usability assessment of a prototype Empower Stars! mobile video game grounded in behavioral and exercise theories with the purpose of coupling physical exercise with empowerment over disease in children with cancer. METHODS: Academic faculty, health-care providers, and community video game developers collaborated in this project. The iPadAir was selected as a delivery platform for its accelerometer and gyroscope features facilitating exercise design. Unity multiplatform technology provided animation and audiovisual features for immediate player feedback. Javascript, C#, Photoshop, Flash, and SketchUp were used for coding, creating graphical assets, Sprite sheets, and printing files, respectively. 3D-printed handles and case backing were used to adapt the iPad for physical exercise. Game usability, engagement, and enjoyment were assessed via a multilevel study of children undergoing cancer chemotherapy, their parents, and pediatric cancer health-care providers. Feedback crucial for ongoing game development was analyzed. RESULTS: A prototype Empower Stars! mobile video game was developed for children 7-14 years old with cancer. Active, sedentary, educational, and empowerment-centered elements intermix for 20 min of exercise within a 30 min "one-day treatment" gameplay session involving superheroes, space exploration, metaphorical cancer challenges, life restoration on a barren planet, and innumerable star rewards. No player "dies." Usability assessment data analyses showed widespread enthusiasm for integrating exercise with empowerment over cancer and the game itself. Favorite elements included collecting star rewards and planet terraforming. Traveling in space and the Healthy Food Choice game were least liked. The need for improved gameplay instructions was expressed by all groups. The usability study provided essential feedback for converting the prototype into alpha version of Empower Stars! CONCLUSION: Adapting exercise empowerment-promoting video game technology to mobile platforms facilitates usability and widespread dissemination for children with cancer. We discuss broader therapeutic applicability in diverse chronic pediatric diseases, including obesity, asthma, cystic fibrosis, diabetes, and juvenile idiopathic arthritis.

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions.

It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the "Parrot AR.Drone2.0" quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.