Milk and/or whey protein plus resistance exercise (RT) increase strength and muscle size, and optimize body composition in adult males and females. Greek yogurt (GY) contains similar muscle-supporting nutrients as milk yet it is different in several ways including being a semi-solid food, containing bacterial cultures and having a higher protein content (mostly casein) per serving. GY has yet to be investigated in the context of a RT program. The purpose of this study was to assess the effects of GY consumption plus RT on strength, muscle thickness and body composition in lean, untrained, university-aged males. Thirty untrained, university-aged (20.6 ± 2.2 years) males were randomized to 2 groups ( n = 15/group): fat-free, plain GY or a Placebo Pudding (PP; isoenergetic carbohydrate-based pudding) and underwent a combined RT/plyometric training program 3 days/week for 12 weeks. They consumed either GY (20 g protein/dose) or PP (0 g protein/dose) daily, 3 times on training days and 2 times on non-training days. After 12 weeks, both groups significantly increased strength, muscle thickness and fat-free mass (FFM) ( p < 0.05). The GY group gained more total strength (GY; 98 ± 37 kg, PP; 57 ± 15 kg), more biceps brachii muscular thickness (GY; 0.46 ± 0.3 cm, PP; 0.12 ± 0.2 cm), more FFM (GY; 2.4 ± 1.5 kg, PP; 1.3 ± 1.3 kg), and reduced % body fat (GY; −1.1 ± 2.2%, PP; 0.1 ± 2.6%) than PP group ( p < 0.05 expressed as absolute change). Thus, consumption of GY during a training program resulted in improved strength, muscle thickness and body composition over a carbohydrate-based placebo. Given the results of our study, the general benefits of consuming GY and its distinctiveness from milk, GY can be a plausible, post-exercise, nutrient-rich alternative for positive strength, muscle, and body composition adaptations.

Procedural Content Generation (PCG) is the process of automating the construction of media types for use in game development, the movie industry, and other creative fields. By approaching the process of media creation as a search for content which is evaluated to express desirable features in a well-defined manner, we are able to apply evolutionary techniques such as genetic programming. This can greatly decrease the effort required to bring a project to completion by allowing artists and developers to focus on guiding the creation process. The specific generation process addressed is that of map creation for dungeon crawler video games. The search method proposed allows artists and developers to guide the generation process by specifying a set of tiles that define the composition of each map, and a fitness function that defines its structure.

Parkinson's disease (PD) is a degenerative disorder of the central nervous system that has many debilitating symptoms which affect the patient's motor system and can cause significant changes in their gait. By using genetic programming, we aim to develop descriptive symbolic nonlinear models of PD patient gait from time series data recorded from pressure sensors under subjects' feet. When compared to popular types of linear regression (OLS and LASSO), the nonlinear models fit their data better and generalize to unseen data significantly better. It was found that models developed for healthy control subjects generalized to other control subjects well, however the models trained on subjects with PD did not generalize well to other PD patients, which complicates the issue of being able to detect the progression of the disease. It is suspected that health care professionals can have difficulty classifying PD due to a lack of accurate data from patient reports; having individually trained models for active monitoring of patients would help in effectively diagnosing PD.

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Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologicallymotivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.