Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Tracking economic growth by evolving expectations via genetic programming: a two-step approach

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents’ to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland.Preprin

The Prevalence of Osteoarthritis in Wild vs Captive Great Ape Skeletons

This research examined whether the prevalence and skeletal distribution of osteoarthritis (OA) differed between wild and captive great ape skeletons. A secondary, but important, aspect of this research focused on the development of improved aging techniques based on methods commonly used on human osteological samples. Tests were conducted pertaining to the effect that wild versus captive status, sex, and species has on vertebral body lipping, marginal lipping, and eburnation. Age was considered a co-factor. Of the aging methods examined, use of the basilar suture to distinguish between adult and old adult specimens proved to be very imprecise. The ribs and auricular surface proved to be of limited value in aging the ape skeletons, while the acetabulum demonstrated potential for use as an aging indicator, although it is not recommended for use in isolation. Molar dental wear proved to be the most viable single indicator of age explaining over 78% of the variation seen. However, a model that combined wear of molars 1 and 2 with certain features of the acetabulum explained over 90% of the variation seen and was the model chosen for aging the apes in this sample. The effect that wild versus captive status, sex, and species had on vertebral body lipping, eburnation, and marginal lipping was analyzed, with age as a co-factor. It was found that status is a significant predictor of the prevalence of both vertebral body lipping and marginal lipping, but not of eburnation with captive apes suffering significantly more vertebral body lipping than wild apes. Sex is not a significant predictor of disease prevalence for any skeletal marker. Species differences are evident in vertebral body lipping and marginal lipping, but not in eburnation. In general, chimpanzees are the least frequently affected and gorillas the most frequently affected. Age has an effect, primarily in vertebral body lipping and marginal lipping, with older individuals being more affected than younger individuals. In summary, while wild versus captive status, species\u27 differences, and age are factors in the development of vertebral body lipping and marginal lipping in many joints, the presence of eburnation is extremely rare in the great apes with very few individuals being affected regardless of status, sex, species, or age. Thus, the results highlight the complex nature of osteoarthritis and enforce the idea that osteoarthritis is markedly multi-factorial and that disease prevalence and patterns are not easily understood or interpreted.\u2

Tracking economic growth by evolving expectations via genetic programming : a two-step approach

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents' expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents' to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland

Mobile Diagnosis 2.0

Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostic tools, environmental monitoring, and citizen science, among many others. The importance of low-cost mobile technologies has been underlined during this current COVID-19 pandemic, particularly for applications such as the detection of pathogens, including bacteria and viruses, as well as for prediction and management of different diseases and disorders. This book focuses on some of these application areas and provides a timely summary of cutting-edge results and emerging technologies in these interdisciplinary fields