Living Shorelines As Alternative Methods Of Shoreline Protection

Louisiana’s marshes and coastal estuaries are losing habitat at an alarming rate. High rates of sea level rise coupled with coastal subsidence are turning once thriving marsh land into open water. The sediment starved Mississippi River Delta is drowning, making families homes, property, and livelihoods increasingly vulnerable every year. Significant funding is being allocated for projects to offset the increasing erosion including but not limited to diversions, marsh creation, ridge restoration, and shoreline protection projects. Living shorelines, for the sake of this study, can be defined as a form of shoreline protection which helps shoreline stabilization and erosion reduction while still providing estuarine habitat and other ecosystem services. Living shorelines offer an eco-friendly alternative to traditional shoreline protection measures which can be costly, offer little ecosystem services, and often become ineffective by sinking below water level due to highly compactable bottom sediments. This study investigates some of the living shoreline projects completed along the Louisiana coast for porosity and wave attenuation and the subsequent effect on volumetric soil erosion rates. Wave attenuation, while important for reducing wave energy in the shoreward regions, did not show a strong correlation with volumetric erosion rates. However, increasing porosity, ranging from 0% to 35%, was shown to have a relationship with soil volume change rates in that the more porous a structure was, less erosion, and in some cases accretion, was shown across all projects

News From USO Shows Press Release

Press release announcing colleges accepted for the 1970-1971 USO Tour.https://digitalcommons.usm.maine.edu/theatre-uso-tour/1005/thumbnail.jp

Escapist environments, restorative experiences, and consumer self-regulation

The study of atmospherics recognizes shoppers engage in consumption for more than its utilitarian function. The concept of the recreational shopper recognizes the value-producing process of the consumption experience. This research furthers the understanding of consumption by delving into the value-enhancing process of escaping during the experience, as well as measuring the mediating effects of fascination and authenticity. In this study, a test of an individual\u27s self-regulating behavior and the moderating effects on the consumption experience help determine if predetermination affects an escape experience. Pictured scenes of restaurants were pretested for their ability to produce fascination and represent and authentic experience. Selecting two scenes rated on the high and low end of the scale of authenticity and fascination, Photoshop is used to change the atmospherics that signal a high and low fascinating experience. From these two pictures, four pictures are created representing the four possible manipulations of authenticity and fascination. The final analysis indicated that the level of escape in a built environment is positively related to positive affect. When consumers are able to engage in a dining experience that takes them away from their normal lives, they exhibit excitement, happiness, and relaxation. Escape also is a partial mediating factor in fascination, defined as involuntary attention that is effortless. Research in fascination is in natural settings; however, this study confirms its relationship in a built environment. Fascination reduces fatigue and leads to a restoration of cognitive effectiveness. This study finds that escape partially explains the fascination experience. When provided an escape setting, the consumer is more likely to relax and more open to enjoy the restorative qualities of a fascinating experience. Interestingly, in this study authenticity is not found to have a significant effect in the escape experience. Indexicality is an important quality in authentic experiences, and once the consumer accepts items and places as authentic, they will become more personally involved with items and places. This research, however, found indexicality is not necessarily required, and is not important in an escape experience. This means escape can be found in environments that are truly unique and untied to the indexicality of the authentic. Self-regulating behavior is found not be a significant contributor the escape experience. Action-oriented individuals are rated high in the active disposition of their actions and avoid difficulties in the completion of a task. This research finds that action-oriented individuals are no differently affected in the escape experience than are state-oriented individuals, who are more willing to go with the flow, have difficulty in completing tasks, and are generally more likely to fail due to an inability to filter out obstructions. This supports the supposition that the escape from the mundane is a sought after quality desired by both action-oriented and state-oriented individuals. This study supports the desirable characteristics of an escape experience and its relationship to fascination. Escape is related to positive feelings, which lead to the desire to stay, engage in social behavior, and ultimately add to the value of the consumption experience

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