Disease transmission promotes evolution of host spatial patterns

Ecological dynamics can produce a variety of striking patterns. On ecological time scales, pattern formation has been hypothesized to be due to the interaction between a species and its local environment. On longer time scales, evolutionary factors must be taken into account. To examine the evolutionary robustness of spatial pattern formation, we construct a spatially explicit model of vegetation in the presence of a pathogen. Initially, we compare the dynamics for vegetation parameters that lead to competition induced spatial patterns and those that do not. Over ecological time scales, banded spatial patterns dramatically reduced the ability of the pathogen to spread, lowered its endemic density and hence increased the persistence of the vegetation. To gain an evolutionary understanding, each plant was given a heritable trait defining its resilience to competition; greater competition leads to lower vegetation density but stronger spatial patterns. When a disease is introduced, the selective pressure on the plant's resilience to the competition parameter is determined by the transmission of the disease. For high transmission, vegetation that has low resilience to competition and hence strong spatial patterning is an evolutionarily stable strategy. This demonstrates a novel mechanism by which striking spatial patterns can be maintained by disease-driven selection

Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments

In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value

The Exsolution of Cu Particles from Doped Barium Cerate Zirconate via Barium Cuprate Intermediate Phases

\ua9 2023 Wiley-VCH GmbH.As a low-cost alternative to noble metals, Cu plays an important role in industrial catalysis, such as water-gas shift reaction, methanol or ethanol oxidation, hydrogenation of oils, CO oxidation, among many others. An important step in optimizing Cu catalyst performance is control of nanoparticles size, distribution, and the interface with the support. While proton conducting perovskites can enhance the metal catalytic activity when acting as the support, there has been limited investigation of in situ growth of Cu metal nanoparticles from the proton conductors and its catalytic performance. Here, Cu nanoparticles are tracked exsolved from an A-site-deficient proton-conducting barium cerate-zirconate using scanning electron microscopy, revealing a continuous phase change during exsolution as a function of reduction temperature. Combined with the phase diagram and cell parameter change during reduction, a new exsolution mechanism is proposed for the first time which provides insight into tailoring metal particles interfaces at proton conducting oxide surfaces. Furthermore, the catalytic behavior in the CO oxidation reaction is explored and, it is observed that these new nanostructures can rival state of the art catalysts over long term operation

Increased ventral striatal volume in college-aged binge drinkers

BACKGROUND Binge drinking is a serious public health issue associated with cognitive, physiological, and anatomical differences from healthy individuals. No studies, however, have reported subcortical grey matter differences in this population. To address this, we compared the grey matter volumes of college-age binge drinkers and healthy controls, focusing on the ventral striatum, hippocampus and amygdala. METHOD T1-weighted images of 19 binge drinkers and 19 healthy volunteers were analyzed using voxel-based morphometry. Structural data were also covaried with Alcohol Use Disorders Identification Test (AUDIT) scores. Cluster-extent threshold and small volume corrections were both used to analyze imaging data. RESULTS Binge drinkers had significantly larger ventral striatal grey matter volumes compared to controls. There were no between group differences in hippocampal or amygdalar volume. Ventral striatal, amygdalar, and hippocampal volumes were also negatively related to AUDIT scores across groups. CONCLUSIONS Our findings stand in contrast to the lower ventral striatal volume previously observed in more severe forms of alcohol use disorders, suggesting that college-age binge drinkers may represent a distinct population from those groups. These findings may instead represent early sequelae, compensatory effects of repeated binge and withdrawal, or an endophenotypic risk factor

Fine sediment reduces vertical migrations of Gammarus pulex (Crustacea: Amphipoda) in response to surface water loss

Surface and subsurface sediments in river ecosystems are recognized as refuges that may promote invertebrate survival during disturbances such as floods and streambed drying. Refuge use is spatiotemporally variable, with environmental factors including substrate composition, in particular the proportion of fine sediment (FS), affecting the ability of organisms to move through interstitial spaces. We conducted a laboratory experiment to examine the effects of FS on the movement of Gammarus pulex Linnaeus (Crustacea: Amphipoda) into subsurface sediments in response to surface water loss. We hypothesized that increasing volumes of FS would impede and ultimately prevent individuals from migrating into the sediments. To test this hypothesis, the proportion of FS (1–2 mm diameter) present within an open gravel matrix (4–16 mm diameter) was varied from 10 to 20% by volume in 2.5% increments. Under control conditions (0% FS), 93% of individuals moved into subsurface sediments as the water level was reduced. The proportion of individuals moving into the subsurface decreased to 74% at 10% FS, and at 20% FS no individuals entered the sediments, supporting our hypothesis. These results demonstrate the importance of reducing FS inputs into river ecosystems and restoring FS-clogged riverbeds, to promote refuge use during increasingly common instream disturbances

Dielectric Barrier Plasma Discharge Exsolution of Nanoparticles at Room Temperature and Atmospheric Pressure

Exsolution of metal nanoparticles (NPs) on perovskite oxides has beendemonstrated as a reliable strategy for producing catalyst-support systems.Conventional exsolution requires high temperatures for long periods of time,limiting the selection of support materials. Plasma direct exsolution isreported at room temperature and atmospheric pressure of Ni NPs from amodel A-site deficient perovskite oxide (La 0.43 Ca 0.37 Ni 0.06 Ti 0.94 O2.955 ). Plasmaexsolution is carried out within minutes (up to 15 min) using a dielectricbarrier discharge configuration both with He-only gas as well as with He/H2gas mixtures, yielding small NPs (<30 nm diameter). To prove the practicalutility of exsolved NPs, various experiments aimed at assessing their catalyticperformance for methanation from synthesis gas, CO, and CH4 oxidation arecarried out. Low-temperature and atmospheric pressure plasma exsolution aresuccessfully demonstrated and suggest that this approach could contribute tothe practical deployment of exsolution-based stable catalyst systems