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Contraction is a well‐documented phenomenon occurring within two months of mesh implantation. Its etiology is unknown, but it is suggested to occur as a result of inadequate tissue ingrowth into the mesh and has been associated with hernia recurrence. In continuation of our previous studies, we compared tissue ingrowth characteristics of large patches of polyester (PE) and heavyweight polypropylene (PP) and their effect on mesh contraction. The materials used were eight PE and eight PP meshes measuring 10 × 10 cm2. After random assignment to the implantation sites, the meshes were fixed to the abdominal wall fascia of swine using interrupted polypropylene sutures. A necropsy was performed three months after surgery for evaluation of mesh contraction/shrinkage. Using a tensiometer, tissue ingrowth was assessed by measuring the force necessary to detach the mesh from the fascia. Histologic analysis included inflammatory and fibroblastic reactions, scored on a 0–4 point scale. One swine developed a severe wound infection that involved two PP meshes and was therefore excluded from the study. The mean area covered by the PE meshes (87 ± 7 cm2) was significantly larger than the area covered by the PP meshes (67 ± 14 cm2) (p = 0.006). Tissue ingrowth force of the PE meshes (194 ± 37 N) had a trend toward being higher than that of the PP meshes (159 ± 43 N), although it did not reach statistical significance. There was no difference in histologic inflammatory and fibroblastic reactions between mesh types. There was a significant correlation between tissue ingrowth force and mesh size (p = 0.03, 95% CI: 0.05–0.84). Our results confirm those from previous studies in that mesh materials undergo significant contraction after suture fixation to the fascia. PE resulted in less contraction than polypropylene. A strong integration of the mesh into the tissue helps prevent this phenomenon, which is evidenced by a significant correlation between tissue ingrowth force and mesh size.

CuFeO 2 is a promising photocathode for H 2 evolution and CO 2 reduction reactions. To better understand the complex defect chemistry and role of impurity phases in this material and their effect on the photochemical performance, we employ visible light transient absorption spectroscopy and density functional theory (DFT) calculations to investigate the electron dynamics in electrochemically deposited Cu-Fe oxide thin films. Kinetic analysis of carrier lifetime shows a fast, sub-ps contribution to relaxation followed by persistence of a long-lived state to time delays greater than 2 ns. Increasing amplitude of the long-lived state is shown to correlate with the rate of fast initial relaxation, and this is explained in terms of a competition between charge carrier trapping and charge separation. Charge separation in CuFeO 2 occurs via hole thermalization from O 2p to Cu 3d valence band states leading to segregation of electrons and holes across layers in the CuFeO 2 lattice. Correlation between transient absorption measurements and DFT calculations suggest that Cu vacancies enhance photochemical performance by facilitating charge separation kinetics. In contrast, O interstitials are predicted to switch the relative positions of O 2p and Cu 3d valence band states, which would inhibit charge separation by inter-band hole thermalization. Finally, we find no evidence for electron injection from CuFeO 2 to CuO suggesting that charge separation at this heterostructure interface does not play a role in the carrier lifetime or photochemical performance of the catalysts studied here.

The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations – proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.

Background: Rising use of electronic nicotine delivery systems (ENDS) and tobacco products among rural youth challenges tobacco control efforts, with higher use rates compared to urban youth. Exposure to tobacco retail outlets (TROs) and marketing influences tobacco use, but the longitudinal pathways of these factors in rural youth are underexplored. Purpose: This study examines the feasibility of app-based ecological momentary assessment (EMA) in capturing real-time exposure to TROs and tobacco marketing and its link to tobacco use and susceptibility among rural youth. Methods: Participants (n = 25) residing in Southwest Virginia were recruited for a 14-day EMA sub-study via the Effortless Assessment Research System (EARS) app. We assessed exposure to TROs and tobacco marketing as predictors and tobacco use susceptibility and past 24-hour tobacco use as outcomes. GPS data identified exposure to TROs within 100 m, and associations with tobacco outcomes were examined using generalized linear mixed-effect regression models. Results: Most participants were 9th graders (36%; range 9th grade--12th grade) and white (56%), with slightly more female (56%) than male (44%). One-third were tobacco-susceptible, and 13% had used tobacco. TRO exposure was higher in activity space outside of the home and school (M = 72 exposures) than near home (4.1) or school (2.1). Over 14 days, 328 EMA responses were collected from 25 participants (72.9% response rate), demonstrating EMA's feasibility. TRO exposure was positively associated with recent tobacco use (P = .04) and negatively associated with recalling combustible tobacco ads (P = .03). Conclusions: This study examined the feasibility of app-based EMAs to track rural youth's exposure to TROs, tobacco marketing, and tobacco outcomes. Findings demonstrate the feasibility of EMA for capturing tobacco-related exposures among adolescents. Rural youth have higher tobacco use rates than their urban peers. While prior research has linked tobacco retail outlets (TROs) and marketing to tobacco use, less is known about how to capture these exposures in real-world settings or how they influence tobacco daily use and susceptibility among rural youth. This study used a smartphone app to track daily real-world exposure to TROs and tobacco marketing. We found that exposure to TROs in activity spaces outside the home and school was linked to recent tobacco use but negatively associated with recall of tobacco ads. The study shows that mobile apps can effectively monitor these exposures and suggests that proximity to TROs may play a role in how often youth use tobacco.

Neural decoding is often limited to tasks with known stimuli and limited response options . Real world tasks, however, are often completely stimulus free with unconstrained user response possibilities. Real time decoding of internal decision making would allow for more complex and interactive Huma Machine Teaming in a way that is not currently possible. To address this problem, we present here a novel method of decoding moments of recognition and their associated internal value judgments in the context of highly complex software reverse engineering tasks. This is done through a combination of P300 detection (a neural marker of recognition) and the Engagement Index (a ratio of neural band powers) to determine whether an item has been identified as relevant to the task (to be further explored) or irrelevant to the task (to be quickly ignored). Artificial neural networks were trained to identify P300s in each subject during the reverse engineering tasks. Dimensionality reduction of neural data during the tasks showed the existence of separately clustering subgroups of P300s with differences in Engagement Index. Subgroups of P300s differentiated by Engagement were further verified as distinct groupings with pupil dilation and user behavior metrics. This decoded information could be used to aid in the reverse engineering process via cognitive offloading of the user’s own decision making on to the visual interface in a completely automated and personalized fashion. This represents a significant advance in domain of real-time neural decoding, and opens up many further possibilities for usage in a broad range of intelligent human systems integration applications.