A metal-insulator-semiconductor (MIS) structure holds great potential to promote photoelectrochemical (PEC) reactions, such as water splitting and CO2 reduction, for the storage of solar energy in chemical bonds. The semiconductor absorbs photons, creating electron-hole pairs; the insulator facilitates charge separation; and the metal collects the desired charge and facilitates its use in the electrochemical reaction. Despite these attractive features, MIS photoelectrodes are significantly limited by their photovoltage, a combination of the voltage generated from photon absorption minus the potential drop across the insulator. Herein, we use multiscale continuum modeling of the carrier, electrolyte, and interfacial transport to identify strategies for mitigating the deleterious potential drop across the insulator and enabling high MIS photovoltages. To this end, we model Ni/SiO2/n-Si photoanodes that employ a planar Ni film or Ni nanoparticles (np-MIS) and validate both models using experimental polarization curves and photovoltage measurements from the literature. The simulations reveal that the insulator potential drop is lower and hence achieves higher photovoltages for np-MIS structures than MIS structures because the electrolyte screens charge trapped at defect states between the semiconductor and the insulator. This electrolyte charge screening phenomenon can be further leveraged by using low loadings or small nanoparticles, which not only minimize the interfacial potential drop but also improve the photocurrent by enabling more light absorption. These insights contribute to the optimization of the np-MIS structures for sustainable energy conversion.

Tissue buckling is an increasingly appreciated mode of morphogenesis in the embryo, but it is often unclear how geometric and material parameters are molecularly determined in native developmental contexts to generate diverse functional patterns. Here, we study the link between differential mechanical properties and the morphogenesis of distinct anteroposterior compartments in the intestinal tract-the esophagus, small intestine, and large intestine. These regions originate from a simple, common tube but adopt unique forms. Using measured data from the developing chick gut coupled with a minimal theory and simulations of differential growth, we investigate divergent lumen morphologies along the entire early gut and demonstrate that spatiotemporal geometries, moduli, and growth rates control the segment-specific patterns of mucosal buckling. Primary buckling into wrinkles, folds, and creases along the gut, as well as secondary buckling phenomena, including period-doubling in the foregut and multiscale creasing-wrinkling in the hindgut, are captured and well explained by mechanical models. This study advances our existing knowledge of how identity leads to form in these regions, laying the foundation for future work uncovering the relationship between molecules and mechanics in gut morphological regionalization.

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The decomposition of macrophytes plays a crucial role in the nutrient cycles of macrophyte-dominated eutrophication lakes. While research on plant decomposition mechanisms and microbial influences has rapid developed, it is curious that plant decomposition models have remained stagnant at the single-stage model from 50 years ago, without endeavor to consider any important factors. Our research conducted in-situ experiments and identified the optimal metrics for decomposition-related microbes, thereby establishing models for microbial impacts on decomposition rates (k_RDR). Using backward elimination in stepwise regression, we found that the optimal subset of independent variables-specifically Gammaproteobacteria-Q-L, Actinobacteriota-Q-L, and Ascomycota-Q-L-increased the adjusted R-squared (Ra2) to 0.93, providing the best modeling for decomposition rate (p = 0.002). Additionally, k_RDR can be modeled by synergic parameters of ACHB-Q-L, LDB-Q-L, and AB-Q-L for bacteria, and SFQ for fungi, albeit with a slightly lower Ra2 of 0.7-0.9 (p < 0.01). The primary contribution of our research lies in two key aspects. Firstly, we introduced optimal metrics for modeling microbes, opting for debris surface microbes over sediment microbes, and prioritizing absolute abundance over relative abundance. Secondly, our model represents a noteworthy advancement in debris modeling. Alongside elucidating the focus and innovative aspects of our work, we also addressed existing limitations and proposed directions for future research. SYNOPSIS: This study explores optimum metrics for decomposition-related microbes, offering precise microbial models for enhanced lake nutrient cycle simulation.

Free water surface constructed wetlands (FWSCWs) for the treatment of various wastewater types have evolved significantly over the last few decades. With an increasing need and interest in FWSCWs applications worldwide due to their cost-effectiveness and other benefits, this paper reviews recent literature on FWSCWs\' ability to remove different types of pollutants such as nutrients (i.e., TN, TP, NH4-N), heavy metals (i.e., Fe, Zn, and Ni), antibiotics (i.e., oxytetracycline, ciprofloxacin, doxycycline, sulfamethazine, and ofloxacin), and pesticides (i.e., Atrazine, S-Metolachlor, imidacloprid, lambda-cyhalothrin, diuron 3,4-dichloroanilin, Simazine, and Atrazine) that may co-exist in wetland inflow, and discusses approaches for simulating hydraulic and pollutant removal processes. A bibliometric analysis of recent literature reveals that China has the highest number of publications, followed by the USA. The collected data show that FWSCWs can remove an average of 61.6%, 67.8%, 54.7%, and 72.85% of inflowing nutrients, heavy metals, antibiotics, and pesticides, respectively. Optimizing each pollutant removal process requires specific design parameters. Removing heavy metal requires the lowest hydraulic retention time (HRT) (average of 4.78 days), removing pesticides requires the lowest water depth (average of 0.34 m), and nutrient removal requires the largest system size. Vegetation, especially Typha spp. and Phragmites spp., play an important role in FWSCWs\' system performance, making significant contributions to the removal process. Various modeling approaches (i.e., black-box and process-based) were comprehensively reviewed, revealing the need for including the internal process mechanisms related to the biological processes along with plants spp., that supported by a further research with field study validations. This work presents a state-of-the-art, systematic, and comparative discussion on the efficiency of FWSCWs in removing different pollutants, main design factors, the vegetation, and well-described models for performance prediction.

We examined the effects of elevated temperatures and biocides on survivability of food isolates of Cronobacter spp. (C. sakazakii) and concomitant enterobacteriaceae obtained in microbiological control of infant nutrition products. Increased resistance of certain strains of Cronobacter, Enterobacter cloacae, and Pantoea spp. to thermal processing was revealed. Salmonella, Pantoea, and Cronobacter bacteria were least sensitive to antimicrobial action of chlorine-containing agents. The above properties varied in the strains of the same species. Specifically, only two of three examined isolates of Cronobacter spp. demonstrated lower sensitivity to heat in comparison with the enterobacterial test-cultures of other species.

The majority of human brain folding occurs during the third trimester of gestation. Although many studies have investigated the physical mechanisms of brain folding, a comprehensive understanding of this complex process has not yet been achieved. In mechanical terms, the \"differential growth hypothesis\" suggests that the formation of folds results from a difference in expansion rates between cortical and subcortical layers, which eventually leads to mechanical instability akin to buckling. It has also been observed that axons, a substantial component of subcortical tissue, can elongate or shrink under tensile or compressive stress, respectively. Previous work has proposed that this cell-scale behavior in aggregate can produce stress-dependent growth in the subcortical layers. The current study investigates the potential role of stress-dependent growth on cortical surface morphology, in particular the variations in folding direction and curvature over the course of development. Evolution of sulcal direction and mid-cortical surface curvature were calculated from finite element simulations of three-dimensional folding in four different initial geometries: (i) sphere; (ii) axisymmetric oblate spheroid; (iii) axisymmetric prolate spheroid; and (iv) triaxial spheroid. The results were compared to mid-cortical surface reconstructions from four preterm human infants, imaged and analyzed at four time points during the period of brain folding. Results indicate that models incorporating subcortical stress-dependent growth predict folding patterns that more closely resemble those in the developing human brain.
UNASSIGNED: Cortical folding is a critical process in human brain development. Aberrant folding is associated with disorders such as autism and schizophrenia, yet our understanding of the physical mechanism of folding remains limited. Ultimately mechanical forces must shape the brain. An important question is whether mechanical forces simply deform tissue elastically, or whether stresses in the tissue modulate growth. Evidence from this paper, consisting of quantitative comparisons between patterns of folding in the developing human brain and corresponding patterns in simulations, supports a key role for stress-dependent growth in cortical folding.

UNASSIGNED: Healthcare professionals and educators closely monitor the occurrence of climacteric symptoms in women\'s primes. Knowledge and perception of menopause play a crucial role in improving quality of life. This study aimed to assess the knowledge and perceptions of menopause among Saudi women and identify its predictors. This study is the first of its kind in the southern region of Saudi Arabia.
UNASSIGNED: Conducted in accordance with the STROBE guidelines, this cross-sectional study was carried out in the Jazan region from May 2022 to January 2023 and involved 480 Saudi women who provided consent. Data were collected through interviews using a validated questionnaire and random sampling. The questionnaire consisted of four parts: informed consent, demographics, 21 knowledge questions, and ten menopause perception questions. The validity of the content and the internal consistency were evaluated before data collection. Primary healthcare centers were randomly selected from four governorates with a proportional sample size to the population. Descriptive analysis, Pearson correlation, and multivariate logistic regression analyses were performed using IBM-SPSS.
UNASSIGNED: Among the participants, 64 % were under 40 years old, 80 % had experienced menarche between the ages of 10 and 15, 48 % were employed, approximately half held a bachelor\'s degree, and they had a good family income. The mean knowledge score of the participants was 48.87 ± 11.72, with a minimum score of 27 and a maximum score of 78. In terms of knowledge categories, 56.3 % of the participants (N = 270) were classified as having low knowledge, while 43.8 % (N = 210) were classified as having high knowledge. Most of the participants had positive perceptions and agreed that menopause is a natural event in women\'s lives. There was a significant positive correlation between knowledge and perception (R = 0.219, P < 0.01). Variable findings were observed regarding the role of explanatory variables in women\'s knowledge of menopause between univariate and multivariate models. The results of the multivariate model showed that age (46-50 years, OR = 0.42), having children (OR = 1.09), residence (OR = 0.45-5.73) and family income categories (medium: OR = 3.98, good: OR = 3.78, and excellent: OR = 1.95) had a significant impact on knowledge, highlighting the correlation between demographic factors and knowledge.
UNASSIGNED: Based on the study findings, we recommend implementing workplace and community-based activities to increase women\'s awareness of menopause and incorporating it as an integral part of counseling sessions for women in this age group. Therefore, the results of the study will be shared with the relevant authorities responsible for women\'s health, enabling them to effectively support and educate women.

This Article shares the proceedings from the August 29th, 2023 (day 1) workshop \"Physiologically Based Biopharmaceutics Modeling (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives\". The focus of the day was on model parametrization; regulatory authorities from Canada, the USA, Sweden, Belgium, and Norway presented their views on PBBM case studies submitted by industry members of the IQ consortium. The presentations shared key questions raised by regulators during the mock exercise, regarding the PBBM input parameters and their justification. These presentations also shed light on the regulatory assessment processes, content, and format requirements for future PBBM regulatory submissions. In addition, the day 1 breakout presentations and discussions gave the opportunity to share best practices around key questions faced by scientists when parametrizing PBBMs. Key questions included measurement and integration of drug substance solubility for crystalline vs amorphous drugs; impact of excipients on apparent drug solubility/supersaturation; modeling of acid-base reactions at the surface of the dissolving drug; choice of dissolution methods according to the formulation and drug properties with a view to predict the in vivo performance; mechanistic modeling of in vitro product dissolution data to predict in vivo dissolution for various patient populations/species; best practices for characterization of drug precipitation from simple or complex formulations and integration of the data in PBBM; incorporation of drug permeability into PBBM for various routes of uptake and prediction of permeability along the GI tract.

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.