Sodium alginate based artificial biofilms polymerized by electrophoretic deposition for microbial hydrogen generation.

This study developed an artificial biofilm of Rhodospirillum rubrum bacteria immobilized within an alginate matrix using electrophoretic deposition (EPD) on an electrode. The resulting biofilm immobilized bacteria effectively and maintained a high survival rate, facilitating stable and high-efficiency hydrogen generation for longer periods compared to biofilms produced using free bacteria. Hydrogen production efficiency remained constant when the substrate was periodically replaced, indicating that the bacteria could survive within the biofilm for long-term hydrogen production. EPD produced mechanically stable large-scale biofilms economically and rapidly, which effectively overcame operational limitations such as culture medium temperature, pH, and flow rate. Therefore, this proposed method has the potential to accelerate the commercialization of biohydrogen production systems through large-scale biofilm production to facilitate continuous hydrogen generation. The technique can be utilized in various hydrogel-based applications, providing a cost-effective and efficient manufacturing process with customized biological and mechanical properties. The developed biofilms have implications beyond biohydrogen production and could be applied to hydrogel-based medical, cosmetic, and food applications. This study highlights the importance of immobilizing bacteria for stable and efficient hydrogen generation and demonstrates the potential of EPD in fabricating mechanically stable biofilms for large-scale production.

In-plane and out-of-plane excitonic coupling in 2D molecular crystals.

Understanding the nature of molecular excitons in low-dimensional molecular solids is of paramount importance in fundamental photophysics and various applications such as energy harvesting, switching electronics and display devices. Despite this, the spatial evolution of molecular excitons and their transition dipoles have not been captured in the precision of molecular length scales. Here we show in-plane and out-of-plane excitonic evolution in quasilayered two-dimensional (2D) perylene-3, 4, 9, 10-tetracarboxylic dianhydride (PTCDA) crystals assembly-grown on hexagonal boron nitride (hBN) crystals. Complete lattice constants with orientations of two herringbone-configured basis molecules are determined with polarization-resolved spectroscopy and electron diffraction methods. In the truly 2D limit of single layers, two Frenkel emissions Davydov-split by Kasha-type intralayer coupling exhibit energy inversion with decreasing temperature, which enhances excitonic coherence. As the thickness increases, the transition dipole moments of newly emerging charge transfer excitons are reoriented because of mixing with the Frenkel states. The current spatial anatomy of 2D molecular excitons will inspire a deeper understanding and groundbreaking applications of low-dimensional molecular systems.

Structural characterization and fatty acid epoxidation of CYP184A1 from Streptomyces avermitilis.

The genome of Streptomyces avermitilis contains 33 cytochrome P450 genes. Among the P450 gene products of S. avermitilis, we characterized the biochemical function and structural aspects of CYP184A1. Ultra-performance liquid chromatography-tandem mass spectrometry analysis showed that CYP184A1 induced an epoxidation reaction to produce 9,10-epoxystearic acid. Steady-state kinetic analysis yielded a kcat value of 0.0067 min-1 and a Km value 10 µM. The analysis of its crystal structures illustrated that the overall CYP184A1 structure adopts the canonical scaffold of cytochrome P450 and possesses a narrow and deep substrate pocket architecture that is required for binding to linear chain fatty acids. In the structure of the CYP184A1 oleic acid complex (CYP184A1-OA), C9-C10 of oleic acid was bound to heme for the productive epoxidation reaction. This study elucidates the roles of P450 enzymes in the oxidative metabolism of fatty acids in Streptomyces species.

Structural Insights for Core Scaffold and Substrate Specificity of B1, B2, and B3 Metallo-ß-Lactamases.

Metallo-ß-lactamases (MBLs) hydrolyze almost all ß-lactam antibiotics, including penicillins, cephalosporins, and carbapenems; however, no effective inhibitors are currently clinically available. MBLs are classified into three subclasses: B1, B2, and B3. Although the amino acid sequences of MBLs are varied, their overall scaffold is well conserved. In this study, we systematically studied the primary sequences and crystal structures of all subclasses of MBLs, especially the core scaffold, the zinc-coordinating residues in the active site, and the substrate-binding pocket. We presented the conserved structural features of MBLs in the same subclass and the characteristics of MBLs of each subclass. The catalytic zinc ions are bound with four loops from the two central ß-sheets in the conserved αß/ßα sandwich fold of MBLs. The three external loops cover the zinc site(s) from the outside and simultaneously form a substrate-binding pocket. In the overall structure, B1 and B2 MBLs are more closely related to each other than they are to B3 MBLs. However, B1 and B3 MBLs have two zinc ions in the active site, while B2 MBLs have one. The substrate-binding pocket is different among all three subclasses, which is especially important for substrate specificity and drug resistance. Thus far, various classes of ß-lactam antibiotics have been developed to have modified ring structures and substituted R groups. Currently available structures of ß-lactam-bound MBLs show that the binding of ß-lactams is well conserved according to the overall chemical structure in the substrate-binding pocket. Besides ß-lactam substrates, B1 and cross-class MBL inhibitors also have distinguished differences in the chemical structure, which fit well to the substrate-binding pocket of MBLs within their inhibitory spectrum. The systematic structural comparison among B1, B2, and B3 MBLs provides in-depth insight into their substrate specificity, which will be useful for developing a clinical inhibitor targeting MBLs.

Heme Oxygenase 1 in Schwann Cells Regulates Peripheral Nerve Degeneration Against Oxidative Stress.

During Wallerian degeneration, Schwann cells lose their characteristic of myelinating axons and shift into the state of developmental promyelinating cells. This recharacterized Schwann cell guides newly regrowing axons to their destination and remyelinates reinnervated axons. This Schwann cell dynamics during Wallerian degeneration is associated with oxidative events. Heme oxygenases (HOs) are involved in the oxidative degradation of heme into biliverdin/bilirubin, ferrous iron, and carbon monoxide. Overproduction of ferrous iron by HOs increases reactive oxygen species, which have deleterious effects on living cells. Thus, the key molecule for understanding the exact mechanism of Wallerian degeneration in the peripheral nervous system is likely related to oxidative stress-mediated HOs in Schwann cells. In this study, we demonstrate that demyelinating Schwann cells during Wallerian degeneration highly express HO1, not HO2, and remyelinating Schwann cells during nerve regeneration decrease HO1 activation to levels similar to those in normal myelinating Schwann cells. In addition, HO1 activation during Wallerian degeneration regulates several critical phenotypes of recharacterized repair Schwann cells, such as demyelination, transdedifferentiation, and proliferation. Thus, these results suggest that oxidative stress in Schwann cells after peripheral nerve injury may be regulated by HO1 activation during Wallerian degeneration and oxidative-stress-related HO1 activation in Schwann cells may be helpful to study deeply molecular mechanism of Wallerian degeneration.

Probing Evolution of Twist-Angle-Dependent Interlayer Excitons in MoSe2/WSe2 van der Waals Heterostructures.

Interlayer excitons were observed at the heterojunctions in van der Waals heterostructures (vdW HSs). However, it is not known how the excitonic phenomena are affected by the stacking order. Here, we report twist-angle-dependent interlayer excitons in MoSe2/WSe2 vdW HSs based on photoluminescence (PL) and vdW-corrected density functional theory calculations. The PL intensity of the interlayer excitons depends primarily on the twist angle: It is enhanced at coherently stacked angles of 0° and 60° (owing to strong interlayer coupling) but disappears at incoherent intermediate angles. The calculations confirm twist-angle-dependent interlayer coupling: The states at the edges of the valence band exhibit a long tail that stretches over the other layer for coherently stacked angles; however, the states are largely confined in the respective layers for intermediate angles. This interlayer hybridization of the band edge states also correlates with the interlayer separation between MoSe2 and WSe2 layers. Furthermore, the interlayer coupling becomes insignificant, irrespective of twist angles, by the incorporation of a hexagonal boron nitride monolayer between MoSe2 and WSe2.

Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models.

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

The significance of endogeneity problems in crash models: an examination of left-turn lanes in intersection crash models.

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes 'appear' to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.