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Laccases from white-rot fungi catalyze lignin depolymerization, a critical first step to upgrading lignin to valuable biodiesel fuels and chemicals. In this study, a wildtype laccase from the basidiomycete Fomitiporia mediterranea (Fom_lac) and a variant engineered to have a carbohydrate-binding module (Fom_CBM) were studied for their ability to catalyze cleavage of β-O-4 ether and C-C bonds in phenolic and non-phenolic lignin dimers using a nanostructure-initiator mass spectrometry-based assay. Fom_lac and Fom_CBM catalyze β-O-4 ether and C-C bond breaking, with higher activity under acidic conditions (pH < 6). The potential of Fom_lac and Fom_CBM to enhance saccharification yields from untreated and ionic liquid pretreated pine was also investigated. Adding Fom_CBM to mixtures of cellulases and hemicellulases improved sugar yields by 140% on untreated pine and 32% on cholinium lysinate pretreated pine when compared to the inclusion of Fom_lac to the same mixtures. Adding either Fom_lac or Fom_CBM to mixtures of cellulases and hemicellulases effectively accelerates enzymatic hydrolysis, demonstrating its potential applications for lignocellulose valorization. We postulate that additional increases in sugar yields for the Fom_CBM enzyme mixtures were due to Fom_CBM being brought more proximal to lignin through binding to either cellulose or lignin itself.

In this 4.0 era, where technology has been a part of our daily lives, the integration of smart devices into education is an indispensable movement that we are striving for. Hence, the application of mobile devices into learning, M-learning is becoming a trendy approach and a quality assurance's measurement for almost every academic institution. Technology makes education available for students almost anywhere, anytime, and offers unlimited access to learning resources. Furthermore, M-learning is also reported to have a positive impact on students' learning autonomy. Hence, this paper aims at validating possibilities to amplify this aspect of M-learning and suggesting an appropriate M-learning model at Nguyen Tat Thanh university (NTTU). The paper investigates students' needs, requirements, ability to adapt to the new trend of M-learning plus the external elements affecting their learning autonomy to come up with the learning model. Surveys were collected from students in the Faculty of Foreign Languages and analyzed using a qualitative method to provide educators and teachers at NTTU with insightful information and careful preparation before launching new approaches to M-learning.

Objective: To document the pattern of particulate matter 2.5 (PM2.5) concentration in urban and suburban schools in Ho Chi Minh City (HCMC) and to estimate the prevalence of wheezing and rhinitis among schoolchildren according to sociodemographic characteristics and school-related conditions. Material and Methods: A cross-sectional study was conducted in two suburban and two urban schools in HCMC. PM2.5 concentrations were monitored hourly from August to December in 2022. Children aged 9-13 years (N=1,033) provided data on wheezing and rhinitis symptoms in the previous year. The daily levels of PM2.5 were monitored in each school. Respiratory symptoms were compared between areas and across school-related conditions. Results: Geometric means of the daily PM2.5 concentration in the suburban and urban areas were 61.2 μg/m3 and 31.0 μg/m3, respectively (p-value

OBJECTIVES Depression in medical students is concerning, potentially fueled by many stressors including career choice-relating stress. Choosing Medicine is a life-long commitment, and low intrinsic motivation or excessive dependence on family can complicate this decision and adding stress throughout their training. This stress intensifies in the final year, as students lacking personal drive struggle to see themselves continuing the career. Given limited studies on this crucial topic in Asia and Vietnam, we explored direct linkage between career choice motivation and depression in final-year medical students. METHODS A cross-sectional study was conducted with 569 final-year students between June and July 2020. The Vietnamese Patient Health Questionnaire 9 (PHQ-9) and 16-item CCM questionnaire were used as survey tools. Univariate analysis was used for descriptive statistics (absolute and relative frequency, mean (M), standard deviation (SD). Multinomial logistic regression models were used to explore the relationship between variables using STATA 5.1. RESULTS The depression among participants was about 24.6% (PHQ-9 cut-off ≥ 12). No difference in gender was found regarding depression. The most acknowledged motivator is securing employment (M = 4.14, SD = 1.02) and the least is parental wishes (M = 3.17, SD = 1.32). Familial influence on career choice significantly increased odds of having “ moderately-severe depression ” (OR = 1.17, 95% CI 1.04-1.32) and “ severe depression ” (OR = 1.36, 95% CI 1.10-1.68), whereas, career-choice motivators including satisfaction (OR = .76, 95% CI .60-.97), self-competence (OR = .80, 95% CI .66-.97) and career success (OR = .84, 95% CI .71-.99) were found to be protective factors for depression. CONCLUSIONS Roughly a quarter of final-year medical students encountered depression. Occupational security ranked as the primary motivator, with parental wish being the least. Familial influence heightened depression risk, while career prospects, satisfaction and self-efficacy acted as protective factors. Medical career paths should align with intrinsic motivations and personal interests for better mental health outcomes.

Development and deployment of commercial biorefineries based on conversion of lignocellulosic biomass into biofuels and bioproducts faces many challenges that must be addressed before they are commercially viable. One of the biggest challenges faced is the efficient and scalable valorization of lignin, one of the three major components of the plant cell wall. Lignin is the most abundant aromatic biopolymer on earth, and its presence hinders the extraction of cellulose and hemicellulose that is essential to biochemical conversion of lignocellulose to fuels and chemicals. There has been a significant amount of work over the past 20 years that has sought to develop innovative processes designed to extract and recycle lignin into valuable compounds and help reduce the overall costs of the biorefinery process. Due to the complex matrix of lignin, which is essential for plant survival, the development of a reliable and efficient lignin conversion technology has been difficult to achieve. One approach that has received significant interest relies on the use of enzymes, notably laccases, a class of multi‑copper green oxidative enzymes that catalyze bond breaking in lignin to produce smaller oligomers. In this review, we first assess the different innovations of lignin valorization using laccases within the context of a biorefinery process, and then assess the latest economical advances that these innovations offered. Finally, we review laccase characterization and optimization, as well as the prospects and bottlenecks of this class of enzymes within the industrial and biorefining sectors.

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

BackgroundLignin peroxidases catalyze a variety of reactions, resulting in cleavage of both β-O-4' ether bonds and C-C bonds in lignin, both of which are essential for depolymerizing lignin into fragments amendable to biological or chemical upgrading to valuable products. Studies of the specificity of lignin peroxidases to catalyze these various reactions and the role reaction conditions such as pH play have been limited by the lack of assays that allow quantification of specific bond-breaking events. The subsequent theoretical understanding of the underlying mechanisms by which pH modulates the activity of lignin peroxidases remains nascent. Here, we report on combined experimental and theoretical studies of the effect of pH on the enzyme-catalyzed cleavage of β-O-4' ether bonds and of C-C bonds by a lignin peroxidase isozyme H8 from Phanerochaete chrysosporium and an acid stabilized variant of the same enzyme.ResultsUsing a nanostructure initiator mass spectrometry assay that provides quantification of bond breaking in a phenolic model lignin dimer we found that catalysis of degradation of the dimer to products by an acid-stabilized variant of lignin peroxidase isozyme H8 increased from 38.4% at pH 5 to 92.5% at pH 2.6. At pH 2.6, the observed product distribution resulted from 65.5% β-O-4' ether bond cleavage, 27.0% Cα-C1 carbon bond cleavage, and 3.6% Cα-oxidation as by-product. Using ab initio molecular dynamic simulations and climbing-image Nudge Elastic Band based transition state searches, we suggest the effect of lower pH is via protonation of aliphatic hydroxyl groups under which extremely acidic conditions resulted in lower energetic barriers for bond-cleavages, particularly β-O-4' bonds.ConclusionThese coupled experimental results and theoretical explanations suggest pH is a key driving force for selective and efficient lignin peroxidase isozyme H8 catalyzed depolymerization of the phenolic lignin dimer and further suggest that engineering of lignin peroxidase isozyme H8 and other enzymes involved in lignin depolymerization should include targeting stability at low pH.