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Abstract Soluble methane monooxygenase (sMMO) oxidizes a wide range of carbon feedstocks (C1 to C8) directly using intracellular NADH and is a useful means in developing green routes for industrial manufacturing of chemicals. However, the high-throughput biosynthesis of active recombinant sMMO and the ensuing catalytic oxidation have so far been unsuccessful due to the structural and functional complexity of sMMO, comprised of three functionally complementary components, which remains a major challenge for its industrial applications. Here we develop a catalytically active miniature of sMMO (mini-sMMO), with a turnover frequency of 0.32 s−1, through an optimal reassembly of minimal and modified components of sMMO on catalytically inert and stable apoferritin scaffold. We characterise the molecular characteristics in detail through in silico and experimental analyses and verifications. Notably, in-situ methanol production in a high-cell-density culture of mini-sMMO-expressing recombinant Escherichia coli resulted in higher yield and productivity (~ 3.0 g/L and 0.11 g/L/h, respectively) compared to traditional methanotrophic production.

IntroductionDoping remains a persistent concern in sports, compromising fair competition. The Athlete Biological Passport (ABP) has been a standard anti-doping measure, but confounding factors challenge its effectiveness. Our study introduces an artificial intelligence-driven approach for identifying potential doping suspicious, utilizing the Athlete’s Performance Passport (APP), which integrates both demographic profiles and performance data, among elite female weightlifters.MethodsAnalyzing publicly available performance data in female weightlifting from 1998 to 2020, along with demographic information, encompassing 17,058 entities, we categorized weightlifters by age, body weight (BW) class, and performance levels. Documented anti-doping rule violations (ADRVs) cases were also retained. We employed AI-powered algorithms, including XGBoost, Multilayer Perceptron (MLP), and an Ensemble model, which integrates XGBoost and MLP, to identify doping suspicions based on the dataset we obtained.ResultsOur findings suggest a potential doping inclination in female weightlifters in their mid-twenties, and the sanctioned prevalence was the highest in the top 1% performance level and then decreased thereafter. Performance profiles and sanction trends across age groups and BW classes reveal consistently superior performances in sanctioned cases. The Ensemble model showcased impressive predictive performance, achieving a 53.8% prediction rate among the weightlifters sanctioned in the 2008, 2012, and 2016 Olympics. This demonstrated the practical application of the Athlete’s Performance Passport (APP) in identifying potential doping suspicions.DiscussionOur study pioneers an AI-driven APP approach in anti-doping, offering a proactive and efficient methodology. The APP, coupled with advanced AI algorithms, holds promise in revolutionizing the efficiency and objectivity of doping tests, providing a novel avenue for enhancing anti-doping measures in elite female weightlifting and potentially extending to diverse sports. We also address the limitation of a constrained set of APPs, advocating for the development of a more accessible and enriched APP system for robust anti-doping practices.

Osmotically assisted reverse osmosis (OARO) is an innovative process that shows promising potential in the treatment of brine produced by conventional reverse osmosis (RO) systems. This study presents a theoretical and experimental analysis of the OARO process, focusing on its application to achieve minimum liquid discharge (MLD). This theoretical analysis includes the development of a mathematical model to describe the transport phenomena occurring during OARO. By considering mass balance equations coupled with transport equations, the theoretical model allows for the simulation of a full-scale system consisting of a single-stage RO and a four-stage OARO. Experimental investigations are also conducted to validate the theoretical model and to evaluate the performance of the OARO process. A laboratory-scale OARO system is designed and operated using a synthetic RO brine. Various operating conditions, including applied pressure, feed concentration, and draw concentration, are varied to investigate their effects on process performance. The experimental results demonstrate the feasibility of OARO as an MLD solution and also validate the predictions of the theoretical model, confirming its reliability for process optimization and design. The results of the theoretical analysis show that OARO has the potential to significantly improve water recovery compared to conventional RO. Based on the simulation, the optimal operating conditions are explored, leading to a significant reduction (up to 89%) in the volume of brine discharge.

Background. Many Type 2 diabetes (T2DM) patients in Korea take Korean Red Ginseng (KRG) for various reasons. In this study, we investigated the effects of KRG administration on diabetic peripheral neuropathy in T2DM patients. Methods. This study was a randomized, double-blind, placebo-controlled trial. Participants were randomly allocated to either the placebo or KRG group and took corresponding tablets for 24 weeks. The primary outcomes were changes in current perception threshold (CPT) at week 24. Secondary outcomes were altered fasting plasma glucose, HbA1c, and various metabolic and inflammatory markers at week 24. Results. Sixty-one patients completed the study. The CPT of the lower extremities at various frequencies exhibited significant improvements at week 24 in the KRG group. Other metabolic parameters were not altered after 24 weeks in both groups. In the subgroup analysis, CPT levels were improved in those with a longer diabetes duration or who already had neuropathy at the beginning of the study, and insulin resistance was improved in patients with a shorter diabetes duration. Conclusion. Twenty-four week administration of KRG in T2DM patients resulted in a significant improvement in neuropathy, especially in those with a longer diabetes duration. A further, larger population study with a longer follow-up period is warranted to verify the effects of KRG on diabetic neuropathy.

Understanding the energy efficiency of direct contact membrane distillation (DCMD) is important for the widespread application and practical implementation of the process. This study analyzed the available energy, known as exergy, in a DCMD system using computational fluid dynamics (CFD). A CFD model was developed to investigate the hydrodynamic and thermal conditions in a DCMD module. After the CFD model was verified, it was used to calculate the temperature polarization coefficient (TPC) and exergy destruction magnitudes under various operating conditions. The results revealed that slight decreases and increases in the TPC occurred with distance from the inlet in the module. The TPC was found to increase as the feed temperature was reduced and the feed and permeate flow rates were increased. The exergy destruction phenomenon was more significant under higher feed temperatures and higher flux conditions. Although the most significant exergy destruction in the permeate occurred near the feed inlet, the effect became less influential closer to the feed outlet. An analysis of exergy flows revealed that the efficiency loss in the permeate side corresponded to 32.9–45.3% of total exergy destruction.

Membrane distillation (MD), which uses hydrophobic porous membranes with a temperature gradient to produce pure water, has the potential to treat high-salinity wastewater. However, it cannot directly treat wastewater containing surfactants, which lower the surface tension and thus result in membrane wetting. To overcome this limitation, this study proposed a hybrid process consisting of powdered activated carbon (PAC) adsorption and MD, where PAC removes the surfactants in the wastewater to alleviate the wetting of the MD membranes. A bench-scale vacuum-assisted air gap MD (V-AGMD) equipment was adopted for the treatment of synthetic wastewater containing inorganic salts and surfactants. The conductivity of the permeate from V-AGMD was continuously monitored to detect membrane wetting. Without the use of PAC, the MD membrane was wetted within a short period, which decreased as the surfactant concentration increased. On the other hand, the addition of PAC retards the onset of wetting even at higher surfactant concentrations. The effectiveness of the PAC addition to the MD system on wetting control was examined under various conditions to elucidate its mechanism. [ABSTRACT FROM AUTHOR]

Multi-tiered storage, where each tier consists of one type of storage device (e.g., SSD, HDD, or disk arrays), is a commonly used approach to achieve both high performance and cost efficiency in large-scale systems that need to store data with vastly different access characteristics. By aligning the access characteristics of the data, either fixed-sized extents or variable-sized files, to the characteristics of the storage devices, a higher performance can be achieved for any given cost. This article presents ExaPlan, a method to determine both the data-to-tier assignment and the number of devices in each tier that minimize the system's mean response time for a given budget and workload. In contrast to other methods that constrain or minimize the system load, ExaPlan directly minimizes the system's mean response time estimated by a queueing model. Minimizing the mean response time is typically intractable as the resulting optimization problem is both nonconvex and combinatorial in nature. ExaPlan circumvents this intractability by introducing a parameterized data placement approach that makes it a highly scalable method that can be easily applied to exascale systems. Through experiments that use parameters from real-world storage systems, such as CERN and LOFAR, it is demonstrated that ExaPlan provides solutions that yield lower mean response times than previous works. It supports standalone SSDs and HDDs as well as disk arrays as storage tiers, and although it uses a static workload representation, we provide empirical evidence that underlying dynamic workloads have invariant properties that can be deemed static for the purpose of provisioning a storage system. ExaPlan is also effective as a load-balancing tool used for placing data across devices within a tier, resulting in an up to 3.6-fold reduction of response time compared with a traditional load-balancing algorithm, such as the Longest Processing Time heuristic. [ABSTRACT FROM AUTHOR]

Parallel processing is a way to use resources efficiently by processing several jobs simultaneously on different servers. In a well-controlled environment where the status of the servers and the jobs are well known, everything is nearly deterministic and replicating jobs on different servers is obviously a waste of resources. However, in a poorly controlled environment where the servers are unreliable and/or their capacity is highly variable, it is desirable to design a system that is robust in the sense that it is not affected by the poorly performing servers. By replicating jobs and assigning them to several different servers simultaneously, we not only achieve robustness but we can also make the system more efficient under certain conditions so that the jobs are processed at a faster rate overall. In this paper we consider the option of replicating jobs and study how the performance of different 'degrees' of replication, ranging from no replication to full replication, affects the performance of a system of parallel servers. [ABSTRACT FROM AUTHOR]