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Engineering modular enzyme assembly: synthetic interface strategies for natural products biosynthesis applications
No full textCovering: 2020 to 2025Natural products remain indispensable sources of therapeutic and bioactive compounds, yet traditional discovery strategies are constrained by compound rediscovery. Modular biosynthetic enzymes, such as type I polyketide synthases (PKSs) and type A non-ribosomal peptide synthetases (NRPSs), offer promising platforms for combinatorial biosynthesis owing to their programmable architectures. However, practical implementation is frequently limited by inter-modular incompatibility and domain-specific interactions. This review highlights recent advances in modular enzyme assembly enabled by synthetic interfaces-including cognate docking domains, synthetic coiled-coils, SpyTag/SpyCatcher, and split inteins-which function as orthogonal, standardized connectors to facilitate post-translational complex formation. These interfaces support rational investigations into substrate specificity, module compatibility, and pathway derivatization as well as general enzyme clustering applications beyond PKS and NRPS systems. Synthetic interfaces can be integrated with computational tools to support a more systematic and scalable framework for modular enzyme engineering by providing predictive insights into domain compatibility and interface design. These approaches within iterative design-build-test-learn workflows can accelerate the programmable assembly of biosynthetic systems and expand the accessible chemical space for natural products.
Harnessing Bifunctional N-Benzoyloxyamides for Photoredox Amidative Dual Functionalizations of Alkenes
No full textHere, we present a photocatalytic strategy for the intermolecular dual functionalizations of alkenes using N & horbar;O bifunctional reagents in an atom-economical fashion. By leveraging N-benzoyloxyamides as bifunctional precursors for generating both amidyl radical and internal O-nucleophiles, this approach achieves chemoselective olefin amidation with simultaneous incorporation of additional functional groups. The current method readily accesses a range of doubly functionalized amino products through 1,2-amidooxygenation, amidoazidation, and formal anti-Markovnikov hydroamidation. Mechanistic studies revealed that the selective interplay of radical and ionic pathways is operative to enable a unified platform for this olefin dual functionalization.
Torsional stiffness-softening effect of torque-dependent variation in moment arm length for torsional vibration isolation
No full textIn this study, we propose a novel torsional-spring mechanism employing a dual extension spring configuration to isolate forced torsional vibrations in mechanical systems. Unlike traditional negative stiffness mechanisms that rely on compression springs, the proposed mechanism avoids drawbacks inherently associated with compression springs, such as spring buckling, structural complexity, and large size, by employing extension springs. The mechanism transmits the average or slowly modulated torque components while isolating unwanted high-frequency pulsating components. When subjected to torque inputs, the two extension springs generate equal-magnitude forces in opposite directions, producing a force couple. The arm length of this force couple decreases as angular displacement increases, imparting stiffness-softening characteristics to the isolation system. As the average torque level rises, the shorter couple arm length, adapted to the adjusted nominal state, implies that the increased average torque is successfully transmitted while enhancing the isolation of pulsating torque owing to the softened stiffness. Because the amplitude of pulsating torque is often proportional to the average torque, this adaptive enhancement can be beneficial. To demonstrate the torsional stiffness-softening behavior and the adaptive isolation performance, we develop a simplified but accurate mathematical model incorporating a modified quintic term in a truncated Taylor series expansion. Through harmonic balance and bifurcation analyses, we propose a rigorous design approach to identify permissible specifications of average and pulsating torques, thereby preventing dynamic instabilities originating from stiffness nonlinearity and offering effective vibration isolation.
Using deep learning to generate key variables in global mitigation scenarios
No full textIntegrated assessment models (IAMs) are the dominant tools for projecting mitigation scenarios. However, IAM-based scenarios often face challenges such as modelling biases and large computational burden. Here we develop a deep learning framework to generate key variables through synthetic mitigation scenarios aligned with the Sixth Assessment Report (AR6) Scenarios Database. By analysing 1,202 scenarios from a diverse set of IAMs, we select key drivers that enable a more detailed sectoral representation. Next, we trained three generative deep learning models to produce 30,000 synthetic scenarios at low computational cost across various IPCC AR6 climate categories, replicating variable distributions and correlations while also demonstrating physical consistency in power sector variables through internal validation checks. We found that the variational autoencoder achieved the highest label transferring accuracy among three frameworks. This study illustrates the potential of deep learning to complement IAM approaches and provides a basis for handling complex mitigation scenario generation tasks.
Analysis of multi-load distribution channel characteristics for air conditioner application
No full textThis paper presents an analysis of multi-load distribution channel characteristics for air conditioner communications using Recommended Standard-485 (RS-485). In this communication environment, the communication channels have distributed long characteristics because a transmitter (Tx) is connected to numerous receivers (Rx) in specific areas, such as buildings and factories. Due to these conditions, impedance matching characteristics are affected according to the data rate. Therefore, it is necessary to analyze the multi-load distribution channel characteristics for high data rate. For characterizing the two distinctive features of this channel, we initially analyzed the communication cable in terms of transmission line theory. Combining the cable and RS-485 circuit in the circuit-level simulation, we constructed the multi-load distribution channel. We validated the RLGC model, propagation constant and characteristic impedance of the cable, forming the channel and utilized these parameters to evaluate eye diagram integrity. As a result, in an established specific scenario, the maximum error rate of eye width and height between simulated and measured results was 9.46% at Rx4, and 6.55% at Rx1, at bit rate of 57.6 kbps, respectively.
Analysis and experimental validation of a design method for 5 K precooled Joule-Thomson coolers
No full textA 2-stage precooled Joule-Thomson (J-T) refrigerator for potential cooling of superconducting nanowire single photon detector (SNSPD) is conceived, analyzed and tested. In the analysis, theoretical tools aimed at aiding the design process are presented. The effect of the precooling temperatures is discussed in detail. To validate the analysis, an experimental apparatus is built employing a commercial Gifford-McMahon (G-M) cryocooler. The system is built by repurposing commercial compressors for use with 4He. The complete system is composed of the G-M auxiliary cryocooler; the J-T refrigerator heat exchangers and expansion orifice, and the compressors with an oil removal system (ORS). The model associated with the analysis predicted the cold-tip temperature with a maximum error lower of 0.5 K. The whole system achieved a cooling power of 10 mW at 5 K.
Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
No full textGrayscale digital light processing (g-DLP) is gaining recognition for its capability to create material property gradients within a single resin system, enabling programmable mechanical responses, enhanced shape accuracy, and improved toughness. However, research on the mechanical robustness of g-DLP is constrained by the limited range of tailorable properties in photocurable resins and insufficient exploration of structural optimization for complex geometries. This study presents a synergistic g-DLP strategy that integrates the synthesis of dynamic bond-controlled polyurethane acrylate (PUA) with a machine learning-based multi-objective optimization, enabling mechanically robust 3D-printed gradient materials. A PUA-based resin system is developed that expands the achievable elastic modulus from 8.3 MPa to 1.2 GPa, while maintaining superior damping performance, making it suitable for diverse applications. Furthermore, a multi-objective Bayesian optimization framework is constructed to efficiently identify optimal gradient structures, reducing strain concentrations and controlling effective stiffness. This approach is applicable to various 3D and arbitrary geometries, achieving a significant strain concentration reduction of up to 83% and demonstrating delayed crack initiation. By combining the developed material with this optimization framework, a versatile platform is established for creating mechanically robust g-DLP printed components, applicable in areas ranging from biomimetic artificial cartilage to automotive energy-absorbing structures.
Elucidating Ligand Exchange Dynamics of Hexacyanochromate-Based Redox Mediators in Aqueous Iron-Chromium Redox Flow Batteries
No full textAqueous redox flow batteries (AQRFBs) are revolutionizing energy storage by integrating sustainability with cutting-edge innovation. Among them, Iron-Chromium RFBs (Fe-Cr RFBs), which utilize aqueous-based electrolytes, effectively address critical challenges in renewable energy integration while offering unparalleled safety, low-cost scalability and environmental compatibility. Potassium hexacyanochromate (K3[Cr(CN)6]) has emerged as a promising negolyte material in Fe-Cr RFBs due to its favorable electrochemical properties. However, enhancing its long-term stability and elucidating its structural transformations remain crucial for optimized performance. Investigations into ligand exchange mechanism reveal connections to detrimental side reactions, notably hydrogen evolution reaction (HER) and hexacyanochromate instability, highlighting pathways for targeted improvement. Density functional theory (DFT) calculations illuminate the effects of ligand exchange dynamics and structural variations on redox stability, providing mechanistic insights into electrolyte behavior. By strategically incorporating sodium hydroxide with sodium cyanide as supporting electrolytes, our study demonstrates significantly improved stability of the redox couple, achieving a stable cycling performance over 250 cycles with an energy density of 13.91 Wh L-1 and energy efficiencies exceeding 76%-77%. This research provides valuable insights into the degradation pathways of hexacyanochromate-based negolyte and emphasizes the importance of optimized electrolyte design for advancing sustainable energy storage technologies.
Microbiota-Dependent Longevity Effects of Aronia Berry (Aronia melanocarpa) in Drosophila melanogaster
No full textAronia berry (Aronia melanocarpa) is rich in polyphenolic compounds with reported antioxidant and prebiotic activities, including potential life-extending effects. Although previous studies have highlighted its impact on the gut microbiota, the extent to which commensal microbes contribute to its longevity effects remains poorly understood. Here, we aimed to determine whether the lifespan- and health-promoting effects of Aronia berry are mediated by commensal microbes and to elucidate the underlying mechanisms. To address this, we investigated the effects of Aronia berry supplementation (5 µg/mL in food) on lifespan and physiological traits in Drosophila melanogaster under both conventional and axenic (germ-free) conditions. Assays included survival, antioxidant activity, microbial load, and immune gene expression. Aronia berry significantly extended the lifespan of conventional flies but failed to do so in axenic flies, suggesting a microbiota-dependent mechanism. In conventional flies, supplementation of Aronia berry reduced microbial load without altering diversity, enhanced oxidative stress resistance, and upregulated antimicrobial peptide genes via Toll pathway activation. These effects were not observed in axenic flies. Genetic activation of the Toll pathway mimicked the Aronia -induced microbial suppression and lifespan extension, suggesting a causal role for Toll signaling in mediating the health benefits of Aronia berry. Our findings demonstrate that Aronia berry exerts its longevity-promoting effects through commensal microbe-mediated activation of host immunity and oxidative stress reduction. This study highlights the essential role of gut microbes in mediating the health benefits of polyphenol-rich diets and offers insights into dietary strategies for promoting healthy aging.
Dysregulation of FGFR1 signaling in the hippocampus facilitates depressive disorder
No full textMajor depressive disorder (MDD) is a complex psychological disorder with a sophisticated molecular etiology. Although its connection with fibroblast growth factor receptor 1 (FGFR1) in the hippocampus is known, the precise mechanisms underlying its pathophysiology remain unclear. Here we conduct a comprehensive analysis of the molecular profile of the hippocampus in patients with MDD. We identified a distinct overexpression of FGFR1 specifically within the dentate gyrus of patients with MDD. Through the use of optogenetic techniques for the in vivo spatiotemporal dissection of FGFR1 signaling, we uncovered a sequential FGFR1-Notch-brain-derived neurotrophic factor (BDNF) pathway within the dentate gyrus, which can ultimately induce adult hippocampal neurogenesis, significantly contributing to antidepressant effects. We discovered that the dysregulation of this axis by the protein Numb, which demonstrates an age-related increase in individuals with MDD, is closely associated with the development of depressive phenotypes. Remarkably, targeting Numb to restore this axis effectively reversed the depressive phenotype, thus offering new insights into potential therapeutic strategies.