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Acylhydrazone-Linked Covalent Organic Frameworks
An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears
Vibrationally-Resolved Electrochemical Impedance Spectroscopy
Loss of Brg1 prevents the progression of pulmonary hypertension by inhibiting Nrf2 expression
Abstract Brahma-related gene 1 (Brg1) is a major factor in regulation of chromatin remodeling and is involved in different cellular processes, including cell proliferation, apoptosis, and differentiation. However, the role of Brg1 in pulmonary artery hypertension (PAH) has not been elucidated. This study aims to assess the role of Brg1 in PAH using hypoxia-induced PAH rat model, Monocrotaline(MCT)-induced PAH rat model, and hypoxia-induced arterial smooth muscle cell model. We found that Brg1 expression was highly expressed in hypoxia-induced PAH rat model and MCT-induced PAH rat model. Moreover, knockdown of Brg1 by injection of adenoviral Brg1 shRNA inhibited Nrf2 expression and its downstream molecular HO-1, attenuated apoptosis resistance, and ameliorated PAH progression in PAH rats. Meanwhile, knockdown of Brg1 suppressed cell proliferation through downregulating the expression of mTOR/P70S6K signaling pathway in vitro and in vivo. In addition, the Nrf2 inhibitor Brusatol treatment significantly inhibited cell proliferation, whereas abrogated by loss of Brg1. In summary, these results demonstrated that loss of Brg1 inhibited PAH progression by downregulating Nrf2 to attenuate cell proliferation and enhance apoptosis, which may provide a new therapeutic target to improve cardiopulmonary function in PAH patients.
Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide
Sulfoxaflor reduces food intake and learning efficiency of solitary Osmia bicornis bees
Emerging Roles of Photoredox Catalysis in Biomedical Research
Degradation-constrained multi-agent reinforcement learning with centralized training and decentralized execution for vehicle-to-grid optimization in renewable-dominated distribution networks
Abstract High renewable penetration and large-scale electric vehicle integration impose voltage instability, frequency deviation, and network congestion challenges in distribution systems while accelerating battery degradation. This study proposes a degradation constrained multi agent reinforcement learning framework based on centralized training with decentralized execution for coordinated vehicle to grid optimization. The method integrates DC power flow constraints, stochastic renewable uncertainty, and electrochemical battery aging dynamics within a unified control architecture to ensure network aware and lifecycle aware scheduling. The framework is evaluated on the IEEE 33 bus distribution network with electric vehicle penetration up to 50% and compared against two baselines: a rule based conventional V2G scheduler and a single agent deep reinforcement learning controller without coordinated network constraints or degradation penalization. Grid stability is quantified using a normalized composite index derived from frequency and voltage magnitude deviations. Across repeated simulation trials, the proposed approach improves the mean stability index by approximately 10% relative to the rule-based method and 7% relative to the single agent baseline. Renewable utilization increases by about 18% age points, peak load reduction reaches 40% under high penetration scenarios, and cumulative battery aging decreases by nearly 13% over a 24-hour horizon, demonstrating enhanced coordinated control performance within standardized simulation environments.
Continuous optimization strategies based on data insights an analysis of LinkedIn’ s operational model
Interstitial-Hydrogen-Modulated Subnanometer PdPtIrCoNiH High-Entropy Hydride Nanowires for Efficient Hydrogen Electrocatalysis
Probing Mesoscopic Solvation Dynamics via Comparable-Sized Nanomolecular Clusters
Rational design of a modular mRNA vaccine platform for rapid adaptation to SARS-CoV-2 variants
Abstract The ongoing emergence of novel SARS-CoV-2 variants due to viral mutations poses a persistent challenge to the efficacy of existing vaccines. To address this challenge, we engineered and comprehensively tested three optimized mRNA vaccine candidates, evaluating the kinetics, quality, and magnitude of antibody responses as well as antigen-specific T cell immunity during a prime-boost vaccination regimen in mice. Among the tested candidates, TP2A encoding secreted receptor-binding domains (RBDs) derived from SARS-CoV-2 wild type (WT), Delta and Omicron variants demonstrated superior immunogenicity, inducing an early IgG2a-dominated antibody response against distinct SARS-CoV-2 spike (S) glycoprotein variants. In addition, TP2A elicited IFN-γ-producing T cells in both spleen and draining lymph nodes and antigen-specific cytotoxic T lymphocytes. Notably, beyond broad immunity induced by the vaccine, TP2A functions as a modular platform, thus enabling flexible antigen assembly and rapid vaccine adaptation to newly emerging variants or even other viral pathogens. These findings position TP2A as a promising next-generation mRNA vaccine candidate.
Photocatalytic Ammonia Synthesis using Fe-Based MOFs: The Role of Ligand Functionalization
Optimal DG allocation using the Dingo Optimization Algorithm: robust power loss reduction with concomitant voltage stability improvement in distribution and transmission networks
Abstract The paper develops a comprehensive model for optimal allocation and sizing of DG units using the Dingo Optimization Algorithm (DOA) targeting active power loss reduction with a concomitant improvement in voltage stability across both distribution (DN) and transmission networks (TN). The novel methodology evaluates the effectiveness of operation of renewable DG units under two different network structures. In the IEEE 33-node DN, DG units comprising PV are optimally placed, with consideration for a constant amount of reactive compensation, considering the practical limitations of the used inverters. In the IEEE 118-node TN, which is a more complicated, meshed grid, PV alone, as well as hybrid configurations combining both PV and Wind technologies, operating at an optimal power factor of 0.95 are considered. In addition, a dispatch factor of 0.3 is applied for hybrid systems under light load to mitigate the high penetration of the renewable energy DG units. Results from the evaluation of IEEE 33-bus network indicate the effectiveness of DOA in securing substantial reductions in active power losses at percentages of 81.63%, 48.37%, and 79.45% in cases of normal, light, and heavy loadings, respectively, coupled with marked improvement in the voltage stability level of the power grid. For instance, during regular operations, the lowest Voltage Stability Index (VSI) rose from a critical point of 0.695 to a safe point of 0.898, whereas the highest Voltage Deviation Index (VDI) fell from 0.087 to 0.027. Application of the algorithm to the 118-bus TN indicates that placing three PV DG units optimally results in reductions in active power losses by 16.98%, 8.39%, and 16.24% in normal, light, and heavy loadings, respectively. Incorporating a hybrid system involving three PV units and three wind power DG units reduces the active power losses to 18.07% in the case of normal loading and 23.98% for heavy loading. Finally, deployment of the dispatch factor achieves a positive reduction in power losses of 12.61% in the light load scenario. Additionally, network stability improved in the hybrid topology when the network was under high traffic loads, leading to the minimum Voltage Deviation Index (VDI) being reduced from 0.1015 to 0.0863, while the maximum Fast Voltage Stability Index (FVSI) was 0.2817.
Toward Solar-Powered Growth of Autotrophic <i>Escherichia coli</i> Using Photoelectrochemistry
Suppressive effect of chrysin on macrophage LC3B autophagy through miR-204-5p in diabetic atherosclerosis
Torsional Ordering as a Prerequisite for Zeolite Crystallization Revealed by X-ray Emission Spectroscopy
Development and field evaluation of a low-cost fuzzy logic irrigation controller for vineyards in semi-arid regions
Abstract Water scarcity in arid and semi-arid regions calls for irrigation strategies that adapt to real-time field conditions. This pilot study presents a low-cost fuzzy logic–based irrigation controller for vineyards in Malekan County (East Azerbaijan, Iran). The controller uses three environmental inputs (soil moisture, air temperature, and a solar-radiation index derived from light intensity (LDR)) and applies a rule-based fuzzy inference system to determine irrigation duration (with pump activation derived from the duration). A rainfall-gating rule is also used to prevent unnecessary irrigation during rainfall events. The system was implemented on an Arduino Uno using off-the-shelf sensors and evaluated through MATLAB surface analysis and a field pilot conducted from March to August 2024. In addition, an adjacent-plot pilot comparison against conventional irrigation was performed to examine seasonal water use and plot-level grape performance indicators (cluster weight, °Brix, acidity, and rotten cluster rate). The proposed controller was associated with a 34.6% reduction in irrigation water use (520 → 340 m 3 /ha) and descriptive improvements in cluster weight, °Brix, and rotten cluster incidence. These pilot results demonstrate the technical feasibility and operational reliability of the fuzzy logic–based controller under real-world semi-arid vineyard conditions, alongside promising descriptive improvements in water-use efficiency and key grape performance indicators. However, as this was a single-season, unreplicated adjacent-plot pilot study, the observed differences in yield and quality parameters should be interpreted as exploratory. Larger-scale, multi-season field trials are therefore required to statistically validate these preliminary findings and confirm their generalizability.