Sulforaphane’s Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2)-Dependent and -Independent Mechanism of Anti-SARS-CoV-2 Activity

It is well established that Nrf2 plays a crucial role in anti-oxidant and anti-inflammatory functions. However, its antiviral capabilities remain less explored. Despite this, several Nrf2 activators have demonstrated anti-SARS-CoV-2 properties, though the mechanisms behind these effects are not fully understood. In this study, using two mouse models of SARS-CoV-2 infection, we observed that the absence of Nrf2 significantly increased viral load and altered inflammatory responses. Additionally, we evaluated five Nrf2 modulators. Notably, epigallocatechin gallate (EGCG), sulforaphane (SFN), and dimethyl fumarate (DMF) exhibited significant antiviral effects, with SFN being the most effective. SFN did not impact viral entry but appeared to inhibit the main protease (M^Pro) of SARS-CoV-2, encoded by the Nsp5 gene, as indicated by two protease inhibition assays. Moreover, using two Nrf2 knockout cell lines, we confirmed that SFN's antiviral activity occurs independently of Nrf2 activation in vitro. Paradoxically, in vivo tests using the MA30 model showed that SFN's antiviral function was completely lost in Nrf2 knockout mice. Thus, although SFN and potentially other Nrf2 modulators can inhibit SARS-CoV-2 independently of Nrf2 activation in cell models, their Nrf2-dependent activities might be crucial for antiviral defense under physiological conditions. 

High Entropy Oxides: Next-Generation Air Electrodes for Reversible Protonic Solid Oxide Cells

Reversible protonic solid oxide cell (P-SOC) operating at intermediate-temperature exhibits excellent potential as a power generation and green hydrogen production device in fuel cell and electrolysis cell modes because of the high conversion efficiency. However, the lack of efficient air electrodes is the main challenge to obtain P-SOC with remarkable performance. Typically, air electrodes should possess high proton, oxygen ion and electron conductivity, outstanding catalytic ability for oxygen reduction reaction and H₂O splitting, and also long-term durability. Recently, high entropy oxides (HEO) have become popular due to their various potential applications in terms of outstanding properties, including catalysis ability, conductivity, thermal stability, etc. HEO air electrodes have been confirmed to show good electrochemical performance in P-SOC, but the complex compositions and structure make it difficult to study HEO by traditional experimental methods. Machine learning (ML) has been regarded as a powerful tool in materials research and can solve the drawbacks in the discovery of HEO in a traditional way. In this perspective, we not only discuss the current utilization of HEO in P-SOC but also provide a possible process to use ML to guide the development of HEO.

Green Chemical Technology: A New Journal for Green Engineering and Green Technology

Cytosine Deaminase-Assisted Mutator for Genome Evolution in Cupriavidus necator

Cupriavidus necator H16 has been intensively explored for its potential as a versatile microbial cell factory, especially for its CO₂ fixation capability over the past few decades. However, rational metabolic engineering remains challenging in the construction of microbial cell factories with complex phenotypes due to the limited understanding of its metabolic regulatory network. To overcome this obstacle, laboratory adaptive evolution emerges as an alternative. In the present study, CAM (cytosine deaminase-assisted mutator) was established for the genome evolution of C. necator, addressing the issue of low mutation rates. By fusing cytosine deaminase with single-stranded binding proteins, CAM introduced genome-wide C-to-T mutations during DNA replication. This innovative approach could boost mutation rates, thereby expediting laboratory adaptive evolution. The applications of CAM were demonstrated in improving cell factory robustness and substrate utilization, with H₂O₂ resistance and ethylene glycol utilization as illustrative case studies. This genetic tool not only facilitates the development of efficient cell factories but also opens avenues for exploring the intricate phenotype-genotype relationships in C. necator.

Unraveling Novel Strategies: Targeting Miz1 for Degradation to Enhance Antiviral Defense against Influenza A Virus

The ubiquitin system has been shown to play an important role in regulation of immune responses during viral infection. In a recent article published in Science Signaling, Wu and colleagues revealed that transcriptional factor Miz1 plays a pro-viral role in influenza A virus (IAV) infection by suppressing type I interferons (IFNs) production through recruiting HDAC1 to ifnb1 promoter. They show that a series of E3 ligases combinatorially regulates Miz1 ubiquitination and degradation and modulates IFNs production and viral replication.

Rurality: Why This Is Important

Biobased Vitrimers: A Sustainable Future

Vitrimers are crosslinked polymers containing dynamic covalent linkages. Because of their crosslinked structure, they are stable as thermosets at their service temperatures. At high enough temperatures, dynamic exchange reactions occur and rearrange the polymer network, thus vitrimers become malleable and reprocessable like thermoplastics. The dynamic covalent bonds can also undergo dissociative cleavage reactions under specific conditions, so vitrimers are inherently degradable. To achieve a sustainable future, various biomass resources have been used as raw materials in vitrimer preparation. This review summarizes recent developments in biobased vitrimers and highlights their preparation methods. The limitations of current biobased vitrimers are also discussed.

Optimizing Performance and Design Simulation of a 100 KW Single Rotor Horizontal Axis Wind Turbine

As wind energy becomes increasingly vital in global energy strategies, optimizing wind turbine design is essential. This research focuses on the development of a 100 kW single rotor horizontal axis wind turbine (HAWT) tailored to meet the energy needs of Jamshoro, Pakistan. The turbine design leverages SolidWorks for structural modeling and is validated through comprehensive simulations using ANSYS and Q-Blade. Operating at an optimal wind speed of 6.9 m/s, the turbine achieves maximum efficiency, as indicated by the highest power factor. This efficiency translates to an estimated power output of approximately 100 kW, suitable for common household consumption. The study integrates regional climatic data and wind conditions to enhance turbine performance and durability. The findings offer a sustainable energy solution for Jamshoro, contributing to Pakistan’s renewable energy infrastructure and addressing local energy demands effectively. The focus of this study will be Jamshoro, a region in Pakistan as a case study. The software simulations will consider a variety of elements, including as wind speeds, variable loads, and environmental factors unique to the chosen region (Jamshoro). This research proposes a sustainable solution for addressing the energy demands in Jamshoro by integrating accurate data based on software analysis with real-world concerns, adding to the larger goal of developing sustainable sources of energy in Pakistan.

Dual Genetic Tracing Reveals the Origin of Alveolar Stem Cells after Lung Injury

As alveolar epithelial stem cells, alveolar type II (AT2) cells play a pivotal role in sustaining alveolar homeostasis and facilitating repair processes. However, the sources of AT2 cell regeneration have remained contentious due to the non-specific labeling limitations of traditional single recombinase-based lineage tracing techniques. To address this issue, we employed dual recombination systems to develop more precise lineage tracing methodologies, effectively bypassing the shortcomings of conventional approaches and enabling specific labeling of lung epithelial cells. Our findings demonstrate that, following lung injury, regenerated AT2 cells do not originate from alveolar type I (AT1) cells, but instead derive from bronchiolar club cells and bronchioalveolar stem cells (BASCs), alongside the self-renewal of resident AT2 cells. Furthermore, we discovered that the transition of club cells and BASCs into AT2 cells is distinctly modulated by the Notch signaling pathway. This study not only provides novel insights into lung regeneration, but the innovative lineage tracing technology developed herein also holds promise as a technical support for research in diverse fields.

Optimized Real Time Single-Drone Path Planning for Harvesting Information from a Wireless Sensor Network

We consider a remote sensing system in which fixed sensors are placed in a region, and a single drone flies over the region to collect information from cluster heads. We assume that the drone has a fixed maximum range and that the energy consumption for information transmission from the cluster heads increases with distance according to a power law. Given these assumptions, we derive local optimum conditions for a drone path that either minimizes the total or maximum energy required by the cluster heads to transmit information to the drone. We show how a homotopy approach can produce a family of solutions for different drone path lengths so that a locally optimal solution can be found for any drone range. We implement the homotopy solution in Python and demonstrate the tradeoff between drone range and cluster head power consumption for several geometries. Execution time is sufficiently rapid for the computation to be performed in real time so that the drone path can be recalculated on the fly. The solution is shown to be globally optimal for sufficiently long drone path lengths. A proof of concept implementation in Python is available on GitHub. For future work, we indicate how the solution can be modified to accommodate moving sensors.