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.

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.

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.

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.

Immune Discovery: A New Open-access Journal to Share Your High-quality Research in Immunology

Investigating and Analyzing the Impact of a Bidirectional Multiport Power Electronic Transformer Interface on the Power Quality and Stability of Interconnected Microgrids

This paper investigates the potential benefits of a bidirectional multi-port power electronic transformer (MPPET) to interface multiple microgrids with utility distribution networks in terms of power quality and stability. The main concept is based on the interaction between the utility grid, the connected microgrids, and the MPPET in controlling the disturbances that lead to grid instability and power quality issues. The proposed MPPET does not require any serious communication infrastructure for operation. In addition, the MPPET can respond to reverse power flow caused by excess power generation on the grid. Due to the intermittent nature of the renewable energy sources and the different stages involved in the design of the proposed MPPET, the system is liable to internal DC voltage fluctuation, causing grid instability; thus, an energy storage system (ESS) is incorporated to avert the challenges. The networks under investigation and the proposed MPPET are designed and simulated using MATLAB and Simulink software. The electrical isolation capability of the proposed bidirectional MPPET is verified through simulation. Several case studies have been carried out to evaluate the behavior of the system under different operating conditions and to check the feasibility of MPPET for power quality improvements. It was observed that the MPPET is proficient in regulating power quality issues, thus enhancing grid stability. It is also varied that the proposed MPPET prevents the escalation of the impact of faults or disturbances all over the grid. At the same time, it is verified that the proposed bidirectional energy storage systems enhance energy transfer between the utility grid and microgrids, which improves the system’s stability.

Initial Stages of Development of an Automated Measurement Technique on Incisors

Teeth are an important object of studies in many scientific disciplines and, among various study techniques, measurements have one of the most promising prospects for further improvements supported by progress in computer sciences, imaging and image processing. Our recent work on automated odontometric algorithms for premolars and molars has gradually come to develop similar methods for another group of teeth—incisors. Using 3D reconstructions of teeth obtained through micro-focus tomographic scanning, we propose landmarks, which correspond to main morphological features of incisors and enable their formal description. In this article we present an orientation and measurement technique, based on an interpretation of incisor morphology, as a system which is able to perform in a fully automated mode. Since the primary objective of the current paper is to introduce methodological improvements, data on measurements and their results are shown at the most basic level.

Advancing Green Infrastructure Solutions in Rural Regions: Economic Impacts and Capacity Challenges in Southwest Ontario, Canada

Green infrastructure (GI) is a growing topic in urban planning, asset management, and climate change adaptation. However, rural regions have been under-represented in the discourse. This paper explores the benefits and challenges associated with the implementation and management of GI through a regional study of rural communities in southwestern Ontario. Our focus concerns the inter-relationships between GI, economic resilience, and the development of rural places. Findings show rural communities benefit from GI initiatives like natural stormwater management, park naturalization, and natural heritage restoration, which provide low-cost municipal services, conserve agricultural soils, and contribute to the amenity appeal of rural places. Challenges surrounding awareness, organizational capacity, and environmental regulation have slowed the uptake of GI and led to inconsistencies across jurisdictions. A mix of supportive policies, funding of demonstration projects with economic monitoring, and training to build professional capacity will advance the use and efficacy of GI across rural regions.

Solid Waste Recycling in Textile Processing Industries: A Case Study of India’s Clothing Hubs

This study investigated the type and amount of solid waste generation from textile wet processing industries and analyzed the disposal and recycling strategies implemented for its utilization. The method involved industrial interactions with textile processing mills. Data was gathered based on a field survey of manufacturing units and their compliance management teams. The solid waste generated in textile processing stages against input raw materials and fuel sources was recorded. The challenges in recycling solid waste are identified and further scope for its valorization is suggested. The results indicate that significant solid waste produced during the wet processing of textiles arises from waste fabric cuttings, combustion of fuels used in processing stages, and sludge generated from the post-effluent treatment. Around 80% of solid waste generated during the wet processing of textiles can find applications in the construction industry. Effective management of solid waste and its potential applications in construction are elaborated in detail.