Onions with glossy leaves possess an unexpected advantage: natural resistance to thrips, one of the crop’s most damaging pests. Researchers identified a premature stop codon mutation in the AcCER2 gene, disrupting the formation of epicuticular wax—a layer that normally gives onion leaves their frosty-white look. Multi-omics analyses revealed that the loss of wax alters fatty acid elongation, gene expression, and metabolite accumulation, resulting in higher flavonoid levels and reduced soluble sugars. These changes appear to make the glossy onions less attractive to thrips. The discovery of AcCER2’s regulatory role provides a new genetic target for breeding pest-resistant onion varieties.

Shanghai Jiao Tong University researchers have developed a data-driven method to recognize the coordinated intentions of unmanned aerial vehicle (UAV) swarms.

By combining a simplified flight motion model with an artificial neural network, the approach can predict swarm behavior early and accurately—advancing aerial surveillance and autonomous defense systems.

The innovation features of this research are: Treating a UAV swarm as a single intelligent entity and combining the Dubins motion model with an artificial neural network to achieve early and highly accurate intention recognition of coordinated swarm behaviors.

Fresh leafy vegetables such as pakchoi rapidly lose quality after harvest due to leaf yellowing and senescence. This study uncovers the molecular mechanism through which the plant hormone 2,4-epibrassinolide (EBR), a brassinosteroid analog, delays leaf senescence in pakchoi. Researchers identified BrWRKY8, a nucleus-localized transcription factor that promotes leaf aging by activating chlorophyll degradation (BrSGR2) and brassinosteroid degradation (BrCHI2) genes. EBR treatment suppresses BrWRKY8 expression, thereby maintaining chlorophyll and hormone balance, leading to extended postharvest freshness. These findings reveal a critical regulatory pathway linking EBR and BrWRKY8 in delaying leaf senescence.

Tomato improvement through genome editing has long been hindered by the difficulty of generating transgenic plants. Researchers have now developed a virus-induced genome editing (VIGE) platform that enables heritable mutations in tomato (Solanum lycopersicum) without the need for tissue culture. By engineering a tobacco rattle virus (TRV) system carrying mobile guide RNAs derived from the tomato Flowering Locus T (SlFT) gene, and pairing it with a SlUBI10-driven Cas9 expression line, they successfully produced knockout tomato seeds with up to 100% heritability. This innovative system dramatically reduces time and labor costs for tomato gene editing, opening the door to rapid functional studies and breeding applications.

Trichomes—the tiny hair-like structures covering tomato surfaces—serve as a crucial frontline defense against environmental stress and pest attacks. However, the mechanisms behind their multicellular formation remain poorly understood. A new study identifies two C2H2-type zinc finger proteins, SlH3 and SlH4, as essential regulators promoting multicellular trichome initiation and elongation in tomato. Using CRISPR-Cas9 knockout mutants and molecular assays, researchers showed that SlH3 and SlH4 work cooperatively to enhance the expression of Woolly (Wo), a master transcription factor controlling trichome development. These findings reveal a previously unknown regulatory layer that fine-tunes trichome patterning and strengthen the genetic framework for improving crop resilience through natural plant structures.

Abstract

Purpose – We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

Design/methodology/approach – We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

Findings – We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

Originality/value – We examine four types of expected return proxies and use the newly developed methodology in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

A just energy transition (JET) to low-carbon fuels, such as green hydrogen, is critical for mitigating climate change. Countries with abundant renewable energy resources are well-positioned to meet the growing global demand for green hydrogen. However, to improve the volumetric energy density and facilitate transport and distribution over long distances, green hydrogen needs to be converted into an energy carrier such as green ammonia. This study conducted a comparative life cycle assessment (LCA) to evaluate the environmental impacts of green ammonia production, with a particular focus on greenhouse gas (GHG) emissions. The boundary of the study was from cradle-to-production gate, and the design was based on a coastal production facility in South Africa, which uses renewable energy to desalinate seawater, produce hydrogen, and synthesise ammonia. The carbon intensity of production was 0.79 kg CO₂-eq per kg of ammonia. However, if co-products of oxygen, argon and excess electricity are sold to market and allocated a portion of GHG emissions, the carbon intensity was 0.28 kg CO₂-eq per kg of ammonia. Further, without the sale of co-products but excluding the embodied emissions of the energy supply system, as defined in the recent international standard (ISO/TS 19870), the carbon intensity was 0.11 kg CO₂-eq per kg of ammonia. Based on the hydrogen content of ammonia, this is equivalent to 0.60 kg CO₂-eq per kg of hydrogen, which is well below the current threshold for certification as a low-carbon fuel. The process contributing most to the overall environmental impacts was electrolysis (68%), with particulate matter (55%) and global warming potential (33%) as the dominant impact categories. This reflects the energy intensity of electrolysis and the carbon intensity of the energy used to manufacture the infrastructure and capital goods required for green ammonia production. These findings support the adoption of green ammonia as a low-carbon fuel to mitigate climate change and help achieve net-zero carbon emissions by 2050. However, achieving this goal requires the rapid decarbonisation of energy supply systems to reduce embodied emissions from manufacturing infrastructure.