Biology

HR38 and E75 are early 20-hydroxyecdysone (20E)-responsive nuclear receptors that play important roles in insect molting and metamorphosis. Here, we cloned and characterized HvHR38 and HvE75 from Heortia vitessoides and analyzed their conserved domains and phylogenetic positions. Both genes exhibited distinct stage- and tissue-specific expression patterns closely associated with ecdysteroid-regulated developmental processes. Hormone-induction assays further demonstrated that the transcription of HvHR38 and HvE75 was strongly activated by 20E. RNA interference targeting either gene resulted in significant transcript knockdown, accompanied by incomplete molting, pupal deformities, and molting failure, ultimately leading to markedly reduced survival, with dsHvE75 causing the highest lethality. Collectively, these results suggest that HR38 and E75 function as key components of the early 20E-responsive transcriptional network involved in molting regulation, and highlight their potential as RNAi targets for species-specific and environmentally sustainable pest management.

Background: Artificial Intelligence (AI) has emerged as a transformative tool in in vitro fertilization (IVF) as it has done in other sectors. In IVF, AI offers advancements in embryo selection, treatment personalization, and outcome prediction. It does so by leveraging deep learning and computer vision, as well as AI-driven platforms such as ERICA, iDAScore, and IVY where the goal is to address the limitations of traditional embryo assessment. Key amongst them are the issues of subjectivity, labor intensity, and limited predictive power. Despite rapid technological progress, the integration of AI into routine IVF practice faces key challenges. These are issues related to clinical validation, ethical dilemmas, and workflow adaptation. Rationale/Objectives: This review synthesizes current evidence to evaluate the role of AI in IVF, focusing on six critical dimensions: (1) the evolution of AI from traditional embryology to algorithmic assessment, (2) clinical validation and regulatory considerations, (3) limitations and ethical challenges, (4) pathways for clinical integration, (5) real-world applications and outcomes, and (6) future directions and policy recommendations. The objective is to provide a comprehensive roadmap for the responsible adoption of AI in reproductive medicine. Outcomes: AI demonstrates significant potential to improve the precision and efficiency of IVF. Studies report that AI models can achieve 10 to 25% higher accuracy in predicting embryo viability and implantation potential compared to traditional morphological assessment by embryologists. This enhanced predictive power supports more consistent embryo ranking, facilitates elective single-embryo transfer (eSET) strategies, and is associated with 30 to 50% reductions in embryologist workload per embryo cohort. Early adopters report promising trends. However, large-scale randomized controlled trials have yet to conclusively demonstrate a statistically significant increase in live birth rates per transfer compared to expert embryologist selection. The most immediate and evidenced value of AI lies in hybrid decision-making models. This is where it augments embryologists by providing data-driven, objective support, thereby standardizing workflows and reducing subjectivity. Wider Implications: The sustainable integration of AI into IVF banks on three key aspects: robust evidence generation, interdisciplinary collaboration, and global standardization. To foster these, policymakers ought to establish regulatory frameworks for transparency and bias mitigation. On their part, clinicians need training to interpret AI outputs critically. Ethically, safeguarding patient trust and equity is non-negotiable. Future innovations, mainly AI-enhanced genomics and real-time monitoring, could further personalize care. However, their success depends on addressing current limitations. By balancing innovation with ethical vigilance, AI holds the potential to revolutionize IVF while upholding the highest standards of patient care.

Woody debris decomposition is a key process in forest ecosystem material cycles, with invertebrate communities playing a vital role. Distinct physicochemical properties of woody debris types lead to varying effects on these communities. Taking woody debris in Saihanba’s Larix principis-rupprechtii plantations, Betula platyphylla natural secondary forests, and larch–birch mixed forests (northern China) as objects, we collected woody debris-inhabiting invertebrates via hand-sorting. We studied how tree species (larch/birch), forest types (pure/mixed), and decay stages (I–V) collectively regulate invertebrate community assembly. Results showed significant differences in woody debris physicochemical properties across these factors. Phytophagous groups dominated early decay stages (I–III) and decreased significantly (p < 0.05) with reduced wood density. In contrast, saprophagous and predatory groups increased with decay, correlated with higher TN and were more abundant in mixed than pure forests. NMDS indicated significant community differences among tree species/forest types in early decay, converging later. PLS-PM further confirmed functional groups’ response pathways to woody debris characteristics. Thus, preserving woody debris integrity and diversity in plantations is crucial for maintaining invertebrate diversity, promoting nutrient cycling, and enhancing forest ecosystem functions.