ABSTRACT The genus Elysia Risso, a diverse group of sea slugs within the order Sacoglossa, comprises highly specialised herbivores capable of photosynthesis. Despite their leaf-like appearance being relatively consistent across species, Elysia exhibits considerable morphological diversity in internal systems among clades. Ecological strategies and behaviours may also vary. Prior descriptions have often relied solely on external traits or radular morphology, a limitation that has contributed to persistent taxonomic misinterpretations. In this study, a high-resolution, comprehensive examination of external and internal morphological features was conducted on four key Caribbean species to elucidate taxonomic uncertainties and establish a detailed morphological baseline. The investigation involved detailed comparative analyses of these traits to assess morphological similarities among evolutionarily related species and identify potentially informative characters for accurate species descriptions. The analyses revealed high intraspecific variability in some external traits, limiting their reliability for species identification. However, specific features were identified, such as dorsal sinus patterns, renopericardial complex arrangement and parapodial shape, that are taxonomically informative. Internal systems also showed significant variation in the renopericardial complex, ganglion and buccal mass morphology, and reproductive structures, reflecting potential adaptive responses to ecological niches and providing valuable insights into species relationships. Overall, our study underscores the importance of integrating detailed morphological analysis of multiple internal systems, helping in resolving taxonomic uncertainties and enhancing our understanding of the evolutionary dynamics within Elysia. Integrating morphological analyses with molecular data holds promise for unravelling the intricate evolutionary history and ecological adaptations of these enigmatic marine organisms.

Adaptive laboratory evolution of Lactobacillus gasseri-01 toward a safe probiotic for enhanced sulforaphane transformation.

Biobanks are essential for intelligent medicine but face fragmentation and heterogeneity. No standardized framework exists for assessing biobank data value using public information; this study addresses this gap. We systematically evaluated 94 global biobanks (2010-2024) through a literature review and structured data extraction. Based on 12 international standards, we developed the 5-dimensional SHARE principle (Standardization, Hierarchical structuring, Analytical compatibility, Regulatory compliance, Evolutionary adaptability), operationalized into 10 indicators with a 4-tier scoring system. GPT-4o provided AI-supported prescoring, which was validated by 10 experts and through case studies, including RARPKB. The SHARE principle and a classification map of 94 biobanks were generated. AI and expert scoring showed substantial consistency (κ = 0.62; 95% CI, 0.54-0.70). Biobanks were categorized into 4 tiers: Traditional (60-69), Data-Driven (70-79), Knowledge-Guided (80-89), and Generative and Reasoning-oriented Biobank (90-100). Case validation confirmed utility for disease-specific biobanks. We highlight the principal findings, critically examine the reliance on public documentation, propose mitigation strategies, and discuss indicator weighting and implications for translational informatics. The SHARE principle provides a scalable, standardized method for assessing biobank data value, supporting biobank development, resource discovery, and the development of AI-driven biomedical ecosystems for intelligent medicine.

Selenoproteins represent a distinct class of proteins that incorporate selenocysteine (Sec), whose biosynthesis and translational integration are dependent on selenium availability and the presence of a selenocysteine insertion sequence (SECIS). These proteins are indispensable for redox regulation, antioxidant defense, and thyroid hormone metabolism, among other vital biological processes. Remarkably, selenoproteins act as critical regulators of cellular fate decisions, a function that hinges on Sec-a residue whose biosynthesis and translational incorporation into protein involve machinery far more intricate than that of canonical amino acids. This evolutionary adaptation, whether arising from stochastic mutational events or as an obligatory trade-off for functional precision, underscores the sophisticated molecular regulatory strategies in living organisms. In this review, we comprehensively outline the uptake and metabolic pathways of selenoamino acids in eukaryotes, with particular emphasis on the biosynthetic mechanism of Sec and its unique translational incorporation into selenoproteins. We systematically elucidate the multi-layered regulatory networks that govern these biological processes within cells. Furthermore, we present a taxonomic classification and functional synthesis of eukaryotic selenoproteins, accompanied by an in-depth analysis of their molecular roles in various pathological states. Special emphasis is placed on the glutathione peroxidase (GPX) family, especially GPX4, in ferroptosis regulation and its sophisticated control mechanisms. Additionally, this review summarizes key challenges in current selenoproteins research and explores potential therapeutic strategies for cancer treatment by targeting selenoproteins.

Adaptive evolution and transcriptional plasticity of the cytochrome P450 superfamily underpin sulfide tolerance in the Urechis unicinctus

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical assessment alone. This study proposes an attention-based deep learning framework for classifying PD from resting-state EEG with minimal preprocessing and leakage-safe evaluation. Raw EEG recordings were first partitioned at the subject level. Within each fold, the selected motor-related EEG channel was decomposed into canonical sub-bands using discrete wavelet transform, and the resulting sub-band signals were then segmented into overlapping temporal windows. Each sub-band window was transformed into a time-frequency spectrogram using the short-time Fourier transform and classified using a ResNet-101 backbone enhanced with dual channel-spatial attention. Hyperparameters were optimized using an Enhanced Adaptive Hybrid Covariance Matrix Adaptation Evolution Strategy (AH-CMA-ES), applied only within training/internal-validation subjects in each fold. Model performance was evaluated on two independent public EEG datasets, UC San Diego and University of Iowa, using subject-wise nested leave-one-subject-out cross-validation. In each fold, the held-out subject was excluded from training, augmentation, hyperparameter optimization, early stopping, and model selection. The proposed framework achieved 95.2% segment-level and 96.77% subject-level accuracy on UCSD, and 92.1% segment-level and 92.86% subject-level accuracy on Iowa, with subject-level decisions obtained by majority voting over non-augmented test segments. In addition to classification, sub-band topographical analysis provided exploratory neurophysiological interpretation across canonical EEG rhythms, revealing patterns consistent with reported PD-related oscillatory alterations. These findings suggest that resting-state EEG combined with attention-based deep learning can support robust, interpretable PD classification, while larger heterogeneous cohorts are needed to further validate clinical generalizability.

Adaptive evolution of engineered Saccharomyces cerevisiae in favored and unusual chemical environments.

Advances in the HAK/KUP/KT Potassium Transporter Family in Regulating Na+/K+ Homeostasis and Salt Tolerance in Plants.

A general theory of adaptation is one that accounts for both the quantitative and functional properties of adaptive evolution. To date, the field has focused more on the former and less on the latter. Here I build on the results of adaptive laboratory evolution experiments in microbes to present a sketch for functional theory of adaptation built around the idea that the primary source of selection in the initial stages of adaptation often involves rapid re-growth in the presence of stress. Consequently, the genes targeted during adaptation are often those involved in the regulation of gene expression, including global regulators associated with managing the stress response. These mutations, especially those involving high level gene regulation, are often highly pleiotropic, in contrast to the expectation derived from the quantitative theory of adaptation. Whether this theory can be used to make sense of adaptation more generally beyond microbial evolution experiments remains an open issue.

Engineering robust Saccharomyces cerevisiae for high-yield sabinene production from lignocellulosic hydrolysate.

Mechanistic insights into fermentation performance and innovative breeding strategies for industrial brewing yeast.

High-titer nicotinamide adenine dinucleotide production via artificially designed pseudo-de novo biosynthesis pathway.

Morlet-controlled parameter adaptive differential evolution with diversity-triggered restart for high-precision photovoltaic model parameter identification

Research on intelligent beam tuning for 2x3.0 MV tandem accelerator based on enhanced parameter adaptive differential evolution algorithm

Biotechnology holds great potential for sustainable industrial production, with methanol emerging as a promising alternative carbon source due to its availability, cost-effectiveness, and high degree of reduction. Natural methylotrophic microorganisms, such as Komagataella phaffii, are well-suited for methanol-based processes. However, the native xylulose monophosphate (XuMP) cycle in K. phaffii is less energy-efficient than the bacterial ribulose monophosphate (RuMP) cycle, which requires fewer ATP molecules for methanol assimilation. In this study, we introduced the bacterial RuMP cycle as the sole methanol assimilation pathway in K. phaffii. The resulting strain, RuMPi, grew on methanol as its sole carbon and energy source, achieving a specific growth rate of μ=0.007±0.001 h-1. Optimization and adaptive laboratory evolution (ALE) improved the strain's performance, resulting in the final strain, RuMPi_mc_fba-ta_evo, with a growth rate of μ=0.030±0.001 h-1. The final strain exhibited biomass yields and methanol uptake rates comparable to the wild type at lower growth rates. This work demonstrates the feasibility of engineering K. phaffii with a heterologous RuMP cycle and provides insights for optimizing methanol utilization in methylotrophic yeasts for industrial applications.

The chloroplast genomes of Verbascum speciosum and V. sinuatum reveal lineage-specific structural variations and hypervariable loci (ycf3_1-trnS-GGA, rps15, matK) that serve as powerful markers for species identification. Signatures of positive selection in ndhB, ycf3, and ycf4 suggest adaptive evolution in plastid genes, while phylogenomic analyses confirm that plastome-scale data are essential for resolving species-level relationships in this medicinally valuable genus. The genus Verbascum (Scrophulariaceae) is both species-rich and notoriously difficult to classify, with many members valued for their medicinal properties but hampered by vague morphological boundaries and a lack of reliable molecular data. To address this, we sequenced and assembled the complete chloroplast genomes of two Iranian medicinal species, Verbascum speciosum and Verbascum sinuatum. The two plastomes measured 153,325bp and 153,038bp, respectively, each containing the typical quadripartite structure and 131 genes. Comparative work uncovered lineage-specific shifts at the inverted repeat boundaries, most notably involving the rpl23 gene, as well as clear differences in simple sequence repeat abundance, with V. sinuatum showing far fewer SSRs than its relative. Scanning across the genomes, we identified several hypervariable spots (rps15, rps16, matK, ycf1, and even some tRNA genes) that could serve as useful barcoding markers. While most coding regions are under strong purifying selection, we found signs of positive selection at specific codon sites within ndhB, ycf3, and ycf4. Phylogenomic analysis using whole plastome data strongly supported the monophyly of Verbascum and confidently placed both new species within the genus. In contrast, standard single-locus barcodes failed to resolve species-level relationships. This work provides the first complete cp genomes for these two species and demonstrates that plastome-scale data offer a real step forward for untangling Verbascum systematics, identifying informative markers, and guiding future taxonomic work.

The significant temperature variations across northern and southern China have driven the adaptive evolution of Chinese native cattle breeds, allowing them to thrive in diverse and extreme bioclimate environments. Understanding how these breeds have adapted to varying temperatures is essential for identifying genetic factors that contribute to their survival in such conditions. In this study, using whole-genome sequence data of 336 individuals (with an average sequencing depth of 30.12 ×) from 21 cattle breeds, including 8 breeds from cold regions, 3 from warm regions, and 10 from hot regions, clear genetic differentiation among the three groups of breeds was revealed. Using whole-genome SNP, InDel, and SV data, a series of selective genomic regions, genes, and variants/SVs associated with cold or hot temperature adaptability were identified. Key genes, including KLB, HSPA4, ECSCR, DNAJC18 and SLC9A1 are speculated to be responsible for cold/hot adaptability based on the extreme difference in allele frequency of the selective variants/SVs harbored by these genes, their known biological functions, protein-protein interaction network, findings from previous studies on their relation to environmental adaptation, and their tissue specificities. By integrating SNP, InDel, and SV data, this study provides a comprehensive genetic framework for understanding selective environmental adaptation. These findings enhance our understanding of the mechanisms underlying temperature adaptation in cattle and offer a molecular foundation for the development of new breeds.

A neural-driven constructive heuristic for the flexible job shop scheduling problem: An efficient alternative to complex deep learning methods

Advancing metal organic frameworks screening and remediation condition optimization for removing persistent organic pollutant via integrated machine learning and theoretical calculation insights.

Ontogenetic co-option of myotoxin expression variation in island Eastern Diamondback Rattlesnake (Crotalus adamanteus) venoms.