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Understanding the molecular mechanisms of carcinogenesis, including disruptions in the homologous recombination system, is fundamental to understanding malignant transformation. Dysfunction of homologous recombination genes, such as BRCA1 and BRCA2, contributes to genomic instability and the development of more aggressive tumor clones. The use of chemical carcinogens enables the modeling of tumor formation and the monitoring of changes in molecular genetic parameters. This approach is important for understanding how tumor cells adapt to genotoxic stress and for advancing the development of personalized cancer therapies. The objective of this study was to evaluate the expression of key homologous recombination system genes in a model of chemically induced carcinogenesis in mice. Materials and Methods: Male outbred ICR (CD-1) laboratory mice (n = 40) were used to study chemically induced carcinogenesis. The animals were divided into four groups: two control groups and two experimental groups, which received 3-methylcholanthrene (MC) or trichloroacetic acid (TCA). Tumor cells were identified by histological analysis of autopsy material using light microscopy after standard hematoxylin and eosin staining. RNA and DNA were extracted from cell suspensions using the RNeasy Plus Mini Kit and QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), respectively. The expression levels of homologous recombination genes were assessed by RT-PCR and microarray analysis. Digital PCR was performed to assess chromosomal aberrations in the Brca1 gene. Results: Tumor formations were identified in laboratory animals two months after 3-methylcholanthrene. Histological analysis revealed morphological changes in a pleomorphic cell tumor, forming diverse, multidirectional fascicular and swirling structures, as well as large solid foci composed of markedly polymorphic spindle-shaped and epithelioid cells. Analysis of copy number aberrations in the examined samples showed that the frequency of Brca1 deletions was 60%, while 40% of animals had normal gene copy number. To further characterize the molecular changes, we assessed gene expression levels through expression microarray analysis. A total of 14 genes were hypoexpressed in the tumor compared to the normal tissue, with p < 0.05. A high level of differential expression was characteristic for Rad50, Rad51, Brca1, Brca2, and Pold4. Two genes, Rad52 and Bard1, exhibited increased expression levels. It was shown that as the tumor mass increased, so did the frequency of homologous recombination genes with hypoexpression. Conclusions: Our findings confirm that MC and TCA influence tumor formation and reveal that suppression of homologous recombination genes may contribute to this process. In addition, it has been established that as tumors progress, the expression of DNA repair genes declines and aberrant gene states accumulate. These data emphasize the importance of studying the state of DNA repair genes for the development of more effective strategies for cancer diagnosis and therapy. Full article

Systematic, spatially explicit tree inventories are increasingly implemented in cities worldwide, as they are crucial for evidence-based green infrastructure planning. Currently, different approaches are adopted, which differ in methodological framework and parameter standardization, limiting comparative assessments and coordinated monitoring. This study presents a replicable protocol for a field-based digital street tree census, applied in a densely built central area and in a low-density suburban area of Rome. Field surveys documented a set of 15 parameters, including species identity, dendrometric and tree pit parameters, acquired using open-source QGIS/QField tools. Subsequent analysis evaluated floristic diversity, population structure, and climate suitability at the neighborhood scale, enabling the identification of context-specific vulnerabilities. The testing of the methodology shown in this work involved 13,017 georeferenced tree pits, pointing out substantial pit restoration needs and insufficient soil conditions in the most densely urbanized area, whereas the suburban area shows optimal conditions with extensive road verge green spaces. Joint interpretation of the considered parameters reveals that high floristic diversity alone does not guarantee climate resilience: high-diversity neighborhoods can exhibit substantial non-climate-resilient species and limited alignment with local species recommendations, demonstrating that comprehensive evaluation of street tree populations requires integrated analysis. The operationalized protocol establishes a replicable, municipally scalable methodological framework, providing policymakers with fine-scale, actionable insights enabling differentiated urban forestry strategies addressing both infrastructure deficits and long-term species climate suitability. Full article

Recent shifts in teacher perspectives indicate that generative artificial intelligence (GenAI) has begun to transform long-standing patterns of routine and repetition in educational practice. This study investigates how educators across different educational levels within an Arab educational context perceive the role of GenAI in fostering sustainable teaching and learning. It examines its influence on learning processes, instructional practices, and educational continuity. Adopting a qualitative research design, the study draws on focus group discussions to capture teachers’ perspectives and applies thematic analysis to explore shared experiences of AI integration in classroom settings. The analysis identified six interconnected themes reflecting a move toward more open and generative learning, the sustainability of learning activities through diversity and personalization, support for teachers in planning, implementation, and assessment, the empowerment of students’ understanding, thinking, achievement, and learning continuity, the central role of ethical considerations, and the challenges and requirements associated with sustainable implementation. The findings demonstrate that the educational value of GenAI is shaped by how it is meaningfully integrated to sustain teaching and learning practices over time. GenAI can contribute to quality and inclusive education in ways that support the long-term aims of Sustainable Development Goal 4. Full article

Simian foamy viruses (SFVs) are ancient retroviruses that co-evolve with nonhuman primates (NHPs), although genomic data from Asian and African monkeys are limited. We report the characterization of three new SFV colobine genomes from two Asian species (Trachypithecus francoisi (Tfr) and Pygathrix nemaeus (Pne)) and one African monkey (Colobus guereza, Cgu), obtained via metagenomics analysis of peripheral blood leukocyte tissue culture isolates. Genomic analyses found conserved structural, enzymatic, and auxiliary genes flanked by long terminal repeats, with all major transcriptional and structural motifs highly preserved. An in-frame Δtas mutation in tissue culture and ex vivo specimens was identified in the SFVpne genome, which may promote viral latency. Phylogenetic analyses revealed that these colobine SFVs have distinct evolutionary trajectories without clustering together, contradicting a strict virus–host co-evolution. We developed a new generic SFV PCR assay using these genomes with increased detection sensitivity for Colobinae SFVs and identified four new human infections with Cgu-derived SFV in the Democratic Republic of Congo. Our findings indicate that SFV evolution in colobine monkeys is shaped by host switching, cross-species transmission, and high viral diversity. Our study underscores the importance of broadening SFV genomic sampling to better understand viral evolution, zoonotic risk, and improved diagnostic capabilities. Full article

Empoasca onukii severely damages tea plants as a major sap-sucking pest, leading to the increasing adoption of biopesticides as a sustainable alternative to chemical control. However, existing research has largely focused on the final lethal effects of these agents, while their short-term interference patterns on pest feeding behavior remain unclear. In this study, six biopesticides—azadirachtin, matrine, Beauveria bassiana, Metarhizium anisopliae CQMa421, Mamestra brassicae nucleopolyhedrovirus (MbNPV), and Bacillus thuringiensis (Bt)—were evaluated using the electrical penetration graph (EPG) technique to precisely analyze their interference on the short-term (6 h) feeding behavior of E. onukii, alongside field trials to validate control efficacy. EPG analysis revealed that different types of biopesticides significantly disrupted feeding in distinct ways. The two botanical pesticides and CQMa421 mainly prolonged the non-probing phase (waveform Np) and reduced active non-phloem feeding (C waveform) (p < 0.05); Bt and B. bassiana significantly extended the resting phase (waveform R) and decreased the frequency of passive phloem feeding (waveform E) (p < 0.05), whereas MbNPV exhibited a combined effect, simultaneously prolonging both Np and R waveforms while reducing waveform C (p < 0.05). Field trials showed that all tested treatments achieved complete control (100%) at 21 days post-application. Moreover, across a wide range of concentrations, they all demonstrated excellent and stable control performance. These findings provide diverse agent options for the green control of E. onukii in tea plantations and lay a foundation for constructing a green integrated pest management system centered on biological control for tea plant pests. Full article

In response to growing global concerns about the impacts of environmental changes on marine ecosystems, scientists have increasingly turned their attention to the role of aquatic environments in shaping biodiversity. This study aimed to assess the biodiversity of northern Liaodong Bay in the context of environmental changes and to elucidate the mechanisms by which aquatic environmental factors influenced different biological groups. Based on the 2024 survey data of phytoplankton, zooplankton, macrobenthos and nekton, the average Marine Biodiversity Index (MBI, a comprehensive index) was calculated as 53.08, corresponding to a moderate evaluation level. This suggests a relatively rich diversity of marine species and a fairly even distribution. A correlation analysis between water quality factors and biological community structure reveals that biodiversity in the bay is under pressure from multiple environmental stressors, including elevated COD and heavy metal contamination (e.g., Pb). The study recommends targeted biodiversity conservation strategies and ecosystem management measures to enhance the resilience of the bay’s ecosystem and mitigate the effects of these environmental stressors. Full article

Eucalyptus is a highly diverse genus of the Myrtaceae family that is planted worldwide. Many changes occur during callus development, an important process during in vitro plant regeneration. In this study, we conducted methylome and transcriptome analyses to reveal such changes. The results showed that differentially expressed genes between E. camaldulensis (voucher ID: c0009; high embryogenic potential) and E. grandis × urophylla (voucher ID: j0017; low embryogenic potential) during callus development were enriched in plant hormone signal transduction and MAPK (Mitogen-activated protein kinase) signaling pathways. qRT-PCR analysis showed AHP, BAK1, BSK, CRE1, GID1, MKS1, PR-1, PYL, RbohD, and TCH4 could be involved in the callus development and plantlet regeneration capacity. The differences observed in regenerative potential during callus maturation between the two species under study provide a reliable molecular basis for the study of Eucalyptus regeneration mechanisms. Full article

Although existing Chinese word segmentation systems have achieved substantial progress on standard benchmarks, prediction disagreements among heterogeneous models remain prevalent when processing texts containing complex ambiguities and out-of-vocabulary words, and traditional static ensemble methods such as majority voting often fail to make reliable decisions in low-consensus scenarios. To address this issue, this paper proposes SegFusion, a stacked heterogeneous ensemble framework for Chinese word segmentation based on word lattice re-scoring. The framework first constructs a candidate word lattice to consolidate diverse outputs from heterogeneous segmenters into a unified lattice representation, and then incorporates unsupervised statistical features, including mutual information and branching entropy, as external discriminative evidence to perform dynamic arbitration at the word level, followed by global decoding to obtain the optimal segmentation path. Experimental results on multiple standard datasets demonstrate that SegFusion consistently outperforms individual models and mainstream ensemble baselines in terms of overall segmentation performance and out-of-vocabulary (OOV) recall. In particular, on the MSR dataset with severe ambiguity, SegFusion achieves improvements of 3.71% in F1 score and 4.10% in OOV recall. Further fine-grained analysis shows that the introduction of unsupervised statistical features effectively mitigates model consistency bias in low-support scenarios. These results indicate that integrating language statistical priors independent of training data into the ensemble arbitration stage is an effective way to enhance the robustness and consistency of Chinese word segmentation systems. Full article

Non-specific lipid transfer proteins (nsLTPs) play a crucial role in lipid transport across membranes, contributing to cellular integrity and structural stability. These proteins are characterized by the presence of eight conserved cysteine residues that form four disulfide bonds and a hydrophobic cavity that is essential for lipid binding and transport. Interactions of nsLTPs with diverse ligands enable them to participate in key biological processes, including signal transduction, protein folding, membrane stabilization, and cell wall organization. Additionally, these proteins are integral to plant responses to abiotic and biotic stresses and to developmental processes, including growth, germination, and flowering. The interaction between nsLTPs and plant signaling molecules activates regulatory networks that modulate stress-responsive gene expression, reinforcing plant resilience under adverse conditions. Despite their functional significance, the evolutionary trajectory, subcellular localization, and regulatory mechanisms governing nsLTP expression remain limited, as reflected in previous reviews on nsLTPs. This review provides a comprehensive analysis of nsLTP evolution, roles in plant defense and signaling, functional diversity, updated subcellular localization, and future research directions based on recent findings. Full article

Sweeteners occupy a pivotal role in the global transition toward sustainable, health-aligned, and resource-efficient food systems. Conventional sucrose production carries significant environmental burdens, while escalating metabolic health concerns intensify demand for viable alternatives. This paper reframes sweeteners not as commodity ingredients, but as digitally engineered, biologically manufactured, and circularity-optimized materials within the emerging bioeconomy. Advances in artificial intelligence (AI), metabolic engineering, precision fermentation, and lignocellulosic valorization are fundamentally reshaping sweetener innovation. We introduce the Sweetener Innovation 4.0 framework, in which AI functions as the integrative engine linking molecular design, bioprocess optimization, and system-level sustainability. Across diverse sweetener classes, including steviol glycosides, mogrosides, rare sugars, sweet proteins, and forestry-derived polyols, AI accelerates discovery, improves metabolic flux control, optimizes downstream processing and enables more adaptive manufacturing systems. This digital–biological convergence is progressively decoupling sweetness production from land-intensive agriculture, reducing dependence on geographically constrained crops, and enabling resilient, low-carbon manufacturing pathways. Comparative life-cycle assessments highlight substantial sustainability gains, but also reveal persistent methodological gaps, particularly in accounting for downstream-processing energy and digital infrastructure emissions. Socioeconomic analysis further underscores the importance of equitable transitions, transparent labeling, and effective consumer communication as fermentation-derived sweeteners enter global markets. Looking forward, we identify key frontiers for Sweetener Innovation 4.0, including de novo AI-designed sweeteners, autonomous fermentation systems, carbon-negative feedstocks, personalized sweetness modulation, and integrated circular biorefineries. Together, these developments position sweeteners as a top domain for demonstrating how AI, biotechnology, and sustainability principles can jointly reshape ingredient development and industrial systems within the 21st-century circular-economy. Full article

Schools are increasingly shaped by societal change and growing cultural diversity, calling for refined approaches to understanding the acculturation of minoritized students. This study examined acculturation profiles among minoritized primary students (n = 736) in Switzerland, applying a tridimensional framework that incorporates a multicultural orientation, beyond heritage and majority orientation. Using a three-stage latent profile analysis, four distinct acculturation profiles emerged: Multiculturalists (33.3%), Heritage-oriented Multiculturalists (29.9%), Majority-oriented Multiculturalists (29.2%), and a smaller group of Assimilationists (7.6%). The number of parents born abroad, religious practice, Swiss citizenship, and socioeconomic status predicted students’ profile membership. Comparisons of academic self-concept showed that only Majority-oriented Multiculturalists differed from the other profiles. Our findings suggest that a high multicultural orientation may support students’ academic self-concept mainly when paired exclusively with a strong majority orientation. In contrast, our results demonstrate that a strong heritage orientation may be less favorably related to academic self-concept, even when paired with a high multicultural orientation. However, given the cross-sectional design, the results call for further longitudinal research. Nonetheless, the results of this study indicate a necessity for more differentiated acculturation frameworks that consider the multidimensionality of acculturation in contemporary culturally diverse classrooms. Full article

Background: Individuals with type 2 diabetes mellitus (T2DM) are at markedly increased risk of developing coronary heart disease (CHD); however, the generalizability and transportability of existing prediction models remain uncertain. Objective: To identify and evaluate multivariable prognostic models developed to predict CHD in adults with T2DM. Methods: We conducted a PRISMA-guided systematic review and meta-analysis of multivariable prognostic models predicting CHD in T2DM populations. Model characteristics and performance metrics were extracted following the CHARMS and TRIPOD-SRMA frameworks, and pooled discrimination was estimated on the logit-transformed AUC scale using a random-effects model (REML, Hartung–Knapp adjustment). Between-study heterogeneity and 95% prediction intervals were quantified, while risk of bias and applicability were assessed using the PROBAST tool. Results: Thirteen studies encompassing clinical, imaging-based, and omics-augmented models met the inclusion criteria. The pooled AUC was 0.69 (95% CI: 0.66–0.71), with high heterogeneity (I² = 97.4%; τ² = 0.0979) and a wide 95% prediction interval (0.54–0.81). Classical regression-based models demonstrated modest discrimination, whereas machine learning, imaging, and proteomic approaches achieved higher AUC estimates but were frequently constrained by small sample sizes, internal-only validation, and poor calibration reporting. The analysis domain emerged as the principal source of bias in PROBAST evaluations, and applicability issues were most frequent in models requiring advanced imaging or molecular platforms. Conclusions: Prognostic models for CHD in T2DM demonstrate moderate-to-good discrimination but substantial heterogeneity and frequent miscalibration across studies. Their clinical utility depends on rigorous external validation and local recalibration, particularly when incorporating imaging or molecular predictors. Future research should prioritize standardized CHD outcomes, consistent calibration reporting, decision-analytic assessments, and the development of transportable multimodal prediction models across diverse populations. Full article

Background: This study explores how healthcare staff perceptions of their physical work environment influence their satisfaction. Methods: A qualitative research design involving semi-structured interviews was adopted. The study sample comprised ten healthcare staff, including both clinical and nonclinical employees, working in a healthcare facility. The participants represented a range of professional roles and work areas, allowing for diverse perspectives on the physical environment. The data were analyzed using thematic analysis. The interview transcripts were systematically coded, and recurring patterns and themes were identified through an iterative analytical process reflecting participants’ perceptions and experiences of the physical work environment. Results: The analysis revealed seven main themes: impact of spatial layout on workflow; need for relaxation and break spaces; connection to nature, furniture and comfort; influence of color on mood; ambient features and environmental control; and natural light and well-being. Conclusions: This study highlights the critical role of the healthcare physical environment in shaping employee satisfaction and offers practical recommendations for healthcare facility design, emphasizing the need for ergonomic workspaces, greenspaces, and safe workplaces. This study contributes to a deeper understanding of how the physical environment can be optimized to support employees in healthcare settings. Full article

Deep learning has achieved expert-level performance in medical imaging analysis. However, models often fail to generalize across patient populations due to cross-population domain shifts, distributional differences arising from demographic variability, variations in imaging protocols, scanner hardware, and differences in disease prevalence. This challenge limits the real-world deployment and can increase health inequities. This review systematically examines the nature, causes, and impact of cross-population domain shift in deep learning-based medical imaging analysis. We analyzed 50 peer-reviewed studies from 2020 to 2025, evaluating the proposed methodologies for handling population shifts, the datasets employed, and the metrics used to assess performance. Our findings demonstrate that performance degradation ranged from 10–25% when models were tested on unseen populations, emphasizing the substantial impact of domain shifts on model generalizability. The literature reveals that mitigation strategies broadly fall into two categories: data-centric approaches, such as augmentation and harmonization, and model-centric approaches, including domain adaptation, transfer learning, adversarial learning, multi-task learning, and continual learning. While domain adaptation and transfer learning are the most widely used, their performance gains across populations remain modest, ranging from 5–15%, and are not supported by external validation. Our synthesis reveals a significant reliance on large, publicly available datasets from limited regions, with an underrepresentation of data from low- and middle-income countries. Evaluation practices are inconsistent, with few studies employing standardized external test sets. This review provides a structured taxonomy of mitigation techniques, a refined analysis of domain shift characteristics, and an in-depth critique of methodological challenges. We highlight the urgent need for more geographically and demographically inclusive datasets, adaptable modeling techniques, and standardized evaluation protocols to enable accurate and equitable AI-driven diagnostics across diverse populations. Finally, we outline future research directions to guide the development of robust, generalizable, and fair models for medical imaging analysis. Full article

Brassica juncea is a widely cultivated leafy vegetable species in Northeast Asia, including Korea, Japan, and China. Under shade conditions, B. juncea exhibits shade avoidance syndrome (SAS), which negatively impacts its market quality. However, B. juncea is cultivated in diverse climates worldwide, including regions with frequent foggy days, highlighting the need to understand its adaptation to shade conditions to improve cultivation quality. To investigate the relationship between SAS phenotypes and environmental factors, including daylength, precipitation, and temperature, we analyzed 30 clones and six commercial cultivars of B. juncea. After 7 days of growth, all six commercial cultivars exhibited a canonical SAS response, with hypocotyl length increasing by 3.25- to 5.18-fold under dim light compared to white light conditions. Among the 30 clones, shade responsiveness varied widely, with hypocotyl elongation ranging from 1.42- to 8.54-fold change. A simple correlation analysis revealed that environmental factors were not highly correlated with shade responsiveness due to their complex interactions. To address this, we applied six machine learning models and found that the random forest algorithm provided the most accurate predictions of environmental influences on hypocotyl length. Using this model, we identified daylength, precipitation, and temperature as key environmental factors contributing to SAS phenotypes in B. juncea. Our findings not only identify clones that can be cultivated under low-light conditions with reduced SAS effects but also establish a link between SAS phenotypes and natural environmental conditions. These insights provide a foundation for future breeding strategies to improve shade adaptation in B. juncea. Full article