Search Results (48)

Domestication has profoundly shaped the phenotypic differentiation and genetic architecture of Perilla. However, analyses of the morphological difference between its cultivated and weedy forms across its varieties remains incomplete. This study analyzed morphological variation, genetic diversity, population structure, and marker–trait associations of 45 accessions representing the cultivated and weedy forms of two Perilla varieties (P. frutescens var. frutescens and var. crispa) collected from South Korea and Japan. Analyses of ten qualitative and quantitative agronomic traits revealed clear domestication-related differentiation. Cultivated var. frutescens showed larger and heavier seeds, whereas cultivated var. crispa and the weedy accessions were characterized by longer inflorescences and higher floret numbers but smaller seeds. Strong positive correlations were observed among seed-related traits, particularly between seed size and seed weight (r = 0.932), indicating coordinated selection of seed traits. Genetic diversity analysis using 70 SSR markers identified 330 alleles consistent with domestication bottlenecks in cultivated forms while higher diversity was generally retained in the weedy accessions. Population structure, UPGMA clustering, and principal coordinate analyses broadly differentiated the cultivated and weedy accessions, although partial admixture indicated shared ancestry and historical gene flow. Association mapping using Q-based GLM and Q + K MLM models identified 23 significant marker–trait associations involving 16 SSR markers consistently detected across both models. Several markers were associated with multiple traits, implying pleiotropy or tight genetic linkage. Notably, five SSR markers (KNUPF192, KNUPF202, KNUPF207, KNUPF230, and KNUPF238) may represent potential candidate loci for marker-assisted selection to improve seed-related traits in var. frutescens and leaf-related traits in var. crispa. Full article

Perilla frutescens(L.) Britt. (P. frutescens) is an important medicinal and aromatic plant, whose leaf color and chemotype strongly influence its medicinal quality and economic value. All the previously discovered perillene (PL)-type P. frutescens are double-sided green, and whether the PL-type trait is tightly linked with the green-leaf trait in genetics remains to be clarified. This study aimed to address this question and attempt to create purple-leaf PL-type germplasm through perillaldehyde (PA) × PL hybridization. Three parallel experiments were conducted using purple-leaf PA-type P. frutescens as male parents and green-leaf PL-type P. frutescens as female parents. Chemotypes were identified by gas chromatography (GC). Association analyses between leaf color and chemotype were performed in segregating F₂ populations. Genes involved in leaf color formation and PL biosynthesis were mapped onto the published Hoko-3 reference genome to provide genomic evidence for the genetic relationship between the two traits. All F₁ individuals were uniformly PA-type. The three F₂ populations exhibited distinct leaf color–chemotype association patterns: Z01 (n = 118) showed a strong association (Fisher’s exact p = 9.13 × 10⁻¹⁰; φ = 0.564), Z02 (n = 117) showed no detectable association (p = 0.9; φ = 0.012), and Z03 (n = 88) showed a moderate association (p = 0.00669; φ = 0.289). Importantly, purple-leaf PL-type recombinants were obtained in F₂ populations and stably maintained through subsequent generations (F₃–F₅), demonstrating that the PL-type trait is not tightly linked with the green-leaf trait in P. frutescens. Genomic mapping genes related to leaf color and PL biosynthesis are distributed across multiple chromosomes and usually present as multiple loci, which is consistent with the pattern of incomplete linkage. The PL-type trait is recessive and not genetically tightly linked to the green-leaf traits in P. frutescens. The successful creation of a purple-leaf PL-type germplasm breaks the historical phenotypic constraint and provides a novel material for further dissection of the molecular mechanisms regulating secondary metabolism and organ coloration in P. frutescens. Full article

Background: Starch accumulation contributes substantially to maize grain yield and quality. Starch synthase III (SSIII) is a key component of the starch biosynthetic enzyme complex. However, its regulatory role in starch accumulation in maize endosperm remains incompletely understood. Methods: The du1-2018 mutant arose spontaneously during a conventional maize breeding program. Phenotypic characterization, storage compound contents, and starch structure were compared between the mutant and wild-type lines. BSA-seq, genetic linkage analysis, and transcriptomic analysis were employed to identify the candidate gene responsible for the mutant phenotype. Transcriptome sequencing was performed on developing kernels to evaluate the genome-wide effects of the du1-2018 mutation. Results: The du1-2018 mutant exhibited dull, glassy, and mildly shrunken kernels, with decreased starch levels and elevated soluble sugar and protein contents. The du1-2018 mutation disrupted starch accumulation, resulting in smaller, irregularly shaped starch granules and significant changes in starch composition and fine structure. This mutation was identified as a severe loss-of-function allele of the dull1 (du1) gene, evidenced by almost undetectable Du1 transcripts in developing kernels. Notably, transcriptomic analysis revealed that a substantial proportion of differentially expressed genes (DEGs) were involved in amino acid and protein metabolism. Conclusions: The novel du1 allelic variant, du1-2018, disrupts starch biosynthesis in maize endosperm, leading to reduced starch accumulation, altered starch structure, and transcriptional changes in nitrogen-related metabolic pathways. Our results provide new insights into the regulatory mechanisms underlying SSIII function in starch synthesis and endosperm development, and suggest potential links to carbon/nitrogen balance, with implications for future genetic improvement of maize grain quality. Full article

Background: Diabetes mellitus is characterized by elevated blood sugar due to absolute or relative insulin deficiency. Diabetes is classified as type 1 (T1D) or type 2 diabetes (T2D), gestational diabetes, and other types, such as monogenic diabetes, exocrine pancreatic disorders, and medication-induced diabetes. Objectives: This review article provides an overview of diabetes genetics, covering polygenic, monogenic, and syndromic forms of the disorder with emphasis on aspects to help clinicians in diagnosis, management, and counseling, but also to foster valuable knowledge for diabetic researchers in identifying phenotypes that will help inform gene discovery. Key Findings: Most cases of T1D and T2D are polygenic with environmental triggers. T1D results from autoimmune destruction of pancreatic beta cells leading to absolute insulin deficiency. Genetic studies of T1D have focused on the identification of loci associated with increased susceptibility to T1D. Early studies showed a linkage between T1D and several human leukocyte antigen (HLA) susceptibility loci on chromosome 6. Genome-wide association studies (GWAS) have identified more than 100 HLA- and non-HLA loci that increase susceptibility to T1D. It has been well established that a substantial portion of the genetic risk for T1D is encoded in the HLA locus. The non-HLA loci INS, CTLA4, IL2RA, IFIH1, and PTPN22 make moderate contributions to T1D risk. Many other non-HLA loci have small effects to the phenotype and are relevant to autoimmunity, but they are yet to be identified. T2D, on the other hand, is associated with obesity and insulin resistance with relative insulin deficiency. Thousands of gene variants that are common and contribute small effects have also been identified through GWAS to contribute to T2D risk, but the rarer variants may confer significant risk to an individual’s risk. Common variants in the TCF7L2 locus consistently carry one of the largest risks associated with T2D with a reported 1.7-fold disease odds for homozygous carriers. The usefulness of individual variants for genetic counseling in the common forms of diabetes has been limited in clinical settings in the past. The development of polygenic risk scores (PRS) and partitioned polygenic risk scores (PPRS), statistics derived from GWAS, are being used to predict and classify diabetes. The performance of PRS and PPRS varies by ancestry and type of diabetes. The PRS performs better with T1D, with an area under the curve and receiver operating characteristics (AUC-ROC) ranging from 0.87 to 0.93, compared to 0.72–0.75 for T2D. The genetic architecture of T2D is markedly more polygenic than T1D, and the PPRS has been useful in assessing risk in that setting. Monogenic diabetes comprises several dysglycemic disorders that include neonatal diabetes, maturity-onset diabetes of the young (MODY), and other genetic syndromes that have diabetes either as an associated finding and/or as a complication. Some of the monogenic diabetes gene variants have incomplete penetrance and variable expressivity leading to different ages of onset and variable presentation even within the same family. Hence some patients with these conditions have been previously diagnosed as having T1D or T2D. Many monogenic disorders follow Mendelian inheritance patterns, so genetic counseling is relatively straightforward if pathogenic variants are found to be inherited from a parent. Counseling for forms of diabetes due to maternally inherited mitochondrial cytopathies, such as MELAS and Kearns–Sayres syndrome, is not straightforward due to the occurrence of two or more populations of genetically distinct mitochondrial DNAs in the cells (heteroplasmy); the higher the percent of pathogenic variants in a cell or tissue, the greater the chance for affectation of disorder. Implications: Early stages of diabetes may be asymptomatic, and improvement in methodologies to identify individuals at high risk is important so prevention strategies can be targeted to susceptible individuals to slow or obviate the onset of disease and to minimize complications. Conclusions: Diabetes is a heterogeneous disorder, and accurate definition of phenotypes in the setting of non-syndromic and syndromic forms, development of powerful statistical methodologies, use of next-generation sequencing applications to interrogate the genome, incorporation of epigenetic mechanisms in statistical modeling and accurate curation of gene variants, will help us to realize application of genomic medicine and to inform diabetes care. Full article

Global water management faces a critical challenge: whilst scholarly consensus recognises that multiple, interacting drivers fundamentally shape water availability and management capacity, operational governance frameworks fail to systematically incorporate this understanding. This disconnect is particularly acute in public good contexts where incomplete knowledge, diverse stakeholder values, and statutory planning mandates create distinct challenges. Using Australia’s Murray–Darling Basin as a pilot case, this research develops and demonstrates a rapid, policy-relevant heuristic for identifying, prioritising, and incorporating drivers of change in complex socio-ecological water systems. Through structured participatory deliberation with 70 experts spanning research, policy, industry, and community sectors across three sequential workshops and 15 semi-structured interviews, we systematically identified key drivers across environmental, governance, economic, social, and legacy dimensions. A risk and sensitivity assessment framework enabled prioritisation based on impact, vulnerability, and urgency. Climate change, drought, water quality events, and cumulative impacts emerged as the highest-priority future drivers, with climate change acting as a threat multiplier, whilst governance drivers show declining relative significance. Using these methodological innovations, we synthesise the I-PLAN heuristic: five interdependent dimensions (Integrative Knowledge, Prioritisation for Management, Linkages between Drivers, Adaptive Agendas, and Normative Collaboration) that provide water planners with a transferable, operational tool for driver identification and bridging to planning and management in data-sparse contexts. Full article

Background: Osteoporosis is highly prevalent in postmenopausal women, yet genome-wide association studies often miss disease-relevant variants because of incomplete single nucleotide polymorphism (SNP) coverage and platform-specific limitations. We aimed to identify genetic contributors to osteoporosis risk by integrating two exome-based genotyping platforms with multilayer analytic approaches. Methods: We analyzed extreme osteoporosis phenotypes in Korean postmenopausal women from the Korean Genome and Epidemiology Study (KoGES) Ansan–Anseong cohorts using the Illumina Infinium HumanExome BeadChip and the Affymetrix Axiom Exome Array. After standard quality control, single-SNP logistic regression, cross-platform overlap analysis, and three machine-learning models were applied. Predicted functional impact was evaluated using multiple in silico algorithms and conservation scores. Finally, datasets from both platforms were merged, and cross-platform linkage disequilibrium (LD) blocks were defined to identify loci containing SNPs with p < 1 × 10⁻⁴. Results: No overlapped SNP reached genome-wide significance, but rs2076212 in PNPLA3 achieved suggestive significance (p < 1 × 10⁻⁵) only on the Illumina array. Cross-platform analysis identified 111 overlapping SNPs in 70 genes. Integrated machine-learning, in silico, and conservation evidence prioritized ARMS2, CCDC92, NQO1, ZNF510, PTPRB, and DYNC2H1 as candidate genes. LD-block analysis revealed 10 blocks with at least one SNP at p < 1 × 10⁻⁴, including four chromosome 12 loci (NAV2, BICD1, CCDC92, ZNF664) that became apparent only when LD patterns were evaluated jointly across platforms. Conclusions: Combining dual exome arrays with LD-block analysis, machine learning, and functional prediction improved sensitivity for detecting low bone mineral density-related loci and highlighted CCDC92, DYNC2H1, NQO1, and related genes as biologically plausible candidates for future validation. Full article

Background: As pediatric head and neck cancer (pHNC) incidence increases, the development of new surgical oncology techniques to reduce morbidity are essential. Intraoperative navigation (iNav) represents the most translatable technology among both the model-comparative and integrative surgical navigation technologies to optimize surgical outcomes. Methods: A scoping review of the literature was performed according to PRISMA guidelines from 1970 to present (February 2025), investigating the use of iNav in cases of pHNC. Patient case details and authors’ perception of iNav’s utility were analyzed. A single-center retrospective case series review (September 2022 to September 2025) of the senior authors’ experience employing iNav in pHNC cases was also performed. Results: The scoping review identified twenty-seven cases of pHNC from sixteen studies that both utilized iNav and met the inclusion criteria. Many of the authors commented favorably on the utility of iNav technology, while concurrently agreeing upon its limitations. The case series review identified five cases of pHNC that met the inclusion criteria. This small case series revealed a 100% R0 resection rate with the use of iNav in four pHNC resections. The fifth case used iNav for biopsy site selection. Conclusions: The results of our scoping review as well as our institutional experience with this technology demonstrate its utility in guiding surgical approach, confirming depth of resection, and navigating marginal assessment. This study was limited by incidental and incomplete reporting of iNav’s clinical application to pHNC; several extensive institutional reports had to be excluded due to insufficiently detailed data linkage. Our review builds upon the existing pediatric surgical literature, anchoring the evidentiary justification for the application of iNav to pediatric head and neck surgery. Full article

Pepino mosaic virus (PepMV) is a highly infectious potexvirus that poses a significant threat to tomato cultivation in greenhouses worldwide. The threat posed by this virus is attributed to by its genetic complexity, characterized by the presence of multiple genotypes in circulation, mixed infections, and ongoing genotype turnover. Surveys of wild Solanum species have identified promising sources of resistance; however, this resistance is often incomplete, manifesting as symptomless, yet virus-positive, plants. When resistance is identified, introgressing of these traits into elite backgrounds is frequently impeded by reproductive barriers and linkage drag. Consequently, there are currently no commercially available cultivars with durable resistance to PepMV. Current control measures rely on stringent hygiene practices, seed health protocols, and the use of mild isolate cross-protection, which can mitigate fruit symptoms when carefully genotype-matched and closely monitored. Looking forward, achieving durable control will likely require host-centered strategies. Loss-of-susceptibility mutations and RNA interference-based approaches have demonstrated strong potential in experimental studies. Future solutions may involve the integration of genome editing with RNA-based technologies, supported by regulatory harmonization and socioeconomic viability considerations. Full article

Entity resolution in real-world datasets remains a persistent challenge, particularly for identifying households and detecting co-residence patterns within noisy and incomplete data. While Large Language Models (LLMs) show promise, monolithic approaches often suffer from limited scalability and interpretability. This study introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes household entity resolution into coordinated, task-specialized agents implemented using LangGraph. The system includes four agents responsible for direct matching, transitive linkage, household clustering, and residential movement detection, combining rule-based preprocessing with LLM-guided reasoning. Evaluation on synthetic S12PX dataset segments containing 200–300 records demonstrates 94.3% accuracy on name variation matching and a 61% reduction in API calls compared to single-LLM baselines, while maintaining transparent and traceable decision processes. These results indicate that coordinated multi-agent specialization improves efficiency and interpretability, providing a structured and extensible approach for entity resolution in census, healthcare, and other administrative data domains. Full article

Background/Objectives: Cervical cancer remains a major cause of morbidity and mortality in Eastern Europe and Central Asia (EECA), where screening implementation remains fragmented. This study provides a comparative assessment of national cervical screening programs across the region, highlighting structural strengths and policy gaps. Methods: National self-reported data were collected through a standardized UNFPA questionnaire from 18 submissions representing 16 EECA countries and territories. Descriptive analyses compared organized and opportunistic approaches, and an illustrative Program Maturity Index (PMI) was constructed from eight structural and performance indicators aligned with WHO and European standards. Results: Fifteen submissions reported national screening guidelines and seventeen defined intervals, most commonly every three or five years. Organized systems achieved higher participation (median 57.2%) than opportunistic models (15%). Follow-up of screen-positive women was the weakest component, with rates ranging from below 2% to above 90%. The regional mean PMI was 0.73, reflecting intermediate-to-advanced maturity overall, but persistent gaps in monitoring and patient linkage to care. Conclusions: This study offers the first regional comparison of cervical screening systems in EECA. Despite policy progress, weak follow-up and incomplete data systems limit impact. Strengthening electronic registries, financing linkage to outcomes, and regional collaboration are essential to meet the WHO’s 90–70–90 elimination targets. Full article

Background: Non-small cell lung cancer (NSCLC) progression is driven by dysregulated competing endogenous RNA (ceRNA) networks, where non-coding RNAs sequester miRNAs to modulate oncogene expression. The tumor-suppressor miR-145 is frequently downregulated in NSCLC, but its lncRNA-mediated regulation remains incompletely characterized. Methods: Integrated transcriptomic analysis of NSCLC datasets (GSE135304: blood RNA from 712 patients; GSE203510: plasma miRNAs) was used to identify dysregulated genes (|log2FC| > 0.1, p < 0.05) and miRNAs (|log2FC| > 1, p < 0.05). Experimentally validated targets from miRTarBase/TarBase were intersected with dysregulated genes, followed by WikiPathways/GO enrichment. ceRNA networks were constructed via co-expression analysis. RT-qPCR validated miR-145-3p expression in A549/MRC-5 cells and NSCLC tissues. GEPIA assessed FN1/CCND1 clinical relevance. Results: We identified 8271 dysregulated genes and 52 miRNAs. miR-145-3p, critical in immune regulation, was significantly downregulated (log2FC = −1.24, p = 0.036). Intersection analysis revealed 27 miR-145-3p targets (e.g., FN1, CCND1, SMAD3) enriched in immune pathways (FDR < 0.05) and TGF-β-mediated EMT within the dysregulated geneset. Six immune-linked hub genes emerged. LncRNAs LOC729919 and LOC100134412 showed strong co-expression with hub genes and competitively bind miR-145-3p, derepressing the expression of the metastasis drivers FN1 (ECM regulator) and CCND1 (cell cycle controller). This ceRNA axis operates within a broader dysregulation of ATM-dependent DNA damage, Hippo signaling, and cell cycle pathways. RT-qPCR confirmed significant miR-145-3p suppression in NSCLC models (p < 0.05). GEPIA revealed a significant FN1-CCND1 co-expression (p = 0.0017). Conclusions: We characterize a novel LOC729919/LOC100134412–miR-145–FN1/CCND1 ceRNA axis in NSCLC pathogenesis. FN1’s prognostic value and functional linkage to CCND1 underscores its potential clinical relevance for therapeutic disruption. Full article

Background and Objectives: Thrombophilia is a prothrombotic disorder that increases the risk of blood clotting and can pose serious health problems. It is considered a condition of gene–gene or gene–environment interactions. Variation in the prevalence of thrombophilia mutations and their interaction among populations necessitates localized genetic assessments. However, population-based genetic data remains limited for developing effective preventive strategies. Materials and Methods: This cross-sectional observational study was conducted over five years (2020–2024) at a tertiary university hospital in Northern Greece. A total of 2961 individuals aged 18–85 years (mean: 50.5) were registered based on family or medical history of venous thromboembolism (VTE) or clinical symptoms of VTE. The final analysis included 2078 participants comprising 1143 males (55%) and 935 females (45%), who met all the inclusion criteria. Inclusion criteria were absence of acute illness or malignancy, informed consent, and an adequate DNA quantity for genotyping, whereas excluded criteria included incomplete laboratory data, active inflammatory or malignant disease, and cognitive or psychiatric conditions. Peripheral blood samples were collected in 2 mL K₃-EDTA tubes, and genomic DNA was analyzed using real-time polymerase chain reaction (PCR) with melting curve analysis and hybridization probes (LightMix^® in vitro diagnostics, TIB MolBiol, Berlin, Germany). Five thrombophilia-related polymorphisms, Factor V Leiden (F5 G1691A), prothrombin (F2 G20210A), methylenetetrahydrofolate reductase (MTHFR C677T and MTHFR A1298C), and Plasminogen Activator Inhibitor-1 (PAI-1) 4G/5G, were examined for allele and genotype frequencies, Hardy–Weinberg equilibrium testing, pairwise linkage disequilibrium (D′ and r²), and power analysis. For subjects tested for Factor V Leiden (n = 1476), the activated protein C resistance (APC) ratio was additionally evaluated using the ACL TOP 750 analyzer. Results: Allele frequencies were 7.3% for FV Leiden and 3.7% for FII. The PAI-1 allele was distributed at 44%, while the MTHFR (C677T and A1298C) alleles were each present at 33%. Significant linkage disequilibrium was identified between MTHFR (C677T and A1298C) and between MTHFR A1298C and PAI-1. No evolutionary pressure or demographic bias was found in the Hardy–Weinberg equilibrium. The APC ratio demonstrated a high sensitivity (99.2%) and specificity (96.6%), indicating that it may serve as a reliable screening method. Conclusions: Our findings highlight informative patterns in the genetic predisposition to thrombophilia, which may help develop rule-based strategies for implementing thromboprophylaxis guidelines and personalized medical interventions. Full article

This study utilized policy text quantification, the entropy weighting technique, and the standard setting of the vertical policy synergetic degree to measure the synergy status of the environmental protection and governance policies in the Bohai Rim Region horizontal space (2001–2021). The standard setting of the vertical policy synergetic degree encompasses three dimensions, namely the policy subjects, policy objectives, and policy instruments. A comprehensive text database was established to facilitate analysis with 122 pieces of Bohai Sea environmental governance policies. After policy text quantification, this research found that the weight coefficients of the three indicators of policy subjects, policy instruments, and policy objectives were slightly different. This study found that provinces should balance the roles of policy issuers, the selection of policy instruments, and the setting of objectives to enhance compatibility between local and central governance policies; the vertical synergy of policies is closely related to the scientific nature of policy formulation. The incompleteness of the vertical synergy of policies affects the effectiveness of Bohai Sea environmental governance. In the future, the Bohai Rim Region’s environmental governance should continue to deepen the synergy of policies, strengthen scientific governance, promote regional linkage, and improve the scientificity of the policy system. Full article

Effective and evidence-based biosecurity measures are essential to prevent trade disruption, protect industries and contain the chains of biological invasions. There are increasing demands for analysts to use quantitative data to monitor this system, with the goals of early detection and forecasting. However, standard approaches often struggle with the incomplete and complex nature of trade data, which tends to include non-normality, temporal and spatial autocorrelation, and limited observations. In this study, a time series of open access import data spanning three years was used to generate measures of diversity indices and network topology, alongside detailed analyses of import pathways and interception records of harmful organisms, revealing their dynamic patterns across different trade routes. Patterns of annual seasonality were evident across the board. A combination of Inverse Simpson’s diversity and network Linkage density optimised the monitoring power of import data for interceptions of harmful taxa. Traditional correlations to total number of interceptions remained intractable, but machine learning tools demonstrated predictive power to forecast these temporal patterns. Combined, these methods provide a novel approach for biosecurity monitoring in plant and animal trade across international borders. These indicators complement more conventional economic metrics, giving actionable insights into trade complexity and biosecurity status. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article