Search Results (3,196)

Background/Objectives: Sweetness is a key determinant of the eating quality of sweet corn, primarily governed by the soluble sugar content in kernels. The soluble sugar content decreases rapidly during the postharvest shelf life, which directly affects the flavor and quality. Relatively few studies have been conducted on the shelf life of sweet corn. Methods: An F₆ recombinant inbred line (RIL) population was constructed from two super sweet inbred lines with contrasting soluble sugar degradation rates: D174 (low degradation rate) and D179 (high degradation rate). Extreme phenotype pools were established using soluble sugar content as the target trait. Based on bulked segregant analysis sequencing, we identified chromosomal segments associated with postharvest soluble sugar reduction in sweet corn, annotated the gene information within these segments, and analyzed the functions of the annotated genes using the Gene Ontology and Genomes databases. Results: Results revealed three associated regions located at 44,205,775–45,290,843 bp on chromosome 4, 6,250,656–6,744,665 bp on chromosome 2, and 135,428,709–136,732,132 bp on chromosome 10. This interval contained 195 genes. Integrated analysis of gene expression, gene annotations, and quantitative real-time PCR indicated that Zm00001eb069070, which is highly expressed in kernels with a prolonged shelf life, might be a key candidate gene regulating soluble sugar degradation in sweet corn. Conclusions: This study provides valuable genetic resources for the improvement of favorable agronomic traits and the advancement of molecular breeding strategies for sweet corn. Full article

Background: Pigs are one of the most important livestock species for providing meat products in the world. Deciphering the genetic architecture of feed efficiency-related traits is beneficial to improve the genetic progress of these traits and save the total cost of pork production. However, the genetic architecture of feed efficiency-related traits remains unclear. Methods: To address this problem, we collected 1301 genotyped Yorkshire pigs with three feed efficiency-related traits, including days at 100 kg (DAYS_100), backfat thickness at 100 kg (BFT_100), and feed conversion ratio from 30 to 100 kg (FCR_30_100), to explore the genetic parameters and genetic basis of these traits. Results: The heritability of DAYS_100, BFT_100, and FCR_30_100 was 0.25 ± 0.04, 0.40 ± 0.05, and 0.23 ± 0.04, respectively. Additionally, BFT_100 and DAYS_100 had a weak negative genetic correlation (−0.01 ± 0.12), while trait FCR_30_100 showed a positive genetic correlation with DAYS_100 (0.51 ± 0.11) and BFT_100 (0.28 ± 0.12). A genome-wide association study identified 7, 5, and 4 SNPs independently associated with BFT_100, DAYS_100, and FCR_30_100, respectively. Further analysis found that the candidate gene ETV4 was significantly associated with DAYS_100 and the candidate gene ENSSSCG00000045751 was associated with FCR_30_100. The functional annotation of candidate genes was enriched in the bile acid metabolic process and protein ubiquitination terms. Conclusions: This study discovered 16 quantitative trait loci associated with feed efficiency-related traits, providing a comprehensive insight for understanding the genetic basis of feed efficiency-related traits in pigs. The candidate genes, such as ETV4 gene in DAYS_100, CAMK1D gene for BFT_100, and ENSSSCG00000045751 gene for FCR_30_100, could be used for further investigation. Full article

The accurate assessment of tailings dam operational status and timely risk warnings are critical for ensuring their safe operation. To address the limitations of existing models in managing complex environments and multidimensional risk factors, this study proposes an early warning model for tailings dam operational status based on a two-dimensional cloud model. First, a comprehensive early warning system is developed to assess the probability and consequences of dam failure, using risk probability and consequences as two-dimensional coordinates, incorporating the randomness and fuzziness of uncertainty described by cloud theory, and transforming qualitative data into quantitative conclusions. Next, a genetic algorithm optimizes the projection pursuit model to determine weights, and weighted numerical features are utilized to enhance the classification of early warning levels. Furthermore, the two-dimensional cloud model is enhanced by introducing a proximity coefficient to replace the membership function, with the resulting cloud map visualized using a forward cloud generator. Finally, the early warning level of the tailings dam’s operational status is determined based on the clustering of cloud droplets and the proximity coefficient. Empirical application to five tailings dams in Hubei Province confirms the model’s effectiveness and practicality. The results demonstrate that the model effectively addresses the complexity and uncertainty of tailings dam operational status, delivers accurate warnings, and provides robust decision support for emergency response. Full article

Atopic dermatitis (AD) is a chronic inflammatory skin disease with a complex and highly polygenic genetic architecture, in which immune-mediated mechanisms play a central role. Here, we integrated single-cell cis-expression quantitative trait loci from 14 immune cell types with AD GWAS summary statistics using a two-sample Mendelian Randomization (MR) framework to resolve cell-specific genetically mediated transcriptional effects. We identified 303 significant cell-specific gene–trait associations with limited overlaps across cell types. A multi-step prioritization strategy refined these findings to 35 genes across all 14 cell types. A comparison with whole blood cis-eQTLs revealed a limited concordance, suggesting an attenuation of cell-specific regulatory effects in bulk transcriptomic approaches. Intersecting single-cell and bulk evidence identified 22 high-confidence genes with a relatively independent mechanism of action. Integrative annotation implicated several immune-relevant and druggable genes, including IL2RA, with distinct cell-specific effects. Our findings demonstrate diverse mechanisms of risk genes for AD at the single-cell level that act across immune cell states and pathways, with implications for therapeutic interventions. Full article

Passiflora germplasm represents an important genetic resource for improving fruit yield and quality in breeding programs targeting semi-arid environments. This study aimed to assess the phenotypic diversity, genetic parameters, and breeding potential of Passiflora accessions conserved in the Passion Fruit Active Germplasm Bank of Embrapa Semiárido. A total of 55 accessions, predominantly Passiflora cincinnata Mast., were evaluated using morphoagronomic descriptors related to plant, flower, and fruit traits. Quantitative data were analyzed using mixed linear models (REML/BLUP) to estimate genetic parameters, and multivariate analyses were applied to characterize phenotypic divergence. Substantial phenotypic variability was observed, particularly for fruit-related traits. Fruit weight ranged from 43.25 to 142.88 g, pulp weight ranged from 7.86 to 51.37 g, and pulp yield ranged from 17.06% to 40.27% among accessions. Broad-sense heritability estimates for key fruit traits were moderate to high, reaching 0.50 for fruit weight, 0.49 for pulp weight, and 0.36 for pulp yield, indicating favorable prospects for selection. Principal Component Analysis explained 66.0% of the total variation in the first two components, with fruit size, pulp-related traits, and seed number contributing most strongly to accession differentiation. Multivariate analyses consistently identified accessions 1 and 16 as superior for fruit weight and pulp yield, whereas accession 55 combined high fruit weight with elevated soluble solid content (up to 14.24 °Brix) but lower pulp yield. Overall, the observed variability highlights the relevance of Passiflora germplasm conserved under semi-arid conditions as a valuable resource for breeding programs focused on fruit yield, quality, and adaptation to water-limited environments. Full article

Gibberellins (GAs) play a crucial regulatory role in the growth and development of cotton (Gossypium hirsutum L.). Through bioinformatics analyses, we identified a total of 39 GA2ox genes (encoding gibberellin 2-oxidases) in the cotton genome, designated GhGA2ox1 to GhGA2ox39. Based on phylogenetic analysis, these genes were classified into five groups. We further examined their gene structures, conserved motifs, and chromosomal distributions, revealing that members within the same group shared similar structural and motif organizations. Collinearity and cis-element analyses provided important insights into the evolutionary history and regulatory potential of the GA2ox gene family in cotton. Notably, using nucleotide diversity (π) and population differentiation (F_ST) analyses across the entire family, we screened and identified nine candidate genes that underwent strong artificial selection during cotton domestication and improvement. Further haplotype-phenotype association analysis identified GH_D09G0919 (GhGA2ox31) as a key regulator of Plant Height (PH). To validate their regulatory roles, we analyzed the genotype distribution in accessions with extreme phenotypes. The results revealed divergent selection histories for these two loci: the favorable allele of GH_D01G0720 (GhGA2ox23) was already fixed in the tested population, whereas GH_D09G0919 maintained significant natural variation. Specifically, the Hap2 allele of GH_D09G0919 was significantly enriched in the shortest accessions compared to the tallest ones. Importantly, quantitative real-time polymerase chain reaction (qRT-PCR) analysis confirmed that the Hap2 allele drives significantly higher gene expression in leaves, suggesting that enhanced GA catabolism underlies the compact phenotype. Additionally, transcriptomic profiling revealed the tissue-specific expression patterns of candidate genes, implying their functional roles in development. Furthermore, functional validation using the Arabidopsis mutant of the homologous gene (AtGA2ox8) confirmed its conserved role in regulating plant height, as the mutant exhibited a distinct short-stature phenotype. These results uncover valuable genetic resources for molecular breeding to shape compact cotton architecture. Collectively, this study aims to analyze the evolutionary patterns of the cotton GA2ox gene family and to identify key genes that regulate plant height under artificial selection, providing theoretical support for molecular breeding of compact plant types. Full article

This paper presents an analysis of the impact of full and partial curfews on water demand and production, as imposed in Kuwait during the meteorological spring (March, April, and May) of 2020, in response to the COVID-19 pandemic. We consider all desalination technologies used in Kuwait: Multi-Stage Flash (MSF), Multi-Effect Thermal Vapor Compression (MED-TVC), and Reverse Osmosis (RO). Historical data and predictive models are combined and analyzed via a statistical genetic algorithm. The environmental and economic implications of the lockdown measures were assessed through quantitative evaluation, comparing actual 2020 water demand and production data with values predicted under normal operating conditions. During the 2020 COVID-19 pandemic, water consumption surged, with maximum daily consumption climbing by 3.6%, and average daily consumption by 5.2%. These values were significant increases relative to 2019, for which the corresponding figures were 2.1% and 1.6%. The study assesses the economic and environmental consequences quantitatively, specifically the increase in CO, CO₂, and NO_x emissions, due to the increase in fuel consumption at desalination and power plants. Water demand and production across the national water network were simulated using mathematical models specifically designed for this purpose, developed from data provided by the Meteorological Department of Civil Aviation and the Ministry of Electricity, Water, and Renewable Energy. Full article

Coastal marine sediments and beach sands receive microbial and genetic inputs from wastewater discharge, urban runoff, aquaculture, wildlife, and recreational activity, yet their role as coupled microbial–genetic interfaces linking environmental processes and human exposure remains incompletely synthesized. This review integrates quantitative evidence from culture-based studies, qPCR surveys, metagenomic analyses, and multi-year monitoring investigations focused on coastal sediments and sands. Reported antibiotic resistance gene (ARG) concentrations in coastal sediments reach 2.2 × 10⁹ copies g⁻¹ (wet weight) for sul1 in wastewater-impacted systems, with total ARG abundances commonly ranging from 1.59 × 10⁷ to 2.88 × 10⁸ copies g⁻¹ in effluent-receiving zones and tetM reported at 1.43 × 10⁷ copies g⁻¹. Beach sands contain measurable resistance markers, including intI1 at 9–3823 copies g⁻¹ and blaTEM up to 14 copies g⁻¹ in wet sand. Viable fecal indicator bacteria and pathogens have been cultured directly from sands, including Staphylococcus aureus at 0–8710 CFU g⁻¹ and methicillin-resistant S. aureus at 0–605 CFU g⁻¹. Collectively, the evidence indicates that coastal sediments and sands function as structured microbial and genetic reservoirs requiring integrated assessment of benthic retention, hydrodynamic redistribution, and exposure-relevant interpretation. Full article

With global warming, high-temperature stress has become a primary abiotic factor limiting maize yield and quality. Exposure to heat stress induces sunscald on maize leaves, which severely impairs photosynthesis and ultimately leads to yield reduction. In this study, we used the heat-tolerant inbred line Zheng58 and the heat-sensitive inbred line HSBN, both of which are cultivated maize (Zea mays L. subsp. mays) inbred lines, as parents to construct F₂ and F_2:3 populations consisting of 257 lines. Phenotyping for sunscald at the flowering stage was performed across three field environments. The F₂ population was genotyped using the Maize 10K SNP array to construct a genetic map containing 1728 single nucleotide polymorphism (SNP) markers. The map spanned 1406.22 cM, with an average marker density of 0.81 cM per marker. Eight quantitative trait loci (QTLs) associated with heat tolerance were identified in the F₂/F_2:3 populations, distributed on chromosomes 1, 4, 5, and 8, collectively explaining 3.43% to 35.44% of the phenotypic variation. Among them, the stable QTL qHT1-2 on chromosome 1 was consistently detected across all three environments, explaining 11.41% to 35.44% of the phenotypic variation. Additionally, a major QTL, qHT1-3, was identified on the same chromosome, accounting for 33.70% of the phenotypic variation. Transcriptome analysis of flowering-stage leaves from both parents revealed 9262 differentially expressed genes (DEGs). Of these, 21 DEGs were co-localized within the eight QTL intervals. The genes Zm00001eb013260, Zm00001eb012720, Zm00001eb013600, and Zm00001eb013100 exhibited highly significant differential expression between the parental lines, these four genes are identified as candidate genes in response to heat stress in maize, and their specific biological functions require further functional validation. Full article

Genome-wide association studies (GWAS) are a powerful approach for elucidating the genetic architecture of complex traits. The widespread application of GWAS across diverse crop species has generated vast amounts of GWAS summary statistics, creating a pressing need for effective tools to identify high-confidence quantitative trait loci (QTLs) from these data. Here, we present Candidate QTL Detector (CQD), a tool that enables the rapid and accurate identification of confident QTL regions from GWAS summary statistics. To evaluate the performance of CQD, we reanalyzed 64 phenotypic traits from a previously published maize diversity panel. CQD identified 108 high-confidence QTLs from 4179 significant GWAS signals, and these QTLs harbored several well-characterized genes, including NS2 (a regulator of tassel branch number), DRL1 (a key regulator of plant architecture), and ZmACS6 (an ACC synthase involved in ethylene biosynthesis), demonstrating the reliability of the QTLs detected by CQD. Overall, CQD provides an efficient and flexible framework for extracting robust QTLs from GWAS summary statistics, thereby facilitating the genetic improvement of complex traits and advancing crop functional genomics. Full article

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies. Full article

The salivary gland is a key organ in insects that plays essential roles in food digestion, nutrient absorption, and energy metabolism, thereby highlighting the importance of studying salivary gland function for gaining a better understanding of nutritional utilization and insect–plant interactions. To date, however, a lack of salivary gland-specific promoters has limited functional analyses of salivary gland genes in Lepidoptera. In this study, based on microarray and salivary gland transcriptome data, we identified nine candidate genes characterized by high salivary gland expression. Semi-quantitative PCR analysis confirmed cholinesterase (BmCe, BGIBMGA010988) as the optimal candidate for promoter cloning. Temporal expression analysis revealed that the expression of BmCe reaches a peak during days 2–4 of the fifth larval instar. A 2152 bp fragment upstream of the transcription initiation site of BmCe was selected as the putative promoter sequence (designated BmCeP) and cloned to construct a piggyBac transgenic vector driving DsRed expression. Transgenic silkworms were obtained via embryonic microinjection and tissue expression analysis on day three of fifth-instar larvae revealed the predominant localization of DsRed expression in the salivary glands. In this study, we thus identified a gene promoter characterized by salivary gland-predominant expression in Bombyx mori, which we believe could serve as a valuable genetic tool for investigating the molecular mechanisms underlying silkworm nutritional utilization and interactions with its host plant, mulberry. Full article

X-ray computed microtomography (µCT) provides an important complement to optical imaging for understanding the three-dimensional (3D) organization and function of xylem and wood. Unlike conventional sectioning, µCT is a non-destructive process that produces high-quality data sets that can be rotated, resliced and, following image segmentation, quantified. We highlight examples in which quantitative processing of 3D µCT sets has provided quantitative understanding of xylem and wood including the development and refilling of xylem embolisms, tree ring analyses and the development of interlocked grain. We also highlight two ways through which the µCT imaging of wood, and plants in general, will be improved. While the current staining protocols for plants are non-specific, developments in specific labeling techniques, including modifications of traditional electron microscopy stains for cell walls and recent developments in µCT imaging in non-plant specimens for studying antibody labeling and transgenes, should allow significant improvements in the imaging of xylem and wood by µCT. We also highlight machine learning which is already facilitating improvements in image segmentation and quantification of µCT data sets. When coupled with the recent advances in molecular genetics of the vascular cambium, these improvements in µCT should dramatically increase our understanding of xylem formation. Full article

In applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can flexibly fit population growth trajectories, but higher orders substantially increase computational complexity. Instead of using Legendre polynomials, we first estimated fewer individual-specific parameters using biologically meaningful non-linear models and then associated these phenotypic regressions with genetic markers using a multivariate linear mixed model (mvLMM). After performing a canonical transformation of the regressions based on the pre-estimated covariance matrices under the null genomic mvLMM, we decomposed the mvLMM into mutually independent univariate models and incorporated EMMAX to enable rapid genome-wide mixed-model associations for each transformed phenotype. Simulations for longitudinal association analysis in maize and GWAS for the growth trajectories of body weights in mice demonstrated the advantages of hierarchical non-linear mixed models in computing efficiency and statistical power for detecting quantitative trait loci (QTL), compared with mvLMM for multiple growth points and the hierarchical random regression model using Legendre polynomials as sub-models. Full article

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detection of GM-derived ingredients to support traceability, regulatory compliance, and accurate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article