Search Results (4,526)

Cotton fiber initiation determines the fiber yield, yet the genetic basis underlying lint and fuzz initiation has still not been fully uncovered. Here, map-based cloning was carried out to identify the fiberless mutant genes derived from a cross between Gossypium hirsutum acc. WT and a natural fiberless mutant, fbl_SHZ. The 12:3:1 segregation ratio in F₂ populations (including 1848 and 3100 individuals that were developed in 2016 and 2018, respectively) revealed dominant epistasis, with the fuzz gene exerting dominance over the lint gene. Genetic linkage analysis revealed that GhMYB25like_A12 controls fuzz fiber initiation, while both GhMYB25like_A12 and GhMYB25like_D12 regulate lint fiber development. Sequencing analyses showed that the fbl_SHZ mutant exhibited a K104M mutation in the R2R3 domain of GhMYB25like_A12 and a transposable element insertion in GhMYB25like_D12, leading to fiberless seeds. Knockout of GhMYB25like_A12 produced fuzzless seeds, knockout of GhMYB25like_D12 led to no obvious change in seeds, and knockout of both (GhMYB25like_A12&D12) resulted in fiberless seeds. The 12:3:1 ratio reappeared in the F₂ population developed from the GhMYB25like_A12&D12 mutated plants as female and Jin668 as the male, which further confirmed the genetic interaction observed in fbl_SHZ. RNA-seq analysis revealed that GhMYB25like regulates cotton fiber initiation through multiple pathways, especially fatty acid metabolism. This study elucidates the key genes and their genetic interaction mechanisms governing cotton fiber initiation, providing a theoretical foundation for genetic improvement of cotton fiber traits. Full article

The improvement in yield in cultivar mixtures has been well established. Despite increasing knowledge of the improvement involving within-species diversification and resource use efficiency, little is known about the benefits arising from relatedness-mediated intraspecific interactions in cultivar mixtures. This study used a relatedness gradient of rice cultivars to test whether neighbor relatedness contributes to improvements in grain yields in cultivar mixtures. We experimentally demonstrated the grain yield of rice cultivar mixtures with varying genetic relatedness under both field and controlled conditions. As a result, a closely related cultivar mixture had increased grain yield compared to monoculture and distantly related mixtures by optimizing the root-to-shoot ratio and accelerating flowering. The benefits over monoculture were most pronounced when compared to the significant yield reductions observed in distantly related mixtures. The relatedness-mediated improvement in yields depended on soil volume and nitrogen use level, with effects attenuating under larger soil volumes or nitrogen deficiency. Furthermore, neighbor relatedness enhanced the richness and diversity of both bacterial and fungal communities in the rhizosphere soil, leading to a significant restructuring of the microbial community composition. These findings suggest that neighbor relatedness may improve the grain yield of rice cultivar mixtures. Beneficial plant–plant interactions may be generated by manipulating cultivar kinship within a crop species. A thorough understanding of kinship strategies in cultivar mixtures offers promising prospects for increasing crop production. Full article

Lotus corniculatus L. is a valuable fodder legume, recognized for its ecological adaptability and high potential for production and fodder quality. In this study, 18 genotypes collected from wild flora were analyzed to highlight genetic variability and facilitate the selection of genotypes with superior potential. The collection area was in the western part of Romania and featured a diverse topography, including parts of the Banat Plain, the Banat Hills, and the Southern and Western Carpathians. The genotypes selected from the wild flora were cultivated and evaluated for morpho-productive and forage quality traits, including pod weight, average number of seeds/pods, green mass production, and protein percentage. PCA highlighted the main components explaining the variability, and K-means clustering allowed for the identification of groups of genotypes with similar performances. ANOVA showed statistically significant differences (p < 0.001) for all traits analyzed. According to the results, genotypes LV-LC-3, LV-LC-4, LV-LC-6, and LV-LC-16 showed high productive potential and were highlighted as the most valuable for advancing in the breeding program. The moderate relationships between traits confirm the importance of integrated selection. The identified genetic variability and selected genotypes support the implementation of effective breeding strategies to obtain high-performance Lotus corniculatus L., adapted to local soil and climate conditions and with a superior forage yield. Full article

Potato (Solanum tuberosum L.) crop yields may be reduced by nitrogen deficiency stress tolerance. An evaluation of 144 tetraploid potato genotypes was carried out during two consecutive seasons (2019 and 2020), with the objective of characterizing their variability in key physiological and agronomic parameters. Physiological parameters included chlorophyll content and fluorescence, stomatal conductance, NDVI, leaf area, and perimeter, while agronomic characteristics such as yield, tuber fresh weight, tuber number, starch content, dry matter, and reducing sugars were evaluated. To genotype the population, the GGP V3 Potato array was used, generating 18,259 high-quality SNP markers. Marker–trait association analysis was conducted using the GWASpoly package in R, applying Q + K linear mixed models to enhance precision. This methodology enabled the identification of 18 SNP markers that exhibited statistically significant associations with the traits analyzed in both trials and periods, relating them to genes whose functional implication has already been described. Genetic loci associated with chlorophyll content and tuber number were detected across non-stress and stress treatments, while markers linked to leaf area and leaf perimeter were identified specifically under nitrogen deficiency stress. The genomic distribution of these markers revealed that genetic markers or single-nucleotide polymorphisms (SNPs) correlated with phenotypic traits under non-stress conditions were predominantly located on chromosome 11, whereas SNPs linked to stress responses were mainly identified on chromosomes 2 and 3. These findings contribute to understanding the genetic mechanisms underlying potato tolerance to nitrogen deficiency stress, offering valuable insights for the development of future marker-assisted selection programs aimed at improving nitrogen use efficiency and stress resilience in potato breeding. Full article

Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximity but also by customer preference characteristics. This study presents a novel heuristic framework that integrates multi-view K-means clustering with customer behavior modeling reinforced by a co-regularization mechanism to align clustering results across heterogeneous data views. By jointly exploiting spatial and behavioral information, the framework clusters customers and facilities into meaningful market segments. Within each segment, a bilevel optimization model is applied to represent the sequential decision-making of competing entities—where a leader first selects facility locations, followed by a reactive follower. An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). Concurrently, it yields an overall profit of 16,104.17, which is an approximate 0.72% increase over the Direct CFLBP-CB profit of 15,988.27 and is only 0.21% lower than the genetic algorithm’s highest profit of 16,137.75. Moreover, comparative analysis reveals that the proposed multi-view clustering with co-regularization outperforms all single-view baselines, including K-means, spectral, and hierarchical methods. This superiority is evidenced by an approximate 5.21% increase in overall profit and a simultaneous reduction in optimization time, thereby demonstrating its effectiveness in capturing complementary spatial and behavioral structures for competitive facility location. Notably, the proposed two-stage approach achieves high-quality solutions with significantly shorter computation times, making it suitable for large-scale or time-sensitive competitive facility planning tasks. Full article

Progress in establishing environmental risk factors and, consequently, prophylactic measures for glial tumors, particularly for glioblastomas, is of utmost importance, considering the dismal prognosis and limited treatment options. This report surveyed updates on established and recently identified factors that can predispose a patient to glioma formation while highlighting possible mechanistic links and further research directions. In addition to established factors that increase the risk of glioma, i.e., brain irradiation and several genetic syndromes, another group consists of likely factors contributing to such risks, such as the use of tobacco and those yielding ambiguous results (e.g., UV exposure). Oxidative stress is a common denominator for several types of exposure, and a mechanistic background for other factors remains elusive. Nevertheless, the analysis of clinical and basic research strongly suggests that, apart from the effect of environmental stressors on DNA alterations and mutation burden, the impact of modifying the tumor microenvironment should be considered. Identifying the involvement of environmental hazards in gliomagenesis and glial tumor progression would lower overall risk by modifying clinical practice, patient management, and lifestyle choices. Further verifying the environmental hazards in glioma formation and progression would have far-reaching implications for neurologists, neurosurgeons, and patients. Full article

Soybean (Glycine max [L.] Merr.) cultivation is expanding in Central Europe due to the development of early-maturing cultivars and growing demand for plant-based protein produced without the use of genetically modified organisms. However, nitrogen (N) management remains a major challenge in temperate climates, where variable weather conditions can significantly affect nodulation and yield. This study evaluated the effects of three nitrogen fertilisation doses (0, 30, and 60 kg N·ha⁻¹), applied in the form of ammonium nitrate (34% N) and two commercial rhizobial inoculants—HiStick Soy (containing Bradyrhizobium japonicum strain 532C) and Nitragina (including a Polish strain of B. japonicum)—on nodulation, nitrogen uptake, and seed yield. A three-year field experiment (2017–2019) was conducted in southwestern Poland using a two-factor randomized complete block design. Nodulation varied significantly across years, with the highest values recorded under favourable early-season moisture and reduced during drought. In the first year, inoculation with HiStick Soy significantly increased nodule number and seed yield compared to Nitragina and the uninoculated control. Nitrogen fertilisation consistently improved seed yield, although it had no significant effect on nodulation. The highest nitrogen use efficiency was observed with moderate nitrogen input (30 kg N·ha⁻¹) combined with inoculation. These findings highlight the importance of integrating effective rhizobial inoculants with optimized nitrogen fertilisation to improve soybean productivity and nitrogen efficiency under variable temperate climate conditions. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. Full article

Vegetable oils are essential for human nutrition and industrial applications. With growing global demand, increasing oil content in oilseed crops has become a top priority. This review synthesizes recent progress in understanding the genetic, environmental, and molecular mechanisms regulating oil content, and presents biotechnological strategies to enhance oil accumulation in major oilseed crops. Oil biosynthesis is governed by intricate genetic–environmental interactions. Environmental factors and agronomic practices significantly impact oil accumulation dynamics. Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) have identified key loci and candidate genes involved in lipid biosynthesis pathways. Transcription factors and epigenetic regulators further fine-tune oil accumulation. Biotechnological approaches, including marker-assisted selection (MAS) and CRISPR/Cas9-mediated genome editing, have successfully generated high-oil-content variants. Future research should integrate multi-omics data, leverage AI-based predictive breeding, and apply precision genome editing to optimize oil yield while maintaining seed quality. This review provides critical references for the genetic improvement and breeding of high- and ultra-high-oil-content varieties in oilseed crops. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Background: Gossypium hirsutum L. is one of the main economic crops worldwide, and increasing the size/weight of its seeds is a potential strategy to improve its seed-related yield. AINTEGUMENTA (ANT) is an organogenesis transcription factor mediating cell proliferation and expansion in Arabidopsis, but little is known about its candidate function in upland cotton seed. Results: In this study, functional characterization of GhANT in the cotton seed development stage was performed. The expression pattern analysis showed that GhANT was predominantly expressed in the ovules, and its expression was consistent with the ovules’ development stage. Heterologous expression of GhANT in Arabidopsis promoted plant organ growth and led to larger seeds. Importantly, specific expression of GhANT by the TFM7 promoter in the cotton ovules enlarged the seeds and increased the cotton seed yield, as compared with the wild-type in a three-year field trial. Furthermore, transcription level analysis showed that numerous genes involved in cell division were up-regulated in the ovules of TFM7::GhANT lines in comparison to the wild-type. These results indicate that GhANT is a potential genetic resource for improving cotton seed yield through its molecular links with cell cycle controllers. Full article

Cannabis sativa L. is a versatile plant with applications in various sectors such as agriculture, medicine, food, and cosmetics. The therapeutic properties of cannabis are often linked to its secondary compounds. The worldwide cannabis market is undergoing swift changes due to varying legal frameworks. Medicinal cannabis (as a heterozygous and dioecious species) is distinct from most annual crops grown in controlled environments, typically propagated through stem cutting rather than seeds to ensure genetic uniformity. Consequently, as with any commercially cultivated crop, biomass yield plays a crucial role in overall productivity. The key factors involved in cultivation conditions, such as successful root establishment, stress tolerance, and the production cycle duration, are critical for safeguarding, improving, and optimizing plant yield. Grafting is a long-established horticultural practice that mechanically joins the scion and rootstock of distinct genetic origins by merging their vascular systems. This approach can mitigate undesirable traits by leveraging the strengths of particular plants, proving beneficial to various applications. Grafting is not used commercially in Cannabis. Only three very recent investigations suggest that grafting holds significant promise for enhancing both the agronomic and medicinal potential of Cannabis. This review critically examines the latest advancements in cannabis grafting and explores prospects for improving biomass (stem, root, flower, etc.) yield and secondary metabolite production. Full article

The yak (Bos grunniens) is a key species in high-altitude rangelands of Asia. Despite their ecological and economic importance, yak production faces persistent challenges, including low milk yields, vulnerability to climate changes, emerging diseases, and a lack of systematic breeding programs. This review presents the genomic, physiological, and environmental dimensions of yak biology and husbandry. Genes such as EPAS1, which encodes hypoxia-inducible transcription factors, underpin physiological adaptations, including enlarged cardiopulmonary structures, elevated erythrocyte concentrations, and specialized thermoregulatory mechanisms that enable their survival at elevations of 3000 m and above. Copy number variations (CNVs) and single nucleotide polymorphisms (SNPs) present promising markers for improving milk and meat production, disease resistance, and metabolic efficiency. F1 and F2 generations of yak–cattle hybrids show superior growth and milk yields, but reproductive barriers, such as natural mating or artificial insemination, and environmental factors limit the success of these hybrids beyond second generation. Infectious diseases, such as bovine viral diarrhea and antimicrobial-resistant and biofilm-forming Enterococcus and E. coli, pose risks to herd health and food safety. Rising ambient temperatures, declining forage biomass, and increased disease prevalence due to climate changes risk yak economic performance and welfare. Addressing these challenges by nutritional, environmental, and genetic interventions will safeguard yak pastoralism. This review describes the genes associated with different yak traits and provides an overview of the genetic adaptations of yaks (Bos grunniens) to environmental stresses at high altitudes and emphasizes the need for conservation and improvement strategies for sustainable husbandry of these yaks. Full article

The mining sequence of ionic rare earth mineral mining districts is related to the effective utilization of rare earth mineral resources and the protection of ecological environment. This study establishes an optimization model for the mining sequence of ion-adsorption rare earth mining districts that incorporates environmental costs, using the net present value (NPV) of the mining district and the net present value of environmental costs (C_E) as objective functions. The model is applied to optimize the mining sequence of Mining District L. The results demonstrate that (1) Four algorithms, namely NSGA-II, NSGA-III, IBEA, and MOEA/D, were selected for comparison. The analysis based on the distribution of solutions, hypervolume values (HV), and computational time revealed that the IBEA exhibited superior performance. (2) The IBEA was employed to solve the multi-objective optimization problem, yielding a set of 30 optimal solutions. Different NPVs corresponded to different C_E values, with the C_E value increasing correspondingly as the NPV increased. (3) The weighted method was employed to transform the multi-objective optimization problem into a single-objective formulation. Using a genetic algorithm (GA), the optimal solution yielded a decision variable sequence for mining order as [2, 5, 8, 4, 1, 9, 6, 7, 3, 10, 11], with the net present value (NPV) of mining district profits reaching CNY 76,640.65 million and the environmental cost NPV amounting to CNY 19,469.18 million. Compared with the mining sequence optimization scheme that did not consider C_E, although the NPV decreased by CNY 3.3266 million, the C_E was reduced by CNY 10.6993 million. The mining sequence optimization model with environmental costs constructed in this paper provides a scientific decision-making basis for mining enterprises to consider the mining sequence in mining districts, minimize the damage to the ecological environment, and promote the coordinated progress of resource development and sustainable development. Full article

Porcine Rotavirus (PoRV), a predominant causative agent of neonatal diarrhea in piglets, shares substantial genetic homology with human rotavirus and represents a considerable threat to both public health and the global swine industry in the absence of specific antiviral interventions. The VP6 protein, an internal capsid component, is characterized by exceptional sequence conservation and robust immunogenicity, rendering it an ideal candidate for viral genotyping and vaccine development. In the present study, the recombinant plasmid pET28a(+)-VP6 was engineered to facilitate the high-yield expression and purification of the VP6 antigen. BALB/c mice were immunized to generate monoclonal antibodies (mAbs) through hybridoma technology, and the antigenic specificity of the resulting mAbs was stringently validated. Subsequently, a panel of truncated protein constructs was designed to precisely map linear B-cell epitopes, followed by comparative conservation analysis across diverse PoRV strains. Functional validation demonstrated that all three mAbs exhibited high-affinity binding to VP6, with a peak detection titer of 1:3,000,000 and exclusive specificity toward PoRVA. These antibodies effectively recognized representative genotypes such as G3 and X1, while exhibiting no cross-reactivity with unrelated viral pathogens; however, their reactivity against other PoRV serogroups (e.g., types B and C) remains to be further elucidated. Epitope mapping identified two novel linear B-cell epitopes, ¹²⁸YIKNWNLQNR¹³⁷ and ¹³⁸RQRTGFVFHK¹⁴⁷, both displaying strong sequence conservation among circulating PoRV strains. Collectively, these findings provide a rigorous experimental framework for the functional dissection of VP6 and reinforce its potential as a valuable diagnostic and immunoprophylactic target in PoRV control strategies. Full article