Search Results (2,730)

Hepatocyte nuclear factor 4α (HNF4A) is a critical transcription factor that regulates the differentiation and metabolism of intestinal epithelial cells. However, its role in piglet growth remains unclear. In this study, the tissue expression of HNF4A was examined using RT-qPCR, and the putative functional SNPs were analyzed by integrating bioinformatics and DNA sequencing. Association analysis was performed in 156 Min pigs and 160 Landraces, and the biological function of the identified genetic variant was explored using a dual-luciferase reporter assay. The results showed that HNF4A was widely expressed in liver, kidney and gastrointestinal tissues, with significantly higher expression in the liver of adult pigs than in newborn piglets (p < 0.05). A 20 bp InDel was identified in the first intron of porcine HNF4A. Allele frequency analysis showed that the Del allele (20 bp deletion) was dominant in Landrace and Duroc pigs, while the In allele (20 bp insertion) was dominant in Min and Jinhua pigs. Association analysis revealed that Min pigs with the In/Del genotype had significantly higher body weights at 14, 21, 28 and 35 days and higher average daily gain (ADG) than those of the In/In animals (p < 0.05). Landrace piglets with the Del/Del genotype exhibited significantly higher body weight at 21 and 28 days than those of the In/Del genotype (p < 0.05). The dual-luciferase reporter assay suggested that the plasmid carrying the In allele exhibited higher transcriptional activity than the Del allele (p < 0.05). Notably, the genotype associated with superior growth performance differed between the two breeds. Collectively, a 20 bp InDel within HNF4A was identified, which might affect piglet growth partially by modulating its transcription, and further study in other populations with different genetic backgrounds is needed before its application in pig breeding. Full article

In recent decades, conservation of local poultry breeds has gained relevance to preserve genetic resources adapted to low-input systems and to enhance their valorization. This study addresses a key knowledge gap by providing a comprehensive characterization of the endangered Murciana chicken breed, native to southeastern Spain. We jointly evaluate recent population dynamics, conservation framework, morphology and morphometrics, growth patterns, and reproductive and productive traits. Data includes census and pedigree records, standardized morphological assessments, growth modeling, and production data from the conservation nucleus. The population increased from fewer than 150 registered animals in 2017 to more than 550 in 2024, indicating stabilization. The breed showed characteristics of slow-growing dual-purpose Mediterranean genotypes, with marked sexual dimorphism, Gompertz relative growth rates of 0.020 d⁻¹ (males) and 0.023 d⁻¹ (females), and adult weights of 3.2 kg and 2.4 kg, respectively. Carcass yield was moderate (61.9%), with higher leg (36.7%) than breast proportion (16.9%). Reproductive (fertility 88.6%, hatchability 80.6%) and laying performance (116.6 eggs/hen/year) were consistent with local extensive systems. These results provide a robust baseline to support conservation, genetic management, and sustainable use of the Murciana chicken breed, contributing to its long-term preservation and valorization. Full article

Genomic prediction (GP) is now widely used in crop improvement, but the rapid expansion of GP methods has outpaced the development of standardized evaluation practices. Reported gains from deep learning and other complex models are difficult to interpret when studies use different datasets, baselines, validation schemes, metrics, tuning budgets, and reporting practices. In this Perspective, we argue that plant GP requires a shift from model-centric performance claims toward standardized, scenario-aware, and application-oriented benchmarking. We highlight four sources of complexity that shape model performance: species and genetic-background diversity, trait architecture, population structure, and genotype-by-environment interaction. We then review current plant GP resources and draw lessons from benchmarking efforts in gene regulatory network inference, single-cell model assessment, and protein structure prediction. On this basis, we propose a nine-step workflow covering curated datasets, harmonized preprocessing and metadata, marker-density scenarios, required baselines, controlled hyperparameter tuning, fixed validation splits, multi-dimensional evaluation metrics, reproducibility reporting, and breeder-facing recommendations. Such benchmarks would make reported gains easier to verify, reduce selective reporting, support cost-aware deployment, and transform plant GP from a collection of fragmented performance claims into a reproducible, comparable, and practically deployable framework for modern breeding. Full article

Background: Plasma biomarkers are promoted as scalable tools for the staging of Alzheimer’s disease (AD), yet head-to-head comparisons against the clinical scales used to define diagnostic labels remain scarce. Reported gains from machine learning fusion of clinical and biomarker features may reflect label circularity rather than biological signals, and quantifying this circularity is a central aim of the present work. Methods: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we assembled 655 participants (CN = 296, MCI = 168, and AD = 191) with concurrent plasma biomarkers (pT217, A$\beta$42/40, NfL, and GFAP), clinical scales (MMSE, CDR-SB, and FAQ), APOE genotype, and demographics. Three pre-specified feature sets (clinical-only, biomarker plus demographic–genetic, and full fusion) were compared across four classifiers (Logistic Regression, SVM, Random Forest, and XGBoost) using repeated, nested cross-validation (5-fold × 3 outer, 5-fold inner) with balanced class weighting. Because the external Center for Neurodegeneration and Translational Neuroscience (CNTN) cohort ($n=130$) measures pT181 rather than pT217 and lacks A$\beta$42/40, external evaluation used a separate reduced feature panel (NfL, GFAP, APOE, age, sex, and education), not the proposed pT217-inclusive panel. Results: Clinical scales alone reached a three-class AUC-OVR of $0.9539\pm 0.0041$, and fusion reached $0.9559\pm 0.0046$, an indistinguishable gain. Because MMSE, CDR-SB, and FAQ partly determine ADNI diagnostic labels, both estimates are circularity-inflated upper bounds and do not reflect independent classification power. Independent of this circularity, the internal plasma plus demographic–genetic model still achieved AUC-OVR $=0.7455\pm 0.0150$, with pT217 as the dominant contributor. Pairwise discrimination was excellent for CN vs. AD ($1.0000$) and MCI vs. AD ($0.9739$) but markedly weaker for CN vs. MCI ($0.9302$ for fused and $0.6972$ for plasma only). The separate reduced-feature model, which contains neither pT217 nor A$\beta$42/40, transferred to CNTN with AUC-OVR $=0.702$ (95% CI $0.635$–$0.764$). Conclusions: Apparent fusion gains in ADNI are largely a consequence of label circularity. After removing the circular clinical features, the internal pT217-inclusive plasma model supports three-class CN/MCI/AD screening at AUC $\approx 0.74$ and a reduced panel without pT217 transfers to an independent cohort at AUC $\approx 0.70$. These values provide a realistic performance estimate for blood-based AD staging under the current feature set, diagnostic label structure, and cohort design, and richer feature sets or pathology-anchored labels may shift this estimate. MCI detection remains the principal bottleneck. Full article

Tomato is an important vegetable crop suited to both open-field and protected cultivation. Indeterminate genotypes with high yield potential and desirable quality traits are especially suited to off-season production under protected cultivation. The present study evaluated 57 indeterminate tomato genotypes over two consecutive years under protected conditions to assess genetic variability, genetic divergence, and trait associations across 16 important yield-attributing and quality traits. The analysis of variance depicted significant differences among genotypes for all traits under study. The traits, viz., fruit weight and number of fruits per cluster, exhibited high heritability and high genetic gain, suggesting the predominance of additive gene action and the possibility of direct selection. A significant, positive correlation between fruit weight and the number of plant clusters and yield was observed. Analysis of genetic divergence following Mahalanobis D² statistics classified the genotypes into seven clusters. The number of flowers per cluster and fruit width were the top contributors to the total genetic divergence. Cluster VI outperformed for earliness and yield, Cluster V outperformed for nutritional quality, while Cluster VII was superior for fruit size. Principal Component Analysis revealed that the first five components cumulatively explained 83.3% of the total variation, with PC1 defined by fruit number trait and PC2 by yield and earliness traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) was used to select the best-performing genotypes, highlighting PIDGT-39, PIDGT-42, and PIDGT-29 as elite. Thus, the findings of the present study provide deeper insights into the genetic makeup of indeterminate tomato genotypes and potential parental accessions for tomato improvement, to enhance yield and quality under protected conditions. Full article

Background/Objectives: Autophagy is a highly conserved eukaryotic cellular process whose dysfunction results in human pathologies including cancer and neurodegenerative disease. First identified in yeast, ATG genes are central players in autophagy. Mutations in core autophagy genes ATG5 and ATG7 have been previously reported to cause rare genetic disorders with autosomal recessive inheritance. Methods: Here we report, for the first time, variants in human ATG9B gene as causative factors for a rare neurodevelopmental disease with autosomal recessive inheritance. Three distinct mutations were detected in three independent families with consanguinity, five patients affected in total. Results: The first variant is an 11-nucleotide deletion resulting in a frameshift. A premature stop codon is added and the C-terminal cytosolic domain of ATG9B protein is truncated. The second one is a point mutation that changes a critical amino acid in the transmembrane domain. The third variant is a 2-nucleotide deletion causing a different truncation product. Patients presented with diverse neurodevelopmental anomalies including intellectual disability, behavioral abnormalities, congenital cerebellar ataxia, mild cerebellar atrophy, and microcephaly. Since human ATG9B is expressed specifically in the placenta, we hypothesized that the disease pathology originates during placental development. To characterize the effects of the first frameshift mutation and gain insight into the specific functions of ATG9B in a physiological setting, we used mammalian cells and a knock-in mouse model. Truncated ATG9B was not stable when expressed in cells. It was localized to perinuclear vesicles like the WT protein, but not to peripheral vesicles. Homozygous knock-in mice were viable, fertile, and displayed no gross phenotypical abnormalities. Histomorphometry analysis of the placenta layers did not reveal a significant difference between mutant and control embryos. The assessments of neurobehavioral tests were similar in wild-type and homozygous knock-in mice. However, knock-in mice had a reduced fear memory trend, which is an amygdala-involved response. Conclusions: In this study, we describe a new rare disease linked to ATG9, including cerebellar ataxia and atrophy, as described for ATG5 and ATG7. Full article

The flying-wing configuration offers inherent advantages in aerodynamic efficiency and stealth; however, conventional fixed-wing designs face fundamental performance trade-offs when tasked with multi-role missions. This paper introduces a multidisciplinary design optimization (MDO) framework for a morphing wing unmanned aerial vehicle (UAV) to overcome this limitation. The proposed UAV integrates four complementary morphing strategies—shear-type variable sweep, variable span, morphing wingtip, and a continuously variable camber trailing edge—to adapt its geometry for different flight phases. An automated parametric modeling platform is developed, enabling the dynamic generation of 3D CAD models driven by design variables. This geometry is coupled with a suite of analysis modules for aerodynamics, propulsion, weight estimation, flight performance, and radar cross-section. The multi-mission profile, including takeoff, climb, cruise, turning, and landing, is decomposed into several phase-specific single-objective optimization subproblems, which are solved using an elitist real-coded genetic algorithm. The results quantify the optimal morphing configurations for each phase, demonstrating significant performance gains over the baseline, such as a 17% increase in range. Critically, the study analyzes the trade-off between aerodynamic benefits and the weight penalty of morphing mechanisms, revealing that both range and maneuverability are the most sensitive to the added weight. The proposed framework uses mission-phase-specific optimum geometries to define the required morphing envelope, actuation ranges, and net performance benefit of a candidate morphing flying-wing UAV after considering mechanism-induced mass penalties. This framework provides a quantitative basis for mission-driven morphing decisions and establishes a viable approach for designing highly adaptive next-generation UAVs. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

Upland cotton is an important fiber and oilseed crop. Cottonseed size, measured by seed index, is an important seed quality trait that affects seed germination, seedling vigor, fiber yield, and cottonseed nutrient content. However, genetic variation in cottonseed size is highly limited within upland cotton, limiting the genetic gain in cottonseed size. Introgression breeding can alleviate this bottleneck effect by introducing desirable genes from pima to upland cotton. This study was conducted to analyze the seed size from both fuzzy and acid-delinted seeds and to assess the appropriate cottonseed size. In 2022, a population of 1600 cotton introgression lines (ILs) was grown at Leyendecker Plant Science Center, NMSU, while three field tests were conducted in 2023, including NM with all the ILs and MS and TX each with 1000 ILs. The analysis of variance of seed size showed that genotypic and environmental variation were found in both types of seeds. The acid-delinted and fuzzy cottonseeds had a mean seed index of 9.58 g and 11.26 g, while the broad sense heritability was 0.56 and 0.32, respectively. Furthermore, the seed index was not significantly correlated with cottonseed oil and different fatty acids. Full article

Accurate maize yield prediction across diverse environments is pivotal for modern breeding programs. While machine learning (ML) excels at capturing non-linear environmental effects, Genomic Best Linear Unbiased Prediction (GBLUP) remains a benchmark for modeling polygenic small-effect contributions. However, principled integration of these paradigms—while explicitly accounting for genotype-by-environment interaction (G×E)—remains a formidable challenge. We propose a two-step framework evaluated on the Genomes to Fields (G2F) 2022 dataset. In Step 1, ML models are employed to fit environmental main effects; in Step 2, genomic residuals are modeled via additive-dominance relationship matrices, augmented by an explicit low-rank G×E matrix. Candidate interaction markers were screened through plasticity-based genome-wide association studies (GWAS) across six phenotypic stability metrics and used to construct a low-rank candidate G×E representation, with a cross-validation-selected scaling parameter applied to control the contribution of the predicted G×E component. TwoStep_G×E_alpha0.33, achieved a within–environment Pearson correlation coefficient (PCC) of 0.376, outperformed both GBLUP and the competition-winning model (PCC = 0.357) in within-environment ranking. Furthermore, environment-adaptive selection yielded a genetic gain of 0.454 Mg ha⁻¹, representing a 34.7% improvement over GBLUP. Overall, the proposed framework provides a practical approach for environment-specific yield prediction and adaptive selection in maize breeding. Full article

Recently, the pre-positioned warehouse mode has gained popularity among fresh-product e-commerce, due to its proximity to customers, flexibility, and low operation and maintenance costs. However, product diversity and perishability cause higher inventory and transportation costs, and warehouse and distribution problems hinder the sustainability of the pre-positioned warehouses. This paper considers such key factors as product diversity, food spoilage, and transportation mode and investigates the warehousing and distribution strategy for fresh-product e-commerce’s central warehouse and pre-positioned warehouse. A warehousing and distribution two-stage model for the central warehouse and pre-positioned warehouse is proposed with the objective function, which minimizes the inventory maintenance cost, spoilage cost, and transportation cost, and is solved based on a genetic algorithm. The results serve as a reference for the replenishment and allocation decisions on different product categories in the pre-positioned warehouse mode and centralized purchase decisions at the central warehouse. Full article

The body’s microbiota plays a fundamental role in maintaining homeostasis and influences immune function, metabolism, and tissue integrity. A growing body of research suggests that fluctuations in the composition and abundance of individual microbiota populations may influence cancer development and the effectiveness of therapy. The condition of microbiota dysbiosis has been demonstrated to induce chronic inflammation, immune system dysregulation, and, most significantly, modulation of molecular pathways that promote tumorigenesis. The efficacy and toxicity of cancer treatment can be influenced by the composition of the microbiota. Bacteria can modify the effectiveness and toxicity of chemotherapy and immunotherapy by affecting drug metabolism and the body’s immune response. In contrast, the development of anticancer therapies that utilize bacteria is gaining increasing interest. This alternative to conventional treatment utilizes the natural ability of certain bacterial species to selectively colonize hypoxic and necrotic environments. The exploration of natural and genetically modified bacteria as vectors for the delivery of cytotoxins, immunomodulators, or therapeutic genes in the combat of cancer is a current area of research. In addition, their capacity to stimulate an antitumor immune response is also exploited. Preclinical investigations in animals have demonstrated the efficacy of this therapeutic approach, underscoring the promise of bacterial therapies as either an adjunct to conventional treatment or as a standalone strategy for combating cancer. This article synthesizes the current knowledge regarding the role of microbiota in carcinogenesis in animals and discusses recent developments in the field of bacterial therapies. The text also addresses the challenges, safety considerations, and future perspectives associated with translating microbiota-targeted and bacterial therapies into veterinary and comparative oncology. Full article

Saprolegnia parasitica (Oomycota) causes saprolegniosis and poses significant ecological and economic challenges in aquaculture. Experimental research on this pathogen is constrained by the lack of reliable long-term preservation methods, as routine maintenance by serial subculturing is labor-intensive and may result in genetic and phenotypic instability. Deep eutectic solvents (DESs), tunable low-melting mixtures, have recently gained attention as alternative cryoprotectants. However, their application has not been evaluated in oomycetes. Here, twelve glycerol-based two- and multicomponent DESs were assessed for cryopreservation of S. parasitica at −80 °C and compared with glycerol as a conventional cryoprotectant. Cryopreservation efficiency was assessed based on post-thaw survival and mycelial regeneration. Several two-component DESs, particularly glycerol-trehalose, supported 100% survival and high post-thaw mycelial regeneration, performing comparably to glycerol under the tested conditions. Shorter pre-incubation (30 min vs. 1 h and 3 h) and controlled-rate freezing (vs. direct freezing) significantly improved post-thaw growth. Although survival remained 100% under optimized conditions, extending storage from 7 to 32 days significantly reduced mycelial regeneration in the glycerol–trehalose treatment, indicating that survival alone, as done in existing literature, does not reflect physiological recovery. Overall, our results support the use of selected DESs as alternative cryoprotectants in oomycetes and contribute to the development of cryopreservation strategies for S. parasitica. Full article

The biosynthesis and accumulation of plant secondary metabolites are tightly regulated by environmental fluctuations, serving as a crucial interface mediating plant–environment interactions. Nevertheless, the phenotypic instability of secondary metabolism-related traits induced by environmental variability has hampered the precise breeding of stress-resistant cultivars. Pinus taeda is an key timber tree species in southern China, and its foliar catechins exhibit substantial stress-resistant potential. However, phenotypic variation driven by seasonal changes has limited the germplasm innovation and genetic selection of this species. In this study, 54 half-sib families of P. taeda were used as experimental materials. Combined with near-infrared spectroscopy (NIRS) and the BLUP model, we systematically analyzed the seasonal variation characteristics, genetic parameters of catechin content (CC), and genetic gains under different breeding strategies across four seasons. Our results demonstrated that family and season had extremely significant effects on CC (p < 0.001), whereas the season × family interaction effect was not significant, indicating that the genetic expression of CC is stable across seasons. CC was higher in spring and winter but lower in summer and autumn; specifically, the mean CC in summer was 47% lower than the peak value in spring (26.95 ± 0.46 μg·g⁻¹), reflecting a resource trade-off between growth and defense metabolism. Genetic parameter analysis revealed that family-mean heritability (0.373–0.714) was higher than individual heritability and within-family heritability, with August identified as the optimal selection season. The maximum genetic gain across the three breeding strategies (individual selection, family selection, and combined selection) reached 7.86%, among which individual selection exhibited the smallest fluctuation in genetic gain. Finally, three superior families and 14 superior individuals were screened out. This study elucidates the seasonal genetic pattern of foliar CC in P. taeda, clarifies the optimal selection stage and efficient breeding strategies, and provides theoretical guidance and material support for the genetic improvement, germplasm innovation, and resource utilization of secondary metabolic traits in this ecologically and economically important tree species. Full article

Ensuring global food security under accelerating climate change requires transformative approaches to crop improvement that extend beyond the limits of traditional breeding and gene editing. While domestication and modern agriculture have delivered substantial gains in productivity, these advances often came at the cost of genetic diversity, stress resilience, and developmental plasticity. Plants, however, inherently exhibit remarkable flexibility in their morphology and development, as evidenced by the vast diversity of organ shapes, cell types, and adaptive responses that have evolved across lineages. This natural design space provides a foundation for reimagining plant architecture using synthetic biology. Recent advances in plant synthetic biology, including programmable transcription factors, CRISPR-based regulatory systems, synthetic gene circuits, orthogonal signalling pathways, and plant artificial chromosomes, now enable precise, modular, and environmentally responsive manipulation of developmental processes. These tools allow researchers to rewire hormone pathways, tune quantitative gene expression, integrate multiple environmental signals, and create novel regulatory modules that operate independently of endogenous networks. Beyond understanding plant development, these capabilities open avenues for engineering crops with dynamic architectures, enhanced plasticity, and improved resilience to complex and fluctuating stresses. In this review, we synthesise insights from natural diversity, developmental biology, and synthetic regulatory engineering to outline how plant architecture can be rationally redesigned. We argue that integrating synthetic biology with modern breeding and modelling frameworks will be essential for generating the next generation of programmable crops; i.e., varieties capable of sustaining productivity and stability in an era of unprecedented environmental and geopolitical changes. Full article