7141 research outputs found
Sort by
Graduate Catalogue 2025 - 2026
Full text linkhttps://digitalscholarship.tnstate.edu/graduatecatalogues/1025/thumbnail.jp
Undergraduate Catalogue 2025 - 2026
Full text linkhttps://digitalscholarship.tnstate.edu/undergraduatecatalogues/1070/thumbnail.jp
Microbiome–Immune Interaction and Harnessing for Next-Generation Vaccines Against Highly Pathogenic Avian Influenza in Poultry
No full textHighly pathogenic avian influenza (HPAI) remains a persistent threat to global poultry production and public health. Current vaccine platforms show limited cross-clade efficacy and often fail to induce mucosal immunity. Recent advances in microbiome research reveal critical roles for gut commensals in modulating vaccine-induced immunity, including enhancement of mucosal IgA production, CD8+ T-cell activation, and modulation of systemic immune responses. Engineered commensal bacteria such as Lactococcus lactis, Bacteroides ovatus, Bacillus subtilis, and Staphylococcus epidermidis have emerged as promising live vectors for antigen delivery. Postbiotic and synbiotic strategies further enhance protective efficacy through targeted modulation of the gut microbiota. Additionally, artificial intelligence (AI)-driven tools enable predictive modeling of host–microbiome interactions, antigen design optimization, and early detection of viral antigenic drift. These integrative technologies offer a new framework for mucosal, broadly protective, and field-deployable vaccines for HPAI control. However, species-specific microbiome variation, ecological safety concerns, and scalable manufacturing remain critical challenges. This review synthesizes emerging evidence on microbiome–immune crosstalk, commensal vector platforms, and AI-enhanced vaccine development, emphasizing the urgent need for One Health integration to mitigate zoonotic adaptation and pandemic emergence
The Effects of Tea Polyphenols on the Emulsifying and Gelling Properties of Minced Lamb After Repeated Freeze–Thaw Cycles
No full textMinced lamb remains one of the most produced meat products in the meat industry, across both the food service and retail sectors. Tea polyphenols (TPs), renowned for their diverse biological activities, are increasingly being employed as natural food additives in research and development. Tea polyphenols at concentrations of 0.00% (CG), 0.01% (TP1), 0.10% (TP2), and 0.30% (TP3) were added to lamb which had undergone a series of freeze–thaw cycles. The presence of tea polyphenols led to a significant decrease in the number of disulfide bonds, resulting in a slower oxidation rate. In addition, the surface hydrophobicity and juice loss of the minced lamb supplemented with tea polyphenols were 91.23 ± 0.22 and 20.00 ± 0.46, respectively, representing a reduction of 1.5% and 7.59% compared to the group without the addition of tea polyphenols. However, the addition of high-dose tea polyphenols also led to a reduction in emulsification stability, alterations in protein conformation, and changes in water migration. Furthermore, the incorporation of a minimal quantity of tea polyphenols (0.01%) resulted in enhanced emulsification stability, water retention, textural properties, and microstructures in minced lamb. This suggests that tea polyphenols have the potential to improve the quality of minced lamb following freezing and thawing processes
Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data
Full text linkDeep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices