Search Results (8,064)

Mastitis represents one of the most economically devastating diseases in dairy production, causing billions of dollars in annual losses through reduced milk quality and quantity. Recent advances in microbiome research have unveiled a critical gut–mammary axis that fundamentally influences mastitis susceptibility and pathogenesis in dairy cattle. Through comprehensive analysis of microbial communities across multiple anatomical sites, we demonstrate that mastitis development involves systematic disruption of both mammary and gastrointestinal microbiomes, characterized by reduced beneficial bacterial populations and increased pathogenic species. Healthy animals maintain balanced microbial ecosystems dominated by protective taxa including Firmicutes, Bacteroidetes, and beneficial Lactobacillus species, while mastitis-affected animals exhibit dysbiotic shifts toward Proteobacteria dominance, elevated Streptococcus and Staphylococcus populations, and compromised microbial diversity. Mechanistic investigations reveal that gut microbiota disruption compromises systemic immune competence, alters metabolite production including short-chain fatty acids and bile acids, and influences inflammatory mediators that circulate to mammary tissue. Therapeutic interventions targeting this axis, including probiotics, prebiotics, and plant-derived compounds, demonstrate significant efficacy in restoring microbiome homeostasis and reducing mastitis severity. These findings establish the gut–mammary axis as a fundamental regulatory mechanism in mastitis pathogenesis, opening new avenues for microbiome-based prevention and treatment strategies that could significantly enhance dairy health management while addressing antimicrobial resistance concerns. Full article

Cowpea production in Nigeria, the world’s largest producer, is insufficient to meet domestic demand due to significant yield gaps caused by various production constraints. Several high-yielding improved cowpea varieties have been developed and disseminated among smallholder farmers to improve productivity, but their adoption is low because breeding efforts have not adequately incorporated farmers’ and consumers’ preferred traits. To address this, a study was conducted to evaluate the performance of newly developed cowpea lines and identify those with traits preferred by farmers and consumers. Twenty-four cowpea lines were evaluated in multiple environments under sole and intercropped systems in Nigeria. The study revealed significant (p < 0.001) genotypic and genotype-by-environment interaction effects for grain yield, fodder yield, and other key agronomic traits. Three genotypes consistently outperformed the standard check, with UAM15-2157-4 exhibiting a 57.6% higher grain yield and superior seed quality. UAM15-2157-4 produced the highest grain yield (1289 kg ha⁻¹) under the intercropping system. GGE biplot analysis identified UAM15-2157-4 as the most stable genotype across all tested environments. This genotype, along with other promising lines, possesses desirable traits such as Striga resistance, large seed size, and preferred seed coat color, making them suitable for release and adoption to improve cowpea productivity in the region. Full article

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article

Multiple Salmonella outbreaks linked to milk powders call for the need for effective pasteurization processes. Understanding the thermal inactivation kinetics of Salmonella in milk powder is crucial; however, the influence of macronutrient content (protein, fat, and carbohydrate) on these kinetics remains unclear. This study investigated the effects of common reconstitution temperature (45, 70, and 99 °C) on Salmonella survival in infant milk powder to raise public awareness about contamination risks. Seven milk powders with varying macronutrient compositions were used as model systems. After equilibrating to a uniform water activity (a_w = 0.2), the thermal resistance of Salmonella was determined at 75, 80, and 85 °C. The goodness-of-fit of two primary models (log-linear and Weibull) was compared, and secondary response surface models were developed to predict the combined effects of temperature and macronutrient composition on Salmonella inactivation. Results showed that Salmonella could proliferate or be resuscitated when contaminated milk powder was reconstituted at conventional preparation temperatures (45 °C and 70 °C). Across the seven formulations, Salmonella thermal resistance (D-value) increased with protein content (10.44–90.18%) and decreased with carbohydrate content (0.35–63.24%). A significant protein–temperature interaction was observed, whereby the effect of protein content on the Salmonella D-value decreased as temperature increased from 75 to 85 °C. Fat content did not significantly affect thermal inactivation (p > 0.05). The log-linear model provided a better fit than the Weibull model in this study. Overall, this research quantifies how macronutrient composition impacts Salmonella thermal resistance and offers predictive models to improve pasteurization strategies for milk powder. Full article

Background: Three-dimensional (3D) printed scaffolds have emerged as promising tools for bone regeneration, but the optimal structural design and pore size remain unclear. Polylactic acid (PLA) reinforced with graphene oxide (GO) offers enhanced mechanical and biological performance, yet systematic evaluation of architecture and pore size is limited. Methods: Two scaffold architectures (lattice-type and dode-type) with multiple pore sizes were fabricated using UV-curable PLA/GO resin. Physical accuracy, porosity, and mechanical properties were assessed through compression and fatigue testing. Based on in vitro screening, four pore sizes (930 μm, 690 μm, 558 μm, 562 μm) within the dode-type structure were analyzed. The 558 μm and 562 μm scaffolds, showing distinct fracture thresholds, were further evaluated in rat and rabbit calvarial defect models for inflammation and bone regeneration. Results: In vitro testing revealed that while 930 μm and 690 μm scaffolds exhibited superior compressive strength, the 562 μm scaffold showed a unique critical fracture behavior, and the 558 μm scaffold offered comparable stability with higher resistance to premature failure. In vivo studies confirmed excellent biocompatibility in both groups, with early bone formation favored in the 558 μm scaffold and more continuous and mature bone observed in the 562 μm scaffold at later stages. Conclusions: This stepwise strategy—from structural design to pore size screening and preclinical validation—demonstrates that threshold-level mechanical properties can influence osteogenesis. PLA/GO scaffolds optimized at 558 μm and 562 μm provide a translationally relevant balance between mechanical stability and biological performance for bone tissue engineering. Full article

Przewalski’s gazelle (Procapra przewalskii) is an endangered ungulate endemic to the Qinghai–Tibet Plateau, with a small population size and exposure to multiple ecological pressures. Its gut microbiota may play a crucial role in host environmental adaptation. To investigate the functional divergence of gut microbial communities, we performed high-throughput metagenomic sequencing on 105 wild fecal samples collected from 10 geographic regions around Qinghai Lake. The results revealed significant regional differentiation in key functional modules related to metabolism, antibiotic resistance mechanisms, and virulence-associated pathways. All populations showed enrichment in core metabolic pathways such as carbohydrate and amino acid metabolism, with carbohydrate-active enzymes dominated by glycoside hydrolases (GHs) and glycosyltransferases (GTs), exhibiting overall functional conservation. Although populations shared many antibiotic- and virulence-related reference genetic markers, the marker composition associated with distinct resistance mechanisms and pathogenic processes exhibited clear population-specific patterns, suggesting differential microbial responses to local environmental pressures. Correlation network analysis further identified core taxa (e.g., Arthrobacter and Oscillospiraceae/Bacteroidales lineages) as key genera linking community structure with core metabolic, resistance-related, and virulence-associated marker functions. Overall, the gut microbiota of Przewalski’s gazelle exhibits a complex spatially structured functional differentiation, reflecting host–microbiome co-adaptation under region-specific ecological pressures. These findings provide critical methodological and theoretical support for microecological health assessment and regionally informed conservation management of this endangered species. Full article

Rice sheath rot has progressively developed into a growing threat to global rice production, particularly in intensively managed systems conducive to disease development. Therefore, accurate identification of the causal pathogen and the development of sustainable management strategies represent urgent scientific requirements. In this study, we isolated the causal organism of rice sheath rot from infected rice tissues and identified it as Fusarium verticillioides based on multi-locus sequence analysis. Eight endophytic bacterial strains were recovered from healthy rice root systems. Among the isolates, Bacillus velezensis isolate 7-A exhibited the strongest antifungal activity against F. verticillioides. This isolate demonstrated broad-spectrum antifungal activity, with inhibition rates ranging from 54.8% to 71.8%. Phylogenetic analysis based on 16S rRNA and gyrB gene sequences identified it as B. velezensis. Further characterization revealed that B. velezensis 7-A is capable of secreting proteases and synthesizing siderophores. The filtered liquid from sterile fermentation markedly inhibited the growth of mycelium in F. verticillioides and induced marked morphological abnormalities. Liquid LC-MS analysis identified multiple antifungal active substances, including camphor, ginkgolides B, salicin, cinnamic acid, hydroxygenkwanin, stearamide, β-carotene, and others. A pot experiment demonstrated that the fermentation broth of B. velezensis 7-A effectively suppressed the occurrence of rice sheath rot, achieving a relative control efficacy of 61.3%, which is comparable to that of a 10% carbendazim water-dispersible granule (WDG). Additionally, isolate 7-A enhances plant disease resistance by activating the activities of key defense enzymes. These findings provide preliminary insights into its potential application in integrated and sustainable disease management programs. Full article

Two-component systems (TCSs) in bacteria are often involved in the global regulation of various physiological activities and behaviours. This study investigated the GacSA TCS and DJ41_1407 transcriptional sensor adjacent to GacA in Acinetobacter baumannii ATCC 19606. The relationship between GacS, GacA, and DJ41_1407 and their functions and signal transduction mechanisms are described. A. baumannii ATCC 19606 mutants, ∆gacS, ∆gacA, and ∆DJ41_1407, were generated using markerless mutation and cultured in LB medium, then collected for RNA sequencing. It was found that GacS, GacA, and DJ41_1407 regulate a series of genes involved in carbon metabolism. Quantitative reverse transcription PCR (qRT-PCR) results showed that DJ41_1407 and GacA may regulate the expression of adh4, ipdC, iacH, and paa. Phos-tag™ results revealed that GacS plays a more significant role in GacA phosphorylation. GacA regulated colony size and growth conditions in rich medium. Compared to the wild-type strain, the ∆gacA and ∆gacSA mutants exhibited smaller colony sizes, and mutation of the gacS, gacA, and DJ41_1407 genes also reduced bacterial virulence as determined by the Galleria mellonella infection assay. GacA also plays a crucial role in modulating antibiotic resistance, and the ∆gacA∆DJ41_1407 mutant demonstrated greater susceptibility to antibiotics. These results highlight the multiple functions regulated by the GacSA global TCS in A. baumannii ATCC 19606. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small-cell lung cancer (NSCLC) being the most prevalent subtype. NSCLC is marked by a complex genetic makeup, involving numerous driver mutations and epigenetic changes that drive tumor growth and resistance to treatment. While several approaches, including chemotherapy and targeted therapy, have been used for lung cancer treatment, their overall responses remain dismal, indicating the need to explore alternative targets implicated in cancer growth. Among various candidates, peroxisome proliferator-activated receptor-gamma (PPARγ), which plays critical roles in regulating cellular functions related to tumorigenesis, has been explored as a promising target for NSCLC intervention. To that end, thiazolidinediones, including pioglitazone, that target PPARγ have shown promise in multiple cellular and preclinical models of NSCLC. Mechanistically, pioglitazone inhibits cancer growth and induces apoptosis via downregulating key signaling pathways, including mitogen-activated protein kinase (MAPK), which play critical roles in regulating cellular activities such as epithelial-to-mesenchymal transition (EMT), cellular bioenergetics, and glucose metabolism. This review highlights the recent updates on the mechanistic insights and the efficacy of PPARγ agonist-based approaches, with an emphasis on pioglitazone, for the treatment of NSCLC. We logically discuss the experimental evidence from the in vitro and in vivo studies exploring pioglitazone’s effect on metabolic pathways, chemical-carcinogen-induced tumorigenesis, the targeting of cell signaling pathways, and then its combination with other therapeutic agents. We also present clinical studies that support pioglitazone’s potential in chemoprevention and underscore its further exploration in large cohorts of NSCLC patients. Full article

Background/Objectives: Bacteriophages are considered promising alternatives for the treatment of multidrug-resistant (MDR) Pseudomonas aeruginosa infections. Methods: Five bacteriophages with lytic activity against MDR P. aeruginosa were isolated from lake and sewage samples and characterized for their biological properties, host range, and efficacy in biofilm and in vitro infection models. Results: The phages displayed broad host ranges, producing zones of lysis in 40–53% of MDR isolates. The average burst size was 112 ± 70 PFU per cell. All phages, either individually or in combination, inhibited biofilm formation and were capable of disrupting preformed biofilms. While treatment with single phages led to bacterial regrowth, the cocktail of all five phages achieved complete bacterial lysis with no regrowth observed. In an in vitro wound and burn infection model, the phage cocktail significantly enhanced cell proliferation and promoted healing. Transmission electron microscopy (TEM) analysis identified phage PA2 as a Myovirus based on its morphology. Conclusions: The phage isolates demonstrated strong activity in multiple in vitro models, effectively targeting both planktonic and biofilm-associated P. aeruginosa. Notably, the five-phage combination prevented the emergence of bacterial resistance, supporting its potential as a biocontrol strategy against MDR P. aeruginosa. Full article

Background: Burkholderia pseudomallei, the causative agent of melioidosis, is a neglected tropical pathogen that has been increasingly encountered in Europe through travel-related infections. Clinical manifestations range from localized abscesses to life-threatening sepsis, posing diagnostic challenges in non-endemic regions. Methods: We report two travel-associated melioidosis cases confirmed in Hungary between 2008 and 2024. Whole-genome sequencing (WGS), multilocus sequence typing (MLST), and core-genome MLST (cgMLST) were performed for molecular characterization. In parallel, a systematic review of travel-related melioidosis cases reported in Europe (1980–2025) was conducted according to PRISMA 2020 guidelines. Data were retrieved from PubMed, Scopus, Google Scholar, and the PubMLST database. Results: In silico MLST identified two distinct sequence types (STs): a novel ST1643, and ST1051, previously reported in Asia and Australia. Both isolates clustered within the Asian clade, confirming an imported origin. Virulence profiling revealed major determinants, including the Yersinia-like fimbriae (YLF) cluster, fhaB3, and ITS type C. The ST1643 isolate carried the bimA_Bm variant and multiple resistance genes (blaOXA-57, blaPenI, and amrAB efflux system), while ST1051 harbored blaOXA-59. The literature review identified 82 studies encompassing 195 European cases, most originating from Southeast Asia, with pneumonia, followed by septic form and abscess as the predominant presentation. We found only eight neuromelioidosis cases in Europe. Conclusions: This study represents the first report of neuromelioidosis in Hungary, and the first global description of ST1643. Combined genomic and epidemiological data highlight the need for improved clinical awareness, genomic surveillance, and diagnostic preparedness in non-endemic regions, as global travel and climate change expand the distribution of melioidosis. Full article

The development of grapevine varieties combining powdery mildew (Erysiphe necator) resistance with acceptable wine quality represents an important goal for sustainable viticulture. This study evaluated the oenological potential of five advanced breeding lines carrying Run1 or Run1Ren1 resistance loci, developed through marker-assisted selection to achieve 99.2–99.6% Vitis vinifera genome content. Genotypes were assessed under Chilean conditions during the 2024–2025 seasons, analyzing disease resistance, berry characteristics, and wine chemical parameters. All resistant genotypes exhibited complete powdery mildew resistance (OIV scores 9) without fungicide applications. Wine analyses showed pH 3.4–3.9, titratable acidity 3.7–7.8 g/L, and total phenolics 229.2–1356.1 mg GAE/L, values within ranges reported in the literature for commercial wines. Two genotypes evaluated across both seasons showed different patterns of year-to-year variation, with AJ-T2 showing 4.7% variation in anthocyanin content, while AJ-T6 exhibited greater variation in phenolic parameters. HPLC analysis revealed anthocyanin profiles dominated by malvidin-3-glucoside without diglucoside forms, consistent with V. vinifera patterns. These preliminary results from single-plant evaluations suggest that marker-assisted breeding may contribute to developing disease-resistant varieties with wine chemical parameters within commercial ranges, though multi-plant trials with appropriate controls are essential for validation. Full article

Besides genomic and proteomic analyses of bulk and individual cancer cells, cancer research focuses on the mechanical analysis of cancers, such as cancer cells. Throughout the oncogenic evolution of cancer, mechanical inputs are stored as epigenetic memory, which ensures versatile coding of malignant characteristics and a quicker response to external environmental influences in comparison to solely mutation-based clonal evolutionary mechanisms. Cancer’s mechanical memory is a proposed mechanism for how complex details such as metastatic phenotypes, treatment resistance, and the interaction of cancers with their environment could be stored at multiple levels. The mechanism appears to be similar to the formation of memories in the brain and immune system like epigenetic alterations in individual cells and scattered state changes in groups of cells. Carcinogenesis could therefore be the outcome of physiological multistage feedback mechanisms triggered by specific heritable oncogenic alterations, resulting in a tumor-specific disruption of the integration of the target site/tissue into the overall organism. This review highlights and discusses the impact of the ECM on cancer cells’ mechanical memory during their metastatic spread. Additionally, it demonstrates how the emergence of a mechanical memory of cancer can give rise to new degrees of individuality within the host organism, and a connection to the cancer entity is established by discussing a connection to the metastasis cascade. The aim is to identify common mechanical memory mechanisms of different types of cancer. Finally, it is emphasized that efforts to identify the malignant potency of tumors should go way beyond sequencing approaches and include a functional diagnosis of cancer physiology and a dynamic mechanical assessment of cancer cells. Full article

Acid mine drainage (AMD) environments feature extreme acidity (pH ≤ 2) and high heavy metal concentrations. Acidophiles survive these conditions through unique genetic adaptations and secondary metabolite (SM) pathways. Leptospirillum ferriphilum, known for its acid and heavy metal resistance, serves as a model for AMD bioremediation, though systematic multi-omics studies on its key SMs and biosynthesis pathways remain underexplored. In this study, L. ferriphilum YR01 was isolated and identified from the AMD of the Zijinshan copper mine, China. Pangenomic analysis revealed that YR01 possesses the largest number of genes (2623) among the eight sequenced L. ferriphilum strains. Comparative genomics, antiSMASH, BiG-SCAPE, and metabolomic analyses (LC-MS and HPLC-MS) were integrated to comprehensively explore its biosynthetic capacity. A total of 39 biosynthetic gene clusters (BGCs) were identified, of which 60% shared <50% similarity with known clusters, indicating substantial novel biosynthetic potential. The sequence alignment of SM biosynthetic gene clusters (BGCs) demonstrated the potential of L. ferriphilum to synthesize conserved clusters for ectoine, choline, carotenoids, terpenoids, and terpene precursors. YR01 harbors complete BGCs for all five SM types. Notably, key nonribosomal peptide synthetase (NRPS) modules implicated in N-acyl homoserine lactone (AHL) synthesis were identified. Untargeted metabolomics (LC-MS) revealed the production of diverse SMs (18 types) putatively involved in environmental adaptation, including phosphocholine, carotenoids (e.g., anteraxanthin), cholera autoinducer-1 (CAI-1), and multiple AHLs. Targeted detection (HPLC-MS) further confirmed that YR01 could produce ectoine (0.10 ng/mL) and specific AHLs (C₁₄-HSL, C₁₂-HSL, C₁₂-OH-HSL), which were beneficial for the survival of the strain in extremely acidic environments and interspecies communication through SMs. This study represents the first comprehensive multi-omics characterization of BGCs in L. ferriphilum and experimentally validates the production of key SMs. Collectively, this study provides a comprehensive elucidation of the SM biosynthetic repertoire and environmental adaptation strategies in L. ferriphilum, advancing our understanding of microbial adaptation and interspecies communication in AMD systems, and offering potential implications for biomining applications. Full article

With the rapid advancement of hyperspectral remote sensing technology, the security of hyperspectral images (HSIs) has become a critical concern. However, traditional image encryption methods—designed primarily for grayscale or RGB images—fail to address the high dimensionality, large data volume, and spectral-domain characteristics inherent to HSIs. Existing chaotic encryption schemes often suffer from limited chaotic performance, narrow parameter ranges, and inadequate spectral protection, leaving HSIs vulnerable to spectral feature extraction and statistical attacks. To overcome these limitations, this paper proposes a novel hyperspectral image encryption algorithm based on a newly designed two-dimensional cross-coupled hyperchaotic map (2D-CSCM), which synergistically integrates Cubic, Sinusoidal, and Chebyshev maps. The 2D-CSCM exhibits superior hyperchaotic behavior, including a wider hyperchaotic parameter range, enhanced randomness, and higher complexity, as validated by Lyapunov exponents, sample entropy, and NIST tests. Building on this, a layered encryption framework is introduced: spectral-band scrambling to conceal spectral curves while preserving spatial structure, spatial pixel permutation to disrupt correlation, and a bit-level diffusion mechanism based on dynamic DNA encoding, specifically designed to secure high bit-depth digital number (DN) values (typically >8 bits). Experimental results on multiple HSI datasets demonstrate that the proposed algorithm achieves near-ideal information entropy (up to 15.8107 for 16-bit data), negligible adjacent-pixel correlation (below 0.01), and strong resistance to statistical, cropping, and differential attacks (NPCR ≈ 99.998%, UACI ≈ 33.30%). The algorithm not only ensures comprehensive encryption of both spectral and spatial information but also supports lossless decryption, offering a robust and practical solution for secure storage and transmission of hyperspectral remote sensing imagery. Full article