Sany (China)

Ofloxacin (OFL) overuse leads to hazardous residues in food and the environment, necessitating sensitive and accurate detection. Traditional gold nanoparticle (AuNP)-based lateral flow immunoassays (LFIAs) suffer from low sensitivity, compromised accuracy, and reliance on single-mode signals, limiting their reliability. We synthesized Au@PdPt nanoparticles (NPs) via a one-step reduction method, integrating a plasmonic Au core with a Pd-Pt alloy shell to enable dual-mode colorimetric and photothermal detection of OFLwith enhanced precision. The NPs exhibited broad-spectrum absorption, high photothermal efficiency, and stability. Based on this material, a dual-mode LFIA was developed for OFL, achieving limits of detection (LOD) of 0.059ngmL⁻1 (colorimetric mode) and 0.038ngmL⁻1 (photothermal mode), surpassing AuNPs-LFIA by 1.64- and 2.55-fold, respectively. Recovery test in river water and milk samples ranged from 80.72 to 115.24% with relative standard deviations (RSD) below 12.75%, demonstrating high accuracy and reliability in complex matrices. This study offers a reliable, user-friendly platform for OFL detection with potential applications in food safety and environmental monitoring.

Herein, we mainly focus on developing a new two-grid Crank-Nicolson (CN) mixed finite element (MFE) (TGCNMFE) method for the generalized nonlinear time fractional fourth-order reaction diffusion equation. To do so, by introducing an auxiliary function, the nonlinear time fractional fourth-order reaction diffusion equation is first split into two second-order nonlinear equations. Thereafter, a new time semi-discrete mixed CN (TSDMCN) scheme is constructed through discretizing the time derivative and time fractional derivative by the CN difference quotient, and the existence, steadiness, and errors of the TSDMCN solutions are analysed. Next, a new TGCNMFE method is developed through using two-grid MFE technique to discretize the spacial variables, and the existence, steadiness, and error estimations for the TGCNMFE solutions are discussed. Lastly, the correctness of theory results and the superiority of the TGCNMFE method are verified by some numerical experiments.

The gut serves as the main site for nutrient digestion and absorption. Simultaneously, it functions as the body's largest immune organ, playing a dual role in sustaining physiological equilibrium and offering immunological defense against intestinal ailments. Maintaining the structural and functional integrity of the intestine is paramount for ensuring animal health and productivity. Puerarin, a naturally derived isoflavonoid from the Pueraria species, exhibits multifaceted bioactivities, such as antioxidant, anti-inflammatory, antimicrobial, and immunomodulatory properties. Emerging evidence highlights puerarin's capacity to enhance gut health in farm animals through four pivotal mechanisms: (1) optimization of intestinal morphology via crypt-villus architecture remodeling, (2) augmentation of systemic and mucosal antioxidant defenses through Nrf2/ARE pathway activation, and (3) reinforcement of intestinal barrier function by regulating tight junction proteins (e.g., ZO-1, occludin), mucin secretion, intestinal mucosal immune barrier, the composition of microbiota, and the derived beneficial metabolites; (4) regulating the function of the intestinal nervous system via reshaping the distribution of intestinal neurons and neurotransmitter secretion function. This review synthesizes current knowledge on puerarin's protective effects on intestinal physiology in farm animals, systematically elucidates its underlying molecular targets (including TLR4/NF-κB, MAPK, and PI3K/Akt signaling pathways), and critically evaluates its translational potential in mitigating enteric disorders such as post-weaning diarrhea and inflammatory bowel disease in agricultural practices.

This study investigated the protective effects of corynoxeine, a natural alkaline compound, on colistin-caused nephrotoxicity using a murine model. Forty mice were divided randomly into control, corynoxeine-only (20 mg/kg/day, intraperitoneal injection), colistin-only (20 mg/kg/day, intraperitoneal injection), and colistin (20 mg/kg/day) + corynoxeine (5 and 20 mg/kg/day) groups (8 mice in each group). All treatments were maintained for seven consecutive days. Results showed that colistin treatment at 20 mg/kg/day for seven days significantly increased serum urea nitrogen and creatinine levels and induced the loss and degeneration of renal tubular epithelial cells, which were markedly ameliorated by corynoxeine co-treatment at 5 or 20 mg/kg/day. Corynoxeine supplementation also markedly attenuated colistin-induced increases in malondialdehyde levels and decreases in reduced glutathione levels and superoxide dismutase and catalase activities in the kidneys. Furthermore, corynoxeine supplementation significantly decreased the expression of transforming growth factor β (TGF-β) and nicotinamide adenine dinucleotide phosphate hydrogen oxidase 4 (NOX4) proteins and nuclear factor kappa B (NF-κB), interleukin-1beta (IL-1β), IL-6, and tumor necrosis factor-α mRNAs, while it significantly increased the expression of erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) proteins in the kidneys. In conclusion, these results reveal that corynoxeine can protect against colistin-induced nephrotoxicity in mice by inhibiting oxidative stress and inflammation, which may partly be attributed to its ability on the activation of the Nrf2/HO-1 pathway and the inhibition of the TGF-β/NOX4 and NF-κB pathways.

The grounding accuracy of existing video captioners is still behind the expectation. The majority of existing methods perform grounded video captioning on sparse entity annotations. However, grounded captioning models rely on deliberate grounding annotations as supervision, which are relatively hard to obtain. Moreover, the captioning accuracy often suffers from degenerated object appearances on the annotated area such as motion blur and video defocus, and these models seldom consider the complex interactions among entities. In this paper, we propose a comprehensive visual grounding network to improve video captioning, by using inexpensive pseudo annotation while avoiding the need to collect large amounts of manual annotations. Specifically, the network consists of spatial-temporal entity grounding and action grounding. The proposed entity grounding encourages the attention mechanism to focus on informative spatial areas across video frames. The action grounding dynamically associates the verbs to related subjects and the corresponding context, which keeps fine-grained spatial and temporal details for action prediction. Both entity grounding and action grounding are formulated as a unified task guided by a soft grounding supervision. More importantly, the grounding objective is supervised by pseudo annotations automatically produced by a grounding annotation generation module, thus our model can be easily applied to the challenging dataset without any grounding annotation provided. We conduct extensive experiments on three benchmark datasets and demonstrate significant performance improvements of +2.4 CIDEr on MSR-VTT, +4.7 CIDEr on MSVD, and +5.1 CIDEr on ActivityNet-Entities compared to state-of-the-arts.

Morinda citrifolia L. (M. citrifolia), commonly referred to as noni, a Polynesian medicinal plant with over 2000 years of traditional use, has garnered global interest for its rich repertoire of antioxidant phytochemicals, including flavonoids (kaempferol, rutin), iridoids (aucubin, asperulosidic acid, deacetylasperulosidic acid, asperuloside), polysaccharides (nonioside A), and coumarins (scopoletin). This comprehensive review synthesizes recent advances (2018-2023) on noni's bioactive constituents, pharmacological properties, and molecular mechanisms, with a focus on its antioxidant potential. Systematic analyses reveal that noni-derived compounds exhibit potent free radical scavenging capacity (e.g., 2,2-Diphenyl-1-picrylhydrazyl/2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonicacid) (DPPH/ABTS) inhibition), upregulate endogenous antioxidant enzymes (Superoxide Dismutase (SOD), Catalase (CAT), Glutathione Peroxidase (GPx)), and modulate key pathways such as Nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1 (Nrf2/Keap1) and Nuclear Factor kappa-B (NF-κB). Notably, polysaccharides and iridoids demonstrate dual antioxidant and anti-inflammatory effects via gut microbiota regulation. This highlights the plant's potential for innovation in the medical and pharmaceutical fields. However, it is also recognized that further research is needed to clarify its mechanisms of action and ensure its safety for widespread application. We emphasize the need for mechanistic studies to bridge traditional knowledge with modern applications, particularly in developing antioxidant-rich nutraceuticals and sustainable livestock feed additives. This review underscores noni's role as a multi-target antioxidant agent and provides a roadmap for future research to optimize its health benefits.

The authors have nothing to report.

Video captioning remains a challenging task due to the diverse video content and the complex relationships between visual and textual elements. Recent efforts predominantly focus on multimodal architecture designs trained with paired video-caption data. Nonetheless, the learning paradigm suffers from the “one-to-many” corresponding problem, since one source video is mapped to multiple caption annotations. The difficulty of video captioning is further exacerbated by the poor-written captions, which mislead the captioner with irrelevant information. Essentially, the problem stems from the inadequate alignment between video and caption. In this work, we propose a Text-Conditional Alignment Transformer, which fully exploits the rich information provided by diverse labeled captions, and avoids the impacts of label ambiguity and noise. To alleviate the challenge of the “one-to-many” correspondence, we introduce Text-conditioned Video Encoding, which diversifies the video representation by emphasizing the spatial-temporal visual areas relevant to the given descriptions while filtering out redundant visual information. The refined video representation is well-aligned to match the corresponding text description, and naturally converts the “one-to-many” mapping to “one-to-one” mapping. To deal with the noisy annotations, we propose Quality-aware Caption Decoding. We first dynamically measure the qualities of different captions corresponding to the same video in a reference-free manner. Then the estimated qualities are further utilized as auxiliary signals, guiding the model to perform quality-aligned learning from noisy captions. We conduct extensive experiments on MSR-VTT, MSVD, VATEX and ActivityNet-Entities datasets, and demonstrate their consistent performance improvements compared to state-of-the-arts.