Objective
To study the role of serine protease encoding gene prtP carried by Lactobacillus paracasei (Lp) in regulating mouse intestinal immunity.
Methods
Female BALB/c mice were randomly divided into seven groups and respectively administered with PBS, medium (5×108 CFU/ml) and high dose (1×109 CFU/ml) of Lp, Lp△prtP and ReLp△prtP for 21 consecutive days by intragastric gavage. Parameters including immune organ index, splenic lymphocyte transformation rate, energy metabolism in celiac macrophages, phagocytic ability of celiac macrophages and cytokines were detected. Quantitative changes in CD11c+ CD80+ cells in culture supernatants of splenic mononuclear cells were analyzed by flow cytometry.
Results
Compared with Lp△prtP, Lp could significantly promote the natural transformation of splenic lymphocytes, increase the level of energy metabolism in celiac macrophages, enhance the phagocytic ability of celiac macrophages, and up-regulate the expression of IFN-α, IL-10 and IFN-γ and the percentage of CD11c+ CD80+ cells.
Conclusion
Serine protease encoding gene prtP in Lp can regulate the mucosal immune system in mice.
Key words:
Lactobacillus paracasei; prtP gene; Intestinal immunity
In AI-facilitated teaching, leveraging various query styles to interpret abstract text descriptions is crucial for ensuring high-quality teaching. However, current retrieval models primarily focus on natural text-image retrieval, making them insufficiently tailored to educational scenarios due to the ambiguities in the retrieval process. In this paper, we propose a diverse expression retrieval task tailored to educational scenarios, supporting retrieval based on multiple query styles and expressions. We introduce the STEM Education Retrieval Dataset (SER), which contains over 24,000 query pairs of different styles, and the Uni-Retrieval, an efficient and style-diversified retrieval vision-language model based on prompt tuning. Uni-Retrieval extracts query style features as prototypes and builds a continuously updated Prompt Bank containing prompt tokens for diverse queries. This bank can updated during test time to represent domain-specific knowledge for different subject retrieval scenarios. Our framework demonstrates scalability and robustness by dynamically retrieving prompt tokens based on prototype similarity, effectively facilitating learning for unknown queries. Experimental results indicate that Uni-Retrieval outperforms existing retrieval models in most retrieval tasks. This advancement provides a scalable and precise solution for diverse educational needs.
Table S1. Statistics of the RNA-seq results for the secondary dormancy of 12 samples. Table S2. Correlation analyses for each sample among three repetitions. Table S3. Detailed information of RNA-seq analysis. Table S4. DEGs isolated by the second round of selection based on the threshold |log2(Va vs Vb)-log2(Ha vs Hb)| ≥ 1. Table S5. Detailed GO enrichment analysis of the DEGs. Table S6. Detailed KEGG enrichment analysis of the DEGs. Table S7. Detailed list of all the DEGs corresponding to MapMan functional categories. Table S8. Detailed list of the DEGs involved in indole GLS-linked auxin biosynthesis. Table S9. List of genes involved in aliphatic and aromatic GLSs. Table S10. Differentially expressed transcription factors identified via RNA-seq analysis. Table S11. Differentially expressed epigenetic modifiers identified via RNA-seq analysis. Table S12. Seed quality differences in cultivars H and V. (XLSX 31002 kb)
The design of temperature-adaptive Zn–air batteries (ZABs) with long life spans and high energy efficiencies is challenging owing to sluggish oxygen reduction reaction (ORR) kinetics and an unstable Zn/electrolyte interface. Herein, a quasi-solid-state ZAB is designed by combining atomically dispersed Fe–N–C catalysts containing pyridinic N vacancies (FeNC-VN) with a polarized organo-hydrogel electrolyte. First-principles calculation predicts that adjacent VN sites effectively enhance the covalency of Fe–Nx moieties and moderately weaken *OH binding energies, significantly boosting the ORR kinetics and stability. In situ Raman spectra reveal the dynamic evolution of *O2– and *OOH on the FeNC-VN cathode in the aqueous ZAB, proving that the 4e– associative mechanism is dominant. Moreover, the ethylene glycol-modulated organo-hydrogel electrolyte forms a zincophilic protective layer on the Zn anode surface and tailors the [Zn(H2O)6]2+ solvation sheath, effectively guiding epitaxial deposition of Zn2+ on the Zn (002) plane and suppressing side reactions. The assembled quasi-solid-state ZAB demonstrates a long life span of over 1076 h at 2 mA cm–2 at −20 °C, outperforming most reported ZABs.
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graph structures. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications such as computer vision and recommender systems. This remarkable success has also attracted interest in applying LLMs to the graph domain. Increasing efforts have been made to explore the potential of LLMs in advancing Graph ML's generalization, transferability, and few-shot learning ability. Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability. Given the rapid progress of this research direction, a systematic review summarizing the latest advancements for Graph ML in the era of LLMs is necessary to provide an in-depth understanding to researchers and practitioners. Therefore, in this survey, we first review the recent developments in Graph ML. We then explore how LLMs can be utilized to enhance the quality of graph features, alleviate the reliance on labeled data, and address challenges such as graph heterogeneity and out-of-distribution (OOD) generalization. Afterward, we delve into how graphs can enhance LLMs, highlighting their abilities to enhance LLM pre-training and inference. Furthermore, we investigate various applications and discuss the potential future directions in this promising field.
Apple exhibits typical gametophytic self-incompatibility, in which self-S-RNase can arrest pollen tube growth, leading to failure of fertilization. To date, there have been few studies on how to resist the toxicity of self-S-RNase. In this study, pollen tube polyamines were found to respond to self-S-RNase and help pollen tubes defend against self-S-RNase. In particular, the contents of putrescine, spermidine, and spermine in the pollen tube treated with self-S-RNase were substantially lower than those treated with non-self-S-RNase. Further analysis of gene expression of key enzymes in the synthesis and degradation pathways of polyamines found that the expression of DIAMINE OXIDASE 4 (MdDAO4) as well as several polyamine oxidases such as POLYAMINE OXIDASES 3 (MdPAO3), POLYAMINE OXIDASES 4 (MdPAO4), and POLYAMINE OXIDASES 6 (MdPAO6) were significantly up-regulated under self-S-RNase treatment, resulting in the reduction of polyamines. Silencing MdPAO6 in pollen tubes alleviates the inhibitory effect of self-S-RNase on pollen tube growth. In addition, exogenous polyamines also enhance pollen tube resistance to self-S-RNase. Transcriptome sequencing data found that polyamines may communicate with S-RNase through the calcium signal pathway, thereby regulating the growth of the pollen tubes. To summarize, our results suggested that polyamines responded to the self-incompatibility reaction and could enhance pollen tube tolerance to S-RNase, thus providing a potential way to break self-incompatibility in apple.
Glenohumeral osteoarthritis (GOA) is characterized by chronic inflammation leading to joint damage. Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) are promising therapies because of their immunomodulatory functions. The anti-inflammatory effects of EVs from human Adipose-derived MSCs (hADSCs) overexpressing microRNA (miR)-146a were investigated in experimental GOA in this study. hADSCs were transfected with a mimic negative control or miR-146a mimics. GOA was induced in C57/Bl6j mice, and subsequently, the animals were treated intra-articularly with phosphate-buffered saline, miR-146a EVs, or miR-control EVs. The expression of miR-146a and its targeted cytokines interleukin (IL)-4, IL-10, tumor necrosis factor-alpha (TNF-α), IL-17, and interferon-gamma (IFN-γ) were analyzed in the spleen of mice by enzyme-linked immunosorbent assay and in the articular cartilage by real-time polymerase chain reaction. miR-146a EVs showed enrichment of miR-146a. In GOA mice, miR-146a EV treatment significantly reduced expression levels of inflammatory cytokines IFN-γ, IL-17, and TNF-α and increased the anti-inflammatory cytokine IL-10 and IL-4 compared to controls. miR-146a EV treatment raised the anti-inflammatory cytokines and reduced the pro-inflammatory cytokines of the spleen in treated mice. This study demonstrates that EVs derived from hADSCs overexpressing miR-146a have enhanced anti-inflammatory potential in GOA by modulating cytokine expression and production. EVs engineered with inflammation-related miRNAs could be a cell-free therapeutic approach for GOA.
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