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Compared with noble metals, semiconductors with surface plasmon resonance effect are another type of SERS substrate materials. The main obstacles so far are that the semiconducting materials are often unstable and easy to be further oxidized or decomposed by laser irradiating or contacting with corrosive substances. Here, we report that metallic MoO 2 can be used as a SERS substrate to detect trace amounts of highly risk chemicals including bisphenol A (BPA), dichloropheno (DCP), pentachlorophenol (PCP) and so on. The minimum detectable concentration was 10 −7  M and the maximum enhancement factor is up to 3.75 × 10 6 . To the best of our knowledge, it may be the best among the metal oxides and even reaches or approaches to Au/Ag. The MoO 2 shows an unexpected high oxidation resistance, which can even withstand 300 °C in air without further oxidation. The MoO 2 material also can resist long etching of strong acid and alkali. Semiconducting materials are potential SERS substrates as alternatives to noble metals, but often suffer from poor stabilities and sensitivities. Here, the authors use molybdenum dioxide as a SERS material, showing high enhancement factors and stability to oxidation even at high temperatures.

To research the value of Autostrain right ventricular (RV) technology in detecting and preventing right ventricular myocardial injury in patients undergoing breast cancer chemotherapy by providing an imaging basis for early identification. To examine the changes in various cardiac function parameters before and after chemotherapy, two-dimensional echocardiography was employed 48 h before chemotherapy, 48 h after the fourth cycle of chemotherapy, and 48 h after the eighth cycle of chemotherapy, respectively. The patients included those with breast cancer who underwent surgery and were primarily administered anthracycline-based chemotherapeutic drugs. (1) Compared with the pre-chemotherapy period, the absolute values of the right ventricular global longitudinal strain (RV4CSL) and right ventricular free-wall longitudinal strain (RVFWSL) decreased after the fourth chemotherapy cycle, and no significant differences were observed in tricuspidannular plane systolic excursion (TAPSE), right ventricular Tei index, and right ventricular fractional area change (FAC); (2) Compared with the pre-chemotherapy period, the absolute values of RV4CSL and RVFWSL decreased after the eighth chemotherapy cycle. TAPSE and FAC decreased, the right ventricular Tei index increased; (3) Compared with the end of the fourth chemotherapy cycle, the absolute values of RV4CSL and RVFWSL decreased at the end of the eighth chemotherapy cycle. TAPSE, right ventricular Tei index and FAC were not significantly different. (4) Pearson correlation analysis revealed a correlation between the absolute value of RV4CSL, the absolute value of RVFWSL, right ventricular Tei index, TAPSE and FAC. The absolute values of RV4CSL and RVFWSL are sensitive indices that reflect changes in the right ventricular myocardium in the early stages of chemotherapy. They can reflect the effects of anthracycline on the right ventricular myocardium of patients with breast cancer earlier than the TAPSE, FAC and right ventricular Tei indices. A relationship exists between the absolute value of RVFWSL, the absolute value of RV4CSL, right ventricular Tei index, TAPSE, FAC and anthracycline-induced alterations in the right ventricular myocardium. This study is helpful for early detection of right ventricular myocardial function injury caused by anthracyclines in breast cancer patients, and provides imaging basis for early clinical detection and prevention of right ventricular myocardial injury.

Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM). After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV). After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

Bone metastasis in breast cancer remains a major therapeutic challenge because current osteoclast-targeted therapies do not fully disrupt the tumor–bone vicious cycle. Osteocytes, the most abundant bone cells, are increasingly recognized as key regulators of bone–tumor crosstalk. Previous work has shown that osteocyte-specific overexpression of the Wnt co-receptor LRP5 inhibits breast cancer-induced osteolysis and generates conditioned medium (CM) with tumor-suppressive activity. Proteomic analysis identified LIM domain and actin-binding protein 1 (LIMA1) as a central mediator that interacts with Myosin Vb (MYO5B), suggesting the role of the LIMA1/MYO5B regulatory axis. This study demonstrates that CM derived from LRP5-overexpressing osteocytes suppresses EO771 breast cancer cell proliferation, migration, and invasion, and downregulates tumor-promoting proteins, including MMP9, Snail, IL-6, and TGF-β1, while upregulating the apoptosis-related protein cleaved caspase-3. These effects were largely reversed by knockdown of LIMA1 or MYO5B. In syngeneic mouse models of mammary tumors and bone metastasis, systemic administration of LRP5-overexpressing osteocyte-derived CM reduced tumor burden and osteolytic bone destruction, whereas genetic knockdown of LIMA1 in osteocytes or MYO5B in tumor cells abrogated these protective effects. Collectively, these findings indicate that LRP5 activation in osteocytes engages the LIMA1/MYO5B signaling axis that inhibits breast cancer progression and osteolysis, disrupts tumor–stromal interactions, and restores bone–tumor homeostasis, thereby providing a potential therapeutic strategy to break the vicious cycle of bone metastasis in breast cancer.

Cucumber mosaic virus (CMV) infection could induce mosaic symptoms on a wide-range of host plants. However, there is still limited information regarding the molecular mechanism underlying the development of the symptoms. In this study, the coat protein (CP) was confirmed as the symptom determinant by exchanging the CP between a chlorosis inducing CMV-M strain and a green-mosaic inducing CMV-Q strain. A yeast two-hybrid analysis and bimolecular fluorescence complementation revealed that the chloroplast ferredoxin I (Fd I) protein interacted with the CP of CMV-M both in vitro and in vivo , but not with the CP of CMV-Q. The severity of chlorosis was directly related to the expression of Fd1, that was down-regulated in CMV-M but not in CMV-Q. Moreover, the silencing of Fd I induced chlorosis symptoms that were similar to those elicited by CMV-M. Subsequent analyses indicated that the CP of CMV-M interacted with the precursor of Fd I in the cytoplasm and disrupted the transport of Fd I into chloroplasts, leading to the suppression of Fd I functions during a viral infection. Collectively, our findings accentuate that the interaction between the CP of CMV and Fd I is the primary determinant for the induction of chlorosis in tobacco.

Mesoporous nanostructures represent a unique class of photocatalysts with many applications, including splitting of water, degradation of organic contaminants and reduction of carbon dioxide. In this work, we report a general Lewis acid catalytic template route for the high–yield producing single– and multi–component large–scale three–dimensional (3D) mesoporous metal oxide networks. The large-scale 3D mesoporous metal oxide networks possess large macroscopic scale (millimeter–sized) and mesoporous nanostructure with huge pore volume and large surface exposure area. This method also can be used for the synthesis of large–scale 3D macro/mesoporous hierarchical porous materials and noble metal nanoparticles loaded 3D mesoporous networks. Photocatalytic degradation of Azo dyes demonstrated that the large–scale 3D mesoporous metal oxide networks enable high photocatalytic activity. The present synthetic method can serve as the new design concept for functional 3D mesoporous nanomaterials.

So far royal jelly (RJ) has been widely used as a kind of popular and traditional food for health promotion. However, the quality of RJ is vulnerable to improper storage conditions. In order to prohibit the low quality RJ products entering the market and consequently affect the health of humans, it is necessary to define the quality parameters and establish corresponding detection methods for the freshness of RJ. In this research, we applied two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight mass spectrometry to research major royal jelly proteins (MRJPs) changes under different storage conditions after 6-month storage, looking for a stable and reliable protein marker which was also feasible to detect the freshness of RJ. Further research with the help of Western blotting analysis (WB) confirmed that, under room temperature, MRJP5 began to hydrolyze within 30 days and would completely degrade within 75 days, indicating that MRJP5 can be adapted as a freshness marker for RJ products. Moreover, the assessment results of the freshness of 12 commercial RJ products with WB showed that MRJP5 was present in twelve RJ samples at different abundance levels which further confirmed the detection of MRJP5 could be a feasible method to assess the quality and freshness of commercial RJ products.

Credit risk management within supply chains has emerged as a critical research area due to its significant implications for operational stability and financial sustainability. The intricate interdependencies among supply chain participants mean that credit risks can propagate across networks, with impacts varying by industry. This study explores the application of Generative Adversarial Networks (GANs) to enhance credit risk identification in supply chains. GANs enable the generation of synthetic credit risk scenarios, addressing challenges related to data scarcity and imbalanced datasets. By leveraging GAN-generated data, the model improves predictive accuracy while effectively capturing dynamic and temporal dependencies in supply chain data. The research focuses on three representative industries-manufacturing (steel), distribution (pharmaceuticals), and services (e-commerce) to assess industry-specific credit risk contagion. Experimental results demonstrate that the GAN-based model outperforms traditional methods, including logistic regression, decision trees, and neural networks, achieving superior accuracy, recall, and F1 scores. The findings underscore the potential of GANs in proactive risk management, offering robust tools for mitigating financial disruptions in supply chains. Future research could expand the model by incorporating external market factors and supplier relationships to further enhance predictive capabilities. Keywords- Generative Adversarial Networks (GANs); Supply Chain Risk; Credit Risk Identification; Machine Learning; Data Augmentation

This study proposes an automated data mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. Through the encoding-decoding structure, the autoencoder can capture the data's potential characteristics and achieve noise reduction and anomaly detection, providing an efficient and stable solution for the data mining process. The experiment compared the performance of the autoencoder with traditional dimensionality reduction methods (such as PCA, FA, T-SNE, and UMAP). The results showed that the autoencoder performed best in terms of reconstruction error and root mean square error and could better retain data structure and enhance the generalization ability of the model. The autoencoder-based framework not only reduces manual intervention but also significantly improves the automation of data processing. In the future, with the advancement of deep learning and big data technology, the autoencoder method combined with a generative adversarial network (GAN) or graph neural network (GNN) is expected to be more widely used in the fields of complex data processing, real-time data analysis and intelligent decision-making.

In the field of computer vision, multimodal image generation has become a research hotspot, especially the task of integrating text, image, and style. In this study, we propose a multimodal image generation method based on Generative Adversarial Networks (GAN), capable of effectively combining text descriptions, reference images, and style information to generate images that meet multimodal requirements. This method involves the design of a text encoder, an image feature extractor, and a style integration module, ensuring that the generated images maintain high quality in terms of visual content and style consistency. We also introduce multiple loss functions, including adversarial loss, text-image consistency loss, and style matching loss, to optimize the generation process. Experimental results show that our method produces images with high clarity and consistency across multiple public datasets, demonstrating significant performance improvements compared to existing methods. The outcomes of this study provide new insights into multimodal image generation and present broad application prospects.