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Objective: To broaden the selection of strains for fermenting fresh rice noodle and develop the application of different strains in fresh rice noodle （FRN）. Methods: With gelatinization, texture, cooking stretching, electronic nose, electronic tongue and sensory evaluation as evaluation indexes, and L. fermentum FR-21, A. orientalis AO-21, L. plantarum YI-Y2013 and blank samples as influencing factors, the eating quality of FRN fermented by different strains was analyzed. Results: L. fermentum FR-21 fermentation improved the gelatinization indexes of indica rice flour, such as valley viscosity, disintegration value, final viscosity and retrogradation value, and reduced the texture viscosity of FRN, and improved its sensory hardness and typical fermented flavor （smell 4.0 points）. The FRN fermented by A. orientalis AO-21 had the lowest cooking loss （1.63%）, high texture hardness and chewability, relatively strong aftertaste, and obvious acidic odor （smell 3.7 points）. L. plantarum YI-Y2013 was similar to L. fermentum FR-21 in gelatinization index and textural viscosity, such as valley viscosity, disintegration value, final viscosity and retrogradation value （P＜0.05）, while the FRN fermented by L. plantarum YI-Y2013 had the highest extensibility （48.83±1.00）, and the smell was fragrant and pure （smell 4.6 points）. Conclusion: The controlled fermentation of three different strains can make FRN present different taste qualities, and rice noodles with different flavor qualities can be produced by using their fermentation characteristics.

Abstract Dual-interfacial structure within catalysts is capable of mitigating the detrimentally completive adsorption during the catalysis process, but its construction strategy and mechanism understanding remain vastly lacking. Here, a highly active dual-interfaces of CeO2-x /CoO1-x /Co is constructed using the pronounced interfacial interaction from surrounding small CeO2-x islets, which shows high activity in catalyzing the water-gas shift reaction. Kinetic evidence and in-situ characterization results revealed that CeO2-x modulates the oxidized state of Co species and consequently generates the dual active CeO2-x /CoO1-x /Co interface during the WGS reaction. A synergistic redox mechanism comprised of independent contribution from dual functional interfaces, including CeO2-x /CoO1-x and CoO1-x /Co, is authenticated by experimental and theoretical results, where the CeO2-x /CoO1-x interface alleviates the CO poison effect, and the CoO1-x /Co interface promotes the H2 formation. The results may provide guidance for fabricating dual-interfacial structures within catalysts and shed light on the mechanism over multi-component catalyst systems.

ObjectiveWine grapes are severely affected by leafroll disease, which affects their growth, and reduces the quality of the color, taste, and flavor of wine. Timely and accurate diagnosis of leafroll disease severity is crucial for preventing and controlling the disease, improving the wine grape fruit quality and wine-making potential. Unmanned aerial vehicle (UAV) remote sensing technology provides high-resolution images of wine grape vineyards, which can capture the features of grapevine canopies with different levels of leafroll disease severity. Deep learning networks extract complex and high-level features from UAV remote sensing images and perform fine-grained classification of leafroll disease infection severity. However, the diagnosis of leafroll disease severity is challenging due to the imbalanced data distribution of different infection levels and categories in UAV remote sensing images.MethodA novel method for diagnosing leafroll disease severity was developed at a canopy scale using UAV remote sensing technology and deep learning. The main challenge of this task was the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. To address this challenge, a method that combined deep learning fine-grained classification and generative adversarial networks (GANs) was proposed. In the first stage, the GANformer, a Transformer-based GAN model was used, to generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity. To further analyze the image generation effect of GANformer. The t-distributed stochastic neighbor embedding (t-SNE) to visualize the learned features of real and simulated images. In the second stage, the CA-Swin Transformer, an improved image classification model based on the Swin Transformer and channel attention mechanism was used, to classify the patch images into different classes of leafroll disease infection severity. CA-Swin Transformer could also use a self-attention mechanism to capture the long-range dependencies of image patches and enhance the feature representation of the Swin Transformer model by adding a channel attention mechanism after each Transformer layer. The channel attention (CA) mechanism consisted of two fully connected layers and an activation function, which could extract correlations between different channels and amplify the informative features. The ArcFace loss function and instance normalization layer was also used to enhance the fine-grained feature extraction and downsampling ability for grapevine canopy images. The UAV images of wine grape vineyards were collected and processed into orthomosaic images. They labeled into three categories: healthy, moderate infection, and severe infection using the in-field survey data. A sliding window method was used to extract patch images and labels from orthomosaic images for training and testing. The performance of the improved method was compared with the baseline model using different loss functions and normalization methods. The distribution of leafroll disease severity was mapped in vineyards using the trained CA-Swin Transformer model.Results and DiscussionsThe experimental results showed that the GANformer could generate high-quality virtual canopy images of grapevines with an FID score of 93.20. The images generated by GANformer were visually very similar to real images and could produce images with different levels of leafroll disease severity. The T-SNE visualization showed that the features of real and simulated images were well clustered and separated in two-dimensional space, indicating that GANformer learned meaningful and diverse features, which enriched the image dataset. Compared to CNN-based deep learning models, Transformer-based deep learning models had more advantages in diagnosing leafroll disease infection. Swin Transformer achieved an optimal accuracy of 83.97% on the enhanced dataset, which was higher than other models such as GoogLeNet, MobileNetV2, NasNet Mobile, ResNet18, ResNet50, CVT, and T2TViT. It was found that replacing the cross entropy loss function with the ArcFace loss function improved the classification accuracy by 1.50%, and applying instance normalization instead of layer normalization further improved the accuracy by 0.30%. Moreover, the proposed channel attention mechanism, named CA-Swin Transformer, enhanced the feature representation of the Swin Transformer model, achieved the highest classification accuracy on the test set, reaching 86.65%, which was 6.54% higher than using the Swin Transformer on the original test dataset. By creating a distribution map of leafroll disease severity in vineyards, it was found that there was a certain correlation between leafroll disease severity and grape rows. Areas with a larger number of severe leafroll diseases caused by Cabernet Sauvignon were more prone to have missing or weak plants.ConclusionsA novel method for diagnosing grapevine leafroll disease severity at a canopy scale using UAV remote sensing technology and deep learning was proposed. This method can generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity using GANformer, and classify them into different classes using CA-Swin Transformer. This method can also map the distribution of leafroll disease severity in vineyards using a sliding window method, and provides a new approach for crop disease monitoring based on UAV remote sensing technology.

Objectives: Accumulating evidence are available on the efficacy of high-dose isoniazid (INH) for multidrug-resistant tuberculosis (MDR-TB) treatment. We aimed to perform a systematic review and meta-analysis to compare clinical efficacy and safety outcomes of high-dose INH- containing therapy against other regimes.Methods: We searched the following databases PubMed, Embase, Scopus, Web of Science, CINAHL, the Cochrane Library, and ClinicalTrials.gov. We considered and included any studies comparing treatment success, treatment unsuccess, or adverse events in patients with MDR-TB treated with high-dose INH (>300 mg/day or >5 mg/kg/day).Results: Of a total of 3,749 citations screened, 19 studies were included, accounting for 5,103 subjects, the risk of bias was low in all studies. The pooled treatment success, death, and adverse events of high-dose INH-containing therapy was 76.5% (95% CI: 70.9%–81.8%; I2: 92.03%), 7.1% (95% CI: 5.3%–9.1%; I2: 73.75%), and 61.1% (95% CI: 43.0%–77.8%; I2: 98.23%), respectively. The high-dose INH administration is associated with significantly higher treatment success (RR: 1.13, 95% CI: 1.04–1.22; p < 0.01) and a lower risk of death (RR: 0.45, 95% CI: 0.32–0.63; p < 0.01). However, in terms of other outcomes (such as adverse events, and culture conversion rate), no difference was observed between high-dose INH and other treatment options (all p > 0.05). In addition, no publication bias was observed.Conclusion: In MDR-TB patients, high-dose INH administration is associated with a favorable outcome and acceptable adverse-event profile.Systematic review registration: identifier CRD42023438080

Abstract Neuropeptides are ubiquitous intercellular signaling molecules in the CNS and play diverse roles in modulating physiological functions by acting on specific G-protein coupled receptors (GPCRs). Among them, the elevenin signaling system is now believed to be present primarily in protostomes. Although elevenin was first identified from the L11 neuron of the abdominal ganglion in mollusc Aplysia californica, no receptors have been described in Aplysia, nor in any other molluscs. Here, using two elevenin receptors in annelid Platynereis dumerilii, we found three putative elevenin GPCRs in Aplysia. We cloned the three receptors and tentatively named them apElevR1, apElevR2, and apElevR3. Using an inositol monophosphate (IP1) accumulation assay, we demonstrated that Aplysia elevenin with the disulfide bond activated the three putative receptors with low EC50 values (ranging from 1.2 to 25 nM), supporting that they are true receptors for elevenin. In contrast, elevenin without the disulfide bond could not activate the receptors, indicating that the disulfide bond is required for receptor activity. Using alanine substitution of individual conserved residues other than the two cysteines, we showed that these residues appear to be critical to receptor activity, and the three different receptors had different sensitivities to the single residue substitution. Finally, we examined the roles of those residues outside the disulfide bond ring by removing these residues and found that they also appeared to be important to receptor activity. Thus, our study provides an important basis for further study of the functions of elevenin and its receptors in Aplysia and other molluscs.

Background:P. polyphylla var. yunnanensis, as a near-threatened and ethnic medicine in China, used to be a key ingredient in traditional Chinese medicine in treatment of traumatic injuries, sore throat, snakebites, and convulsions for thousands of years. However, there were no reports on the inverse relationship between the contents of heavy metals and saponins and its anti-breast cancer pharmacological activity in P. polyphylla var. yunnanensis.Methods: The present study aimed to reveal the characteristics of heavy metal contents and saponins and its anti-breast cancer pharmacological activity and their interrelationships in P. polyphylla var. yunnanensis from different production areas. The contents of heavy metal and steroidal saponins in P. polyphylla var. yunnanensis were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) and the high-performance liquid chromatography technique, respectively. The Pearson correlation was used to study the correlation between saponins and heavy metals. 4T1 mouse mammary tumor cells were selected and cultivated for antitumor studies in vitro. Cell Counting Kit-8 (CCK-8) assay, Hoechst staining, and flow cytometry analysis were used for the examination of the proliferation and apoptosis of 4T1 tumor cells. Mouse breast cancer 4T1 cells were subcutaneously injected into BALB/c mice to construct a tumor model to explore the in vivo inhibitory effect on breast cancer. TUNEL assay and immunohistochemistry were used for the examination of the effect of P. polyphylla var. yunnanensis from different origins on cancer cell proliferation and apoptosis induction in 4T1 tumor mice.Results: Heavy metal contents were highly correlated with the content of steroidal saponins. The overall content of 10 metals in the three producing origins was of the order C3 >C2 >C1. The total content of eight steroidal saponins in the extracts of P. polyphylla var. yunnanensis from three different origins was C1 >C2 >C3. The Pearson correlation study showed that in all of the heavy metals, the contents of Cd and Ba were positively correlated with the main steroidal saponins in P. polyphylla var. yunnanensis, while Al, Cr, Cu, Fe, Zn, As, Hg, and Pb showed a negative correlation. In vitro experiments showed that the extracts of P. polyphylla var. yunnanensis from three origins could inhibit the proliferation and induce cell apoptosis of 4T1 cells in a concentration- and time-dependent manner, especially in the C1 origin. In vivo experiments showed that the extract of P. polyphylla var. yunnanensis from the three origins could inhibit the growth of tumors and induce the apoptosis of tumor cells. In the three origins, C1 origin had the lowest total heavy metal level but the highest total steroidal saponin level. Therefore, it showed a better effect in reducing the expression of the human epidermal growth factor receptor 2 (HER2) and Kiel 67 (Ki67) and increasing the expression of p53 in tumor tissues compared to the other origins. In conclusion, in the three origins, C1 origin exhibits antitumor pharmacological effects in vivo and in vitro which are better than those in the other origins.Conclusion: In this study, we found that with the increase of the heavy metal content, the content of steroid saponins and anti-breast cancer activity decreased. The results showed that the high content of the total heavy metals may not be conducive to the accumulation of steroidal saponins in P. polyphylla var. yunnanensis and lead to the low anti-breast cancer activity. The results of this study suggest that the content of heavy metals should be controlled in the artificial cultivation process of P. polyphylla var. yunnanensis.