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Feeding habits and competitive interactions among dominant grasshopper species in the Xinjiang grasslands (China) were studied under natural conditions through microscopic analyses of insect crop contents. Sex-specific and interindividual differences in feeding habits and interspecific competition were investigated. Analyses of ecological niche width and overlap revealed potential competition among grasshoppers. The results showed significant difference in the sex-specific variations in the feeding habits of the seven grasshoppers; sex-specific variations in feeding range and preferred plants were observed, with females feeding more extensively on host plants, and female and male adults choosing to feed on different plant species. Individuals of all seven grasshoppers showed different degrees of dietary variance, with oligophagous grasshoppers (Oedaleus decorus males, Dericorys annulata, and Bryodema gebleri males) showing a smaller degree of individual dietary variance than polyphagous grasshoppers (Oedipoda caerulescens, Calliptamus coelesyriensis females, Calliptamus barbarus, and Notostaurus albicornis); Calliptamus coelesyriensis and Notostaurus albicornis showed the greatest individual variance in their diets. Oedaleus decorus, Bryodema gebleri, and Calliptamus barbarus consumed primarily Poaceae and exhibited varying foot-plant choice. For example, Oedaleus decorus was observed to have high- preference feeding for Poa annua, Bryodema gebleri for Stipa capillata, and Calliptamus barbarus for Setaria viridis. Dericorys annulata fed primarily on Amaranthaceae, Notostaurus albicornis fed primarily on Poaceae and Amaranthaceae, and Oedipoda caerulescens fed primarily on Asteraceae. Calliptamus barbarus exhibited strong interspecific competition with Oedaleus decorus and Calliptamus coelesyriensis, and Bryodema gebleri demonstrated the strongest interspecific competition with all six other species. Considering the influence of sex on interspecific competition among grasshoppers enhances our understanding of interspecific competitive relationships.

Attention mechanisms have found extensive application in Deep Neural Networks (DNNs), with numerous experiments over time showcasing their efficacy in improving the overall performance of DNNs. Considering the black-box nature of DNNs, it is unclear how attention mechanisms affect model decision-making processes. Therefore, it is of great significance to explore the internal relationship between the focused area of attention mechanisms and the prediction results. For the first time, an explainable framework for the attention mechanism of Synthetic Aperture Radar (SAR) image analytics is proposed, integrating DNN and eXplainable Artificial Intelligence (XAI), which realizes the effective explanation for attention mechanisms across different locations. The framework consists of three parts: the water extraction network which contains the attention mechanism at different locations; the proposed Attention- Gradient Class Activation Mapping (A-GCAM) method for attribution analysis of attention mechanisms; and we invent Category-specific channel Score of Confidence Mapping (CSCM) to perform geo-visualization for the output features of attention mechanisms. Experiments are conducted with the widely used Sentinel-1 system for water detection, in which three attention mechanisms with different characteristics are added to the encoder and decoder in DNNs. The results show that the framework can make the decision-making process of attention mechanisms transparent, thus improving their comprehensiveness and trustworthiness in various tasks and providing a reliable approach to selecting suitable attention mechanisms for the given task.

The present study was aimed to investigate the effects of Phaeodactylum tricornutum oil (PO) and Laminaria japonica oil (LO) against obesity and other metabolic disorders in a high-fat diet (HFD) induced obese mouse model. The results revealed that PO or LO supplementation significantly reduced HFD-induced body weight gain, hyperlipemia, and fat accumulation in mice. PO or LO supplementation also inhibited SREBP-1-mediated de novo lipogenesis and PPARγ-mediated adipogenesis, as well as enhanced PPARα-mediated fatty acid β-oxidation in the WAT and liver of HFD-fed mice. Moreover, PO or LO supplementation attenuated low-grade chronic inflammation, hyperglycemia and insulin resistance in obese mice. Furthermore, Dietary PO or LO also modulated HFD-induced gut microbiota dysbiosis and increased the SCFAs production. More importantly, our results also manifested that the ameliorative effect of PO and LO against HFD-induced obesity, IR and gut microbiota dysbiosis was comparable, but was superior than that of krill oil and fish oil.

Due to the advantages of large surface area, easy functional structure, good biocompatibility, and easy integration into signal transduction system, hydrogel-based nanocomposite is an excellent electrode modification material. Several common synthesis strategies of hydrogel-based nanocomposites and their significance as electrochemical modified electrode materials were summarized in this paper. The preparation of hydrogel-based nanocomposites modified electrode and their sensing mechanism for the electrochemical detection were discussed. Finally, the preparation of hydrogel-based nanocomposites and their applications in electrochemical sensors are summarized and prospected, which will have importance to promote the development of electrochemical sensing detection technology.

African swine fever virus (ASFV) is a lethal pathogen that causes severe threats to the global swine industry and it has already had catastrophic socio-economic effects. To date, no licensed prophylactic vaccine exists. Limited knowledge exists about the major immunogens of ASFV and the epitope mapping of the key antigens. As such, there is a considerable requirement to understand the functional monoclonal antibodies (mAbs) and the epitope mapping may be of utmost importance in our understanding of immune responses and designing improved vaccines, therapeutics, and diagnostics. In this study, we generated an ASFV antibody phage-display library from ASFV convalescent swine PBMCs, further screened a specific ASFV major capsid protein (p72) single-chain antibody and fused with an IgG Fc fragment (scFv-83-Fc), which is a specific recognition antibody against ASFV Pig/HLJ/2018 strain. Using the scFv-83-Fc mAb, we selected a conserved epitope peptide (221MTGYKH226) of p72 retrieved from a phage-displayed random peptide library. Moreover, flow cytometry and cell uptake experiments demonstrated that the epitope peptide can significantly promote BMDCs maturation in vitro and could be effectively uptaken by DCs, which indicated its potential application in vaccine and diagnostic reagent development. Overall, this study provided a valuable platform for identifying targets for ASFV vaccine development, as well as to facilitate the optimization design of subunit vaccine and diagnostic reagents.

The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.