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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.

Quantum fluctuations disrupt the cyclic motions of dissipative Kerr solitons (DKSs) in nonlinear optical microresonators and consequently cause timing jitter of the emitted pulse trains. This problem is translated to the performance of several applications that employ DKSs as compact frequency comb sources. Recently, device manufacturing and noise reduction technologies have advanced to unveil the quantum properties of DKSs. Here we investigate the quantum decoherence of DKSs existing in normal-dispersion microresonators known as dark pulses. By virtue of the very large material nonlinearity, we directly observe the quantum decoherence of dark pulses in an AlGaAs-on-insulator microresonator, and the underlying dynamical processes are resolved by injecting stochastic photons into the microresonators. Moreover, phase correlation measurements show that the uniformity of comb spacing of quantum-limited dark pulses is better than 1.2 × 10-16 and 2.5 × 10-13 when normalized to the optical carrier frequencies and repetition frequencies, respectively. Comparing DKSs generated in different material platforms explicitly confirms the advantages of dark pulses over bright solitons in terms of quantum-limited coherence. Our work establishes a critical performance assessment of DKSs, providing guidelines for coherence engineering of chip-scale optical frequency combs.

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.

BackgroundGlioblastoma (GBM), a prevalent malignant neoplasm within the neuro-oncological domain, has been a subject of considerable scrutiny. Macrophages, serving as the principal immunological constituents, profoundly infiltrate the microenvironment of GBM. However, investigations elucidating the intricate immunological mechanisms governing macrophage involvement in GBM at the single-cell level remain notably limited.MethodsWe conducted a comprehensive investigation employing single-cell analysis, aiming to redefine the intricate cellular landscape within both the core and peripheral regions of GBM tumors. Our analytical focus extended to the profound study of macrophages, elucidating their roles within the context of oxidative stress, intercellular information exchange, and cellular trajectories concerning GBM and its assorted subpopulations. We pursued the identification of GBM prognostic genes intricately associated with macrophages. Utilizing experimental research to investigate the relevance of MANBA in the context of GBM.ResultsOur investigations have illuminated the central role of macrophages in the intricate interplay among various subpopulations within the GBM microenvironment. Notably, we observed a pronounced intensity of oxidative stress responses within macrophages when compared to their GBM counterparts in other subpopulations. Moreover, macrophages orchestrated intricate cellular communication networks, facilitated by the SPP1-CD44 axis, both internally and with neighboring subpopulations. These findings collectively suggest the potential for macrophage polarization from an M1 to an M2 phenotype, contributing to immune suppression within the tumor microenvironment. Furthermore, our exploration unearthed GBM prognostic genes closely associated with macrophages, most notably MANBA and TCF12. Remarkably, MANBA appears to participate in the modulation of neuroimmune functionality by exerting inhibitory effects on M1-polarized macrophages, thereby fostering tumor progression. To bolster these assertions, experimental validations unequivocally affirmed the promotional impact of MANBA on GBM, elucidated through its capacity to curb cell proliferation, invasiveness, and metastatic potential.ConclusionThese revelations represent a pivotal step towards unraveling the intricate immunological mechanisms governing the interactions between macrophages and diverse subpopulations within the GBM milieu. Furthermore, they lay the foundation for the development of an innovative GBM prognostic model, with MANBA at its epicenter, and underscore the potential for novel immunotherapeutic targets in the ongoing pursuit of enhanced treatment modalities for this formidable malignancy.

This article proposes an integer ambiguity determination method based on Beidou system-reflectometry (Beidou-R) observations of the carrier phase at the B1I and B3I frequencies. To enhance the accuracy of sea surface height (SSH) estimation, this study introduces a parallel filtering algorithm and an adaptive iterative fusion algorithm, enabling data fusion based on the variance at B1I and B3I frequencies. To validate and evaluate the proposed method, a coastal experiment was conducted at the Shenxian River. In this experiment, reflected signals from GEO and IGSO satellites were collected. Data analysis reveals that the method is effective, demonstrating that the root mean square error (RMSE) of SSH achieves 2.85 cm and 2.89 cm for PRN 04 and PRN 33, respectively. Furthermore, the impact of the elevation angle on measurement accuracy is analyzed. This study aims to propose a method to enhance coastal sea surface height estimation, offering potential advancements in sea surface altimetry.