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Healthy soils are essential for sustainable agricultural development and soil health requires careful assessment with increasing societal concern over environmentally friendly agricultural development. Soil health is the capacity of soil to function within ecological boundaries to sustain productivity, maintain environmental quality, and promote plant and animal health. Physical, chemical and biological indicators are used to evaluate soil health; the biological indicators include microbes, protozoa and metazoa. Nematodes are the most abundant metazoa and they vary in their sensitivity to pollutants and environmental disturbance. Soil nematode communities are useful biological indicators of soil health, with community characteristics such as abundance, diversity, community structure and metabolic footprint all closely correlated with the soil environment. The community size, complexity and structure reflect the condition of the soil. Both free-living and plant-parasitic nematodes are effective ecological indicators, contributing to nutrient cycling and having important roles as primary, secondary and tertiary consumers in food webs. Tillage inversion, cropping patterns and nutrient management may have strong effects on soil nematodes, with changes in soil nematode communities reflecting soil disturbance. Some free-living nematodes serve as biological models to test soil condition in the laboratory and because of these advantages soil nematodes are increasingly being used as biological indicators of soil health.

Objective: To determine the critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages during periodontitis. Background: Cathepsin S (CatS) is a cysteine protease and exerts important roles in the immune response. Elevated CatS has been found in the gingival tissues of periodontitis patients and is involved in alveolar bone destruction. However, the underlying mechanism of CatS‐driven IL‐6 production in periodontitis remains unclear. Methods: Western blot was applied to measure mature cathepsin S(mCatS) and IL‐6 expression in gingival tissues from periodontitis patients and RAW264.7 cells exposed to lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). Immunofluorescence was applied to confirm the localization of PU.1, and CatS in the gingival tissues of periodontitis patients. ELISA was performed to determine IL‐6 production by the P.g. LPS‐exposed RAW264.7 cells. Knockdown by shRNA was used to determine the effects of PU.1 on p38/ nuclear factor (NF)‐κB activation, mCatS expression and IL‐6 production in RAW264.7 cells. Results: The expressions mCatS and IL‐6 were significantly upregulated in gingival macrophages. In cultured RAW264.7 cells, increased mCatS and IL‐6 protein paralleled the activation of p38 and NF‐κB after exposure to P.g. LPS. CatS knockdown by shRNA significantly decreased P.g. LPS‐induced IL‐6 expression and p38/NF‐κB activation. PU.1 was significantly increased in P.g. LPS‐exposed RAW264.7 cells, and PU.1 knockdown dramatically abolished the P.g. LPS‐induced upregulation of mCatS and IL‐6 and the activation of p38 and NF‐κB. Furthermore, PU.1 and CatS colocalized in macrophages within the gingival tissues of periodontitis patients. Conclusion: PU.1‐dependent CatS drives IL‐6 production in macrophages by activating p38 and NF‐κB in periodontitis. [ABSTRACT FROM AUTHOR]

As one of the important vital features of the human body, the acquisition of a speech signal plays an important role in human–computer interaction. In this study, voice sounds are gathered and identified using Doppler radar. The skin on the neck vibrates when a person speaks, which causes the vocal cords to vibrate as well. The vibration signal received by the radar will produce a unique micro-Doppler signal according to words with different pronunciations. Following the conversion of these signals into micro-Doppler feature maps, these speech signal maps are categorized and identified. The speech recognition method used in this paper is on neural networks. CNN convolutional neural networks have a lower generalization and accuracy when there are insufficient training samples and sample extraction bias, and the training model is not suitable for use on mobile terminals. MobileViT is a lightweight transformers-based model that can be used for image classification tasks. MobileViT uses a lightweight attention mechanism to extract features with a faster inference speed and smaller model size while ensuring a higher accuracy. Our proposed method does not require large-scale data collection, which is beneficial for different users. In addition, the learning speed is relatively fast, with an accuracy of 99.5%. [ABSTRACT FROM AUTHOR]