Biological Characteristics, Domesticated Cultivation Protocol, Antioxidant Activity, and Protective Effects against Cellular Oxidative Stress of an Underutilized Medicinal Mushroom: Fomitopsis palustris.

Brown-rot fungus is one of the important medicinal mushrooms, which include some species within the genus Fomitopsis. This study identified wild macrofungi collected from a broad-leaved tree in Liaoning Province as Fomitopsis palustris using both morphological and molecular methods. To elucidate the potential medicinal and economic value of F. palustris, we conducted single-factor and orthogonal tests to optimize its mycelium culture conditions. Subsequently, we completed liquid culture and domestic cultivation based on these findings. Furthermore, crude polysaccharides were extracted from the cultivated fruiting bodies of F. palustris and their antioxidant activity was evaluated using chemical methods and cell-based models. The results showed that the optimal culture conditions for F. palustris mycelium were glucose as the carbon source, yeast extract powder as the nitrogen source, pH 6.0, and a temperature of 35 °C. Moreover, temperature was found to have the most significant impact on mycelial growth. The liquid strains were fermented for 6 days and then inoculated into a cultivation substrate composed of broadleaf sawdust, resulting in mature fruiting bodies in approximately 60 days. The crude polysaccharides extracted from the cultivated fruiting bodies of F. palustris (FPPs) possess in vitro scavenging abilities against DPPH radicals and OH radicals, as well as a certain ferric-reducing antioxidant power. Additionally, FPPs effectively mitigated H2O2-induced oxidative stress in RAW264.7cells by enhancing the intracellular activity of antioxidant enzymes such as SOD and CAT, scavenging excess ROS, and reducing MDA levels. This study provides preliminarily evidence of the potential medicinal and economic value of F. palustris and offers initial data for the future development and utilization of this species.

Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification.

Accurate and efficient crop classification using remotely sensed data can provide fundamental and important information for crop yield estimation. Existing crop classification approaches are usually designed to be strong in some specific scenarios but not for multi-scenario crop classification. In this study, we proposed a new deep learning approach for multi-scenario crop classification, named Cropformer. Cropformer can extract global features and local features, to solve the problem that current crop classification methods extract a single feature. Specifically, Cropformer is a two-step classification approach, where the first step is self-supervised pre-training to accumulate knowledge of crop growth, and the second step is a fine-tuned supervised classification based on the weights from the first step. The unlabeled time series and the labeled time series are used as input for the first and second steps respectively. Multi-scenario crop classification experiments including full-season crop classification, in-season crop classification, few-sample crop classification, and transfer of classification models were conducted in five study areas with complex crop types and compared with several existing competitive approaches. Experimental results showed that Cropformer can not only obtain a very significant accuracy advantage in crop classification, but also can obtain higher accuracy with fewer samples. Compared to other approaches, the classification performance of Cropformer during model transfer and the efficiency of the classification were outstanding. The results showed that Cropformer could build up a priori knowledge using unlabeled data and learn generalized features using labeled data, making it applicable to crop classification in multiple scenarios.

Metagenomics analysis of the effects of Agaricus bisporus mycelia on microbial diversity and CAZymes in compost.

Agaricus bisporus growth alters the lignocellulosic composition and structure of compost. However, it is difficult to differentiate the enzyme activities of A. bisporus mycelia from the wider microbial community owing to the complication of completely speareting the mycelia from compost cultures. Macrogenomics analysis was employed in this study to examine the fermentation substrate of A. bisporus before and after mycelial growth, and the molecular mechanism of substrate utilization by A. bisporus mycelia was elucidated from the perspective of microbial communities and CAZymes in the substrate. The results showed that the relative abundance of A. bisporus mycelia increased by 77.57-fold after mycelial colonization, the laccase content was significantly increased and the lignin content was significantly decreased. Analysis of the CAZymes showed that AA10 family was extremely differentiated. Laccase-producing strains associated with AA10 family were mostly bacteria belonging to Thermobifida and Thermostaphylospora, suggesting that these bacteria may play a synergistic role in lignin decomposition along with A. bisporus mycelia. These findings provide preliminary evidence for the molecular mechanism of compost utilization by A. bisporus mycelia and offer a reference for the development and utilization of strains related to lignocellulose degradation.