Dendroctonus valens LeConte, an invasive bark beetle, has caused severe damage in pine forests and has the potential to disperse into new geographic ranges in China. Although the gut microbiota of D. valens and its fundamental role in host fitness have been investigated widely, little is known about the relationship between the seasonal shifts of both cold tolerance and the gut microbiome of D. valens during overwintering, which occurs at the larval stage. In this study, to examine seasonal variations in the composition of the microbiome, we collected D. valens larvae in September (autumn), January (winter), and May (spring), and then analyzed the bacterial and fungal communities of the gut via sequencing of partial 16S rRNA and ITS genes. In addition, changes in the supercooling capacity and antioxidant enzyme activities of D. valens larvae collected in the different seasons were evaluated. Overwintering resulted in changes to microbial communities. In particular, the abundances of Enterobacter, Serratia, Erwinia, and Klebsiella decreased during overwintering. Concurrent with these changes, the cold tolerance of D. valens larvae was enhanced during overwintering, and the activities of the antioxidant enzymes catalase and peroxidase were reduced. We hypothesize that seasonal shifts in the gut microbiome may be connected to changes in cold tolerance and antioxidant enzyme activity in D. valens. It will be worthwhile to confirm whether seasonal changes in the microbiome contribute to the success of host overwintering.

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In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection.