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ObjectiveDefining whether a suspected case was due to scrub typhus through laboratory testing, to understand the prevalence of scrub typhus in Shijiazhuang City, Hebei Province.MethodsAn epidemiological investigation was conducted on the suspected case, utilizing Weil-Felix test and indirect immunofluorescence assay (IFA) to detect specific antibodies against O. tsutsugamushi in serum specimens. Additionally, PCR amplification of the 56-kDa and groEL genes was performed, followed by constructing a phylogenetic tree to identify the genotype.ResultsThe acute phase titer of the Weil-Felix test for the case was 1:160, which increased to 1:320 in the recovery phase. IFA assay revealed IgG titers against O. tsutsugamushi of 1:64 in the acute phase and 1:256 in the recovery phase. Sequence alignment of the PCR amplified fragment showed the highest similarity with the O. tsutsugamushi genotype. Kawasaki sequence, ranging from 99.71 to 100.00%. The strain exhibited the closest genetic relationship with the known O. tsutsugamushi Kawasaki genotype.ConclusionThis study confirms the presence of O. tsutsugamushi in Shijiazhuang City, Hebei Province, with the identified strain belonging to the Kawasaki genotype, marking the first diagnosis of this strain in the region.

Abstract Deep learning can be used to achieve single‐image superresolution (SR) reconstruction. To address problems encountered during this process, such as the number of network parameters, high training requirements on equipment performance, and inability to downsample certain SR images accurately, an image SR reconstruction algorithm based on deep residual network optimization is proposed. The model introduces wavelet transforms based on the original U‐Net, where the U‐Net is trained to obtain SR wavelet feature images at multiple scales simultaneously. This approach reduces the mapping space for the network to learn low‐ to high‐resolution image mapping, which in turn reduces the training difficulty of the model. In terms of network details, the inverse wavelet transform is used in image upsampling to enhance the sparsity of the reconstruction layer in the original network. The network structure of the U‐Net upsampling is adjusted slightly to enable the network to distinguish wavelet images from feature images, thereby improving the richness of the features extracted by the model. The experimental results show that the peak signal‐to‐noise ratio (PSNR) of the fourfold SR model is 32.35 and 28.68 on the Set5 and Set14 validation sets, respectively. Compared with networks that use wavelet prediction mechanisms, such as the deep wavelet prediction SR (DWSR) and deep wavelet prediction‐based residual SR (DWRSR) models, the PSNR for all the tested public datasets is improved by 0.5. The method yields superior results in terms of both visual effect and PSNR, demonstrating the feasibility of the wavelet prediction mechanism in SR reconstruction and thus offering application value and research significance.

Abstract The problem that the texture details of low‐resolution (LR) digital images of ancient murals are ambiguous persists. To solve this problem, this study proposes a super‐resolution (SR) reconstruction method for fuzzy murals based on a generative adversarial network with self‐attention (SA). The network uses a blur kernel and realistic noise data to add blur and noise, respectively, to a high‐resolution (HR) image to obtain an original LR image. Then, a feature image with the same size as that of the input image is obtained through a SA module. Finally, the feature information extracted from the image is input into the high‐resolution image space by using a subpixel convolution layer to realize the image enlargement process from an LR to an HR. Experiments evaluate the proposed approach both objectively and subjectively. The objective evaluation results show that compared with other SR reconstruction algorithms, the proposed algorithm's peak signal‐to‐noise ratio (PSNR) is increased by 0.04 to 3.78 dB on average, and its structural similarity is increased by 0.002 to 0.191. A subjective perception evaluation shows that the developed algorithm can better reconstruct the texture details of murals, thus better meeting the visual perception needs of the public. The method proposed in this study can satisfactorily reconstruct the texture details of murals, which may provide technical guidance for the development of mural protection plans. Furthermore, it may be of certain practical significance for the SR reconstruction of ancient murals.