Background: The diagnosis of early mycosis fungoides (MF) is a big challenge to dermatologists and dermatopathologists because it lacks specific clinicopathologic features. Methods: Fifty‐two paraffin‐embedded skin samples from 50 patients, including 31 with suspected MF, 10 with typical MF and 9 with benign inflammatory dermatosis (BID), were obtained from our archives. DNA was extracted both by traditional phenol‐chloroform method and by the laser‐capture microdissection (LCM)‐proteinase K approach. The T VG /T JG , V 2–5 /V 8–12 /JGT 1 and BIOMED‐2‐TCR‐γ primers were used to assess TCR‐γ monoclonal rearrangement as measured by polymerase chain reaction (PCR). Results: In the suspected MF group, clonal TCR‐γ gene rearrangements were detected in 11/31 cases (35.5%) by phenol‐chloroform DNA extraction and in 25/31 cases (80.7%) by LCM‐proteinase K extraction (p < 0.05). While T‐cell clonality was detected in 8/10 cases (80%) by the phenol‐chloroform method and 10/10 cases (100%) by LCM (p > 0.05) in the typical MF group, no TCR‐γ monoclonal rearrangement was detected in the BID group. Conclusions: The strategy of multiple PCR/heteroduplex analysis for TCR‐γ gene rearrangement combined with LCM increases the detection rate of clonal TCR‐γ gene rearrangement in early MF cases and could provide strong evidence to confirm the diagnosis of early MF. Yang H, Xu C, Tang Y, Wan C, Liu W, Wang L. The significance of multiplex PCR/heteroduplex analysis‐based TCR ‐γ gene rearrangement combined with laser‐capture microdissection in the diagnosis of early mycosis fungoides.
For the market model, this paper first uses the ARIMA time series model to analyze why the ARIMA model can predict the time series of gold price changes. After retesting the model, we use the model to predict the value and growth of gold. Then the LSTM neural network is used to effectively avoid the big error caused by the significant deviation in individual cases. It can accurately predict bitcoin quotations through subtle control of memory gates, forgetting gates, and output doors in LSTM. This model is also suitable for the accuracy test of the LSTM model based on MSE, MAE, and other parameters.
Mathematic sense is of great importance in exploring the mathematical world, the motivation of studying mathematical problems, and a guide to a scientific thinking in handling and solving mathematic problems.
Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively.
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