A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures

Intrahepatic cholestasis of pregnancy worsening perinatal depressive tendency: A follow-up study from the second trimester to the sixth week postpartum

The total bile acid (TBA) is usually used to diagnose intrahepatic cholestasis of pregnancy (ICP) as a common clinical index. Recently many research reports on the microbiota-gut-brain axis (MGB axis) suggest that bile acids have an influence on human mental illnesses such as anxiety and depression, linked closely to intestinal microbial population. However, there is still a lack of clinical data to support intrinsic relationships about human cases. In this study, we conducted a follow-up study of 25 ICP and 98 healthy pregnant women to investigate the influence of ICP disease on perinatal depression. To further explore the effect of TBA concentration, we reviewed data of another 41 ICP women then added their cross-sectional data. The results showed that ICP disease increased mental scale scores but a conventional efficient treatment by using ursodeoxycholic acid (UDCA) could not decrease scores, suggesting intrahepatic cholestasis might make some key bile acids not to be processed by gut microbiota. UDCA could not replace the function of gut microbiota for easing depression and the change of bile acid composition in intestines worsened perinatal depressive tendency through the MGB axis

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis

Objectives: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB). Methods: A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model. The testing cases were first divided into T-SPOT-positive and -negative groups, and the DL model was then used to separate the cases into four subgroups further. Results: The precision was found to be 91.7% for the subgroup of T-SPOT-negative and DL classified as NTM-PD, and 89.8% for T-SPOT-positive and DL classified as PTB, which covered 66.9% of the total cases, compared with the accuracy rate of 80.3% of T-SPOT alone. In the other two remaining groups, where the T-SPOT prediction was inconsistent with the DL model, the accuracy was 73.0% and 52.2%, separately. Conclusion: Our study shows that the new diagnostic system combining T-SPOT with DL based computed tomography image analysis can greatly improve the classification precision of NTM-PD and PTB when the two methods of prediction are consistent

Diversification of ranunculaceous petals in shape supports a generalized model for plant lateral organ morphogenesis and evolution.

Peltate organs, such as the prey-capturing traps of carnivorous plants and nectary-bearing petals of ranunculaceous species, are widespread in nature and have intrigued and perplexed scientists for centuries. Shifts in the expression domains of adaxial/abaxial genes have been shown to control leaf peltation in some carnivorous plants, yet the mechanisms underlying the generation of other peltate organs remain unclear. Here, we show that formation of various peltate ranunculaceous petals was also caused by shifts in the expression domains of adaxial/abaxial genes, followed by differentiated regional growth sculpting the margins and/or other parts of the organs. By inducing parameters to specify the time, position, and degree of the shifts and growth, we further propose a generalized modeling system, through which various unifacial, bifacial, and peltate organs can be simulated. These results demonstrate the existence of a hierarchical morphospace system and pave the way to understand the mechanisms underlying plant organ diversification