In the current study, we aimed to determine the association of single nucleotide polymorphism rs189037 in ataxia-telangiectasia mutated (ATM) gene with cardiac structure and human longevity. Based on the China Hainan Centenarian Cohort Study performed in 18 cities and counties of Hainan Province, China, the current study enrolled 547 centenarians, 250 young participants aged 20–45 years, and 250 middle-aged and elderly participants aged 46–90 years. The frequency of TT genotype was significantly higher and that of CC genotype was significantly lower in middle-aged and elderly participants than in young (P = 0.012) and centenarian (P = 0.041) participants. There were no significant differences in the genotype and allele frequencies of SNP rs189037 between young and centenarian participants. Compared with CT genotype, TT genotype was positively and significantly associated with interventricular septum thickness (IVST) and left ventricular posterior wall thickness (LVPWT) in centenarian (IVST: P = 0.049; LVPWT: P = 0.047) and middle-aged and elderly (IVST: P = 0.008; LVPWT: P = 0.004) participants. Compared with CC genotype, TT genotype was positively and significantly associated with LVPWT in centenarian (P = 0.030) and middle-aged and elderly (P = 0.013) participants. Compared with CC genotype, CT genotype was negatively and significantly associated with left ventricular end-diastolic diameter (LVEDD) in centenarian (P = 0.011) and middle-aged and elderly (P = 0.040) participants. The current study demonstrated that mutant rs189037 in the ATM gene was more commonly identified in middle-aged and elderly participants than in young and centenarian participants, was significantly associated with increased left ventricular wall thickness and volume, and could induce left ventricular eccentric hypertrophy and shorten human lifespan. Therefore, rs189037 without mutation might be an indicator of youth health and successful aging, whereas mutant rs189037 might hinder human longevity.

The deep learning model is a data-driven model and more high-quality data will bring it better results. In the task of Unmanned Surface Vessel’s object detection based on optical images or videos, the object is sparser than the target in the natural scene. The current datasets of sea scenes often have some disadvantages such as high image acquisition costs, wide range of changes in object size, imbalance in the number of different objects and so on, which limit the generalization of the model for the detection of sea surface objects. In order to solve problems of insufficient scene and poor effect in current sea surface object detection, an object-level data augmentation for sea surface objects called SOMC is proposed. According to the different scenarios faced by the USV when performing autonomous obstacle avoidance, patrol and other tasks, SOMC generates suitable scenarios by mixing and copying targets conveniently, providing the possibility of unlimited expansion of the sea surface object. The experiment selected images in the video taken by the camera on top of the USV. A sufficient amount of comparative experiment prove that the SOMC integrates with existing excellent data augmentations and achieved an improvement in the detection effect, which proves the effectiveness and practicability of the SOMC in the perception task of the USV.

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Zhang et al.: Detection of hyperthyroidism by the modified LeNet-5 networkTo study the automatic detection method of hyperthyroidism based on the deep learning of the modified LeNet-5 network and to establish a detection method with higher precision and better performance, a total of 180 facial images of patients with hyperthyroidism in the ultrasound imaging department of the HwaMei hospital were collected as well as 180 facial images of healthy persons were collected to serve as the control group. A method was proposed for the detection of hyperthyroidism based on the modified LeNet-5. Three test groups were designed by randomized controlled experiment design for the detection of hyperthyroidism, in which the control group was tested manually by experienced doctors, the LeNet-5 network learning group adopted the classical LeNet-5 network learning algorithm and the experimental group used the modified LeNet-5 network learning algorithm. Evaluation indices included the accuracy, detection efficiency, sensitivity, specificity and F1 score of the detection. At the same time, differences between the two algorithms in the detection of thyroid nodules were compared. There was no significant difference between the 3 groups in the accuracy and specificity of detection of hyperthyroidism. In terms of the detection efficiency and sensitivity, the performance of the network learning algorithm group and learning group was better than that of the control group. Both the network learning algorithm group and experimental group could detect the thyroid nodules accurately, but there was no significant difference in the accuracy of detecting the type of thyroid nodules. The correct rate of malignant thyroid nodules was significantly higher than that of the benign thyroid nodules. The modified LeNet-5 network algorithm showed acceptable consistency with experienced doctors in the detection of hyperthyroidism, and this method was useful for the exclusion of thyroid malignant tumours and can be used as a simple method to exclude and identify malignant thyroid tumours.