In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

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This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

In this article, we present in situ U-Pb and Lu-Hf isotope data for Upper Triassic detritus in the Sichuan region of northwestern South China, which was a foreland basin during the Late Triassic. The aim is to determine the provenance of sediments in the foreland basin and to constrain the evolution of the surrounding mountain belts. U-Pb age data for the Late Triassic detrital zircons generally show populations at 2.4-2.6 Ga, 1.7-1.9 Ga, 710-860 Ma, 410-460 Ma, and 210-300 Ma. By fitting the zircon data into the tectonic, sedimentologic, and palaeographic framework, we propose that the north Yangtze Block and South Qinling-Dabie Orogen were the important source areas of sediments in the northern part of the foreland basin, whereas the Longmen Shan thrust-fold belt was the main source region for detritus in other parts of the foreland basin. The South Qinling-Dabie Orogen could also have served as a physical barrier to block most detritus shed from the southern North China Block into the foreland basin during the sedimentation of the Xujiahe Formation. Our results also reveal that part of the flysch from the eastern margin of the Songpan-Ganzi region had been displaced into the Longmen Shan thrust-fold belt before the deposition of the foreland basin sediments. In addition, the Lu-Hf data indicate that Phanerozoic igneous rocks in central China show insignificant formation of the juvenile crust.