The orientation effect on blistering phenomenon in H implanted Si was studied for (100), (111), and (110) Si wafers. It was found that substrate orientation has no observable effects on the underlying blistering mechanisms. Furthermore, the implantation damage, Si–H complex formation in as-implanted samples and surface roughness of the transferred layer appeared to be unaffected by the orientation. However, the blistering kinetics are orientation dependent, with (100) Si having the fastest blistering rate, and (110) Si the slowest. This dependence was attributed to the different density of ruptured Si–Si bonds of different orientations. The magnitude of the observed in-plane compressive stress in the H-implanted Si wafers is rationalized in terms of the formation of platelets in the samples.
In this study, an on-line two-dimensional high-speed counter-current chromatography system based on a six-port valve was developed. Target-guided by ultrafiltration with high-performance liquid chromatography, the one-step isolation of three potential α-amylase inhibitors from Abelmoschus esculentus (L).Moench was achieved by employing the developed orthogonal system and extrusion elution mode. The purities of three potential α-amylase inhibitors were all over 95% as determined by high-performance liquid chromatography. Furthermore, UV, mass spectrometry and H NMR spectroscopy were applied to the structural identification of the isolated three target compounds, their structures were assigned as quercetin-3-O-sophoroside (i), 5,7,3',4'-tetrahydroxy flavonol-3-O-[β-d-rhamnopyranosil-(1→2)]-β-d-glucopyranoside (ii ) and isoquercitrin (iii), respectively. The Results demonstrated that the proposed method was highly efficient to screen and isolate enzyme inhibitors from complex natural products extracts, and on-line two-dimensional high-speed counter-current chromatography can effectively increase the peak resolution of target compounds.
Background
Psoriasis is a chronic inflammatory skin disease, which holds a high incidence in China. However, professional dermatologists who can diagnose psoriasis early and correctly are insufficient in China, especially in the rural areas. A smart approach to identify psoriasis by pictures would be highly adaptable countrywide and could play a useful role in early diagnosis and regular treatment of psoriasis.
Objectives
Design and evaluation of a smart psoriasis identification system based on clinical images (without relying on a dermatoscope) that works effectively similar to a dermatologist.
Methods
A set of deep learning models using convolutional neural networks (CNNs) was explored and compared in the system for automatic identification of psoriasis. The work was carried out on a standardized dermatological dataset with 8021 clinical images of 9 common disorders including psoriasis along with full electronic medical records of patients built over the last 9 years in China. A two‐stage deep neural network was designed and developed to identify psoriasis. In the first stage, a multilabel classifier was trained to learn the visual patterns for each individual skin disease. In the second stage, the output of the first stage was utilized to distinguish psoriasis from other skin diseases.
Results
The area under the curve (AUC) of the two‐stage model reached 0.981 ± 0.015, which outperforms a single‐stage model. And, the classifier showed superior performance (missed diagnosis rate: 0.03, misdiagnosis rate: 0.04) than 25 Chinese dermatologists (missed diagnosis rate: 0.19, misdiagnosis rate: 0.10) in the diagnosis of psoriasis on 100 clinical images.
Conclusions
Using clinical images to identify psoriasis is feasible and effective based on CNNs, which also builds a solid technical base for smart care of skin diseases especially psoriasis using mobile/tablet applications for teledermatology in China.
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