| 1. |
- Li, Wenlong, et al.
(author)
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Sterically hindered diarylethenes with a benzobis(thiadiazole) bridge : photochemical and kinetic studies
- 2015
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In: Journal of Materials Chemistry C. - : Royal Society of Chemistry (RSC). - 2050-7526 .- 2050-7534. ; 3:33, s. 8665-8674
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Journal article (peer-reviewed)abstract
- Four rationally designed diarylethenes (DAEs) 1-4 with a benzobis(thiadiazole) bridge are specifically designed for gaining insights into steric effects on photochromic performances. It is shown that, upon increasing steric hindrance, the exchanging rate between two main conformers in the ring-open form gradually slows down, offering the opportunity for isolating photoactive anti-parallel conformers. Impressively, the separated anti-parallel conformer shows high cyclization quantum yields over the unresolved common DAEs. The typical donor-pi-acceptor (D-pi-A) feature in ring-open DAEs 1-4 endows their prominent fluorescence, which can be conveniently modulated by photocyclization. In the ring-closed form, the excess steric hindrance is found to seriously disrupt the thermal bistability, and particularly 3c fades quickly with a half-life of several hours at ambient temperature. In contrast, both 1c and 2c exhibit excellent stability, which originates from the stabilization effects of intramolecular hydrogen bonds. This work demonstrates the steric effects on the photochemical and kinetic behaviors of DAEs, providing a unique approach to develop photochromic DAEs with high photosensitivity.
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| 2. |
- Yu, Wenjin, et al.
(author)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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In: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Journal article (peer-reviewed)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 3. |
- Li, Yunsong, et al.
(author)
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Dynamics modeling and modal experimental study of high speed motorized spindle
- 2017
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In: Journal of Mechanical Science and Technology. - : Springer Science and Business Media LLC. - 1738-494X .- 1976-3824. ; 31:3, s. 1049-1056
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Journal article (peer-reviewed)abstract
- This paper presents a dynamical model of high speed motorized spindles in free state and work state. In the free state, the housing is modeled as a rotor with equivalent masses including bearing pedestals, motor stator and rear end cover. As a consequence, a double rotor dynamics can be modeled for high speed motorized spindles by a bearing element which connects the housing and bearing pedestals. In the work state, the housing is fixed and the system becomes a bearing-rotor dynamical model. An excitation-measurement test in the free state is designed to analyze the cross spectral density and auto spectral density of input and output signals. Then the frequency response function of system and coherence function of input and output signals which are used to analyze the inherent characteristics of the double- rotor model can be obtained. The other vibration test in the work state is designed to research the dynamical supporting characteristics of bearings and the effects from bearings on the inherent characteristics of the system. The good agreement between the experimental data and theoretical results indicates that the dynamical model in two states is capable of accurately predicting the dynamic behavior of high speed motorized spindles.
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