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| 1 | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning显示文摘Parasitic infections constitute a major global public health issue.Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis.Here,we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism.Based on this principle,a cost-effective and mobile instrument,which rapidly screens~3.2 mL of fluid sample in three dimensions,was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns.We demonstrate the capabilities of our platform by detecting trypanosomes,which are motile protozoan parasites,with various species that cause deadly diseases affecting millions of people worldwide.Using a holographic speckle analysis algorithm combined with deep learningbased classification,we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid(CSF)samples,achieving a limit of detection of ten trypanosomes per mL of whole blood(~five-fold better than the current state-of-the-art parasitological method)and three trypanosomes per mL of CSF.We further demonstrate that this platform can be applied to detect other motile parasites by imaging Trichomonas vaginalis,the causative agent of trichomoniasis,which affects 275 million people worldwide.With its costeffective,portable design and rapid screening time,this unique platform has the potential to be applied for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions. | Yibo Zhang Hatice Ceylan Koydemir Michelle M.Shimogawa Sener Yalcin Alexander Guziak Tairan Liu Ilker Oguz Yujia Huang Bijie Bai Yilin Luo Yi Luo Zhensong Wei Hongda Wang Vittorio Bianco Bohan Zhang Rohan Nadkarni Kent Hill Aydogan Ozcan | 2018 | Light(Science & Applications)2018，7，1： | 6 |
| 2 | Ethylenediurea(EDU) pretreatment alleviated the adverse effects of elevated O3 on Populus alba 'Berolinensis' in an urban area显示文摘Ethylenediurea(EDU)has been used as a chemical protectant against ozone(O3).However,its protective effect and physiological mechanisms are still uncertain.The present study aimed to investigate the changes of foliar visible injury,physiological characteristics and emission rates of volatile organic compounds(VOCs)in one-year-old Populus alba'Berolinensis'saplings pretreated with EDU and exposed to elevated O3(EO,120μg/m3).The results showed that foliar visible injury symptoms under EO were significantly alleviated in plants with EDU application(p<0.05).Under EO,net photosynthetic rate,the maximum photochemical efficiency of PSII and the photochemical efficiency of PSII of plants pretreated with 300 and600 mg/L EDU were similar to unexposed controls and significantly higher compared to EOstressed plants without EDU pretreatment,respectively.Malondialdehyde content was highest in EO without EDU and decreased significantly by 14.9%and 21.3%with 300 and600 mg/L EDU pretreatment,respectively.EDU pretreatment alone increased superoxide dismutase activity by 10-fold in unexposed plants with further increases of 88.4%and 37.5%in EO plants pretreated with 300 and 600 mg/L EDU pretreatment,respectively(p<0.05).Abscisic acid content declined under EO relative to unexposed controls with the effect partially reversed by EDU pretreatments.Similarly,VOCs emission rate declined under EO relative to unexposed plants with a recovery of emission rate observed with 300 and 600 mg/L EDU pretreatment.These findings provided significant evidence that EDU exerted a beneficial effect and protection on the tested plants against O3 stress. | Sheng Xu Xingyuan He Kent Burkey Wei Chen Pin Li Yan Li Bo Li Yijing Wang | 2019 | Journal of Environmental Sciences2019，31，10： | 4 |
| 3 | Comparison of entrepre- neurial intention among college students in the USA and China 显示文摘 | Wei Lu Wenjun Wang J Kent Millington | 2010 | International Journal of Pluralism and Economics Education2010，1，4： | 1 |
| 4 | Loss of Hsp70 in Drosophila is pleiotropic,with effects on thermotolerance,recovery from heat shock,and neurodegeneration显示文摘 | Kent G Golic | 2006 | Genetics2006，172，1： | 1 |
| 5 | Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients显示文摘The exponential spread of COVID-19 worldwide is evident,with devastating outbreaks primarily in the United States,Spain,Italy,the United Kingdom,France,Germany,Turkey and Russia.As of 1 May 2020,a total of 3,308,386 confirmed cases have been reported worldwide,with an accumulative mortality of 233,093.Due to the complexity and uncertainty of the pathology of COVID-19,it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases,with consistency.The more than 300 laboratory features,coupled with underlying disease,all combine to complicate proper and rapid patient diagnosis.However,such screening is necessary for early triage,diagnosis,assignment of appropriate level of care facility,and institution of timely intervention.A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020,who were admitted to Tongji Hospital,in Wuhan,China.A softmax neural network-based machine learning model was established to categorise patient severity levels.According to the analysis of 2662 cases using clinical and laboratory data,the present model can be used to reveal the top 30 of more than 300 laboratory features,yielding 86.30%blind test accuracy,0.8195 F1-score,and 100%consistency using a two-way patient classification of severe/critical to general.For severe/critical cases,F1-score is 0.9081(i.e.recall is 0.9050,and precision is 0.9113).This model for classification can be accomplished at a mini-second-level computational cost(in contrast to minute-level manual).Based on available COVID-19 patient diagnosis and therapy,an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100%consistency to significantly improve diagnostic and classification efficiency.The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines,thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection.Such early differentiation will help the assignment of the appropriate level of care for individual patients. | Yang Xiao Li Yan Mingyang Zhang Kent E.Pinkerton Haosen Cao Ying Xiao Wei Li Shuai Li Yancheng Wang Shusheng Li Zhiguo Cao Gary Wing-Kin Wong Hui Xu Hai-Tao Zhang | 2021 | IET Cyber-Systems and Robotics2021，3，1： | 1 |
| 6 | Explaining the crosssection of stock returns in Japan:Factors or characteristics? 显示文摘 | Daniel Kent Titman Sheridan Wei K C John | 2001 | Journal of Finance2001，，56： | 1 |
| 7 | Experimental tomographic methods for analysing flow dynamics of gas-oil- water flows in horizontal pipeline显示文摘Gas-oil-water three phase flow is of practical significance in oil and gas industries. An insight into the dynamics of such multiphase flows is significantly valuable to obtain optimal design parameters and operational conditions. Since flow patterns are sensitive to pipe geometry, flow conditions and thermophysical properties of fluid, it is extremely challenging to provide a universal solution for visualisation of three phase horizontal flows. This study deals with a fully developed, turbulent three phase flow and presents the outcomes of electrical tomographic imaging techniques to visualise gas-oil-water flows in a horizontal pipeline. | Qiang WANG Jiri POLANSKY Bishal KARKI Mi WANG Kent WEI Changhua QIU Asaad KENBAR David MILLINGTON | 2016 | Journal of Hydrodynamics2016，28，6： | 0 |
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