| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Tong, Yang, et al.
(författare)
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Progress of the key materials for organic solar cells
- 2020
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Ingår i: Science in China Series B. - Beijing, China : SCIENCE PRESS. - 1674-7291 .- 1869-1870. ; 63:6, s. 758-765
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Forskningsöversikt (refereegranskat)abstract
- Organic solar cells have attracted academic and industrial interests due to the advantages like lightweight, flexibility and roll-to-roll fabrication. Nowadays, 18% power conversion efficiency has been achieved in the state-of-the-art organic solar cells. The recent rapid progress in organic solar cells relies on the continuously emerging new materials and device fabrication technologies, and the deep understanding on film morphology, molecular packing and device physics. Donor and acceptor materials are the key materials for organic solar cells since they determine the device performance. The past 25 years have witnessed an odyssey in developing high-performance donors and acceptors. In this review, we focus on those star materials and milestone work, and introduce the molecular structure evolution of key materials. These key materials include homopolymer donors, D-A copolymer donors, A-D-A small molecular donors, fullerene acceptors and nonfullerene acceptors. At last, we outlook the challenges and very important directions in key materials development.
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| 3. |
- Shen, Ting, et al.
(författare)
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A structure of CdS/CuxS quantum dots sensitized solar cells
- 2016
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Ingår i: Applied Physics Letters. - : AIP Publishing. - 0003-6951 .- 1077-3118. ; 108:21
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Tidskriftsartikel (refereegranskat)abstract
- This work introduces a type of CdS/CuxS quantum dots (QDs) as sensitizers in quantum dot sensitized solar cells by in-situ cationic exchange reaction method where CdS photoanode is directly immersed in CuCl2 methanol solution to replace Cd2+ by Cu2+. The p-type CuxS layer on the surface of the CdS QDs can be considered as hole transport material, which not only enhances the light harvesting of photoanode but also boosts the charge separation after photo-excitation. Therefore, both the electron collection efficiency and power conversion efficiency of the solar cell are improved from 80% to 92% and from 1.21% to 2.78%, respectively.
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| 4. |
- Yu, Wenjin, et al.
(författare)
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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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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)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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