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| 3. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- 2019
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Tidskriftsartikel (refereegranskat)
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- Ablikim, M., et al.
(författare)
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Measurement of the integrated Luminosities of cross-section scan data samples around the psi(3770) mass region
- 2018
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Ingår i: Chinese Physics C. - : SCIENCE PRESS. - 1674-1137 .- 2058-6132. ; 42:6
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Tidskriftsartikel (refereegranskat)abstract
- To investigate the nature of the psi(3770) resonance and to measure the cross section for e(+)e(-) -> D (D) over bar, a cross-section scan data sample, distributed among 41 center-of-mass energy points from 3.73 to 3.89 GeV, was taken with the BESIII detector operated at the BEPCII collider in the year 2010. By analyzing the large angle Bhabha scattering events, we measure the integrated luminosity of the data sample at each center-of-mass energy point. The total integrated luminosity of the data sample is 76.16 +/- 0.04 +/- 0.61 pb(-1), where the first uncertainty is statistical and the second systematic.
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| 6. |
- Ablikim, M., et al.
(författare)
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Measurements of (XcJ)-> K+K-K+K- decays
- 2006
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Ingår i: Physics Letters B. - : Elsevier BV. - 0370-2693 .- 1873-2445. ; 642:3, s. 197-202
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Tidskriftsartikel (refereegranskat)abstract
- Using 14M psi(2S) events taken with the BESII detector, chi(cJ) -> 2(K+K-) decays are studied. For the four-kaon final state, the branching fractions are B(chi(c0,1,2) ->.2(K+K-)) = (3.48 +/- 0.23 +/- 0.47) x 10(-3), (0.70 +/- 0.13 +/- 0.10) x 10(-3), and (2.17 +/- 0.20 +/- 0.31) x 10(-3). For the phi K+K- final state, the branching fractions, which are measured for the first time, are B(chi(c0,1,2) -> phi K+K-) = (1.03 +/- 0.22 +/- 0.15) x 10(-3), (0.46 +/- 0.16 +/- 0.06) x 10(-3), and (1.67 +/- 0.26 +/- 0.24) x 10(-4). For the phi phi final state, B(chi(c0,2) -> phi phi) = (0.94 +/- 0.21 +/- 0.13) x 10(-3) and (1.70 +/- 0.30 +/- 0.25) x 10(-3).
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| 7. |
- Kristan, Matej, et al.
(författare)
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The first visual object tracking segmentation VOTS2023 challenge results
- 2023
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Ingår i: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
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| 9. |
- 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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| 10. |
- Zhang, Qi, et al.
(författare)
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Muscle-like Artificial Molecular Actuators for Nanoparticles
- 2018
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Ingår i: Chem. - : Cell Press. - 2451-9308 .- 2451-9294. ; 4:11, s. 2670-2684
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Tidskriftsartikel (refereegranskat)abstract
- Muscle tissue performs crucial contraction/extension motions that generate mechanical force and work by consuming chemical energy. Inspired by this naturally created biomolecular machine, artificial molecular muscles are designed and synthesized to undertake linear actuation functions. However, most of these muscle-like actuators are performed at large ensembles, while to realize the nanoscale actuation at the single-to few-molecule level remains challenging. Herein, we developed an artificial muscle-like molecular actuator that can reversibly control the proximity of the attached nano-objects, gold nanoparticles, within the single-molecule length level by its stimuli-responsive muscle-like linear contraction/extension motion. The molecular actuation motion is accompanied by an optical signal output resulting from the plasmonic resonance properties of gold nanoparticles. Meanwhile, the thermal noise of the muscle-like molecular actuator can be overcome by integrating the optical signal over a sufficiently long period.
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