| 1. |
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| 2. |
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| 3. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 4. |
- 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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| 5. |
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| 6. |
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| 7. |
- 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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| 8. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 9. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t
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| 10. |
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| 11. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 12. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 13. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 14. |
- Aad, G., et al.
(författare)
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Readiness of the ATLAS Tile Calorimeter for LHC collisions
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:4, s. 1193-1236
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Tidskriftsartikel (refereegranskat)abstract
- The Tile hadronic calorimeter of the ATLAS detector has undergone extensive testing in the experimental hall since its installation in late 2005. The readout, control and calibration systems have been fully operational since 2007 and the detector has successfully collected data from the LHC single beams in 2008 and first collisions in 2009. This paper gives an overview of the Tile Calorimeter performance as measured using random triggers, calibration data, data from cosmic ray muons and single beam data. The detector operation status, noise characteristics and performance of the calibration systems are presented, as well as the validation of the timing and energy calibration carried out with minimum ionising cosmic ray muons data. The calibration systems' precision is well below the design value of 1%. The determination of the global energy scale was performed with an uncertainty of 4%.
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| 15. |
- Aad, G., et al.
(författare)
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Studies of the performance of the ATLAS detector using cosmic-ray muons
- 2011
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 71:3
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Tidskriftsartikel (refereegranskat)abstract
- Muons from cosmic-ray interactions in the atmosphere provide a high-statistics source of particles that can be used to study the performance and calibration of the ATLAS detector. Cosmic-ray muons can penetrate to the cavern and deposit energy in all detector subsystems. Such events have played an important role in the commissioning of the detector since the start of the installation phase in 2005 and were particularly important for understanding the detector performance in the time prior to the arrival of the first LHC beams. Global cosmic-ray runs were undertaken in both 2008 and 2009 and these data have been used through to the early phases of collision data-taking as a tool for calibration, alignment and detector monitoring. These large datasets have also been used for detector performance studies, including investigations that rely on the combined performance of different subsystems. This paper presents the results of performance studies related to combined tracking, lepton identification and the reconstruction of jets and missing transverse energy. Results are compared to expectations based on a cosmic-ray event generator and a full simulation of the detector response.
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| 16. |
- Aad, G., et al.
(författare)
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The ATLAS Inner Detector commissioning and calibration
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 787-821
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Tidskriftsartikel (refereegranskat)abstract
- The ATLAS Inner Detector is a composite tracking system consisting of silicon pixels, silicon strips and straw tubes in a 2 T magnetic field. Its installation was completed in August 2008 and the detector took part in data-taking with single LHC beams and cosmic rays. The initial detector operation, hardware commissioning and in-situ calibrations are described. Tracking performance has been measured with 7.6 million cosmic-ray events, collected using a tracking trigger and reconstructed with modular pattern-recognition and fitting software. The intrinsic hit efficiency and tracking trigger efficiencies are close to 100%. Lorentz angle measurements for both electrons and holes, specific energy-loss calibration and transition radiation turn-on measurements have been performed. Different alignment techniques have been used to reconstruct the detector geometry. After the initial alignment, a transverse impact parameter resolution of 22.1 +/- 0.9 mu m and a relative momentum resolution sigma (p) /p=(4.83 +/- 0.16)x10(-4) GeV(-1)xp (T) have been measured for high momentum tracks.
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| 17. |
- Aad, G., et al.
(författare)
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The ATLAS Simulation Infrastructure
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 823-874
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Tidskriftsartikel (refereegranskat)abstract
- The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.
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| 18. |
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| 19. |
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| 20. |
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| 21. |
- 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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| 22. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 23. |
- Aad, G., et al.
(författare)
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- 2013
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swepub:Mat__t (refereegranskat)
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| 24. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 25. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 26. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 27. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 28. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 29. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 30. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 31. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 32. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 33. |
- Aad, G., et al.
(författare)
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- 2012
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Tidskriftsartikel (refereegranskat)
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| 34. |
- Aad, G., et al.
(författare)
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- 2012
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swepub:Mat__t (refereegranskat)
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| 35. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 36. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 37. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 38. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 39. |
- Aad, G., et al.
(författare)
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- 2011
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Tidskriftsartikel (refereegranskat)
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| 40. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 41. |
- Aad, G., et al.
(författare)
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- 2011
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Tidskriftsartikel (refereegranskat)
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| 42. |
- Aad, G., et al.
(författare)
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- 2011
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Tidskriftsartikel (refereegranskat)
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| 43. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 44. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 45. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 46. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 47. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 48. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 49. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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| 50. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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