1. |
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- 2019
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Journal article (peer-reviewed)
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2. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)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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6. |
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7. |
- Klionsky, Daniel J., et al.
(author)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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In: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Research review (peer-reviewed)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. |
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9. |
- Aad, G., et al.
(author)
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- 2013
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swepub:Mat__t (peer-reviewed)
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10. |
- Aad, G., et al.
(author)
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- 2012
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Journal article (peer-reviewed)
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11. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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12. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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13. |
- Kristan, Matej, et al.
(author)
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The first visual object tracking segmentation VOTS2023 challenge results
- 2023
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In: 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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Conference paper (peer-reviewed)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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14. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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15. |
- Aad, G., et al.
(author)
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- 2011
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Journal article (peer-reviewed)
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16. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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17. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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18. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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19. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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20. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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21. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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22. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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23. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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24. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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25. |
- Aad, G., et al.
(author)
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- 2012
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swepub:Mat__t (peer-reviewed)
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26. |
- Aad, G., et al.
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- 2011
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swepub:Mat__t (peer-reviewed)
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27. |
- Aad, G., et al.
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- 2011
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swepub:Mat__t (peer-reviewed)
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28. |
- Aad, G., et al.
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- 2011
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swepub:Mat__t (peer-reviewed)
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29. |
- Aad, G., et al.
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- 2011
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swepub:Mat__t (peer-reviewed)
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30. |
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swepub:Mat__t (peer-reviewed)
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31. |
- Aad, G., et al.
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- 2012
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swepub:Mat__t (peer-reviewed)
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32. |
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swepub:Mat__t (peer-reviewed)
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33. |
- Aad, G., et al.
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- 2012
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Journal article (peer-reviewed)
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34. |
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- 2012
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swepub:Mat__t (peer-reviewed)
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swepub:Mat__t (peer-reviewed)
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36. |
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swepub:Mat__t (peer-reviewed)
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37. |
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swepub:Mat__t (peer-reviewed)
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38. |
- Aad, G., et al.
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- 2012
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swepub:Mat__t (peer-reviewed)
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39. |
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- 2011
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swepub:Mat__t (peer-reviewed)
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40. |
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- 2011
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swepub:Mat__t (peer-reviewed)
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41. |
- Aad, G., et al.
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- 2012
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Journal article (peer-reviewed)
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42. |
- Aad, G., et al.
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- 2011
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Journal article (peer-reviewed)
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43. |
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swepub:Mat__t (peer-reviewed)
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44. |
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swepub:Mat__t (peer-reviewed)
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45. |
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swepub:Mat__t (peer-reviewed)
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46. |
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swepub:Mat__t (peer-reviewed)
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47. |
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Journal article (peer-reviewed)
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48. |
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Journal article (peer-reviewed)
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49. |
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swepub:Mat__t (peer-reviewed)
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swepub:Mat__t (peer-reviewed)
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