| 1. |
- 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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| 2. |
- Kristan, Matej, et al.
(author)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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In: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 3. |
- Murphy, Shannon G., et al.
(author)
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Class a penicillin-binding protein-mediated cell wall synthesis promotes structural integrity during peptidoglycan endopeptidase insufficiency in vibrio cholerae
- 2021
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In: mBio. - : American Society for Microbiology. - 2161-2129 .- 2150-7511. ; 12:2
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Journal article (peer-reviewed)abstract
- The bacterial cell wall is composed primarily of peptidoglycan (PG), a poly-aminosugar that is essential to sustain cell shape, growth, and structural integrity. PG is synthesized by class A/B penicillin-binding proteins (a/bPBPs) and shape, elongation, di-vision, and sporulation (SEDS) proteins like RodA (as part of the Rod system cell elongation machinery) and degraded by "autolytic" enzymes to accommodate growth processes. It is thought that autolysins (particularly endopeptidases [EPs]) are required for PG synthesis and incorporation by creating gaps that are patched and paved by PG syn-thases, but the exact relationship between autolysins and PG synthesis remains incom-pletely understood. Here, we have probed the consequences of EP depletion for PG synthesis in the diarrheal pathogen Vibrio cholerae. We found that EP depletion resulted in severe morphological and division defects, but these cells continued to increase in mass and aberrantly incorporated new cell wall material. Mass increase proceeded in the presence of Rod system inhibitors, but cells lysed upon inhibition of aPBPs, suggesting that aPBPs are required for structural integrity under these conditions. The Rod system, although not essential for the observed mass increase, remained functional even after prolonged EP depletion. Last, heterologous expression of an EP from Neisseria gonorrhoeae fully complemented growth and morphology of an EP-insufficient V. cholerae, highlighting the possibility that the PG synthases may not necessarily function via direct interaction with EPs. Overall, our findings suggest that during EP insufficiency in V. cholerae, aPBPs become essential for structural integrity while the Rod system is unable to promote proper cell expansion.IMPORTANCE: Synthesis and turnover of the bacterial cell wall must be tightly coordinated to avoid structural integrity failure and cell death. Details of this coordination are poorly understood, particularly if and how cell wall turnover enzymes are required for the activity of the different cell wall synthesis machines, the aPBPs and the Rod system. Our results suggest that in Vibrio cholerae, one class of turnover enzymes, the endopeptidases, are necessary for proper cell elongation and division. aPBPs become essential for maintaining structural integrity during EP insufficiency, while the Rod system remains active but contributes little to cell expansion under these conditions. Our results suggest that aPBPs are more versatile than the Rod system in their ability to recognize cell wall gaps formed by autolysins other than the major endopeptidases, adding to our understanding of the coordination between autolysins and cell wall synthases. A detailed understanding of autolysin biology may promote the development of antibiotics that target these essential turnover processes.
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| 4. |
- Zhao, Ziyi, et al.
(author)
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3D Pose Based Feedback for Physical Exercises
- 2023
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In: ACCV 2022. - Cham : Springer Nature. ; , s. 189-205
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Conference paper (peer-reviewed)abstract
- Unsupervised self-rehabilitation exercises and physical training can cause serious injuries if performed incorrectly. We introduce a learning-based framework that identifies the mistakes made by a user and proposes corrective measures for easier and safer individual training. Our framework does not rely on hard-coded, heuristic rules. Instead, it learns them from data, which facilitates its adaptation to specific user needs. To this end, we use a Graph Convolutional Network (GCN) architecture acting on the user's pose sequence to model the relationship between the body joints trajectories. To evaluate our approach, we introduce a dataset with 3 different physical exercises. Our approach yields 90.9% mistake identification accuracy and successfully corrects 94.2% of the mistakes. Our code and dataset are available at https://github.com/Jacoo-Zhao/3D-Pose-Based-Feedback-For-Physical-Exercises.
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