| 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. |
- 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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| 3. |
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| 4. |
- Chen, Kun-Chih, et al.
(author)
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A Lego-Based Neural Network Design Methodology With Flexible NoC
- 2021
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In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2156-3357 .- 2156-3365. ; 11:4, s. 711-724
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Journal article (peer-reviewed)abstract
- Deep Neural Networks (DNNs) have shown superiority in solving the problems of classification and recognition in recent years. However, DNN hardware implementation is challenging due to the high computational complexity and diverse dataflow in different DNN models. 'lb mitigate this design challenge, a large body of research has focused on accelerating specific DNN models or layers and proposed dedicated designs. However, dedicated designs for specific DNN models or layers limit the design flexibility. In this work, we take advantage of the similarity among different DNN models and propose a novel Lego-based Deep Neural Network on a Chip (DNNoC) design methodology. We work on common neural computing units (e.g., multiply-accumulation and pooling) and create some neuron computing units called NeuLego processing elements (NeuLego(PE)(s)). These NeuLego(PE)(s) are then interconnected using a flexible Network-on-Chip (NoC), allowing to construct different DNN models. To support large-scale DNN models, we enhance the reusability of each NeuLego(PE) by proposing a Lego placement method. The proposed design methodology allows leveraging different DNN model implementations, helping to reduce implementation cost and time-to-market. Compared with the conventional approaches, the proposed approach can improve the average throughput by 2,802% for given DNN models. Besides, the corresponding hardware is implemented to validate the proposed design methodology, showing on average 12,523% hardware efficiency improvement by considering the throughput and area overhead simultaneously.
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| 5. |
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| 6. |
- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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| 7. |
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| 8. |
- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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