| 1. |
- 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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| 2. |
- Teng, Yiran, et al.
(författare)
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Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial]
- 2024
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 16:7, s. C76-C87
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Tidskriftsartikel (refereegranskat)abstract
- The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements.
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| 3. |
- Teng, Yiran, et al.
(författare)
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DRL-based RMSCA for SDM Networks with Core Switching in Multi-Core Fibres
- 2023
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Ingår i: Proceedings of the 2023 Photonics in Switching and Computing. - 9798350323702
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Konferensbidrag (refereegranskat)abstract
- We designed a DRL-based routing, modulation, spectrum, and core assignment algorithm to fully exploit the core switching capabilities of space division multiplexing networks with multi-core fibres. The proposed method shows up to 53\% better blocking probability performance compared to RMSCA benchmarks in different traffic loads.
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