Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm 2 /min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.
Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a unique geometry and, thus, a unique optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, light-weight and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and non-iterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ~28 µs, which is orders of magnitude faster compared to other computational spectroscopy approaches. When blindly tested on unseen new spectra (N = 14,648) with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable and sensitive high-resolution spectroscopy tools.
Problem definition: For many supply chains, deep-tier suppliers, due to their small size and lack of access to capital, are most vulnerable to disruptions. We study the use of advance payment (AP) as a financing instrument in a multitier supply chain to mitigate the supply disruption risk in a traditional system (with limited visibility) and a blockchain-enabled system (with perfect visibility). The main goal of this paper is to shed light on how blockchain adoption impacts agents’ operational and financial decisions as well as profit levels in a multitier supply chain. Academic/practical relevance: Traditionally, because of the limited visibility in the deep tiers, powerful downstream manufacturers’ financing schemes offered to their immediate upstream suppliers are not effective in instilling capital into the deep tiers. Advancements in blockchain technology improve the supply chain visibility and enable the manufacturer to better devise deep-tier financing to improve supply chain resilience. Methodology: We develop a three-tier supply chain model and take a game-theoretic approach to compare how blockchain-enabled deep-tier financing schemes affect a financially constrained supply chain’s optimal risk-mitigation and financial strategies. Results: We find that although improved visibility via blockchain adoption can help the manufacturer make informed supply chain financing decisions, whether it can benefit all supply chain members depends on the financing schemes in use. Blockchain-enabled delegate financing increases risk-mitigation investments and benefits all three tiers of the supply chain only when the tier 2 supplier is severely capital-constrained with the working capital below a threshold. Because delegate financing endows the intermediary tier 1 supplier with leverage over the manufacturer, the inefficiency inhibits an all-win outcome when the tier 2 supplier is not severely capital-constrained. Blockchain-enabled cross-tier direct financing exhibits a compelling performance as it always leads to win-win-win outcomes (and is thus ubiquitously implementable) regardless of the suppliers’ working capital profile. Managerial implications: Our insights help firms assess opportunities and challenges associated with enhancing supply chain visibility via blockchain adoption.
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h. This...
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