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- 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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- Luo, Yifei, et al.
(author)
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Technology Roadmap for Flexible Sensors
- 2023
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In: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
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Research review (peer-reviewed)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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- Jin, Ying-Hui, et al.
(author)
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Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19 : An evidence-based clinical practice guideline (updated version)
- 2020
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In: Military Medical Research. - : Springer Science and Business Media LLC. - 2054-9369. ; 7:1
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Journal article (peer-reviewed)abstract
- The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, coronavirus disease 2019 (COVID-19), affecting more than seventeen million people around the world. Diagnosis and treatment guidelines for clinicians caring for patients are needed. In the early stage, we have issued "A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)"; now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based guideline. We formed a working group of clinical experts and methodologists. The steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas: chemoprophylaxis, diagnosis, treatments, and discharge management. We searched the literature for direct evidence on the management of COVID-19, and assessed its certainty generated recommendations using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. Finally, we issued 34 statements. Among them, 6 were strong recommendations for, 14 were weak recommendations for, 3 were weak recommendations against and 11 were ungraded consensus-based statement. They covered topics of chemoprophylaxis (including agents and Traditional Chinese Medicine (TCM) agents), diagnosis (including clinical manifestations, reverse transcription-polymerase chain reaction (RT-PCR), respiratory tract specimens, IgM and IgG antibody tests, chest computed tomography, chest x-ray, and CT features of asymptomatic infections), treatments (including lopinavir-ritonavir, umifenovir, favipiravir, interferon, remdesivir, combination of antiviral drugs, hydroxychloroquine/chloroquine, interleukin-6 inhibitors, interleukin-1 inhibitors, glucocorticoid, qingfei paidu decoction, lianhua qingwen granules/capsules, convalescent plasma, lung transplantation, invasive or noninvasive ventilation, and extracorporeal membrane oxygenation (ECMO)), and discharge management (including discharge criteria and management plan in patients whose RT-PCR retesting shows SARS-CoV-2 positive after discharge). We also created two figures of these recommendations for the implementation purpose. We hope these recommendations can help support healthcare workers caring for COVID-19 patients.
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- Abbafati, Cristiana, et al.
(author)
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- 2020
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Journal article (peer-reviewed)
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| 7. |
- Xu, Jiawei, et al.
(author)
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A Memristor Model with Concise Window Function for Spiking Brain-Inspired Computation
- 2021
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In: 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS. - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- This paper proposes a concise window function to build a memristor model, simulating the widely-observed non-linear dopant drift phenomenon of the memristor. Exploiting the non-linearity, the memristor model is applied to the in-situ neuromorphic solution for a cortex-inspired spiking neural network (SNN), spike-based Bayesian Confidence Propagation Neural Network (BCPNN). The improved memristor model utilizing the proposed window function is able to retain the boundary effect and resolve the boundary lock and inflexibility problem, while it is simple in form that can facilitate large-scale neuromorphic model simulation. Compared with the state-of-the-art general memristor model, the proposed memristor model can achieve a 5.8x reduction of simulation time at a competitive fitting level in cortex-comparable large-scale software simulation. The evaluation results show an explicit similarity between the non-linear dopant drift phenomenon of the memristor and the BCPNN learning rule, and the memristor model is able to emulate the key traces of BCPNN with a correlation coefficient over 0.99.
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