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1. Ultrasensitive Detection of Dopamine, IL‐6 and SARS‐CoV‐2 Proteins on Crumpled Graphene FET Biosensor

   https://doi.org/10.1002/admt.202100712  

   Hwang, Michael_Taeyoung; Park, Insu; Heiranian, Mohammad; Taqieddin, Amir; You, Seungyong; Faramarzi, Vahid; Pak, Angela_A; van_der_Zande, Arend_M; Aluru, Narayana_R; Bashir, Rashid (August 2021, Advanced Materials Technologies)

   Abstract Universal platforms for biomolecular analysis using label‐free sensing modalities can address important diagnostic challenges. Electrical field effect‐sensors are an important class of devices that can enable point‐of‐care sensing by probing the charge in the biological entities. Use of crumpled graphene for this application is especially promising. It is previously reported that the limit of detection (LoD) on electrical field effect‐based sensors using DNA molecules on the crumpled graphene FET (field‐effect transistor) platform. Here, the crumpled graphene FET‐based biosensing of important biomarkers including small molecules and proteins is reported. The performance of devices is systematically evaluated and optimized by studying the effect of the crumpling ratio on electrical double layer (EDL) formation and bandgap opening on the graphene. It is also shown that a small and electroneutral molecule dopamine can be captured by an aptamer and its conformation change induced electrical signal changes. Three kinds of proteins were captured with specific antibodies including interleukin‐6 (IL‐6) and two viral proteins. All tested biomarkers are detectable with the highest sensitivity reported on the electrical platform. Significantly, two COVID‐19 related proteins, nucleocapsid (N‐) and spike (S‐) proteins antigens are successfully detected with extremely low LoDs. This electrical antigen tests can contribute to the challenge of rapid, point‐of‐care diagnostics. 

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2. Scan-based eye tracking measures are predictive of workload transition performance

   Shannon P Devlin, Noelle L (November 2022, Applied Ergonomics)

   Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research. 

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3. Scan-based eye tracking measures are predictive of workload transition performance

   https://doi.org/10.1016/j.apergo.2022.103829  

   Devlin, S.P; Brown, N.L.; Drollinger, S.; Sibley, C.; Alami, J.; Riggs, S.L. (November 2022, Applied Ergonomics)

   Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research. 

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4. Continuous Levels of Detail for Light Field Networks

   Li, David; Feng, Brandon; Varshney, Amitabh (November 2023, Proceedings of the 34th British Machine Vision Conference (BMVC 2023))

   Recently, several approaches have emerged for generating neural representations with multiple levels of detail (LODs). LODs can improve the rendering by using lower resolutions and smaller model sizes when appropriate. However, existing methods generally focus on a few discrete LODs which suffer from aliasing and flicker artifacts as details are changed and limit their granularity for adapting to resource limitations. In this paper, we propose a method to encode light field networks with continuous LODs, allowing for finely tuned adaptations to rendering conditions. Our training procedure uses summed-area table filtering allowing efficient and continuous filtering at various LODs. Furthermore, we use saliency-based importance sampling which enables our light field networks to distribute their capacity, particularly limited at lower LODs, towards representing the details viewers are most likely to focus on. Incorporating continuous LODs into neural representations enables progressive streaming of neural representations, decreasing the latency and resource utilization for rendering. 

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5. Digital Microfluidics for the Detection of Selected Inorganic Ions in Aerosols

   https://doi.org/10.3390/s20051281  

   Huang, Shuquan; Connolly, Jessica; Khlystov, Andrei; Fair, Richard B. (March 2020, Sensors)

   A prototype aerosol detection system is presented that is designed to accurately and quickly measure the concentration of selected inorganic ions in the atmosphere. The aerosol detection system combines digital microfluidics technology, aerosol impaction and chemical detection integrated on the same chip. Target compounds are the major inorganic aerosol constituents: sulfate, nitrate and ammonium. The digital microfluidic system consists of top and bottom plates that sandwich a fluid layer. Nozzles for an inertial impactor are built into the top plate according to known, scaling principles. The deposited air particles are densely concentrated in well-defined deposits on the bottom plate containing droplet actuation electrodes of the chip in fixed areas. The aerosol collection efficiency for particles larger than 100 nm in diameter was higher than 95%. After a collection phase, deposits are dissolved into a scanning droplet. Due to a sub-microliter droplet size, the obtained extract is highly concentrated. Droplets then pass through an air/oil interface on chip for colorimetric analysis by spectrophotometry using optical fibers placed between the two plates of the chip. To create a standard curve for each analyte, six different concentrations of liquid standards were chosen for each assay and dispensed from on-chip reservoirs. The droplet mixing was completed in a few seconds and the final droplet was transported to the detection position as soon as the mixing was finished. Limits of detection (LOD) in the final droplet were determined to be 11 ppm for sulfate and 0.26 ppm for ammonium. For nitrate, it was impossible to get stable measurements. The LOD of the on-chip measurements for sulfate was close to that obtained by an off-chip method using a Tecan spectrometer. LOD of the on-chip method for ammonium was about five times larger than what was obtained with the off-chip method. For the current impactor collection air flow (1 L/min) and 1 h collection time, the converted LODs in air were: 0.275 μg/m3 for sulfate, 6.5 ng/m3 for ammonium, sufficient for most ambient air monitoring applications. 

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