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US CDC Real-Time Reverse Transcription PCR Panel for Detection of Severe Acute Respiratory Syndrome Coronavirus 2
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the etiologic agent associated with coronavirus disease, which emerged in late 2019. In response, we developed a diagnostic panel consisting of 3 real-time reverse transcription PCR assays targeting the nucleocapsid gene and evaluated use of these assays for detecting SARS-CoV-2 infection. All assays demonstrated a linear dynamic range of 8 orders of magnitude and an analytical limit of detection of 5 copies/reaction of quantified RNA transcripts and 1 x 10
50% tissue culture infectious dose/mL of cell-cultured SARS-CoV-2. All assays performed comparably with nasopharyngeal and oropharyngeal secretions, serum, and fecal specimens spiked with cultured virus. We obtained no false-positive amplifications with other human coronaviruses or common respiratory pathogens. Results from all 3 assays were highly correlated during clinical specimen testing. On February 4, 2020, the Food and Drug Administration issued an Emergency Use Authorization to enable emergency use of this panel.
Journal Article
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation
Low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, leading to a considerable decrease in accuracy. We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for real-time semantic segmentation. For this purpose, we propose an efficient and effective architecture with a good trade-off between speed and accuracy, termed Bilateral Segmentation Network (BiSeNet V2). This architecture involves the following: (i) A detail branch, with wide channels and shallow layers to capture low-level details and generate high-resolution feature representation; (ii) A semantics branch, with narrow channels and deep layers to obtain high-level semantic context. The detail branch has wide channel dimensions and shallow layers, while the semantics branch has narrow channel dimensions and deep layers. Due to the reduction in the channel capacity and the use of a fast-downsampling strategy, the semantics branch is lightweight and can be implemented by any efficient model. We design a guided aggregation layer to enhance mutual connections and fuse both types of feature representation. Moreover, a booster training strategy is designed to improve the segmentation performance without any extra inference cost. Extensive quantitative and qualitative evaluations demonstrate that the proposed architecture shows favorable performance compared to several
state-of-the-art
real-time semantic segmentation approaches. Specifically, for a
2048
×
1024
input, we achieve 72.6% Mean IoU on the Cityscapes test set with a speed of 156 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods, yet we achieve better segmentation accuracy. The code and trained models are available online at
https://git.io/BiSeNet
.
Journal Article
Skin‐Inspired Piezoelectric Tactile Sensor Array with Crosstalk‐Free Row+Column Electrodes for Spatiotemporally Distinguishing Diverse Stimuli
Real‐time detection and differentiation of diverse external stimuli with one tactile senor remains a huge challenge and largely restricts the development of electronic skins. Although different sensors have been described based on piezoresistivity, capacitance, and triboelectricity, and these devices are promising for tactile systems, there are few, if any, piezoelectric sensors to be able to distinguish diverse stimuli in real time. Here, a human skin‐inspired piezoelectric tactile sensor array constructed with a multilayer structure and row+column electrodes is reported. Integrated with a signal processor and a logical algorithm, the tactile sensor array achieves to sense and distinguish the magnitude, positions, and modes of diverse external stimuli, including gentle slipping, touching, and bending, in real time. Besides, the unique design overcomes the crosstalk issues existing in other sensors. Pressure sensing and bending sensing tests show that the proposed tactile sensor array possesses the characteristics of high sensitivity (7.7 mV kPa−1), long‐term durability (80 000 cycles), and rapid response time (10 ms) (less than human skin). The tactile sensor array also shows a superior scalability and ease of massive fabrication. Its ability of real‐time detection and differentiation of diverse stimuli for health monitoring, detection of animal movements, and robots is demonstrated.
Human skin‐inspired piezoelectric tactile sensor array can sense and distinguish the magnitude, positions, and modes of diverse external stimuli in real time. The dual‐layer comb structures of the sensor array with row+column electrodes eliminate crosstalk and reduce the number of connection wires. It excavates enormous applications in various settings, such as health monitoring, detection of animal movements, and robots.
Journal Article
Real-time electrocardiogram P-QRS-T detection–delineation algorithm based on quality-supported analysis of characteristic templates
Abstract The main objective of this study is to introduce a simple, low-latency, and accurate algorithm for real-time detection of P-QRS-T waves in the electrocardiogram (ECG) signal. In the proposed method, real-time signal preprocessing, which includes high frequency noise filtering and baseline wander reduction, is performed by applying discrete wavelet transform (DWT). A method based on signal first-order derivative and adaptive threshold adjustment is employed for real-time detection of the QRS complex. Moreover, detection and delineation of P- and T-waves are achieved by correlation analysis conducted between signal and their templates. Besides, signal quality is investigated online, and if the quality of the analysis window is unacceptable, then the algorithm will guess (estimate) the locations of P- and T-waves. The operating characteristics of the proposed algorithm are evaluated by its implementation to an artificially generated ECG signal whose quality is adjustable from the best (Quality, 100%) to the worst (Quality, ≤40%) cases based on the random-walk noise theory. The algorithm was applied to the MIT-BIH arrhythmia database, QT database, and Physionet/CinC challenge 2011competition database. The obtained results, which were based on the QT database, showed sensitivity and positive predictivity of Se=99.63% and P+=99.83%, Se=99.83% and P+=99.98%, and Se=99.74% and P+=99.89% for the detection of P-, QRS-, and T-waves, respectively, and the obtained results, which were based on the MIT-BIH arrhythmia database, showed Se=99.81% and P+=99.70% for the detection of the QRS complex. Moreover, it will be shown that the results of the proposed method are reliable for a minimum signal quality value of 70%. According to numerical assessments, 8-ms after the occurrence of R-wave, its location will be identified by the computer code of the proposed algorithm. This parameter is 198-ms and 177-ms for P- and T-waves, respectively.
Journal Article
Cloud analytics with Google Cloud Platform : an end-to-end guide to processing and analyzing big data using Google Cloud Platform
\"With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you're planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation.\"--Publisher description.
Book
Comparative analysis of dynamic cell viability, migration and invasion assessments by novel real-time technology and classic endpoint assays
Cell viability and motility comprise ubiquitous mechanisms involved in a variety of (patho)biological processes including cancer. We report a technical comparative analysis of the novel impedance-based xCELLigence Real-Time Cell Analysis detection platform, with conventional label-based endpoint methods, hereby indicating performance characteristics and correlating dynamic observations of cell proliferation, cytotoxicity, migration and invasion on cancer cells in highly standardized experimental conditions.
Dynamic high-resolution assessments of proliferation, cytotoxicity and migration were performed using xCELLigence technology on the MDA-MB-231 (breast cancer) and A549 (lung cancer) cell lines. Proliferation kinetics were compared with the Sulforhodamine B (SRB) assay in a series of four cell concentrations, yielding fair to good correlations (Spearman's Rho 0.688 to 0.964). Cytotoxic action by paclitaxel (0-100 nM) correlated well with SRB (Rho>0.95) with similar IC(50) values. Reference cell migration experiments were performed using Transwell plates and correlated by pixel area calculation of crystal violet-stained membranes (Rho 0.90) and optical density (OD) measurement of extracted dye (Rho>0.95). Invasion was observed on MDA-MB-231 cells alone using Matrigel-coated Transwells as standard reference method and correlated by OD reading for two Matrigel densities (Rho>0.95). Variance component analysis revealed increased variances associated with impedance-based detection of migration and invasion, potentially caused by the sensitive nature of this method.
The xCELLigence RTCA technology provides an accurate platform for non-invasive detection of cell viability and motility. The strong correlations with conventional methods imply a similar observation of cell behavior and interchangeability with other systems, illustrated by the highly correlating kinetic invasion profiles on different platforms applying only adapted matrix surface densities. The increased sensitivity however implies standardized experimental conditions to minimize technical-induced variance.
Journal Article