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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
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
Quantitative and dynamic profiling of human gut core microbiota by real-time PCR
The human gut microbiota refers to a diverse community of microorganisms that symbiotically exist in the human intestinal system. Altered microbial communities have been linked to many human pathologies. However, there is a lack of rapid and efficient methods to assess gut microbiota signatures in practice. To address this, we established an appraisal system containing 45 quantitative real-time polymerase chain reaction (qPCR) assays targeting gut core microbes with high prevalence and/or abundance in the population. Through comparative genomic analysis, we selected novel species-specific genetic markers and primers for 31 of the 45 core microbes with no previously reported specific primers or whose primers needed improvement in specificity. We comprehensively evaluated the performance of the qPCR assays and demonstrated that they showed good sensitivity, selectivity, and quantitative linearity for each target. The limit of detection ranged from 0.1 to 1.0 pg/µL for the genomic DNA of these targets. We also demonstrated the high consistency (Pearson’s
r
= 0.8688,
P
< 0.0001) between the qPCR method and metagenomics next-generation sequencing (mNGS) method in analyzing the abundance of selected bacteria in 22 human fecal samples. Moreover, we quantified the dynamic changes (over 8 weeks) of these core microbes in 14 individuals using qPCR, and considerable stability was demonstrated in most participants, albeit with significant individual differences. Overall, this study enables the simple and rapid quantification of 45 core microbes in the human gut, providing a promising tool to understand the role of gut core microbiota in human health and disease.
Key points
•
A panel of original qPCR assays was developed to quantify human gut core microbes.
•
The qPCR assays were evaluated and compared with mNGS using real fecal samples.
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This method was used to dynamically profile the gut core microbiota in individuals.
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
Social interactions in the metaverse: Framework, initial evidence, and research roadmap
Real-time multisensory social interactions (RMSIs) between people are at the center of the metaverse, a new computer-mediated environment consisting of virtual “worlds” in which people act and communicate with each other in real-time via avatars. This research investigates whether RMSIs in the metaverse, when accessed through virtual-reality headsets, can generate more value for interactants in terms of interaction outcomes (interaction performance, evaluation, and emotional responses) than those on the two-dimensional (2D) internet (e.g., Zoom meetings). We combine theoretical logic with extensive field-experimental probes (which support the value-creation potential of the virtual-reality metaverse, but contradict its general superiority) to develop and refine a framework of how RMSIs in the metaverse versus on the 2D internet affect interaction outcomes through interactants’ intermediate conditions. The refined framework serves as foundation for a research roadmap on RMSIs in the metaverse, in which we highlight the critical roles of specific mediating and moderating forces along with interactional formats for future investigations of the metaverse and also name key business areas and societal challenges that deserve scholarly attention.
Journal Article