scholarly journals High-Performance Passive Plasma Separation on OSTE Pillar Forest

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 355
Author(s):  
Zhiqing Xiao ◽  
Lexin Sun ◽  
Yuqian Yang ◽  
Zitao Feng ◽  
Sihan Dai ◽  
...  
Keyword(s):  
Whole Blood ◽  
High Performance ◽  
State Of The Art ◽  
Lateral Flow ◽  
Protein Recovery ◽  
Biomarker Detection ◽  
Plasma Separation ◽  
Filtration Membrane ◽  
Uniform Microstructure ◽  
Lateral Flow Tests

Plasma separation is of high interest for lateral flow tests using whole blood as sample liquids. Here, we built a passive microfluidic device for plasma separation with high performance. This device was made by blood filtration membrane and off-stoichiometry thiol–ene (OSTE) pillar forest. OSTE pillar forest was fabricated by double replica moldings of a laser-cut polymethylmethacrylate (PMMA) mold, which has a uniform microstructure. This device utilized a filtration membrane to separate plasma from whole blood samples and used hydrophilic OSTE pillar forest as the capillary pump to propel the plasma. The device can be used to separate blood plasma with high purity for later use in lateral flow tests. The device can process 45 μL of whole blood in 72 s and achieves a plasma separation yield as high as 60.0%. The protein recovery rate of separated plasma is 85.5%, which is on par with state-of-the-art technologies. This device can be further developed into lateral flow tests for biomarker detection in whole blood.

scholarly journals Determination of 19 Psychoactive Substances in Premortem and Postmortem Whole Blood Samples Using Ultra-High-Performance Liquid Chromatography–Tandem Mass Spectrometry

Separations ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 78
Author(s):  
Sevasti Karampela ◽  
Jessica Smith ◽  
Irene Panderi
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Formic Acid ◽  
Whole Blood ◽  
High Performance ◽  
Tandem Mass ◽  
Psychoactive Substances ◽  
Blood Samples ◽  
Whole Blood Samples

An ever-increasing need exists within the forensic laboratories to develop analytical processes for the qualitative and quantitative determination of a broad spectrum of new psychoactive substances. Phenylethylamine derivatives are among the major classes of psychoactive substances available on the global market and include both amphetamine analogues and synthetic cathinones. In this work, an ultra-high-performance liquid chromatography-positive ion electrospray ionization tandem mass spectrometric method (UHPLC-ESI-MS/MS) has been developed and fully validated for the determination of 19 psychoactive substances, including nine amphetamine-type stimulants and 10 synthetic cathinone derivatives, in premortem and postmortem whole blood. The assay was based on the use of 1 mL premortem or postmortem whole blood, following solid phase extraction prior to the analysis. The separation was achieved on a Poroshell 120 EC-C18 analytical column with a gradient mobile phase of 0.1% formic acid in acetonitrile and 0.1% formic acid in water in 9 min. The dynamic multiple reaction monitoring used in this work allowed for limit of detection (LOD) and lower limit of quantitation (LOQ) values of 0.5 and 2 ng mL−1, respectively, for all analytes both in premortem and postmortem whole blood samples. A quadratic calibration model was used for the 12 quantitative analytes over the concentration range of 20–2000 ng mL−1, and the method was shown to be precise and accurate both in premortem and postmortem whole blood. The method was applied to the analysis of real cases and proved to be a valuable tool in forensic and clinical toxicology.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Integrating high-performing electrochemical transducers in lateral flow assay

2021 ◽  
Author(s):  
Antonia Perju ◽  
Nongnoot Wongkaew
Keyword(s):  
High Performance ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Lateral Flow ◽  
Analytical Performance ◽  
Label Free ◽  
High Performing ◽  
Lateral Flow Assays ◽  
Electrochemical Transducers

AbstractLateral flow assays (LFAs) are the best-performing and best-known point-of-care tests worldwide. Over the last decade, they have experienced an increasing interest by researchers towards improving their analytical performance while maintaining their robust assay platform. Commercially, visual and optical detection strategies dominate, but it is especially the research on integrating electrochemical (EC) approaches that may have a chance to significantly improve an LFA’s performance that is needed in order to detect analytes reliably at lower concentrations than currently possible. In fact, EC-LFAs offer advantages in terms of quantitative determination, low-cost, high sensitivity, and even simple, label-free strategies. Here, the various configurations of EC-LFAs published are summarized and critically evaluated. In short, most of them rely on applying conventional transducers, e.g., screen-printed electrode, to ensure reliability of the assay, and additional advances are afforded by the beneficial features of nanomaterials. It is predicted that these will be further implemented in EC-LFAs as high-performance transducers. Considering the low cost of point-of-care devices, it becomes even more important to also identify strategies that efficiently integrate nanomaterials into EC-LFAs in a high-throughput manner while maintaining their favorable analytical performance.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Communication Failure Resilient Distributed Neural Network for Edge Devices

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1614
Author(s):  
Jonghun Jeong ◽  
Jong Sung Park ◽  
Hoeseok Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
State Of The Art ◽  
Wearable Devices ◽  
Communication Failure ◽  
Canadian Institute ◽  
Multiple Devices ◽  
Knowledge Distillation ◽  
Partitioning Technique

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

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