scholarly journals Applications of Converged Various Forces for Detection of Biomolecules and Novelty of Dielectrophoretic Force in the Applications

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3242 ◽  
Author(s):  
Seungjun Lee ◽  
Seong Min Roh ◽  
Eunji Lee ◽  
Yejin Park ◽  
Byung Chul Lee ◽  
...  
Keyword(s):  
Deoxyribonucleic Acid ◽  
State Of The Art ◽  
Selective Detection ◽  
Microfluidic Chips ◽  
Dielectrophoretic Force ◽  
Advanced Technologies ◽  
Highly Sensitive ◽  
Different Types ◽  
Detection Of Biomolecules ◽  
Electrothermal Flow

Since separation of target biomolecules is a crucial step for highly sensitive and selective detection of biomolecules, hence, various technologies have been applied to separate biomolecules, such as deoxyribonucleic acid (DNA), protein, exosome, virus, etc. Among the various technologies, dielectrophoresis (DEP) has the significant advantage that the force can provide two different types of forces, attractive and repulsive DEP force, through simple adjustment in frequency or structure of microfluidic chips. Therefore, in this review, we focused on separation technologies based on DEP force and classified various separation technologies. First, the importance of biomolecules, general separation methods and various forces including DEP, electrophoresis (EP), electrothermal flow (ETF), electroosmosis (EO), magnetophoresis, acoustophoresis (ACP), hydrodynamic, etc., was described. Then, separating technologies applying only a single DEP force and dual force, moreover, applying other forces simultaneously with DEP force were categorized. In addition, advanced technologies applying more than two different kinds of forces, namely complex force, were introduced. Overall, we critically reviewed the state-of-the-art of converged various forces for detection of biomolecules with novelty of DEP.

Carbon nanoparticle-protected aptamers for highly sensitive and selective detection of biomolecules based on nuclease-assisted target recycling signal amplification

2014 ◽  
Vol 50 (57) ◽  
pp. 7646-7648 ◽  
Author(s):  
Xiaoyan Lin ◽  
Liang Cui ◽  
Yishun Huang ◽  
Ya Lin ◽  
Yi Xie ◽  
...  
Keyword(s):  
Signal Amplification ◽  
Carbon Nanoparticles ◽  
Sensitive Detection ◽  
Selective Detection ◽  
Carbon Nanoparticle ◽  
Target Recycling ◽  
Amplification Method ◽  
Highly Sensitive ◽  
Detection Of Biomolecules ◽  
Highly Sensitive Detection

A nuclease-assisted target recycling signal amplification method based on carbon nanoparticles for highly sensitive detection of biomolecules was developed.

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.

AIE-active Schiff base compounds as fluorescent probe for the highly sensitive and selective detection of Al3+ ions

2021 ◽  
Vol 233 ◽  
pp. 117911
Author(s):  
Minmin Wang ◽  
Linxia Lu ◽  
Wenwu Song ◽  
Xunyue Wang ◽  
Tongming Sun ◽  
...  
Keyword(s):  
Schiff Base ◽  
Fluorescent Probe ◽  
Selective Detection ◽  
Highly Sensitive

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Highly sensitive and selective colorimetric detection of dual metal ions (Hg2+ and Sn2+) in water: an eco-friendly approach

RSC Advances ◽  
2021 ◽  
Vol 11 (24) ◽  
pp. 14700-14709
Author(s):  
Rintumoni Paw ◽  
Moushumi Hazarika ◽  
Purna K. Boruah ◽  
Amlan Jyoti Kalita ◽  
Ankur K. Guha ◽  
...  
Keyword(s):  
Metal Ions ◽  
Ag Nanoparticles ◽  
Limit Of Detection ◽  
Colorimetric Detection ◽  
Garlic Extract ◽  
Selective Detection ◽  
Highly Sensitive ◽  
Dual Metal

Synthesis of Ag nanoparticles using Allin based garlic extract for highly sensitive and selective detection of metal ions Hg2+ and Sn2+ in water. The limit of detection (LoD) for Hg2+ and Sn2+ ions were found as 15.7 nM and 11.25 nM respectively.

Sign in / Sign up

Export Citation Format

Share Document