Recent advances in epigenomics in NSCLC: real-time detection and therapeutic implications

Epigenomics ◽  
2016 ◽  
Vol 8 (8) ◽  
pp. 1151-1167 ◽  
Author(s):  
Antonello Di Paolo ◽  
Marzia Del Re ◽  
Iacopo Petrini ◽  
Giuseppe Altavilla ◽  
Romano Danesi
Keyword(s):  
Real Time ◽  
Therapeutic Implications ◽  
Recent Advances ◽  
Real Time Detection

scholarly journals Recent advances in fast-scan cyclic voltammetry

The Analyst ◽  
2020 ◽  
Vol 145 (4) ◽  
pp. 1087-1102 ◽  
Author(s):  
Pumidech Puthongkham ◽  
B. Jill Venton
Keyword(s):  
Cyclic Voltammetry ◽  
Real Time ◽  
Fast Scan Cyclic Voltammetry ◽  
Recent Advances ◽  
Future Challenges ◽  
Real Time Detection

We reviewed recent advances and future challenges in fast-scan cyclic voltammetry for real-time detection of neurotransmitters.

scholarly journals Recent Advances in Aptamer Discovery and Applications

Molecules ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 941 ◽  
Author(s):  
Yang Zhang ◽  
Bo Lai ◽  
Mario Juhas
Keyword(s):  
Food Safety ◽  
Real Time ◽  
Basic Research ◽  
Therapeutic Agents ◽  
Clinical Diagnostics ◽  
Future Directions ◽  
Single Stranded Dna ◽  
High Affinity ◽  
Recent Advances ◽  
Real Time Detection

Aptamers are short, single-stranded DNA, RNA, or synthetic XNA molecules that can be developed with high affinity and specificity to interact with any desired targets. They have been widely used in facilitating discoveries in basic research, ensuring food safety and monitoring the environment. Furthermore, aptamers play promising roles as clinical diagnostics and therapeutic agents. This review provides update on the recent advances in this rapidly progressing field of research with particular emphasis on generation of aptamers and their applications in biosensing, biotechnology and medicine. The limitations and future directions of aptamers in target specific delivery and real-time detection are also discussed.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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