scholarly journals Detection of cellular microRNAs with programmable DNA nanoswitches

2018 ◽  
Author(s):  
Arun Richard Chandrasekaran ◽  
Molly MacIsaac ◽  
Paromita Dey ◽  
Oksana Levchenko ◽  
Lifeng Zhou ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Biological Sample ◽  
Medical Diagnostics ◽  
Northern Blotting ◽  
Single Step ◽  
Detection Methods ◽  
Biological Research ◽  
Clinical Tool ◽  
Single Nucleotide ◽  
Detection Techniques

AbstractMicroRNAs are short non-coding regulatory RNAs that are increasingly used as disease biomarkers. Detection of microRNAs can be arduous and expensive, and often requires amplification, labeling, or radioactive probes. Here we report a single-step, non-enzymatic detection assay using conformationally responsive DNA nanoswitches. Termed miRacles (microRNAactivatedconditionallooping ofengineeredswitches), our assay has sub-attomole sensitivity and single-nucleotide specificity using an agarose gel electrophoresis readout. We detect cellular microRNAs from nanogram-scale RNA extracts of differentiating muscle cells, and demonstrate multiplexed detection of several microRNAs from one biological sample. We demonstrate one-hour detection without expensive equipment or reagents, making this assay a compelling alternative to qPCR and Northern blotting.Significance statementDetection of microRNAs play a key role in biological research and medical diagnostics, and current detection methods are expensive and require sophisticated processes. We presentmicroRNAactivatedconditionallooping ofengineeredswitches (miRacles), a mix-and-read strategy that is based on conformational changes of DNA nanoswitches upon binding a target microRNA. MiRacles has a sensitivity of ∼4 copies/cell and specificity of a single nucleotide, and can be performed in one hour at a fraction of the cost of traditional microRNA detection techniques. Our method can also be multiplexed to detect multiple microRNAs from one biological sample. The minimalistic miRacles assay has immediate application in biomedical research and longer term potential as a clinical tool.

scholarly journals Cellular microRNA detection with miRacles: microRNA- activated conditional looping of engineered switches

2019 ◽  
Vol 5 (3) ◽  
pp. eaau9443 ◽  
Author(s):  
Arun Richard Chandrasekaran ◽  
Molly MacIsaac ◽  
Paromita Dey ◽  
Oksana Levchenko ◽  
Lifeng Zhou ◽  
...  
Keyword(s):  
Quantitative Polymerase Chain Reaction ◽  
Northern Blotting ◽  
Single Step ◽  
Single Nucleotide ◽  
Disease Biomarkers ◽  
Microrna Detection ◽  
Detection Assay ◽  
Polymerase Chain ◽  
Regulatory Rnas ◽  
Nucleotide Specificity

MicroRNAs are short noncoding regulatory RNAs that are increasingly used as disease biomarkers. Detection of microRNAs can be arduous and expensive and often requires amplification, labeling, or radioactive probes. Here, we report a single-step, nonenzymatic microRNA detection assay using conformationally responsive DNA nanoswitches. Termed miRacles (microRNA-activated conditional looping of engineered switches), our assay has subattomole sensitivity and single-nucleotide specificity using an agarose gel electrophoresis readout. We detect cellular microRNAs from nanogram-scale RNA extracts of differentiating muscle cells and multiplex our detection for several microRNAs from one biological sample. We demonstrate 1-hour detection without expensive equipment or reagents, making this assay a compelling alternative to quantitative polymerase chain reaction and Northern blotting.

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

scholarly journals Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

2021 ◽  
Vol 11 (12) ◽  
pp. 5685
Author(s):  
Hosam Aljihani ◽  
Fathy Eassa ◽  
Khalid Almarhabi ◽  
Abdullah Algarni ◽  
Abdulaziz Attaallah
Keyword(s):  
Attack Detection ◽  
Detection Methods ◽  
Software Systems ◽  
Distributed Software ◽  
Detection Techniques ◽  
Detection Systems ◽  
Novel Approach ◽  
Validation Experiment ◽  
Critical Issues ◽  
Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

State of Research on Negative Pressure Techniques Applied to Leak Detection in Liquid Pipelines

2004 ◽  
Author(s):  
Wei Liang ◽  
Lai-bin Zhang ◽  
Zhao-hui Wang
Keyword(s):  
Pressure Wave ◽  
Analytical Techniques ◽  
Leak Detection ◽  
Point Of View ◽  
Detection Methods ◽  
Propagation Mechanism ◽  
Detection Techniques ◽  
Wave Detection ◽  
State Of Research ◽  
Signal Processing Methods

In China, the rarefaction-pressure wave techniques are widely used to diagnose the leakage fault for liquid pipelines. Many leaking propagating assumptions, such as stable single-phased flow hypothesis and none rarefaction wave front hypothesis, are often uncertain in the process of leak detection, which can easily result in some errors. Thus the rarefaction-pressure wave techniques should be integrated with other analytical techniques to compute a more accurate leak location. Additionally, the development trends of rarefaction-pressure wave techniques lie in three aspects. First, rarefaction-pressure wave detection techniques will be integrated with other compatible detection techniques and modern signal processing methods to solve the complex problems encountered in leak detection. Second, studies of rarefaction-pressure wave techniques have advanced to a new stage. The deductions on propagation mechanism of rarefaction-pressure wave have been successfully applied to determine leaks qualitatively. Third, analysis on rarefaction-pressure wave detection techniques will be made from a quantitative point of view. The quantitative data have been used to deduce leak amounts and location. The purpose of this paper is to present the recent achievements in the study of improved rarefaction-pressure wave detection techniques. The rarefaction-pressure wave detection methods, effects of incomplete information conditions, the improvements of rarefaction-pressure wave detection techniques with modified factors and propagation mechanisms are comprehensively investigated. The disfigurements of rarefaction-pressure wave are analyzed. The corresponding methods for resolving such problems as ill diagnostic information and weak amplitude values are put forward. Several methods for stronger small leakage detection ability, higher leakage positioning precision, lower false alarm rates are proposed. The application of rarefaction-pressure wave detection techniques to safety protection of liquid pipelines is also introduced. Finally, the prospect of rarefaction-pressure wave detection techniques is predicted.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

Partial Discharge Detection Using Acoustic and Optical Methods in High Voltage Power Equipments: A Review

2013 ◽  
Vol 845 ◽  
pp. 283-286 ◽  
Author(s):  
Malik Abdul Razzaq Al Saedi ◽  
Mohd Muhridza Yaacob
Keyword(s):  
Acoustic Emission ◽  
High Voltage ◽  
Power Transformer ◽  
Partial Discharge ◽  
Optical Detection ◽  
Detection Threshold ◽  
Optical Methods ◽  
Detection Methods ◽  
Detection Techniques ◽  
Advantages And Disadvantages

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

The Detection of Crime

2019 ◽  
pp. 91-112
Author(s):  
Brian H. Bornstein ◽  
Jeffrey S. Neuschatz
Keyword(s):  
Deception Detection ◽  
Lie Detection ◽  
The Body ◽  
Detection Methods ◽  
Major Focus ◽  
The Unconscious ◽  
Detection Techniques ◽  
Psychology And Law ◽  
Physiologically Based ◽  
Better Than

The deception detection method Münsterberg advocates is grounded on principles of association. Although this approach derives partially from a Freudian view of the unconscious, it is not terribly dissimilar to more modern, physiologically based lie detection methods. In recent years, deception detection has become a major focus within psychology and law. Research shows that humans’ ability to detect deception is limited but, summarizing across the body of studies, slightly better than chance. However, most police investigators believe they can detect when suspects are lying. This chapter covers the reliability of modern deception detection techniques with the exception of the polygraph, which is covered in the next chapter.

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