scholarly journals A Multichannel Pattern-Recognition-Based Protein Sensor with a Fluorophore-Conjugated Single-Stranded DNA Set

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5110
Author(s):  
Mari Okada ◽  
Hiroka Sugai ◽  
Shunsuke Tomita ◽  
Ryoji Kurita
Keyword(s):  
Pattern Recognition ◽  
Optical Response ◽  
Disease Diagnosis ◽  
Detection Methods ◽  
Fluorescence Signal ◽  
Signal Acquisition ◽  
Response Patterns ◽  
Protein Sensing ◽  
Composition Ratios ◽  
Protein Sensor

Recently, pattern-recognition-based protein sensing has received considerable attention because it offers unique opportunities that complement more conventional antibody-based detection methods. Here, we report a multichannel pattern-recognition-based sensor using a set of fluorophore-conjugated single-stranded DNAs (ssDNAs), which can detect various proteins. Three different fluorophore-conjugated ssDNAs were placed into a single microplate well together with a target protein, and the generated optical response pattern that corresponds to each environment-sensitive fluorophore was read via multiple detection channels. Multivariate analysis of the resulting optical response patterns allowed an accurate detection of eight different proteases, indicating that fluorescence signal acquisition from a single compartment containing a mixture of ssDNAs is an effective strategy for the characterization of the target proteins. Additionally, the sensor could identify proteins, which are potential targets for disease diagnosis, in a protease and inhibitor mixture of different composition ratios. As our sensor benefits from simple construction and measurement procedures, and uses accessible materials, it offers a rapid and simple platform for the detection of proteins.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

The Integrated Mechanical Function Test System of Material and Structural Fatigue Damage

2012 ◽  
Vol 532-533 ◽  
pp. 398-402
Author(s):  
Yu Lan Wei ◽  
Bing Li ◽  
Sui Ying Jin ◽  
Kai Kai Chen
Keyword(s):  
Optimal Design ◽  
Test System ◽  
Integrated System ◽  
Detection Methods ◽  
Signal Acquisition ◽  
Mechanical Function ◽  
Structural Fatigue ◽  
Material Functions ◽  
Function Test ◽  
And Function

An integrated system to measure mechanical functions of material or structure is introduced. This system is able to provide more detection methods and experiment environments. And it can discover the characteristics and mechanisms of damnification and breakage in materials, considering the effects of loading and environments. Material functions were analyzed in many aspects, including loading, strain, light, sound, temperature and infrared to ensure the safety of materials and configuration of unmanned plane. Current study has laid a foundation for realizing optimal design of unmanned plane. In this paper, theory, components, and function of the system were discussed, as well as signal acquisition and analysis.

scholarly journals Polymer-Based Chemical Nose Systems for Optical Pattern Recognition of Gut Microbiota

2020 ◽  
Author(s):  
Shunsuke Tomita ◽  
Hiroyuki Kusada ◽  
Naoshi Kojima ◽  
Sayaka Ishihara ◽  
Koyomi Miyazaki ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Gut Microbiota ◽  
Health Management ◽  
Clinical Settings ◽  
Response Patterns ◽  
Bacterial Strains ◽  
Optical Pattern Recognition ◽  
Sensing Systems ◽  
Optical Pattern ◽  
The Status

Understanding the status of gut microbiota has been recognized as crucial in health management and disease treatment. To meet the demands of medical and biological applications where rapid evaluation of gut microbiota in limited research environment is essential, we developed new sensing systems able to readout the overall characteristics of complex microbiota. Response patterns generated by a synthetic library of 12 charged block-copolymers with aggregation-induced emission units were analyzed with pattern recognition algorithms, allowing to identify the species/phyla of 16 axenic cultures of intestinal bacterial strains. More importantly, our method clearly classified artificial models of obesity-associated gut microbiota, and further succeeded in detecting sleep disorders in mice through comparative analysis of the normal/abnormal mouse gut microbiota. Our techniques can analyze complex bacterial samples far more quickly, simply and inexpensively than common metagenome-based methods, offering a powerful and complementary tool for gut microbiome analysis for practical use, e.g., in clinical settings.

A novel duplex ddPCR assay for the diagnosis of schistosomiasis japonica: proof of concept in an experimental mouse model

Parasitology ◽  
2017 ◽  
Vol 144 (8) ◽  
pp. 1005-1015 ◽  
Author(s):  
KOSALA G. WEERAKOON ◽  
CATHERINE A. GORDON ◽  
PENGFEI CAI ◽  
GEOFFREY N. GOBERT ◽  
MARY DUKE ◽  
...  
Keyword(s):  
Mouse Model ◽  
Large Scale ◽  
Public Health Problem ◽  
Digital Pcr ◽  
Disease Diagnosis ◽  
World Health ◽  
Schistosomiasis Japonica ◽  
Detection Methods ◽  
Clinical Samples ◽  
Additional Tool

SUMMARYThe current World Health Organization strategic plan targets the elimination of schistosomiasis as a public health problem by 2025 and accurate diagnostics will play a pivotal role in achieving this goal. DNA-based detection methods provide a viable alternative to some of the commonly used tests, notably microscopy and serology, for the diagnosis of schistosomiasis. The detection of parasite cell-free DNA in different clinical samples is a recent valuable advance, which provides significant benefits for accurate disease diagnosis. Here we validated a novel duplex droplet digital PCR assay for the diagnosis of Chinese (SjC) and Philippine (SjP) strains of Schistosoma japonicum infection in a mouse model. The assay proved applicable for both SjC and SjP infections and capable of detecting infection at a very early intra-mammalian stage in conveniently obtainable samples (urine and saliva) as well as in serum and feces. The target DNA copy numbers obtained in the assay showed a positive correlation with the infection burden assessed by direct traditional parasitology. The potential to detect parasite DNA in urine and saliva has important practical implications for large-scale epidemiological screening programmes in the future, particularly in terms of logistical convenience, and the assay has the potential to be a valuable additional tool for the diagnosis of schistosomiasis japonica.

Development of a Fluorescence-Based Caries Scoring System for an Intraoral Scanner: An in vitro Study

2020 ◽  
Vol 54 (4) ◽  
pp. 324-335
Author(s):  
Stavroula Michou ◽  
Ana Raquel Benetti ◽  
Christoph Vannahme ◽  
Pétur Gordon Hermannsson ◽  
Azam Bakhshandeh ◽  
...  
Keyword(s):  
Scoring System ◽  
State Of The Art ◽  
Assessment System ◽  
Detection Methods ◽  
Caries Detection ◽  
Fluorescence Signal ◽  
Radiographic Assessment ◽  
Intraoral Scanner ◽  
Posterior Teeth ◽  
Occlusal Surfaces

<b><i>Objectives:</i></b> To develop an automated fluorescence-based caries scoring system for an intraoral scanner and to<i></i>test the performance of the system compared to state-of-the-art methods. <b><i>Methods:</i></b> Seventy-three permanent posterior teeth were scanned with a three-dimensional (3D) intraoral scanner prototype which emitted light at 415 nm. An overlay representing the fluorescence signal from the tissue was mapped onto 3D models of the teeth. Multiple examination sites (<i>n</i> = 139) on the occlusal surfaces were chosen, and their red and green fluorescence signal components were extracted. These components were used to calculate 4 mathematical functions upon which a caries scoring system for the scanner prototype could be based. Visual-tactile (International Caries Detection and Assessment System, ICDAS), radiographic (ICDAS), and histological assessments were conducted on the same examination sites. <b><i>Results:</i></b> Most index tests showed significant correlation with histology. The strongest correlation was observed for the visual-tactile examination (<i>r</i><sub>s</sub> = 0.80) followed by the scanner supported by the caries classification function that quantifies the overall fluorescence compared to sound surfaces (<i>r</i><sub>s</sub> = 0.78). Additionally, this function resulted in the highest intra-examiner reliability (κ = 0.964), and the highest sum of sensitivity (SE) and specificity (SP) (sum SE-SP: 1.60–1.84) at the 2 histological levels where the comparison with visual-tactile assessment was possible (κ = 0.886, sum SE-SP = 1.57–1.81) and at the 3 out of 4 histological levels where the comparison with radiographic assessment was possible (κ = 0.911, sum SE-SP = 1.37–1.78); the only exception was for the lesions in the outer third of dentin, where the radiographic assessment showed the highest sum SE-SP (1.78). <b><i>Conclusion:</i></b> A fluorescence-based caries scoring system was developed for the intraoral scanner showing promising performance compared to state-of-the-art caries detection methods. The intraoral scanner accompanied by an automated caries scoring system may improve objective caries detection and increase the efficiency and effectiveness of oral examinations. Furthermore, this device has the potential to support reliable monitoring of early caries lesions.

scholarly journals A Probabilistic Multispectral Pattern Recognition Method for Detection of Overshooting Cloud Tops Using Passive Satellite Imager Observations

2016 ◽  
Vol 55 (9) ◽  
pp. 1983-2005 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Konstantin Khlopenkov
Keyword(s):  
Pattern Recognition ◽  
Detection Rate ◽  
Earth Science ◽  
Weather Conditions ◽  
The United States ◽  
Detection Methods ◽  
False Detection Rate ◽  
False Detection ◽  
Weather And Climate ◽  
Climate Analysis

AbstractDeep convective updrafts often penetrate through the surrounding cirrus anvil and into the lower stratosphere. Cross-tropopause transport of ice, water vapor, and chemicals occurs within these “overshooting tops” (OTs) along with a variety of hazardous weather conditions. OTs are readily apparent in satellite imagery, and, given the importance of OTs for weather and climate, a number of automated satellite-based detection methods have been developed. Some of these methods have proven to be relatively reliable, and their products are used in diverse Earth science applications. Nevertheless, analysis of these methods and feedback from product users indicate that use of fixed infrared temperature–based detection criteria often induces biases that can limit their utility for weather and climate analysis. This paper describes a new multispectral OT detection approach that improves upon those previously developed by minimizing use of fixed criteria and incorporating pattern recognition analyses to arrive at an OT probability product. The product is developed and validated using OT and non-OT anvil regions identified by a human within MODIS imagery. The product offered high skill for discriminating between OTs and anvils and matched 69% of human OT identifications for a particular probability threshold with a false-detection rate of 18%, outperforming previously existing methods. The false-detection rate drops to 1% when OT-induced texture detected within visible imagery is used to constrain the IR-based OT probability product. The OT probability product is also shown to improve severe-storm detection over the United States by 20% relative to the best existing method.

scholarly journals Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

2021 ◽  
Author(s):  
Hongzuo Chu ◽  
Yong Cao ◽  
Jin Jiang ◽  
Jiehong Yang ◽  
Mengyin Huang ◽  
...  
Keyword(s):  
Task Difficulty ◽  
Detection Method ◽  
Mental Workload ◽  
Systems Design ◽  
Detection Methods ◽  
Complex Signal ◽  
Detection Accuracy ◽  
Signal Acquisition ◽  
Detection Techniques ◽  
Channel Configuration

Abstract Background: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating EEG and fNIRS signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.Method: The signal acquisition configuration was optimized and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical MATB task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.Results: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 78.25±4.71%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of 𝛽1 and 𝛽2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.Conclusions: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 78.25±4.71% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

scholarly journals Detection of Powdery Mildew Disease of Beans in India : A Review

2016 ◽  
Vol 9 (3) ◽  
pp. 226-234
Author(s):  
Kuldeep Singh ◽  
Satish Kumar ◽  
Pawan Kaur
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Powdery Mildew ◽  
Management Strategies ◽  
Cost Effective ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Spectroscopic Techniques ◽  
Powdery Mildew Disease

Powdery mildew disease of beans in India causes major economic losses in agriculture. For sustainable agriculture detection and identification of diseases in plants is very important. In this review, we are trying to identify the powdery mildew disease of beans crop by using some image processing and pattern recognition techniques and comparing with molecular and spectroscopic techniques. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. The present review recognizes the need for developing a rapid, cost-effective, and reliable health monitoring techniques that would facilitate advancements in agriculture. These technologies include image processing and pattern recognition based plant disease detection methods

scholarly journals The Analysis of Plant’s Organic Volatiles Compounds with Electronic Nose and Pattern Recognition Techniques

2011 ◽  
pp. 115-126
Author(s):  
Reza Ghaffari ◽  
Fu Zhang ◽  
Daciana Iliescu ◽  
Evor Hines ◽  
Mark Leeson ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Electronic Nose ◽  
Disease Diagnosis ◽  
Performance Comparison ◽  
Plant Pest ◽  
Organic Volatiles ◽  
Greenhouse Plant ◽  
Diagnosis Tool ◽  
Volatiles Compounds

In this chapter, the authors introduce the principles of some of the most widely used supervised and unsupervised Pattern Recognition (PR) techniques and assess behaviour and performances. A dataset acquired from a set of experiments conducted at University of Warwick is employed to construct a case study in which the techniques will be applied. The chapter will also evaluate the integration of PR methods with an Electronic Nose (EN) device to develop and implement a plant diagnosis tool based on discriminating the Organic Volatile Compounds (VOC) released by plants when attacked by pest. The chapter concludes with a performance comparison and a brief discussion of how an appropriate PR technique can be coupled with an EN to produce a greenhouse plant pest and disease diagnosis system for day-to-day utilisation. Some consideration of further work is also presented.

Sign in / Sign up

Export Citation Format

Share Document