scholarly journals Machine Learning-Based Protein Microarray Digital Assay Analysis

2020 ◽  
Author(s):  
Yujing Song ◽  
Jingyang Zhao ◽  
Tao Cai ◽  
Shiuan-Haur Su ◽  
Erin Sandford ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Protein Microarray ◽  
High Capacity ◽  
Image Data ◽  
High Sensitivity ◽  
Sample Volume ◽  
Protein Biomarkers ◽  
T Cell Therapy ◽  
Serial Measurement

AbstractSerial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, we developed a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay achieved 14-plexed biomarker detection at concentrations < 10pg/mL with a sample volume < 10 μL, including all processes from sampling to analyzed data delivery within 30 min, while only requiring a 5-min assay incubation. The assay procedure applied both a spatial-spectral microfluidic encoding scheme and an image data analysis algorithm based on machine learning with a convolutional neural network (CNN) for pre-equilibrated single-molecule protein digital counting. This unique approach remarkably reduced errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 14 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy manifested the powerful biomarker profiling capability and great potential of the assay for its translation to near-real-time bedside immune status monitoring.

scholarly journals Bayesian machine learning analysis of single-molecule fluorescence colocalization images

2021 ◽  
Author(s):  
Yerdos A. Ordabayev ◽  
Larry J. Friedman ◽  
Jeff Gelles ◽  
Douglas L. Theobald
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Specific Binding ◽  
Image Data ◽  
Automated Analysis ◽  
Surface Binding ◽  
Single Molecule Fluorescence ◽  
Binding Characteristics ◽  
Wide Range ◽  
Downstream Analysis

AbstractMulti-wavelength single-molecule fluorescence colocalization (CoSMoS) methods allow elucidation of complex biochemical reaction mechanisms. However, analysis of CoSMoS data is intrinsically challenging because of low image signal-to-noise ratios, non-specific surface binding of the fluorescent molecules, and analysis methods that require subjective inputs to achieve accurate results. Here, we use Bayesian probabilistic programming to implement Tapqir, an unsupervised machine learning method based on a holistic, physics-based causal model of CoSMoS data. This method accounts for uncertainties in image analysis due to photon and camera noise, optical non-uniformities, non-specific binding, and spot detection. Rather than merely producing a binary “spot/no spot” classification of unspecified reliability, Tapqir objectively assigns spot classification probabilities that allow accurate downstream analysis of molecular dynamics, thermodynamics, and kinetics. We both quantitatively validate Tapqir performance against simulated CoSMoS image data with known properties and also demonstrate that it implements fully objective, automated analysis of experiment-derived data sets with a wide range of signal, noise, and non-specific binding characteristics.

Direct Read and Quantify Damage Nucleotide from an Oligonucleotide Using a Single Molecule Interface

2019 ◽  
Author(s):  
Jiajun Wang ◽  
Meng-Yin Li ◽  
Jie Yang ◽  
Ya-Qian Wang ◽  
Xue-Yuan Wu ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Molecule ◽  
Genetic Diseases ◽  
High Sensitivity ◽  
Dna Lesions ◽  
Lesion Detection ◽  
The Novel ◽  
Structural Variations ◽  
Dna Lesion ◽  
Base Lesion

DNA lesion such as metholcytosine(<sup>m</sup>C), 8-OXO-guanine(<sup>O</sup>G), inosine(I) <i>etc</i> could cause the genetic diseases. Identification of the varieties of lesion bases are usually beyond the capability of conventional DNA sequencing which is mainly designed to discriminate four bases only. Therefore, lesion detection remain challenge due to the massive varieties and less distinguishable readouts for minor structural variations. Moreover, standard amplification and labelling hardly works in DNA lesions detection. Herein, we designed a single molecule interface from the mutant K238Q Aerolysin, whose confined sensing region shows the high compatible to capture and then directly convert each base lesion into distinguishable current readouts. Compared with previous single molecule sensing interface, the resolution of the K238Q Aerolysin nanopore is enhanced by 2-order. The novel K238Q could direct discriminate at least 3 types (<sup>m</sup>C, <sup>O</sup>G, I) lesions without lableing and quantify modification sites under mixed hetero-composition condition of oligonucleotide. Such nanopore could be further applied to diagnose genetic diseases at high sensitivity.

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

scholarly journals Nanopore Technology for the Application of Protein Detection

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1942
Author(s):  
Xiaoqing Zeng ◽  
Yang Xiang ◽  
Qianshan Liu ◽  
Liang Wang ◽  
Qianyun Ma ◽  
...  
Keyword(s):  
Single Molecule ◽  
Protein Detection ◽  
High Sensitivity ◽  
Single Molecule Detection ◽  
Sequence Detection ◽  
Fast Detection ◽  
Material Basis ◽  
Sensing Technology ◽  
Detection Technology ◽  
Structure Recognition

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

scholarly journals Single-copy detection of somatic variants from solid and liquid biopsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana-Luisa Silva ◽  
Paulina Klaudyna Powalowska ◽  
Magdalena Stolarek ◽  
Eleanor Ruth Gray ◽  
Rebecca Natalie Palmer ◽  
...  
Keyword(s):  
Real Time ◽  
Single Molecule ◽  
Real Time Pcr ◽  
Clinical Decision Making ◽  
High Sensitivity ◽  
Clinical Decision ◽  
Single Copy ◽  
Turnaround Time ◽  
Copy Detection ◽  
Allele Specific

AbstractAccurate detection of somatic variants, against a background of wild-type molecules, is essential for clinical decision making in oncology. Existing approaches, such as allele-specific real-time PCR, are typically limited to a single target gene and lack sensitivity. Alternatively, next-generation sequencing methods suffer from slow turnaround time, high costs, and are complex to implement, typically limiting them to single-site use. Here, we report a method, which we term Allele-Specific PYrophosphorolysis Reaction (ASPYRE), for high sensitivity detection of panels of somatic variants. ASPYRE has a simple workflow and is compatible with standard molecular biology reagents and real-time PCR instruments. We show that ASPYRE has single molecule sensitivity and is tolerant of DNA extracted from plasma and formalin fixed paraffin embedded (FFPE) samples. We also demonstrate two multiplex panels, including one for detection of 47 EGFR variants. ASPYRE presents an effective and accessible method that simplifies highly sensitive and multiplexed detection of somatic variants.

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

IMPROVED TOW-END DETECTION FOR FIBER PLACEMENT INSPECTION USING MACHINE LEARNING

2021 ◽  
Author(s):  
ADRIANA W. (AGNES) BLOM-SCHIEBER ◽  
WEI GUO ◽  
EKTA SAMANI ◽  
ASHIS BANERJEE
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Image Data ◽  
Detection Algorithm ◽  
Automated Inspection ◽  
Machine Learning Algorithm ◽  
Fiber Placement ◽  
Inspection Systems ◽  
Automated Fiber Placement ◽  
The University

A machine learning approach to improve the detection of tow ends for automated inspection of fiber-placed composites is presented. Automated inspection systems for automated fiber placement processes have been introduced to reduce the time it takes to inspect plies after they are laid down. The existing system uses image data from ply boundaries and a contrast-based algorithm to locate the tow ends in these images. This system fails to recognize approximately 10% of the tow ends, which are then presented to the operator for manual review, taking up precious time in the production process. An improved tow end detection algorithm based on machine learning is developed through a research project with the Boeing Advanced Research Center (BARC) at the University of Washington. This presentation shows the preprocessing, neural network and post‐processing steps implemented in the algorithm, and the results achieved with the machine learning algorithm. The machine learning algorithm resulted in a 90% reduction in the number of undetected tows compared to the existing system.

scholarly journals A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Johannes Jordan ◽  
Elli Angelopoulou ◽  
Andreas Maier
Keyword(s):  
Hyperspectral Image ◽  
Imaging Modality ◽  
High Capacity ◽  
Interactive Visualization ◽  
Image Data ◽  
Hyperspectral Image Analysis ◽  
Supervised Segmentation ◽  
Hyperspectral Image Data ◽  
Visualization Techniques ◽  
Application Specific

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

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