scholarly journals Rapid and sensitive mycoplasma detection system using image-based deep learning

2021 ◽  
Author(s):  
Hiroko Iseoka ◽  
Masao Sasai ◽  
Shigeru Miyagawa ◽  
Kazuhiro Takekita ◽  
Satoshi Date ◽  
...  
Keyword(s):  
Cell Therapy ◽  
Detection System ◽  
Low Cost ◽  
Negative Control ◽  
Training Data ◽  
Cell Counting ◽  
Test Time ◽  
Manufacturing Cost ◽  
Mycoplasma Detection ◽  
Manual Counting

AbstractA major concern in the clinical application of cell therapy is the manufacturing cost of cell products, which mainly depends on quality control. The mycoplasma test, an important biological test in cell therapy, takes several weeks to detect a microorganism and is extremely expensive. Furthermore, the manual detection of mycoplasma from images requires high-level expertise. We hypothesized that a mycoplasma identification program using a convolutional neural network could reduce the test time and improve sensitivity. To this end, we developed a program comprising three parts (mycoplasma detection, prediction, and cell counting) that allows users to evaluate the sample and verify infected/non-infected cells identified by the program. In experiments conducted, stained DNA images of positive and negative control using mycoplasma-infected and non-infected Vero cells, respectively, were used as training data, and the program results were compared with those of conventional methods, such as manual counting based on visual observation. The minimum detectable mycoplasma contaminations for manual counting and the proposed program were 10 and 5 CFU (colony-forming unit), respectively, and the test time for manual counting was 20 times that for the proposed program. These results suggest that the proposed system can realize a low-cost and streamlined manufacturing process for cellular products in cell-based research and clinical applications.

scholarly journals Spam Detection Based on Feature Evolution to Deal with Concept Drift

2021 ◽  
Vol 27 (4) ◽  
pp. 364-386
Author(s):  
Marcia Henke ◽  
Eulanda Santos ◽  
Eduardo Souto ◽  
Altair O Santin
Keyword(s):  
Knowledge Transfer ◽  
Detection System ◽  
Concept Drift ◽  
Low Cost ◽  
Training Data ◽  
Classification Model ◽  
Easy Access ◽  
Spam Detection ◽  
Concept Drift Detection ◽  
E Mail

Electronic messages are still considered the most significant tools in business and personal applications due to their low cost and easy access. However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users. Several approaches have been proposed to detect spam, such as filters implemented in e-mail servers and user-based spam message classification mechanisms. A major problem with these approaches is spam detection in the presence of concept drift, especially as a result of changes in features over time. To overcome this problem, this work proposes a new spam detection system based on analyzing the evolution of features. The proposed method is divided into three steps: 1) spam classification model training; 2) concept drift detection; and 3) knowledge transfer learning. The first step generates classification models, as commonly conducted in machine learning. The second step introduces a new strategy to avoid concept drift: SFS (Similarity-based Features Se- lection) that analyzes the evolution of the features taking into account similarity obtained between the feature vectors extracted from training data and test data. Finally, the third step focuses on the following questions: what, how, and when to transfer acquired knowledge? The proposed method is evaluated using two public datasets. The results of the experiments show that it is possible to infer a threshold to detect changes (drift) in order to ensure that the spam classification model is updated through knowledge transfer. Moreover, our anomaly detection system is able to perform spam classification and concept drift detection as two parallel and independent tasks.

scholarly journals An Alternative Approach to Detecting Cancer Cells by Multi-Directional Fluorescence Detection System Using Cost-Effective LED and Photodiode

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2301
Author(s):  
Kyoungrae Cho ◽  
Jeong-hyeok Seo ◽  
Gyeongyong Heo ◽  
Se-woon Choe
Keyword(s):  
Cancer Cells ◽  
Light Source ◽  
Fluorescence Detection ◽  
Detection System ◽  
Low Cost ◽  
Cost Effective ◽  
Flow Cytometer ◽  
Cell Counting ◽  
Fluorescence Characteristics ◽  
Fluorescence Detection System

The enumeration of cellular proliferation by covering from hemocytometer to flow cytometer is an important procedure in the study of cancer development. For example, hemocytometer has been popularly employed to perform manual cell counting. It is easily achieved at a low-cost, however, manual cell counting is labor-intensive and prone to error for a large number of cells. On the other hand, flow cytometer is a highly sophisticated instrument in biomedical and clinical research fields. It provides detailed physical parameters of fluorescently labeled single cells or micro-sized particles depending on the fluorescence characteristics of the target sample. Generally, optical setup to detect fluorescence uses a laser, dichroic filter, and photomultiplier tube as a light source, optical filter, and photodetector, respectively. These components are assembled to set up an instrument to measure the amount of scattering light from the target particle; however, these components are costly, bulky, and have limitations in selecting diverse fluorescence dyes. Moreover, they require multiple refined and expensive modules such as cooling or pumping systems. Thus, alternative cost-effective components have been intensively developed. In this study, a low-cost and miniaturized fluorescence detection system is proposed, i.e., costing less than 100 US dollars, which is customizable by a 3D printer and light source/filter/sensor operating at a specific wavelength using a light-emitting diode with a photodiode, which can be freely replaceable. The fluorescence detection system can quantify multi-directional scattering lights simultaneously from the fluorescently labeled cervical cancer cells. Linear regression was applied to the acquired fluorescence intensities, and excellent linear correlations (R2 > 0.9) were observed. In addition, the enumeration of the cells using hemocytometer to determine its performance accuracy was analyzed by Student’s t-test, and no statistically significant difference was found. Therefore, different cell concentrations are reversely calculated, and the system can provide a rapid and cost-effective alternative to commercial hemocytometer for live cell or microparticle counting.

scholarly journals Machine learning-based automated yeast cell counting under a complicated background with ilastik and ImageJ

2021 ◽  
Author(s):  
Chenxi Li ◽  
Xiaoyu Ma ◽  
Jing Deng ◽  
Jiajia Li ◽  
Yanjie Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yeast Cell ◽  
Cell Biology ◽  
Low Cost ◽  
Batch Processing ◽  
Yeast Cells ◽  
Cell Counting ◽  
Industrial Fermentation ◽  
Manual Counting ◽  
Set Up

Measuring the concentration and viability of yeast cells is an important and fundamental procedure in scientific research and industrial fermentation. In consideration of the drawbacks of manual cell counting, large quantities of yeast cells require methods that provide easy, objective, and reproducible high-throughput calculations, especially for samples in complicated backgrounds. To answer this challenge, we explored and developed an easy-to-use yeast cell counting pipeline that combined the machine learning-based ilastik tool with the freeware ImageJ, as well as a conventional photomicroscope. Briefly, learning from labels provided by the user, ilastik performs segmentation and classification automatically in batch processing mode for large numbers of images and thus discriminates yeast cells from complex backgrounds. The files processed through ilastik can be recognized by ImageJ, which can set up customizable parameters based on cell size, perimeter, roundness and so on. In this work, we programmed an ImageJ macro, “Yeast Counter”, to compute the numeric results of yeast cells for automatic batch processing. Taking the yeast Cryptococccus deneoformans as an example, we observed that the customizable software algorithm for yeast counting with ilastik and ImageJ reduced inter-operator errors significantly and achieved accurate and objective results in the spotting test, while manual counting with a haemocytometer exhibited some errors between repeats and required more time. In summary, a convenient, rapid, reproducible and extremely low-cost method to count yeast cells is described here that can be applied to multiple kinds of yeasts in genetics, cell biology and industrial fermentation.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals Revisiting Electrochemical Biosensing in the 21st Century Society for Inflammatory Cytokines Involved in Autoimmune, Neurodegenerative, Cardiac, Viral and Cancer Diseases

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 189
Author(s):  
Susana Campuzano ◽  
Paloma Yáñez-Sedeño ◽  
José Manuel Pingarrón
Keyword(s):  
Point Of Care ◽  
Low Cost ◽  
Accurate Determination ◽  
Test Time ◽  
Electrochemical Biosensing ◽  
Recent Developments ◽  
Inflammatory Processes ◽  
Human Skills ◽  
Cancer Diseases

The multifaceted key roles of cytokines in immunity and inflammatory processes have led to a high clinical interest for the determination of these biomolecules to be used as a tool in the diagnosis, prognosis, monitoring and treatment of several diseases of great current relevance (autoimmune, neurodegenerative, cardiac, viral and cancer diseases, hypercholesterolemia and diabetes). Therefore, the rapid and accurate determination of cytokine biomarkers in body fluids, cells and tissues has attracted considerable attention. However, many currently available techniques used for this purpose, although sensitive and selective, require expensive equipment and advanced human skills and do not meet the demands of today’s clinic in terms of test time, simplicity and point-of-care applicability. In the course of ongoing pursuit of new analytical methodologies, electrochemical biosensing is steadily gaining ground as a strategy suitable to develop simple, low-cost methods, with the ability for multiplexed and multiomics determinations in a short time and requiring a small amount of sample. This review article puts forward electrochemical biosensing methods reported in the last five years for the determination of cytokines, summarizes recent developments and trends through a comprehensive discussion of selected strategies, and highlights the challenges to solve in this field. Considering the key role demonstrated in the last years by different materials (with nano or micrometric size and with or without magnetic properties), in the design of analytical performance-enhanced electrochemical biosensing strategies, special attention is paid to the methods exploiting these approaches.

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

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