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

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.

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Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform

Journal of The Royal Society Interface ◽

10.1098/rsif.2016.0705 ◽

2017 ◽

Vol 14 (127) ◽

pp. 20160705 ◽

Cited By ~ 27

Author(s):

Cristian Versari ◽

Szymon Stoma ◽

Kirill Batmanov ◽

Artémis Llamosi ◽

Filip Mroz ◽

...

Keyword(s):

Cell Biology ◽

High Performance ◽

Yeast Cells ◽

Additional Information ◽

New Variant ◽

Long Term Experiments ◽

Community Effort ◽

Set Up ◽

Evaluation Platform

With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar , a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.

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An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Scientific Reports ◽

10.1038/s41598-021-81115-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Francy Shu ◽

Jeff Shu

Keyword(s):

Machine Learning ◽

Detection System ◽

Low Cost ◽

Fall Detection ◽

Detection Methods ◽

Surveillance Systems ◽

Nursing Facilities ◽

Risk Zones ◽

Set Up ◽

Current Detection

AbstractFalls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

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Low-cost photolithography system for cell biology labs

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-2140 ◽

2021 ◽

Vol 7 (2) ◽

pp. 550-553

Author(s):

Benjamin K. Naggay ◽

Kerstin Frey ◽

Markus Schneider ◽

Kiriaki Athanasopulu ◽

Günter Lorenz ◽

...

Keyword(s):

Light Source ◽

Cell Biology ◽

Soft Lithography ◽

Uv Light ◽

Low Cost ◽

Cost Effective ◽

Working Environment ◽

Light Emitting ◽

Set Up ◽

High Level

Abstract Soft lithography, a tool widely applied in biology and life sciences with numerous applications, uses the soft molding of photolithography-generated master structures by polymers. The central part of a photolithography set-up is a mask-aligner mostly based on a high-pressure mercury lamp as an ultraviolet (UV) light source. This type of light source requires a high level of maintenance and shows a decreasing intensity over its lifetime, influencing the lithography outcome. In this paper, we present a low-cost, bench-top photolithography tool based on ninety-eight 375 nm light-emitting diodes (LEDs). With approx. 10 W, our presented lithography set-up requires only a fraction of the energy of a conventional lamp, the LEDs have a guaranteed lifetime of 1000 h, which becomes noticeable by at least 2.5 to 15 times more exposure cycles compared to a standard light source and with costs less than 850 C it is very affordable. Such a set-up is not only attractive to small academic and industrial fabrication facilities who want to enable work with the technology of photolithography and cannot afford a conventional set-up, but also microfluidic teaching laboratories and microfluidic research and development laboratories, in general, could benefit from this cost-effective alternative. With our self-built photolithography system, we were able to produce structures from 6 μm to 50 μm in height and 10 μm to 200 μm in width. As an optional feature, we present a scaled-down laminar flow hood to enable a dust-free working environment for the photolithography process.

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

Journal of Artificial Organs ◽

10.1007/s10047-021-01282-4 ◽

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.

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Improved Technique for Electron Microscope Visualization of Yeast Membrane Structure

Microscopy and Microanalysis ◽

10.1007/s10005-001-0020-4 ◽

2001 ◽

Vol 7 (6) ◽

pp. 530-534 ◽

Cited By ~ 18

Author(s):

Christoph Bauer ◽

Volker Herzog ◽

Matthias F. Bauer

Keyword(s):

Electron Microscopy ◽

Cell Wall ◽

Yeast Cell ◽

Cell Biology ◽

Membrane Structure ◽

Membrane Integrity ◽

Enzymatic Digestion ◽

Yeast Cells ◽

Digestion Technique ◽

Organelle Morphology

AbstractYeast cells represent a powerful model system in cell biology mainly due to their amenability to genetic manipulations. Increasingly, studies focus on mutant genes resulting in alterations of cellular structures and organelles. To ascertain the phenotypic changes involved, it is often desirable to use the resolving power of electron microscopy. In contrast to higher eukaryotic cells, yeast cells are particularly difficult to preserve mainly due to the presence of a thick cell wall that acts as a barrier against diffusion of fixatives. Although several procedures are targeted to overcome these difficulties, none of them have become established as a standard procedure. As a consequence, electron microscopy is still not used routinely as a tool in yeast cell biology. This prompted us to develop an easy-to-follow protocol for yeast transmission electron microscopy that should be useful in all cases where membrane integrity and organelle morphology is emphasized. One means of making the yeast cytoplasm more attainable to fixation and staining solutions is by enzymatic digestion of the cell wall. Following this approach, we were able to reliably preserve yeast cells and their cellular organelles. Enzymatic treatment with zymolyase 20T to partially remove the yeast cell wall allowed the fixation, preservation, and visualization of the yeast cytoplasm revealing detailed ultrastructure. The advancement of this technique is demonstrated with mitochondria as a model organelle. Our studies on various yeast mutants clearly show the power of the enzymatic digestion technique in visualizing subtle changes of membrane structure and organelle morphology.

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Development of a Capacitor Probe to Detect Subsurface Deterioration in Concrete

MRS Proceedings ◽

10.1557/proc-503-231 ◽

1997 ◽

Vol 503 ◽

Cited By ~ 2

Author(s):

B. K. Diefenderfer ◽

I. L. Al-Qadi ◽

J. J. Yoho ◽

S. M. Riad ◽

A. Loulizi

Keyword(s):

Dielectric Constant ◽

Portland Cement ◽

Electromagnetic Waves ◽

Low Cost ◽

Complex Dielectric Constant ◽

Life Cycle Costs ◽

Parallel Plates ◽

Portland Cement Concrete ◽

Cement Concrete ◽

Set Up

ABSTRACTPortland cement concrete (PCC) structures deteriorate with age and need to be maintained or replaced. Early detection of deterioration in PCC (e.g., alkali-silica reaction, freeze/thaw damage, or chloride presence) can lead to significant reductions in maintenance costs. However, it is often too late to perform low-cost preventative maintenance by the time deterioration becomes evident. By developing techniques that would enable civil engineers to evaluate PCC structures and detect deterioration at early stages (without causing further damage), optimization of life-cycle costs of the constructed facility and minimization of disturbance to the facility users can be achieved.Nondestructive evaluation (NDE) methods are potentially one of the most useful techniques ever developed for assessing constructed facilities. They are noninvasive and can be performed rapidly. Portland cement concrete can be nondestructively evaluated by electrically characterizing its complex dielectric constant. The real part of the dielectric constant depicts the velocity of electromagnetic waves in PCC. The imaginary part, termed the “loss factor,” describes the conductivity of PCC and the attenuation of electromagnetic waves.Dielectric properties of PCC have been investigated in a laboratory setting using a parallel plate capacitor operating in the frequency range of 0.1 to 40.1MIHz. This capacitor set-up consists of two horizontal-parallel plates with an adjustable separation for insertion of a dielectric specimen (PCC). While useful in research, this approach is not practical for field implementation. A new capacitor probe has been developed which consists of two plates, located within the same horizontal plane, for placement upon the specimen to be tested. Preliminary results show that this technique is feasible and results are promising; further testing and evaluation is currently underway.

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Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

10.26434/chemrxiv.7263596.v2 ◽

2019 ◽

Author(s):

Qiannan Duan ◽

Jianchao Lee ◽

Jinhong Gao ◽

Jiayuan Chen ◽

Yachao Lian ◽

...

Keyword(s):

Machine Learning ◽

Acute Toxicity ◽

Model Building ◽

Low Cost ◽

Enzyme Reaction ◽

Chemical Effect ◽

Technological Innovations ◽

Acute Toxicity Test ◽

Toxicity Prediction ◽

Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

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Fault Localization of Power-Ground Short via Signal Injection and Oscilloscope Technique

ISTFA 2010: Conference Proceedings from the 36th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2010p0202 ◽

2010 ◽

Author(s):

Binh Nguyen

Keyword(s):

Low Cost ◽

Short Circuit ◽

Fault Localization ◽

Fault Isolation ◽

Test Apparatus ◽

Good Working ◽

Signal Injection ◽

Set Up ◽

Non Destructive ◽

Short Circuit Condition

Abstract For those attempting fault isolation on computer motherboard power-ground short issues, the optimal technique should utilize existing test equipment available in the debug facility, requiring no specialty equipment as well as needing a minimum of training to use effectively. The test apparatus should be both easy to set up and easy to use. This article describes the signal injection and oscilloscope technique which meets the above requirements. The signal injection and oscilloscope technique is based on the application of Ohm's law in a short-circuit condition. Two experiments were conducted to prove the effectiveness of these techniques. Both experiments simulate a short-circuit condition on the VCC3 power rail of a good working PC motherboard and then apply the signal injection and oscilloscope technique to localize the short. The technique described is a simple, low cost and non-destructive method that helps to find the location of the power-ground short quickly and effectively.

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