Ultrasound based noninvasive real-time cell proliferation process monitoring

In Vitro Approaches to Determine the Potential Carcinogenic Risk of Environmental Pollutants

One important environmental/health challenge is to determine, in a feasible way, the potential carcinogenic risk associated with environmental agents/exposures. Since a significant proportion of tumors have an environmental origin, detecting the potential carcinogenic risk of environmental agents is mandatory, as regulated by national and international agencies. The challenge mainly implies finding a way of how to overcome the inefficiencies of long-term trials with rodents when thousands of agents/exposures need to be tested. To such an end, the use of in vitro cell transformation assays (CTAs) was proposed, but the existing prevalidated CTAs do not cover the complexity associated with carcinogenesis processes and present serious limitations. To overcome such limitations, we propose to use a battery of assays covering most of the hallmarks of the carcinogenesis process. For the first time, we grouped such assays as early, intermediate, or advanced biomarkers which allow for the identification of the cells in the initiation, promotion or aggressive stages of tumorigenesis. Our proposal, as a novelty, points out that using a battery containing assays from all three groups can identify if a certain agent/exposure can pose a carcinogenic risk; furthermore, it can gather mechanistic insights into the mode of the action of a specific carcinogen. This structured battery could be very useful for any type of in vitro study, containing human cell lines aiming to detect the potential carcinogenic risks of environmental agents/exposures. In fact, here, we include examples in which these approaches were successfully applied. Finally, we provide a series of advantages that, we believe, contribute to the suitability of our proposed approach for the evaluation of exposure-induced carcinogenic effects and for the development of an alternative strategy for conducting an exposure risk assessment.

The Design of Low-Cost Stand-Alone Microcontroller-Based Wireless Ultrasonic System for Process Monitoring and Analysis

OBJECTIVES

Ultrasound technology is currently used in many areas, such as imaging, analysis, and process monitoring. The noninvasive implementation, nondestructive effect on the material to be applied, and low cost of the needed components give an advantage to the ultrasonic systems when compared to other methods for analysis and process monitoring studies. However, the current ultrasonic analysis setups used in the studies require additional devices such as a signal generator and oscilloscope. These devices used in the setup increase the cost, size, usage difficulty of the system and, most importantly, decrease the portability and stability. In order to prevent these disadvantages, an ultrasonic system that can work in real-time and its software are developed to be used in analysis and process monitoring without any additional devices.

METHODS

This system was designed by using a microcontroller. The developed system is portable, has a small size, and a Bluetooth Low Energy connection. It has a battery for using standalone.

RESULTS

Therefore, it can be easily used in different small and closed measurement environments such as incubators and controlled remotely. In addition, a mixture was analyzed with both the designed system and a commercial module. When the results are compared, two systems are found highly correlated r 2 = 1 . CONCLUSION: In this study, an embedded ultrasonic measurement system and its software are developed to be used in analysis studies, density measurements, and real-time process monitoring as a stand-alone device.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made.