Projects

This collaborative project is an integrated fundamental study to (i) elaborate and tune approaches for fast detection of the CWA and their transformation products and (ii) propose decontamination formulation(s) for fast and irreversibly decontamination with as lowest impact to the personnel and the environment as possible. The versatile portable and user-friendly all-weather and all-terrain kits for certified First Responders and spontaneous volunteers are to design. The methods and formulations to be developed are based on the recent research developments and designed in accordance with the requirements of green and sustainable chemistry, including green chemistry metrics approach.

The main aim of the study is to review the published scientific data and, based on the analysis, to develop the expert opinion by the Estonian scientific community about the possible health effects caused by nonionizing radiation, including 5G generation mobile communication. Based on the analyses, the recommendations for modification of the nonionizing radiation safety requirements will be proposed. The additional aim of the study is to measure the actual levels of radiofrequency radiation and to determine the worst case in the sufficient number of points in areas close to base stations, including 5G base stations, for analyzing the radiofrequency situation in the public environment in Estonia. As the result of the study, the conclusions will be drawn up in radiofrequency EMF health effects and possible modification will be proposed for the regulations for health protection of the general population.

Chiral pollution is an environmental topic of crucial importance, considering that a large number of chemicals spreading into the environment, for example pesticides, are chiral substances. However, usually the stereoisomerism of contaminants is not considered, although the biological activity of enantiomers is significantly different, making their recognition critical for environmental control. Enantiomeric excess is currently determined by off-site analysis, requiring collection, transportation, eventual pre-treating of the sample, and expensive instrumentations and specifically trained staff. Thus, providing devices able to allow for rapid on site detection and chiral discrimination of target analytes would have a dramatic impact in all the fields of environmental control with significant economic benefits. The development of chemical sensors has been conceived to bypass restrictions related to classical analytical protocols and supports the use of conventional laboratory techniques for environmental control. While the technological foundation for chemical sensors already exists, it has been difficult to apply them to chiral discrimination and analysis, due to the lack of suitable solid state receptors. The main aims of the project are: a) the development of novel molecular receptors, mainly based on porphyrin derivatives, b) integration of the receptors with different nanostructures and characterization of their solid state organization, c) deposition of the structures onto transducer surfaces, d) testing and validation of the new chemical sensor devices with enantiomeric pairs of model analytes. The synergistic complementary know-how of six academic units and two private companies will allow a breakthrough development through delivery of sensing probes ranging from the synthesis of macrocyclic molecular receptors to the building and testing of analytical and electronic parts for final, field-capable devices.

The project is focused on developing molecular diagnostics tools for early detection of schizophrenia. The research program of SZ_TEST will include three interrelated lines of research: 1: Deciphering molecular mechanisms of schizophrenia. 2: Identifying molecular biomarkers for early detection of schizophrenia. 3: Developing reliable protocols for diagnostic use of newly identified biomarkers in clinical settings.

Drug Hunter is a portable analyzer for the detection of narcotics in saliva. The project's goal was to integrate machine learning methods into an expert system for substance detection to reduce the competence required of the analyzer's user to interpret the results and thereby reduce errors in substance detection. Various machine learning methods based on supervised learning - artificial neural networks - were tested during the project. In order to prepare (annotate) the data necessary for training the neural network, a cloud service was created, where the technical parameters of all analyzes and the obtained electropherograms, on which the substances to be analyzed were marked, were stored. The best results in detecting electrophoretic patterns were obtained using a convolutional neural network (CNN). The analyzer was tested in cooperation with Estonian Police at roadside drug testing raids in Estonia.

Wireless biomedical sensors should dramatically reduce the costs and risks associated with personal health care while being more and more exploited by telemedicine and efficient e-health systems. However, because of the large power consumption of continuous wireless transmission, the battery life of the sensors is reduced for long-term use. Sub-Nyquist continuous-time discrete-amplitude (CTDA) sampling approaches using level-crossing analogto- digital converters (ADCs) have been developed to reduce the sampling rate and energy consumption of the sensors. However, traditional machine learning techniques and architectures are not compatible with the non-uniform sampled data obtained from levelcrossing ADCs. This project aims to develop analog algorithms, circuits, and systems for the implementation of machine learning techniques in CTDA sampled data in wireless biomedical sensors. This “near-sensor computing” approach, will help reduce the wireless transmission rate and therefore the power consumption of the sensor. The output rate of the CTDA is directly proportional to the activity of the analog signal at the input of the sensor. Therefore, artificial intelligence hardware that processes CTDA data should consume significantly less energy. For demonstration purposes, a prototype biomedical sensor for the detection and classification of sleep apnea will be developed using integrated circuit prototypes and a commercially available analog front-end interface. The sensor will acquire electrocardiogram and bioimpedance signals from the subject and will use data fusion techniques and machine learning techniques to achieve high accuracy.

New viral species and strains are continuously emerging over time around the world. They can originate either from fully unknown taxa or related to known viruses that evolve changing their geographical distribution and/or ecological niche. Cereals are the most important crops for human food and animal feed. Therefore, there is a paramount need to monitor carefully viruses able to jump on cereals and to investigate in detail the mechanisms of viral emergence. In this project, our aim is to understand how cereal sobemoviruses and common ancestors emerged from wild plants and diverged to different viral species with different host ranges. Investigating the evolvability of cereal sobemoviruses, we will contribute to evaluate the risk of host jumps and viral emergence on cereals. To fulfill this aim, we plan to implement two parallel approaches: (i) a comparative one using taxonomy analyses based on protein structure alignments in addition to sequence alignments and (ii) an experimental one searching for a common host plant for different sobemoviruses and assessing viral fitness of each species.

Design and testing of antihypertriglyceridemic peptides in human plasma

The project is focused on the development of a new energy and source efficient transformation, which can be potentially integrated into an industrial process. Synthetic electrochemistry attracts more and more attention as an alternative to classical thermochemical reactions that require toxic and expensive transition-metal-catalysts or stoichiometric amounts of harmful and wasteful oxidants. In electrochemical reactions, electrons are used as “traceless and green reagents” to generate highly reactive radical particles under the mild reaction condition providing access to the previously unapproachable reaction pathways. Moreover, the potential to harvest sustainable solar or wind energy makes electrochemistry even more attractive. In the framework of this project, we have successfully demonstrated a novel electrochemical approach for the synthesis of aryl oxygen compounds that does not require the use of oxidants nor metal catalysts. These type of compounds are widely used precursors for synthesis of valuable polymers, complex bioactive molecules and are common structural motifs in many natural products. In our research we have used an inexpensive self-manufactured setup to perform electrochemical transformation in continuous-flow, which allowed us to develop a reliable and scalable process that makes it particularly interesting for the chemical industry.

The most significant results of this project include developing a user-friendly tool for microbiology studies. This tool was used to figure out how growth of individual bacteria cells varies during exposure to different metals. This vital knowledge will contribute to ongoing experiments about the possibility of metals affecting individual bacteria cells’ ability to grow in the presence of antibiotics, and to overall research in antibacterial substances and antibiotic resistance. The newly developed tool has also opened the door for future experiments and new collaboration projects both regionally and internationally. Researchers and students alike, no matter their educational or financial background, will be able to apply the tool for their own research topic. Through this project the PI has obtained valuable insight into leadership, experimental procedures, networking, and writing which has contributed to development into an independent researcher.