Projects

The consumption of dietary fibres (DF) should be increased to comply with the dietary guidelines and fuel various beneficial metabolites produced by the gut microbiota such as short chain fatty acids (SCFA). Microbiota also produces gases that, in sensitive persons including irritable bowel syndrome patients (IBS), trigger disturbing and at worst disabling gastrointestinal (GI) symptoms, leading to the avoidance of fibre-rich foods. The gas formation patterns and amounts are highly individual and interrelated to GI parameters such as gut pH and transit rate. FIBRE-MATCH develops and validates a concept to match DF types to gut microbiome subtypes for optimal metabolic output. The project identifies major DF-metabolising microbiome types prevalent in Europeans using existing metagenomic, dietary and GI symptom data, considering also endogenous glycans. Representative microbiomes and DF will be characterized in vitro to identify metabolic phenotypes. DF combinations yielding an optimal gas to SCFA ratio in vitro will be used to develop fermented food prototypes for proof-of-concept studies in healthy volunteers and IBS patients, to study whether consumption of the microbiome-tailored food improves GI symptoms and IBS markers compared to unmatched fibre, using breath monitoring, metagenome, metabolome and glycan analyses. Biosamples from the healthy subjects will be used to evaluate the effects of DF on risk markers of noncommunicable diseases using metabolomics approaches. A novel database of chemical composition of DF in high-fibre foods will be developed to enable analysis of nutrition-microbiome interactions at functional and molecular level. FIBRE-MATCH fits to the Precision Nutrition Challenge portfolio as it develops fundamental knowledge, capabilities and resources that foster precision nutrition innovations related to individually-tailored microbiome-targeting foods, microbiome-based stratification, and ultimately decreasing the fibre gap.

The overall goal of the project is to increase the number of healthy life years of the population. Currently, Estonia has recorded one of the lowest number of healthy life years at birth in the EU. To achieve this goal, three closely related areas of digital health are researched, developed and piloted. We use the standardized data exchange environment and digital data of the Estonian health information system (EHIS) to develop applications that increase the use of data collected by the person for health promotion, prevention and control of chronic conditions. Second, we focus on sensors and digital applications supported by artificial intelligence (AI) to allow a person to collect both biosignals and textual data in machine-readable form. With this, we speed up the detection of health risks and reduce the healthcare workload. Thirdly, we develop various AI methods by combining the data in EHIS and the Health Insurance Fund's database, as well as the data collected by the person.

This project is based on the new paradigm of "flow as information", a groundbreaking approach for underwater sensing of multiscale flows in Nature. It will lead to new, optimized devices and methods to measure, classify and explore the underwater environment when traditional methods are too expensive or simply do not work. Flow as information is inspired by aquatic animals who have evolved advanced sensory systems which combine sensing and information processing into a single framework. The proposal will advance TalTech's underwater sensing technologies from working prototypes (TRL3 to TRL5) to tests in relevant operational environments (TRL6), and support technology transfer to Estonian and international firms. These devices and methods will provide researchers, industry and authorities with new and reliable sources of flow data during extreme climate and weather events where conventional devices fail and when critical infrastructure is at risk, such as during storm surges and floods.

The importance of antimicrobial membranes has significantly grown during the recent COVID pandemic era. Nanofibrous antimicrobial membranes have seen novel applications in biomedicine, such as face masks against viral threats or wound dressings used in chronic patient care. Composite electrospun nanofiber meshes are convenient to use as antimicrobial membranes. At present, the lack of automated, inline quality control limits both the pilot and large scale production of multi-material multilayer composite membranes. The alternative, manual re-calibration greatly limits production throughput and thus commercial viability. The goal of this R&D activity is to create technology for scalable inline quality control of electrospun nanofiber meshes. Using cognitive electronics, the system will be capable of continuous multiparameter monitoring and electrospinning process control to maintain optimal product quality and distribution.

The general goal is the development of electronic devices for clinical measurement of soft tissues. Specific result is a device for continuous monitoring of the condition of the heart muscle during heart surgery. Heart disease is the most common cause of death (WHO 2019, https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death). Surgery plays an important role in the treatment of advanced heart diseases, with a risk of damage to the muscle (of a stopped heart!) during surgery. Current heart protection methods are poorly controlled, as based on schemes developed by trial and error. The unique solution being created will ensure the real-time usage of protective solutions based on objective heart muscle indicators. The technology being created, also allows measuring of various other muscle, fat and connective tissues, and could also distinguish benign and malignant tissues. The solutions created use inventive electrical impedance spectroscopy technologies of TalTech.

Wood or lignocellulosic biomass more generally, is a readily available renewable resource, offering sustainable solutions for our growing human population. The core wood polymers - cellulose, hemicellulose, and lignin - serve as fundamental components, extending beyond paper production to produce valuable wood sugars, textile fibers, thermoplastics, and fine chemicals. In our project, we are developing enzyme technologies utilizing extremophilic microbe-derived enzymes to break down and modify lignin, remove toxic phenolic compounds, convert cellulose into wood sugars, and advance enzyme-catalyzed cellulose technologies. Additionally, the project focuses on advancing technologies for converting kraft, hydrolysis (and organosolv) and synthetic lignins into porous materials, thermoplastics, and cutting-edge catalysts.

Sensing, capturing and separating enantiomers is important for environmental safety, agricultural chemistry, and drug design. The use of hemicucurbiturils is an effective strategy because of the combination of various monomers in a single-step templated mechanochemical synthesis. Due to the absence of bulk solvent the self-organizing efficiency is amplified and there is less waste - the process is green and sustainable. The current work will study the fundamentals of self-organization of hemicucurbiturils, the binding (capturing) of chiral molecules, and detecting chirality using supramolecular complexes. In the long term, the empirical observations will be combined with results from computational chemistry and cheminformatics to build models for predicting necessary monomers and reaction conditions to form macrocycles with desired properties. The outcomes of the project are expected to be highly useful for organizations and industries that monitor, use, or manufacture chiral compounds

TIRAMISU “Training and Innovation in Reliable and Efficient Chip Design for Edge AI” is a European HORIZON MSCA Doctoral Network project. The general research objective of TIRAMISU is a practical methodology for reliable and energy-efficient Edge AI hardware backbone design and innovation management. The action will provide strong interdisciplinary training for future European engineers and researchers driving the innovation for reliable and energy-efficient Edge AI chips. The consortium is strategically designed to foster cross-disciplinary synergies, by seamlessly integrating innovation management research with the technical aspects of Edge AI design. The non-academic sector is represented by a European flagship R&D hub for nanoelectronics - IMEC, a global leader in industrial electronics and the largest semiconductor manufacturer in Germany - Infineon, a trusted automotive solutions provider - Dumarey, the worldwide leader in EDA tools development - Cadence. The academic excellence is established by the top ICT and Technology Innovation engineering universities and Europe's largest application-oriented research organisation - Fraunhofer.

The study contributes in the framework of the measure „Enhancing the Knowledge Intensity of Entrepreneurship in Ida-Viru: Developing Research Capacity in Ida-Viru County to Establish a RDI Network” during 2024-2028 to the development of a theoretically grounded framework for transition processes as well as methodology and models for empirical monitoring of relevant aspects of transition. The study consists of three themes: just transition governance, Ida-Viru innovation system, and changing business models of Ida-Viru companies. The aforementioned three thematic work packages contribute together with the remaining four (monitoring and prognosis of Ida-Viru employment, development of Ida-Viru technology intensive innovation niches, vulnerability of local population and innovation potential, health impacts of transition and development of health services) to the development of the transition processes framework and are implemented in cooperation with UT research teams.

The nervous system consists of multiple cell types with distinct physiological specializations and gene expression patterns. In tissue, these cells form a complex, intertwined network that is subject to constant interaction between different cell types. This complexity poses a challenge for researchers in both separating cell types for analysis as well as studying interactions and information transfer between cells. In this application, we propose a molecular neuroscience study addressing both aspects. First, we are developing proteomics methods to allow analysis of newly synthesized proteins on a cell type-specific basis. Second, we shall use novel genetic tools for cell type-specific stimulation and gene expression analysis in primary co-cultures of neurons and astroglial cells. We shall use this system to probe gene expression signatures in neuron-astrocyte communication and determine the transmitters that form the basis of this communication.