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3D-bioprinted Gelatin/Alginate loaded with Carbon Nanotubes for tissue engineering application
Partovi Nasr, Minoo ; Zumberg, Inna ; Chmelíková, Larisa ; Fohlerová, Zdenka ; Provazník, Valentine
The objective of utilizing 3D-bioprinted Gelatin/Alginate loaded with Carbon Nanotubes (CNTs) in tissue engineering applications is to create scaffolds that closely mimic the natural extracellular matrix (ECM), thereby enhancing cell growth, proliferation, and differentiation. Gelatin and Alginate, both biocompatible materials, have been widely researched for their potential in bioprinting due to their similarity to the ECM, offering a conducive environment for cell encapsulation and tissue regeneration. The addition of CNTs to these hydrogels significantly improves the mechanical properties and stability of the scaffolds, making them more suitable for supporting tissue development. CNTs, known for their unique properties such as high tensile strength and electrical conductivity, contribute to the development of scaffolds that not only support mechanical stability but also can influence cellular behavior and tissue formation. This integration aims at enhancing the functionality of 3D-bioprinted scaffolds, enabling them to better support the formation and maturation of engineered tissues. Furthermore, the electrical conductivity of CNTs-loaded scaffolds can be exploited to stimulate electrical activity in tissues, such as cardiac and neural tissues, promoting organized tissue development and functionality. The strategic combination of Gelatin/Alginate with CNTs in 3D bioprinting offers a promising approach to tissue engineering, aiming to address the critical challenge of replicating the complex structure and function of natural tissues. This innovative methodology not only enhances the mechanical and structural properties of the scaffolds but also introduces new possibilities in tissue engineering through the electrical stimulation of tissues, paving the way for the creation of more complex and functional tissue constructs.
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Design of analysis and measurement methodology for evaluation of dynamic physiological changes in plants
Rumlerová, Tereza ; Provazník, Valentine (referee) ; Kolář, Radim (advisor)
Tato diplomová práce s názvem „Návrh analýzy a metodiky měření pro hodnocení dynamických fyziologických změn rostlin“ zkoumá vývoj a aplikaci inovativního algoritmu pro frekvenční analýzu oscilačních signálů odpovědi rostlin na dynamicky se měnící světlo s využitím Fourierovy transformace. Algoritmus, speciálně přizpůsobený pro použití v prostředí C# a integrovaný do softwarové sady FluorCam10 od společnosti Photon Systems Instruments (PSI), významně zlepšuje schopnosti zobrazování chlorofylové fluorescence – zásadního ukazatele zdraví rostlin a fotosyntetické efektivity. Výzkum zakládá pevný teoretický základ v fenotypizaci rostlin a zobrazování chlorofylové fluorescence, přezkoumává stávající metodologie a připravuje půdu pro pokročilé prozkoumání dynamiky fotosyntézy. Jádro práce se zabývá technickými detaily aplikace Fourierovy transformace pro dekompozici oscilačních signálů za účelem posouzení fyziologických dopadů environmentálních změn na mechanismy rostlin. To zahrnuje důkladné hodnocení několika knihoven FFT, aby bylo možné identifikovat nejefektivnější a nejpřesnější integraci algoritmu, což zajišťuje optimální výkon při zpracování fyziologických dat rostlin. Praktické aplikace jsou demonstrativně prezentovány integrací algoritmu s softwarem FluorCam10 (FC10), vyvinutým společností PSI. Tato integrace umožňuje detailní analýzu na úrovni pixelů a oblastí zájmu, rozšiřuje funkcionalitu softwaru a umožňuje podrobné zkoumání reakcí rostlin v dynamických environmentálních podmínkách. Kromě toho rozšiřuje funkčnost softwaru FC10 o spolupráci s nástrojem PlantScreen SW, který podporuje vysoce výkonná měření a řízení složitých, víceprotokolových experimentů v plně automatizovaném rámci. Práce rovněž popisuje komplexní metodiku pro standardizaci experimentálních nastavení pro studium odpovědí rostlin na dynamicky se měnící světlo, detailně rozpracovává vývoj protokolů měření a akvizice za účelem zajištění robustního sběru dat, která přesně odrážejí fyziologické změny způsobené definovanými environmentálními proměnnými. Experimentální validace je poskytována prostřednictvím studií na rostlinách Arabidopsis thaliana a rajčat, které demonstrují účinnost algoritmu při detekci významných fyziologických odpovědí na různé frekvence světelné oscilace a přispívají k poznatkům o genetické variabilitě v podmínkách environmentálního stresu. Tato práce posouvá obor dynamických fyziologických studií rostlin integrací akademického výzkumu s praktickými průmyslovými aplikacemi. Klade základ pro budoucí výzkumné spolupráce zaměřené na další prozkoumání a zpřesnění našeho porozumění dynamickým odpovědím rostlin. Tato úsilí mají potenciál významně zvýšit přesnost a použitelnost měření fotosyntézy v reálných podmínkách. Práce zdůrazňuje vznikající obor dynamické fenotypizace s výkonným měřením jako slibnou oblast studia s významnými důsledky jak pro akademický výzkum, tak pro praktické zemědělské aplikace.
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Genomic prediction based on deep learning using LSTM networks
Komjaty, Daniel ; Provazník, Valentine (referee) ; Schwarzerová, Jana (advisor)
This bachelor's thesis deals with the problem of genomic prediction using machine learning based prediction methods. The first part of the thesis deals with theoretical review with a narrower focus on genomic prediction and its application to plant data. Thesis then discusses prediction algorithms and machine learning based models that are used for genomic prediction. The following section contains a more detailed description of the used genomic and metabolomic data, provided by the thesis supervisor. The fourth section describes the actual implementation of the selected machine learning models. The last fifth section deals with the evaluation of the machine learning models and discussion of the results.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Smital, Lukáš (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. In the first chapter the heart and its electrical activity measurement is described shortly. In addition to that, the abnormalities which are going to be classified in this thesis are also briefly described. In the second chapter, it is described how the ECG was diagnosed earlier, by classical methods that preceded deep learning. Some of the shortcomings that the classical methods have compared to deep learning are also described here. The third part already pays attention to deep learning itself, and its contribution and advantages compared to classical methods. Convolutional neural networks and their individual blocks are also described here, later attention is paid to selected architectures that were used in some studies. The fourth chapter already focuses on the practical part, in which the data used from the PhysioNet database, the proposed algorithm and its implementation are described in more detail. In the fifth chapter the results are discussed and compared to the corresponding publications.
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Correction of the concept of drift in prediction models
Michálková, Eva ; Provazník, Valentine (referee) ; Schwarzerová, Jana (advisor)
The main goal of this bachelor thesis is the analysis of concept drift in metabolomics. Concept drift is an undesirable phenomenon and can be caused by nonstationary data. It can have a negative impact on the performance and reliability of predictive modelling. This challenge can be solved by concept drift detection and subsequent correction. One of the fields where this issue has recently emerged is metabolomic diagnostics. Metabolomic data analysis can lead to early detection of several serious diseases, which can help with the recovery process. When diagnosing an illnes predictive models present a way to make the process more efficient, faster and give the option of personalization. The first part of this thesis specifies concept drift, it’s detection and correction methods and the importance of metabolomics and prediction models. The second part deals with the implementation of some available algorithms for concept drift detection and correction and the implementation of automatic concept drift correction. Finally, in the second part results and their discussion are described.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Chmelík, Jiří (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. This problem is solved by standard methods such as random forest, artificial neural networks or K-nearest neighbors. However, thanks to its ability to independently extract symptoms, deep learning methods are also popular. All these methods are described in the theoretical part. In the practical part, deep learning models were designed, functionality support was verified using data from the PhysioNet database. Two pilot models were created and subsequently optimized. From the entire parameter optimization procedure, three models are available, of which the best accuracy achieves an F1 score of 87.35% and 83.7%, and the second best achieves an F1 score of 77.74% and an accuracy of 84.53%. The results achieved are discussed and compared with those of similar publications.
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Correction of the concept of drift in prediction models
Michálková, Eva ; Provazník, Valentine (referee) ; Schwarzerová, Jana (advisor)
The main goal of this bachelor thesis is the analysis of concept drift in metabolomics. Concept drift is an undesirable phenomenon and can be caused by nonstationary data. It can have a negative impact on the performance and reliability of predictive modelling. This challenge can be solved by concept drift detection and subsequent correction. One of the fields where this issue has recently emerged is metabolomic diagnostics. Metabolomic data analysis can lead to early detection of several serious diseases, which can help with the recovery process. When diagnosing an illnes predictive models present a way to make the process more efficient, faster and give the option of personalization. The first part of this thesis specifies concept drift, it’s detection and correction methods and the importance of metabolomics and prediction models. The second part deals with the implementation of some available algorithms for concept drift detection and correction and the implementation of automatic concept drift correction. Finally, in the second part results and their discussion are described.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Smital, Lukáš (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. In the first chapter the heart and its electrical activity measurement is described shortly. In addition to that, the abnormalities which are going to be classified in this thesis are also briefly described. In the second chapter, it is described how the ECG was diagnosed earlier, by classical methods that preceded deep learning. Some of the shortcomings that the classical methods have compared to deep learning are also described here. The third part already pays attention to deep learning itself, and its contribution and advantages compared to classical methods. Convolutional neural networks and their individual blocks are also described here, later attention is paid to selected architectures that were used in some studies. The fourth chapter already focuses on the practical part, in which the data used from the PhysioNet database, the proposed algorithm and its implementation are described in more detail. In the fifth chapter the results are discussed and compared to the corresponding publications.
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