Darbo tikslas – nustatyti aminorūgščių, esančių skystųjų amidinio azoto trąšų sudėtyje, panaudotų papildomam žieminių kviečių tręšimui per lapus poveikį augalų biologiniam potencialui. Ginamieji disertacijos teiginiai: 1. Aminorūgščių trąšos skatina žieminių kviečių lapų fotosintezės aktyvumą bei didina fotosintetinių rodiklių parametrus. 2. Skirtingų aminorūgščių koncentracijų tirpalai, naudojami įvairiais augalų vystymosi tarpsniais, nevienodai įtakoja žieminių kviečių grūdų derlingumą. 3. Aminorūgštys, naudojamos tręšimui per lapus, teigiamai veikia žieminių kviečių grūdų kokybę bei baltymų ir aminorūgščių kiekybinę ir kokybinę sudėtį. / Aim of the work The study was designed to establish the effects of amino acids, present in the composition of liquid amide nitrogen fertilizers, applied as additional foliar feeding on winter wheat. Proposition to be defended: 1. Amino acid fertilization promotes winter wheat leaf photosynthetic activity and increases parameters of photosynthetic indicators. 2. Different concentrations of amino acids applied at various plant growth stages highlight the differences of their efficacy for winter wheat grain yield. 3. Foliar–applied amino acids positively affect winter wheat grain quality and quantitative and qualitative composition of proteins and amino acids.

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Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.

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xvi, 227 leaves : ill. ; 26 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Plant Physiology, 1971

Introduction of soft ionization techniques, such as electrospray ionization (ESI), has resulted in extensive use of mass spectrometry based techniques to study biomolecules in the gas phase. Despite thorough studies of the gas-phase chemistry of even-electron biomolecules, the examination of their odd-electron counterparts has to this point been much less extensive due to the inefficiency of ESI in generating such species. Among various methods that could be employed to generate and study odd-electron biomolecules in the gas phase, redox processes involving metal ions and homolytic cleavage of metallated amino acid or peptide derivatives would be attractive from a chemical perspective since, in principle, a wide range of metals and biomolecules or biomolecule derivatives could be explored. An important aspect of these approaches is that they can be carried out on a wide range of tandem mass spectrometers equipped with electrospray ionization and collision induced dissociation capabilities. (For complete abstract open document)

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 4 uppsatser.