The Peruvian Andes are one of the most important water sources for the country. Therefore, their exploitation might pose critical threats for local farming activities, national economy, and water quantity and quality. At ...

Die Entwicklung gastroretentiver Arzneiformen ist komplex, da neben der kontrollierten Freisetzung des Wirkstoffs zusätzlich die Verlängerung der Verweilzeit im Magen gewährleistet werden muss. Hierfür werden meist hydrophile ...

Ziel der Arbeit war, ein robustes System zur intermolekularen Palladium katalysierten regiodivergenten Carboaminierung zu entwickeln und die Grenzen der Substratpalette dieses Prozesses zu erforschen. Der Fokus der Arbeit ...

Phospholipases D (PLDs) are involved in different plant stress responses, exclusively to abiotic stresses like cold, salinity, and drought. The PLD enzymes catalyse the hydrolysis of membrane phospholipids into phosphatidic acid (PA) and a free head group. PA is a membranous second-messenger molecule that is involved in various stress-dependent signal transduction pathways. Earlier, an unknown protein PLDrp1 recognised as a putative target protein downstream of the PLD α1 signalling pathway. The substantial dependence of this protein from PLD α1 could be diminished under water stress conditions, raising the question about the mechanisms involved in this observation. Interestingly, the homolog of PLDrp1 with a 60% sequence similarity At3g29075 protein was identified in A. thaliana. To characterise unknown protein, At3g29075 was the main motive of this study.<br /> This study exhibits that the At3g29075 protein is a class V glycine-rich protein, with more than 21.1% lysine residues. The C-terminal end of the unknown protein At3g29075 has no similar protein or motif in the whole protein database. The protein localisation has confirmed to the cytosol and nucleus. Dehydration stress induces the expression of At3g29075. Wild Type and mutant plants were grown and phenotypically monitored upon standard growth conditions to define the protein’s function. The overexpression lines of AT3G29075 showed early flowering compared to the wild type, and at3g29075 knockdown mutant....

In this thesis, we consider problems arising from the physical phenomenon of particle sedimentation. We focus on non-Brownian particles in fluids at zero Reynolds number. Microscopically, the particle system is described ...

Pollination syndromes represent groups of floral phenotypes that have originated and diversified in interaction with biotic and abiotic pollen vectors. Plant trait pattern that constitute respective syndromes have been ...

Nearly 50 million people worldwide suffer from epilepsy, with 1/3 of the patients remaining without seizure control. This high number emphasizes the importance to develop new anti-epileptic drugs (AEDs) and to understand ...

Pflegeheimbewohner sind häufig von Polymedikation und damit einhergehenden arzneimittelbezogenen Problemen (ABP) betroffen. Medikationsanalysen können die Angemessenheit der Medikation bei Älteren verbessern. Mit der ...

Agglomerative clustering (AC) is a very popular greedy method for computing hierarchical clusterings in practice, yet its theoretical properties have been studied relatively little. We consider AC with respect to the most ...

Frequent itemset mining is a classical data mining task with a broad range of applications, including fraud discovery and product recommendation. The enumeration of frequent itemsets has two main benefits for such applications: First, frequent itemsets provide a human-understandable representation of knowledge. This is crucial as human experts are involved in designing systems for these applications. Second, many efficient algorithms are known for mining frequent itemsets. This is essential as many of today’s realworld applications produce ever-growing data streams. Examples of these are online shopping, electronic payment or phone call transactions. With limited physical main memory, the analysis of data streams can, in general, be only approximate. State-ofthe-art algorithms for frequent itemset mining from such streams bound their error by processing the transactions in blocks of fixed size, either each transaction individually or in mini-batches. In theory, single transaction-based updates provide the most up-todate result after each transaction, but this enumeration is inefficient in practice as the number of frequent itemsets for a single transaction can be exponential in its cardinality. Mini-batch-based algorithms are faster but can only produce a new result at the end of each batch. In this thesis, the binary choice between up-to-date results and speed is eliminated. To provide more flexibility, we develop new algorithms with a probabilistic error bound that can process an arbitrary number of transactions in each batch.<br/>State-of-the-art algorithms mining frequent itemsets from data streams with minibatches derive the size of the mini-batch from a user-defined error parameter and hence couple their error bound to the size of the update. By introducing a dynamic error bound that adapts to the length of the data stream the error is decoupled from the size of the update. The benefits of this approach are twofold: First, the dynamic error bound is independent of the size of the update. Hence, an arbitrary number of transactions can be processed without losing the error bound. Second, the bound becomes tighter as more transactions arrive and thus the tolerated error decreases, in contrast to algorithms with static thresholds. Our approach is extensively compared to the state-of-the-art in an empirical evaluation. The results confirm that the dynamic approach is not only more flexible but also outperforms the state-of-the-art in terms of F-score for a large number of data streams.<br/>As it is easier for experts to extract knowledge from a smaller collection, we consider mining a compact pattern set. Especially useful are parameterized pattern classes for which the expert can regulate the size of the output. An example of such a parameterized pattern class are strongly closed itemsets. Additionally, they are stable against small changes in the data stream. We present an algorithm mining strongly closed itemsets from data streams. It builds on reservoir sampling and is thus capable of producing a result after any number of transactions, once the initial sample is complete. The high approximation quality of the algorithm is empirically demonstrated and the potential of strongly closed patterns for two stream mining tasks is shown: concept drift detection and product configuration recommendation....