Machine Learning Methods for Effectively Discovering Complex Relationships in Graph Data

Graphs are extensively employed in many systems due to their capability to capture the interactions (edges) among data (nodes) in many real-life scenarios. Social networks, biological networks and molecular graphs are some of the domains where data have inherent graph structural information. Built graphs can be used to make predictions in Machine Learning (ML) such as node classifications, link predictions, graph classifications, etc. But, existing ML algorithms hold a core assumption that data instances are independent of each other and hence prevent incorporating graph information into ML. This irregular and variable sized nature of non-Euclidean data makes learning underlying patterns of the graph more sophisticated. One approach is to convert the graph information into a lower dimensional space and use traditional learning methods on the reduced space. Meanwhile, Deep Learning has better performance than ML due to convolutional layers and recurrent layers which consider simple correlations in spatial and temporal data, respectively. This proves the importance of taking data interrelationships into account and Graph Convolutional Networks (GCNs) are inspired by this fact to exploit the structure of graphs to make better inference in both node-centric and graph-centric applications. In this dissertation, the graph based ML prediction is addressed in terms of both node classification and link prediction tasks. At first, GCN is thoroughly studied and compared with other graph embedding methods specific to biological networks. Next, we present several new GCN algorithms to improve the prediction performance related to biomedical networks and medical imaging tasks. A circularRNA (circRNA) and disease association network is modeled for both node classification and link prediction tasks to predict diseases relevant to circRNAs to demonstrate the effectiveness of graph convolutional learning. A GCN based chest X-ray image classification outperforms state-of-the-art transfer learning methods. Next, the graph representation is used to analyze the feature dependencies of data and select an optimal feature subset which respects the original data structure. Finally, the usability of this algorithm is discussed in identifying disease specific genes by exploiting gene-gene interactions

Effects of experimental parameters on the growth of GaN nanowires on Ti-film/Si(1 0 0) and Ti-foil by molecular beam epitaxy

Gallium nitride (GaN) nanostructures are used in optoelectronic applications due to their unique optical and electronic properties. For some optoelectronic applications and potential photocatalytic systems, the growth of GaN nanowires on metallic substrates instead of expensive single crystalline semiconductors can be beneficial due to specific properties of metals. In this study, GaN nanowire systems were grown on 300 nm Ti-film/Si(1 0 0) and Ti-foil by plasma assisted molecular beam epitaxy (PA-MBE) and characterized in situ by Auger electron spectroscopy (AES) and ex situ by scanning electron microscopy (SEM). Effects of (i) the nature of substrate surface, (ii) Ga flux, and (iii) substrate temperature on the growth of GaN nanowires were investigated. Nearly vertical nanowires can be grown on Ti-films covered with amorphous TiOx or TiOxNy, which is formed during the nitridation process. To grow nearly vertical nanowires on Ti-foils, pre-nitridation of the substrate surface was found to be important. The orientation of GaN nanowires grown on nitridated Ti-foil is determined by the grain alignment of the original Ti-foil, however, GaN nanowires grown on nitridated Ti-foils are uniformly oriented to one direction within an individual grain, which is most likely due to the epitaxial relation between the nanowires and the underneath grains of the polycrystalline Ti-foils. Both the orientation and nanowire density vary on different grains

Reactions of ethanol over CeO2 and Ru/CeO2 catalysts

The reaction of ethanol has been investigated on Ru/CeO2 in steady state conditions as well as with temperature programmed desorption (TPD). High resolution transmission electron microscopy (HRTEM) images indicated that the used catalyst contained Ru particles with a mean size of ca. 1.5 nm well dispersed on CeO2 (of about 12–15 nm in size). Surface uptake of ethanol was measured by changing exposure to ethanol followed by TPD. Saturation coverage is found to be between 0.25 and 0.33 of a monolayer for CeO2 that has been prior heated with O2 at 773 K. The main reactions of ethanol on CeO2 during TPD are: re-combinative desorption of ethanol; dehydrogenation to acetaldehyde; and dehydration to ethylene. The dehydration to ethylene occurs mainly in a small temperature window at about 700 K and it is attributed to ethoxides adsorbed on surface-oxygen defects. The presence of Ru considerably modified the reaction of ceria towards ethanol. It has switched the desorption products to CO, CO2, CH4 and H2. These latter products are typical reforming products. Ethanol steam reforming (ESR) conducted on Ru/CeO2 indicated that optimal reaction activity is at about 673 K above which CO2 production declines (together with that of H2) due to reverse water gas shift. This trend was well captured during ethanol TPD where CO2 desorbed about 50 K below than CO on both oxidized and reduced Ru/CeO2 catalysts.Peer ReviewedPostprint (author's final draft

3\u27 end additions by T7 RNA polymerase are RNA self-templated, distributive and diverse in character––RNA-Seq analyses

Synthetic RNA is widely used in basic science, nanotechnology and therapeutics research. The vast majority of this RNA is synthesized in vitro by T7 RNA polymerase or one of its close family members. However, the desired RNA is generally contaminated with products longer and shorter than the DNA-encoded product. To better understand these undesired byproducts and the processes that generate them, we analyze in vitro transcription reactions using RNA-Seq as a tool. The results unambiguously confirm that product RNA rebinds to the polymerase and self-primes (in cis) generation of a hairpin duplex, a process that favorably competes with promoter driven synthesis under high yield reaction conditions. While certain priming modes can be favored, the process is heterogeneous, both in initial priming and in the extent of priming, and already extended products can rebind for further extension, in a distributive process. Furthermore, addition of one or a few nucleotides, previously termed ‘nontemplated addition,’ also occurs via templated primer extension. At last, this work demonstrates the utility of RNA-Seq as a tool for in vitro mechanistic studies, providing information far beyond that provided by traditional gel electrophoresis

Design of a swimming snake robot

This paper presents the design and realization of a bioinspired snake robot that can move on the water surface. This robot mimics the locomotion strategies of anguilliform fishes such as eels and lampreys, which have a thin, long, cylindrical body and whose movement resembles the crawling of a snake. An autonomous underwater vehicle with such a shape can pass through narrow crevices and reach places inaccessible to other swimming robots. Moreover, this locomotion entails a high energy efficiency and outstanding agility in maneuvers. The body of the bioinspired robot consists of a modular structure in which each module contains a battery, the electronic board, and a servo motor that drives the following module. The head of the robot has a different shape as it contains a camera and an ultrasonic sensor used to detect obstacles. In addition to the design of the robot, this paper also describes the implementation of the kinematic model

Bacterial chemoreceptor signaling complexes control kinase activity by stabilizing the catalytic domain of CheA

Motile bacteria have a chemotaxis system that enables them to sense their environment and direct their swimming toward favorable conditions. Chemotaxis involves a signaling process in which ligand binding to the extracellular domain of the chemoreceptor alters the activity of the histidine kinase, CheA, bound ~300 Å away to the distal cytoplasmic tip of the receptor, to initiate a phosphorylation cascade that controls flagellar rotation. The cytoplasmic domain of the receptor is thought to propagate this signal via changes in dynamics and/or stability, but it is unclear how these changes modulate the kinase activity of CheA. To address this question, we have used hydrogen deuterium exchange mass spectrometry to probe the structure and dynamics of CheA within functional signaling complexes of the Escherichia coli aspartate receptor cytoplasmic fragment, CheA, and CheW. Our results reveal that stabilization of the P4 catalytic domain of CheA correlates with kinase activation. Furthermore, differences in activation of the kinase that occur during sensory adaptation depend on receptor destabilization of the P3 dimerization domain of CheA. Finally, hydrogen exchange properties of the P1 domain that bears the phosphorylated histidine identify the dimer interface of P1/P1’ in the CheA dimer and support an ordered sequential binding mechanism of catalysis, in which dimeric P1/P1’ has productive interactions with P4 only upon nucleotide binding. Thus stabilization/destabilization of domains is a key element of the mechanism of modulating CheA kinase activity in chemotaxis, and may play a role in the control of other kinases

Evaluation of concept importance in concept maps mined from lecture notes: computer vs human

Concept maps are commonly used tools for organising and representing knowledge in order to assist meaningful learning. Although the process of constructing concept maps improves learners’ cognitive structures, novice students typically need substantial assistance from experts. Alternatively, expert-constructed maps may be given to students, which increase the workload of academics. To overcome this issue, automated concept map extraction has been introduced. One of the key limitations is the lack of an evaluation framework to measure the quality of machine-extracted concept maps. At present, researchers in this area utilise human experts’ judgement or expert-constructed maps as the gold standard to measure the relevancy of extracted knowledge components. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information that has to be organised. Therefore, this paper introduces a machine-based approach which studies the relative importance of knowledge components and organises them hierarchically. We compare machine-extracted maps with human judgment, based on expert knowledge and perception. This paper describes three ranking models to organise domain concepts. The results show that the auto-generated map positively correlates with human judgment (rs~1) for well-structured courses with rich grammar (well-fitted contents).Thushari Atapattu, Katrina Falkner and Nickolas Falkne