Machine Learning Using U-Net Convolutional Neural Networks for the Imaging of Sparse Seismic Data

Machine learning using convolutional neural networks (CNNs) is investigated for the imaging of sparsely sampled seismic reflection data. A limitation of traditional imaging methods is that they often require seismic data with sufficient spatial sampling. Using CNNs for imaging, even if the spatial sampling of the data is sparse, good imaging results can still be obtained. Therefore, CNNs applied to seismic imaging have the potential of producing improved imaging results when spatial sampling of the data is sparse. The imaged model can then be used to generate more densely sampled data and in this way be used to interpolate either regularly or irregularly sampled data. Although there are many approaches for the interpolation of seismic data, here seismic imaging is performed directly with sparse seismic data once the CNN model has been trained. The CNN model is found to be relatively robust to small variations from the training dataset. For greater deviations, a larger training dataset would likely be required. If the CNN is trained with a sufficient amount of data, it has the potential of imaging more complex seismic profiles

Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting

This thesis focuses on two fundamental machine learning problems:unsupervised learning, where no label information is available, and semi-supervised learning, where a small amount of labels are given in addition to unlabeled data. These problems arise in many real word applications, such as Web analysis and bioinformatics,where a large amount of data is available, but no or only a small amount of labeled data exists. Obtaining classification labels in these domains is usually quite difficult because it involves either manual labeling or physical experimentation. This thesis approaches these problems from two perspectives: graph based and distribution based. First, I investigate a series of graph based learning algorithms that are able to exploit information embedded in different types of graph structures. These algorithms allow label information to be shared between nodes in the graph---ultimately communicating information globally to yield effective unsupervised and semi-supervised learning. In particular, I extend existing graph based learning algorithms, currently based on undirected graphs, to more general graph types, including directed graphs, hypergraphs and complex networks. These richer graph representations allow one to more naturally capture the intrinsic data relationships that exist, for example, in Web data, relational data, bioinformatics and social networks. For each of these generalized graph structures I show how information propagation can be characterized by distinct random walk models, and then use this characterization to develop new unsupervised and semi-supervised learning algorithms. Second, I investigate a more statistically oriented approach that explicitly models a learning scenario where the training and test examples come from different distributions. This is a difficult situation for standard statistical learning approaches, since they typically incorporate an assumption that the distributions for training and test sets are similar, if not identical. To achieve good performance in this scenario, I utilize unlabeled data to correct the bias between the training and test distributions. A key idea is to produce resampling weights for bias correction by working directly in a feature space and bypassing the problem of explicit density estimation. The technique can be easily applied to many different supervised learning algorithms, automatically adapting their behavior to cope with distribution shifting between training and test data

Study on Colour Reaction of Vanadium(V) with 2-(2-Quinolylazo)-5-Diethylaminophenol and Its Application

A sensitive, selective and rapid method has been developed for the determination of vanadium based on the rapid reaction of vanadium(V) with 2-(2-quinolylazo)-5-diethylaminophenol (QADEAP). The QADEAP reacts with V(V) in the presence of citric acid-sodium hydroxide buffer solution (pH =3.5) and cetyl trimethylammonium bromide (CTMAB) medium to form a violet chelate of a molar ratio 1:2 (V(V) to QADEAP). The molar absorptivity of the chelate is 1.23 x 105 L mol-1 cm-1 at 590 nm in the measured solution. Beer's law is obeyed in the range of 0.01~0.6 mg mL-1. This method was applied to the determination of vanadium(v) with good results. South African Journal of Chemistry Vol.57 2004: 15-1

The Composition, Diversity and Predictive Metabolic Profiles of Bacteria Associated With the Gut Digesta of Five Sea Urchins in Luhuitou Fringing Reef (Northern South China Sea)

Sea urchins strongly affect reef ecology, and the bacteria associated with their gut digesta have not been well studied in coral reefs. In the current study, we analyze the bacterial composition of five sea urchin species collected from Luhuitou fringing reef, namely Stomopneustes variolaris, Diadema setosum, Echinothrix calamaris, Diadema savignyi, and Tripneustes gratilla, using high-throughput 16S rRNA gene-based pyrosequencing. Propionigenium, Prolixibacter, and Photobacterium were found to be the dominant bacterial genera in all five species. Interestingly, four sea urchin species, including S. variolaris, D. setosum, E. calamaris, and D. savignyi, displayed a higher mean total abundance of the three bacterial genera (69.72 ± 6.49%) than T. gratilla (43.37 ± 13.47%). Diversity analysis indicated that the gut digesta of sea urchin T. gratilla displayed a higher bacterial α-diversity compared with the other four species. PCoA showed that the four groups representing D. setosum, D. savignyi, E. calamaris, and S. variolaris were overlapping, but distant from the group representing T. gratilla. Predictive metagenomics performed by PICRUSt revealed that the abundances of genes involved in amino acid metabolism and metabolism of terpenoid and polyketide were higher in T. gratilla, while those involved in carbohydrate metabolism were higher in the other four sea urchin species. Therefore, our results indicated that the composition, diversity and predictive metabolic profiles of bacteria associated with the gut digesta of T. gratilla were significantly different from those of the other four sea urchin species in Luhuitou fringing reef

The Molecular Mechanisms of OPA1-Mediated Optic Atrophy in Drosophila Model and Prospects for Antioxidant Treatment

Mutations in optic atrophy 1 (OPA1), a nuclear gene encoding a mitochondrial protein, is the most common cause for autosomal dominant optic atrophy (DOA). The condition is characterized by gradual loss of vision, color vision defects, and temporal optic pallor. To understand the molecular mechanism by which OPA1 mutations cause optic atrophy and to facilitate the development of an effective therapeutic agent for optic atrophies, we analyzed phenotypes in the developing and adult Drosophila eyes produced by mutant dOpa1 (CG8479), a Drosophila ortholog of human OPA1. Heterozygous mutation of dOpa1 by a P-element or transposon insertions causes no discernable eye phenotype, whereas the homozygous mutation results in embryonic lethality. Using powerful Drosophila genetic techniques, we created eye-specific somatic clones. The somatic homozygous mutation of dOpa1 in the eyes caused rough (mispatterning) and glossy (decreased lens and pigment deposition) eye phenotypes in adult flies; this phenotype was reversible by precise excision of the inserted P-element. Furthermore, we show the rough eye phenotype is caused by the loss of hexagonal lattice cells in developing eyes, suggesting an increase in lattice cell apoptosis. In adult flies, the dOpa1 mutation caused an increase in reactive oxygen species (ROS) production as well as mitochondrial fragmentation associated with loss and damage of the cone and pigment cells. We show that superoxide dismutase 1 (SOD1), Vitamin E, and genetically overexpressed human SOD1 (hSOD1) is able to reverse the glossy eye phenotype of dOPA1 mutant large clones, further suggesting that ROS play an important role in cone and pigment cell death. Our results show dOpa1 mutations cause cell loss by two distinct pathogenic pathways. This study provides novel insights into the pathogenesis of optic atrophy and demonstrates the promise of antioxidants as therapeutic agents for this condition