A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data. In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical explanation of the brain map, for data visualisation, classification, and clustering is presented

A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering

Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving ma

Insights into genomics and gene expression of oleaginous yeasts of the genus Rhodoturula

The Rhodotorula glutinis cluster includes oleaginous yeast species with high biotechnological potential as cell factories for lipid, carotenoid, and oleochemical production. They can grow on various low-cost residues, such as lignocellulose and crude glycerol. The accumulated microbial lipids have a fatty acid composition similar to vegetable oils, representing a sustainable alternative in food, feed, and biofuel industries. Strain manipulation could further increase its economic attractiveness. A basic prerequisite for this is to understand the genome organization of Rhodotorula spp. and the lipid and carotenoid metabolism on residual products in detail. This doctoral thesis made a substantial contribution to this:Hybrid genome assemblies from R. toruloides, R. glutinis, and R. babjevae were built de novo using a combination of short- and long-read sequencing. By doing this, high-quality genomes could be achieved, showing high completeness and contiguity (near-chromosome level). Among other things, this enabled the prediction of ploidy and the discovery of extrachromosomal structures. Phylogenetic analyses revealed high intraspecific divergence in R. babjevae. Using RNA-seq data generated from different growth conditions, gene annotation for R. toruloides could be significantly increased, and comprehensive gene expression data could be obtained. These allowed conclusions to be drawn about the activation and mechanism of lipid accumulation in R. toruloides grown on residual products such as crude glycerol and hemicellulosic hydrolysate

Fault Analysis of Electromechanical Systems using Information Entropy Concepts

Fault analysis of mechanical and electromechanical systems has been a subject of considerable interest in the systems and control research community. Entropy, under its various formulations is an important variable, which is unrivaled when it comes to measuring order (or organization) and/or disorder (or disorganization). Researchers have successfully used entropy based concepts to solve various challenging problems in engineering, mathematics, meteorology, biotechnology, medicine, statistics etc. This research tries to analyze faults in electromechanical systems using information entropy concepts. The objectives of this research are to develop a method to evaluate signal entropy of a dynamical system using only input/output measurements, and to use this entropy measure to analyze faults within a dynamical system. Given discrete-time signals corresponding to the three-phase voltages and currents of an electromechanical system being monitored, the problem is to analyze whether or not this system is healthy. The concepts of Shannon entropy and relative entropy come from the field of Information Theory. They measure the degree of uncertainty that exists in a system. The main idea behind this approach is that the system's dynamics may have regularities hidden in measurements that are not obvious to see. The Shannon entropy and relative entropy measures are calculated by using probability distribution functions (PDF) that are formed by sampling the time series currents and voltages of a system. The system's health is monitored by, first, sampling the currents and voltages at certain time intervals, then generating the corresponding PDFs and, finally, calculating the information entropy measures. If the system dynamics are unchanged, or in other words, the system continues to be healthy, then the relative entropy measures will be consistently low or constant. But, if the system dynamics change due to damage, then the corresponding relative entropy and Shannon entropy measures will be increasing compared to the entropy of the system with less damage

Single-Cell (Meta-)Genomics of a Dimorphic Candidatus Thiomargarita nelsonii Reveals Genomic Plasticity

The genus Thiomargarita includes the world's largest bacteria. But as uncultured organisms, their physiology, metabolism, and basis for their gigantism are not well understood. Thus, a genomics approach, applied to a single Candidatus Thiomargarita nelsonii cell was employed to explore the genetic potential of one of these enigmatic giant bacteria. The Thiomargarita cell was obtained from an assemblage of budding Ca. T. nelsonii attached to a provannid gastropod shell from Hydrate Ridge, a methane seep offshore of Oregon, USA. Here we present a manually curated genome of Bud S10 resulting from a hybrid assembly of long Pacific Biosciences and short Illumina sequencing reads. With respect to inorganic carbon fixation and sulfur oxidation pathways, the Ca. T nelsonii Hydrate Ridge Bud S10 genome was similar to marine sister taxa within the family Beggiatoaceae. However, the Bud S10 genome contains genes suggestive of the genetic potential for lithotrophic growth on arsenite and perhaps hydrogen. The genome also revealed that Bud 510 likely respires nitrate via two pathways: a complete denitrification pathway and a dissimilatory nitrate reduction to ammonia pathway. Both pathways have been predicted, but not previously fully elucidated, in the genomes of other large, vacuolated, sulfur-oxidizing bacteria. Surprisingly, the genome also had a high number of unusual features for a bacterium to include the largest number of metacaspases and introns ever reported in a bacterium. Also present, are a large number of other mobile genetic elements, such as insertion sequence (IS) transposable elements and miniature inverted-repeat transposable elements (MITEs). In some cases, mobile genetic elements disrupted key genes in metabolic pathways. For example, a MITE interrupts hupL, which encodes the large subunit of the hydrogenase in hydrogen oxidation. Moreover, we detected a group I intron in one of the most critical genes in the sulfur oxidation pathway, dsrA. The dsrA group I intron also carried a MITE sequence that, like the hupL MITE family, occurs broadly across the genome. The presence of a high degree of mobile elements in genes central to Thiomargarita's core metabolism has not been previously reported in free-living bacteria and suggests a highly mutable genome

Single Cell Sequencing Facilitates Genome-enabled Biology in Uncultured Fungi and Resolves Deep Branches on the Fungal Tree of Life

Microbial life on Earth is the most diverse life on Earth. The magnitude of microbial diversity is obscured by their small statures, relatively short list of defining morphological characteristics, and general recalcitrance to being separated from nature and brought into the laboratory. Most microbes cannot be grown under axenic conditions (i.e., uncultured), a simple reality that impedes their discovery in complex natural systems and downstream studies to understand their biology. A point no less important in the age of genome-enabled biological research, the uncultured status of most microbes precludes sequencing of their genomes via conventional high-throughput sequencing, which requires ample input material. Single cell sequencing offers a viable workaround to this central obstacle by enabling the amplification of genomic DNA from individual cells up to amounts more than sufficient for sequencing. That said, this workaround introduces biases to sequence composition and exacerbates contamination, both of which present unique challenges to downstream genome-scale analyses. Fungi constitute a diverse lineage of heterotrophic eukaryotes that sometimes blur the line between microbial and macroscopic life. Our understanding of fungi is wildly incomplete and biased toward fungi that produce macroscopic forms or those that can be grown under axenic conditions. Even in the age of genome-enabled biological research, most fungi that are microscopic, uncultured, or especially both remain poorly understood. In this dissertation, I use single cell sequencing, sometimes combined with conventional genome sequencing, to address this gap by conducting genome-enabled biological research in uncultured or under-sampled sectors of the fungal tree of life. In Chapter 2, I design and deploy a novel computational approach to filtering the biased and often contaminated sequence data associated with single cell sequencing. I demonstrate its ability to outperform available filtering approaches using genuine and mock datasets. In Chapter 3, I use single cell sequencing of predatory fungi to discover novel endohyphal bacteria colonizing fungi in a phylum where this type of symbiosis was entirely unknown. Genome-scale phylogenetic analyses implicate recent interphylum host switches for bacteria thought to transmit predominantly vertically. The novel bacterial endosymbionts discovered have similar genomes to other endohyphal bacteria but have, in some cases, acquired and retained horizontally transferred genes from animals. In Chapter 4, I use genome-scale data to infer a robustly supported phylogeny of zoosporic fungi. Mapping of genetic traits and ploidy inferred from sequence data suggests that fungal evolution has been driven by gradual loss and that most early diverging lineages have diploid-dominant life cycles. In Chapter 5, I use genome-scale data to resolve a disagreement between classical taxonomy and molecular phylogenetics revolving around the phylogenetic placement of the enigmatic, arthropod-mummifying fungal genus Neozygites. Through the development of novel computational methods, genome-scale phylogenetics, and a comparative approach, this dissertation demonstrates the utility of single cell sequencing in closing vast gaps in our understanding of fungi.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169656/1/amsesk_1.pd

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19