Artificial Mixture Methods for Correlated Nominal Responses and Discrete Failure Time.

Multinomial logit model with random effects is a common choice for modeling correlated nominal responses. But due to the presence of random effects and the complex form of the multinomial probabilities, the computation is often costly. We generalize the artificial mixture method for independent nominal response to correlated nominal responses. Our method transforms the complex multinomial likelihood to Poisson-type likelihoods and hence allows for the estimates to be obtained iteratively solving a set of independent low-dimensional problems. The methodology is applied to real data and studied by simulations. For discrete failure time data in large data sets, there are often many ties and a large number of distinct event time points. This poses a challenge of a high-dimensional model. We explore two ideas with the discrete proportional odds (PO) model due to its methodological and computational convenience. The log-likelihood function of discrete PO model is the difference of two convex functions; hence difference convex algorithm (DCA) carries over and brings computational efficiency. An alternative method proposed is a recursive procedure. As a result of simulation studies, these two methods work better than Quasi-Newton method in terms of both accuracy and computational time. The results from the research on the discrete PO model motivate us to develop artificial mixture methods to discrete failure time data. We consider a general discrete transformation model and mediate the high-dimensional optimization problem by changing the model form at the “complete-data” level (conditional on artificial variables). Two complete data representations are studied: proportional hazards (PH) and PO mixture frameworks. In the PH mixture framework, we reduce the high-dimensional optimization problem to many one-dimensional problems. In the PO mixture framework, both recursive solution and DCA can be synthesized into the M-step of EM algorithm leading to simplification in the optimization. PO mixture method is recommended due to its simplicity. It is applied to real data sets to fit a discrete PH and PHPH models. Simulation study fitting discrete PH model shows that the advocated PO mixture method outperforms Quasi-Newton method in terms of both accuracy and speed.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91531/1/sfwang_1.pd

Automated Morphology Analysis of Nanoparticles

The functional properties of nanoparticles highly depend on the surface morphology of the particles, so precise measurements of a particle's morphology enable reliable characterizing of the nanoparticle's properties. Obtaining the measurements requires image analysis of electron microscopic pictures of nanoparticles. Today's labor-intensive image analysis of electron micrographs of nanoparticles is a significant bottleneck for efficient material characterization. The objective of this dissertation is to develop automated morphology analysis methods. Morphology analysis is comprised of three tasks: separate individual particles from an agglomerate of overlapping nano-objects (image segmentation); infer the particle's missing contours (shape inference); and ultimately, classify the particles by shape based on their complete contours (shape classification). Two approaches are proposed in this dissertation: the divide-and-conquer approach and the convex shape analysis approach. The divide-and-conquer approach solves each task separately, taking less than one minute to complete the required analysis, even for the largest-sized micrograph. However, its separating capability of particle overlaps is limited, meaning that it is able to split only touching particles. The convex shape analysis approach solves shape inference and classification simultaneously for better accuracy, but it requires more computation time, ten minutes for the biggest-sized electron micrograph. However, with a little sacrifice of time efficiency, the second approach achieves far superior separation than the divide-and-conquer approach, and it handles the chain-linked structure of particle overlaps well. The capabilities of the two proposed methods cannot be substituted by generic image processing and bio-imaging methods. This is due to the unique features that the electron microscopic pictures of nanoparticles have, including special particle overlap structures, and large number of particles to be processed. The application of the proposed methods to real electron microscopic pictures showed that the two proposed methods were more capable of extracting the morphology information than the state-of-the-art methods. When nanoparticles do not have many overlaps, the divide-and-conquer approach performed adequately. When nanoparticles have many overlaps, forming chain-linked clusters, the convex shape analysis approach performed much better than the state-of-the-art alternatives in bio-imaging. The author believes that the capabilities of the proposed methods expedite the morphology characterization process of nanoparticles. The author further conjectures that the technical generality of the proposed methods could even be a competent alternative to the current methods analyzing general overlapping convex-shaped objects other than nanoparticles

Bacterial characterization of Louisiana groundwater contaminated by DNAPL-containing chloroethanes and other solvents

In support of an effort to determine the feasibility of using an in-situ bioremediation strategy for cleanup of groundwater in an area containing chlorinated solvents present as a dense non-aqueous phase liquid (DNAPL), the bacterial population in the groundwater at a Superfund site located near Baton Rouge, Louisiana was characterized. More than 3¡Á107 cells/mL in the groundwater were observed via microscopy. Universal bacterial and ¡°Dehalococcoides¡±-specific 16S rRNA gene libraries were constructed and analyzed. Universal clones grouped into 18 operational taxonomic units (OTUs), as defined by sequence similarity ¡Ý97.0%, which included several as yet undescribed organisms. Multiple unique sequences closely related to ¡°Dehalococcoides ethenogenes¡± were detected. Sequences of 168 anaerobically grown isolates grouped into 18 OTUs, of which only three were represented in the clone library. Collectively, these results revealed that large numbers of novel microorganisms are present in groundwater within the DNAPL source zone, including bacteria closely related to known dechlorinators, fermentors, and hydrogen producers. This suggests that the population contains the bacterial components necessary to carry out reductive dechlorination. To further characterize the functional role that they may play in chlorinated solvent biotransformation, representatives from nine isolate OTUs and nine Clostridium type strains were tested to determine their ability to fermentatively produce hydrogen in the presence of 1,2-dichloroethane (DCA), 1,1,2-trichloroethane (TCA), and tetrachloroethene (PCE). All of the strains produced hydrogen in the presence of at least 7.4 mM DCA, 2.4 mM TCA, and 0.31 mM PCE. These results demonstrated that many Clostridium species are tolerant of chlorinated solvents and can produce hydrogen even in the presence of high concentrations of this class of contaminants. This suggests that Clostridum species may functionally support chlorinated solvent bioremediation through intra-species hydrogen transfer. Four isolates were characterized using a polyphasic approach to establish their taxonomic status. Three of the strains were phylogenetically and phenotypically identical. Phenotypic and chemotaxonomic properties differentiated these isolates from their closest phylogenetic relative. Phylogenetic analysis of the fourth strain revealed it was divergent from other Clostridium species. Based on these results, two new Clostridium species are proposed with names of Clostridium hydrogeniformans sp. nov. and Clostridium cavendishii sp. nov

Ecological patterns, community classification, and a comparison of approaches for predicting biological and habitat reference conditions in New Hampshire streams

Reference conditions play a vital role in many challenges facing both conservation and natural resources management. This dissertation sought to establish minimally-impacted reference conditions for stream biota and habitat in New Hampshire and explore alternative statistical methodologies to predict reference conditions for biological and habitat assessments. The fish, stream-dwelling salamander, macroinvertebrate and periphyton assemblages as well as the co-occurring physical habitat and riparian conditions of 76 minimally-impacted first to fourth order streams in New Hampshire were estimated using USEPA Environmental Monitoring and Assessment Program protocols over a four year period. Several statistical approaches and data standardizations for classifying multi-taxonomic assemblages were investigated for the strength of the classification they produced; log transformed abundances classified using TWINSPAN produced the best classification as measured using specific criteria. Seven natural biotic community types primarily arranged along the longitudinal stream profile were classified. Geographic classifications based on ecoregions and watersheds poorly explained organism distributions and abundances. Organism distributions were primarily associated with substrate characteristics, elevation, latitude, and the proportion of mesohabitat types (e.g. pool, riffle, etc.). A new approach to constructing a biological assessment index that is based on the Bray-Curtis percent similarity between the observed and predicted communities was developed to allow taxa density information into the multivariate predictive assessments. Separate linear regression models to predict the densities of each taxon resulted in the most accurate predictions of expected community structure. Multivariate predictive models that included classification steps were not in general less accurate than approaches based on continuous prediction of taxon densities such as nearest-neighbor or ordination-based analyses. Including abundance information into the predictive models did not increase relative prediction error compared to an AusRivAS-style assessment index based solely on predicted taxon occurrences. Habitat prediction followed similar results. Inter-annual variation in three streams sampled every year of the study was highest in the vertebrates and lowest in the macroinvertebrates. In contrast, vertebrate assemblages were more resistant to a summer spate than the macroinvertebrates. Greater sampling intensity in the field and laboratory are probably the only remaining avenues for increasing assessment accuracies and reducing unexplained variation in reference conditions

Probabilistic Graphical Models for Medical Image Segmentation

Image segmentation constitutes one of the elementary tasks in computer vision. Various variations exists, one of them being the segmentation of layers that entail a natural ordering constraint. One instance of that problem class are the cell layers in the human retina. In this thesis we study a segmentation approach for this problem class, that applies the machinery of probabilistic graphical models. Linked to probabilistic graphical models is the task of inference, that is, given an input scan of the retina, how to obtain an individual prediction or, if possible, a distribution over potential segmentations of that scan. In general, exact inference is unfeasible which is why we study an approximative approach based on variational inference, that allows to efficiently approximate the full posterior distribution. A distinguishing feature of our approach is the incorporation of a prior shape model, which is not restricted to local information. We evaluate our approach for different data sets, including pathological scans, and demonstrate how global shape information yields state-of-the-art segmentation results. Moreover, since we approximatively infer the full posterior distribution, we are able to assess the quality of our prediction as well as rate the scan in terms of its abnormality. Motivated by our problem we also investigate non-parametric density estimation with a log-concavity constraint. This class of density functions is restricted to the convex hull of the empirical data, which naturally leads to shape distributions that comply with the ordering constraint of retina layers, by not assigning any probability mass to invalid shape configurations. We investigate a prominent approach from the literature, show its extensions from 2-D to N-D and apply it to retina boundary data

Automated Morphology Analysis of Nanoparticles

The functional properties of nanoparticles highly depend on the surface morphology of the particles, so precise measurements of a particle's morphology enable reliable characterizing of the nanoparticle's properties. Obtaining the measurements requires image analysis of electron microscopic pictures of nanoparticles. Today's labor-intensive image analysis of electron micrographs of nanoparticles is a significant bottleneck for efficient material characterization. The objective of this dissertation is to develop automated morphology analysis methods. Morphology analysis is comprised of three tasks: separate individual particles from an agglomerate of overlapping nano-objects (image segmentation); infer the particle's missing contours (shape inference); and ultimately, classify the particles by shape based on their complete contours (shape classification). Two approaches are proposed in this dissertation: the divide-and-conquer approach and the convex shape analysis approach. The divide-and-conquer approach solves each task separately, taking less than one minute to complete the required analysis, even for the largest-sized micrograph. However, its separating capability of particle overlaps is limited, meaning that it is able to split only touching particles. The convex shape analysis approach solves shape inference and classification simultaneously for better accuracy, but it requires more computation time, ten minutes for the biggest-sized electron micrograph. However, with a little sacrifice of time efficiency, the second approach achieves far superior separation than the divide-and-conquer approach, and it handles the chain-linked structure of particle overlaps well. The capabilities of the two proposed methods cannot be substituted by generic image processing and bio-imaging methods. This is due to the unique features that the electron microscopic pictures of nanoparticles have, including special particle overlap structures, and large number of particles to be processed. The application of the proposed methods to real electron microscopic pictures showed that the two proposed methods were more capable of extracting the morphology information than the state-of-the-art methods. When nanoparticles do not have many overlaps, the divide-and-conquer approach performed adequately. When nanoparticles have many overlaps, forming chain-linked clusters, the convex shape analysis approach performed much better than the state-of-the-art alternatives in bio-imaging. The author believes that the capabilities of the proposed methods expedite the morphology characterization process of nanoparticles. The author further conjectures that the technical generality of the proposed methods could even be a competent alternative to the current methods analyzing general overlapping convex-shaped objects other than nanoparticles

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird