Learning and using taxonomies for visual and olfactory classification

Humans are able of distinguishing more than 5000 visual categories even in complex environments using a variety of different visual systems all working in tandem. We seem to be capable of distinguishing thousands of different odors as well. In the machine learning community, many commonly used multi-class classifiers do not scale well to such large numbers of categories. This thesis demonstrates a method of automatically creating application-specific taxonomies to aid in scaling classification algorithms to more than 100 cate- gories using both visual and olfactory data. The visual data consists of images collected online and pollen slides scanned under a microscope. The olfactory data was acquired by constructing a small portable sniffing apparatus which draws air over 10 carbon black polymer composite sensors. We investigate performance when classifying 256 visual categories, 8 or more species of pollen and 130 olfactory categories sampled from common household items and a standardized scratch-and-sniff test. Taxonomies are employed in a divide-and-conquer classification framework which improves classification time while allowing the end user to trade performance for specificity as needed. Before classification can even take place, the pollen counter and electronic nose must filter out a high volume of background “clutter” to detect the categories of interest. In the case of pollen this is done with an efficient cascade of classifiers that rule out most non-pollen before invoking slower multi-class classifiers. In the case of the electronic nose, much of the extraneous noise encountered in outdoor environments can be filtered using a sniffing strategy which preferentially samples the visensor response at frequencies that are relatively immune to background contributions from ambient water vapor. This combination of efficient background rejection with scalable classification algorithms is tested in detail for three separate projects: 1) the Caltech-256 Image Dataset, 2) the Caltech Automated Pollen Identification and Counting System (CAPICS) and 3) a portable electronic nose specially constructed for outdoor use.

In vivo analysis of interactions between trans-acting factors and their target genes

The investigations presented in this thesis use various in vivo techniques to understand how trans-acting factors control gene expression. The first part addresses the transcriptional regulation of muscle creatine kinase (MCK). MCK expression is activated during the course of development and is found only in differentiated muscle. Several in vivo footprints are observed at the enhancer of this gene, but all of these interactions are limited to cell types that express MCK. This is interesting because two of the footprints appear to represent muscle specific use of general transcription factors, while the other two correspond to sites that can bind the myogenic regulator, MyoD1, in vitro. MyoD1 and these general factors are present in myoblasts, but can bind to the enhancer only in myocytes. This suggests that either the factors themselves are post-translationally modified (phosphorylation or protein:protein interactions), or the accessibility of the enhancer to the factors is limited (changes in chromatin structure). The in vivo footprinting study of MCK was performed with a new ligation mediated, single-sided PCR (polymerase chain reaction) technique that I have developed.

The second half of the thesis concerns the regulation of mouse metallothionein (MT). Metallothioneins are a family of highly conserved housekeeping genes whose expression can be induced by heavy metals, steroids, and other stresses. By adapting a primer extension method of genomic sequencing to in vivo footprinting, I've observed both metal inducible and noninducible interactions at the promoter of MT-I. From these results I've been able to limit the possible mechanisms by which metal responsive trans-acting factors induce transcription. These interpretations correlate with a second line of experiments involving the stable titration of positive acting factors necessary for induction of MT. I've amplified the promoter of MT to 10^2-10^3 copies per cell by fusing the 5' and 3' ends of the MT gene to the coding region of DHFR and selecting cells for methotrexate resistance. In these cells, there is a metal-specific titration effect, and although it acts at the level of transcription, it appears to be independent of direct DNA binding factors.