Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Genetic Studies Identify Critical Biomarkers and Refine the Classification of Malignant Gliomas

Gliomagenesis is driven by a complex network of genetic alterations and while the glioma genome has been a focus of investigation for many years; critical gaps in our knowledge of this disease remain. The identification of novel molecular biomarkers remains a focus of the greater cancer community as a method to improve the consistency and accuracy of pathological diagnosis. In addition, novel molecular biomarkers are drastically needed for the identification of targets that may ultimately result in novel therapeutics aimed at improving glioma treatment. Through the identification of new biomarkers, laboratories will focus future studies on the molecular mechanisms that underlie glioma development. Here, we report a series of genomic analyses identifying novel molecular biomarkers in multiple histopathological subtypes of glioma and refine the classification of malignant gliomas. We have completed a large scale analysis of the WHO grade II-III astrocytoma exome and report frequent mutations in the chromatin modifier, alpha thalassemia mental retardation x-linked (ATRX), isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), and mutations in tumor protein 53 (TP53) as the most frequent genetic mutations in low grade astrocytomas. Furthermore, by analyzing the status of recurrently mutated genes in 363 brain tumors, we establish that highly recurrent gene mutational signatures are an effective tool in stratifying homogeneous patient populations into distinct groups with varying outcomes, thereby capable of predicting prognosis. Next, we have established mutations in the promoter of telomerase reverse transcriptase (TERT) as a frequent genetic event in gliomas and in tissues with low rates of self renewal. We identify TERT promoter mutations as the most frequently mutated gene in primary glioblastoma. Additionally, we show that TERT promoter mutations in combination with IDH1 and IDH2 mutations are able to delineate distinct clinical tumor cohorts and are capable of predicting median overall survival more effectively than standard histopathological diagnosis alone. Taken together, these data advance our understanding of the genetic alterations that underlie the transformation of glial cells into neoplasms and we provide novel genetic biomarkers and multi – gene mutational signatures that can be utilized to refine the classification of malignant gliomas and provide opportunity for improved diagnosis.

Life-narrative and word-cued autobiographical memories in centenarians: comparisons with 80-year-old control, depressed, and dementia groups.

Centenarians provided autobiographical memories to either a request for a life narrative or a request to produce autobiographical memories to cue words. Both methods produced distributions with childhood-amnesia, reminiscence-bump, and recency components. The life-narrative method produced relatively more bump memories at the expense of recent memories. The life-narrative distributions were similar to those obtained from 80-year-old adults without clinical symptoms and from 80-year-old Alzheimer's dementia and depression patients, except that the centenarians had an additional 20-year period of relatively low recall between the bump and recency components. The centenarians produced more emotionally neutral memories than the other three groups and produced fewer and less detailed memories than the non-clinical 80-year-old sample.

Properties of word cues for autobiographical memory.

A sample of 124 words were used to cue autobiographical memories in 120 adults varying in age from 20 to 73 years. Individual words reliably cued autobiographical memories of different ages with different speeds. For all age groups, words rated high in imagery produced older memories and faster reaction times.

Distribution of important and word-cued autobiographical memories in 20-, 35-, and 70-year-old adults.

For word-cued autobiographical memories, older adults had an increase, or bump, from the ages 10 to 30. All age groups had fewer memories from childhood than from other years and a power-function retention for memories from the most recent 10 years. There were no consistent differences in reaction times and rating scale responses across decades. Concrete words cued older memories, but no property of the cues predicted which memories would come from the bump. The 5 most important memories given by 20- and 35-year-old participants were distributed similarly to their word-cued memories, but those given by 70-year-old participants came mostly from the single 20-to-30 decade. No theory fully accounts for the bump.

The Toronto Word Pool: Norms for imagery, concreteness, orthographic variables, and grammatical usage for 1,080 words

Imagery and concreteness norms and percentage noun usage were obtained on the 1,080 verbal items from the Toronto Word Pool. Imagery was defined as the rated ease with which a word aroused a mental image, and concreteness was defined in relation to level of abstraction. The degree to which a word was functionally a noun was estimated in a sentence generation task. The mean and standard deviation of the imagery and concreteness ratings for each item are reported together with letter and printed frequency counts for the words and indications of sex differences in the ratings. Additional data in the norms include a grammatical function code derived from dictionary definitions, a percent noun judgment, indexes of statistical approximation to English, and an orthographic neighbor ratio. Validity estimates for the imagery and concreteness ratings are derived from comparisons with scale values drawn from the Paivio, Yuille, and Madigan (1968) noun pool and the Toglia and Battig (1978) norms. © 1982 Psychonomic Society, Inc.