Beneficial effects of sun exposure on cancer mortality

For more than 50 years, there has been documentation in the medical literature suggesting that regular sun exposure is associated with substantial decreases in death rates from certain cancers and a decrease in overall cancer death rates. Recent research suggests that this is a causal relationship that acts through the body's vitamin D metabolic pathways. The studies reviewed here show that (a) sunlight activation is our most effective source of vitamin D; (b) regular sunlight/vitamin D "intake" inhibits growth of breast and colon cancer cells and is associated with substantial decreases in death rates from these cancers; (c) metabolites of vitamin D have induced leukemia and lymphoma cells to differentiate, prolonged survival of leukemic mice, and produced complete and partial clinical responses in lymphoma patients having high vitamin D metabolite receptor levels in tumor tissue; (d) sunlight has a paradoxical relationship with melanoma, in that severe sunburning initiates melanoma whereas long-term regular sun exposure inhibits melanoma; (e) frequent regular sun exposure acts to cause cancers that have a 0.3% death rate with 2,000 U.S. fatalities per year and acts to prevent cancers that have death rates from 20-65% with 138,000 U.S. fatalities per year; (f) there is support in the medical literature to suggest that the 17% increase in breast cancer incidence during the 1991-1992 year may be the result of the past decade of pervasive anti-sun advisories from respected authorities, coinciding with effective sunscreen availability; and (g) trends in the epidemiological literature suggest that approximately 30,000 U.S. cancer deaths yearly would be averted by the widespread public adoption of regular, moderate sunning.(ABSTRACT TRUNCATED AT 250 WORDS)     

Classification of nonstationary narrowband signals using segmented chirp features and hidden Gauss-Markov models

A method is provided for classifying finite-duration signals with narrow instantaneous bandwidth and dynamic instantaneous frequency (IF). In this method, events are partitioned into nonoverlapping segments, and each segment is modeled as a linear chirp, forming a piecewise-linear IF model. The start frequency, chirp rate, signal energy, and noise energy are estimated in each segment. The resulting sequences of frequency and rate features for each event are classified by evaluating their likelihood under the probability density function (PDF) corresponding to each narrowband class hypothesis. The class-conditional PDFs are approximated using continuous-state hidden Gauss-Markov models (HGMMs), whose parameters are estimated from labeled training data. Previous HGMM algorithms are extended by dynamically weighting the output covariance matrix by the ratio of the estimated signal and noise energies from each segment. This covariance weighting discounts spurious features from segments with low signal-to-noise ratio (SNR), making the algorithm more robust in the presence of dynamic noise levels and fading signals. The classification algorithm is applied in a simulated three-class cross-validation experiment, for which the algorithm exhibits percent correct classification greater than 97% as low as -7 dB SNR.     

Observations on oblique projectors and pseudoinverses

Oblique projectors (which were examined by Behrens and Scharf (see ibid., vol.42, no.6, p.1413-24, 1994)) are related to earlier work on oblique pseudoinverses and constrained least-squares methods and are reconsidered from the standpoint of orthogonal basis vectors and QR factorizations. Construction algorithms are presented that are numerically more stable than the normal-equations constructions previously used     

Full Newton and constraint methods for semilinear signal modeling

The authors derive an expression for the Hessian matrix of the variable projection functional (VPF) and implement the Hessian using QR factorization. This is incorporated into a full Newton variable projection (FNVP) algorithm for estimating parameters in semilinear signals. They introduce a deflation technique for constraining the VPF to contain known basis vectors. For modeling exponential signals, the computational cost of the FNVP algorithm is shown to vary linearly with N, the size of the data vector, while other algorithms vary as N² or N log ₂ N     

Hidden Gauss-Markov models for signal classification

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the Baum (1972), Viterbi (1962), and Baum-Welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss-Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that the Baum and Viterbi algorithms for HGMMs are implemented by two different formulations of the fixed-interval Kalman smoother. The measurement likelihoods obtained from the forward pass of the HGMM Baum algorithm and from the Kalman-filter innovation sequence are shown to be equal. A direct link between the Baum-Welch training algorithm and an existing expectation-maximization (EM) algorithm for Gaussian models is demonstrated. A new expression for the cross covariance between time-adjacent states in HGMMs is derived from the off-diagonal block of the conditional joint covariance matrix. A parameter invariance structure is noted for the HGMM likelihood function. CS-HMMs and HGMMs are extended to incorporate mixture densities for the a priori density of the initial state. Application of HGMMs to signal classification is demonstrated with a three-class test simulation     

A B-wavelet-based noise-reduction algorithm

A wavelet-based method is introduced for removing structured noise (e.g., impulsive spikes or unwanted harmonic components) from data. For this type of noise, the time- and frequency-localization capabilities of wavelets provide better noise detection and less signal distortion than direct filtering of data. The procedure is applied to time-series data with impulsive noise and transfer-function data with multipath interference. The authors use a single set of scaling and wavelet bases that can effectively represent a wide range of signals, and then they perform noise reducing operations on the scaling and wavelet coefficients. Multiresolution analysis is introduced in order to describe the noise reduction algorithm