Contrast enhanced ultrasound eases interpretation of an unclear renal tumor in addition to CT, MRI and histological findings--a case report in a young patient

Renal cancer represents accounts for approximately 3% of all adult malignancies with a rising incidence. Incidental diagnosis is mostly based upon ultrasound (US). US and Computed tomography (CT) are the standard imaging modalities for detecting renal cell cancer (RCC). Differentiation between malignant and benign renal tumors is of utmost importance. Contrast enhanced ultrasound (CUS) seems to be a promising new diagnostic option for diagnosis and preoperative treatment planning for patients with renal cancer. It is an additional examination to baseline ultrasound and CT. We report a case of a 37-year-old woman with a papillary renal cell cancer in which CUS helped to differentiate dignity of the tumor. CUS is an additional examination to baseline ultrasound and CT. It is a less invasive technique than contrast enhanced CT and shows even slight tumor blood flow. In addition it may allow a more rapid diagnosis, because of its bedside availability.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Cognitive Differences in Chart Reading: A Comparison of Nurses and Physicians

This pilot study compares the mental models of a patient constructed by nurses and physicians while reading an electronic medical record. Preliminary results suggest that the participants' summaries were both quantitatively and qualitatively different. The physician made more inferences and focused on deeper relationships in the record, whereas the nurse focused on the descriptive surface structure of the record.

Good just isn't Good enough. Best System Probabilities and the Foundations of Statistical Mechanics

Statistical physicists assume a probability distribution over micro-states to explain thermodynamic behavior. The question of this paper is whether these probabilities are part of a best system and can thus be interpreted as Humean chances. I consider two strategies, viz. a globalist as suggested by Loewer, and a localist as advocated by Frigg and Hoefer. Both strategies fail because the system they are part of have rivals that are roughly equally good, while ontic probabilities should be part of a clearly winning system. I conclude with the diagnosis that well-defined micro-probabilities under-estimate the robust character of explanations in statistical physics.

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Stratigraphic Variation Within Polar Firn Caused by Differential Accumulation and Ice Flow: Interpretation of a 400 Mhz Short-Pulse Radar Profile from West Antarctica

We investigate causes of the stratigraphic variation revealed in a 177 km, 400 MHz short-pulse radar profile of firn from West Antarctica. The profile covers 56 m depth, and its direction was close to those of the ice flow and mean wind. The average, near-surface accumulation rates calculated from the time delays of one radar horizon consistently show minima on leeward slopes and maxima on windward slopes, confirming an earlier study based on stake observations. The stratigraphic variation includes up to 30 m depth variation in individual horizons over tens of km, fold limbs that become progressively steeper with depth, and fold-hinge loci that change direction or propagate down-ice with depth over distances far less than predicted by the ice speeds. We use an accumulation rate model to show how local rate anomalies and the effect of ice speed upon a periodic variation in accumulation rate cause these phenomena, and we reproduce two key features seen in the stratigraphic variations. We conclude that the model provides an explanation of changes in spatial stratigraphy and local measures of accumulation history given the constraints of surface topography, ice and wind velocities, and a general accumulation rate for an area.

Dating the Siple Dome (Antarctica) Ice Core By Manual and Computer Interpretation of Annual Layering

The Holocene portion of the Siple Dome (Antarctica) ice core was dated by interpreting the electrical, visual and chemical properties of the core. The data were interpreted manually and with a computer algorithm. The algorithm interpretation was adjusted to be consistent with atmospheric methane stratigraphic ties to the GISP2 (Greenland Ice Sheet Project 2) ice core, (BE)-B-10 stratigraphic ties to the dendrochronology C-14 record and the dated volcanic stratigraphy. The algorithm interpretation is more consistent and better quantified than the tedious and subjective manual interpretation.

Low reliability of perceptual priming: consequences for the interpretation of functional dissociations between explicit and implicit memory

In this study, three experiments are presented that investigate the reliability of memory measures. In Experiment 1, the well-known dissociation between explicit (recall, recognition) and implicit memory (picture clarification) as a function of age in a sample of 335 persons aged between 65 and 95 was replicated. Test-retest reliability was significantly lower in implicit than in explicit measures. In Experiment 2, parallel-test reliabilities in a student sample confirmed the finding of Experiment 1. In Experiment 3, the reliability of cued recall and word stem completion was investigated. There were significant priming effects and a dissociation between explicit and implicit memory as a function of levels of processing. However, the reliability of implicit memory measures was again substantially lower than in explicit tests in all test conditions. As a consequence, differential reliabilities of direct and indirect memory tests should be considered as a possible determinant of dissociations between explicit and implicit memory as a function of experimental or quasi-experimental manipulations.