De-biased Populations of Kuiper Belt Objects from the Deep Ecliptic Survey

The Deep Ecliptic Survey (DES) discovered hundreds of Kuiper Belt objects from 1998-2005. Follow-up observations yielded 304 objects with good dynamical classifications (Classical, Scattered, Centaur, or 16 mean-motion resonances with Neptune). The DES search fields are well documented, enabling us to calculate the probability of detecting objects with particular orbital parameters and absolute magnitudes at a randomized point in each orbit. Grouping objects together by dynamical class leads, we estimate the orbital element distributions (a, e, i) for the largest three classes (Classical, 3:2, and Scattered) using maximum likelihood. Using H-magnitude as a proxy for the object size, we fit a power law to the number of objects for 8 classes with at least 5 detected members (246 objects). The best Classical slope is alpha=1.02+/-0.01 (observed from 5<=H<=7.2). Six dynamical classes (Scattered plus 5 resonances) are consistent in slope with the Classicals, though the absolute number of objects is scaled. The exception to the power law relation are the Centaurs (non-resonant with perihelia closer than Neptune, and thus detectable at smaller sizes), with alpha=0.42+/-0.02 (7.5<H<11). This is consistent with a knee in the H-distribution around H=7.2 as reported elsewhere (Bernstein et al. 2004, Fraser et al. 2014). Based on the Classical-derived magnitude distribution, the total number of objects (H<=7) in each class are: Classical (2100+/-300 objects), Scattered (2800+/-400), 3:2 (570+/-80), 2:1 (400+/-50), 5:2 (270+/-40), 7:4 (69+/-9), 5:3 (60+/-8). The independent estimate for the number of Centaurs in the same H range is 13+/-5. If instead all objects are divided by inclination into "Hot" and "Cold" populations, following Fraser et al. (2014), we find that alphaHot=0.90+/-0.02, while alphaCold=1.32+/-0.02, in good agreement with that work.Comment: 26 pages emulateapj, 6 figures, 5 tables, accepted by A

Buoyancy waves in Pluto's high atmosphere: Implications for stellar occultations

We apply scintillation theory to stellar signal fluctuations in the high-resolution, high signal/noise, dual-wavelength data from the MMT observation of the 2007 March 18 occultation of P445.3 by Pluto. A well-defined high wavenumber cutoff in the fluctuations is consistent with viscous-thermal dissipation of buoyancy waves (internal gravity waves) in Pluto's high atmosphere, and provides strong evidence that the underlying density fluctuations are governed by the gravity-wave dispersion relation.Comment: Accepted 18 June 2009 for publication in Icaru

TNOs are Cool: A survey of the trans-Neptunian region V. Physical characterization of 18 Plutinos using Herschel PACS observations

We present Herschel PACS photometry of 18 Plutinos and determine sizes and albedos for these objects using thermal modeling. We analyze our results for correlations, draw conclusions on the Plutino size distribution, and compare to earlier results. Flux densities are derived from PACS mini scan-maps using specialized data reduction and photometry methods. In order to improve the quality of our results, we combine our PACS data with existing Spitzer MIPS data where possible, and refine existing absolute magnitudes for the targets. The physical characterization of our sample is done using a thermal model. Uncertainties of the physical parameters are derived using customized Monte Carlo methods. The correlation analysis is performed using a bootstrap Spearman rank analysis. We find the sizes of our Plutinos to range from 150 to 730 km and geometric albedos to vary between 0.04 and 0.28. The average albedo of the sample is 0.08 \pm 0.03, which is comparable to the mean albedo of Centaurs, Jupiter Family comets and other Trans-Neptunian Objects. We were able to calibrate the Plutino size scale for the first time and find the cumulative Plutino size distribution to be best fit using a cumulative power law with q = 2 at sizes ranging from 120-400 km and q = 3 at larger sizes. We revise the bulk density of 1999 TC36 and find a density of 0.64 (+0.15/-0.11) g cm-3. On the basis of a modified Spearman rank analysis technique our Plutino sample appears to be biased with respect to object size but unbiased with respect to albedo. Furthermore, we find biases based on geometrical aspects and color in our sample. There is qualitative evidence that icy Plutinos have higher albedos than the average of the sample.Comment: 18 pages, 8 figures, 8 tables, accepted for publication in A&

The Properties and Origins of Kuiper Belt Object Arrokoth's Large Mounds

We report on a study of the mounds that dominate the appearance of Kuiper Belt Object (KBO) (486958) Arrokoth's larger lobe, named Wenu. We compare the geological context of these mounds, measure and intercompare their shapes, sizes/orientations, reflectance, and colors. We find the mounds are broadly self-similar in many respects and interpret them as the original building blocks of Arrokoth. It remains unclear why these building blocks are so similar in size, and this represents a new constrain and challenge for solar system formation models. We then discuss the interpretation of this interpretation.Comment: 24 pages, 8 figure

Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)

A four-kallikrein panel for the prediction of repeat prostate biopsy: data from the European Randomized Study of Prostate Cancer Screening in Rotterdam, Netherlands

Background: Most men with elevated levels of prostate-specific antigen (PSA) do not have prostate cancer, leading to a large number of unnecessary biopsies. A statistical model based on a panel of four kallikreins has been shown to predict the outcome of a first prostate biopsy. In this study, we apply the model to an independent data set of men with previous negative biopsy but persistently elevated PSA. Methods: The study cohort consisted of 925 men with a previous negative prostate biopsy and elevated PSA (≥3 ng ml-1), with 110 prostate cancers detected (12%). A previously published statistical model was applied, with recalibration to reflect the lower positive biopsy rates on rebiopsy. Results: The full-kallikrein panel had higher discriminative accuracy than PSA and DRE alone, with area under the curve (AUC) improving from 0.58 (95% confidence interval (CI): 0.52, 0.64) to 0.68 (95% CI: 0.62, 0.74), P<0.001, and high-grade cancer (Gleason 7) at biopsy with AUC improving from 0.76 (95% CI: 0.64, 0.89) to 0.87 (95% CI: 0.81, 0.94), P<0.003). Application of the panel to 1000 men with persistently elevated PSA after initial negative biopsy, at a 15% risk threshold would reduce the number of biopsies by 712; would miss (or delay) the diagnosis of 53 cancers, of which only 3 would be Gleason 7 and the rest Gleason 6 or less. Conclusions: Our data constitute an external validation of a previously published model. The four-kallikrein panel predicts the result of repeat prostate biopsy in men with elevated PSA while dramatically decreasing unnecessary biopsies