Lateral Abdominal Wall Hematoma Mimicking Aortic Dissection Presentation: A Case Report.

Lateral abdominal wall hematoma is a rare clinical entity but a great mimicker of other diseases' clinical presentations. In this case report, we present a 42-year-old male patient with a constellation of signs and symptoms that were mistaken for aortic dissection before the lateral abdominal wall hematoma diagnosis was confirmed with computed tomography (CT) imaging. Uncontrolled hypertension and persistent cough were most likely predisposing factors; the patient was managed conservatively and discharged in a stable condition.

A hidden Markov model for population-level cervical cancer screening data.

The Cancer Registry of Norway has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers. In this article, we present a continuous-time, time-inhomogeneous hidden Markov model which was developed to understand the screening process and cervical cancer carcinogenesis in detail. By leveraging 1.7 million individual's multivariate time-series of medical exams performed over a 25-year period, we simultaneously estimate all model parameters. We show that an age-dependent model reflects the Norwegian screening program by comparing empirical survival curves from observed registry data and data simulated from the proposed model. The model can be generalized to include more detailed individual-level covariates as well as new types of screening exams. By utilizing individual screening histories and covariate data, the proposed model shows potential for improving strategies for cancer screening programs by personalizing recommended screening intervals.

Predictive modeling techniques for nanosecond-laser damage growth in fused silica optics.

Empirical numerical descriptions of the growth of laser-induced damage have been previously developed. In this work, Monte-Carlo techniques use these descriptions to model the evolution of a population of damage sites. The accuracy of the model is compared against laser damage growth observations. In addition, a machine learning (classification) technique independently predicts site evolution from patterns extracted directly from the data. The results show that both the Monte-Carlo simulation and machine learning classification algorithm can accurately reproduce the growth of a population of damage sites for at least 10 shots, which is extremely valuable for modeling optics lifetime in operating high-energy laser systems. Furthermore, we have also found that machine learning can be used as an important tool to explore and increase our understanding of the growth process.

Probability of growth of small damage sites on the exit surface of fused silica optics.

Growth of laser damage on fused silica optical components depends on several key parameters including laser fluence, wavelength, pulse duration, and site size. Here we investigate the growth behavior of small damage sites on the exit surface of SiO2 optics under exposure to tightly controlled laser pulses. Results demonstrate that the onset of damage growth is not governed by a threshold, but is probabilistic in nature and depends both on the current size of a damage site and the laser fluence to which it is exposed. We also develop models for use in growth prediction. In addition, we show that laser exposure history also influences the behavior of individual sites.

Exploration of the multiparameter space of nanosecond-laser damage growth in fused silica optics.

Historically, the rate at which laser-induced damage sites grow on the exit surface of SiO2 optics under subsequent illumination with nanosecond-laser pulses of any wavelength was believed to depend solely on laser fluence. We demonstrate here that much of the scatter in previous growth observations was due to additional parameters that were not previously known to affect growth rate, namely the temporal pulse shape and the size of a site. Furthermore, the remaining variability observed in the rate at which sites grow is well described in terms of Weibull statistics. The effects of site size and laser fluence may both be expressed orthogonally in terms of Weibull coefficients. In addition, we employ a clustering algorithm to explore the multiparameter growth space and expose average growth trends. Conversely, this analysis approach also identifies sites likely to exhibit growth rates outside the norm. The ability to identify which sites are likely to grow abnormally fast in advance of the manifestation of such behavior will significantly enhance the accuracy of predictive models over those based on average growth behaviors.

Practical data mining and machine learning for optics applications: introduction to the feature issue.

Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.

Architectural Implications for Spatial Object Association Algorithms.

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).