Simulated X-ray Spectra From Ionized Wind-Blown Nebulae around Massive Stars

Using an ionization gasdynamics code, we simulate a model of the wind-blown bubble around a 40 solar mass star. We use this to compute the X-ray spectra from the bubble, which can be directly compared to observations. We outline our methods and techniques for these computations, and contrast them with previous calculations. Our simulated X-ray spectra compare reasonably well with observed spectra of Wolf-Rayet bubbles. They suggest that X-ray nebulae around massive stars may not be easily detectable, consistent with observations.Comment: 7 pages, 2 figures. Revised and shortened version following referee comments. Accepted to High Energy Density Physic

Unfolding Rates for the Diffusion-Collision Model

In the diffusion-collision model, the unfolding rates are given by the likelihood of secondary structural cluster dissociation. In this work, we introduce an unfolding rate calculation for proteins whose secondary structural elements are $\alpha$-helices, modeled from thermal escape over a barrier which arises from the free energy in buried hydrophobic residues. Our results are in good agreement with currently accepted values for the attempt rate.Comment: Shorter version of cond-mat/0011024 accepted for publication in PR

Localization of diagnostically relevant regions of interest in whole slide images

Whole slide imaging technology enables pathologists to screen biopsy images and make a diagnosis in a digital form. This creates an opportunity to understand the screening patterns of expert pathologists and extract the patterns that lead to accurate and efficient diagnoses. For this purpose, we are taking the first step to interpret the recorded actions of world-class expert pathologists on a set of digitized breast biopsy images. We propose an algorithm to extract regions of interest from the logs of image screenings using zoom levels, time and the magnitude of panning motion. Using diagnostically relevant regions marked by experts, we use the visual bag-of-words model with texture and color features to describe these regions and train probabilistic classifiers to predict similar regions of interest in new whole slide images. The proposed algorithm gives promising results for detecting diagnostically relevant regions. We hope this attempt to predict the regions that attract pathologists' attention will provide the first step in a more comprehensive study to understand the diagnostic patterns in histopathology. © 2014 IEEE

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine

Multi-instance multi-label learning for whole slide breast histopathology

Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis systems. However, the whole slide images also provide two new challenges to image analysis algorithms. The first one is the need for simultaneous localization and classification of malignant areas in these large images, as different parts of the image may have different levels of diagnostic relevance. The second challenge is the uncertainty regarding the correspondence between the particular image areas and the diagnostic labels typically provided by the pathologists at the slide level. In this paper, we exploit a data set that consists of recorded actions of pathologists while they were interpreting whole slide images of breast biopsies to find solutions to these challenges. First, we extract candidate regions of interest (ROI) from the logs of pathologists' image screenings based on different actions corresponding to zoom events, panning motions, and fixations. Then, we model these ROIs using color and texture features. Next, we represent each slide as a bag of instances corresponding to the collection of candidate ROIs and a set of slide-level labels extracted from the forms that the pathologists filled out according to what they saw during their screenings. Finally, we build classifiers using five different multi-instance multi-label learning algorithms, and evaluate their performances under different learning and validation scenarios involving various combinations of data from three expert pathologists. Experiments that compared the slide-level predictions of the classifiers with the reference data showed average precision values up to 62% when the training and validation data came from the same individual pathologist's viewing logs, and an average precision of 64% was obtained when the candidate ROIs and the labels from all pathologists were combined for each slide. © 2016 SPIE

Scattering of elastic waves by periodic arrays of spherical bodies

We develop a formalism for the calculation of the frequency band structure of a phononic crystal consisting of non-overlapping elastic spheres, characterized by Lam\'e coefficients which may be complex and frequency dependent, arranged periodically in a host medium with different mass density and Lam\'e coefficients. We view the crystal as a sequence of planes of spheres, parallel to and having the two dimensional periodicity of a given crystallographic plane, and obtain the complex band structure of the infinite crystal associated with this plane. The method allows one to calculate, also, the transmission, reflection, and absorption coefficients for an elastic wave (longitudinal or transverse) incident, at any angle, on a slab of the crystal of finite thickness. We demonstrate the efficiency of the method by applying it to a specific example.Comment: 19 pages, 5 figures, Phys. Rev. B (in press

Modeling the natural history of ductal carcinoma in situ based on population data

Background: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. Methods: Two well-established population models evaluated six possible DCIS natural history submodels. The submodels assumed 30%, 50%, or 80% of breast lesions progress from undetectable DCIS to preclinical screen-detectable DCIS; each model additionally allowed or prohibited DCIS regression. Preclinical screen-detectable DCIS could also progress to clinical DCIS or invasive breast cancer (IBC). Applying US population screening dissemination patterns, the models projected age-specific DCIS and IBC incidence that were compared to Surveillance, Epidemiology, and End Results data. Models estimated mean sojourn time (MST) in the preclinical screen-detectable DCIS state, overdiagnosis, and the risk of progression from preclinical screen-detectable DCIS. Results: Without biopsy and surgical excision, the majority of DCIS (64-100%) in the preclinical screen-detectable state progressed to IBC in submodels assuming no DCIS regression (36-100% in submodels allowing for DCIS regression). DCIS overdiagnosis differed substantially between models and submodels, 3.1-65.8%. IBC overdiagnosis ranged 1.3-2.4%. Submodels assuming DCIS regression resulted in a higher DCIS overdiagnosis than submodels without DCIS regression. MST for progressive DCIS varied between 0.2 and 2.5 years. Conclusions: Our findings suggest that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to IBC and that the MST is relatively short. Nevertheless, due to the heterogeneity of DCIS, more research is needed to understand the progression of DCIS by grades and molecular subtypes

Relativistic wave equations for interacting massive particles with arbitrary half-intreger spins

New formulation of relativistic wave equations (RWE) for massive particles with arbitrary half-integer spins s interacting with external electromagnetic fields are proposed. They are based on wave functions which are irreducible tensors of rank $n ($n=s-\frac12$) antisymmetric w.r.t. n pairs of indices, whose components are bispinors. The form of RWE is straightforward and free of inconsistencies associated with the other approaches to equations describing interacting higher spin particles

A Measurement of Psi(2S) Resonance Parameters

Cross sections for e+e- to hadons, pi+pi- J/Psi, and mu+mu- have been measured in the vicinity of the Psi(2S) resonance using the BESII detector operated at the BEPC. The Psi(2S) total width; partial widths to hadrons, pi+pi- J/Psi, muons; and corresponding branching fractions have been determined to be Gamma(total)= (264+-27) keV; Gamma(hadron)= (258+-26) keV, Gamma(mu)= (2.44+-0.21) keV, and Gamma(pi+pi- J/Psi)= (85+-8.7) keV; and Br(hadron)= (97.79+-0.15)%, Br(pi+pi- J/Psi)= (32+-1.4)%, Br(mu)= (0.93+-0.08)%, respectively.Comment: 8 pages, 6 figure