Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).We developed prognostic models for breast cancer-specific survival (BCSS) that consider anatomic stage and other important determinants of prognosis and survival in breast cancer, such as age, grade, and receptor-based subtypes with the intention to demonstrate that these factors, conditional on stage, improve prediction of BCSS. A total of 20,928 patients with stage I-III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1990 and 2016, who received surgery as an initial treatment were identified to generate prognostic models by Fine-Gray competing risk regression model. Model predictive accuracy was assessed using Harrell's C-index. The Aalen-Johansen estimator and a selected Fine-Gray model were used to estimate the 5-year and 10-year BCSS probabilities. The performance of the selected model was evaluated by assessing discrimination and prediction calibration in an external validation dataset of 29,727 patients from the National Comprehensive Cancer Network (NCCN). The inclusion of age, grade, and receptor-based subtype in addition to stage significantly improved the model predictive accuracy (C-index 0.774 (95% CI 0.755-0.794) vs. 0.692 for stage alone, p  less then  0.0001). Young age ( less then 40), higher grade, and TNBC subtype were significantly associated with worse BCSS. The selected model showed good discriminative ability but poor calibration when applied to the validation data. After recalibration, the predictions showed good calibration in the training and validation data. More refined BCSS prediction is possible through a model that has been externally validated and includes clinical and biological factors. © The Author(s) 2020.Thermophysical properties of highly doped Si50Ge50 melt were measured contactlessly in the electromagnetic levitation facility ISS-EML on board the International Space Station. The sample could be melted, overheated by about 375 K, and cooled down in 350 mbar Argon atmosphere. A large undercooling of about 240 K was observed and a quasi-homogeneous nucleation on the droplet surface occurred. During the cooling phase, high-resolution videos were taken from the side and the top. The density and thermal expansion were evaluated with digital image processing; the viscosity and the surface tension were measured by means of the oscillating drop technique. Inductive measurements of the electrical resistivity were conducted by a dedicated electronics. All data were taken as a function of temperature T from the overheated melt down to the undercooled range. We found a nonlinear thermal expansion, suggesting a many body effect in the liquid beyond the regular pair interaction, an enhanced damping of surface oscillations likely related to an internal turbulent flow, and an increment of the electrical resistivity with decreased T in the undercooled range regarding a demixing of the components. © The Author(s) 2020.When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. Piperlongumine cell line We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).Purpose For the focal spot measurement of x-ray tubes, we propose a practical method in which only a metal edge and a digital detector are used, together with a process of removing detector blur inherently associated. Approach The evaluation was made through the optical transfer function (OTF) measurements using the edge response of a 1-mm-thick tungsten plate. First, we made the acquisition of a geometrically magnified edge response, which consists of focal spot penumbra and detector blur, followed by the acquisition of nonmagnified edge response, which includes only detector blur. Then the detector blur was removed by taking the ratio of the two OTFs. Finally, the focal spot profile was obtained by the inverse Fourier transform of this ratio. Results Resultant full widths at the half-maximum of a small focus profile were 0.529 ± 0.005    mm for the proposed method and 0.527 ± 0.020    mm for the conventional slit method with film, indicating excellent agreement between both methods. Comparing between results obtained using two flat panel detectors with different pixel pitches (0.143 and 0.175 mm) confirmed no differences with these variations. Conclusion Through the whole study, the accuracy and the practicality of the proposed method were demonstrated, indicating a possibility of the method to be widely used to evaluate the effective focal spot size and profile of x-ray systems. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).A cold atmospheric pressure plasma device was developed using two parallel plates of Low Temperature Co-fired Ceramic with embedded electrodes. The 2.4 cm wide by 1 mm deep plasma discharge operates at 20 kHz with a 2-5 kV AC drive signal across a 0.25 mm gap. Mixed Argon/oxygen plasmas were directed between the plates to flow toward a bacterial biofilm sample for treatment. Results showed that at 4-5 kV the plasma etched away a bacterial biofilm on glass in 10 minutes. In addition, we showed that short plasma treatments rapidly killed biofilm resident bacteria with ED90 values of less then 15 s.