Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN

The effect of rosiglitazone on PPARg expression in human adipose tissue Is limited by continued exposure to thymidine NRTI

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In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery

We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, are varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.Comment: 13 pages, 11 figures, submitted to Scientific Dat

Vibrational Properties of Nanoscale Materials: From Nanoparticles to Nanocrystalline Materials

The vibrational density of states (VDOS) of nanoclusters and nanocrystalline materials are derived from molecular-dynamics simulations using empirical tight-binding potentials. The results show that the VDOS inside nanoclusters can be understood as that of the corresponding bulk system compressed by the capillary pressure. At the surface of the nanoparticles the VDOS exhibits a strong enhancement at low energies and shows structures similar to that found near flat crystalline surfaces. For the nanocrystalline materials an increased VDOS is found at high and low phonon energies, in agreement with experimental findings. The individual VDOS contributions from the grain centers, grain boundaries, and internal surfaces show that, in the nanocrystalline materials, the VDOS enhancements are mainly caused by the grain-boundary contributions and that surface atoms play only a minor role. Although capillary pressures are also present inside the grains of nanocrystalline materials, their effect on the VDOS is different than in the cluster case which is probably due to the inter-grain coupling of the modes via the grain-boundaries.Comment: 10 pages, 7 figures, accepted for publication in Phys. Rev.

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

© 2018, The Author(s). Abstract: To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients. Methods: Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy. Results: 2493 patients, with median age 78–80 years (DK–Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7–8.6 vs. 5.8 95% CI 5.6–5.9) and Danish mean 7.1 (95% CI 6.6–7.5 vs. 5.5 95% CI 5.4–5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL’s accuracy was only modest for in-hospital death prediction in either setting. Conclusions: The modified CriSTAL tool (with CFS instead of Fried’s frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician’s confidence in initiating earlier end-of-life discussions

Modelling the dispersion of particle numbers in five European cities

We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximatelyPeer reviewe