Early detection is vital for future neuroprotective treatments of Parkinson's disease (PD). Resting state electroencephalographic (EEG) recording has shown potential as a cost-effective means to aid in detection of neurological disorders such as PD. In this study, we investigated how the number and placement of electrodes affects classifying PD patients and healthy controls using machine learning based on EEG sample entropy. We used a custom budget-based search algorithm for selecting optimized sets of channels for classification, and iterated over variable channel budgets to investigate changes in classification performance. Our data consisted of 60-channel EEG collected at three different recording sites, each of which included observations collected both eyes open (total N = 178) and eyes closed (total N = 131). Our results with the data recorded eyes open demonstrated reasonable classification performance (ACC = .76; AUC = .76) with only 5 channels placed far away from each other, the selected regions including right-frontal, left-temporal and midline-occipital sites. Comparison to randomly selected subsets of channels indicated improved classifier performance only with relatively small channel-budgets. The results with the data recorded eyes closed demonstrated consistently worse classification performance (when compared to eyes open data), and classifier performance improved more steadily as a function of number of channels. In summary, our results suggest that a small subset of electrodes of an EEG recording can suffice for detecting PD with a classification performance on par with a full set of electrodes. Furthermore our results demonstrate that separately collected EEG data sets can be used for pooled machine learning based PD detection with reasonable classification performance.

The contemporary e-learning landscape at universities consists of various tools, platforms and services. At the same time, an increasingly bigger proportion of learning happens online. When creating exams or exercises for students in such a setting, teachers often face the following issues: (1) having to create exercises for multiple platforms, (2) lack of support for creating complex exercises, and (3) having to transfer exercises between platforms. To solve these issues we created a platformin-dependent tool for creating e-learning exercises and exams. The system relies on the structured Markdown format, which is then parsed and exported to learning management systems (LMS) such as Moodle, or to PDF. Scripts can be used to fully customize and randomize the Markdown exercises for each student, which mitigates cheating through copying answers, and enables holding exams that students take asynchronously. Overall, we argue that as digital learning ecosystems are becoming increasingly complex, educational institutions and teachers should strive more strongly for platform independence. In this study, we demonstrate how this can be done with exams and exercises.

Motivation: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. Results: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. Availability and implementation: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary For determining distances (fetch lengths) from points to polygons in a two‐dimensional Euclidean plane, cell‐based algorithms provide a simple and effective solution. They divide the input area into a grid of cells that cover the area. The objects are stored into the appropriate cells, and the resulting structure is used for solving the problem. When the input objects are distributed unevenly or the cell size is small, most of the cells may be empty. The representation is then called sparse. In the method proposed in this work, each cell contains information about its distance to the nonempty cells. It is then possible to skip over several empty cells at a time without memory accesses. A cell‐based fetch length algorithm is implemented on a graphics processing unit (GPU). Because control flow divergence reduces its performance, several methods to reduce the divergence are studied. While many of the explicit attempts turn out to be unsuccessful, sorting of the input data and sparse traversal are observed to greatly improve performance: compared with the initial GPU implementation, up to 45‐fold speedup is reached. The speed improvement is greatest when the map is very sparse and the points are given in a random order. Copyright © 2015 John Wiley & Sons, Ltd.

Software houses sell their products by transferring usage licenses of various software components to the customers. Depending on the kind of software, there are several different license types that allow controlled access of services. The two most popular types are the fixed license, which gives access rights for an identified workstation, and the floating license, which restricts the number of simultaneous users to a certain bound. The latter of these types is advantageous when the users do not demand full-time services and occasional lack of access is bearable. The problem of deciding the number of floating licenses is studied in the present paper. Based on the expected usage profile of the software, we calculate the minimal number of licenses that guarantees that the customers get service better than a given lower bound. The problem is studied by using certain queuing models, known as the Erlang toss system, the Erlang delay system, and the Engset model. None of these analytic models consider, however, the transient period that we analyze by means of simulation and by the so-called modified offered load approximation. We also give simple formulas presenting how the number of software licenses needed to keep the probability of nonaccess below a given blocking level grows as a function of the offered load, which is the proportion of the time used in the case that all requests were successful. Results of the study may be used for setting license prices and for determining the proper number of licenses.