BACKGROUND: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. METHODS: Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. RESULTS: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4. CONCLUSIONS: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.

For many Services Research and Service Design are emerging, crossing and in some cases redefining disciplinary boundaries. The user, value and worth centred ethos of human–computer interaction (HCI...

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.

There has been an increase in the relevance of and interest in services and services research. There is a acknowledgement that the emerging field of services science will need to draw on multiple disciplines and practices. There is a growing body of work from Human–Computer Interaction (HCI) researchers and practitioners that consider services, but there has been limited interaction between service researchers and HCI. We argue that HCI can provide two major elements of interest to service science: (1) the user centred mindset and techniques; and (2) concepts and frameworks applicable to understanding the nature of services. This second option is of major concern in this paper, where we consider Long’s work (undertaken with John Dowell) on a Conception for HCI. The conception stands as an important antecedent to our own work on a framework that: (a) relates the various strands of servicer research; and (b) can be used to provide high-level integrative models of service systems. Core concepts of the UCL Conception such as domain, task, and structures and behaviours partially help to relate systematically different streams of services research, and provide richer descriptions of them. However, if the UCL Conception is moved towards services additional issues and challenges arise. For example, the kinds of domain changes that are made in services differ; services exist in a wider environment; and that effectiveness judgements are dependent on values. We explore these issues and provide reflections on the status of HCI and Service Science.

Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.

Ring topology local area networks (LAN's) using the "buffer insertion" access method have as yet received relatively little attention. In this paper we present details of a LAN of this,type, called SILK-system for integrated local communication (in German, "Kommunikation"). Sections of the paper describe the synchronous transmission technique of the ring channel, the time-multiplexed access of eight ports at each node, the "braided" interconnection for bypassing defective nodes, and the role of interface transformation units and user interfaces, as well as some traffic characteristics and reliability aspects. SILK's modularity and open system concept are demonstrated by the already implemented applications such as distributed text editing, local telephone or teletex exchange, and process control in a TV studio.