Collecting the Painted Netherlands: The Art Collection of Ernest of Austria in Brussels

The (art) collection of Archduke Ernest of Austria (1553-1595) is widely unknown when it comes to early-modern Habsburg collections. Ernest, younger brother of Emperor Rudolf II (b. 1552) and educated at the Madrid court, was appointed Governor-General of the Netherlands by King Philip II of Spain, his uncle, in summer 1593. Ernest relocated his court from Vienna to Brussels in early 1594 and was welcomed there with lavish festivities: the traditional Blijde Inkomst, Joyous Entry, of the new sovereign. Unfortunately, the archduke died in February 1595 after residing in Brussels for a mere thirteen months. This investigation aims to shed new light on the archduke and his short-lived collecting ambitions in the Low Countries, taking into account that he had the mercantile and artistic metropolis Antwerp in his immediate reach. I argue, that his collecting ambitions can be traced back to one specific occasion: Ernest’s Joyous Entry into Antwerp in June 1594. There the archduke received a series of six paintings of Pieter Bruegel the Elder (1525/30-1569) known as The Months (painted in 1565), hanging today in separate locations in Vienna, New York and Prague. These works of art triggered Ernest’s collecting ambitions and prompted him to focus mainly on works of art and artefacts manufactured at or traded within the Netherlands during the last eight months of his lifetime. Additionally, it will be shown that the archduke was inspired by the paintings’ motifs and therefore concentrated on acquiring works of art depicting nature and landscape scenes from the 1560s and 1590s. On the basis of the archduke’s recently published account book (Kassabuch) and of the partially published inventory of his belongings, it becomes clear that Ernest of Austria must be seen in line with the better-known Habsburg collectors and that his specific collection of “the painted Netherlands” can be linked directly to his self-fashioning as a rightful sovereign of the Low Countries.

Report from the National Institute of Allergy and Infectious Diseases workshop on drug allergy

Allergic reactions to drugs are a serious public health concern. In 2013, the Division of Allergy, Immunology, and Transplantation of the National Institute of Allergy and Infectious Diseases sponsored a workshop on drug allergy. International experts in the field of drug allergy with backgrounds in allergy, immunology, infectious diseases, dermatology, clinical pharmacology, and pharmacogenomics discussed the current state of drug allergy research. These experts were joined by representatives from several National Institutes of Health institutes and the US Food and Drug Administration. The participants identified important advances that make new research directions feasible and made suggestions for research priorities and for development of infrastructure to advance our knowledge of the mechanisms, diagnosis, management, and prevention of drug allergy. The workshop summary and recommendations are presented herein.

SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients

SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity

Pulse wave velocity (PWV) is a surrogate of arterial stiffness and represents a non-invasive marker of cardiovascular risk. The non-invasive measurement of PWV requires tracking the arrival time of pressure pulses recorded in vivo, commonly referred to as pulse arrival time (PAT). In the state of the art, PAT is estimated by identifying a characteristic point of the pressure pulse waveform. This paper demonstrates that for ambulatory scenarios, where signal-to-noise ratios are below 10 dB, the performance in terms of repeatability of PAT measurements through characteristic points identification degrades drastically. Hence, we introduce a novel family of PAT estimators based on the parametric modeling of the anacrotic phase of a pressure pulse. In particular, we propose a parametric PAT estimator (TANH) that depicts high correlation with the Complior(R) characteristic point D1 (CC = 0.99), increases noise robustness and reduces by a five-fold factor the number of heartbeats required to obtain reliable PAT measurements.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.