Anomaly detection in elderly daily behavior in ambient sensing environments

Current ubiquitous computing applications for smart homes aim to enhance people’s daily living respecting age span. Among the target groups of people, elderly are a population eager for “choices for living arrangements”, which would allow them to continue living in their homes but at the same time provide the health care they need. Given the growing elderly population, there is a need for statistical models able to capture the recurring patterns of daily activity life and reason based on this information. We present an analysis of real-life sensor data collected from 40 different households of elderly people, using motion, door and pressure sensors. Our objective is to automatically observe and model the daily behavior of the elderly and detect anomalies that could occur in the sensor data. For this purpose, we first introduce an abstraction layer to create a common ground for home sensor configurations. Next, we build a probabilistic spatio-temporal model to summarize daily behavior. Anomalies are then defined as significant changes from the learned behavioral model and detected using a cross-entropy measure. We have compared the detected anomalies with manually collected annotations and the results show that the presented approach is able to detect significant behavioral changes of the elderly

The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department

The hypothalamic-pituitary-adrenal axis is activated in response to stress. One of the activated hypothalamic hormones is arginine vasopressin, a hormone involved in hemodynamics and osmoregulation. Copeptin, the C-terminal part of the arginine vasopressin precursor peptide, is a sensitive and stable surrogate marker for arginine vasopressin release. Measurement of copeptin levels has been shown to be useful in a variety of clinical scenarios, particularly as a prognostic marker in patients with acute diseases such as lower respiratory tract infection, heart disease and stroke. The measurement of copeptin levels may provide crucial information for risk stratification in a variety of clinical situations. As such, the emergency department appears to be the ideal setting for its potential use. This review summarizes the recent progress towards determining the prognostic and diagnostic value of copeptin in the emergency department

Proteomics of industrial fungi: trends and insights for biotechnology

Filamentous fungi are widely known for their industrial applications, namely, the production of food-processing enzymes and metabolites such as antibiotics and organic acids. In the past decade, the full genome sequencing of filamentous fungi increased the potential to predict encoded proteins enormously, namely, hydrolytic enzymes or proteins involved in the biosynthesis of metabolites of interest. The integration of genome sequence information with possible phenotypes requires, however, the knowledge of all the proteins in the cell in a system-wise manner, given by proteomics. This review summarises the progress of proteomics and its importance for the study of biotechnological processes in filamentous fungi. A major step forward in proteomics was to couple protein separation with high-resolution mass spectrometry, allowing accurate protein quantification. Despite the fact that most fungal proteomic studies have been focused on proteins from mycelial extracts, many proteins are related to processes which are compartmentalised in the fungal cell, e.g. β-lactam antibiotic production in the microbody. For the study of such processes, a targeted approach is required, e.g. by organelle proteomics. Typical workflows for sample preparation in fungal organelle proteomics are discussed, including homogenisation and sub-cellular fractionation. Finally, examples are presented of fungal organelle proteomic studies, which have enlarged the knowledge on areas of interest to biotechnology, such as protein secretion, energy production or antibiotic biosynthesis