Co-Expression Analysis of Airway Epithelial Transcriptome in Asthma Patients with Eosinophilic vs. Non-Eosinophilic Airway Infiltration.

Asthma heterogeneity complicates the search for targeted treatment against airway inflammation and remodeling. We sought to investigate relations between eosinophilic inflammation, a phenotypic feature frequent in severe asthma, bronchial epithelial transcriptome, and functional and structural measures of airway remodeling. We compared epithelial gene expression, spirometry, airway cross-sectional geometry (computed tomography), reticular basement membrane thickness (histology), and blood and bronchoalveolar lavage (BAL) cytokines of n = 40 moderate to severe eosinophilic (EA) and non-eosinophilic asthma (NEA) patients distinguished by BAL eosinophilia. EA patients showed a similar extent of airway remodeling as NEA but had an increased expression of genes involved in the immune response and inflammation (e.g., KIR3DS1), reactive oxygen species generation (GYS2, ATPIF1), cell activation and proliferation (ANK3), cargo transporting (RAB4B, CPLX2), and tissue remodeling (FBLN1, SOX14, GSN), and a lower expression of genes involved in epithelial integrity (e.g., GJB1) and histone acetylation (SIN3A). Genes co-expressed in EA were involved in antiviral responses (e.g., ATP1B1), cell migration (EPS8L1, STOML3), cell adhesion (RAPH1), epithelial-mesenchymal transition (ASB3), and airway hyperreactivity and remodeling (FBN3, RECK), and several were linked to asthma in genome- (e.g., MRPL14, ASB3) or epigenome-wide association studies (CLC, GPI, SSCRB4, STRN4). Signaling pathways inferred from the co-expression pattern were associated with airway remodeling (e.g., TGF-ß/Smad2/3, E2F/Rb, and Wnt/ß-catenin).

Intelligent Sensors for Human Motion Analysis.

Currently, the analysis of human motion is one of the most interesting and active research topics in computer science, especially in computer vision [...].

Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System.

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.

Reticular Basement Membrane Thickness Is Associated with Growth- and Fibrosis-Promoting Airway Transcriptome Profile-Study in Asthma Patients.

Airway remodeling in asthma is characterized by reticular basement membrane (RBM) thickening, likely related to epithelial structural and functional changes. Gene expression profiling of the airway epithelium might identify genes involved in bronchial structural alterations. We analyzed bronchial wall geometry (computed tomography (CT)), RBM thickness (histology), and the bronchial epithelium transcriptome profile (gene expression array) in moderate to severe persistent (n = 21) vs. no persistent (n = 19) airflow limitation asthmatics. RBM thickness was similar in the two studied subgroups. Among the genes associated with increased RBM thickness, the most essential were those engaged in cell activation, proliferation, and growth (e.g., CDK20, TACC2, ORC5, and NEK5) and inhibiting apoptosis (e.g., higher mRNA expression of RFN34, BIRC3, NAA16, and lower of RNF13, MRPL37, CACNA1G). Additionally, RBM thickness correlated with the expression of genes encoding extracellular matrix (ECM) components (LAMA3, USH2A), involved in ECM remodeling (LTBP1), neovascularization (FGD5, HPRT1), nerve functioning (TPH1, PCDHGC4), oxidative stress adaptation (RIT1, HSP90AB1), epigenetic modifications (OLMALINC, DNMT3A), and the innate immune response (STAP1, OAS2). Cluster analysis revealed that genes linked with RBM thickness were also related to thicker bronchial walls in CT. Our study suggests that the pro-fibrotic profile in the airway epithelial cell transcriptome is associated with a thicker RBM, and thus, may contribute to asthma airway remodeling.

Human fall detection on embedded platform using depth maps and wireless accelerometer.

Since falls are a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.

Detecting human falls with 3-axis accelerometer and depth sensor.

Previous work demonstrated that Kinect sensor can be very useful for fall detection. In this work we present a novel approach to fall detection that allows us to achieve reliable fall detection in larger areas through person detection and tracking in dense depth map sequences acquired by an active pan-tilt 3D camera. We demonstrate that both high sensitivity and specificity can be obtained using dense depth images acquired by a ceiling mounted Kinect and executing the proposed algorithms for lying pose detection and motion analysis. The person is extracted using depth region growing and person detection.