The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein-protein interaction and signalling networks

Motivation: Network visualizations of complex biological datasets usually result in 'hairball' images, which do not discriminate network modules. Results: We present the EntOptLayout Cytoscape plug-in based on a recently developed network representation theory. The plug-in provides an efficient visualization of network modules, which represent major protein complexes in protein-protein interaction and signalling networks. Importantly, the tool gives a quality score of the network visualization by calculating the information loss between the input data and the visual representation showing a 3- to 25-fold improvement over conventional methods. Availability: The plug-in (running on Windows, Linux, or Mac OS) and its tutorial (both in written and video forms) can be downloaded freely under the terms of the MIT license from: http://apps.cytoscape.org/apps/entoptlayout. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Real-time tracking with an embedded 3D camera with FPGA processing

People tracking is a crucial component of many intelligent video surveillance systems and recent developments in embedded computing architectures and algorithms allow us to design compact, lightweight and energy efficient systems aimed at tackling this problem. In particular, the advent of cheap RGBD sensing devices enables to exploit depth information as additional cue. In this paper we propose a 3D tracking system aimed to become the basic node of a distributed system for business analytics applications. In the envisioned distributed system each node would consist of a custom stereo camera with on-board FPGA processing coupled with a compact CPU based board. In the basic node proposed in this paper, aimed at raw people tracking within the sensed area of a single device, the custom stereo camera delivers, in real time and with minimal energy requirements, accurate dense depth maps according to state-of-the-art computer vision algorithms. Then, the CPU based system, by processing this information enables reliable 3D people tracking. In our system, deploying the FPGA front-end, the main constraint for real time 3D tracking is concerned with the computing requirement of the CPU based board and, in this paper, we propose a fast and effective node for 3D people tracking algorithm suited for implementation on embedded devices

Functional brain network topology discriminates between patients with minimally conscious state and unresponsivewakefulness syndrome

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients