Towards a Formalism-Based Toolkit for Automotive Applications

The success of a number of projects has been shown to be significantly improved by the use of a formalism. However, there remains an open issue: to what extent can a development process based on a singular formal notation and method succeed. The majority of approaches demonstrate a low level of flexibility by attempting to use a single notation to express all of the different aspects encountered in software development. Often, these approaches leave a number of scalability issues open. We prefer a more eclectic approach. In our experience, the use of a formalism-based toolkit with adequate notations for each development phase is a viable solution. Following this principle, any specific notation is used only where and when it is really suitable and not necessarily over the entire software lifecycle. The approach explored in this article is perhaps slowly emerging in practice - we hope to accelerate its adoption. However, the major challenge is still finding the best way to instantiate it for each specific application scenario. In this work, we describe a development process and method for automotive applications which consists of five phases. The process recognizes the need for having adequate (and tailored) notations (Problem Frames, Requirements State Machine Language, and Event-B) for each development phase as well as direct traceability between the documents produced during each phase. This allows for a stepwise verification/validation of the system under development. The ideas for the formal development method have evolved over two significant case studies carried out in the DEPLOY project

NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification

Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001;mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis

Gut microbiome patterns correlate with higher postoperative complication rates after pancreatic surgery

Abstract Background Postoperative complications are of great relevance in daily clinical practice, and the gut microbiome might play an important role by preventing pathogens from crossing the intestinal barrier. The two aims of this prospective clinical pilot study were: (1) to examine changes in the gut microbiome following pancreatic surgery, and (2) to correlate these changes with the postoperative course of the patient. Results In total, 116 stool samples of 32 patients undergoing pancreatic surgery were analysed by 16S-rRNA gene next-generation sequencing. One sample per patient was collected preoperatively in order to determine the baseline gut microbiome without exposure to surgical stress and/or antibiotic use. At least two further samples were obtained within the first 10 days following the surgical procedure to observe longitudinal changes in the gut microbiome. Whenever complications occurred, further samples were examined. Based on the structure of the gut microbiome, the samples could be allocated into three different microbial communities (A, B and C). Community B showed an increase in Akkermansia, Enterobacteriaceae and Bacteroidales as well as a decrease in Lachnospiraceae, Prevotella and Bacteroides. Patients showing a microbial composition resembling community B at least once during the observation period were found to have a significantly higher risk for developing postoperative complications (B vs. A, odds ratio = 4.96, p &lt; 0.01**; B vs. C, odds ratio = 2.89, p = 0.019*). Conclusions The structure of the gut microbiome is associated with the development of postoperative complications

3D movement extent (ME) analysis and movement laterality of a seizure MOI.

<p>The solid line inside each rounding box represents movement extent, calculated as the maximum volume traveled by the MOI and limited by the three-dimensional maxima 1, 2, 3, 4, 5 and 6. The dotted line represents the medial planes, used as reference to calculate the movement laterality in each direction (R-right, L-left, F-front, B-back). In this example, a mostly right, down and front laterality MOI is represented.</p

Aspects of the 3Dvideo-EEG system console installed in the routine of University of Munich EMU.

<p>Notice the KiT time-code is inserted in the video-EEG system for complete synchronization purposes. The system does not interferes with the EMU routine.</p

KiT first integration in the routine of University of Munich EMU (circa 2012).

<p>KiT first integration in the routine of University of Munich EMU (circa 2012).</p

KiT v2 from LMU Bed: Trial tests to assess the new sensor robustness.

<p>In the primary and secondary source, the new color and bodyindex stream, as well as the 3D joints estimations of the human body are presented.</p

Schematic representation of the developed algorithm.

<p>The yellow and green colors are associated with the depth of the centroid in two consecutive frames. In frame n, the user selects and ellipsoid mask over the aimed ROI (Fig A). The resulting velocity vector is estimated (based on the OF pixel velocity vectors—in blue) and is used to calculate the next centroid (Fig B). In frame n + 1, the new centroid is calculated. If the depth of the estimated <i>P</i>_<i>n</i>+1(<i>x</i>, <i>y</i>) is concordant with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145669#pone.0145669.e002" target="_blank">Eq 2</a>, the estimation is accepted (Fig C).</p

NeuroKinect architecture deployed inside the University of Munich EMU.

<p>NeuroKinect architecture deployed inside the University of Munich EMU.</p