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Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much higher energy consumption. In light of this, we want to mimic flying insects in terms of their processing capabilities, and consequently show the efficiency of this approach in the real world. This letter does so through evolving spiking neural networks for controlling landings of micro air vehicles using optical flow divergence from a downward-looking camera. We demonstrate that the resulting neuromorphic controllers transfer robustly from a highly abstracted simulation to the real world, performing fast and safe landings while keeping network spike rate minimal. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learned with only a single spiking neuron. To the best of our knowledge, this work is the first to integrate spiking neural networks in the control loop of a real-world flying robot. Videos of the experiments can be found at https://bit.ly/neuro-controller ., 8 pages, 6 figures, camera-ready version to appear in IEEE Robotics and Automation Letters

The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems, namely feature extraction and local and global motion perception. Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer. The proposed solution is validated using synthetic and real event sequences. Along with this paper, we provide the cuSNN library, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at https://github.com/tudelft/cuSNN., Comment: 14 pages, 14 figures

et al., [Objective]: Peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) is a transcriptional coactivator that has been proposed to play a protective role in mouse models of cardiac ischemia and heart failure, suggesting that PGC-1α could be relevant as a prognostic marker. Our previous studies showed that the estimation of peripheral mRNA PGC-1α expression was feasible and that its induction correlated with the extent of myocardial necrosis and left ventricular remodeling in patients with myocardial infarction. In this study, we sought to determine if the myocardial and peripheral expressions of PGC-1α are well correlated and to analyze the variability of PGC-1α expression depending on the prevalence of some metabolic disorders. [Methods]: This was a cohort of 35 consecutive stable heart failure patients with severe aortic stenosis who underwent an elective aortic valve replacement surgery. mRNA PGC-1α expression was simultaneously determined from myocardial biopsy specimens and blood samples obtained during surgery by quantitative PCR, and a correlation between samples was made using the Kappa index. Patients were divided into two groups according to the detection of baseline expression levels of PGC-1α in blood samples, and comparisons between both groups were made by chi-square test or unpaired Student’s t-test as appropriate. [Results]: Based on myocardial biopsies, we found that mRNA PGC-1α expression in blood samples showed a statistically significant correlation with myocardial expression (Kappa index 0.66, p, Grant from Sociedad Valenciana de Cardiología, 2013 to Óscar Fabregat-Andrés.

OBJECTIVES: We aim to describe our experience in coronary artery bypass grafting (CABG) with or without cardiopulmonary bypass by comparing intraoperative and postoperative outcomes. METHODS: From January 1993 to June 2013, 3097 patients underwent consecutive emergency and scheduled CABG surgery. A total of 1770 patients underwent on-pump CABG (ONCABG) and 1327 off-pump CABG (OPCABG). A propensity score matching was performed to identify appropriate matched-pair patients; univariable and multivariable logistic regression analyses were performed to assess significant predictors of hospital and 30-day morbidity and mortality composite end-points. Morbidity composite end-point was defined as any renal, pulmonary, cardiovascular and neurological complication that occurred during hospital stay. We collected all-cause mortality data during the study period. RESULTS: We identified 1004 patients in each group. There were no significant differences in thirty day mortality, 2.8 vs 3.8%, in OPCABG and ONCABG, respectively (P = 0.21). Cardiovascular, neurological, respiratory and renal complications were more frequent in the ONCABG group: 13.9 vs 8.7% (P < 0.001), 3.9 vs 2.2% (P = 0.03), 13.5 vs 7.5% (P < 0.001), 7.1 vs 5.3% (P = 0.095), respectively. The long-term all-cause mortality rate was 12.3 vs 12.9% in the OPCABG versus ONCABG group (P = 0.42), respectively. In both uni- and multivariable analysis preoperative renal failure, chronic obstructive pulmonary disease and ONCABG were independent predictors of mortality and morbidity composite end-points. CONCLUSIONS: OPCABG is associated with less postoperative morbimortality and shorter hospital and intensive care unit length of stay. ONCABG resulted as an independent predictor of morbidity and mortality composite end-point. No statistically significant differences were observed in long-term all-cause mortality between groups.