Postconditioning: a form of "modified reperfusion" protects the myocardium by activating the phosphatidylinositol 3-kinase-akt pathway

Brief intermittent episodes of ischemia and reperfusion, at the onset of reperfusion after a prolonged period of ischemia, confer cardioprotection, a phenomenon termed "ischemic postconditioning" (Postcond). We hypothesized that this phenomenon may just represent a modified form of reperfusion that activates the reperfusion injury salvage kinase (RISK) pathway. Isolated perfused rat hearts were subjected to: (a) 35 minutes of ischemia and 120 minutes of reperfusion, and infarct size was determined by tetrazolium staining; or (b) 35 minutes of ischemia and 7 minutes of reperfusion, and the phosphorylation states of Akt, endothelial NO synthase (eNOS), and p70S6K were determined. Postcond reduced infarct size from 51.2±3.4% to 31.5±4.1% (P<0.01), an effect comparable with ischemic preconditioning (IPC; 27.5±2.3%; P<0.01). Of interest, the combined protective effects of IPC and Postcond were not additive (30.1±4.8% with IPC+Postcond; P=NS). Inhibiting phosphatidylinositol 3-kinase (PI3K) at reperfusion using LY or Wortmannin (Wort) during the first 15 minutes of reperfusion completely abolished Postcond-induced protection (31.5±4.1% with Postcond versus 51.7±4.5% with Postcond+LY, P<0.01; 56.2±10.1% with Postcond+ Wort; P<0.01), suggesting that Postcond protects the heart by activating PI3K-Akt. Western blot analysis demonstrated that Postcond induced a significant increase in phosphorylation of Akt, eNOS, and p70S6K in an LY- and Wort-sensitive manner. In conclusion, we show for the first time that ischemic Postcond protects the myocardium by activating the prosurvival kinases PI3K-Akt, eNOS, and p70S6K in accordance with the RISK pathway

Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question. In this paper, we propose a hybrid approach to address this problem. It combines sparse smart meters with deep learning methods, e.g. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs), to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset

Disclosure Style and Its Determinants in Integrated Reports

Integrated Reporting promotes a more cohesive and efficient approach to corporate reporting and aims to improve the quality of information available to providers of financial capital. The purpose of this paper was to investigate the determinants of readability and optimism which build the disclosure style of integrated reports. Our research draws on impression management theory and legitimacy theory, while also taking into consideration the cultural system of Hofstede with its further developments by Gray. Our sample consisted of 30 annual reports, extracted randomly from the Integrated Reporting examples database set up by the International Integrated Reporting Council. For the purposes of our investigation, we have carried out a multivariate regression analysis. Firstly, our results show that the higher the revenues of the reporting company, the more balanced their integrated reports, while younger companies use a more optimistic tone when reporting. Additionally, optimism seems to be inversely correlated with the length of the reports. Secondly, entities based in countries with a stronger tendency towards transparency surprisingly provide less readable integrated reports. It was also revealed that companies operating in non-environmentally sensitive industries, as well as International Financial Reporting Standards adopters deliver foggier and thus less readable integrated reports

Hu-bot:promoting the cooperation between humans and mobile robots

peer reviewedThis paper investigates human–robot collaboration in a novel setup: a human helps a mobile robot that can move and navigate freely in an environment. Specifically, the human helps by remotely taking over control during the learning of a task. The task is to find and collect several items in a walled arena, and Reinforcement Learning is used to seek a suitable controller. If the human observes undesired robot behavior, they can directly issue commands for the wheels through a game joystick. Experiments in a simulator showed that human assistance improved robot behavior efficacy by 30% and efficiency by 12%. The best policies were also tested in real life, using physical robots. Hardware experiments showed no significant difference concerning the simulations, providing empirical validation of our approach in practice

Predicting battery depletion of neighboring wireless sensor nodes

With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy inference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

Fluorescence Properties of Photonic Crystals Doped with Perylenediimide

This study aims to present the fabrication of colloidal photonic crystals (PC) with increased fluorescence properties. The use of a highly fluorescent perylenediimide derivate (PDI) during the soap-free emulsion polymerization of styrene–acrylic acid resulted in monodisperse core–shell particles which allowed the fabrication of PC films. The properties of the hybrid material were studied in comparison with hybrid materials obtained by impregnation of films with chromophore solutions. In both cases an increase of the fluorescence response was observed in addition to a blue shift for the PDI core particles, proving the incorporation of the dye inside the copolymer particles

Sparse Training Theory for Scalable and Efficient Agents

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study