Exercise improves memory acquisition and retrieval in the Y-maze task: Relationship with hippocampal neurogenesis van der Borght, K.; Havekes, Robbert; Bos, T.; Eggen, Bartholomeus; van der Zee, Eelke University of Groningen Enhanced physical activity is associated with improvements in cognitive function in rodents as well as in humans. The authors examined in detail which aspects of learning and memory are influenced by exercise, using a spatial Y-maze test combined with a 14-day exercise paradigm at different stages of learning. The authors show that 14 days of wheel running promotes memory acquisition, memory retention, and reversal learning. The exercise paradigm that was employed also significantly increased the number of maturing neurons, suggesting that an increase in neurogenesis underlies the positive effects of exercise on Y-maze performance. Finally, the authors show that memory acquisition in itself does not have a major impact on the number of immature neurons. However, memory retention testing and reversal learning both cause a significant reduction in the number of doublecortin and Ser133-phosphorylated pCREB-positive cells, indicating that a decrease in neurogenesis might be a prerequisite for optimal memory retrieval.
Ultrasound waves pose a promising alternative to the commonly used electromagnetic waves for intra-body communication. This due to the lower ultrasound wave attenuation, the reduced health risks and the reduced external interference. Current state-of-the-art ultrasound designs, however, are limited in their practical in-body deployment and reliability. This stems from their use of bulky, focused transducers, the use of simple modulation schemes or the absence of a realistic test environment and corresponding realistic channel models. Therefore, this work proposes a new, ultrasound, static emulation test bed consisting of small, omnidirectional transducers and custom gelatin phantoms with additional scattering materials. Using this test bed, we investigate different in-body communication scenarios. Multiple communication channels were extracted and mapped onto FIR channel models, which are verified and open sourced for future research. Furthermore, a basic QAM modem was built to assess the communication performance under various modulation schemes. A link was established using 4-QAM and 200kbit/s resulting in a BER <1e-4 at received Eb/No of 13dB. Identical results were obtained by computer simulations on the FIR channels, which makes the extracted FIR channels suitable for the design of future ultrasound in-body communication schemes.
Implanted medical devices need a reliable, lowenergy and secure in-body communication link. Ultrasound wave propagation is promising over other techniques due to its lower body attenuation, inherent security and well-known health risks. While US communication systems have been developed for inbody communication, state-of-the-art systems fail to provide small-size solutions capable of operating under realistic channel conditions. This paper focuses on communication with smallscale (2 mm size) and omni-directional transducers (1.2 MHz center frequency), and discusses the high multipath delay in such channels. To cope with these channel characteristics, an ultrasound in-body optimized OFDM communication scheme is proposed and implemented. In experiments through real tissue, the modem achieves a bit error rate below 1e-4 until a throughput of 340 kbps across 10 cm of beef tissue.
Financial investors make trades based on available information. Previous research has proved that microblogs are a useful source for supporting stock market decisions. However, the financial domain lacks specific sentiment lexicons that could be utilized to extract the sentiment from these microblogs. In this research, we investigate automatic approaches that can be used to build financial sentiment lexicons. We introduce weighted versions of the Pointwise Mutual Information approaches to build sentiment lexicons automatically. Furthermore, existing sentiment lexicons often neglect negation while building the sentiment lexicons. In this research, we also propose two methods (Negated Word and Flip Sentiment) to extend the sentiment building approaches to take into account negation when constructing a sentiment lexicon. We build the financial sentiment lexicons by leveraging 200,000 messages from StockTwits. We evaluate the constructed financial sentiment lexicons in two different sentiment classification tasks (unsupervised and supervised). In addition, the created financial sentiment lexicons are compared with each other and with other existing sentiment lexicons. The best performing financial sentiment lexicon is built by combining our Weighted Normalized Pointwise Mutual Information approach with the Negated Word approach. It outperforms all the other sentiment lexicons in the two sentiment classification tasks. In the unsupervised sentiment classification task, it has, on average, a balanced accuracy of 69.4%, and in the supervised setting, a balanced accuracy of 75.1%. Moreover, the various sentiment classification tasks confirm that the sentiment lexicons could be improved by taking into account negation while building the sentiment lexicons. The improvement could be made by using one of the proposed methods to incorporate negation in the sentiment lexicon construction process.
For wireless LAN applications and the wireless ATM network demonstrator system "Magic WAND", a phase locked oscillator was built with standard, low cost components. Although a simple architecture is chosen, the oscillator has a phase noise of -87 dBc/Hz at 10 kHz frequency offset and an output power of 0 dBm at-16.2 GHz. By changing the reference frequency, the output frequency can be tuned from 15.61 GHz to 16.34 GHz without degradation of the phase noise.
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