The moss Physcomitrella patens (P. patens) is a useful model to study abiotic stress responses since it is highly tolerant to drought, salt and osmotic stress. However, very little is known about the defense mechanisms activated in this moss after pathogen assault. In this study, we show that P. patens activated multiple and similar responses against Pythium irregulare and Pythium debaryanum, including the reinforcement of the cell wall, induction of the defense genes CHS, LOX and PAL, and accumulation of the signaling molecules jasmonic acid (JA) and its precursor 12-oxo-phytodienoic acid (OPDA). However, theses responses were not sufficient and infection could not be prevented leading to hyphae colonization of moss tissues and plant decay. Pythium infection induced reactive oxygen species production and caused cell death of moss tissues. Taken together, these data indicate that Pythium infection activates in P. patens common responses to those previously characterized in flowering plants. Microscopic analysis also revealed intracellular relocation of chloroplasts in Pythium-infected tissues toward the infection site. In addition, OPDA, JA and its methyl ester methyl jasmonate induced the expression of PAL. Our results show for the first time JA and OPDA accumulation in a moss and suggest that this defense pathway is functional and has been maintained during the evolution of plants.

Background: Vascular plants respond to pathogens by activating a diverse array of defense mechanisms. Studies with these plants have provided a wealth of information on pathogen recognition, signal transduction and the activation of defense responses. However, very little is known about the infection and defense responses of the bryophyte, Physcomitrella patens, to well-studied phytopathogens. The purpose of this study was to determine: i) whether two representative broad host range pathogens, Erwinia carotovora ssp. carotovora (E.c. carotovora) and Botrytis cinerea (B. cinerea), could infect Physcomitrella, and ii) whether B. cinerea, elicitors of a harpin (HrpN) producing E.c. carotovora strain (SCC1) or a HrpN-negative strain (SCC3193), could cause disease symptoms and induce defense responses in Physcomitrella.

No abstract

No abstract

This paper presents experimental measurements of power consumption for core logic of a 65-nm Cyclone III FPGA and its comparison with the value predicted by the power estimation tool. The laboratory work is described, including the measurement setup, the benchmark circuits, and the CAD flows utilized to obtain power estimations. The selected circuits used as benchmarks were different type of multipliers implemented in LUTs and in embedded blocks both with or without pipelining stages. Three type of results are presented: first, the error between power measurements and power estimations; second, the power savings by using pipeline stages, and third, the quantification of power savings by using embedded blocks.

No abstract

This work presents a wireless multichannel electroencephalogram (EEG) recording system featuring lossless and near-lossless compression of the digitized EEG signal. Two novel, low-complexity, efficient compression algorithms were developed and tested in a low-power platform. The algorithms were tested on six public EEG databases comparing favorably with the best compression rates reported up to date in the literature. In its lossless mode, the platform is capable of encoding and transmitting 59-channel EEG signals, sampled at 500 Hz and 16 bits per sample, at a current consumption of 337 A per channel; this comes with a guarantee that the decompressed signal is identical to the sampled one. The near-lossless mode allows for significant energy savings and/or higher throughputs in exchange for a small guaranteed maximum per-sample distortion in the recovered signal. Finally, we address the tradeoff between computation cost and transmission savings by evaluating three alternatives: sending raw data, or encoding with one of two compression algorithms that differ in complexity and compression performance. We observe that the higher the throughput (number of channels and sampling rate) the larger the benefits obtained from compression.

This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.