A Conserved Role for Atlastin GTPases in Regulating Lipid Droplet Size

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Nutritional assessment and fate of dna of soybean meal from roundup ready or conventional soybeans using rats

This study was conducted to compare the safety of soybean meal prepared from genetically modified (GM) glyphosate-tolerant (Roundup Ready; RR) soybeans and conventional soybeans. Eighty Sprague-Dawley rats (40 males and 40 females) were randomly allotted to one of four groups according to sex and body weight for a 13-week feeding experiment. The rats were fed corn-based diets containing 60% conventional soybean meal, a mixture of 30% conventional and 30% RR soybean meal, 60% or 90% RR soybean meal. All diets were adjusted to an identical nutrient level except the 90% RR diet. The two soybean meals were similar in chemical analysis and amino acid composition. During the 13-week growth trial, body weight (P?P?cp4 epsps gene specific for the GM constructs from RR soybean meal or a 407?bp of lec gene from endogenous soybean DNA could not be detected in investigated masseter muscle samples. No adverse effects of glyphosate-tolerant soybean meal on rats were seen even at levels as high as 90% of the diet.     

Bag-of-words representation for biomedical time series classification

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is originally developed for text document analysis is extended for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.     

Observer-based tracking control of abnormal oscillations in demyelination symptom

Fast axonal conduction of action potentials in mammals relies on myelin insulation. Demyelination can cause slowed, blocked, desynchronized, or paradoxically excessive spiking that underlies the symptoms observed in demyelination diseases. Feedback control via functional electrical stimulation (FES) seems to be a promising treatment modality in such diseases. However, there are challenges to implementing such method for neurons: high nonlinearity, biological tissue constrains and unobservable ion channel states. To address this problem, we propose an estimating and tracking control strategy for systems based on Kalman filter, in order to enhance the action potential propagation reliability of demyelinated neuron via FES. Unscented Kalman filter (UKF) is employed to estimate the unobservable states and parameters in the demyelination neuron model from membrane potential dynamics. Our method could promote the design of new closed-loop electrical stimulation systems for patients suffering from different nerve system dysfunctions.