Acitve noise control in three dimension enclosure using piezoceramics

Analysis and experiment are undertaken to attenuate the three dimension enclosure noise using piezoceramics. A flexible aluminium plate and five wood walls are constructed with clamped four edges conditions. Noise is generated by speaker and transmitted to the three dimension enclosure. Piezoceramics are used to control the noise inside. A microphone is put inside to monitor the noise. State space method is used to identify the system, the vibration mode and acoustic mode is also researched. Different arithmetic is used to control the noise inside. The sound pressure level reduction at selected point is observed

A theoretical foundation for program transformations to reduce cache thrashing due to true data sharing

AbstractCache thrashing due to true data sharing can degrade the performance of parallel programs significantly. Our previous work showed that parallel task alignment via program transformations can be quite effective for the reduction of such cache thrashing. In this paper, we present a theoretical foundation for such program transformations. Based on linear algebra and the theory of numbers, our work analyzes the data dependences among the tasks created by a fork-join parallel program and determines at compile time how these tasks should be assigned to processors in order to reduce cache thrashing due to true data sharing. Our analysis and program transformations can be easily performed by compilers for parallel computers

Bootstrap-based Support of HGT Inferred by Maximum Parsimony

Background: Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. Results: In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrapbased measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. Conclusions: We have implemented our method in the NEPAL software tool (available publicly a

The circRNA and Role in Alzheimer’s Disease: From Regulation to Therapeutic and Diagnostic Targets

Alzheimer\u27s disease (AD) is a devastating neurodegenerative disorder and the most common form of dementia worldwide. Although the great progress on the prevention and treatment of AD, no effective therapies are available as yet. With the increasing incidence of AD, it has brought a growing burden to the family and society. Histopathologically, AD is characterized by the presence of myloid β (Aβ) plaques composed of Aβ and neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau proteins, which lead to neuronal loss. However, the full spectrum of precise molecular mechanism that contribute to AD pathogenesis remains largely unknown. circular RNAs (circRNAs) are a novel class of endogenous non-coding RNAs that play a vital role in post-transcriptional regulation. Recent reports showed circRNAs to be an important player in the development of neurodegenerative diseases like AD. In this chapter, we review recent progress on understanding the role of circRNAs in AD, and many studies implicating specific circRNAs in the development of the disease. Moreover, we explore the potential promise of these findings for future diagnosis and treatment

Validity of oral fluid test for Delta-9-tetrahydrocannabinol in drivers using the 2013 National Roadside Survey Data

Background Driving under the influence of marijuana is a serious traffic safety concern in the United States. Delta 9-tetrahydrocannabinol (THC) is the main active compound in marijuana. Although blood THC testing is a more accurate measure of THC-induced impairment, measuring THC in oral fluid is a less intrusive and less costly method of testing. Methods We examined whether the oral fluid THC test can be used as a valid alternative to the blood THC test using a sensitivity and specificity analysis and a logistic regression, and estimate the quantitative relationship between oral fluid THC concentration and blood THC concentration using a correlation analysis and a linear regression on the log-transformed THC concentrations. We used data from 4596 drivers who participated in the 2013 National Roadside Survey of Alcohol and Drug Use by Drivers and for whom THC testing results from both oral fluid and whole blood samples were available. Results Overall, 8.9% and 9.4% of the participants tested positive for THC in oral fluid and whole blood samples, respectively. Using blood test as the reference criterion, oral fluid test for THC positivity showed a sensitivity of 79.4% (95% CI: 75.2%, 83.1%) and a specificity of 98.3% (95% CI: 97.9%, 98.7%). The log-transformed oral fluid THC concentration accounted for about 29% of the variation in the log-transformed blood THC concentration. That is, there is still 71% of the variation in the log-transformed blood THC concentration unexplained by the log-transformed oral fluid THC concentration. Back-transforming to the original scale, we estimated that each 10% increase in the oral fluid THC concentration was associated with a 2.4% (95% CI: 2.1%, 2.8%) increase in the blood THC concentration. Conclusions The oral fluid test is a highly valid method for detecting the presence of THC in the blood but cannot be used to accurately measure the blood THC concentration

Predicting Disease-Related Genes Using Integrated Biomedical Networks

Background: Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery. Results: We propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery. Conclusions: The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets