Classifying and Predicting Respiratory Function Based on Gait Analysis

The human walking behaviour can express the physiological information of human body, and gait analysis methods can be used to access the human body condition. In addition, the respiratory parameters from pulmonary spirometer are the standard of accessing the body condition of the subjects. Therefore, we want to show the correlation between gait analysis method and the respiratory parameters. We propose a vision sensor-based gait analysis method without wearing any sensors. Our method proposed features such as D′p, V′p and γυ to prove the correlation by classification and prediction experiment. In our experiment, the subjects are divided into three levels depending on the respiratory index. We run classifying and predicting experiment with the extracted features: V′p and γυ. In the classifying experiment, the accuracy result is 75%. In predicting experiment, the correlations of predicting the forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) are 0.69 and 0.67, respectively. Therefore, there is a correlation between the pulmonary spirometer and our method. The radar system is a tool using impulse to record the moving of the subjects’ chest. Combining the features of radar system with our features improves the classification result from 75 to 81%. In predicting FEV1/FVC, the correlation also improves from 25 to 42%. Therefore, cooperating with radar system improves the correlation

Candida lipolytica candidemia as a rare infectious complication of acute pancreatitis: A case report and literature review

Candida lipolytica candidemia is a rare but an emerging pathogenic yeast infection in humans. It can gain access to the bloodstream through intravascular catheterization, especially through central venous catheters in immunocompromised or critically ill patients during hospitalization. In this report, we present a noncatheter-related C. lipolytica candidemia infection in an 84-year-old man who was admitted due to acute pancreatitis. The possible pathogenesis and management of C. lipolytica candidemia are highlighted. It was an unusual infectious complication of acute pancreatitis. Clinicians should be aware that such an opportunistic pathogen can lead to invasive candidemia infection. In clinical practice, systemic antifungal therapy and the removal of the potentially infected central venous catheter might be recommended for the treatment of C. lipolytica candidemia

Planning and Implantation of NetFPGA Platform on Network Emulation Testbed

The concepts of cloud computing and Internet applications have expanded gradually and have become more and more important. Researchers need a new, high-speed network to build experimental environments for testing new network protocolswithout affecting existing traffic. In this paper, we describe a way to integrate NetFPGA platform, OpenFlow concept and NetFPGA reference designs into anetwork testbed to improve the packet processing speed and the dynamic adjustability for network emulation experiments. Furthermore, combined with Tunneling and VPLS, the proposed network testbed can be connected to distributed network, thus providing researchers a traffic-controllable and NIC-programmable experimental networking testbed in intra-communicating part

Survival Prediction of Initial Blood pH for Nontraumatic Out-of-hospital Cardiac Arrest Patients in the Emergency Department

SummaryBackgroundMost nontraumatic out-of-hospital cardiac arrest (NTOHCA) patients who fail in prehospital resuscitation receive continued cardiopulmonary resuscitation in the emergency department (ED). Initial blood pH, which can be assessed rapidly in the ED, was examined to see whether it is a strong survival predictor for these patients.MethodsA 1-year retrospective study included consecutive 225 NTOHCA patients at a medical center in northern Taiwan who presented through the emergency medical services system. On arrival at the ED, these patients received continued cardiopulmonary resuscitation, and their initial blood pH data were assessed.ResultsThe pH value was positively correlated with variables such as return of spontaneous circulation, witnessed arrest, short prehospital time (≤20 minutes), and survival. The best cut-off value of initial blood pH, revealed by the receiver operating characteristic curve, was 7.068. The lowest pH value of the survivors was 6.856. The results of logistic regression model analysis shows that the odds ratios of survival was 10.0 (95% confidence interval [CI], 2.1–47.7) for patients with initial blood pH ≥ 7.068, 5.3 (95% CI, 1.48–18.9) for those with nonasystole rhythm, 4.0 (95% CI, 1.1–14.8) for those with prehospital time ≤20 minutes, and 9.1 (95% CI, 2.3–35.2) for those without NaHCO3 administration during resuscitation, respectively.ConclusionA cut-off value of an initial blood pH of 7.068 can serve as a predictor for survival among NTOHCA patients. In addition, patients whose initial blood pH is lower than 6.85 in the ED may not survive until hospital discharge

A Study on the Luminescence and Energy Transfer of Green-Emitting Ca 9 Y(PO 4 ) 7 :Ce 3+ ,Tb 3+ Phosphor for Fluorescent Lamp Application

Abstrac

Drastic population fluctuations explain the rapid extinction of the passenger pigeon

To assess the role of human disturbances in species' extinction requires an understanding of the species population history before human impact. The passenger pigeon was once the most abundant bird in the world, with a population size estimated at 3-5 billion in the 1800s; its abrupt extinction in 1914 raises the question of how such an abundant bird could have been driven to extinction in mere decades. Although human exploitation is often blamed, the role of natural population dynamics in the passenger pigeon's extinction remains unexplored. Applying high-throughput sequencing technologies to obtain sequences from most of the genome, we calculated that the passenger pigeon's effective population size throughout the last million years was persistently about 1/10,000 of the 1800's estimated number of individuals, a ratio 1,000-times lower than typically found. This result suggests that the passenger pigeon was not always super abundant but experienced dramatic population fluctuations, resembling those of an "outbreak" species. Ecological niche models supported inference of drastic changes in the extent of its breeding range over the last glacial-interglacial cycle. An estimate of acorn-based carrying capacity during the past 21,000 y showed great year-to-year variations. Based on our results, we hypothesize that ecological conditions that dramatically reduced population size under natural conditions could have interacted with human exploitation in causing the passenger pigeon's rapid demise. Our study illustrates that even species as abundant as the passenger pigeon can be vulnerable to human threats if they are subject to dramatic population fluctuations, and provides a new perspective on the greatest human-caused extinction in recorded history

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p