A Simple Fiber Bragg Grating-Based Sensor Network Architecture with Self-Protecting and Monitoring Functions

A novel fiber Bragg grating (FBG)-based passive sensor architecture, which can be used to protect the fiber cut and monitor the multiple sensors simultaneously, is proposed and experimentally demonstrated. Here, we employ a wavelength-tunable erbium-doped fiber (EDF) laser scheme with 25 km cavity length acting as the detecting light source in central office (CO). Each FBG sensor, serving as a feedback element, is used in proposed sensor architecture. By tuning the tunable bandpass filter (TBF) placing inside cavity to match the corresponding Bragg wavelength of FBG over the amplification bandwidth, we can retrieve the related wavelength lasing for the FBG sensing and monitoring simultaneously. Moreover, the survivability and capacity of the passive FBG sensor architecture can be also enhanced

Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition

<p>Abstract</p> <p>Background</p> <p>Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased.</p> <p>Methods</p> <p>This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR).</p> <p>Results</p> <p>The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (<it>P </it>< 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis.</p> <p>Conclusions</p> <p>The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.</p

Measurement of Organic Chemical Refractive Indexes Using an Optical Time-Domain Reflectometer

In this investigation, we propose and experimentally demonstrate a method for measuring the refractive index (RI) of liquid organic chemicals. The scheme is based on a single-mode fiber (SMF) sensor and an optical time-domain reflectometer (OTDR). Here, due to the different reflectance (R) between the SMF and organic liquid chemicals, the reflected power level of the backscattering light (BSL) measured by the OTDR would be different. Therefore, we can measure the RI of chemical under test via the measured BSL level. The proposed RI sensor is simple and easy to manipulate, with stable detected signals, and has the potential to be a valuable tool for use in biological and chemical applications

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

First-order magnetic and structural phase transitions in Fe1+y_{1+y}Sex_xTe1−x_{1-x}

We use bulk magnetic susceptibility, electronic specific heat, and neutron scattering to study structural and magnetic phase transitions in Fe$_{1+y}$Se% $_x$Te$_{1-x}$. Fe$_{1.068}$Te exhibits a first order phase transition near 67 K with a tetragonal to monoclinic structural transition and simultaneously develops a collinear antiferromagnetic (AF) order responsible for the entropy change across the transition. Systematic studies of FeSe$$Te$_x$ system reveal that the AF structure and lattice distortion in these materials are different from those of FeAs-based pnictides. These results call into question the conclusions of present density functional calculations, where FeSe$_{1-x}$Te$_x$ and FeAs-based pnictides are expected to have similar Fermi surfaces and therefore the same spin-density-wave AF order.Comment: 5 pages, 3 figure

Association of metabolic syndrome with erosive esophagitis and Barrett’s esophagus in a Chinese population

AbstractBackgroundMetabolic syndrome has been highlighted as a risk factor for several gastrointestinal diseases, including gastroesophageal reflux disease and Barrett’s esophagus (BE). The aim of this study was to investigate the association of metabolic syndrome with erosive esophagitis (EE) and BE.MethodsData were retrospectively collected from patients who visited the Medical Screening Center at Taichung Veterans General Hospital, Taichung, Taiwan from January 2006 to December 2009. All patients underwent an open-access transoral upper gastrointestinal endoscopy, and serum laboratory data were collected. The exclusion criteria included prior gastric surgery, or presence of esophageal varices or peptic ulcers. These patients were assigned to groups according to their endoscopic findings as follows: (1) normal group; (2) EE group; and (3) BE group. Metabolic syndrome was diagnosed based on the International Diabetes Federation criteria.ResultsThere were 560/6499 (8.6%) patients, 214/1118 (9.6%) patients, and 19/95 (20%) patients with metabolic syndrome in the normal, EE, and BE groups, respectively. There was a significantly higher percentage of cases with hypertriglyceridemia in the EE group (67%) compared with the other groups. The BE group had significantly higher rates of central obesity (33%) and hypertension (29.5%) compared with rates in the normal and EE groups. After adjusting for confounders, the positive association with metabolic syndrome still existed in both the EE group (adjusted odds ratio=2.43; 95% confidence interval=1.02–3.44) and the BE group (adjusted odds ratio=2.82; 95% confidence interval=2.05–3.88).ConclusionOur research indicated that in fact there is a greater risk of concurrent metabolic syndrome in patients with EE or BE