Effects of lead exposure on hippocampal metabotropic glutamate receptor subtype 3 and 7 in developmental rats

Background A complete explanation of the mechanisms by which Pb2+ exerts toxic effects on developmental central nervous system remains unknown. Glutamate is critical to the developing brain through various subtypes of ionotropic or metabotropic glutamate receptors (mGluRs). Ionotropic N-methyl-D-aspartate receptors have been considered as a principal target in lead-induced neurotoxicity. The relationship between mGluR3/mGluR7 and synaptic plasticity had been verified by many recent studies. The present study aimed to examine the role of mGluR3/mGluR7 in lead-induced neurotoxicity. Methods Twenty-four adult and female rats were randomly selected and placed on control or 0.2% lead acetate during gestation and lactation. Blood lead and hippocampal lead levels of pups were analyzed at weaning to evaluate the actual lead content at the end of the exposure. Impairments of short -term memory and long-term memory of pups were assessed by tests using Morris water maze and by detection of hippocampal ultrastructural alterations on electron microscopy. The impact of lead exposure on mGluR3 and mGluR7 mRNA expression in hippocampal tissue of pups were investigated by quantitative real-time polymerase chain reaction and its potential role in lead neurotoxicity were discussed. Results Lead levels of blood and hippocampi in the lead-exposed rats were significantly higher than those in the controls (P < 0.001). In tests using Morris Water Maze, the overall decrease in goal latency and swimming distance was taken to indicate that controls had shorter latencies and distance than lead-exposed rats (P = 0.001 and P < 0.001 by repeated-measures analysis of variance). On transmission electron microscopy neuronal ultrastructural alterations were observed and the results of real-time polymerase chain reaction showed that exposure to 0.2% lead acetate did not substantially change gene expression of mGluR3 and mGluR7 mRNA compared with controls. Conclusion Exposure to lead before and after birth can damage short-term and long-term memory ability of young rats and hippocampal ultrastructure. However, the current study does not provide evidence that the expression of rat hippocampal mGluR3 and mGluR7 can be altered by systemic administration of lead during gestation and lactation, which are informative for the field of lead-induced developmental neurotoxicity noting that it seems not to be worthwhile to include mGluR3 and mGluR7 in future studies. Background

Bottlenecks detection of track allocation schemes at rail stations by Petri nets

Robustness of the track allocation problem is rarely addressed in literatures and the obtained track allocation schemes (TAS) embody some bottlenecks. Therefore, an approach to detect bottlenecks is needed to support local optimization. First a TAS is transformed to an executable model by Petri nets. Then disturbances analysis is performed using the model and the indicators of the total trains' departure delays are collected to detect bottlenecks when each train suffers a disturbance. Finally, the results of the tests based on a rail hub linking six lines and a TAS about thirty minutes show that the minimum buffer time is 21 seconds and there are two bottlenecks where the buffer times are 57 and 44 seconds respectively, and it indicates that the bottlenecks do not certainly locate at the area where there is minimum buffer time. The proposed approach can further support selection of multi schemes and robustness optimization.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

Metabolic urinary profiling of alcohol hepatotoxicity and intervention effects of Yin Chen Hao Tang in rats using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry

This paper was designed to study metabonomic characters of the hepatotoxicity induced by alcohol and the intervention effects of Yin Chen Hao Tang (YCHT), a classic traditional Chinese medicine formula for treatment of jaundice and liver disorders in China. Urinary samples from control, alcohol- and YCHT-treated rats were analyzed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) in positive ionization mode. The total ion chromatograms obtained from the control, alcohol- and YCHT-treated rats were easily distinguishable using a multivariate statistical analysis method such as the principal components analysis (PCA). The greatest difference in metabolic profiling was observed from alcohol-treated rats compared with the control and YCHT-treated rats. The positive ions m/z 664.3126 (9.00 min) was elevated in urine of alcohol-treated rats, whereas, ions m/z 155.3547 (10.96 min) and 708.2932 (9.01 min) were at a lower concentration compared with that in urine of control rats, however, these ions did not indicate a statistical difference between control rats and YCHT-treated rats. The ion m/z 664.3126 was found to correspond to ceramide (d18:1/25:0), providing further support for an involvement of the sphingomyelin signaling pathway in alcohol hepatotoxicity and the intervention effects of YCHT.

Achieving world leadership in sustainable buildings - a Brisbane case study

In Australia, the building and construction industry is taking significant steps towards the enhancement of environmental performance of the built environment. A large number of world class sustainable buildings have been constructed in recent years, offering researchers and practitioners alike a good opportunity to identify the best practices and real life experiences in delivering high performance buildings. A case study of ONE ONE ONE Eagle Street, a 6 Star Green Star office building in Brisbane, was conducted to investigate the best practice in achieving this “world leader” green office building. The study identified a number of key factors relating to project delivery system, contractor selection method, client’s early commitment, design integration, communication as major contributors to the successful delivery of this project. Additionally, key environmentally sustainable features and their cost implications were explored through in-depth interviews with the main contractor. The findings of this study will shed lights on the successful delivery of sustainable buildings and provide practical implications for different stakeholders.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Visible-light-induced selective photocatalytic oxidation of benzylamine into imine over supported Ag/AgI photocatalysts

Titanate nanotubes (TNT) supported AgI nanoparticles were prepared by a two-step method: the deposition of Ag2O on titanate nanotubes from AgNO3 solution and the subsequent I-adsorption process from NaI solution. It is found that the supported AgI samples exhibited excellent photoactivity for the selective oxidation of benzylamine to the corresponding imine under visible light illumination and the photocatalyst can be used for many times without apparent activity loss. X-ray diffraction studies, transmission electron microscopy, diffuse reflectance UV-Vis spectroscopy and nitrogen adsorption measurements were used for the characterization of the as-prepared and recycled AgI samples. It is found that under visible light irradiation, AgI partially decomposed to produce Ag/AgI nanostructure and thus stabilized. The photoactivity of supported Ag/AgI for the selective oxidation of benzylamine was studied in terms of the light intensity, wavelength, temperature and substituent. It is proposed that the formation of plasmonic Ag nanoparticles should be responsible for the high activity and selectivity.