Estimation of the burden of malignant neoplasms by DALYs in Shandong Province [应用伤残调整寿命年测量山东省恶性肿瘤疾病负担的研究]

Objective: To evaluate the burden of malignant neoplasms in Shandong Province in order to provide scientific evidence for policy-making. Methods: The main data for this study were from Shandong third cause of death sampling survey in 2006 and Shandong 2007 cancer prevalence survey. YLLs, YLDs, DALYs and disability weights of each type of cancers were calculated according to the global burdens of disease （GBD） methodology. The direct method was used to estimate YLDs. The uncertainty analysis was conducted following the methodology in GBD study. Results: The total cancers burden in Shandong population was 1 383 thousands DALYs. Lung cancer, liver cancer, stomach cancer and esophagus cancer were the top four cancers with the highest health burden. The burden of the four major cancers together accounted for 71.45% of the total burden of all cancers. 95% of the total burden of malignant tumors was caused by premature death, and only 5.26% of the total cancer burden was due to disability. The uncertainty of total burden estimate was around±11%， the uncertainty of YLDs was bigger than that of YLLs. Conclusion: The health burden due to cancers in Shandong population is heavier than that of the national average level. Liver cancer, lung cancer and stomach cancer should be the major cancers for disease control and prevention in Shandong.

DRB2, DRB3 and DRB5 function in a non-canonical microRNA pathway in Arabidopsis thaliana

DOUBLE-STRANDED RNA BIN DIN G (DRB) proteins have been functionally characterized in viruses, prokaryotes and eukaryotes and are involved in all aspects of RNA biology. Arabidopsis thaliana (Arabidopsis) encodes five closely related DRB proteins, DRB1 to DRB5. DRB1 and DRB4 are required by DICER-LIKE (DCL) proteins DCL1 and DCL4 to accurately and efficiently process structurally distinct double-stranded RNA (dsRNA) precursor substrates in the microRNA (miRNA) and trans-acting small-interfering RNA (tasiRNA) biogenesis pathways respectively. We recently reported that DRB2 is also involved in the biogenesis of specific miRNA subsets. Furthermore, the severity of the developmental phenotype displayed by the drb235 triple mutant plant, compared with those expressed by either drb2, drb3 and drb5 single mutants, or double mutant combinations thereof, indicates that DRB3 and DRB5 function in the same non-canonical miRNA pathway as DRB2. Through the use of our artificial miRNA (amiRNA) plant expression vector, pBlueGreen 2,3 we demonstrate here that unlike DRB2, DRB3 and DRB5 are not involved in the dsRNA processing stages of the miRNA biogenesis pathway, but are required to mediate RNA silencing of target genes of DRB2-associated miRNA s. © 2012 Landes Bioscience.

Rice ragged stunt oryzavirus genome segment S4 could encode an RNA dependent RNA polymerase and a second protein of unknown function

The complete nucleotide sequence of genome segment S4 of rice ragged stunt oryzavirus (RRSV, Thai-isolate) was determined. The 3823 bp sequence contains two large open reading frames (ORFs). ORF1, spanning nucleotides 12 to 3776, is capable of encoding a protein of M(r) 141,380 (P4a). The P4a amino acid sequence predicted from the nucleotide sequence contains sequence motifs conserved in RNA-dependent RNA polymerases (RDRPs). When compared for evolutionary relationships with RDRPs of other reoviruses using the amino acid sequences around the conserved GDD motif, P4a was shown to be more related to Nilaparvata lugens reovirus and reovirus serotype 3 than to rice dwarf phytoreovirus, bovine rotavirus or bluetongue virus. The ORF2, spanning nucleotides 491 to 1468, is out of frame with ORF1 and is capable of encoding a protein of 36, 920 (P4b). Coupled in vitro transcription-translation from cloned ORF2 in wheat germ extract confirmed the existence of ORF2 but in vivo production and possible function of P4b is yet to be determined.

Consistent HMM parameter estimation using Kerridge inaccuracy rates

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on Kerridge inaccuracy rate (KIR) concepts. Under mild identifiability conditions, we prove that our online KIR-based estimator is strongly consistent. In simulation studies, we illustrate the convergence behaviour of our proposed online KIR-based estimator and provide a counter-example illustrating the local convergence properties of the well known recursive maximum likelihood estimator (arguably the best existing solution).

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

Identification and estimation of equivalent area parameters using synchronised phasor measurements

A new approach is proposed for obtaining a non-linear area-based equivalent model of power systems to express the inter-area oscillations using synchronised phasor measurements. The generators that remain coherent for inter-area disturbances over a wide range of operating conditions define the areas, and the reduced model is obtained by representing each area by an equivalent machine. The parameters of the reduced system are identified by processing the obtained measurements, and a non-linear Kalman estimator is then designed for the estimation of equivalent area angles and frequencies. The simulation of the approach on a two-area system shows substantial reduction of non-inter-area modes in the estimated angles. The proposed methods are also applied to a ten-machine system to illustrate the feasibility of the approach on larger and meshed networks.

Sampling designs on stream networks using the pseudo-Bayesian approach

Monitoring stream networks through time provides important ecological information. The sampling design problem is to choose locations where measurements are taken so as to maximise information gathered about physicochemical and biological variables on the stream network. This paper uses a pseudo-Bayesian approach, averaging a utility function over a prior distribution, in finding a design which maximizes the average utility. We use models for correlations of observations on the stream network that are based on stream network distances and described by moving average error models. Utility functions used reflect the needs of the experimenter, such as prediction of location values or estimation of parameters. We propose an algorithmic approach to design with the mean utility of a design estimated using Monte Carlo techniques and an exchange algorithm to search for optimal sampling designs. In particular we focus on the problem of finding an optimal design from a set of fixed designs and finding an optimal subset of a given set of sampling locations. As there are many different variables to measure, such as chemical, physical and biological measurements at each location, designs are derived from models based on different types of response variables: continuous, counts and proportions. We apply the methodology to a synthetic example and the Lake Eacham stream network on the Atherton Tablelands in Queensland, Australia. We show that the optimal designs depend very much on the choice of utility function, varying from space filling to clustered designs and mixtures of these, but given the utility function, designs are relatively robust to the type of response variable.

Traffic state estimation from partial Bluetooth and volume observations : case study in the Brisbane metropolitan area

The application of the Bluetooth (BT) technology to transportation has been enabling researchers to make accurate travel time observations, in freeway and arterial roads. The Bluetooth traffic data are generally incomplete, for they only relate to those vehicles that are equipped with Bluetooth devices, and that are detected by the Bluetooth sensors of the road network. The fraction of detected vehicles versus the total number of transiting vehicles is often referred to as Bluetooth Penetration Rate (BTPR). The aim of this study is to precisely define the spatio-temporal relationship between the quantities that become available through the partial, noisy BT observations; and the hidden variables that describe the actual dynamics of vehicular traffic. To do so, we propose to incorporate a multi- class traffic model into a Sequential Montecarlo Estimation algorithm. Our framework has been applied for the empirical travel time investigations into the Brisbane Metropolitan region.

The α5 subunit regulates the expression and function of α4*-containing neuronal nicotinic acetylcholine receptors in the ventral-tegmental area

Human genetic association studies have shown gene variants in the α5 subunit of the neuronal nicotinic receptor (nAChR) influence both ethanol and nicotine dependence. The α5 subunit is an accessory subunit that facilitates α4* nAChRs assembly in vitro. However, it is unknown whether this occurs in the brain, as there are few research tools to adequately address this question. As the α4*-containing nAChRs are highly expressed in the ventral tegmental area (VTA) we assessed the molecular, functional and pharmacological roles of α5 in α4*-containing nAChRs in the VTA. We utilized transgenic mice α5+/+(α4YFP) and α5-/-(α4YFP) that allow the direct visualization and measurement of α4-YFP expression and the effect of the presence (α5+/+) and absence of α5 (-/-) subunit, as the antibodies for detecting the α4* subunits of the nAChR are not specific. We performed voltage clamp electrophysiological experiments to study baseline nicotinic currents in VTA dopaminergic neurons. We show that in the presence of the α5 subunit, the overall expression of α4 subunit is increased significantly by 60% in the VTA. Furthermore, the α5 subunit strengthens baseline nAChR currents, suggesting the increased expression of α4* nAChRs to be likely on the cell surface. While the presence of the α5 subunit blunts the desensitization of nAChRs following nicotine exposure, it does not alter the amount of ethanol potentiation of VTA dopaminergic neurons. Our data demonstrates a major regulatory role for the α5 subunit in both the maintenance of α4*-containing nAChRs expression and in modulating nicotinic currents in VTA dopaminergic neurons. Additionally, the α5α4* nAChR in VTA dopaminergic neurons regulates the effect of nicotine but not ethanol on currents. Together, the data suggest that the α5 subunit is critical for controlling the expression and functional role of a population of α4*-containing nAChRs in the VTA.

Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature, body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations were developed using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indices as independent variables and percentage body fat (%BF) determined from deuterium dilution technique as the reference. All equations were then tested in the cross-validation group. Results from the reference method were also compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), and Gurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.77- 0.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%, and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001) underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly (p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of vibration amplitude in Fourier domain optical coherence tomography interferometric signals from Doppler spectrum

In presented method combination of Fourier and Time domain detection enables to broaden the effective bandwidth for time dependent Doppler Signal that allows for using higher-order Bessel functions to calculate unambiguously the vibration amplitudes.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.