Simultaneous spectrophotometric determination of trace iron (III) and aluminum (III) with hybrid linear analysis

Hybrid linear analysis (HLA) was applied to resolution of overlapping spectra of Fe3+-salicylfluorone and Al3+-salicylfluorone complexes and simultaneous spectrophotometric determination of Fe3+ and Al3+. The absorbance matrix of 7 standard mixtures at 41 measuring points ranged from the wavelength of 550 nm to 630 nm was used for calibration. To avoid the effect of interaction between the two components on the determination, the column vector of K matrix obtained from the standard mixtures with least squares was used as the pure spectrum of component. The recoveries of the two elements for the analysis of the synthetic samples were 93.3% similar to 107.5% in the range of the concentration ratio of Fe3+:Al3+ = 10:1 to 1:8. Comparing with the partial least squares (PIS) model, the HLA method was simple, accuracy and precise.

Factor analysis used to peak position optimization and spectral interference correction in inductively coupled plasma atomic emission spectrometry

Target transformation factor analysis was used to correct spectral interference in inductively coupled plasma atomic emission spectrometry (ICP-BES) for the determination of rare earth impurities in high purity thulium oxide. Data matrix was constructed with pure and mixture vectors and background vector. A method based on an error evaluation function was proposed to optimize the peak position, so the influence of the peak position shift in spectral scans on the determination was eliminated or reduced. Satisfactory results were obtained using factor analysis and the proposed peak position optimization method.

FACTOR-ANALYSIS SPECTROPHOTOMETRY FOR THE SIMULTANEOUS DETERMINATION OF VALENCE STATES OF METALS

Two M(n+)-2-(5-bromo-2-pyridylazo)-5-diethylaminophenol systems for the simultaneous determination of the valence states of Cr and Fe using factor analysis were studied. (1) At pH 4.0, Cr(III) and Cr(VI) react with the reagent to form stable complexes and a slight difference in the wavelengths of maximum absorption (lambda(max.)) between the two complexes is observed when the sodium lauryl sulfate, which also acts as a solubilizing and sensitizing agent, is added, viz., 590 nm for Cr(III) and 593 nm for Cr(VI) complexes. (2) In the presence of ethanol, both Fe(II) and Fe(III) form 1:2 complexes with the reagent at pH 2.5-3.5 and the lambda(max.) of the Fe(II) and Fe(III) complexes is at 557 and 592 nm, respectively. In the target transformation factor analysis, the K coefficients calculated from the standard mixtures by classical least-squares analysis and a non-zero intercept added to each wavelength are used as the target vector instead of the pure component standards; this can decrease the analysis errors introduced by the interaction between the two species and by deviations from Beer's law.

VEGF gene silencing by cytomegalovirus promoter driven ShRNA expression vector results in vascular development defects in zebrafish

Zebrafish has been generally considered as an excellent model in case of drug screening, disease model establishment, and vertebrate embryonic development study. In this work, the ability of human cytomegalovirus immediate early promoter (CMV promoter)-driven short hairpin RNA (shRNA) expression vector to induce shRNA against VEGF gene in zebrafish was tested, and its effect on vascular development was assed, too. Using RT-qPCR, blood vessel staining, and in situ hybridization, we confirmed certain transcriptional activity and down regulation of gene expression by the vector. In situ hybridization analysis indicated selective inhibition of NRP1 expression in the VEGF gene loss of function model, which might imply in turn that VEGF could not only activate endothelial cells directly but also could contribute to stimulating angiogenesis in vivo by a mechanism that involved up-regulation of its cognate receptor expression in zebrafish. This contributed to a better understanding of molecular mechanisms of cardiovascular development. The system improved the success rate in making inducible knockdown and widened the possibilities for better therapeutic targets in zebrafish.

Sequence analysis and functional study of thymidylate synthase from zebrafish, Danio rerio

The thymidylate synthase (TS), an important target for many anticancer drugs, has been cloned from different species. But the cDNA property and function of TS in zebrafish are not well documented. In order to use zebrafish as an animal model for screening novel anticancer agents, we isolated TS cDNA from zebrafish and compared its sequence with those from other species. The open reading frame (ORF) of zebrafish TS cDNA sequence was 954 nucleotides, encoding a 318-amino acid protein with a calculated molecular mass of 36.15 kDa. The deduced amino acid sequence of zebrafish TS was similar to those from other organisms, including rat, mouse and humans. The zebrafish TS protein was expressed in Escherichia coli and purified to homogeneity. The purified zebrafish TS showed maximal activity at 28 degrees C with similar K-m value to human TS. Western immunoblot assay confirmed that TS was expressed in all the developmental stages of zebrafish with a high level of expression at the 1-4 cell stages. To study the function of TS in zebrafish embryo development, a short hairpin RNA (shRNA) expression vector, pSilencer 4.1-CMV/TS, was constructed which targeted the protein-coding region of zebrafish TS mRNA. Significant change in the development of tail and epiboly was found in zebrafish embryos microinjected pSilencer4.1-CMV/TS siRNA expression vector.

attB site disruption in marine Actinomyces sp M048 via DNA transformation of a site-specific integration vector

An efficient conjugation method has been developed for the marine Actinomyces sp. isolate M048 to facilitate the genetic manipulation of the chandrananimycin biosynthesis gene cluster. A phi C31-derived integration vector pIJ8600 containing oriT and attP fragments was introduced into strain M048 by bi-parental conjugation from Escherichia coli ET12567 to strain M048. Transformation efficiency was (6.38 +/- 0.41) x 10(-5) exconjugants per recipient spore. Analysis of eight exconjugants showed that the plasmid pIJ8600 was stably integrated at a single chromosomal site (attB) of the Actinomyces genome. The DNA sequence of the attB was cloned and shown to be conserved. The results of antimicrobial activity analysis indicated that the insertion of plasmid pIJ8600 seemed to affect the biosynthesis of antibiotics that could strongly inhibit the growth of E. coli and Mucor miehei (Tu284). HPLC-MS analysis of the extracts indicated that disruption of the attB site resulted in the complete abolition of chandrananimycin A-C production, proving the identity of the gene cluster. Instead of chandrananimycins, two bafilomycins were produced through disruption of the attB site from the chromosomal DNA of marine Actinomyces sp. M048.

Expression, purification of recombinant flounder MRF4 protein in Escherichia coli and analysis of its polyclonal antibodies

MRF4 is one of muscle regulatory factors and plays critical roles during skeletal muscle development. The muscle development is important for the fish growth which is an important economic factor for the fish culture. To analyze the function of MRF4 in fish, the founder MRF4 antibody was prepared. The flounder MRF4 was cloned, ligated into prokaryotic expression vector pET-30b and expressed in strain E. coli BL21 (130). The recombinant flounder MRF4 fusion protein was soluble and purified with cobalt IMAC resins. To prepare MRF4 polyclonal antibodies, rabbits were immunized with the soluble protein and the increasing level of antibodies was determined by Western blot. Also, the endogenous flounder MRF4 was recognized by the anti-serum. The result further proved the existence of the anti-MRF4 antibody in the anti-serum, which will be useful for studies on the function of flounder MRF4.

Wind-vector estimation for RADARSAT-1 SAR images: Validation of wind-direction estimates based upon geometry diversity

In this letter, a new wind-vector algorithm is presented that uses radar backscatter sigma(0) measurements at two adjacent subscenes of RADARSAT-1 synthetic aperture radar (SAR) images, with each subscene having slightly different geometry. Resultant wind vectors are validated using in situ buoy measurements and compared with wind vectors determined from a hybrid wind-retrieval model using wind directions determined by spectral analysis of wind-induced image streaks and observed by colocated QuikSCAT measurements. The hybrid wind-retrieval model consists of CMOD-IFR2 [applicable to C-band vertical-vertical (W) polarization] and a C-band copolarization ratio according to Kirchhoff scattering. The new algorithm displays improved skill in wind-vector estimation for RADARSAT-1 SAR data when compared to conventional wind-retrieval methodology. In addition, unlike conventional methods, the present method is applicable to RADARSAT-1 images both with and without visible streaks. However, this method requires ancillary data such as buoy measurements to resolve the ambiguity in retrieved wind direction.

Genetic analysis of bacteriophages from clinical and environmental samples

Bacteriophages, viruses infecting bacteria, are uniformly present in any location where there are high numbers of bacteria, both in the external environment and the human body. Knowledge of their diversity is limited by the difficulty to culture the host species and by the lack of the universal marker gene present in all viruses. Metagenomics is a powerful tool that can be used to analyse viral communities in their natural environments. The aim of this study was to investigate diverse populations of uncultured viruses from clinical (a sputum of patient with cystic fibrosis, CF) and environmental samples (a sludge from a dairy food wastewater treatment plant) containing rich bacterial populations using genetic and metagenomic analyses. Metagenomic sequencing of viruses obtained from these samples revealed that the majority of the metagenomic reads (97-99%) were novel when compared to the NCBI protein database using BLAST. A large proportion of assembled contigs were assignable as novel phages or uncharacterised prophages, the next largest assignable group being single-stranded eukaryotic virus genomes. Sputum from a cystic fibrosis patient contained DNA typical of phages of bacteria that are traditionally involved in CF lung infections and other bacteria that are part of the normal oral flora. The only eukaryotic virus detected in the CF sputum was Torque Teno virus (TTV). A substantial number of assigned sequences from dairy wastewater could be affiliated with phages of bacteria that are typically found in the soil and aquatic environments, including wastewater. Eukaryotic viral sequences were dominated by plant pathogens from the Geminiviridae and Nanoviridae families, and animal pathogens from the Circoviridae family. Antibiotic resistance genes were detected in both metagenomes suggesting phages could be a source for transmissible antimicrobial resistance. Overall, diversity of viruses in the CF sputum was low, with 89 distinct viral genotypes predicted, and higher (409 genotypes) in the wastewater. Function-based screening of a metagenomic library constructed from DNA extracted from dairy food wastewater viruses revealed candidate promoter sequences that have ability to drive expression of GFP in a promoter-trap vector in Escherichia coli. The majority of the cloned DNA sequences selected by the assay were related to ssDNA circular eukaryotic viruses and phages which formed a minority of the metagenome assembly, and many lacked any significant homology to known database sequences. Natural diversity of bacteriophages in wastewater samples was also examined by PCR amplification of the major capsid protein sequences, conserved within T4-type bacteriophages from Myoviridae family. Phylogenetic analysis of capsid sequences revealed that dairy wastewater contained mainly diverse and uncharacterized phages, while some showed a high level of similarity with phages from geographically distant environments.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Using decision analysis to improve malaria control policy making.

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets (ITNs) and artemesinin combination therapies (ACTs) for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

Feasibility of a Clinical Chemical Analysis Approach to Predict Misuse of Growth Promoting Hormones in Cattle

A study was performed to determine if targeted metabolic profiling of cattle sera could be used to establish a predictive tool for identifying hormone misuse in cattle. Metabolites were assayed in heifers (n ) 5) treated with nortestosterone decanoate (0.85 mg/kg body weight), untreated heifers (n ) 5), steers (n ) 5) treated with oestradiol benzoate (0.15 mg/kg body weight) and untreated steers (n ) 5). Treatments were administered on days 0, 14, and 28 throughout a 42 day study period. Two support vector machines (SVMs) were trained, respectively, from heifer and steer data to identify hormonetreated animals. Performance of both SVM classifiers were evaluated by sensitivity and specificity of treatment prediction. The SVM trained on steer data achieved 97.33% sensitivity and 93.85% specificity while the one on heifer data achieved 94.67% sensitivity and 87.69% specificity. Solutions of SVM classifiers were further exploited to determine those days when classification accuracy of the SVM was most reliable. For heifers and steers, days 17-35 were determined to be the most selective. In summary, bioinformatics applied to targeted metabolic profiles generated from standard clinical chemistry analyses, has yielded an accurate, inexpensive, high-throughput test for predicting steroid abuse in cattle.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.