Correlation between BRAF mutation and the clinicopathological parameters in papillary thyroid carcinoma with particular reference to follicular variant

Mutation of the BRAF gene is common in thyroid cancer. Follicular variant of papillary thyroid carcinoma is a variant of papillary thyroid carcinoma that has created continuous diagnostic controversies among pathologists. The aims of this study are to (1) investigate whether follicular variant of papillary thyroid carcinoma has a different pattern of BRAF mutation than conventional papillary thyroid carcinoma in a large cohort of patients with typical features of follicular variant of papillary thyroid carcinoma and (2) to study the relationship of clinicopathological features of papillary thyroid carcinomas with BRAF mutation. Tissue blocks from 76 patients with diagnostic features of papillary thyroid carcinomas (40 with conventional type and 36 with follicular variant) were included in the study. From these, DNA was extracted and BRAF V600E mutations were detected by polymerase chain reaction followed by restriction enzyme digestion and sequencing of exon 15. Analysis of the data indicated that BRAF V600E mutation is significantly more common in conventional papillary thyroid carcinoma (58% versus 31%, P = .022). Furthermore, the mutation was often noted in female patients (P = .017), in high-stage cancers (P = .034), and in tumors with mild lymphocytic thyroiditis (P = .006). We concluded that follicular variant of papillary thyroid carcinoma differs from conventional papillary thyroid carcinoma in the rate of BRAF mutation. The results of this study add further information indicating that mutations in BRAF play a role in thyroid cancer development and progression.

Quantitative analysis of survivin in colorectal adenocarcinoma : increased expression and correlation with telomerase activity

The aims of the present study are to quantitatively analyze survivin expression, its clinicopathologic roles, and correlation with telomerase activity in a large cohort of patients with colorectal adenocarcinoma. Real-time polymerase chain reaction was used to quantitate expression level of survivin messenger RNA and human telomerase reverse transcriptase messenger RNA (telomerase activity) in 51 patients with colorectal adenocarcinomas. The findings were correlated with the clinicopathologic features of patients, which were prospectively collected into a computerized database. Survivin messenger RNA was expressed in all tumor samples. The level of expression in tumor tissues was increased in comparison with matched nontumor mucosa in the same patient (P = .01). The level of expression of survivin was significantly correlated with the level of human telomerase reverse transcriptase expression (P = .008) and size of the colorectal adenocarcinomas (P = .004). Survival of the patients with colorectal adenocarcinoma was associated with the TNM stages (P = .001) and not with the level of expression of survivin. Thus, survivin activity was altered in colorectal adenocarcinoma. The high prevalence of survivin expression and correlation with telomerase activity are important factors for consideration in gene targeting therapy for colorectal adenocarcinoma.

A digital watermarking framework with application to medical image security

Dealing with digital medical images is raising many new security problems with legal and ethical complexities for local archiving and distant medical services. These include image retention and fraud, distrust and invasion of privacy. This project was a significant step forward in developing a complete framework for systematically designing, analyzing, and applying digital watermarking, with a particular focus on medical image security. A formal generic watermarking model, three new attack models, and an efficient watermarking technique for medical images were developed. These outcomes contribute to standardizing future research in formal modeling and complete security and computational analysis of watermarking schemes.

Digital image watermarking: its formal model, fundamental properties and possible attacks

While formal definitions and security proofs are well established in some fields like cryptography and steganography, they are not as evident in digital watermarking research. A systematic development of watermarking schemes is desirable, but at present their development is usually informal, ad hoc, and omits the complete realization of application scenarios. This practice not only hinders the choice and use of a suitable scheme for a watermarking application, but also leads to debate about the state-of-the-art for different watermarking applications. With a view to the systematic development of watermarking schemes, we present a formal generic model for digital image watermarking. Considering possible inputs, outputs, and component functions, the initial construction of a basic watermarking model is developed further to incorporate the use of keys. On the basis of our proposed model, fundamental watermarking properties are defined and their importance exemplified for different image applications. We also define a set of possible attacks using our model showing different winning scenarios depending on the adversary capabilities. It is envisaged that with a proper consideration of watermarking properties and adversary actions in different image applications, use of the proposed model would allow a unified treatment of all practically meaningful variants of watermarking schemes.

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

Attracting and Retaining Staff : The Role of Branding and Industry Image

In increasingly competitive labour markets, attracting and retaining talent has become a prime concern of organisations. Employers need to understand the range of factors that influence career decision making and the role of employer branding in attracting human capital that best fits and contributes to the strategic aims of an organisation. This chapter identifies the changing factors that attract people to certain employment and industries and discusses the importance of aligning employer branding with employee branding to create a strong, genuine and lasting employer brand. Whilst organisations have long used marketing and branding practices to engender loyalty in customers, they are increasingly expanding this activity to differentiate organisations and make them attractive from an employee perspective. This chapter discusses employer branding and industry image as two important components of attraction strategies and describes ways companies can maximise their brand awareness in the employment market to both current and future employees.

Topological localization using optical flow descriptors

We propose a topological localization method based on optical flow information. We analyse the statistical characteristics of the optical flow signal and demonstrate that the flow vectors can be used to identify and describe key locations in the environment. The key locations (nodes) correspond to significant scene changes and depth discontinuities. Since optical flow vectors contain position, magnitude and angle information, for each node, we extract low and high order statistical moments of the vectors and use them as descriptors for that node. Once a database of nodes and their corresponding optical flow features is created, the robot can perform topological localization by using the Mahalanobis distance between the current frame and the database. This is supported by field trials, which illustrate the repeatability of the proposed method for detecting and describing key locations in indoor and outdoor environments in challenging and diverse lighting conditions.

Surface plasmon-enhanced zeolite catalysis under light irradiation and its correlation with molecular polarity of reactants

Enhanced catalytic performance of zeoltes via the plasmonic effect of gold nanoparticles has been discovered to be closely correlated with the molecular polarity of reactants. The intensified polarised electrostatic field of Na+ in NaY plays a critical role in stretching the C=O bond of aldehydes to improve the reaction rate.

Evaluation of the spatial distribution of γH2AX following ionizing radiation

An early molecular response to DNA double-strand breaks (DSBs) is phosphorylation of the Ser-139 residue within the terminal SQEY motif of the histone H2AX1,2. This phosphorylation of H2AX is mediated by the phosphatidyl-inosito 3-kinase (PI3K) family of proteins, ataxia telangiectasia mutated (ATM), DNA-protein kinase catalytic subunit and ATM and RAD3-related (ATR)3. The phosphorylated form of H2AX, referred to as γH2AX, spreads to adjacent regions of chromatin from the site of the DSB, forming discrete foci, which are easily visualized by immunofluorecence microscopy3. Analysis and quantitation of γH2AX foci has been widely used to evaluate DSB formation and repair, particularly in response to ionizing radiation and for evaluating the efficacy of various radiation modifying compounds and cytotoxic compounds Given the exquisite specificity and sensitivity of this de novo marker of DSBs, it has provided new insights into the processes of DNA damage and repair in the context of chromatin. For example, in radiation biology the central paradigm is that the nuclear DNA is the critical target with respect to radiation sensitivity. Indeed, the general consensus in the field has largely been to view chromatin as a homogeneous template for DNA damage and repair. However, with the use of γH2AX as molecular marker of DSBs, a disparity in γ-irradiation-induced γH2AX foci formation in euchromatin and heterochromatin has been observed5-7. Recently, we used a panel of antibodies to either mono-, di- or tri- methylated histone H3 at lysine 9 (H3K9me1, H3K9me2, H3K9me3) which are epigenetic imprints of constitutive heterochromatin and transcriptional silencing and lysine 4 (H3K4me1, H3K4me2, H3K4me3), which are tightly correlated actively transcribing euchromatic regions, to investigate the spatial distribution of γH2AX following ionizing radiation8. In accordance with the prevailing ideas regarding chromatin biology, our findings indicated a close correlation between γH2AX formation and active transcription9. Here we demonstrate our immunofluorescence method for detection and quantitation of γH2AX foci in non-adherent cells, with a particular focus on co-localization with other epigenetic markers, image analysis and 3Dmodeling.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials

The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization campaigns automatically. © 2010 International Union of Crystallography Printed in Singapore-all rights reserved.