RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes

Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Hub-based reliable gene expression algorithm to classify ER+ and ER- breast cancer subtypes

Identifying gene signatures that are associatedwith the estrogen receptor based breast cancer samples is achallenging problem that has significant implications in breastcancer diagnosis and treatment. Various existing approaches foridentifying gene signatures have been developed but are not ableto achieve the satisfactory results because of their severallimitations. Subnetwork-based approaches have shown to be arobust classification method that uses interaction datasets suchas protein-protein interaction datasets. It has been reported thatthese interaction datasets contain many irrelevant interactionsthat have no biological meaning associated with them, and thusit is essential to filter out those interactions which can improvethe classification results. In this paper, we therefore, proposed ahub-based reliable gene expression algorithm (HRGE) thateffectively extracts the significant biologically-relevantinteractions and uses hub-gene topology to generate thesubnetwork based gene signatures for ER+ and ER- breastcancer subtypes. The proposed approach shows the superiorclassification accuracy amongst the other existing classifiers, inthe validation dataset.

Access to breast prostheses via a government-funded service in Victoria, Australia

For many women, the only alternative to breast reconstruction following a mastectomy is to use external prostheses, which need replacing regularly at a cost of up to $395 per prosthesis. Commonwealth and state governments across Australia have responded to this need by providing subsidies to assist in the purchase of breast prostheses. However, current arrangements have been highly variable and sometimes difficult to access. As part of a larger review of breast pros-thesis services in Victoria, Australia, the aim of this research was to evaluate client satisfaction among Victorian women who accessed funds through the State Government's Aids and Equipment Program, compare the responses of the program service providers with the experiences of clients accessing funding, and identify opportunities to improve service provision.

Kernel-based naive bayes classifier for breast cancer prediction

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis.

Psychosocial factors and survival of young women with breast cancer: a population-based prospective cohort study

Purpose: Most women with early-stage breast cancer believe that psychosocial factors are an important influence over whether their cancer will recur. Studies of the issue have produced conflicting results.

Patients and Methods: A population-based sample of 708 Australian women diagnosed before age 60 years with nonmetastatic breast cancer was observed for a median of 8.2 years. Depression and anxiety, coping style, and social support were assessed at a median of 11 months after diagnosis. Hazard ratios for distant disease-free survival (DDFS) and overall survival (OS) associated with psychosocial factors were estimated separately using Cox proportional hazards survival models, with and without adjustment for known prognostic factors.

Results: Distant recurrence occurred in 209 (33%) of 638 assessable patients, and 170 (24%) of 708 patients died during the follow-up period. There were no statistically significant associations between any of the measured psychosocial factors and DDFS or OS from the adjusted analyses. From unadjusted analyses, associations between greater anxious preoccupation and poorer DDFS and OS were observed (P = .02). These associations were no longer evident after adjustment for established prognostic factors; greater anxious preoccupation was associated with younger age at diagnosis (P = .03), higher tumor grade (P = .02), and greater number of involved axillary nodes (P = .008).

Conclusion: The findings do not support the measured psychosocial factors being an important influence on breast cancer outcomes. Interventions for adverse psychosocial factors are warranted to improve quality of life but should not be expected to improve survival.

Age-specific norms and determinants of anxiety and depression in 731 women with breast cancer recruited through a population-based cancer registry

The aim of this study was to determine population norms and determinants of anxiety and depression in a population-based sample of 731 women with breast cancer (aged 23–60 years) with the Hospital Anxiety and Depression scale (HADS). The prevalence of ‘probable’ psychological morbidity due to anxiety was 23% and due to depression was 3%. When the women identified as ‘possible’ cases were included, the respective proportions were 45 and 12%. Higher anxiety was present in younger, less educated women not born in Australia. There was no clear pattern of risk factors for depression. These population-based findings highlight the need for clinicians to be aware that age, education and country of birth may identify a particularly vulnerable subgroup. While brief scales such as the HADS are limited in their ability to accurately predict a clinical diagnosis, high scores identify those who may warrant referral for clinical evaluation.

Early breast cancer identification : Which way to go? Microarray or image based computer aided diagnosis!

The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

Zirconium, calcium, and strontium contents in magnesium based biodegradable alloys modulate the efficiency of implant-induced osseointegration

Development of new biodegradable implants and devices is necessary to meet the increasing needs of regenerative orthopedic procedures. An important consideration while formulating new implant materials is that they should physicochemically and biologically mimic bone-like properties. In earlier studies, we have developed and characterized magnesium based biodegradable alloys, in particular magnesium-zirconium (Mg-Zr) alloys. Here we have reported the biological properties of four Mg-Zr alloys containing different quantities of strontium or calcium. The alloys were implanted in small cavities made in femur bones of New Zealand White rabbits, and the quantitative and qualitative assessments of newly induced bone tissue were carried out. A total of 30 experimental animals, three for each implant type, were studied, and bone induction was assessed by histological, immunohistochemical and radiological methods; cavities in the femurs with no implants and observed for the same period of time were kept as controls. Our results showed that Mg-Zr alloys containing appropriate quantities of strontium were more efficient in inducing good quality mineralized bone than other alloys. Our results have been discussed in the context of physicochemical and biological properties of the alloys, and they could be very useful in determining the nature of future generations of biodegradable orthopedic implants.

Fast vision-based catheter 3D reconstruction

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters, where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors can not be employed in the way they are in rigid robotic manipulators. In this paper, a high speed vision-based shape sensing algorithm for real-time 3D reconstruction of continuum robots based on the views of two arbitrary positioned cameras is presented. The algorithm is based on the closed-form analytical solution of the reconstruction of quadratic curves in 3D space from two arbitrary perspective projections. High-speed image processing algorithms are developed for the segmentation and feature extraction from the images. The proposed algorithms are experimentally validated for accuracy by measuring the tip position, length and bending and orientation angles for known circular and elliptical catheter shaped tubes. Sensitivity analysis is also carried out to evaluate the robustness of the algorithm. Experimental results demonstrate good accuracy (maximum errors of  ±0.6 mm and  ±0.5 deg), performance (200 Hz), and robustness (maximum absolute error of 1.74 mm, 3.64 deg for the added noises) of the proposed high speed algorithms.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.

Mathematical modelling of the refractive index reconstruction through global optimization

We examine a mathematical model of non-destructive testing of planar waveguides, based on numerical solution of a nonlinear integral equation. Such problem is ill-posed, and the method of Tikhonov regularization is applied. To minimize Tikhonov functional, and find the parameters of the waveguide, we use two new optimization methods: the cutting angle method of global optimization, and the discrete gradient method of nonsmooth local optimization. We examine how the noise in the experimental data influences the solution, and how the regularization parameter has to be chosen. We show that even with significant noise in the data, the numerical solution is of high accuracy, and the method can be used to process real experimental da.ta..

A computational approach to the reconstruction of surface geometry from early temple superstructures

Recovering the control or implicit geometry underlying temple architecture requires bringing together fragments of evidence from field measurements, relating these to mathematical and geometric descriptions in canonical texts and proposing "best-fit" constructive models. While scholars in the field have traditionally used manual methods, the innovative application of niche computational techniques can help extend the study of artefact geometry. This paper demonstrates the application of a hybrid computational approach to the problem of recovering the surface geometry of early temple superstructures. The approach combines field measurements of temples, close-range architectural photogrammetry, rule-based generation and parametric modelling. The computing of surface geometry comprises a rule-based global model governing the overall form of the superstructure, several local models for individual motifs using photogrammetry and an intermediate geometry model that combines the two. To explain the technique and the different models, the paper examines an illustrative example of surface geometry reconstruction based on studies undertaken on a tenth century stone superstructure from western India. The example demonstrates that a combination of computational methods yields sophisticated models of the constructive geometry underlying temple form and that these digital artefacts can form the basis for in depth comparative analysis of temples, arising out of similar techniques, spread over geography, culture and time.

Reconstruction in Aceh: building whose capacity?

One of the dominant themes in development programmes over the last fifteen years has been a commitment to capacity building. This paper investigates the forms of capacity building in Aceh, Indonesia, since the devastating earthquake and tsunami that hit the province on 26 December 2004. Despite the preference of the Acehnese people for reconstruction processes based on the principles of community development, local people have been largely marginalized by both the Indonesian government and the international aid and development agencies. The paper suggests some of the reasons for this marginalization.