Prognostic impact of vascular and lymphovascular invasion in early lung cancer

Background The prognostic significance of vascular and lymphatic invasion in non-small-cell lung cancer is under continuous debate. We analyzed the effect of tumor aggressiveness (lymphatic and/or vessel invasion) on survival and relapse in stage I and II non-small-cell lung cancer. Methods We retrospectively analyzed prospectively collected data of 457 patients with stage I and II non-small-cell lung cancer from 1998 to 2008. Specimens were analyzed for intratumoral vascular invasion and lymphovascular space invasion. Overall survival and disease-free survival were estimated using the Kaplan-Meier method, and differences were determined by the logrank test. Cox regression analysis was performed to identify independent risk factors. Results: The incidence of intratumoral vascular invasion was 23.4%, and this correlated significantly with grade of differentiation, visceral pleural involvement, lymphovascular space invasion, and N status. The incidence of lymphovascular space invasion was 5.5%, and this correlated significantly with grade of differentiation, lymph nodes involved, and intratumoral vascular invasion. On multivariate analyses, intratumoral vascular invasion proved to be an significant independent risk factor for overall survival but not for disease-free survival. Lymphovascular space invasion was associated significantly with early tumor recurrence but not with overall survival. Conclusions: Vascular and lymphatic invasion can serve as independent prognostic factors in completely resected nonsmall- cell lung cancer. Intratumoral vascular invasion and lymphovascular space invasion in early stage non-small-cell lung cancer are important factors in overall survival and early tumor recurrence. Further large scale studies with more recent patient cohorts and refined histological techniques are warranted.

Predicting positive and negative impacts of cancer among long-term endometrial cancer survivors

OBJECTIVE Although the survival outcomes among women diagnosed with endometrial cancer are very favorable, little is known about the long-term impact of their cancer experience. This study identifies the extent of positive and negative impacts of cancer and factors associated with this, amongst long-term survivors of endometrial cancer. METHODS Australian women diagnosed with endometrial cancer (N=632) were sent questionnaires at the time of diagnosis and 3-5 years later. Hierarchical multiple regression models were used to examine whether a range of variables at diagnosis/treatment predicted subsequent scores on the Impact of Cancer Scale, which examines positive (e.g. health awareness) and negative (e.g. appearance concerns) impacts amongst cancer survivors. RESULTS Overall, women had a higher mean score for the positive than negative impact scales (M=3.5 versus M=2.5, respectively). An intermediate grade of endometrial cancer, a prior diagnosis of cancer and lower levels of education were significant, but weak, predictors of higher scores on the positive impact scale. Higher scores on the negative impact scale were predicted by a higher grade of cancer, poor physical and mental health, a younger age, being single or having lower levels of education. CONCLUSIONS The study demonstrates that factors that predict positive impact in cancer survivors differ to those that predict negative impact, suggesting that interventions to optimize cancer survivors' quality of life will need to be multi-dimensional, and this supports the need for tailored intervention.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Copula-based spatial modelling of geometallurgical variables

The most important aspect of modelling a geological variable, such as metal grade, is the spatial correlation. Spatial correlation describes the relationship between realisations of a geological variable sampled at different locations. Any method for spatially modelling such a variable should be capable of accurately estimating the true spatial correlation. Conventional kriged models are the most commonly used in mining for estimating grade or other variables at unsampled locations, and these models use the variogram or covariance function to model the spatial correlations in the process of estimation. However, this usage assumes the relationships of the observations of the variable of interest at nearby locations are only influenced by the vector distance between the locations. This means that these models assume linear spatial correlation of grade. In reality, the relationship with an observation of grade at a nearby location may be influenced by both distance between the locations and the value of the observations (ie non-linear spatial correlation, such as may exist for variables of interest in geometallurgy). Hence this may lead to inaccurate estimation of the ore reserve if a kriged model is used for estimating grade of unsampled locations when nonlinear spatial correlation is present. Copula-based methods, which are widely used in financial and actuarial modelling to quantify the non-linear dependence structures, may offer a solution. This method was introduced by Bárdossy and Li (2008) to geostatistical modelling to quantify the non-linear spatial dependence structure in a groundwater quality measurement network. Their copula-based spatial modelling is applied in this research paper to estimate the grade of 3D blocks. Furthermore, real-world mining data is used to validate this model. These copula-based grade estimates are compared with the results of conventional ordinary and lognormal kriging to present the reliability of this method.

Evidence-based recommendations for the monitoring and treatment of ankylosing spondylitis: Results from the Australian 3E initiative in rheumatology

Aim: To develop a set of Australian recommendations for the monitoring and treatment of ankylosing spondylitis (AS) through systematic literature review combined with the opinion of practicing rheumatologists. Methods: A set of eight questions, four in each domain of monitoring and treatment, were formulated by voting and the Delphi method. The results of a systematic literature review addressing each question were presented to the 23 participants of the Australian 3E meeting. All participants were clinical rheumatologists experienced in the daily management of AS. Results: After three rounds of breakout sessions to discuss the findings of the literature review, a set of recommendations was finalized after discussion and voting. The category of evidence and strength of recommendation were determined for each proposal. The level of agreement among participants was excellent (mean 84%, range 64-100%). Conclusions: The 12 recommendations developed from evidence and expert opinion provide guidance for the daily management of AS patients. For most recommendations, we found a paucity of supportive evidence in the literature highlighting the need for additional clinical studies.

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

Data modelling in the beginning school years

This paper argues for a renewed focus on statistical reasoning in the beginning school years, with opportunities for children to engage in data modelling. Some of the core components of data modelling are addressed. A selection of results from the first data modelling activity implemented during the second year (2010; second grade) of a current longitudinal study are reported. Data modelling involves investigations of meaningful phenomena, deciding what is worthy of attention (identifying complex attributes), and then progressing to organising, structuring, visualising, and representing data. Reported here are children's abilities to identify diverse and complex attributes, sort and classify data in different ways, and create and interpret models to represent their data.

Grading scales for contact lens complications

Purpose. To devise and validate artist-rendered grading scales for contact lens complications Methods. Each of eight tissue complications of contact lens wear (listed under 'Results') was painted by a skilled ophthalmic artist (Terry R. Tarrant) in five grades of severity: 0 (normal), 1 (trace), 2 (mild), 3 (moderate) and 4 (severe). A representative slit lamp photograph of a tissue response of each of the eight complications was shown to 404 contact lens practitioners who had never before used clinical grading scales. The practitioners were asked to grade each tissue response to the nearest 0.1 grade unit by interpolation. Results. The standard deviation (± s.d.) of the 404 responses for each tissue complication is tabulated below:_ing_ 0.5 Endothelial pplymegethisjij-4 0.7 Epithelial microcysts 0.5 Endothelial blebs_ 0.4 Stromal edema_onjunctiva! hyperemia 0.4 Stromal neovascularization 0.4 Papillary conjunctivitis 0.5 The frequency distributions and best-fit normal curves were also plotted. The precision of grading (s.d. x 2) ranged from 0.8 to 1.4, with a mean precision of 1.0. Conclusions. Grading scales afford contact lens practitioners with a method of quantifying the severity of adverse tissue responses to contact lens wear. It is noteworthy that the statistically verified precision of grading (1.0 scale unit) concurs precisely with the essential design feature of the grading scales that each grading step of 1.0 corresponds to clinically significant difference in severity. Thus, as a general rule, a difference or change in grade of > 1.0 can be taken to be both clinically and statistically significant when using these grading scales. Trained observers are likely to achieve even greater grading precision. Supported by Hydron Limited.

A reciprocal collaborative method using relevance feedback and feature importance

In a people-to-people matching systems, filtering is widely applied to find the most suitable matches. The results returned are either too many or only a few when the search is generic or specific respectively. The use of a sophisticated recommendation approach becomes necessary. Traditionally, the object of recommendation is the item which is inanimate. In online dating systems, reciprocal recommendation is required to suggest a partner only when the user and the recommended candidate both are satisfied. In this paper, an innovative reciprocal collaborative method is developed based on the idea of similarity and common neighbors, utilizing the information of relevance feedback and feature importance. Extensive experiments are carried out using data gathered from a real online dating service. Compared to benchmarking methods, our results show the proposed method can achieve noticeable better performance.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Serum HE4 as a prognostic marker in endometrial cancer : a population based study

Objective HE4 has emerged as a promising biomarker in gynaecological oncology. The purpose of this study was to evaluate serum HE4 as a biomarker for high-risk phenotypes in a population-based endometrial cancer cohort. Methods Peri-operative serum HE4 and CA125 were measured in 373 patients identified from the prospective Australian National Endometrial Cancer Study (ANECS). HE4 and CA125 were quantified on the ARCHITECT instrument in a clinically accredited laboratory. Receiver operator curves (ROC), Spearman rank correlation coefficient, and chi-squared and Mann–Whitney tests were used for statistical analysis. Survival analysis was performed using Kaplan–Meier and Cox multivariate regression analyses. Results Median CA125 and HE4 levels were higher in stage III and IV tumours (p < 0.001) and in tumours with outer-half myometrial invasion (p < 0.001). ROC analysis demonstrated that HE4 (area under the curve (AUC) = 0.76) was a better predictor of outer-half myometrial invasion than CA125 (AUC = 0.65), particularly in patients with low-grade endometrioid tumours (AUC 0.77 vs 0.64 for CA125). Cox multivariate analysis demonstrated that elevated HE4 was an independent predictor of recurrence-free survival (HR = 2.40, 95% CI 1.19–4.83, p = 0.014) after adjusting for stage and grade of disease, particularly in the endometrioid subtype (HR = 2.86, 95% CI 1.25–6.51, p = 0.012). Conclusion These findings demonstrate the utility of serum HE4 as a prognostic biomarker in endometrial cancer in a large, population-based study. In particular they highlight the utility of HE4 for pre-operative risk stratification to identify high-risk patients within low-grade endometrioid endometrial cancer patients who might benefit from lymphadenectomy.

A multidimensional collaborative filtering fusion approach with dimensionality reduction

Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.