Background:The role of tumour-infiltrating inflammation in the prognosis of patients with colorectal cancer (CRC) has not been fully evaluated. The primary objective of our meta-analysis was to determine the impact of tumour-infiltrating inflammation on survival outcomes.Methods:Ovid MEDLINE and EMBASE were searched to identify studies reporting the prognostic significance of tumour-infiltrating inflammation for patients with CRC. The primary outcome measures were overall survival (OS), cancer-specific survival (CS) and disease-free survival (DFS).Results:A total of 30 studies involving 2988 patients were identified. Studies were subdivided into those considering the associations between CRC survival and generalised tumour inflammatory infiltrate (n=12) and T lymphocyte subsets (n=18). Pooled analyses revealed that high generalised tumour inflammatory infiltrate was associated with good OS (HR, 0.59; 95% CI, 0.48–0.72), CS (HR, 0.40; 95% CI, 0.27–0.61) and DFS (HR, 0.72; 95% CI, 0.57–0.91). Stratification by location and T lymphocyte subset indicated that in the tumour centre, CD3+, CD8+ and FoxP3+ infiltrates were not statistically significant prognostic markers for OS or CS. In the tumour stroma, high CD8+, but not CD3+ or FoxP3+ cell infiltrates indicated increased OS. Furthermore, high CD3+ cell infiltrate was detected at the invasive tumour margin in patients with good OS and DFS; and high CCR7+ infiltrate was also indicated increased OS.Conclusion:Overall, high generalised tumour inflammatory infiltrate could be a good prognostic marker for CRC. However, significant heterogeneity and an insufficient number of studies underscore the need for further prospective studies on subsets of T lymphocytes to increase the robustness of the analyses.

Collaborative team members usually come from diverse disciplines; their demands for information are also different from each other. This paper is mainly concerned with how to capture designers’ demands for information in a collaborative team. From a workflow perspective, designers’ information demand is modelled considering three aspects: members’, roles’, and tasks’ requirements for information. Based on a static model of information demand and information filtering technologies, a dynamic model is proposed so that the designers’ information demand could be derived automatically. In addition, the appropriate volume of information for designers’ needs could also be determined intelligently. With the information demand model, an information supply system could be developed to realize the following objective: the delivery of an appropriate volume of information within an appropriate domain, delivered to the appropriate user in the collaborative team.

Automatic and accurate fault diagnosis is very important for condition-based maintenance. In this study, an intelligent fault diagnosis method based on relevance vector machines (RVM) is proposed for automatic fault diagnosis of rotating machinery. First, the global optimal features from all node energies of full wavelet packet tree are obtained by combining wavelet packet transform with an improved Fisher feature selection method. Individual salient feature subsets are selected for each pair of classes separately. Then, RVM method is adopted to train the intelligent fault diagnosis model. The multi-class RVM classifier is constructed by combining several RVM binary classifiers using 'max-probability-win' strategy. Moreover, improved from Gaussian radial basis function, a new kernel function denoted variance radial basis function is developed and used for RVM to adaptively balance the difference between the scales of different features. The proposed method was carried out to develop a multi-class bearing fault diagnosis model under varying load conditions, resulting in high accuracy around 99.58 per cent. Experimental results demonstrate that the proposed method is promising for intelligent fault diagnosis of rotating machinery.

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