Gene expression profiling of mucinous ovarian tumors and comparison with upper and lower gastrointestinal tumors identifies markers associated with adverse outcomes.

PURPOSE: Advanced-stage mucinous ovarian carcinoma (MOC) has poor chemotherapy response and prognosis and lacks biomarkers to aid stage I adjuvant treatment. Differentiating primary MOC from gastrointestinal (GI) metastases to the ovary is also challenging due to phenotypic similarities. Clinicopathologic and gene-expression data were analyzed to identify prognostic and diagnostic features. EXPERIMENTAL DESIGN: Discovery analyses selected 19 genes with prognostic/diagnostic potential. Validation was performed through the Ovarian Tumor Tissue Analysis consortium and GI cancer biobanks comprising 604 patients with MOC (n = 333), mucinous borderline ovarian tumors (MBOT, n = 151), and upper GI (n = 65) and lower GI tumors (n = 55). RESULTS: Infiltrative pattern of invasion was associated with decreased overall survival (OS) within 2 years from diagnosis, compared with expansile pattern in stage I MOC [hazard ratio (HR), 2.77; 95% confidence interval (CI), 1.04–7.41, P = 0.042]. Increased expression of THBS2 and TAGLN was associated with shorter OS in MOC patients (HR, 1.25; 95% CI, 1.04–1.51, P = 0.016) and (HR, 1.21; 95% CI, 1.01–1.45, P = 0.043), respectively. ERBB2 (HER2) amplification or high mRNA expression was evident in 64 of 243 (26%) of MOCs, but only 8 of 243 (3%) were also infiltrative (4/39, 10%) or stage III/IV (4/31, 13%). CONCLUSIONS: An infiltrative growth pattern infers poor prognosis within 2 years from diagnosis and may help select stage I patients for adjuvant therapy. High expression of THBS2 and TAGLN in MOC confers an adverse prognosis and is upregulated in the infiltrative subtype, which warrants further investigation. Anti-HER2 therapy should be investigated in a subset of patients. MOC samples clustered with upper GI, yet markers to differentiate these entities remain elusive, suggesting similar underlying biology and shared treatment strategies

Operations management teaching on European MBA programmes

A comprehensive review of the literature established that several investigations have been made of operations management teaching in the USA, whereas almost nothing has been published on European teaching. Therefore, an exploratory investigation was made of operations management teaching on the MBA courses of ten leading European business schools. The results show that course content is similar across schools, but there are large variations on three dimensions: the time allocated by schools to the subject; the balance between operations strategy and tools and techniques in teaching; and the level of emphasis given to service operations. The results also indicate the emerging importance of integrating operations management with other subjects in the MBA curriculum and the key challenge facing faculty - the need to raise the perceived importance of operations management. The comparison of courses will be of interest to all operations management faculty who teach core courses and particularly those who are looking for ideas on how to re-design courses

Comprehending Feature Models Expressed in CVL

Feature modeling is a common way to present and manage variability of software and systems. As a prerequisite for effective variability management is comprehensible representation, the main aim of this paper is to investigate difficulties in understanding feature models. In particular, we focus on the comprehensibility of feature models as expressed in Common Variability Language (CVL), which was recommended for adoption as a standard by the Architectural Board of the Object Management Group. Using an experimental approach with participants familiar and unfamiliar with feature modeling, we analyzed comprehensibility in terms of comprehension score, time spent to complete tasks, and perceived difficulty of different feature modeling constructs. The results showed that familiarity with feature modeling did not influence the comprehension of mandatory, optional, and alternative features, although unfamiliar modelers perceived these elements more difficult than familiar modelers. OR relations were perceived as difficult regardless of the familiarity level, while constraints were significantly better understood by familiar modelers. The time spent to complete tasks was higher for familiar modelers.acceptedVersio