Loss of TRAIL-receptors is a recurrent feature in pancreatic cancer and determines the prognosis of patients with no nodal metastasis after surgery

INTRODUCTION Agonistic antibodies targeting TRAIL-receptors 1 and 2 (TRAIL-R1 and TRAIL-R2) are being developed as a novel therapeutic approach in cancer therapy including pancreatic cancer. However, the cellular distribution of these receptors in primary pancreatic cancer samples has not been sufficiently investigated and no study has yet addressed the issue of their prognostic significance in this tumor entity. AIMS AND METHODS Applying tissue microarray (TMA) analysis, we performed an immunohistochemical assessment of TRAIL-receptors in surgical samples from 84 consecutive patients affected by pancreatic adenocarcinoma and in 26 additional selected specimens from patients with no lymph nodes metastasis at the time of surgery. The prognostic significance of membrane staining and staining intensity for TRAIL-receptors was evaluated. RESULTS The fraction of pancreatic cancer samples with positive membrane staining for TRAIL-R1 and TRAIL-R2 was lower than that of cells from surrounding non-tumor tissues (TRAIL-R1: p<0.001, TRAIL-R2: p = 0.006). In addition, subgroup analyses showed that loss of membrane staining for TRAIL-R2 was associated with poorer prognosis in patients without nodal metastases (multivariate Cox regression analysis, Hazard Ratio: 0.44 [95% confidence interval: 0.22-0.87]; p = 0.019). In contrast, analysis of decoy receptors TRAIL-R3 and -R4 in tumor samples showed an exclusively cytoplasmatic staining pattern and no prognostic relevance. CONCLUSION This is a first report on the prognostic significance of TRAIL-receptors expression in pancreatic cancer showing that TRAIL-R2 might represent a prognostic marker for patients with early stage disease. In addition, our data suggest that loss of membrane-bound TRAIL-receptors could represent a molecular mechanism for therapeutic failure upon administration of TRAIL-receptors-targeting antibodies in pancreatic cancer. This hypothesis should be evaluated in future clinical trials.

Non-comparative versus Comparative Advertising of Quality

Two firms produce a good with a horizontal and a vertical character- istic called quality. The difference in the unobservable quality levels determines how the firms share the market. We consider two scenar- ios: In the first one, firms disclose quality; in the second one, they send costly signals thereof. Under non-comparative advertising a firm advertises its own quality, under comparative advertising a firm adver- tises the quality differential. In either scenario, under comparative ad- vertising the firms never advertise together which they may do under non-comparative advertising. Moreover, under comparative advertis- ing firms do not advertise when the informational value to consumers is small.

Communion-over-Agency effects on advertising effectiveness

Gender-stereotypical portrayals of communal women and agentic men are highly common in advertising. But past research indicates that advertising effectiveness is higher when endorsers are portrayed as communal – irrespective of their gender. The aim of the current research is to explore this communion-over-agency effect on advertising effectiveness and its underlying mechanism. Two studies provide evidence for a communion-over-agency effect on advertising effectiveness (i.e., attitude toward the ad and brand). These studies show that the communion-over-agency effect on attitude toward the ad is mediated by endorser likeability (simple mediation). The effect on attitude toward the brand is mediated by endorser likeability and attitude toward the ad (serial multiple mediation). In concert, this research underscores the significance of communion (and agency) on endorser evaluation and advertising effectiveness.

Synchronization of two independently moving cameras without feature correspondences

In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and/or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

“Therapeutic Advertising”: When Do Female Consumers Really Benefit?

This research examines whether female consumers benefit from brand strategies that attempt to decrease their self-discrepancies by setting more realistic ideals (i.e., therapeutic advertising, such as Body Shop, Aerie, and Always). The results of our preliminary study reveal that therapeutic advertising leads to stronger self-conscious emotions than idealistic advertising. More specifically, it leads to stronger emotions of both pride and shame. However, the latter only holds true for female consumers low in self-liking and high difficulties in abandoning unattainable goals. Female consumers who like themselves and are able to abandon unattainable goals do not feel more ashamed when being exposed to therapeutic advertising compared to idealized advertising. These findings have implications for marketing managers and policy makers.