Prognostic value of p53 gene mutations in a large series of node- negative breast cancer patients

The most important subgroup of breast cancer patients for which reliable prognostic factors are needed are women without axillary lymph node involvement. Although overall, these patients have a good prognosis, it is known that 20-30% will experience a recurrence of the disease. To determine the prognostic significance of P53 tumor suppressor gene mutation, specimens from 113 primary breast cancers were evaluated for the presence of P53 alterations, as detected by cDNA sequencing of the entire coding sequence of the gene. The median follow-up for patients was 105 months. P53 gene mutation was an independent prognostic marker of early relapse and death. Our results suggest that P53 gene mutations could be an important factor to identify node-negative patients who have a poor prognosis in the absence of adjuvant therapy. Prospective studies should be designed to determine which therapy should be performed in this subgroup of patients

Proba-V cloud detection Round Robin: Validation results and recommendations

This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparison of different cloud detection algorithms for Proba-V. Clouds detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking directly translates into significant uncertainty in the retrieved downstream geophysical products. Cloud detection is particularly challenging for Proba-V due to the presence of a limited number of spectral bands and the lack of thermal infrared bands. The main objective of the project was the inter-comparison of several cloud detection algorithms for Proba-V over a wide range of surface types and environmental conditions. Proba-V Level 2a products have been distributed to six different algorithm providers representing companies and research institutes in several European countries. The considered cloud detection approaches are based on different strategies: Neural Network, Discriminant Analysis, Multi-spectral and Multi-textural Thresholding, Self-Organizing Feature Maps, Dynamic Thresholding, and physically-based retrieval of Cloud Optical Thickness. The results from all algorithms were analysed and compared against a reference dataset, consisting of a large number (more than fifty thousands) of visually classified pixels. The quality assessment was performed according to a uniform methodology and the results provide clear indication on the potential best-suited approach for next Proba-V cloud detection algorithm

Dual-phase evolution in complex adaptive systems

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle