Background:Metastatic prostate cancer (PCa) has no curative treatment options. Some forms of PCa are indolent and slow growing, while others metastasise quickly and may prove fatal within a very short time. The basis of this variable prognosis is poorly understood, despite considerable research. The aim of this study was to identify markers associated with the progression of PCa.Methods:Artificial neuronal network analysis combined with data from literature and previous work produced a panel of putative PCa progression markers, which were used in a transcriptomic analysis of 29 radical prostatectomy samples and correlated with clinical outcome.Results:Statistical analysis yielded seven putative markers of PCa progression, ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13. Two data transformation methods were utilised with only markers that were significant in both selected for further analysis. ANPEP and EFNA1 were significantly correlated with Gleason score. Models of progression co-utilising markers ANPEP and ABL1 or ANPEP and PSCA had the ability to correctly predict indolent or aggressive disease, based on Gleason score, in 89.7% and 86.2% of cases, respectively. Another model of TRIP13 expression in combination with preoperative PSA level and Gleason score was able to correctly predict recurrence in 85.7% of cases.Conclusion:This proof of principle study demonstrates a novel association of carcinogenic and tumourigenic gene expression with PCa stage and prognosis.

Greek mythology is filled with examples of beings made of mixtures of several creatures fused together to form a single one. Perhaps the best example of this was a combination of lion, goat and serpent, known as the Chimaera. Today the term has come to describe organisms composed of two or more different, genetically distinct cell lines. For scientists involved in forensic DNA testing this concept is troubling as the analysis of such individuals would lead to spurious results that could be problematic to interpret. It is therefore important to understand what chimaerism is, how prevalent it is in the population and ultimately how this may affect the outcome of forensic DNA testing. This paper will explore the literature surrounding this phenomenon and propose some answers to the questions that arise. RÉSUMÉ La mythologie grecque est remplie d'exemples d'êtres formés d'une fusion de plusieurs créatures pour n'en faire qu'une. Un bon exemple est la combinaison d'un lion, d'une chèvre et d'un serpent pour former la chimère. De nos jours, ce terme désigne des organismes formés de deux ou plusieurs différentes lignes de cellules génétiquement distinctes. Pour les scientifiques effectuant des tests d'identité parl'ADN, ce concept est préoccupant puisque l'analyse de tels individus mènerait à de résultats spécieux qui s'avèreraient difficiles à interpréter. Il est donc primordial de comprendre ce qu'est le chimérisme, à quel point il est répandu dans la population et en bout de ligne, comment ceci affecterait les résultats de l'identité par ADN. Cet article examinera la littérature se rapportant à ce phénomène et suggèrera quelques réponses aux questions qui émergent.

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Mobile robots are often tasked with repeatedly navigating through an environment whose traversability changes over time. These changes may exhibit some hidden structure, which can be learned. Many studies consider reactive algorithms for online planning, however, these algorithms do not take advantage of the past executions of the navigation task for future tasks. In this paper, we formalize the problem of minimizing the total expected cost to perform multiple start-togoal navigation tasks on a roadmap by introducing the Learned Reactive Planning Problem. We propose a method that captures information from past executions to learn a motion policy to handle obstacles that the robot has seen before. We propose the LAMP framework, which integrates the generated motion policy with an existing navigation stack. Finally, an extensive set of experiments in simulated and real-world environments show that the proposed method outperforms the state-of-theart algorithms by 10% to 40% in terms of expected time to travel from start to goal. We also evaluate the robustness of the proposed method in the presence of localization and mapping errors on a real robot.