Wave energy exploitation in the Ionian Sea Hellenic coasts: spatial planning of potential wave power stations

Wave energy represents the ‘new entry’ to Renewable Energy Sources discussion, in the context of clean and sustainable energy solutions in the electricity production sector. This research describes a geo-spatial Multiple-Criteria Decision Analysis, based on the Geographic Information Systems technology, for the identification of the best location to deploy a potential Wave Energy Farm in the Ionian Sea, an area offshore the Greek mainland West coast. For this purpose, several factors are taken into consideration; restrictions such as protected areas, military exercise areas etc. and weighted factors such as distance to power grid, wave height etc. The wave resource assessment is completed through real data measurements and numerical wave model approximations. The results pinpoint the most suitable areas for installing the proposed wave power plants, i.e. near the SW coast of Corfu, in the West side of the Straits between Kefallonia and Zakynthos and near the broader area of Pylos, in the SW coast of Peloponnesus. The suggested methodology can be equally applied in other spatial planning cases too, being considered as a checklist, addressed to policy-makers and private investors.     

Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer

Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.