| 1 | Human papillomavirus does not have a causal role in colorectal carcinogenesis显示文摘AIM: To investigate the presence of human papillomavirus(HPV) DNA along with the integration,the quantification and the expression of the HPV16 in colorectal cancers.METHODS: A prospective series of colorectal tumors were genotyped for HPV DNA.The clinical and pathological variables of the HPV-positive tumors were compared to those of HPV-negative samples.The integration status of HPV16 was evaluated by calculating E2/E6 ng ratios.HPV16-positive tumors were also evaluated for(1) E2,E4,E5,E6 and E7 viral gene ng quantification;(2) relative quantification compared to W12 cells; and(3) viral E2,E4,E5,E6 and E7 mR NA transcripts by real-time polymerase chain reaction.RESULTS: HPV infection was detected in 16.9% of all tumors examined,and HPV16 was the most frequent type detected(63.6% of positive tissues).Notably,the clinical and pathological features of HPV-positive colorectal cancers were not significantly different than those of HPV-negative cancers(χ2 and t-test for all clinical and pathological features of HPV-positive vs HPV-negative colorectal cancers: p ns).HPV16 DNA was present exclusively in episomal form,and the HPV16 E2,E4,E5,E6 and E7 genes were detected in tracenanogram quantities.Furthermore,the HPV16 genes ranged from 10-3 to 10-9 compared to W12 cells at an episomal stage.Although the extractions were validated by housekeeping gene expression,all the HPV16 positive tissues were transcriptionally inactive for the E2,E4,E5,E6 and E7 mR NAs.CONCLUSION: Based on our results,HPV is unlikely involved in colorectal carcinogenesis. | Laura Lorenzon Francesca Mazzetta Emanuela Pilozzi Giordana Uggeri Maria Rosaria Torrisi Mario Ferri Vincenzo Ziparo Deborah French | 2015 | World Journal of Gastroenterology2015，21，1： | 3 |
| 2 | Big Data Analytics for Earth Sciences:the EarthServer approach显示文摘Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures.Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations.However,Earth Science data and applications present specificities in terms of relevance of the geospatial information,wide heterogeneity of data models and formats,and complexity of processing.Therefore,Big Earth Data Analytics requires specifically tailored techniques and tools.The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets,built around a high performance array database technology,and the adoption and enhancement of standards for service interaction(OGC WCS and WCPS).The EarthServer solution,led by the collection of requirements from scientific communities and international initiatives,provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization.The result is demonstrated and validated through the development of lighthouse applications in the Marine,Geology,Atmospheric,Planetary and Cryospheric science domains. | Peter Baumann Paolo Mazzetti Joachim Ungar Roberto Barbera Damiano Barboni Alan Beccati Lorenzo Bigagli Enrico Boldrini Riccardo Bruno Antonio Calanducci Piero Campalani Oliver Clements Alex Dumitrua,Mike Grant Pasquale Herzig George Kakaletris John Laxton Panagiota Koltsida Kinga Lipskoch Alireza Rezaei Mahdiraji Simone Mantovani Vlad Merticariu Antonio Messina Dimitar Misev Stefano Natali Stefano Nativi Jelmer Oosthoek Marco Pappalardo James Passmore Angelo Pio Rossi Francesco Rundo Marcus Sen Vittorio Sorbera Don Sullivan Mario Torrisi Leonardo Trovato Maria Grazia Veratelli Sebastian Wagner | 2016 | International Journal of Digital Earth2016，9，1： | 1 |
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