2,163 research outputs found
Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer
No full text: In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients' treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure
MP53-14 MULTIMODAL TREATMENT FOR HIGH-RISK PROSTATE CANCER WITH HIGH-DOSE INTENSITY-MODULATED RADIATION THERAPY, CONCURRENT INTENSIFIED-DOSE DOCETAXEL AND LONG-TERM ANDROGEN DEPRIVATION THERAPY AFTER RADICAL PROSTATECTOMY AND LYMPHADENECTOMY: RESULTS OF A PROSPECTIVE PHASE II TRIAL
No full textThe Poem of Memory. "Triumphi"
No full textWriting of the "Triumphi" Fabio Finotti finds that its author undercuts a medieval idea of ascent to God in the structural progression from Love to Chastity, Death, Fame, Time, and Eternity. Here Petrarch programmatically counters Dante, transforming a universal, eschatological vision into a subjective, cultural and psychological experience
Fabio Tronchetti
No full textFabio Tronchetti is an Associate Professor of Law at the School of Law of the Harbin Institute of Technology, People’s Republic of China, where he also serves as Director of the International Law Department. Since January 2014 he works as an Adjunct Professor of Comparative National Space Law at the the School of Law of the University of Mississippi, United States. Earlier in his career he was Lecturer and Academic Coordinator at the International Institute of Air and Space Law, Leiden University, the Netherlands.
Professor Tronchetti is regularly invited to give lectures at several European and Chinese Universities, including the Cologne University (Germany), the Leiden University (the Netherlands) and the Beihang University (Beijing, China) and has participated as a speaker at numerous international conferences.
Prof. Tronchetti’s scholarly is primarily in the areas of international space law and public international law. His publications include two books and more than 20 articles in internationally peer-reviewed space law and policy journals, such as Space Policy, the German Journal of Air and Space Law, the Journal of Space Law, etc.
He holds a PhD in International Space Law (Leiden University) and an Advanced LL.M in International Relations (Bologna University, Italy). He is Member of the International Institute of Space Law (IISL), European Centre for Space Law (ECSL), and the Asian Society of International Law (ASIL). He is the recipient of the 2007 Diederiks-Verschoor award for the best paper submitted by an author not older than 40 years to the International Institute of Space Law (IISL) during the 58th International Astronautical Congress of the International Astronautical Federation (IAF).https://commons.erau.edu/stm-images/1076/thumbnail.jp
Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
No full textIn recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented
Per un ritratto di André Tosel
No full textIn memory of André Tosel, who passed away last March 14th in his hometown Nice, «Gramsciana» publishes an article on Gramsci in France that he had sent to this journal as a contribution to the section «My Gramsci». The editor, Fabio Frosini, prefaces the text with a quick portrait of Tosel as a philosophy professor, an influential Marxist intellectual, a critic of contemporary capitalism, as well as the author of landmark books on Spinoza, Kant and Marx and, above all, one of the most important Gramsci scholars of the last 50 years
NEW DEVELOPMENTS IN LIDAR UAS SURVEYS. PERFORMANCE ANALYSES AND VALIDATION OF THE DJI ZENMUSE L1
No full textThanks to the latest technological developments LiDAR (Light Detection And Ranging) sensors are no longer an exclusive feature of manned airborne platforms but they are close to becoming a commercial solution in the UAS (Uncrewed Aerial Systems) domain. The release on the market of the Zenmuse L1 by DJI (Dà-Jiāng Innovations) is a step further in this direction, thanks also to a substantial work of enhancement made by the Chinese company not only on the hardware side, but also on the software one. The research presented in this work is focused on the use of the L1 LiDAR for the 3D survey of built heritage, analysing the results of different tests to highlight first considerations on its performances and the point cloud quality. Considering its recent release, this sensor is still yet to be thoroughly analysed and validated and its performances to be assessed. LiDAR data has been acquired on a selected test site, documented also with traditional Terrestrial Laser Scanner (TLS) and UAS photogrammetry. The latter techniques (supported also by a topographic survey) will thus be exploited to generate the ground reference and to assess the quality and accuracy of the L1 dataset
Comparing machine and deep learning methods for large 3D heritage semantic segmentation
No full textIn recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented
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