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Ion implantation in GaN at liquid-nitrogen temperature: structural characteristics and amorphization
(1998)
GaN is implanted with 90 keV Mg-ions and afterwards annealed at 1150°C in a rapid thermal annealing furnace. In order to study the dependence of damage generation during implantation on the different implantation parameters, the dose is varied between 1×1014 and 5×1015 Mg+ cm−2, the substrate temperature during implantation is varied from 25 to 550°C and the ion current density is varied between 0.5 and 20 μA cm−2. The defect concentration is examined by Rutherford backscattering spectroscopy/channeling. Up to doses of 2.5×1015 Mg+ cm−2, the implantation-damage can readily be reduced by annealing. Variation of the ion current density has no influence on defect generation. Implantation at higher temperatures shows an unusual behaviour as it results in an increase in damage.
N-BLR, a primate-specific non-coding transcript leads to colorectal cancer invasion and migration
(2017)
Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
