Cairo University

About: Cairo University is a education organization based out in Giza, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 33532 authors who have published 55581 publications receiving 792654 citations. The organization is also known as: Fuad I University & King Fuad I University.

Experimental determination of the maximum post-process annealing temperature for standard CMOS wafers

Abstract: This paper reports on the experimental determination of the maximum post-process annealing temperature for standard 035 /spl mu/m CMOS wafers with aluminum based interconnections and tungsten plugs, without introducing significant modifications to their standard characteristics The impact of increasing the post-processing temperature from 475/spl deg/C to 575/spl deg/C, for periods varying between 30 and 90 min, on both the front and back end is analyzed 035 /spl mu/m CMOS technologies with different Al alloys, Al-1wt%Si-05wt%Cu (AlSiCu) or Al-05wt%Cu (AlCu), and different back end structures are considered It is illustrated that the maximum annealing temperature is a function of the structure and composition of the interconnection layers and their maximum allowable resistance increase It is also demonstrated that the transistor characteristics, the silicide quality and the leakage currents are as good as unaffected by annealing for 90 min at temperatures up to 525/spl deg/C

Efficacy and safety of deferasirox, an oral iron chelator, in heavily iron-overloaded patients with β-thalassaemia: the ESCALATOR study

Abstract: Objective: Many patients with transfusional iron overload are at risk for progressive organ dysfunction and early death and poor compliance with older chelation therapies is believed to be a major contributing factor. Phase II/III studies have shown that oral deferasirox 20–30 mg/kg/d reduces iron burden, depending on transfusional iron intake.

Structured crowdsourcing enables convolutional segmentation of histology images.

Abstract: Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.