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.

The effect of phosphorus deficiency on nutrient uptake, nitrogen fixation and photosynthetic rate in mashbean, mungbean and soybean

Abstract: Phosphorous (P) fertilization is the major mineral nutrient yield determinant among legume crops. However, legume crops vary widely in the ability to take up and use P during deficiency. The aim here was to compare P uptake and translocation, biological nitrogen fixing ability and photosynthetic rate among mashbean (Vigna aconitifolia cv. ‘Mash-88’), mungbean (Vigna radiata cv. ‘Moong-6601’) and soybean (Glycine max L. cv. ‘Tamahomare’) during deficiency in hydroponics. Two treatments, the withdrawal of P from the solution (P-deprivation) and continued P at 160 μM (P sufficient) were effected at the pod initiation stage. Plants were grown for 20 days. Short-term labeling with 32P showed the uptake and distribution of P into plant parts. Withdrawal of P from the solution reduced biomass, photosynthetic activity, and nitrogen fixing ability in mungbean, and mashbean more than in soybean. P deprivation decreased P accumulation more than N accumulation. The decrease was more severe in mungbean and mashbean than soybean. More P was translocated and distributed into leaves in soybean than in mungbean and mashbean. Leaf P amount was more correlated to leaf area than to photosynthetic rate per unit leaf area among all three legume species. The results indicate that selection for increased efficiency of P utilization and leaf area may be used to improve leguminous crops.

A Novel Template Reduction Approach for the $K$ -Nearest Neighbor Method

Abstract: The K-nearest neighbor (KNN) rule is one of the most widely used pattern classification algorithms. For large data sets, the computational demands for classifying patterns using KNN can be prohibitive. A way to alleviate this problem is through the condensing approach. This means we remove patterns that are more of a computational burden but do not contribute to better classification accuracy. In this brief, we propose a new condensing algorithm. The proposed idea is based on defining the so-called chain. This is a sequence of nearest neighbors from alternating classes. We make the point that patterns further down the chain are close to the classification boundary and based on that we set a cutoff for the patterns we keep in the training set. Experiments show that the proposed approach effectively reduces the number of prototypes while maintaining the same level of classification accuracy as the traditional KNN. Moreover, it is a simple and a fast condensing algorithm.

Pregnancy Outcomes in Women With Rheumatic Mitral Valve Disease: Results From the Registry of Pregnancy and Cardiac Disease

Abstract: Background:Cardiac disease is 1 of the major causes of maternal mortality. We studied pregnancy outcomes in women with rheumatic mitral valve disease. Methods:The Registry of Pregnancy and Cardiac ...