University of Jordan

About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Health care. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Mutations in PYCR1 cause cutis laxa with progeroid features

Abstract: Autosomal recessive cutis laxa (ARCL) describes a group of syndromal disorders that are often associated with a progeroid appearance, lax and wrinkled skin, osteopenia and mental retardation. Homozygosity mapping in several kindreds with ARCL identified a candidate region on chromosome 17q25. By high-throughput sequencing of the entire candidate region, we detected disease-causing mutations in the gene PYCR1. We found that the gene product, an enzyme involved in proline metabolism, localizes to mitochondria. Altered mitochondrial morphology, membrane potential and increased apoptosis rate upon oxidative stress were evident in fibroblasts from affected individuals. Knockdown of the orthologous genes in Xenopus and zebrafish led to epidermal hypoplasia and blistering that was accompanied by a massive increase of apoptosis. Our findings link mutations in PYCR1 to altered mitochondrial function and progeroid changes in connective tissues.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Abstract: Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

An efficient hybrid multilayer perceptron neural network with grasshopper optimization

Abstract: This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

Endoscopic exploration of Red Sea coral reefs reveals dense populations of cavity-dwelling sponges

Abstract: Framework cavities are the largest but least explored coral reef habitat1. Previous dive studies of caverns, spaces below plate corals, rubble and artificial cavities1,2,3 suggest that cavity-dwelling (coelobite) filter-feeders are important in the trophodynamics of reefs2,4,5. Quantitative community data are lacking, however, as the bulk of the narrow crevices interlacing the reef framework are inaccessible to conventional analysis methods6. Here we have developed endoscopic techniques to explore Red Sea framework crevices up to 4 m into the carbonate rock, revealing a large internal surface (2.5–7.4 m2 per projected m2 reef) dominated by encrusting filter-feeders. Sponges alone provided up to 60% of coelobite cover, outweighing epi-reefal filter-feeder biomass by two orders of magnitude. Coelobite community filtration removed more than 60% of the phytoplankton in the course of its less than 5-minute passage through the crevices, corresponding to an uptake of roughly 0.9 g carbon m-2 d-1. Mineralization of the largely allochthonous organic material is a principal source of nutrients supporting coral and algal growth. The supply of new material by coelobites may provide a key to understanding the ‘coral reef paradox’—a rich ecosystem thriving in nutrient-poor water.

Nanoparticle-protein interactions: a thermodynamic and kinetic study of the adsorption of bovine serum albumin to gold nanoparticle surfaces.

Abstract: Investigating the adsorption process of proteins on nanoparticle surfaces is essential to understand how to control the biological interactions of functionalized nanoparticles. In this work, a library of spherical and rod-shaped gold nanoparticles (GNPs) was used to evaluate the process of protein adsorption to their surfaces. The binding of a model protein (bovine serum albumin, BSA) to GNPs as a function of particle shape, size, and surface charge was investigated. Two independent comparative analytical methods were used to evaluate the adsorption process: steady-state fluorescence quenching titration and affinity capillary electrophoresis (ACE). Although under favorable electrostatic conditions kinetic analysis showed a faster adsorption of BSA to the surface of cationic GNPs, equilibrium binding constant determinations indicated that BSA has a comparable binding affinity to all of the GNPs tested, regardless of surface charge. BSA was even found to adsorb strongly to GNPs with a pegylated/neutral surf...