Mutation analysis of the MEN1 gene in multiple endocrine neoplasia type 1, familial acromegaly and familial isolated hyperparathyroidism

Multiple endocrine neoplasia type 1 (MEN 1) is an autosomal dominant disease characterized by neoplasia of the parathyroid glands, the endocrine pancreas, and the anterior pituitary gland. In addition, families with isolated endocrine neoplasia, notably familial isolated hyperparathyroidism (FIHP) and familial acromegaly, have also been reported. However, whether these families constitute MEN 1 variants or separate entities remains speculative as the genetic bases for these diseases are unclear. The gene for MEN 1 has recently been cloned and characterized. Using single strand conformation analysis (SSCA) and sequencing, we performed mutation analysis in: a) a total of 55 MEN 1 families from 7 countries, b) 13 isolated MEN 1 cases without family history of the disease, c) 8 acromegaly families, and d) 4 FIHP families. Mutations were identified in 27 MEN 1 families and 9 isolated cases. The 22 different mutations spread across most of the 9 translated exons and included frameshift (11), nonsense (6), splice (2), missense mutations (2), and in-frame deletions (1). Among the 19 Finnish MEN 1 probands, a 1466del12 mutation was identified in 6 families with identical 11q13 haplotypes and in 2 isolated cases indicating a common founder. One frameshift mutation caused by 359del4 (GTCT) was found in 1 isolated case and 4 kindreds of different origin and haplotypes; this mutation therefore represents a common "warm" spot in the MEN1 gene. By analyzing the DNA of the parents of an isolated case one mutation was confirmed to be de novo. No mutation was found in any of the acromegaly and small FIHP families, suggesting that genetic defects other than the MEN1 gene might be involved and that additional such families need to be analyzed.     

An exploratory study for investigating the critical success factors for cloud migration in the Saudi Arabian higher education context

Saudi universities have at their disposal a huge number of low cost IT resources to aid in teaching, research and learning. By migrating to cloud services, Saudi universities will be moving data and programs from local servers to the internet, thereby providing users with the ability to access and share information at any time from multiple devices. The migration to cloud-based IT resources is not yet widespread in Saudi universities due to several challenges including security, legal policies and implementation. At present, there is lack of research and guidance for Saudi universities on how to overcome these challenges and how contextual factors can influence the successful migration to the educational clouds.This research presents a framework for the successful migration to cloud technology in the Saudi Arabian universities. In this research, a set of key critical success factors (CSFs) were identified by synthesizing components from studies concerned with the migration of cloud for higher education and factors identified from the successful implementation of WBL (Web Based Learning) and ERP (Enterprise Resource Planning) on higher education in Saudi Arabia. Based on this knowledge, the proposed framework was evaluated via expert review and a survey by IT specialists from the Saudi universities. The initial CSFs were updated based on the expert reviews and the results were analysed. Based on the findings at this stage, additional CSFs were added to the framework as suggested by the experts. Subsequently, in order to confirm the reviewed CSFs, additional investigation via a structured online questionnaire was conducted and the outcome was analysed via one-sample t-test with the data integrity analysed via Cronbach’s alpha. The outcome indicated the majority of CSFs to be statistically significant except the Physical Location CSF. Potential future study and contributions are discussed.     

A novel cellular automata based approach to storm sewer design

Optimal storm sewer design aims at minimizing capital investment on infrastructure whilst ensuring good system performance under specified design criteria. An innovative sewer design approach based on cellular automata (CA) principles is introduced in this paper. Cellular automata have been applied as computational simulation devices in various scientific fields. However, some recent research has indicated that CA can also be a viable and efficient optimization engine. This engine is heuristic and largely relies on the key properties of CA: locality, homogeneity, and parallelism. In the proposed approach, the CA-based optimizer is combined with a sewer hydraulic simulator, the EPA Storm Water Management Model (SWMM). At each optimization step, according to a set of transition rules, the optimizer updates all decision variables simultaneously based on the hydraulic situation within each neighbourhood. Two sewer networks (one small artificial network and one large real network) have been tested in this study. The CA optimizer demonstrated its ability to obtain near-optimal solutions in a remarkably small number of computational steps in a comparison of its performance with that of a genetic algorithm.     

A Facet‐Specific Quantum Dot Passivation Strategy for Colloid Management and Efficient Infrared Photovoltaics

Colloidal nanocrystals combine size‐ and facet‐dependent properties with solution processing. They offer thus a compelling suite of materials for technological applications. Their size‐ and facet‐tunable features are studied in synthesis; however, to exploit their features in optoelectronic devices, it will be essential to translate control over size and facets from the colloid all the way to the film. Larger‐diameter colloidal quantum dots (CQDs) offer the attractive possibility of harvesting infrared (IR) solar energy beyond absorption of silicon photovoltaics. These CQDs exhibit facets (nonpolar (100)) undisplayed in small‐diameter CQDs; and the materials chemistry of smaller nanocrystals fails consequently to translate to materials for the short‐wavelength IR regime. A new colloidal management strategy targeting the passivation of both (100) and (111) facets is demonstrated using distinct choices of cations and anions. The approach leads to narrow‐bandgap CQDs with impressive colloidal stability and photoluminescence quantum yield. Photophysical studies confirm a reduction both in Stokes shift (≈47 meV) and Urbach tail (≈29 meV). This approach provides a ≈50% increase in the power conversion efficiency of IR photovoltaics compared to controls, and a ≈70% external quantum efficiency at their excitonic peak.     

General Ribbons: A Model for Stylus-Generated Images

General ribbons are presented as a mathematical model of stylus-generated images which is based on the image formation process. One purpose of the model is to provide a formal basis for the development of thinning algorithms for the stroke components of stylus-generated images. Before the stroke components can be thinned they must be segmented from the blob components of the image, which do not require thinning. The second purpose of the model is to provide a formal basis for the development of blob/stroke segmentation algorithms. Two blob/stroke segmentation algorithms based on the model are presented.     

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.     

Collaborative test of determination of iodine value in fish oils. 2. Carbon tetrachloride or cyclohexane/acetic acid as solvents

Nine laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150; four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane/acetic acid (AOCS Recommended Practice Cd 1d-92) as solvent and 1 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each method (open duplicates). Replacing carbon tetrachloride with cyclohexane/acetic acid resulted in similar mean values for both low- and high-IV oils and similar estimates of repeatability and reproducibility. The repeatability standard deviation (s _r ), based on hidden duplicates, with carbon tetrachloride and cyclohexane/acetic acid were 1.71 and 1.55, respectively. The corresponding reproducibility standard deviations were 1.81 and 1.98.