Reforming Fiscal Institutions in Resource-Rich Arab Economies: Policy Proposals

This paper traces the evolution of fiscal institutions of Resource Rich Arab Economies (RRAEs) over time since their pre-oil days, through the discovery of oil to their build-up of oil exports. It then identifies challenges faced by RRAEs and variations in their severity among the different countries over time. Finally, it articulates specific policy reforms, which, if implemented successfully, could help to overcome these challenges. In some cases, however, these policy proposals may give rise to important trade-offs that will have to be evaluated carefully in individual cases

A proposed multi-scale approach with automatic scale selection for image change detection

In this paper a system for multi-scale change detection with automatic scale selection is proposed. The generation of the multi-scale data set is performed based on the fractal net evolution approach (FNEA). The set of scales used by FNEA are optimally selected from the scale domain ensuring that the selected levels present a good enough representation of the scale domain. A pattern search module is used to select good enough set of scales with the least redundancy. The change detection is performed on each scale individually. For each individual object in a specific scale change indicators are extracted for the pixels corresponding to this object in the base-scale images. After extracting the change indicators for each scale, the extracted indicators are thresholded to obtain a per-scale binary change map. To obtain the final change map, a scale-driven fusion of all the extracted change maps is performed. The fusion is based on detecting for each pixel the preferred scale to obtain its change information. The best scale for an object is the scale where the object area keeps static/almost static while moving from one scale to the next scale(s). The proposed system proves advantageous over other change detection systems

Caffeic acid methyl and ethyl esters exert potential antidiabetic effects on glucose and lipid metabolism in cultured murine insulin-sensitive cells through mechanisms implicating activation of AMPK

Context: Caffeic acid methyl (CAME) and ethyl (CAEE) esters stimulate glucose uptake and AMP-activated protein kinase (AMPK) in C2C12 myocytes (ATCC® CRL-1772TM). Objective: Effects of CAME and CAEE were now assessed on myocyte glucose transporter GLUT4 activity and expression, on hepatic gluconeogenesis and on adipogenesis as well as major underlying signaling pathways. Materials and methods: GLUT4 protein translocation was studied in L6 GLUT4myc cells, glucose-6-phospatase (G6Pase) in H4IIE hepatocytes and adipogenesis in 3T3-L1 adipocytes. Key modulators were measured using western immunoblot. Cells were treated for 18 h with either CAME or CAEE at various concentrations (12.5–100 μM). Results: Myocyte glucose uptake rose from 10.1 ± 0.5 to 18.7 ± 0.8 and 21.9 ± 1.0 pmol/min/mg protein in DMSO-, CAME- and CAEE-stimulated cells, respectively, similar to insulin (17.7 ± 1.2 pmol/min/mg protein), while GLUT4myc translocation increased significantly by 1.70 ± 0.18, by 1.73 ± 0.18- and by 1.95 ± 0.30-fold (relative to DMSO), following insulin, CAME and CAEE stimulation, respectively. CAME and CAEE suppressed hepatocyte G6Pase by 62.0 ± 6.9% and 62.7 ± 6.0% with IC50 of 45.93 and 22.64 μM, respectively, comparable to insulin (70.7 ± 2.3% inhibition). Finally, CAME and CAEE almost abrogated adipogenesis (83.3 ± 7.2% and 97.3 ± 3.0% at 100 μM; IC50 of 13.8 and 12.9 μM, respectively). The compounds inhibited adipogenic factors C/EBP-β and PPAR-γ and stimulated AMPK activity in the three cell-lines. Discussion and conclusions: CAME and CAEE exerted antidiabetic activities in insulin-responsive cells through insulin-independent mechanisms involving AMPK and adipogenic factors

Node classification with graph neural network based centrality measures and feature selection

Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs are used as important components for developing and training neural networks. GNN always learns the weight importance of the neighbour for perform message aggregation in which the feature vectors of all neighbors are aggregated without considering whether the features are useful or not. Using such more informative features positively affect the performance of the GNN model. So, in this paper i) after selecting a subset of features to define important node features, we present new graph features’ explanation methods based on graph centrality measures to capture rich information and determine the most important node in a network. Through our experiments, we find that selecting certain subsets of these features and adding other features based on centrality measure can lead to better performance across a variety of datasets and ii) We introduce a major design strategy for graph neural networks. Specifically, we suggest using batch renormalization as normalization over GNN layers. Combining these techniques, representing features based on centrality measures that passed to multilayer perceptron (MLP) layer which is then passed to adjusted GNN layer, the proposed model achieves greater accuracy than modern GNN models

Significance of microbiota in obesity and metabolic diseases and the modulatory potential by medicinal plant and food ingredients

Metabolic syndrome is a cluster of three or more metabolic disorders including insulin resistance, obesity, and hyperlipidemia. Obesity has become the epidemic of the twenty-first century with more than 1.6 billion overweight adults. Due to the strong connection between obesity and type 2 diabetes, obesity has received wide attention with subsequent coining of the term "diabesity." Recent studies have identified unique contributions of the immensely diverse gut microbiota in the pathogenesis of obesity and diabetes. Several mechanisms have been proposed including altered glucose and fatty acid metabolism, hepatic fatty acid storage, and modulation of glucagon-like peptide (GLP)-1. Importantly, the relationship between unhealthy diet and a modified gut microbiota composition observed in diabetic or obese subjects has been recognized. Similarly, the role of diet rich in polyphenols and plant polysaccharides in modulating gut bacteria and its impact on diabetes and obesity have been the subject of investigation by several research groups. Gut microbiota are also responsible for the extensive metabolism of polyphenols thus modulating their biological activities. The aim of this review is to shed light on the composition of gut microbes, their health importance and how they can contribute to diseases as well as their modulation by polyphenols and polysaccharides to control obesity and diabetes. In addition, the role of microbiota in improving the oral bioavailability of polyphenols and hence in shaping their antidiabetic and antiobesity activities will be discussed