Inter-Alpha-Inhibitor: A Protein Family Involved in the Inhibition of Calcium Oxalate Crystallization

Inter-α-inhibitor (IαI) is a serine protease inhibitor present in human plasma. It has a molecular weight of about 220 kDa which encompasses 3 chains including two heavy chains and one light chain. The light chain, known as bikunin, is responsible for the antitryptic activity of IαI in the inhibition of various enzymes, such as trypsin and chymotrypsin. Under physiologic or certain pathologic circumstances, several macromolecules related to IαI appear in plasma and urine. However, the physiologic role of IαI remains unclear. As far as urolithiasis is concerned, two urinary macromolecules related to IαI have been isolated and shown to be potent inhibitors of calcium oxalate formation. One of these inhibitors, uronic-acid-rich protein (UAP), has been identified and well characterized. The sequence of the first 18 amino acid residues of UAP is identical with that of bikunin. Furthermore, the immunoreaction between UAP and IαI antibody using immunoblot analysis was positive. UAP isolated from the urine of stone formers exhibited less inhibitory activity towards calcium oxalate crystallization than that derived from the urine of healthy subjects. This suggests a structural abnormality of the inhibitor obtained from stone patients. The organic matrix extracted from kidney stones contained a protein antigenically related to IαI. We conclude that UAP is a member of IαI family taking part in inhibiting calcium oxalate crystallization, and modulating the formation of stones in the urinary tract

Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques

Robust off-line text independent writer identification using bagged discrete cosine transform features

Efficient writer identification systems identify the authorship of an unknown sample of text with high confidence. This has made automatic writer identification a very important topic of research for forensic document analysis. In this paper, we propose a robust system for offline text independent writer identification using bagged discrete cosine transform (BDCT) descriptors. Universal codebooks are first used to generate multiple predictor models. A final decision is then obtained by using the majority voting rule from these predictor models. The BDCT approach allows for DCT features to be effectively exploited for robust hand writer identification. The proposed system has first been assessed on the original version of hand written documents of various datasets and results have shown comparable performance with state-of-the-art systems. Next, blurry and noisy documents of two different datasets have been considered through intensive experiments where the system has been shown to perform significantly better than its competitors. To the best of our knowledge this is the first work that addresses the robustness aspect in automatic hand writer identification. This is particularly suitable in digital forensics as the documents acquired by the analyst may not be in ideal conditions

Efficiency measurement of Islamic and conventional banks in Saudi Arabia:an empirical and comparative analysis

Saudi Arabia, beside Malaysia and many other Muslim countries, is one of those countries where Islamic and conventional banking operate in parallel. Over the last decade, the country’s banking industry is growing at rapid pace that accounts for the largest share in GCC. The present study measures and compares the performance of Saudi conventional and Islamic banking industry and identifies the areas where the strategic measures are required to improve the banking performance. It applies non-parametric Data Envelopment Analysis (DEA) for the data from 2008-2016 of Saudi banking industry and provides comprehensive empirical results at individual bank vis-a-vis industry levels. The empirical results demonstrate a mix trend among the banks in achieving technical, pure technical and scale efficiency. It is observed that with the common pledge to expanding market share and performance, both conventional and Islamic banks have been successful in improving their levels of efficiency. At individual bank level, Al-Rajhi is the only bank that has achieved the highest score in terms of technical, pure technical and scale efficiency, while in the conventional banking group, both Saudi Hollandi and National Commercial banks are found on the top position. Despite the growth of incomes and deposits of entire banking industry in Saudi Arabia, this study particularly recommends for the Islamic banks to redirect their short term and long-term marketing strategies and to focus on improving their management skills at the branch level

An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks

Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist Learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as the “standard” PSO, Adaptive PSO, BAT, and Dolphin algorithm

Application of Ethnobotanical Indices on the Use of Traditional Medicines against Common Diseases

The present study was aimed at documenting the detailed ethnomedicinal knowledge of an unexplored area of Pakistan. Semistructured interviews were taken with 55 informants randomly chosen regarding detailed ethnomedicinal and sociocultural information. The study exposed 67 medicinal plant species used to prepare 110 recipes and the major modes of herbal formulation were decoction and powdering (20% each). The disease categories with the highest Fic values were gastrointestinal and dermatological (0.87 each). The study determined 3 plant species, i.e., Acacia modesta Wall., Caralluma tuberculata R.Br., and Withania somnifera (L.) Dunal with a FL of 100%. DMR results showed that Olea ferruginea (Sol.) Steud. ranked first, Morus alba L. ranked second, and Melia azedarach L. ranked third. Among the 55 informants, the male concentration was high (61%) and most of them were over 40 years old while a leading quantity of respondents (45%) was uneducated. There is a dire need to take necessary steps for the conservation of important medicinal plants by inhibiting overgrazing and providing alternate fuel resources. Young generations should be educated regarding the importance of ethnomedicinal knowledge and plants with high Fic and FL values should be further checked chemically and pharmacologically for future exploration of modern medicine

The Role of Extracellular Vesicles as Modulators of the Tumor Microenvironment, Metastasis and Drug Resistance in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers worldwide, with high morbidity and mortality rates. A number of factors including modulation of the tumor microenvironment, high metastatic capability, and resistance to treatment have been associated with CRC disease progression. Recent studies have documented that tumor-derived extracellular vesicles (EVs) play a significant role in intercellular communication in CRC via transfer of cargo lipids, proteins, DNA and RNAs to the recipient tumor cells. This transfer influences a number of immune-related pathways leading to activation/differentiation/expression of immune cells and modulation of the tumor microenvironment that plays a significant role in CRC progression, metastasis, and drug resistance. Furthermore, tumor-derived EVs are secreted in large amounts in biological fluids of CRC patients and as such the expression analysis of EV cargoes have been associated with prognosis or response to therapy and may be a source of therapeutic targets. This review aims to provide a comprehensive insight into the role of EVs in the modulation of the tumor microenvironment and its effects on CRC progression, metastasis, and drug resistance. On the other hand, the potential role of CRC derived EVs as a source of biomarkers of response and therapeutic targets will be discussed in detail to understand the dynamic role of EVs in CRC diagnosis, treatment, and management