The Changes in the Lipid Composition of Mung Bean Seeds as Affected by Processing Methods

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This study was conducted to assess in detail the possible effects of some technological processes such as soaking, germination, cooking, soaking + cooking, and germination + cooking on the lipid composition of mung bean seeds of Giza 1 variety. TLC analysis of mung bean lipids showed that the phospholipids and triglycerides recorded the highest percentage among lipid fractions (32.26 and 30.10%), while the 1,3 diglycerides constituted the least percentage (2.80%) in mung bean seeds. The soaking, germination and cooking processes caused a decrease in the phospholipids, triglycerides and hydrocarbons accompanied with an increase in monoglycerides, 1,2-(2,3)-diglycerides, sterols and free fatty acids. Eleven fractions were separated from phospholipids class of the studied samples; seven of these fractions were identified. The major component of phospholipids was phosphatidyl choline, amounting to 21.30, 17.84, 16.21, 13.87, 13.20 and 11.47% of the total phospholipids in raw, soaked, germinated, raw-cooked, soaked-cooked and germinated-cooked mung bean seeds, respectively. Gas liquid chromatography of the total lipids of mung bean seeds showed that the unsaturated fatty acids represented 69.58, 64.35, 63.3, 63.16, 61.84 and 61.12%, while the levels of saturated fatty acids were low being 30.37, 34.05, 35.66, 34.64, 37.93 and 38.75% of the total fatty acids in raw, soaked, germinated, raw-cooked, soaked-cooked and germinated-cooked, respectively. The total essential fatty acids (linoleic and linolenic) represented the highest proportion of fatty acids (50.10% of the total fatty acids)

Assessment of circulating MCP-1 level and 2518A>G gene polymorphism in systemic lupus erythematosus

Lupus nephritis (LN) is a major contributor to morbidity and mortality in patients with Systemic Lupus Erythematosus (SLE). This study aims to investigate the possible role of a functional polymorphism in the regulatory region of the monocyte chemo-attractant protein-1 (MCP-1) gene and MCP-1 blood level in the diagnosis of LN and in correlating the MCP-1 blood levels with disease activity. The study included 56 SLE patients and 56 controls. All the SLE patients suffered from LN. An analysis of MCP-1 gene polymorphism by polymerase chain reaction was performed followed by restriction fragment length polymorphism (PCR-RFLP) analysis and MCP-1 blood level was determined using the ELISA technique. Calculation of Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) was performed. Serologic tests included the determination of antinuclear antibody (ANA) and anti-double-stranded (ds) DNA antibodies, Complement C3 and C4 levels. A significant increase in the frequency of genotype A/G and a decrease in the frequency of genotype A/A were found among patients with active LN compared to inactive LN. There was a statistically significant difference in the blood level of MCP-1 between LN patients and controls. Also, MCP-1 blood levels were significantly higher in active LN patients than inactive LN. A significant positive linear correlation was detected between MCP-1 blood level and SLEDAI, creatinine, and 24 hours protein in LN patients. These results suggest that an A/G genotype together with the measurement of the blood level of MCP-1 can be a useful tool for detection and follow up of active LN

Multi-Agent based Intelligent Decision Support Systems for Cancer Classification

There is evidence that early detection of cancer diseases can improve the treatment and increase the survival rate of patients. This paper presents an efficient CAD system for cancer diseases diagnosis by gene expression profiles of DNA microarray datasets. The proposed CAD system combines Intelligent Decision Support System (IDSS) and Multi-Agent (MA) system. The IDSS represents the backbone of the entire CAD system. It consists of two main phases; feature selection/reduction phase and a classification phase. In the feature selection/reduction phase, eight diverse methods are developed. While, in the classification phase, three evolutionary machine learning algorithms are employed. On the other hand, the MA system manages the entire operation of the CAD system. It first initializes several IDSSs (exactly 24 IDSSs) with the aid of mobile agents and then directs the generated IDSSs to run concurrently on the input dataset. Finally, a master agent selects the best classification, as the final report, based on the best classification accuracy returned from the 24 IDSSs The proposed CAD system is implemented in JAVA, and evaluated by using three microarray datasets including; Leukemia, Colon tumor, and Lung cancer. The system is able to classify different types of cancer diseases accurately in a very short time. This is because the MA system invokes 24 different IDSS to classify the diseases concurrently in parallel processing manner before taking the decision of the best classification result

An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi

The metabolomic analysis of five Mentha species: cytotoxicity, anti-Helicobacter assessment, and the development of polymeric micelles for enhancing the anti-Helicobacter activity

Mentha species are medicinally used worldwide and remain attractive for research due to the diversity of their phytoconstituents and large therapeutic indices for various ailments. This study used the metabolomics examination of five Mentha species (M. suaveolens, M. sylvestris, M. piperita, M. longifolia, and M. viridis) to justify their cytotoxicity and their anti-Helicobacter effects. The activities of species were correlated with their phytochemical profiles by orthogonal partial least square discriminant analysis (OPLS-DA). Tentatively characterized phytoconstituents using liquid chromatography high-resolution electrospray ionization mass spectrometry (LC-HR-ESI-MS) included 49 compounds: 14 flavonoids, 10 caffeic acid esters, 7 phenolic acids, and other constituents. M. piperita showed the highest cytotoxicity to HepG2 (human hepatoma), MCF-7 (human breast adenocarcinoma), and CACO2 (human colon adenocarcinoma) cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. OPLS-DA and dereplication studies predicted that the cytotoxic activity was related to benzyl glucopyranoside-sulfate, a lignin glycoside. Furthermore, M. viridis was effective in suppressing the growth of Helicobacter pylori at a concentration of 50 mg mL−1. OPLS-DA predicted that this activity was related to a dihydroxytrimethoxyflavone. M. viridis extract was formulated with Pluronic® F127 to develop polymeric micelles as a nanocarrier that enhanced the anti-Helicobacter activity of the extract and provided minimum inhibitory concentrations and minimum bactericidal concentrations of 6.5 and 50 mg mL−1, respectively. This activity was also correlated to tentatively identified constituents, including rosmarinic acid, catechins, carvone, and piperitone oxide

Hepatitis C virus infection and global kidney health: the consensus proceedings of the International Federation of Kidney Foundations

Hepatitis C virus (HCV) infection is an important cause of major morbidities including chronic liver disease, liver cancer, acute kidney injury and chronic kidney disease (CKD). Among patients with kidney disease who have HCV infection, the clinical outcomes are worse. The prevalence of HCV infection is exceptionally high among dialysis and kidney transplant patients throughout the globe. It is estimated that 5% to 25% or more of dialysis-dependent patients are affected. Almost half of all deaths in CKD patients, including HCV-infected patients, are due to cardiovascular disease, and HCV-infected patients have higher mortality. Given the importance and impact of the HCV epidemic on global kidney health, and the status of Egypt as the nation with the highest prevalence of HCV infection in the world along with its initiatives to eradicate HCV, the International Federation of Kidney Foundations convened a consensus conference in Cairo in December 2017. This article reflects the opinions and recommendations of the contributing experts and reiterates that, with the current availability of highly effective and well tolerated pharmacotherapy, CKD patients should be given priority for the treatment of HCV, as an important step towards the World Health Organization’s goal of eliminating viral hepatitis as a public health problem by 2030

SVD Audio Watermarking: A Tool to Enhance the Security of Image Transmission over ZigBee Networks, Journal of Telecommunications and Information Technology, 2011, nr 4

The security is important issue in wireless networks. This paper discusses audio watermarking as a tool to improve the security of image communication over the IEEE 802.15.4 ZigBee network. The adopted watermarking method implements the Singular-Value Decomposition (SVD) mathematical technique. This method is based on embedding a chaotic encrypted image in the Singular Values (SVs) of the audio signal after transforming it into a 2-D format. The objective of chaotic encryption is to enhance the level of security and resist different attacks. Experimental results show that the SVD audio watermarking method maintains the high quality of the audio signals and that the watermark extraction and decryption are possible even in the presence of attacks over the ZigBee network

An Efficient Chaotic Interleaver for Image Transmission over IEEE 802.15.4 Zigbee Network, Journal of Telecommunications and Information Technology, 2011, nr 2

This paper studies a vital issue in wireless communications, which is the transmission of images over wireless networks. IEEE ZigBee 802.15.4 is a short-range communication standard that could be used for small distance multimedia transmissions. In fact, the ZigBee network is a wireless personal area network (WPAN), which needs a strong interleaving mechanism for protection against error bursts. This paper presents a novel chaotic interleaving scheme for this purpose. This scheme depends on the chaotic Baker map. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes

Oligogenic heterozygosity in individuals with high-functioning autism spectrum disorders

Autism spectrum disorders (ASDs) are a heterogeneous group of neuro-developmental disorders. While significant progress has been made in the identification of genes and copy number variants associated with syndromic autism, little is known to date about the etiology of idiopathic non-syndromic autism. Sanger sequencing of 21 known autism susceptibility genes in 339 individuals with high-functioning, idiopathic ASD revealed de novo mutations in at least one of these genes in 6 of 339 probands (1.8%). Additionally, multiple events of oligogenic heterozygosity were seen, affecting 23 of 339 probands (6.8%). Screening of a control population for novel coding variants in CACNA1C, CDKL5, HOXA1, SHANK3, TSC1, TSC2 and UBE3A by the same sequencing technology revealed that controls were carriers of oligogenic heterozygous events at significantly (P < 0.01) lower rate, suggesting oligogenic heterozygosity as a new potential mechanism in the pathogenesis of ASDs