Novel multiplex PCR-RFLP assay discriminates bovine,porcine and fish gelatin substitution in Asian pharmaceuticals capsule shells

Gelatin is widely used in pharmaceuticals as a protective coating, such as soft and hard capsule shells. However, the animal source of gelatin is a sensitive issue because certain gelatins such as porcine and bovine gelatins are not welcome in Halal, Kosher and Hindus’ consumer goods. Recently, we have documented DNA barcoding and multiplex PCR platforms for discriminating porcine, bovine and fish gelatins in various fish and confectionary products; but those assays were not self-authenticating and also not tested in highly refined pharmaceutical products. To address this knowledge gap, here we report a self-authenticating multiplex PCR-restriction fragment length polymorphism (RFLP) assay to identify animal sources of various gelatin in pharmaceutical capsules. Three different restriction enzymes, BsaAI, Hpy188I and BcoDI were used to yield distinctive RFLP patterns for gelatin-based bovine (26, 94 bp), fish (97, 198 bp) and porcine (17, 70 bp) DNA in control experiments. The specificity was cross-tested against 16 non-target species and the optimised assay was used to screen gelatin sources in 30 halal-branded pharmaceuticals capsule shells. Bovine and porcine DNA was found in 27 and 3 of the 30 different capsules products. The assay was suitable for detecting 0.1 to 0.01 ng total DNA extracted from pure and mixed gelatins. The study might be useful to authenticate and monitor halal, kosher, vegetarian and Hindu compliant pharmaceuticals, foods and cosmetics.     

Fly Ash-based Geopolymer Lightweight Concrete Using Foaming Agent

In this paper, we report the results of our investigation on the possibility of producing foam concrete by using a geopolymer system. Class C fly ash was mixed with an alkaline activator solution (a mixture of sodium silicate and NaOH), and foam was added to the geopolymeric mixture to produce lightweight concrete. The NaOH solution was prepared by dilute NaOH pellets with distilled water. The reactives were mixed to produce a homogeneous mixture, which was placed into a 50 mm mold and cured at two different curing temperatures (60 °C and room temperature), for 24 hours. After the curing process, the strengths of the samples were tested on days 1, 7, and 28. The water absorption, porosity, chemical composition, microstructure, XRD and FTIR analyses were studied. The results showed that the sample which was cured at 60 °C (LW2) produced the maximum compressive strength for all tests, (11.03 MPa, 17.59 MPa, and 18.19 MPa) for days 1, 7, and 28, respectively. Also, the water absorption and porosity of LW2 were reduced by 6.78% and 1.22% after 28 days, respectively. The SEM showed that the LW2 sample had a denser matrix than LW1. This was because LW2 was heat cured, which caused the geopolymerization rate to increase, producing a denser matrix. However for LW1, microcracks were present on the surface, which reduced the compressive strength and increased water absorption and porosity.     

Role of Glycosylation in Vascular Calcification

Glycosylation is an important step in post-translational protein modification. Altered glycosylation results in an abnormality that causes diseases such as malignancy and cardiovascular diseases. Recent emerging evidence highlights the importance of glycosylation in vascular calcification. Two major types of glycosylation, N-glycosylation and O-glycosylation, are involved in vascular calcification. Other glycosylation mechanisms, which polymerize the glycosaminoglycan (GAG) chain onto protein, resulting in proteoglycan (PG), also have an impact on vascular calcification. This paper discusses the role of glycosylation in vascular calcification.     

Maternal N-Acetyl-Cysteine Prevents Neonatal Hypoxia-Induced Brain Injury in a Rat Model

Perinatal hypoxia is a major cause of infant brain damage, lifelong neurological disability, and infant mortality. N-Acetyl-Cysteine (NAC) is a powerful antioxidant that acts directly as a scavenger of free radicals. We hypothesized that maternal-antenatal and offspring-postnatal NAC can protect offspring brains from hypoxic brain damage.Sixty six newborn rats were randomized into four study groups. Group 1: Control (CON) received no hypoxic intervention. Group 2: Hypoxia (HYP)-received hypoxia protocol. Group 3: Hypoxia-NAC (HYP-NAC). received hypoxia protocol and treated with NAC following each hypoxia episode. Group 4: NAC Hypoxia (NAC-HYP) treated with NAC during pregnancy, pups subject to hypoxia protocol. Each group was evaluated for: neurological function (Righting reflex), serum proinflammatory IL-6 protein levels (ELISA), brain protein levels: NF-κB p65, neuronal nitric oxide synthase (nNOS), TNF-α, and IL-6 (Western blot) and neuronal apoptosis (histology evaluation with TUNEL stain). Hypoxia significantly increased pups brain protein levels compared to controls. NAC administration to dams or offspring demonstrated lower brain NF-κB p65, nNOS, TNF-α and IL-6 protein levels compared to hypoxia alone. Hypoxia significantly increased brain apoptosis as evidenced by higher grade of brain TUNEL reaction. NAC administration to dams or offspring significantly reduce this effect. Hypoxia induced acute sensorimotor dysfunction. NAC treatment to dams significantly attenuated hypoxia-induced acute sensorimotor dysfunction. Prophylactic NAC treatment of dams during pregnancy confers long-term protection to offspring with hypoxia associated brain injury, measured by several pathways of injury and correlated markers with pathology and behavior. This implies we may consider prophylactic NAC treatment for patients at risk for hypoxia during labor.     

Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application

This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.     

Multilayered Cylindrical Triboelectric Nanogenerator to Harvest Kinetic Energy of Tree Branches for Monitoring Environment Condition and Forest Fire

Forest fires present a great threat as they can rapidly grow and become large, resulting in tragic loss of life and property when occurring near occupied land. Here a self‐powered fire alarm system based on a novel multilayered cylindrical triboelectric nanogenerator (MC‐TENG) that can produce electrical power for the detection sensors by harvesting the kinetic energy of moving tree branches in a forest is presented. The major parameters for harvesting the kinetic energy using the proposed MC‐TENG are investigated, including the number of triboelectric layers, the frequency, the amplitude of external excitation, and the orientation between motion direction and device configuration. The fabricated MC‐TENG results in a peak power of 2.9 mW and a maximum average power of 1.2 mW at a low frequency of 1.25 Hz. The integrated self‐powered forest fire alarm system, consisting of fire sensors, a carbon‐based micro‐supercapacitor, and the MC‐TENG, is demonstrated to be able to report fire risk or hazard efficiently, accurately, and robustly. This study provides a new solution to reduce the forest fire risk through a portable and sustainable alarm system by effectively harvesting kinetic energies in natural environment with TENG technology.     

Feature selection using swarm-based relative reduct technique for fetal heart rate

Fetal heart rate helps in diagnosing the well-being and also the distress of fetal. Cardiotocograph (CTG) monitors the fetal heart activity to estimate the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of fetal heart rate signal, uterine contraction activity and fetal movements. Generally CTG comprises more number of features. Feature selection also called as attribute selection is a process of selecting a subset of highly relevant features which is responsible for future analysis. In general, medical datasets require more number of features to predict an activity. This paper aims at identifying the relevant and ignores the redundant features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using unsupervised particle swarm optimization (PSO)-based relative reduct (US-PSO-RR) and compared with unsupervised relative reduct and principal component analysis. The proposed method is then tested by applying various classification algorithms such as single decision tree, multilayer perceptron neural network, probabilistic neural network and random forest for maximum number of classes and clustering accuracies like root mean square error, mean absolute error, Davies–Bouldin index and Xie–Beni index for minimum number of classes. Empirical results show that the US-PSO-RR feature selection technique outperforms the existing methods by producing sensitivity of 72.72 %, specificity of 97.66 %, F-measure of 74.19 % which is remarkable, and clustering results demonstrate error rate produced by US-PSO-RR is less as well.     

Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection

Foreground segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated video surveillance, human-machine interface, and optical motion capture. Many models have been introduced to deal with the problems of modeling the background and detecting the moving objects in the scene. One of the successful solutions to these problems is the use of the well-known adaptive Gaussian mixture model. However, this method suffers from some drawbacks. Modeling the background using the Gaussian mixture implies the assumption that the background and foreground distributions are Gaussians which is not always the case for most environments. In addition, it is unable to distinguish between moving shadows and moving objects. In this paper, we try to overcome these problem using a mixture of asymmetric Gaussians to enhance the robustness and flexibility of mixture modeling, and a shadow detection scheme to remove unwanted shadows from the scene. Furthermore, we apply this method to real image sequences of both indoor and outdoor scenes. The results of comparing our method to different state of the art background subtraction methods show the efficiency of our model for real-time segmentation.