Reduced Photoinhibition under Low Irradiance Enhanced Kacip Fatimah (Labisia pumila Benth) Secondary Metabolites, Phenyl Alanine Lyase and Antioxidant Activity

A randomized complete block design experiment was designed to characterize the relationship between production of total flavonoids and phenolics, anthocyanin, photosynthesis, maximum efficiency of photosystem II (Fv/Fm), electron transfer rate (Fm/Fo), phenyl alanine lyase activity (PAL) and antioxidant (DPPH) in Labisia pumila var. alata, under four levels of irradiance (225, 500, 625 and 900 μmol/m(2)/s) for 16 weeks. As irradiance levels increased from 225 to 900 μmol/m(2)/s, the production of plant secondary metabolites (total flavonoids, phenolics and antocyanin) was found to decrease steadily. Production of total flavonoids and phenolics reached their peaks under 225 followed by 500, 625 and 900 μmol/m(2)/s irradiances. Significant positive correlation of production of total phenolics, flavonoids and antocyanin content with Fv/Fm, Fm/Fo and photosynthesis indicated up-regulation of carbon-based secondary metabolites (CBSM) under reduced photoinhibition on the under low light levels condition. At the lowest irradiance levels, Labisia pumila extracts also exhibited a significantly higher antioxidant activity (DPPH) than under high irradiance. The improved antioxidative activity under low light levels might be due to high availability of total flavonoids, phenolics and anthocyanin content in the plant extract. It was also found that an increase in the production of CBSM was due to high PAL activity under low light, probably signifying more availability of phenylalanine (Phe) under this condition.     

Effects of varying degrees of doneness on the formation of Heterocyclic Aromatic Amines in chicken and beef satay

The study was carried out to determine the effect of cooking method on Heterocyclic Aromatic Amines (HAs) concentration in grilled chicken and beef (satay). Six common HAs were investigated: 2-amino-3-methylimidazo[4,5-f]quinolone (IQ), 2amino 3,4dimethylimidazo [4,5f]quinoline (MeIQ), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), 2-amino-3,4,8 trimethylimidazo[4,5-f]quinoxaline (4,8-DiMeIQx), 2-amino-3,7,8trimethylimidazo [4,5-f]quinoxaline (7,8-DiMeIQx), and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP). Chicken and beef satay samples were grilled to medium and well done level of doneness. Charcoal grilled (treatment A), microwave pre-treatment prior to grilling (treatment B), and microwave-deep fried (treatment C) were applied to beef and chicken satay samples. The satay samples which were microwaved prior to grilling (B) showed significantly (p < 0.05) lower HAs concentration as compared to those charcoal grilled (A). Both medium and well done cooked beef and chicken satay samples that were microwaved and deep fried (C) as an alternative method to grilling were proven to produce significantly lesser HAs as compared to charcoal-grilled (A) and microwaved prior to grilling (B).     

Effect of Different Light Intensities on Total Phenolics and Flavonoids Synthesis and Anti-oxidant Activities in Young Ginger Varieties (Zingiber officinale Roscoe)

Nowadays, phytochemicals and antioxidants in plants are raising interest in consumers for their roles in the maintenance of human health. Phenolics and flavonoids are known for their health-promoting properties due to protective effects against cardiovascular disease, cancers and other disease. Ginger (Zingiber officinale) is one of the traditional folk medicinal plants and it is widely used in cooking in Malaysia. In this study, four levels of glasshouse light intensities (310, 460, 630 and 790 μmol m(-2)s(-1)) were used in order to consider the effect of light intensity on the production, accumulation and partitioning of total phenolics (TP), total flavonoids (TF) and antioxidant activities in two varieties of Malaysian young ginger (Zingiber officinale). TF biosynthesis was highest in the Halia Bara variety under 310 μmol m(-2)s(-1) and TP was high in this variety under a light intensity of 790 μmol m(-2)s(-1). The highest amount of these components accumulated in the leaves and after that in the rhizomes. Also, antioxidant activities determined by the 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) assay in both of varieties, increased significantly (p ≤ 0.01) with increasing TF concentration, and high antioxidant activity was observed in the leaves of Halia Bara grown under 310 μmol m(-2)s(-1). The ferric reducing (FRAP) activity of the rhizomes was higher than that of the leaves in 310 μmol m(-2)s(-1) of sun light. This study indicates the ability of different light intensities to enhance the medicinal components and antioxidant activities of the leaves and young rhizomes of Zingiber officinale varieties. Additionally, this study also validated their medicinal potential based on TF and TP contents.     

Rheological wall slip velocity prediction model based on artificial neural network

Wall slip is a phenomenon in which particles migrate from solid boundaries, leaving a thin liquid rich layer adjacent to a wall, which can affect the measurement of the rheological properties. Currently, analyses of wall slip are normally carried out through experimental study using a rheometer. These traditional methods are generally time consuming, as several experiment sets are usually required. The aim of this research is to develop an alternative, more efficient approach, by formulating a mathematical model able to predict the wall slip velocity with an acceptable level of accuracy. Specifically, this study investigates a Multi-Layer Perceptron Neural Network (MLP-NN) as an advanced method to predict wall slip velocity. It develops and tests several MLP-NN architectures that accommodate a range of fixed input variables including shear stress, concentration, temperature and particle sizes, with estimated wall slip velocity as the output variable. Using this method, users can perform wall slip velocity analyses by simply plugging different patterns of the proposed input variables into the recommended architecture. Our tests show an MLP-NN model with one hidden layer consisting of nine hidden neurons to be the best architecture for such purposes, producing a strong overall performance with an R² value of 0.9994 and maximum error of 28%. This research study is innovative in its use of artificial intelligence to predict wall slip velocity in rheological applications.     

Design and construct optimum dosimeter to detect airborne radon and thoron gas: Experimental study

Aim of this work is to design and select optimum dimension of a radon and thoron dosimeter within the measure optimum value of the calibration factor, using CR-39 Nuclear Track Detectors (NTDs). The results show that the best dimension to detect and measure real values of airborne radon and thoron concentrations is 6 cm and 7 cm for diameter and height, respectively. Calibration factors (K) for radon and thoron at this dimension were 2.68 ± 0.03 cm and 0.83 ± 0.01 cm, respectively, and these factors relatively depend on the detector efficiency. Therefore, the efficiency of CR-39NTDs to register alpha particles and their effects on the calibration factor estimated. It is found that the calibration factor increased exponentially with detector efficiency. Moreover, detector efficiency was equal to 80.3 ± 1.23% at the optimum dosimeter.     

Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals

This paper considers the prediction of noisy time series data, specifically, the prediction of financial signals. A novel Dynamic Ridge Polynomial Neural Network (DRPNN) for financial time series prediction is presented which combines the properties of both higher order and recurrent neural network. In an attempt to overcome the stability and convergence problems in the proposed DRPNN, the stability convergence of DRPNN is derived to ensure that the network posses a unique equilibrium state. In order to provide a more accurate comparative evaluation in terms of profit earning, empirical testing used in this work encompass not only on the more traditional criteria of NMSE, which concerned at how good the forecasts fit their target, but also on financial metrics where the objective is to use the networks predictions to generate profit. Extensive simulations for the prediction of one and five steps ahead of stationary and non-stationary time series were performed. The resulting forecast made by DRPNN shows substantial profits on financial historical signals when compared to various neural networks; the Pi-Sigma Neural Network, the Functional Link Neural Network, the feedforward Ridge Polynomial Neural Network, and the Multilayer Perceptron. Simulation results indicate that DRPNN in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and rapid convergence over other network models.     

Toward collective organizational values: a case study in UK construction

Despite privatization, many government‐linked companies (GLCs) still continue to operate in Malaysia. Many have objectives that include the redressing of ethnic economic imbalance. Government‐linked construction companies (GLCCs) were created within these larger public groups. A study was conducted to explore whether the GLCCs are still valid in Malaysia's present construction industry scenario, and if not, what actions should be taken in respect of them. Four aspects (i.e. social obligation, competitiveness, efficiency and income generation) were looked into. To answer the research questions, the viewpoints of ‘A’ Class bumiputera contractors were solicited which were then investigated on four GLCCs, three belonging to state economic development corporations (SEDCs) and one to a land development agency (LDA). The exploratory study found that three case study GLCCs have limited public value. This paper concludes by recommending changes that should be made to them. There is much that policy makers in developing countries with GLCCs can learn from the Malaysian experience. At the very least, it underscores the need to assess their raison d'être periodically. This study also hopes to encourage other scholars to look into a much under‐researched area—that of a particular set of actors commonly created to promote local construction industry development.     

MicroRNA Expression Profile of Neural Progenitor-Like Cells Derived from Rat Bone Marrow Mesenchymal Stem Cells under the Influence of IGF-1, bFGF and EGF

Insulin-like growth factor 1 (IGF-1) enhances cellular proliferation and reduces apoptosis during the early differentiation of bone marrow derived mesenchymal stem cells (BMSCs) into neural progenitor-like cells (NPCs) in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). BMSCs were differentiated in three groups of growth factors: (A) EGF + bFGF, (B) EGF + bFGF + IGF-1, and (C) without growth factor. To unravel the molecular mechanisms of the NPCs derivation, microarray analysis using GeneChip^® miRNA arrays was performed. The profiles were compared among the groups. Annotated microRNA fingerprints ({"type":"entrez-geo","attrs":{"text":"GSE60060","term_id":"60060"}}GSE60060) delineated 46 microRNAs temporally up-regulated or down-regulated compared to group C. The expressions of selected microRNAs were validated by real-time PCR. Among the 46 microRNAs, 30 were consistently expressed for minimum of two consecutive time intervals. In Group B, only miR-496 was up-regulated and 12 microRNAs, including the let-7 family, miR-1224, miR-125a-3p, miR-214, miR-22, miR-320, miR-708, and miR-93, were down-regulated. Bioinformatics analysis reveals that some of these microRNAs (miR-22, miR-214, miR-125a-3p, miR-320 and let-7 family) are associated with reduction of apoptosis. Here, we summarize the roles of key microRNAs associated with IGF-1 in the differentiation of BMSCs into NPCs. These findings may provide clues to further our understanding of the mechanisms and roles of microRNAs as key regulators of BMSC-derived NPC maintenance.