The present study highlights the role of calcium, copper, iron, phosphorus, magnesium and zinc in the pathogenesis of breast cancer. The estimation of serum levels of these metal ions has a potential role in early detection and monitoring of breast cancer.

The present study focused on phytofabrication of selenium nanoparticles (SeNPs) from Carica papaya extract and exploration of their multi-biofunctional features. Total phenolics and flavonoids of C. papaya fruit extract were determined as 23.30 ± 1.88 mg gallic acid equivalents and 19.21 ± 0.44 mg quercetin equivalents per gram, respectively, which suggested that C. papaya fruit extract could be a competitive reducing and stabilizing agent during phytofabrication of nanoparticles. UV–Vis and FTIR spectroscopy showed the formation of SeNPs from sodium selenite, which could be related to the reducing and stabilizing activities of C. papaya fruit extract. The SeNPs were found to be stable with a Zeta potential of −32 mV. The average hydrodynamic size of SeNPs was found as 159 nm by dynamic light scattering. The SeNPs showed a broader XRD pattern with no sharp Bragg’s peaks and found to be amorphous. SEM showed that SeNPs were spherical in shape and EDX pattern showed that SeNPs were made up of Se (71.81%), C (11.41%), and O (14.88%). The HR-TEM picture showed that SeNPs were spherical in morphology and have a size range of 101–137 nm. The SeNPs exhibited potent antioxidant activity and their EC50 values (effective concentration required to inhibit 50% of radicals) were 45.65 ± 2.01 and 43.06 ± 3.80 μg/ml in DPPH and ABTS assays, respectively. The antimicrobial action of SeNPs was found as a broad spectrum and suppressed microbial pathogens in ascending order: fungi > Gram-positive bacteria > Gram-negative bacteria. The SeNPs have been demonstrated to reduce the growth and ochratoxin A (OTA) of mycotoxigenic Aspergillus ochraceus and Penicillium verrucosum at 40 μg/ml in broth culture, which is noteworthy. The SeNPs reduced cancer cell proliferation (RAW 264.7, Caco-2, MCF-7, and IMR-32) more preferentially than normal cells (Vero), found to be highly biocompatible. Lower doses of SeNPs (up to 50 μg/ml) were shown to be less toxic and did not cause death in Danio rerio (zebrafish) embryos, implying that lower doses of SeNPs could be beneficial for biological purposes. The present study concluded that phytofabricated SeNPs have multiple biofunctional properties, including antioxidant, antimicrobial, antimycotoxin, and anticancer activities, as well as high biocompatibility.

Abstract. The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

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Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.

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The human malarial parasite Plasmodium falciparum is one of the world's most devastating pathogen. Its capability to regulate its genes under various stages of its life cycle as well as under unfavourable environmental conditions has led to the development of vaccine resistant strains. Similarly, under drug pressure it develops mutations in the target genes. These mutations confer mid and high-level resistance to the antimalarial drugs. Increasing a resistance of malaria parasites to conventional antimalarial drugs is an important factor contributing to the persistence of the disease as a major health threat. This article reviews current knowledge of stage specific malarial targets, antimalarial drugs and the mutations that have led to the emergence of resistant strains.