Robust and High Accuracy Algorithm for Detection of Pupil Images

Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHMCEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Świrski, ExCuSe, Else, and labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.     

On the design and implementation of a dual fisheye camera-based surveillance vision system

Multimedia Tools and Applications - Image processing and computer vision have been a focus of researchers for decades in various application domains. This research is continuously rising with the...     

Green synthesis of titanium dioxide nanoparticles with volatile oil of Eugenia caryophyllata for enhanced antimicrobial activities

Different chemo‐physical methods are used to synthesise titanium oxide nanoparticles (TiO₂ NPs), which are often expensive, unfriendly to the environment, toxic, not biocompatible, with a small yield. To resolve these problems, the researchers use green procedures to synthesise TiO₂ ‐NPs by plant extracts of Capsicum annum L. and Allium cepa (onion) and characterise using atomic force microscopy, scanning electron microscopy, transmission electronic microscopy, X‐ray diffraction, ultraviolet (UV)–visible (Vis) spectra and Fourier transform infrared spectroscopy. The results indicate that most NPs synthesised by the first and second procedures of onion had an average diameter of 95.7 and 89.1 nm, while NPs synthesised by C. annum had an average diameter of 103.60 and 90.07 nm, respectively. In UV–Vis spectra, strong absorption was below 470 nm, and energy gap was 3.3 eV in each of the first procedure of A. cepa and the second procedure of C. annum compared with 270 nm, 6.3 eV for each of the second procedure of A. cepa and the first procedure of C. annum. The antimicrobial activities of NPs were evaluated and an attempt was made to enhance these activities by Eugenia caryophyllata plant''s oil in combination therapies. There were synergistic effects between NPs and plant''s oil.Inspec keywords: scanning electron microscopy, visible spectra, nanofabrication, titanium compounds, ultraviolet spectra, X‐ray diffraction, nanoparticles, atomic force microscopy, antibacterial activity, transmission electron microscopy, Fourier transform infrared spectra, nanomedicine, semiconductor materials, semiconductor growthOther keywords: cepa, green synthesis, titanium dioxide nanoparticles, volatile oil, eugenia caryophyllata, capsicum annum, atomic force microscopy, electron microscopy, transmission electronic microscopy, UV‐visible spectra, plant extracts, antimicrobial activities, chemophysical methods, Capsicum annum L., Allium cepa, scanning electron microscopy, X‐ray diffraction, Fourier transform infrared spectroscopy, onion, plant oil, TiO₂      

The xenometabolome and novel contaminant markers in fish exposed to a wastewater treatment works effluent

Organisms exposed to wastewater treatment works (WwTW) effluents accumulate complex mixtures of xenobiotics but there is a scarcity of information on the nature and impacts of these chemical mixtures. We applied metabolomics techniques as a novel approach to identify xenobiotics and their metabolites (the xenometabolome) that bioconcentrate in fish exposed to a WwTW effluent. Exposed juvenile rainbow trout (Oncorhynchus mykiss) accumulated surfactants, naphthols, chlorinated xylenols, and phenoxyphenols, chlorophenes, resin acids, mefenamic acid, oxybenzone, and steroidal alkaloids in the bile or plasma, and there were perturbations in the plasma concentrations of bile acids and lipids. Exposure of adult roach (Rutilus rutilus) to 50% or 100% concentrations of the same effluent resulted in dose-dependent increases in plasma concentrations of xenometabolites as well as cyprinol sulfate and taurocholic acid, lysophospholipids, and a decrease in sphingosine levels (a key component of cell membrane lipids). Our findings reveal the highly complex nature of xenobiotics accumulating in effluent-exposed fish, and the great potential of metabolomics for both identifying plasma marker (bio)chemicals for monitoring exposure to wastewater effluents, and for targeting studies on potential consequent impacts on fish health.     

Determination of heavy metal uptake in transgenic plants harbouring the rabbit CYP450 2E1 using X-ray fluorescence analysis

Transgenic Sesbania grandiflora (L.) pers (Fabaceae) and Arabidopsis thaliana (L.) (Brassicaceae) plants harbouring the rabbit cytochrome p450 2E1 enzyme were evaluated for their ability to accumulate heavy metals, potassium (K), calcium (Ca), manganese (Mn), zinc (Zn), copper (Cu), iron (Fe), lead (Pb) and bromine (Br), using X-ray Fluorescence analysis. When grown for 15?days on heavy metal-contaminated soils, transgenic cuttings of S. grandiflora and T3 A. thaliana plants recorded higher dry and fresh weight compared with their respective controls (A. thaliana and S. grandiflora plants transformed with an empty vector). Dry weight of transgenic S. grandiflora plants (0.321?g) was seven times higher than that of the wildtype (0.049?g), and the fresh weight (4.421?g) was about 4.6 times higher. Likewise, the dry weight of CYP450 2E1 A. thaliana (0.198?g) was more than eight times higher than that seen in the control (0.024?g). Moreover, Fe, Mn, K, and Ca concentrations in transgenic plants were significantly higher than those in their corresponding controls. For instance, concentrations of accumulated K (~3000 and 2000?mg/kg dry weight in S. grandiflora and A. thaliana, respectively) were significantly higher than those recorded in their corresponding controls (2500 and 1500?mg/kg, respectively). In the same vein, translocation of all studied metals from soils cultured with transgenic plants was higher than in those cultured with the control plants. In conclusion, the obtained results show the potential in using transgenic Sesbania and Arabidopsis plants harbouring the rabbit CYP450 2E1 for phytoremediation of mixed environmental contaminants.     

Propagation of a Thermoelastic Wave in a Half-Space of a Homogeneous Isotropic Material Subjected to the Effect of Rotation and Initial Stress

The propagation of thermoelastic waves in a homogeneous, isotropic elastic semi-infinite space is subjected to rotation and initial stress, which is at temperature T₀ - initially, and whose boundary surface is subjected to heat source and load moving with finite velocity. Temperature and stress distribution occurring due to heating or cooling and have been determined using certain boundary conditions. Numerical results have been given and illustrated graphically in each case considered. Comparison is made with the results predicted by the theory of thermoelasticity in the absence of rotation and initial stress. The results indicate that the effect of the rotation and initial stress is very pronounced.     

The Discovery of Potent SHP2 Inhibitors with Anti-Proliferative Activity in Breast Cancer Cell Lines

Despite available treatments, breast cancer is the leading cause of cancer-related death. Knowing that the tyrosine phosphatase SHP2 is a regulator in tumorigenesis, developing inhibitors of SHP2 in breast cells is crucial. Our study investigated the effects of new compounds, purchased from NSC, on the phosphatase activity of SHP2 and the modulation of breast cancer cell lines’ proliferation and viability. A combined ligand-based and structure-based virtual screening protocol was validated, then performed, against SHP2 active site. Top ranked compounds were tested via SHP2 enzymatic assay, followed by measuring IC50 values. Subsequently, hits were tested for their anti-breast cancer viability and proliferative activity. Our experiments identified three compounds 13030, 24198, and 57774 as SHP2 inhibitors, with IC50 values in micromolar levels and considerable selectivity over the analogous enzyme SHP1. Long MD simulations of 500 ns showed a very promising binding mode in the SHP2 catalytic pocket. Furthermore, these compounds significantly reduced MCF-7 breast cancer cells’ proliferation and viability. Interestingly, two of our hits can have acridine or phenoxazine cyclic system known to intercalate in ds DNA. Therefore, our novel approach led to the discovery of SHP2 inhibitors, which could act as a starting point in the future for clinically useful anticancer agents.     

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion of Intelligent Water Drops (IWDs) and AntMiner+. It was subjected to a four-stage sensitivity analysis to optimize its performance. For this purpose, 21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine. Interestingly, there were only limited differences in performance between IWD-Miner variants which is suggestive of its robustness. Finally, using the same 21 datasets, we compared the performance of the optimized IWD-Miner against two extant algorithms, AntMiner+ and J48. The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner, as confirmed by the Wilcoxon nonparametric statistical test. Results suggest that IWD-Miner is more efficient than AntMiner+ as measured by the average number of fitness evaluations to a solution (1,386,621.30 vs. 2,827,283.88 fitness evaluations, respectively). J48 exhibited higher accuracy on average than IWD-Miner (79.58 vs. 73.65, respectively) but produced larger models (32.82 leaves vs. 8.38 terms, respectively).