Retinal imaging analysis based on vessel detection

With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.     

Textural,rheological and sensorial properties of mayonnaise fortified with Asian sea bass bio-calcium

Asian sea bass bio-calcium (ASBC) can be fortified in low calcium food product such as mayonnaise. To ensure the smoothness of fortified products, the particle size and fishy compounds of ASBC must be reduced. Ultrasonicated ASBC (U-ASBC) was applied for different times (5–15 min) in the presence of hexane. ASBC and U-ASBC had mean particle size (D₄₃) of 21.87 ± 19.54 and 7.83 ± 7.10 μm, respectively. U-ASBC had lowered volatile compounds. Mayonnaise fortified with ASBC had the augmented firmness, consistency and cohesiveness with increasing ASBC levels (0%–10%) (p < 0.05). ASBC raised lightness (L*) and total color difference (ΔE*), but decreased a* and b*-values of mayonnaise in a dose dependent manner (p < 0.05). Higher G′, G″, viscosity and shear stress value were observed in mayonnaise sample added with ASBC. However, lower acceptability was attained when mayonnaise was added with ASBC at level higher than 2.5% due to fishy odor and grittiness perceived by panelists. The lower droplet size with higher zeta potential of mayonnaise added with ASBC or U-ASBC was related with the lower creaming index. Mayonnaise added with 7.5% U-ASBC (M-UBC-7.5) had higher viscosity with lower creaming and thermal creaming index (p < 0.05) than control and that containing 2.5% ASBC (M-BC-2.5). Denser and smaller droplet sizes were observed for M-BC-2.5 and M-UBC-7.5 than control. Lowest moisture, fat and carbohydrate contents were attained for M-UBC-7.5 with the higher protein, ash and calcium content (p < 0.05). Fortification of mayonnaise with ASBC at 2.5% or U-ASBC at 7.5% increased calcium content by 54 or 174 times, respectively, without sensorial changes.     

Novel processing,testing and characterization of copper/carbon nanotube (Cu/CNT) yarn composite conductor

Sustainable electrified aircraft propulsion (EAP) is likely to lead to an increase in the electrical wiring contained within a single aircraft. Since the electrical resistance and mass of copper (Cu) conductors are associated with power losses, it is desirable to design high-conductivity lightweight conductor materials, thus reducing the mass of components like motor windings, low-voltage signal cables, and transmission cables for data and power to improve the overall energy efficiency. This paper describes a unique framework for manufacturing metalized carbon nanotube (CNT) composite conductors, measuring their electrical conductivity and strength, and modeling the overall conductivity and current sharing within such composites. Tensile testing was conducted on the processed composite conductor cables with the use of acoustic emission and electrical resistivity to determine stress-dependent-failure mechanisms while monitoring the electrical conductivity. The average of measured electrical conductivities of annealed Cu/CNT samples from batch 5 was greater than theoretical predictions by 9.8 percent and was also greater than the conductivity of pure annealed Cu by 4.8 percent and had comparable ultimate tensile strengths. Additionally, those Cu/CNT samples provide a 13.5% weight saving over current state of the art copper wires. Theories explaining improved intrinsic conductivity are discussed.     

Effects of repeated frying on physicochemical characteristics,oxidative stress,and free radical scavenging potential of canola oil and ghee

The repeated use of cooking oils and ghee for the deep frying of food materials may affect their nutritional quality. The present study evaluated the effect of repeated frying on the physicochemical characteristics and antiradical potential of canola oil and ghee. The oil and ghee were used for frying of fish and chicken for 2, 4, 6, 8, and 10 frying cycles followed by the analysis of physicochemical, oxidative stress, and antiradical parameters. Regression analysis of the data showed a frying cycle-dependent significant linear increase in saponification (R² = 0.9507–0.9748), peroxide and acid values (R² = 0.956–0.9915), and malondialdehyde (MDA) production (R² = 0.9058–0.9557) of canola oil and ghee subjected to fish and chicken frying but exponential increase in saponification value (R² = 0.9778) and MDA production (R² = 0.7407) of canola oil and ghee used for fish frying. The increase in the number of frying cycles linearly decreased the iodine value (R² = 0.9781–0.9924), and 1, 1-diphenyl-2-picrylhydrazyl, hydroxyl, and 2, 2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) radical scavenging potential (R² = 0.9089–0.9979) of canola oil and ghee. Repeated frying in cooking oil and ghee increases oxidative stress and decreases their physicochemical and antioxidant qualities. Canola oil was comparatively more oxidative resistant than canola ghee. The regression equations derived from regression analysis will guide researchers to conduct similar types of univariate studies.     

Additive manufacturing of novel 3D ceramic electrodes for high-power-density batteries

The development of batteries with high-specific power and energy densities will enable more efficient implementation of all-electric aircraft and urban air mobility technologies. Additive manufacturing technologies can be leveraged to produce engineered three-dimensional electrode structures with increased electrolyte/electrode interfacial area and high density, yielding increased power and energy densities. In this work, a novel engineered three-dimensional interdigitated LiFePO₄ cathode structure was designed and manufactured using direct-write additive manufacturing technology, which allowed for the deposition of highly solid-loaded inks with excellent dimensional accuracy. Ink rheology was adjusted to optimize material characteristics of the final electrodes, including the addition of carbon nanoparticles to increase the final electrode conductivity. Printed cathodes were then sintered and characterized. The sintered electrodes possessed greater structural stability and a surface area approximately 190% greater than similarly produced devices reported in the literature. Finally, the characterization of the manufactured electrodes showed uniform dispersion of conductive carbon nanopowder throughout the microstructure, which could increase the final conductivity of the electrodes.     

Ni3V2O8 Nanosheets Grafted on 3D Helical-shaped Carbon Nanocoils as A Binder-free Hierarchical Composite for Efficient Non-enzymatic Glucose Sensing

High fabrication cost, chemical instability, and complex immobilization of enzyme molecules are critical issues of enzyme-based glucose sensors. Designing state-of-the-art, binder-free, and non-enzymatic glucose sensing probes plays an imperative role to cope with the aforementioned issues. 3D carbonaceous nanomaterials coated with transition metal vanadates (TMVs) are a favorable biomimetic platform for glucose quantification. Peculiar hierarchical structure, enhanced conductivity, synergistic interaction, multiple oxidation states, and high catalytic activity would make such composite a potential contender for non-enzymatic glucose sensing. Herein, 3D helical-shaped carbon nanocoils (CNCs) are grown on nickel foam (NF) via chemical vapor deposition method to prepare a robust CNCs/NF scaffold. Then, a hydrothermal route is followed to grow interconnected free-standing Ni₃V₂O₈ nanosheets (NSs) on CNCs/NF scaffold. This novel and binder-free Ni₃V₂O₈ NSs/CNCs/NF hierarchical composite possesses superior electrochemical active area (ECSA) and exceptional electrochemical efficacy. Amperometric analysis exhibits extremely prompt detection time (0.1 s), elevated sensitivity (5214 µA mM⁻¹ cm⁻²), and low detection limit (0.04 µM). Developed sensor demonstrates appreciable recoveries (93.3 to 103.3%) regarding glucose concentration in human serum. The appealing analytical results show that deployment of a 3D helical-shaped hierarchical smart scaffold can be an effective strategy for developing efficient and advanced non-enzymatic glucose sensors.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.     

Unraveling the physicochemical and toxicological properties of food product isolated E341/E171

Nanostructured materials have been extensively exploited in the food sector for nutrient delivery, sensing, packaging, and food additives. It has exhibited size, shape, chemical nature, and surface area-dependent physicochemical and biological properties. Currently, nanoscale ingredients are identified in approved food additives. Still, food-grade nanostructured material's impact on biological systems is unexplored well. Thus, in this study, we have identified and analyzed the physicochemical properties of nanoscale ingredients in commercial food product isolate E341 and E171 combinations. Also, we have evaluated the E341/E171 combination cytotoxic potential on human mesenchymal stem cells. The food isolated E341/E171 exhibits spherical nature with a 60–200 nm diameter. The E341 and E171 combination exposure increased the cell viability loss to ~36% at a high dose in human mesenchymal stem cells (hMSCs). The E341/E171 treatment-induced cellular and nuclear damage at a moderate dose. The mitochondrial membrane potential loss and ROS generation were observed in E341/E171 treated cells. The E341/E171 alters the gene expression pattern in hMSCs dose-dependently. The GSR and POR gene expression is upregulated significantly in E341/E171 treatment. Our study results revealed that E341/E171 induced toxicity in hMSCs at high concentrations. Thus, E341/E171 potential impact on the biological system should be reexamined for food industrial implications.