High pressure-based hurdle interventions for raw and processed meat: a clean-label prospective

Today's consumers demand clean-label healthy products to which no artificial preservatives have been added. Distribution of fresh, safe raw meat or meat products requires reduced numbers of bacteria on its surface when it leaves the processing plant under strict maintenance of low temperatures throughout the supply chain. Means of controlling or even improving the food integrity aim to decontaminate the carcasses or products during or at the end of the production line. In the past decades, high-pressure processing (HPP) has been investigated as an alternative non-thermal preservation technology to match all these demands without compromising safety. HPP treatments could efficiently inactivate the vegetative microorganisms (related to foodborne diseases), but not spores. However, the combination of several non-thermal and conventional preservation techniques under the so-called hurdle technology has been explored to enhance their efficiency.     

Variable thermal conductivity influence on micropolar nanofluid flow triggered by a stretchable disk with Arrhenius activation energy

The analysis of magnetized micro–nanoliquid flows generated by the movable disk is executed in this study. The disk is contained under the porous zone influence. The heat generation, heat sink, and temperature-dependent conductance analysis are reported through the energy equation. The activation energy in terms of a chemical reaction is incorporated through the mass equation. The flow model is normalized through the implementation of similarity transformations. The numerical algorithm Runge–Kutta–Fehlberg is used to solve the reduced system. Results are plotted graphically and in tabular format to investigate the velocity, thermal, and concentration fields. Numeric benchmarks of couple and shear stresses, thermal and concentration rates are also computed. The temperature is augmented against the incremented thermophoretic, variable conductivity, and Brownian movement parameters. The presence of variable conductivity parameter resulted in a weaker rate of heat transportation. The heat transportation rate is boosted with an incremented Prandtl number.     

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.     

Understanding the Diffusion-Dominated Properties of MOF-Derived Ni–Co–Se/C on CuO Scaffold Electrode using Experimental and First Principle Study

Batteries and supercapacitors continue to be one of the most researched topics in the class of energy storage devices. The continuous development of battery and supercapacitor cell components has shown promising development throughout the years—from slabs of pure metal to porous and tailored structures of metal-based active materials. In this direction, metal–organic frameworks (MOFs) serve great advantages in improving the properties and structure of the derived metal-based active materials. This research provides a novel electrode material, Ni–Co–Se/C@CuO, derived from Ni–Co-MOF integrated with pre-oxidized Cu mesh. The superior electrochemical performance of Ni–Co–Se/C@CuO over Ni–Co-MOF@CuO is evident through its higher specific capacity, lower resistivity, richer redox activity, and more favorable diffusion-dominated storage mechanism. When assembled as a hybrid supercapacitor (HSC), the hybrid device using rGO and Ni–Co–Se/C@CuO as electrodes exhibits a high energy density of 42 W h kg⁻¹ at a power density of 2 kW kg⁻¹, and maintains its capacity retention even after 20 000 cycles. The improved capacity performance is also evaluated using first-principle investigations, revealing that the unique and preserved heterostructure of Ni–Co–Se/C@CuO portrays enhanced metallic properties. Such evaluation of novel electrodes with superior properties may benefit next-generation electrodes for supercapacitor devices.     

Multimodal information fusion for android malware detection using lazy learning

Multimedia Tools and Applications - Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the...     

Synthesis of magnesium and Mg@Ni core-shell nanoparticles by microemulsion for hydrogen storage applications

Journal of Materials Science: Materials in Electronics - In the present work magnesium nanoparticles (MgNPs) and Mg@Ni core-shell nanoparticles has been synthesized first time by microemulsion...     

Graphene nanoplatelets/Cr2O3 nanocomposites as novel nanoantibiotics: Towards control of multiple drug resistant bacteria

Drug resistance resulting due to the abuse of antibiotics can possibly be fatal for human beings. It, therefore, is required to develop novel nanoantibiotics to fight with the bacterial infections. In this work, we report graphene nanoplatelets/Cr₂O₃ nanocomposites (GNPs/Cr₂O₃) as a potential nanomedicine. Antibacterial characteristics of GNPs/Cr₂O₃ nanocomposites have been investigated against Pseudomonas aeruginosa and Staphylococcus aureus. GCr₂O₃-II with the optimized GNPs’ content shows excellent antibacterial performance with 84.25% growth inhibition of S. aureus (Gram-positive bacteria) and 80.76% growth inhibition of -P. aeruginosa (Gram-negative bacteria). This can be attributed to the synergistic contribution of Cr₂O₃ nanoflakes and GNPs/Cr₂O₃ nanocomposites, towards bacterial membrane disruption, that may be caused by the sharp edges of GNPs and induction of the oxidative stress by Cr₂O₃ nanoflakes. Therefore, this study suggests that GNPs/Cr₂O₃ nanocomposites can be employed as an innovative nanoantibiotics for pathogen control.     

Programmed Editing of Rice (Oryza sativa L.) OsSPL16 Gene Using CRISPR/Cas9 Improves Grain Yield by Modulating the Expression of Pyruvate Enzymes and Cell Cycle Proteins

Rice (Oryza sativa L.) is one of the major crops in the world and significant increase in grain yield is constant demand for breeders to meet the needs of a rapidly growing population. The size of grains is one of major components determining rice yield and a vital trait for domestication and breeding. To increase the grain size in rice, OsSPL16/qGW8 was mutagenized through CRISPR/Cas9, and proteomic analysis was performed to reveal variations triggered by mutations. More specifically, mutants were generated with two separate guide RNAs targeting recognition sites on opposite strands and genomic insertions and deletions were characterized. Mutations followed Mendelian inheritance and homozygous and heterozygous mutants lacking any T-DNA and off-target effects were screened. The mutant lines showed a significant increase in grain yield without any change in other agronomic traits in T₀, T₁, and T₂ generations. Proteomic screening found a total of 44 differentially expressed proteins (DEPs), out of which 33 and 11 were up and downregulated, respectively. Most of the DEPs related to pyruvate kinase, pyruvate dehydrogenase, and cell division and proliferation were upregulated in the mutant plants. Pathway analysis revealed that DEPs were enriched in the biosynthesis of secondary metabolites, pyruvate metabolism, glycolysis/gluconeogenesis, carbon metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and citrate cycle. Gene Ontology (GO) analysis presented that most of the DEPs were involved in the pyruvate metabolic process and pyruvate dehydrogenase complex. Proteins related to pyruvate dehydrogenase E1 component subunit alpha-1 displayed higher interaction in the protein-protein interaction (PPI) network. Thus, the overall results revealed that CRISPR/Cas9-guided OsSPL16 mutations have the potential to boost the grain yield of rice. Additionally, global proteome analysis has broad applications for discovering molecular components and dynamic regulation underlying the targeted gene mutations.     

Characterization of CaO-CaF2-TiO2-SiO2 Based Welding Slags for Physicochemical and Thermophysical Properties

Silicon - The work is aimed at characterizing the properties of slag obtained from CaO-CaF2- TiO2-SiO2 based coatings developed for welding the offshore structures. Extreme vertices methodology was...