Green facile synthesis of low-toxic superparamagnetic iron oxide nanoparticles (SPIONs) and their cytotoxicity effects toward Neuro2A and HUVEC cell lines

Superparamagnetic iron oxide (Fe₃O₄) nanoparticles (SPIONs) were synthesized by co-precipitation using polyvinyl alcohol (PVA) as a capping agent under alkaline condition. The produced X-ray diffraction (XRD) pattern evidenced the presence of peaks corresponding to the inverse spinel structure of the prepared SPIONs. Debye-Scherrer and field emission scanning microscopy (FESEM) showed the prepared SPIONs to be well-defined with about <?50?nm size. Likewise, the superparamagnetic properties of the SPIONs measured by Vibrating Sample Magnetometer (VSM) showed high saturation magnetization (~ 65.36?emu/g). The in vitro cytotoxicity studies on Neuro2A and HUVEC cells have mentioned low toxic and non-toxic SPIONs, respectively in a range of concentrations (1.17–150?μg/ml), thus, we reckon that the synthesized SPIONs will have persistent utilization in different fields of medical applications.     

Data-Fusion Approaches and Applications for Construction Engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.     

Bioprinting Via a Dual-Gel Bioink Based on Poly(Vinyl Alcohol) and Solubilized Extracellular Matrix towards Cartilage Engineering

Various hydrogel systems have been developed as biomaterial inks for bioprinting, including natural and synthetic polymers. However, the available biomaterial inks, which allow printability, cell viability, and user-defined customization, remains limited. Incorporation of biological extracellular matrix materials into tunable synthetic polymers can merge the benefits of both systems towards versatile materials for biofabrication. The aim of this study was to develop novel, cell compatible dual-component biomaterial inks and bioinks based on poly(vinyl alcohol) (PVA) and solubilized decellularized cartilage matrix (SDCM) hydrogels that can be utilized for cartilage bioprinting. In a first approach, PVA was modified with amine groups (PVA-A), and mixed with SDCM. The printability of the PVA-A/SDCM formulations cross-linked by genipin was evaluated. On the second approach, the PVA was functionalized with cis-5-norbornene-endo-2,3-dicarboxylic anhydride (PVA-Nb) to allow an ultrafast light-curing thiol-ene cross-linking. Comprehensive experiments were conducted to evaluate the influence of the SDCM ratio in mechanical properties, water uptake, swelling, cell viability, and printability of the PVA-based formulations. The studies performed with the PVA-A/SDCM formulations cross-linked by genipin showed printability, but poor shape retention due to slow cross-linking kinetics. On the other hand, the PVA-Nb/SDCM showed good printability. The results showed that incorporation of SDCM into PVA-Nb reduces the compression modulus, enhance cell viability, and bioprintability and modulate the swelling ratio of the resulted hydrogels. Results indicated that PVA-Nb hydrogels containing SDCM could be considered as versatile bioinks for cartilage bioprinting.     

Comparison of functional properties and SDS-PAGE patterns between fish protein isolate and surimi produced from silver carp

The purpose of this study was to investigate and compare the physicochemical properties of fish protein isolate (FPI) produced from silver carp (Hypophthalmichthys molitrix), which is a relatively cheap fish with low consumption in Fars (a state of Iran). In this research, proteins were isolated using pH shifts method. Results showed that in terms of production efficiency, protein recovery, lipid reduction and functional properties such as water-holding capacity, emulsifying capacity, foaming capacity and water absorption, FPI was better than conventional surimi. The treated samples at pH?=?2.5 represented better functional properties relative to other samples. The electrophoretic patterns demonstrated that acidic pH led to vanish high molecular weight protein bands because these proteins were converted to low molecular weight proteins, whereas basic pH caused the formation of high molecular weight proteins relative to myosin heavy chain. It seems that in basic pH, the proteins were linked together after denaturation and then aggregated. Overall, the results showed that physicochemical qualitative properties of FPI were better than those of conventional surimi.     

Virtual learning environment to predict withdrawal by leveraging deep learning

The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision-making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision-making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decision-making. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short-term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall.     

The Effect of Electromagnetic Stirrer on the Size and Morphology of Intermetallic Particles in Al-Si Metal Matrix Composite

Silicon - The electromagnetic stirrer method was developed to overcome the problems caused by the presence of a mechanical stirrer in the slurry; this method is nowadays considered to be the most...