Poly(2-hydroxyethylmethacrylate-co-2-folate ethylmethacrylate) and Folic acid/Poly(2-hydroxyethylmethacrylate) Solid Solution: Preparation and Drug Release Investigation

Poly(2-hydroxyethylmethacrylate-co-2-folatethylmethacrylate) was synthesized by free radical polymerization of 2-hydroxyethylmethacrylate with 2-folatethylmethacrylate and folic acid/poly(2-hydroxyethylmethacrylate) solid solutions was prepared by mixing folic acid with poly(2-hydroxyethylmethacrylate) using the solution casting method. The structure and the homogeneity distribution of folic acid in the polymer matrix are characterized by different methods. The diffusion behaviors of water and folic acid through poly(2-hydroxyethylmethacrylate-co-2-folatethylmethacrylate) and poly(2-hydroxyethylmethacrylate) matrixes were found obey to the Fick models. The in vitro cytotoxicity assessed by microculture tetrazolium test assay and the antioxidant activity of poly(2-hydroxyethylmethacrylate-co-2-folatethylmethacrylate) systems determined by 1,1-diphenyl-2-picryl-hydrazyl method revealed no significant toxicity of these systems and has excellent free radical scavenger property which can be as safe candidate in drug-carrier system. The solubility enhancement of folic acid in different pH media is also investigated and the results obtained reveal a maximum of 399–400?mg?L^?1. The release dynamic of folic acid from the poly(2-hydroxyethylmethacrylate-co-2-folatethylmethacrylate) containing 5?mol% of folic acid and that from folic acid/poly(2-hydroxyethylmethacrylate) system containing 10?mol% of folic acid have the best intestine/stomach ratio.     

Processing and Characterization of Oxygen Sensors Prepared From Freeze-Dried Calcia-Stabilized Zirconia Powders

Precursor powders of 15 and 22 mol% calcia-stabilized zirconia solid electrolytes were synthesized by a freeze-drying method and calcined at 900°C. Sensor elements were fabricated via uniaxial pressing of the calcined powders and subsequently sintered at 1650°C. X-ray diffraction and scanning electron microscopy techniques were used to analyze the phase composition and microstructure. The electrical response of the gas sensors to oxygen and the complex impedance in air were investigated at various operating temperatures. The correlation between electrical properties and sensor material chemical compositions as well as microstructure is discussed.     

Partially oxidized cobalt species in nitrogen-doped carbon nanotubes: Enhanced catalytic performance to water-splitting

It's still an ongoing research challenge to explore non-precious metal-based catalysts for substituting precious metal catalysts during full water electrocatalysis. Herein, we reported the partially oxidized cobalt species in nitrogen-doped carbon nanotubes hierarchical structures to produce dual-functionality towards oxygen/hydrogen evolution reactions. The in situ transformation of carbon nanotubes and well-exposed metal-oxide contributes to mass diffusion and greater electrolyte-accessible surface area. The as-synthesized catalyst displays low overpotentials of 287 mV and 171 mV for oxygen and hydrogen evolution reactions at 10 mA cm^?2 of current density with remarkable performance during long-term stability. Furthermore, when employed as cathode and anode, a respectable performance of 1.68 V demonstrated our catalyst as an efficient bifunctional material for conducting water-splitting operation.     

Threshold computation for fault detection in linear discrete‐time Markov jump systems

This paper proposes a threshold computation scheme for an observer‐based fault detection (FD) in linear discrete‐time Markovian jump systems. An observer‐based FD scheme typically consists of two stages known as residual generation and residual evaluation. Even information of faults is contained inside a residual signal, a decision of faults occurrence is consequently made by a residual evaluation stage, which consists of residual evaluation function and threshold setting. For this reason, a successful FD strongly depends on a threshold setting for a given residual evaluation function. In this paper, Kalman filter (KF) is used as a residual generator. Based on an accessibility of Markov chain to KF, two types of residual generations are considered, namely mode‐dependent and mode‐independent residual generation. After that threshold is computed in a residual evaluation stage such that a maximum fault detection rate is achieved, for a given false alarm rate. Without any knowledge of a probability density function of residual signal before and after fault occurrence, a threshold is computed by using an estimation of residual evaluation function variance in a fault‐free case. Finally, a detection performance is demonstrated by a numerical example. Copyright © 2013 John Wiley & Sons, Ltd.     

Canadian Bridge Design Code Provisions for Fiber-Reinforced Structures

This paper presents a synthesis of the design provisions of the Canadian Highway Bridge Design Code for fiber-reinforced structures. These include structures reinforced with fiber-reinforced polymer and fiber-reinforced concrete (FRC). The provisions apply to fully, or partially, prestressed concrete beams and slabs, non-prestressed concrete beams, slabs, and deck slabs, FRC deck slabs of slab-on-girder bridges, stressed wood decks, and barrier walls. Test methods to confirm the tensile strength of fiber-reinforced polymer and the postcracking strength of FRC are given.     

Aggregation and Phase Separation Phenomenon of Amitriptyline Hydrochloride Under the Influence of Pharmaceutical Excipients

The interaction between the amphiphilic drug amitriptyline hydrochloride (AMT) and the nonionic surfactants used in drug delivery has been investigated. Herein, we report the micellization behavior of AMT in presence of ethoxylated alkyl phenols in aqueous medium and the clouding phenomenon in the absence and presence of different nonionic surfactants in buffer solution. The values of critical micelle concentration (CMC) of AMT obtained using the conductivity method, decrease as nonionic surfactant concentration increases. With an increase in temperature, the CMC first increases and then decreases. At 303.15 K, the maximum CMC values were obtained with or without nonionic surfactant. The results obtained indicate attractive interactions (synergism) between the two mixing amphiphiles in solution. The experimentally obtained critical micelle concentration (CMC) values are always lower than ideal CMC values. Micellar mole fraction (X₁) values, calculated by different proposed models, show the contribution of nonionic surfactant concentration. At a fixed drug concentration (50 mmol kg^?1) and pH (=6.7) nonionic surfactants show continuous increase in cloud point (CP). Increase in drug concentration and pH, in the presence of fixed amounts of nonionic surfactant, increases and decreases the CP, respectively.     

Electrical detection of cancer biomarker using aptamers with nanogap break-junctions

Epidermal growth factor receptor (EGFR) is a cell surface protein overexpressed in cancerous cells. It is known to be the most common oncogene. EGFR concentration also increases in the serum of cancer patients. The detection of small changes in the concentration of EGFR can be critical for early diagnosis, resulting in better treatment and improved survival rate of cancer patients. This article reports an RNA aptamer based approach to selectively capture EGFR protein and an electrical scheme for its detection. Pairs of gold electrodes with nanometer separation were made through confluence of focused ion beam scratching and electromigration. The aptamer was hybridized to a single stranded DNA molecule, which in turn was immobilized on the SiO(2) surface between the gold nanoelectrodes. The selectivity of the aptamer was demonstrated by using control chips with mutated non-selective aptamer and with no aptamer. Surface functionalization was characterized by optical detection and two orders of magnitude increase in direct current (DC) was measured when selective capture of EGFR occurred. This represents an electronic biosensor for the detection of proteins of interest for medical applications.     

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Due to a tremendous increase in mobile traffic, mobile operators have started to restructure their networks to offload their traffic. New research directions will lead to fundamental changes in the design of future Fifth-generation (5G) cellular networks. For the formal reason, the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory. Any number that can be uniquely calculated by a graph is known as a graph invariant. During the last two decades, innumerable numerical graph invariants have been portrayed and used for correlation analysis. In any case, no efficient assessment has been embraced to choose, how much these invariants are connected with a network graph. This paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical networks. Since K-banhatti sombor invariants (KBSO) and Contrharmonic-quadratic invariants (CQIs) are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or networks. As the hexagonal networks are used in mobile base stations in layered, forms called honeycomb. The review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced forms. The deduced outcomes can be utilized for the modeling of mobile cellular networks, multiprocessors interconnections, microchips, chemical compound synthesis and memory interconnection networks. The results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network (MBSN) for the high load of traffic and minimal traffic also.     

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI.