An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Green-Inspired Fabrication of Silver Nanoparticles and Examine its Potential In-Vitro Cytotoxic and Antibacterial Activities

Journal of Inorganic and Organometallic Polymers and Materials - The present work demonstrates the non-hazardous and environmentally benevolent green fabrication of silver nanoparticles using...     

Thermoplastic elastomer blend based on EMA and NBR; optimization of process parameters

EMA–NBR has been explored to be a potential thermoplastic elastomer blend having good thermal stability as well as oil resistance property. The present investigation reports the optimization of process parameters for the novel polymer blends based on poly(ethylene-co-methyl acrylate) (EMA) and poly(acrylonitrile-co-butadiene) rubber (NBR) with criteria based on the statistical design of experiment (Taguchi L9 orthogonal array). In this case, the polymer blends were prepared by changing the polymer blending conditions such as mixing temperature, mixing time and rotor speed as per Taguchi's L9 orthogonal array. Optimization of the process parameters was carried out based on the physicomechanical properties such as tensile strength, elongation at break, hardness, and tensile impact strength of the resulting EMA/NBR blend. Each processing parameter has been optimized from the experimental data, which are converted into signal-to-noise ratio. The standard statistical technique of analysis of variance result was used to evaluate the proportional role of the different control variables. It has been found that the mixing temperature play very significant role trailed by rotor speed and mixing time in controlling droplet matrix morphology of the EMA/NBR blends. Predominantly, these factors affect the size of the NBR domain and its distribution in the EMA matrix, which in turn have a notable contribution to the physicomechanical properties of the blends. By the optimization of processing conditions, the NBR matrix domain size greatly decreases, leading to significant improvement in physicomechanical properties of the EMA/NBR blends. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48900.     

Metal oxide-assisted chemical synthesis of poly(α-naphthylamine) and characterizations

α-Naphthylamine (NA) was chemically polymerized using peroxy disulfate (PDS) as a chemical initiator under inert atmosphere at 45 °C with vigorous stirring under various experimental conditions such as different concentrations of NA, PDS and different % weight of nanosized metal oxides like Sb₂O₃, CrO₃, V₂O₅, Al₂O₃, As₂O₃ and ammonium heptamolybdate (AHM). Chemical kinetics inferred that rate of polymerization (R _p) is 1.75 order of reaction with [NA] and 1.0 with [PDS]. FTIR spectroscopy confirmed that both benzenoid and quinonoid rings built up the structure of poly(α-naphthylamine) (PNA). Added metal oxides influenced the R _p, char forming capability of PNA and conductivity too through their surface catalytic effect. AFM confirmed the presence of nanosized materials in the polymer–nanocomposite structure. HRTEM was used to confirm the nanosize of metal oxides and the intercalation of PNA chains into the basal spacing of metal oxides.     

Ionic conductivity and Raman investigations on the phase transformations of Na4P2O7

The ionic conductivity and thermo-Raman spectra of anhydrous sodium pyrophosphate Na₄P₂O₇ were measured dynamically in the temperature range from 25 to 600 °C with a heating rate of 2 °C min⁻¹ to understand the structural evolution and phase transformation involved. The DSC thermogram was also measured in the same thermal process for the phase transformation investigation. The spectral variations observed in the thermo-Raman investigation indicated the transformation of Na₄P₂O₇ from low temperature phase () to high temperature phase () proceeded through pre-transitional region from 75 to 410 °C before the major orientational disorder at 420 °C and minor structural modifications at 511, 540 and 560 °C. The activation energies and enthalpies of the proposed phase transformations were determined. The possible mechanism for temperature dependent conductivity in Na₄P₂O₇ was discussed with the available data.     

Designing biological apatite suitable for neomycin delivery

Bovine-derived biological apatite has a long history for use in human health care as bone substitute. This article reports an elegant method of designing bovine-derived hydroxyapatite (BHA) blocks suitable for drug delivery. Neomycin sulphate (NS) was selected as a model drug and its loading and release studies were performed under in-vitro physiological conditions. The results indicate that phase purity and structural integrity of the BHA are retained even after entrapment of drug molecules. It was noticed that the rate of neomycin delivery was directly proportional to the porosity of the BHA blocks. The in-vitro release studies suggest that BHA may be used as a good carrier system for neomycin delivery.     

Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field,Semi-Empirical,and Density Functional Theory Calculations

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer’s disease (AD), the extracellular aggregates originate from amyloid-β proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-β peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to β-sheet like secondary structures of sequences from amyloid-β protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and β-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.     

Structural and optical characterization of Ni-doped CdS quantum dots

Ni-doped CdS quantum dots have been prepared by chemical precipitation technique. The X-diffraction results indicated that the particle size of Ni-doped CdS nanoparticles is smaller than that of undoped CdS and no secondary phase was observed. The average grain size of the nanoparticles is found to lie in the range of 2.7–4 nm. The compositional analysis results show that Cd, Ni, and S are present in the samples. HRTEM studies reveal that the average particle size of undoped and Ni-doped CdS quantum dots is 2 and 3 nm, respectively. Raman spectra shows that 1LO, 2LO, and 3LO peaks of the Ni-doped CdS samples are slightly red shifted when compared to that of undoped CdS. The absorption edge of Ni-doped CdS nanoparticles is found to shift towards the higher-wavelength (red shift) side when compared to that of undoped CdS and the band gap is observed to lie in the range of 3.79–3.95 eV. This band gap is higher than that of the bulk CdS and is due to quantum confinement effect present in CdS nanoparticles.     

ANFIS‐EM approach for PET brain image reconstruction

In this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS‐EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS‐EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS‐EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015