Analysis of surface layers on bioactive glasses

FT-Raman spectroscopy proves to be a powerful technique to study surface reactions on bioactive glasses and it eliminates the fluorescence of the organic phase of whole bone, thereby making it possible to compare the reaction layers formed on bioactive glasses with the mineral phase of bone. The spectrum of hydroxycarbonate apatite (HCA) developed on the bioactive glasses is closer to that of bone than synthetic hydroxyapatite (HA) and closely matches that of bone mineral obtained by deproteination of whole human femoral cortical bone.     

Dynamics of redox processes in ionic liquids and their interplay for discriminative electrochemical sensing

Motivated by the use of ionic liquids (ILs) as green replacers of traditional electrolytes, a mechanistic study has been systematically conducted to comprehend various design principles responsible for electrochemical profiling of redox-active species in ILs. The full spectrum of properties associated with ILs is exploited to assess the viability of this platform, thus revealing the correlation between the redox properties and the physiochemical parameters of the species involved. This includes the evaluation of (1) the variation of redox responses toward analytes with similar molecular structures or functionalities of ILs, (2) the influence in terms of physical criteria of the system such as viscosity and conductivity as well as chemical structure of ILs, and (3) the sustainability in harsh conditions (high temperature or humidity) and interferences. The principle is exemplified via trinitrotoluene (TNT) and dinitrotoluene (DNT) with inherent redox activity as analytes and IL membranes as solvents and electrolytes using glassy carbon (GC) electrodes. A discrete response pattern is generated that is analyzed through linear discriminant analysis (LDA) leading to 100% classification accuracy even for the mixture of analytes. Quantitative analysis through square wave voltammetry (SWV) gave rise to the detection limits in liquid phase of 190 and 230 nM for TNT and DNT, respectively, with a linear range up to 100 μM. Gas-phase analysis shows strong redox signals for the estimated concentrations of 0.27 and 2.05 ppm in the gas phase for TNT and DNT, respectively, highlighting that ILs adopt a role as a preconcentrator to add on sensitivity with enhanced selectivity coming from their physiochemical diversity, thus addressing the major concerns usually referred to most sensor systems.     

Analysis of phytohormones and phosphate solubilization in Photorhabdus spp.

Bacteria of the genus Photorhabdus are nematodes (Heterorhabditidae) symbiont and highly entomopathogenic in nature. Present study was concerned to investigate the effects of Photorhabdus temperata M1021 and Photorhabdus luminescens TT01 on plant morphology and physiology (‘Dongjin-beyo’ and ‘waitoc’). A significant (p<0.05) increase in the plant growth attributes including total plant length, root length, biomass, and chlorophyll contents were observed after treatment with culture extracts. Moreover indole-3-acetic acid (IAA) production by respective bacteria was confirmed by GCMS analysis. IAA production through tryptophan independent pathway was confirmed in P. temperata M1021 and P. luminescens TT01. Phosphate solubilization capability was also investigated by growing them on pikovskaya (PVK) agar as well as on PVK liquid medium with pH 7 at 28±2°C for 14 days. IAA production and phosphate solubilization capability of these bacteria could be the potential factors for plant growth promotion.     

RETRACTED ARTICLE: Document image analysis: issues,comparison of methods and remaining problems

Image analysis is an interesting research area with a large variety of challenging applications. Researchers have worked from decades on this topic, as witnessed by the scientific literature. However, document image analysis is the special case in image analysis as their spatial properties are different from natural images. Therefore, the main focus of this paper is to describe image denoising issues in general and document image issues in particular. Since the field of document processing is relatively new, it is also dynamic, so current methods have room for improvement and innovations are still being made. Several algorithms proposed in the literature are described. Critical discussions are reported about the current status of the field and open problems are highlighted. It is also demonstrated that, there are rarely definitive techniques for all cases of a certain problem. We surveyed the state of art, analyzed recent trends and tried to identify challenges for future research in this field.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

Optimal production-inventory policy for make-to-order versus make-to-stock based on the M/Er/1 queuing model

This paper analyzes a multi-product production / inventory system where demands for each item arrive according to a Poisson process and the production time for each product has an Erlang distribution. The paper proposes an optimality condition that specifies whether each product should be produced make-to-stock or make-to-order. In the event a product should be produced make-to-stock, an approach for computing the optimal base-stock level is proposed. Numerical examples are given for illustrative purpose.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Enhancement in Physical Properties of Silver-Doped Fe–Ni Invar Nano-alloy Using Chemical Reduction Method

Journal of Superconductivity and Novel Magnetism - Silver-substituted Fe–Ni nano invar alloy is a new and innovative field of research due to their interesting invar, magnetic and electrical...