CuxNi1-xO nanostructures and their nanocomposites with reduced graphene oxide: Synthesis,characterization, and photocatalytic applications

NiO nanostructure was synthesized using a simple co-precipitation method and was embedded on reduced graphene oxide surface via ultrasonication. Structural investigations were made through X-ray diffraction (XRD) and functional groups were confirmed by Fourier transform infrared spectroscopy (FTIR). XRD analysis revealed the grain size reduction with doping. Fourier transform infrared spectroscopy confirmed the presence of metal-oxygen bond in pristine and doped NiO nanostructure as well as the presence of carbon containing groups. Scanning electron microscopy (SEM) indicated that the particle size decreased when NiO nanostructure was doped with copper. BET surface area was found to increase almost up to 43 m²/g for Cu doped NiO nanostructure/rGO composite. Current-voltage measurements were performed using two probe method. UV–Visible spectroscopic profiles showed the blue and red shift for Cu doped NiO nanostructure and Cu doped NiO Nanostructure/rGO composite respectively. Rate constant for Cu doped NiO nanostructure/rGO composite found to increase 4.4 times than pristine NiO nanostructure.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

Copper substituted nickel ferrite nanoparticles anchored onto the graphene sheets as electrode materials for supercapacitors fabrication

Nickel ferrites with high theoretical capacitance value as compared to the other metal oxides have been applied as electrode material for energy storage devices i.e. batteries and supercapacitors. High tendency towards aggregation and less specific surface area make the metal oxides poor candidate for electrochemical applications. Therefore, the improvements in the electrochemical properties of nickel ferrites (NiFe₂O₄) are required. Here, we report the synthesis of graphene nano-sheets decorated with spherical copper substituted nickel ferrite nanoparticles for supercapacitors electrode fabrication. The copper substituted and unsubstituted NiFe₂O₄ nanoparticles were prepared via wet chemical co-precipitation route. Reduced graphene oxide (rGO) was prepared via well-known Hummer's method. After structural characterization of both ferrite (Ni_1-xCu_xFe₂O₄) nanoparticles and rGO, the ferrite particles were decorated onto the graphene sheets to obtain Ni_1-xCu_xFe₂O₄@rGO nanocomposites. The confirmation of preparation of these nanocomposites was confirmed by scanning electron microscopy (SEM). The electrochemical measurements of nanoparticles and their nanocomposites (Ni_0.9Cu_0.1Fe₂O₄@rGO) confirmed that the nanocomposites due to highly conductive nature and relatively high surface area showed better capacitive behavior as compared to bare nanoparticles. This enhanced electrochemical energy storage properties of nanocomposites were attributed to the graphene and also supported by electrical (I-V) measurements. The cyclic stability experiments results showed ~65% capacitance retention after 1000 cycles. However this retention was enhanced from 65% to 75% for the copper substituted nanoparticles (Ni_0.9Cu_0.1Fe₂O₄) and 65–85% for graphene based composites. All this data suggest that these nanoparticles and their composites can be utilized for supercapacitors electrodes fabrication.     

Binary phase solid-state photopolymerization of acrylates: design,characterization and biomineralization of 3D scaffolds for tissue engineering

Porous polymer scaffolds designed by the cryogel method are attractive materials for a range of tissue engineering applications. However, the use of toxic crosslinker for retaining the pore structure limits their clinical applications. In this research, acrylates (HEA/PEGDA, HEMA/PEGDA and PEGDA) were used in the low-temperature solid-state photopolymerization to produce porous scaffolds with good structural retention. The morphology, pore diameter, mineral deposition and water absorption of the scaffold were characterized by SEM and water absorption test respectively. Elemental analysis and cytotoxicity of the biomineralized scaffold were revealed by using XRD and MTT assay test. The PEGDA-derived scaffold showed good water absorption ability and a higher degree of porosity with larger pore size compared to others. XRD patterns and IR results confirmed the formation of hydroxyapatite crystals from an alternative socking process. The overall cell proliferation was excellent, where PEGDA-derived scaffold had the highest and the most uniform cell growth, while HEMA/PEGDA scaffold showed the least. These results suggest that the cell proliferation and adhesion are directly proportional to the pore size, the shape and the porosity of scaffolds.     

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.     

Probiotics for cure of Helicobacter pylori infection: A review

Human physiology normally contains pathogenic and non-pathogenic microorganisms in the gastrointestinal flora. Disturbance of these microorganism balance results in the formation of infection. Extensive use of antibiotics for cure of these disturbances like Helicobacter pylori (H. pylori) infection leads to patient discomfort and associated side-effects. There is a need to adopt adjunct or alternative approach in order to minimize such conditions. Probiotics is one of the potential therapies to cure gastrointestinal discomforts especially associated with H. pylori. It competes through non-immune and immune systems. This review article concludes that probiotics are used to eradicate the infection at increased rate, and decreased associated side-effects are caused by triple therapy. A proper evaluation of these probiotics is demanded before their use in future as a commercial product. Furthermore, their effect on immune system requires more research work so that their usage for other chronic disorders can also be considered.     

工艺参数设置和厚度对电子束熔融加工Ti-6Al-4V合金微观结构的影响

研究了试件尺寸和工艺参数(电子束强度,扫描速度,焦点偏移量和扫描长度)对电子束熔融(EBM)加工Ti-6Al-4V合金微观结构的影响。结果表明,可以观察到EBM加工的Ti-6Al-4V合金的微观结构由原始β相的柱状晶粒组成。在柱状晶粒内部观察到典型的(α+β)结构,即魏氏体α片和在细小的α晶粒的界面上形成的杆状β相。还发现沿原始β柱状晶粒的晶界形成的α层晶界。随着试件厚度、电子束能量密度和扫描长度的增加,先前的β柱状晶粒的直径增大,并且生长的方向与加工方向一致。同时,柱状晶粒直径随着试件高度的增加而减小。随着试件厚度和电子束能量密度的增加,α片会变得更粗大。     

N‐halamine‐modified polyglycolide (PGA) multifilament as a potential bactericidal surgical suture: In vitro study

In this study, new cationic homopolymer and anionic copolymer were synthesized, and deposited onto polyglycolide sutures using a layer‐by‐layer assembly technique. The coated sutures were rendered antibacterial by chlorinating with dilute solution of household bleach solution at pH 7. The chlorination treatment transformed the N? H groups of anionic copolymer into N‐halamine structures. The N‐halamine‐modified sutures were challenged with Staphylococcus aureus and Escherichia coli O157:H7 bacteria at different contact times. The suture with chlorine loading of 0.22% completely inactivated both bacterial strains in 30 min contact time. Fourier transform infrared spectroscopy, scanning electron microscopy, and analytical titration confirmed the successful deposition of the N‐halamine multilayers. The effect of layer‐by‐layer coatings of polyelectrolytes on the chlorine loading and antibacterial efficacy of sutures was evaluated. The straight‐pull and knot‐pull strength tests performed on the sutures reported slight decline in tensile properties after chlorination treatment. The in vitro hemolysis and cytocompatibility tests revealed that the N‐halamines‐based antibacterial sutures were biocompatible. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42483.     

Enhanced Catalytic,Antibacterial and Anti-cancer Activities of Erythromycin Capped Gold Nanoparticles

Journal of Inorganic and Organometallic Polymers and Materials - In this study, the nanosized spherical erythromycin capped gold nanoparticles (eryth-Au(0)NPs) were fabricated for the first time....