Nanohydroxyapatite-coated hydroxyethyl cellulose/poly (vinyl) alcohol electrospun scaffolds and their cellular response

The present study focused on the preparation of nanohydroxyapatite (nHA)-coated hydroxyethyl cellulose/polyvinyl alcohol (HEC/PVA) nanofibrous scaffolds for bone tissue engineering application. The electrospun HEC/PVA scaffolds were mineralized via alternate soaking process. FESEM revealed that the nHA was formed uniformly over the nanofibers. The nHA mineralization enhanced the tensile strength and reduced the elongation at breakage of scaffolds. The wettability of the nanofibrous scaffolds was significantly improved. The in vitro biocompatibility of scaffolds was evaluated with human osteosarcoma cells. nHA-coated scaffolds had a favorable effect on the proliferation and differentiation of osteosarcoma cell and could be a potential candidate for bone regeneration.     

Preparation and photoluminescent analysis of Sm3+ complexes based on unsymmetrical conjugated chromophoric ligand

Journal of Materials Science: Materials in Electronics - The octa-coordinated complexes of Sm(III) with β-diketone and nitrogen-heterocyclic bidentate auxiliary moiety were prepared and...     

Optimal linear representations of images for object recognition

Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.     

Design and Fabrication of Fiber-Optic Nanoprobes for Optical Sensing

This paper describes the design and fabrication of fiber-optic nanoprobes developed for optical detection in single living cells. It is critical to fabricate probes with well-controlled nanoapertures for optimized spatial resolution and optical transmission. The detection sensitivity of fiber-optic nanoprobe depends mainly on the extremely small excitation volume that is determined by the aperture sizes and penetration depths. We investigate the angle dependence of the aperture in shadow evaporation of the metal coating onto the tip wall. It was found that nanoaperture diameters of approximately 50 nm can be achieved using a 25° tilt angle. On the other hand, the aperture size is sensitive to the subtle change of the metal evaporation angle and could be blocked by irregular metal grains. Through focused ion beam (FIB) milling, optical nanoprobes with well-defined aperture size as small as 200 nm can be obtained. Finally, we illustrate the use of the nanoprobes by detecting a fluorescent species, benzo[a]pyrene tetrol (BPT), in single living cells. A quantitative estimation of the numbers of BPT molecules detected using fiber-optic nanoprobes for BPT solutions shows that the limit of detection was approximately 100 molecules.     

Fabrication of poly (vinyl alcohol)/ovalbumin/cellulose nanocrystals/nanohydroxyapatite based biocomposite scaffolds

Biocomposite scaffolds composed of PVA, ovalbumin, cellulose nanocrystals, and nanohydroxyapatite were fabricated by freeze-drying method. The results revealed that the different fractions of nanohydroxyapatite and cellulose nanocrystals provide the mechanical strength and stiffness to the desired biocomposite scaffolds. In vitro biomineralization showed the formation of apatite onto the surface of obtained biocomposite scaffolds and increased as amount of nanohydroxyapatite increased. The obtained results suggest that the different combinations of these four biomaterials can be used to fabricate highly porous scaffolds with desired mechanical performance and degradation rate by adjusting ratio for potential use in low load-bearing applications.     

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.     

Statistical analysis of manual segmentations of structures in medical images

The problem of extracting anatomical structures from medical images is both very important and difficult. In this paper we are motivated by a new paradigm in medical image segmentation, termed Citizen Science, which involves a volunteer effort from multiple, possibly non-expert, human participants. These contributors observe 2D images and generate their estimates of anatomical boundaries in the form of planar closed curves. The challenge, of course, is to combine these different estimates in a coherent fashion and to develop an overall estimate of the underlying structure. Treating these curves as random samples, we use statistical shape theory to generate joint inferences and analyze this data generated by the citizen scientists. The specific goals in this analysis are: (1) to find a robust estimate of the representative curve that provides an overall segmentation, (2) to quantify the level of agreement between segmentations, both globally (full contours) and locally (parts of contours), and (3) to automatically detect outliers and help reduce their influence in the estimation. We demonstrate these ideas using a number of artificial examples and real applications in medical imaging, and summarize their potential use in future scenarios.     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

     

Dynamic adaptive autonomy in multi-agent systems

Multi-agent systems require adaptability to perform effectively in complex and dynamic environments. This article shows that agents should be able to benefit from dynamically adapting their decision-making frameworks. A decision-making framework describes the set of multi-agent decision-making interactions exercised by members of an agent group in the course of pursuing a goal or set of goals. The decision-making interaction style an agent adopts with respect to other agents influences that agent's degree of autonomy. The article introduces the capability of Dynamic Adaptive Autonomy (DAA), which allows an agent to dynamically modify its autonomy along a defined spectrum (from command-driven to consensus to locally autonomous/master) for each goal it pursues. This article presents one motivation for DAA through experiments showing that the ‘best’ decision-making framework for a group of agents depends not only on the problem domain and pre-defined characteristics of the system, but also on run-time factors that can change during system operation. This result holds regardless of which performance metric is used to define ‘best’. Thus, it is possible for agents to benefit by dynamically adapting their decision-making frameworks to their situation during system operation.