In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Nanomaterials, such as nanoparticles, nanorods, nanosphere, nanoshells, and nanostars, are very commonly used in biomedical imaging and cancer therapy. They make excellent drug carriers, imaging contrast agents, photothermal agents, photoacoustic agents, and radiation dose enhancers, among other applications. Recent advances in nanotechnology have led to the use of nanomaterials in many areas of functional imaging, cancer therapy, and synergistic combinational platforms. This review will systematically explore various applications of nanomaterials in biomedical imaging and cancer therapy. The medical imaging modalities include magnetic resonance imaging, computed tomography, positron emission tomography, single photon emission computerized tomography, optical imaging, ultrasound, and photoacoustic imaging. Various cancer therapeutic methods will also be included, including photothermal therapy, photodynamic therapy, chemotherapy, and immunotherapy. This review also covers theranostics, which use the same agent in diagnosis and therapy. This includes recent advances in multimodality imaging, image-guided therapy, and combination therapy. We found that the continuous advances of synthesis and design of novel nanomaterials will enhance the future development of medical imaging and cancer therapy. However, more resources should be available to examine side effects and cell toxicity when using nanomaterials in humans.

/pdf/application-of-nanomaterials-in-biomedical-imaging-and-46iiw39o6f.pdf

Application of Nanomaterials in Biomedical Imaging and Cancer Therapy.

The brain is the body's control center, so when a disease affects it, the outcomes are devastating. Alzheimer's and Parkinson's disease, and multiple sclerosis are brain diseases that cause a large number of human deaths worldwide. Curcumin has demonstrated beneficial effects on brain health through several mechanisms such as antioxidant, amyloid β-binding, anti-inflammatory, tau inhibition, metal chelation, neurogenesis activity, and synaptogenesis promotion. The therapeutic limitation of curcumin is its bioavailability, and to address this problem, new nanoformulations are being developed. The present review aims to summarize the general bioactivity of curcumin in neurological disorders, how functional molecules are extracted, and the different types of nanoformulations available.

Curcumin’s Nanomedicine Formulations for Therapeutic Application in Neurological Diseases

Although carbon nanoparticles or quantum dots (C-dots) have been studied extensively for a variety of applications (e.g., photocatalysis, metal ion sensing, antibacterial, cell labeling), a greener...

Mushroom-Derived Carbon Dots for Toxic Metal Ion Detection and as Antibacterial and Anticancer Agents

The aging process is known to be associated with heightened oxidative stress and related systemic inflammation. Therefore, antioxidant supplementation is regarded as a promising strategy to combat aging and associated pathological conditions. Food-grade antioxidants from plant-derived extracts are the most common ingredients of these supplements. Phyto-bioactive compounds such as curcumin, resveratrol, catechins, quercetin are among the most commonly applied natural compounds used as potential modulators of the free radical-induced cellular damages. The therapeutic potential of these compounds is, however, restricted by their low bioavailability related to poor solubility, stability, and absorbance in gastrointestinal tract. Recently, novel nanotechnology-based systems were developed for therapeutic delivery of natural antioxidants with improved bioavailability and, consequently, efficacy in clinical practice. Such systems have provided many benefits in preclinical research over the conventional preparations, including superior solubility and stability, extended half-life, improved epithelium permeability and bioavailability, enhanced tissue targeting, and minimized side effects. The present review summarizes recent developments in nanodelivery of natural antioxidants and its application to combat pathological conditions associated with oxidative stress.

/pdf/nanodelivery-of-natural-antioxidants-an-anti-aging-mna9v30zxt.pdf

Nanodelivery of Natural Antioxidants: An Anti-aging Perspective.

The preeminent treatments for neurodegenerative disease are often unavailable due to the poor accessibility of therapeutic drugs. Moreover, the blood-brain barrier (BBB) effectively blocks the transfer of cells, particles and large molecules, ie, drugs, across the brain. The most important challenge in the treatment of neurodegenerative diseases is the development of targeted drug delivery system. Theranostic strategies are known to combine therapeutic and diagnostic capabilities together. The aim of this review was to record the response to treatment and thereby improve drug safety. Nanotechnology offers a platform for designing and developing theranostic agents that can be used as an efficient nano-carrier system. This is achieved by the manipulation of some of the properties of nanoparticles (NPs), thereby enabling the attachment of suitable drugs onto their surface. The results provide revolutionary treatments by stimulation and thus interaction with targeted sites to promote physiological response with minimum side effects. This review is a brief discussion of the administration of drugs across the brain and the advantages of using NPs as an effective theranostic platform in the treatment of Alzheimer's, Parkinson's, epilepsy and Huntington's disease.

/pdf/theranostic-applications-of-nanoparticles-in-18xl02y8l4.pdf

Theranostic applications of nanoparticles in neurodegenerative disorders.

A study on ECG signal characterization and practical implementation of some ECG characterization techniques

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Fuzzy C Means (FCM) clustering technique is widely used in medical image segmentation. The classical FCM is sensitive to noise and the objective function often gets trapped in local optima. In this work, we have combined FCM with various optimization techniques to find the optimum cluster centres. The Artificial Bee Colony (ABC), Firefly, Cuckoo and Simulated Annealing (SA) are the widely used optimization techniques in segmentation as well as for classification problems. Here we are proposing the Crow Search (CS) Optimization for the image segmentation using FCM. The CS optimization was found to produce satisfactory and promising results when compared with ABC, Firefly, Cuckoo and SA algorithm in the segmentation of abdomen CT images using FCM. In addition, a thorough investigation has been made in choosing the appropriate validation functions for the clustering techniques. The algorithms have been developed in Matlab 2015a and tested on CT abdomen datasets. Finally, the hardware implementation of FCM-CS algorithm was done in Raspberry Pi B+ model embedded board.

Fuzzy-Crow Search Optimization for Medical Image Segmentation

The role image processing role is inevitable in the computer vision, robotics, agriculture, and medical field. This work proposes K-means clustering algorithm for the detection of leaf disease and classification. Prior to segmentation and classification, preprocessing was performed by the color median filter. The image in the RGB color model was converted into L*a*b model for segmentation. The color texture feature is extracted and fed to multiclass SVM classifier. The algorithms are developed in MATLAB 2015a and tested on real time images. The classification accuracy on an average for SVM was found to be greater than 95%.

A. Lenin Fred

Papers

Theranostic applications of nanoparticles in neurodegenerative disorders.

A study on ECG signal characterization and practical implementation of some ECG characterization techniques

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Fuzzy-Crow Search Optimization for Medical Image Segmentation

K-means Clustering and SVM for Plant Leaf Disease Detection and Classification