Synthesis of 3D nanostructured metal alloy of immiscible materials induced by megahertz-repetition femtosecond laser pulses

In this work, we have proposed a concept for the generation of three-dimensional (3D) nanostructured metal alloys of immiscible materials induced by megahertz-frequency ultrafast laser pulses. A mixture of two microparticle materials (aluminum and nickel oxide) and nickel oxide microparticles coated onto an aluminum foil have been used in this study. After laser irradiation, three different types of nanostructure composites have been observed: aluminum embedded in nickel nuclei, agglomerated chain of aluminum and nickel nanoparticles, and finally, aluminum nanoparticles grown on nickel microparticles. In comparison with current nanofabrication methods which are used only for one-dimensional nanofabrication, this technique enables us to fabricate 3D nanostructured metal alloys of two or more nanoparticle materials with varied composite concentrations under various predetermined conditions. This technique can lead to promising solutions for the fabrication of 3D nanostructured metal alloys in applications such as fuel-cell energy generation and development of custom-designed, functionally graded biomaterials and biocomposites.     

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain-related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas-Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.     

Selenium (Se) improves drought tolerance in crop plants – a myth or fact?

Climate change has emerged as one of the most complex challenges of the 21st century and has become an area of interest in the past few decades. Many countries of the world have become extremely vulnerable to the impacts of climate change. The scarcity of water is a serious concern for food security of these countries and climate change has aggravated the risks of extreme events like drought. Oxidative stress, caused by a variety of active oxygen species formed under drought stress, damages many cellular constituents, such as carbohydrates, lipids, nucleic acids and proteins, which ultimately reduces plant growth, respiration and photosynthesis. Se has become an element of interest to many biologists owing to its physiological and toxicological importance. It plays a beneficial role in plants by enhancing growth, reducing damage caused by oxidative stress, enhancing chlorophyll content under light stress, stimulating senesce to produce antioxidants and improving plant tolerance to drought stress by regulating water status. Researchers have adopted different strategies to evaluate the role of selenium in plants under drought stress. Some of the relevant work available regarding the role of Se in alleviating adverse effect of drought stress is discussed in this paper. © 2015 Society of Chemical Industry     

Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images

Multimedia Tools and Applications - Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the...     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.     

Remote health monitoring of elderly through wearable sensors

Multimedia Tools and Applications - Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for...     

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

Reliable brain tumor radiology is one of the serious mortality issues of medical hospitals and on priority of healthcare departments. In this research, the presence of brain tumor and its type (if exists) is automatically diagnosed from magnetic resonance imaging (MRI). The first step is most important where suitable parameters from Gabor texture analysis are extracted and then classified with a support vector machine. The drive of this research activity is to verify robustness of the proposed model on cross datasets, so that it could deal with variability and multiformity present in MRI data. Further to this, the developed approach is able to deploy as a real application in the local environment. Therefore, once a model has been trained and tested on an openly available benchmarked dataset, it is retested on a different dataset acquired from a local source. Standard evaluation measures, that is, accuracy, specificity, sensitivity, precision, and AUC-values have been used to evaluate the robustness of the proposed method. It has been established that the proposed method has the ability to deal with multiformity, variability, and local medical traits present in brain MRI data.