Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, ...are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.
Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce ...chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.
Modern societies have concerns about growing annoyance due to noise in private dwellings and in commercial worksites. People are exposed to the noise from neighbours, adjacent offices and road ...traffic which causes disturbance in sleep, physical or mental work impairments. Though ISO (International Standards Organization) provides sound insulation guidelines to protect citizens from the noise exposures, these guidelines do not provide an optimal acoustic satisfaction especially for specific sounds, for example a conversation varying in intelligibility. This work addresses the challenges in traditional sound insulation models, filters and auralization techniques, and establishes an interface between psychoacoustic research and building acoustics in audio-visual VR environments. Improvements are made in sound insulation prediction methods, filters construction and rendering techniques for sound insulation auralization. The virtual building acoustic framework (VBA) is developed toward real-time interactive audio-visual technology, to be able to introduce more realism and, hence, contextual features into psychoacoustic experiments. Listening experiments close to real-life situations are carried which showed that the VBA can be used as an alternate to design test paradigms which help to better analyse and interpret the noise impacts in built-up environments situations depending on the actual activities.
The brain is a vital organ, susceptible to alterations under genetic influences and environmental experiences. Social isolation (SI) acts as a stressor which results in alterations in reactivity to ...stress, social behavior, function of neurochemical and neuroendocrine system, physiological, anatomical and behavioral changes in both animal and humans. During early stages of life, acute or chronic SIS has been proposed to show signs and symptoms of psychiatric and neurological disorders such as anxiety, depression, schizophrenia, epilepsy and memory loss. Exposure to social isolation stress induces a variety of endocrinological changes including the activation of hypothalamic–pituitary–adrenal (HPA) axis, culminating in the release of glucocorticoids (GCs), release of catecholamines, activation of the sympatho-adrenomedullary system, release of Oxytocin and vasopressin. In several regions of the central nervous system (CNS), SIS alters the level of neurotransmitter such as dopamine, serotonin, gamma aminobutyric acid (GABA), glutamate, nitrergic system and adrenaline as well as leads to alteration in receptor sensitivity of N-methyl-D-aspartate (NMDA) and opioid system. A change in the function of oxidative and nitrosative stress (O&NS) mediated mitochondrial dysfunction, inflammatory factors, neurotrophins and neurotrophicfactors (NTFs), early growth response transcription factor genes (Egr) and C-Fos expression are also involved as a pathophysiological consequences of SIS which induce neurological and psychiatric disorders.
Milk and dairy products are integral part of human nutrition and they are considered as the carriers of higher biological value proteins, calcium, essential fatty acids, amino acids, fat, water ...soluble vitamins and several bioactive compounds that are highly significant for several biochemical and physiological functions. In recent years, foods containing natural antioxidants are becoming popular all over the world as antioxidants can neutralize and scavenge the free radicals and their harmful effects, which are continuously produced in the biological body. Uncontrolled free radicals activity can lead to oxidative stresses, which have been implicated in breakdown of vital biochemical compounds such as lipids, protein, DNA which may lead to diabetes, accelerated ageing, carcinogenesis and cardiovascular diseases. Antioxidant capacity of milk and milk products is mainly due to sulfur containing amino acids, such as cysteine, phosphate, vitamins A, E, carotenoids, zinc, selenium, enzyme systems, superoxide dismutase, catalase, glutathione peroxidase, milk oligosaccharides and peptides that are produced during fermentation and cheese ripening. Antioxidant activity of milk and dairy products can be enhanced by phytochemicals supplementation while fermented dairy products have been reported contained higher antioxidant capacity as compared to the non-fermented dairy products. Literature review has shown that milk and dairy products have antioxidant capacity, however, information regarding the antioxidant capacity of milk and dairy products has not been previously compiled. This review briefly describes the nutritional and antioxidant capacity of milk and dairy products.
Big data analytics for preventive medicine Razzak, Muhammad Imran; Imran, Muhammad; Xu, Guandong
Neural computing & applications,
05/2020, Letnik:
32, Številka:
9
Journal Article
Recenzirano
Odprti dostop
Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from ...complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.
Developing countries are facing the problem of environmental degradation. Environmental degradation is caused by the use of non-renewable energy consumptions for economic growth but the consequences ...of environmental degradation cannot be ignored. This primary purpose of this study is to investigate the nexus between energy consumption, economic growth and CO2 emission in Pakistan by using annual time series data from 1965 to 2015. The estimated results of ARDL indicate that energy consumption and economic growth increase the CO2 emissions in Pakistan both in short run and long run. Based on the estimated results it is recommended that policy maker in Pakistan should adopt and promote such renewable energy sources that will help to meet the increased demand for energy by replacing old traditional energy sources such as coal, gas, and oil. Renewable energy sources are reusable that can reduce the CO2 emissions and also ensure sustainable economic development of Pakistan.
Mobile data traffic grew by 74% in 2015 and it is expected to grow eight-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. ...Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity, and additional backhaul limitation. Hence, small CoMP clusters will need to be formed in the network. This paper surveys the state-of-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organization as an important concept for effective CoMP clustering to maximize CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organization and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilization of big data in cellular context to support proactive CoMP clustering based on prediction modeling. Finally, we conclude this paper with a summary of lessons learned in this field. This paper aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.
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Many food-derived phytochemicals and their derivatives represent a cornucopia of new anti-cancer compounds. Luteolin (3,4,5,7-tetrahydroxy flavone) is a flavonoid found in different ...plants such as vegetables, medicinal herbs, and fruits. It acts as an anticancer agent against various types of human malignancies such as lung, breast, glioblastoma, prostate, colon, and pancreatic cancers. It also blocks cancer development in vitro and in vivo by inhibition of proliferation of tumor cells, protection from carcinogenic stimuli, and activation of cell cycle arrest, and by inducing apoptosis through different signaling pathways. Luteolin can additionally reverse epithelial-mesenchymal transition (EMT) through a mechanism that involves cytoskeleton shrinkage, induction of the epithelial biomarker E-cadherin expression, and by down-regulation of the mesenchymal biomarkers N-cadherin, snail, and vimentin. Furthermore, luteolin increases levels of intracellular reactive oxygen species (ROS) by activation of lethal endoplasmic reticulum stress response and mitochondrial dysfunction in glioblastoma cells, and by activation of ER stress-associated proteins expressions, including phosphorylation of eIF2α, PERK, CHOP, ATF4, and cleaved-caspase 12. Accordingly, the present review article summarizes the progress of recent research on luteolin against several human cancers.
Microalgae have recently attracted considerable interest worldwide, due to their extensive application potential in the renewable energy, biopharmaceutical, and nutraceutical industries. Microalgae ...are renewable, sustainable, and economical sources of biofuels, bioactive medicinal products, and food ingredients. Several microalgae species have been investigated for their potential as value-added products with remarkable pharmacological and biological qualities. As biofuels, they are a perfect substitute to liquid fossil fuels with respect to cost, renewability, and environmental concerns. Microalgae have a significant ability to convert atmospheric CO
to useful products such as carbohydrates, lipids, and other bioactive metabolites. Although microalgae are feasible sources for bioenergy and biopharmaceuticals in general, some limitations and challenges remain, which must be overcome to upgrade the technology from pilot-phase to industrial level. The most challenging and crucial issues are enhancing microalgae growth rate and product synthesis, dewatering algae culture for biomass production, pretreating biomass, and optimizing the fermentation process in case of algal bioethanol production. The present review describes the advantages of microalgae for the production of biofuels and various bioactive compounds and discusses culturing parameters.
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