MRI brain features in nitrobenzene toxic encephalopathy

Mosquito-borne diseases such as malaria, filariasis, chikunguniya, yellow fever, dengue and Japanese encephalitis are the major cause of remarkable morbidity and mortality in livestock and humans worldwide. Since ancient times, aromatic plants are used for their medicinal value. Essential oils derived from these plants may be used as effective alternatives/adjuvants in pharmaceuticals, biomedical, cosmetic, food, veterinary and agriculture applications. These oils have also gained popularity and interest for prevention and treatment of various disorders. However, several reports on adverse effects including skin eruption, contact artricaria or toxic encephalopathy in children are available for synthetic repellent in the literature. Thus, natural insect repellents like essential oils have been explored recently as an alternative. One such essential oil studied widely, is citronella oil, extracted mainly from Cymbopogon nardus. This essential oil has exhibited good efficacy against mosquitoes. It is a mixture of components including citronellal, citronellol, geraniol as major constituents contributing to various activities (antimicrobial, anthelmintic, antioxidant, anticonvulsant antitrypanosomal and wound healing), besides mosquito repellent action. Citronella essential oil is registered in US EPA (Environmental protection agency) as insect repellent due to its high efficacy, low toxicity and customer satisfaction. However, poor stability in the presence of air and high temperature limits its practical applications. Since specific knowledge on properties and chemical composition of oil is fundamental for its effective application, the present review compiles and discusses biological properties of citronella oil. It also sheds light on various formulations and applications of this essential oil.

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Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve

Current Signal Transduction Therapy ◽

10.2174/1574362411666160616124516 ◽

2016 ◽

Vol 11 (2) ◽

pp. 114-120 ◽

Cited By ~ 2

Author(s):

C. Peter Devadoss ◽

Balasubramanian Sankaragomathi ◽

Thirugnanasambantham Monica

Keyword(s):

Image Compression ◽

Region Of Interest ◽

Space Filling ◽

Brain Image ◽

Space Filling Curve ◽

Mri Brain ◽

Filling Curve ◽

Mri Brain Image

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Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

Current Bioinformatics ◽

10.2174/1574893614666191017100657 ◽

2020 ◽

Vol 15 (4) ◽

pp. 287-299

Author(s):

Jie Zhang ◽

Junhong Feng ◽

Fang-Xiang Wu

Keyword(s):

Brain Function ◽

Brain Networks ◽

Discrete Particle Swarm Optimization ◽

Mri Brain ◽

Mutation Operators ◽

Neural Unit ◽

Crossover And Mutation ◽

The Brain ◽

Index Table ◽

Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

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