Satellite Observations of Separator Line Geometry of Three-Dimensional Magnetic Reconnection

Detection of a separator line that connects magnetic nulls and the determination of the dynamics and plasma environment of such a structure can improve our understanding of the three-dimensional (3D) magnetic reconnection process. However, this type of field and particle configuration has not been directly observed in space plasmas. Here we report the identification of a pair of nulls, the null-null line that connects them, and associated fans and spines in the magnetotail of Earth using data from the four Cluster spacecraft. With di and de designating the ion and electron inertial lengths, respectively, the separation between the nulls is found to be ~0.7di and an associated oscillation is identified as a lower hybrid wave with wavelength ~ de. This in situ evidence of the full 3D reconnection geometry and associated dynamics provides an important step toward to establishing an observational framework of 3D reconnection.Comment: 10 pages, 3 figures and 1 tabl

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p

ARTIFICIAL INTELLIGENCE APPROACH TO CLASSIFY UNIPOLAR and BIPOLAR DEPRESSIVE DISORDERS

Machine learning (ML) approaches for medical decision making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs) successfully meet the first goal with its adaptive engine while nature inspired algorithms are focusing on the feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar methods for medical data processing. Classification of psychiatric disorders is one of major focus of medical informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases, bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this context, the paper puts forward a study using two-step hybridized methodology, particle swarm optimization (PSO) algorithm for feature selection process and ANN for training process. The noteworthy performance of ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89% overall classification accurac