Chromaticity of Gravitational Microlensing Events

In this paper, we investigate the color changes of gravitational microlensing events caused by the two different mechanisms of differential amplification for a limb-darkened extended source and blending. From this investigation, we find that the color changes of limb-darkened extended source events (color curves) have dramatically different characteristics depending on whether the lens transits the source star or not. We show that for a source transit event, the lens proper motion can be determined by simply measuring the turning time of the color curve instead of fitting the overall color or light curves. We also find that even for a very small fraction of blended light, the color changes induced by the blending effect is equivalent to those caused by the limb-darkening effect, causing serious distortion in the observed color curve. Therefore, to obtain useful information about the lens and source star from the color curve of a limb-darkened extended source event, it will be essential to eliminate or correct for the blending effect. We discuss about the methods for the efficient correction of the blending effect.Comment: total 18 pages, including 5 figures and no table, MNRAS, submitte

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

The conventional CNN, widely used for two-dimensional images, however, is not directly applicable to non-regular geometric surface, such as a cortical thickness. We propose Geometric CNN (gCNN) that deals with data representation over a spherical surface and renders pattern recognition in a multi-shell mesh structure. The classification accuracy for sex was significantly higher than that of SVM and image based CNN. It only uses MRI thickness data to classify gender but this method can expand to classify disease from other MRI or fMRI dataComment: 29 page

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications

Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor

Thalamotomy at the ventralis intermedius nucleus for essential tremor is known to cause changes in motor circuitry, but how a focal lesion leads to progressive changes in connectivity is not clear. To understand the mechanisms by which thalamotomy exerts enduring effects on motor circuitry, a quantitative analysis of directed or effective connectivity among motor-related areas is required. We characterized changes in effective connectivity of the motor system following thalamotomy using (spectral) dynamic causal modeling (spDCM) for resting-state fMRI. To differentiate long-lasting treatment effects from transient effects, and to identify symptom-related changes in effective connectivity, we subject longitudinal resting-state fMRI data to spDCM, acquired 1 day prior to, and 1 day, 7 days, and 3 months after thalamotomy using a non-cranium-opening MRI-guided focused ultrasound ablation technique. For the group-level (between subject) analysis of longitudinal (between-session) effects, we introduce a multilevel parametric empirical Bayes (PEB) analysis for spDCM. We found remarkably selective and consistent changes in effective connectivity from the ventrolateral nuclei and the supplementary motor area to the contralateral dentate nucleus after thalamotomy, which may be mediated via a polysynaptic thalamic–cortical–cerebellar motor loop. Crucially, changes in effective connectivity predicted changes in clinical motor-symptom scores after thalamotomy. This study speaks to the efficacy of thalamotomy in regulating the dentate nucleus in the context of treating essential tremor. Furthermore, it illustrates the utility of PEB for group-level analysis of dynamic causal modeling in quantifying longitudinal changes in effective connectivity; i.e., measuring long-term plasticity in human subjects non-invasively

Metal/graphene sheets as p-type transparent conducting electrodes in GaN light emitting diodes

We demonstrate the use of graphene based transparent sheets as a p-type current spreading layer in GaN light emitting diodes (LEDs). Very thin Ni/Au was inserted between graphene and p-type GaN to reduce contact resistance, which reduced contact resistance from similar to 5.5 to similar to 0.6 Omega/ cm(2), with no critical optical loss. As a result, LEDs with metal-graphene provided current spreading and injection into the p-type GaN layer, enabling three times enhanced electroluminescent intensity compared with those with graphene alone. We confirmed very strong blue light emission in a large area of the metal-graphene layer by analyzing image brightness.open281

Optimal set of grid size and angular increment for practical dose calculation using the dynamic conformal arc technique: a systematic evaluation of the dosimetric effects in lung stereotactic body radiation therapy

Purpose To recommend the optimal plan parameter set of grid size and angular increment for dose calculations in treatment planning for lung stereotactic body radiation therapy (SBRT) using dynamic conformal arc therapy (DCAT) considering both accuracy and computational efficiency. Materials and methods Dose variations with varying grid sizes (2, 3, and 4 mm) and angular increments (2°, 4°, 6°, and 10°) were analyzed in a thorax phantom for 3 spherical target volumes and in 9 patient cases. A 2-mm grid size and 2° angular increment are assumed sufficient to serve as reference values. The dosimetric effect was evaluated using dose–volume histograms, monitor units (MUs), and dose to organs at risk (OARs) for a definite volume corresponding to the dose–volume constraint in lung SBRT. The times required for dose calculations using each parameter set were compared for clinical practicality. Results Larger grid sizes caused a dose increase to the structures and required higher MUs to achieve the target coverage. The discrete beam arrangements at each angular increment led to over- and under-estimated OARs doses due to the undulating dose distribution. When a 2° angular increment was used in both studies, a 4-mm grid size changed the dose variation by up to 3–4% (50 cGy) for the heart and the spinal cord, while a 3-mm grid size produced a dose difference of \u3c1% (12 cGy) in all tested OARs. When a 3-mm grid size was employed, angular increments of 6° and 10° caused maximum dose variations of 3% (23 cGy) and 10% (61 cGy) in the spinal cord, respectively, while a 4° increment resulted in a dose difference of \u3c1% (8 cGy) in all cases except for that of one patient. The 3-mm grid size and 4° angular increment enabled a 78% savings in computation time without making any critical sacrifices to dose accuracy. Conclusions A parameter set with a 3-mm grid size and a 4° angular increment is found to be appropriate for predicting patient dose distributions with a dose difference below 1% while reducing the computation time by more than half for lung SBRT using DCAT