Nanoantenna-enhanced ultrafast nonlinear spectroscopy of a single gold nanoparticle

Optical nanoantennas are a novel tool to investigate previously unattainable dimensions in the nanocosmos. Just like their radio-frequency equivalents, nanoantennas enhance the light-matter interaction in their feed gap. Antenna enhancement of small signals promises to open a new regime in linear and nonlinear spectroscopy on the nanoscale. Without antennas especially the nonlinear spectroscopy of single nanoobjects is very demanding. Here, we present for the first time antenna-enhanced ultrafast nonlinear optical spectroscopy. In particular, we utilize the antenna to determine the nonlinear transient absorption signal of a single gold nanoparticle caused by mechanical breathing oscillations. We increase the signal amplitude by an order of magnitude which is in good agreement with our analytical and numerical models. Our method will find applications in linear and nonlinear spectroscopy of nanoobjects, ranging from single protein binding events via nonlinear tensor elements to the limits of continuum mechanics

Appraisal patterns of envy and related emotions

Envy is a frustrating emotion that arises from upward social comparison. Two studies investigated the appraisals that distinguish benign envy (aimed at improving one’s own situation) from malicious envy (aimed at pulling down the superior other). Study 1 found that appraisals of deservingness and control potential differentiated both types of envy. We manipulated these appraisals in Study 2 and found that while both did not influence the intensity of envy, they did determine the type of envy that resulted. The more a situation was appraised as undeserved, the more participants experienced malicious envy. Benign envy was experienced more when the situation was not undeserved, and the most when the situation was appraised as both deserved and controllable. The current research also clarifies how the types of envy differ from the related emotions admiration and resentment

Retrospective head motion estimation in structural brain MRI with 3D CNNs

Head motion is one of the most important nuisance variables in neuroimaging, particularly in studies of clinical or special populations, such as children. However, the possibility of estimating motion in structural MRI is limited to a few specialized sites using advanced MRI acquisition techniques. Here we propose a supervised learning method to retrospectively estimate motion from plain MRI. Using sparsely labeled training data, we trained a 3D convolutional neural network to assess if voxels are corrupted by motion or not. The output of the network is a motion probability map, which we integrate across a region of interest (ROI) to obtain a scalar motion score. Using cross-validation on a dataset of n=48 healthy children scanned at our center, and the cerebral cortex as ROI, we show that the proposed measure of motion explains away 37% of the variation in cortical thickness. We also show that the motion score is highly correlated with the results from human quality control of the scans. The proposed technique can not only be applied to current studies, but also opens up the possibility of reanalyzing large amounts of legacy datasets with motion into consideration: we applied the classifier trained on data from our center to the ABIDE dataset (autism), and managed to recover group differences that were confounded by motion

Effect of fenugreek (Trigonella foenum-graecum L.) intake on glycemia: A meta-analysis of clinical trials

10.1186/1475-2891-13-7Nutrition Journal131