We have previously shown that individuals with high depression scores demonstrate impaired behavioral and neural responses during social learning. Given that depression is associated with altered dopamine (DA) and serotonin (5-HT) functioning, the current study aimed to elucidate the role of these neurotransmitters in the social learning process using a dietary depletion manipulation. In a double-blind design, 70 healthy volunteers were randomly allocated to a 5-HT depletion (N = 24), DA depletion (N = 24), or placebo (N = 22) group. Participants performed a social learning task during fMRI scanning, as part of which they learned associations between name cues and rewarding (happy faces) or aversive (fearful faces) social outcomes. Behaviorally, 5-HT depleted subjects demonstrated impaired social reward learning compared to placebo controls, with a marginal effect in the same direction in the DA depletion group. On the neural level, computational modeling-based fMRI analyses revealed that 5-HT depletion altered social reward prediction signals in the insula, temporal lobe, and prefrontal cortex, while DA depletion affected social reward prediction encoding only in the prefrontal cortex. These results indicate that 5-HT depletion impairs learning from social rewards, on both the behavioral and the neural level, while DA depletion has a less extensive effect. Interestingly, the behavioral and neural responses observed after 5-HT depletion in the current study closely resemble our previous findings in individuals with high depression scores using the same task. It may thus be the case that decreased 5-HT levels contribute to social learning deficits in depression.Achieving good quality Ohmic contacts to van der Waals materials is a challenge, since at the interface between metal and van der Waals material different conditions can occur, ranging from the presence of a large energy barrier between the two materials to the metallization of the layered material below the contacts. In black phosphorus (bP), a further challenge is its high reactivity to oxygen and moisture, since the presence of uncontrolled oxidation can substantially change the behavior of the contacts. Here we study three of the most commonly used metals as contacts to bP, Chromium, Titanium, and Nickel, and investigate their influence on contact resistance against the variability between different flakes and different samples. We investigate the gate dependence of the current-voltage characteristics of field-effect transistors fabricated with these metals on bP, observing good linearity in the accumulation regime for all metals investigated. Using the transfer length method, from an analysis of ten devices, both at room temperature and at low temperature, Ni results to provide the lowest contact resistance to bP and minimum scattering between different devices. Moreover, we observe that our best devices approach the quantum limit for contact resistance both for Ni and for Ti contacts. © 2020 IOP Publishing Ltd.Magnetic Resonance Imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating "semi-synthetic" CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the "semi-synthetic" CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images.  https://www.selleckchem.com/products/Nolvadex.html  The resulting "semi-synthetic" CT images were paired with their corresponding MR images and used to tute of Physics and Engineering in Medicine.PURPOSE Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground-truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio in TRUS. In this study, we proposed a weakly supervised deep learning approach to address these issues. METHODS We employed deep learning techniques to combine image segmentation, affine and non-rigid registration to perform a deformable MRI-TRUS registration. First, we trained two separate fully convolutional neural networks to perform MRI and TRUS prostate segmentation. Then, a convolutional neural network was used to rigidly register MRI-TRUS images via affine registration. Third, a UNET-like network was applied for non-rigid registration. For both affine and non-rigid registration. Due to the unavailability of ground truth correspondences and the lack of accuratistration performance in terms of Dice, TRE, MSD and HD. © 2020 Institute of Physics and Engineering in Medicine.Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by means of (real-time) adaptive radiotherapy. The noise navigator, which is independent of the MR signal, could serve as a secondary motion detection method in synergy with MR imaging.The feasibility of respiratory motion detection by means of the noise navigator was demonstrated previously. Furthermore, from electromagnetic simulations we know that the noise navigator is sensitive to tissue displacement and thus could in principle be used for the detection of various types of motion. In this study we demonstrate the detection of various types of motion for three anatomical use cases of MRI-guided radiotherapy, i.e.