P-274: Activated endothelin system in polyglobulia

The role of the endothelin system, the functional counterpart of NO, in the pathophysiology of polyglobulia remains still elusive. Therefore a novel erythropoietin overexpressing mouse was generated, with hematocrit levels of about 80%. Hence, we analyzed vascular contractions to ET-1 and big endothelin-1 (big ET-1), endothelin-1 (ET-1) promoter activity, ET-1 immunochemistry, endothelin-1 (ET-1)-protein tissue levels, ETA/B-receptor mRNA expression in this novel transgenic model of severe polyglobulia. For analysis of ET-1 promotor activity, EPO transgenic mice were mated with homozygous transgenic mice expressing the lacZ gene under control of the human ET-1 promoter and immunochistochemistry for gal blue was performed in lacZ transgenic animals. Notwithstanding markedly increased eNOS expression, NO-mediated endothelium-dependent relaxation and circulating and vascular tissue NO levels indicating enhanced bioavailability of NO, ET-1 tissue levels were also augmented in heart, kidney, liver and aorta (2.2±0.3 vs. 0.5±0.1 pg/mg tissue; P<0.01) of transgenic polyglobulic animals. Accordingly, immunohistochemistry demonstrated enhanced expression of ET-1 protein in the vascular wall of polyglobulic animals as compared to controls (p< 0.05), while increase of ET-1 promoter activity was confined to the perivascular tissue (P<0.05). NOS inhibition with L-NAME unmasked increased vascular reactivity to ET-1 and bigET-1 and aortic ETA/B receptor mRNA gene expression was enhanced (p<0.05 vs. controls). Administration of the NOS inhibitor L-NAME led to acute vasoconstriction of peripheral resistance vessels, hypertension and death of transgenic mice within 2 days, while wildtypes did not show increased mortality. Treatment with the ETA antagonist darusentan doubled survival time of transgenic polyglobulic mice after NO synthase inhibition (p<0.01 vs placebo). In conclusion, in this study we provide first evidence that the tissue endothelin system is activated by polyglobulia. Together with a stimulated NO system it contributes to cardiovascular regulation in pathophysiological conditions associated with increased hematocri

Counteracting incentive sensitization in severe alcohol dependence using deep brain stimulation of the nucleus accumbens: clinical and basic science aspects

The ventral striatum / nucleus accumbens has been implicated in the craving for drugs and alcohol which is a major reason for relapse of addicted people. Craving might be induced by drug-related cues. This suggests that disruption of craving-related neural activity in the nucleus accumbens may significantly reduce craving in alcohol-dependent patients. Here we report on preliminary clinical and neurophysiological evidence in three male patients who were treated with high frequency deep brain stimulation of the nucleus accumbens bilaterally. All three had been alcohol dependent for many years, unable to abstain from drinking, and had experienced repeated relapses prior to the stimulation. After the operation, craving was greatly reduced and all three patients were able to abstain from drinking for extended periods of time. Immediately after the operation but prior to connection of the stimulation electrodes to the stimulator, local field potentials were obtained from the externalized cables in two patients while they performed cognitive tasks addressing action monitoring and incentive salience of drug related cues. LFPs in the action monitoring task provided further evidence for a role of the nucleus accumbens in goal-directed behaviors. Importantly, alcohol related cue stimuli in the incentive salience task modulated LFPs even though these cues were presented outside of the attentional focus. This implies that cue-related craving involves the nucleus accumbens and is highly automatic

From dense to sparse design: Optimal rates under the supremum norm for estimating the mean function in functional data analysis

We derive optimal rates of convergence in the supremum norm for estimating the H\"older-smooth mean function of a stochastic process which is repeatedly and discretely observed with additional errors at fixed, multivariate, synchronous design points, the typical scenario for machine recorded functional data. Similarly to the optimal rates in $L_2$ obtained in \citet{cai2011optimal}, for sparse design a discretization term dominates, while in the dense case the parametric $\sqrt n$ rate can be achieved as if the $n$ processes were continuously observed without errors. The supremum norm is of practical interest since it corresponds to the visualization of the estimation error, and forms the basis for the construction uniform confidence bands. We show that in contrast to the analysis in $L_2$, there is an intermediate regime between the sparse and dense cases dominated by the contribution of the observation errors. Furthermore, under the supremum norm interpolation estimators which suffice in $L_2$ turn out to be sub-optimal in the dense setting, which helps to explain their poor empirical performance. In contrast to previous contributions involving the supremum norm, we discuss optimality even in the multivariate setting, and for dense design obtain the $\sqrt n$ rate of convergence without additional logarithmic factors. We also obtain a central limit theorem in the supremum norm, and provide simulations and real data applications to illustrate our results

Bad Guys, Bad Brands? How product placement on cool movie antagonists alters brand recall, purchase intention and brand perception

Masteroppgave(MSc) in Master of Science in Strategic Marketing Management - Handelshøyskolen BI, 2024This thesis addresses a gap in the product placement literature by addressing the effects of placing products on movie antagonists. The research explores if different characteristics of these antagonists will alter the effects. Drawn from the literature on coolness and aesthetics we manipulate a cool and an uncool antagonist to test their effects on brand recall, purchase intention, and brand perception. In addition, the study accounts for mediating effects of fit, and moderating effects of product placement attitudes are tested. Utilizing a cross-sectional research design in the form of an online survey, three character manipulations are tested to add a quasi-experimental element. The findings uncover interesting insight regarding the product placement literature. Surprisingly, these findings suggest that the cool antagonist shows a negative effect on the consumers brand recall, suggesting their complexity may obscure the brand message. The same counts for purchase intention as the cool antagonist yet again shows negative effects, heightening the possibility for potential confusion among consumers. However, the uncool antagonist displays a positive influence on purchase intentions. Moreover, for brand perceptions the risk of brand transgressions is revealed, as the uncool antagonist shows adverse effects. Additionally, the results indicate that fit maintains its importance in product placements, and product placement attitudes have positive interaction effects. The results extend todays literature on product placement as it explores these placements on unconventional characters. Thus, the theoretical and managerial implications highlight the opportunities that lie within these placements

Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance valuation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively little image overlap, which can be challenging for NeRF-based approaches that are typically trained with significantly more images and varying camera angles. We show that despite lower quality compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually convincing results in challenging areas. Furthermore, our study shows that in particular increasing the number of sub-modules and predicting the visibility using an additional neural network improves the quality of the resulting reconstructions significantly

Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions

Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery

Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy {\delta}1.25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions