Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.

Mixture Density Networks (MDN) belong to a class of models that can be applied to data which cannot be sufficiently described by a single distribution since it originates from different components of the main unit and therefore needs to be described by a mixture of densities. In some situations, however, MDNs seem to have problems with the proper identification of the latent components. While these identification issues can to some extent be contained by using custom initialization strategies for the network weights, this solution is still less than ideal since it involves subjective opinions. We therefore suggest replacing the hidden layers between the model input and the output parameter vector of MDNs and estimating the respective distributional parameters with penalized cubic regression splines. Applying this approach to data from Gaussian mixture distributions as well gamma mixture distributions proved to be successful with the identification issues not playing a role anymore and the splines reliably converging to the true parameter values.

Background and aims Transjugular intrahepatic portosystemic shunt (TIPS) implantation is an established procedure to treat portal hypertension. Impact of administration of aspirin on transplant-free survival after TIPS remains unknown. Methods A multicenter retrospective analysis including patients with TIPS implantation between 2011 and 2018 at three tertiary German Liver Centers was performed. N = 583 patients were included. Survival analysis was performed in a matched cohort after propensity score matching. Patients were grouped according to whether aspirin was (PSM-aspirin-cohort) or was not (PSM-no-aspirin-cohort) administered after TIPS. Primary endpoint of the study was transplant-free survival at 12 months after TIPS. Results Aspirin improved transplant-free survival 12 months after TIPS with 90.7% transplant-free survival compared to 80.0% (p = 0.001) after PSM. Separated by TIPS indication, aspirin did improve transplant-free survival in patients with refractory ascites significantly (89.6% vs. 70.6% transplant-free survival, p < 0.001), while no significant effect was observed in patients with refractory variceal bleeding (91.1% vs. 92.2% transplant-free survival, p = 0.797). Conclusion This retrospective multicenter study provides first data indicating a beneficial effect of aspirin on transplant-free survival after TIPS implantation in patients with refractory ascites.