No abstract

Abstract:The success of medical implant innovation as a complex field of science and technology requires efforts and strategies in accompanying communication. Fostering public understanding and acceptance of biomedical research and its methods (animal experimentation in particular) is important to counteract wrong assumptions and expectations that journalists, lay people, and (future) patients may hold and that could impede effective takeup of true innovation in the clinical context. Two studies on media coverage of the term "personalized medicine" (n = 118 newspaper articles) and on medical implants (n = 256 newspaper articles) in Germany (published between 2008 and 2013) illustrate patterns of how journalists offer diverse frames of interpretation to lay audiences. Scholars in biomedical implant innovation and their institutions can benefit from taking these frames into account in order to bridge gaps of missing background knowledge or to counteract overgeneralization of past scandals with (breast) implants. This way, empirically informed communication strategies will satisfy public requirements for transparency and science-public dialogue so that sustainable trust in biomedical implant innovations can evolve.

The primary goal of discriminant analysis (DA) is to explain and predict a categorical variable by a combination of interval predictor variables. Its use is twofold: (i) DA determines predictors that separate best between cases. (ii) It assigns new cases into the existing groups according to their values regarding the independent variables. The article describes assumptions, general concepts, and evaluation criteria of discriminant analysis and gives an overview of different classification rules. Additionally, it indicates potential problems, limitations, and difficulties inherent to DA and compares the method to alternative procedures.

No abstract

Modeling of rainfall-runoff is very critical for flood prediction studies in decision making for disaster management. Deep learning methods have proven to be very useful in hydrological prediction. To increase their acceptance in the hydrological community, they must be physic-informed and show some interpretability. They are several ways this can be achieved e.g. by learning from a fully-trained hydrological model which assumes the availability of the hydrological model or to use physic-informed data. In this work we developed a Graph Attention Network (GAT) with learnable Adjacency Matrix coupled with a Bi-directional Gated Temporal Convolutional Neural Network (2DGAT-BiLSTM). Physic-informed data with spatial information from Digital Elevation Model and geographical data is used to train it. Besides, precipitation, evapotranspiration and discharge, the model utilizes the catchment area characteristic information, such as instantaneous slope, soil type, drainage area etc. The method is compared to two different current developments in deep learning structures for streamflow prediction, which also utilize all the spatial and temporal information in an integrated way. One, namely Graph Neural Rainfall-Runoff Models (GNRRM) uses timeseries prediction on each node and a Graph Neural Network (GNN) to route the information to the target node and another one called STA-LSTM is based on Spatial and temporal Attention Mechanism and Long Short Term Memory (LSTM) for prediction. The different methods were compared in their performance in predicting the flow at several points of a pilot catchment area. With an average prediction NSE and KGE of 0.995 and 0.981, respectively for 2DGAT-BiLSTM, it could be shown that graph attention mechanism and learning the adjacency matrix for spatial information can boost the model performance and robustness, and bring interpretability and with the inclusion of domain knowledge the acceptance of the models.