The common approach in morphological analysis of dendritic spines of mammalian neuronal cells is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia shaped. The corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate the synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist. We propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests. We compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify the differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. The source code of our software is freely available for non-commercial use1. Contact: d.plewczynski@cent.uw.edu.pl.
In this paper, we present work-in-progress of a recently started research effort that aims at understanding the hidden temporal dynamics in online food communities. In this context, we have mined and analyzed temporal patterns in terms of recipe production and consumption in a large German community platform. As our preliminary results reveal, there are indeed a range of hidden temporal patterns in terms of food preferences and in particular in consumption and production. We believe that this kind of research can be important for future work in personalized Web-based information access and in particular recommender systems.
Since innovation plays an important role in the context of food, as evident in how successful chefs, restaurants or cuisines in general evolve over time, we were interested in exploring this dimension from a more virtual perspective. In particular, the paper presents results of a study that was conducted in the context of a large-scale German online food community forum to explore another important dimension of online food recipe production, namely known as online food innovation. The study shows interesting findings and temporal patterns in terms of how online food recipe innovation takes place.
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
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