Fusion Techniques in Biomedical Information Retrieval

For difficult cases clinicians usually use their experience and also the information found in textbooks to determine a diagnosis. Computer tools can help them supply the relevant information now that much medical knowledge is available in digital form. A biomedical search system such as developed in the Khresmoi project (that this chapter partially reuses) has the goal to fulfil information needs of physicians. This chapter concentrates on information needs for medical cases that contain a large variety of data, from free text, structured data to images. Fusion techniques will be compared to combine the various information sources to supply cases similar to an example case given. This can supply physicians with answers to problems similar to the one they are analyzing and can help in diagnosis and treatment planning

Carbon Stocks and Fluxes in Tropical Lowland Dipterocarp Rainforests in Sabah, Malaysian Borneo

Deforestation in the tropics is an important source of carbon C release to the atmosphere. To provide a sound scientific base for efforts taken to reduce emissions from deforestation and degradation (REDD+) good estimates of C stocks and fluxes are important. We present components of the C balance for selectively logged lowland tropical dipterocarp rainforest in the Malua Forest Reserve of Sabah, Malaysian Borneo. Total organic C in this area was 167.9 Mg C ha−1±3.8 (SD), including: Total aboveground (TAGC: 55%; 91.9 Mg C ha−1±2.9 SEM) and belowground carbon in trees (TBGC: 10%; 16.5 Mg C ha−1±0.5 SEM), deadwood (8%; 13.2 Mg C ha−1±3.5 SEM) and soil organic matter (SOM: 24%; 39.6 Mg C ha−1±0.9 SEM), understory vegetation (3%; 5.1 Mg C ha−1±1.7 SEM), standing litter (<1%; 0.7 Mg C ha−1±0.1 SEM) and fine root biomass (<1%; 0.9 Mg C ha−1±0.1 SEM). Fluxes included litterfall, a proxy for leaf net primary productivity (4.9 Mg C ha−1 yr−1±0.1 SEM), and soil respiration, a measure for heterotrophic ecosystem respiration (28.6 Mg C ha−1 yr−1±1.2 SEM). The missing estimates necessary to close the C balance are wood net primary productivity and autotrophic respiration

Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context

Analysis of Informative Features for Negative Selection in Protein Function Prediction

Negative examples in automated protein function prediction (AFP), that is proteins known not to possess a given protein function, are usually not directly stored in public proteome and genome databases, such as the Gene Ontology database. Nevertheless, most computational methods need negative examples to infer new predictions. A variety of algorithms has been proposed in AFP for negative selection, ranging from network- and feature-based heuristics, to hierarchy-based and hierarchy-less strategies. Moreover, several bio-molecular information sources about proteins, such as gene co-expression, genetic and protein-protein interactions data, are naturally encoded in protein networks, where nodes are proteins and edges connect proteins sharing common characteristics. Although selecting negatives in biological networks is thereby a central and challenging problem in computational biology, detecting the characteristics proteins should have to be considered as negative is still a difficult task. It this work, we show that a few protein features extracted from the network help in detecting reliable negatives. We tested such features in two real world experiments: predicting unreliable negatives with an SVM classifier through temporal holdout on model organisms for AFP, and selecting reliable negatives with a clustering-based state-of-the-art negative selection procedure