Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Measures and Limits of Models of Fixation Selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection . We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced

The Protein Partners of GTP Cyclohydrolase I in Rat Organs

GTP cyclohydrolase I (GCH1) is the rate-limiting enzyme for tetrahydrobiopterin biosynthesis and has been shown to be a promising therapeutic target in ischemic heart disease, hypertension, atherosclerosis and diabetes. The endogenous GCH1-interacting partners have not been identified. Here, we determined endogenous GCH1-interacting proteins in rat.A pulldown and proteomics approach were used to identify GCH1 interacting proteins in rat liver, brain, heart and kidney. We demonstrated that GCH1 interacts with at least 17 proteins including GTP cyclohydrolase I feedback regulatory protein (GFRP) in rat liver by affinity purification followed by proteomics and validated six protein partners in liver, brain, heart and kidney by immunoblotting. GCH1 interacts with GFRP and very long-chain specific acyl-CoA dehydrogenase in the liver, tubulin beta-2A chain in the liver and brain, DnaJ homolog subfamily A member 1 and fatty aldehyde dehydrogenase in the liver, heart and kidney and eukaryotic translation initiation factor 3 subunit I (EIF3I) in all organs tested. Furthermore, GCH1 associates with mitochondrial proteins and GCH1 itself locates in mitochondria.GCH1 interacts with proteins in an organ dependant manner and EIF3I might be a general regulator of GCH1. Our finding indicates GCH1 might have broader functions beyond tetrahydrobiopterin biosynthesis

Amyloids - A functional coat for microorganisms

Amyloids are filamentous protein structures ~10 nm wide and 0.1–10 µm long that share a structural motif, the cross-β structure. These fibrils are usually associated with degenerative diseases in mammals. However, recent research has shown that these proteins are also expressed on bacterial and fungal cell surfaces. Microbial amyloids are important in mediating mechanical invasion of abiotic and biotic substrates. In animal hosts, evidence indicates that these protein structures also contribute to colonization by activating host proteases that are involved in haemostasis, inflammation and remodelling of the extracellular matrix. Activation of proteases by amyloids is also implicated in modulating blood coagulation, resulting in potentially life-threatening complications.