Aortic neck dilatation after endovascular abdominal aortic aneurysm repair: a word of caution

Continuing aortic neck dilatation, most probably an expression of ongoing aneurysmal wall degeneration of the infrarenal aortic segment, has been shown to seriously impair clinical results after endovascular abdominal aortic aneurysm repair. However, conflicting data on the extent and clinical relevance on this observation have recently been published. This article reviews the recent literature, summarizing our current understanding of the role of aortic neck dilatation after open surgical and endovascular abdominal aortic aneurysm repair. In addition, differences in methodology of studies on aortic neck dilatation are highlighted.

Modulating the granularity of category formation by global cortical states

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients.

Origins of the word "phenology"

Observing and documenting life cycle stages of plants and animals have been tradition and necessity for humans throughout history. Phenological observations—as called by their modern scientific name—were key to successful hunting and farming because the precise knowledge of animal behavior and plant growth, as well as their timing with changing seasons, was critical for survival. In today's context of environmental awareness and climate change research, phenological observations have become prime indicators of documenting altered life cycles due to environmental change in disciplines from biology to climatology, geography, and environmental history. Observations on the ground, from space, and from models of different complexity describe intra-annual and interannual changes of life cycles at individual, pixel, or grid box scale.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.