Sex Differences in Semantic Processing: Event-Related Brain Potentials Distinguish between Lower and Higher Order Semantic Analysis during Word Reading

Behavioral studies suggest that women and men differ in the strategic elaboration of verbally encoded information especially in the absence of external task demand. However, measuring such covert processing requires other than behavioral data. The present study used event-related potentials to compare sexes in lower and higher order semantic processing during the passive reading of semantically related and unrelated word pairs. Women and men showed the same early context effect in the P1-N1 transition period. This finding indicates that the initial lexical-semantic access is similar in men and women. In contrast, sexes differed in higher order semantic processing. Women showed an earlier and longer lasting context effect in the N400 accompanied by larger signal strength in temporal networks similarly recruited by men and women. The results suggest that women spontaneously conduct a deeper semantic analysis. This leads to faster processing of related words in the active neural networks as reflected in a shorter stability of the N400 map in women. Taken together, the findings demonstrate that there is a selective sex difference in the controlled semantic analysis during passive word reading that is not reflected in different functional organization but in the depth of processing.

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.

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.