A biosemiotic interpretation of certain genital morphological structures in the spiders Dysdera erythrina and Dysdera crocata (Araneae: Dysderidae).

A biosemiotic approach to the interpretation of morphological data is apt to highlight morphological traits that have hitherto gone unnoticed for their crucial roles in intraspecific sign interpretation and communication processes. Examples of such traits include specific genital structures found in the haplogyne spiders Dysdera erythrina (Walckenaer 1802) and Dysdera crocata (Koch 1838). In both D. erythrina and D. crocata, the distal sclerite of the male bulb and the anterior diverticulum of the female endogyne exhibit a striking, previously unreported correspondence in size and shape, allowing for a precise match between these structures during copulation. In D. erythrina, the sclerite at the tip of the bulb and the anterior diverticulum are semi-circular in shape, whereas in D. crocata they are rectangular. From the perspective of biosemiotics, which studies the production and interpretation of signs and codes in living systems, these structures are considered the morphological zones of an intraspecific sign interpretation process. This process constitutes one of the necessary prerequisites for sperm transfer and the achievement of fertilization. Therefore, these morphological elements deserve particular attention as they hold higher taxonomic value compared to morphological traits of the bulb for which a relevant role in mating and fertilization has not been proven. Thus, an approach to species delimitation based on biosemiotics, with its specific evaluation of morphological structures, provides new insights for the multidisciplinary endeavour of modern integrative taxonomy.

A neural-network technique to learn concepts from electroencephalograms.

A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms (EEGs). A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the EEG segments presented by spectral and statistical features. This technique has been applied to the EEG data recorded from 65 sleeping healthy newborns in order to learn a brain maturation concept of newborns aged between 35 and 51 weeks. The 39,399 and 19,670 segments from these data have been used for learning and testing the concept, respectively. As a result, the concept has correctly classified 80.1% of the testing segments or 87.7% of the 65 records.

The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns.

In this paper, we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle, and noise artifacts and, as a consequence, some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible classification rules while also attempting to keep their classification error down. This technique is shown to out-perform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.