The loss of alveolar bone supporting the maxillary central incisors and the general periodontal conditions were evaluated after 14 years in the 12 patients remaining from an original group of 47 under treatment in Malaysia. Alveolar bone loss was minimal during this period even in the presence of periodontal inflammation. These data suggest that treatment protects patients with leprosy from alveolar bone loss and suggests that other skeletal deformities might respond similarly.
Multiple-choice questions (MCQ) are widely used to evaluate students in the health sciences, including anatomy. Unusual responses in 90 simple MCQ examinations have been identified and classified as to cause, including a number of illustrated examples. About one-quarter of these errors were attributable to the teacher and could have been avoided by a critical analysis of the questions before use. The increasing use of sophisticated formats of the MCQ in medical education indicates that teachers need to analyze their questions more carefully before and after actual tests to minimize errors.
The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.