New Members and Foreign Associates Elected to the National Academy of Sciences on May 1, 2001: 72 New Members Chosen by the Academy

The Academy has elected 72 new members and 15 foreign associates from 10 countries in recognition of their distinguished and continuing achievements in original research. The election was held during the business session of the 138th annual meeting of the Academy. Election to membership in the Academy is considered one of the highest honors that can be accorded a U.S. scientist or engineer. Foreign associates are non-voting members of the Academy, with citizenship outside of the United States.

New Members and Foreign Associates Elected to the National Academy of Sciences on May 2, 2000: 60 New Members Chosen by the Academy

The Academy has elected 60 new members and 15 foreign associates from 9 countries in recognition of their distinguished and continuing achievements in original research. The election was held during the business session of the 137th annual meeting of the Academy. Election to membership in the Academy is considered one of the highest honors that can be accorded a U.S. scientist or engineer. Foreign associates are non-voting members of the Academy, with citizenship outside of the United States.

Archaeal-eubacterial mergers in the origin of Eukarya: phylogenetic classification of life.

A symbiosis-based phylogeny leads to a consistent, useful classification system for all life. "Kingdoms" and "Domains" are replaced by biological names for the most inclusive taxa: Prokarya (bacteria) and Eukarya (symbiosis-derived nucleated organisms). The earliest Eukarya, anaerobic mastigotes, hypothetically originated from permanent whole-cell fusion between members of Archaea (e.g., Thermoplasma-like organisms) and of Eubacteria (e.g., Spirochaeta-like organisms). Molecular biology, life-history, and fossil record evidence support the reunification of bacteria as Prokarya while subdividing Eukarya into uniquely defined subtaxa: Protoctista, Animalia, Fungi, and Plantae.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

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