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Explores the archaeology of Mississippian communities and households using new data and advances in method and theory   Published in 1995, Mississippian Communities and Households , edited by J.Daniel Rogers and Bruce D.

The Recorrido Arqueológico de Coixtlahuaca (RAC) presents period-by-period settlement pattern maps for the valley of Coixtlahuaca in the northern Mixteca Alta. The RAC project made improvements in full-coverage survey methods. We identify limitations and suggest that similar projects in the future need to resolve several management and budget problems. The survey revealed two periods of heavy occupation, 700–300 BC and AD 1200–1520, separated by a long period of lower population. Archaeological and historical data indicate that during the AD 1200–1520 period, and probably earlier, small landholders organized in strong communities managed an intensive agroecosystem, investing in landesque capital. Urbanization was impressive, yet cities were aggregations of communities and barrios. Today local citizens pose questions about how the large prehispanic population could have organized and sustained itself; these questions coincide with anthropological interest in collective agency, property, landesque capital, and collapse. En este artículo se presentan los mapas del patrón de asentamiento por período del valle de Coixtlahuaca, en el norte de la región de la Mixteca Alta, en el estado de Oaxaca, México. Estos datos fueron generados por el proyecto Recorrido Arqueológico de Coixtlahuaca (RAC). En el proyecto RAC se realizaron avances sobre los métodos de prospección de cobertura total. En este artículo se identifican ciertas limitaciones y se sugiere que proyectos similares a realizarse en el futuro deberán prever y resolver diversos problemas de presupuesto y gestión. Como resultado de la prospección se reconocieron dos periodos de intensa ocupación, 700-300 a.C. y 1200-1520 d.C., separados por un largo periodo de menor densidad poblacional. Los datos arqueológicos e históricos indican que durante el periodo de 1200 a 1520 d.C., y quizás antes, los pequeños productores agrícolas lograron el manejo de un agroecosistema intensivo, invirtiendo en capital en tierras (“landesque capital”) y organizándose en fuertes comunidades locales. Aunque la urbanización fue impresionante, estas ciudades eran agregados de comunidades y barrios. Las preguntas planteadas por los ciudadanos locales modernos acerca de cómo la numerosa población prehispánica pudo mantenerse y organizarse son relevantes para los temas antropológicos de agencia colectiva, propiedad, capital en la forma de enmiendas a la tierra y colapso.

1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

Neurofibromatosis type 1 (NF1) is a commonly inherited autosomal dominant disorder. Previous studies indicated that mice homozygous for a null mutation in Nf1 exhibit mid-gestation lethality, whereas heterozygous mice have an increased predisposition to tumors and learning impairments. Here we show that mice lacking the alternatively spliced exon 23a, which modifies the GTPase-activating protein (GAP) domain of Nf1, are viable and physically normal, and do not have an increased tumor predisposition, but show specific learning impairments. Our findings have implications for the development of a treatment for the learning disabilities associated with NF1 and indicate that the GAP domain of NF1 modulates learning and memory.