Open fetal surgery for myelomeningocele

Despite efforts at prevention through the use of preconception folic acid, spina bifida remains one of the most common congenital anomalies of the central nervous system that is compatible with life. It is, however, associated with a significant degree of lifelong morbidity. The development of open fetal surgery for myelomeningocele (MMC) has been a long process but one that serves as a model for how new procedures and technologies need to be properly evaluated before being brought into mainstream medical practice. Even so, risks and benefits need to be evaluated for each patient. The currently available studies have been carried out on a highly selected patient population where the fetal findings provided the maximum opportunity for benefit from prenatal closure of the MMC defect. There is the potential that as the surgery becomes more widely available, pressure will be brought to bear to perform surgery in cases where the likelihood for benefit is decreased and yet the risks are not. The only way to duplicate the results of the current studies is to follow the methodology and criteria that were used in the studies. This will mean that not every fetus with an MMC will be a candidate for in utero surgery. The balance of risk to benefit will continue to evolve as further technological advances are evaluated and more follow-up information is obtained. Copyright © 2011 John Wiley & Sons, Ltd.     

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.