Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest

Specificity of facial expression labeling deficits in childhood psychopathology

Background: We examined whether face-emotion labeling deficits are illness-specific or an epiphenomenon of generalized impairment in pediatric psychiatric disorders involving mood and behavioral dysregulation. Method: Two hundred fifty-two youths (7-18 years old) completed child and adult facial expression recognition subtests from the Diagnostic Analysis of Nonverbal Accuracy (DANVA) instrument. Forty-two participants had bipolar disorder (BD), 39 had severe mood dysregulation (SMD; i.e., chronic irritability, hyperarousal without manic episodes), 44 had anxiety and/or major depressive disorders (ANX/MDD), 35 had attention-deficit/hyperactivity and/or conduct disorder (ADHD/CD), and 92 were controls. Dependent measures were number of errors labeling happy, angry, sad, or fearful emotions. Results: BD patients made more errors than ANX/MDD, ADHD/CD, or controls when labeling all emotional expressions, whether those expressions were on the faces of children or adults. SMD also showed emotion-labeling deficits, in particular as compared to ANX/MDD patients and controls. Conclusions: Face-emotion labeling deficits differentiate BD and SMD patients from those with ANX/MDD or ADHD/CD and controls. The extent to which such deficits cause vs. result from emotional dysregulation requires further study

Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma

SummaryWe report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine