Nitrogen, Phosphorus, and Suspended Solids Concentrations in Tributaries to the Great Bay Estuary Watershed in 2011

Nitrogen, phosphorus, and sediment loads to the Great Bay Estuary are a growing concern. The Piscataqua Region Estuaries Partnership (PREP) calculates the nitrogen load from tributaries to the Great Bay Estuary for its State of the Estuaries reports. Therefore, the purpose of this study was to collect representative data on nitrogen, phosphorus, and suspended sediment concentrations in tributaries to the Great Bay Estuary in 2011. The study design followed the tributary sampling design which was implemented by the New Hampshire Department of Environmental Services between 2001 and 2007 and by the University of New Hampshire in 2008 and 2010, so as to provide comparable data to the previous loading estimates

Nitrogen, Phosphorus, and Suspended Solids Concentrations in Tributaries to the Great Bay Estuary Watershed in 2010

Nitrogen, phosphorus, and sediment loads to the Great Bay Estuary are a growing concern. The Piscataqua Region Estuaries Partnership (PREP) calculates the nitrogen load from tributaries to the Great Bay Estuary for its State of the Estuaries reports. Therefore, the purpose of this study was to collect representative data on nitrogen, phosphorus, and suspended sediment concentrations in tributaries to the Great Bay Estuary in 2010. The study design followed the tributary sampling design which was implemented by the New Hampshire Department of Environmental Services between 2001 and 2007 and by the University of New Hampshire in 2008 and 2009, so as to provide comparable data to the previous loading estimates

Shellfish Tissue Monitoring in Piscataqua Region Estuaries 2010 and 2011

The goal of this project was to provide data for two PREP indicators of estuarine condition: TOX1 and TOX3. These two indicators report on “Shellfish tissue concentrations relative to FDA standards” and “Trends in shellfish tissue contaminant concentrations”, respectively. Both of these indicators depend on data from the Gulfwatch Program. In particular, TOX3 requires annual data at benchmark sites to assess trends. In 2010 and 2011, PREP supported the collection and analysis of tissue samples from benchmark mussel sites in Hampton-Seabrook Harbor and Dover Point

Kufor-Rakeb syndrome, pallido-pyramidal degeneration with supranuclear upgaze paresis and dementia, maps to 1p36

Kufor-Rakeb syndrome is an autosomal recessive nigro-striatal-pallidal-pyramidal neurodegeneration. The onset is in the teenage years with clinical features of Parkinson’s disease plus spasticity, supranuclear upgaze paresis, and dementia. Brain scans show atrophy of the globus pallidus and pyramids and, later, widespread cerebral atrophy. We report linkage in Kufor- Rakeb syndrome to a 9 cM region of chromosome 1p36 delineated by the markers D1S436 and D1S2843, with a maximum multipoint lod score of 3.6. (J Med Genet 2001;38:680–682

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework

Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function

This paper presents a number of proofs that equate the outputs of a Multi-Layer Perceptron (MLP) classifier and the optimal Bayesian discriminant function for asymptotically large sets of statistically independent training samples. Two broad classes of objective functions are shown to yield Bayesian discriminant performance. The first class are “reasonable error measures,” which achieve Bayesian discriminant performance by engendering classifier outputs that asymptotically equate to a posteriori probabilities. This class includes the mean-squared error (MSE) objective function as well as a number of information theoretic objective functions. The second class are classification figures of merit (CFMmono ), which yield a qualified approximation to Bayesian discriminant performance by engendering classifier outputs that asymptotically identify themaximum a posteriori probability for a given input. Conditions and relationships for Bayesian discriminant functional equivalence are given for both classes of objective functions. Differences between the two classes are then discussed very briefly in the context of how they might affect MLP classifier generalization, given relatively small training sets

Adaptive working memory strategy training in early Alzheimer's disease: randomised controlled trial.

BACKGROUND: Interventions that improve cognitive function in Alzheimer's disease are urgently required. AIMS: To assess whether a novel cognitive training paradigm based on 'chunking' improves working memory and general cognitive function, and is associated with reorganisation of functional activity in prefrontal and parietal cortices (trial registration: ISRCTN43007027). METHOD: Thirty patients with mild Alzheimer's disease were randomly allocated to receive 18 sessions of 30 min of either adaptive chunking training or an active control intervention over approximately 8 weeks. Pre- and post-intervention functional magnetic resonance imaging (fMRI) scans were also conducted. RESULTS: Adaptive chunking training led to significant improvements in verbal working memory and untrained clinical measures of general cognitive function. Further, fMRI revealed a bilateral reduction in task-related lateral prefrontal and parietal cortex activation in the training group compared with controls. CONCLUSIONS: Chunking-based cognitive training is a simple and potentially scalable intervention to improve cognitive function in early Alzheimer's disease