Objective-Individuals with schizophrenia exhibit disturbances in a number of cognitive, affective, sensory, and motor functions that depend on the circuitry of different cortical areas. The cognitive deficits associated with dysfunction of the dorsolateral prefrontal cortex result, at least in part, from abnormalities in GABA neurotransmission, as reflected in a specific pattern of altered expression of GABA-related genes. Consequently, the authors sought to determine whether this pattern of altered gene expression is restricted to the dorsolateral prefrontal cortex or could also contribute to the dysfunction of other cortical areas in subjects with schizophrenia.Method-Real-time quantitative polymerase chain reaction was used to assess the levels of eight GABA-related transcripts in four cortical areas (dorsolateral prefrontal cortex, anterior cingulate cortex, and primary motor and primary visual cortices) of subjects (N=12) with schizophrenia and matched normal comparison subjects.Results-Expression levels of seven transcripts were lower in subjects with schizophrenia, with the magnitude of reduction for each transcript comparable across the four areas. The largest reductions were detected for mRNA encoding somatostatin and parvalbumin, followed by moderate decreases in mRNA expression for the 67-kilodalton isoform of glutamic acid decarboxylase, the GABA membrane transporter GAT-1, and the α1 and δ subunits of GABA A receptors. In contrast, the expression of calretinin mRNA did not differ between the subject groups in any of the four areas.Conclusions-Because the areas examined represent the major functional domains (e.g., association, limbic, motor, and sensory) of the cerebral cortex, our findings suggest that a conserved set of molecular alterations affecting GABA neurotransmission contribute to the pathophysiology of different clinical features of schizophrenia.The core features of schizophrenia include disturbances in critical cognitive functions, such as working memory, that are mediated by the neural circuitry of the dorsolateral prefrontal cortex (1,2). In the dorsolateral prefrontal cortex of subjects with schizophrenia, markers of inhibitory neurotransmission appear to be impaired (3). For example, reduced levels of mRNA encoding the 67-kilodalton isoform of glutamic acid decarboxylase (GAD 67 ), the enzyme principally responsible for GABA synthesis (4), and the GABA membrane transporter GAT-1, which regulates the reuptake of synaptically released GABA, have been schizophrenia (5-12). These alterations in markers of GABA neurotransmission appear to involve specific subsets of GABA neurons. For example, mRNA encoding parvalbumin and somatostatin, each of which is expressed in a separate subset of GABA neurons, was decreased, whereas mRNA encoding calretinin, which is expressed in a third subset of GABA neurons, was unchanged in subjects with schizophrenia (11,13). Furthermore, reduced GABA synthesis might be selectively mediated by a deficit in GAD 67 , because neither mRNA nor protein leve...

BackgroundWhile farmers’ markets are a potential strategy to increase access to fruits and vegetables in rural areas, more information is needed regarding use of farmers’ markets among rural residents. Thus, this study’s purpose was to examine (1) socio-demographic characteristics of participants; (2) barriers and facilitators to farmers’ market shopping in southern rural communities; and (3) associations between farmers’ market use with fruit and vegetable consumption and body mass index (BMI).MethodsCross-sectional surveys were conducted with a purposive sample of farmers’ market customers and a representative sample of primary household food shoppers in eastern North Carolina (NC) and the Appalachian region of Kentucky (KY). Customers were interviewed using an intercept survey instrument at farmers’ markets. Representative samples of primary food shoppers were identified via random digit dial (RDD) cellular phone and landline methods in counties that had at least one farmers’ market. All questionnaires assessed socio-demographic characteristics, food shopping patterns, barriers to and facilitators of farmers’ market shopping, fruit and vegetable consumption and self-reported height and weight. The main outcome measures were fruit and vegetable consumption and BMI. Descriptive statistics were used to examine socio-demographic characteristics, food shopping patterns, and barriers and facilitators to farmers’ market shopping. Linear regression analyses were used to examine associations between farmers’ market use with fruit and vegetable consumption and BMI, controlling for age, race, education, and gender.ResultsAmong farmers’ market customers, 44% and 55% (NC and KY customers, respectively) reported shopping at a farmers’ market at least weekly, compared to 16% and 18% of NC and KY RDD respondents. Frequently reported barriers to farmers’ market shopping were market days and hours, “only come when I need something”, extreme weather, and market location. Among the KY farmers’ market customers and NC and KY RDD respondents, fruit and vegetable consumption was positively associated with use of farmers’ markets. There were no associations between use of farmers’ markets and BMI.ConclusionsFruit and vegetable consumption was associated with farmers’ market shopping. Thus, farmers’ markets may be a viable method to increase population-level produce consumption.

This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regression problem in learning theory. A novel regularization approach is presented, which yields satisfactory learning rates. The rates depend on the approximation property and the capacity of the reproducing kernel Hilbert space measured by covering numbers. When the kernel is C ∞ and the regression function lies in the corresponding reproducing kernel Hilbert space, the rate is m −ζ with ζ arbitrarily close to 1, regardless of the variance of the bounded probability distribution.Short Title: Least-square Regularized Regression

A family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve multi-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these multi-kernel regularized classifiers when the loss functions achieve the zero value. The error analysis consists of two parts: regularization error and sample error. Allowing multi-kernels in the algorithm improves the regularization error and approximation error, which is one advantage of the multi-kernel setting. For a general loss function, we show how to bound the regularization error by the approximation in some weighted L q spaces. For the sample error, we use a projection operator. The projection in connection with the decay of the regularization error enables us to improve convergence rates in the literature even for the one-kernel schemes and special loss functions: leastsquare loss and hinge loss for support vector machine soft margin classifiers. Existence of the optimization problem for the regularization scheme associated with multi-kernels is verified when the kernel functions are continuous with respect to the index set. Concrete examples, including Gaussian kernels with flexible variances and probability distributions with some noise conditions, are used to illustrate the general theory.

Both in vivo and post-mortem investigations have demonstrated smaller volumes of the whole brain and of certain brain regions in individuals with schizophrenia. It is unclear to what degree such smaller volumes are due to the illness or to the effects of antipsychotic medication treatment. Indeed, we recently reported that chronic exposure of macaque monkeys to haloperidol or olanzapine, at doses producing plasma levels in the therapeutic range in schizophrenia subjects, was associated with significantly smaller total brain weight and volume, including an 11.8-15.2% smaller gray matter volume in the left parietal lobe. Consequently, in this study we sought to determine whether these smaller volumes were associated with lower numbers of the gray matter's constituent cellular elements. The use of point counting and Cavalieri's principle on Nissl-stained sections confirmed a 14.6% smaller gray matter volume in the left parietal lobe from antipsychotic-exposed monkeys. Use of the optical fractionator method to estimate the number of each cell type in the gray matter revealed a significant 14.2% lower glial cell number with a concomitant 10.2% higher neuron density. The numbers of neurons and endothelial cells did not differ between groups. Together, the findings of smaller gray matter volume, lower glial cell number, and higher neuron density without a difference in total neuron number in antipsychotic-exposed monkeys parallel the results of post-mortem schizophrenia studies, and raise the possibility that such observations in schizophrenia subjects might be due, at least in part, to antipsychotic medication effects.