Expression of D1 receptor mRNA in projections from the forebrain to the ventral tegmental area

The presence of subunit proteins, 1H9 for the alpha-subunit and 2B6 for the beta-subunit, of H(+)-K+ ATPase and its activity in tubulovesicles and intracellular canaliculi of gastric parietal cells were immunocytochemically and enzyme cytochemically examined. Specimens were taken from healthy human volunteers by endoscopic biopsy in resting, tetragastrin-stimulated and omeprazole-inhibited conditions. H(+)-K+ ATPase was present in both intracellular canaliculi and tubulovesicles in these three conditions. Gold particles of the alpha-subunit decreased in number, and those showing the beta-subunit increased under both gastrin-stimulating and omeprazole-inhibiting conditions compared with parietal cells in the resting state. We suggest that the administration of tetragastrin and omeprazole alter the turnover rate of each subunit of H(+)-K+ ATPase, resulting in the difference of the proportions of alpha- and beta-subunits. Moreover, the activity of H(+)-K+ ATPase was detected strongly beneath the membrane of microvilli and weakly in that of tubulovesicles under these three conditions. After 7 days of daily oral omeprazole intake, H(+)-K+ ATPase in parietal cells were detected in intracellular canaliculi and tubulovesicles. However, the H(+)-K+ ATPase activity in tubulovesicles was diminished 1 h after omeprazole intake, and the activity in intracellular canaliculi was completely inhibited even 3 h after omeprazole administration. These results show that omeprazole inhibited the H(+)-K+ ATPase activity in both intracellular canaliculi and tubulovesicles.     

Dopamine depletion augments endogenous opioid-induced locomotion in the nucleus accumbens using both mu 1 and delta opioid receptors

The aim of this study is to analyze further the opioid receptor subtypes involved in the augmentation of behavioral activity after dopamine depletion in the nucleus accumbens of rats. Initially, the opioid receptors involved in the augmentation of locomotion produced by endogenous opioids were evaluated by microinjection of kelatorphan, an inhibitor of proteolytic enzymes that inactivates enkephalin, with or without specific antagonists for mu 1 or delta-opioid receptors, naloxonazine or naltrindole, respectively. Kelatorphan produced a dose-dependent increase in horizontal photocell counts and vertical movements. At all doses examined the behavioral response was augmented in rats sustaining accumbal dopamine lesions. The augmentation in dopamine-depleted rats was partially blocked by naloxonazine or naltrindole. Since the motor stimulant response to intra-accumbens microinjection of the delta-opioid agonist, [D-penicillamine2,5]-enkephalin, was not augmented in a previous study, we tested the behavioral response to a new endogenous delta-opioid agonist, [D-Ala2] deltorphin I. The locomotor response to deltorphin was slightly augmented in dopamine-depleted rats. These data suggest that the augmentation in the motor response elicited by endogenous opioids after dopamine lesions in the nucleus accumbens involves both mu 1, and delta-opioid receptors.     

A topographically organized gamma-aminobutyric acid projection from the ventral pallidum to the nucleus accumbens in the rat

Anatomical and electrophysiological studies have indicated that a reciprocal projection from the ventral pallidum back to the nucleus accumbens exists and has functional relevance. In this study, the topographical projection from the ventral pallidum to the nucleus accumbens was examined by using retrograde tracing with fluoro-gold iontophoresed in subcompartments of the nucleus accumbens in rats combined with either in situ hybridization for glutamic acid decarboxylase and preproenkephalin mRNA or substance P immunoreactivity. Deposits made into the medial nucleus accumbens preferentially labeled neurons in the medial ventral pallidum, while deposits into the dorsolateral nucleus accumbens, at or lateral to the anterior commissure, labeled primarily cells in the dorsal and lateral ventral pallidum. A mediolateral to rostrocaudal topography was also observed, with the medial deposits preferentially labeling cells in rostral ventral pallidum and the lateral deposits resulting in retrogradely labeled cells in the ventral pallidum below the crossing of the posterior anterior commissure (subcommissural) as well as below the globus pallidus (sublenticular). The majority of cells retrogradely labeled with fluoro-gold were double-labeled for glutamic acid decarboxylase mRNA. In contrast, very few retrogradely labeled neurons in the ventral pallidum were double labeled for mRNA for preproenkephalin. These data demonstrate a topographically organized projection from the ventral pallidum to the nucleus accumbens that is primarily gamma-aminobutyric acid (GABA)-ergic and reciprocal to the GABAergic projection from the nucleus accumbens to the ventral pallidum.     

Utility-preserving transaction data anonymization with low information loss

Transaction data record various information about individuals, including their purchases and diagnoses, and are increasingly published to support large-scale and low-cost studies in domains such as marketing and medicine. However, the dissemination of transaction data may lead to privacy breaches, as it allows an attacker to link an individual's record to their identity. Approaches that anonymize data by eliminating certain values in an individual's record or by replacing them with more general values have been proposed recently, but they often produce data of limited usefulness. This is because these approaches adopt value transformation strategies that do not guarantee data utility in intended applications and objective measures that may lead to excessive data distortion. In this paper, we propose a novel approach for anonymizing data in a way that satisfies data publishers' utility requirements and incurs low information loss. To achieve this, we introduce an accurate information loss measure and an effective anonymization algorithm that explores a large part of the problem space. An extensive experimental study, using click-stream and medical data, demonstrates that our approach permits many times more accurate query answering than the state-of-the-art methods, while it is comparable to them in terms of efficiency.     

The MinMax k-Means clustering algorithm

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and optimizes a weighted version of the k-Means objective. Weights are learned together with the cluster assignments, through an iterative procedure. The proposed weighting scheme limits the emergence of large variance clusters and allows high quality solutions to be systematically uncovered, irrespective of the initialization. Experiments verify the effectiveness of our approach and its robustness over bad initializations, as it compares favorably to both k-Means and other methods from the literature that consider the k-Means initialization problem.     

Efficient reactor concepts for coupling of endothermic and exothermic reactions

Multifunctional autothermal reactors are a novel concept in process integration and intensification. They can be implemented as a countercurrent or reverse-flow reactor. A promising field of application is the coupling of endothermic and exothermic reactions. Methane steam reforming coupled with methane combustion is considered as a particular example. Several novel reactor configurations with co- and countercurrent flow in the reaction zone will be discussed by numerical simulation and an example for experimental verification will be presented.     

Molecular diffusion model of neurotransmitter homeostasis around synapses supporting gradients

Neurotransmitter homeostasis in and around a synapse involves complex random processes such as diffusion, molecular binding, and uptake by glial transporters. A three-dimensional stochastic diffusion model of a synapse was developed to provide molecular-level details of neurotransmitter homeostasis not predicted by alternative models based on continuum approaches. The development was illustrated through an example case cortico-accumbens synapse that successfully integrated neuroadaptations observed after chronic cocaine. By incorporating cystine-glutamate exchanger as a nonsynaptic release site for glutamate, the stochastic model was used to quantify the relative contributions of synaptic and nonsynaptic sources to extracellular concentration and to estimate molecular influx rates into the perisynapse. A perturbation analysis showed that among the parameters considered, variation in surface density of glial transporters had the largest effect on glutamate concentrations. The stochastic diffusion model of the example synapse was further generalized to characterize glial morphology by studying the role of diffusion path length in supporting neurotransmitter gradients and isolating the synapse. For the same set of parameters, diffusion path length was found to be proportional to the gradient supported.     

Tracking recurring contexts using ensemble classifiers: an application to email filtering

Concept drift constitutes a challenging problem for the machine learning and data mining community that frequently appears in real world stream classification problems. It is usually defined as the unforeseeable concept change of the target variable in a prediction task. In this paper, we focus on the problem of recurring contexts, a special sub-type of concept drift, that has not yet met the proper attention from the research community. In the case of recurring contexts, concepts may re-appear in future and thus older classification models might be beneficial for future classifications. We propose a general framework for classifying data streams by exploiting stream clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual representation model is proposed. The clustering algorithm is then applied in order to group batches of examples into concepts and identify recurring contexts. The ensemble is produced by creating and maintaining an incremental classifier for every concept discovered in the data stream. An experimental study is performed using (a) two new real-world concept drifting datasets from the email domain, (b) an instantiation of the proposed framework and (c) five methods for dealing with drifting concepts. Results indicate the effectiveness of the proposed representation and the suitability of the concept-specific classifiers for problems with recurring contexts.     

Conclusive local interpretation rules for random forests

Data Mining and Knowledge Discovery - In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be...