Quality Determination of Hydraulic Pumps with Adaptive Fuzzy Pattern Classifiers to Reduce the Risk for Quality Management

Automated production of complex assemblies such as hydraulic pumps also requires reliable detection of defects utilizing functional tests. In principle, this is a classification task in good/bad, which, however, is often not to be made sharply but should provide gradations for detailed error analysis. From this, conclusions can be drawn, for example, about the type or location of the defects, wear, or aging of components in the production chain. A high-dimensional vector of data from static or dynamic measurements including is generally available as the basis for the fault detection model. Modeling such complex nonlinear systems under various load conditions with dynamic test procedures leads to uncertainties that should also be reflected in the diagnostic model. For this, the design of the classification model (the classifier) should be largely automatic during the training phase for time and cost reasons. In addition, online updating under actual operating conditions is also often desired. These challenging goals can be met through the artificial intelligence (AI) methodology of fuzzy pattern classification. This chapter deals with the development of a fuzzy classifier for the application case of the final inspection of hydraulic axial piston pumps. The focus is on the automatic training of the classifier employing a new adaptation procedure and permanently (until termination) evaluates the resp. current classifier using performance measures. Using real experimental data, the procedure and the step-by-step adaptation results for different links between the current classification model and the new data are presented and compared

Genetic inhibition of neurotransmission reveals role of glutamatergic input to dopamine neurons in high-effort behavior

Midbrain dopamine neurons are crucial for many behavioral and cognitive functions. As the major excitatory input, glutamatergic afferents are important for control of the activity and plasticity of dopamine neurons. However, the role of glutamatergic input as a whole onto dopamine neurons remains unclear. Here we developed a mouse line in which glutamatergic inputs onto dopamine neurons are specifically impaired, and utilized this genetic model to directly test the role of glutamatergic inputs in dopamine-related functions. We found that while motor coordination and reward learning were largely unchanged, these animals showed prominent deficits in effort-related behavioral tasks. These results provide genetic evidence that glutamatergic transmission onto dopaminergic neurons underlies incentive motivation, a willingness to exert high levels of effort to obtain reinforcers, and have important implications for understanding the normal function of the midbrain dopamine system.Fil: Hutchison, M. A.. National Institutes of Health; Estados UnidosFil: Gu, X.. National Institutes of Health; Estados UnidosFil: Adrover, Martín Federico. National Institutes of Health; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Lee, M. R.. National Institutes of Health; Estados UnidosFil: Hnasko, T. S.. University of California at San Diego; Estados UnidosFil: Alvarez, V. A.. National Institutes of Health; Estados UnidosFil: Lu, W.. National Institutes of Health; Estados Unido

Methamphetamine induces Shati/Nat8L expression in the mouse nucleus accumbens via CREB- and dopamine D1 receptor-dependent mechanism

Shati/Nat8L significantly increased in the nucleus accumbens (NAc) of mice after repeated methamphetamine (METH) treatment. We reported that Shati/Nat8L overexpression in mouse NAc attenuated METH-induced hyperlocomotion, locomotor sensitization, and conditioned place preference. We recently found that Shati/Nat8L overexpression in NAc regulates the dopaminergic neuronal system via the activation of group II mGluRs by elevated Nacetylaspartylglutamate following N-acetylaspartate increase due to the overexpression. These findings suggest that Shati/Nat8L suppresses METH-induced responses. However, the mechanism by which METH increases the Shati/Nat8L mRNA expression in NAc is unclear. To investigate the regulatory mechanism of Shati/Nat8L mRNA expression, we performed a mouse Shati/Nat8L luciferase assay using PC12 cells. Next, we investigated the response of METH to Shati/Nat8L expression and CREB activity using mouse brain slices of NAc, METH administration to mice, and western blotting for CREB activity of specific dopamine receptor signals in vivo and ex vivo. We found that METH activates CREB binding to the Shati/Nat8L promoter to induce the Shati/Nat8L mRNA expression. Furthermore, the dopamine D1 receptor antagonist SCH23390, but not the dopamine D2 receptor antagonist sulpiride, inhibited the upregulation of Shati/Nat8L and CREB activities in the mouse NAc slices. Thus, the administration of the dopamine D1 receptor agonist SKF38393 increased the Shati/Nat8L mRNA expression in mouse NAc. These results showed that the Shati/ Nat8L mRNA was increased by METH-induced CREB pathway via dopamine D1 receptor signaling in mouse NAc. These findings may contribute to development of a clinical tool for METH addiction

SHANK3 controls maturation of social reward circuits in the VTA.

Haploinsufficiency of SHANK3, encoding the synapse scaffolding protein SHANK3, leads to a highly penetrant form of autism spectrum disorder. How SHANK3 insufficiency affects specific neural circuits and how this is related to specific symptoms remains elusive. Here we used shRNA to model Shank3 insufficiency in the ventral tegmental area of mice. We identified dopamine (DA) and GABA cell-type-specific changes in excitatory synapse transmission that converge to reduce DA neuron activity and generate behavioral deficits, including impaired social preference. Administration of a positive allosteric modulator of the type 1 metabotropic glutamate receptors mGluR1 during the first postnatal week restored DA neuron excitatory synapse transmission and partially rescued the social preference defects, while optogenetic DA neuron stimulation was sufficient to enhance social preference. Collectively, these data reveal the contribution of impaired ventral tegmental area function to social behaviors and identify mGluR1 modulation during postnatal development as a potential treatment strategy

Short-Time Prediction Based on Recognition of Fuzzy Time Series Patterns

Abstract. This article proposes knowledge-based short-time prediction methods for multivariate streaming time series, relying on the early recognition of local patterns. A parametric, well-interpretable model for such patterns is presented, along with an online, classification-based recognition procedure. Subsequently, two options are discussed to predict time series employing the fuzzified pattern knowledge, accompanied by an example. Special emphasis is placed on comprehensible models and methods, as well as an easy interface to data mining algorithms