50 resultados para Neo-Fuzzy Neuron


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Individuals with an inherited deficiency in gonadotropin-releasing hormone (GnRH) have impaired sexual reproduction. Previous genetic linkage studies and sequencing of plausible gene candidates have identified mutations associated with inherited GnRH deficiency, but the small number of affected families and limited success in validating candidates have impeded genetic diagnoses for most patients. Using a combination of exome sequencing and computational modeling, we have identified a shared point mutation in semaphorin 3E (SEMA3E) in 2 brothers with Kallmann syndrome (KS), which causes inherited GnRH deficiency. Recombinant wild-type SEMA3E protected maturing GnRH neurons from cell death by triggering a plexin D1-dependent (PLXND1-dependent) activation of PI3K-mediated survival signaling. In contrast, recombinant SEMA3E carrying the KS-associated mutation did not protect GnRH neurons from death. In murine models, lack of either SEMA3E or PLXND1 increased apoptosis of GnRH neurons in the developing brain, reducing innervation of the adult median eminence by GnRH-positive neurites. GnRH neuron deficiency in male mice was accompanied by impaired testes growth, a characteristic feature of KS. Together, these results identify SEMA3E as an essential gene for GnRH neuron development, uncover a neurotrophic function for SEMA3E in the developing brain, and elucidate SEMA3E/PLXND1/PI3K signaling as a mechanism that prevents GnRH neuron deficiency.

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Accurate perception of taste information is crucial for animal survival. In adult Drosophila, gustatory receptor neurons (GRNs) perceive chemical stimuli of one specific gustatory modality associated with a stereotyped behavioural response, such as aversion or attraction. We show that GRNs of Drosophila larvae employ a surprisingly different mode of gustatory information coding. Using a novel method for calcium imaging in the larval gustatory system, we identify a multimodal GRN that responds to chemicals of different taste modalities with opposing valence, such as sweet sucrose and bitter denatonium, reliant on different sensory receptors. This multimodal neuron is essential for bitter compound avoidance, and its artificial activation is sufficient to mediate aversion. However, the neuron is also essential for the integration of taste blends. Our findings support a model for taste coding in larvae, in which distinct receptor proteins mediate different responses within the same, multimodal GRN.

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The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.