HCN2 channels in local inhibitory interneurons constrain LTP in the hippocampal direct perforant path

Neuronal hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are known to modulate spontaneous activity, resting membrane potential, input resistance, afterpotential, rebound activity, and dendritic integration. To evaluate the role of HCN2 for hippocampal synaptic plasticity, we recorded long-term potentiation (LTP) in the direct perforant path (PP) to CA1 pyramidal cells. LTP was enhanced in mice carrying a global deletion of the channel (HCN2^−/−) but not in a pyramidal neuron-restricted knockout. This precludes an influence of HCN2 located in postsynaptic pyramidal neurons. Additionally, the selective HCN blocker zatebradine reduced the activity of oriens-lacunosum moleculare interneurons in wild-type but not HCN2^−/− mice and decreased the frequency of spontaneous inhibitory currents in postsynaptic CA1 pyramidal cells. Finally, we found amplified LTP in the PP of mice carrying an interneuron-specific deletion of HCN2. We conclude that HCN2 channels in inhibitory interneurons modulate synaptic plasticity in the PP by facilitating the GABAergic output onto pyramidal neurons.     

Biochemical dysfunction and memory loss: the case of Alzheimer's dementia

Among the different types of cognitive impairment that appear with increasing age, Alzheimer's disease (AD) is rated as the most frequent. Despite intensive research, key questions concerning AD aetiology remain elusive, but it appears that many biochemical events crucial for neuronal communication and synaptic plasticity fail during the course of the disease. The aim of this review is therefore to provide an overview of intracellular cascades involved in AD pathology. For almost all factors. it is a matter of controversy whether their contribution should be considered to be cause or effect. However, intracellular signalling may be crucial as it is in learning and memory mechanisms and malfunction of biochemical pathways may be a common denominator in neurodegenerative processes, thus providing new venues for treatment and therapeutic strategies.     

Data Mining as an Enabler for Customer Data Driven Vehicle Development

对于汽车等面向消费者的行业,数据驱动的产品开发是一项关键的系统工程。数据驱动方法的前提是数据本身,由于现代车辆的联网能力不断提高,使得汽车制造商能够以内部总线信号的形式记录并储存客户数据。由于这些数据并不用于外部,只用于内部通信以保证车辆的安全性和功能性,这给这些数据的应用带来了一定的困难,因此汽车行业的主要问题在于如何利用数据挖掘技术从这些数据中提取客户需求及相关信息。为此,对上述数据应用问题进行了文献调研,并在此基础上开展数据挖掘的模拟研究,以确定现有数据挖掘过程在需求获取领域的适用性;进而提出一种扩展程序,使得数据挖掘技术能够应用于相关客户数据之中,从而加快车辆开发的进程。     

Further evidence for the existence of a bottleneck in the metabolism of Saccharomyces cerevisiae

The growth physiology of Saccharomyces cerevisiae strains H1022 and Whi2+ has been studied in aerobic batch and continuous (chemostat) cultures. Results from the measurement of biomass and medium components (off-line) together with oxygen, carbon dioxide and heat measurements (on-line) have been used in an attempt to explore the existence of 'overflow' or 'bottleneck' metabolism as opposed to catabolite repression (Crabtree effect) in these strains. Chemostat experiments indicated that specific oxygen uptake rate (qO2) was linearly related to the dilution rate (D) at values below the critical dilution rate (D crit), becoming constant above D crit, which is in agreement with the bottleneck theory. However, batch culture experiments indicated negligible oxygen consumption during the initial glucose growth phase, the culture exhibiting purely anaerobic metabolism. The bottleneck theory would propose that qO2 has a constant (maximum) value under these conditions. The results presented here suggest that while the bottleneck theory can be adequately used to describe chemostat growth of S. cerevisiae, some other control mechanism must be operating under conditions of high glucose concentrations, such as those initially prevailing in the batch culture experiments.     

Probability distributions,trading strategies and leverage: an application of Gaussian mixture models

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a one‐day‐ahead forecasting task of the EUR/USD time series. This is implemented using a Gaussian mixture model neural network, benchmarking the results against standard forecasting models, namely a naïve model, a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA), a logistic regression model (LOGIT) and a multi‐layer perceptron network (MLP). Secondly, to examine the possibilities of improving the trading performance of those models with confirmation filters and leverage. While the benchmark models perform best without confirmation filters and leverage, the Gaussian mixture model outperforms all of the benchmarks when taking advantage of the possibilities offered by a combination of more sophisticated trading strategies and leverage. This might be due to the ability of the Gaussian mixture model to identify successfully trades with a high Sharpe ratio. Copyright © 2004 John Wiley & Sons, Ltd.