Enhanced expression of a specific hyperpolarization-activated cyclic nucleotide-gated cation channel (HCN) in surviving dentate gyrus granule cells of human and experimental epileptic hippocampus.

Changes in the expression of ion channels, contributing to altered neuronal excitability, are emerging as possible mechanisms in the development of certain human epilepsies. In previous immature rodent studies of experimental prolonged febrile seizures, isoform-specific changes in the expression of hyperpolarization-activated cyclic nucleotide-gated cation channels (HCNs) correlated with long-lasting hippocampal hyperexcitability and enhanced seizure susceptibility. Prolonged early-life seizures commonly precede human temporal lobe epilepsy (TLE), suggesting that transcriptional dysregulation of HCNs might contribute to the epileptogenic process. Therefore, we determined whether HCN isoform expression was modified in hippocampi of individuals with TLE. HCN1 and HCN2 expression were measured using in situ hybridization and immunocytochemistry in hippocampi from three groups: TLE with hippocampal sclerosis (HS; n = 17), epileptic hippocampi without HS, or non-HS (NHS; n = 10), and autopsy material (n = 10). The results obtained in chronic human epilepsy were validated by examining hippocampi from the pilocarpine model of chronic TLE. In autopsy and most NHS hippocampi, HCN1 mRNA expression was substantial in pyramidal cell layers and lower in dentate gyrus granule cells (GCs). In contrast, HCN1 mRNA expression over the GC layer and in individual GCs from epileptic hippocampus was markedly increased once GC neuronal density was reduced by >50%. HCN1 mRNA changes were accompanied by enhanced immunoreactivity in the GC dendritic fields and more modest changes in HCN2 mRNA expression. Furthermore, similar robust and isoform-selective augmentation of HCN1 mRNA expression was evident also in the pilocarpine animal model of TLE. These findings indicate that the expression of HCN isoforms is dynamically regulated in human as well as in experimental hippocampal epilepsy. After experimental febrile seizures (i.e., early in the epileptogenic process), the preserved and augmented inhibition onto principal cells may lead to reduced HCN1 expression. In contrast, in chronic epileptic HS hippocampus studied here, the profound loss of interneuronal and principal cell populations and consequent reduced inhibition, coupled with increased dendritic excitation of surviving GCs, might provoke a "compensatory" enhancement of HCN1 mRNA and protein expression

Solubility of Tris(hydroxymethyl)aminomethane in Water + Methanol +1-Propanol Mixtures at Various Temperatures

Article on the solubility of tris(hydroxymethyl)aminomethane in water + methanol + 1-propanol mixtures at various temperatures

Solubility of Sodium Acetate in Ternary Mixtures of Methanol, 1-Propanol, Acetonitrile, and Water at 298.2 K

Article on the solubility of sodium acetate in ternary mixtures of methanol, 1-propanol, acetonitrile, and water at 298.2 K

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Solubility of Tris(hydroxymethyl)aminomethane in Water + 1-Propanol Mixtures at Various Temperatures

Article on the solubility of tris(hydroxymethyl)aminomethane in water + 1-propanol mixtures at various temperatures

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications