Dendritic distributions of l\u3csub\u3eh\u3c/sub\u3e channels in experimentally-derived multi-compartment models of oriens-lacunosum/moleculare (O-LM) hippocampal interneurons

The O-LM cell type mediates feedback inhibition onto hippocampal pyramidal cells and gates information flow in the CA1. Its functions depend on the presence of voltage-gated channels (VGCs), which affect its integrative properties and response to synaptic input. Given the challenges associated with determining densities and distributions of VGCs on interneuron dendrites, we take advantage of computational modeling to consider different possibilities. In this work, we focus on hyperpolarization-activated channels (h-channels) in O-LM cells. While h-channels are known to be present in O-LM cells, it is unknown whether they are present on their dendrites. In previous work, we used ensemble modeling techniques with experimental data to obtain insights into potentially important conductance balances. We found that the best O-LM models that included uniformly distributed h-channels in the dendrites could not fully capture the “sag” response. This led us to examine activation kinetics and non-uniform distributions of h-channels in the present work. In tuning our models, we found that different kinetics and non-uniform distributions could better reproduce experimental O-LM cell responses. In contrast to CA1 pyramidal cells where higher conductance densities of h-channels occur in more distal dendrites, decreasing conductance densities of h-channels away from the soma were observed in O-LM models. Via an illustrative scenario, we showed that having dendritic h-channels clearly speeds up back-propagating action potentials in O-LM cells, unlike when h-channels are present only in the soma. Although the present results were morphology-dependent, our work shows that it should be possible to determine the distributions and characteristics of O-LM cells with recordings and morphologies from the same cell. We hypothesize that h-channels are distributed in O-LM cell dendrites and endow them with particular synaptic integration properties that shape information flow in hippocampus

Semantic Segmentation Using Super Resolution Technique as Pre-Processing

Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation

Updated constraints on Georgi-Machacek model, and its electroweak phase transition and associated gravitational waves

With theoretical constraints such as perturbative unitarity and vacuum stability conditions and updated experimental data of Higgs measurements and direct searches for exotic scalars at the LHC, we perform an updated scan of the allowed parameter space of the Georgi-Machacek (GM) model. With the refined global fit, we examine the allowed parameter space for inducing strong first-order electroweak phase transitions (EWPTs) and find only the one-step phase transition is phenomenologically viable. Based upon the result, we study the associated gravitational wave (GW) signals and find most of which can be detected by several proposed experiments. We also make predictions on processes that may serve as promising probes to the GM model in the near future at the LHC, including the di-Higgs productions and several exotic scalar production channels.Comment: 42 pages, 11 figures, 9 table

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

Investigation of a Potential Scintigraphic Tracer for Imaging Apoptosis: Radioiodinated Annexin V-Kunitz Protease Inhibitor Fusion Protein

Radiolabeled annexin V (ANV) has been widely used for imaging cell apoptosis. Recently, a novel ANV-Kunitz-type protease inhibitor fusion protein, ANV-6L15, was found to be a promising probe for improved apoptosis detection based on its higher affinity to phosphatidylserine (PS) compared to native ANV. The present paper investigates the feasibility of apoptosis detection using radioiodinated ANV-6L15. Native ANV and ANV-6L15 were labeled with iodine-123 and iodine-125 using Iodogen method. The binding between the radioiodinated proteins and erythrocyte ghosts or chemical-induced apoptotic cells was examined. ANV-6L15 can be radioiodinated with high yield (40%−60%) and excellent radiochemical purity (>95%). 123I-ANV-6L15 exhibited a higher binding ratio to erythrocyte ghosts and apoptotic cells compared to 123I-ANV. The biodistribution of 123I-ANV-6L15 in mice was also characterized. 123I-ANV-6L15 was rapidly cleared from the blood. High uptake in the liver and the kidneys may limit the evaluation of apoptosis in abdominal regions. Our data suggest that radiolabled ANV-6L15 may be a better scintigraphic tracer than native ANV for apoptosis detection

Does feedback trading drive returns of cross-listed shares?

This paper examines the role of cross-listing in stock return dynamics with particular reference to feedback trading based on a sample of five most frequently traded cross-listed shares. We find that a long-run equilibrium relationship among the cross-listed share prices exists, but find no evidence of long-run co-movements among different shares traded in the same exchange. Furthermore, the VAR Granger causality tests indicate bi-directional feedback relations among the returns of cross-listed shares, while there is no consistent causality among different stocks within the markets. We also find that the cross-listed shares demonstrate strong volatility spillovers, which is driven by the covariance structure that are formed by variance and correlation terms. In addition, we report liquidity spillover effects and spillovers running from liquidity to volatility for some firms but no evidence that spillover effects run from volatility to liquidity

An investigation into customer perception and behaviour through social media research – an empirical study of the United Airline overbooking crisis

Airlines have been adopting yield management to optimise the perishable seat control problem and overbooking is a common strategy. This study outlines the connections between yield management, crises, and crisis communication. Using big data captured on a social media platform, this study aims to combine traditional yield management with emerging social big data analytics. As part of this, we use the twitter data on the 2017 United Airline (UA) to analyse the overbooking crisis. Our findings shed light on the importance of a more effective orchestration of yield management to avoid the escalation of crises during crisis communication phases

Bagged fuzzy clustering for fuzzy data: An application to a tourism market.

Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered