Studies on astrocyte function : potential roles in brain water homeostasis and neuroprotection

Astrocytes are essential in brain homeostasis and function, including maintenance of water and ion balance. Astrocytes express the water channel aquaporin 4 (AQP4), implicated in both physiological functions and injury processes associated with brain edema, a common consequence of brain diseases. As part of the tripartite synapse astrocytes are tightly coupled to normal brain function via neuron-astrocyte interactions and by providing metabolic support to neurons as well as controlling extracellular potassium and glutamate. The overall aim was to explore the regulation of astrocyte water permeability and study aspects of astrocyte function of relevance for the interplay between astrocytes and neurons in physiology and ischemia. The molecular mechanisms involved in short term regulation of astrocyte AQP4 were investigated by exploring the effects of glutamate and potassium on astrocyte water permeability. Glutamate was found to significantly increase astrocyte water permeability via activation of group I metabotropic glutamate receptors (mGluRs), an effect attributable to an effect on AQP4. An essential conclusion in this study is that AQP4 can be short-term regulated via gating. The evidence supports that this effect is dependent on phosphorylation, that the AQP4 serine 111 residue is a molecular target for the regulation and that this residue can be phosphorylated by particular protein kinases directly. Next we showed that elevations in extracellular potassium increase astrocyte water permeability via a cAMP-dependent mechanism involving AQP4. The role of AQP4 serine 111 in the regulation of astrocyte water permeability was confirmed in this study. A prolonged upregulation of astrocyte water permeability was dependent on Kirchannel function. The effect could be modulated by calcium when such signaling was triggered by high extracellular potassium. The findings point to a functional coupling between water transport and potassium handling in astrocytes. Hence, as fundamental ‘messengers’ from neurons, glutamate and potassium were found to regulate astrocyte water permeability. The results indicate that astrocyte water permeability can be dynamically regulated in response to neuronal activity and that modulation of astrocyte signaling is dependent on both dose and duration of exposure to its regulators. Due to its neuroprotective potential, the effects of EPO on astrocyte function were examined with regard to water permeability and aspects of astrocyte metabolism. EPO was found to counteract the glutamate-induced upregulation of astrocyte water permeability and significantly reduce neurological symptoms in an animal model of brain edema. It was shown that EPO modifies mGluR-mediated intracellular calcium signaling. In oxygen-glucose deprivation, a cellular model of ischemia, EPO was found to enhance astrocyte glutamate uptake. The effect was depended on the sodium pump Na,K-ATPase and coupled to intracellular pH regulation. The evidence also suggested that astrocyte metabolism is enhanced by EPO under oxygen-glucose-free conditions, a finding that indirectly supports a therapeutic potential of EPO or EPO analogs in ischemia. Taken together, our data indicate a dynamic role for astrocytes in the regulation of brain water and ion homeostasis. Upregulation of AQP4 water permeability facilitates water transport across the plasma membrane. In conditions associated with perturbed brain water balance, this may accelerate or attenuate brain edema depending on the phase of edema formation or resolution. EPO protects against water overload by modulation of astrocyte water permeability. Moreover, by enhanced astrocyte metabolism and restored astrocyte function in ischemia, EPO should favor local homeostasis and promote neuroprotection via astrocytes

An XGBoost Algorithm for Predicting Purchasing Behaviour on E-Commerce Platforms

To improve and enhance the predictive ability of consumer purchasing behaviours on e-commerce platforms, a new method of predicting purchasing behaviour on e-commerce platforms is created in this paper. This study introduced the basic principles of the XGBoost algorithm, analysed the historical data of an e-commerce platform, pre-processed the original data and constructed an e-commerce platform consumer purchase prediction model based on the XGBoost algorithm. By using the traditional random forest algorithm for comparative analysis, the K-fold cross-validation method was further used, combined with model performance indicators such as accuracy rate, precision rate, recall rate and F1-score to evaluate the classification accuracy of the model. The characteristics of the importance of the results were found through visual analysis. The results indicated that using the XGBoost algorithm to predict the purchasing behaviours of e-commerce platform consumers can improve the performance of the method and obtain a better prediction effect. This study provides a reference for improving the accuracy of e-commerce platform consumers\u27 purchasing behaviours prediction, and has important practical significance for the efficient operation of e-commerce platforms

Entropic Explanation of Power Set

A power set of a set S is defined as the set of all subsets of S, including set S itself and empty set, denoted as P(S) or 2S. Given a finite set S with |S|=n hypothesis, one property of power set is that the amount of subsets of S is |P(S)| = 2n.  However, the physica meaning of power set needs exploration. To address this issue, a possible explanation of power set is proposed in this paper. A power set of n events can be seen as all possible k-combination, where k ranges from 0 to n. It means the power set extends the event space in probability theory into all possible combination of the single basic event. From the view of power set, all subsets or all combination of basic events, are created equal. These subsets are assigned with the mass function, whose uncertainty can be measured by Deng entropy. The relationship between combinatorial number, Pascal's triangle and power set is revealed by Deng entropy quantitively from the view of information measure.&nbsp

CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

Geo-tagged images are publicly available in large quantities, whereas labels such as object classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has achieved tremendous success in various natural image and language tasks with limited labeled data. However, existing methods fail to fully leverage geospatial information, which can be paramount to distinguishing objects that are visually similar. To directly leverage the abundant geospatial information associated with images in pre-training, fine-tuning, and inference stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images, which can be transferred to downstream supervised tasks such as image classification. Experiments show that CSP can improve model performance on both iNat2018 and fMoW datasets. Especially, on iNat2018, CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.Comment: In: ICML 2023, Jul 23 - 29, 2023, Honolulu, Hawaii, US

Intriguing Properties of Text-guided Diffusion Models

Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: (i) natural-looking text prompts generating images with the wrong content, or (ii) different random samples of the latent variables that generate vastly different, and even unrelated, outputs despite being conditioned on the same text prompt. In this work, we aim to study and understand the failure modes of TDMs in more detail. To achieve this, we propose SAGE, an adversarial attack on TDMs that uses image classifiers as surrogate loss functions, to search over the discrete prompt space and the high-dimensional latent space of TDMs to automatically discover unexpected behaviors and failure cases in the image generation. We make several technical contributions to ensure that SAGE finds failure cases of the diffusion model, rather than the classifier, and verify this in a human study. Our study reveals four intriguing properties of TDMs that have not been systematically studied before: (1) We find a variety of natural text prompts producing images that fail to capture the semantics of input texts. We categorize these failures into ten distinct types based on the underlying causes. (2) We find samples in the latent space (which are not outliers) that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured. (3) We also find latent samples that lead to natural-looking images which are unrelated to the text prompt, implying a potential misalignment between the latent and prompt spaces. (4) By appending a single adversarial token embedding to an input prompt we can generate a variety of specified target objects, while only minimally affecting the CLIP score. This demonstrates the fragility of language representations and raises potential safety concerns.Comment: Code will be available at: https://github.com/qihao067/SAG

Are business intelligence systems different to decision support systems and other business information systems?

A common view of information systems (IS) researchers is that business intelligence (BI) systems are essentially a type of decision support systems (DSS). This approach to knowledge implies that DSS theory can be transferred to BI systems in order to explain and predict their action. Further, some researchers feel that BI systems can also be adequately researched using general IS theory. This paper is the first from a project that is examining if BI systems have significant differences to operational IS and DSS. This first exploration is informed by a focus group of senior BI professionals. The study illuminates some differences between BI and other types of business IS and indicates that context could be significant for BI theorizing and that care is needed in transferring operational IS and DSS theory to BI systems research. In practice, these differences could be a source of project failure

Reconfigurable Holographic Surface: A New Paradigm to Implement Holographic Radio

Ultra-massive multiple-input multiple-output (MIMO) is one of the key enablers in the forthcoming 6G networks to provide high-speed data services by exploiting spatial diversity. In this article, we consider a new paradigm termed holographic radio for ultra-massive MIMO, where numerous tiny and inexpensive antenna elements are integrated to realize high directive gain with low hardware cost. We propose a practical way to enable holographic radio by a novel metasurface-based antenna, i.e., reconfigurable holographic surface (RHS). Specifically, RHSs incorporating densely packed tunable metamaterial elements are capable of holographic beamforming. Based on the working principle and hardware design of RHSs, we conduct full-wave analyses of RHSs and build an RHS-aided point-to-point communication platform supporting real-time data transmission. Both simulated and experimental results show that the RHS has great potential to achieve high directive gain with a limited size, thereby substantiating the feasibility of RHS-enabled holographic radio. Moreover, future research directions for RHS-enabled holographic radio are also discussed.Comment: 7 pages, 7 figure