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Astrocytes are essential for normal brain development and functioning. They respond to brain injury and disease through a process referred to as reactive astrogliosis, where the reactivity is highly heterogenous and context-dependent. Reactive astrocytes are active contributors to brain pathology and can exert beneficial, detrimental, or mixed effects following brain insults. Transforming growth factor-β (TGF-β) has been identified as one of the key factors regulating astrocyte reactivity. The genetic and pharmacological manipulation of the TGF-β signaling pathway in animal models of central nervous system (CNS) injury and disease alters pathological and functional outcomes. This review aims to provide recent understanding regarding astrocyte reactivity and TGF-β signaling in brain injury, aging, and neurodegeneration. Further, it explores how TGF-β signaling modulates astrocyte reactivity and function in the context of CNS disease and injury.

Grain boundaries (GBs) can be treated as two‐dimensional (2‐D) interfacial phases (also called “complexions”) that can undergo interfacial phase‐like transitions. As bulk phase diagrams and calculation of phase diagram (CALPHAD) methods serve as a foundation for modern materials science, we propose to extend them to GBs to have equally significant impacts. This perspective article reviews a series of studies to compute the GB counterparts to bulk phase diagrams. First, a phenomenological interfacial thermodynamic model was developed to construct GB lambda diagrams to forecast high‐temperature GB disordering and related trends in sintering and other properties for both metallic and ceramic materials. In parallel, an Ising‐type lattice statistical thermodynamic model was utilized to construct GB adsorption (segregation) diagrams, which predicted first‐order GB adsorption transitions and critical phenomena. These two simplified thermodynamic models emphasize the GB structural (disordering) and chemical (adsorption) aspects, respectively. Subsequently, hybrid Monte Carlo and molecular dynamics atomistic simulations were used to compute more rigorous and accurate GB “phase” diagrams. Computed GB diagrams of thermodynamic and structural properties were further extended to include mechanical properties. Moreover, machine learning algorithms were combined with atomistic simulations to predict GB properties as functions of four independent compositional variables and temperature in a 5‐D space for a given GB in high‐entropy alloys or as functions of five GB macroscopic (crystallographic) degrees of freedom plus temperature and composition for a binary alloy in a 7‐D space. Other relevant studies are also examined. Future perspective and outlook, including two emerging fields of high‐entropy grain boundaries (HEGBs) and electrically (or electrochemically) induced GB transitions, are discussed. As bulk phase diagrams serve as a foundation for materials science, computing their grain boundary counterparts can have equally significant impacts.

Introduction Behcet’s syndrome (BS) is a complex, heterogeneous disorder. However, classification of its subgroups is still debated. The purpose of this study was to investigate the clinical features and aggregation of patients with BS in China, based on manifestations and organ involvements. Methods This was a cross-sectional study of BS patients in Huadong Hospital of Fudan University between September 2012 and January 2020. We calculated relative risks (RRs) of clinical variables according to sex. Moreover, we conducted a hierarchical cluster analysis applied according to eighteen variables to determine subgroups of patients. Results A total of 860 BS patients were included. Male sex was associated with ocular involvement (RR 2.32, 95% CI 1.67, 3.22, P  < 0.0001), vascular involvement (RR 2.00, 95% CI 1.23, 3.23, P  = 0.004), cardiac lesion (RR 5.46, 95% CI 2.33, 12.77, P  < 0.0001), and central nervous system involvement (RR 2.95, 95% CI 1.07, 6.78, P  = 0.007) and was negatively associated with genital ulcers (RR 0.84, 95% CI 0.79, 0.91, P  < 0.0001). Five clusters (C1–C5) were observed. C1 ( n  = 307) showed the skin and mucosa type. In C2 ( n  = 124), all had articular involvement, barely having major organ involvement except for 18 cases with intestinal lesions. In C3 ( n  = 156), the gastrointestinal type, 144 patients presented with intestinal involvement, and 36 patients with esophageal ulcers. In C4 ( n  = 142), all subjects presented with uveitis. C5 ( n  = 131) consisted of 44 patients with cardiac lesions, 58 with vascular involvement, and 26 cases having central nervous system involvement. Conclusion Our analysis confirmed sex differences in phenotypes of BS. Cluster analysis identified gastrointestinal, uveitis, and cardiovascular involvement cluster separately in different subsets, which represents the most commonly involved organs. Further research is required to replicate and clarify the patterns of phenotype in BS.

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.

High-entropy ceramics (HECs) have quickly gained attention since 2015. To date, nearly all work has focused on five-component, equimolar compositions. This perspective article briefly reviews different families of HECs and selected properties. Following a couple of our most recent studies, we propose a step forward to expand HECs to compositionally complex ceramics (CCCs) to include medium-entropy and non-equimolar compositions. Using defective fluorite and ordered pyrochlore oxides as two primary examples, we further consider the complexities of aliovalent cations and anion vacancies as well as ordered structures with two cation sublattices. Better thermally insulating yet stiff CCCs have been found in non-equimolar compositions with optimal amounts of oxygen vacancies and in ordered pyrochlores with substantial size disorder. It is demonstrated that medium-entropy ceramics can prevail over their high-entropy counterparts. The diversifying classes of CCCs provide even more possibilities than HECs to tailor the composition, defects, disorder/order, and, consequently, various properties.

Along with the rapid development of the Internet, the form of destination marketing is becoming increasingly innovative and diverse. Celebrity endorsement via social media, as an effective marketing strategy, has been widely used by various tourism companies to attract and engage with their customers. Previous studies have investigated the various aspects of such endorsement (e.g. the effect of endorser's attributes) in different contexts (e.g. Hotel, Restaurant, Airline). However, little research has focused on the influence of the tourists' parasocial relationship with endorsers on destination marketing. Accordingly, the current study aims to explore the role of parasocial relationships on tourists' destination attitude and visit intention. This study adopted quantitative method and conducted Partial Least Square-Structural Equation Modelling (PLS-SEM) for data analysis. 498 valid questionnaires were collected from Weibo (One of China's most popuar social media platforms). Results suggested that endorser's credibility positively influenced follower's parasocial relationship (p<0.001), which in turn positively influenced destination attitude and visit intention (p<0.001). Additionally, the parasocial relationship significantly mediated the relationship between credibility and destination attitude as well as visit intention (p<0.001). The emerging trend of celerbity endorsement via live-streaming makes tourism destination marketing more diverse and even effective. Through exploring the underlying mechanism of celebrity endorsement, this study can provide destination marketers with insights about how to build and strengthen the tourist's attitude and visit intention towards destiantion by developing their parasocial relationships with endorsers. This study also adds to the literature of using Partial Least Square-Structural Equation Modelling (PLS-SEM) in tourism and hospiatlity research.

Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling. Key Points A novel approach for describing complex subsurface structures using sparse observations (hard data) and auxiliary variables (soft data) The proposed deep learning network is able to establish the implicit relationship among multiple observations The proposed approach can be easily extended and widely used for reservoir characterization, hydrogeophysical modeling, and other fields

Genome-wide association studies (GWASs) have identified over 180 independent schizophrenia risk loci. Nevertheless, how the risk variants in the reported loci confer schizophrenia susceptibility remains largely unknown. Here we systematically investigate the gene regulatory mechanisms underpinning schizophrenia risk through integrating data from functional genomics (including 30 ChIP-Seq experiments) and position weight matrix (PWM). We identify 132 risk single nucleotide polymorphisms (SNPs) that disrupt transcription factor binding and we find that 97 of the 132 TF binding-disrupting SNPs are associated with gene expression in human brain tissues. We validate the regulatory effect of some TF binding-disrupting SNPs with reporter gene assays (9 SNPs) and allele-specific expression analysis (10 SNPs). Our study reveals gene regulatory mechanisms affected by schizophrenia risk SNPs (including widespread disruption of POLR2A and CTCF binding) and identifies target genes for mechanistic studies and drug development. Our results can be accessed and visualized at SZDB database ( http://www.szdb.org/ ). We know a large number of risk SNPs for schizophrenia, but little about how these SNPs contribute to the disorder. Here, the authors use functional genomics to identify risk SNPs that disrupt transcription factor binding and validate the regulatory effects of the transcription factor binding-disrupting SNPs.

Motor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain. To achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations. Empirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.

Background Immune and inflammatory responses occurring in the spinal cord play a pivotal role in the progression of radicular pain caused by intervertebral disk herniation. Interleukin-33 (IL-33) orchestrates inflammatory responses in a wide range of inflammatory and autoimmune disorders of the nervous system. Thus, the purpose of this study is to investigate the expression of IL-33 and its receptor ST2 in the dorsal spinal cord and to elucidate whether the inhibition of spinal IL-33 expression significantly attenuates pain-related behaviors in rat models of noncompressive lumbar disc herniation. Methods Lentiviral vectors encoding short hairpin RNAs that target IL-33 (LV-shIL-33) were constructed for gene silencing. Rat models of noncompressive lumber disk herniation were established, and the spines of rats were injected with LV-shIL-33 (5 or 10 μl) on the first day after the operation. Mechanical thresholds were evaluated during an observation period of 21 days. Moreover, the expression levels of spinal tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and cyclooxygenase 2 (COX-2) and the activation of the mitogen-activated protein kinases (MAPK) and nuclear factor-κB (NF-κB) pathways were evaluated to gain insight into the mechanisms related to the contribution of IL-33/ST2 signaling to radicular pain. Results The application of nucleus pulposus (NP) to the dorsal root ganglion (DRG) induced an increase in IL-33 and ST2 expression in the spinal cord, mainly in the dorsal horn neurons, astrocytes, and oligodendrocytes. Spinally delivered LV-shIL-33 knocked down the expression of IL-33 and markedly attenuated mechanical allodynia. In addition, spinal administration of LV-shIL-33 reduced the overexpression of spinal IL-1β, TNF-α, and COX-2 and attenuated the activation of C-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and NF-κB/p65 but not p38. Conclusions This study indicates that spinal IL-33/ST2 signaling plays an important role in the development and progression of radicular pain in rat models of noncompressive lumber disk herniation. Thus, the inhibition of spinal IL-33 expression may provide a potential treatment to manage radicular pain caused by intervertebral disk herniation.