AIM2 inflammasome: A potential therapeutic target in ischemic stroke.

Ischemic stroke (IS) is a significant global public health issue with a high incidence, disability, and mortality rate. A robust inflammatory cascade with complex and wide-ranging mechanisms occurs following ischemic brain injury. Inflammasomes are multiprotein complexes in the cytoplasm that modulate the inflammatory response by releasing pro-inflammatory cytokines and inducing cellular pyroptosis. Among these inflammasomes, the Absent in Melanoma 2 (AIM2) inflammasome shows the ability to detect a wide range of pathogen DNAs, thereby triggering an inflammatory response. Recent studies have indicated that the aberrant expression of AIM2 inflammasome in various cells is closely associated with the pathological processes of ischemic brain injury. This paper summarizes the expression and regulatory role of AIM2 in CNS and peripheral immune cells and discusses current therapeutic approaches targeting AIM2 inflammasome. These findings aim to serve as a reference for future research in this field.

NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups.

Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods.

User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function.

Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.

Nitrosporeusine A attenuates sepsis-associated acute kidney injury through the downregulation of IL-6/sIL-6R axis activation-mediated PGC-1α suppression.

Nitrosporeusine A (NA) has been recently reported to exert anti-inflammatory and renal protective effects, but whether NA can attenuate sepsis-associated acute kidney injury (AKI) has not yet been reported. In this study, our results found that cecal ligation and puncture (CLP) reduced renal PGC-1α expression and induced oxidative stress in C57BL/6 mice. PGC-1α overexpression attenuated CLP-induced AKI with decreased oxidative stress, whereas worsened AKI with excessive reactive oxygen species (ROS) production was observed in renal specific PGC-1α knockout (NiPKO) mice. In addition, PGC-1α expression is retained in IL-6-/- mice and wild-type (WT) C57BL/6 mice received JAK2/STAT3 inhibition. Finally, administration of NA attenuated CLP-induced AKI with decreased IL-6/sIL-6R axis activation, increased PGC-1α expression, and diminished ROS production in injured kidneys. However, NA failed to attenuate CLP-induced AKI in NiPKO mice. Together, these results suggested that NA attenuates sepsis-associated AKI through the downregulation of IL-6/sIL-6R axis activation-mediated renal PGC-1α suppression.

Towards generalizable Graph Contrastive Learning: An information theory perspective.

Graph Contrastive Learning (GCL) is increasingly employed in graph representation learning with the primary aim of learning node/graph representations from a predefined pretext task that can generalize to various downstream tasks. Meanwhile, the transition from a specific pretext task to diverse and unpredictable downstream tasks poses a significant challenge for GCL's generalization ability. Most existing GCL approaches maximize mutual information between two views derived from the original graph, either randomly or heuristically. However, the generalization ability of GCL and its theoretical principles are still less studied. In this paper, we introduce a novel metric GCL-GE, to quantify the generalization gap between predefined pretext and agnostic downstream tasks. Given the inherent intractability of GCL-GE, we leverage concepts from information theory to derive a mutual information upper bound that is independent of the downstream tasks, thus enabling the metric's optimization despite the variability in downstream tasks. Based on the theoretical insight, we propose InfoAdv, a GCL framework to directly enhance generalization by jointly optimizing GCL-GE and InfoMax. Extensive experiments validate the capability of InfoAdv to enhance performance across a wide variety of downstream tasks, demonstrating its effectiveness in improving the generalizability of GCL.

Buyang huanwu decoction alleviates stroke-induced immunosuppression in MCAO mice by reducing splenic T cell apoptosis triggered by AIM2 inflammasome.

ETHNOPHARMACOLOGICAL RELEVANCE: Ischemic stroke is a serious disabling and fatal disease that places a heavy burden on the world. Stroke induces a state of systemic immunosuppression that is strongly associated with an increased risk of infection and severe outcomes. Buyang Huanwu Decoction (BYHWD) is an ancient Chinese traditional formula with a good clinical and experimental basis. However, the role of BYHWD on post-stroke immunomodulation, especially immunosuppression, is unclear. AIM OF THE STUDY: The aim of this study was to investigate the pharmacological mechanism of BYHWD to alleviate ischemic stroke by analyzing splenic T cells apoptosis triggered by the AIM2 inflammasome activation cascade. MATERIALS AND METHODS: An ischemic stroke model in C57BL/6 J mice was constructed using the MCAO method. The mNSS test and the hanging wire test were conducted to evaluate neurological impairment in mice. Histopathological damage was visualized by Nissl staining and HE staining. The protective effects of BYHWD on the spleen were determined by splenic index and spleen HE staining. The inhibition of AIM2 inflammasome cascade by BYHWD were explored through immunofluorescence (IF), flow cytometry, enzyme-linked immunosorbent assay (ELISA) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Flow cytometry was used to assess the apoptosis of splenic T cells. RESULTS: BYHWD significantly reduced infarct size, improved neurological function scores, and alleviated histopathological damage in middle cerebral artery occlusion (MCAO) mice. At the same time, BYHWD salvaged spleen atrophy. BYHWD significantly ameliorated apoptosis of splenic T lymphocytes. Key proteins and factors in the AIM2/IL-1ß/FasL/Fas axis are effectively inhibited from expression after BYHWD treatment. CONCLUSION: It is the first study to demonstrate that BYHWD can improve stroke-induced immunosuppression by down-regulating Fas-dependent splenic T-cell apoptosis triggered by peripheral AIM2 inflammasome-driven signaling cascade.

Visual Reasoning: From State to Transformation.

Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e., transformation. They are solely defined to test how well machines understand concepts and relations within static settings, like one image. Such state driven visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning (TVR) task. Given both the initial and final states, the target becomes to infer the corresponding intermediate transformation. Following this definition, a new synthetic dataset namely TRANCE is first constructed on the basis of CLEVR, including three levels of settings, i.e., Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Next, we build another real dataset called TRANCO based on COIN, to cover the loss of transformation diversity on TRANCE. Inspired by human reasoning, we propose a three-staged reasoning framework called TranNet, including observing, analyzing, and concluding, to test how recent advanced techniques perform on TVR. Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event, View, and TRANCO. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and new problems need to be investigated in this direction.

UniSKGRep: A unified representation learning framework of social network and knowledge graph.

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.

Location-aware convolutional neural networks for graph classification.

Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classification due to their powerful capability in pattern learning. The varying numbers of neighbor nodes and the lack of canonical order of nodes on graphs pose challenges in constructing receptive fields for CNNs. Existing methods generally follow a heuristic ranking-based framework, which constructs receptive fields by selecting a fixed number of nodes and dropping the others according to predetermined rules. However, such methods may lose important structure information through dropping nodes, and they also cannot learn task-oriented graph patterns. In this paper, we propose a Location learning-based Convolutional Neural Networks (LCNN) for graph classification. LCNN constructs receptive fields by learning the location of each node according to its embedding that contains structures and features information, then standard CNNs are applied to capture graph patterns. Such a location learning mechanism not only retains the information of all nodes, but also provides the ability for task-oriented pattern learning. Experimental results show the effectiveness of the proposed LCNN, and visualization results further illustrate the valid pattern learning ability of our method for graph classification.

The prediction of fluctuation in the order-driven financial market.

Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation-absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader's behaviours-order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

Effects of metformin, insulin on COVID-19 patients with pre-existed type 2 diabetes: A multicentral retrospective study.

AIMS: Type 2 diabetes is considered to be one of the essential risks of adverse outcomes in coronavirus disease 2019 (COVID-19).1 Metformin and insulin were suggested to affect the outcomes. However, divergent views are still expressed. We aim to gain further insight into metformin and insulin in both pre-admission and in-hospital usage in COVID-19 patients with pre-existed type 2 diabetes. MAIN METHODS: This is a multicentral retrospective study of the hospital confirmed COVID-19 patients between January 19 to April 09, 2020, who admitted to 3 main hospitals in Xiangyang city, China. The effect of type 2 diabetes, metformin, and insulin on COVID-19 were analyzed, respectively. Clinical characteristics, blood laboratory indices, clinical observational indices, and outcomes of these cases were collected. KEY FINDINGS: A total of 407 confirmed COVID-19 patients (including 50 pre-existed type 2 diabetes) were eligible in our study. COVID-19 patients with type 2 diabetes had more adverse outcomes than non-diabetes (OR2: mortality: 1.46 [95% CI3 1.11, 1.93]; P < 0.001). Pre-admission metformin usage showed a declined intensive care unit admission rate in a dose-dependent fashion (OR 0.04 [95% CI 0.00, 0.99]; adjust P = 0.049). While in-hospital insulin usage attempted to increase the invasive ventilation (8 [34.8%] vs. 1 [3.7%], adjust P = 0.043), independent of age and blood glucose. SIGNIFICANCE: Our study indicated that pre-admitted metformin usage may have beneficial effects on COVID-19 with pre-existed type 2 diabetes, insulin should be used sparingly in the hospital stay.

Conductivity and Stability Properties of Anion Exchange Membranes: Cation Effect and Backbone Effect.

The rise of heterocycle cations, a new class of stable cations, has fueled faster growth of research interest in heterocycle cation-attached anion exchange membranes (AEMs). However, once cations are grafted onto backbones, the effect of backbones on properties of AEMs must also be taken into account. In order to comprehensively study the influence of cations effect and backbones effect on AEMs performance, a series of AEMs were prepared by grafting spacer cations, heterocycles cations, and aromatic cations onto brominated poly(2,6-dimethyl-1,4-phenylene oxide) (BPPO) or poly(vinylbenzyl chloride) (PVB) backbones, respectively. Spacer cation [trimethylamine (TMA), N,N-dimethylethylamine (DMEA)]-attached AEMs showed general ion transportation and stability behaviors, but exhibited high cationic reaction efficiency. Heterocycle cation [1-methylpyrrolidine (MPY), 1-methylpiperidine (MPrD)]-attached AEMs showed excellent chemical stability, but their ion conduction properties were unimpressive. Aromatic cation [1-methylimidazole (MeIm), N,N-dimethylaniline (DMAni)]-attached AEMs exhibited superior ionic conductivity, while their poor cations stabilities hindered the application of the membranes. Besides, it was found that PVB-based AEMs had excellent backbone stability, but BPPO-based AEMs exhibited higher OH- conductivity and cation stability than those of the same cations grafted PVB-based AEMs due to their higher water uptake (WU). For example, the ionic conductivities (ICs) of BPPO-TMA and PVB-TMA at 80 °C were 53.1 and 38.3 mS cm-1 , and their WU was 152.3 and 95.1 %, respectively. After the stability test, the IC losses of BPPO-TMA and PVB-TMA were 21.4 and 32.2 %, respectively. The result demonstrated that the conductivity and stability properties of the AEMs could be enhanced by increasing the WU of the membranes. These findings allowed the matching of cations to the appropriate backbones and reasonable modification of the AEM structure. In addition, these results helped to fundamentally understand the influence of cation effect and backbone effect on AEM performance.

Clinical retrospective study on the efficacy of Qingfei Paidu decoction combined with Western medicine for COVID-19 treatment.

BACKGROUND: Coronavirus disease 2019 (COVID-19)2 has emerged as a global pandemic. However, as effective treatments for this disease are still unclear, safe and efficient therapies are urgently needed. Qingfei Paidu decoction (QPD)3 is strongly recommended in the Chinese Novel Coronavirus Pneumonia Diagnosis and Treatment Plan (Provisional 6th Edition). However, clinical research data on the effects of QPD on COVID-19 are scarce. Our study aimed to explore the effects of combined treatment with QPD and Western medicine on COVID-19. METHODS: In this study, 63 patients with confirmed COVID-19 were analyzed. During the first 14 days of hospitalization, patients with deteriorating symptoms were administered QPD along with Western medicine therapy (the antiviral medicine selected from interferon, lopinavir, or arbidol). The clinical characteristics and blood laboratory indices (blood routine, inflammatory factors, and multi-organ biochemical indices) were examined, and the total lung severity scores were evaluated in each patient by reviewing chest computed tomography before treatment and at the end of treatment. RESULTS: Before QPD treatment, the combined treatment group showed higher blood C-reactive protein levels and more severe pulmonary inflammation and clinical symptoms than the Western medicine treatment group. Both groups met the discharge criteria after a similar length of hospitalization. At the end of treatment, circulating white blood cells, total lymphocyte count, and glutamic-oxaloacetic transaminase levels improved dramatically in both groups (P <  0.05). In contrast, C-reactive protein, creatine kinase, creatine kinase-myocardial band, lactate dehydrogenase, and blood urea nitrogen levels were improved only in the combined treatment group (P <  0.05), and C-reactive protein and creatine kinase were the most pronounced (P <  0.01). Compared with baseline, at the end of treatment, the proportion of patients with normal values of C-reactive protein, total lymphocyte count, and lactate dehydrogenase were increased in the combined treatment group (P < 0.05), whereas no significant difference was observed in the Western medicine treatment group (P >  0.05). CONCLUSION: The combination of QPD with Western medicine demonstrated significant anti-inflammatory effects compared with those of only Western medicine in patients with mild and moderate COVID-19; however, neither mortality nor length of hospitalization was affected. Moreover, the combined treatment tended to mitigate the extent of multi-organ impairment. Long-term randomized controlled trials with follow-up evaluations are required to confirm the results presented here.

Fluid resuscitation with preventive peritoneal dialysis attenuates crush injury-related acute kidney injury and improves survival outcome.

BACKGROUND: In-hospital renal replacement therapy (RRT) is widely used for the treatments of acute kidney injury (AKI) in crush injury (CI) victims. This study was designed to investigate whether preventive peritoneal dialysis (PPD) is useful for renal protection in CI. METHODS: Animals received hindlimb compressions for 6 h to induce CI. Then, animals were untreated or treated with PPD and/or massive fluid resuscitation (MFR) for 8 h since the onset of compression release. Blood and renal tissue samples were collected at various time points for biological and morphological analysis. RESULTS: PPD attenuated lactic acidosis and reduced serum K+ and myoglobin levels in CI animals. In addition, PPD was effective in removing blood urea nitrogen (BUN) and creatinine, and reduced renal expressions of neutrophil gelatinase-associated lipocalin (NGAL). The combination of PPD and MFR furtherly attenuated AKI with significantly decreased histological scores (p = 0.037) and reduced NGAL expressions (p = 0.0002) as compared with the MFR group. Moreover, MFR + PPD group had a significantly higher survival rate than that in the MFR and the PPD groups (p < 0.05, respectively). CONCLUSION: The use of PPD at the onset of compression release is beneficial for renal protection and survival outcome in a rabbit model of CI.

Anomaly detection in Bitcoin market via price return analysis.

The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Trading network predicts stock price.

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

Temporal scaling in information propagation.

For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.

Cumulative effect in information diffusion: empirical study on a microblogging network.

Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.

A network for scene processing in the macaque temporal lobe.

Spatial navigation is a complex process, but one that is essential for any mobile organism. We localized a region in the macaque occipitotemporal sulcus that responds preferentially to images of scenes. Single-unit recording revealed that this region, which we term the lateral place patch (LPP), contained a large concentration of scene-selective single units. These units were not modulated by spatial layout alone but were instead modulated by a combination of spatial and nonspatial factors, with individual units coding specific scene parts. We further demonstrate by microstimulation that LPP is connected with extrastriate visual areas V4V and DP and a scene-selective medial place patch in the parahippocampal gyrus, revealing a ventral network for visual scene processing in the macaque.