5Z-7-Oxozaenol attenuates cuprizone-induced demyelination in mice through microglia polarization regulation.

INTRODUCTION: Demyelination is a key factor in axonal degeneration and neural loss, leading to disability in multiple sclerosis (MS) patients. Transforming growth factor beta activated kinase 1 (TAK1) is a critical molecule involved in immune and inflammatory signaling pathways. Knockout of microglia TAK1 can inhibit autoimmune inflammation of the brain and spinal cord and improve the outcome of MS. However, it is unclear whether inhibiting TAK1 can alleviate demyelination. METHODS: Eight-week-old male c57bl/6j mice were randomly divided into five groups: (a) the control group, (b) the group treated with cuprizone (CPZ) only, (c) the group treated with 5Z-7-Oxozaenol (OZ) only, and (d) the group treated with both cuprizone and 15 µg/30 µg OZ. Demyelination in the mice of this study was induced by administration of CPZ (ig) at a daily dose of 400 mg/kg for consecutive 5 weeks. OZ was intraperitoneally administered at mentioned doses twice a week, starting from week 3 after beginning cuprizone treatment. Histology, rotarod test, grasping test, pole test, Western blot, RT-PCR, and ELISA were used to evaluate corpus callosum demyelination, behavioral impairment, oligodendrocyte differentiation, TAK1 signaling pathway expression, microglia, and related cytokines. RESULTS: Our results demonstrated that OZ protected against myelin loss and behavior impairment caused by CPZ. Additionally, OZ rescued the loss of oligodendrocytes in CPZ-induced mice. OZ inhibited the activation of JNK, p65, and p38 pathways, transformed M1 polarized microglia into M2 phenotype, and increased brain-derived neurotrophic factor (BDNF) expression to attenuate demyelination in CPZ-treated mice. Furthermore, OZ reduced the expression of proinflammatory cytokines and increases anti-inflammatory cytokines in CPZ-treated mice. CONCLUSION: These findings suggest that inhibiting TAK1 may be an effective approach for treating demyelinating diseases.

Coronavirus disease 2019 and acute cerebrovascular events: a comprehensive overview.

Since the Corona Virus Disease 2019 (COVID-19) pandemic, there has been increasing evidence that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with acute cerebrovascular events such as cerebral infarction, cerebral hemorrhage, and cerebral venous thrombosis. Although the mechanism of cerebrovascular complications among COVID-19 patients has not been adequately elucidated, the hypercoagulable state, excessive inflammation and ACE-2-associated alterations in the renin-angiotensin-aldosterone system after SARS-CoV-2 infection probably play an essential role. In this overview, we discuss the possible mechanisms underlying the SARS-CoV-2 infection leading to acute cerebrovascular events and review the characteristics of COVID-19-related acute cerebrovascular events cases and treatment options available worldwide.

Snake net with a neural network for detecting multiple phases in the phase diagram.

Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett. 120, 176401 (2018)0031-900710.1103/PhysRevLett.120.176401]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this work we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, a balloon force is introduced, which helps the snake to leave a wrong initial position and thus may allow for greater freedom in the initialization of the snake. Our method is helpful in determining the phase diagram with multiple phases using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.

Sphingosine-1-phosphate receptor modulators in stroke treatment.

Sphingosine-1-phosphate (S1P) is a bioactive lysophospholipid that can influence a broad range of biological processes through its binding to five distinct G-protein-coupled receptors. S1P receptor modulators are a new group of immunosuppressive agents currently used in the immunotherapy of multiple sclerosis. Inflammation following stroke can exacerbate neuronal injury. Given that S1P signaling is linked to multiple immune processes, therapies targeting the S1P axis may be suitable for treating stroke. In this review, we outline S1P metabolism and S1P receptors, discuss the mechanisms of action of S1P receptor modulators in lymphocyte migration and their direct action on cells of the central nervous system, and provide a concise summary of the efficacy of S1P receptor modulators in animal studies and clinical trials on treatments for stroke.

The Alteration Profiles of m6A-Tagged circRNAs in the Peri-Infarct Cortex After Cerebral Ischemia in Mice.

The N6-methyladenosine (m6A) modification acts as a dynamic regulatory factor in diseases by regulating the metabolism and function of the transcriptome, especially mRNAs. However, little is known regarding the functional effects of m6A modifications on circRNAs. In this research, we established a distal middle cerebral artery occlusion (MCAO) model in adult C57BL/6J mice. The mice were divided into three groups: sham surgery, 3 days after MCAO (3d), and 7 days after MCAO (7d). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) demonstrated that the mRNA expression levels of m6A-related methyltransferases (METTL3, METTL14), demethylases (FTO, ALKBH5), and reading proteins (YTHDF1, YTHDF3) altered compared to the sham group. Furthermore, the translation level of ALKBH5 and YTHDF3 was significantly decreased in the 3d group while increased in 7d group. Methylated RNA immunoprecipitation (MeRIP) and circRNA microarray indicated 85 hypermethylated and 1621 hypomethylated circRNAs in the 3d group. In the 7d group, the methylation level increased in 57 and decreased in 66 circRNAs. Subsequently, our results were verified by MeRIP-qPCR. Bioinformatics analysis was performed to analyze the functions of differentially m6A-modified circRNAs. We found some m6A modified-circRNAs associated with cerebral infarction, providing a new direction for the molecular mechanism of stroke.

Machine learning for percolation utilizing auxiliary Ising variables.

Machine learning for phase transition has received intensive research interest in recent years. However, its application in percolation still remains challenging. We propose an auxiliary Ising mapping method for the machine learning study of the standard percolation as well as a variety of statistical mechanical systems in correlated percolation representation. We demonstrate that unsupervised machine learning is able to accurately locate the percolation threshold, independent of the spatial dimension of system or the type of phase transition, which can be first-order or continuous. Moreover, we show that, by neural network machine learning, auxiliary Ising configurations for different universalities can be classified with a high confidence level. Our results indicate that the auxiliary Ising mapping method, despite its simplicity, can advance the application of machine learning in statistical and condensed-matter physics.

Phase transitions in a three-dimensional Ising model with cluster weight studied by Monte Carlo simulations.

The loop model is an important model of statistical mechanics and has been extensively studied in two-dimensional lattices. However, it is still difficult to simulate the loop model directly in three-dimensional lattices, especially in lattices with coordination numbers larger than 3. In this paper, a cluster weight Ising model is proposed by introducing an additional cluster weight n in the partition function of the traditional Ising model. This model is equivalent to the loop model on the two-dimensional lattice, but on the three-dimensional lattice, it is still not very clear whether or not these models have the same universality. By using a Monte Carlo method with cluster updates and color assignment, we obtain the global phase diagram containing the paramagnetic and ferromagnetic phases. The phase transition between the two phases is second order at 1≤n

Machine learning of phase transitions in the percolation and XY models.

In this paper, we apply machine learning methods to study phase transitions in certain statistical mechanical models on the two-dimensional lattices, whose transitions involve nonlocal or topological properties, including site and bond percolations, the XY model, and the generalized XY model. We find that using just one hidden layer in a fully connected neural network, the percolation transition can be learned and the data collapse by using the average output layer gives correct estimate of the critical exponent ν. We also study the Berezinskii-Kosterlitz-Thouless transition, which involves binding and unbinding of topological defects, vortices and antivortices, in the classical XY model. The generalized XY model contains richer phases, such as the nematic phase, the paramagnetic and the quasi-long-range ferromagnetic phases, and we also apply machine learning method to it. We obtain a consistent phase diagram from the network trained with only data along the temperature axis at two particular parameter Δ values, where Δ is the relative weight of pure XY coupling. Aside from using the spin configurations (either angles or spin components) as the input information in a convolutional neural network, we devise a feature engineering approach using the histograms of the spin orientations in order to train the network to learn the three phases in the generalized XY model and demonstrate that it indeed works. The trained network by using system size L×L can be used to the phase diagram for other sizes (L^{'}×L^{'}, where L^{'}≠L) without any further training.

Percolation of the site random-cluster model by Monte Carlo method.

We propose a site random-cluster model by introducing an additional cluster weight in the partition function of the traditional site percolation. To simulate the model on a square lattice, we combine the color-assignation and the Swendsen-Wang methods to design a highly efficient cluster algorithm with a small critical slowing-down phenomenon. To verify whether or not it is consistent with the bond random-cluster model, we measure several quantities, such as the wrapping probability Re, the percolating cluster density P∞, and the magnetic susceptibility per site χp, as well as two exponents, such as the thermal exponent yt and the fractal dimension yh of the percolating cluster. We find that for different exponents of cluster weight q=1.5, 2, 2.5, 3, 3.5, and 4, the numerical estimation of the exponents yt and yh are consistent with the theoretical values. The universalities of the site random-cluster model and the bond random-cluster model are completely identical. For larger values of q, we find obvious signatures of the first-order percolation transition by the histograms and the hysteresis loops of percolating cluster density and the energy per site. Our results are helpful for the understanding of the percolation of traditional statistical models.

Critical properties of the Hintermann-Merlini model.

Many critical properties of the Hintermann-Merlini model are known exactly through the mapping to the eight-vertex model. Wu [J. Phys. C 8, 2262 (1975)] calculated the spontaneous magnetizations of the model on two sublattices by relating them to the conjectured spontaneous magnetization and polarization of the eight-vertex model, respectively. The latter conjecture remains unproved. In this paper we numerically study the critical properties of the model by means of a finite-size scaling analysis based on transfer matrix calculations and Monte Carlo simulations. All analytic predictions for the model are confirmed by our numerical results. The central charge c=1 is found for the critical manifold investigated. In addition, some unpredicted geometric properties of the model are studied. Fractal dimensions of the largest Ising clusters on two sublattices are determined. The fractal dimension of the largest Ising cluster on the sublattice A takes a fixed value D(a)=1.888(2), while that for sublattice B varies continuously with the parameters of the model.