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Aims Hypoxic–ischemic brain injury (HIBI) often results in cognitive impairments. Herein, we investigated the roles of ferroptosis in HIBI and the underlying signaling pathways. Methods Ferrostatin‐1 (Fer‐1) or resveratrol (Res) treatments were administered intracerebroventricularly 30 min before HIBI in 7‐day‐old rats. Glutathione peroxidase 4 (GPx4) expression, malondialdehyde (MDA) concentration, iron content, mitochondrial morphology, and the expression of silent information regulator factor 2‐related enzyme 1 (SIRT1) and nuclear factor erythroid‐2‐related factor 2 (Nrf2) were measured after HIBI. Additionally, the weight ratio of left/right hemisphere, brain morphology, Nissl staining, and the Morris water maze test were conducted to estimate brain damage. Results At 24‐h post‐HIBI, GPx4 expression was decreased, and MDA concentration and iron content were increased in the hippocampus. HIBI led to mitochondrial atrophy, brain atrophy/damage, and resultant learning and memory impairments, which were alleviated by Fer‐1‐mediated inhibition of ferroptosis. Furthermore, Res‐mediated SIRT1 upregulation increased Nrf2 and GPx4 expression, thereby attenuating ferroptosis, reducing brain atrophy/damage, and improving learning and memory abilities. Conclusion The results demonstrated that during HIBI, ferroptosis occurs via the SIRT1/Nrf2/GPx4 signaling pathway, suggesting it as a potential therapeutic target for inhibiting ferroptosis and ameliorating HIBI‐induced cognitive impairments. Ferroptosis occurs in a neonatal rat model of HIBI. Ferroptosis inhibition via Fer‐1 administration attenuates brain atrophy and learning and memory impairments. Activation of SIRT1/Nrf2/GPx4 signaling following Res treatment represents a potential therapeutic strategy for inhibiting ferroptosis and ameliorating HIBI‐induced learning and memory impairments.

Deep brain regions such as hippocampus, insula, and amygdala are involved in neuropsychiatric disorders, including chronic insomnia and depression. Our recent reports showed that transcranial alternating current stimulation (tACS) with a current of 15 mA and a frequency of 77.5 Hz, delivered through a montage of the forehead and both mastoids was safe and effective in intervening chronic insomnia and depression over 8 weeks. However, there is no physical evidence to support whether a large alternating current of 15 mA in tACS can send electrical currents to deep brain tissue in awake humans. Here, we directly recorded local field potentials (LFPs) in the hippocampus, insula and amygdala at different current strengths (1 to 15 mA) in 11 adult patients with drug-resistant epilepsy implanted with stereoelectroencephalography (SEEG) electrodes who received tACS at 77.5 Hz from 1 mA to 15 mA at 77.5 Hz for five minutes at each current for a total of 40 min. For the current of 15 mA at 77.5 Hz, additional 55 min were applied to add up a total of 60 min. Linear regression analysis revealed that the average LFPs for the remaining contacts on both sides of the hippocampus, insula, and amygdala of each patient were statistically associated with the given currents in each patient ( p  < 0.05–0.01), except for the left insula of one subject ( p  = 0.053). Alternating currents greater than 7 mA were required to produce significant differences in LFPs in the three brain regions compared to LFPs at 0 mA ( p  < 0.05). The differences remained significant after adjusting for multiple comparisons ( p  < 0.05). Our study provides direct evidence that the specific tACS procedures are capable of delivering electrical currents to deep brain tissues, opening a realistic avenue for modulating or treating neuropsychiatric disorders associated with hippocampus, insula, and amygdala.

Emotional decoding and automatic identification of major depressive disorder (MDD) is helpful to doctors in diagnosis of the disease on time, and electroencephalogram (EEG) is sensitive to the changes of functional state of human brain, showing its potential to help to diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross correlation with EEG signals is proposed and tested on 32 subjects (16 MDD and 16 healthy controls (HC)). First, the structure feature and connectivity feature of theta, alpha and beta band are extracted on the preprocessed and segmented EEG. Second, the structure feature matrix of theta, alpha and beta are added to and subtracted the connectivity feature matrix respectively to obtain the mixed features. Finally, the structure feature, connectivity feature and the mixed features are fed to six classifiers respectively to select the suitable features for the classification, and it is found that we have the best classification results using the mixed features. The results are also compared with those from some of the state-of-the-art methods, and we achieve accuracy of 94.13%, sensitivity of 95.74%, specificity of 93.52% and f1_score of 95.62% on the data from the Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a Brain–computer interfacing(BCI) system that may help for clinical purposes.

Abnormal lipid metabolism is known to increases the risk for metabolic diseases, such as type 2 diabetes mellitus(T2DM). The relationship between baseline ratio of triglyceride to HDL cholesterol (TG/HDL-C) and T2DM in Japanese adults was investigated in this study. Our secondary analysis included 8419 male and 7034 female Japanese subjects who were free of diabetes at baseline. The correlation between baseline TG/HDL-C and T2DM was analyzed by a proportional risk regression model, the nonlinear correlation between baseline TG/HDL-C and T2DM was analyzed by a generalized additive model (GAM), and the threshold effect analysis was performed by a segmented regression model. We conducted subgroup analyses in different populations. During the median 5.39 years follow-up, 373 participants, 286 males and 87 females, developed diabetes mellitus. After full adjustment for confounders, the baseline TG/HDL-C ratio positively correlated with the risk of diabetes (hazard ratio 1.19, 95% confidence interval 1.09–1.3), and smoothed curve fitting and two-stage linear regression analysis revealed a J-shaped relationship between baseline TG/HDL-C and T2DM. The inflection point for baseline TG/HDL-C was 0.35. baseline TG/HDL-C > 0.35 was positively associated with the development of T2DM (hazard ratio 1.2, 95% confidence interval 1.10–1.31). Subgroup analysis showed no significant differences in the effect between TG/HDL-C and T2DM in different populations. A J-shaped relationship was observed between baseline TG/HDL-C and T2DM risk in the Japanese population. When TG/HDL-C was higher than 0.35, there was a positive relationship between baseline TG/HDL-C and the incidence of diabetes mellitus.

The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

Long non‐coding RNAs (lncRNAs) have emerged as important regulators of cancer, including colorectal cancer (CRC). The exact expression pattern of long intergenic noncoding RNA 00312 (LINC00312) in CRC and its mechanisms of action have not been reported. Here, we found that LINC00312 is underexpressed in CRC tissues and cell lines. Functional experiments suggested that LINC00312 suppresses growth, migration and invasion of CRC cells in vitro and attenuates tumour proliferation and metastasis in vivo. Mechanistically, LINC00312 was found to regulate the malignancy of CRC cells by binding to miR‐21 and by functioning as a tumour suppressor targeting PTEN. Overexpression of miR‐21 or knockdown of PTEN attenuated the LINC00312‐mediated inhibition of CRC cell proliferation and invasion. Taken together, our results elucidate the role of the LINC00312–miR‐21–PTEN axis in CRC cell proliferation and tumour progression and may lead to new lncRNA‐based diagnostics or therapeutics for CRC.

The analysis of slope failure modes is essential for understanding slope stability. This study investigated the failure modes and triggering factors of a rock slope using the limit equilibrium method, finite differences method, and exploratory factor analysis. First, the limit equilibrium method was used to identify potential sliding surfaces. Then, the finite differences method was employed to study deformation and failure features in a slope. Stability factors were calculated considering specific conditions such as rainfall, prestressing loss, and earthquakes using the strength reduction method. Finally, exploratory factor analysis was utilized to identify the triggering factors of each failure mode. The results revealed that failure modes were categorized into two types based on the positions of the sliding surface. The main triggering factors for Failure Mode 1 were rainfall and prestress loss, while for Failure Mode 2 they were earthquake loading and prestress loss. This study offers a comprehensive exploration of potential failure modes and their triggering factors from mechanical and statistical perspectives, enriching our understanding of potential failure modes in rock slopes.

Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. To improve the classification performance of a single machine learning (ML) model, this study selects logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), and proposes a novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability in a typical landslide area in Yanbian Prefecture, China. Firstly, a dataset consisting of 1531 historical landslides was collected from field investigations and historical records, and a spatial database containing 16 predisposing factors was established. The dataset was divided into a training set and a test set in a ratio of 7:3. The results showed that SWA effectively improved the Accuracy, AUC, and robustness of the model compared to a single ML model. The SWA achieved the best classification results (Accuracy = 91.39% and AUC = 0.967). To verify the generalization ability of SWA, we selected published landslide datasets from Yanshan country and Yongxin country in China for testing. SWA also performed well, with an AUC of 0.871 and 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is the factor that has the greatest impact on landslide occurrence. The landslide susceptibility maps obtained from this study will provide an effective reference program for land use planning and disaster prevention and mitigation projects in Yanbian Prefecture, China.

Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

High-Mg adakite rocks preserve crucial information about the crust-mantle interactions during the magma evolution. The Luxi Terrane, southeastern North China Craton, stores a set of Early Cretaceous high-Mg adakite rocks; nevertheless, their petrogenesis remains controversial. In this study, we present new whole-rock geochemistry, zircon U-Pb-Hf isotopes in the Tiezhai, Jinxingtou, and Sanshanyu complexes which are composed of gabbroic diorite, diorites, syenites, and monzonites. Field observations and zircon U-Pb dating indicate that all of the rock units crystallized contemporaneously at ca. 125-120 Ma. They are characterized by high Al2O3 and Sr contents, and low MgO, Y, Yb, and heavy rare earth elements contents, coupled with high Sr/Y values (42-163), showing adakitic affinities. The magma mixing process is supported by the following ample evidence: (1) the disequilibrium mineral textures and mafic enclaves; (2) high Mg# values (37-69, Mean = 58); and (3) widely zircons εHf(t) values (-25.6 to +7.8). The signature geochemical characteristics support that the adakites were generated by magma mixing of ancient crust-derived melts and relatively mafic melts from metasomatized mantle source. In combined with regional geology, the Early Cretaceous high-Mg adakites in the Luxi Terrane represent the magmatic response of intensive crust-mantle interaction caused by the underplating of voluminous mantle-derived magma in an extension intracontinental setting.