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Alzheimer’s disease (AD) is a progressive and degenerative disorder accompanied by cognitive decline, which could be promoted by mitochondrial dysfunction induced by mitochondrial Ca 2+ (mCa 2+) homeostasis Mitochondrial calcium uniporter (MCU), a key channel of mCa 2+ uptake, may be a target for AD treatment. In the present study, we reveal for the first time that MCU knockdown in hippocampal neurons improves the memory performance of APP/PS1/tau mice through radial arm maze task. Western blot analysis, transmission electron microscopy (TEM), Golgi staining, immunohistochemistry (IHC) and ELISA results demonstrate that MCU knockdown in hippocampal neurons upregulates the levels of postsynaptic density protein 95 (PSD95) and synaptophysin (SYP), and increases the numbers of synapses and dendritic spines. Meanwhile, MCU knockdown in hippocampal neurons decreases the neuroinflammatory response induced by astrogliosis and high levels of IL-1β and TNF-α, and improves the PINK1-Parkin mitophagy signaling pathway and increases the level of Beclin-1 but decreases the level of P62. In addition, MCU knockdown in hippocampal neurons recovers the average volume and number of mitochondria. These data confirm that MCU knockdown in hippocampal neurons improves the memory performance of APP/PS1/tau mice through ameliorating the synapse structure and function, relieving the inflammation response and recovering mitophagy, indicating that MCU inhibition has the potential to be developed as a novel therapy for AD.

The interferometric inverse synthetic aperture radar (InISAR) system realises the interferometric processing on the imaging results obtained from three radars on a set of orthogonal baselines and can get the three‐dimensional (3D) imaging for the ship target. The ship targets experience complex motion characteristics such as roll, pitch and yaw in the course of navigation. These motions lead to the blurred two‐dimensional (2D) images and further affect the interference performance during the InISAR procedure. Therefore, this paper proposes a 3D InISAR imaging algorithm for the ship targets based on the cubic phase signal (CPS) model. This method approximates the azimuth echo signal as a CPS and utilises the particle swarm optimization algorithm to estimate the higher‐order phase coefficients. While preserving the phase information, the 3D image result can be achieved by the interference processing between the high‐resolution 2D images. The effectiveness of the algorithm is validated through the simulation experiments under different baseline lengths and signal‐to‐noise ratios. [ABSTRACT FROM AUTHOR]

With the increasing number of cancer patients, how to care for cancer survivors and establish a sound cancer survivorship care model has become a hot topic in China and even the world. The development of cancer survivorship care model in China is still in its infancy. There are some deficiencies in cancer care and the management needs to be improved. We reviewed the latest developments in foreign cancer care models, and analyzed the aspects that merit attention, such as care process improvement, resource convergence and model innovation, then based on current situation of cancer care in China, we advised from five aspects to facilitate domestic development of cancer survivorship care models: using tiered diagnosis and treatment to individualizedly manage cancer survivors, developing and improving cancer care guidelines, building a strong cancer care workforce, innovating cancer care management, and strengthening policy and institutional measures to reduce the cancer burden.

In view of China's "carbon peak, carbon neutral" strategy, especially the important point of reaching the carbon peak by 2035 and achieving socialist modernisation, it is important to select a number of priority end-use energy consumption technology topics to promote the company's technological innovation. Based on the results of the two rounds of the Delphi survey, eight end-use energy consumption technologies with a "dual carbon" focus should be prioritised, namely: virtual power plant technology, electric vehicle technology, impact assessment and costing of carbon emissions and reduction, and carbon dioxide capture and storage technology. The results of the Delphi survey show that R&D investment, policy mechanisms and market viability are the main constraints to the completion of these technologies. In response to these constraints, relevant recommendations are made in terms of funding input, institutional development and market development.

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (

Objective: microRNA‐450b (miR‐450b) plays an important role in cancer progression; however, its function in oral squamous cell carcinoma (OSCC) remains largely unknown. This study aimed to investigate the action mechanisms of miR‐450b in OSCC. Materials and Methods: OSCC animal model was established via continuous induction with single‐drug 7, 12‐dimethylbenzo[a]anthracene (DMBA). Animal tissue samples were pathologically typed using haematoxylin–eosin (HE) staining. The Cancer Genome Atlas (TCGA) database was used to predict miR‐450b and SERPINB2 expression in head and neck squamous cell carcinoma (HNSCC). qRT‐PCR and Western blotting were used to detect gene and protein expression in OSCC tissue and cells, respectively. OSCC cell proliferation, growth, migration and invasion were detected using CCK‐8, colony formation, transwell migration and matrigel invasion assays, respectively. Bioinformatic tools were used to predict miR‐450b target genes. Dual‐luciferase reporter assay was used to verify targeting between miR‐450b and SERPINB2. Finally, small interfering RNA (siRNA) was used to reduce SERPINB2 expression to detect its effect on tumourigenesis. Results: Four stages of OSCC carcinogenesis (normal oral epithelium, simple epithelial hyperplasia, dysplasia and OSCC) were identified. miR‐450b was found to be overexpressed in OSCC animal samples, HNSCC samples and human OSCC cells. Upregulation of miR‐450b significantly promoted OSCC cell proliferation, colony formation, migration and invasion, while its downregulation had the opposite effect. SERPINB2 was found to be a miR‐450b target gene, and its expression was negatively correlated with miR‐450b expression. Altering SERPINB2 expression effectively inhibited OSCC cell invasion, metastasis and epithelial‐mesenchymal transition (EMT). Conclusions: miR‐450b plays a key role in OSCC tumourigenesis by regulating OSCC cell migration, invasion and EMT via SERPINB2. [ABSTRACT FROM AUTHOR]

Under global warming, the availability of water resources is one of the most important factors affecting trait evolution and plant species distribution across terrestrial ecosystems, and the relationships between drought resistance strategies and the hydrological niche characteristics of plants are worth studying. We continuously monitored physiological drought response parameters such as gs, Tr, proline, soluble sugar, gene expression and activities of SOD, POD, and CAT to assess drought resistance strategies of Platycarya longipes and Lindera communis; determined plant soil hydrological niche separation by stable H and O isotope analysis; and analysed the effects of interspecific water competition by comparing the differences in morphological and physiological parameters between solo and mixed planting. Under drought stress, L. communis exhibited a drought avoidance strategy, and P. longipes exhibited a drought tolerance strategy. L. communis utilized the water within the shallow soil layer, while P. longipes mainly utilized the water in the deeper soil layer; there were fewer parameters with significant differences between the solo planting and the mixed planting of L. communis compared to P. longipes. Overall, P. longipes benefited from coexistence with L. communis under drought stress, which may be because L. communis employs a drought avoidance strategy, reducing soil water consumption in the drought environment. These results suggested that differences in functional traits or resistance strategies among species benefit species' coexistence in a community under drought stress. [ABSTRACT FROM AUTHOR]

There has been a growing interest in studying the communication of gut microbial metabolites between the gut and the liver as liver fibrosis progresses. Although 3-Indolepropionic acid (IPA) is regarded as a clinically valuable gut metabolite for the treatment of certain chronic diseases, the effects of oral administration of IPA on hepatic fibrosis in different animal models have been conflicting. While some mechanisms have been proposed to explain these contradictory effects, the direct impact of IPA on hepatic fibrosis remains unclear. In this study, we found that IPA could directly activate LX-2 human hepatic stellate cells in vitro. IPA upregulated the expression of fibrogenic marker genes and promoted the features associated with HSCs activation, including proliferation and contractility. IPA also increased reactive oxygen species (ROS) in mitochondria and the expression of inflammation-related genes in LX-2 cells. However, when a ROS-blocking agent was used, these effects were reduced. p38 and JNK, the downstream signaling cascades of ROS, were found to be required for the activation of LX-2 induced by IPA. These findings suggest that IPA can directly activate hepatic stellate cells through ROS-induced JNK and p38 signaling pathways. [ABSTRACT FROM AUTHOR]

Monitoring the risk of intussusception after the introduction of rotavirus vaccines is recommended by the World Health Organization (WHO). Although the validity of intussusception monitoring using electronic health records (EHRs) has been confirmed previously, no similar studies have been conducted in China. We aimed to verify the diagnosis and determine an algorithm with the best performance for identification of intussusception using Chinese EHR databases. Using the Regional Health Information Platform in Ningbo, patients aged 0–72 months from 2015 to 2021 with any related visits for intussusception were included. The algorithms were based on diagnostic codes or keywords in different clinical scenarios, and their performance was evaluated with positive predictive value (PPV) and sensitivity in line with the Brighton guidelines. Brighton level 1 intussusception was confirmed in 2958 patients with 3246 episodes. Fine-tuned algorithms combining the appearance of the relevant ICD-10 codes or the Chinese keyword 'Chang Tao' in any diagnostic reports with the results of enema treatments or related surgeries showed the highest sensitivity, while the highest PPV was obtained by further criteria based on typical radiographic appearances. Intussusception could be identified and validated internally using EHRs in Ningbo. [ABSTRACT FROM AUTHOR]

Background: The CRISPR/Cas9 system is widely used for genome editing in human, rat and mouse cells. In this study, we established Fzd6 mutant mice using CRISPR/Cas9 technology, and obtained Fzd6 homozygous mutant (Fzd6Q152E) mice through breeding. Fzd6 plays a role in depression, but there are few related reports. We used this model to investigate the mechanism of Fzd6 involved in depression, and build a solid foundation for subsequent in-depth studies. Methods and Results: The target of Fzd6 mutation was obtained by CRISPR/Cas9 technology and hippocampal tissue was collected for Nissl staining and histological analysis. Blood was collected for enzyme linked immunosorbent assay (ELISA); The gene expression of Fzd6 and the related genes expression in wnt pathway was quantified by quantitative real-time PCR (qRT-PCR), and then expression of Fzd6 and proteins in the Wnt pathway were identified by western blotting. ELISA results showed that the expression levels of brain derived neurotrophic factor (BDNF), 5-hydroxytryptamine (5-HT), and Noradrenaline (NE) in serum were significantly decreased in Fzd6Q152E mice, whereas the mRNA expression of Lrp5, Lrp6, and Dkk2 is increased. The western blotting revealed that the expression of Fzd6 and Lrp6 is decreased, although the expression of Dkk2 and Gsk-3β increased. Conclusion: Our study successfully established homozygous Fzd6 mutant mice model. The relationship between Fzd6-Wnt and depression was preliminarily clarified, which provides an ideal animal model for subsequent research on diseases induced by the Fzd6 mutation. [ABSTRACT FROM AUTHOR]

High-precision wind power prediction could reference the optimal dispatch and stable operation of the power system. This paper proposes an adaptive hybrid optimization algorithm that integrates decomposition and reconstruction to effectively explore the potential characteristics and related factors of wind power output and improve the accuracy of short-term wind power prediction. First, the extreme-point symmetric mode decomposition is used to analyze the periodicity, trend, and abrupt characteristics in the original wind power sequence and form multiple intrinsic mode functions with local time-domain characteristics. Then, considering the similarity of the feature sequence and the efficiency of the prediction algorithm, the permutation entropy is used to reconstruct the components with close time-domain characteristics to form subsequences that could reflect different spectral characteristics. Then, the improved maximum relevance minimum redundancy-the long short-term memory-the adaptive boosting algorithm model is used to determine the prediction model structure, parameters, and optimal feature factors of the subsequences. Finally, the prediction results of each subsequence are integrated to obtain the final wind power. Taking a wind farm in northern Shaanxi as the application object, the prediction accuracy and efficiency of the methods proposed in this paper are compared in terms of the decomposition method, prediction model, and prediction timeliness. The results show that in the 15 min to 3 h forecast periods, compared with other models, the mean absolute error and root mean square error of the proposed model are increased. At the same time, as the forecast period grows, the superiority of the proposed method is more prominent. [ABSTRACT FROM AUTHOR]

Transfer learning is the method that makes use of knowledge from other fields to solve problems in related fields. It has been shown that it can deal with the problem of insufficient labeled data for new users or new tasks in the brain-computer interface. Domain adaptation is one of the transfer learning methods which is widely used for its excellent performance. Here, the offline cross-subject EEG signal classification is mainly focused on. The unlabeled EEG trials of the new user are classified by using the EEG trials with labels from source subjects. In this paper, a novel transfer learning method called multi-source fusion adaptation regularization (MFAR) is proposed. MFAR preprocesses the EEG signal by aligning the motor imagery trials to their resting state trials, and can reduce the differences among subjects. It also defines a learning framework by combining weighted balanced distribution adaptation (W-BDA), source empirical risk, and manifold regularization to further reduce the variation between source and target domains. We validated the method on two BCI Competition IV datasets for motor imagery tasks. In the absence of labeled EEG trials of the target subject, compared with the excellent counterparts, the classification accuracy increases by 9.28% and 11.73%. After the alignment algorithm is added, the accuracy of the MFAR is improved by 9.36% and 4.17% on the basis. The experimental results show that our learning framework outperformed several state-of-the-art transfer learning algorithms. Even when the training data from the new user are sufficient, the proposed approach achieves good performance. [ABSTRACT FROM AUTHOR]

In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification. [ABSTRACT FROM AUTHOR]

Background and hypothesis: Sepsis is still one of the reasons for serious infectious diseases in pediatric intensive care unit patients despite the use of anti-infective therapy and organ support therapy. As it is well-known, the effect of single gene or pathway does not play a role in sepsis. We want to explore the interaction of two more genes or pathways in sepsis patients for future works. We hypothesize that the discovery from the available gene expression data of pediatric sepsis patients could know the process or improve the situation. Methods and results: The gene expression profile dataset GSE26440 of 98 septic shock samples and 32 normal samples using whole blood-derived RNA samples were generated. A total of 1,108 upregulated and 142 downregulated differentially expressed genes (DEGs) were identified in septic shock children using R software packages. The Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed using DAVID software; Gene Set Enrichment Analysis method was also used for enrichment analysis of the DEGs. The protein–protein interaction (PPI) network and the top 10 hub genes construction of the DEGs were constructed via plug-in Molecular Complex Detection and cytoHubba of Cytoscape software. From the PPI network, the top 10 hub genes, which are all upregulated DEGs in the septic shock children, were identified as GAPDH, TNF, EGF, MAPK3, IL-10, TLR4, MAPK14, IL-1b, PIK3CB, and TLR2. Some of them were involved in one or more significant inflammatory pathways, such as the enrichment of tumor necrosis factor (TNF) pathway in the activation of mitogen-activated protein kinase activity, toll-like receptor signaling pathway, nuclear factor-κB signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway. These findings support future studies on pediatric septic shock. [ABSTRACT FROM AUTHOR]

Circadian rhythm disturbance (CRD) is one of the iconic manifestations in Alzheimer’s disease (AD), a disease tightly associated with age, but the characteristics and gender difference of CRD occurred in AD have not been well demonstrated. Using 6-month-old triple transgenic AD mouse model (3xTg-AD) without obvious brain pathological changes, we demonstrated the gender difference of CRD at this age. We further showed abnormal Per gene expression in the central clock suprachiasmatic nucleus (SCN) of the 3xTg-AD mice. Specifically, compared with the wide type (WT) mice, the 3xTg-AD mice showed disrupted circadian locomotor rhythms both at LD (light-dark 12 h:12 h) and DD (constant dark) conditions, such as increased activities in the resting phase, decreased and scattered activities in the active phase, decreased overall activity intensities, amplitude, robustness, and increased intradaily variability. We further observed that 3xTg-AD female mice showed obviously less CRD compared with the 3xTg-AD male mice, and female mice of both WT and 3xTg-AD were more active in locomotor activity. Accordingly, 3xTg-AD mice showed a phase delay in the expression of Per1 and Per2 mRNA in the SCN, with the levels of Per1 and Per2 mRNA were significantly lower than that of WT mice at specific time points. We conclude that 3xTg-AD mice exhibit behavioral CRD at the age of six months with male gender preference, and these phenomena are at least partly associated with the alteration of Per1 and Per2 transcription patterns in the SCN. [ABSTRACT FROM AUTHOR]

Apolipoprotein E4 ( APOE4 ) allele located on chromosome 19 is a major genetic risk factor for developing Alzheimer’s disease (AD). However, the direct effects of ApoE4 on the cognitive function and long-term synaptic plasticity have not fully investigated. At the same time, although amyloid beta protein (Aβ)-ApoE complexes are principal components of AD-associated brain damage, there is still lack of in vivo study on the effects of co-existed Aβ1-40 and ApoE4. In the present study, we examined the effects of ApoE4 on the spatial memory and hippocampal long term potentiation (LTP) by using Morris water maze test and in vivo field potential recording, compared the neurotoxicity of Aβ1-40 and ApoE4, and investigated the effects of co-application of Aβ1-40 and ApoE4 on cognitive behavior and synaptic plasticity. The results showed that intracerebrovenrticular (i.c.v.) injection of Aβ1-40 or ApoE4 significantly and similarly impaired spatial learning and memory, and depressed the high-frequency stimulus (HFS) induced LTP. Importantly, compared to the effects of Aβ1-40 or ApoE4 alone, co-injection of Aβ1-40 and ApoE4 produced much heavier damages in cognitive behaviors and long term synaptic plasticity. These results demonstrated that ApoE4 not only exerted direct neurotoxicity but also enhanced the neurotoxicity of Aβ1-40 on spatial cognitive function and hippocampal LTP, which maybe partly elucidates the mechanism by which APOE4 allele exerted negative effects as a major genetic risk factor for developing AD. [ABSTRACT FROM AUTHOR]