The deubiquitinase USP6 affects memory and synaptic plasticity through modulating NMDA receptor stability

人类与其他动物相比的重要区别在于人类拥有高等认知能力，这种能力集中体现在学习记忆和语言表达方面。厦门大学医学院神经科学研究所王鑫教授团队发现人科动物特异性基因USP6作为一个新的NMDA受体调控因子，可通过去泛素化途径调节NMDA型谷氨酸受体的降解和稳定性，进而调控突触可塑性和学习记忆能力。 本研究工作由王鑫教授指导完成，博士生曾凡伟、马学海与硕士生朱琳为共同第一作者，王鑫教授为通讯作者。Ubiquitin-specific protease (USP) 6 is a hominoid deubiquitinating enzyme previously implicated in intellectual disability and autism spectrum disorder. Although these findings link USP6 to higher brain function, potential roles for USP6 in cognition have not been investigated. Here, we report that USP6 is highly expressed in induced human neurons and that neuron-specific expression of USP6 enhances learning and memory in a transgenic mouse model. Similarly, USP6 expression regulates N-methyl-D-aspartate-type glutamate receptor (NMDAR)-dependent long-term potentiation and long-term depression in USP6 transgenic mouse hippocampi. Proteomic characterization of transgenic USP6 mouse cortex reveals attenuated NMDAR ubiquitination, with concomitant elevation in NMDAR expression, stability, and cell surface distribution with USP6 overexpression. USP6 positively modulates GluN1 expression in transfected cells, and USP6 down-regulation impedes focal GluN1 distribution at postsynaptic densities and impairs synaptic function in neurons derived from human embryonic stem cells. Together, these results indicate that USP6 enhances NMDAR stability to promote synaptic function and cognition.This work was partially supported by the National Natural Science Foundation of China (31871077, 81822014, 81571176 to XW; 81701349 to Hongfeng Z.; 81701130 to QZ; and 81471160 to HS), the National Key R&D Program of China (2016YFC1305900 to XW and HS), the Natural Science Foundation of Fujian Province of China (2017J06021 to XW), the Fundamental Research Funds for the Chinese Central Universities (20720150061 to XW and 20720180040 to ZS), Open Research Fund of State Key Laboratory of Cellular Stress Biology, Xiamen University (SKLCSB2019KF012 to QZ), and China Postdoctoral Science Foundation (2017M612130 to QZ).该研究得到了国家自然科学基金面上项目和优秀青年基金项目的支持

Preparation of Well-Defined Propargyl-Terminated Tetra-Arm Poly(N-isopropylacrylamide)s and Their Click Hydrogels Crosslinked with β-cyclodextrin

As an important class of reversible deactivation radical polymerization (RDRP), reversible addition fragmentation chain transfer (RAFT) polymerization has attracted great attention attributed to its facile and flexible features to prepare well-defined polymers with different complex structures. In addition, the combination of RAFT with click chemistry provides more effective strategies to fabricate advanced functional materials. In this work, a series of temperature responsive tetra-arm telechelic poly(N-isopropylacrylamide)s (PNIPAs) with propargyl end groups were prepared for the first time through RAFT and subsequent aminolysis/Michael addition modification. The temperature sensitivities of their aqueous solutions were researched via turbidity measurement. It was found that the phase transition temperature of obtained PNIPAs increased with their molecular weights ascribed to their distinctions in the hydrophobic/hydrophilic balance. Subsequently, β-cyclodextrin (β-CD) functionalized with azide moieties was used to crosslink the prepared propargyl-terminated tetra-arm PNIPAs through click chemistry, fabricating corresponding hydrogels with thermoresponse. Similar to their precursors, the hydrogels demonstrated the same dependence of volume phase transition temperature (VPTT) on their molecular weights. In addition, the incorporation of β-CD and the residual groups besides crosslinking may provide a platform for imparting additional functions such as inclusion and adsorption as well as further functionalization

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities

Predictions and Evolution Characteristics of Failure Modes of Degenerate RC Piers

During the service process, piers are often in harsh chloride ion erosion environments. The failure mode evolution of reinforced concrete (RC) piers may occur under the action of continuous corrosion. Accurately identifying the failure mode types and evolution characteristics of corroded RC bridge piers is a prerequisite for the lifetime seismic performance evaluations of bridges. First, based on Fisher’s theory and 174 RC pier columns as the analysis samples, a two-stage discrimination formula for the pier failure modes was established and compared with the existing theoretical discrimination methods. Then, based on Fisher’s discriminant grouping, and combined with Bayes’ formula and chloride erosion theory, a failure mode discrimination method for corrosion-damaged bridge piers that considers probability was developed. Finally, taking a medium-span concrete bridge as an example, the failure modes of the corroded pier in different service periods were predicted, and the influences of the various parameters on the failure mode evolution process of the corroded pier were studied. The results show that the accuracy of the proposed discriminant model was significantly improved compared with those of previous theoretical studies. The development of the failure mode features depends on how the distinct RC pier material qualities degrade under the influence of chloride ions. The degradation of the stirrups and concrete accelerates the nonductile failure of RC bridge piers, while the degradation of the longitudinal reinforcements delays it

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO₂ Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities

Spatiotemporal Dynamics of Ecological Security Pattern of Urban Agglomerations in Yangtze River Delta Based on LUCC Simulation

Urbanization has not only promoted economic development, but also significantly changed land use and development strategy. The environmental problems brought by urbanization threaten ecological security directly. Therefore, it is necessary to introduce changes in land use when constructing an ecological security pattern. This study takes the Yangtze River Delta urban agglomeration, one of the most economically developed regions in China, as the research area. Based on its land use status, the Cellular Automata–Markov model was used to predict the quantitative change and transfer of land-use types in 2025, and three types of land-use patterns were simulated under different scenarios. Combined with the pressure–state–response model, the Entropy TOPSIS comprehensive evaluation model is used to evaluate the three phases in the years of 2005, 2010, and 2015, and the results indicated that the safety level dropped from 85.45% to 82.94%. Five spatial associations were obtained from the spatial autocorrelation analysis using GeoDA, and the clustering distribution of the three phases was roughly the same. Based on the requirements of “Natural Growth” scenario, “Urban Sprawl” scenario, and “Ecological Protection” scenario, the transfer matrix of the various land-use types were modified rationally. The results of scenario simulations illustrated that the level of urbanization was inversely proportional to the level of ecological security. The surrounding cities in the northern part of Taihu Lake were developing rapidly, with low levels of ecological security. The hilly cities in the southern part, in contrast, developed slowly and had a high level of ecological security. Based on the temporal and spatial changes in the ecosystem, an ecosystem optimization model was proposed to determine the ecological functional areas. The nature of each functional area provided the basis to formulate urban construction and management plans and achieve sustainable urban development

The preparation of intrinsic DOPO-Cinnamic flame-retardant cellulose and its application for lithium-ion battery separator

A renewable and superior intrinsic thermal-resistant cellulose-based nonwoven was explored as lithium-ion battery separator via phase separation mechanism. Herein, we sparked a robust strategy for improving the flammability of cellulose, namely DOPO- Cinnamoyl Cellulose (DCC) with intrinsic flame retardant was obtained via the incorporation of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) and Cinnamoyl Chloride attached on the backbone of cellulose. It demonstrates that the heat release rate and total heat release significantly reduced. Meanwhile the membrane displayed excellent self-extinction. Additionally, after the DCC membrane assembled into lithium battery, under the optimum formulation situation, the electrochemical properties established that the LIBs showed superior electrochemical performance compared with PP separator. The interface impedance of DCC separator was less than 300 Ω, which was much smaller than that of commercial separator of 410 Ω. After 50 cycles, the battery with DCC-0.11 separator retained 84.2% of its initial discharge capacity, which was higher than the commercial polypropylene separator with the numeric of 79.1%. In sum, this novel, environmental friendly and intrinsic DOPO-Cinnamic flame-retardant cellulose based separator can be considered as an expectant candidate for lithium ion battery separator with high performance

Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension

Hypertension is a common and chronic disease and causes severe damage to patients’ health. Blood pressure of a human being is controlled by the autonomic nervous system. Heart rate variability (HRV) is an impact of the autonomic nervous system and an indicator of the balance of the cardiac sympathetic nerve and vagus nerve. HRV is a good method to recognize the severity of hypertension due to the specificity for prediction. In this paper, we proposed a novel fine-grained HRV analysis method to enhance the precision of recognition. In order to analyze the HRV of the patient, we segment the overnight electrocardiogram (ECG) into various scales. 18 HRV multidimensional features in the time, frequency, and nonlinear domain are extracted, and then the temporal pyramid pooling method is designed to reduce feature dimensions. Multifactor analysis of variance (MANOVA) is applied to filter the related features and establish the hypertension recognizing model with relevant features to efficiently recognize the patients’ severity. In this paper, 139 hypertension patients’ real clinical ECG data are applied, and the overall precision is 95.1%. The experimental results validate the effectiveness and reliability of the proposed recognition method in the work

Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries

Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input–output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas