Reciprocal interplay between OTULIN-LUBAC determines genotoxic and inflammatory NF-κB signal responses.

Targeting nuclear factor-kappa B (NF-κB) represents a highly viable strategy against chemoresistance in cancers as well as cell death. Ubiquitination, including linear ubiquitination mediated by the linear ubiquitin chain assembly complex (LUBAC), is emerging as a crucial mechanism of overactivated NF-κB signaling. Ovarian tumor family deubiquitinase OTULIN is the only linear linkage-specific deubiquitinase; however, the molecular mechanisms of how it counteracts LUBAC-mediated NF-κB activation have been largely unknown. Here, we identify Lys64/66 of OTULIN for linear ubiquitination facilitated in a LUBAC-dependent manner as a necessary event required for OTULIN-LUBAC interaction under unstressed conditions, which becomes deubiquitinated by OTULIN itself in response to genotoxic stress. Furthermore, this self-deubiquitination of OTULIN occurs intermolecularly, mediated by OTULIN dimerization, resulting in the subsequent dissociation of OTULIN from the LUBAC complex and NF-κB overactivation. Oxidative stress induces OTULIN dimerization via cysteine-mediated covalent disulfide bonds. Our study reveals that the status of the physical interaction between OTULIN and LUBAC is a crucial determining factor for the genotoxic NF-κB signaling, as measured by cell survival and proliferation, while OTULIN loss of function resulting from its dimerization and deubiquitination leads to a dissociation of OTULIN from the LUBAC complex. Of note, similar molecular mechanisms apply to the inflammatory NF-κB signaling in response to tumor necrosis factor α. Hence, a fuller understanding of the detailed molecular mechanisms underlying the disruption of the OTULIN-LUBAC interaction will be instrumental for developing future therapeutic strategies against cancer chemoresistance and necroptotic processes pertinent to numerous human diseases.

Numerical modeling of the effects of the shape and aspect ratio of 3D curved fiber on the percolation threshold and electrical conductivity of conductive polymer composites.

For designing conductive polymer composites (CPCs), understanding how the fiber curvature affects the percolation behavior of curved conductive fibers is essential for determining the effective electrical conductivity σeff of the CPCs. In this work, CPCs were considered as a polymer matrix filled with the random packing of overlapped curved spherocylinders. The geometries of the curved spherocylinders were defined, and inter-curved spherocylinder contact-detecting and system-spanning fiber cluster searching algorithms were developed. The finite-size-scaling method was used to explore how the aspect ratio α and bending central angle θ of a curved spherocylinder affect the percolation threshold ϕc of an overlapped curved spherocylinder system in 3D space. The findings suggest that ϕc decreases as α increases and increases initially before declining as θ increases. An empirical approximation formula was proposed to quantify the effect of the curved spherocylinder's morphology, characterized by the dimensionless excluded volume Vdex of the curved spherocylinder, on ϕc. The new rigorous bound for ϕc of the soft-curved spherocylinder system was further proposed. A random resistor network model was constructed, and the reliability of this model was validated by comparing the simulations and published data. Finally, a fitting formula was developed to assess the impacts of the normalized reduced density (η - ηc)/ηc and Vdex on the σeff of CPCs. A distinct linear correlation between σeff and (η - ηc)/ηc was constructed, denoted as σeff ∼ [(η - ηc)/ηc]t(α,θ). An empirical approximation model was proposed to establish the relationship between the fiber shape and conductivity exponent t. Our study may provide a theoretical hint for the design of CPCs.

Robust Ru/Ce@Co Catalyst with an Optimized Support Structure for Propane Oxidation.

Short carbon chain alkanes, as typical volatile organic compounds (VOCs), have molecular structural stability and low molecular polarity, leading to an enormous challenge in the catalytic oxidation of propane. Although Ru-based catalysts exhibit a surprisingly high activity for the catalytic oxidation of propane to CO2 and H2O, active RuOx species are partially oxidized and sintered during the oxidation reaction, leading to a decrease in catalytic activity and significantly inhibiting their application in industrial processes. Herein, the Ru/Ce@Co catalyst is synthesized with a specific structure, in which cerium dioxide is dispersed in a thin layer on the surface of Co3O4, and Ru nanoparticles fall preferentially on cerium oxide with high dispersity. Compared with the Ru/CeO2 and Ru/Co3O4 catalysts, the Ru/Ce@Co catalyst demonstrates excellent catalytic activity and stability for the oxidation of propane, even under severe operating conditions, such as recycling reaction, high space velocity, a certain degree of moisture, and high temperature. Benefiting from this particular structure, the Ru/Ce@Co (5:95) catalyst with more Ce3+ species leads to the Ru species being anchored more firmly on the CeO2 surface with a low-valent state and has a strong potential for adsorption and activation of propane and oxygen, which is beneficial for RuOx species with high activity and stability. This work provides a novel strategy for designing high-efficiency Ru-based catalysts for the catalytic combustion of short carbon alkanes.

Dissecting the chiral recognition of TLR4/MD2 with Neoseptin-3 enantiomers by molecular dynamics simulations.

Toll-like receptor 4 (TLR4) is a pivotal innate immune recognition receptor that regulates intricate signaling pathways within the immune system. Neoseptin-3 (Neo-3), a recently identified small-molecule agonist for mouse TLR4/MD2, exhibits chiral recognition properties. Specifically, the L-enantiomer of Neo-3 (L-Neo-3) effectively activates the TLR4 signaling pathway, while D-Neo-3 fails to induce TLR4 activation. However, the underlying mechanism by which TLR4 enantioselectively recognizes Neo-3 enantiomers remains poorly understood. In this study, in silico simulations were performed to investigate the mechanism of chiral recognition of Neo-3 enantiomers by TLR4/MD2. Two L-Neo-3 molecules stably resided within the cavity of MD2 as a dimer, and the L-Neo-3 binding stabilized the (TLR4/MD2)2 dimerization state. However, the strong electrostatic repulsion between the hydrogen atoms on the chiral carbon of D-Neo-3 molecules caused the relative positions of two D-Neo-3 molecules to continuously shift during the simulation process, thus preventing the formation of D-Neo-3 dimer as well as their stable interactions with the surrounding residues in (TLR4/MD2)2. Considering that L-Neo-3 could not sustain a stable dimeric state in the bulk aqueous environment, it is unlikely that L-Neo-3 entered the cavity of MD2 as a dimeric unit. Umbrella sampling simulations revealed that the second L-Neo-3 molecule entering the cavity of MD2 exhibited a lower binding energy (-25.75 kcal mol-1) than that of the first L-Neo-3 molecule (-14.31 kcal mol-1). These results imply that two L-Neo-3 molecules enter the cavity of MD2 sequentially, with the binding of the first L-Neo-3 molecule facilitating the entry of the second one. This study dissects the binding process of Neo-3 enantiomers, offering a comprehensive understanding of the atomic-level mechanism underlying TLR4's chiral recognition of Neo-3 molecules.

Per- and polyfluoroalkyl substances in waterbird feathers around Poyang Lake, China: Compound and species-specific bioaccumulation.

As a nondestructive means of environmental monitoring, bird feathers have been used to analyze levels of per- and polyfluoroalkyl substances (PFASs) in specific environments. In this study, feather samples from 10 waterbird species around Poyang Lake were collected, and a pretreatment method for PFASs in feathers was optimized. The results showed that a combined cleaning method using ultrapure water and n-hexane effectively removed external PFASs. Twenty-three legacy and emerging PFASs were identified in the feathers of waterbirds, of which hexafluoropropylene oxides (HFPOs), chlorinated polyfluoroalkyl ether sulfonates (Cl-PFESAs), and sodium p-perfluorinated noneoxybenzene sulfonate (OBS) were reported for the first time, with their concentrations ranging from 0.060-2.4 ng·g-1 dw, 0.046-30 ng·g-1 dw, and lower than the method detection limit to 30 ng·g-1 dw, respectively. Compound- and species-specific bioaccumulation of PFASs was observed in the feathers of different waterbird species, suggesting that different PFAS types can be monitored through the selection of different species. Moreover, the concentrations of most PFCAs (except perfluorobutyric acid), perfluorooctane sulfonate (PFOS), and perfluorooctane sulfonamide (FOSA) were significantly positively correlated with δ15N (p < 0.05), while the concentrations of HFPOs, Cl-PFESAs, and OBS had significant positive correlations with δ13C. This indicates that the bioaccumulation of legacy and emerging PFASs in waterbird feathers is affected by their trophic level, feeding habits, and foraging area.

Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density.

In the process of data transmission in mobile ad hoc networks, it is essential to establish optimal routes from source nodes to destination nodes. However, as network density increases, this process is often accompanied by a significant rise in network overhead. To address this issue, the ND-AODV (neighborhood density AODV) protocol has been introduced, which reduces the probability of transmitting control information in high-density node environments to mitigate network overhead. Nevertheless, this may come at the cost of reduced routing accuracy, potentially leading to unnecessary resource wastage in certain scenarios. Furthermore, ND-AODV does not comprehensively consider the location of the receiving nodes, which limits its ability to reduce network overhead effectively. To overcome these limitations, this paper introduces a novel routing approach, known as CND-AODV (common neighborhood density AODV). In comparison to ND-AODV, CND-AODV offers a more comprehensive solution to the challenges posed by high-density network environments. It intelligently processes control information based on the special positioning of the receiving nodes, thereby significantly reducing unnecessary network overhead. Through simulation experiments comparing performance metrics such as throughput, packet delivery rate, and latency, the results clearly indicate that CND-AODV substantially decreases network overhead, enhancing network performance. Compared to ND-AODV, this innovative routing approach exhibits significant advantages. It provides a more efficient and reliable solution for ad hoc networks in high-density environments.

Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal.

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

Heat shock factor HSF1 regulates BDNF gene promoters upon acute stress in the hippocampus, together with pCREB.

Heat shock factor 1 (HSF1) is a master stress-responsive transcriptional factor, protecting cells from death. However, its gene regulation in vivo in the brain in response to neuronal stimuli remains elusive. Here, we investigated its direct regulation of the brain-derived neurotrophic factor (BDNF) gene (Bdnf) in response to acute neuronal stress stimuli in the brain. The results of immunohistochemistry and chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) showed that administration of kainic acid (a glutamate receptor agonist inducing excitotoxity) to young adult mice induced HSF1 nuclear translocation and its binding to multiple Bdnf promoters in the hippocampus. Footshock, a physical stressor used for learning, also induced HSF1 binding to selected Bdnf promoters I and IV. This is, to our knowledge, the first demonstration of HSF1 gene regulation in response to neuronal stimuli in the hippocampus in vivo. HSF1 binding sites (HSEs) in Bdnf promoters I and IV were also detected when immunoprecipitated by an antibody of phosphorylated (p)CREB (cAMP-responsive element-binding protein), suggesting their possible interplay in acute stress-induced Bdnf transcription. Interestingly, their promoter binding patterns differed by KA and footshock, suggesting that HSF1 and pCREB orchestrate to render fine-tuned promoter control depending on the types of stress. Further, HSF1 overexpression increased Bdnf promoter activity in a luciferase assay, while virus infection of constitutively active-form HSF1 increased levels of BDNF mRNA and protein in vitro in primary cultured neurons. These results indicated that HSF1 activation of Bdnf promoter was sufficient to induce BDNF expression. Taken together, these results suggest that HSF1 promoter-specific control of Bdnf gene regulation plays an important role in neuronal protection and plasticity in the hippocampus in response to acute stress, possibly interplaying with pCREB.

White matter damage as a consequence of vascular dysfunction in a spontaneous mouse model of chronic mild chronic hypoperfusion with eNOS deficiency.

Vascular cognitive impairment and dementia (VCID) is the second most common form of dementia after Alzheimer's disease (AD). Currently, the mechanistic insights into the evolution and progression of VCID remain elusive. White matter change represents an invariant feature. Compelling clinical neuroimaging and pathological evidence suggest a link between white matter changes and neurodegeneration. Our prior study detected hypoperfused lesions in mice with partial deficiency of endothelial nitric oxide (eNOS) at very young age, precisely matching to those hypoperfused areas identified in preclinical AD patients. White matter tracts are particularly susceptible to the vascular damage induced by chronic hypoperfusion. Using immunohistochemistry, we detected severe demyelination in the middle-aged eNOS-deficient mice. The demyelinated areas were confined to cortical and subcortical areas including the corpus callosum and hippocampus. The intensity of demyelination correlated with behavioral deficits of gait and associative recognition memory performances. By Evans blue angiography, we detected blood-brain barrier (BBB) leakage as another early pathological change affecting frontal and parietal cortex in eNOS-deficient mice. Sodium nitrate fortified drinking water provided to young and middle-aged eNOS-deficient mice completely prevented non-perfusion, BBB leakage, and white matter pathology, indicating that impaired endothelium-derived NO signaling may have caused these pathological events. Furthermore, genome-wide transcriptomic analysis revealed altered gene clusters most related to mitochondrial respiratory pathways selectively in the white matter of young eNOS-deficient mice. Using eNOS-deficient mice, we identified BBB breakdown and hypoperfusion as the two earliest pathological events, resulting from insufficient vascular NO signaling. We speculate that the compromised BBB and mild chronic hypoperfusion trigger vascular damage, along with oxidative stress and astrogliosis, accounting for the white matter pathological changes in the eNOS-deficient mouse model. We conclude that eNOS-deficient mice represent an ideal spontaneous evolving model for studying the earliest events leading to white matter changes, which will be instrumental to future therapeutic testing of drug candidates and for targeting novel/specific vascular mechanisms contributing to VCID and AD.

Percolation threshold and electrical conductivity of conductive polymer composites filled with curved fibers in two-dimensional space.

Quantifying the influence of fiber curvature on the percolation behavior of flexible conductive fiber and further on the electrical conductivity of conductive polymer composites (CPCs) is crucial for the design of CPCs. This study considers CPCs as a random packing of soft curved discorectangles (CDCRs) in a polymer matrix. The geometry of CDCR is developed, and an inter-CDCR contact detection algorithm is used to generate a random packing structure of CDCRs. The effects of aspect ratio α and bending central angles θ of CDCR on the percolation threshold ϕc of the overlapped CDCR system in a two-dimensional plane are then investigated using the finite-size scaling method. The result reveals that ϕc decreases monotonically as α grows and increases monotonically as θ rises. A shape-independent power law formula, denoted as ϕc = 2.2015 A-0.8172dex is developed to quantify the relationship between the Adex and ϕc. A comparison of our numerical simulations, published data, and predictions verifies the reliability and universality of the fitting model. Subsequently, a resistor network searching algorithm (RNSA) is proposed to construct the random resistor network model (RRNM). A power law model, denoted as is developed to evaluate the effects of the normalized reduced density (η - ηc)/ηc on the effective conductivity σeff of CPC. Comparing our predictions with data from the literature and our simulation verifies the reliability of our RNSA and the fitting model. This paper's methodology and findings may provide a theoretical hint for the CPC's design.

Design, synthesis and evaluation of monoketene compounds as novel potential Parkinson's disease agents by suppressing ER stress via AKT.

Curcumin is identified that it has the potential to treat Parkinson's disease (PD), but its instability limits its further application in clinic. The mono-carbonyl analogs of curcumin (MACs) with diketene structure can effectively improve its stability, but it is highly toxic. In the present study, a less cytotoxic and more stable monoketene MACs skeleton S2 was obtained, and a series of monoketene MACs were synthesized by combining 4-hydroxy-3­methoxy groups of curcumin. In the 6-OHDA-induced PD's model in-vitro, some compounds exhibited significant neurotherapeutic effect. The quantitative structure-activity relationship (QSAR) model established by the random forest algorithm (RF) for the cell viability rate of above compounds showed that the statistical results are good (R2 = 0.883507), with strong reliability. Among all compounds, the most active compound A4 played an important role in neuroprotection in the PD models both in vitro and in vivo by activating AKT pathway, and then inhibiting the apoptosis of cells caused by endoplasmic reticulum (ER) stress. In the PD model in-vivo, compound A4 significantly improved survival of dopaminergic neurons and the contents of neurotransmitters. It also enhanced the retention of nigrostriatal function which was better than the effect in the mice treated by Madopar, a classical clinical drug for PD. In summary, we screened out the compound A4 with high stability, less cytotoxic monoketene compounds. And these founding provide evidence that the compound A4 can protect dopaminergic neurons via activating AKT and subsequently suppressing ER stress in PD.

Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems.

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.

The Effects Analysis of Contact Stiffness of Double-Row Tapered Roller Bearing under Composite Loads.

Double-row tapered roller bearings have been widely used in various equipment recently due to their compact structure and ability to withstand large loads. The dynamic stiffness is composed of contact stiffness, oil film stiffness and support stiffness, and the contact stiffness has the most significant influence on the dynamic performance of the bearing. There are few studies on the contact stiffness of double-row tapered roller bearings. Firstly, the contact mechanics calculation model of double-row tapered roller bearing under composite loads has been established. On this basis, the influence of load distribution of double-row tapered roller bearing is analyzed, and the calculation model of contact stiffness of double-row tapered roller bearing is obtained according to the relationship between overall stiffness and local stiffness of bearing. Based on the established stiffness model, the influence of different working conditions on the contact stiffness of the bearing is simulated and analyzed, and the effects of radial load, axial load, bending moment load, speed, preload, and deflection angle on the contact stiffness of double row tapered roller bearings have been revealed. Finally, by comparing the results with Adams simulation results, the error is within 8%, which verifies the validity and accuracy of the proposed model and method. The research content of this paper provides theoretical support for the design of double-row tapered roller bearings and the identification of bearing performance parameters under complex loads.

Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice.

Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their own. Adherence to and quality of these home task practices are often limited, which is likely a factor reducing rehabilitation effectiveness post-stroke. However, home adherence is typically measured by self-reports that are known to be inconsistent with objective measurement. The objective of this study was to develop algorithms to enable the objective identification of task type and quality. Twenty neurotypical participants wore an IMU sensor on the wrist and performed four representative tasks in prescribed fashions that mimicked correct, compensatory, and incomplete movement qualities typically seen in stroke survivors. LSTM classifiers were trained to identify the task being performed and its movement quality. Our models achieved an accuracy of 90.8% for task identification and 84.9%, 81.1%, 58.4%, and 73.2% for movement quality classification for the four tasks for unseen participants. The results warrant further investigation to determine the classification performance for stroke survivors and if quantity and quality feedback from objective monitoring facilitates effective task practice at home, thereby improving motor recovery.

Examining the Role of Body Image Instability in Young Adult Women: Conceptualization, Development, and Psychometric Evaluation of the Vacillating Body Image Scale (VBIS).

The current study conceptualized body image instability as a maladaptive tendency to vacillate between different self-perceptions of one's overall body image and developed a corresponding measure to assess body image instability. Results from a series of studies of young adult women demonstrated the validity, reliability, and utility of the Vacillating Body Image Scale (VBIS) as a meaningful measure of body image instability. In Study 1, we found that body image instability, as assessed by the VBIS, represents a unidimensional and reliable construct. In Study 2, we found evidence for both the convergent and discriminant validity of the VBIS in relation to other individual differences measures (i.e., self-concept schema, broad personality factors). In Study 3, the concurrent criterion validity of the VBIS was supported for young adult women in relation to a range of adjustment measures. Finally, in Study 4, we found consistent evidence for the incremental validity of the VBIS in predicting subsequent variations in eating disturbances, even after controlling for global self-esteem and self-concept instability. Overall, our findings offer promising support for our contention that body image instability, as measured by the VBIS, represents an important construct for understanding eating-related disturbances and other health outcomes in young adult women.

Popularity Status Insecurity as a Risk Factor for Adolescents' Maladaptive Weight-Related Cognitions and Behaviors: Examining a Moderated Mediation Model.

Previous studies have identified popularity as a risk factor for adolescents' body image concerns and disordered eating behaviors, yet little is known about how adolescents' insecure feelings about their popularity status may be associated with these outcomes. To address this gap, this study examined whether popularity status insecurity was linked to weight-related cognitions and behaviors one year later and whether these links were mediated by body dissatisfaction and moderated by popularity status. A total of 233 Chinese 10th and 11th grade adolescents (41% girls; Mage = 15.81 years, SD = 0.68) participated in the study. The results showed that adolescents' popularity status insecurity was positively and indirectly related to greater future drive for thinness and restrained eating through the mediation of dissatisfied feelings about their own body shape only among those with average and low popularity, and these indirect effects were strengthened as adolescents' popularity decreased. Implications for prevention and intervention of eating disturbances for adolescents are discussed.

Does the construction of innovative cities promote urban green innovation?

As a typical example of an innovative governance environment, innovative city has become the focus of political and academic circles. Discussing the green innovation effect of this policy is beneficial in providing decision support for enhancing urban green innovation capability and solving the dilemma of urban resources. Based on data from 241 cities in China from 2005 to 2017, this paper takes innovative city construction as a "quasi-natural experiment" and uses the difference-in-differences method to empirically study the impact of China's innovative city construction on urban green innovation. The results show that innovative urban construction: has a positive effect on improving urban green innovation, and that this effect will gradually increase with time; promotes urban green innovation by expanding the scope of technology application, improving the efficiency of resource allocation and promoting the adjustment of industrial structure; and presents heterogeneity in cities' location, size and hierarchy. Finally, this paper proposes that the government should promote innovative policies in an orderly manner on the basis of following the principle of adapting measures to local conditions, as well as incorporating green innovation performance into the evaluation system for innovative city construction.

Identification of key priority areas under different ecological restoration scenarios on the Qinghai-Tibet Plateau.

With the intensification of climate warming and human activities, the ecosystems on the Qinghai-Tibet Plateau (QTP) are facing increasing threats which leads to extensive ecological degradation. Ecological restoration measures need to be implemented to improve biodiversity and ecosystem services to mitigate the impact of climate change and human disturbances. However, the key priority areas (KPAs) for ecological restoration are not clear on the QTP, and the benefits of ecosystem services for ecological restoration are often ignored. In this study, we are the first to identify the KPAs based on the quantitative evaluation method and multicriteria optimization algorithm under five restoration scenarios aiming at ecosystem service improvement on the QTP. Results showed that: (1) The benefits of ecological restoration for climate change mitigation and associated costs under different scenarios showed generally similar spatial variability, exhibiting higher in the south and lower in the west, which were different from those for biodiversity. (2) The restoration priorities in Sichuan and Yunnan were generally higher under scenarios Ⅱ and Ⅴ, while in Xinjiang, Sichuan, Yunnan, and western and southern Tibet were higher under scenarios Ⅰ, Ⅲ and Ⅳ. (3) For different ecosystems, the similarities lied in that the restoration priorities of wetland ecosystem were the highest, while those of desert ecosystem were the lowest under five restoration scenarios. (4) When the restoration area requirement was 25% of the total degraded area, the highest restoration priority levels under scenarios Ⅰ, Ⅲ and Ⅳ were mainly distributed in Guinan, Renbu, Nierong and Chayu counties, and under scenarios Ⅱ and Ⅴ were mainly distributed in Renbu, Lang and Guinan counties. When the restoration area requirements were 50% and 75% of the total degraded area, the counties with higher restoration priority levels under scenario Ⅱ were different from those under other four scenarios. This study identified the KPAs under different restoration scenarios, which provided references for the restoration measures implementation on the QTP.

Core-shell structured monodisperse carbon-rich SiO1.31C1.46H0.81 ceramic spheres as anodes for high-capacity lithium-ion batteries.

SiOC ceramic material is a promising anode material for lithium-ion batteries. However, due to its intrinsically low electronic conductivity, it often suffers from a much lower specific capacity than the theoretical value, poor rate capability and serious potential hysteresis. In this paper, we report a core-shell structured monodisperse carbon-rich SiO1.31C1.46H0.81 submicron ceramic sphere with a free carbon content of 13.7 wt%, which is synthesized by directly annealing polysiloxane spheres derived from vinyltrimethoxysilane without adding external carbon resources. The SiO1.31C1.46H0.81 sphere has a unique microstructure, the core of which is organically assembled by large amounts of SiO1.31C1.46H0.81 primary particles of less than 20 nm and coated by a shell of 20-50 nm. As anodes for lithium-ion batteries, it presents much higher reversible capacity, initial Coulomb efficiency (ICE) and rate performance than the SiOC-based ceramic materials reported in the literature to date. At 100 mA g-1, its first reversible capacity and ICE reach ∼1107 mAh g-1 and 78.2%, respectively. At 1600 mA g-1, its stable discharge capacity is still as high as 610 mAh g-1. The excellent electrochemical performance is attributed to the moderate composition, spherical morphology and unique microstructure of the synthesized material.