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Abstract Given the aggressive nature and robust survival capabilities of the alligator gar (Atractosteus spatula), if it was to exist in a new environment as an invasive species, it could cause significant disruption to the invaded ecosystem. Building on the continuity and completeness of the existing draft genome were not optimal, this study has updated a high-quality genome of the alligator gar at the chromosome level, which was assembled using Oxford Nanopore Technology and chromatin interaction mapping (Hi-C) sequencing techniques. In summary, the alligator gar genome in this study was 1.05 Gb in size with a contig N50 of 15.7 Mb and scaffold N50 of 56.8 Mb. We captured 98.26% of assembled bases in 28 pseudochromosomes. The completeness of the final chromosome-level genome reached 96.7%. Meanwhile, a total of 19,103 protein-coding genes were predicted, of which 99.83% could be predicted with functions. Taken together, the present high-quality alligator gar chromosome-level genome provides a valuable resource for exploring the underlying genomic basis to comprehend the functional genomics, chromosome evolution, and population management of this species.

Abstract MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder—machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve.

In this study, Ce2[Zr1−x(Ba1/3Nb2/3)x]3(MoO4)9 (0.02 ≤ x ≤ 0.1, CZ1−xNx) ceramics were sintered at 600 °C and 700 °C using the traditional solid-state method. An analysis conducted through XRD and Rietveld refinement confirmed that all the CZ1−xNx ceramics displayed a single phase with a trigonal structure (space group R-3c). The observed increases in cell volume with increasing x values indicate the successful substitution of (Ba1/3Nb2/3)4+. The high densification of the synthesized phase was validated by the density and SEM results. Additionally, the P-V-L theory demonstrates a strong correlation between the Ce-O bond and εr, as well as τf, and between the Mo-O bond and Q×f. Notably, the CZ0.98N0.02 ceramics demonstrated superior performance at 675 °C, exhibiting εr = 10.41, Q×f = 53,296 GHz, and τf = −23.45 ppm/°C. Finally, leveraging CZ0.98N0.02 ceramics as substrate materials enabled the design of a patch antenna suitable for the 5G communication band, demonstrating its significant potential in this field.

BackgroundAnemia is considered a risk factor for cardiovascular disease. However, there is little evidence regarding the relationship between hemoglobin (HB) and cerebral infarction after revascularization in patients with moyamoya disease (MMD). This study aimed to explore the relationship between postoperative cerebral infarction and HB in patients with MMD and to establish a predictive model.MethodsDemographic information and different HB levels (the preoperative and postoperative HB, highest and lowest HB, and mean HB during hospitalization) of 112 patients with MMD were collected, of which 11 had cerebral infarction after revascularization.ResultsIn the binomial logistic regression analysis, low HB levels were an independent risk factor for cerebral infarction after revascularization, which also led to a worse long-term prognosis in patients with MMD. The risk factors, including Pre-HB, Post-HB, type of MMD, and hypertension (HTN), were incorporated into the receiver operating characteristic curve, which yielded an area under the curve (AUC) of 0.83.ConclusionThe prediction model was visualized using a nomogram, and a clinical decision curve was drawn to evaluate the net benefit of clinical decisions.

Fault detection in charging piles is crucial for the widespread adoption of electric vehicles and the reliability of charging infrastructure. Currently, due to the lack of sufficient fault data for charging piles, achieving stable and accurate fault identification is challenging. Moreover, distinctive fault features are key to accurate fault recognition. To address this, we designed a simulated charging pile system and collected fault data at multiple power levels by manually introducing faults. Furthermore, we proposed a fault identification algorithm based on spatiotemporal feature fusion using machine learning. This algorithm first collects fault data through a sliding window and utilizes Fourier transform to extract frequency domain information to construct temporal features. These features are then fused with spatial current amplitude information to form a distinctive feature set, enabling fault identification based on a machine learning model. Extensive experiments conducted on the constructed dataset show that this method can accurately identify charging pile faults. Compared with random forest and gradient boosted decision tree, the proposed method improves the macro-average score by 2.99% and 7.28%, respectively. We also explored the importance of each feature for fault identification results and the impact of window length on identification outcomes, demonstrating the necessity of the extracted features and the robustness of the proposed method to data resolution.

Visual geo-localization using prior map of known environments has extensive applications in the fields such as self-driving, augmented reality and navigation. Currently, such prior maps are usually constructed by visual SLAM or SFM. However, the recent advances in LiDAR SLAM technology with RGB camera demonstrate great potential in efficient and accurate prior map construction and visual geo-localization. In this research, we developed a novel prior map construction approach which seamlessly integrates a handheld LiDAR SLAM system, a global LiDAR localization algorithm and a sparse set of terrestrial LiDAR scans as accurate control within a factor graph optimization framework. The developed LiDAR SLAM system augmented by terrestrial LiDAR scans achieves an improved mapping accuracy of 8 cm under GNSS-denied conditions in the areas over 50,000 m2 while the state-of-art FAST-LIO2 alone fails and produces a mapping error over 30 m. The results show that a competitive accuracy of visual geo-localization on a mobile phone using the constructed prior map is achieved (i.e. 60 and 76 cm in two tests respectively). Finally, this work also demonstrates a novel LiDAR point cloud fusion method that produces optimized and consistent coarse-to-fine 3D reconstruction in large and complex scenes.

The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at https://github.com/wuhaitao2178827/DNE-YOLO.

Abstract Background High-altitude cerebral edema (HACE) is considered an end-stage acute mountain sickness (AMS) that typically occurs in people after rapid ascent to 2500 m or more. While hypoxia is a fundamental feature of the pathophysiological mechanism of HACE, emerging evidence suggests that inflammation serves as a key risk factor in the occurrence and development of this disease. However, little is known about the molecular mechanism underlying their crosstalk. Methods A mouse HACE model was established by combination treatment with hypobaric hypoxia exposure and lipopolysaccharides (LPS) stimulation. Lactylated-proteomic analysis of microglia was performed to reveal the global profile of protein lactylation. Molecular modeling was applied to evaluate the 3-D modeling structures. A combination of experimental approaches, including western blotting, quantitative real-time reverse transcriptionpolymerase chain reaction (qRT-PCR), and enzyme-linked immunosorbent assay (ELISA), confocal microscopy and RNA interference, were used to explore the underlying molecular mechanisms. Results We found that hypoxia exposure increased the lactate concentration and lactylation in mouse HACE model. Moreover, hypoxia aggravated the microglial neuroinflammatory response in a lactate-dependent manner. Global profiling of protein lactylation has shown that a large quantity of lysine-lactylated proteins are induced by hypoxia and preferentially occur in protein complexes, such as the NuRD complex, ribosome biogenesis complex, spliceosome complex, and DNA replication complex. The molecular modeling data indicated that lactylation could affect the 3-D theoretical structure and increase the solvent accessible surface area of HDAC1, MTA1 and Gatad2b, the core members of the NuRD complex. Further analysis by knockdown or selectively inhibition indicated that the NuRD complex is involved in hypoxia-mediated aggravation of inflammation. Conclusions These results revealed a comprehensive profile of protein lactylation in microglia and suggested that protein lysine lactylation plays an important role in the regulation of protein function and subsequently contributes to the neuroinflammatory response under hypoxic conditions.

Abstract Sign language is an important way to provide expression information to people with hearing and speaking disabilities. Therefore, sign language recognition has always been a very important research topic. However, many sign language recognition systems currently require complex deep models and rely on expensive sensors, which limits the application scenarios of sign language recognition. To address this issue, based on computer vision, this study proposed a lightweight, dual-path background erasing deep convolutional neural network (DPCNN) model for sign language recognition. The DPCNN consists of two paths. One path is used to learn the overall features, while the other path learns the background features. The background features are gradually subtracted from the overall features to obtain an effective representation of hand features. Then, these features are flatten into a one-dimensional layer, and pass through a fully connected layer with an output unit of 128. Finally, use a fully connected layer with an output unit of 24 as the output layer. Based on the ASL Finger Spelling dataset, the total accuracy and Macro-F1 scores of the proposed method is 99.52% and 0.997, respectively. More importantly, the proposed method can be applied to small terminals, thereby improving the application scenarios of sign language recognition. Through experimental comparison, the dual path background erasure network model proposed in this paper has better generalization ability.

The development of dielectric materials with low permittivity and low loss is a great challenge in wireless communication. In this study, LiLn(PO3)4 (Ln = La, Sm, Eu) ceramic systems were successfully prepared using the traditional solid-state method. X-ray diffraction analysis indicated that the LiLn(PO3)4 ceramics crystallized in a monoclinic structure when sintered at 850–940 ℃. The characteristic peak shifted to higher angles with variations in the Ln element, which was ascribed to a reduction in the cell volume. Further analysis by structure refinement revealed that the reduction in the cell volume resulted from the decrease in chemical bond lengths and the compression of [LiO4] and [PO4] tetrahedra. Remarkably, the LiLn(PO3)4 ceramic system displayed exceptional performance at low sintering temperatures (910–925 ℃), including a high quality factor (Q·f) of 41,607–75,968 GHz, low temperature coefficient of resonant frequency (τf) ranging from −19.64 to −47.49 ppm/℃, low permittivity (εr) between 5.04 and 5.26, and low density (3.04–3.26 g/cm3). The application of Phillips–van Vechten–Levine (P–V–L) theory revealed that the increased Q·f value of the LiLn(PO3)4 systems can be attributed to the enhanced packing fraction, bond covalency, and lattice energy, and the stability of τf was associated with the increase in the bond energy. Furthermore, a prototype microstrip patch antenna using LiEu(PO3)4 ceramics was fabricated. The measurement results demonstrated excellent antenna performance with a bandwidth of 360 MHz and a peak gain of 5.11 dB at a central frequency of 5.08 GHz. Therefore, low-εr LiLn(PO3)4 ceramic systems are promising candidates for microwave/millimeter-wave communication.

Body size and reproductive strategies are usually connected to several important ecological processes and have drawn the interest of numerous researchers. While numerous studies have investigated the patterns of body size or reproductive mode across elevation gradients, few studies focused on the variations in body size along environmental gradients between taxa in different reproductive modes. Here, we investigated how abundance and body size of sexual and parthenogenetic oribatid mites change along an altitudinal gradient in Changbai Mountain. The results indicated that the community-weighted mean (CWM) body size of sexual taxa was lowest at 1800 m mainly attributing to precipitation. The percentage of parthenogenetic individuals was higher at 600 m, 1600 m and 2200 m compared to other altitudes. Temperature and precipitation accounted for most of the variation in soil oribatid mite communities. Parthenogenetic species accounted for almost half of the total species suggesting parthenogenetic reproduction wins in the harsh environment. However, parthenogenetic species were more frequent in litter than soil indicating they were more susceptible to resource availability. Sexual taxa were considered exploiting resources more efficiently in soil layer as they produce genetically diverse offspring. Further, sexual taxa had larger body size in litter compared to soil habitats, their body size positively correlated with C:N ratio indicating the food resources are important in body size variations between different reproductive modes. The body size of parthenogenetic taxa was negatively related to C:P and N:P ratio suggesting that phosphorus probably increased the availability of certain food resources. The different use of resources contribute to variations in body size between sexual and parthenogenetic taxa. Our findings highlight the variations of functional traits across environmental gradients, provide new insight into climate and resource driving shifts in body size and reproductive mode among oribatid mites inhabiting mountain ranges.

In the era of globalization, enhancing construction supply chain resilience (CSCR) has become essential. Traditional supply chains face issues like information asymmetry, trust deficits, and inefficiencies, weakening their resilience. Blockchain offers a solution by improving transparency, trust, and coordination, but its impact on CSCR remains unclear. This study addresses the gap by developing a moderated mediation model to explore how blockchain influences CSCR through supply chain integration and the moderating role of environmental uncertainty. Data from 310 senior managers in Chinese construction firms were analyzed using structural equation modeling. The results showed that blockchain significantly improves CSCR, mediated by operational, informational, and relational integration, particularly under high uncertainty environments. The findings highlight blockchain’s potential to strengthen CSCR and provide theoretical insights into its mechanisms, offering practical guidance for construction companies to enhance supply chain processes through blockchain implementation.

The ecological environment of wetlands in semi-arid regions has deteriorated, and vegetation succession has accelerated due to climate warming-induced aridification and human interference. The nutrient acquisition strategies and biomass allocation patterns reflect plant growth strategies in response to environmental changes. However, the impact of nutrient acquisition strategies on biomass allocation in successional vegetation remains unclear. We investigated 87 plant communities from 13 wetland sites in the semi-arid upper Yellow River basin. These communities were divided into three successional sequences: the herbaceous community (HC), the herbaceous–shrub mixed community (HSC), and the shrub community (SC). The nutrient composition of stems and leaves, as well as the biomass distribution above and belowground, were investigated. Results revealed that aboveground biomass increased with succession while belowground biomass decreased. Specifically, SC exhibited the highest stem biomass of 1,194.53 g m−2, while HC had the highest belowground biomass of 2,054.37 g m−2. Additionally, significant positive correlations were observed between leaf and stem biomasses in both HC and SC. The nitrogen (N) and phosphorus (P) contents within aboveground parts displayed an evident upward trend along the succession sequence. The highest N and P contents were found in SC, followed by HSC, and the lowest in HC. Stem N was negatively correlated with stem, leaf, and belowground biomass but positively correlated with root–shoot ratio. Leaf P displayed positive correlations with aboveground biomass while showing negative correlations with belowground biomass and root–shoot ratio. The ratios of C:N, C:P, and N:P in stem and leaf exhibited positive correlations with belowground biomass. The random forest model further demonstrated that stem N and leaf P exerted significant effects on aboveground biomass, while leaf P, stem N and P, and leaf C:P ratio had significant effects on belowground components. Additionally, the root–shoot ratio was significantly influenced by leaf P, leaf C:P ratio, and stem N, P, and C:P ratio. Therefore, the aboveground and belowground biomasses exhibited asynchronism across successional sequences, while plant nutrient acquisition strategies, involving nutrient levels and stoichiometric ratios, determined the biomass allocation pattern. This study offers valuable insights for assessing vegetation adaptability and formulating restoration plans in the semi-arid upper Yellow River basin.

Novel YbxCe1−xO2−0.5x (x = 0–0.8) ceramics, designed by replacing Ce4+ with Yb3+ ions were prepared by conventional oxide reaction, and the structural stability of the cubic fluorite structure was assessed using lattice energy and ionic properties of Ce/Yb–O bonds. The oxygen vacancy caused by unequal substitution, which played a decisive role in bond ionicity and lattice energy, was analyzed experimentally by XPS and also theoretically by first principles. The YbxCe1−xO2−0.5x ceramics maintain a stable cubic fluorite structure when x ≤ 0.47, corresponding to the minimum lattice energy of 4142 kJ/mol with the lowest ionicity as ƒi = 87.57%. For microwave dielectric properties, when the YbxCe1−xO2−0.5x (x = 0–0.4) ceramics are pure phase, the porosity-corrected permittivity is dependent on the bond ionicity. The Q׃ values are related to the lattice energy and grain distribution. The temperature coefficient of resonance frequency has been analyzed using bond valence. When the YbxCe1−xO2−0.5x (x = 0.5–0.8) ceramics are multiple phases, the microwave dielectric properties are associated with the phase composition and grain growth.

This paper systematically explores the impact of the digital economy on the resilience of food production and its mechanism of action based on panel data from 30 provincial-level administrative regions in China from 2011 to 2022. This study shows that the digital economy significantly enhances the ability of the food production system to cope with external shocks by improving resource allocation efficiency and mitigating factor mismatch. Specifically, the digital economy directly improves the stability and resilience of food production through the widespread application of digital technology; at the same time, it indirectly contributes to the enhancement of food production resilience by alleviating the mismatch of labor and capital factors. Heterogeneity analyses show that there are regional differences in the impact of the digital economy, in which the main food-producing regions benefit more significantly due to their agricultural resource advantages and policy support, and the improvement of food production resilience in the central region is particularly prominent. This study provides an important theoretical basis and practical reference for exploring the potential of the application of digital economy in agriculture and formulating policies to enhance food production resilience.

Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC. Methods: Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The “Climbing senecio-Hepatocellular Carcinoma Targets” network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio’s bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio’s primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization. Results: Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC. Conclusions: This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation.

Soil organic pollution (such as heavy metals, PAHs, etc.) has caused serious environmental problems, which have resulted in unexpected effects on contaminated soil ecosystems. However, knowledge of the interactions between environmental PAHs and bacterial and fungal communities is still limited. In this study, soil samples from different PAH-contaminated areas including non-contaminated areas (NC), low-contaminated areas (LC), and high-contaminated areas (HC) were selected. Results of toxic equivalent quantity (TEQ) indicated that Benzo[a]pyrene (BaP) and Dibenzo[a,h]anthracene (DBahA) constituted the main TEQs of ∑16PAHs. Incremental lifetime cancer risk (ILCR) assessment revealed that the main pathway of exposure to soil PAHs was dermal contact in adults and children. Furthermore, adults faced a higher total cancer risk (including dermal contact, ingestion, and inhalation) from soil PAHs than children. The microbial community composition analysis demonstrated that soil PAHs could decrease the diversity of bacterial and fungal communities. The relative abundance of Acidobacteriota, Gemmatimonadota, Fimicutes, Bacteroidota, Ascomycota, and Basidiomycota exhibited varying degrees of changes under different concentrations of PAHs. Benzo[a]anthracene (BaA) and Chrysene (Chr) drove the bacterial community composition, while BaP and DBahA drove the fungal community compositions. Co-occurrence network analysis revealed the high contamination levels of PAHs that could change the relationships among different microorganisms and reduce the complexity and stability of fungal and bacterial networks. Overall, these findings provide comprehensive insight into the responses of bacterial and fungal communities to PAHs.

BackgroundDisorders of consciousness (DoC) represent a spectrum of neurological conditions that pose significant treatment challenges. Percutaneous short-term spinal cord stimulation (SCS) has emerged as a promising experimental diagnostic treatment to assess and potentially improve consciousness levels. However, the effectiveness of this intervention is frequently compromised by the shift of electrodes, particularly in the cervical region, which can negatively affect therapeutic outcomes.MethodsThis retrospective study aimed to study if electrodes shift in percutaneous short-term SCS in patients with DoC would affect the outcome. We analyzed the relationship between electrode shift length and patient outcome, as well as the correlation with various anatomical parameters, including the actual length of the cervical spine, linear length, spinal canal transverse diameter, spinal canal diameter, and C2 cone height, in a cohort of patients undergoing the procedure.ResultsOur findings revealed that in patients with better outcome, there are significant less patient with electrode shift (p = 0.019). Further, a linear correlation was found between the length of electrode shift and patients’ outcome (Rho = 0.583, p = 0.002), with longer shift lengths associated with poorer outcomes. Contrary to our expectations, there was no significant association between the measured anatomical parameters and the extent of electrode shift. However, a trend was found between the actual length of the cervical spine and the shift of the electrode (p = 0.098). Notably, the shorter spinal canal transverse diameter was found to be significantly associated with better outcome in patients with DoC receiving percutaneous short-term SCS (p = 0.033).ConclusionThese results highlight the clinical importance of electrode stability in the cervical region during SCS treatment for patients with DoC. Ensuring secure placement of electrodes may play a crucial role in enhancing patients’ outcome and minimize postoperative complications. Given the lack of association with expected anatomical parameters, future research should investigate other factors that could impact electrode stability to optimize this therapeutic intervention.

Abstract Numerous factors influence mountain biodiversity variation across elevational gradients and recognizing the relative importance is vital for understanding species distribution mechanisms. We examined oribatid mites at nine elevations (from 600 to 2200 m a.s.l) and four vegetation types from mixed coniferous and broad‐leaved forests to alpine tundra on Changbai Mountain. We assessed the contribution of environmental factors (climatic and local factors) and spatial processes (geographic or elevation distances) to oribatid mite community assembly and identified 59 oribatid mite species from 38 families and 51 genera. With increasing elevation, species richness and the Shannon index declined significantly, whereas abundance followed a hump‐shaped trend. Soil TP, NH4+‐N, MAT, MAP, and elevation were the critical variables shaping oribatid mite communities based on random forest analysis. Moreover, environmental and spatial factors, and oribatid mite communities were significantly correlated based on Mantel and partial Mantel tests. Local characteristics (3.9%), climatic factors (1.9%), and spatial filtering (8.8%) played crucial roles in determining oribatid mite communities across nine elevational bands (based on variation partitioning analyses of abundance data). Within the same vegetation types, spatial processes had relatively little effects, with local characteristics the dominant drivers of oribatid mite community variation. Environmental and spatial filters together shape oribatid mite community assembly and their relative roles varied with elevation and vegetation type. These findings are crucial for the conservation, restoration, and management of Changbai mountain ecosystems in the context of climate change, along with the prediction of future vertical biotic gradient pattern evolution.

Study region: The study region of this paper is the Wusuli River basin, which is comprised of nine sub-basins. The basin is located on the border between China and Russia. Study focus: We investigated the hydrological connectivity of the Wusuli River basin, an international boundary river that has received little attention. The spatial and temporal evolution of hydrological connectivity over the last 38 years was characterized using a geostatistical connectivity function combined with the remote sensing technique. In particular, some key sub-basins were identified to help strengthen China-Russian cooperation on ecological protection of the basin. New hydrological insights for the region: The results show that the hydrological connectivity in the northwestern region of the Wusuli River basin was unstable with several surges from 1984 to 2021. Analyzed from a temporal perspective, the effective range of hydrological connectivity is expanding and connectivity is showing an overall upward trend due to increased fluctuations in rainfall. Analyzed from a spatial perspective, the connectivity of the right bank of the Wusuli River is better than that of the left bank due to the influence of topography. Stable connectivity pathways are a must for efficient hydrological connectivity within a watershed. Another finding is that the capacity of the Wusuli River basin to resist extreme rainfall is weakening.

Abstract Background Attention provides the foundation for cognitions, which was shown to be affected by microwave (MW) radiation. With the ubiquitous of microwaves, public concerns regarding the impact of MW radiation on attention has hence been increased. Our study aims to investigate the potential effect and mechanism of acute microwave exposure on attention. Results We identified obvious impairment of attention in mice by the five-choice serial reaction time (5-CSRT) task. Proteomic analysis of the cerebrospinal fluid (CSF) revealed neuroinflammation and microglial activation potentially due to acute MW exposure. Moreover, biochemical analysis further confirmed microglial activation in the prefrontal cortex (PFC) of mice subjected to acute MW exposure. Finally, minocycline, a commercially available anti-inflammatory compound, attenuated neuroinflammation, inhibited the upregulation of N-methyl-D-aspartic acid receptor (NMDAR) including NR2A and NR2B, and also accelerated the attentional recovery after MW exposure. Conclusions We believe that microglial activation and NMDAR upregulation likely contribute to inattention induced by acute MW exposure, and we found that minocycline may be effective in preventing such process.

The tradeoff between spatial and spectral resolution in sensor design is inevitable, and spatial–spectral fusion aims to use low spatial resolution hyperspectral image (HSI) and high spatial resolution (HR) multispectral image (MSI) obtained at the same time and in the same area to reconstruct HR HSI. Recently, a large number of deep-learning methods have been applied in this field and achieved success. However, these methods do not fully utilize the characteristics of data for network design, and cannot guarantee effective computational efficiency in extracting local and global features. To solve the above problems, we designed a spatial–spectral interaction super-resolution convolutional neural network (CNN)–Mamba fusion network for satellite HSI and MSI, which uses mutual guidance to improve the spatial and spectral resolution of different data, and obtains the final fused image through feature fusion. In addition, we combined Mamba with CNN to effectively explore global and local features of images. Extensive experiments have proven that our method can reconstruct fused images of high quality and is superior to current state-of-the-art fusion methods.

Abstract Air gap impulse discharge characteristics of railway tunnel‐catenary at high‐altitude are of great significance to the electrical insulation design. The existing altitude correction methods for non‐uniform air gaps are not applicable to tunnel‐catenary. To investigate the impulse discharge characteristics of the tunnel‐catenary, a typical structural model was built in an artificial climate chamber. Different altitudes were simulated by adjusting the atmospheric pressure. The 50% breakdown voltages of the 300–700 mm tunnel‐catenary gap were tested at 243–4000 m. The test found that the altitude/atmospheric pressure and gap length have an evident effect on the 50% impulse breakdown voltage, and the influence of humidity is relatively small. The polarity effect was analysed by streamer theory. The {d, P, h} parameter method was employed to obtain the fitting formulas for the impulse breakdown voltage. A high‐altitude correction method for the tunnel‐catenary breakdown voltage based on 1 km altitude was proposed. Field natural tests were conducted to verify the accuracy of the proposed method. The results show that the breakdown voltage obtained by the correction method has an average error of 3.66% in the results of the field tests, indicating that the method has engineering application prospects.

In order to study the influence of moisture on seepage characteristics of coal during the complete stress-strain process, the complete stress-strain process with seepage experiment of dry coal sample and full-water coal sample were carried out, and the complete stress-strain characteristics and permeability evolution laws of two coal samples were analyzed. The experimental results showed that the complete stress-strain curve of dry coal sample had five stages, while the full-water coal sample had only four stages. After the peak stress destruction, the axial stress of full-water coal sample fell by 36.82%, and the strain softening phase disappeared in the full stress-strain curve. Compared with dry coal samples, the compressive strength, elastic modulus and peak stress of saturated coal samples decreased by 8.95%, 8.54%, 8.90% and 10.87% respectively. The permeability of dry coal sample increases with the increase of axial strain, and the relationship is linear. The relationship between permeability and strain of saturated coal sample has obvious stages, which can be divided into stable seepage stage and rapid seepage stage. The existence of water will inhibit the gas seepage process of coal body, so the drainage of coal seam should be paid attention to in the gas extraction project.

In order to expand the application of melatonin in gel food, convenient gummies loaded with melatonin and Lycium barbarum L. were developed in this study. The comprehensive effects of gelatin dose, Lycium barbarum L. powder level and drying time on the properties of melatonin gummies were investigated by color difference analysis, texture analysis, low field NMR analysis and sensory evaluation. The results showed that the gummies containing with 0.4% Lycium barbarum L. powder, 18% gelatin, 5% carrageenan, 17.5% sorbitol, 17.5% erythritol and drying for 18 h were elastic and palatable with smooth surface. The gummies had moisture content less than 20% and melatonin retention rate of 92.0%~97.7%, and high sensory evaluation with potential practical application.

Dissimilatory iron reduction (DIR) coupled with carbon cycling is increasingly being recognized as an influential process in freshwater wetland soils and sediments. The role of DIR in organic matter (OM) mineralization, however, is still largely unknown in lake sediment environments. In this study, we clarified rates and pathways of OM mineralization in two shallow lakes with seasonal hydrological connectivity and different eutrophic situations. We found that in comparison with the domination of DIR (55%) for OM mineralization in Lake Xiaoxingkai, the contribution of methanogenesis was much higher (68%) in its connected lake (Lake Xingkai). The differences in rates and pathways of sediment OM mineralization between the two lakes were attributed to higher concentrations of carbonate associated iron oxides (Fecarb) in Lake Xiaoxingkai compared to Lake Xingkai (P = 0.002), due to better deposition mixing, more contributions of terrigenous detrital materials, and higher OM content in Lake Xiaoxingkai. Results of structural equation modeling showed that Fecarb and total iron content (TFe) regulated 25% of DIR in Lake Xiaoxingkai and 76% in Lake Xingkai, accompanied by a negative effect of TFe on methanogenesis in Lake Xingkai. The relative abundance and diversity of Fe-reducing bacteria were significantly different between the two lakes, and showed a weak effect on sediment OM mineralization. Our findings emphasize the role of iron minerals and geochemical characterizations in regulating rates and pathways of OM mineralization, and deepen the understanding of carbon cycling in lake sediments.

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

The proposed microgrid provides a new way to explore and makes usage of available solar energy resources. In order to realize the energy management of microgrid, this paper describes a multi-mode coordinated operation control strategy with the main control objective of ensuring the DC bus voltage stability and the mode division depends on the battery charge state and the source-load power state inside the microgrid system. The simulation model is established in PSCAD/EMTDC, and the simulation results verify the rationality and effectiveness of the proposed coordinated control strategy.

Background: The primary objective of this investigation was to assess the impact of pyrroline-5-carboxylate reductase 1 (PYCR1) on the progression of gastric cancer (GC), specifically focusing on tumor growth and metastatic potential. Methods: Surgical specimens from patients with different stages of GC were assayed for PYCR1 expression using immunohistochemistry. PYCR1 expression was manipulated by depletion or overexpression approaches in GC cells, and these cells were applied to explore the functional roles of PYCR1. Expression of apoptosis– and metastasis–related markers was quantified through quantitative real-time PCR and Western blot. Results: Higher PYCR1 expression was ascertained in surgical specimens from patients with GC as compared to noncancerous adjacent tissues. Additionally, PYCR1 overexpression in GC tissues was linked to adverse clinical outcomes. The depletion of PYCR1 in GC cells resulted in a pronounced reduction in proliferation, the induction of apoptosis, and the attenuation of invasion and metastasis. Conversely, its ectopic expression notably augmented proliferation, restricted apoptosis, and stimulated invasion and metastasis. In addition, the knockdown of PYCR1 resulted in a significant elevation in the activation of caspase 3, a key protein involved in apoptosis. This depletion also led to a decrease in the activation or expression of proteins associated with metastasis, such as phosphorylated (p)-phosphatidylinositol 3-kinase (PI3K), p-AKT serine/threonine kinase (AKT), and snail family transcriptional repressor 1 (Snail). Additionally, it resulted in an upregulation of E-cadherin expression. Conversely, the overexpression of PYCR1 notably increased the levels of p-PI3K, p-AKT, and Snail, while simultaneously reducing E-cadherin expression. Conclusion: PYCR1, by activating PI3K/AKT signaling, assumes a crucial role in governing malignant characteristics of GC cells, including proliferation, apoptosis, and metastasis. These findings underscore the promising potential of PYCR1 as a diagnostic biomarker and a target for tailored therapeutic interventions in patients with GC.

Summary: Oligodendrocyte progenitor cells (OPCs) differentiate into myelin-producing cells and modulate neuronal activity. Defects in OPC development are associated with neurological diseases. N6-methyladenosine (m6A) contributes to neural development; however, the mechanism by which m6A regulates OPC development remains unclear. Here, we demonstrate that PRRC2B is an m6A reader that regulates OPC development and myelination. Nestin-Cre-mediated Prrc2b deletion affects neural stem cell self-renewal and glial differentiation. Moreover, the oligodendroglia lineage-specific deletion of Prrc2b reduces the numbers of OPCs and oligodendrocytes, causing hypomyelination and impaired motor coordination. Integrative methylated RNA immunoprecipitation sequencing, RNA sequencing, and RNA immunoprecipitation sequencing analyses identify Sox2 as the target of PRRC2B. Notably, PRRC2B, displaying separate and cooperative functions with PRRC2A, stabilizes mRNA by binding to m6A motifs in the coding sequence and 3′ UTR of Sox2. In summary, we identify the posttranscriptional regulation of PRRC2B in OPC development, extending the understanding of PRRC2 family proteins and providing a therapeutic target for myelin-related disorders.

Mountain ecosystems exhibit rapid changes in vegetation cover and climate conditions along elevation gradients, offering an ideal natural experiment to examine variations in the molecular composition of soil organic carbon (SOC) responsing to environmental change. We examined molecular composition, diversity, and network complexity of SOC using the pyrolysis-gas chromatography/mass spectrometry technology and the Shannon diversity index (SHDI) in Changbai Mountain (CBS). Results indicated that molecular composition of SOC differed greatly across different vegetation zones, which were predominated by mixed coniferous and broad-leaved forests (HJL), mixed coniferous forests (ZYL), birch forest covered (YHL), and tundra vegetation (TYD). ZYL had remarkably more abundant moieties with aromatic structures, 36% on average, than those in HJL, TYD, and YHL, 18.31%, 12.75% and 16.51% on average, respectively. O-alkyl, alkyl, and terpene compounds showed an increasing trend, whereas aromatic, lignin and nitrogen-containing compounds exhibited a notable decrease with elevation to a significant level. Either alpha diversity or beta diversity of SOC varied significantly among TYD, YHL, ZYL, and HJL, with HJL exhibiting the lowest alpha diversity. SOC from ZYL was the most complex, showing the highest number of edges and graph density. Leaf litter elements greatly regulated the molecular diversity and network complexity of SOC. Positive correlations between SHDI and SOC contents, as well as ratios of SOC to total iron, suggested that high molecular diversity promoted SOC persistence in mountain ecosystems.

Understanding composition-structure-property relationships is critical to developments and applications of novel microwave dielectric ceramics used in the field of wireless communications. Herein, a series of CeZr1·14Bi0.045A0.045Mo4·5O18 (abbreviated as CZA, A = Nb, Ta and Sb) ceramics were prepared through a solid-state reaction method. Density and scanning electron microscope results revealed dense and homogeneous microstructures. X-ray diffraction analysis indicated the formation of solid solutions. Accordingly, the effects of extrinsic factors were negligible and thus composition-structure-property relationships were systematically investigated by the combination of the chemical bond theory, Raman spectroscopy, far-infrared (FIR) spectroscopy and terahertz time-domain spectroscopy (THz-TDS) from an intrinsic perspective. Interestingly, the measured dielectric constants (εr) and quality factors (Q × f) are quite close to the extrapolated values from FIR and THz-TDS, and these values show a similar trend with varying components. In addition, Mo–O bonds play an important role in the coefficient of thermal expansion (α) and bond energy (E), and the composition dependent temperature coefficient of the resonance frequency (τf) can be well explained by variations of mean α and E of Mo–O bonds. Typically, excellent properties of Q × f = 116,485 GHz (at 9.58 GHz), εr = 9.91 and τf = −19.20 ppm °C−1 were achieved for the CZSb ceramic sintered at 700 °C. Notably, Q × f values of CZSb and CZTa ceramics are 6.13 and 4.16 times larger than that of the matrix, respectively. Ultra-high Q × f, low sintering temperatures (600 − 700 °C) and low εr make them potential candidates for low temperature co-fired ceramics (LTCC) applications.

Using panel data from 30 provinces in China from 2005 to 2020, this paper uses a spatial double difference model to evaluate the policy impact of the “grain-to-feed” policy on feed grain production in pilot areas and adjacent spatial areas. Research has found that the “grain-to-feed” policy has a significant impact on the feed grain production in pilot areas and can significantly increase the feed grain production in pilot areas by about 2.71 million tons. The “grain-to-feed” policy has strengthened the positive connection between pilot areas and adjacent pilot areas, increased feed grain production, and has a significant spatial spillover effect. Robustness analysis shows that whether using different methods to measure spatial adjacency or using different standards to distribute subsidies, the “grain-to-feed” policy can significantly increase feed grain production, narrow the supply and demand gap of feed grain, and ensure feed grain security. Further analysis shows that the “grain-to-feed” policy can not only ensure the security of feed grain for the current and next periods but also promote the increase in farmers’ income, which is long-term and sustainable. Compared with non-pilot areas, the “grain-to-feed” policy can mitigate the negative impact of wage–price signals on feed grain production in pilot areas. It is recommended that government departments accelerate the transformation of food security concepts, establish a “Big Food Perspective”, gradually promote the pilot of the “grain-to-feed” policy nationwide, increase the subsidy amount of the “grain-to-feed” policy, increase financial support for scientific and technological research and achievement transformation in the field of feed grain, prevent the impact of economic price signal fluctuations on feed grain production, and effectively ensure the security of feed grain in China.

GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers.

This paper designs a two-degrees-of-freedom (DOF) compliant positioning stage with antagonistic piezoelectric actuation. Two pairs of PEAs are arranged in an antagonistic configuration to generate reciprocating motions. Flexure mechanisms are intentionally adopted to construct the fixtures for PEAs, whose elastic deformations can help to reduce the stress concentration on the PEA caused by the extension of the PEA in the other direction. Subsequently, the parameter and performance of the 2-DOF compliant positioning stage is optimized and verified by finite element analysis. Finally, a prototype is fabricated and tested. The experimental results show that the developed positioning stage achieves a working stroke of 28.27 μm × 27.62 μm. Motion resolutions of both axes are 8 nm and natural frequencies in the working directions are up to 2018 Hz, which is promising for high-precision positioning control.

Regional green technology innovation (RGTI) carries ecological and technological externalities, serving as a crucial force in aligning economic development with the imperative of environmental preservation. Industrial digitalization (indig) exhibits innovative, quality-enhancing, and efficiency-enhancing traits. Yet, the exact effects of indig on RGTI have not been extensively studied. In this study, based on provincial data from 2013-2019 in China, we investigated the linear and nonlinear relationships, spatial characteristics and underlying mechanisms between indig and RGTI through a Hansen threshold model and a mediating effect model. The results showed that: i. indig fluctuates while RGTI increases steadily, both displaying evident spatial agglomeration traits and a unipolar characteristic of regional heterogeneity; ii. indig has effectively boosted RGTI at both national and regional levels, with the exception of the northeastern region; iii. There was a single threshold for industrial digitalization and the positive driving trend exhibited a “marginally increasing” characteristic; iv. Two critical elements, namely industrial structure advancement and marketization, have partial intermediary roles. Basing on these results, this study presents several policy recommendations.

The Chinese government aims to use environmental policy measures to improve society's overall green total factor productivity (GTFP) and create a new development pattern in which the economy and the environment are equally important. Based on the perspective of indirect financing, this study divides 272 prefecture-level industry–finance cooperation cities in China from 2015 to 2021 into 109 resource-based cities and 163 nonresource-based cities; we use the PSM-difference-in-differences (DID) model to explore their impact on GTFP. The research results are as follows. First, industry–finance cooperation significantly promotes GTFP. Second, although the nonresource-based cities have insignificant industry–finance cooperation, resource-based cities have significantly positive industry–finance cooperation. Third, from the perspective of indirect financing, industry–finance cooperation can effectively improve GTFP. Fourth, the reliability of the conclusions is verified by successively using the parallel trend test, the placebo test, the dynamic time window test, the counterfactual test, and the elimination of other policy interferences. Based on the above findings, the Chinese government must always adhere to the important “comprehensive regulation and measures according to local conditions” policy and fully use “information technology and green credit.”

Abstract Depression is a highly prevalent and disabling mental disorder, involving numerous genetic changes that are associated with abnormal functions in multiple regions of the brain. However, there is little transcriptomic‐wide characterization of chronic social defeat stress (CSDS) to comprehensively compare the transcriptional changes in multiple brain regions. Spatial transcriptomics (ST) was used to reveal the spatial difference of gene expression in the control, resilient (RES) and susceptible (SUS) mouse brains, and annotated eight anatomical brain regions and six cell types. The gene expression profiles uncovered that CSDS leads to gene synchrony changes in different brain regions. Then it was identified that inhibitory neurons and synaptic functions in multiple regions were primarily affected by CSDS. The brain regions Hippocampus (HIP), Isocortex, and Amygdala (AMY) present more pronounced transcriptional changes in genes associated with depressive psychiatric disorders than other regions. Signalling communication between these three brain regions may play a critical role in susceptibility to CSDS. Taken together, this study provides important new insights into CSDS susceptibility at the ST level, which offers a new approach for understanding and treating depression.

Wetland restoration has received increasing attention to compensate for the continuing loss of natural wetlands and revive biodiversity and associated ecological functions. The development of methods for assessing the effectiveness of restoration measures is important for wetland management. However, most studies assessing restoration success in freshwater ecosystems are based on taxonomic biodiversity; trait-based approaches remain limited, particularly in wetland ecosystems. Here, we assess the effectiveness of wetland restoration in the Sanjiang Plain, Northeast China, based on the trait composition and functional diversity of aquatic insect assemblages. We found that functional richness in restored wetlands was significantly higher than impacted wetlands but lower than natural wetlands. Compared to natural wetlands, functional beta diversity of impacted wetlands was higher. In terms of dissimilarity between wetland types, trait composition of aquatic insects in restored wetlands showed a higher similarity to natural wetlands compared to those in impacted wetlands. Trait compositions of aquatic insect assemblages in restored wetlands were more similar to impacted wetlands in May but showed a greater similarity to natural wetlands in October. Our study shows that restoration measures have facilitated the partial recovery of functional diversity and trait composition of aquatic insect assemblages in the Sanjiang Plain wetlands. Our study confirms the potential of using trait-based approach to assess the effectiveness of wetland restoration. Given that assessments based on taxonomic and trait data can provide complementary information, we recommend incorporating both taxonomic and functional metrics and considering seasonal dynamics of wetlands in post-restoration monitoring and assessment in wetlands.

Efficient utilization of clean energy is iminent for achieving the ambitious “dual-carbon” goal, digitalization provides favorable conditions for decarbonzation. Based on the data of 30 provinces in China, the biggest developing country, this study measures clean energy efficiency with improved DEA and digitalization with entropy method, and empirically explores the linear and nonlinear relationships from multidimensions. The major study findings are as follows. (1) On the whole from 2013 to 2017, digitalization and clean energy efficiency represent a steady growth trend, and the former increases by 8.98 % annually with a relatively low average level of 0.2286, while the latter increases by 1.24 % annually with a high average level of 0.9695. Meanwhile, regional heterogeneity of clean energy efficiency is significant. (2) digitalization can significantly promote clean energy efficiency with the marginal effect of 0.676, and clean energy efficiency increases by 0.0525 in the study period, indicating that 5.4 % of the improvement in clean energy efficiency caused by digitalization. (3) Hansen threshold analysis shows three nonlinear critical factors with single threshold and heterogeneous trends: technology innovation, energy consumption structure and population aggregation. Finally, according to the conclusions, corresponding policy suggestions are put forward.

By optimizing the design of the conventional low-voltage current limiting controller (VDCOL), the continuous commutation failure on the receiving end of the traditional LCC-HVDC transmission system is effectively suppressed. In order to overcome low insensitivity of conventional VDCOL under mild and moderate faults, the dc current deviation value is superimposed into the starting voltage of the VDCOL in the form of a compensation voltage to enhance sensitivity of the VDCOL to the dynamic changes of the dc current during mild and moderate faults. In the face of severe faults, the conventional VDCOL adopts a linear adjustment method with a fixed slope, which is bad for suppressing continuous commutation failure. Aiming at this problem, a nonlinear VDCOL strategy is adopted, which the dc current command and the dc voltage are a nonlinear function relationship with a variable slope with the increasing or decreasing of the dc voltage. In the PSCAD/EMTDC simulation platform, the proposed control strategy is verified using the International Council on Large Electric Systems (CIGRE) standard model. Simulation results reveal that the proposed nonlinear VDCOL strategy with improved voltage compensation method can well suppress continuous commutation failures in the case of single-phase and three-phase short-circuit faults, and it is beneficial to restore dc voltage and transmission power and ensure the normal operation of the HVDC transmission system.

Microwave dielectric ceramics (MWDCs) with low dielectric constant and low dielectric loss are desired in contemporary society, where the communication frequency is developing to high frequency (sub-6G). Herein, Nd2(Zr1−xTix)3(MoO4)9 (NZ1−xTxM, x = 0.02–0.10) ceramics were prepared through a solid-phase process. According to X-ray diffraction (XRD) patterns, the ceramics could form a pure crystal structure with the R3¯c (167) space group. The internal parameters affecting the properties of the ceramics were calculated and analyzed by employing Clausius–Mossotti relationship, Shannon’s rule, and Phillips–van Vechten–Levine (P–V–L) theory. Furthermore, theoretical dielectric loss of the ceramics was measured and analyzed by a Fourier transform infrared (IR) radiation spectrometer. Notably, when x = 0.08 and sintered at 700 ℃, optimal microwave dielectric properties of the ceramics were obtained, including a dielectric constant (εr) = 10.94, Q·f = 82,525 GHz (at 9.62 GHz), and near-zero resonant frequency temperature coefficient (τf) = −12.99 ppm/℃. This study not only obtained an MWDC with excellent properties but also deeply analyzed the effects of Ti4+ on the microwave dielectric properties and chemical bond characteristics of Nd2Zr3(MoO4)9 (NZM), which laid a solid foundation for the development of rare-earth molybdate MWDC system.

Most greenhouse gases come from biological activities and industry which will lead to global warming and show an impact on human life. With the need of green transformation of the global economic structure and seeking for higher quality of human life, the detection and management of greenhouse gases, as well as most hazardous gases in the environment, are increasingly demanding. Applications in different fields require sensors that can detect gas volume fractions with magnitudes from 10–9 to 10–4. Greenhouse gas detection plays an important role both in the agriculture and industry field. In this review, we first summarize the mechanism of several common gas detectors used currently. Then, the advantages of nanostructured gas sensors are discussed. Finally, the applications of infrared gas sensors based on nanophotonic devices are described in detail. This review has been an outlook on the future development of infrared gas sensors based on nanophotonic devices.

Abstract How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles’ transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments’ effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Abstract The molecular mechanism underlying gastric cancer (GC) peritoneal metastasis (PM) remains unclear. Here, we identified LINC00924 as a GC PM-related lncRNA through Microarray sequencing. LINC00924 was highly expressed in GC, and its high expression is associated with a broad range of PM. Via RNA sequencing, RNA pulldown assay, mass spectrometry, Seahorse, Lipidomics, spheroid formation and cell viability assays, we found that LINC00924 promoted fatty acid (FA) oxidation (FAO) and FA uptake, which was essential for matrix-detached GC cell survival and spheroid formation. Regarding the mechanism, LINC00924 regulated the alternative splicing (AS) of Mnk2 pre-mRNA by binding to hnRNPC. Specifically, LINC00924 enhanced the binding of hnRNPC to Mnk2 pre-mRNA at e14a, thus downregulating Mnk2a splicing and regulating the p38 MAPK/PPARα signaling pathway. Collectively, our results demonstrate that LINC00924 plays a role in promoting GC PM and could serve as a drug target.

Peatlands store 20–30 % of the global soil carbon (C). Earthworms simultaneously increase net C stabilization and net C mineralization. But little is known about the impact of earthworm invasion on the C cycling in peat soils. Earthworm excreta, such as casts and mucus, have been shown to affect soil chemistry and microbes, the former representing gut process and the latter are secretions from earthworms as they dig burrows. Therefore, this study evaluates the separate effects of the presence of earthworms, casts, and mucus on the soil chemistry, soil microbial community, and the enzymes of peat soil. The results indicated that earthworms and their casts had significantly negative effects on peat soil dissolved organic C, microbial biomass C, pH and polyphenol oxidase, and had positive effects on phenolic matters, hydrolase activity, bacterial diversity, and metabolic functions. The effects of mucus on polyphenol oxidase, phenolic matters and bacterial evenness were similar to those of earthworms, but the effects on other soil properties were different. Earthworms and their excreta selectively promote or inhibit the survival of some soil bacteria, e.g., the relative abundance of Bacteroidetes was increased and Acidobacteria and Rokubacteria was reduced after earthworm entering. This research provides evidence that earthworms change soil properties primarily through gut processes (casts), indicates the negative effect of earthworms on peat soil C, and call for the monitoring of soil fauna in degraded peatland to protecting C stocks in peatlands.

Hydrological connectivity is crucial for supporting aquatic biodiversity and serving ecosystem function in river floodplain wetlands. Although the effects of longitudinal connectivity in the composition of snail (Mollusca: Gastropoda) assemblages is well understood, the effects of lateral connectivity are less evaluated. Here, we evaluated the effects of lateral and longitudinal connectivity on multiple facets of alpha and beta diversity (i.e., taxonomic, functional, and phylogenetic) of freshwater snails in 38 floodplain wetlands in the Da Xing'an Mountains of Northeast China. A total of 9,784 snails subjected to 5 orders, 14 families, 24 genera, and 42 species were collected. Overall, the snail alpha diversity was higher in the nearly isolated wetlands and downstream areas. Multivariate analyses revealed that the taxonomic, functional, and phylogenetic structures of snail assemblages in floodplain wetlands differed significantly across the lateral and longitudinal gradients. Waterscape and climate variables appeared to equivalently explain a large proportion of variations in the taxonomic, functional, and phylogenetic structures of snail assemblages. Lateral connectivity and river order were paramount variables that explain the greatest variation in taxonomic, functional, and phylogenetic structures of the snail assemblages. Our study suggests that hydrological connectivity is a key factor controlling the multiple facets of snail diversity in floodplains. Attention should be paid to the effects of changing climate and waterscape on the multi-faceted diversity of snail assemblages. Due to the extant patterns in assemblage structures, floodplain restoration and management should consider the full spectrum of longitudinal and laterally connectedness to maximize aquatic biodiversity.