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Abstract One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning has become the main method for lung nodule classification. However, successful deep learning models usually require large number of parameters and carefully annotated data. In the field of medical images, the availability of such data is usually limited, which makes deep networks often perform poorly on new test data. In addition, the model based on the linear stacked single branch structure hinders the extraction of multi-scale features and reduces the classification performance. In this paper, to address this problem, we propose a lightweight interleaved fusion integration network with multi-scale feature learning modules, called MIFNet. The MIFNet consists of a series of MIF blocks that efficiently combine multiple convolutional layers containing 1 × 1 and 3 × 3 convolutional kernels with shortcut links to extract multiscale features at different levels and preserving them throughout the block. The model has only 0.7 M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. The proposed MIFNet conducted exhaustive experiments on the reconstructed LUNA16 dataset, achieving impressive results with 94.82% accuracy, 97.34% F1 value, 96.74% precision, 97.10% sensitivity, and 84.75% specificity. The results show that our proposed deep integrated network achieves higher performance than pre-trained deep networks and state-of-the-art methods. This provides an objective and efficient auxiliary method for accurately classifying the type of lung nodule in medical images.

Abstract Objective Type 2 diabetes mellitus (T2DM) poses a substantial global health concern. Statins are widely used among T2DM patients for managing dyslipidemia, preventing cardiovascular disease (CVD), and offering renal protection. However, the extent to which their renal protective effects contribute to reducing the incidence of severe renal complications, including chronic kidney disease (CKD) and renal failure, is not well-defined. Methods This investigation scrutinizes the impact of simvastatin versus placebo on renal outcomes among T2DM patients utilizing data from the ACCORD trial. It encompasses incidence rate comparisons, Kaplan-Meier estimates, Cox proportional hazards models, and mediation analyses. Results The study consisted of 3,619 individuals diagnosed with T2DM, among which 2,753 were treated routinely with simvastatin, while 866 did not receive any statin therapy. After adjusting for baseline characteristics and time-dependent covariates, simvastatin treatment was associated with a 71% reduction in the risk of CKD (HR 0.29, 95% CI 0.27–0.31, p

A growing body of evidence exhibits the ubiquitous presence and accumulation of micro- and nanoplastics (MNPs) in the air, drinking water, food, and even inside the body, which has raised concerns about their potential impact on reproductive and developmental health. To comprehensively examine the current state of knowledge regarding MNPs-induced reproductive and developmental toxicity, we conducted this systematic review by focusing on the prevalence of MNPs determined in reproductive tissues and their influences on parental reproduction and offspring development. Our findings demonstrate the detection of MNPs in various human reproductive tissues, including semen, placenta, and ovarian follicular fluid, as well as in reproductive tissues of diverse animal species. We show a potential relationship between MNP exposure and increased prevalence of infertility and adverse pregnancy outcomes based on the fact that MNPs exert detrimental effects on reproductive parameters, including sperm quality, ovarian function, and steroidogenesis. In male reproductive systems, MNPs disrupt testicular tissue structure, impair reproductive endocrinology, and reduce sperm quality. In females, MNPs affect ovarian tissue structure and function, interfere with hormone secretion, and impact the endometrium and embryo implantation. Additionally, MNPs cause developmental toxicity in animal models, affecting embryonic development and offspring health, and produce transgenerational effects. Notably, in-depth literature study suggests a crucial role for oxidative stress, inflammation, and epigenetic modification in MNPs-induced toxicity. In conclusion, we integrated systematic knowledge on MNPs-induced reproductive and developmental toxicity, and the systematic finding underscores future study to fully elucidate the risks posed by MNPs to reproductive and developmental health and to inform policy decisions and public health interventions aimed at mitigating their harmful effects.

Fine particulate matter (PM2.5) exposure has been extensively linked to reproductive and developmental dysfunctions, yet the underlying mechanisms remain elusive. This study employed single-cell RNA sequencing (scRNA-seq) to investigate PM2.5-induced changes in uterine cell populations and gene expression profiles in mice during estrus and early pregnancy. Methodologically, we intranasally inoculated mice with 20 μL of 4.0 mg/mL PM2.5 suspension during their estrus and early pregnancy periods. Utilizing scRNA-seq analysis, we revealed significant alterations in cell type composition following PM2.5 exposure. Notably, we observed a marked decrease in the proportion of natural killer (NK) cells in PM2.5-exposed mice (2.00 % vs. 8.97 % in controls). Further functional enrichment analysis identified suppression of the IL-17 signaling pathway in NK cells as a key mechanism of PM2.5-induced toxicity. GSEA analysis showed in-depth details of the downregulated genes in this pathway, including Fosb, S100a8, Tnfaip3, IL-17a, and S100a9. PM2.5 exposure also disrupted intercellular communication within the uterine microenvironment, with the number of cell interactions decreasing from 483 to 315 and interaction strength reducing from 12.43 to 6.78 compared to controls. Histological examination revealed that PM2.5 exposure led to thinning of the endometrium and less prominent main branches in uterine tissues, and immunofluorescence assays corroborated the altered expression of IL-17 pathway components, showing enhanced Hsp90ab1 expression and reduced FOSB, S100A8, and S100A9 expression in PM2.5-exposed uterine tissues. These findings provide novel insights into the cellular mechanisms of PM2.5-induced reproductive toxicity, highlighting the IL-17 signaling pathway in uterine NK cells as a potential target for therapeutic interventions. Our results underscore the need for air quality regulations and open new avenues for developing biomarkers and targeted therapies to mitigate the reproductive risks associated with PM2.5 exposure.

Excessive erosion caused by the continuous collision of sand-carrying annular flow with the gas well wellbore can lead to serious production accidents. This study combined the multifrequency response characteristics of sand particle-wall collision with a deep learning algorithm to improve the recognition accuracy of sand particle information in annular flow. The findings showed that sand-wall collision strength was closely related to the velocity, size, and number of sand particles and that the shielding effect generated by the collision behavior between multiple particles had a protective effect on the elbow. In addition, sand-wall collision strength increased with increases in gas velocity and particle size and decreased with an increase in liquid velocity. The shear effect, the secondary flow effect, and the liquid film buffering effect were shown to be key factors affecting the transportation behavior and spatial distribution of sand particles in annular flow. Furthermore, the fast Fourier transform (FFT) and short-time Fourier transform (STFT) analysis results showed that the multifrequency collision response characteristics of sand carrying annular flow were complex and that the main frequency response of sand-wall collision was concentrated in the high frequency range of 50–80 kHz. Moreover, the recognition accuracy results of convolutional neural network (CNN) models for particle size, gas velocity, and liquid velocity were 93.8%, 91.7%, and 91%, respectively, which were significantly higher than the results for the long short-term memory (LSTM) model. The combination of multifrequency collision response and deep learning effectively characterized sand particle feature information in strong gas-liquid turbulence, providing a reference for the accurate monitoring of sand particle information in high-yield water-bearing gas wells.

We investigate the Λ particle motion and cluster states of BeΛ9−11 using a novel multiple cooling approach guided by the Control Neural Networks method (Ctrl.NN). The numerical results are well reproduced, and the Ctrl.NN method shows rapid convergence concerning the set of the basis states in comparison to traditional generator coordinate method (GCM) calculations. Taking advantage of this merit, we calculate ground state energies and rotational bands of BeΛ9−11 which agree well with experimental observations. By analyzing the density distributions of the main basis, a slight short-range repulsive correlation between Λ particle and α clusters in directions perpendicular to the axis of the Be8 core is revealed. The Λ particle presents a significant broad distribution as a consequence of this correlation, which we call an “analog π-orbit” structure. Furthermore, we prove that this correlation originates from the competition between kinetic energy and Λ-N interaction, as indicated by the energy curves for different configurations of BeΛ9. In the ground states of BeΛ10 and BeΛ11, the Λ particle is confined in a cage-like structure of core nuclei formed by two α clusters and valence neutrons. These structures lead to the development of more compact Λ particle configurations, and the shrinkage of α-α relative distance is also well reproduced.

Sunlight-driven photocatalysis, which can produce clean fuels and mitigate environmental pollution, has received extensive research attention due to its potential for addressing both energy shortages and environmental crises. Bismuth (Bi)-based photocatalysts with broad spectrum solar-light absorption and tunable structures, exhibit promising applications in solar-driven photocatalysis. Oxygen vacancy (OV) engineering is a widely recognized strategy that shows great potential for accelerating charge separation and small molecule activation. Based on OV engineering, this review focuses on Bi-based photocatalysts and provides a comprehensive overview including synthetic methods, regulation strategies, and applications in photocatalytic field. The synthetic methods of Bi-based photocatalysts with OVs (BPOVs) are classified into hydrothermal, solvothermal, ultraviolet light reduction, calcination, chemical etching, and mechanical methods based on different reaction types, which provide the possibility for the structural regulation of BPOVs, including dimensional regulation, vacancy creation, elemental doping, and heterojunction fabrication. Furthermore, this review also highlights the photocatalytic applications of BPOVs, including CO2 reduction, N2 fixation, H2 generation, O2 evolution, pollutant degradation, cancer therapy, and bacteria inactivation. Finally, the conclusion and prospects toward the future development of BPOVs photocatalysts are presented.

Abstract Exosomes are nanoscale extracellular vesicles present in bodily fluids that mediate intercellular communication by transferring bioactive molecules, thereby regulating a range of physiological and pathological processes. Exosomes can be secreted from nearly all cell types, and the biological function of exosomes is heterogeneous and depends on the donor cell type and state. Recent research has revealed that the levels of exosomes released from the endosomal system increase in cells undergoing programmed cell death. These exosomes play crucial roles in diseases, such as inflammation, tumors, and autoimmune diseases. However, there is currently a lack of systematic research on the differences in the biogenesis, secretion mechanisms, and composition of exosomes under different programmed cell death modalities. This review underscores the potential of exosomes as vital mediators of programmed cell death processes, highlighting the interconnection between exosome biosynthesis and the regulatory mechanisms governing cell death processes. Furthermore, we accentuate the prospect of leveraging exosomes for the development of innovative biomarkers and therapeutic strategies across various diseases.

Abstract Plants with the C4 photosynthesis pathway typically respond to climate change differently from more common C3-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C4 and C3 vegetation distributions. However, current C4 vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C4 vegetation. We find that global C4 vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C4 natural grass cover due to elevated CO2 favoring C3-type photosynthesis, and an increase in C4 crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C4 vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C4 plants in the contemporary global carbon cycle.

Abstract Exosomes are nanoscale extracellular vesicles secreted by cells and enclosed by a lipid bilayer membrane containing various biologically active cargoes such as proteins, lipids, and nucleic acids. Engineered exosomes generated through genetic modification of parent cells show promise as drug delivery vehicles, and they have been demonstrated to have great therapeutic potential for treating cancer, cardiovascular, neurological, and immune diseases, but systematic knowledge is lacking regarding optimization of drug loading and assessment of delivery efficacy. This review summarizes current approaches for engineering exosomes and evaluating their drug delivery effects, and current techniques for assessing exosome drug loading and release kinetics, cell targeting, biodistribution, pharmacokinetics, and therapeutic outcomes are critically examined. Additionally, this review synthesizes the latest applications of exosome engineering and drug delivery in clinical translation. The knowledge compiled in this review provides a framework for the rational design and rigorous assessment of exosomes as therapeutics. Continued advancement of robust characterization methods and reporting standards will accelerate the development of exosome engineering technologies and pave the way for clinical studies. Graphical Abstract

Pomegranate seeds are rich in polyphenols, known for their significant health benefits. In this study, we developed microcapsules to encapsulate pomegranate seed polyphenols (PSP) using a combination of zeaxylated protein (Zein) and chitosan (Cs). The optimization process involved a Zein to Cs volume ratio of 1:1, a chitosan concentration of 1.0 mg mL−1, and a polyphenol concentration of 2.0 mg mL−1. Scanning electron microscopy confirmed the formation of Zein/Cs/PSP complex coacervates, indicating the successful encapsulation. Fourier-transform infrared spectroscopy provided further evidence of electrostatic attraction between PSP and the Zein/Cs matrix. Differential scanning calorimetry analysis demonstrated the successful incorporation of PSP into the Zein/Cs wall material. Moreover, the encapsulated core material exhibited exceptional storage stability and retained its antioxidant activity. Notably, more than 85% of the encapsulated PSP remained intact after 5 h of simulated intestinal fluid digestion. This study underscores the potential of pomegranate seeds for comprehensive utilization in food engineering.

Abstract Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.

Foodborne spores are ubiquitous with extremely strong resistance, and pose a serious threat to food safety and human health. Therefore, rapid, sensitive, and selective detection of spores are crucial. In this study, a fluorescent probe was developed based on lanthanide ion (Eu3+)-labeled nano-silver-modified graphene oxide (GO-AgNPs-Eu3+) for the detection of 2,6-dipicolinic acid (DPA), a biomarker unique to spores, to allow quantitative spores detection. The GO-AgNPs-Eu3+ nano-fluorescent probe was loaded onto a polyvinylidene fluoride microfiltration membrane, and a smartphone-assisted portable GO-AgNPs-Eu3+ nanoparticles-based paper visual sensor was designed for rapid on-site quantitative and real-time online detection of spores. The results indicated that the developed probe achieved equilibrium binding with DPA within 5 min, and enhanced fluorescence emission through antenna effect. The fluorescence detection presented a good linear relationship in the DPA concentration range of 0–45 μM, with a DPA detection limit of 4.62 nM and spore detection limit of 104 cfu/mL. The developed sensor showed a change in fluorescence from blue to red with increasing DPA concentration, and this color change was quantitatively detected through smartphone RGB variations, with a detection limit of 13.1 μM for DPA and 6.3 cfu/mL for Bacillus subtilis spores. Subsequently, the sensitivity and selectivity of the developed sensor were verified using actual milk and water samples spiked with B. subtilis spores. The results of this study provided objective technological support for rapid detection of spores, which is important for reducing the occurrence of foodborne diseases and improving food safety.

Silica nanoparticle (SiNP) exposure induces severe pulmonary inflammation and fibrosis, but the pathogenesis remains unclear, and effective therapies are currently lacking. To explore the mechanism underlying SiNPs-induced pulmonary fibrosis, we constructed in vivo silica exposure animal models and in vitro models of silica-induced macrophage pyroptosis and fibroblast transdifferentiation. We found that SiNP exposure elicits upregulation of pulmonary proteins associated with pyroptosis, including NLRP3, ASC, IL-1β, and GSDMD, while the immunofluorescence staining co-localized NLRP3 and GSDMD with macrophage-specific biomarker F4/80 in silica-exposed lung tissues. However, the NLRP3 inhibitor MCC950 and classical anti-fibrosis drug pirfenidone (PFD) were found to be able to alleviate silica-induced collagen deposition in the lungs. In in vitro studies, we exposed the fibroblast to a conditioned medium from silica-induced pyroptotic macrophages and found enhanced expression of α-SMA, suggesting increased transdifferentiation of fibroblast to myofibroblast. In line with in vivo studies, the combined treatment of MCC950 and PFD was demonstrated to inhibit the expression of α-SMA and attenuate fibroblast transdifferentiation. Mechanistically, we adopted high throughput RNA sequencing on fibroblast with different treatments and found activated signaling of relaxin and osteoclast differentiation pathways, where the expression of the dysregulated genes in these two pathways was examined and found to be consistently altered both in vitro and in vivo. Collectively, our study demonstrates that SiNP exposure induces macrophage pyroptosis, which subsequently causes fibroblast transdifferentiation to myofibroblasts, in which the relaxin and osteoclast differentiation signaling pathways play crucial roles. These findings may provide valuable references for developing new therapies for pulmonary fibrosis.

Background: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity. However, the global landscape of immune-derived lncRNA signatures has not yet been explored in colorectal cancer.Methods: In this study, we applied DESeq2 to identify differentially expressed lncRNAs in colon cancer. Next, we performed an integrative analysis to globally identify immune-driven lncRNA markers in CRC, including immune-associated pathways, tumor immunogenomic features, tumor-infiltrating immune cells, immune checkpoints, microsatellite instability (MSI) and tumor mutation burden (TMB).Results: We also identified dysregulated lncRNAs, such as LINC01354 and LINC02257, and their clinical relevance in CRC. Our findings revealed that the differentially expressed lncRNAs were closely associated with immune pathways. In addition, we found that RP11-354P11.3 and RP11-545G3.1 had the highest association with the immunogenomic signature. As a result, these signatures could serve as markers to assess immunogenomic activity in CRC. Among the immune cells, resting mast cells and M0 macrophages had the highest association with lncRNAs in CRC. The AC006129.2 gene was significantly associated with several immune checkpoints, for example, programmed cell death protein 1 (PD-1) and B and T lymphocyte attenuator (BTLA). Therefore, the AC006129.2 gene could be targeted to regulate the condition of immune cells or immune checkpoints to enhance the efficacy of immunotherapy in CRC patients. Finally, we identified 15 immune-related lncRNA-generated open reading frames (ORFs) corresponding to 15 cancer immune epitopes.Conclusion: In conclusion, we provided a genome-wide immune-driven lncRNA signature for CRC that might provide new insights into clinical applications and immunotherapy.

With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods.

Spatiotemporal data fusion technologies have been widely used for land surface phenology (LSP) monitoring since it is a low-cost solution to obtain fine-resolution satellite time series. However, the reliability of fused images is largely affected by land surface heterogeneity and input data. It is unclear whether data fusion can really benefit LSP studies at fine scales. To explore this research question, this study designed a sophisticated simulation experiment to quantify effectiveness of 2 representative data fusion algorithms, namely, pair-based Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and time series-based Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data in extracting pixel-wise spring phenology (i.e., the start of the growing season, SOS) and its spatial gradient and temporal variation. Our results reveal that: (a) STARFM can improve the accuracy of pixel-wise SOS by up to 74.47% and temporal variation by up to 59.13%, respectively, compared with only using Landsat images, but it can hardly improve the retrieval of spatial gradient. For SSFIT, the accuracy of pixel-wise SOS, spatial gradient, and temporal variation can be improved by up to 139.20%, 26.36%, and 162.30%, respectively; (b) the accuracy improvement introduced by fusion algorithms decreases with the number of available Landsat images per year, and it has a large variation with the same number of available Landsat images, and (c) this large variation is highly related to the temporal distributions of available Landsat images, suggesting that fusion algorithms can improve SOS accuracy only when cloud-free Landsat images cannot capture key vegetation growth period. This study calls for caution with the use of data fusion in LSP studies at fine scales.

The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM2.5 reduction strategies.

Autotetraploid rice is developed from diploid rice by doubling the chromosomes, leading to higher nutritional quality. Nevertheless, there is little information about the abundances of different metabolites and their changes during endosperm development in autotetraploid rice. In this research, two different kinds of rice, autotetraploid rice (AJNT-4x) and diploid rice (AJNT-2x), were subjected to experiments at various time points during endosperm development. A total of 422 differential metabolites, were identified by applying a widely used metabolomics technique based on LC-MS/MS. KEGG classification and enrichment analysis showed the differences in metabolites were primarily related to biosynthesis of secondary metabolites, microbial metabolism in diverse environments, biosynthesis of cofactors, and so on. Twenty common differential metabolites were found at three developmental stages of 10, 15 and 20 DAFs, which were considered the key metabolites. To identify the regulatory genes of metabolites, the experimental material was subjected to transcriptome sequencing. The DEGs were mainly enriched in starch and sucrose metabolism at 10 DAF, and in ribosome and biosynthesis of amino acids at 15 DAF, and in biosynthesis of secondary metabolites at 20 DAF. The numbers of enriched pathways and the DEGs gradually increased with endosperm development of rice. The related metabolic pathways of rice nutritional quality are cysteine and methionine metabolism, tryptophan metabolism, lysine biosynthesis and histidine metabolism, and so on. The expression level of the genes regulating lysine content was higher in AJNT-4x than in AJNT-2x. By applying CRISPR/Cas9 gene-editing technology, we identified two novel genes, OsLC4 and OsLC3, negatively regulated lysine content. These findings offer novel insight into dynamic metabolites and genes expression variations during endosperm development of different ploidy rice, which will aid in the creation of rice varieties with better grain nutritional quality.

Respirable silica dust is a common hazard faced by occupational workers and prolonged exposure to this dust can lead to pulmonary inflammation, fibrosis and, in severe cases, silicosis. However, the underlying mechanism by which silica exposure causes these physical disorders is not yet understood. In this study, we aimed to shed light on this mechanism by establishing in vitro and in vivo silica exposure models from the perspective of macrophages. Our results showed that compared to the control group, silica exposure resulted in an upregulation of the pulmonary expression of P2X7 and Pannexin-1, but this effect was suppressed by treatment with MCC950, a specific inhibitor of NLRP3. Our in vitro studies showed that silica exposure induced mitochondrial depolarization in macrophages, which led to a reduction of intracellular ATP and an influx of Ca2+. Furthermore, we found that creating an extracellular high potassium environment by adding KCl to the macrophage medium inhibited the expression of pyroptotic biomarkers and pro-inflammatory cytokines such as NLRP3 and IL-1β. Treatment with BBG, a P2X7 antagonist, also effectively inhibited the expression of P2X7, NLRP3, and IL-1β. On the other hand, treatment with FCF, a Pannexin-1 inhibitor, suppressed the expression of Pannexin-1 but had no effect on the expression of pyroptotic biomarkers such as P2X7, NLRP3, and IL-1β. In conclusion, our findings suggest that silica exposure triggers the opening of P2X7 ion channels, resulting in intracellular K+ efflux, extracellular Ca2+ influx, and the assembly of the NLRP3 inflammasome, ultimately leading to macrophage pyroptosis and pulmonary inflammation.

BackgroundMacrophages are essential components of the natural immune system. They play a significant role in resisting foreign bodies in the respiratory tract and maintaining the homeostasis of the internal environment of lung tissue.ObjectiveTo investigate the mechanism of macrophage pyroptosis induced by silica dust with different particle sizes.MethodsThe modified murine macrophage cell line, RAW-ASC cells, was cultured and divided into a blank control group, a lipopolysaccharide (LPS) group (1 μg·mL−1 LPS), a nano-SiO2 group (1 μg·mL−1 LPS+100 μg·mL−1 nano-SiO2), a micro-SiO2 group (1 μg·mL−1 LPS+750 μg·mL−1 micro-SiO2), and a positive control group [1 μg·mL−1 LPS+3 mmol·L−1 adenosine triphosphate (ATP)]. Apart from the blank control group, cells in other groups were pretreated with LPS for 6 h, and then exposed to SiO2 or ATP for 4 h. According to the molecular target NOD-like receptor pyrin domain-containing protein 3 (NLRP3) and reactive oxygen species (ROS), we applied MCC950 (NLRP3 inhibitor) and N-acetyl cysteine (NAC, ROS scavenger) to macrophages. CCK-8 assay was used to detect cell viability; 5-ethynyl-2'-deoxyuridine (EdU) staining was used to detect cell proliferation; lactate dehydrogenase (LDH) assay kit was used to detect LDH in supernatant; calcein AM/PI fluorescent double-staining was applied to evaluate cell rupture; 2',7'-dichlorofluorescin diacetate (DCFH-DA) fluorescent probe was used to measure the content of ROS; Western blotting was used to measure the expressions of NLRP3, apoptosis-associated speck-like protein containing CARD (ASC), Caspase-1, gasdermin D (GSDMD), and interleukin-1β (IL-1β).ResultsCompared with the blank group, 100 μg·mL-1nano-SiO2 and 750 μg·mL-1micro-SiO2 dust exposure reduced the cell viability to 40% and 68% (P

In the context of energy transformation and power market system reform, it is crucial to address the network risks associated with enhancing the integration of the “Energy–Information–Market” paradigm. This necessitates research on multi-energy market trading modes and the corresponding offensive and defensive technologies. This paper proposes a novel approach centered around a node-local Energy Hub (EH) that represents large industrial users with diverse energy demands. To facilitate multi-energy two-way trading, a price-oriented Transactive Energy (TE) market clearing strategy is developed. Building upon this transaction network framework, a data-driven attack strategy targeting the state estimator of the Transmission System Operator (TSO) is introduced and implemented in two stages, encompassing real-time topology estimation and False Data Injection attacks. By leveraging Matrix Transfer Entropy (MTE), the optimal attack target is identified to disrupt the economic stability of the system and the profit of the attacker increases significantly. The proposed attack strategy is validated through simulations conducted on a 30-node system, yielding conclusive evidence of its effectiveness while offering vital insights for system defense.

Studies have shown that prenatal ozone exposure is associated with an increased risk of adverse pregnancy outcomes, among which abnormal birth weight is a detrimental factor for diseases in adulthood, but the association between birth weight and ozone is inconclusive. Herein, we conducted this study by enrolling 407 couples of pregnant women and collected their demographical materials, their exposure to ambient ozone was assessed according to the place of their residence. The hourly monitored ozone was first averaged to the daily level, then monthly and whole-gestationally levels. After adjusting confounders, we processed a multivariate generalized addictive analysis to predict the association between prenatal ozone exposure and birth weight. We also divided the cohort into two categories according to whether the infant met the standard of macrosomia, and the occurrence of macrosomia was studied via univariate and multivariate logistic regression analyses as extreme conditions of the effects of ozone exposure on birth weight. We found that the ground-level ozone in Jinan changed with temperature periodically, higher in summer and lower in winter. Over the past 8 years from 2014, the ambient ozone increased by 1.74 μg/m3 per year. Of the 407 singleton-pregnant women, 21 infants were diagnosed with macrosomia. After adjusting confounders, we found that each unit increase in prenatal ozone exposure caused 8.80% [ORozone90%CI: 0.912 (0.850, 0.978)] decreased risk of macrosomia, but the splined ambient ozone exposure data was not statistically associated with birth weight, which is probably due to the limited sample size. In conclusion, prenatal ozone exposure is associated with decreased risk of macrosomia but is weakly linked to birth weight.

Background and objectivesThe glymphatic system is a recently discovered cerebrospinal fluid transport system and little is known about its dynamic changes after stroke. This study aimed to dynamically observe the structural and functional changes of the impaired glymphatic system in the thalamus after ischemic stroke by pathology and MRI.Materials and methodsIschemic stroke was induced by the middle cerebral artery occlusion (MCAO) model. A total of 20 Sprague-Dawley rats were randomly assigned into four groups: sham, MCAO 1 week, MCAO 2 week, and MCAO 2 month. All rats successively underwent neurological examination, dynamic contrast-enhanced MRI (DCE-MRI), and immunofluorescence staining. Immunofluorescence staining of glial fibrillary acidic protein (GFAP), aquaporin-4 (AQP4), ionized calcium-binding adaptor molecule 1 (Iba1), and beta-amyloid precursor protein (APP) were done in thalamus ventroposterior nucleus.ResultsThe astrocyte and microglial activation and the APP deposition in the MCAO 2 week group were the highest (P < 0.05 for all). The AQP4 polarization rates of the MCAO 2 week and 2 month groups were the lowest (P < 0.05 for all). Although there was no correlation between histological changes and MRI metrics in all four groups (P > 0.05 for all), the tendency of the APP deposition was nearly consistent with the one of the contrast agent retention in DCE-MRI.ConclusionThe glymphatic system in the thalamus was severely impaired at 2nd week after MCAO, and may be revealed by DCE-MRI. This study may provide a relevant theoretical basis for making a thorough inquiry of the mechanism of brain injury after stroke and clinical treatment of ischemic stroke and help readers appreciate the importance of DCE-MRI.

Abstract Extensive application of nanomaterials has dramatically increased the risk of silica nanoparticle (SiNP, SiO2) exposure, yet their biological effect on reproduction has not been fully elucidated. By tracking the uterine biodistribution of SiNP in pregnant mice, this study was conducted to evaluate the biological effect of SiNP on reproduction. First, SiNP was conjugated with FITC, and then the FITC-SiNP was administrated to trophoblast (100 µg/mL, 24 h) in vitro and pregnant mice (0.25 mg/mouse, 2–24 h) in vivo. It was found that the FITC-SiNP was internalized by trophoblast and deposited in the uterus. The internalization of SiNP caused trophoblast dysfunction and apoptosis, while SiNP accumulation in the uterus induced diffuse inflammatory infiltration. The genome-wide alteration of gene expression was studied by high throughput sequencing analysis, where 75 genes were found to be dysregulated after SiNP exposure, among which ACOT2, SCD1, and CPT1A were demonstrated to regulate the biosynthesis of unsaturated fatty acids. Moreover, the suppression of unsaturated fatty acids caused mitochondrial overload of long-chain fatty acyl-CoA (LACoA), which further induced both trophoblast apoptosis and endometrial inflammation. In conclusion, the successful conjugation of FITC onto SiNP facilitated the tracking of SiNP in vitro and in vivo, while exposure to FITC-SiNP induced uterine metabolic disorder, which was regulated by the ACOT/CPT1A/SCD1 axis through the biosynthesis of unsaturated fatty acids signaling pathway.

Satellites have different revisit frequencies (i.e., temporal resolutions), ranging from daily to monthly. The satellite revisit frequencies suitable for accurately monitoring the phenology of deciduous broad-leaved forests (DBF) are not well-known. To fill this knowledge gap, this study used MODIS Daily Nadir BRDF-Adjusted images to simulate EVI time series with a wide range of temporal resolutions from daily to 52 days, to investigate the impacts of satellite revisit frequency on monitoring spatial and temporal patterns of spring phenology, i.e., the start of season (SOS), of DBF in North America. Then, these EVI time series were used to extract SOS by two common phenology extraction methods (i.e., relative threshold and curvature methods). Our results reveal that 1) low temporal resolutions cannot accurately reconstruct real vegetation growth profile, which generally causes a false early SOS detection, 2) the impact of temporal resolutions is nonlinear. The accuracy of SOS detection from data with relatively high frequencies (e.g., 7 days) is only slightly lower than that from daily time series but the accuracy decreases largely with low frequencies, and 3) validation with ground observations from PhenoCam Network stations and an experiment using three real satellite datasets (i.e., MODIS, Landsat 8, and Sentinel-2) confirm the findings from our simulation study. This study suggests that satellites with medium temporal resolutions, such as Sentinel-2 and Landsat 8, could extract reliable phenology metrics in non-cloudy regions.

High-quality fine-resolution satellite time series data are important for monitoring land surface dynamics in heterogeneous areas. However, the quality of raw satellite time series is affected by clouds and the revisit frequency. Currently, there are two major strategies to reconstruct high-quality fine-resolution time series: the interpolation of the missing pixels using auxiliary data from the same satellite (known as filling) and the fusion of fine-resolution and coarse-resolution images (known as fusing). These two strategies use different principles and input data to reach the same goal, but which one is superior in different scenarios is not known. Therefore, this study fills this research gap by comparing two representative methods from filling and fusing: the Neighborhood Similar Pixel Interpolator (NSPI) for filling and the Flexible Spatiotemporal DAta Fusion (FSDAF) for fusing. The potential factors affecting the accuracy of the two methods were investigated using two simulated experiments. The results show that (1) the accuracy of both methods decreases with the time interval between the image to be reconstructed and the auxiliary image; (2) NSPI is generally better than FSDAF for reconstructing images with small cloud patches but this superiority is insignificant in homogeneous areas; (3) the accuracy of NSPI significantly decreases with cloud size, and NSPI is worse than FSDAF for reconstructing images with large clouds; and (4) the performance of FSDAF is significantly affected by the scale difference between the fine- and coarse-resolution images, especially for heterogeneous areas. The findings of this study can help users select the appropriate method to reconstruct satellite time series for their specific applications.

BackgroundIschemic stroke is one of the leading causes of human death and disability. Brain edema and peri-infarct astrocyte reactivity are crucial pathological changes, both involving aquaporin-4 (AQP4). Studies revealed that acute inhibition of AQP4 after stroke diminishes brain edema, however, its effect on peri-infarct astrocyte reactivity and the subacute outcome is unclear. And if diffusion-weighted imaging (DWI) could reflect the AQP4 expression patterns is uncertain.MethodsRats were subjected to middle cerebral artery occlusion (MCAO) and allocated randomly to TGN 020-treated and control groups. One day after stroke, brain swelling and lesion volumes of the rats were checked using T2-weighted imaging (T2-WI). Fourteen days after stroke, the rats successively underwent neurological examination, T2-WI and DWI with standard b-values and ultra-high b-values, apparent diffusion coefficient (ADC) was calculated correspondingly. Finally, the rats’ brains were acquired and used for glial fibrillary acidic protein (GFAP) and AQP4 immunoreactive analysis.ResultsAt 1 day after stroke, the TGN-020-treated animals exhibited reduced brain swelling and lesion volumes compared with those in the control group. At 14 days after stroke, the TGN-020-treated animals showed fewer neurological function deficits and smaller lesion volumes. In the peri-infarct region, the control group showed evident astrogliosis and AQP4 depolarization, which were reduced significantly in the TGN-020 group. In addition, the ultra-high b-values of ADC (ADCuh) in the peri-infarct region of the TGN-020 group was higher than that of the control group. Furthermore, correlation analysis revealed that peri-infarct AQP4 polarization correlated negatively with astrogliosis extent, and ADCuh correlated positively with AQP4 polarization.ConclusionWe found that acutely inhibiting AQP4 using TGN-020 promoted neurological recovery by diminishing brain edema at the early stage and attenuating peri-infarct astrogliosis and AQP4 depolarization at the subacute stage after stroke. Moreover, ADCuh could reflect the AQP4 polarization.

Silica is an essential substrate of various materials, and inhaling silica induces pulmonary diseases potentially associated with macrophage pyroptosis. Utilizing silica of micro- and nano- sizes, we explored the role of macrophage pyroptosis in silica-induced pulmonary inflammation. Under the transmission electron microscopy, we found that the internalization of silica nanoparticle induced membrane rupture and increased the number of intracellular vacuoles, and both sizes of silica could suppress cell viability and proliferation. Also, silica-exposed macrophages generated higher levels of ROS, together with the upregulated expression of NLRP3, ASC, Caspase-1, GSDMD, IL-1β, and IL-6. However, the expression of these proteins was suppressed after removing ROS or NLRP3. In addition, we found increased expression of TLR4 and NF-κB responsible for silica recognition and pyroptosis priming after silica exposure. For in vivo studies, we established animal model by intratracheally instilling 5 mg of silica into mice with/without NLRP3 inhibition. Four weeks later, we found diffused infiltration of inflammatory cells and enhanced collagen hyperplasia partially reversed by additional treatment with MCC950, so as the expression of pyroptotic molecules and proinflammatory cytokines. In particular, the dual immunofluorescent staining showed co-expression of macrophage-specific biomarker F4/80 and NLRP3 within the cells, and silica of nano-size showed more potent toxicity and pathogenicity than that of the micro-sized particles both in vitro and in vivo. To sum up, macrophage pyroptosis is an upstream event of silica-induced pulmonary inflammation promoted by ROS through the TLR4/NLRP3/NF-κB signaling axis.

Abstract Background Frequent freeze-thaw phenomena, together with widely used deicing salt and intense acid precipitation, often occur in northeastern China, causing damage to various aspects of plants, such as the permeability of biological membranes, osmotic adjustment, and photosystems. Aiming to explore the resistance of alfalfa to freezing-thawing (F), acid precipitation (A) and deicing salt (D), this study used Medicago sativa cv. Dongmu-70 as the experimental material, and the contents of malondialdehyde (MDA), soluble protein, soluble sugars, proline and chlorophyll were evaluated. Results As the temperature decreased, the MDA content in the seedlings of the group under combined stress (A-D-F) increased and was significantly higher than that of group F (by 69.48 ~ 136.40%). Compared with those in the control (CK) group, osmotic substances such as soluble sugars and proline in the treatment groups were higher, while the soluble protein content was lower. The chlorophyll contents in the seedlings of the treatment groups were lower than those of the CK group; however, the chlorophyll content displayed a non-significant change during the free-thaw cycle. Conclusion Injury to the permeability of the biological membranes and photosystems of alfalfa results from stress. Moreover, alfalfa maintains osmotic balance by adaptively increasing the potential of osmotic substances such as soluble sugars and proline. Furthermore, the influence of stress from freezing-thawing and deicing salt is highly substantial, but the combined stresses of acid precipitation with the two factors mentioned above had little effect on the plants.

Background: We sought to determine if the morphological and compositional features of chronic internal carotid artery occlusion (CICAO), as assessed by MR vessel wall imaging (MR-VWI), initially predict successful endovascular recanalization. Methods: Consecutive patients with CICAO scheduled for endovascular recanalization were recruited. MR-VWI was performed within 1 week prior to surgery for evaluating the following features: proximal stump morphology, extent of occlusion, occlusion with collapse, arterial tortuosity, the presence of hyperintense signals (HIS) and calcification in the occluded C1 segment. Multivariate logistic regression was used to identify features associated with technical success and construct a prediction model. Results: Eighty-three patients were recruited, of which fifty-seven (68.7%) were recanalized successfully. The morphological and compositional characteristics of CICAO were associated with successful recanalization, including occlusions limited to C1 and extensive HIS, as well as the absence of extensive calcification, absence of high tortuosity, and absence of artery collapse. The MR CICAO score that comprised the five predictors showed a high predictive ability (area under the curve: 0.888, p < 0.001). Conclusion: the MR-VWI characteristics of CICAO predicted the technical success of endovascular recanalization and may be leveraged for identifying patients with a high probability of successful recanalization.

Many studies have investigated the impacts of typhoons on natural vegetation, but the influencing factor of urban vegetation damage from super typhoon is not clear. Therefore, this study investigated the vegetation damage patterns in eight cities affected by Typhoon Mangkhut (the 2nd strongest tropical storm worldwide in 2018) using the normalized difference vegetation index (NDVI) derived from Sentinel-2 images. The vegetation damage maps have an overall accuracy of 97% using the very high-resolution WorldView-3 images as reference data. The results show that (1) The typhoon-induced vegetation damage show high spatial heterogeneity in urban areas and varies with land cover types. Residential greenspace and street trees are more susceptible to typhoon disturbance than natural vegetation. (2) Wind intensity is still an important factor in urban vegetation damage (r2 = 0.43, P value

Previous studies have demonstrated that silica nanoparticle (SiNP) exposure induces pulmonary and cardiovascular diseases, yet their transportation and degradation in vivo have not been fully elucidated. From the perspective of reproduction, this study was implemented to examine the uterine accumulation of SiNP and explore its reproductive toxicity and pathogenic mechanisms. First, we coupled FITC onto SiNPs and intratracheally instilled them into pregnant mice on the fifth gestational day, and the toxic effect of SiNP was evaluated in vitro and in vivo. It was found that SiNP penetrated the trophoblast membrane, leading to apoptosis and suppression of cell proliferation, tube formation, and invasion in a dose-dependent manner. Mechanistically, SiNP dysregulated the expression of Scd1, Slc27a1, and Cpt1a, and induced over synthesis and efflux obstruction of fatty acid through the PPARγ signaling pathway. The downregulation of Caspase-3 triggered apoptosis of trophoblast, which was causally associated with intracellular fatty acid accumulation as revealed by the correlation analysis. Besides, SiNP induced uterine inflammation in vivo, which aggravated with the observation prolongation within 24 h. Overall, SiNPs were visualized by coupling with FITC, and the uterine accumulation of SiNP induced fatty acid metabolic disorder, biological dysfunction, and trophoblast apoptosis, which were mediated in part by the PPARγ signaling pathway. These findings would contribute to understanding the environmental impacts of SiNP better, as well as the development of control measures for environmental pollution.

Autophagy is essential for cellular metabolism and plays pivotal roles in carcinogenesis, while excessive autophagy induces toxicity and cell death. Our previous studies have suggested that let-7a-5p/BCL-xL might regulate autophagy in lung cancer, but the regulatory mechanism is unclear. The central goal of the study was to figure out the role of let-7a-5p/BCL-xL in the initiation of autophagy and its effect on the migration, invasion, and proliferation of A549 cells as well as its therapeutic potential in lung cancer. Based on the genome-wide expression profiles of lung cancer, BCL-xL and let-7a-5p were found to be dysregulated and negatively correlated in lung adenocarcinoma, which was associated with the survival of lung cancer. The crosstalk between BCL-xL and let-7a-5p was then investigated using dual-luciferase reporter assay, and it was found to suppress the migration and invasion of A549 cells. Further, we found that the crosstalk between BCL-xL and let-7a-5p could lead to toxic autophagy and cell death through activating the PI3K-signaling pathway, which was independent of apoptosis or pyroptosis. These findings indicate that let-7a-5p is a sensitive initiator for toxic autophagy in A549 lung cancer cells and is an appealing target for lung cancer therapy. Keywords: lung adenocarcinoma, toxic autophagy, apoptosis, BCL-xL, let-7a-5p

Redox flow batteries are promising for large-scale energy storage, but are hindered by cost, stability, and safety issues. Here the authors construct an all-polymer particulate slurry battery to bypass solubility limits and apply insoluble redox-active materials.

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.

We describe comparatively cryogenically cooled Tm, Ho: GdVO4 lasers with an emission wavelength of 2.05 μm under continuous wave and pulse operating mode. By varying the transmittance of output couplers to be 0.40 for a continuous wave laser, the maximum output power of 7.4 W was generated with a slope efficiency of 43.3% when the absorbed pump power was increased to 18.7 W. For passively Q-switched lasers, the output characteristics were researched through altering pump mode radius. When the pump mode radius focused into the Tm, Ho: GdVO4 center equaled near 600 μm, the peak power was increased to be the maximum value of 9.9 kW at the absorbed pump power of 11.8 W. The pulse energy of 0.39 mJ was achieved at the same absorbed pump power with repetition of 5.7 kHz.

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

Air pollution is one of the most concerning environmental problems in cities. Hourly data on pollutant concentrations from 11 automatic atmospheric monitoring stations and meteorological data in Shenyang from 2017 to 2019 were used to analyze the spatio-temporal variation rules of CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), O3 (ozone), PM2.5 and PM10 (PM particles with an aerodynamic diameters of not more than 2.5 µm and 10 µm) and their relationships with meteorological parameters. Meanwhile, the regional transmission route of pollutants was analyzed by the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The results showed that the concentration of O3 in the northern area of the city was higher than that in the south; CO, SO2 and NO2 were relatively high in the urban center; and PM2.5 and PM10 were relatively high in the southwest. The average concentration of pollutants was lowest in 2019. The concentration of O3 was the highest in spring, while CO showed no significant variations between different seasons. The remaining pollutant concentrations appeared to be high in winter and low in summer. The cumulative concentrations of the six pollutants were the highest in March, and relatively low in July–September. The diurnal concentration variations of O3, CO and SO2 exhibited a “single peak,” while others showed a “double peak and double valley.” Temperature was positively correlated with O3 concentration and negatively correlated with others. Wind speed was negatively correlated with the concentration of PM2.5, NO2, and O3. The air quality of the main urban area in spring and summer was mainly affected by the coastal air flow, while it was mostly affected by the northwest air flow in autumn and winter.

Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural−urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural−urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural−urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.

Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.