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Objective To explore the current status of serum phosphorus level in patients on maintenance hemodialysis （MHD）, examine its relationship with clinical parameters and determine the influencing factors of serum phosphorus level. Methods For this multicenter cross-sectional study, 2667 patients on MHD at 27 domestic dialysis centers were recruited from 2019 to 2020. Demographic profiles, primary diseases and laboratory tests were recorded. According to serum phosphorus （P） level, they were assigned into three groups of low blood phosphorus （P

Called the “Water Tower”, the water resource in Qinghai Province plays a vital role in the ecological environment of northwestern China. However, significant uncertainty exists regarding its spatial-temporal variations. In this study, the data on the surface water of Qinghai Province from 1986 to 2018 was extracted and a systemic analysis of its spatial-temporal variations and responses to climate change was conducted using the Google Earth Engine (GEE) cloud processing platform. Our findings revealed that the surface water in Qinghai Province was primarily concentrated in the Qaidam Basin, the Hoh Xil Nature Reserve, and Qinghai Lake. The surface water area in Qinghai Province exhibited an overall increasing trend, with the temperature and precipitation being the primary drivers of this expansion. These results provide crucial insights into the variations of surface water variations in Qinghai Province, northwestern China, under the influence of climate change.

This study focuses on the scientific problem of whether farmland scale utilization is feasible in China and which areas of farmland are suitable for scale utilization. Based on multi-source data, we adopted TOPSIS and Hotspot analysis models to quantitatively evaluate and reveal the spatio-temporal evolution law of the suitability for farmland scale utilization in China over the past 30 years, and then used matrix grouping method to carried out the regulatory zoning. The findings suggested that: (1) The spatio-temporal differentiation of suitability for farmland scale utilization in China was significant from 1990 to 2020, 68 % of counties and 81 % of farmland resources were suitable for scale utilization. (2) In terms of spatial pattern, the suitability showed the distribution characteristics of high in the north and low in the south, high in the east and low in the west. In terms of time change, the farmland endowment suitability decreased slightly over the past 30 years, the natural environment suitability was basically stable, the socio-economic suitability increased steadily, and the comprehensive suitability showed a continuous and slow upward trend. (3) The priority development zone, suitable development zone and remediation regulate zone for farmland scale utilization in China accounted for 29.82 %, 31.19 % and 38.99 %, respectively. The regulatory zoning indicated that the suitability level of farmland scale utilization in China needed to be further improved. This study can provide scientific reference for the optimal allocation and utilization mode adjustment of farmland resources in different regions of China, which is of great valuable for promoting the sustainable utilization of farmland resources and ensuring food security in China.

Multiple scattering of laser ray leads to distance calculation error and accumulated intensity error in LiDAR waveform. This study quantitatively assesses multiple scattering in LiDAR canopy waveform and explores its relationship with sensor configurations, forest structure, and leaf biochemistry. Airborne LiDAR measurements were conducted to verify the presence of multiple scattering in spaceborne LiDAR waveforms. A Monte Carlo-based radiative transfer model, DART, was applied to simulate laser multiple scattering. The simulated results were compared with the actual measurements and further revealed how forest characteristics and sensor parameters influence multiple scattering. Results show that multiple scattering contributes significantly (∼30% of canopy subwaveform and ∼18% of total waveform) when there are sufficient scatterers in a large LiDAR footprint, and the scatterers possess a reasonable spatial distribution and a strong albedo at the laser wavelength; contribution of multiple scattering is typically less than 5% for small-footprint LiDAR; multiple scattering of large-footprint LiDAR increases with forest leaf area index and fractional coverage, while also varying with laser pointing angle, crown shape, and leaf angle distribution; leaf biochemical contents affect multiple scattering only at their sensitive wavelength. These findings help to quantitatively explain errors in the simulation, validation, and interpretation of LiDAR signal.

ABSTRACTThe constant need for decarbonization has led to the replacement of artificial light at night (ALAN) with light-emitting diodes (LEDs), inducing blue light pollution and its consequent adverse effects. As a result, there is an urgent need for the development of a technique for the rapid, accurate, and large-scale discrimination of the various illumination sources. The newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) can play this role by supplementing the existing nighttime light data with multispectral and high-resolution features. Along these lines, in this work, a novel approach to identify various types of illumination sources using machine learning in SDGSAT-1 images was proposed, taking Beijing as a worked example. The results indicate that: (1) The method can effectively distinguish the various types of light sources with an overall accuracy of 0.92 for ALAN and 0.95 for streetlights. (2) The illumination patterns can be clearly depicted, indicating distinct spatial heterogeneity in ALAN along Beijing’s 5th Ring Road. (3) Statistically significant disparities between road classes and streetlight types were detected, with a notable increase in LED streetlight usage as the road class diminishes. This work emphasizes the crucial role of SDGSAT-1 in analysing ALAN, providing valuable insights in urban lighting management.

The objective of this study was to elucidate the mechanism of action of the active components of Coptidis rhizoma against porcine epidemic diarrhea and to provide a theoretical foundation for further development of novel anti-PED therapeutic agents based on Coptidis rhizoma. The potential targets of Coptidis rhizoma against PEDV were identified through a comprehensive literature review and analysis using the TCMSP pharmacological database, SwissDrugDesign database, GeneCards database, and UniProt database. Subsequently, the STRING database and Cytoscape 3.7.1 software were employed to construct a protein–protein interaction (PPI) network and screen key targets. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the identified targets. Molecular docking studies were performed using AutoDock 1.5.7 software to analyze the binding energy and modes of interaction between the active components of Coptidis rhizoma and the target proteins. The PyMOL 2.5.0a0 software was employed to visualize the docking results. Through comprehensive analysis, 74 specific targets of active components of Coptidis rhizoma against PEDV were identified. The core gene targets were screened, and an interaction network diagram was subsequently generated. Ultimately, 14 core targets were identified, with STAT3, ESR1, CASP3, and SRC exhibiting the most significant interactions. GO enrichment analysis revealed a total of 215 molecular items, including 48 biological function items, 139 biological process items, and 28 cellular component items. KEGG enrichment analysis identified 140 signaling pathways. Molecular docking analysis demonstrated that epiberberine and palmatine exhibited high binding affinity with STAT3 protein, worenine showed high binding affinity with ESR1 protein, obacunone exhibited high binding affinity with CASP3 protein, and epiberberine, obacunone, berberine, and berberruine exhibited high binding affinity with SRC protein. A network pharmacology and molecular docking technology approach was employed to screen six important active components of Coptidis rhizoma and four important potential targets against PEDV infection. The findings indicated that the active components of Coptidis rhizoma could serve as promising pharmaceutical agents for the prevention and control of PEDV, with significant potential for clinical application.

More and more offshore heavy oil resources are discovered and exploited as the focus of the oil and gas industry shifts from land to sea. However, unlike onshore heavy oil reservoirs, offshore heavy oil reservoirs not only have active edge and bottom water but also have different exploitation methods. In this paper, a typical special heavy oil reservoir in China was analyzed in detail, based on geology–reservoir–engineering integration technology. Firstly, it is identified as a self-sealing bottom water heavy oil reservoir by analyzing its geological characteristics and hydrocarbon accumulation mechanism. Secondly, the water cut is initially controlled by oil viscosity, but subsequently, by reservoir thickness through the analysis of oil and water production data. Thirdly, the bottom oil–water contact of the reservoir was re-corrected to build an accurate 3D geological model, based on the production history matching of a single well and the whole reservoir. Lastly, a scheme of thermal production coupled with cold production was proposed to exploit this special reservoir, and the parameters of steam, N2, and CO2 injection and production were optimized to predict oil production. This work can provide a valuable development model for the efficient exploitation of similar offshore special heavy oil reservoirs.

Key Clinical Message Ingestion of foreign bodies is very common in clinical practice. However, gastrointestinal perforation caused by a foreign body is rare, as most foreign bodies can pass the alimentary tract spontaneously or be removed endoscopically. Ingesting a foreign body causes gastrointestinal tract perforation in less than 1% of cases that require surgery. In the past, the literature about gastrointestinal tract perforation caused by foreign bodies had been widely reported worldwide. However, the case of foreign bodies causing gastrointestinal perforation without significant abdominal infection was rarely documented. A 47‐year‐old woman presented with intermittent left lower abdominal pain associated with a mass for 1 month and had no other symptoms. Laparotomy was performed after clinical assessment. During the operation, a local inflammatory mass that adhered to the abdominal wall, part of the small intestine, and sigmoid colon was found in the left lower quarter of the abdominal cavity. The surrounding intestinal wall was edematous. There were two bony foreign bodies in it. Postoperative pathology suggested an inflammatory mass. A foreign body rarely migrates into the abdominal cavity without symptoms that may be related to the omentum's slow perforation process and good function. The best treatment is surgery and using appropriate antibiotics.

Abstract Background The Controlling Nutritional Status (CONUT) score has proven to be a potential biomarker for determining the prognosis of patients with various types of cancer. Its value in determining the prognosis of patients with gynecological cancer, however, remains unknown. The present study was a meta-analysis that aimed to evaluate the prognostic and clinicopathological significance of the CONUT score in gynecological cancer. Methods The Embase, PubMed, Cochrane Library, Web of Science, and China National Knowledge Infrastructure databases were comprehensively searched through November 22, 2022. A pooled hazard ratio (HR), together with a 95% confidence interval (CI), was used to determine whether the CONUT score had prognostic value in terms of survival outcomes. Using odds ratios (ORs) and 95% CIs, we estimated the relationship between the CONUT score and clinicopathological characteristics of gynecological cancer. Results We evaluated 6 articles, involving a total of 2,569 cases, in the present study. According to the results of our analyses, higher CONUT scores were significantly correlated with decreased overall survival (OS) (n = 6; HR = 1.52; 95% CI = 1.13–2.04; P = 0.006; I2 = 57.4%; Ph = 0.038) and progression-free survival (PFS) (n = 4; HR = 1.51; 95% CI = 1.25–1.84; P

AbstractBackground Numerous studies have explored whether the prognostic nutritional index (PNI) can predict the prognosis of cervical cancer (CC); however, their findings remain controversial. This meta-analysis focused on evaluating the relationship between the PNI and the prognosis of patients with CC.Methods Relevant articles were collected from specific databases up to March 16, 2023. The relationship between the PNI and survival outcomes in patients with CC was estimated using combined hazard ratios (HRs) and associated 95% confidence intervals (CIs). The association of the PNI with clinicopathological features in patients with CC was assessed by combining odds ratios (ORs) and associated 95% CIs.Results Nine articles with 2508 cases were included in the meta-analysis. According to our pooled findings, a decreased PNI showed a significant association with worse overall survival (OS) (HR = 2.98, 95% CI = 2.22–3.99, p 4 cm (OR = 1.73, 95% CI = 1.21–2.46, p = .002). However, the PNI was not significantly associated with histology, differentiation, or FIGO stage.Conclusion In this study, a low PNI predicted dismal OS and PFS in patients with CC, who also tend to suffer from LN metastasis and larger tumor size. PNI is a promising biomarker for predicting the prognosis of patients with CC in clinical practice.

As part of the genus Enteropathogenic Coronaviruses, Porcine Epidemic Diarrhea Virus (PEDV) is an important cause of early diarrhea and death in piglets, and one of the most difficult swine diseases to prevent and control in the pig industry. Previously, we found that PEDV can block Na+ absorption and induce diarrhea in piglets by inhibiting the activity of the sodium-hydrogen ion transporter NHE3 in pig intestinal epithelial cells, but the mechanism needs to be further explored. The epidermal growth factor receptor (EGFR) has been proved to be one of the co-receptors involved in many viral infections and a key protein involved in the regulation of NHE3 activity in response to various pathological stimuli. Based on this, our study used porcine intestinal epithelial cells (IPEC-J2) as an infection model to investigate the role of EGFR in regulating NHE3 activity after PEDV infection. The results showed that EGFR mediated viral invasion by interacting with PEDV S1, and activated EGFR regulated the downstream EGFR/ERK signaling pathway, resulting in decreased expression of NHE3 and reduced NHE3 mobility at the plasma membrane, which ultimately led to decreased NHE3 activity. The low level of NHE3 expression in intestinal epithelial cells may be a key factor leading to PEDV-induced diarrhea in newborn piglets. This study reveals the importance of EGFR in the regulation of NHE3 activity by PEDV and provides new targets and clues for the prevention and treatment of PEDV-induced diarrhea in piglets.

Porcine epidemic diarrhea virus (PEDV) mainly invades the small intestine and promotes an inflammatory response, eventually leading to severe diarrhea, vomiting, dehydration, and even death of piglets, which seriously threatens the economic development of pig farming. In recent years, researchers have found that probiotics can improve the intestinal microenvironment and reduce diarrhea. At the same time, certain probiotics have been shown to have antiviral effects; however, their mechanisms are different. Herein, we aimed to investigate the inhibitory effect of Lactiplantibacillus plantarum supernatant (LP-1S) on PEDV and its mechanism. We used IPEC-J2 cells as a model to assess the inhibitory effect of LP-1S on PEDV and to further investigate the relationship between LP-1S, Ca2+, and PEDV. The results showed that a divalent cation chelating agent (EGTA) and calcium channel inhibitors (Bepridil hydrochloride and BAPTA-acetoxymethylate) could inhibit PEDV proliferation while effectively reducing the intracellular Ca2+ concentration. Furthermore, LP-1S could reduce PEDV-induced loss of calcium channel proteins (TRPV6 and PMCA1b), alleviate intracellular Ca2+ accumulation caused by PEDV infection, and promote the balance of intra- and extracellular Ca2+ concentrations, thereby inhibiting PEDV proliferation. In summary, we found that LP-1S has potential therapeutic value against PEDV, which is realized by modulating Ca2+. This provides a potential new drug to treat PEDV infection.

Abstract The development of simple and effective strategies to prepare electrocatalysts, which possess unique and stable structures comprised of metal/nonmetallic atoms for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is currently an urgent issue. Herein, an efficient bifunctional electrocatalyst featured by ultralong N, S‐doped carbon nano‐hollow‐sphere chains about 1300 nm with encapsulated Co nanoparticles (Co‐CNHSCs) is developed. The multifunctional catalytic properties of Co together with the heteroatom‐induced charge redistribution (i.e., modulating the electronic structure of the active site) result in superior catalytic activities toward OER and ORR in alkaline media. The optimized catalyst Co‐CNHSC‐3 displays an outstanding electrocatalytic ability for ORR and OER, a high specific capacity of 1023.6 mAh gZn−1, and excellent reversibility after 80 h at 10 mA cm−2 in a Zn‐air battery system. This work presents a new strategy for the design and synthesis of efficient multifunctional carbon‐based catalysts for energy storage and conversion devices.

China has become a major investor and constructor of electrical power plants in developing countries. However, the impacts of China's overseas power stations (COPSs) on the developing countries hosting them are poorly understood. Here, a novel method is proposed to evaluate the influence of COPSs in 80 host countries. First, their electricity consumption from 1971 to 2017 was estimated using data provided by the World Bank, International Energy Agency, and World Resources Institute. Regression analysis was then used to predict consumption from 2018 to 2025. Finally, three parameters were used to evaluate the influences of COPSs. The results show that: 1) COPSs significantly increased the total installed capacity of 35 of the host countries by > 20%. 2) The power generated by COPSs is greater than the growing demands of 32 of the host countries. 3) COPSs will increase the per capita electricity consumption of all 80 host countries. 4) Among the 437 COPSs existing in 2000–2019, renewable power plants (including hydropower) were most numerous, accounting for 51.3%. This proportion increased significantly after 2013 and renewable plants will continue to dominate as China will no longer invest in new coal-fired power stations after 2021.

Abstract Background Placement of a levonorgestrel-releasing intrauterine system (LNG-IUS) is an effective treatment for adenomyosis, especially for patients who have severe dysmenorrhea symptoms but a strong desire to preserve fertility. Nonetheless, for patients with adenomyosis accompanied by an enlarged uterus, expulsion of the ring is a troublesome problem. In this study, we sewed and fixed the LNG-IUS in the uterus, which provides a good solution to this problem. Methods In this prospective case series approved by the Ethics Committee of Hangzhou Women’s Hospital, 12 patients with adenomyosis were successfully enrolled after providing informed consent, and all patients underwent long-term postoperative follow-up. Results Twelve patients with adenomyosis underwent suture fixation with an LNG-IUS, and during the long-term postoperative follow-up, every patient experienced complete remission of their symptoms: a significant decrease in menstrual flow, relief of dysmenorrhea, and improvement in quality of life. Only one person reported expulsion a year later. Conclusion In patients with adenomyosis suffering from dysmenorrhea or excessive menstrual blood loss, suture fixation of an LNG-IUS using the hysteroscopic cold knife surgery system is a minimally invasive and effective alternative treatment for adenomyosis and decreases the risk of LNG-IUS expulsion.

Solid-state photochemical reactions of olefinic compounds provide access to organic cyclic molecules with specific configurations but the precise control of the stereochemistry in these reactions remains challenging. Here, the authors demonstrate control of the regiospecific configurations of C=C groups and the intermediates by varying temperatures in multi-step thermal and photoinduced ring opening and closing reactions using a single-crystal coordination polymer platform.

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

With the challenges brought about by the COVID-19 pandemic, China’s real-estate market has been facing new bottlenecks. The solution lies in an in-depth understanding of regional real-estate conditions. In the study of housing, remote sensing technology can help to extract building height as well as to calculate the number of floors and estimate the total amount of housing. It is more efficient and accurate compared to conventional statistical and sampling methods. Remote sensing is widely used in real-estate research and building height estimation, whereas it is less frequently used for the total estimation of urban housing. In this context, we used Chinese satellite GF-7 stereopair images, point of interest (POI) data, and other data combined with the digital surface model (DSM) and shadow methods to calculate the height of residential buildings. An efficient and accurate method system was then established for estimating the total housing and per capita living area (PCLA). According to the calculation of the PCLA of each district in Ningbo City (China), it was found that different regions were suitable for different development paths. Based on this, the driving factor model was derived and the real-estate development potential of Ningbo city was quantitatively analyzed. The results showed that Ningbo City, a first-tier city with a large population inflow, still has potential for real-estate development.

Plant growth and its changes over space and time are effective indicators for signifying ecosystem health. However, large uncertainties remain in characterizing and attributing vegetation changes in the ecologically fragile South China Karst region, since most existing studies were conducted at a coarse spatial resolution or covered limited time spans. Considering the highly fragmented landscapes in the region, this hinders their capability in detecting fine information of vegetation dynamics taking place at local scales and comprehending the influence of climate change usually over relatively long temporal ranges. Here, we explored the spatiotemporal variations in vegetation greenness for the entire South China Karst region (1.9 million km2) at a resolution of 30m for the notably increased time span (1987-2018) using three decadal Landsat images and the cloud-based Google Earth Engine. Moreover, we spatially attributed the vegetation changes and quantified the relative contribution of driving factors. Our results revealed a widespread vegetation recovery in the South China Karst (74.80%) during the past three decades. Notably, the area of vegetation recovery tripled following the implementation of ecological engineering compared with the reference period (1987-1999). Meanwhile, the vegetation restoration trend was strongly sustainable beyond 2018 as demonstrated by the Hurst exponent. Furthermore, climate change contributed only one-fifth to vegetation restoration, whereas major vegetation recovery was highly attributable to afforestation projects, implying that anthropogenic influences accelerated vegetation greenness gains in karst areas since the start of the new millennium during which ecological engineering was continually established. Our study provides additional insights into ecological restoration and conservation in the highly heterogeneous karst landscapes and other similar ecologically fragile areas worldwide.

Bone cancer pain (BCP) is excruciating for cancer patients, with limited clinical treatment options and significant side effects, due to the complex and unclear pathogenesis of bone cancer pain. Peripheral sensitization in dorsal root ganglion (DRG) neurons is a recognized cellular mechanism for bone cancer pain. The pathological mechanism of chronic pain is increasingly being affected by epigenetic mechanisms. In this study, we unbiasedly showed that the DNA hydroxymethylase ten-eleven translocation 1 (TET1) expression was significantly increased in the L4–6 DRG of BCP rats and ten-eleven translocation 2 (TET2) expression did not change significantly. Notably, TET1 inhibition by intrathecal injection of Bobcat339 (a TET1 inhibitor) effectively relieved mechanical hyperalgesia in BCP rats. Peripheral sensitization in chronic pain relies on the activation and overexpression of ion channels on neurons. Here, we demonstrated that TRPV4, one of the transient receptor potential ion channel family members, was significantly elevated in the L4–6 DRG of BCP rats. In addition, TRPV4 inhibition by intrathecal injection of HC067047 (a TRPV4 inhibitor) also significantly attenuated mechanical hyperalgesia in BCP rats. Interestingly, we found that TET1 inhibition downregulated TRPV4 expression in the L4–6 DRG of BCP rats. As a result, these findings suggested that TET1 may contribute to bone cancer pain by upregulating TRPV4 expression in the L4–6 DRG of BCP rats and that TET1 or TRPV4 may become therapeutic targets for bone cancer pain.

Various coronaviruses have emerged as a result of cross-species transmission among humans and domestic animals. Porcine epidemic diarrhea virus (PEDV; family Coronaviridae, genus Alphacoronavirus) causes acute diarrhea, vomiting, dehydration, and high mortality in neonatal piglets. Porcine small intestinal epithelial cells (IPEC-J2 cells) can be used as target cells for PEDV infection. However, the origin of PEDV in pigs, the host range, and cross-species infection of PEDV remain unclear. To determine whether PEDV has the ability to infect human cells in vitro, human small intestinal epithelial cells (FHs 74 Int cells) were inoculated with PEDV LJX and PEDV CV777 strains. The results indicated that PEDV LJX, but not PEDV CV777, could infect FHs 74 Int cells. Furthermore, we observed M gene mRNA transcripts and N protein expression in infected FHs 74 Int cells. A one-step growth curve showed that the highest viral titer of PEDV occurred at 12 h post infection. Viral particles in vacuoles were observed in FHs 74 Int cells at 24 h post infection. The results proved that human small intestinal epithelial cells are susceptible to PEDV infection, suggesting the possibility of cross-species transmission of PEDV.

Abstract Background Hormonal reproductive factors have been suggested to play an important role in the etiology of rheumatoid arthritis (RA), an autoimmune inflammatory disorder affecting primarily women. We conducted a two-sample Mendelian randomization (MR) study examining three relevant exposures, age at menarche (AAM), age at natural menopause (ANM), and age at first birth (AFB) with the risk of RA. Methods We collected summary statistics from the hitherto largest GWAS conducted in AAM (N = 329,345), ANM (N = 69,360), AFB (N = 251,151), and RA (N case = 14,361, N control = 43,923), all of European ancestry. We constructed strong instruments using hundreds of exposure-associated genetic variants and estimated causal relationship through different MR approaches including an inverse-variance weighted method, an MR-Egger regression and a weighted median method. We conducted a multivariable MR to control for pleiotropic effect acting in particular through obesity and socioeconomic status. We also performed important sensitivity analyses to verify model assumptions. Results We did not find any evidence in support for a causal association between genetically predicted reproductive factors and risk of RA (ORper-SD increment in AAM = 1.06 [0.98–1.15]; ORper-SD increment in ANM = 1.05 [0.98–1.11], OR per-SD increment in AFB = 0.85 [0.65–1.10]). Results remained consistent after removing palindromic SNPs (ORper-SD increment in AAM = 1.06 [0.97–1.15], ORper-SD increment in ANM = 1.05 [0.98–1.13], ORper-SD increment in AFB = 0.81 [0.61–1.07]) or excluding SNPs associated with potential confounding traits (ORper-SD increment in AAM = 1.03 [0.94–1.12], ORper-SD increment in ANM = 1.04 [0.95–1.14]). No outlying instrument was identified through the leave-one-out analysis. Conclusions Our MR study does not convincingly support a casual effect of reproductive factors, as reflected by age at menarche, age at menopause, and age at first birth, on the development of RA. Despite the largely augmented set of instruments we used, these instruments only explained a modest proportion of phenotypic variance of exposures. Our knowledge regarding this topic is still insufficient and future studies with larger sample size should be designed to replicate or dispute our findings.

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration (Rs) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with R2 of 0.72 and RMSE of 0.96 g·C·m−2·day−1. The performance was also good when predicting across-site (R2 = 0.75) and seasonal patterns (R2 = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and Rs. This study indicates the importance of considering the influence of water-related variables and Rs in the estimation of NEE at the global scale.

Significant interest in waste-cement recycling has been stimulated because of the high contents of calcium and silicon in waste cement. The reactivity of calcium and silicon in the raw material is one of the important factors for the hydrothermal synthesis of xonotlite. Therefore, the effect of the reactivity of calcium and silicon in the waste cement on the hydrothermal synthesis of xonotlite was studied in this paper. Portland cement that was hydrated for 6 months, with the aim of simulating the waste cement, was used for the first time as the calcium and silicon source in the hydrothermal synthesis of xonotlite. As calcination would raise the reactivity of the hydrated cement, the effect of calcination of the Portland hydrated cement on the hydrothermal synthesis of xonotlite was investigated. The hydrated cement was calcined at 900 °C, and the hydrothermal synthesis was carried out at 220 °C for different times. The phases of the hydrothermal products were analyzed by XRD and TG-DSC, and it was noted that the calcination of hydrated cement affected the formation rate of xonotlite. The content of xonotlite increased from 18% (synthesized with hydrated cement without calcination) to 74% (synthesized from hydrated cement with calcination at 900 °C) during a reaction time of 24 h. Furthermore, the micromorphologies of xonotlite using calcined and hydrated cement were compared and discussed from the perspective of the reactivity of the starting materials.

Land-surface greening has been reported globally over the past decades. While often seen to represent ecosystem recovery, the impacts on biodiversity and society can also be negative. Greening has been widely reported from rangelands, where drivers and processes are complex due to its high environmental heterogeneity and societal dynamics. Here, we assess the complexity behind greening and assess its links to various drivers in an iconic, heterogeneous rangeland area, the Iberá Wetlands and surroundings, in Argentina. Time-series satellite imagery over the past 19 years showed overall net greening, but also substantial local browning both in protected and unprotected areas, linking to land use, temporal changes in surface water, fire, and weather. We found substantial woody expansion mainly in the unprotected land, with 37% contributed by tree plantations and the remaining 63% by spontaneous woody expansion, along with widespread transitions from terrestrial land to seasonal surface water. Fire occurrences tended to reduce greening with unprotected areas experiencing widespread and frequent fire. However, protected areas had more browning in unburnt areas than burned areas. Temporal variation in annual precipitation and temperature tended to nonlinearly influence fire occurrences with an interplay of human fire management, further shaping the vegetation greening, pointing to high complexity behind the observed rangeland greening involving interactions among local drivers. Our findings highlight that the observed overall greening is an outcome of multiple trends with clear negative impacts on biodiversity and the local livestock-oriented culture (notably expanding tree plantations) and spontaneous vegetation dynamics, partly involving spontaneous woody expansion. The latter has positive potential for biodiversity and ecosystem services in terms of woodland recovery, but can become negative in such a natural savanna region if expansions develop on a too broad scale, highlighting the importance of ensuring recovery of natural fire and herbivory regimes in protected areas along with sustainable rangeland management elsewhere.

The separation of xylene isomers remains a great challenge in industry due to their similar molecular structure and physical properties. Here the authors demonstrate adaptively molecular discrimination of xylene isomers by employing a NbOF5 2−-pillared metal–organic framework with rotational anionic sites.

Designing and building unique cage assemblies attract increasing interest from supramolecular chemists but remain synthetically challenging. Herein, we propose the use of a flexible vertex with adjustable angles to selectively form highly distorted tetrahedral and octahedral cages, for the first time, in which the flexible vertex forms from the synergistic effect of coordination and covalent interactions. The inherent interligand angle of the vertex can be modulated by guest anions present, which allows for the fine-tuning of different cage geometries. Furthermore, the reversible structural transformation between tetrahedral and octahedral cages was achieved by anion exchange monitored by mass spectrometric technique, the smaller anions favoring tetrahedral cages, while the larger anions supporting octahedral cages. Additionally, the KBr-based cage thin films exhibited prominent enhancement of their third-order NLO responses in two or three orders of magnitude compared to those obtained for their corresponding solutions. This work not only provides a new methodology to build irregular polyhedral structures in a controlled and tunable way but also provides access to new kinds of promising functional optical materials.

Abstract Background Acute renal injury (AKI) caused by hypotension often occurs in elderly patients after gastrointestinal tumor surgery. Although vasoactive drugs can increase effective filtration pressure, they may increase renal vascular resistance and reduce renal blood flow. The effect of methoxamine on renal function is not clear. Methods After obtaining written informed consent, 180 elderly patients undergoing elective gastrointestinal tumor surgery were randomly allocated into two groups: M group (continuous infusion of methoxamine at 2 μg/kg/min) and N group (continuous infusion of normal saline). The patients’ mean arterial pressure was maintained within 20% of baseline by a continuous infusion of methoxamine or normal saline. Maintenance fluid was kept at 5 mL/kg/h. According to Kidney disease improve global outcome (KDIGO) guidelines, creatinine was measured at 1, 2 and 7 days after operation, and urine volume at 6, 12 and 24 h after operation was measured to evaluate the occurrence of AKI. 162 patients were included in the final data analysis. Results Significant differences in the incidence of postoperative Acute kidney injury (M group: 7.5%; N group: 18.3%; P

Airborne LiDAR has been adopted as a powerful survey tool for overhead power transmission line (TL) cruising so that the operator can quickly search for, locate and eliminate the risk objects at the power line corridor scene. However, the TLs are moving objects, which positions are dynamically affected by working conditions (e.g. temperature variation, wind-induced conductor motion) while they are acquired by airborne LiDAR. The point clouds data acquired by airborne LiDAR only reflect the geometric relationship between the TL and its surrounding objects at the transient moment of the data acquisition. In order to overcome the shortcomings of the instantaneously acquired laser scanning data, this article presents an approach for simulating the dynamic TL shape under different working conditions based on mechanical computation of the overhead line. The proposed approach considers the tension variation of TL resulting from weather conditions, such as temperature, wind and ice, while simulates TL sag curve using the parabolic catenary equation combined with the tension. A performance evaluation was conducted over the TLs data with multiple voltage levels. Experiments results show that the proposed approach is effective for simulating the 3D shape of TL under different working conditions. The simulation error achieved was less than 0.65m and the maximum Diff-Ratio was about 1.57%. This provides a scientifically sound predicting and modeling approach for TL risk assessment and warning along the corridor.

Tree cover, which has been widely studied using various remote sensing techniques, serves an essential indicator of forest productivity and habitat quality. Spatial heterogeneity in tree cover has received less attention despite links with critical phenomena such as biodiversity, tree re-expansion into formerly deforested areas, and degradation of existing forest landscapes. Few remote sensing studies have explored how tree cover heterogeneity varies across space and time, or how it may determine local or large-scale forest dynamics. This study used a global vegetation time-series product to map spatio-temporal dynamics in global tree cover heterogeneity over a 35-year period from 1982 to 2016, with heterogeneity quantified using the diversity metric Rao’s Q. We first explored the underlying relationship between tree cover and its heterogeneity at landscape scale across the globe. We then investigated how tree cover heterogeneity varied across tree cover gradients, biogeographic biomes, stand history, land protection status, and forest landscape intact status. Finally, nine possible combinations of variation trends in tree cover and its heterogeneity were used to generate a new map of forest dynamics categories. Our results show that monotonic spatio-temporal changes in tree cover are not necessarily associated with changes in tree cover heterogeneity. This suggests that simply using variation trends of tree cover without considering the context of local spatial heterogeneity does not fully capture forest dynamics. We show that remotely sensed tree cover heterogeneity can easily distinguish tree plantations from primary and secondary forests and that temporal change in tree cover heterogeneity is sensitive to spatial heterogeneity of open forest landscapes. The new forest dynamics categories map captures ongoing woody expansion in the Sahel region, forest degradation in the Amazon and central Africa, and widespread forest regrowth in Europe and Asia. Thereby, it is clear that spatio-temporal variation in tree cover heterogeneity supplements existing remote sensing studies in depicting global forest degradation, succession, and recovery.

Central nervous system (CNS) disorders feature the progressive and selective loss of normal brain functions. CNS disorders often include an irreversible physiological and anatomical loss of neurons that can lead to dysfunction in various parts of the brain and eventually death. Glioblastoma multiforme (GBM) and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are hard to be diagnosed at an early stage for the prevention of disease propagation. Such diagnosis is vital for the timely commencement of actual treatments. Nanotechnology brings new diagnosis hope for CNS disorders as it provides ultrasensitive detection for more specific biomarkers. Herein, the recent progress in techniques development for detecting pathological biomarkers for GBM, AD, and PD is summarized, in particular, the principles that govern the design of these sensors, blood–brain barrier (BBB) dysfunction, and its integrity during disease development. Finally, a perspective on future directions to further advance and improve the early‐stage diagnosis of CNS disorders is presented.

Abstract Background Goal-directed volume expansion is increasingly used for fluid management in mechanically ventilated patients. The Pleth Variability Index (PVI) has been shown to reliably predict preload responsiveness; however, a lot of research on PVI has been published recently, and update of the meta-analysis needs to be completed. Methods We searched PUBMED, EMBASE, Cochrane Library, Web of Science (updated to November 7, 2018) and the associated references. Relevant authors and researchers had been contacted for complete data. Results Twenty-five studies with 975 mechanically ventilated patients were included in this meta-analysis. The area under the curve (AUC) of receiver operating characteristics (ROC) to predict preload responsiveness was 0.82 (95% confidence interval (CI) 0.79–0.85). The pooled sensitivity was 0.77 (95% CI 0.67–0.85) and the pooled specificity was 0.77 (95% CI 0.71–0.82). The results of subgroup of patients without undergoing surgery (AUC =0.86, Youden index =0.65) and the results of subgroup of patients in ICU (AUC =0.89, Youden index =0.67) were reliable. Conclusion The reliability of the PVI is limited, but the PVI can play an important role in bedside monitoring for mechanically ventilated patients who are not undergoing surgery. Patients who are expanded with colloid may be more suitable for PVI.

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions.

Remote sensing fractional vegetation cover (FVC) requires both finer-resolution and high-frequency in climate and ecosystem research. The increasing availability of finer-resolution (≤ 30 m) remote sensing data makes this possible. However, data from different satellites have large differences in spatial resolution, spectral response function, and so on, making joint use difficult. Herein, we showed that the vegetation index (VI)-based mixture model with the appropriate VI values of pure vegetation (Vv) and bare soil (Vs) from the MODIS BRDF product via the multi-angle VI method (MultiVI) was feasible to estimate FVC with multiple satellite data. Analyses of the spatial resolution and spectral response function differences for MODIS and other satellites including Landsat 8, Chinese GF 1, and ZY 3 predicted that (1) the effect of Vv and Vs downscaling on FVC estimation uncertainty varied from satellite to satellite due to the positioning differences, and (2) after spectral normalization, the uncertainty (RMSDs) for FVC estimation decreased by ~2.6% compared with the results without spectral normalization. FVC estimation across multiple satellite data will help to improve the spatiotemporal resolution of FVC products, which is an important development for numerous biophysical applications. Herein, we proved that the VI-based mixture model with Vv and Vs from MultiVI is a strong candidate.

The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

Access to electricity (the proportion of the population with access to electricity) is a key indica for of the United Nations’ Sustainable Development Goal 7 (SDG7), which aims to provide affordable, reliable, sustainable, and modern energy services for all. Accurate and timely global data on access to electricity in all countries is important for the achievement of SDG7. Current survey-based access to electricity datasets suffers from short time spans, slow updates, high acquisition costs, and a lack of location data. Accordingly, a new method for identifying the electrification status of built-up areas based on the remote sensing of nighttime light is proposed in this study. More specifically, the method overlays global built-up area data with night-time light remote sensing data to determine whether built-up areas are electrified based on a threshold night-time light value. By using our approach, electrified and unelectrified built-up areas were extracted at 500 m resolution on a global scale for the years 2014 and 2020. The acquired results show a significant reduction in an unelectrified built-up area between 2014 and 2020, from 51,301.14 km2 to 22,192.52 km2, or from 3.05% to 1.32% of the total built-up area. Compared to 2014, 117 countries or territories had improved access to electricity, and 18 increased their proportion of unelectrified built-up area by >0.1%. The identification accuracy was evaluated by using a random sample of 10,106 points. The accuracies in 2014 and 2020 were 97.29% and 98.9%, respectively, with an average of 98.1%. The outcomes of this method are in high agreement with the spatial distribution of access to electricity data reported by the World Bank. This study is the first to investigate the global electrification of built-up areas by using remote sensing. It makes an important supplement to global data on access to electricity, which can aid in the achievement of SDG7.

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (Hcanopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 Hcanopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 Hcanopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 Hcanopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted Hcanopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in Hcanopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.

COVID-19 has had a huge impact on many industries around the world. Internationally-funded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China’s industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019; (2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks.

The proportion of the rural population who live within 2 km of an all-season road is an indicator of the United Nations’ Sustainable Development Goals (SDGs) 9.1.1. This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. Three monitoring methods are proposed for different spatial regions based on the five expressways built by China’s Belt and Road Initiative Project. These methods are based on remote sensing and WorldPop and The High Resolution Settlement Layer (HRSL) population data. The results indicate that (1) the WorldPop population statistics show that the five expressways built by China’s Belt Project have increased the rural population of the 2 km buffer zone by 192,016 between the start of construction and eight years after its completion. By the end of 2019, the population increased by 329,291 accounting for 1.17% of the rural population. (2) Based on populations estimated form built-up index (NDBI) building areas, the rural populations within the 2 km buffer area of the Bejaia-Haniff Expressway in 2011, 2015, and 2019 were 273,118, 306,430, and 375,408, respectively. (3) HRSL population grid statistics indicate that, in 2015, the populations were: East-West Expressway = 911,549, Bejaia Expressway = 127,471, Tipaza Expressway = 71,411, North-South Expressway = 30,583, and Cherchell Ring Expressway = 41,657. (4) A visual interpretation method based on Google Earth imagery was used to count the number of buildings and number of building floors in the town of Tikhramtath. Based on the estimated population of each building and floor, the population of Tikhramtath town in 2011, 2015, 2017, and 2019 was estimated as 1,790, 2,785, 3,365, and 3,870, respectively. (5) Through analysis and accuracy assessment, the appropriate statistical methods for different regions were determined.

Transmissible gastroenteritis (TGE), caused by transmissible gastroenteritis virus (TGEV), is one many gastrointestinal inflections in piglets, characterized by diarrhea, and high mortality. Probiotics are ubiquitous bacteria in animal intestines, which have many functions, such as promoting intestinal peristalsis and maintaining the intestinal balance. We found that the supernatant of the Lp-1 strain of Lactobacillus plantarum, isolated in our laboratory, and named Lp-1s had marked anti-TGEV effect on IPEC-J2 cells. Lp-1s could induce large amounts of interferon-β in IPEC-J2 cells in the early stage (6 h) of infection with TGEV, and increased the level of phosphorylated signal transducer and activator of transcription and its nuclear translocation in the late stage (24–48 h) of infection. This resulted in upregulated expression of interferon-stimulated genes, and increased the transcription and protein expression of antiviral proteins, resulting in an anti-TGEV effect.

The original moth-flame optimization (MFO) algorithm neither generates high-performance flames nor utilizes the flames to offer enough effective search guidance for moths in solution spaces, causing the degeneration of the global search capability and convergence speed in confronting complicated problems. To overwhelm those imperfections, this paper proposes a double-evolutionary learning MFO algorithm (DELMFO), where two different evolutionary learning strategies, namely, the differential evolution flame generation (DEFG) and dynamic flame guidance (DFG) strategy, are presented to generate high-performance flames and dynamically guide the search of moths, respectively. By constructing the cascading collaboration between DEFG and DFG, the DELMFO offers a positive feedback channel that makes the personal best historical solutions (PBHSs), flames, and moths promote each other. This improves the global search capability and accelerates convergence speed. The DELMFO is compared with six MFO algorithms and nine popular stochastic optimization algorithms on the CEC2013 test suite. Furthermore, the DELMFO also is further compared with 10 stochastic optimization algorithms on the CEC2017 test suite. Experimental results show that the DELMFO obtains the competitive performance on the global search capability, convergence speed, and scalability among all the algorithms.

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

Background Vegetation in the Mongolian Plateau is very sensitive to climate change, which has a significant impact on the regulation of terrestrial carbon cycle. Methods We analyzed spatio-temporal changes of both growing season and the seasonal Normalized Difference Vegetation Index (NDVI) using simple linear trend analysis. Besides, correlation analysis was applied to explore the climate factors’ effects on vegetation growth at temporal and spatial scale. Potential effects of human factors on vegetation growth were also explored by residual trend analysis. Results The results indicated that vegetation growth showed a greening trend in the Mongolian Plateau over the past 30 years. At the temporal scale, the growing season NDVI showed an insignificant increasing trend (at a rate of 0.0003 yr−1). At the spatial scale, a large region (53.8% of the whole Mongolian Plateau) with an increasing growing season NDVI, was primarily located in the southern and northern parts of the plateau. The correlation analysis suggested that temperature and precipitation were the main limiting factors that affected vegetation growth in spring and the growing season, respectively. The residual trend analysis showed that human activities primarily stimulated the growth of grasslands and shrublands, while croplands displayed a decreasing trend due to human disturbances, implying that anthropogenic factors may lead to croplands abandonment in favor of grasslands restoration. Our results provided detailed spatial and temporal changes of vegetation growth, and explored how climate and human factors affected vegetation growth, which may offer baseline data and scientific suggestions for local land and resources management, and facilitate the sustainable development of the terrestrial ecosystems.

Ports have been built or expanded in a number of countries to cater to increasing maritime trade in the 21st century. Port expansion is associated with economic and environmental impacts on the local and regional scales, and these impacts can be studied using remote sensing. The present study presents new results from multi-source remote sensing monitoring of the Ajmr Port expansion. An analysis of land use and vegetation coverage at the port is used to monitor the impact of port construction on the local ecology, while changes in roads, buildings, and lights are used to monitor the economic impact. The results show that: (1) After nine years of expansion, the port area has gradually expanded from the central to the southern coastal area, with an increase of 21.68 hectares during the expansion period. After the expansion, the area of builidings and construction in the study area increased significantly, while the area of water and green areas decreased significantly, indicating that the port construction changed the land use structure of the area. (2) From the perspective of vegetation coverage, the vegetation coverage within 5 km from the port is in good condition. After 9 years, the vegetation coverage in the region between 0.6 and 1 increased from 43.71% to 44.25%, reflecting the higher overall greening level in the region. (3) By analyzing the increase in roads and buildings, it can be seen that the port’s comprehensive transportation capacity has improved, the population of the region has increased significantly. As the scale of construction has been continuously expanded , the prosperity as increased. (4) By analyzing the changes in the light index, the light data from the northeast to the southwest in the region is very obvious, and it is clearly located along the coast, indicating that the economic development of the coastal zone is faster than other regions, and the coastal region has promoted the development of the inland region.

Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units.

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects.

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata−Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner.