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Calibrating an accurate soil organic matter (SOM) satellite mapping model requires a sufficient number of representative satellite samples (including actual ground-based SOM values and corresponding satellite spectral data). However, collecting these samples is challenging due to complex geological environments and inconvenient transportation conditions across large areas. To address this issue, a satellite sample simulation strategy was developed to transform ground-based local-area soil spectral samples into satellite-simulated spectral samples. Subsequently, a transfer learning (TL) approach was implemented to encode the spectra-SOM relationship learned from the satellite-simulated sample set into a basic neural network model. Finally, by fine-tuning this model with only a small number of satellite spectral samples, a robust SOM satellite mapping model was established. The results indicated that a total of 846 satellite-simulated spectral samples were generated, and the differences between the satellite-simulated spectral samples and the satellite spectral samples were minimized. The optimal TL-based SOM satellite mapping model (TL-half model) had an R2 of 0.90 and RPIQ of 4.02, representing a 12.50% improvement over the traditional SOM mapping model (R2 = 0.80; RPIQ = 3.10). The TL-based SOM satellite mapping method proposed in this study offers an effective technical approach for regional SOM monitoring and global carbon storage management.

Abstract Purpose Active radiation skin injury (ARSI) has the highest incidence of acute adverse reactions caused by radiotherapy (RT) in patients with head and neck cancer (HNC). This study aimed to screen risk factors that can facilitate the identification of HNC patients at high risk of ARSI. Methods Data from 255 stage III-IV HNC patients who underwent intensity-modulated radiation therapy (IMRT) were collected. The data from our medical records, including clinical characteristics and hematological indices before RT, were retrospectively collected and arranged. The Common Terminology Criteria for Adverse Events Criteria (CTCAE), Radiation Therapy Oncology Group Criteria (RTOG), World Health Organization Criteria (WHO), Oncology Nursing Society (ONS), Acute Radiation Dermatitis Graduation Scale, Douglas & Fowler and Radiation Dermatitis Severity Scale (RDSS) were used to assess ARSI. Of these, CTCAE was used for further analysis. Binary logistic regression analyses were used to identity risk factors. To establish the correction between each risk factor and the ARSI score, the odds ratio (OR) and 95% confidence interval (CI) were computed. Results The assessment results of the CTCAE with RTOG, WHO, ONS, Graduation Scale, Douglas & Fowler and RDSS have good consistency. After radiotherapy, 18.4% of patients had at least 3 (3 +) grade ARSI. Multivariate logistic regression analysis revealed that the KPS score, blood glucose level, white blood cell count, and plasma free thyroxine (FT4) concentration were independent risk factors for 3 + grade ARSI. A nomogram was constructed on the basis of these risk factors, which demonstrated good predictive power according to the area under the ROC curve (AUC). The satisfactory consistency and clinical efficacy of the nomogram were confirmed by calibration curves and decision curve analysis (DCA). Conclusion A low KPS score, high blood glucose level, high white blood cell count, and high thyroid hormone prior to radiotherapy for stage III-IV HNC are independent risk factors for grade 3 + RSI.

Abstract Urban populations face heightened extreme heat risks attributed to urban heat islands and high population densities. Although previous studies have examined global urban population exposure to heatwaves, the influence of urbanization-induced warming is still not quantified. Here, leveraging satellite-derived near-surface air temperature data, we assess the impacts of urbanization-induced warming on heat exposure in 1028 cities worldwide. Additionally, we investigate its role in shaping disparities in heat exposure between global North and South cities. Our findings reveal that urbanization-amplified compound heatwaves exacerbate heat exposure risk in more than 90% of cities, and that this amplification is stronger in high urbanization areas. Moreover, our analysis highlights the potential for overestimating disparities between global North and South cities if urbanization-induced warming is overlooked. The inequality of higher heat exposure in the global South cities than in the global North cities will be narrowed in real scenarios due to more intense urbanization-induced warming in the global North cities. We emphasize the pivotal role of urbanization-induced heatwave intensification in heat exposure assessments and call for its inclusion in future population vulnerability evaluations to extreme heat.

Sufficient and equitable exposure to blue-green spaces enhances human well-being. Existing studies on people’s exposure to blue-green spaces have not adequately considered the different contributions of various components in the blue-green spaces. We proposed an improved model to estimate people’s exposure to blue-green spaces and inequality by distinguishing the contributions of blue-green space components via ecosystem services value. The results showed that people’s exposure to blue-green spaces in Chinese cities was low (0.067). Spatially, the exposure was higher in the south and lower in the north, while higher in the west and lower in the east. Compared with 2001, the exposure declined by 23% in 2020. Additionally, the spatial distribution and the component configuration of the blue-green spaces were not reasonable. Meanwhile, the exposure among the population exhibited extremely high inequality, with an average Gini coefficient of 0.845. During 2001–2020, the overall inequality continued to worsen (+3%). The intensification trend demonstrated spatial differences, with a more pronounced pattern in eastern cities. Further research confirmed that inequality existed among different genders and age groups. Finally, spatial correlation analysis showed that cities with high exposure and equality tended to be in southern China. Low exposure was more pronounced in northern cities, and inequality was a more pressing issue in southern cities. We hope this study can provide a reference for blue-green space planning and boost the sustainability of cities.

In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, along with the high mobility of vehicles, an unmanned aerial vehicle cannot continuously cover and serve the same vehicle, leading to interruptions in vehicular application services. Therefore, this paper proposes a joint optimization strategy for task migration and power allocation based on soft actor-critic (JOTMAP-SAC). First, communication models, computational resource allocation models, and computation models are established sequentially based on the computational resource and dynamic coordinate of each node. The joint optimization problem of task migration and power allocation is then formulated. Considering the dynamic nature of the unmanned aerial vehicle-assisted internet of vehicles environment and the continuity of the action space, a soft actor-critic based algorithm for task migration and power allocation is designed. This algorithm iteratively finds the optimal solution to the joint optimization problem, thereby reducing the processing delay in unmanned aerial vehicle-assisted internet of vehicles and ensuring the continuity of internet of vehicles task processing.

In this study, an Infrared Small-Target Detection Transformer (IST-DETR) is introduced, a novel model specifically designed to tackle the challenges of low-resolution infrared images and small target scales. IST-DETR integrates a backbone network, a hybrid encoder featuring, a Mutual Feature Screening (MFS) module, and a decoder with auxiliary prediction heads. The hybrid encoder employs Learning Position Encoding to reduce information redundancy and employs a mutual feature screening mechanism to enhance the interaction between high-level semantic features and low-level positional features, facilitating more accurate detection of small infrared targets. What’s more, a customized IoU metric and a novel sample weighting function are employed to effectively address dataset imbalance, significantly improving detection performance. Experiments conducted on the FLIR Dataset, HIT-UAV Dataset, and IVFlying Dataset yielded an average precision (AP) of 44.1%, 34.0%, and 58.5%, respectively, with a processing speed of 74 frames per second. IST-DETR outperforms contemporary algorithms such as Yolov8, CO-DETR, and DINO, demonstrating a superior balance of speed and accuracy, particularly in recognizing small infrared targets across diverse and complex scenarios.

Land surface temperature (LST) is an important parameter for the study related to land–air coupled systems. Satellite-based passive microwave (PMW) sensor is a significant approach to retrieving LSTs, which can penetrate the atmosphere conditions. However, soil moisture is one of the variables affecting PMW brightness temperature, yet few existing methods have considered it in the procedure of retrieving LSTs. On this basis, we propose a retrieval algorithm for PMW-based LST that comprehensively considers soil moisture changes and land scenarios (RA-PLST-SM) to take the soil moisture into account. Additionally, two fusion strategies one of which fuses the landform and soil moisture, and the other further fusing with land cover are proposed to construct the land environment description. Besides, we also propose a fusion strategy to integrate the LSTs from the time-interval-based models of month, quarter, and year. The mean root-mean-squared error (RMSE) referring to MODIS LSTs shows that RA-PLST-SM LSTs gain 3.13 and 2.32 K at day/night time. Also, referring to advanced LST data product, RA-PLST-SM LSTs present a yearly mean STD of 4.12 and 2.48 K at day/night time. Furthermore, the metric results according to six in situ stations demonstrate that RA-PLST-SM LST is significantly enhancing the consistency of actual LSTs, obtaining a mean RMSE of 4.42 K/3.48 K at day/night time. Moreover, the LST fusion strategy proposed in this article can effectively improve the quality of results. The progress of this article can promote the research fields to acquire cloudy LSTs and even provide the technique reference for PMW-based LST retrieval.

The accurate extraction of the installation area of the photovoltaic power station is an important basis for the management of the photovoltaic power generation system. Deep learning has proven to be a powerful tool for rapidly detecting the distribution of photovoltaic panels in remote sensing images. The wealth of information from various remote sensing sensors aids in distinguishing photovoltaic pixels within complex backgrounds. However, the distinct imaging characteristics of different sensors present challenges for deep learning models. In this article, we propose a deep learning extraction method for photovoltaic panels that effectively improves the spatial and spectral differences inherent in remote sensing images. Considering the characteristics of different sensors, two attention modules and a feature fusion module are applied to suppress the inconsistency of spatial resolution and spectral resolution. Based on the Unet model, we implement the photovoltaic power station identification method and compare it with several commonly used semantic segmentation models. Qualitative and quantitative accuracy assessments show that the PV-Unet method can effectively overcome the spatial and spectral differences of remote sensing images. It achieves 98.04% F1 score and 96.15% IoU on the test dataset, verifying the superiority of this method. PV-Unet method has the potential for identifying photovoltaic panels from multisource remote sensing data.

Abstract Background Locally advanced non-small cell lung cancer (NSCLC) with N1/N2 lymph node metastasis is challenging with poor survival. Neo-adjuvant chemo-immunotherapy has gained benefits in a proportion of these patients. However no specific biomarker has been proved to predict the effect before therapy. In addition, the relationship of nodal status and survival after neo-adjuvant chemo-immunotherapy is still not well stated. Methods A total of 75 resectable NSCLC patients with N1/N2 stage who received neo-adjuvant chemo-immunotherapy plus surgery were retrospectively studied. The clinical characteristics, surgical information and safety parameters were collected. The correlations of major pathological response (MPR) and pathological complete response (pCR) with clinical data were analyzed. The progression free disease(PFS) and overall survival(OS) were evaluated with pathological response and nodal status. Results Of the 75 patients, 69 (92%) patients experienced treatment related adverse effects, while grade 3–4 adverse effects occurred in 8 (10%) patients. All the patients received surgical R0 resection with a MPR rate of 60% and a pCR rate of 36%. 67% of N1 patients and 77% of N2 patients had nodal clearance after neo-adjuvant treatment. A significant difference was observed between pathological response with age, histology and multiple lymph node metastasis. The PFS was better in the MPR cohort. The PFS was 90.1% and 83.6% at the nodal clearance group at the time of 12 and 18 months, compared with 70.1% and 63.7% at the nodal residual group. Conclusions The neo-adjuvant chemo-immunotherapy for locally advanced NSCLC with nodal positive was safe and feasible. The patients with elder age and squamous-cell carcinoma (SCC) were more likely to have better pathological response, while multiple nodal metastasis was a negative predictor. The clearance of lymph node resulted in significantly longer PFS and OS.

Identifying the precise moment before the onset of hepatocellular carcinoma (HCC) remains a significant challenge in the medical field. The existing biomarkers fall short of pinpointing the critical point preceding HCC formation. This study aimed to determine the exact tipping point for the transition from cirrhosis to HCC, identify the core Dynamic Network Biomarker (DNB), and elucidate its regulatory effects on HCC. A spontaneous HCC mouse model was established to mimic HCC formation in patients with chronic hepatitis. Using the DNB method, C1q and tumor necrosis factor (TNF) related 1 (C1QTNF1) protein was identified as the key DNB at the crucial tipping time of spontaneous HCC development. Both in vitro and in vivo studies showed that C1QTNF1 could inhibit tumor growth. Overexpression of C1QTNF1 before the tipping point effectively prevented HCC occurrence. Patients with elevated C1QTNF1 expression demonstrated improved overall survival (OS) (P = 0.03) and disease-free survival (DFS) (P = 0.03). The diagnostic value of C1QTNF1 was comparable to that of alpha-fetoprotein (AFP) (area under the curve [AUC] = 0.84; sensitivity 85%; specificity 80%). Furthermore, our research indicated that platelet-expressed C1QTNF1 is involved in cancer-associated signaling pathways. Our findings introduce a novel perspective by highlighting C1QTNF1 as the pivotal biomarker at the tipping point of primary HCC formation using DNB. We propose C1QTNF1 as a prognostic biomarker for HCC, potentially influencing tumor development through a platelet-related cancer signaling pathway.

TetR-family transcriptional regulators are widely distributed among bacteria and involved in various cellular processes such as multidrug and inhibitor resistance. Zymomonas mobilis is a industrial bacterium for lignocellulosic ethanol production. Although TetR-family regulators and their associated RND-family efflux pumps in Z. mobilis have been identified to be differentially expressed under various inhibitors and stressful conditions, there are no systematic investigation yet. In this study, bioinformatic analyses indicated that there are three TetR-family transcriptional regulators (ZMO0281, ZMO0963, ZMO1547) and two RND-family efflux pumps (ZMO0282-0285, ZMO0964-0966) adjacent to corresponding TetR-family regulators of ZMO0281 and ZMO0963 in Z. mobilis. Genetics studies were then carried out with various mutants of TetR-family regulators constructed, and ZMO0281 was characterized to be related to acetate tolerance. Combining transcriptomics and dual-reporter gene system, this study demonstrated that three TetR-family regulators repressed their adjacent genes specifically. Moreover, TetR-family regulator ZMO0281 might also be involved in other cellular processes in the presence of acetate. In addition, the upregulation of RND-family efflux pumps due to ZMO0281 deletion might lead to an energy imbalance and decreased cell growth in Z. mobilis under acetate stress. The systematic investigation of all three TetR-family regulators and their roles on a major lignocellulosic inhibitor acetate tolerance in Z. mobilis thus not only unravels the molecular mechanisms of TetR-family regulators and their potential cross-talks on regulating RND-family efflux pumps and other genes in Z. mobilis, but also provides guidance on understanding the roles of multiple regulators of same family in Z. mobilis and other microorganisms for efficient lignocellulosic biochemical production.

The novel AZ31B/SiCps composite was prepared by wire-arc directed energy deposition (DED-Arc) method. The effects of SiCps content in the range of 1.0–3.0 wt% on grain orientation, average grain size, grain boundary angle distribution, and mechanical properties of AZ31B magnesium alloy were studied. The results showed that SiCps, as a reinforcing phase uniformly dispersed in the matrix, could effectively reduce the free energy of the α-Mg matrix, and promoted non-uniform nucleation as a heterogeneous nucleation point. At the same time, SiCps was also pushed to the grain boundary by the solid-liquid interface, hindering the columnar grain of epitaxial growth, which helped to reduce the subcooling of components at the grain growth front (ΔTcs = ΔTn-min). With the increase of SiCps mass fraction, the transformation of columnar crystals to equiaxed crystals was realized. The average grain size of the deposited specimen and the texture strength of the crystal face of the α-Mg matrix (0001) gradually decreased. Finally, the AZ31B magnesium alloy with a mass fraction of 2.0 wt% SiCps exhibited excellent mechanical properties with a tensile strength of 233.35 MPa and an elongation of 24.04%. The anisotropy of the deposited sample was converted to isotropy.

Conclusions: AF is not only an isolated disease with abnormal electrophysiological activity but might also share a common genetic basis and biological process with tumors and inflammatory diseases as well as cardiovascular diseases. Moreover, the six novel genes inferred from network analysis might help detect the missing AF risk loci.

Background: Circular RNA (circRNA) plays a critical role in tumour progression. Circ-CCT3, a particularly abundant circRNA, was proposed to be involved in tumorigenesis. However, the role of circ-CCT3 in hepatocellular carcinoma remains elusive.Methods: Here, circ-CCT3 (a circRNA derived from exons 3, 4 and 5 of the CCT3 gene, hsa_circ_0004680) was identified by circRNA microarray and validated by qRT-PCR. RNA immunoprecipitation (RIP) was performed to confirm the binding between ALKBH5 along with METTL3 and circ-CCT3. Methylated RNA Immunoprecipitation (MeRIP) was used to detect the N6-methyladenosine (m 2A) levels of circ-CCT3. CircRNAs in vivo precipitation, luciferase reporter assay, biotin-coupled microRNA capture, and fluorescence in situ hybridization were conducted to assess the interaction between circ-CCT3 and miR-378a-3p. The functions of circ-CCT3 in HCC were evaluated both in vitro and in vivo.Results: We demonstrated that circ-CCT3 was highly expressed in HCC which indicated the poor prognosis. Circ-CCT3 expression served as an independent risk factor for overall survival in patients with HCC. Knocking-down of circ-CCT3 inhibited the proliferation, invasion and migration of HCC cells, and angiogenesis of HUVEC. Mechanistically, ALKBH5 and METTL3 could bind and regulate m A-modification of circ-CCT3. Further, circ-CCT3 upregulated the expression of FLT-1 by sponging miR-378a-3p.Conclusions: Circ-CCT3 was significantly up-regulated in HCC and promoted liver cancer development via miR-378a-3p-FLT1 axis. It was also found that circ-CCT3 was under m A-modification mediated by ALKBH5 and METTL3. Our study highlights circ-CCT3 as a potential therapeutic target of HCC treatment, which provides a novel understanding on mechanisms of circRNAs in HCC progression.

A suitable calibration sample set is extremely important to acquire an accurate spectral-based model for estimating soil organic matter (SOM). However, an unrepresentative calibration sample set was frequently collected due to the inappropriate samplings pattern caused by problematic transportation logistics and complex geographic conditions, which resulted in fairly poor generalization and low accuracy of the spectroscopic model. Thus, we hypothesized that a soil sample dataset equivalent to natural soil samples could be prepared under controlled laboratory conditions, and increase the accuracy of spectroscopic estimation of SOM content by use of a coverage assessment method that added laboratory-simulated near-natural samples to the natural samples set in order to enhance the representative sample size and variability of the calibration set. The results showed that the near-natural samples enhanced (NSE) calibration set contained 42 natural soil samples and 28 near-natural soil samples. This set exhibited sufficient coverage and better information integrity within estimators space than the initial calibration set that included 43 natural soil samples. Random forest model based on the NSE calibration set (R2 = 0.90; RPIQ = 4.17) more accurately estimated SOM content than the spectral-based model built with the initial calibration set (R2 = 0.73; RPIQ = 2.32); the SOM chemical compositions (e.g., lipids, polysaccharides, and lignin) and their relative abundance from the laboratory-simulated near-natural soil samples were basically consistent with those of natural soil samples. The inclusion of near-natural soil samples in the calibration set improved the SOM spectral-based estimation model, and was observed to be a practical method. Our results provided a calibration set enhancement strategy that effectively supports spectroscopic estimation model of SOM contents in the case of pattern-biased field samplings at the local scale.

A detailed investigation of the microclimate is beneficial for optimizing the urban inner/spatial pattern to enhance thermal comfort or even reduce heatwave disasters, whereas accurately classifying local climate zones (LCZs) severely restricts analysis of thermal characterization. Generally, deep learning-based approaches are effective for adaptive LCZ mapping, yet they often have poor accuracy because inadequate cascade feature extraction patterns may not adapt to the fuzzy LCZ boundaries produced by intricate urban textures, especially when using large-scale datasets. To address these issues, we propose a novel CNN model in which we design a strategy that incorporates a triple feature fusion pattern to enhance LCZ recognition based on the So2sat LCZ 42 large-scale annotated dataset. The approach connects multilayer cascaded information to participate in category judgment, which avoids the loss of effective feature information via continuous cascade transformation as much as possible. The results show that the overall accuracy and kappa coefficient of the proposed model reach 0.70 and 0.68, respectively, manifesting an improvement of approximately 4.47% and 6.25% over advanced LCZ classification approaches. In particular, the accuracy of the proposed approach improves even further after the fusion structure or layer depth is partially removed or reduced, respectively. Finally, we discuss several items, including the effectiveness of different parameters and cascaded feature fusion patterns, the superiority of multilayer cascade feature fusion, the mapping impact of seasons and cloud cover, and even some other issues in LCZ mapping. This article will facilitate improvements in the research precision of urban thermal environments.

ObjectiveWe aimed to identify the clinical factors associated with lymph node metastasis (LNM) based on ultrasound characteristics and clinical data, and develop a nomogram for personalized clinical decision-making.MethodsA retrospective analysis was performed on 252 patients with papillary thyroid carcinoma (PTC). The patient’s information was subjected to univariate and multivariate logistic regression analyses to identify risk factors. A nomogram to predict LNM was established combining the risk factors. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, cross-validation, decision curve analysis (DCA), and clinical impact curve.ResultsThere are significant differences between LNM and non-LNM groups in terms of age, sex, tumor size, hypoechoic halo around the nodule, thyroid capsule invasion, lymph node microcalcification, lymph node hyperechoic area, peak intensity of contrast (PI), and area under the curve (AUC) of the time intensity curve of contrast (P

Abstract Background Circular RNAs (circRNAs) play a significant role in the initiation and progression of various cancers, including hepatocellular carcinoma (HCC). Circular syntaxin 6 (circSTX6, also known as hsa_circ_0007905) has been identified as a microRNA (miRNA) sponge in pancreatic adenocarcinoma. However, its full range of functions in terms of protein scaffold and translation remain largely unexplored in the context of HCC. Methods The expression of circSTX6 and its encoded protein was examined in HCC tumour tissues. N6‐methyladenosine (m6A) on circSTX6 was verified and quantified by methylated RNA immunoprecipitation (Me‐RIP), RIP and dual luciferase reporter assays. The biological functions of circSTX6 and its encoded protein in HCC were clarified by in vitro and in vivo experiments. Mechanistically, the interaction between circSTX6 and heterogeneous nuclear ribonucleoprotein D (HNRNPD) was investigated by RNA pull‐down, RIP and fluorescence in situ hybridization (FISH)/IF. The regulatory effects of circSTX6 and HNRNPD on activating transcription factor 3 (ATF3) mRNA were determined by mRNA stability and RIP assays. Furthermore, the presence of circSTX6‐encoded protein was verified by mass spectrometry. Results CircSTX6 and its encoded 144 amino acid polypeptide, circSTX6‐144aa, were highly expressed in HCC tumour tissues and served as independent risk factors for overall survival in HCC patients. The expression of circSTX6 was regulated by METTL14 in an m6A‐dependent manner. Functionally, circSTX6 accelerated HCC proliferation and tumourigenicity and reinforced tumour metastasis in vitro and in vivo. Mechanistically, circSTX6 acted as a sponge for HNRNPD protein, facilitating its binding to ATF3 mRNA, consequently promoting ATF3 mRNA decay. Meanwhile, circSTX6‐144aa promoted HCC proliferation, migration and invasion independent of circSTX6 itself. Conclusion Collectively, our study reveals that m6A‐modified circSTX6 drives malignancy in HCC through the HNRNPD/ATF3 axis, while its encoded circSTX6‐144aa contributes to HCC progression independent of circSTX6. CirSTX6 and its encoded protein hold promise as potential biomarkers and therapeutic targets in HCC.

Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.

High-order transverse-mode lasers have important potential application value in many fields. To address the current issue of the limited controllability of modes in high-order transverse-mode lasers, we have designed a self-matching photonic lantern (SMPL). The SMPL is formed by introducing a few-mode fiber into the input fiber array of the traditional photonic lantern. The parameters of the few-mode fiber match those of the tapered few-mode port of the SMPL; thus, it can transmit high-order modes in a closed loop. The designed SMPL exhibits dual-band multiplexing characteristics at 980/1550 nm, manifesting specifically as high-order mode selectivity excitation at 980 nm and mode preservation at 1550 nm. These characteristics have been validated through simulation and preliminary experiments. The SMPL is designed for constructing all few-mode fiber ring cavity lasers, enabling the pumping of the 980 nm fundamental mode to high-order modes and the transmission of multiple high-order transverse-mode lasers at 1550 nm in a closed loop. The proposed SMPL extends the configuration and functionality of the photonic lantern family, offering a flexible and effective approach to facilitate the generation of multiple high-order transverse-mode lasers. The SMPL combined with fiber laser systems could effectively broaden communication channels and enhance communication bandwidth. It also holds significant value in optical sensing, high-resolution imaging, laser micro-processing, and other fields.

During the operational phase of the expressway, a significant challenge arises concerning substantial differential settlement in the transition zone connecting the culvert and the general subgrade, affecting its smoothness. In order to address the issue of abrupt stiffness variations within the transition section and to mitigate the occurrence of differential settlement, a gradient pile–reinforced-concrete slab composite foundation was implemented for the first time within an expressway culvert–subgrade transition section. At the same time, an in situ vibration test was conducted through the SBZ30 vibration exciter to comprehensively understand the vertical dynamic responses in the culvert–subgrade transition section under various axle loads and speed conditions. Furthermore, continuous monitoring was conducted to track the long-term settlement of the roadbed. The findings indicate that the utilization of gradient pile–reinforced-concrete slab composite foundations can significantly mitigate the amplitude of the dynamic response parameters. Moreover, dynamic parameters and attenuation coefficients exhibit a gradual reduction as the depth increases. Dynamic stresses, acceleration, and displacements on the roadbed surface exhibited positive correlations with both the axle weight and vehicle speed. However, at deeper depths, the load weight exerted a more pronounced influence. As the speed rose, acceleration decayed faster, affecting a shallower depth. Conversely, the increased load slowed the acceleration decay. The cumulative deformation of the roadbed and the number of excitations followed exponential function characteristics. Settlement values progressively increased while the settlement rate gradually diminished, eventually reaching a stable state, ultimately stabilizing within 4.7 mm. These research outcomes offer valuable guidance and serve as a reference for the implementation of gradient pile–reinforced-concrete slab composite foundations within the culvert–subgrade transition section.

A self-designed ultrasonic-assisted welding platform was built to improve the poor microstructure and properties of conventional TIG welded ferritic stainless steel. The ultrasonic vibration was transmitted to the weld pool through the base metal in the manner of point–surface contact in the optimal position after calculation. The results show that the coarse columnar grains in the welded joint can be transformed into very fine equiaxed grains under ultrasonic vibration, especially the coarse columnar grains near the fusion line where cliff-like refinement occurs. The maximum grain size in the weld seam is reduced from 420 μm to 260 μm, and the average size is reduced by 60%. At the same time, the grain orientation tends to be harmonized. The microhardness of the welded joint is greatly improved on the whole, and the softening of the heat-affected zone caused by grain coarsening is effectively inhibited. Compared with the welded joints without ultrasonic assistance, the tensile strength and yield strength can be increased by 61 MPa and 47 MPa, respectively, under 130 W ultrasonic vibration. By strengthening the weak part of the welded joint, the weldability and toughness reserve of 441 ferritic stainless steel can be significantly improved.

Genome minimization is an effective way for industrial chassis development. In this study, Zymomonas mobilis ZMNP, a plasmid-free mutant strain of Z. mobilis ZM4 with four native plasmids deleted, was constructed using native type I-F CRISPR-Cas system. Cell growth of ZMNP under different temperatures and industrial effluent of xylose mother liquor were examined to investigate the impact of native plasmid removal. Despite ZMNP grew similarly as ZM4 under different temperatures, ZMNP had better xylose mother liquor utilization than ZM4. In addition, genomic, transcriptomic, and proteomic analyses were applied to unravel the molecular changes between ZM4 and ZMNP. Whole-genome resequencing result indicated that an S267P mutation in the C-terminal of OxyR, a peroxide-sensing transcriptional regulator, probably alters the transcription initiation of antioxidant genes for stress responses. Transcriptomic and proteomic studies illustrated that the reason that ZMNP utilized the toxic xylose mother liquor better than ZM4 was probably due to the upregulation of genes in ZMNP involving in stress responses as well as cysteine biosynthesis to accelerate the intracellular ROS detoxification and nucleic acid damage repair. This was further confirmed by lower ROS levels in ZMNP compared to ZM4 in different media supplemented with furfural or ethanol. The upregulation of stress response genes due to the OxyR mutation to accelerate ROS detoxification and DNA/RNA repair not only illustrates the underlying mechanism of the robustness of ZMNP in the toxic xylose mother liquor, but also provides an idea for the rational design of synthetic inhibitor-tolerant microorganisms for economic lignocellulosic biochemical production.

ObjectivesTo explore the diagnostic efficacy of ultrasound (US), two-dimensional and three-dimensional shear-wave elastography (2D-SWE and 3D-SWE), and contrast-enhanced ultrasound (CEUS) in breast neoplasms in category 4 based on the Breast Imaging Reporting and Data System (BI-RADS) from the American College of Radiology (ACR) and to develop a risk-prediction nomogram based on the optimal combination to provide a reference for the clinical management of BI-RADS 4 breast neoplasms.MethodsFrom September 2021 to April 2022, a total of 104 breast neoplasms categorized as BI-RADS 4 by US were included in this prospective study. There were 78 breast neoplasms randomly assigned to the training cohort; the area under the receiver-operating characteristic curve (AUC), 95% confidence interval (95% CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 2D-SWE, 3D-SWE, CEUS, and their combination were analyzed and compared. The optimal combination was selected to develop a risk-prediction nomogram. The performance of the nomogram was assessed by a validation cohort of 26 neoplasms.ResultsOf the 78 neoplasms in the training cohort, 16 were malignant and 62 were benign. Among the 26 neoplasms in the validation cohort, 6 were malignant and 20 were benign. The AUC values of 2D-SWE, 3D-SWE, and CEUS were not significantly different. After a comparison of the different combinations, 2D-SWE+CEUS showed the optimal performance. Least absolute shrinkage and selection operator (LASSO) regression was used to filter the variables in this combination, and the variables included Emax, Eratio, enhancement mode, perfusion defect, and area ratio. Then, a risk-prediction nomogram with BI-RADS was built. The performance of the nomogram was better than that of the radiologists in the training cohort (AUC: 0.974 vs. 0.863). In the validation cohort, there was no significant difference in diagnostic accuracy between the nomogram and the experienced radiologists (AUC: 0.946 vs. 0.842).ConclusionsUS, 2D-SWE, 3D-SWE, CEUS, and their combination could improve the diagnostic efficiency of BI-RADS 4 breast neoplasms. The diagnostic efficacy of US+3D-SWE was not better than US+2D-SWE. US+2D-SWE+CEUS showed the optimal diagnostic performance. The nomogram based on US+2D-SWE+CEUS performs well.

Abstract The function of the N6-methyladenosine (m6A) methyltransferase RNA-binding motif protein 15 (RBM15) in hepatocellular carcinoma (HCC) has not been thoroughly investigated. Here we determined the clinical value, biological functions, and potential mechanisms of RBM15 in HCC. Expression of RBM15 was identified using tissue microarrays and online databases. A risk-prediction model based on RBM15 was developed and validated. We determined the biological role of RBM15 on HCC cells in vitro and in vivo. RNA sequencing was used to screen candidate targets of RBM15. Subsequently, the m6A dot blot assay, methylated RNA immunoprecipitation qPCR, dual-luciferase reporter assays, RNA decay assay, and RNA immunoprecipitation qPCR were employed to explore the mechanisms of RBM15. Our study showed that RBM15 was highly expressed in HCC, and overexpression of RBM15 indicated a worse outcome. A new nomogram combining RBM15 with age and TNM stage was developed and validated to predict the outcome of HCC patients; our nomogram increased the prediction accuracy of the TNM system. Functionally, RBM15 facilitates the proliferation and invasiveness of HCC. RBM15-mediated m6A modification contributed to a post-transcriptional activation of YES proto-oncogene 1 (YES1) in an insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1)-dependent manner. In addition, YES1 was confirmed as an oncogene in HCC cells by activating the mitogen-activated protein kinase (MAPK) pathway. In conclusion, RBM15-mediated m6A modification might facilitate the progression of HCC via the IGF2BP1–YES1–MAPK axis. RBM15 may be a promising biomarker in the outcome prediction of HCC.

Thermal sharpening expands the application of land surface temperature due to the trade-off between spatial and temporal resolutions. Fixed kernels (FK) are widely applied in two window strategies: local window strategies (LWS) and object-based window strategies (OWS). However, the fixed regression kernel effect on LWS and OWS has rarely been considered in existing studies. Due to the heterogeneity among different windows, it is important to select suitable kernels for each window either in OWS or LWS. This article presents an auto-adjusted kernel (AAK) method to resolve this issue and examines certain simple kernel selection rules in thermal sharpening aiming to balance accuracy and efficiency. We test the AAK method with Landsat 8 data and compare it to the FK method with both OWS and LWS. The results reveal that the AAK method generally performs better than the FK method. Compared to the FK method, the AAK method improves the OWS accuracy by 0.283 K on average at three downscaling ratios, and the accuracy improvement increases with increasing downscaling ratio (from 3 to 9). Especially when the downscaling ratio reaches to 9, there is an evident improvement with 0.425 K of AAK. Moreover, the AAK method enhances the mean LWS accuracy by 0.179 K overall and decreases the difference between OWS and LWS. Furthermore, the AAK method increases the accuracy in specific areas and reduces extreme-value points. These findings indicate the potential of the AAK method in thermal sharpening with OWS and LWS, which resolves kernel selection problems.

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

Opencast iron mine and mine solid waste (OIM-MSW) are closely associated with a wide range of environmental issues, such as destroying soil structure, affecting air quality, and damaging biodiversity. Therefore, accurately grasping the distribution of OIM-MSW is of great significance to monitoring and managing environmental conditions. In order to reduce the impact of background features on the identification of OIM-MSW, we constructed an index based on medium spatial resolution satellite data, and proposed a semiautomatic method to determine the threshold value of the index. Experimental results indicated that: 1) The proposed index (OIM-MSWI) can effectively identify the OIM-MSW of which the OA (overall accuracy) and K (kappa coefficient) value are 0.98 and 0.75, respectively. 2) The index owns satisfying performance in identifying OIM-MSW in different months, and its recognition ability in plant growing season is better than that in the nonplant growing season. 3) Both Landsat 8 images and Sentinel 2 images have the potential to identify OIM-MSW via the proposed index, but the recognition accuracy of Landsat 8 images (OA = 0.98, K = 0.75) is slightly better than Sentinel 2 images (OA = 0.96, K = 0.64). (4) OIM-MSWI is effective in different climatic conditions, with OA and K of 0.98, 0.75 and 0.97, 0.63 in the cases of semiarid and humid zones, respectively. In conclusion, OIM-MSWI is a simple and effective index for identifying OIM-MSW, and can provide support for OIM-MSW monitoring and management.

Abstract Background N6-methyladenosine (m6A) modification is an emerging layer of epigenetic regulation which is widely implicated in the tumorigenicity of hepatocellular carcinoma (HCC), offering a novel perspective for investigating molecular pathogenesis of this disease. The role of AlkB homolog 5 (ALKBH5), one of the m6A demethylases, has not been fully explored in HCC. Here we clarify the biological profile and potential mechanisms of ALKBH5 in HCC. Methods Expression of ALKBH5 and its correlation with clinicopathological characteristics of HCC were evaluated using tissue microarrays and online datasets. And biological effects of ALKBH5 in HCC were determined in vitro and in vivo. Subsequently, methylated RNA immunoprecipitation sequencing (MeRIP-seq) combined with RNA sequencing (RNA-seq), and following m6A dot blot, MeRIP-qPCR, RIP-qPCR or dual luciferase reporter assays were employed to screen and validate the candidate targets of ALKBH5. Results We demonstrated that ALKBH5 was down-regulated in HCC, and decreased ALKBH5 expression was an independent prognostic factor of worse survival in HCC patients. Functionally, ALKBH5 suppressed the proliferation and invasion capabilities of HCC cells in vitro and in vivo. Mechanistically, ALKBH5-mediated m6A demethylation led to a post-transcriptional inhibition of LY6/PLAUR Domain Containing 1 (LYPD1), which could be recognized and stabilized by the m6A effector IGF2BP1. In addition, we identified that LYPD1 induced oncogenic behaviors of tumors in contrast to ALKBH5. Dysregulation of ALKBH5/LYPD1 axis impelled the progression of HCC. Conclusion Our study reveals that ALKBH5, characterized as a tumor suppressor, attenuates the expression of LYPD1 via an m6A-dependent manner in HCC cells. Our findings enrich the landscape of m6A-modulated tumor malignancy, and provide new insights into potential biomarkers and therapeutic targets of HCC treatment.

Abstract Background Acid pretreatment is a common strategy used to break down the hemicellulose component of the lignocellulosic biomass to release pentoses, and a subsequent enzymatic hydrolysis step is usually applied to release hexoses from the cellulose. The hydrolysate after pretreatment and enzymatic hydrolysis containing both hexoses and pentoses can then be used as substrates for biochemical production. However, the acid-pretreated liquor can also be directly used as the substrate for microbial fermentation, which has an acidic pH and contains inhibitory compounds generated during pretreatment. Although the natural ethanologenic bacterium Zymomonas mobilis can grow in a broad range of pH 3.5 ~ 7.5, cell growth and ethanol fermentation are still affected under acidic-pH conditions below pH 4.0. Results In this study, adaptive laboratory evolution (ALE) strategy was applied to adapt Z. mobilis under acidic-pH conditions. Two mutant strains named 3.6M and 3.5M with enhanced acidic pH tolerance were selected and confirmed, of which 3.5M grew better than ZM4 but worse than 3.6M in acidic-pH conditions that is served as a reference strain between 3.6M and ZM4 to help unravel the acidic-pH tolerance mechanism. Mutant strains 3.5M and 3.6M exhibited 50 ~ 130% enhancement on growth rate, 4 ~ 9 h reduction on fermentation time to consume glucose, and 20 ~ 63% improvement on ethanol productivity than wild-type ZM4 at pH 3.8. Next-generation sequencing (NGS)-based whole-genome resequencing (WGR) and RNA-Seq technologies were applied to unravel the acidic-pH tolerance mechanism of mutant strains. WGR result indicated that compared to wild-type ZM4, 3.5M and 3.6M have seven and five single nucleotide polymorphisms (SNPs), respectively, among which four are shared in common. Additionally, RNA-Seq result showed that the upregulation of genes involved in glycolysis and the downregulation of flagellar and mobility related genes would help generate and redistribute cellular energy to resist acidic pH while keeping normal biological processes in Z. mobilis. Moreover, genes involved in RND efflux pump, ATP-binding cassette (ABC) transporter, proton consumption, and alkaline metabolite production were significantly upregulated in mutants under the acidic-pH condition compared with ZM4, which could help maintain the pH homeostasis in mutant strains for acidic-pH resistance. Furthermore, our results demonstrated that in mutant 3.6M, genes encoding F1F0 ATPase to pump excess protons out of cells were upregulated under pH 3.8 compared to pH 6.2. This difference might help mutant 3.6M manage acidic conditions better than ZM4 and 3.5M. A few gene targets were then selected for genetics study to explore their role in acidic pH tolerance, and our results demonstrated that the expression of two operons in the shuttle plasmids, ZMO0956–ZMO0958 encoding cytochrome bc1 complex and ZMO1428–ZMO1432 encoding RND efflux pump, could help Z. mobilis tolerate acidic-pH conditions. Conclusion An acidic-pH-tolerant mutant 3.6M obtained through this study can be used for commercial bioethanol production under acidic fermentation conditions. In addition, the molecular mechanism of acidic pH tolerance of Z. mobilis was further proposed, which can facilitate future research on rational design of synthetic microorganisms with enhanced tolerance against acidic-pH conditions. Moreover, the strategy developed in this study combining approaches of ALE, genome resequencing, RNA-Seq, and classical genetics study for mutant evolution and characterization can be applied in other industrial microorganisms.

Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this article proposes a method combining adjustment from annual temperature circle to reconstruct LST based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with moderate resolution imaging spectroradiometer (MODIS) LST, ground surface temperature, and in situ observations from the Heihe Watershed Allied Telemetry Experimental Research, respectively. In the strategy 1, mean root mean square error (RMSE) of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K, and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K, and 2.87 K at day, and 2.80 K, 3.61 K, and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction.

Abstract Background N6-methyladenosine (m6A) methylation, a well-known modification with new epigenetic functions, has been reported to participate in the tumorigenesis of hepatocellular carcinoma (HCC), providing novel insights into the molecular pathogenesis of this disease. However, as the key component of m6A methylation, Wilms tumor 1-associated protein (WTAP) has not been well studied in HCC. Here we investigated the biological role and underlying mechanism of WTAP in liver cancer. Methods We determined the expression of WTAP and its correlation with clinicopathological features using tissue microarrays and the Cancer Genome Atlas (TCGA) dataset. And we clarified the effects of WTAP on HCC cells using cell proliferation assay, colony formation, Edu assay and subcutaneous xenograft experiments. We then applied RNA sequencing combined with gene expression omnibus (GEO) data to screen candidate targets of WTAP. Finally, we investigated the regulatory mechanism of WTAP in HCC by m6A dot blot assay, methylated RNA immunoprecipitation (MeRIP) assay, dual luciferase reporter assay, RNA immunoprecipitation (RIP) assay and Chromatin immunoprecipitation (ChIP) assay. Results We demonstrated that WTAP was highly expressed in HCC which indicated the poor prognosis, and that WTAP expression served as an independent predictor of HCC survival. Functionally, WTAP promoted the proliferation capability and tumor growth of HCC cells in vitro and in vivo. Furthermore, ETS proto-oncogene 1 (ETS1) was identified as the downstream effector of WTAP. The m6A modification regulated by WTAP led to post-transcriptional suppression of ETS1, with the implication of Hu-Antigen R (HuR) as an RNA stabilizer. Then ETS1 was found to inhibit the progression of HCC and could rescue the phenotype induced by WTAP deficiency. Moreover, WTAP modulated the G2/M phase of HCC cells through a p21/p27-dependent pattern mediated by ETS1. Conclusion We have identified that WTAP is significantly up-regulated in HCC and promotes liver cancer development. WTAP-guided m6A modification contributes to the progression of HCC via the HuR-ETS1-p21/p27 axis. Our study is the first to report that WTAP-mediated m6A methylation has a crucial role in HCC oncogenesis, and highlights WTAP as a potential therapeutic target of HCC treatment.

The methane (CH4)/oxygen (O2) gas supply ratios significantly affect the cell growth and metabolic pathways of aerobic obligate methanotrophs. However, few studies have explored the CH4/O2 ratios of the inlet gas, especially for the CH4 concentrations within the explosion range (5∼15% of CH4 in air). This study thoroughly investigated the molecular mechanisms associated with the impact of different CH4/O2 ratios on cell growth of a model type I methanotroph Methylomicrobium buryatense 5GB1 cultured at five different CH4/O2 supply molar ratios from 0.28 to 5.24, corresponding to CH4 content in gas mixture from 5% to 50%, using RNA-Seq transcriptomics approach. In the batch cultivation, the highest growth rate of 0.287 h–1 was achieved when the CH4/O2 supply molar ratio was 0.93 (15% CH4 in air), and it is crucial to keep the availability of carbon and oxygen levels balanced for optimal growth. At this ratio, genes related to methane metabolism, phosphate uptake system, and nitrogen fixation were significantly upregulated. The results indicated that the optimal CH4/O2 ratio prompted cell growth by increasing genes involved in metabolic pathways of carbon, nitrogen and phosphate utilization in M. buryatense 5GB1. Our findings provided an effective gas supply strategy for methanotrophs, which could enhance the production of key intermediates and enzymes to improve the performance of bioconversion processes using CH4 as the only carbon and energy source. This research also helps identify genes associated with the optimal CH4/O2 ratio for balancing energy metabolism and carbon flux, which could be candidate targets for future metabolic engineering practice.

For decades, optical fiber interferometers have been extensively studied and applied for their inherent advantages. With the rapid development of science and technology, fiber sensors with higher detection sensitivity are needed on many occasions. As an effective way to improve measurement sensitivity, Vernier effect fiber sensors have drawn great attention during the last decade. Similar to the Vernier caliper, the optical Vernier effect uses one interferometer as a fixed part of the Vernier scale and the other as a sliding part of the Vernier scale. This paper first illustrates the principle of the optical Vernier effect, then different configurations used to produce the Vernier effect are classified and discussed. Finally, the outlook for Vernier effect fiber sensors is presented.

China has experienced varying degrees of increase or decrease in daytime and nighttime heatwaves, but studies have mostly been at the site or grid scale, and it remains unclear how daytime and nighttime heatwave events in China vary regionally when spatial scales are considered. Here, we redefine the different types of heatwave events in China from 1979 to 2018 as combined day and night heatwave events (CDNHWEs) and independent daytime (nighttime) heatwave events. Due to more pronounced spatiotemporal characteristics, CDNHWEs are the dominant heatwave events in China. Further analysis of the dominant heatwave events indicates that their increase in intensity is stronger at night than during the day, i.e. for CDNHWEs, the daytime-dominated events are gradually replaced by nighttime-dominated events. Compared to 1979–2003, there has been an increase in dominant heatwave events mainly in the south during the day and a nationwide increase at night since 2004. For CDNHWEs, daytime and nighttime processes are regulated by different mechanisms. During the daytime, fewer clouds enhance solar shortwave radiation, favouring daytime heatwaves; however, the increase in aerosols in northern China suppresses solar shortwave radiation. At night, the increase in humidity allows for increased longwave radiation, which favours the formation of nighttime heatwaves across the country. These findings further demonstrate the regional variability of heatwave hazards experienced in China and that targeted heatwave-mitigation measures should be developed based on regional characteristics.

Urban building morphology has a significant impact on the urban thermal environment (UTE). The sky view factor (SVF) is an important structure index of buildings and combines height and density attributes. These factors have impact on the land surface temperature (LST). Thus, it is crucial to analyze the relationship between SVF and LST in different spatial-temporal scales. Therefore, we tried to use a building vector database to calculate the SVF, and we used remote sensing thermal infrared band to retrieve LST. Then, we analyzed the influence between SVF and LST in different spatial and temporal scales, and we analyzed the seasonal variation, day–night variation, and the impact of building height and density of the SVF–LST relationship. We selected the core built-up area of Beijing as the study area and analyzed the SVF–LST relationship in four periods in 2018. The temporal experimental results indicated that LST is higher in the obscured areas than in the open areas at nighttime. In winter, the maximum mean LST is in the open areas. The spatial experimental results indicate that the SVF and LST relationship is different in the low SVF region, with 30 m and 90 m pixel scale in the daytime. This may be the shadow cooling effect around the buildings. In addition, we discussed the effects of building height and shading on the SVF–LST relationship, and the experimental results show that the average shading ratio is the largest at 0.38 in the mid-rise building area in winter.

Soil salt content (SSC) is normally featured with obvious spatiotemporal variations in arid and semi-arid regions. Space factors such as elevation, temperature, and spatial locations are usually used as input variables for a model to estimate the SSC. However, whether temporal patterns of salt-affected soils (identified as temporal spectral patterns) can indicate the SSC level and be applied as a covariate in a model to estimate the SSC remains unclear. Hence, temporal changes in soil spectral patterns need to be characterized and explored as to their use as an input variable to improve SSC estimates. In this study, a total of 54 field samples and a time-series of Sentinel-2 multispectral images taken at monthly intervals (from October 2017 to April 2018) were collected in the Yinbei area of western China. Then, two-date satellite images were used to quantify significant spectral changes over time using spectral change vector analysis, and four two-date-based index methods were used to characterize soil spectral changes. Lastly, the optimal two-date-based spectral indices and multispectral bands were used as input variables to build the estimation models using a random forest algorithm. Results showed that the two-date-based spectral index could be applied as an input variable to improve the accuracy of SSC estimation at a regional scale. Temporal changes in salt-induced spectral patterns can be indicated by the band difference in the wavelength range from 400 nm to 900 nm. Three two-date-based indices designated as D28a (i.e., the band difference between band 2 from an image acquired in April 2018 and band 8a from an image acquired in December 2017), D22, and D28 were the optimal parameters for characterizing salt-induced spectral changes, which were dominated by the total brightness, chloride, and sulfate accumulation of the soils. The model did not yield satisfactory estimation results (RPD = 1.49) when multispectral bands were used as the input variables. Multispectral bands coupled with two two-date-based indices (D22 and D28a) used as the input variables produced the best estimation result (R2 = 0.92, RPD = 3.27). Incorporating multispectral bands and two-date-based indices into the random forest model provides a remotely-sensed strategy that effectively supports the monitoring of soil salt content.

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements.

Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap.

Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

Daylight factor has long been the predominant metric to evaluate daylight performance. Recently, the profession has moved toward annual dynamic daylight metrics such as useful daylight illuminance and daylight autonomy, which are based on absolute values of time varying daylight illuminance for a period of full year. As opposed to static daylight metrics that only concentrate on individual sky conditions, such as the widely used daylight factor, these metrics provide a more comprehensive way to measure illuminance for a wide range of sun positions and sky conditions. Although there is a growing consensus assigning importance to dynamic daylight metrics, there is no common understanding of how to integrate the preference and behaviour of building occupants in assessing the applicability of these metrics. In fact, it is when these occupancy observations and quantitative measurements are taken together that the importance of daylight performance metrics is fully realized. This study seeks to investigate the extent to which the influence of daylight on behaviour can be predicted, and for this the behaviour investigated is seating preferences of occupants in open plan, hot-desking spaces in two university libraries in Sheffield: Western Bank Library and the Information Commons. The results suggest that the association between daylight and seat choice may not be strong, and that any effect is better associated with daylight factor than with useful daylight illuminance or daylight autonomy.

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research.

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.

Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult to extract the shaded area at the subpixel scale and to connect such areas with the LST at the pixel scale. In this study, we combined the sun angle, building height, building footprint and building occlusion to extract the seasonal spatial distribution of BSs in the central area of Beijing. The effect of BSs on the LST was analyzed using LST retrieved from Landsat-8 thermal infrared sensor data. First, the relationship between the LST patch fragmentation and proportion of BSs in the sample areas was modeled without vegetation. Then, we quantitatively studied the mitigated intensity of the LST in pure impervious surfaces (IS) and vegetation pixels covered by BSs; next, we analyzed the LST sensitivity of these pixels to BSs. The results showed that the existence of BSs influences the fragmentation of the low LST patches strongly from summer to winter. On the other hand, pure IS pixels totally covered by BSs experienced a greater cooling effect, with 3.16 K on 10 July, and the lowest cooling occurred between 14 and 25 December, with a mean of 1.24 K. Without considering the relationship in winter, the LST is nonlinearly correlated to the building shadows ratio (BSR) in pixels, and an approximate 10% increase in the BSR resulted in decreases in the LST of approximately 0.33 K (mean of 16 April and 10 May), 0.37 K (10 July) and 0.24 K (28 September) for pure IS pixels, and 0.18 K, 0.20 K and 0.15 K, respectively, for pure vegetation pixels. Further analysis indicates that the LST of pure IS pixels is more sensitive to BSs than that of vegetation because the self-regulation mechanism of vegetation reduces the cooling effect of BSs. These findings can help urban planners understand the cooling characteristics of BSs and design suitable urban forms to resist urban heat islands (UHIs).

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous.

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the “regression-then-fusion” (R-F) and “fusion-then-regression” (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa.