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The healing of large skin defects remains a significant challenge in clinical settings. The lack of epidermal sources, such as autologous skin grafting, limits full-thickness skin defect repair and leads to excessive scar formation. Skin organoids have the potential to generate a complete skin layer, supporting in-situ skin regeneration in the defect area. In this study, skin organoid spheres, created with human keratinocytes, fibroblasts, and endothelial cells, showed a specific structure with a stromal core surrounded by surface keratinocytes. We selected an appropriate bioink and innovatively combined an extrusion-based bioprinting technique with dual-photo source cross-linking technology to ensure the overall mechanical properties of the 3D bioprinted skin organoid. Moreover, the 3D bioprinted skin organoid was customized to match the size and shape of the wound site, facilitating convenient implantation. When applied to full-thickness skin defects in immunodeficient mice, the 3D bioprinted human-derived skin organoid significantly accelerated wound healing through in-situ regeneration, epithelialization, vascularization, and inhibition of excessive inflammation. The combination of skin organoid and 3D bioprinting technology can overcome the limitations of current skin substitutes, offering a novel treatment strategy to address large-area skin defects.

ABSTRACTAmidst rapid global urbanization, comprehending urban area growth (UAG) dynamics is vital for urban planning and sustainability. Despite ample urban development research, the historical developmental patterns of UAG remain underexplored. Long-term UAG exhibits an initial acceleration followed by deceleration, thus, using 200-years UAG data from the U.S., we meticulously compare the S-shaped curve (Logistics and Gompertz models) with other time-series models, and unveil insights into UAG's intricacies by examining how S-shaped models perform across different urban stages. Notably, the Logistics model emerges as the more accurate modeling tool, boasting an RMSE of 0.019, which surpasses the Gompertz model's 0.032. Moreover, the parameters of the S-curve explicitly describe the fundamental properties of UAG, and we unveil the remarkable stability of the maximum growth rate in the S-shaped model through a thorough parameter analysis, which underscores its role as a reliable and universally applicable assessment tool for UAG. Fundamentally, this meticulously analytical research delves deep into the complexities of the UAG curve, robustly confirming the S-shaped trend as the ‘realistic’ consequence of UAG over time. Importantly, this enduring S-shaped trend remains consistent across historical and contemporary contexts. These findings significantly advance our understanding of UAG dynamics for informed urban planning and development.

Urban form, which refers to the land use patterns and the structure of transport network within a city, indirectly influences carbon dioxide emissions in cities by impacting fossil fuel consumption in the transportation and residential sectors. Previous studies on the relationships between urban form and carbon emissions have mainly focused on general analysis at city level, usually considering urban built-up areas as homogenous units, while ignoring the spatial heterogeneity of functions within urban built-up areas. To fill the gap, this study aims to examine the impact of urban functional form on carbon emissions in 31 major cities in China by introducing urban functional zones data to capture the spatial heterogeneity of functions within cities. Firstly, ODIAC (Open-source Data Inventory for Anthropogenic Carbon dioxide) was preprocessed to obtain gridded carbon dioxide emission data as dependent variables. Secondly, a comprehensive indicator system was established to quantify the functional form of a city in terms of both land use patterns and transport networks structure as independent variables. Thirdly, a scale sensitivity analysis was then conducted to determine the optimal scale for analyzing the impacts. Finally, lasso regression was utilized to investigate the impact of urban functional form on carbon emissions. Experimental results indicate that 1) the grid size of 6 km is the optimal scale for studying the relationships between urban functional form and carbon emissions; 2) the topology of road networks is more important than its size for influencing carbon emissions; 3) specific relationships between land use patterns and carbon emissions has been revealed, highlighting the importance of considering functional heterogeneity when studying the relationship between urban form and carbon emissions, while simultaneously providing significant guidance for the construction of low-carbon cities. These findings not only provide an important reference for future research (e.g., optimal scale), but also further highlight the theoretical and practical significance of urban functional form adjustment in low-carbon and sustainable urban development. The study offers solid theoretical basis for urban planning, construction and management, demonstrating that urban greenhouse gas emissions can be effectively reduced by optimizing urban form planning, and provides reliable decision support for the development of specific optimization schemes.

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan–Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

Urban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and sustainable development. Traditional remote sensing-based methods for mapping UFZs primarily capture the physical attributes of ground objects (such as land cover and spatial patterns) while overlooking the inherent social and economic characteristics, as well as the comprehensiveness, heterogeneity, and scale-dependency. With the rapid development of intelligent sensors, the available geospatial big data, reflecting individual human activities, have greatly increased and enable users to analyze UFZs from both physical and socioeconomic aspects. In this study, we provide a comprehensive review of the existing literature on UFZ mapping using remote sensing and geospatial big data. Specifically, this study summarizes the state of the art from three perspectives: spatial analysis units, representation features derived from multi-source data, and the function classification methods of UFZs. Spatial analysis units encompass regular grids, road blocks, image segmentation units, traffic analysis zones, and buildings. Data features consist of the remote sensing image-derived features (such as visual, spatial pattern, and abstract features) and the geospatial big data-derived features (such as spatial, attribute, and temporal features). For function classification, kernel density estimation, cluster analysis, supervised machine learning, probabilistic topic models, and deep learning methods have been applied. Finally, this study discusses the challenges and limitations of UFZ mapping units, the bias issues of geospatial big data, and the integration of remote sensing and geospatial big data. Meanwhile, future opportunities to these issues and the expansion of functions from 2D to 3D are discussed, in order to formulate an enhanced UFZ mapping framework.

Abstract Background Clear cell renal cell carcinoma (ccRCC) is characterized as one of the most common types of urological cancer with high degrees of malignancy and mortality. Due to the limited effectiveness of existing traditional therapeutic methods and poor prognosis, the treatment and therapy of advanced ccRCC patients remain challenging. Tryptophan metabolism has been widely investigated because it significantly participates in the malignant traits of multiple cancers. The functions and prognostic values of tryptophan metabolism-related genes (TMR) in ccRCC remain virtually obscure. Methods We employed the expression levels of 40 TMR genes to identify the subtypes of ccRCC and explored the clinical characteristics, prognosis, immune features, and immunotherapy response in the subtypes. Then, a model was constructed for the prediction of prognosis based on the differentially expressed genes (DEGs) in the subtypes from the TCGA database and verified using the ICGC database. The prediction performance of this model was confirmed by the receiver operating characteristic (ROC) curves. The relationship of Risk Score with the infiltration of distinct tumor microenvironment cells, the expression profiles of immune checkpoint genes, and the treatment benefits of immunotherapy and chemotherapy drugs were also investigated. Results The two subtypes revealed dramatic differences in terms of clinical characteristics, prognosis, immune features, and immunotherapy response. The constructed 6-gene-based model showed that the high Risk Score was significantly connected to poor overall survival (OS) and advanced tumor stages. Furthermore, increased expression of CYP1B1, KMO, and TDO2 was observed in ccRCC tissues at the translation levels, and an unfavorable prognosis for these patients was also found. Conclusion We identified 2 molecular subtypes of ccRCC based on the expression of TMR genes and constructed a prognosis-related model that may be used as a powerful tool to guide the prediction of ccRCC prognosis and personalized therapy. In addition, CYP1B1, KMO, and TDO2 can be regarded as the risk prognostic genes for ccRCC.

Abstract Background Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions. Methods RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses. Results ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients. Conclusions The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Abstract Background Discoid meniscus (DM) and femoral trochlear dysplasia (FTD) are common knee disorders. Both as congenital malformation, whether there is a connection between them is unclear and the research on their prevalence in the general population is inadequate. This study aimed to investigate the prevalence of FTD and DM in the general population through a large sample size, and to explore the relationship between them. Study design Retrospective study. Methods Patients undergoing knee magnetic resonance imaging (MRI) examinations at our outpatient clinic were screened and 1003 patients were enrolled in DM group with 989 patients in non-DM (NDM) group. The type of DM and FTD was classified with Watanabe classification and Dejour’s classification, respectively. The prevalence of FTD and DM in the general population and the relationship between them were evaluated. Results The prevalence of DM and FTD was 10.0% and 14.5%, respectively. The overall percentage of FTD was higher in DM group (P

The start of the growing season (SOS) plays a pivotal role in the intricate interplay between climate and terrestrial ecosystems. Since the late 1990 s, a slowdown in SOS advancing trend has been documented across numerous regions in the Northern Hemisphere. However, it remains uncertain whether a commensurate slowdown in the SOS advancing trend has manifested in northern temperate and boreal forests. Therefore, our effort is dedicated to unraveling the change in SOS both before and after the late 1990 s and to elucidate the underlying mechanisms causing this phenomenon. Our findings show that despite an overall increase in preseason temperature in northern temperate and boreal forests, an unexpected slowdown in the SOS advancing trend has occurred between 1999 and 2015, primarily due to a decrease in SOS temperature sensitivity. Furthermore, we have identified two distinct effects of preseason precipitation on SOS temperature sensitivity. As preseason precipitation increases, SOS temperature sensitivity experiences an increment of 0.34 days/°C∙10 mm, up to a preseason precipitation threshold of 70 mm, after which it undergoes a reduction at a rate of 0.12 days/°C∙10 mm. Our results provide valuable insights into understanding how SOS responds to ongoing climate warming in northern temperate and boreal forests. In addition, we have, for the first time, identified two distinct regulatory mechanisms governing SOS temperature sensitivity in relation to preseason precipitation before and after the 70 mm threshold, opening new avenues for the development of more accurate phenological models.

Accurately spatiotemporal distribution of water bodies is of great importance in the fields of ecology and environment. Recently, convolutional neural networks (CNN) have been widely used for this purpose due to their powerful features extraction ability. However, the CNN methods have two limitations in extracting water bodies. First, the large variations in both the spatial and spectral characteristics of water bodies require that the CNN-based methods have the ability of extracting multi-scale features and using multi-layer features. Second, collecting enough samples is a difficult problem in the training phase of CNN. Therefore, this paper proposed a multi-scale features extraction network (MSFENet) for water extraction, and its advantages are contributed to two distinct features: (1) scale features extractor (MSFE) is designed to extract multi-layer multi-scale features of water bodies; (2) contrastive learning (CL) is adopted to reduce the sample size requirement. Experimental results show that MSFE can effectively improve the small water body extraction performance, and the CL can significantly improve the extraction accuracy when the training sample size is small. Compared with other methods, MSFENet achieves the highest F1-score and kappa coefficient in two datasets. Furthermore, spectral variability analysis shows that MSFENet is more robust than other neural networks in a spectrum variation scenario.

Low-carbon and efficient city, a sustainable city development mode, requires the land to carry more people and economy, but cause no ecological damage, which is essentially a trade-off among diverse land functional zones (LFZs), e.g. living, productive, and ecological lands, from a land system perspective. Unfortunately, there is little knowledge about how to trade off LFZs for building low-carbon and efficient cities. To fill the gap, the study first proposes a fine-grained LFZ mapping method to sort the land into seven kinds of LFZs based on multimodal geographic data; then, the study defines carbon efficiency (CE) as a reference to evaluate efficient cities considering their economics, society, and ecology; finally, the study presents a cross lasso regression method to explore how LFZ patterns as well as their temporal changes influence CE, based on which LFZ features are optimized at both national and city levels for improving CE and facilitating efficient cities. The proposed methods are verified in 30 major Chinese cities from 2015 to 2020. The experimental results indicate that the 30 cities have different LFZ patterns in area proportion, morphology, and temporal change, where LFZ proportions are master control factors to CE; and LFZ feature trade-off at national level works better than per city, as it can improve CE by 58.5% compared to that in 2020, which increases population carrying capacity by 88.5% and economy producing capacity by 81.0% with no change in carbon emission, or reduces carbon emission by 55.2% by keeping population and economy constant. These findings directly contribute to low-carbon and efficient city planning, and facilitating sustainable city development.

IntroductionThe tumor microenvironment of hepatocellular carcinoma is composed of multiple cells, and the interactive communication between cells drives tumor progression and characterizes the tumor. Communication between cells is mainly achieved through signal transduction between receptor ligands, and the rise of single-cell technology has made it possible to analyze the communication network between cells.MethodsWe applied a train of bioinformatic techniques and in vitro experiments. We analyzed the composition of the microenvironment of liver cancer by combining single-cell sequencing data and transcriptome sequencing data from liver cancer to construct molecular typing and risk models for LRs. Then, we analyzed association of it with prognosis, mutation, KEGG, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB) and drug sensitivity in liver cancer. qPCR and was used to identify SLC1A5 expression in LIHC cell lines and CCK8, transwell and cell colony formation were performed to validate the function of SLC1A5. Meanwhile, we also performed polarization of macrophages.ResultsIn this experiment, we found that liver cancer tissues are rich in immune and mesenchymal cells, and there is extensive signaling between individual cells, so we constructed molecular typing and risk models for LRs. Combining clinical data revealed significant differences in clinical characteristics, prognosis and mutated genes between the molecular typing of receptor-ligand pairs, as well as in sensitivity to drugs; similarly, there were significant prognostic differences between the risk models. There were also notable differences in activated signaling pathways, infiltrating immune cells and immune subtypes. Subsequently, we used siRNA to knock down SLC1A5 in hepatocellular carcinoma cells and found that cell proliferation, migration and invasion were diminished.ConclusionsIn conclusion, our LRs model may become a marker to guide clinical treatment and prognosis.

As basic units of urban areas, urban functional zones (UFZs) are fundamental to urban planning, management, and renewal. UFZs are mainly determined by human activities, economic behaviors, and geographical factors, but existing methods 1) do not fully use multimodal geographic data owing to a lack of semantic modeling and feature fusion of geographic objects and 2) are composed of multiple stages, which lead to the accumulation of errors through multiple stages and increase the mapping complexity. Accordingly, this study designs a multimodal data fusion framework (MDFF) to map fine-grained UFZs end-to-end, which effectively integrates very-high-resolution remote sensing images and social sensing data. The MDFF extracts physical attributes from remote sensing images and models socioeconomic semantics of geographic objects from social sensing data, and then fuses multimodal information to classify UFZs where object semantics guide the fine-grained classification. Experimental results in Beijing and Shanghai, two major cities of China, show that the MDFF greatly improves the quality of UFZ mapping with the accuracy about 5% higher than state-of-the-art methods. The proposed method significantly reduces the complexity of UFZ mapping to complete the urban structure analysis conveniently.

Background: Clear cell renal cell carcinoma (ccRCC) is a common urinary cancer. Although diagnostic and therapeutic approaches for ccRCC have been improved, the survival outcomes of patients with advanced ccRCC remain unsatisfactory. Fatty acid metabolism (FAM) has been increasingly recognized as a critical modulator of cancer development. However, the significance of the FAM in ccRCC remains unclear. Herein, we explored the function of a FAM-related risk score in the stratification and prediction of treatment responses in patients with ccRCC. Methods: First, we applied an unsupervised clustering method to categorize patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets into subtypes and retrieved FAM-related genes from the MSigDB database. We discern differentially expressed genes (DEGs) among different subtypes. Then, we applied univariate Cox regression analysis followed by least absolute shrinkage and selection operator (LASSO) linear regression based on DEGs expression to establish a FAM-related risk score for ccRCC. Results: We stratified the three ccRCC subtypes based on FAM-related genes with distinct overall survival (OS), clinical features, immune infiltration patterns, and treatment sensitivities. We screened nine genes from the FAM-related DEGs in the three subtypes to establish a risk prediction model for ccRCC. Nine FAM-related genes were differentially expressed in the ccRCC cell line ACHN compared to the normal kidney cell line HK2. High-risk patients had worse OS, higher genomic heterogeneity, a more complex tumor microenvironment (TME), and elevated expression of immune checkpoints. This phenomenon was validated in the ICGC cohort. Conclusion: We constructed a FAM-related risk score that predicts the prognosis and therapeutic response of ccRCC. The close association between FAM and ccRCC progression lays a foundation for further exploring FAM-related functions in ccRCC.

Abstract Global population growth and land development are highly imbalanced, marked by 43% of population increase but 150% of built-up area expansion from 1990 to 2018. This results in the widely concerned ghost city phenomenon and runs against the sustainable development goals. Existing studies identify ghost cities by population densities, but ignore the spatial heterogeneity of land carrying capacities (LCC). Accordingly, this study proposes a general concept termed underload city to define cities carrying fewer people and lower economic strength than their LCC. The underload city essentially describes imbalanced human-land relationship and is understood in a broader context than the usually applied ghost city. In this study, very high-resolution satellite images are analyzed to obtain land functional structures, and further combined with population and GDP data to derive LCC. We empirically identify eight underload cities among 81 major Chinese cities, differing from previous findings of ghost cities. Accordingly, the proposed underload city considers heterogeneous human-land relationships when assessing city loads and contributes to sustainable city developments.

Abstract Background Surgical treatment for recurrent patellar dislocation (RPD) could yield good outcomes. While, unsatisfactory recovery still exists in some cases. For all prognostic factors, serum biomarkers have rarely been investigated. This study aimed to evaluate the prognostic value of preoperative serum calcium level, a widely used serum biomarker, in surgical treatment for RPD. Study design Retrospective study. Methods Ninety-nine patients with RPD were enrolled in the study. Preoperative serum calcium was acquired from routinely tested blood 1 day prior to operation. Demographic data, characteristics of RPD, postoperative functional outcomes were obtained. The association between preoperative calcium and postoperative functional outcomes (Kujala, Lysholm, Tegner, IKDC and KOOS score) was determined by correlation analysis and multivariate linear regression analysis. Poor recovery was determined as Kujala score below 80. The receiver operating characteristic (ROC) curve was used to assess the prognostic value of preoperative calcium. Results Patients were followed up for a mean period of 2.45 ± 1.33 years. All clinical scores showed significant improvement at the latest follow-up. Correlation and multivariate linear analyses indicated that serum calcium level was an important factor related with the prognosis of surgical treatment for RPD. According to the ROC curve, the cut-off value for preoperative calcium was 2.225 mmol/L. The clinical outcomes of patients with a preoperative blood calcium

ObjectivesTo develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system.MethodsThe clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong’s test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram.ResultsLogistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice.ConclusionsThe nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system.

Abstract Background Dysfunctional p53 signaling is one of the major causes of hepatocellular carcinoma (HCC) tumorigenesis and development, but the mechanisms underlying p53 inactivation in HCC have not been fully clarified. The role of Krüppel-associated box (KRAB)-type zinc-finger protein ZNF498 in tumorigenesis and the underlying mechanisms are poorly understood. Methods Clinical HCC samples were used to assess the association of ZNF498 expression with clinicopathological characteristics and patient outcomes. A mouse model in which HCC was induced by diethylnitrosamine (DEN) was used to explore the role of ZNF498 in HCC initiation and progression. ZNF498 overexpression and knockdown HCC cell lines were employed to examine the effects of ZNF498 on cellular proliferation, apoptosis, ferroptosis and tumor growth. Western blotting, immunoprecipitation, qPCR, luciferase assays and flow cytometry were also conducted to determine the underlying mechanisms related to ZNF498 function. Results ZNF498 was found to be highly expressed in HCC, and increased ZNF498 expression was positively correlated with advanced pathological grade and poor survival in HCC patients. Furthermore, ZNF498 promoted DEN-induced hepatocarcinogenesis and progression in mice. Mechanistically, ZNF498 directly interacted with p53 and suppressed p53 transcriptional activation by inhibiting p53 Ser46 phosphorylation. ZNF498 competed with p53INP1 for p53 binding and suppressed PKCδ- and p53INP1-mediated p53 Ser46 phosphorylation. In addition, functional assays revealed that ZNF498 promoted liver cancer cell growth in vivo and in vitro in a p53-dependent manner. Moreover, ZNF498 inhibited p53-mediated apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation. Conclusions Our results strongly suggest that ZNF498 suppresses apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation in hepatocellular carcinogenesis, revealing a novel ZNF498-PKCδ-p53INP1-p53 axis in HCC cells that would enrich the non-mutation p53-inactivating mechanisms in HCC.

Land covers provide essential information for understanding and detecting ecosystem, resources, and environmental dynamics. However, they are generally mapped at coarser temporal scales to study the inter-annual changes, while scant attention has been paid to map intra-annual land cover dynamics at finer temporal scales. Moreover, existing studies are still limited in intra-annual land cover mapping with dense satellite image time series (SITS). Accordingly, this study proposed a novel approach to accurately classify dense SITS for mapping intra-annual land cover dynamics. First, dense SITS is segmented at multiple spatiotemporal scales to generate optimal spatiotemporal cubes (ST-cubes), which are chosen as classification units. Second, the ST-cubes based on spectral, textural, spatial, and temporal features are integratively defined and employed in SITS classification. Third, the spatiotemporal context is modeled by a spatiotemporally extended conditional random field model that measures both spatiotemporal features and semantic correlation between geographic objects. Finally, the proposed method is applied to map the intra-annual land cover dynamics. Comparative experiments of SITS classification are carried out between our method and three existing competitors in a suburban area in Beijing, China, with a dense Sentinel-2 SITS. Moreover, based on the classification results, we analyzed the quantitative intra-annual dynamics of land cover. The result shows that our approach achieves significant improvements in classification accuracy over existing methods, indicating the effectiveness and superiority of the proposed method in mapping intra-annual land cover dynamics with dense SITS.

Long non-coding RNA (lncRNA) is a subtype of noncoding RNA that has more than 200 nucleotides. Numerous studies have confirmed that lncRNA is relevant during multiple biological processes through the regulation of various genes, thus affecting disease progression. The lncRNA DRAIC, a newly discovered lncRNA, has been found to be abnormally expressed in a variety of diseases, particularly cancer. Indeed, the dysregulation of DRAIC expression is closely related to clinicopathological features. It was also reported that DRAIC is key to biological functions such as cell proliferation, autophagy, migration, and invasion. Furthermore, DRAIC is of great clinical significance in human disease. In this review, we discuss the expression signature, clinical characteristics, biological functions, relevant mechanisms, and potential clinical applications of DRAIC in several human diseases.

Background: Epithelial–mesenchymal transition (EMT) and the immune microenvironment play important roles in the progression of gastric cancer (GC), but the joint role of both in GC is not clear.Methods: We identified EMT- and immune-related genes (EIRGs), and the molecular subtypes of EIRGs were identified by unsupervised cluster analysis. Then, we constructed an accurate EIRG_score model by using differential genes of molecular subtypes. The correlation of EIRG_score with prognosis, immune infiltration, gene mutation, chemotherapeutic drug sensitivity, and immunotherapy response was comprehensively analyzed. In addition, we investigated the biological function of EIRG_score via in vitro experiments.Results: A total of 808 GC patients were classified into two molecular subtypes, which were enriched in EMT and immune-related biological pathways and significantly correlated with prognosis and immune infiltration. The constructed EIRG_score had an important role in predicting prognosis and immunotherapeutic response. The higher EIRG_score was associated with worse prognosis, higher abundance of immunosuppressive cell infiltration, lower immune checkpoint genes expression, lower tumor mutation burden, microsatellite instability-high, lower chemotherapeutic drug sensitivity, and poorer immunotherapeutic response.Conclusion: EIRG_score may be used as a biomarker to assess prognosis and guide precise treatment.

Background: The function of olfactomedin-like 2B (OLFML2B), as a member of the olfactomedin domain-containing protein family, remains ambiguous, especially in tumors. The current study explores the possible correlation between OLFML2B, prognosis, and immune infiltration in pan-cancer.Methods: We applied a number of bioinformatics techniques to probe the prospective function of OLFML2B, consisting of its association with prognosis, clinicopathology, alteration, GSEA, tumor microenvironment (TME), immune-associated genes, immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity in several cancer types. qPCR and immunohistochemistry were used to identify OLFML2B expression in LIHC cell lines and liver cancer tissues.Results: We discovered that OLFML2B was overexpressed in 14 cancers and positively related to several cancer type prognoses. The expression of OLFML2B was further validated in the LIHC cell lines. OLFML2B expression was bound up with TMB in 13 cancers, MSI in 10 cancers, and TME in almost all cancers. Furthermore, OLFML2B was highly co-expressed with genes encoding immune activators and immune suppressors. We further found that OLFML2B played a role in infiltrating different types of immune cells, such as macrophages and cancer-associated fibroblasts. OLFML2B may influence various cancer and immune-related pathways, such as the PI3K-Akt signaling pathway, ECM–receptor interaction, focal adhesion, and leukocyte transendothelial migration. In addition, OLFML2B may increase drug resistance of binimetinib, cobimentinib, and trametinib.Conclusion: Our outcomes reveal that OLFML2B may act as a prognostic marker and a potential target in immunotherapy for diverse tumors due to its oncogenesis function and immune infiltration.

Kinesin superfamily member 2C (KIF2C) is an essential regulator of the cell cycle and its aberrant expression can promote tumor progression. However, the mechanism of KIF2C in pan-cancer is unclear.Data were obtained from public databases, including The Cancer Genome Atlas (TCGA), UALCAN, TIMER and CellMiner. The data came from public databases such as The Cancer Genome Atlas (TCGA), UALCAN, TIMER, and CellMiner. We analyzed the correlation of KIF2C with expression, prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repairs (MMR), immune infiltration and anticancer drug sensitivity by R language.KIF2C was highly expressed in several tumors and correlated with poor prognosis. KIF2C expression was significantly correlated with TMB, MSI, MMRs, and immune checkpoint genes, and with the level of immune cell infiltration such as tumor-associated macrophage (TAM), cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs) and Tregs. The GO and KEGG results suggest that KIF2C is involved in immune regulation in addition to cell cycle regulation.In addition, KIF2C is associated with DNA methylation, m6A modifications and m7G modifications. Our data suggest that KIF2C is a prognostic biomarker linked to immunosuppression, targeting KIF2C may improve the outcome of immunotherapy. Our findings indicate that KIF2C is a prognostic biomarker associated with immunosuppression, and that targeting KIF2C may improve the outcome of immunotherapy.

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved astonishing performance for semantically labeling very high resolution (VHR) remote sensing images. However, it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results. Consequently, this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model (DeepLabv3+) and object-based image analysis (OBIA), wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images. The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+ network on spectral image and a random forest (RF) classifier on hand-crafted features, respectively. These two predictions are then integrated by Dempster-Shafer (D-S) evidence theory to be fed into an object-constrained higher-order conditional random field (CRF) framework to estimate the final semantic labeling results with the consideration of the spatial contextual information. The proposed method is applied to the ISPRS 2D semantic labeling benchmark, and competitive overall accuracies of 90.6% and 85.0% are achieved for Vaihingen and Potsdam datasets, respectively.

Abstract We extend the Fermi gas approach to a class of ABJM-like necklace quiver theories without parity invariance. The resulting partition function on S 3 retains the form of an Airy function, but now includes a phase that scales as Nk in the large-N limit where k is an overall Chern-Simons level. We demonstrate the presence of this phase both analytically and numerically in the case of a three node quiver.

Numerous long noncoding RNAs (lncRNAs) have been identified as powerful regulators of human diseases. The lncRNA FOXD3-AS1 is a novel lncRNA that was recently shown to exert imperative roles in the initialization and progression of several diseases. Emerging studies have shown aberrant expression of FOXD3-AS1 and close correlation with pathophysiological traits of numerous diseases, particularly cancers. More importantly, FOXD3-AS1 was also found to ubiquitously impact a range of biological functions. This study aims to summarize the expression, associated clinicopathological features, major functions and molecular mechanisms of FOXD3-AS1 in human diseases and to explore its possible clinical applications.

Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping.

In this paper, a comprehensive computational modeling study of the unsteady aerodynamic environment around a warship with a helicopter is performed. An experimental validation exercise is also conducted, comparing computational fluid dynamics (CFD) results of the airwake calculated for a reduced-scale model of the isolated Landing Helicopter Assault (LHA) model with high-quality particle image velocimetry experimental data provided by the NASA AMES Research Center. Comparisons of the results generally obtain agreement, indicating that the CFD numerical method is able to resolve the large-scale turbulent airflow. Building on this, a numerical simulation of a real Robin helicopter, immersed in the unsteady airwakes of a full-scale Amphibious Assault Ship (AAS), is performed. The aerodynamic simulation of the influence on the coupled airflow of warship–helicopter is explored and compared with that of the solitary ship airflow field and the superposition airwakes, where the vortex patterns and pressure on the ship surface, as well as the velocity distribution, are circumvented. As a further step, dynamic landing analysis of the airflow field for a shipborne helicopter is implemented at an important location through the landing path for headwind. The aerodynamic characteristics of a helicopter during a flight deck landing are also explored for the unsteady ship airwakes impacting on rotor force during shipboard landings. In addition, different shipboard landing paths of the helicopter are comparatively investigated for obtaining an optimal landing path decision. The present study demonstrates an effective aerodynamic analysis and robust numerical approach, which creates a solid foundation supporting further alternative evaluations of ship airflow fields.

With the rapid development of nanotechnology, the bio-barcode assay (BCA), as a new diagnostic tool, has been gradually applied to the detection of protein and nucleic acid targets and small-molecule compounds. BCA has the advantages of high sensitivity, short detection time, simple operation, low cost, good repeatability and good linear relationship between detection results. However, bio-barcode technology is not yet fully formed as a complete detection system, and the detection process in all aspects and stages is unstable. Therefore, studying the optimal reaction conditions, optimizing the experimental steps, exploring the multi-residue detection of small-molecule substances, and preparing immuno-bio-barcode kits are important research directions for the standardization and commercialization of BCA. The main theme of this review was to describe the principle of BCA, provide a comparison of its application, and introduce the single-residue and multi-residue detection of macromolecules and single-residue detection of small molecules. We also compared it with other detection methods, summarized its feasibility and limitations, expecting that with further improvement and development, the technique can be more widely used in the field of stable small-molecule and multi-residue detection. Keywords: Bio-barcode assay, Protein, Application, Multi-residue detection of macromolecules, Single-molecule single-residue detection

In this study, a previously unreported 3-((4-(isopropylamino)-6-(methylthio)-1,3,5-triazin-2-yl) amino) butyric acid hapten was designed and synthesized. This maximized the exposure of the antigen-determinant isopropyl of prometryn to the immune system in animals to induce the production of anticipated highly specific anti-prometryn antibodies. The hapten has a molecular weight of 285.37 Da. The compound was confirmed by nuclear magnetic resonance hydrogen spectroscopy (1H NMR), nuclear magnetic resonance carbon spectroscopy (13C NMR), and high-resolution mass spectrometry (HRMS). By using the active ester approach, immunogens and coated antigens were created. Bovine serum albumin (BSA) was used as an immunogen, along with the successfully produced hapten, to immunize mice. The IC50 value of mouse monoclonal anti-prometryn antibody (mAb) 7D4 (the quantity of analyte that generated 50% prometryn inhibition) was 3.9 ng/mL. The anti-prometryn mAb was of the IgG1 subclass. The IC20 (80% binding level (B/B0) of prometryn)-IC80 (20% binding level (B/B0) of prometryn) range of the anti-prometryn monoclonal antibody standard curve working range was 0.9–18.1 ng/mL. The prepared mAb has good characteristics because it can specifically recognize prometryn, and the cross-reaction rates for ametryn, desmetryn, and terbumeton were 34.77%, 18.09%, and 7.64%, respectively. The cross-reaction rate with the other seven triazines was less than 1%. The hapten structure proposed can serve as an additional tool for modulating selectivity in detecting triazines.

Image segmentation is a decisive process in object-based image analysis, while the uncertainty of segmentation scales can significantly influence the results. To resolve this issue, this study proposes a Self-Adaptive Segmentation (SAS) method which bridges the gap between the inherent scale and segmentation scale of each object. Firstly, SAS is defined as a variable-scale segmentation approach, aiming at generating local optimum results. It is then implemented based on Segmentation by Weighted Aggregation (SWA) method and optimized by selfhood scale estimation technique. Secondly, two images derived from WorldView-2 (an urban area) and Landsat-8 (a farmland area) are employed to verify the effectiveness and adaptability of this method. Thirdly, it is further compared with SWA and mean shift method by reference to two evaluations, including an unsupervised evaluation, i.e., Global Score (GS), and a supervised one named Geometry-based comparison. The experimental results indicate the SAS method presented in this study is effective in solving the scale issues in multiscale segmentations. It shows higher accuracy than SWA through assigning an appropriate segmentation scale for each object. Moreover, the segmentation results achieved by SAS method in both study areas are more accurate than those by mean shift method, which demonstrates the stable performance of SAS method across diverse scenes and data sets. This advantage is more obvious in farmland area than in urban scene, according to the accuracy assessment results.

Polyphenol oxidase (PPO) is responsible for the enzymatic browning of fruits and vegetables, and thus has negative effects on the quality of food. In this study, extraction and purification of PPO from water yam was performed successfully, and the characteristics of PPO and inhibitors of enzyme activity were analysed. The results showed that PPO activity improved by 4.58-fold through the DEAE-Sepharose and Superdex G-75 purification technique, which reached electrophoretic purity. The enzyme molecular weight was estimated to be 32 kDa. The optimum pH and temperature values of PPO were 6.0 and 35°C, respectively, and it was not thermostable at higher temperatures. PPO had the highest substrate affinity towards catechol (Km = 27.82 mmol/L and Vmax = 1.464 △A/min). The most effective inhibitor was L-cysteine followed by ascorbic acid and sodium sulfite.

A rapid, ultra-sensitive and simple fluorescence immunoassay (FLISA) on CdSe/ZnS Quantum dots (QDs) for recognition pesticide triazophos have been established. In this assay, the immunoassay uses (CdSe/ZnS) Quantum dots (QDs) as a probe that carries monoclonal antibody (mAb). The free triazophos and OVA-haptens were competed for binding the mAb on the surface of the QDs. Therefore, the concentration of TRIAZ can get by calculation of the detection of fluorescence value. The assay has a linear response in the 10 ng L−1–25 μg L−1 TRIAZ concentration range, and limit of detection (IC10) was 0.508 ng L−1. The recovery rate in the case of spiked samples ranges from 82.6% to 96.6%, and the RSD is

Anti-AMP scFv were prepared by cloning variable region genes (VH and VL) from hybridoma cell line which secretes antibodies against AMP, and phoA gene was cloned from Escherichia coli strain K12 chromosomal DNA. The resulting scFv and phoA gene were inserted into the expression vector pBV220 to express the scFv–AP fusion protein in E. coli strain BL21. The purified scFv-AP fusion protein was used to develop an ic-CLEIA method for detection of AMP. The limit of detection (IC15) was 0.11 ± 0.004 ng/mL and the IC50 was 0.75 ± 0.04 ng/mL, respectively. The assay was 3 times as sensitive as the corresponding ic-ELISA based on the same fusion protein. IC-CLEIA was used to analyse AMP spiked milk samples, and the validation was confirmed by HPLC. Good correlation between ic-CLEIA and HPLC data was obtained (R2 = 0.9934). It indicated that the developed method can apply to effectively monitor food safety.

This study provides the first design and synthetic protocol for preparing highly sensitive and specific atrazine (ATR) monoclonal antibodies (mAbs). In this work, a previously unreported hapten, 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine, was designed and synthesized, which maximally exposed the characteristic amino group ATR to an animal immune system to induce the expected antibody. The molecular weight of the ATR hapten was 259.69 Da, and its purity was 97.8%. The properties of the anti-ATR mAb were systematically characterized. One 9F5 mAb, which can detect ATR, was obtained with an IC50 value (the concentration of analyte that produced 50% inhibition of ATR) of 1.678 µg/L for ATR. The molecular weight for the purified 9F5 mAb was approximately 52 kDa for the heavy chain and 15 kDa for the light chain. The anti-ATR mAb prepared in this study was the IgG1 type. The working range of the standard curve (IC20 (the concentration of analyte that produced 20% inhibition of ATR)—IC80 (the concentration of analyte that produced 80% inhibition of ATR)) was 0.384 to 11.565 µg/L. The prepared anti-ATR mAb had high specificity, sensitivity, and affinity with low cross-reactivity. The prepared anti-ATR mAb could provide the core raw material for establishing an ATR immunoassay.

The Qaidam Basin is a sensitive climate transition zone revealing a wide spectrum of local climates and their variability. In order to obtain an objective and quantitative expression of local climate regions as well as avoid the challenge to pre-define the number of heterogeneous local climates, the ISODATA cluster method is employed to achieve the fine-grained climate divisions of the Qaidam Basin, which can heuristically alter the number of clusters based on the input of monthly temperature and precipitation data. The fine-grained climate classification extends the traditional Köppen climate classification and represents the complex climate transformation processes in terms of fine-grained climate clusters. The following results are observed: (i) The Qaidam Basin is divided into an arid desert basin area and the surrounding alpine mountainous areas. The climate distribution is affected by both the altitude and the dryness ratio, which, employing the Budyko framework, largely characterizes the local energy–water fluxes at the surface and the related vegetation regimes (biomes). The fine-grained climate classification successfully captures their causal relationships and represents them well by the local climates: the climatic spatial differentiation in the mountainous areas is highly consistent with the topography and reveals an elevation-dependent circular distribution from the edges to the center of the basin; the climate heterogeneity within the basin presents a west-to-east meridional distribution due to the combined effect of the mid-latitude westerlies and the Indian monsoon. (ii) The climate gradients are spatially different over the Qaidam Basin. The surrounding mountainous areas have a large climate gradient compared to the inner basin; the southern mountain edge is governed by a more severe climate change than the north-eastern one; and the climate gradient is larger in the eastern than in the western basin. (iii) The lake regions within the basin show an obvious lake effect and reveal a local lake climate. Spatially, a common structure emerges with a dryer-climate zone or watershed embedding a wetter lake-affected area, which appears to migrate eastward becoming stepwise wetter from the very dry center to the wet eastern boundary of the Qaidam basin. This provides a topographically induced insight of the wet climate expansion of initially arid climates and is crucial to improve the Qaidam Basin’s ecological environment. Finally, although this work mainly focuses on the local-scale climates and their variability in the Qaidam Basin, the data-driven cluster methodology for climate refinement is transferable to regional- even global-scale climate studies, which offers broad application prospects.

High-fat diet (HFD) leads to systemic low-grade inflammation, which has been involved in the pathogenesis of diverse metabolic and inflammatory diseases. Colon is thought to be the first organ suffering from inflammation under HFD conditions due to the pro-inflammatory macrophages infiltration, however, the mechanisms concerning the induction of pro-inflammatory phenotype of colonic macrophages remains unclear. In this study, we show that HFD increased the percentage of gram-positive bacteria, especially genus Clostridium, and resulted in the significant increment of fecal deoxycholic acid (DCA), a gut microbial metabolite produced by bacteria mainly restricted to genus Clostridium. Notably, reducing gram-positive bacteria with vancomycin diminished fecal DCA and profoundly alleviated pro-inflammatory macrophage infiltration in colon, whereas DCA-supplemented feedings to vancomycin-treated mice provoked obvious pro-inflammatory macrophage infiltration and colonic inflammation. Meanwhile, intra-peritoneal administration of DCA also elicited considerable recruitment of macrophages with pro-inflammatory phenotype. Mechanistically, DCA dose-dependently promoted M1 macrophage polarization and pro-inflammatory cytokines production at least partially through toll-like receptor 2 (TLR2) transactivated by M2 muscarinic acetylcholine receptor (M2-mAchR)/Src pathway. In addition, M2-mAchR mediated increase of TLR2 transcription was mainly achieved via targeting AP-1 transcription factor. Moreover, NF-κB/ERK/JNK signalings downstream of TLR2 are involved in the DCA-induced macrophage polarization. In conclusion, our findings revealed that high level DCA induced by HFD may serve as an initiator to activate macrophages and drive colonic inflammation, thus offer a mechanistic basis that modulation of gut microbiota or intervening specific bile acid receptor signaling could be potential therapeutic approaches for HFD-related inflammatory diseases.

Advanced hepatocellular carcinoma (HCC) has limited therapeutic options. Immunotherapy is a promising treatment, while sorafenib is a first-line drug-based treatment for advanced HCC. However, the efficacy of sorafenib and immunotherapy in combination, have not been clearly evaluated. Sorafenib treatment has been shown to promote immunosuppression by increasing hypoxia in orthotopic HCC models. Here, we found that sorafenib treatment in mice with orthotopic HCC increased the expression of inhibitor programmed death-ligand 1 (PD-L1) and T-regulatory cells in tumor tissues. We pulsed dendritic cells with exosomes derived from tumor cells (DC-TEX) and found that the number of T-regulatory cells decreased and the number of CD8+T cells increased. However, combining DC-TEX and sorafenib did not prolong survival in these mice. Moreover, we found that the number of PD-1+CD8+T cells significantly increased after DC-TEX treatment. Therefore, we next added PD-1 antibody (PD-1 Ab) to the treatment regimen to block the PD-1/PD-L1 pathway, and found that the exhausted CD8+T cells were restored, without affecting the number of T-regulatory cells. Thus, our data suggest that the combination of DC-TEX and PD-1 Ab enhanced the efficacy of sorafenib, but treatment with either DC-TEX or PD-1 Ab alone, did not.

Background/Aims: The endocannabinoid signalling (ECS) system has been known to regulate glucose homeostasis. Previous studies have suggested that the cannabinoid 2 (CB2) receptor may play a regulatory role on insulin secretion, immune modulation and insulin resistance. Given that diabetes and insulin resistance are attributable to elevated inflammatory tone, we investigated the role of CB2 receptor on glucose tolerance and insulin sensitivity in high-fat diet (HFD)/streptozotocin (STZ)-induced mice. Methods: Diabetes was induced in male ICR mice by HFD/STZ and exposed to a CB2 receptor agonist, SER601, for 2- or 4-weeks via subcutaneous implantation of osmotic minipumps. Glucose and insulin tolerance tests were performed at the end of treatment. Islets were isolated for assessment of β-cell function. Pancreases and skeletal muscles were also obtained for histological analyses. Results: Despite a lack of impact on glucose tolerance, substantial improvement on insulin sensitivity was observed in SER601-treated mice, which could partly be attributed to improved islet β-cell function, shown as increased glucose-induced insulin secretion and insulin content. No changes on islet macrophage infiltration or skeletal muscle fat deposition were detectable from SER601-treated mice. However, a major decrease in body weight was recorded at the end of 4-week SER601 exposure, accompanied by a lack of epididymal adipose mass in SER601-treated mice. Conclusion: Our data suggest a lipolytic role of SER601 in HFD/STZ-induced diabetic mice, which results in significant improvement of systemic insulin sensitivity. Thus, the CB2 receptor may be considered a promising target for therapeutic development against insulin resistance and obesity-related diabetes.

The fast and accurate creation of land use/land cover maps from very-high-resolution (VHR) remote sensing imagery is crucial for urban planning and environmental monitoring. Geographic object-based image analysis methods (GEOBIA) provide an effective solution using image objects instead of individual pixels in VHR remote sensing imagery analysis. Simultaneously, convolutional neural networks (CNN) have been widely used in the image processing field because of their powerful feature extraction capabilities. This study presents a patch-based strategy for integrating deep features into GEOBIA for VHR remote sensing imagery classification. To extract deep features from irregular image objects through CNN, a patch-based approach is proposed for representing image objects and learning patch-based deep features, and a deep features aggregation method is proposed for aggregating patch-based deep features into object-based deep features. Finally, both object and deep features are integrated into a GEOBIA paradigm for classifying image objects. We explored the influences of segmentation scales and patch sizes in our method and explored the effectiveness of deep and object features in classification. Moreover, we performed 5-fold stratified cross validations 50 times to explore the uncertainty of our method. Additionally, we explored the importance of deep feature aggregation, and we evaluated our method by comparing it with three state-of-the-art methods in a Beijing dataset and Zurich dataset. The results indicate that smaller segmentation scales were more conducive to VHR remote sensing imagery classification, and it was not appropriate to select too large or too small patches as the patch size should be determined by imagery and its resolution. Moreover, we found that deep features are more effective than object features, while object features still matter for image classification, and deep feature aggregation is a critical step in our method. Finally, our method can achieve the highest overall accuracies compared with the state-of-the-art methods, and the overall accuracies are 91.21% for the Beijing dataset and 99.05% for the Zurich dataset.

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

The spectral uncertainty refers to the diversity and variations of spectral characteristics within a single geographic object or across different objects of the same class. Usually, existing methods represent the spectral characteristics as precise single-valued curves. Thus, the spectral variations cannot be modeled, which further restricts the analysis and classification performance of remote sensing images. On the other hand, unsupervised methods have poor performance in classification and modeling uncertainty, while supervised methods need a large number of samples with high quality. Fuzzy semi-supervised clustering (FSSC) methods achieve a high accuracy with limited labelled samples. Thus, currently, FSSC methods attract more and more attention. This paper proposes a novel method to model the spectral uncertainty for very-high-resolution (VHR) images based on interval type-2 fuzzy sets (IT2 FSs), namely the hierarchical semi-supervising and weighted interval type-2 fuzzy c-means for objects (hierarchical SSW-IT2FCM-O) clustering method. In this method, the VHR image is segmented into image objects to reduce spectral uncertainty within objects. Spectral values, spectral indices and textures were weighted for object-based image classification. To further reduce spectral uncertainty across different objects of the same class, the spectral characteristics of land cover types were represented as banded curves with certain widths instead of precise single-valued spectral curves. The experimental results show that the banded spectral curves produced by the hierarchical SSW-IT2FCM-O can effectively model the spectral uncertainty of geographic objects. From the perspective of classification, four typical validity indices along with the confusion matrix and kappa coefficient were used to test the effectiveness of the hierarchical SSW-IT2FCM-O method, and these indices show that the presented method SSW-IT2FCM-O has greater classification accuracy than the existing FSSC methods and, more importantly, it requires smaller training samples than the existing methods.

Background: High stakes medical licensing programs are planning to augment and adapt current examinations to be relevant for a two-decision point model for licensure: entry into supervised practice and entry into unsupervised practice. Therefore, identifying which skills should be assessed at each decision point is critical for informing examination development, and gathering input from residency program directors is important. Methods: Using data from previously developed surveys and expert panels, a web-delivered survey was distributed to 3,443 residency program directors. For each of the 28 procedural and 18 advanced communication skills, program directors were asked which clinical skills should be assessed, by whom, when, and how. Descriptive statistics were collected, and Intraclass Correlations (ICC) were conducted to determine consistency across different specialties. Results: Among 347 respondents, program directors reported that all advanced communication and some procedural tasks are important to assess. The following procedures were considered ‘important’ or ‘extremely important’ to assess: sterile technique (93.8%), advanced cardiovascular life support (ACLS) (91.1%), basic life support (BLS) (90.0%), interpretation of electrocardiogram (89.4%) and blood gas (88.7%). Program directors reported that most clinical skills should be assessed at the end of the first year of residency (or later) and not before graduation from medical school. A minority were considered important to assess prior to the start of residency training: demonstration of respectfulness (64%), sterile technique (67.2%), BLS (68.9%), ACLS (65.9%) and phlebotomy (63.5%). Discussion: Results from this study support that assessing procedural skills such as cardiac resuscitation, sterile technique, and phlebotomy would be amenable to assessment at the end of medical school, but most procedural and advanced communications skills would be amenable to assessment at the end of the first year of residency training or later. Conclusions: Gathering data from residency program directors provides support for developing new assessment tools in high-stakes licensing examinations.

Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities—Beijing, Putian, and Zhuhai—and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts.