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As the number of high-density buildings has increased, the management of property with complex condominium ownership has become an ongoing challenge in property registration and management. The three-dimensional (3D) modeling of condominium ownership has emerged as an effective means of meeting this challenge and has attracted great attention from fields such as geographical information science, urban planning and management, and property administration. Much progress has been made in building 3D models of condominium ownership; however, existing studies are all on a case-by-case basis and have left some critical issues unsolved, such as vague ownership boundaries, spatial rights without physical counterparts, and the unfixed spatial extent. The purpose of this study is to construct a 3D building ownership model with multiple levels of detail in the context of Chinese law to overcome the defects of 3D models above. This 3D model is presented in a case study of China by subdividing ownership boundaries based on clarifying the internal structure of condominium ownership, embedding the apportionment mechanism, and integrating the semantics, attributes, and geometry associated with the physical and legal entity of the condominium. The proposed 3D model is implemented by extending Building Information Modeling (BIM) based on the Industry Foundation Classes (IFC) and inheriting legal information from Land Administration Domain Model (LADM). In this study, examples of condominium ownership from three real legal dispute cases in China are analyzed and tested. The study clearly demonstrates that the proposed model can provide a better cognitive understanding of the legal space of property by rendering unambiguous ownership boundaries and presenting the spatial internal structure of ownership, which offers solid technical support for dealing with property registration and many legal dispute cases about condominium ownership.

Abstract Background Hepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients. Methods A total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation. Conclusion In this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.

Lattice structures are crucial for weight reduction, and laser powder bed fusion (LPBF) is an efficient fabrication method. However, there's a notable research gap in understanding the residual deformation of LPBF-fabricated lattice structures. This study investigates the residual deformation of three basic lattice structures, body centre cell (BCC), face centre cell (FCC), and diamond cell (DC), fabricated via LPBF to reveal their deformation mechanisms. Analysis covers horizontal direction, building direction, and total deformations, unveiling complex structural responses. Lower relative densities exhibit a prevalent structural deformation mode (SDM) causing significant deformations, while higher densities shift to an intrinsic deformation mode (IDM) dominated by contraction during fabrication. The FCC structure shows optimal stability with minimal residual deformation across most densities. This study offers insightful findings and a detailed mechanistic analysis of residual deformation in LPBF-fabricated lattice structures, applicable across various configurations, ensuring design process uniformity and reliability.

Background Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.Methods Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.Results 151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).Conclusion The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

Traditional aluminum alloys are unsuitable for structural use above 200 ℃ due to precipitate coarsening or dissolution. Laser powder bed fusion (LPBF) additive manufacturing technique enables fabricating novel aluminum alloys with enhanced high-temperature properties. This study focuses on investigating the mechanical properties and microstructural evolution of a novel LPBF-fabricated Al-Cu-Li-Sc-Zr alloy at elevated temperatures. The microstructure is characterized by nano-scale grains and precipitates. Excellent grain structure and precipitate stability result in superior high-temperature mechanical properties. This study advances additively manufactured aluminum alloy design for potential high-temperature applications, offering valuable insights into their behavior in extreme environments.

Property disputes are frequent occurrences in rapidly urbanizing regions. Among the legal issues concerning building property rights, solar rights disputes are prevalent because text clauses on solar rights rely on diverse measurements or calculations. Moreover, previous studies on measuring solar rights addressed only the sunlight duration and neglected the daylight factor (daylighting coefficient), which is an integral component of solar rights. Therefore, this study proposes a 3D spatiotemporal model for effectively measuring solar rights by taking both the sunlight duration and the daylight factor into consideration. This model would be technologically valuable in avoiding or settling legal disputes. Taking the specifications of the solar rights of building property in China as a case study, the proposed spatiotemporal model clarifies the complete solar rights of residential units and is implemented by integrating a geometric model from CityGML with a property model, namely, the Land Administration Domain Model (LADM). Three real typical cases are adopted to show the applicability and benefit of the 3D spatiotemporal model in resolving legal property disputes.

Support structures are widely used in laser powder bed fusion (LPBF) for overhanging features. However, the majority of works carried out on support modeling were to obtain the best trade-off between production time, cost, and accuracy. A notable absence in the existing literature pertains to investigate support structure modeling for optimizing heat dissipation and residual stresses for improving the support structure, thus, enhancing the final part quality. In this study, the effects of the support structure on residual stress were investigated systematically. The influences of the support on residual stress in LPBF sample were categorized into the effect of the constraints and heat conduction area. The rigidity and area of the support structure are used to describe the effect of the support structure on the residual stress. The results showed that the constrain of LPBF samples mainly depends on their inherent characteristics, with limited impact from external constraints. The residual stress in the samples increased as the support area increased. Therefore, when designing the support, priority should be given to reduce its thermal conductivity area. In addition, the hardness of the support added sample were lower than that of the sample without the support, but the hardness distribution is uniform along the deposition height regardless of whether the support is added or not. Therefore, the introduction of support does not result in performance irregularities along the sample height direction.

Fabricating high strength high conductivity (HSHC) Cu alloy inner parts by laser powder bed fusion (LPBF) and high-strength steel out layers on Cu alloy surfaces by laser-direct energy deposition (LDED), e.g., laser powder hybrid additive manufacturing (LPH-AM), has shown promise in the development of rocket engine thrust chambers with large thrust forces. In this study, we successfully fabricated a QCr0.8 HSHC Cu alloy/S06 stainless steel bimetal structure via In718 multi-interlayer using LPH-AM. We investigated the relative density, defects, microstructure, and interfacial characteristics of the bimetal structure in detail. The results indicate that the sample achieved up to 99.9% relative density with no naked defects and good metallurgical bonding at the QCr0.8/In718 and In718/S06 interfaces. The LPH-AM-ed QCr0.8/In718 interface exhibited a combination of columnar and equiaxed dendrites. The vertically combined sample exhibited an average ultimate tensile strength (UTS) and break elongation (EL) of 300.3 ± 10.6 MPa and 15.0 ± 1.4%, respectively, with fracture occurring at the QCr0.8 side. This study provides a new method for fabricating complex-shaped Cu/steel bimetal components, such as rocket engine thrust chambers.

Ecological security patterns (ESPs) provide an effective spatial approach for identifying critical conservation areas and ensuring regional ecological security. However, prior research has not paid much attention to the importance of the stability of ecological sources in time-series changes, which is especially critical for maintaining ecological functions in ecologically fragile areas. Focusing on the Ningxia Hui Autonomous Region (NHAR) of China, this study evaluated the spatiotemporal change patterns in ecosystem services importance (ESI) from 2000 to 2020, integrating the spatial principal component analysis (SPCA) and circuit theory to propose a novel ESP construction framework that aims to address the issue of insufficient consideration of source stability. A total of 93 stable ecological sources were identified, with the capacity to ensure the continuous provision of high-level ecosystem services and resistance to external disturbances. The extraction of 234 ecological corridors and 430 ecological nodes effectively enhanced the stable flow of ecological processes and connectivity. The stable ESP, constituted by the above ecological elements, can serve as core ecological space and basic skeleton to maintain the regional sustainable landscape. This study provides scientific references for identifying key priority conservation areas and formulating targeted ecological conservation and restoration strategies in ecologically fragile areas.

RNA modification plays an important role in epigenetics at the posttranscriptional level, and 5-methylcytosine (m5C) has attracted increasing attention in recent years due to the improvement in RNA m5C site detection methods. By influencing transcription, transportation and translation, m5C modification of mRNA, tRNA, rRNA, lncRNA and other RNAs has been proven to affect gene expression and metabolism and is associated with a wide range of diseases, including malignant cancers. RNA m5C modifications also substantially impact the tumor microenvironment (TME) by targeting different groups of immune cells, including B cells, T cells, macrophages, granulocytes, NK cells, dendritic cells and mast cells. Alterations in immune cell expression, infiltration and activation are highly linked to tumor malignancy and patient prognosis. This review provides a novel and holistic examination of m5C-mediated cancer development by examining the exact mechanisms underlying the oncogenicity of m5C RNA modification and summarizing the biological effects of m5C RNA modification on tumor cells as well as immune cells. Understanding methylation-related tumorigenesis can provide useful insights for the diagnosis as well as the treatment of cancer.

Abstract. From being described as “non-A, non-B” hepatitis in 1975 and being identified in 1989, to the emergence of direct-acting antiviral drugs (DAAs), knowledge on hepatitis C virus (HCV) has achieved a qualitative leap in recent decades. Although more than 95% of HCV patients can be cured by DAAs, the high detection rate, high treatment cost, and relative high recurrence rate for some subtypes (eg, type 3b) make it still a public health problem worldwide. Due to the widespread availability of DAAs, vaccine research has received relatively little attention. The purpose of this review is to look back to the discovery of the HCV, its life cycle, innate and adaptive immune responses, and the evolution of treatment options for HCV.

Abstract. Hepatitis B virus (HBV) infection is a public health problem that endangers global health and is the leading cause for the occurrence and death due to hepatocellular carcinoma. Although nucleotide analogs are excellent in controlling virus replication, they have little effect on the production, stability, and transcription of covalently closed circular DNA (cccDNA) in infected hepatocytes. Moreover, only a small fraction of patients with chronic hepatitis B are cured by interferon therapy. During HBV infection, HBV-specific B cells and T cells are produced. HBV-specific T cells exert antiviral effects through cell lysis and non-cytolytic effector functions, reducing viral intermediates and cccDNA. In addition, HBV-specific B cells produce antibodies that eliminate HBV-infected liver cells through antibody-dependent cell-mediated cytotoxicity of natural killer cells. They can also bind to the hepatitis B surface antigen on the surface of the virus particle, inducing antibody-dependent phagocytosis by Kupffer cells. These responses could be combined with immunotherapy based on antiviral therapy, which may achieve a complete cure for hepatitis B. However, patients with chronic hepatitis B have immune dysfunctions, which challenges immunotherapy implementation. This review focuses on advances in adaptive immunotherapy for chronic viral hepatitis B.

Abstract Background Acute-on-chronic liver failure (ACLF) is a comprehensive syndrome characterized by an acute deterioration of liver function and high short-term mortality rates in patients with chronic liver disease. Whether plasma soluble urokinase plasminogen activator receptor (suPAR) is a suitable biomarker for the prognosis of patients with ACLF remains unknown. Method A prospective cohort of 282 patients with ACLF from three hospitals in China was included. 88.4% of the group was hepatitis B virus-related ACLF (HBV-related ACLF). Cox regression was used to assess the impact of plasma suPAR and other factors on 30- and 90-day mortality. Reactive oxygen species (ROS) production were detected to explore the role of suPAR in regulating neutrophil function in HBV-related ACLF. Result There was no difference in plasma suPAR levels between HBV-related and non-HBV-related ACLF. Patients with clinical complications had higher suPAR levels than those without these complications. A significant correlation was also found between suPAR and prognostic scores, infection indicators and inflammatory cytokines. Cox’s regression multivariate analysis identified suPAR ≥ 14.7 ng/mL as a predictor for both day 30 and 90 mortality (Area under the ROC curve: 0.751 and 0.742 respectively), independent of the MELD and SOFA scores in patients with ACLF. Moreover, we firstly discovered suPAR enhanced neutrophil ROS production under E.coli stimulation in patients with HBV-related ACLF. Conclusions suPAR was a useful independent biomarker of short-term outcomes in patients with ACLF and might play a key role in the pathogenesis. Trial registration CNT, NCT02965560.

Abstract Recently, long noncoding RNAs (lncRNAs) have attracted great attention from researchers. LncRNAs are non-protein-coding RNAs of more than 200 nucleotides in length. Multiple studies have been published on the relationship between lncRNA expression and the progression of human diseases. LncRNA small nucleolar RNA host gene 4 (SNHG4), a member of the lncRNA SNHG family, is abnormally expressed in a variety of human diseases, including gastric cancer, renal cell carcinoma, glioblastoma, neuroblastoma, prostate cancer, colorectal cancer, osteosarcoma, cervical cancer, liver cancer, lung cancer, non-small-cell lung cancer, neonatal pneumonia, diabetic retinopathy, neuropathic pain, acute cerebral infarction, acute myeloid leukaemia, and endometriosis. In this paper, the structure of SNHG4 is first introduced, and then studies in humans, animal models and cells are summarized to highlight the expression and function of SNHG4 in the above diseases. In addition, the specific mechanism of SNHG4 as a competing endogenous RNA (ceRNA) is discussed. The findings indicate that SNHG4 can be used as a biomarker for disease prognosis evaluation and as a potential target for disease diagnosis and treatment.

Natural killer T cells (NKTs) are an important part of the immune system. Since their discovery in the 1990s, researchers have gained deeper insights into the physiology and functions of these cells in many liver diseases. NKT cells are divided into two subsets, type I and type II. Type I NKT cells are also named iNKT cells as they express a semi-invariant T cell-receptor (TCR) α chain. As part of the innate immune system, hepatic iNKT cells interact with hepatocytes, macrophages (Kupffer cells), T cells, and dendritic cells through direct cell-to-cell contact and cytokine secretion, bridging the innate and adaptive immune systems. A better understanding of hepatic iNKT cells is necessary for finding new methods of treating liver disease including autoimmune liver diseases, alcoholic liver diseases (ALDs), non-alcoholic fatty liver diseases (NAFLDs), and liver tumors. Here we summarize how iNKT cells are activated, how they interact with other cells, and how they function in the presence of liver disease.

Abstract No‐wait flow shop scheduling problems (NWFSPs) are widespread in practical applications. The authors propose a quantum‐inspired cuckoo co‐evolutionary algorithm for the NWFSP to minimize the makespan. There are three algorithm components: quantum solution construction, quantum population evolution, and an improved neighbourhood local search. They generate initial solutions, search solutions, and improve solution qualities, respectively. Parameters of the proposed algorithm are calibrated statistically. The proposal with calibrated parameters is compared with three existing algorithms on Reeves and Taillard benchmark instances with middle scales. Experimental results show that the proposal outperforms the compared algorithms.

Abstract Background Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. Methods HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. Results Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. Conclusions The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

Heart failure (HF), a common disease in adults, accounts for significantly global morbidity and mortality. Due to population aging, therapeutic progression in acute cardiovascular events, the prevalence of HF is increasing, in spite of the efficacy of multiple therapies for HF patients with decreased ejection fraction. Despite great progress in the underlying molecular mechanisms, it remains incompletely clear of the function of competing endogenous RNA (ceRNA) network in HF pathogenesis. Herein, we established an HF-related ceRNA network on the basis of differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs) as well as mRNAs from GSE136547 and GSE124401 datasets. In brief, the ceRNA network composed of 58 mRNA nodes, 5 miRNA nodes, 82 lncRNA nodes as well as 252 edges. In addition, three lncRNAs (KCNQ1OT1, XIST and AC010336) with higher node degrees than other lncRNAs were chosen as hub nodes. At the same time, we have established five subnetwork of miR-17-5p, miR-20b-5p, miR-107, miR-125a-5p and miR-140-5p centered ceRNA. Pathway analysis revealed the enrichment of ceRNA network in cell cycle pathways. Collectively, our research sheds new lights on the essential functions of ceRNA network in HF development, which also suggests possible application of these hub nodes as diagnostic biomarkers as well as therapeutic targets.

Abstract Interferon-alpha (IFN-α) and nucleot(s)ide analogs (NAs) are first-line drugs for the treatment of chronic hepatitis B virus (HBV) infections. Generally, NAs target the reverse transcription of HBV pregenomic RNA, but they cannot eliminate covalently-closed-circular DNA (cccDNA). Although effective treatment with NAs can dramatically decrease HBV proteins and DNA loads, and even promote serological conversion, cccDNA persists in the nucleus of hepatocytes due to the lack of effective anti-cccDNA drugs. Of the medications currently available, only IFN-α can potentially target cccDNA. However, the clinical effects of eradicating cccDNA using IFN-α in the hepatocytes of patients with HBV are not proficient as well as expected and are not well understood. Herein, we review the anti-HBV mechanisms of IFN-α involving cccDNA modification as the most promising approaches to cure HBV infection. We expect to find indications of promising areas of research that require further study to eliminate cccDNA of HBV in patients.

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

Nonalcoholic fatty liver disease (NAFLD) is a class of metabolic-associated liver diseases. Aberrant lipid consumption plays an important role in NAFLD pathogenesis. It has been shown CD1d can bind to multiple different lysophospholipids and associated with NAFLD progression. However, the mechanism of CD1d regulation in NAFLD is not completely understood. In this study, we established a NAFLD mouse model by feeding C57/BL6J mice a high-fat diet (HFD) for 24 weeks. Subsequently, we performed integrated transcriptomics and metabolomics analyses to thoroughly probe the role of CD1d in NAFLD progression. In the present study, we demonstrate that CD1d expression was significantly decreased in our murine model of NAFLD. Additionally, we show CD1d knockdown (CD1d KO) in HFD-fed wild-type (WT) mice induced NAFLD, which resulted in weight gain, exaggerated liver injury, and hepatic steatosis. We uncover the crucial roles of CD1d deficiency results in accumulated lipid accumulation. We further explored the CD1d deficiency in NAFLD regarding the transcriptional landscapes, microbiota environment, metabolomics change, and transcriptomics differences. In conclusion, our data demonstrate CD1d plays an important role in NAFLD pathogenesis and may represent a potential therapeutic target for the further therapy.

Hepatocellular carcinoma (HCC) is a common primary liver cancer with ∼750,000 annual incidence rates globally. PGE2, usually known as a pro-inflammatory cytokine, is over-expressed in various human malignancies including HCC. PGE2 binds to EP receptors in HCC cells to influence tumorigenesis or enhance tumor progression through multiple pathways such as EP1-PKC-MAPK, EP2-PKA-GSK3β, and EP4-PKA-CREB. In the progression of hepatocellular carcinoma, PGE2 can promote the proliferation and migration of liver cancer cells by affecting hepatocytes directly and the tumor microenvironment (TME) through ERK/COX-2/PGE2 signal pathway in hepatic stellate cells (HSC). For the treatment of hepatocellular carcinoma, there are drugs such as T7 peptide and EP1 antagonist ONO-8711 targeting Cox-2/PGE2 axis to inhibit tumor progression. In conclusion, PGE2 has been shown to be a traditional target with pleiotropic effects in tumorigenesis and progression of HCC that could be used to develop a new potential clinical impact. For the treatment study focusing on the COX-PGE2 axis, the exclusive usage of non-steroidal anti-inflammatory agents (NSAIDs) or COX-2-inhibitors may be replaced by a combination of selective EP antagonists and traditional anti-tumoral drugs to alleviate severe side effects and achieve better outcomes.

The influence of the defocusing distance on melt pool morphology, defect, microstructure and mechanical properties of the laser powder bed fusion (L-PBF) of high strength Al–Cu–Mg–Mn alloy was studied. It is found that because of the different laser energy distribution, the defocusing distance has significant effects on melt pool morphology, defects, microstructure and mechanical properties of the L-PBF parts using same process parameters and laser spot size. When defocusing distance in the range of −3.0 to 0 mm, specimens with cracks, fine grains and keyhole or transition melt mode are obtained. When defocusing distance is positive or −3.0 mm, specimens with no cracks, columnar grains and conduction mode are obtained. The best mechanical properties are obtained when defocusing distance is 1.0 mm in this experiment. This paper provides a novel method for tailoring the microstructure and mechanical properties of L-PBF parts.

Deep learning methods for semantic image segmentation can effectively extract geographical features from very high-resolution (VHR) remote sensing images. However, these methods experience over-segmentation in low-level features and a breakdown in the integrity of objects with fixed patch sizes due to the multi-scaled geographical features. In this study, a dual attention mechanism is introduced and embedded into densely connected convolutional networks (DenseNets) to form a dense-global-entropy network (DGEN) for the semantic segmentation of VHR remote sensing images. In the DGEN architecture, a global attention enhancement module is developed for context acquisition, and a local attention fusion module is designed for detail selection. This network presents the improved semantic segmentation performance of test ISPRS 2D datasets. The experimental results indicate an improvement in the overall accuracy (OA), F1, kappa coefficient and mean intersection over union (MIoU). Compared with the DeeplabV3+ and SegNet models, the OA improves by 2.79% and 1.19%; the mean F1 improves by 3.43% and 0.88%; the kappa coefficient improves by 4.04% and 1.82%; and the MIoU improves by 5.22% and 1.47%, respectively. The experiments showed that the dual attention mechanism presented in this study can improve segmentation and maintain object integrity during the encoding-decoding process.

Bone-related diseases seriously affect the lives of patients and carry a heavy economic burden on society. Treatment methods cannot meet the diverse clinical needs of affected patients. Exosomes participate in the occurrence and development of many diseases through intercellular communication, including bone-related diseases. Studies have shown that exosomes can take-up and “package” non-coding RNAs and “deliver” them to recipient cells, thereby regulating the function of recipient cells. The exosomal non-coding RNAs secreted by osteoblasts, osteoclasts, chondrocytes, and other cells are involved in the regulation of bone-related diseases by inhibiting osteoclasts, enhancing chondrocyte activity and promoting angiogenesis. Here, we summarize the role and therapeutic potential of exosomal non-coding RNAs in the bone-related diseases osteoporosis, osteoarthritis, and bone-fracture healing, and discuss the clinical application of exosomes in patients with bone-related diseases.

Background: 5-Methylcytosine (m5C) plays essential roles in hepatocellular carcinoma (HCC), but the association between m5C regulation and immune cell infiltration in HCC has not yet been clarified.Methods: In this study, we analysed 371 patients with HCC from The Cancer Genome Atlas (TCGA) database, and the expression of 13 m5C regulators was investigated. Additionally, gene set variation analysis (GSVA), unsupervised clustering analysis, single-sample gene set enrichment analysis (ssGSEA), correlation analysis, and immunohistochemical (IHC) staining were performed.Results: Among the 371 patients, 41 had mutations in m5C regulators, the frequency of which was 11.26%. Compared with normal hepatic tissues, the expression of m5C regulators with copy number variations (CNVs) expansion was significantly higher than that in HCC tissues. Then, we identified three m5C modification patterns that had obvious tumour microenvironment (TME) cell infiltration characteristics. The prognostic analysis of the three major m5C modification subtypes showed that Cluster-2 had a clear survival advantage over the others. In addition, we found that DNMT1 was highly expressed in tumour tissues compared with normal tissues in a tissue microarray (TMA) and that it was positively correlated with many TME-infiltrating immune cells. High expression of the m5C regulator DNMT1 was related to a poor prognosis in patients with HCC. Furthermore, we developed three distinct Immu-clusters. Importantly, mRNAs related to the transcription of growth factor β (TGF-β)/EMT pathway were significantly up-regulated in Immu-cluster 2, indicating that this cluster is considered to be the immune rejection phenotype. Immu-cluster 3 showed elevated expression of mRNAs related to immune checkpoint genes.Conclusion: Our work revealed the association between m5C modification and immune regulators in the TME. These findings also suggest that DNMT1 has great potential as a prognostic biomarker and therapeutic target for HCC.

Objectives: Dendrobium officinale polysaccharide (DOP) is the main active ingredient in a valuable traditional Chinese medicine, which exerts several pharmacological activities including hepatoprotection and hypoglycemic effects. However, the effects of DOP on obesity-associated insulin resistance (IR) and lipid metabolism remain unknown. This study aimed to investigate the role of DOP in IR and abnormal lipid metabolism in obese mice.Methods: IR models were established using 3T3-L1 adipocytes, C2C12 myocytes, and primary cultured hepatocytes exposed to palmitate acid. After treatment with DOP, insulin-stimulated glucose uptake, glucose release, and AKT phosphorylation was detected. Fasting blood glucose, fasting serum insulin, the glucose tolerance test (GTT), and the insulin tolerance test (ITT) were measured to evaluate IR of obese mice. Lipid analysis was conducted to evaluate the effects of DOP on lipid metabolism in obese mice.Results:In vitro, DOP treatment ameliorated palmitic acid-induced IR in adipocytes, myocytes, and hepatocytes. DOP regulated cellular insulin sensitivity via the peroxisome proliferator-activated receptor-γ (PPAR-γ). Furthermore, administration of DOP significantly reduced the IR and visceral adipose tissue (VAT) inflammation of diet-induced obese (DIO) and the genetically-induced obesity mice (ob/ob) mouse models. In addition, DOP treatment attenuated the high-fat diet (HFD)-induced liver lipid accumulation by reducing liver triglycerides (TG), plasma free fatty acid (FFA), serum cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels, while increasing HDL-C levels.Conclusion: DOP could improve obesity-associated IR and abnormal lipid metabolism through its activities on PPAR-γ, and may serve as a potential therapeutic agent for obesity-associated insulin resistance and lipid metabolism disorder.

Abstract Background and aims Prostaglandin E receptor 2 (EP2) is an immune modulatory molecule that regulates the balance of immunity. Here we investigated the role of EP2 in immune dysregulation in patients with acute-on-chronic liver failure (ACLF). Methods Plasma Progstaglandin E2 (PGE2) levels and EP2 expression on immune cells were determined in blood samples collected from patients with chronic hepatitis B related ACLF(HB-ACLF), patients with chronic hepatitis B (CHB), acute decompensated cirrhosis without ACLF (AD) and healthy controls (HC). Cytokine production, bacterial phagocytosis and reactive oxygen species (ROS) production were detected to explore the role of EP2 in regulating immune cell functions. Results The plasma PGE2 levels were increased and EP2 expression on CD8+ T cells was decreased in HB-ACLF compared with those in controls. The levels of PGE2 and EP2 were associated with systemic inflammation and disease severity. Small molecular chemicals against EP2 increased both cytokine secretion in PBMCs and ROS production in neutrophils and monocytes, but decreased monocytic phagocytosis. By contrast, an EP2-selective agonist reduced the production of a series of cytokines in PBMCs, but increased G-CSF. Conclusion Altered PGE2-EP2 augmented the excessive inflammation of innate and adaptive immune cells in response to LPS or E. coli in HB-ACLF. EP2 might be a new potential target for HB-ACLF treatment.

Space is the most fundamental organizing dimension for information that forms the basic spatial understanding around which all other temporal and semantic details are situated. Various types of web information are present in our daily lives, and spatial content can facilitate the understanding of that information. Hence, many studies have been conducted to extract the implicit spatial location from different web resources, and they have mainly investigated web textual information. However, the existing studies mainly focus on the extraction or simple presentation of the location of the information, while further exploration and visualization of the information through the comprehensive consideration of its spatial, temporal, and semantic dimensions have rarely been performed. Thus, this paper proposes a novel modeling and visualization framework for location-referenced web textual information. The framework applies an information model to structurally extract and resolve spatial content along with the temporal and semantic elements from the location-referenced information items. Based on the constructed information model, the information items are hierarchically clustered and visualized by combining map mashups with cartographic processes and methods. This framework enables users to interactively index and browses individual information items. Furthermore, the association relationships involved in the information set are explored in our work. With knowledge graphs that are automatically generated based on the information model, a high-level understanding of the information set can be obtained by users.

Abstract Background Cirrhosis always goes with profound immunity compromise, and makes those patients easily be the target of pneumonia. Cirrhotic patients with pneumonia have a dramatically increased mortality. To recognize the risk factors of mortality and to optimize stratification are critical for improving survival rate. Methods Two hundred and three cirrhotic patients with pneumonia at a tertiary care hospital were included in this retrospective study. Demographical, clinical and laboratory parameters, severity models and prognosis were recorded. Multivariate Cox regression analysis was used to identify independent predictors of 30-day and 90-day mortality. Area under receiver operating characteristics curves (AUROC) was used to compare the predictive value of different prognostic scoring systems. Results Patients with nosocomial acquired or community acquired pneumonia indicated similar prognosis after 30- and 90-day follow-up. However, patients triggered acute-on-chronic liver failure (ACLF) highly increased mortality (46.4% vs 4.5% for 30-day, 69.6% vs 11.2% for 90-day). Age, inappropriate empirical antibiotic therapy (HR: 2.326 p = 0.018 for 30-day and HR: 3.126 p

A theoretical model has been proposed to predict the horizontal dimensional accuracy of selective laser melting (SLM) in this paper. It is found that the horizontal dimensional deviation of SLM consists of two parts. The first part is related to the mode of track filling and the track width. The effect of heat accumulation on track width is also taken into account in this model. The second part is the solidification shrinkage which is dominated by the temperature history of the tracks of a layer when the material and dimension size are fixed. Then, the SLMed Ti6Al4V parts fabricated to verify this theoretical model has been performed in this paper too. The results show that the predicted and experimental results has a good correspondence. At last, less than 20μm dimension deviation of SLMed thin-wall samples was achieved by pre-compensation using this theoretical model. Keywords: Theoretical model, Horizontal dimensional accuracy, Selective laser melting, Track width, Solidification shrinkage

Background: COVID-19 not only threatened the public's physical health but also brought unbearable psychological pressure, especially for those vulnerable groups like the elderly. However, studies on the psychological status of older adults during this public health emergency remained scant. This study aims to investigate the mental health status among the elderly Chinese population during COVID-19 pandemic and determine the influencing factors of psychological symptoms.Methods: From February 19 to March 19, 2020, an online survey was administered to Chinese older adults using a convenience sampling method. Information on demographic data, health status and other epidemic related factors were collected. Specifically, the study defined the psychological status as five primary disorder–depression, neurasthenia, fear, anxiety, and hypochondria–which were assessed by the Psychological Questionnaire for Emergent Event of Public Health (PQEEPH). Standard descriptive statistics and multiple logistic regression analyses were conducted to analyze the data.Results: Of 1,501 participants recruited from 31 provinces in China, 1,278 were valid for further analysis. Participants' scores on each sub-scale were described in median and interquartile [M(Q)]: depression [0.00 (0.33)], neurasthenia [0.00 (0.40)], fear [1.00 (0.83)], anxiety [0.00 (0.17)], hypochondria [0.00 (0.50)]. Chronic diseases (depression p = 0.001; neurasthenia p < 0.001; fear p = 0.023; anxiety p < 0.001; hypochondria p = 0.001) and the BMI index (depression p = 0.015; neurasthenia p = 0.046; fear p = 0.016; anxiety p = 0.015; hypochondria p = 0.013) had significant impacts on all of the five sub-scales. Specifically, the rural dwellers had a higher level of neurasthenia, fear, and hypochondria. Besides, education level (p = 0.035) and outbreak risk level (p = 0.004) had significant impacts on the depression. Higher household monthly income per capita (p = 0.031), and the community-level entry/exit control (p = 0.011) are factors against anxiety.Conclusions: Most elderly residents reported mild negative emotions during COVID-19 and more attention should be paid to the recognition and alleviation of fear. Our findings also identified factors associated with the mental health status of the elderly, which is of practical significance in the design and implementation of psychological interventions for this vulnerable population during COVID-19 and future emerging diseases.

Osteoporosis is a chronic age-related disease. During aging, bone marrow-derived mesenchymal stem cells (BMSCs) display increased adipogenic, along with decreased osteogenic, differentiation capacity. The aim of the present study was to investigate the effect of calcitonin gene-related peptide (CGRP) on the osteogenic and adipogenic differentiation potential of BMSC-derived osteoblasts. Here, we found that the level of CGRP was markedly lower in bone marrow supernatant from aged mice compared with that in young mice. In vitro experiments indicated that CGRP promoted the osteogenic differentiation of BMSCs while inhibiting their adipogenic differentiation. Compared with vehicle-treated controls, aged mice treated with CGRP showed a substantial promotion of bone formation and a reduction in fat accumulation in the bone marrow. Similarly, we found that CGRP could significantly enhance bone formation in ovariectomized (OVX) mice in vivo. Together, our results suggested that CGRP may be a key regulator of the age-related switch between osteogenesis and adipogenesis in BMSCs and may represent a potential therapeutic strategy for the treatment of age-related bone loss.

Long non-coding RNAs (lncRNAs) regulate gene expression and carcinogenesis. The lncRNA heart and neural crest derivatives expressed transcript 2 antisense RNA 1 (HAND2‑AS1) suppresses tumor growth, and its expression level was lower in tumor tissues than in adjacent normal tissues of most types of human cancers, including non-small cell lung cancer, ovarian cancer, breast cancer, gastric cancer, colorectal cancer, cervical cancer, endometrial cancer, prostate cancer, and esophagus squamous cell carcinoma. However, one study reported that the HAND2‑AS1 expression was upregulated in hepatocellular carcinoma tissues comparing with non-tumor tissues and it promoted tumor development. The aberrant expression of HAND2-AS1 was strongly linked to tumor progression and prognosis. Moreover, HAND2-AS1 was involved in tumor cell proliferation, differentiation, apoptosis, and cellular glucose metabolism. This review summarizes data on the expression profile, functions, underlying mechanism, and clinical value of HAND2-AS1 in cancer. The expression profile of HAND2-AS1 in 33 tumors was evaluated by bioinformatics analysis of The Cancer Genome Atlas.

Emerging evidence has demonstrated that long noncoding RNAs (lncRNAs) play vital roles in tumorigenesis and progression. LncRNAs can participate in various biological processes, such as cell growth, anti-apoptosis functions, migration, and invasion. Cancer susceptibility candidate 15 (CASC15) is a cancer-related lncRNA that has been reported to play opposite roles in the pathogenesis of different types of cancers. Studies have shown that CASC15 is downregulated in ovarian cancer and neuroblastoma, acting mainly as a tumour suppressor, while it is highly expressed and carcinogenic in hepatocellular carcinoma (HCC), lung cancer, tongue squamous cell carcinoma, gastric cancer, colorectal cancer, cervical cancer, and breast cancer. Furthermore, aberrant CASC15 expression is associated with tumorigenesis, progression, and patient outcomes via regulation of target genes and signalling pathways. In this review, we summarize current data concerning the regulatory functions and underlying mechanisms of CASC15 in tumour development. We also highlight its potential clinical utility as a biomarker for early detection or as a therapeutic target in human cancers.

Introduction Gallbladder cancer (GBC), the sixth most common gastrointestinal tract cancer, poses a significant disease burden in China. However, no national representative data are available on the clinical characteristics, treatment and prognosis of GBC in the Chinese population.Methods and analysis The Chinese Research Group of Gallbladder Cancer (CRGGC) study is a multicentre retrospective registry cohort study. Clinically diagnosed patient with GBC will be identified from 1 January 2008 to December, 2019, by reviewing the electronic medical records from 76 tertiary and secondary hospitals across 28 provinces in China. Patients with pathological and radiological diagnoses of malignancy, including cancer in situ, from the gallbladder and cystic duct are eligible, according to the National Comprehensive Cancer Network 2019 guidelines. Patients will be excluded if GBC is the secondary diagnosis in the discharge summary. The demographic characteristics, medical history, physical examination results, surgery information, pathological data, laboratory examination results and radiology reports will be collected in a standardised case report form. By May 2021, approximately 6000 patient with GBC will be included. The clinical follow-up data will be updated until 5 years after the last admission for GBC of each patient. The study aimed (1) to depict the clinical characteristics, including demographics, pathology, treatment and prognosis of patient with GBC in China; (2) to evaluate the adherence to clinical guidelines of GBC and (3) to improve clinical practice for diagnosing and treating GBC and provide references for policy-makers.Ethics and dissemination The protocol of the CRGGC has been approved by the Committee for Ethics of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine (SHEC-C-2019–085). All results of this study will be published in peer-reviewed journals and presented at relevant conferences.Trial registration number NCT04140552, Pre-results.

In addition to playing a pivotal role in cellular energetics and biosynthesis, mitochondrial components are key operators in the regulation of cell death. In addition to apoptosis, necrosis is a highly relevant form of programmed liver cell death. Differential activation of specific forms of programmed cell death may not only affect the outcome of liver disease but may also provide new opportunities for therapeutic intervention. This review describes the role of mitochondria in cell death and the mechanism that leads to chronic liver hepatitis and liver cirrhosis. We focus on mitochondrial-driven apoptosis and current knowledge of necroptosis and discuss therapeutic strategies for targeting mitochondrial-mediated cell death in liver diseases.

For older adults, self-care begins with daily health behaviors (DHB), which refers to a series of basic behaviors beneficial to health in daily life; it is the foundation for promoting health, preventing disease, and maintaining health with or without the support of a healthcare provider. Thus, this study aimed to observe the changes in DHB among older adults when the COVID-19 pan-demic first erupted in China (at the beginning of 2020) and explore the impact factors on self-care routines in daily life. We applied a cross-sectional study among 1256 (83.7%) valid older Chinese from 19 February 2020 to 19 March 2020, the score of DHB changes (mean ± SD, 14.70 ± 2.140; range, 8–18) presented a significant growth (t1256 = 44.636, p < 0.001) during COVID-19. From 3 hierarchical linear regression models, the older Chinese who received a higher education include high school (β = 0.403, 95% CI [0.009, 0.797], p = 0.045) and college degree and above (β = 0.488, 95% CI [0.034, 0.943], p = 0.035), and lived in the eastern China (β = 0.771, 95% CI [0.392, 1.151], p < 0.001) took DHB more frequently. However, the high-risk infection (β = −0.740, 95% CI [−1.248, −0.231], p = 0.004), overweight/obese character (β = −0.265, 95% CI [−0.526, −0.004], p = 0.047), and alcohol consumption (β = −0.350, 95% CI [−0.634, −0.065], p = 0.016) are significant factors in decreasing a senior’s DHB performance. For China, self-care offers a straightforward strategy among the range of measures required to combat COVID-19 and future health threats. In summary, findings in this study can build a foundation for developing healthcare policy and services for the relevant government and departments on prompting DHB and the importance of self-care among the older population.

Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.

Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.

Selective laser melting (SLM) produces a large amount of residual stress which limits its applicability. Despite the increasing number of studies to understand the relationship between the process parameters and the residual stress, the underlying complex mechanisms are yet to be understood. In this work, a 3D finite element model (FEM) is established to investigate the relationship between the process parameters and the residual stress during SLM. When the process parameters are in different ranges, the residual stress behavior is different. The complex relationship between the residual stress and the process parameters could be the reason for the contradictory parametric effects reported in the literature. The magnitude of the parametric effect on the residual stress is obtained. The influence of the scanning speed is stronger than that of the laser power, which is in turn stronger than that of the hatch spacing. These results can bring insights into the understanding of the residual stress during SLM.

Exploring the spatial patterns of COVID-19 transmission and its key determinants could provide a deeper understanding of the evolution of the COVID-19 pandemic. The goal of this study is to investigate the spatial patterns of COVID-19 transmission in different periods in Singapore, as well as their relationship with demographic and built-environment factors. Based on reported cases from 23 January to 30 September 2020, we divided the research time into six phases and used spatial autocorrelation analysis, the ordinary least squares (OLS) model, the multiscale geographically weighted regression (MGWR) model, and dominance analysis to explore the spatial patterns and influencing factors in each phase. The results showed that the spatial patterns of COVID-19 cases differed across time, and imported cases presented a random pattern, whereas local cases presented a clustered pattern. Among the selected variables, the supermarket density, elderly population density, hotel density, business land proportion, and park density may be particular fitting indicators explaining the different phases of pandemic development in Singapore. Furthermore, the associations between determinants and COVID-19 transmission changed dynamically over time. This study provides policymakers with valuable information for developing targeted interventions for certain areas and periods.

With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

Copper/steel bimetal, one of the most popular and typical multi-material components (MMC), processes excellent comprehensive properties with the high strength of steel and the high thermal conductivity of copper alloy. Additive manufacturing (AM) technology is characterized by layer-wise fabrication, and thus is especially suitable for fabricating MMC. However, considering both the great difference in thermophysical properties between copper and steel and the layer-based fabrication character of the AM process, the optimal processing parameters will vary throughout the deposition process. In this paper, we propose an analytical calculation model to predict the layer-dependent processing parameters when fabricating the 07Cr15Ni5 steel on the CuCr substrate at the fixed layer thickness (0.3 mm) and hatching space (0.3 mm). Specifically, the changes in effective thermal conductivity and specific heat capacity with the layer number, as well as the absorption rate and catchment efficiency with the processing parameters are considered. The parameter maps predicted by the model have good agreement with the experimental results. The proposed analytical model provides new guidance to determine the processing windows for novel multi-material components, especially for the multi-materials whose physical properties are significantly different.

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

Vector tile technology is developing rapidly and has received increasing attention in recent years. Compared to the raster tile, the vector tile has shown incomparable advantages, such as flexible map styles, suitability for high-resolution screens and ease of interaction. Recent studies on vector tiles have mostly focused on improving the efficiency on the server side and have overlooked the efficiency on the client side, which affects user experience. Parallel computing provides solutions to this issue. Parallel visualization of vector tiles is a typical example of embarrassing parallelism; thus, estimating the computing times of each tile accurately and decomposing the workload into multiple computing units evenly are key to the parallel visualization of vector tiles. This article adopts mainstream parallel computing and proposes an efficient tile-based parallel method for accelerating geographical feature visualization by building computational weight functions (CWFs) of geographical feature visualizations. The computing time of each vector tile is estimated by the CWF, and an effective workload decomposition strategy is proposed such that the efficiency of vector tile visualization is improved on the client side. Furthermore, a tile-based reconstruction scheme for geographical features is also proposed. Experiments show that the R-squared value of the estimated computing times of vector tiles is 0.914 and that the computational efficiency of the parallel visualization of vector tiles with the proposed workload decomposition strategy is 18.6% higher than that of common parallel visualization. Finally, users can obtain the entire set of features effectively and accurately based on the proposed reconstruction scheme.

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.