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Studies on immune checkpoint inhibitors targeting B7-CD28 family pathways in esophageal squamous cell carcinoma (ESCC) have shown promising results. However, a comprehensive understanding of B7-CD28 family members in ESCC is still limited. This study aimed to construct a novel B7-CD28 family-based prognosis system to predict survival in patients with ESCC. We collected 179 cases from our previously published microarray data and 86 cases with qPCR data. Specifically, 119 microarray data (GSE53624) were used as a training set, whereas the remaining 60 microarray data (GSE53622), all 179 microarray data (GSE53625) and an independent cohort with 86 qPCR data were used for validation. The underlying mechanism and immune landscape of the system were also explored using bioinformatics and immunofluorescence. We examined 13 well-defined B7-CD28 family members and identified 2 genes (ICSOLG and HHLA2) with the greatest prognostic value. A system based on the combination HHLA2 and ICOSLG (B7-CD28 signature) was constructed to distinguish patients as high- or low-risk of an unfavorable outcome, which was further confirmed as an independent prognostic factor. As expected, the signature was well validated in the entire cohort and in the independent cohort, as well as in different clinical subgroups. The signature was found to be closely related to immune-specific biological processes and pathways. Additionally, high-risk group samples demonstrated high infiltration of Tregs and fibroblasts and distinctive immune checkpoint panels. Collectively, we built the first, practical B7-CD28 signature for ESCC that could independently identify high-risk patients. Such information may help inform immunotherapy-based treatment decisions for patients with ESCC.

The optimal mode of neoadjuvant treatment for esophageal squamous cell carcinoma (ESCC) has not been well characterized. Our study compared neoadjuvant chemotherapy (NCT) with neoadjuvant chemoradiotherapy (NCRT) for patients with ESCC. Data from ESCC patients receiving NCRT or NCT combined with esophagectomy between 2010 and 2018 from the National Cancer Center in China were retrospectively collected. Long-term survival, pathological response, and perioperative mortality and morbidity were compared between the NCRT and NCT groups. A Cox proportional hazards model and propensity score matching (PSM) were used to minimize bias due to potential confounding. Out of 327 eligible patients with ESCC in our study, 90 patients were identified in each group by PSM. The complete pathologic response (pCR) rate in the NCRT group was markedly higher than that in the NCT group (before PSM: 35.1% vs. 6.0%; after PSM: 38.9% vs. 5.6%; both P

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N

The prevalence of early-stage lung adenocarcinoma (LUAD) has increased alongside increased implementation of lung cancer screenings. Robust discrimination criteria are urgently needed to identify those patients who might benefit from additional systemic therapy. Here, to develop a reliable, individualized immune gene-set-based signature to predict recurrence in early-stage LUAD, a novel recurrence-associated immune signature was identified using a least absolute shrinkage and selection operator model, and a stepwise Cox proportional hazards regression model with a training set comprised of 338 early-stage LUAD samples form TCGA, which was subsequently validated in 226 cases from GSE31210 and an independent set of 68 frozen tumor samples with qRT-PCR data. This new classification system remained strongly predictive of prognoses across clinical subgroups and mutation status. Further analysis revealed that samples from high-risk cases were characterized by active interferon signal transduction, distinctive immune cell proportions and immune checkpoint profiles. Moreover, the signature was identified as an independent prognostic factor. In conclusion, the signature is highly predictive of recurrence in patients with early-stage LUAD, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.

Tumor suppressor candidate 3 (TUSC3) is a coding gene responsible for N-glycosylation of many critical proteins. TUSC3 gene plays an oncogenic role in colorectal cancer (CRC), however, the role of TUSC3 in drug resistance of CRC is still unclear. The aim of this study is to investigate the biological function and molecular mechanism of TUSC3 in CRC drug resistance. The expression of TUSC3 in CRC is positively correlated to tumor stage in 90 paired clinical samples, and negatively associated with overall survival and disease-free survival of CRC patients. In vitro, TUSC3 promotes the formation of stemness and induces the drug resistance to 5-fluorouracil and cis-dichlorodiammineplatinum(II) in CRC cells. The tissue microarray assay and bioinformatic analysis indicate that TUSC3 may promote the expression of CD133 and ABCC1 via Hedgehog signaling pathway. Treatment of Hedgehog signaling pathway agonist or inhibitor in TUSC3-silenced or TUSC3-overexpressed cells reverse the effects of TUSC3 in cellular stemness phenotype and drug resistance. Meanwhile, coimmunoprecipitation and immunofluorescence assays indicate a tight relationship between TUSC3 and SMO protein. Our data suggest that TUSC3 promotes the formation of cellular stemness and induces drug resistance via Hedgehog signaling pathway in CRC.

Owing to the paucity of specimens, progress in identifying prognostic and therapeutic biomarkers for small cell lung cancer (SCLC) has been stagnant for decades. Considering that the costimulatory molecules are essential elements in modulating immune responses and determining therapeutic response, we systematically revealed the expression landscape and identified a costimulatory molecule-based signature (CMS) to predict prognosis and chemotherapy response for SCLCs for the first time. We found T cell activation was restrained in SCLCs, and costimulatory molecules exhibited widespread abnormal genetic alterations and expression. Using a LASSO Cox regression model, the CMS was built with a training cohort of 77 cases, which successfully divided patients into high- or low-risk groups with significantly different prognosis and chemotherapy benefit (both P

Power transition is the top priority in energy transition. All existing power transition paths have been studied under the same system; thus far, no basic research has investigated what paths are involved and how they cooperate with each other under the interaction of different systems. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), featuring a “one country, two systems” approach, as an example, this research identified and quantified the best path for the GBA’s power transition and explored the mode of cooperation during the power transition among the three regions under the interaction of different systems. The results showed that a combination of multiple low-carbon technologies is the best option for the GBA’s deep power transition, which can be characterized by the following components: “gas increase, nuclear increase, coal guarantee, and low proportion of renewable energy”. In this scenario, the GBA can achieve a carbon peak of 167 million tons of CO2 in 2023. Before 2030, the GBA needs to first develop class H gas power, photovoltaic power and nuclear power while phasing out subcritical and below thermal power cogeneration, and subcritical and below coal power. After 2030, a significant increase will be needed in the installed capacity of distributed gas power to replace some class E and F gas power units. Distributed rooftop PV power generation will be the mainstream method of renewable energy generation. Power generation through waste incineration can also provide a prominent contribution to urban biomass power. Under the interaction of different systems, breaking the technical barriers among the three regions would represent a breakthrough for establishing a cooperative power transition. A “one primary system, two auxiliary systems” theoretical framework of cooperation is proposed, and the scope of its application is revealed. This study can provide a case reference for the establishment of a win–win cooperation mechanism for energy transition in different countries.

Tumor necrosis factor (TNF) family members participate in the body’s antitumor immunity response and influence tumor prognosis and treatment response. However, little is known about the roles of TNF family members in small cell lung cancer (SCLC). Therefore, we conducted the first comprehensive investigation of TNF family members in patients with SCLC, with the goal of using them to predict prognosis and chemotherapy benefit. Abnormal genetic alterations and expression of TNF family members were found to be widespread in SCLC patients. Using LASSO Cox regression analysis, we constructed a TNF family-based signature that separated SCLC patients in the training set (n=77) into high- and low-risk groups with distinct survival and chemotherapy benefit, and the signature was well-validated in the validation set (n=137) by RT-qPCR. Importantly, the signature exhibited superior predictive performance and was identified as a novel independent prognostic factor. Additionally, different immune phenotypes were found between the low-risk and high-risk groups, and high-risk patients had higher CMTM6 expression, suggesting that these patients could benefit from therapeutic methods targeting CMTM6. We constructed the first clinically applicable TNF family-based signature for predicting prognosis and chemotherapy benefit for patients with SCLC. The findings reported here provide a new method for predicting the prognosis of SCLC patients and optimizing clinical management.

Background: Small cell lung cancer (SCLC) is the deadliest pathological subtype of lung cancer with few precise treatment options. Owing to the paucity of specimens, progress in identifying prognostic and therapeutic biomarkers for SCLC has been stagnant for decades. Considering the costimulatory molecules are essential elements in modulating anti-tumor immune responses and determining therapeutic response, we decided to conduct a comprehensive analyses of costimulatory molecule expression profile with regard to prognosis and chemotherapy response for SCLCs for the first time. Methods: We systematically explored the molecular characteristics and clinical relevance of costimulatory molecules in SCLC. Using a univariate Cox and LASSO Cox model, we then constructed a molecule-based signature (CMS) based on our examination of 208 cases with SCLC from two different cohorts—including an independent cohort that contained 131 cases with qPCR data, to predict the outcome and chemotherapy response for SCLCs. Relationships between the CMS and adjuvant chemotherapy (ACT) benefits and inflammatory landscape were also evaluated.Results: We found T cell activation was restrained in SCLCs, and costimulatory molecules exhibited widespread abnormal genetic alterations and expression. Then, the CMS containing 7 molecules (ICOSLG, EDA2R, TNFRSF25, CD276, RELT, PDCD1, and TNFSF14) was built with a training cohort of 77 cases, which successfully divided patients into high- or low-risk groups with significantly different prognosis and chemotherapy benefit (both PConclusions: We firstly uncovered the expression heterogeneity of costimulatory molecules in SCLC, and successfully constructed a novel predictive CMS. The identified signature contributed to more accurate patient stratification and provided robust prognostic value in estimating survival and the clinical response to chemotherapy, allowing optimization of treatment and prognosis management for patients with SCLC.

Small-cell lung cancer (SCLC) is a devastating subtype of lung cancer with few therapeutic options. Despite the advent of immunotherapy, platinum-based chemotherapy is still the irreplaceable first-line therapy for SCLCs. However, drug resistance will invariably occur in most patients and the outcomes are heterogeneous. Therefore, clinically feasible classification strategies and potential therapeutic targets for overcoming chemotherapy resistance are urgently needed. N6-methyladenosine (m6A) is a novel epigenetic decisive factor that is involved in tumor progression and drug resistance. However, almost nothing is known about m6A modification in SCLC. Here, we assessed 200 SCLC samples from patients who underwent chemotherapy from three different cohorts, including a validation cohort containing 71 cases with qPCR data and an independent cohort containing 79 cases with immunohistochemistry data (quantified as H-score). We systematically characterized the predictive landscape of m6A regulators in SCLC patients following with chemotherapy. Using the LASSO Cox model, we built a seven-regulator-based (ZCCHC4, IGF2BP3, ALKBH5, YTHDF3, METTL5, G3BP1, and RBMX) chemotherapy benefit predictive classifier (m6A score) and subsequently validated the classifier in two other cohorts. Time-dependent ROC and C-index analyses showed that the m6A score to possessed superior predictive power for chemotherapy benefit in comparison with other clinicopathological parameters. A multicohort multivariate analysis revealed that the m6A score is an independent factor that affects survival benefit across multiple cohorts. Our in vitro experimental results revealed that three regulators—ZCCHC4, G3BP1, and RBMX—may serve as promising novel therapeutic targets for overcoming chemoresistance in SCLCs. Our results, for the first time, demonstrate the predictive significance of m6A regulators for chemotherapy benefit, as well as their potential as therapeutic targets for overcoming chemotherapy resistance in SCLC patients. The m6A score was found to be a reliable prognostic tool that may help guide chemotherapy decisions for patients with SCLC. Supplementary Information The online version contains supplementary material available at 10.1186/s13045-021-01173-4.

N6‐methyladenosine (m6A), the newest and most prevalent layer of internal epigenetic modification in eukaryotic mRNA, has been demonstrated to play a critical role in cancer biology. Increasing evidence has highlighted that the interaction between cancer stem cells (CSCs) and the tumor immune microenvironment (TIME) is the root cause of tumorigenesis, metastasis, therapy resistance, and recurrence. In recent studies, the m6A modification has been tightly linked to this CSC‐TIME interplay, participating in the regulation of CSCs and TIME remolding. Interestingly, the m6A modification has also been identified as a novel decisive factor in the efficacy of immunotherapies—particularly anti‐PD‐1/PD‐L1 monotherapies—by changing the plasticity of the TIME. Given the functional importance of the m6A modification in the crosstalk between CSCs and the TIME, targeting m6A regulators will open new avenues to overcome therapeutic resistance, especially for immune checkpoint‐based immunotherapy. In the present review, we summarize the current landscape of m6A modifications in CSCs and the TIME, and also prospect the underling role of m6A modifications at the crossroads of CSCs and the TIME for the first time. Additionally, to provide the possibility of modulating m6A modifications as an emerging therapeutic strategy, we also explore the burgeoning inhibitors and technologies targeting m6A regulators. Lastly, considering recent advances in m6A‐seq technologies and cancer drug development, we propose the future directions of m6A modification in clinical applications, which may not only help to improve individualized monitoring and therapy but also provide enhanced and durable responses in patients with insensitive tumors., In this review, we evaluate the current landscape of m6A modification in the lethal teamwork of CSCs and the TIME for the first time. In addition, we summarize the existing inhibitors targeting m6A regulators and discuss the potential of modulating m6A modification as a therapeutic strategy. Moreover, we propose the future directions of using m6A modification in cancer diagnosis and treatment, as well as providing an overview of the emerging approaches to m6A‐targeted immunotherapy. We believe that this review will provide novel insight into the role of m6A modification in cancer‐specific basic and translational medicine.

Background Costimulatory molecules play significant roles in mounting anti-tumor immune responses, and antibodies targeting these molecules are recognized as promising adjunctive cancer immunotherapies. Here, we aim to conduct a first full-scale exploration of costimulatory molecules from the B7-CD28 and TNF families in patients with lung adenocarcinoma (LUAD) and generated a costimulatory molecule-based signature (CMS) to predict survival and response to immunotherapy. Methods We enrolled 1549 LUAD cases across 10 different cohorts and included 502 samples from TCGA for discovery. The validation set included 970 cases from eight different Gene Expression Omnibus (GEO) datasets and 77 frozen tumor tissues with qPCR data. The underlying mechanisms and predictive immunotherapy capabilities of the CMS were also explored. Results A five gene-based CMS (CD40LG, TNFRSF6B, TNFSF13, TNFRSF13C, and TNFRSF19) was initially constructed using the bioinformatics method from TCGA that classifies cases as high- vs. low-risk groups per OS. Multivariable Cox regression analysis confirmed that the CMS was an independent prognostic factor. As expected, CMS exhibited prognostic significance in the stratified cohorts and different validation cohorts. Additionally, the prognostic meta-analysis revealed that CMS was superior to the previous signature. Samples in high- and low-risk groups exhibited significantly different tumor-infiltrating leukocytes and inflammatory activities. Importantly, we found that signature high-risk patients were optimal candidates for immunotherapy. Conclusion We conducted the first and most comprehensive costimulatory molecule landscape analysis of patients with LUAD and built a clinically feasible CMS for prognosis and immunotherapy response prediction, which will be helpful for further optimize immunotherapies for cancer.

Eimeria stiedai is an apicomplexan protozoan parasite that invades the liver and bile duct epithelial cells in rabbits and causes severe hepatic coccidiosis, resulting in significant economic losses in the domestic rabbit industry. Hepatic coccidiosis lacks the typical clinical symptoms and there is a lack of effective premortem tools to timely diagnose this disease. Therefore, in the present study we cloned and expressed the two microneme proteins i.e., microneme protein 1 (EsMIC1) and microneme protein 3 (EsMIC3) from E. stiedai and used them as recombinant antigens to develop a serodiagnostic method for an effective diagnosis of hepatic coccidiosis. The cDNAs encoding EsMIC1 and EsMIC3 were cloned and the mRNA expression levels of these two genes at different developmental stages of E. stiedai were determined by quantitative real-time PCR analysis (qRT-PCR). The immunoreactivity of recombinant EsMIC1 (rEsMIC1) and EsMIC3 (rEsMIC3) proteins were detected by Western blotting, and indirect enzyme-linked immunosorbent assays (ELISAs) based on these two recombinant antigens were established to evaluate their serodiagnostic potential. Our results showed that the proteins encoded by the ORFs of EsMIC1 (711 bp) and EsMIC3 (891 bp) were approximately 25.89 and 32.39 kDa in predicted molecular weight, respectively. Both EsMIC1 and EsMIC3 showed the highest mRNA expression levels in the merozoites stage of E. stiedai. Western blotting analysis revealed that both recombinant proteins were recognized by E. stiedai positive sera, and the indirect ELISAs using rEsMIC1 and rEsMIC3 were developed based on their good immunoreactivity, with 100% (48/48) sensitivity and 97.9% (47/48) specificity for rEsMIC1 with 100% (48/48) sensitivity and 100% (48/48) specificity for rEsMIC3, respectively. Moreover, rEsMIC1- and rEsMIC3-based indirect ELISA were able to detect corresponding antibodies in sera at days 6, 8, and 10 post E. stiedai infection, with the highest positive diagnostic rate (62.5% (30/48) for rEsMIC1 and 66.7% (32/48) for rEsMIC3) observed at day 10 post infection. Therefore, both EsMIC1 and EsMIC3 can be used as potential serodiagnostic candidate antigens for hepatic coccidiosis caused by E. stiedai.

BackgroundImmunotherapy has achieved surprising success in the treatment of esophageal squamous cell carcinoma (ESCC). However, studies concerning immune phenotypes within the ESCC microenvironment and their relationship with prognostic outcomes are limited. Therefore, we aim to construct and validate an individual immune-related risk signature for patients with ESCC.MethodsWe collected 196 ESCC cases, including 119 samples from our previous public data (GSE53624) to use as a training set. We additionally collected an independent validation cohort consisting of 77 frozen tumor tissues with qPCR data. Head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC) cohorts were also collected for validation. A least absolute shrinkage and selection operator (LASSO) model and a stepwise Cox proportional hazards regression model were used to construct the immune-specific signature. The potential mechanism and inflammatory landscapes of the signature were explored by using bioinformatics and immunofluorescence assay methods.ResultsImmune-related genes were significantly altered in ESCC tissues, and 16 differentially expressed immune-related genes with the most prognostic value were filtered out (PTSPAN2, AMBP, ITLN1, C6, PRLR, and MADCAM1) was generated from the training set. This signature classified the patients into two groups with significantly different overall and relapse-free survival. Furthermore, the signature showed similar prognostic values in different clinical subgroups and in the independent cohort, as well as in patients with HNSCC and LUSC. Multivariable Cox regression analysis confirmed that the signature was an independent prognostic factor for patients with ESCC in different cohorts. Pathway analysis showed that genes related to the signature were mostly involved in cell adhesion, leukocyte transendothelial migration, and cancer progression. Further analysis revealed that the signature was closely associated with specific inflammatory activities (activation of macrophages and T cells signaling transduction). Additionally, high-risk patients were found characterized by distinctive immune checkpoints panel and higher filtration of Tregs and fibroblasts. ConclusionWe constructed the first comprehensive immune-related risk signature for ESCC and furnished new hints of immune profiling of ESCC. Future prospective studies are needed to test the clinical utility of this signature in the individualized management of patients with ESCC.

A pilot carbon emissions trading scheme (ETS) has been launched for three years in the Guangdong (GD) Province in China, with the power industry contributing nearly 66% of the covered CO2 emissions...

No clinically available biomarkers can predict pathological complete response (pCR) for esophageal squamous cell carcinomas (ESCCs) with neoadjuvant chemoradiotherapy (nCRT). Considering that antitumor immunity status is an important determinant for nCRT, we performed an integrative analysis of immune-related gene profiles from pretreatment biopsies and constructed the first individualized immune signature for pCR and outcome prediction of ESCCs through a multicenter analysis. During the discovery phase, 14 differentially expressed immune-related genes (DEIGs) with greater than a twofold change between pCRs and less than pCRs (SERPINE1, MMP12, PLAUR, and EPS8) was built based on the verified DEIGs from 71 cases in a Beijing training cohort, and achieved a high accuracy with an area under the receiver operating characteristic curve (AUC) of 0.970. The signature was further validated in an internal validation cohort and an integrated external cohort (Zhengzhou and Anyang cohorts) with AUCs of 0.890 and 0.859, respectively. Importantly, a multivariate analysis showed that the signature was the only independent predictor for pCR. In addition, patients with high predictive scores showed significantly longer overall and relapse-free survival across multiple centers (P

Carbon emission trading mechanism is an important market-based instrument to address global climate change. The auction ratio and price of carbon emission allowances (CEA) are cores of the carbon emission trading mechanism. What combination of CEA auction ratio and auction price is most conducive to carbon emission reduction for emission control enterprises? There are still relatively few studies conducted on this issue, and there is no clear answer from the views of theory and practice. In this paper, the AIM/Enduse model was used to analyze the changes and differences of installed power capacity, power supply, energy consumption and carbon emissions in the Guangdong-Hong Kong-Macao Great Bay Area (GBA) with different combinations of CEA auction ratios and prices. Moreover, the best pathway of energy transition was explored in the power industry of the GBA, and the best combination of CEA auction ratios and prices was identified in the GBA. The results show that: (1)The carbon emission constraint starts to work only when the auction ratio of CEA is increased to 50% and the auction price is increased to 60 CNY/ton (Scenario p3). When the auction ratio of CEA is 100% and the auction price reaches 120 CNY/ton (Scenario p5), a low-carbon transition of the power sector in the GBA can be achieved. (2) Under scenario p5, the total cost of power transition rises and then falls. And the cost of carbon in the power sector increases exponentially as the auction ratio of CEA and the auction price rise. However, the growth in the cost of power supply is relatively flat, which shows that appropriate carbon trading mechanism can improve the economic benefits of power sectors. With the increase of installed capacity of clean low-carbon energy generation and power supply, as well as the decrease of local power cost in the GBA, the proportion of outsourcing power can be appropriately reduced in the future, with an aim to reduce the external dependence of electricity to ensure the security of energy supply. In the end, it suggests that cities in the GBA should give full play to their regional advantages and establish a unified carbon emission trading market in the GBA as soon as possible. This study provides a theoretical basis for the supplementation and improvement of China's carbon emission trading mechanism.

Tumor Necrosis Factor (TNF) family members play important roles in mounting anti-tumor immune responses, and clinical trials targeting these molecules are ongoing. However, the expression patterns and clinical significance of TNF members in lung adenocarcinoma (LUAD) remain unrevealed. This study involved an integrative analysis of the gene expression profiles of TNF family members in LUAD and constructed a TNF family-based prognosis signature. In total, 1300 LUAD cases from seven different cohorts were collected. Samples from The Cancer Genome Atlas (TCGA) were used as the training set, and the RNA data from five Gene Expression Omnibus (GEO) datasets and qPCR data from 102 samples were used for validation. The immune profiles and immunotherapy response prediction value of the signature were also explored. After univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a TNF family-based signature was constructed in the TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of OS. This signature remained an independent prognostic factor in multivariate analyses. Moreover, the clinical significance of the signature was well validated in different clinical subgroups and independent validation cohorts.Further analysis revealed that signature high-risk patients were characterized by distinctive immune cell proportions and immune-suppressive states. Importantly, signature high-risk cases were considered appropriate candidates for immunotherapy. Collectively, this was the first, reliable, TNF family-based prognostic model for predicting outcomes and immunotherapy response, which may emerge as a clinically useful tool for better prognostic management and increased precision in the application of immunotherapy for LUAD patients. Funding Statement: This work was supported by the CAMS Innovation Fund for Medical Sciences (2017-I2M-1-005, 2016-I2M-1-001), the National Key R&D Program of China (2016YFC1303201), the National Natural Science Foundation of China (81802299, 81502514), the Fundamental Research Funds for the Central Universities (3332018070), the National Key Basic Research Development Plan (2018YFC1312105). Declaration of Interests: The authors declare that they have no conflicts of interest. Ethics Approval Statement: This study was approved by the Institutional Review Boards of The First Affiliated Hospital of Zhengzhou University.

The only joint effort area of provincial and municipal governments resides in Guangdong Province and Shenzhen City in China’s carbon emission trading system (ETS) pilots, which characterize the nat...

Ecological civilization construction in China is in its critical period and the natural resources assets are audited to the administration. However, the natural resources accounting is still in its infancy, especially the atmospheric environmental resources accounting, which refers to the ability of the atmospheric environment to accommodate and purify certain pollutants. This paper established a methodology to calculate the atmospheric resources assets with the index of SO2, a convenient method to calculate the physical accounts for atmospheric environmental resources based on the capacity of SO2 and an accessible way to estimate the monetary accounts with market-based price. Based on the methodology, a calculation was conducted on the assets and liabilities of SO2 capacity resources for 31 provinces of China in 2015. Empirical results showed that the physical accounts for SO2 capacity resources quantify the environmental pollution status in each region, and the monetary accounts reflect whether the industry and energy structures in one region are sound and sustainable. The findings provide specific profit or loss in terms of physical and monetary accounts for each region, which enable to quantify the government's occupancy and affordability of SO2 capacity resources, and contribute to the establishment of natural resource balance sheet and ecological compensation mechanism.

In car navigation applications, the real-time parking information is an important application for the future, but there is no standardized definition for the communication data model of the real-time parking information. The specifications are defined for the real-time parking information, where they are mainly expressed in terms of whether the parking is full or unoccupied, the parking jam information for the exits, fee information, as well as parking information. The three-layer model is defined for the information dissemination to express the different detailed levels parking information.

The visual detection based on structured light is put forward to solve the problem of image matching for stereo vision in recent years. It is paid much attention in the industrial environment, especially in the three-dimensional automatic detection and object recognition. In this paper, it is discussed about the accuracy of visual inspection techniques based on structured light. There are some factors of affecting measurement techniques based on structured light, which are object material, edge detection algorithms, uneven lighting, and so on. The experimental results reveal that these three factors have a great impact on the accuracy of the model measurement of irregular objects.

Label algorithm is the center part in most of the shortest path algorithms, in which there are Label Setting and Label Correcting. Topological network consisting of nodes and arcs is used in Label Setting and Label Correcting algorithms. Open frame and Close frame are built in the course of searching. Then the node whose cost is the minimum will be put into Close frame. If the cost of the node is not the minimum while searching, it is saved into Open frame. The searching will be finished until all the object nodes are all saved into Close frame. However, the searching method is not fully applied to the traffic rule which is limited in turn restriction. Even if the rule which is prohibited passing between arc and arc is established in the topological network and the rule is kept during searching, the shortest path like P-Turn and Michigan-Turn can't be searched out. To search out the shortest path with turn restriction traffic rule, it is put forward a new searching method in which a Turn_open frame on the basis of using the Open frame and Close frame is built in the paper. Owning to making use of these three frames interactively, the shortest path which is proper to the traffic rule will be searched out successfully.