Effects of Seipin on Mouse Mesenchymal Stem Cell Osteo-Adipogenic Balance.

Seipin deficiency is an important cause of type 2 Berardinelli-Seip congenital dyslipidemia (BSCL2). BSCL2 is a severe lipodystrophy syndrome with lack of adipose tissue, hepatic steatosis, insulin resistance, and normal or higher bone mineral density. Bone marrow mesenchymal stem cells (BMSCs) are believed to maintain bone and fat homeostasis by differentiating into osteoblasts and adipocytes. We aimed to explore the role of seipin in the osteogenic/adipogenic differentiation balance of BMSCs. Seipin loxP/loxP mice are used to explore metabolic disorders caused by seipin gene mutations. Compared with wild-type mice, subcutaneous fat deficiency and ectopic fat accumulation were higher in seipin knockout mice. Microcomputed tomography of the tibia revealed the increased bone content in seipin knockout mice. We generated seipin-deficient BMSCs in vitro and revealed that lipogenic genes are downregulated and osteogenic genes are upregulated in seipin-deficient BMSCs. In addition, peroxisome proliferator-activated receptor gamma (PPARγ) signaling is reduced in seipin-deficient BMSCs, while using the PPARγ activator increased the lipogenic differentiation and decreased osteogenic differentiation of seipin-deficient BMSCs. Our findings indicated that bone and lipid metabolism can be regulated by seipin through modulating the differentiation of mesenchymal stem cells. Thus, a new insight of seipin mutations in lipid metabolism disorders was revealed, providing a prospective strategy for MSC transplantation-based treatment of BSCL2.

Sustaining the future: How green capabilities and digitalization drive sustainability in modern business.

In recent years, the unprecedented growth in environmental vulnerabilities has made the firms realize the need for environmental protection. With this, the rapid surge for ecological preservation has made worldwide businesses divert their focus toward greener practices that ensure the firm's financial and environmental performance. This study examines the relationships between green management strategies (green dynamic capabilities, internal green supply chain management and green technology adoption), and organizational outcomes, specifically environmental and financial performance. The data was collected from the 471 employees working in the manufacturing firms. Utilizing the Structural Equation Modeling (SEM) method via Smart-PLS, our findings show the importance of integrating green practices in supply chain management, dynamic capabilities, and technology adoption to enhance both environmental and financial outcomes under the moderating role of industry dynamism and green knowledge acquisition.

Unified Model to Predict gRNA Efficiency across Diverse Cell Lines and CRISPR-Cas9 Systems.

Computationally predicting the efficiency of a guide RNA (gRNA) from its sequence is crucial to designing the CRISPR-Cas9 system. Currently, machine learning (ML)-based models are widely used for such predictions. However, these ML models often show performance imbalance when applied to multiple data sets from diverse sources, hindering the practical utilization of these tools. To address this issue, we propose a Michaelis-Menten theoretical framework that integrates information from multiple data sets. We demonstrate that the binding free energy can serve as a useful invariant that bridges the data from different experimental setups. Building upon this framework, we develop a new ML model called Uni-deepSG. This model exhibits broad applicability on 27 data sets with different cell types, Cas9 variants, and gRNA designs. Our work confirms the existence of a generalized model for predicting gRNA efficiency and lays the theoretical groundwork necessary to finalize such a model.

Impact of the intensive use of urban construction land on carbon emission efficiency: evidence from the urban agglomeration in the middle reaches of the Yangtze River.

Urban construction land, as the main carrier of socioeconomic activities, is also a land type that is associated with large carbon emissions. This study uses statistical data of the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) from 2006 to 2020 to examine the mechanism of the intensive use of urban construction land (IUUCL) on carbon emission efficiency (CEE) from the perspective of urban land resource utilization. The study shows that the capital-intensive and technology-intensive use of urban construction land can significantly increase CEE, while increased labor and energy intensification inhibits CEE. In addition, there is regional heterogeneity in the effect of the IUUCL on CEE. The external control factor industrial structure has the most obvious inhibiting effect on the CEE of the Wuhan urban circle, the intensive use of energy has become the crucial constraint on the carbon emission reduction of the city cluster around Poyang Lake, and the intensive use of science and technology is the key factor in realizing the green and low-carbon development of the Chang-Zhu-Tan city cluster. This study innovatively constructs a theoretical framework of IUUCL versus CEE and conducts a heterogeneous study on the CEE of intensive use of construction land from the perspective of urban agglomerations. By providing a better understanding of the intrinsic influence mechanism of both these processes, this study provides a new perspective for reducing carbon emissions.

Glioblastoma Vascular Plasticity Limits Effector T-cell Infiltration and Is Blocked by cAMP Activation.

Glioblastoma (GBM) is the deadliest form of brain cancer. It is a highly angiogenic and immunosuppressive malignancy. Although immune checkpoint blockade therapies have revolutionized treatment for many types of cancer, their therapeutic efficacy in GBM has been far less than expected or even ineffective. In this study, we found that the genomic signature of glioma-derived endothelial cells (GdEC) correlates with an immunosuppressive state and poor prognosis of patients with glioma. We established an in vitro model of GdEC differentiation for drug screening and used this to determine that cyclic adenosine monophosphate (cAMP) activators could effectively block GdEC formation by inducing oxidative stress. Furthermore, cAMP activators impaired GdEC differentiation in vivo, normalized the tumor vessels, and altered the tumor immune profile, especially increasing the influx and function of CD8+ effector T cells. Dual blockade of GdECs and PD-1 induced tumor regression and established antitumor immune memory. Thus, our study reveals that endothelial transdifferentiation of GBM shapes an endothelial immune cell barrier and supports the clinical development of combining GdEC blockade and immunotherapy for GBM. See related Spotlight by Lee et al., p. 1300.

Omentum provides a special cell microenvironment for ovarian cancer.

BACKGROUND: Ovarian cancer seriously threatens women's health because of its poor prognosis and high mortality. Due to the lack of efficient early detection and screening methods, when patients seek doctors' help with complaints of abdominal distension, back pain and other nonspecific signs, the clinical results always hint at the widespread metastasis of disease. When referring to the metastasis of this disease, the omentum always takes precedence. RECENT FINDINGS: The distinguishing feature of the omentum is adipose tissue, which satisfies the energy demand of cancer cells and supplies a more aggressive environment for ovarian cancer cells. In this review, we mainly focus on three important cell types: adipocytes, macrophages, and mesenchymal stem cells. Besides, several mechanisms underlying cancer-associated adipocytes (CAA)-facilitated ovarian cancer cell development have been revealed, including their capacities for storing lipids and endocrine function, and the release of hormones, growth factors, and adipokines. Blocking the reciprocity among cancer cells and various cells located on the omentum might contribute to ovarian cancer therapy. The inhibition of hormones, growth factors and adipokines produced by adipocytes will be a novel therapeutic strategy. However, a sufficient number of trials has not been performed. In spite of this, the therapeutic potential of metformin and the roles of exercise in ovarian cancer will be worth mentioning. CONCLUSION: It is almost impossible to overcome completely ovarian cancer at the moment. What we can do is trying our best to improve these patients' prognoses. In this process, adipocytes may bring promising future for the therapy of ovarian cancer.

Elevated glucose metabolism driving pro-inflammatory response in B cells contributes to the progression of type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease characterized by the immune system's failure to maintain self-tolerance, resulting in the autoimmune destruction of pancreatic beta cells. Although T1D has conventionally been viewed as a T-cell-dominant disease, recent research has emphasized the contribution of B cells in the onset of the disease. However, the mechanism underlying aberrant B cell responses remains unknown. B cell metabolism is a crucial prerequisite for B cell function and the development of adaptive immune responses. Here, we investigated the metabolic features of B cells, first in a cross-sectional cohort and subsequently in non-obese diabetic (NOD) mice, and revealed that there is an increased frequency of high-glucose-avidity (2-NBDGhigh) B cell population that may contribute to T1D progression. Further characterization of the metabolic, transcriptional and functional phenotype of B cells in NOD mice found that elevated glucose avidity is associated with a greater capacity for co-stimulation, proliferation and inflammatory cytokine production. Mechanistically, elevated Myc signaling orchestrated the glucose metabolism and the pro-inflammatory response of B cells in T1D. In vitro experiments demonstrated that pharmacological inhibition of glucose metabolism using metformin and 2-DG reduced pro-inflammatory cytokine production and B cell proliferation. Moreover, the combination of these inhibitors successfully delayed insulitis development, onset of diabetes, and improved high blood glucose levels in streptozotocin (STZ)-induced diabetic mice model. Taken together, our work has uncovered these high-glucose-avidity B cells as novel adjuvant diagnostic and therapeutic targets for T1D.

Histogram analysis of ultrasonographic images in the differentiation of benign and malignant parotid gland tumors.

OBJECTIVE: We evaluated the diagnostic value of histogram analysis (HA) using ultrasonographic (US) images for differentiation among pleomorphic adenoma (PA), adenolymphoma (AL), and malignant tumors (MT) of the parotid gland. STUDY DESIGN: Preoperative US images of 48 patients with PA, 39 patients with AL, and 17 patients with MT were retrospectively analyzed for gray-scale histograms. Nine first-order texture features derived from histograms of the tumors were compared. Area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of texture features. The Youden index maximum exponent was used to calculate sensitivity and specificity. RESULTS: Statistically significant differences were discovered in Mean and Skewness HA values between PA and AL (P<0.001), and in Mean values between AL and MT (P<0.001). However, comparison of PA and MT showed no statistically significant differences (P>0.01). Excellent discrimination was detected between PA and AL (AUC=0.802), and between AL and MT (AUC=0.822). The combination of Mean plus Skewness improved discrimination between PA and AL (AUC=0.823) with sensitivity values reaching 1.00. However, Mean plus Skewness applied to differentiate PA from AL and Mean values applied to distinguish AL and MT resulted in low specificity, indicating many false positive interpretations. CONCLUSIONS: Histogram analysis is useful for differentiating PA from AL and AL from MT but not PA from MT.

Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms.

In recent years, machine learning methods have been applied successfully in many fields. In this paper, three machine learning algorithms, including partial least squares-discriminant analysis (PLS-DA), adaptive boosting (AdaBoost), and light gradient boosting machine (LGBM), were applied to establish models for predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET for short) properties, namely Caco-2, CYP3A4, hERG, HOB, MN of anti-breast cancer compounds. To the best of our knowledge, the LGBM algorithm was applied to classify the ADMET property of anti-breast cancer compounds for the first time. We evaluated the established models in the prediction set using accuracy, precision, recall, and F1-score. Compared with the performance of the models established using the three algorithms, the LGBM yielded most satisfactory results (accuracy > 0.87, precision > 0.72, recall > 0.73, and F1-score > 0.73). According to the obtained results, it can be inferred that LGBM can establish reliable models to predict the molecular ADMET properties and provide a useful tool for virtual screening and drug design researchers.

Development of a predictive model to identify patients most likely to benefit from surgery in metastatic breast cancer.

Primary tumor resection for metastatic breast cancer (MBC) has demonstrated a survival advantage, however, not all patients with MBC benefit from surgery. The purpose of this study was to develop a predictive model to select patients with MBC who are most likely to benefit from surgery at the primary site. Data from patients with MBC were obtained from the Surveillance, Epidemiology and End Results (SEER) cohort and patients treated at the Yunnan Cancer Hospital. The patients from the SEER database were divided into surgery and non-surgery groups and a 1:1 propensity score matching (PSM) was used to balance baseline characteristics. We hypothesized that patients who underwent local resection of primary tumors had improved overall survival (OS) compared to those who did not undergo surgery. Based on the median OS time of the non-surgery group, patients from the surgery group were further categorized into beneficial and non-beneficial groups. Logistic regression analysis was performed to identify independent factors associated with improved survival in the surgery group and a nomogram was established using the most significant predictive factors. Finally, internal and external validation of the prognostic nomogram was also evaluated by concordance index (C-index) and using a calibration curve. A total of 7759 eligible patients with MBC were identified in the SEER cohort and 92 with MBC patients who underwent surgery at the Yunnan Cancer Hospital. Amongst the SEER cohort, 3199 (41.23%) patients received surgery of the primary tumor. After PSM, the OS between the surgery and non-surgery group was significantly different based on Kaplan-Meier survival analysis (46 vs. 31 months, P < 0.001), In the surgery group, 562 (55.20%) patients survived for longer than 31 months and were classified in the beneficial group. Significant differences were observed in patient characteristics between the beneficial and non-beneficial groups including age, grade, tumor size, liver metastasis, breast cancer subtype and marital status. These factors were used as independent predictors to create a nomogram. The internally and externally validated C-indices of the nomogram were 0.703 and 0.733, respectively, indicating strong consistency between the actual and predicted survival. A nomogram was developed and used to identify MBC patients who are most likely to benefit from primary tumor resection. This predictive model has the potential to improve clinical decision-making and should be considered routine clinical practice.

Spatiotemporal heterogeneity and decoupling decomposition of industrial carbon emissions in the Yangtze River Delta urban agglomeration of China.

The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.

The prognostic value of DAAM2 in lower grade glioma, liver cancer, and breast cancer.

PURPOSE: Dishevelled-associated activator of morphogenesis 2 (DAAM2) is a formin protein and has a potential role in the tumor metastasis. The prognostic value of DAAM2 in pan-cancer is investigated in this study. METHODS: TCGA and GTEx database were downloaded to perform bioinformatics analysis and ROC curves. Then we explored protein-protein interaction and GO-KEGG enrichment to figure out the protein pathways associated with DAAM2 and studied DAAM2-related immune infiltration and methylation. Fifteen pairs of BRCA clinical samples were enrolled to determine the expression and distribution of DAAM2 in BRCA sections by immunohistochemistry. Finally, BRCA cells were transfected with siRNA targeting DAAM2 and subsequently subject to cell proliferation, migration, and invasion assays. RESULTS: DAAM2 was closely related to the diagnosis and clinical characteristics of lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and breast cancer (BRCA). Survival curve analysis demonstrated DAAM2 served as a potential prognostic indicator of LGG and LIHC (P = 0.0029 and P = 0.025, respectively). DAAM2 was mainly participated in signaling pathways mediating cytoskeleton regulation and tumor development. The correlation of DAAM2 with tumor-infiltrating immune cells (TIICs) and methylation levels was conducive to the prediction of novel biomarkers of pan-carcinoma. DAAM2 was highly expressed in BRCA tissues than that in paracancerous tissues. The proliferation, invasion, and migration of BRCA cells were inhibited by DAAM2 siRNA. CONCLUSION: DAAM2 had a specific value in foretelling the prognosis of LGG, LIHC, and BRCA. High expression level of DAAM2 has longer survival rates in LGG and LIHC. The knockdown of DAAM2 retards the proliferation, invasion, and migration of BRCA cells. This study provides a novel sight of DAAM2 into the exploration of a potential biomarker in pan-cancer.

Construction and validation of a prognostic nomogram in metastatic breast cancer patients of childbearing age: A study based on the SEER database and a Chinese cohort.

Introduction: Cancer in patients of childbearing age continues to become increasingly common. The purpose of this study was to explore the impact of metastatic breast cancer (MBC) on overall survival (OS) and cancer-specifific survival (CSS) in patients of childbearing age and to construct prognostic nomograms to predict OS and CSS. Methods: Data from MBC patients of childbearing age were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and the patients were randomly assigned into the training and validation cohorts. Univariate and multivariate Cox analyses were used to search for independent prognostic factors impacting OS and CSS, and these data were used to construct nomograms. The concordance index (C-index), area under the curve (AUC), and calibration curves were used to determine the predictive accuracy and discriminative ability of the nomograms. Additional data were obtained from patients at the Yunnan Cancer Hospital to further verify the accuracy of the nomograms. Results: A total of 1,700 MBC patients of childbearing age were identifified from the SEER database, and an additional 92 eligible patients were enrolled at the Yunnan Cancer Hospital. Multivariate Cox analyses identifified 10 prognostic factors for OS and CSS that were used to construct the nomograms. The calibration curve for the probabilities of OS and CSS showed good agreement between nomogram prediction and clinical observations. The C-index of the nomogram for OS was 0.735 (95% CI = 0.725-0.744); the AUC at 3 years was 0.806 and 0.794 at 5 years.The nomogram predicted that the C-index of the CSS was 0.740 (95% CI = 0.730- 0.750); the AUC at 3 years was 0.811 and 0.789 at 5 years. The same results were observed in the validation cohort. Kaplan- Meier curves comparing the low-,medium-, and high-risk groups showed strong prediction results for the prognostic nomogram. Conclusion: We identifified several independent prognostic factors and constructed nomograms to predict the OS and CSS for MBC patients of childbearing age.These prognostic models should be considered in clinical practice to individualize treatments for this group of patients.

New Constructed EEM Spectra Combined with N-PLS Analysis Approach as an Effective Way to Determine Multiple Target Compounds in Complex Samples.

Excitation-emission matrix (EEM) fluorescence spectroscopy has been applied to many fields. In this study, a simple method was proposed to obtain the new constructed three-dimensional (3D) EEM spectra based on the original EEM spectra. Then, the application of the N-PLS method to the new constructed 3D EEM spectra was proposed to quantify target compounds in two complex data sets. The quantitative models were established on external sample sets and validated using statistical parameters. For validation purposes, the obtained results were compared with those obtained by applying the N-PLS method to the original EEM spectra and applying the PLS method to the extracted maximum spectra in the concatenated mode. The comparison of the results demonstrated that, given the advantages of less useless information and a high calculating speed of the new constructed 3D EEM spectra, N-PLS on the new constructed 3D EEM spectra obtained better quantitative analysis results with a correlation coefficient of prediction above 0.9906 and recovery values in the range of 85.6-95.6%. Therefore, one can conclude that the N-PLS method combined with the new constructed 3D EEM spectra is expected to be broadened as an alternative strategy for the simultaneous determination of multiple target compounds.

Sex Detection of Chicks Based on Audio Technology and Deep Learning Methods.

The sex detection of chicks is an important work in poultry breeding. Separating chicks of different sexes early can effectively improve production efficiency and commercial benefits. In this paper, based on the difference in calls among one-day-old chicks of different sexes, a sex detection method based on chick calls is designed. Deep learning methods were used to classify the calls of chicks and detect their sex. This experiment studies three different varieties of chicks. The short-time zero-crossing rate was used to automatically detect the endpoints of chick calls in audio. Three kinds of audio features were compared: Spectrogram, Cepstrogram and MFCC+Logfbank. The features were used as the input in neural networks, and there were five kinds of neural networks: CNN, GRU, CRNN, TwoStream and ResNet-50. After the cross-comparison experiment of different varieties of chicks, audio features and neural networks, the ResNet-50 neural network trained with the MFCC+Logfbank audio features of three yellow chick calls had the highest test accuracy of 83% when testing Three-yellow chicks' calls. The GRU neural network trained with the Spectrogram audio features of native chick calls had the highest test accuracy of 76.8% when testing Native chicks' calls. The ResNet-50 neural network trained with Spectrogram audio features of flaxen-yellow chick calls had the highest test accuracy of 66.56%when testing flaxen-yellow chick calls. Multiple calls of each chick were detected, and the majority voting method was used to detect the sex of the chicks. The ResNet-50 neural network trained with the Spectrogram of three yellow chick calls had the highest sex detection accuracy of 95% when detecting the three yellow chicks' sex. The GRU neural network trained with the Spectrogram and cepstrogram of native chick calls and the CRNN network trained with the Spectrogram of native chick calls had the highest sex detection accuracy of 90% when detecting the native chicks' sex. The Twostream neural network trained with MFCC+Logfbank of flaxen-yellow chick calls and the ResNet-50 network trained with the Spectrogram of flaxen-yellow chick calls had the highest sex detection accuracy of 80% when detecting the flaxen-yellow chicks' sex. The results of the cross-comparison experiment show that there is a large diversity between the sex differences in chick calls of different breeds. The method is more applicable to chick sex detection in three yellow chicks and less so in native chicks and flaxen-yellow chicks. Additionally, when detecting the sex of chicks of a similar breed to the training chicks, the method obtained better results, while detecting the sex of chicks of other breeds, the detection accuracy was significantly reduced. This paper provides further perspectives on the sex detection method of chicks based on their calls and help and guidance for future research.

A comprehensive pan-cancer analysis on the immunological role and prognostic value of TYMP in human cancers.

Background: The TYMP gene encodes an important nucleoside metabolism enzyme which is a rate-limiting enzyme for chemotherapeutic drug metabolism. Previous studies have shown that TYMP is highly expressed in many different tumors, promoting invasiveness and progression, and that it helps to predict the response to chemotherapeutic drugs. However, the role of TYMP in tumor immunity and prognosis remains largely unclear. The purpose of this pan-cancer analysis was to acquire more data on the function of TYMP function and its clinical significance. Methods: To access the TYMP expression, we accessed datasets from The Cancer Genome Atlas (TCGA), Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Cancer Cell Line Encyclopedia (CCLE) databases, and analyzed its differential expression between paired tumor and normal samples. We employed PrognoScan and Kaplan-Meier plotter for survival analyses. TYMP mutations were analyzed using cBioPortal. Correlations of TYMP with tumor stage, tumor mutational burden (TMB), microsatellite instability (MSI), immune checkpoint genes (ICGs), and immune cell infiltration were estimated via bioinformatics tools and methods. The CellMiner database was used to predict drug response. Gene set enrichment analysis (GSEA) was applied to explore the biological functions of TYMP in different tumors. Results: Our results indicated that TYMP was overexpressed and also significantly associated with a worse prognosis in several human cancers, such as kidney clear cell carcinoma (KIRC) and lower grade glioma (LGG). TYMP was also associated with TMB, MSI, and ICGs across a variety of malignancies. TYMP was most significantly correlated with immune cell infiltration in five tumors, namely, breast cancer (BRCA), cervical cancer (CESC), KIRC, skin cutaneous melanoma (SKCM), and stomach adenocarcinoma (STAD). Moreover, TYMP expression predicted sensitivity to chemotherapy drugs and also influenced relevant biological pathways, according to enrichment analysis. Conclusions: According to the results of this comprehensive analysis, TYMP is associated with prognosis and tumor immunology, which might make it be a potential therapeutic target for cancer treatment.

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data.

Ovarian cancer (OC) is one the most life-threatening cancers affecting women's health worldwide. Immunotherapy has become a promising treatment for a variety of cancers, but the therapeutic effects in OC remain limited. In this study, we constructed a macrophage risk score (MRS) based on M1 and M2 macrophages and a gene risk score (GRS) based on the prognostic genes associated with MRS. Next, cell-cell communication analysis was performed using single-cell RNA (scRNA) sequencing data. Survival status and immune characteristics were compared between the high- and low-score groups separated by MRS or GRS. Our results suggested that MRS and GRS can identify the immune subtypes of OC patients with better overall survival (OS) and inflammatory immune microenvironment. Moreover, M1 and M2 macrophages may affect the prognosis of OC patients through signal communication with CD8 T cells. Finally, functional differences between the two groups separated by GRS were elucidated. Taken together, this study constructed two useful models for the identification of immune subtypes in OC, which has a better prognosis and may have a sensitive response to immune checkpoint inhibitors (ICIs). The hub genes for the construction of GRS may be potential synergetic targets for immunotherapy in OC patients.

Spatiotemporal dynamics and decoupling mechanism of economic growth and carbon emissions in an urban agglomeration of China.

Carbon emissions and economic growth are two contradictions in urban development, and their decoupling is related to the sustainable development of cities. This paper took urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), China, as the study area. The Kaya model, the Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatiotemporal differentiation of carbon emissions, the decoupling of economic activities, and driving factors. The results indicate that (1) carbon emissions increased by 66% in the study period, but the growth momentum was curbed after 2015. Low level and medium level areas continue to decrease, and relatively high level area gradually become dominant. (2) Spatially, carbon emissions are in a pattern of middle-hot and east-cold. Jiangxi is in the sub-cold and coldspot area, while the hotspot area is driven by the transformation from Wuhan's single-core to Wuhan and Changsha's dual-core. (3) Since 2010, most cities have been in a good decoupling state, and weak decoupling cities have risen from 35.5% in the initial period to 87.1% in 2010-2011, but the decoupling situation of industrial cities with more high-energy-consuming industries still rebounded slightly. (4) The economic level and energy intensity effect had the most significant impact on the economic decoupling of carbon emissions, whose absolute contribution rates were greater than 35%. Urbanization and economic level both play a positive role in promoting carbon emissions, and the energy intensity plays a negative role in retarding carbon emissions. The population effect was mainly manifested in carbon increase from 2006 to 2011, and 45.2% of the cities from 2011 to 2017 turned into carbon suppression. Finally, we suggest that decoupling carbon emissions from economic growth requires developing green urbanization and a decarbonized economy, optimizing the structure of energy consumption and guiding rational population flow.