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The economy of China is growing rapidly. With this overwhelming growth, the country is experiencing a higher level of carbon emissions. Amid this backdrop, China is under immense pressure to reduce carbon emissions up to a sustainable level. This study adapted 31 provincial panel data from 2007 to 2017 using factor analysis system SBM-undesirable model to calculate the agro-ecological output of each province respectively and used a carbon transfer network impact analysis panel to calculate ecological performance impacts. Results show that (1) overall agro-ecological efficiency in China shows an upward trend but regional differences are evident. The efficiency in the eastern region is higher than that in the central and western regions but the extent of informatization in the central region is higher than that in the western region. (2) Informatization will significantly promote agro-ecological efficiency. (3) Changes in agricultural planting structure, agricultural value-added per capita, employment of human capital in the agricultural sector, and agricultural scale management are also important factors affecting agro-ecological growth. (4) China’s amount of carbon transfer is growing year by year, and energy-intensive areas and heavy industry bases are undertaking carbon transfer from the eastern coastal regions; (5) Jiangsu, Henan, and Hebei (Hubei) have the highest centers between 2007 and 2012; (6) inter-provincial carbon transmission is concentrated mainly in the metal smelting and rolling processing industries as well as in the coal, heat, and supply industries.

The gastrointestinal (GI) tract plays an essential role in food digestion, absorption, and the mucosal immune system; it is also inhabited by a huge range of microbes. The GI tract is densely innervated by a network of 200–600 million neurons that comprise the enteric nervous system (ENS). This system cooperates with intestinal microbes, the intestinal immune system, and endocrine systems; it forms a complex network that is required to maintain a stable intestinal microenvironment. Understanding how gut microbes influence the ENS and central nervous system (CNS) has been a significant research subject over the past decade. Moreover, accumulating evidence from animal and clinical studies has revealed that gut microbiota play important roles in various neurological diseases. However, the causal relationship between microbial changes and neurological disorders currently remains unproven. This review aims to summarize the possible contributions of GI microbiota to the ENS and CNS. It also provides new insights into furthering our current understanding of neurological disorders.

Achalasia is a rare motility disorder of the esophagus caused by the gradual degeneration of myenteric neurons. Immune-mediated ganglionitis has been proposed to underlie the loss of myenteric neurons. Here, we measure the immune cell transcriptional profile of paired lower esophageal sphincter (LES) tissue and blood samples in achalasia and controls using single-cell RNA sequencing (scRNA-seq). In achalasia, we identify a pattern of expanded immune cells and a specific transcriptional phenotype, especially in LES tissue. We show C1QC + macrophages and tissue-resident memory T cells (T RM ), especially ZNF683 + CD8 + T RM and XCL1 + CD4 + T RM , are significantly expanded and localized surrounding the myenteric plexus in the LES tissue of achalasia. C1QC + macrophages are transcriptionally similar to microglia of the central nervous system and have a neurodegenerative dysfunctional phenotype in achalasia. T RM also expresses transcripts of dysregulated immune responses in achalasia. Moreover, inflammation increases with disease progression since immune cells are more activated in type I compared with type II achalasia. Thus, we profile the immune cell transcriptional landscape and identify C1QC + macrophages and T RM as disease-associated immune cell subsets in achalasia. Achalasia is a rare motility disorder of the esophagus resulting from abnormal immune responses, but the immunologic mechanism is unclear. Here the authors use scRNA-seq of PBMC and esophageal lower sphincter tissue and find C1QC + macrophages and tissue-resident memory T cells with expanded compositions and altered transcriptional profiles in achalasia.

Esophageal squamous cell carcinoma (ESCC) causes aggressive and lethal malignancies with extremely poor prognoses, and accounts for about 90% of cases of esophageal cancer. Neuropilin and tolloid-like 2 (NETO2) protein coding genes have been associated with various human cancers. Nevertheless, little information is reported about the phenotypic expression and its clinical significance in ESCC progression. Here, our study found that NETO2 expression in ESCC patients was associated with tumor clinical stage and lymph node metastasis status. Gain-of-function and loss-of-function analyses showed that NETO2 stimulated ESCC cell proliferation while suppressing apoptosis and enhanced tumor growth . Moreover, knockdown of NETO2 significantly inhibited migration and invasion in combination with regulation of epithelial-mesenchymal transition (EMT) related markers. Mechanistically, overexpression of NETO2 increased the phosphorylation of ERK, PI3k/AKT, and Nuclear factor erythroid-2-related factor 2(Nrf2), whereas silencing NETO2 decreased the phosphorylation of these targets. Our data suggest that Nrf2 was a critical downstream event responsible for triggering the PI3K/AKT and ERK signaling pathways and plays a crucial role in NETO2-mediated tumorigenesis. Taken together, NETO2 acts as an oncogene and might serve as a novel therapeutic target or prognostic biomarker in ESCC patients.

This paper proposes a novel mean-field matrix-analytic method in the study of bike sharing systems, in which a Markovian environment is constructed to express time-inhomogeneity and asymmetry of processes that customers rent and return bikes. To achieve effective computability of this mean-field method, this study provides a unified framework through the following three basic steps. The first one is to deal with a major challenge encountered in setting up mean-field block-structured equations in general bike sharing systems. Accordingly, we provide an effective technique to establish a necessary reference system, which is a time-inhomogeneous queue with block structures. The second one is to prove asymptotic independence (or propagation of chaos) in terms of martingale limits. Note that asymptotic independence ensures and supports that we can construct a nonlinear quasi-birth-and-death (QBD) process, such that the stationary probability of problematic stations can be computed under a unified nonlinear QBD framework. Lastly, in the third step, we use some numerical examples to show the effectiveness and computability of the mean-field matrix-analytic method, and also to provide valuable observation of the influence of some key parameters on system performance. We are optimistic that the methodology and results given in this paper are applicable in the study of general large-scale bike sharing systems.

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic.

Background The Th17 subset and IL-17 have been found in increased frequencies within certain tumors. However, their relevance in cancer biology remains controversial. This study aimed to clarify the biological action of IL-17 on hepatocellular carcinoma (HCC). Methods Effects and underlying molecular mechanisms of IL-17 on human HCC were explored in vitro using exogenous IL-17 stimulation and in nude mice by implanting IL-17 overexpressed HCC cells. The clinical significance of IL-17 was investigated in tissue microarrays containing HCC tissues from 323 patients following hepatectomy using immunohistochemistry. Results Although exogenous IL-17 showed no direct effect on the growth rate of HCC cells in vitro , PCR and ELISA showed that IL-17 selectively augmented the secretion of diverse proinvasive factors and transwell showed a direct promotion of invasion of HCC cells by IL-17. Furthermore, transfection of IL-17 into HCC cells significantly promoted neoangiogenesis, neutrophil recruitment and tumor growth in vivo . Using siRNA mediated knockdown of AKT and STAT3, we suggested that the effects of IL-17 were operated through activation of the AKT signaling in HCC, which resulted in IL-6 production. Then, IL-6 in turn activated JAK2/STAT3 signaling and subsequently up-regulated its downstream targets IL-8, MMP2, and VEGF. Supporting these findings, in human HCC tissues, immunostaining indicated that IL-17 expression was significantly and positively associated with STAT3 phosphorylation, neutrophil infiltration and increased tumor vascularity. The clinical significance of IL-17 was authenticated by revealing that the combination of intratumoral IL-17+ cells and phospho-STAT3 served as a better prognosticator for postoperative tumor recurrence than either marker alone. Conclusions IL-17 mediated tumor-promoting role involves a direct effect on HCC cells through IL-6/JAK2/STAT3 induction by activating the AKT pathway.

Background An increasing number of studies have been conducted on the use of endoscopic ultrasound (EUS)-guided needle sampling for upper gastrointestinal subepithelial lesions (SEL). However, reported diagnostic efficacy varies greatly. Objective To summarize up current evidences on the diagnostic efficacy of EUS-guided needle sampling for upper GI SEL. Method A reproducible strategy was used to search four databases. Search results were evaluated for eligibility, and the quality of eligible studies was assessed by QUADAS-2. Pooled efficacy of EUS-guided needle sampling in upper GI SEL was calculated. Procedure-related complications, diagnostic errors, and independent factors related to a higher success rate were also recorded and analyzed. Results Seventeen studies, comprising 978 attempts of EUS-guided needle sampling, were included in a meta-analysis. Pooled diagnostic rate of EUS-guided needle sampling was 59.9 %, with a heterogeneity I 2 of 55.2 %. Subgroup analysis showed no difference in diagnostic rate among fine needle aspiration (FNA), trucut needle biopsy (TCB), and fine needle biopsy (FNB), or among 19-, 22-, and 25-G needles. Subgroup analysis and meta-regression suggested that the cell block method might be correlated with a higher diagnostic rate. Few severe complications were reported. Diagnosis errors were rare. Conclusion EUS-guided needle sampling is a safe, but only moderately effective method for pathology diagnosis of upper GI SEL. Choice of FNA/TCB/FNB, or 19 G/22 G/25 G does not seem to alter the overall diagnostic rate.

After over three years of COVID-19, it has become clear that infectious diseases are difficult to eradicate, and humans remain vulnerable under their influence in a long period. The presence of presymptomatic and asymptomatic patients is a significant obstacle to preventing and eliminating infectious diseases. However, the long-term transmission of infectious diseases involving asymptomatic patients still remains unclear. To address this issue, this paper develops a novel Markov process for infectious diseases with asymptomatic patients by means of a continuous-time level-dependent quasi-birth-and-death (QBD) process. The model accurately captures the transmission of infectious diseases by specifying several key parameters (or factors). To analyze the role of asymptomatic and symptomatic patients in the infectious disease transmission process, a simple sufficient condition for the stability of the Markov process of infectious diseases is derived using the mean drift technique. Then, the stationary probability vector of the QBD process is obtained by using RG-factorizations. A method of using the stationary probability vector is provided to obtain important performance measures of the model. Finally, some numerical experiments are presented to demonstrate the model's feasibility through analyzing COVID-19 as an example. The impact of key parameters on the system performance evaluation and the infectious disease transmission process are analyzed. The methodology and results of this paper can provide theoretical and technical support for the scientific control of the long-term transmission of infectious diseases, and we believe that they can serve as a foundation for developing more general models of infectious disease transmission.