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The outbreak of Ulva prolifera in the Yellow Sea of China has caused serious economic losses. Strengthening Ulva prolifera disaster monitoring can reduce economic losses, support sustainable marine resource use, and maintain ecological balance. Deep learning-based Ulva prolifera disaster monitoring has gradually become a research hotspot. However, existing deep-learning semantic segmentation methods are insufficient in perceiving the spatial information of Ulva prolifera. On the one hand, downsampling will lose the spatial information of Ulva prolifera, and on the other hand, existing models are difficult to effectively capture the multiscale spatial characteristics of Ulva prolifera. In order to solve these problems, this article proposes an Ulva prolifera extraction network (UPE-Net) based on the U-Net model with three important modifications. First, we develop a novel multifeature downsampling module, MH-downsampling block, specifically designed to mitigate spatial information loss. Second, we propose a dual attention spatial pyramid pooling module that effectively suppresses noise interference while capturing multiscale spatial information on Ulva prolifera. Third, a detail-enhanced attention block is incorporated to enhance the learning of crucial spatial and detail features during the decoding process. The performance of the proposed model was evaluated using the Gaofen-1 wide field view Ulva prolifera dataset. Compared with existing methods, our UPE-Net has significantly enhanced the ability to perceive the spatial information of Ulva prolifera, improving the IoU by at least 8%, the segmentation results of our model are closest to the ground truth. Thereby our UPE-Net can provide a robust basis for monitoring Ulva prolifera disasters.

In order to achieve more optimal resource scheduling effect for logistics networks, it is essential to collaboratively predict throughput amount of different network nodes in future timestamps. However, the logistics networks are actually a kind of connected complex networks, in which a node denotes a single logistics station and all nodes are associated by implicit relationships. When it comes to collaborative forecasting towards logistics network throughput, all the nodes are required to be integrated together as a whole research object. Therefore, this work introduces graph learning to extract graph-level features of logistics networks, and proposes a data-driven collaborative forecasting method for logistics network throughput based on graph learning. Firstly, information characteristics of graph-level logistics networks is defined as vectorized format. Then, the graph learning framework is formulated, so as to fit the nonlinear relationship between logistics networks and dynamic throughput amount. At last, some simulations are also taken to testify performance of the proposal. The research results show that the graph neural network can find the temporal correlation between data and combine preprocessed multi-layer feature vector with temporal attention weight vectors. And the proposal is able to well implement collaborative forecasting towards logistics networks, with the assistance of graph learning.

Plant health is closely related to the soil, where microorganisms play a critical and unique role. For instance, Paeonia suffruticosa is an emerging woody oil crop in China with attractive development and utilization prospects. However, black root rot causes wilting of the aboveground plant parts, which significantly affected its seed yield and quality. Studies found that soil microorganisms are critical in maintaining plant health, but how changes in the soil microbial communities affect the healthy and diseased oil peony is unclear. Therefore, our present study used high throughput sequencing and BIOLOG to analyze the rhizosphere soil microbial communities of healthy and diseased oil peonies. Our results revealed that the physical and chemical properties of the soil of the diseased plants had changed, with the ability to metabolize the carbon source being enhanced. Moreover, our research highlighted that the oil peony-infecting fungal pathogenic genus (Fusarium, Cylindrocarpon, and Neocosmospora) was closely associated with oil peony yield reduction and disease aggravation. Further network analysis demonstrated that the bacterial and fungal networks of the diseased plants were more complex than those of the healthy plants. Finally, the inter-kingdom network among the diseased plants further indicated that the lesions destroyed the network and increased the intraspecific correlation between the fungal groups.

BACKGROUND:In China, the disease burden of cervical cancer remains substantial. Human papillomavirus (HPV) vaccines are expensive and not yet centrally funded. To inform immunization policy, understanding the economic burden of the disease is necessary. This study adopted a societal perspective and investigated costs and quality of life changes associated with cervical cancer from diagnosis to one year after final discharge in Henan province, China. METHODS:Inpatient records of cervical cancer patients admitted to the largest cancer hospital in Henan province between Jan. 2017 and Dec. 2018 were extracted. A telephone interview with four modules was conducted in Jun.-Jul. 2019 with a 40% random draw of patients to obtain direct non-medical costs and indirect costs associated with inpatients, costs associated with outpatient visits, and changes in quality of life status using the EQ-5D-5L instrument. Direct medical expenditures were converted to opportunity costs of care using cost-to-charge ratios obtained from hospital financial reports. For each clinical stage (IA-IV), total costs per case from diagnosis to one year after final discharge were extrapolated based on inpatient records, responses to the telephone interview, and recommendation on outpatient follow-ups by Chinese cervical cancer treatment guidelines. Loss in quality-adjusted life years was obtained using the 'under the curve' method and regression predictions. RESULTS:A total of 3,506 inpatient records from 1,323 patients were obtained. Among 541 randomly selected patients, 309 completed at least one module of the telephone interview. The average total costs per case associated with cervical cancer from diagnosis to one year after final discharge ranged from $8,066-$22,888 (in 2018 US Dollar) and the quality-adjusted life years loss varied from 0.05-0.26 for IA-IV patients. CONCLUSIONS:The economic burden associated with cervical cancer is substantial in Henan province. Our study provided important baseline information for cost-effectiveness analysis of HPV immunization program in China.

Lung cancer is the leading cause of cancer death and morbidity in China. Smoking and exposure to secondhand smoking closely related to lung cancer. Recently, series policies of tobacco control and lung cancer prevention was carried out. However, burden of lung cancer is still serious, and smoking rate in male is still very high, and in never smokers exposure to secondhand smoking is still very extensive. In this paper, epidemic situation of lung cancer, smoking and second hand smoking are described, at the same time, current implementations of tobacco control policies are summarized.

Background and objective Studies found that there were changes in histological subtypes of lung cancer patients in China. This study investigated the effect of smoking on lung cancer histology and its trend in Chinese male. Methods Demographic, smoking history and histological information about male lung cancer patients diagnosed or treated from 2000 to 2012 was collected from Cancer Hospital, Chinese Academy of Medical Science. Trends of histological subtypes calculated with annual percentage change (APC). Results A total of 14,106 patients with lung cancer were enrolled, with smoking 11,750 cases and non-smoking 2,356 cases. The main histological type of smoking lung cancer was squamous cell carcinoma (SCC)(39.38%), followed by adenocarcinoma (ADC)(29.85%). Among smokers, the proportion of SCC decreased from 44.19% to 35.50% (APC=-1.9%, P

Context. Human papillomavirus (HPV) testing is widely used in cervical cancer screening in women; however, its efficiency in triaging women with atypical squamous cells of undetermined significance (ASC-US) needs to be validated. Objective. To evaluate the performance of HPV16/18 in the triage of women with ASC-US. Methods. Women presenting for routine cervical cancer screening had cervical specimens collected, with which both liquid-based cytology (LBC) and hrHPVs were examined; those with ASC-US cytology underwent colposcopy. HPV16/18 and 12 other types were tested with domestic hybridization capture and chemiluminescence signal amplification (DH3). Performance characteristics of HPV test (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) for identification of cervical intraepithelium neoplasma (CIN) grade 2 or worse (CIN2+), and CIN grade 3 or worse (CIN3+)) were determined using standard statistical tests. Results. 317 women with ASC-US were eligible for the study. HrHPV prevalence was 15.77% (50/317); HPV16/18 prevalence was 3.61% (20/317). Sensitivity and specificity of HPV16/18 for detection of CIN 2+ were 64.71% and 97% and 64.29% and 96.37% for detection of CIN 3+, respectively. The positive predictive values (PPVs) and negative predictive values (NPVs) of HPV16/18 were 55.00% and 97.98% for CIN2+ and 45.00% and 98.32% for CIN3+, respectively. Conclusion. HPV16/18 can be considered as an effective method to triage women with ASC-US as its good clinical performance. Trial Registration. This trial is registered with Henan Cancer Hospital Medical Ethics Committee on July 5, 2016 (http://www.anti-cancer.com.cn), with registry no.: 2016037.

To reduce financial pressure and operational risk, and improve match between supply and demand, an increasing number of enterprises are adopting presales to launch new products. In this context, this paper investigates three presale models for innovative products, namely, the no-presale model, the manufacturer presale model and the retailer presale model. A Hotelling model is used to describe the impact of channel preferences and valuation differences on the two-stage competition between innovative products. Aiming at evaluating the purchase behavior of consumers under three presale modes, a game optimization model is established to analyze the presale decision problem for innovative products under different presale entities. The research shows that: (1) Compared with no-presale, presales can help enterprises obtain more profits. The overall profit of the supply chain is optimal under the retailer presale mode. (2) When the difference in channel preferences is small, the manufacturer obtains the greatest profit by choosing the direct presale model. In contrast, the selection of different presale entities has a significant impact on product sales and supply chain enterprise profits. At this point, the manufacturer should choose the retailer presale model. (3) When the product valuation discount is high, the manufacturer can increase the spot and wholesale prices to induce consumers to choose the presale method to purchase the product. In the opposite situation, the manufacturer should lower the presale price to improve the presale utility of consumers and encourage them to participate in the presale. These conclusions provide more targeted suggestions for enterprises to formulate presale strategies, which can help them grasp market demand and improve market competitiveness.

Summary: Endoplasmic reticulum membrane protein complex subunit 6 (EMC6) plays an important function in both physiological and pathological states of cells. Nevertheless, there are few studies focused on the role of EMC6 in tumors. At first, we performed a series of bioinformatics analyses on 33 kinds of cancers, including differential expression analysis, tumor mutational burden analysis, prognostic analysis, and clinicopathological staging analysis. Then, we corroborated the important role of EMC6 in lung cancer by cytological and in vivo experiments. We found that the reduction of EMC6 expression did effectively inhibit the proliferation, invasion, and metastasis of A549. Finally, EMC6 is indeed involved in the regulation of ferroptosis, cuproptosis, and immune response in LUAD. In a word, our study not only comprehensively analyzed the functional mechanisms of EMC6 in all cancers but also validated the regulatory role of EMC6 in lung cancer for the first time.

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.

Abstract Background Colorectal cancer (CRC) has become the second deadliest cancer in the world and severely threatens human health. An increasing number of studies have focused on the role of the RNA helicase DEAD-box (DDX) family in CRC. However, the mechanism of DDX10 in CRC has not been elucidated. Methods In our study, we analysed the expression data of CRC samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed cytological experiments and animal experiments to explore the role of DDX10 in CRC cells. Furthermore, we performed Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein–protein interaction (PPI) network analyses. Finally, we predicted the interacting protein of DDX10 by LC–MS/MS and verified it by coimmunoprecipitation (Co-IP) and qPCR. Results In the present study, we identified that DDX10 mRNA was extremely highly expressed in CRC tissues compared with normal colon tissues in the TCGA and GEO databases. The protein expression of DDX10 was measured by immunochemistry (IHC) in 17 CRC patients. The biological roles of DDX10 were explored via cell and molecular biology experiments in vitro and in vivo and cell cycle assays. We found that DDX10 knockdown markedly reduced CRC cell proliferation, migration and invasion. Then, we constructed a PPI network with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). GO and KEGG enrichment analysis and gene set enrichment analysis (GSEA) showed that DDX10 was closely related to RNA splicing and E2F targets. Using LC–MS/MS and Co-IP assays, we discovered that RPL35 is the interacting protein of DDX10. In addition, we hypothesize that RPL35 is related to the E2F pathway and the immune response in CRC. Conclusions In conclusion, provides a better understanding of the molecular mechanisms of DDX10 in CRC and provides a potential biomarker for the diagnosis and treatment of CRC.

BackgroundUlcerative colitis (UC) is an inflammatory disease of the intestinal mucosa, and its incidence is steadily increasing worldwide. Intestinal immune dysfunction has been identified as a central event in UC pathogenesis. However, the underlying mechanisms that regulate dysfunctional immune cells and inflammatory phenotype remain to be fully elucidated.MethodsTranscriptome profiling of intestinal mucosa biopsies were downloaded from the GEO database. Robust Rank Aggregation (RRA) analysis was performed to identify statistically changed genes and differentially expressed genes (DEGs). Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore potential biological mechanisms. CIBERSORT was used to evaluate the proportion of 22 immune cells in biopsies. Weighted co-expression network analysis (WGCNA) was used to determine key module-related clinical traits. Protein-Protein Interaction (PPI) network and Cytoscape were performed to explore protein interaction network and screen hub genes. We used a validation cohort and colitis mouse model to validate hub genes. Several online websites were used to predict competing endogenous RNA (ceRNA) network.ResultsRRA integrated analysis revealed 1838 statistically changed genes from four training cohorts (adj. p-value < 0.05). GSEA showed that statistically changed genes were enriched in the innate immune system. CIBERSORT analysis uncovered an increase in activated dendritic cells (DCs) and M1 macrophages. The red module of WGCNA was considered the most critical module related to active UC. Based on the results of the PPI network and Cytoscape analyses, we identified six critical genes and transcription factor NF-κB. RT-PCR revealed that andrographolide (AGP) significantly inhibited the expression of hub genes. Finally, we identified XIST and three miRNAs (miR-9-5p, miR-129-5p, and miR-340-5p) as therapeutic targets.ConclusionsOur integrated analysis identified four hub genes (CXCL1, IL1B, MMP1, and MMP10) regulated by NF-κB. We further revealed that AGP decreased the expression of hub genes by inhibiting NF-κB activation. Lastly, we predicted the involvement of ceRNA network in the regulation of NF-κB expression. Collectively, our results provide valuable information in understanding the molecular mechanisms of active UC. Furthermore, we predict the use of AGP and small RNA combination for the treatment of UC.

Tumor-associated macrophages (TAMs) are important immune cells in the tumor microenvironment (TME). Previous studies have shown that TAMs play a dual role in the development of colorectal cancer and promote the additional exploration of the immune escape of colorectal cancer. Studies have confirmed that macrophages utilize amino acid metabolism under the stimulation of some factors released by tumor cells, thus affecting the direction of polarization. Therefore, we investigated the effect of amino acid metabolism on macrophage function and the involved mechanism. Based on the comprehensive analysis of the GSE18804 GEO dataset and amino acid metabolism pathway, we identified the eight key enzymes of amino acid metabolism in colon TAMs, namely, ACADM, ACADS, GPX4, GSR, HADH, HMGCL, HMGCS1 and IDH1. We then evaluated the expression, survival analysis and relationship of clinicopathological features with these eight key enzymes. The results supported the critical role of these eight genes in colorectal cancer. Macrophages phagocytose tumor cells, and these eight key enzymes were identified in combination with GPX4, a critical protein of ferroptosis, suggesting that the change in the expression of these eight key enzymes in TAMs may be involved in the regulation of colorectal cancer through cell death. Correlation analysis of three programmed cell death (PCD) marker genes indicated that these eight key enzymes may cause macrophage death through pyroptosis, leading to immune escape of colorectal cancer. We also investigated the regulation of ACADS in CRC using flow cytometry, qPCR and ELISAs, which demonstrated that an ACADS deficiency polarizes TAMs to M2 macrophages. In summary, the present study revealed the relationship between amino acid metabolism and the cell death of macrophages, providing a new research direction for the molecular mechanism of macrophage polarization.