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Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.

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Recent maritime cybersecurity accidents reveal that shipping is facing increased exposure to cyber threats, especially due to the fast-growing digitalisation of the sector, leaving vessels and their onboard systems vulnerable to cyberattacks. This research aims at evaluating the relationship among the critical dimensions influencing cybersecurity performance in the maritime industry. To achieve this, six critical dimensions related to cybersecurity preparedness are first identified through literature review, namely 'regulations', 'company procedures' from a managerial perspective and 'shipboard systems readiness', 'training and awareness', 'human factor' and 'compliance monitoring' at an operation level. A Likert-scale questionnaire is designed and used to collect empirical data from 133 seafarers and shore-based staff. Structural Equation Modelling (SEM) is applied to examine the causal relationships between the six dimensions and shipboard cybersecurity performance. The results show that 'regulations' positively influence shipping companies' cybersecurity-related 'procedures', which in turn positively affects 'shipboard systems readiness', 'training and awareness', and 'monitoring'. Further, 'training and awareness' positively influences the cybersecurity performance of ships. The results have profound implications for the shipping industry on how to strengthen their cyber practices in order to improve their cybersecurity performance. Recommendations for future academic research related to maritime cybersecurity are also provided.

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BACKGROUND: Gastric ulcer (GU) is a common digestive tract disease, and medical records of GU combined with depression are increasingly common. Currently, the risk factors and pathogenesis of GU complicated with depression remain unclear. Low immune function and gastrointestinal hormone levels may also be significant risk factors. Therefore, this study explored the immune function and gastrointestinal hormone levels in patients with GU combined with depression. AIM: To explore the immune function, gastrointestinal hormone level, and clinical significance of patients with GU combined with depression. METHODS: A retrospective analysis was conducted on 300 patients with GU combined with depression admitted to Guizhou Provincial People's Hospital from January 2021 to June 2022 as the study subjects. According to the Hamilton Depression Scale (HAMD) score, patients were divided into mild-to-moderate (n = 210) and heavy (n = 90) groups. Basic data, immune function indices [immunoglobulin A (IgA), IgM, IgG, serum CD4+ and CD8+ percentage, and CD4+/CD8+ ratio], and gastrointestinal hormone indices [serum gastrin (GAS), cholecystokinin (CCK), and motilin (MTL) levels] were collected. The basic data of the two groups were compared, and the immune function and gastrointestinal hormone indices were analyzed. Multivariate logistic regression was used to analyze the factors influencing the severity of GU complicated with depression. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to analyze the value of the immune function index, gastrointestinal hormone index, and combined index in predicting the severity of GU complicated with depression. RESULTS: There were no marked differences in sex, age, body mass index, abdominal distension, abdominal pain, belching, nausea, vomiting, or sleep disorders between the heavy and mild-to-moderate groups (P > 0.05). There was a marked difference in the family history of depression between the heavy and mild-to-moderate groups (P < 0.05). There were significant differences in serum IgA and IgM levels and serum CD4+, CD8+, and CD4+/CD8+ ratios between the heavy and mild-to-moderate groups (P < 0.05). Multivariate analysis showed that IgA, IgM, GAS, and CCK serum levels influenced the severity of GU with depression (P < 0.05). The AUC of the ROC curve for serum IgA level predicting GU with depression severity was 0.808 [95% confidence interval (CI): 0.760-0.857], the AUC of the serum IgM level was 0.757 (95%CI: 0.700-0.814), the AUC of the serum GAS level was 0.853 (95%CI: 0.810-0.897), the AUC of the serum CCK level was 0.762 (95%CI: 0.709-0.822), the AUC of immune function (IgA, IgM) and gastrointestinal hormone levels (GAS, CCK) for the prediction of GU with depression severity was 0.958 (95%CI: 0.933-0.976). CONCLUSION: Important factors influencing GU complicated with depression are serum IgA, IgM, GAS, and CCK indicators. They can be used as indicators to predict the severity of GU complicated with depression.

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A hostile microenvironment in tumor tissues disrupts endoplasmic reticulum homeostasis and induces the unfolded protein response (UPR). A chronic UPR in both cancer cells and tumor-infiltrating leukocytes could facilitate the evasion of immune surveillance. However, how the UPR in cancer cells cripples the anti-tumor immune response is unclear. Here, we demonstrate that, in cancer cells, the UPR component X-box binding protein 1 (XBP1) favors the synthesis and secretion of cholesterol, which activates myeloid-derived suppressor cells (MDSCs) and causes immunosuppression. Cholesterol is delivered in the form of small extracellular vesicles and internalized by MDSCs through macropinocytosis. Genetic or pharmacological depletion of XBP1 or reducing the tumor cholesterol content remarkably decreases MDSC abundance and triggers robust anti-tumor responses. Thus, our data unravel the cell-non-autonomous role of XBP1/cholesterol signaling in the regulation of tumor growth and suggest its inhibition as a useful strategy for improving the efficacy of cancer immunotherapy.

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Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID-19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first-hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID-19.

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INTRODUCTION: Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision-making can increase the likelihood of a collision. METHODS: A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20-min period of sustained attention to locate a target vessel and (2) a 10-min period of decision-making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15-channel functional near-infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph-theoretic measures. RESULTS: Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision-making. The graph-theoretic analysis revealed that density declined during decision-making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. CONCLUSIONS: The right lateral area of the PFC is sensitive to watchkeeping and decision-making during operational performance. Graph-theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety-critical performance.

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Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance.

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China's fast economic growth contributes to the rapid development of its urbanization process, and also renders a series of industrial accidents, which often cause loss of life, damage to property and environment, thus requiring the associated risk analysis and safety control measures to be implemented in advance. However, incompleteness of historical failure data before the occurrence of accidents makes it difficult to use traditional risk analysis approaches such as probabilistic risk analysis in many cases. This paper aims to develop a new methodology capable of assessing regional industrial safety (RIS) in an uncertain environment. A hierarchical structure for modelling the risks influencing RIS is first constructed. The hybrid of evidential reasoning (ER) and Analytical Hierarchy Process (AHP) is then used to assess the risks in a complementary way, in which AHP is hired to evaluate the weight of each risk factor and ER is employed to synthesise the safety evaluations of the investigated region(s) against the risk factors from the bottom to the top level in the hierarchy. The successful application of the hybrid approach in a real case analysis of RIS in several major districts of Beijing (capital of China) demonstrates its feasibility as well as provides risk analysts and safety engineers with useful insights on effective solutions to comprehensive risk assessment of RIS in metropolitan cities. The contribution of this paper is made by the findings on the comparison of risk levels of RIS at different regions against various risk factors so that best practices from the good performer(s) can be used to improve the safety of the others.

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Hepatitis C virus (HCV) is a major cause of chronic liver disease, infecting approximately 170 million people worldwide. HCV assembly is tightly associated with the lipoprotein pathway. Exchangeable apolipoprotein E (apoE) is incorporated on infectious HCV virions and is important for infectious HCV virion morphogenesis and entry. Moreover, the virion apoE level is positively correlated with its ability to escape E2 antibody neutralization. However, the role of apoE exchange in the HCV life cycle is unclear. In this study, the relationship between apoE expression and cell permissiveness to HCV infection was assessed by infecting apoE knockdown and derived apoE rescue cell lines with HCV. Exchange of apoE between lipoproteins and HCV lipoviral particles (LVPs) was evaluated by immunoprecipitation, infectivity testing, and viral genome quantification. Cell and heparin column binding assays were applied to determine the attachment efficiency of LVPs with different levels of incorporated apoE. The results showed that cell permissiveness for HCV infection was determined by exogenous apoE-associated lipoproteins. Furthermore, apoE exchange did occur between HCV LVPs and lipoproteins, which was important to maintain a high apoE level on LVPs. Lipid-free apoE was capable of enhancing HCV infectivity for apoE knockdown cells but not apoE rescue cells. A higher apoE level on LVPs conferred more efficient LVP attachment to both the cell surface and heparin beads. This study revealed that exogenous apoE-incorporating lipoproteins from uninfected hepatocytes safeguarded the apoE level of LVPs for more efficient attachment during HCV infection. IMPORTANCE: In this study, a neglected but important role of apoE exchange in HCV LVP infectivity after virus assembly and release was identified. The data indicated that apoE expression level in uninfected cells is important for high permissiveness to HCV infection. Secreted apoE-associated lipoprotein specifically enhances infection of HCV LVPs. apoE exchange between HCV LVP and lipoproteins is important to maintain an adequate apoE level on LVPs for their efficient attachment to cell surface. These data defined for the first time an extracellular role of exchangeable apoE in HCV infection and suggested that exchangeable apolipoproteins reach a natural equilibrium between HCV LVPs and lipoprotein particles, which provides a new perspective to the understanding of the heterogeneity of HCV LVPs in composition.

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