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Uncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study. [ABSTRACT FROM AUTHOR]

When reasoning about causes of sustainability problems and possible solutions, sustainability scientists rely on disciplinary-based understanding of cause–effect relations. These disciplinary assumptions enable and constrain how causal knowledge is generated, yet they are rarely made explicit. In a multidisciplinary field like sustainability science, lack of understanding differences in causal reasoning impedes our ability to address complex sustainability problems. To support navigating the diversity of causal reasoning, we articulate when and how during a research process researchers engage in causal reasoning and discuss four common ideas about causation that direct it. This articulation provides guidance for researchers to make their own assumptions and choices transparent and to interpret other researchers' approaches. Understanding how causal claims are made and justified enables sustainability researchers to evaluate the diversity of causal claims, to build collaborations across disciplines, and to assess whether proposed solutions are suitable for a given problem. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate the causal relationship between phosphatidylcholine (PC) levels and dysmenorrhea using Mendelian randomization (MR) analysis. Methods: We conducted a two-sample MR analysis using GWAS data on PC levels and dysmenorrhea. Single nucleotide polymorphisms (SNPs) associated with PC levels were used as instrumental variables. MR-Egger regression and inverse variance weighting (IVW) were used to estimate the causal effect of PC levels on dysmenorrhea. Sensitivity analyses were performed to assess the robustness of the results. Results: The IVW analysis revealed a significant positive association between higher PC levels and dysmenorrhea (OR: 1.533, 95% CI: 1.039-2.262, P = 0.031). The MR-Egger regression did not detect pleiotropy. Sensitivity analyses confirmed the robustness of the results. Conclusion: This study provides evidence suggesting a causal link between increased PC levels and dysmenorrhea. Further research is needed to understand the biological mechanisms underlying this relationship and to explore potential therapeutic implications. [ABSTRACT FROM AUTHOR]

In the context of mines, roof-caving incidents constitute the most common and expensive accidents. To enhance the management and prevention of roof-caving accidents, it is imperative to investigate the factors that contribute to such incidents and comprehend the intricate causal relationships among them. This study aims to classify the causes of these accidents into three categories: basic factors, controllable factors, and sudden factors, based on the mechanism of roof caving. The categorization is primarily determined by two indicators: intervisibility and variability. Furthermore, the study delves into analyzing the mutual influence relationships among these factors and proposes the BCX theory (Basic-Controllable-Sudden causing theory) for roof caving. Subsequently, based on this theory, an index system called BCX is established for roof caving, and the DEMATEL method is employed to analyze the factors within this index system. To attain more accurate results, this study utilizes interval trapezoidal type-2 fuzzy number scale optimization and Tsallis relative entropy to address the limitations of the DEMATEL method. By comparing the outcomes of the traditional and optimal DEMATEL methods, it is observed that the optimal method exhibits superior applicability in the BCX index system of roof caving, with results that align closely with the actual scenario. Therefore, the optimal DEMATEL method's analysis of centrality, importance, and chain relationships between the factors within the BCX index system will offer valuable guidance for preventing roof-caving accidents in mining operations. [ABSTRACT FROM AUTHOR]

Forest fires will do great harm to the ecological environment. At present, the most important research field is the study of the causes of forest fires, and the failure to determine the causes will greatly hinder the prevention, control and safety management of forest fires. In order to solve this problem, this paper constructs a Bayesian network to calculate the state probability and sensitivity after determining the factors related to forest fire. Secondly, on the basis of constructing Bayesian network, the Bow-tie model is constructed according to the consequences of forest fire. The constructed Bow-tie model contains 7 influencing factors and 5 consequences, and 26 corresponding control measures. Finally, through the analysis of Bayesian network and bow tie model, the forest fire prevention system is constructed. According to the calculation results of the Bayesian network, the probability of forest fires being at high risk of occurrence is higher for three factors: agriculture, forestry and water expenditure, rural population density and precipitation. Changes in rural population density and humidity have the greatest impact on the occurrence of forest fires. In combination with the Bow-tie model, human factors are the most important factors leading to the occurrence of forest fires, and the material, environmental, and management factors are the secondary factors. Based on the forest control system, the prevention and management measures are put forward. This research can provide reference for the control of forest fire and reduce the occurrence of similar forest fires. [ABSTRACT FROM AUTHOR]

This work presents a knowledge discovery approach through Causal Bayesian Networks for understanding the conditions under which the performance of an optimization algorithm can be affected by the characteristics of the instances of a combinatorial optimization problem (COP). We introduce a case study for the causal analysis of the performance of two state-of-the-art algorithms for the one-dimensional Bin Packing Problem (BPP). We meticulously selected the set of features associated with the parameters that define the instances of the problem. Subsequently, we evaluated the algorithmic performance on instances with distinct features. Our analysis scrutinizes both instance features and algorithm performance, aiming to identify causes influencing the performance of the algorithms. The proposed study successfully identifies specific values affecting algorithmic effectiveness and efficiency, revealing shared causes within some value ranges across both algorithms. The knowledge generated establishes a robust foundation for future research, enabling predictions of algorithmic performance, as well as the selection and design of heuristic strategies for improving the performance in the most difficult instances. The causal analysis employed in this study did not require specific configurations, making it an invaluable tool for analyzing the performance of different algorithms in other COPs. [ABSTRACT FROM AUTHOR]

Background: Epidemiological and experimental evidence suggests that chronic inflammation plays an important role in the onset and progression of sarcopenia. However, there is inconsistent data on the inflammatory cytokines involved in the pathogenesis of sarcopenia. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to explore the causal relationship between circulating cytokines and sarcopenia-related traits. Methods: The MR analysis utilized genetic data from genome-wide association study that included genetic variations in 41 circulating cytokines and genetic variant data for appendicular lean mass (ALM), hand grip strength, and usual walking pace. Causal associations were primarily explored using the inverse variance-weighted (IVW) method, supplemented by MR-Egger, simple mode, weighted median, and weighted mode analyses. Additionally, sensitivity analyses were also performed to ensure the reliability and stability of the results. Results: Three cytokines [hepatocyte growth factor (HGF), interferon gamma-induced protein 10 (IP-10), and macrophage colony-stimulating factor (M-CSF)] were positively associated with ALM (β: 0.0221, 95% confidence interval (CI): 0.0071, 0.0372, P = 0.0039 for HGF; β: 0.0096, 95%CI: 4e-04, 0.0189, P = 0.0419 for IP-10; and β: 0.0100, 95%CI: 0.0035, 0.0165, P = 0.0025 for M-CSF). Conversely, higher levels of interleukin-7 (IL-7), monocyte chemotactic protein 3 (MCP-3), and regulated on activation, normal T cell expressed and secreted (RANTES) were associated with decreased hand grip strength (b: -0.0071, 95%CI: -0.0127, -0.0014, P = 0.0140 for IL-7; β: -0.0064, 95%CI: -0.0123, -6e-04, P = 0.0313 for MCP-3; and b: -0.0082, 95%CI: -0.0164, -1e-04, P = 0.0480 for RANTES). Similarly, interleukin 1 receptor antagonist (IL-1RA) was negatively correlated with usual walking pace (β: -0.0104, 95%CI: -0.0195, -0.0013, P = 0.0254). Sensitivity analysis confirmed the robustness of these findings. Conclusions: Our study provides additional insights into the pivotal role of specific inflammatory cytokines in the pathogenesis of sarcopenia. Further research is required to determine whether these cytokines can be used as targets for the prevention and treatment of sarcopenia. [ABSTRACT FROM AUTHOR]

Background: Dilated cardiomyopathy (DCM) is a cardiac disease with a poor prognosis of unclear etiology. Previous studies have shown that metabolism is associated with DCM. This study investigates the causal relationship between 1400 metabolites and DCM using a two-sample Mendelian randomization (MR) approach. Methods: The study utilized data from the OpenGWAS database, comprising 355,381 Europeans, including 1,444 DCM cases. A total of 1,400 metabolites were evaluated for their causal association with DCM. Instrumental variables (IVs) were selected based on genetic variation and used in the MR analysis. The primary analysis method was inverse variance weighting (IVW), supplemented by weighted median-based estimation and sensitivity analyses. Results: Of the 1,400 metabolites analyzed, 52 were identified as causally associated with DCM. The analysis revealed both positively and negatively correlated metabolites with DCM risk. Notable findings include the positive correlation of Tryptophan betaine and 5-methyluridine (ribothymidine) levels, and an inverse association of Myristoleate and Erythronate levels with DCM. Conclusions: The study provides significant insights into the metabolites potentially involved in the pathogenesis of DCM. These findings could pave the way for new therapeutic strategies and biomarker identification in DCM management. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium–glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. Methods: Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013–2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. Results: Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (−0.3 mmol/mol [−1.6, 1.1], −0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (−4 mmol/mol [4.8, −3.1], −0.4% [−0.4, −0.3]). Conclusions/interpretation: Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require sufficient labeled training data to achieve good attack detection accuracy. On the other hand, unsupervised machine learning methods are more practical by avoiding training data labeling requirements, but they often suffer from high false alarm rates. In this article, we present a generic self-supervised hybrid learning (SHIL) framework for achieving efficient online security attack detection in containerized systems. SHIL can effectively combine both unsupervised and supervised learning algorithms but does not require any manual data labeling. We have implemented a prototype of SHIL and conducted experiments over 46 real-world security attacks in 29 commonly used server applications. Our experimental results show that SHIL can reduce false alarms by 33%–93% compared to existing supervised, unsupervised, or semi-supervised machine learning schemes while achieving a higher or similar detection rate. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive analysis of the impact of global and regional events on the dynamics of tourist flows. The primary reasons for the decrease in inbound tourist flows have been identified and categorized into three main groups: military-political class, economic class, and natural-technological class. Additionally, a hybrid class was identified, to which COVID-19 was assigned. The article is based on causal and comparative analysis, utilizing statistical data on tourist flows provided by the World Bank and UNWTO, as well as an assessment of similar patterns among countries worldwide. The main characteristics of tourist flows from 1995 to 2022 have been identified, pinpointing leaders among countries in increasing and decreasing the number of tourists per year. During the study, countries with similar patterns of tourist flow dynamics were identified. It was concluded that relying solely on quantitative analysis has limited applicability in identifying these patterns. Therefore, it is recommended that quantitative analysis be complemented with expert assessments for a more comprehensive understanding. The basic strategies for responding to the identified classes of causes were outlined. The research will benefit tourism professionals, governmental authorities, and researchers in socio-economic and political disciplines.

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Abstract Background Mendelian randomization (MR) studies have an advantage over conventional observational studies when studying the causal effect of lifestyle-related risk factors on back pain. However, given the heterogeneous design of existing MR studies on back pain, the reported causal estimates of these effects remain equivocal, thus obscuring the true extent of the biological effects of back pain lifestyle-risk factors. Purpose The purpose of this study was to conduct a systematic review with multiple meta-analyses on the associations between various lifestyle factors and low back pain. Methods We conducted a PRISMA systematic review and specifically included MR studies to investigate the associations between lifestyle factors—specifically, BMI, insomnia, smoking, alcohol consumption, and leisure sedentary behavior—and various back pain outcomes. Each meta-analysis synthesized data from three or more studies to assess the causal impact of these exposures on distinct back pain outcomes, including chronic pain, disability, and pain severity. Quality of studies was assessed according to STROBE-MR guidelines. Results A total of 1576 studies were evaluated and 20 were included. Overall, the studies included were of high quality and had a low risk of bias. Our meta-analysis demonstrates the positive causal effect of BMI (OR IVW−random effects models: 1.18 [1.08–1.30]), insomnia(OR IVW−random effects models: 1.38 [1.10–1.74]), smoking(OR IVW−fixed effects models: 1.30 [1.23–1.36]), alcohol consumption(OR IVW−fixed effects models: 1.31 [1.21–1.42]) and leisure sedentary behaviors(OR IVW−random effects models: 1.52 [1.02–2.25]) on back pain. Conclusion In light of the disparate designs and causal effect estimates presented in numerous MR studies, our meta-analysis establishes a compelling argument that lifestyle-related risk factors such as BMI, insomnia, smoking, alcohol consumption, and leisure sedentary behaviors genuinely contribute to the biological development of back pain.

Background: Mendelian randomization (MR) studies have an advantage over conventional observational studies when studying the causal effect of lifestyle-related risk factors on back pain. However, given the heterogeneous design of existing MR studies on back pain, the reported causal estimates of these effects remain equivocal, thus obscuring the true extent of the biological effects of back pain lifestyle-risk factors. Purpose: The purpose of this study was to conduct a systematic review with multiple meta-analyses on the associations between various lifestyle factors and low back pain. Methods: We conducted a PRISMA systematic review and specifically included MR studies to investigate the associations between lifestyle factors—specifically, BMI, insomnia, smoking, alcohol consumption, and leisure sedentary behavior—and various back pain outcomes. Each meta-analysis synthesized data from three or more studies to assess the causal impact of these exposures on distinct back pain outcomes, including chronic pain, disability, and pain severity. Quality of studies was assessed according to STROBE-MR guidelines. Results: A total of 1576 studies were evaluated and 20 were included. Overall, the studies included were of high quality and had a low risk of bias. Our meta-analysis demonstrates the positive causal effect of BMI (OR IVW−random effects models: 1.18 [1.08–1.30]), insomnia(OR IVW−random effects models: 1.38 [1.10–1.74]), smoking(OR IVW−fixed effects models: 1.30 [1.23–1.36]), alcohol consumption(OR IVW−fixed effects models: 1.31 [1.21–1.42]) and leisure sedentary behaviors(OR IVW−random effects models: 1.52 [1.02–2.25]) on back pain. Conclusion: In light of the disparate designs and causal effect estimates presented in numerous MR studies, our meta-analysis establishes a compelling argument that lifestyle-related risk factors such as BMI, insomnia, smoking, alcohol consumption, and leisure sedentary behaviors genuinely contribute to the biological development of back pain. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Statins, which are medications that lower lipid levels, are extensively used to decrease cardiovascular disease risk. Recently, the use of statins in cancer prevention has attracted considerable interest. However, it is still unclear whether the use of statins has a causal effect on bladder cancer. Methods: The two-sample Mendelian Randomization (MR) was performed to infer the causal relationship between statin therapy (atorvastatin, simvastatin, and rosuvastatin) and bladder cancer. Single-nucleotide polymorphisms (SNP)-based genome-wide association studies (GWAS) of statins (atorvastatin, simvastatin, and rosuvastatin) were gathered from the UK Biobank, involving 462,933 participants. We acquired summary-level genetic data on bladder cancer from a European cohort of 175,121 individuals. The inverse variance weighted (IVW) method was the main analytical technique used, supplemented by MR-Egger, weighted median, weighted mode, and simple mode to estimate causal effects. Additionally, sensitivity analyses were conducted to verify the robustness and reliability of our findings. Results: Based on the IVW analysis, we identified a significant causal association between rosuvastatin use and a decreased risk of bladder cancer, with genetic analysis inferring the substantial reduction in odds (OR = 3.52E-19, 95% CI: 5.48E-32-2.26E-06, p = 0.005). In contrast, the IVW results did not reveal a statistically significant relationship between the genetically estimated use of atorvastatin (OR = 7.42E-03, 95% CI: 6.80E-06-8.084, p = 0.169) or simvastatin (OR = 0.135, 95% CI: 0.008-2.330, p = 0.168) and bladder cancer risk. Conclusion: We investigated the causal link between statin therapy (atorvastatin, simvastatin, and rosuvastatin) and bladder cancer using a two-sample Mendelian Randomization analysis among the European population. Our findings indicated that genetically predicted use of rosuvastatin was associated with a decreased risk of bladder cancer, whereas no significant genetically predicted causal effects were observed for atorvastatin and simvastatin use. [ABSTRACT FROM AUTHOR]

This paper offers an accessible discussion of graphical causal models and how such a framework can be used to help identify causal relations. A graphical causal model represents a researcher's qualitative assumptions. As a result of the credibility revolution, there is growing interest to properly estimate cause-and-effect relationships. Using several examples, we illustrate how graphical models can and cannot be used to identify causation from observational data. Further, we offer a replication of a previous study that explored college enrolment by high school seniors who were eligible for student aid. From the original study, we use a graphical causal model to motivate the quantitative and qualitative modelling assumptions. Using a similar difference-in-difference approach based on propensity score matching, we estimate a smaller average treatment effect than the original study. The smaller estimated effect arguably stems from the graphical causal model's delineation of the original model specification. [ABSTRACT FROM AUTHOR]

Understanding the relationship between unbalanced riverbed scouring or deposition and the evolution of central bars in natural conditions and human activity is useful for river regime control and waterway improvement projects. Toward this end, we utilized the Yangzhong reach in the lower reaches of the Yangtze River as a case study and evaluated runoff, sediment content, and topographical data measured over the past 70 years (1951–2021). With the decrease in the amount of incoming sediment in the river basin, the Yangzhong reach exhibited a continuous state of scouring. The cumulative riverbed scouring volumes of the low-water and flood channels from 1981 to 2021 were 3.97×108 and 4.14×108 m3, respectively, with the riverbed scouring volume of the low-water channel accounting for 95.9% of that of the flood channel. Under quasi-natural runoff–sediment conditions, the evolution of the central bars in the Yangzhong reach was highly correlated with the amount of scouring or deposition. In particular, the Luochengzhou reach could be characterized as a meandering river with scouring on concave riverbanks and deposition on convex riverbanks. In the context of reduced incoming sediment, the beach area of the Yangzhong reach decreased by approximately 9.9% (from 2003 to 2021) and the central bars of the straight section areas decreased. Moreover, following operation of the Three Gorges Reservoir, both the Luocheng central bar and Jiangyin beach areas could be characterized as meandering rivers with convex riverbanks and beaches. In the quasi-natural period before the implementation of the waterway improvement project, the trend of high scouring intensity and increased fluid diversion ratio in the right branch of the Luocheng central bar was in accordance with the law that the short branch of the length is in a developing state under the condition of decreasing river sediment volume. With the control of riverbank protection and the construction of waterway improvement projects in the Yangzhong reach, the shapes of the riverbanks and central bars were effectively stabilized, and the linkage relationship formed in the quasi-natural period was interrupted. Overall, these findings provide a strong foundation for understanding riverbed scouring or deposition and the evolution of central bars under the influence of natural conditions and human activities, and will inform future river management and waterway dimension planning. [ABSTRACT FROM AUTHOR]

When interest lies in causal analysis of the effects of multiple exposures on an outcome, one may be interested in investigating the interaction between the exposures. In such settings, causal analysis requires modeling the joint distribution of exposures given pertinent confounding variables. In the most general setting, this may require modeling the effect of confounding variables on the association between exposures via a second-order regression model. We consider joint modeling of exposures for causal analysis via regression adjustment and inverse weighting. In both frameworks, we also investigate the asymptotic bias of estimators when the dependence model for the generalized propensity score incorrectly assumes conditional independence of exposures or is based on a naive dependence model which does not accommodate the effect of confounders on the conditional association of exposures. We also consider the problem of a semi-continuous bivariate exposure and propose a two-stage estimation technique to study the effects of prenatal alcohol exposure, and the effects of drinking frequency and intensity on childhood cognition. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is the third leading cause of cancer death worldwide, and hypothyroidism has been identified as a potential influencing factor. Despite known associations between hypothyroidism and various cancers, the causal link between hypothyroidism and GC and potential mediators of this relationship remains unclear. This study aimed to clarify these relationships using Mendelian randomization (MR). Methods: Utilizing genetic variant information from the FinnGen and MRC Integrative Epidemiology Unit open genome-wide association studies (GWAS) databases, we conducted univariable and multivariable MR analyses to explore the causal relationship between hypothyroidism and GC risk. The analysis was adjusted for confounders such as BMI, smoking status, and alcohol intake, and included mediator MR analysis to examine the role of high cholesterol. Results: We identified a significant inverse association between hypothyroidism and GC risk (OR = 0.93, 95% CI= 0.89-0.98, P = 0.003), with no evidence of reverse causation or pleiotropy. Adjustments for Helicobacter pylori infection weakened this association. Mediator analysis highlighted high cholesterol levels, chronic hepatitis B infection, and diabetes/endocrine disease status as significant mediators of the protective effect of hypothyroidism on GC risk. Conclusion: Our findings suggest that hypothyroidism may confer a protective effect against GC, mediated in part by high cholesterol and other factors. These results underscore the importance of thyroid function and metabolic health in GC risk, offering new insights for preventive strategies and highlighting the need for further research into these complex associations. [ABSTRACT FROM AUTHOR]

The fundamental methodologies of machine learning and econometrics are reviewed. We also discuss the challenges of integrating the data-driven and model-based causal approaches and conjecture how it may yield new insights to empirical economic studies. [ABSTRACT FROM AUTHOR]

Interactions among atmospheric, root‐soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub‐daily timescales and validate process‐based and data‐driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory‐based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower data sets in the Midwestern U.S. in intensively managed corn‐soybean landscapes. We compare multiple linear regressions, long‐short term memory, and random forests (RF), and evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive and functional performance, which examines a suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance, correctly capturing strong dependencies between radiation and temperature variables with Fc. Regionally trained models demonstrate lower predictive but higher functional performance compared to site‐specific models, suggesting superior reproduction of observed relationships at the expense of predictive accuracy. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models. Plain Language Summary: In an agricultural landscape, exchanges of carbon dioxide between the land and atmosphere occur due to photosynthesis and respiration and depend on weather, soil, and vegetation conditions. Traditional model performance metrics focus on the relationship between observed and modeled outputs, while functional performance considers the relationships between interacting inputs and outputs. We compare several performance measures for three different machine learning (ML) models that simulate sub‐daily carbon fluxes. We look at how drivers such as solar radiation, soil moisture, temperature, humidity, and rainfall provide information to carbon fluxes, and whether different ML models also capture these interactions. In other words: Air, soil, and plants drive carbon's upward path,Models are detectives, interpreting their math.With information theory, we map data's travel courses,To see how models find or miss carbon's causal sources. Key Points: Information theory measures describe individual and joint causal relationships in observed versus modeled vertical carbon dioxide fluxesThree machine learning models overestimate unique information from sources at the expense of synergistic, or joint informationRegionally trained models have improved functional but not predictive performances, indicating a trade‐off [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This 2022 article explores how and why philanthropy has largely rejected the rigorous examination of cause-and-effect relationships in social change strategies. It outlines the myths that, the authors argue, stood in the way of this practice and calls for increased causal analysis in the sector and the use of new ways for doing it, particularly for strategies rooted in complex systems. In the 2024 prologue, the authors describe how they launched Causal Pathways, a collaborative field-building initiative that is responding to the demand for more learning and practice on this topic. They identify two new myths about why more causal analysis is not happening: Evaluations are already causal and there is nothing that needs to change, and causality is not important in evaluations. The authors also encountered practical barriers to applying causal approaches. Funders and evaluators need in-depth and real-life opportunities to try and learn causal approaches. [ABSTRACT FROM AUTHOR]

Qinghai-Tibet Plateau has a special geographical location and large lake groups making it an important research subject of global climate change and regional response. Taking its 149 typical lakes as the research subject, using the relevant features of lake area data from 1985 to 2020, the variation of the lake area, the chronological division of climate change, and the impact of climate features on the lake area growth rate are analyzed. The main methods and results include the following: (1) By using the time series clustering algorithm to mine the shape similarity of lake area change, three patterns of area change, C1, C2 and C3 modes, which show a slow growth trend, a strong fluctuation, and a significant growth trend, respectively, are summarized. (2) When the optimal segmentation point index of the binary sequence is proposed and combined with the clustering results of the lake regional climate to find the year of the optimal segmentation point for all lakes, the regional climate segmentation points of lakes are concentrated from 1995 to 2005. (3) When researching the lakes in C3 mode, using the XGBoost model as the regression model to extract the feature importance affecting lake growth rate, the effect of precipitation on lake area is the highest, followed by surface temperature. When a double machine learning model is established to conduct causal analysis to obtain an unbiased estimation of the lake area growth rate influenced climate features, the effect of precipitation is the highest, followed by evaporation. The methods used and the results show a new perspective in understanding climate change and lake response. [ABSTRACT FROM AUTHOR]

PurposeAge-related macular degeneration (AMD) is the leading cause of low vision and even blindness in the elderly population worldwide. However, no studies have been conducted to analyze the causal relationship between the cathepsin family and AMD. The present study aimed to explore and analyze this potential association using Mendelian randomization (MR).MethodsIn this study, AMD was classified into two types: exudative AMD and atrophic AMD. Inverse-variance weighting (IVW) was used as the main analysis method. The association between nine cathepsins and the two classifications of AMD were analyzed using multivariable Mendelian randomization (MVMR). Sensitivity analysis included Cochran’s Q-test and the MR-Egger intercept test.ResultsTwo-sample MR analysis showed that higher levels of cathepsin L2 were associated with a delay in the development of atrophic AMD (IVW: p = 0.017; OR = 0.885; 95% CI = 0.799–0.979). Reverse MR analysis indicated that cathepsin E levels were increased in individuals with atrophic (IVW: p = 0.023; OR = 1.058; 95% CI = 1.007–1.111) and exudative AMD (IVW: p = 0.018; OR = 1.061; 95% CI 1 = 1.010–1.115). MVMR analysis indicated a causal relationship between cathepsin G (IVW: p = 0.025; OR = 1.124; 95% CI = 1.014–1.245), cathepsin O (IVW: p = 0.043, OR = 1.158, 95% CI = 1.004–1.336), and exudative AMD after coordinating for other types of cathepsin.ConclusionThis study demonstrated a potential link between the cathepsin family and the onset of AMD. Elevated serum concentrations of cathepsin L2 may serve as a protective factor for atrophic AMD, while increased levels of serum cathepsin G and O concentrations may promote the development of exudative AMD. Besides, the development of AMD may be associated with elevated serum concentrations of cathepsin E.

We commend Alt et al.'s innovative approach for analysis with a hybrid control arm while offering insights into two key considerations: the necessity for extrapolation and the potential benefits of curating historical control data before analysis. [ABSTRACT FROM AUTHOR]

Extreme rainfall is often accompanied by mass geological disasters, which seriously endangers the safety of people 's lives and property in prone areas and affects the healthy development of the economy and society. Summarizing and analyzing the time-space distribution characteristics of geological disasters due to extreme rainfall and the effectiveness of early warning is of great significance for improving the comprehensive defense ability against geological disasters. Taking the sudden geological disasters caused by “23•7” heavy rainfall in 2023 as the research object, based on the refined precipitation data from the Beijing sudden geological disaster monitoring and early warning system, the time-space distribution characteristics of “23•7” heavy rainfall and the development and distribution characteristics of geological disasters were analyzed, and the early warning effect of geological disasters was discussed. The results show that the“23•7”heavy rainfall has the characteristics such as a large total amount, strong rainfall, long duration and wide range, and the disasters due to extreme rainfall have the characteristics of group occurrence. The multi-dimensional early warning of geological disaster classification has achieved remarkable results and has achieved the goal of zero casualties due to geological disasters under extreme weather conditions. The research results can provide a reference for actively preventing and scientifically responding to extreme rainfall geological disasters.

The sea‐ice cover in the Barents and Kara Seas (BKS) displays pronounced interannual variability. Both atmospheric and oceanic drivers have been found to influence sea‐ice variability, but their relative strength and regional importance remain under debate. Here, we use the Liang‐Kleeman information flow method to quantify the causal influence of oceanic and atmospheric drivers on the annual sea‐ice cover in the BKS in the Community Earth System Model large ensemble and reanalysis. We find that atmospheric drivers dominate in the northern part, ocean heat transport dominates in the central and northeastern part, and local sea‐surface temperature dominates in the southern part. Furthermore, the large‐scale atmospheric circulation over the Nordic Seas drives ocean heat transport into the Barents Sea, which then influences sea ice. Under future sea‐ice retreat, the atmospheric drivers are expected to become more important. Plain Language Summary: The sea ice in the Barents and Kara Seas (BKS) is melting due to Arctic warming, but this is overlaid by large natural variability. This variability is caused by variations in the ocean and the atmosphere, but it is not clear which is more important in which parts of the region. We use a relatively new method that allows us to quantify cause‐effect relationships between sea ice and atmospheric and oceanic drivers. We find that in the north of the BKS, the atmosphere has the biggest impact, in the central and northeastern parts, it is the heat from the ocean, and in the south, it is the local sea temperature. We also find that wind patterns over the Nordic Seas affect how much oceanic heat comes into the Barents Sea, and that, in turn, affects the sea ice. Looking ahead, as the ice is expected to melt more in the future, the atmosphere is likely to become more important in driving sea ice variability in the BKS. This study helps us better understand how the ocean and atmosphere work together to influence the yearly changes in sea ice in this region. Key Points: Ocean heat transport drives sea‐ice variability in the central and northeastern Barents SeaAtmospheric temperature drives sea‐ice variability in the northern Barents‐Kara SeasAtmospheric circulation over the Nordic Seas drives ocean heat transport, which then influences sea‐ice variability [ABSTRACT FROM AUTHOR]

Predatory journals have been a recent phenomenon, drawing attention from the academic community in the last decade. However, as the open access (OA) movement has gained momentum, the indiscriminate growth of predatory journals has had significant negative impacts on academic communication, scholarly publishing, and effective utilization of scientific resources. This rampant growth poses a serious threat to the healthy development of the OA movement and also undermines the integrity of research and the research ecosystem. Identifying predatory journals from the massive number of OA journals would assist scholars in evading negative consequences in areas of monetary investment, reputation, academic influence, and occupational advancement. Traditional methods for identifying predatory journals have relied heavily on the knowledge of domain experts. However, a large number of predatory journals exhibit latent and covert characteristics, and the growth rate of OA journals is extremely rapid, making it difficult for experts to identify these predatory journals from the vast number of OA journals. This paper proposes an interpretable machine learning model for early warning of predatory OA journals, which identifies predatory journals through the ensemble of multiple machine learning algorithms. Specifically, the proposed methodology first constructs an OA journal early warning indicator system and integrates multiple machine learning algorithms to compute the early warning values of OA journals. Then, the SHAP interpretable framework is introduced to analyze the causal factors of the early warning risks in a novel way. To verify the accuracy of the model's causal factors, we conduct a comparative analysis of domestic and foreign medical OA journals using case studies. The empirical analysis conducted in this study demonstrates the efficacy of the ensemble algorithm in accurately identifying the risk of predatory OA journals. [ABSTRACT FROM AUTHOR]

Background: Previous observational researchers have found an inverse bidirectional link between Alzheimer's disease (AD) and prostate cancer (PCa); yet, the causative nature of this link remains unclear. To investigate the causal interactions between AD and PCa, a bidirectional Mendelian randomization (MR) analysis was conducted. Methods: This study comprised two Genome-Wide Association Study (GWAS) summary statistics for AD (17,008 cases and 37,154 controls) and PCa (79,148 cases and 61,106 controls) in individuals of European ancestry. The inverse-variance weighted (IVW) method was employed as the primary approach, while MR-Egger, weighted median, weighted mode, and simple mode served as supplementary methods for estimating the causal effect. To assess pleiotropy, the MR-PRESSO global test and MR-Egger regression were used. Cochran's Q test was adopted to check heterogeneity, MR Steiger test and the leave-one-out analysis was performed to confirm the robustness and reliability of the results. Results: The causal association genetically inferred of AD on PCa was found using IVW (OR = 0.974, 95% CI = 0.958-0.991, p = 0.003) in forward MR analysis and the causal association genetically inferred of PCa on AD was not found using IVW (OR = 1.000, 95% CI: 0.954-1.049, P = 0.988) in reverse MR analysis. The sensitivity analysis showed that no pleiotropy and heterogeneity was observed. The leave-one-out analysis showed that the findings were not inordinately affected by any instrumental variables. Conclusion: The results of this study demonstrated an absence of bidirectional causality between AD and PCa among the European population, suggested that a genetically predicted possibility of decreased PCa risk in AD patients, and no significant genetically predicted causal effect of PCa on AD. [ABSTRACT FROM AUTHOR]

Global changes in drought and wetness and their future trends in arid regions have recently become a major focus of research attention. The Tarim River Basin (TRB) in Xinjiang, China, is among the most climate-sensitive regions in the world. This study uses data from the past 60 years (1962–2021) to analyze the spatial and temporal features of drought and wetness conditions in the TRB, calculating the Standardized Precipitation Evapotranspiration Index (SPEI). Trend detection for SPEI is performed using the BEAST mutation test, identification of drought events using the theory of operations, and spatial and temporal analyses of dry and wet changes using Empirical Orthogonal Function (EOF) decomposition. Additionally, the CMIP6 dataset is used to estimate future changes. The study results indicate the following: (1) From 1962 to 1998, the TRB exhibited a "warm and wet" trend that suddenly shifted from "wet-to-dry" in 1998 and subsequently transitioned to a pronounced "warm and dry" trend. (2) After the "wet-to-dry" shift, the frequency of drought events noticeably increased. The northern section of the basin witnessed more frequent drought events, albeit with lower severity, while the southern part had fewer occurrences but with higher severity. The spatial distribution of drought event frequency and severity is inconsistent. (3) The EOF decomposition results for SPEI-variable fields at 1-, 3-, and 6-month time scales show that the cumulative variance contribution rate of the first three principal spatial modal feature vectors exceeds 70%. The spatial distribution of the modes includes a consistent pattern across the entire basin, a north–south opposite pattern, and an east–west opposite pattern. (4) The future trend of drought in the TRB is expected to intensify, manifesting a spatial pattern characterized by dryness in the middle of the basin and wetness around the periphery. These research findings can provide support for decisions addressing regional drought risks. [ABSTRACT FROM AUTHOR]

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This paper proposes a methodology to estimate the probability of basic causes of allision accidents between vessels and offshore platforms that overcomes the problem of data scarcity required for causal analysis. The approach uses information derived from incidental data and expert elicitation, processed by a multiple attribute utility method and hierarchical Bayesian analysis. First, the methodology is detailed, briefly describing the adopted approaches. A dataset of allision incidents provided mainly by the UK Health and Safety Executive and other agencies is prepared. The features of the incidents' causation in terms of the causal factors and basic causes are presented and discussed. A novel scheme is proposed to evaluate the annual occurrence rates of basic causes of accidents from the relative importance of each basic cause derived by the Deck of Cards method. Then, a hierarchical Bayesian analysis is conducted to predict the posterior distribution of the occurrence rate of each basic cause in the time frame under analysis. The proposed holistic methodology provides transparent estimates of allision causation probabilities from limited and heterogeneous datasets. [ABSTRACT FROM AUTHOR]

While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConslusions\nHIGHLIGHTSWe investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement.Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant’s own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks.For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods.In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.Movement-related cortical magnetic fields were assessed with Granger causality analysisConnectivity of neural signals to M1 was different between internal and external cue tasks.Connectivity of neural signals from the frontal area was dominant to M1 in the internal cue task.Connectivity of neural signals to M1 from the parietal area was observed in the external cue task. [ABSTRACT FROM AUTHOR]

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions. [ABSTRACT FROM AUTHOR]

Background: Aspirin, as one of the most commonly used drugs, possesses a broad spectrum of therapeutic applications. Presently, the potential association between aspirin usage and the risk elevation of erectile dysfunction (ED) remains inconclusive. The objective of this study employing two-sample Mendelian randomization (MR) was to clarify the causal impact of aspirin use on the risk of ED. Methods: This study incorporated two sets of Genome-Wide Association Study (GWAS) summary statistics, one for aspirin use (46,946 cases and 286,635 controls) and another for ED (6,175 cases and 217,630 controls) in individuals of European ancestry. The inverse-variance weighted (IVW) method was employed as the primary approach, supplemented by MREgger, weighted median, weighted mode, and simple mode to estimate the causal effect of aspirin usage on the risk of ED development. To assess pleiotropy, the MR-PRESSO global test and MR-Egger regression were used. Cochran's Q test was adopted to check heterogeneity, and the leave-oneout analysis was performed to confirm the robustness and reliability of the results. Results: The causal association between genetically inferred aspirin use and ED was found by using inverse variance weighted (OR = 20.896, 95% confidence interval = 2.077-2.102E+2, P = 0.010). The sensitivity analysis showed that no pleiotropy and heterogeneity was observed. Furthermore, the leave-one-out analysis demonstrated that the findings were not significantly affected by any instrumental variables. Conclusion: The results of this study highlighted the significance of aspirin use as a predisposing factor for ED and provided further evidence supporting the causal association between aspirin utilization and ED within European populations. [ABSTRACT FROM AUTHOR]

To assess the indications, timing, and clinical outcomes that result from the early tracheostomy (ET) administration, by causal inference models. A retrospective observational study. Multiinstitutional intensive care unit in the United States The study comprised 626 trauma patients. An ET versus late tracheostomy (LT). Trauma patients with tracheostomy were identified from 2 public databases named Medical Information Mart for the Intensive Care-IV and eICU Collaborative Research Database. Tracheostomy was defined as early (≤7 days) or late (>7 days) from intensive care unit admission. A marginal structural Cox model (MSCM) with inverse probability weighting was employed. For comparison, the authors also used time-dependent propensity-score matching (PSM) to account for differences in the probability of receiving an ET or LT. A total of 626 eligible patients were enrolled in the study, of whom 321 (51%) received a ET. The MSCM and time-dependent PSM indicated that the ET group was associated with reduced ventilation-associated pneumonia (VAP) and a shorter mechanical ventilation (MV) duration than the LT group. Yet, mortality did not show any difference between the two groups. The authors' study observed that ET was not associated with reduced mortality in trauma patients, but it was associated with reduced VAP risk and MV duration. The results warrant further validation in randomized controlled trials. [ABSTRACT FROM AUTHOR]

The northwest China is a climate change-sensitive and ecologically vulnerable area. Under the backdrop of global warming, this region exhibits clear characteristics of warming and wetting. In recent years, the causes of climate change in the northwest China has become a widely-focused topic. Previous research has mainly attributed the increase in precipitation to changes in the westerly belt and enhanced local convective activity caused by global warming. South Asia, beside the Tibetan Plateau from the northwest China, is one of the regions with the fastest growth in global atmospheric pollutant emissions. This study utilizes reanalysis data such as ERA5 and MERRA-2. Statistical methods, including Theil-Sen Median trend analysis and Singular Value Decomposition analysis, are employed to analyze the spatiotemporal characteristics of South Asian aerosols, extreme precipitation in the northwest China, and the correlation between the two. The study reveals the existence of two key aerosol-precipitation response areas. Synthetic analysis of the meteorological elements of the response events in both regions is conducted to explore the possible physical mechanisms behind the correlation between South Asian aerosols and precipitation in the northwest China. The result of this study is to provide a new perspective on the causes of extreme precipitation in the arid region of northwest China.

Abstract The sea‐ice cover in the Barents and Kara Seas (BKS) displays pronounced interannual variability. Both atmospheric and oceanic drivers have been found to influence sea‐ice variability, but their relative strength and regional importance remain under debate. Here, we use the Liang‐Kleeman information flow method to quantify the causal influence of oceanic and atmospheric drivers on the annual sea‐ice cover in the BKS in the Community Earth System Model large ensemble and reanalysis. We find that atmospheric drivers dominate in the northern part, ocean heat transport dominates in the central and northeastern part, and local sea‐surface temperature dominates in the southern part. Furthermore, the large‐scale atmospheric circulation over the Nordic Seas drives ocean heat transport into the Barents Sea, which then influences sea ice. Under future sea‐ice retreat, the atmospheric drivers are expected to become more important.

Abstract Background Observational studies have demonstrated an association between gut microbiota and myasthenia gravis; however, the causal relationship between the two still lacks clarity. Our goals are to ascertain the existence of a bidirectional causal relationship between gut microbiota composition and myasthenia gravis, and to investigate how gut microbiota plays a role in reducing the risk of myasthenia gravis. Methods We acquired gut microbiota data at the phylum, class, order, family, and genus levels from the MiBioGen consortium (N = 18,340) and myasthenia gravis data from the FinnGen Research Project (426 cases and 373,848 controls). In the two-sample Mendelian randomization analysis, we assessed the causal relationship between the gut microbiota and myasthenia gravis. We also conducted bidirectional MR analysis to determine the direction of causality. The inverse variance weighted, mendelian randomization-Egger, weighted median, simple mode, and weighted mode were used to test the causal relationship between the gut microbiota and severe myasthenia gravis. We used MR-Egger intercept and Cochran's Q test to assess for pleiotropy and heterogeneity, respectively. Furthermore, we utilized the MR-PRESSO method to evaluate horizontal pleiotropy and detect outliers. Results In the forward analysis, the inverse-variance weighted method revealed that there is a positive correlation between the genus Lachnoclostridium (OR = 2.431,95%CI 1.047–5.647, p = 0.039) and the risk of myasthenia gravis. Additionally, the family Clostridiaceae1 (OR = 0.424,95%CI 0.202–0.889, p = 0.023), family Defluviitaleaceae (OR = 0.537,95%CI 0.290–0.995, p = 0.048), family Enterobacteriaceae (OR = 0.341,95%CI 0.135–0.865, p = 0.023), and an unknown genus (OR = 0.407,95%CI 0.209–0.793, p = 0.008) all demonstrated negative correlation with the risk of developing myasthenia gravis. Futhermore, reversed Mendelian randomization analysis proved a negative correlation between the risk of myasthenia gravis and genus Barnesiella (OR = 0.945,95%CI 0.906–0.985, p = 0.008). Conclusion Our research yielded evidence of a causality connection in both directions between gut microbiota and myasthenia gravis. We identified specific types of microbes associated with myasthenia gravis, which offers a fresh window into the pathogenesis of this disease and the possibility of developing treatment strategies. Nonetheless, more studies, both basic and clinical, are necessary to elucidate the precise role and therapeutic potential of the gut microbiota in the pathogenesis of myasthenia gravis.

This research endeavors to investigate the development characteristics, spatial-temporal distribution, and cause of karst collapses in Chengnan community, Yingcheng Street, Yingde City, Guangdong Province. Various technological means such as ground investigation, geophysical exploration, and drilling were employed to examine the collapses discovered in the study area, along with systematic data collection on environmental geologic conditions, historical disasters, and water-level monitoring in the study area and its surrounding areas. The results show that: (1) The karst collapses in the study area constitute an extremely large geological disaster, affecting an area of 0.6 km2, with 31 consecutive eruptions. Most of the surface collapse pits in the study area are small-scale, while a few are medium and large-scale. The planar shape of the collapse pits is primarily circular and elliptical, while the cross-section shape is mainly cylindrical. (2) The occurrence of karst collapses in the study area is influenced by geological structure, covering-layer of soil, atmospheric rainfall, hydrogeological characteristics, and human activities. Among these factors, geological structure, covering-layer of soil, and hydrogeological characteristics are considered as primary factors, whereas atmospheric rainfall and human activities act as inducing factors after a comprehensive assessment. (3) To conclude, the karst collapses erupted in the study area can be categorized as “the mode of collapse near rivers in subsurface karst areas”. During the causal analysis, priority should be given to considering water level fluctuations, pumping, and drainage. Subsequently, other influencing factors should be taken into account based on specific conditions, providing valuable insights for future research on karst collapses erupted near rivers.

Abstract Background When ectopically overexpressed, anticancer genes, such as TRAIL, PAR4 and ORCTL3, specifically destroy tumour cells without harming untransformed cells. Anticancer genes can not only serve as powerful tumour specific therapy tools but studying their mode of action can reveal mechanisms underlying the neoplastic transformation, sustenance and spread. Methods Anticancer gene discovery is normally accidental. Here we describe a systematic, gain of function, forward genetic screen in mammalian cells to isolate novel anticancer genes of human origin. Continuing with over 30,000 transcripts from our previous study, 377 cell death inducing genes were subjected to screening. FBLN5 was chosen, as a proof of principle, for mechanistic gene expression profiling, comparison pathways analyses and functional studies. Results Sixteen novel anticancer genes were isolated; these included non-coding RNAs, protein-coding genes and novel transcripts, such as ZNF436-AS1, SMLR1, TMEFF2, LINC01529, HYAL2, NEIL2, FBLN5, YPEL4 and PHKA2-processed transcript. FBLN5 selectively caused inhibition of MYC in COS-7 (transformed) cells but not in CV-1 (normal) cells. MYC was identified as synthetic lethality partner of FBLN5 where MYC transformed CV-1 cells experienced cell death upon FBLN5 transfection, whereas FBLN5 lost cell death induction in MCF-7 cells upon MYC knockdown. Conclusions Sixteen novel anticancer genes are present in human genome including FBLN5. MYC is a synthetic lethality partner of FBLN5. Video Abstract

Abstract Background The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis. Methods In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005–2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014–2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set. Results Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome. Conclusions Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

This work presents a knowledge discovery approach through Causal Bayesian Networks for understanding the conditions under which the performance of an optimization algorithm can be affected by the characteristics of the instances of a combinatorial optimization problem (COP). We introduce a case study for the causal analysis of the performance of two state-of-the-art algorithms for the one-dimensional Bin Packing Problem (BPP). We meticulously selected the set of features associated with the parameters that define the instances of the problem. Subsequently, we evaluated the algorithmic performance on instances with distinct features. Our analysis scrutinizes both instance features and algorithm performance, aiming to identify causes influencing the performance of the algorithms. The proposed study successfully identifies specific values affecting algorithmic effectiveness and efficiency, revealing shared causes within some value ranges across both algorithms. The knowledge generated establishes a robust foundation for future research, enabling predictions of algorithmic performance, as well as the selection and design of heuristic strategies for improving the performance in the most difficult instances. The causal analysis employed in this study did not require specific configurations, making it an invaluable tool for analyzing the performance of different algorithms in other COPs.