Verification Of Causality Research Articles

Causal relationship between 150 skin microbiomes and prostate cancer: insights from bidirectional mendelian randomization and meta-analysis.

Despite relevant research, the relationship between skin microbiomes and prostate cancer remains controversial. This study utilizes bidirectional Mendelian randomization (MR) analysis combined with meta-analysis to explore the potential link between the two. This study aims to identify the causal relationship between 150 skin microbiomes and prostate cancer (PCa) using bidirectional Mendelian randomization (MR) and meta-analysis. This study employed a comprehensive Bidirectional Two-sample MR analysis using publicly available genetic data to ascertain the relationship between 150 skin microbiomes and PCa. We conducted extensive sensitivity analyses, tests for heterogeneity, and assessments of horizontal pleiotropy to ensure the accuracy of our results. Subsequently, we conducted a meta-analysis to strengthen our conclusions' robustness further. Finally, we performed reverse causal verification on the positive skin microbiomes and PCa. After conducting a meta-analysis and multiple corrections of the MR analysis results, our findings reveal a correlation between Neisseria in dry skin and PCa risk, identifying it as a risk factor. The IVW result shows an Odds Ratio (OR) of 1.009 (95% Confidence Interval [CI]: 1.004-1.014, P = 0.027). Furthermore, the reverse MR analysis indicates the absence of an inverse causal relationship between the two. Apart from the identified skin microbiome, no significant associations were found between the other microbiomes and PCa. The study identified a correlation between Neisseria in dry skin, one of the 150 skin microbiomes, and the risk of developing PCa, establishing it as a risk factor for increased susceptibility to PCa.

Causal inference of multivariate time series in complex industrial systems

In complex industrial systems, causal inference plays a crucial role in improving production and tracing faults. The causal inference of industrial systems encompasses two main steps. First, it aims to discover causal relationships between variables. Second, it also attempts to determine time delays between variables that are causally linked, caused by factors such as industrial responses and pipeline transport. However, existing causal inference methods have certain limitations when applied to complex process industrial systems with highly dynamic and noisy environments. Therefore we propose the Causal-Transformer, a causal model based on the deep structure of the transformer. In our model, a multi-head causal attention mechanism is designed to discovery causal relationships, remove indirect coupling effects in causality, and eliminate the effects of confounding factors in causality through a causal verification step. To the best of our knowledge, we propose for the first time a novel delay discovery method to address the dynamic delay problem caused by the environment. In summary, our approach addresses the problem of causal inference in complex industrial systems. We conducted extensive numerical experiments to verify the effectiveness of our model. Moreover, we applied the model to the actual polymerization process of polyester fiber, further validating its performance in a real industrial system.

Temporal tendency, seasonality and relationship with climatic factors of Crimean-Congo Hemorrhagic Fever cases (East of Turkey: 2012–2021)

Crimean-Congo Hemorrhagic Fever continues to be an important public health problem by expanding its borders. To evaluate the temporal trend, seasonality, and relationship with the climatic factors of Crimean-Congo Hemorrhagic Fever. Study data included cases treated in two different tertiary healthcare institutions between 2012 and 2021. The demographic characteristics of the cases and the dates of admission to the hospital were determined, and they were matched with the average of the measurements (temperature, cumulative precipitation, relative humidity, wind speed) of two different meteorology stations in the study area. By calculating the crude incidence rates, the trend in years was investigated. Estimates were created by removing the incidence rates, seasonality, and trend components using the additive decomposition technique. The temporal relationship between incidence rates and climatic factors was evaluated with the help of the Autoregressive Distributed Lag Bound Test. Toda Yamamoto test was used for causality verification. The mean age of the cases (n = 974) included in the study was 47.6 ± 17.7 years, and the majority (57.3%) were in the group above 45 years of age. 56.6% of the cases were male and there was a male predominance in all age groups. Incidence rates ranged from 5.5 to 23.1/100,000 over the ten-year period and there was a significant upward trend (R2 = 0.691, p = 0.003). Cases of Crimean-Congo Hemorrhagic Fever that started in March, peaked in July and ended in October, showed a clear seasonality. A cointegration relationship was observed between case incidence rates and air temperature, cumulative precipitation, and relative humidity (p < 0.05 for all). Climatic factors can only indirectly affect the occurrence of Crimean-Congo Hemorrhagic Fever cases. However, climatic conditions that become progressively more favorable for vector ticks lead to the spread of the disease. The control measures to be taken should be prepared by considering the changing climatic conditions and prioritizing the risk groups. There is a need for information and awareness-raising studies about climate change and the growing dangers associated with it, also outside of endemic regions.

Anabolic Androgenic Steroids from Underground Market: drug Quality and Implications for Research

Currently, a large portion of users of anabolic androgenic steroids purchase these drugs from non-legal sources, the so-called underground market. Studies involving drugs seizures from the underground market, and their respective product chemical analysis, indicated that the adulteration rate can vary between 18 and 86%, plus a potential serious contamination by heavy metals or microorganisms. Inadvertent abuse of underground market products can put users' health at additional risk, leading to more serious side effects or unknown adverse events. The poor quality of these products may also jeopardize proper verification of causality in case report studies or case series.

Epidemiological characteristics, seasonality, trends of dog bite injuries, and relationship with meteorological data.

Animal bites are among the most critical in public health problems. Dogs are the leading cause of bite injuries. The study aimed to investigate the epidemiology and clinical features of dog bite cases admitted to an emergency department, as well as their temporal trends, seasonality, and tr relationship with meteorological data. Study data comprised eight years (2012-2019) emergency room records of a tertiary center. Demographic characteristics of the cases, bite anatomical area, treatment applied, hospitalization, and death rates were determined. The incidence rates and distribution of meteorological data by years were examined using ANOVA and Kruskal Wallis tests. Seasonality and temporal trends were investigated for incidence rates using the additive decomposition technique. The temporal relationship of incidence rates with meteorological data was evaluated using the Autoregressive Distributed Delayed Boundary Test. Causality verification was perfoirmed using the Granger test. Dog bite cases consisted of 1,335 records of partients with a mean age of 26.6±0.2 years. Bite cases were most common in the 20-44 age group (44.7%), males (76.4%), and lower extremities (48.2%). The frequency of hospitalization was 4.1%. Annual incidence rates ranged from 52.7-49.9/100,000, with a non-significant increasing trend. The incidence of bites had two peaks, in June and August. A co-integration relationship was observed between incidence rates and air temperature and humidity levels (p<0.001). Effective implementation of prevention programmes is needed for high-risk demographic groups. In addition, a national monitoring and reporting system could evaluate the effectiveness of any prevention programme and reduce the incedence of dog bites.

PENGARUH KEADILAN PAJAK, SISTEM PERPAJAKAN, PEMAHAMAN PERPAJAKAN DAN SANKSI PAJAK TERHADAP PERSEPSI WAJIB PAJAK MENGENAI ETIKA PENGGELAPAN PAJAK (TAX EVASION)

This study aims to analyze the effect of tax justice, taxation systems, tax understanding, and tax sanctions on taxpayers' perceptions of tax evasion ethics on individual taxpayers of West Kotawaringin. The method used in this research is descriptive method and causality verification. This research was conducted by individual taxpayers of Kotawaringin Barat, totaling 100 samples. The statistical test used is the calculation of the Pearson correlation, validity test, reliability test, multiple regression analysis, hypothesis testing and using the SPSS 25.0 for windows application

Root cause diagnosis for complex industrial process faults via spatiotemporal coalescent based time series prediction and optimized Granger causality

It is essential to conduct root cause diagnosis (RCD) to guarantee the safety of operations of chemical processes and suppress fault deterioration, yet RCD has encountered challenges in the context of the increasing complexity of industrial processes. Granger causality (GC) analysis is one of the most commonly used methods to construct process causal maps and identify root causes. However, its use is subject to a number of limitations for dynamic nonlinear industrial processes. Therefore, an optimized GC analysis is proposed to perform RCD in complex industrial processes based on the spatiotemporal coalescent-based prediction model (SCPM) and causality verification algorithm (CVA). The time series prediction model SCPM is presented as an alternative to GC’s conventional autoregressive model in order to avoid spurious regressive facing nonlinear processes, which stacks dilated convolutional neural networks (DCNN) and bidirectional gated recurrent unit (BiGRU) to coalesce time series nonlinear couplings and long-term dependence information. Moreover, considering data that may deviate from normal distribution after the fault occurs, a nonparametric causality test method CVA based on the Wilcoxon signed-rank test (WSRT) is constructed combined with SCPM to eliminate false causality discovery. Empirical results on the Tennessee Eastman process and the blast furnace process demonstrate the effectiveness of the proposed RCD method.

Developments in Transcranial Magnetic Stimulation to Study Human Cognition.

Developments in Transcranial Magnetic Stimulation to Study Human Cognition.

Causality Verification for the Correlation between the Presence of Nonstarter Bacteria and Flavor Characteristics in Soft-Type Ripened Cheeses.

Flavor characteristics of ripened cheese are established by various bacteria, such as lactic acid bacteria, Actinobacteria, and Proteobacteria, which spontaneously develop during the cheese-manufacturing process. We previously revealed the relationship between bacterial microbiota and flavor components in soft-type ripened cheeses by using a multiomics approach that combined metagenomics and metabolomics; however, we could not establish a causal relationship. This study aimed to substantiate the causal nature of the correlations revealed by the multiomics approach by using cheese-ripening tests with single isolate inoculation. The bacterial diversity and composition in surface mold-ripened cheeses from Japan and France varied, depending on the differences between the milks (pasteurized or raw), cheese positions (core or rind), and manufacturers. Although the volatile compounds did not clearly reflect the distinctive characteristics of the cheese samples, nonstarter lactic acid bacteria, Actinobacteria, and Proteobacteria positively correlated with ketones and sulfur compounds, as evidenced by a Spearman's correlation analysis. Cheese-ripening tests conducted after inoculation with single bacterial strains belonging to the above-mentioned taxa confirmed that these bacteria formed volatile compounds, in agreement with the correlations observed. In particular, various flavor compounds, such as acids, esters, ketones, and sulfur compounds, were detected in cheese inoculated with Pseudoalteromonas sp. TS-4-4 strain. These findings provide important insights into the role of nonstarter bacteria in the development of cheese flavor and into the effectiveness of the multiomics approach in screening for bacteria that can improve the quality of cheese products. IMPORTANCE Our previous study revealed that the existence of various bacteria, such as lactic acid bacteria, Actinobacteria, and Proteobacteria, clearly correlated with the abundance of flavor components, such as volatile compounds, in soft-type ripened cheeses via a multiomics approach that used 16S rRNA gene amplicon sequencing and headspace gas chromatography-mass spectrometry. However, this approach only showed correlations derived from statistical analyses rather than causal relationships. Therefore, in the present study, we performed cheese-ripening tests using nonstarter bacteria to substantiate the correlations revealed by the multiomics approach in soft-type ripened cheese. Our results suggest the capability of nonstarter bacteria, such as Proteobacteria, to impart flavor to cheese and the effectiveness of the multiomics approach in screening for microbial isolates that can improve the quality of cheese. Overall, our research provides new insights into the importance of bacteria in cheese production.

A Comprehensive Method for Fault Location of Active Distribution Network Based on Improved Matrix Algorithm and Optimization Algorithm

The traditional fault location methods are not effective for the multisource active distribution network and are difficult to meet both requirements of timeliness and fault tolerance simultaneously. In this paper, a fault location method for active distribution networks which combines both improved matrix algorithm and optimization algorithm is proposed. The improved matrix algorithm is constructed to determine the fault section hypothesis by using the causal relationship between the alarm information and the fault section. Subsequently, network splitting is used for causal verification to check whether the alarm information is distorted. If the alarm information is normal, the fault sections can be quickly located by the above matrix algorithm. If the alarm information is distorted, the optimization model is constructed by using the fault section hypothesis selected by the matrix algorithm. The discrete particle swarm optimization (DPSO) algorithm is used to solve this optimization model, which can accurately locate the real fault sections. Hence, the advantages of both matrix algorithm and optimization algorithm are complementary. Simulation results show that the proposed method has the advantages of both high timeliness and fault tolerance in a complex active distribution network.

DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

A Study on the Causality Verification between Reading Strategy and Influence Factors of Korean Learners

목적 본 연구는 외국어로서의 한국어 학습자의 읽기 전략과 정의적&#8901;환경적 측면에서 영향 관계에 있는 요인 간의 인과성을 검증함으로써 한국어 읽기 전략 교수 학습에 필요한 실증적인 정보를 얻고 교육적 함의를 도출하는 데 목적이 있다. 방법 이를 위하여 서울, 부산, 대구, 울산 지역 소재의 외국어로서의 한국어 학습자 389명을 대상으로 한국어 읽기 전략 검사, 한국어 읽기 동기 검사, 한국어 읽기 불안 검사, 한국어 읽기 환경 검사를 실시하고, 구조 방정식 모델 분석 및 부트스트래핑 분석을 통해 한국어 읽기 전략과 영향 관계에 있는 요인들의 인과성을 검증하였다. 결과 그 결과 한국어 학습자의 읽기 전략과 정의적 측면에서의 한국어 읽기 동기는 유의미한 정의 상관관계가 검증되었고, 한국어 읽기 불안은 학습자의 읽기 전략 사용과 유의미한 부의 상관관계가 검증되었다. 또한 환경적 측면에서의 한국어 교실 내 상호작용 요인은 학습자의 읽기 전략과 유의미한 정의 상관관계가 검증되었고, 교실 내 물리적 요인은 직접적인 경로에서 유의미한 상관관계가 검증되지 않았으나, 한국어 읽기 동기와 매개하여 한국어 읽기 전략과 유의미한 정의 상관관계가 검증되었다. 이 중 특히 한국어 읽기 동기와 교실 내 상호작용 환경의 경우, 이들 요인이 강화될수록 학습자의 읽기 전략 사용의 증가에 영향을 미치는 것으로 나타났다. 결론 그간 한국어 읽기 교육 분야에서 한국어 학습자의 읽기 전략, 한국어 읽기 동기, 한국어 읽기 불안, 한국어 읽기 환경에 관한 독립적인 연구들이 주로 실시되었으나, 이들의 인과적 관계를 검증하는 종합적이고 거시적인 논의가 부족하였다. 따라서 본 연구의 한국어 학습자의 읽기 전략과 영향 요인에 대한 인과성 검증 결과는 한국어 읽기 교육에서 읽기 전략 교수 학습의 원리와 방법적 설계를 위한 기초적이고 실제적인 정보로 활용될 수 있을 것이다.

Prediction method of construction land expansion speed of ecological city based on BP neural network

In order to solve the problems of low accuracy and poor convergence of traditional urban construction land expansion speed prediction, a new method based on BP neural network for ecological city construction land expansion speed prediction is proposed. On the basis of driving forces state responses (DSR) research framework model, this paper analyses the main components of the driving mechanism model of urban construction land expansion, finds out the driving factors, and verifies the unit root of its time series, as well as causality verification and screening, to establish the driving mechanism model of urban construction land expansion. After preprocessing the data in the model, the BP neural network is constructed to predict the expansion speed of urban construction land. The experimental results show that the proposed method has better convergence and higher prediction accuracy, which provides a reference for related research.

Facial palsy 12 h after a first Moderna jab requires pathophysiological disclosure and verification of causality

We read with interest the article by Cellina et al. about a 33 years old female developing left peripheral facial palsy (House-Brackmann score IIII) 12 h after the first dose of the anti-SARS-CoV-2 Moderna vaccine [ 1. Cellina M. D'Arrigo A. Floridi C. Oliva G. Carrafiello G. Left Bell's palsy following the first dose of mRNA-1273 SARS-CoV-2 vaccine: a case report. Clin Imaging. 2021 Nov; 4: 1-4https://doi.org/10.1016/j.clinimag.2021.10.010 Abstract Full Text Full Text PDF Scopus (2) Google Scholar ]. The general practitioner prescribed prednisone (50 mg/d) for 10 days resulting in complete recovery of the palsy at follow-up 19 days after the vaccine shot [ 1. Cellina M. D'Arrigo A. Floridi C. Oliva G. Carrafiello G. Left Bell's palsy following the first dose of mRNA-1273 SARS-CoV-2 vaccine: a case report. Clin Imaging. 2021 Nov; 4: 1-4https://doi.org/10.1016/j.clinimag.2021.10.010 Abstract Full Text Full Text PDF Scopus (2) Google Scholar ]. A causal relation between the vaccination and the palsy was established. The study is appealing but raises comments and concerns.

Visual Causality Analysis of Event Sequence Data.

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.

Decreased interhemispheric connectivity and increased cortical excitability in unmedicated schizophrenia: A prefrontal interleaved TMS fMRI study

BackgroundPrefrontal abnormalities in schizophrenia have consistently emerged from resting state and cognitive neuroimaging studies. However, these correlative findings require causal verification via combined imaging/stimulation approaches. To date, no interleaved transcranial magnetic stimulation and functional magnetic resonance imaging study (TMS fMRI) has probed putative prefrontal cortex abnormalities in schizophrenia. Objective/Hypothesis: We hypothesized that subjects with schizophrenia would show significant hyperexcitability at the site of stimulation (BA9) and decreased interhemispheric functional connectivity. MethodsWe enrolled 19 unmedicated subjects with schizophrenia and 22 controls. All subjects underwent brain imaging using a 3T MRI scanner with a SENSE coil. They also underwent a single TMS fMRI session involving motor threshold (rMT) determination, structural imaging, and a parametric TMS fMRI protocol with 10 Hz triplet pulses at 0, 80, 100 and 120% rMT. Scanning involved a surface MR coil optimized for bilateral prefrontal cortex image acquisition. ResultsOf the original 41 enrolled subjects, 8 subjects with schizophrenia and 11 controls met full criteria for final data analyses. At equal TMS intensity, subjects with schizophrenia showed hyperexcitability in left BA9 (p = 0.0157; max z-score = 4.7) and neighboring BA46 (p = 0.019; max z-score = 4.47). Controls showed more contralateral functional connectivity between left BA9 and right BA9 through increased activation in right BA9 (p = 0.02; max z-score = 3.4). GM density in subjects with schizophrenia positively correlated with normalized prefrontal to motor cortex ratio of the corresponding distance from skull to cortex ratio (S-BA9/S-MC) (r = 0.83, p = 0.004). ConclusionsSubjects with schizophrenia showed hyperexcitability in left BA9 and impaired interhemispheric functional connectivity compared to controls. Interleaved TMS fMRI is a promising tool to investigate prefrontal dysfunction in schizophrenia.

Causal Verification of Intentional and Unintentional Medication Non-adherence

Causal Verification of Intentional and Unintentional Medication Non-adherence

Pengaruh Good Corporate Governance Terhadap Kualitas Laba Pada Perusahaan Real Estate dan Property

This study aims to analyze the effect of corporate governance on the quality of study earnings in real estate and property companies listed on the Indonesia Stock Exchange (IDX) in 2012-2017. In this study, Good Corporate Governance is analyzed as a factor that can encourage companies to have good earnings quality. This study belongs to the type of descriptive research verification of causality. The population in this study are real estate and property companies listed on the Indonesia Stock Exchange (IDX) in 2012-2017 using the purposive sampling method. The type of data collected and used in this study is secondary data with a method of collecting data through documentation and literature studies. The data analysis method used is simple linear regression analysis that has met the testing of classical assumptions. The results of this study indicate that Good Corporate Governance has no effect on earnings quality because the application of Good Corporate Governance (GCG) in companies in Indonesia has not really been used as a tool to minimize information asymmetry between owners and management, but solely to comply with regulations and conditions that apply only.Key words: Good Coporate Governance, kualitas laba.

Identifying linear absolute instabilities from differential eigenvalue problems using sensitivity analysis

Identifying the convective/absolute instability nature of a local base flow requires an analysis of its linear impulse response. One must find the appropriate singularity in the eigenvalue problem with complex frequencies and wavenumbers and prove causality. One way to do so is to show that the appropriate integration contour of this response, a steepest decent path through the relevant singularity, exists. Due to the inherent difficulties of such a proof, one often verifies instead whether this singularity satisfies the collision criterion. In other words, one must show that the branches involved in the formation of this singularity come from distinct halves of the complex wavenumber plane. However, this graphical search is computationally intensive in a single plane and essentially prohibitive in two planes. A significant computational cost reduction can be achieved when root finding procedures are applied instead of graphical ones to search for singularities. They focus on locating these points, with causality being verified graphically a posteriori for a small parametric sample size. The use of root-finding procedures require auxiliary equations, often derived by applying the zero group velocity conditions to the dispersion relation. This relation, in turn, is derived by applying matrix forming to the differential eigenvalue problem and taking the determinant of the resulting system of algebraic equations. Taking the derivative of the dispersion relation with respect to the wavenumbers generates the auxiliary equations. If the algebraic system is decoupled, this derivation is straightforward. However, its computational cost is often prohibitive when the algebraic system is coupled. Other methods exist, but often they can also be too costly and/or not reliable for two wavenumber plane searches. This paper describes an alternative methodology based on sensitivity analysis and adjoints that allow the zero group velocity conditions to be applied directly to the differential eigenvalue problem. In doing so, the direct and auxiliary differential eigenvalue problems can be solved simultaneously using standard shooting methods to directly locate singularities. Auxiliary dispersion relations no longer have to be derived, although it is shown that they are the algebraic equivalent of the auxiliary differential eigenvalue problems obtained by this alternative methodology. Using the latter dramatically reduces computational costs. The search for arbitrary singularities is then not only accelerated in single wavenumber planes but it also becomes viable in two wavenumber planes. Finally, the new method also allows group velocity calculations, greatly facilitating the verification of causality. Several test cases are presented to illustrate the capabilities of this new method.

Empirical verification of causality between CO&lt;SUB align="right"&gt;2 emissions, energy consumption, foreign direct investment, gross domestic product, and openness of the economy: evidence from India

Empirical verification of causality between CO&lt;SUB align="right"&gt;2 emissions, energy consumption, foreign direct investment, gross domestic product, and openness of the economy: evidence from India