Causal Indicators Research Articles

Epigenetic regulation of cardiovascular diseases induced by behavioral and environmental risk factors: Mechanistic, diagnostic, and therapeutic insights

AbstractBehavioral and environmental risk factors are critical in the development and progression of cardiovascular disease (CVD). Understanding the molecular mechanisms underlying these risk factors will offer valuable insights for targeted preventive and therapeutic strategies. Epigenetic modifications, including DNA methylation, histone modifications, chromatin remodeling, noncoding RNA (ncRNA) expression, and epitranscriptomic modifications, have emerged as key mediators connecting behavioral and environmental risk factors to CVD risk and progression. These epigenetic alterations can profoundly impact on cardiovascular health and susceptibility to CVD by influencing cellular processes, development, and disease risk over an individual's lifetime and potentially across generations. This review examines how behavioral and environmental risk factors affect CVD risk and health outcomes through epigenetic regulation. We review the epigenetic effects of major behavioral risk factors (such as smoking, alcohol consumption, physical inactivity, unhealthy diet, and obesity) and environmental risk factors (including air and noise pollution) in the context of CVD pathogenesis. Additionally, we explore epigenetic biomarkers, considering their role as causal or surrogate indicators, and discuss epigenetic therapeutics targeting the mechanisms through which these risk factors contribute to CVD. We also address future research directions and challenges in leveraging epigenetic insights to reduce the burden of CVD related to behavioral and environmental factors and improve public health outcomes. This review aims to provide a comprehensive understanding of behavioral and environmental epigenetics in CVD and offer valuable strategies for therapeutic intervention.

Household earthquake readiness: Conceptualization and measurement

Decades of research have been dedicated to understanding and measuring the capacity of households to face and deal with earthquakes. A reflective-measure model equating household earthquake readiness to preparedness has been adopted assuming the presence of a common factor. However, using instruments with unknown psychometric properties is prevalent, instruments with known psychometric properties are unaligned with the construct, and information on the source of items is missing. This study proposes an alternative theoretical framework based on a causal indicator model equating household earthquake readiness to hazard adjustments applied to earthquakes; investigates the presence of a common factor; and, suggests a new measure constructed following the literature guidelines and based on prior research on household earthquake readiness: Earthquake Adjustments Checklist (EAC). Factor Analysis was used to analyze the data from 921 residents in the Azores, a Portuguese archipelago vulnerable to seismic risk. The final version of the EAC contains 13 items derived from the literature that are congruent with Portuguese household earthquake recommendations and with the supporting construct. These items were formulated to be applicable elsewhere. A four-factor solution was obtained differing from the three postulated dimensions of hazard adjustments. The results showed weak correlations, low commonalities and internal consistency pointing to a common factor that is nonexistent or at least insufficient to explain the variability in the adoption of household earthquake adjustments. Moreover, a weak correlation was found between two factors. These findings have implications for the understanding and measuring of household earthquake readiness and practice.

INVESTIGATION OF THE CORRELATION BETWEEN EMPLOYEE PRODUCTIVITY AND MOTIVATION IN THE COMPANY "INNOVATOR"

Nino Lazviashvili E-mail: lazviashvilinino11@gtu.ge Professor, Georgian Technical University Tbilisi, Georgia https://orcid.org/0009-0009-1251-4475 Zurab Gegenava E-mail: gege@innovator.ge PhD student, Georgian Technical University Tbilisi, Georgia https://orcid.org/0000-0001-7855-5987 Abstract: In the article - "Investigation of the correlation between employee productivity and motivation in the company "Innovator"" - in the period of 2018-2022 (number of observations n=5), the density of the correlation between productivity (y) and economic motivation (x) in the company "Innovator" is calculated and evaluated. The calculation is made both in terms of the entire number of employees and on average per employee. In any organization there is a resultative event and its cause (or causes). In this particular case, in the company "Innovator" the effective indicator is productivity, i.e. turnover (y), and the causal indicator is labor remuneration (x). The correlation is one-factor, i.e. pairwise, and also linear. In the paper, the density of the correlation connection is calculated by a special formula (Ч_yx ), which is perceived as positively functional if its value is placed in the norm of the numerical connection between the x and y values [-1; 1]. The research established that the correlation coefficient between productivity and motivation in the company "Innovator" is: for the total number of employees - 0.8075 (ie 80.7%) per employee on average - 0.7959 (ie 79.5%) Both these coefficients are placed in the interval between -1 and +1, which indicates the essentiality of this connection. The essentiality (significance) of these coefficients in the paper is substantiated using the student's distribution table. The paper concludes that more than 80% of overall productivity in the company "Innovator" is influenced by external motivation, i.e. the amount of remuneration, and the rest by the internal human factor, i.e. "motivation 3.0". Keywords: correlation; correlational connection; connection density; pairwise correlation; successful event; causal event; correlation coefficient; Student criteria. JEL classification: A2; M12;

Dynamic Successive Assessment of Water Resource Carrying Capacity Based on System Dynamics Model and Variable Fuzzy Pattern Recognition Method

The water resource carrying capacity (WRCC) system comprises multiple complex and non-linear interactions related to society, economy, water resources, and the water environment. A comprehensive comprehension of its internal mechanisms is essential for the continual enhancement of the regional WRCC. This study concentrates on the temporal and spatial variability of the WRCC to investigate a method for dynamic successive assessment. Firstly, the pressure–state–response (PSR) framework is used to develop a systematic and causal indicator system. Then, the variable fuzzy pattern recognition (VFPR) model and an analytic hierarchy process—entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resource carrying capacity in Hebei Province is mainly due to the basic attribute of the decision on the water resource shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which has made the province’s water resource carrying capacity tend to become better gradually. Simultaneously, a system dynamics model (SD) for water resource carrying capacity was established based on an analysis of the model structure. Moreover, three scenarios were designed, including existing continuation, high-efficiency water saving, and cross-regional water transfer. Subsequently, each scenario is further categorized into high- and low-speed economic development and population growth schemes. Afterward, simulations and predictions were conducted for a total of six schemes spanning from 2023 to 2030. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of the economy and population, the efficient water conservation, and the interbasin transfer scenario (scenario 2 with high speed) are the best choices for the sustainable development of water resources and social economy in Hebei.

A causal interactions indicator between two time series using extreme variations in the first eigenvalue of lagged correlation matrices

A causal interactions indicator between two time series using extreme variations in the first eigenvalue of lagged correlation matrices

Development of a new measure of encounters with health care barriers: The Commonly Experienced Health Care Barriers Index.

Common barriers to health care, such as lack of insurance or transportation, hold a key theoretical role in many models attempting to explain problems with health care utilization (not seeking needed health care). However, the assessment of barriers is often post hoc, with no existing scales appropriate for a general population. This study developed and tested a new measure of commonly experienced health care barriers across three studies (Study 1, N = 194; Study 2, N = 206; Study 3, N = 741). Items were developed in line with recommendations for causal indicator models, emphasizing content validity. The measure showed preliminary test-retest reliability, sensitivity to health care inequities between Black and White individuals (beyond socioeconomic status and including association with health care discrimination), expected associations with health care utilization problems and other health experiences and life stressors, and unique associations with health care utilization problems accounting for health experiences and life stressors. The new measure has the potential to identify modifiable factors related to health care inequities and common problems with health care utilization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Compare and Identifying Causal Indicators of Physical Literacy during the Outbreak of Corona Virus in Rich and Low-Income Cities of the Country

Compare and Identifying Causal Indicators of Physical Literacy during the Outbreak of Corona Virus in Rich and Low-Income Cities of the Country

Dynamic successive assessment of Rural Human Settlements Environment in China

The Rural human settlements environment (RHSE) is an important guarantee for the implementation of the rural revitalization strategy and may be affected by many factors such as water quality, economy and environmental protection. This article focuses on the temporal and spatial variability of RHSE to explore a method of dynamic successive assessment. Firstly, a systematic and causal indicator system is developed from four aspects of agricultural origin environment, sanitary conditions, ecological protection, and economic development. Then, The Variable Fuzzy Pattern Evaluation (VFPE) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess RHSE. The proposed method is applied to the dynamic successive assessment of RHSE in China, including inter-province comparisons.The results show that China's RHSE has improved significantly over time, which reflects that the government has achieved remarkable results in rural environmental governance for a long time. In spatial distribution characteristics, the RHSE of Beijing, Tianjin and the southeastern coastal regions maintained the highest, followed by the northeast, north and central regions, and the western region was the lowest. Finally, countermeasures are put forward for the development of RHSE in different regions considering the differences in economic development, geographical environment and infrastructure construction.

Explicit and implicit knowledge-enhanced model for event causality identification

Event Causality Identification (ECI) aims at detecting the causal relation between 2 events, which is a challenging task due to the complexity of causal expressions and the background knowledge needed for identifying certain causal relations. Considerable work has been done on the learning of context and the incorporation of external knowledge. However, none of the work incorporates both explicit and implicit causal knowledge. To this end, we propose an integrative model for event causality identification, integrating both explicit causal indicators and implicit causal knowledge with the data-oriented model. For the data-oriented model, an event pair graph is constructed and Relational Graph Convolutional Network (R-GCN) is employed to better capture interactions between individual pairs. Regarding the explicit causal indicators, their word embeddings are used to initialize the filters of convolutional neural network so as to capture the clues indicating causal relation. We further introduce a cause–effect matching mechanism to better leverage implicit causal knowledge. It measures the possibility of causal relation holding between 2 events based on the possible causes and effects generated by COMET. The proposed method is evaluated on 3 datasets, and experimental results demonstrate the effectiveness and superiority of the proposed method.

Multi-class categorization of reasons behind mental disturbance in long texts

Motivated with recent advances in inferring users’ mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based studies for causal explanation analysis on curated Facebook data. The investigation on transformer-based model for multi-class causal categorization in Reddit posts point to a problem of using long-text which contains as many as 4000 words. Developing end-to-end transformer-based models subject to the limitation of maximum-length in a given instance. To handle this problem, we use Longformer and deploy its encoding on transformer-based classifier. The experimental results show that Longformer achieves new state-of-the-art results on M-CAMS, a publicly available dataset with 62% F1-score. Cause-specific analysis and ablation study prove the effectiveness of Longformer. We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions.

Health in Down syndrome: creating a conceptual model.

Down syndrome (DS) has a unique medical and psychological profile that could impact how health is defined on three dimensions: physical, social and mental well-being. In 2021, we presented our proposed conceptual model to three expert panels, four focus groups of parents of individuals with DS age 0-21years and four focus groups of individuals with DS age 13-21years through videoconferencing technology. Participants gave feedback and discussed the concept of health in DS. Feedback from participants resulted in iterative refinement of our model, retaining the three dimensions of health, and modifying constructs within those dimensions. Experts and parents agreed that individuals with DS have unique health concerns that necessitate the creation and validation of a syndrome-specific health model. We present key themes that we identified and a final conceptual model of health for individuals with DS. Health in DS is a multi-dimensional, multi-construct model focused on relevant constructs of causal and effect indicators. This conceptual model can be used in future research to develop a syndrome-specific measure of health status.

Synergies vs. Clustering Only of Depressive Symptoms in Diabetes and Co-Occurring Conditions: Symmetric Indicators with Asymmetric, Bidirectional Influences in MIMIC Models

I extend multiple indicators multiple causes (MIMIC) models to unveil unbiased, asymmetric, bidirectional influences using indicators of the same items within variable-defined subgroups. The strategy discerns (1) item-variation in interaction (and derivative) terms that capture synergies and cluster together (formative or causal indicators) from (2) item-variation in duplicate terms when items lack synergy and cluster together only (reflective or effect indicators). An item may reveal either or both influences. These symmetric indicators yield estimates of (1) the unique variation and synergy of each formative indicator within the structural model portion of the MIMIC model (based on moderated regression) and (2) the remaining shared variation in the reflective indicator within the measurement model portion (based on confirmatory factor analysis). I reveal two patterns of comorbidity in disease subgroups of a specific co-occurring condition across a community sample of older adults and in age and gender subsamples. First, as structural model indicators, depressive symptoms may display different synergies as they cluster within a disease subgroup of diabetes and a specific co-occurring condition. As measurement model indicators, depressive symptoms capture non-synergistic clustering within the disease subgroup. Second, diabetes may mediate the co-occurring condition when depressive symptoms lack synergies but cluster within the disease subgroup. Researchers should distinguish both comorbidity patterns, which have different implications. I offer insights for adaptive modeling, conceptualizing and screening symptom clusters, metabolomics, and economic or social monitoring.

Modelling direct field nitrogen emissions using a semi-mechanistic leaching model newly implemented in Indigo-N v3

Nitrogen plays a major role in agroecosystems as the key nutrient in agricultural production as well as a source of different emissions, which exceed currently planetary boundaries. N losses, conditioned by both pedoclimatic conditions and agricultural strategies (e.g. rotations, fertilisation), predominantly take the form of ammonia (NH3) volatilisation, nitrate (NO3) leaching, nitrification-driven nitric oxide (NOx) emission to air, and denitrification-driven nitrous oxide (NOx and N2O) emissions to air. The multiplication of initiatives and studies on nitrogen modelling resulted in a broad offer of complex simulation models (Tier 3) on one extreme of the gradient between feasibility and integration of processes. On the other side, a multiplication of initiatives has led to a broad offer of causal indicators in the form of proxies and considering one or a few input variables (Tier 1). A relevant compromise between those extremes lies in the development of operational models using a restricted number of parameters and input variables (Tier 2). Here, we propose a new semi-mechanistic operational model for the estimation of direct field N emissions (NH3, NO3, NOx and N2O) from contrasting agricultural situations: the Indigo-N v3 (I-N3) model. The gaseous emissions are based on Tier 1 (NOx) and Tier 2 (NH3, N2O) methods taken from the literature, with some enhancements, while we developed a totally new semi-mechanistic approach for nitrate leaching. A comparison of I-N3 outputs was performed with measurements of nitrate leaching in three countries (15 arable fields in France, 3 sugar cane fields at Reunion Inland, and 5 cropped fields in Kenya) and showed a reasonable predictive quality for temperate arable fields, and for some of the tropical fields (1 in Reunion and 3 in Kenya). It also performed better than the previous version of Indigo-N (IN-2) and the SALCA/SQCB models. In comparison with previous Tier 2 models, the newly developed Indigo-N v3 presents an original position on the gradient between integration of processes and feasibility of the simulation of processes. Another novelty of I-N3 lies in its broad scope, designed to be valid for temperate and non-temperate crops, including annual field crops, short-cycle vegetables, temporary grasslands and perennial grasses (such as sugarcane, miscanthus or switchgrass). Parameterisation and validation should be continued for further crops, such as associations and short cycle vegetables.

Documenting the Longitudinal Relationship between Group Conflict and Group Cohesion

ABSTRACT Group scholars have devoted a considerable amount of attention to the dynamics of group conflict and group cohesion. This investigation contends that both dynamics are closely related, such that they are related causally. A three-wave longitudinal investigation is reported that examines the extent to which this is the case. Results suggest that group conflict has a strong negative effect on group cohesion over time, and that this is the case despite controlling for the effect of past cohesion on future cohesion (i.e., the cohesion autoregressions). Alternatively, the negative effects of group cohesion on group conflict are much more limited and were deemed trivial. These results suggest that group conflict is a strong causal indicator of group cohesion, but that the causal effect of group cohesion on group conflict is much more limited. Future scholarship is encouraged to investigate the extent to which certain variables moderate the group conflict → group cohesion relationship, such that the negative effects of group conflict are attenuated or perhaps reversed.

Validation of the Sri Lankan Health-Related Quality-of-Life Index for pre-schoolers (SLHQL-P) and school age (SLHQL-S) children with epilepsy

IntroductionHealth-related quality of life (HRQL) in children and adolescents remains an underappreciated problem in Asian region, due to lack of culturally sound HRQL measures. This paper describes the validation process of two age-specific, proxy-rated HRQL indexes namely Sri Lankan Health-Related Quality-of-Life Index for preschoolers (SLHQL-P) and school age children (SLHQL-S) with epilepsy. MethodsPrimary caregivers of children and adolescents with epilepsy aged 1–18 years from three districts in Sri Lanka were invited to fill the SLHQL-P (for 1–5 years) and SLHQLS (for 6–18 years). It was re-administered to a subset of consenting primary caregivers after an interval of two weeks. Scientific soundness of SLHQL-P and SLHQL-S were established based on causal indicator model. ResultsTotal of 98 and 169 primary caregivers responded to SLHQL-P, SLHQL-S respectively. Final questionnaires of SLHQL-P and SLHQL-S consists of 26, 57 items respectively in three multi-item domains measuring the child’s physical functioning (6, 9 items), psychological functioning (6, 31 items) and social functioning (14, 17 items) with higher scores reflecting better HRQL. Construct validity was established by several methods including strong relationship with seizure severity. Good test–retest reliability was also demonstrated for both SLHQL-P and SLHQL-S (r = 0.77, 0.938) ConclusionsBoth SLHQL-P and SLHQL-S are feasible, reliable and valid instruments to measure HRQL in children and adolescents with epilepsy in clinical as well as research settings in Sri Lanka.

Кичик тадбиркорлик ишлаб чиқариш хажмини динамик эконометрик моделлар ёрдамида таҳлил этиш

This article is aimed at determining the changes in the socio-economic development process of small entrepreneurship in the Surkhandarya region, in which the link between the GDP of the region and the volume of small business production was analyzed on the basis of the distributed lag values of dynamic econometric models. Using the Koyk model for the evaluation of the distributed L lag value, the correlation between the causal and factor indicators was evaluated on a quantitative and qualitative basis on a scientific basis, proposals and recommendations were developed

Причинно-следственные полипредикативные конструкции алтайского языка (на материале художественных и научных текстов)

The structural and semantic types of causal polypredicative constructions of the Altai language and their functioning in artistic and scientific texts are considered. The study material includes artistic works of famous Altai writers and scientific monographs of researchers of folklore and literature in the Altai language. Compared to other conditionality relations, causal relations in the Altai language are expressed by the widest variety of means, including monofinite and bifinite polypredicative constructions. In total, 23 models of causal polypredicative constructions have been identified, with 11 models found in scientific texts. With all the multiplicity of causal indicators, the basic coding tools are constructions with general causal semantics. In literary texts, in oral speech, such construction is a participial construction with a causal-purpose postposition učun ‘because of, for’, in scientific texts - a construction with the causal union neniŋ učun deze ‘because of.’ Depending on the actualization of the semantics of cause or effect, two semantic blocks are distinguished: causal and investigative. Causal constructions are divided into proper causal and non-causal constructions. The actual causal constructions represent real relations that objectively exist regardless of the subject of the action. They include constructions with general causal semantics that can be neutral or complicated by positive or negative evaluative semantics. Non-causal constructions include alternative motivation, emotional and physical response to a stimulus, with the meaning of sanctions and with an inferential meaning. Special attention is paid to constructions conveying mainly temporary, conditional, target, explanatory relations, and only sometimes causal relations.

Toward a holistic understanding of performance in Lean Manufacturing: a discussion on the relevance of its indicators

PurposeThis study aims to propose a novel approach to select and prioritize performance indicators in Lean Manufacturing depending on whether they are influencing or being influenced by others, thereby assisting in the decision-making process for improving overall performance.Design/methodology/approachThe methodology comprises two stages. First, a literature review was conducted to identify the performance indicators, and then their interrelationships were analyzed by means of the decision-making trial and evaluation laboratory (DEMATEL) multi-criteria decision-making (MCDM) method.FindingsThe results provide a comprehensive visualization of the performance indicators in Lean Manufacturing, with a total of 50 identified indicators. Among these, 29 were categorized as causal, meaning that their results mainly influence the others, and 21 as influenced, with their results mostly being influenced by others. Among the causal indicators, those related to the human factor (eight indicators) were the most predominant. However, the most-cited performance families in the literature do not stand out as being causal, but rather as mostly influenced.Practical implicationsThis study can help managers improve and analyze performance more effectively, while focusing on the importance of choosing causal over influenced indicators.Originality/valuePerformance measurement plays a crucial role for organizations, but because of the increasing number of metrics, there lacks an established framework. This exploratory study thus opens the discussion on relevance to determine a group of coherent and connected indicators that could help measure performance in a more comprehensive manner, rather than in several isolated parts.

РОЛЬ ПРЕЕМСТВЕННОСТИ ТРУДОВ ГАФУРА ГУЛЯМА В РАЗВИТИИ ДУХОВНО-ПРОСВЕТИТЕЛЬСКИХ ТРАДИЦИЙ БУДУЩИХ ЮРИСТОВ

The article examines the relationship of spiritual values in the poetry of Gafur Gulyam and their relevance to the study of lawyers at the present stage. Since creative thinking is a kind of human thinking in modern science, especially for lawyers. Since Gafur Gulyam played the decisive role in the development of tolerance and friendliness, known in Uzbekistan as an outstanding poet, whose work and interests were primarily in the field of social and even legal sciences. However, there was a period in the biography of the scientist when the subject of his research was the problem of the state and law. They have created works that, according to the deep analysis of the relationship between people, a person and the government, a person and the state, can be assessed as achievements of political and legal thought of Uzbekistan. These issues of the development of society and the state, raised to such a literary height, invariably remain relevant. Therefore, the author pays attention to the spiritual and aesthetic originality and ideological and compositional innovation of Gafur Gulyam. Special attention is also paid to the comprehension of spiritual-value, fundamental signs-axioms, the understanding of moral-psychological and philosophical-ethical issues that determine the essential, causal indicators and vectors of the dynamics of literary and historical-cultural processes from a legal point of view. In the article, the main focus is the vital role of the works of Gafur Gulyam in the development of spiritual and educational traditions of the new Uzbekistan.

Multimorbidity from Diabetes, Heart Failure, and Related Conditions: Assessing a Panel of Depressive Symptoms as Both Formative and Reflective Indicators of a Latent Trait

Through exploring specific conditions (diabetes, heart failure, related vascular/metabolic diagnoses) and their multimorbidities, I develop a more thorough means to adjust confounders of clinical targets within main or interactive contexts in epidemiological panel studies. Regression-based multiple indicators-multiple causes (MIMIC) models combine multiple or moderated regression and confirmatory factor analysis. In a novel specification, each of twenty depressive symptoms is both a “formative” (causal) indicator and a “reflective” (effect) indicator of a latent trait (Depression). Although both indicators provide identical information (under different variable names), formative indicators provide “exogenous” information (outside the model) to estimate, within groups or subgroups, “endogenous” effects (recovered by the model) from the latent trait and its reflective indicators. Formative indicators within the multiple regressions constitute comprehensive proxies for unspecified confounders by completely mediating all unspecified confounder effects on the endogenous latent trait and its reflective indicators, the latter estimated through confirmatory factor analysis. Findings of symptom clusters of Depression in these specific conditions, and in subgroups that capture their synergies, corroborate parallel MIMIC models with instrumental variables that specify several known confounders, but suggest some confounding biases remain. All multimorbidities involve synergy from co-occurring diabetes and heart failure. There may be opportunities to target screening and optimize metformin treatment for these co-occurring conditions. This strategy avoids the need to specify all confounders, which may not be possible or verifiable.