Cause Of Events Research Articles

Analyzing the syntax and salience of causal links embedded within semantic links in concept maps: Implications for temporal flow and learning transfer

Including causal links in concept maps enables learners to meaningfully relate concepts to a larger context or problem in terms of how and where concepts apply within the chains of causal events that lead to a given goal or outcome. Given that higher quality maps are produced when students link and sequence events to flow temporally and sequentially in a consistent direction towards a target outcome in a map, it is highly plausible that students can improve learning transfer (the ability to apply concepts to diagnose and solve problems) by including and making more salient the sequences of causal links nestled in the semantic links in concept maps. To lay the groundwork to empirically test this proposition in future research, this study: 1) analyzes 16 concept maps presented in the Proceedings of the 8th Int. Conference on Concept Mapping to codify the diverse and sometimes incongruent syntaxes used to convey causal relationships; 2) examines how the causal link syntaxes combined with semantic links work jointly (or against each other) to create temporal flow; and 3) explore how causal and semantic links can be integrated to increase the saliency and quality of the causal networks connecting concepts to outcomes. A better understanding of how causal links are expressed, integrated, and made more salient in concept maps can reveal ways to help students create concept maps that are more accurate, meaningful, and effective in improving the ability to apply concepts to solve complex problems.

The Mental Causality Exclusion Argument and the Levels of Organization of Living Objects

The paper aims to demonstrate the possibility of consistently accepting the existence of effective mental causality in the fundamentally physical world. We suppose that the concept of causality in J. Kim’s exclusion argument against mental causation, which implies а generative conception of causal relations, can be revised taking into account the specificity of the multilevel organization of living objects. Rejection of the mechanistic model of causality as a linear process, allows you to maintain commitment to the principle of causal closure of the physical world and at the same time explain how top-down causality at the macro level is possible. For this, we use the model of a fractal tree of causal chains by J. Lowe, in which mental causality plays the role of an indirect cause of a fact. We carry out a meaningful distinction between the causality of facts and events by resorting to the multilevel model of J. Ellis, in which mental causality can be considered as a macro-level fact that has a selective effect on physical events of lower levels, taking into account a wide environmental context.

Стратегия ввода темы посредством представления каузирующего события в аналитической газетной статье (на примере предстоящего события)

The study aims to give a comprehensive description of the strategy of topic introduction by presenting a causative event in analytical newspaper articles. The work specifies the main research units and aspects of theoretical description, gives definitions and an overview for speech strategies of topic introduction in journalistic discourse, namely in analytical articles. Using the example of an analytical article, the work considers the speech strategy in four main directions: the linguistic aspect of speech strategies itself; the cognitive aspect; the pragmatic aspect; the semantic aspect. The study is novel in that it identifies the varieties of topic introduction strategies in analytical articles in journalistic discourse; determines the functional-pragmatic and referential-pragmatic characteristics of the statements implementing the strategy of topic introduction in discourse through the presentation of a causative event. As a result, the researchers have identified the main strategies of topic introduction in analytical articles and have analysed the strategy of topic introduction by presenting a causative event that describes the event that is the main cause of the phenomenon given in the article as a semantic construct acting as a global topic of discourse in its specific text representation.

La expresión de eventos de movimiento causativos mediante verbos de contacto por impacto en alemán y sus correspondencias en español

Abstract The subject of this article is the expression of causative movement events in German and Spanish. It is hypothesized that, being respectively a satellite frame language and a verbal frame language, it is likely that both differ in the syntactic order with which the semantic material of the aforementioned events is expressed, a question already studied for this pair of languages in the intransitive movement events. It is especially interesting to analyse this possible interlingual difference in statements with types of verbs that cannot necessarily be expected to develop the triargumental scheme of these causative events (agent subject, affected object and dynamic locative complement) due to their basic argument structure. This contribution specifically studies the possible development of these triargumental structures by the so-called verbs of contact by impact. To do this, a one-way contrastive analysis is carried out, from German to Spanish, of a corpus of pairs of statements extracted from the PaGeS corpus. The results certify the existence of the phenomenon in both languages, but also point to important interlingual differences in their productivity.

Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies.

Alcohol use is a growing global health concern and economic burden. Alcohol involvement (i.e., initiation, use, problematic use, alcohol use disorder) has been reliably associated with broad spectrum grey matter differences in cross-sectional studies. These findings have been largely interpreted as reflecting alcohol-induced atrophy. However, emerging data suggest that brain structure differences also represent pre-existing vulnerability factors for alcohol involvement. Here, we review evidence from human studies with designs (i.e., family-based, genomic, longitudinal) that allow them to assess the plausibility that these correlates reflect predispositional risk factors and/or causal consequences of alcohol involvement. These studies provide convergent evidence that grey matter correlates of alcohol involvement largely reflect predisposing risk factors, with some evidence for potential alcohol-induced atrophy. These conclusions highlight the importance of study designs that can provide causal clues to cross-sectional observations. An integrative model may best account for these data, in which predisposition to alcohol use affects brain development, effects which may then be compounded by the neurotoxic consequences of heavy alcohol use.

Native Ontological Framework Guides Causal Reasoning: Evidence from Wichi People

Abstract Causal cognition – how we perceive, represent and reason about causal events – are fundamental to the human mind, but it has rarely been approached in its cultural specificity. Here, we investigate this core concept among Wichi people, an indigenous group living in Chaco Forest. We focus on the Wichi, because their epistemological orientations and explanatory frameworks about ecosystem differ importantly from those documented among most Western majority-culture populations. We asked participants to reason about causes of events that involve the hunhat lheley (inhabitants of the earth: humans, non-human animals, plants and spiritual beings) and other entities of their ecosystem (e.g., lagoon). We find a native ontological framework that encompasses three interacting organizing principles. This new evidence highlights ways in which native categories guide causal reasoning. Our research challenge long-held assumptions that dichotomies – nature-culture or natural-supernatural – are universal features of the human mind.

Autism and intention attribution test: a non-verbal evaluation with comic strips

BackgroundDespite autism spectrum disorder (ASD) and mentalization being two words often associated in the literature, the assessment of this ability in individuals with ASD in the clinical setting is still limited. Indeed, there are no standardized Theory of Mind (ToM) tests that are adaptable to different cognitive profiles, such as individuals with language poverty, and intellectual or memory impairments. This study proposes a non-verbal test (Intentions Attribution-Comic Strip Test; IA-CST) to evaluate the ability to infer the intentions of others, a basic component of ToM, in the clinical setting.MethodIn Study 1, the test was administered to 261 healthy individuals and we performed structural validation using Exploratory Graph Analysis. In Study 2, the final version of the test was administered to 32 individuals with ASD to assess the known group validity of the measure by comparing their scores with a sample of IQ-matched controls. Moreover, we performed logistic regression and ROC curve to preliminarily assess the diagnostic performance of the IA-CST.ResultsThe IA-CST resulted in a 3-dimension measure with good structural stability. Group comparison indicated that the ASD group shows significantly lower performance in intention attribution but not in inferring causal consequences. The test demonstrated known group validity and that, preliminarily, it is suitable for implementation within the clinical practice.ConclusionsThe results support the IA-CST as a valid non-verbal task for evaluating intentions attribution in the clinical setting. Difficulties in ToM are early and relevant in ASD, so assessing these aspects is valuable for structuring individualized and evidence-based interventions.

The role of natural language processing techniques versus conventional methods to gain ICI safety insights from unstructured EHR data.

136 Background: Studies using International Classification of Diseases (ICD) codes have aimed to characterize the safety profile of cancer therapies in the Real-World Data setting, but this approach has limitations including an inability to illustrate patient journey and offer insights into drug-Adverse Event (AE) causality. Using data from a cohort of ~9000 patients treated with immune checkpoint inhibitors (ICI) at Mayo Clinic, we recently demonstrated the feasibility of applying Augmented Curation (AC), a natural language processing (NLP)-based innovation, on unstructured electronic health record (EHR) data to detect and characterize immune related AEs (irAEs). While the ability to identify and extract clinical details from EHRs has been previously demonstrated, our approach focuses on applying these techniques to identify implied textual causality of drug-event pairs. This study aims to build upon our prior findings and compare the accuracy, speed, and resource savings enabled by AC analysis versus manual review of patient notes. Methods: We compared the prevalence of adrenal insufficiency, colitis, and hypophysitis (A.C.H) among ICI patients at Mayo Clinic using ICD codes and AC which leverages SciBERT to perform scientific NLP tasks. AC models trained to identify drug-adverse event relationships were used to assess irAEs and subsequent treatments and demonstrated strong performance with F1 scores of 0.85. Manual curation of relevant clinical data, i.e. medications and adverse events, was employed to create a “gold standard” A.C.H. cohort. Time required for manual review compared to AC was documented for overall comparison. Corticosteroid/immunosuppressant use and ICI discontinuations were used as proxies of severity. Results: In our cohort, 540 unique patients experienced at least one of the A.C.H. irAEs. A.C.H patients were found to receive corticosteroids for their respective irAE 79% of the time and discontinued the ICI due to the irAE 7.7% of the time. 6.1% of Colitis patients were treated with a 2L immunosuppressant. Similar to the previous study, AC showed higher sensitivity, PPV and NPV compared to ICD codes alone within the A.C.H. cohort. Sentence extraction and entity classification using AC models on all 9,000-patients took ~10 minutes, whereas manual review of this cohort would take roughly 9 weeks (~10 minutes per patient). Conclusions: The results from this study reinforce that AC models are a valuable tool to comprehensively extract information and to impute relationships between entities, such as therapies and AEs. The AC models demonstrated better accuracy at a much greater speed compared to manual review of patient EHRs. Leveraging AC models provides the ability to manipulate unstructured clinical text married to structured EHR data at scale while minimizing need for manual curation.

Exploring the experiences of researchers in the interdisciplinary humanities research teams on knowledge creation: a qualitative study

PurposeThe process of knowledge creation is recognized as an essential process for organizational learning and innovation. Creating knowledge to solve the problems and complexities of today's world is like opening a black box. Hence, the higher education system and universities are exploring ways to overcome the complexities and cope with global changes. In this regard, interdisciplinary collaborations and activities are crucial in creating knowledge and innovation to counter these changes. This study aimed to know the experiences of Shiraz university interdisciplinary researchers in the field of humanities and also design and explain the conceptual model of knowledge creation in interdisciplinary research teams in the field of humanities.Design/methodology/approachIn this qualitative research, grounded theory was implemented based on Strauss and Corbin's systematic approach. The sampling method was purposeful, and the participants included sixteen faculty members of shiraz university who had at least one experience of performing an interdisciplinary activity in one of the humanities fields. The first participant was selected as a pilot, and the rest were selected by snowball sampling. Semi-structured interviews were also used to collect data and continued until theoretical saturation was attained. After collecting the available information and interviewing the people, the data were organized and analyzed in three stages, open coding, axial coding, and selective coding, using the proposed framework of Strauss and Corbin. Finally, the researcher reached a final and meaningful categorization.FindingsIn this research, the results were presented as a paradigm model of knowledge creation in the interdisciplinary research teams in the field of humanities. The paradigm model of the study consists of causal factors (internal and external factors), main categories (specialized competencies, scientific discourse, understanding of knowledge domains), strategies (structuring and synchronizing), context (individual and organizational), interfering factors (leadership, industry, and society), and consequences (individual and group achievement).Originality/valueThe present study aimed to explore the experiences of researchers in the interdisciplinary humanities research teams on knowledge creation in qualitative research. The study used Strauss and Corbin's systematic approach to recognize the causal factors of knowledge creation and the contexts. Discovering the main category of knowledge creation in interdisciplinary research teams, the authors analyze the strategies and consequences of knowledge creation.

Cholesterol's Function and Origin in the Alzheimer's Disease Brain.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with neuroinflammation and altered lipids in the brain. Cholesterol is a key component of inflammatory lipids. However, the role of cholesterol in AD, specifically in sporadic or late-onset AD, has remained poorly understood due to the belief that most brain cholesterol is separate from circulating blood cholesterol. A new theory suggests that the permeation of circulating cholesterol into the brain is a causal event critical for the onset of AD. As research in this area continues, new hypotheses and insights into AD are expected to emerge.

Designing a talent management model in Mobarakeh Steel Company

OBJECTIVE: The present study aimed to provide a talent management model for Mobarakeh Steel Company (MSC). METHODS: The research method in this study is a mixed method including qualitative and quantitative methods. Qualitative data were collected through interviews with experts and informants and then analyzed by grounded theory. Then, quantitative data were collected from employees and managers and analyzed by Partial Least Square (PLS) analysis. Finally, an interpretation and conclusion were made. In the qualitative section, the research community consists of 15 managers and experts of MSC. In the quantitative section, 300 managers and experts were selected as a sample, and questionnaires were distributed and analyzed. RESULTS: The results of the interview analysis extracted 86 concepts, which were classified into 5 categories including causal conditions, central phenomena, contextual factors, strategies, and consequences. The results of the qualitative section show that causal conditions had a significant effect on the central phenomenon (with a coefficient of 0.89). In addition, the effect of the differentiation of susceptible manpower on contextual factors (with a coefficient of 0.82) was confirmed. Contextual factors have a significant effect on strategies (with a coefficient of 0.93). The results also show that the intervening factors (with a coefficient of 0.74) have a significant effect on the strategies and the talent management process (with a coefficient of 0.92) also has a significant effect on the consequences. CONCLUSIONS: The results provide insights and practical suggestions for managers who decide to design human resource system with a talent management approach. Additionally, the findings of this research help managers to better understand the factors that have an impact on talent management.

Logic and Commonsense-Guided Temporal Knowledge Graph Completion

A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The appendix, source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

Identify Event Causality with Knowledge and Analogy

Event causality identification (ECI) aims to identify the causal relationship between events, which plays a crucial role in deep text understanding. Due to the diversity of real-world causality events and difficulty in obtaining sufficient training data, existing ECI approaches have poor generalizability and struggle to identify the relation between seldom seen events. In this paper, we propose to utilize both external knowledge and internal analogy to improve ECI. On the one hand, we utilize a commonsense knowledge graph called ConceptNet to enrich the description of an event sample and reveal the commonalities or associations between different events. On the other hand, we retrieve similar events as analogy exam- ples and glean useful experiences from such analogous neigh- bors to better identify the relationship between a new event pair. By better understanding different events through exter- nal knowledge and making an analogy with similar events, we can alleviate the data sparsity issue and improve model gener- alizability. Extensive evaluations on two benchmark datasets show that our model outperforms other baseline methods by around 18% on the F1-value on average

A review of how we study coastal and marine conflicts: is social science taking a broad enough view?

Conflict in the marine environment is of increasing relevance as blue growth boundaries are pushed and resource access and use are in dispute. Social science disciplines have a long history and a wide range of approaches for studying conflict. However, understanding the approaches used to study marine conflict is challenging since the literature is large, broad, difficult to navigate, and there is little connection between conflict themes and the associated methods used to analyze these conflicts. In the present study, we take a first step to address this by systematically reviewing 109 peer-reviewed articles that employ empirical social science methods to study marine conflict. We find that studies on marine conflicts have centered on disputed space, mainly at local scale, and natural resources, such as fish. The main parties at the center of the conflicts are small-scale fisheries and public authorities, although with a growing presence of blue growth sectors. Most studies employed qualitative approaches to study marine conflicts. Current gaps in the understanding of marine conflict include gaps in understanding relational interactions and historical causal events. The need for social science research into marine conflict and the application of multiple social science methods is ongoing as different constellations of conflict actors emerge and as disputed ocean spaces expand beyond EEZs, to include polar regions, and the sea floor.

Role of Impaired Astrocyte Gap Junction Coupling in Epileptogenesis.

The gap-junction-coupled astroglial network plays a central role in the regulation of neuronal activity and synchronisation, but its involvement in the pathogenesis of neuronal diseases is not yet understood. Here, we present the current state of knowledge about the impact of impaired glial coupling in the development and progression of epilepsy and discuss whether astrocytes represent alternative therapeutic targets. We focus mainly on temporal lobe epilepsy (TLE), which is the most common form of epilepsy in adults and is characterised by high therapy resistance. Functional data from TLE patients and corresponding experimental models point to a complete loss of astrocytic coupling, but preservation of the gap junction forming proteins connexin43 and connexin30 in hippocampal sclerosis. Several studies further indicate that astrocyte uncoupling is a causal event in the initiation of TLE, as it occurs very early in epileptogenesis, clearly preceding dysfunctional changes in neurons. However, more research is needed to fully understand the role of gap junction channels in epilepsy and to develop safe and effective therapeutic strategies targeting astrocytes.

Dopamine and vesicular monoamine transport loss supports incidental Lewy body disease as preclinical idiopathic Parkinson

Incidental Lewy body disease (ILBD) is a neuropathological diagnosis of brains with Lewy bodies without clinical neuropsychiatric symptoms. Dopaminergic deficits suggest a relationship to preclinical Parkinson’s disease (PD). We now report a subregional pattern of striatal dopamine loss in ILBD cases, with dopamine found significantly decreased in the putamen (−52%) and only to a lower extent in the caudate (−38%, not statistically significant); this is similar to the pattern in idiopathic PD in various neurochemical and in vivo imaging studies. We aimed to find out if our recently reported impaired storage of dopamine in striatal synaptic vesicles prepared from striatal tissue of cases with idiopathic PD might be an early or even causative event. We undertook parallel measurements of [3H]dopamine uptake and vesicular monoamine transporter (VMAT)2 binding sites by the specific label [3H]dihydrotetrabenazine on vesicular preparation from caudate and putamen in ILBD. Neither specific uptake of dopamine and binding of [3H]dihydrotetrabenazine, nor mean values of the calculated ratios of dopamine uptake and VMAT2 binding, a measure of uptake rate per transport site, were significantly different between ILBD and controls. ATP-dependence of [3H]dopamine uptake revealed significantly higher rates in putamen than in caudate at saturating concentrations of ATP in controls, a subregional difference lost in ILBD. Our findings support a loss of the normally higher VMAT2 activity in putamen as a contributing factor to the higher susceptibility of the putamen to dopamine depletion in idiopathic PD. Moreover, we suggest ILBD postmortem tissue as a valuable source for testing hypotheses on processes in idiopathic PD.

Reciprocal interactions between alveolar progenitor dysfunction and aging promote lung fibrosis.

Aging is a critical risk factor in idiopathic pulmonary fibrosis (IPF). Dysfunction and loss of type 2 alveolar epithelial cells (AEC2s) with failed regeneration is a seminal causal event in the pathogenesis of IPF, although the precise mechanisms for their regenerative failure and demise remain unclear. To systematically examine the genomic program changes of AEC2s in aging and after lung injury, we performed unbiased single-cell RNA-seq analyses of lung epithelial cells from uninjured or bleomycin-injured young and old mice, as well as from lungs of IPF patients and healthy donors. We identified three AEC2 subsets based on their gene signatures. Subset AEC2-1 mainly exist in uninjured lungs, while subsets AEC2-2 and AEC2-3 emerged in injured lungs and increased with aging. Functionally, AEC2 subsets are correlated with progenitor cell renewal. Aging enhanced the expression of the genes related to inflammation, stress responses, senescence, and apoptosis. Interestingly, lung injury increased aging-related gene expression in AEC2s even in young mice. The synergistic effects of aging and injury contributed to impaired AEC2 recovery in aged mouse lungs after injury. In addition, we also identified three subsets of AEC2s from human lungs that formed three similar subsets to mouse AEC2s. IPF AEC2s showed a similar genomic signature to AEC2 subsets from bleomycin-injured old mouse lungs. Taken together, we identified synergistic effects of aging and AEC2 injury in transcriptomic and functional analyses that promoted fibrosis. This study provides new insights into the interactions between aging and lung injury with interesting overlap with diseased IPF AEC2 cells.

Logical Foundations for Reasoning in Cyber - Physical Systems

This paper aims at building the causal relations and event structures13 to study the complex and evolving cyber – physical systems with illustrations of the reasoning based on Robotics24 and Policy Analysis25 for Communication Systems. An empirical analysis points to the realism that network security is also a geometric theory with safety and authentication tending to geometric formulae that make the larger structures. Security is very much a matter of perception too. The proposed approach also factors the perceptual aspects of the human mind. This paper includes several interesting possibilities for using linear algebra, discrete mathematics, analysis, and topology in the domains such as economics, game theory, robotics and biology to mention a few.

CEG: A joint model for causal commonsense events enhanced story ending generation.

With the success of pre-trained language models, the performance of story ending generation has been dramatically improved while remaining challenging due to the lack of commonsense reasoning ability. Most previous works mainly focus on using commonsense knowledge to enhance the implicit correlations between words but ignore the hidden causality of sentences or events. In this paper, we propose Causal commonsense Enhanced joint model for story ending Generation (CEG), which incorporates causal commonsense events knowledge to generate a reasonable story ending. Specifically, we first develop a commonsense events inference model trained on GLUCOSE, which converts static knowledge into a dynamic generation model to discover unseen knowledge. It uses prompts to produce various commonsense events behind the stories as pseudo-labels of the dataset. Then, we propose a joint model for the causal events inference task and the story ending generation task to inject inference knowledge into the generation, which consists of a shared encoder, an inference decoder, and a generation decoder. In the causal events inference task, we use the shared encoder and the inference decoder to reason the causal events behind each sentence of the story context to help the model better understand the story and provide long-distance dependencies for the story ending generation. In story ending generation, we combine the hidden states of the causal events with the story context to generate the story ending by the shared encoder and the generation decoder. We jointly train the model on two tasks so that the generation decoder produces the story endings that better match the clues. Experimental results on the ROCStories dataset show that our model outperforms the previous works, demonstrating the effectiveness of the joint model and the generated causal events.

Epigenetic Regulation and its Effects on Aging and Cardiovascular Disease.

Cardiovascular disease (CVD), specifically coronary atherosclerosis, is regulated by an interplay between genetic and lifestyle factors. Most recently, a factorgetting much attention is the role epigenetics play in atherosclerosis; particularly the development of coronary artery disease. Furthermore, it is important to understand the intricate interaction between the environment and each individual genetic material and how this interaction affects gene expression and consequently influences the development of atherosclerosis.Our main goal is to discuss epigenetic regulations; particularly, the factors contributing to coronary atherosclerosis and their role in aging and longevity. We reviewed the current literature and provided a simplified yet structured and reasonable appraisal of this topic. This role has also been recently linked to longevity and aging. Epigenetic regulations (modifications) whether through histone modifications or DNA or RNA methylation have been shown to be regulated by environmental factors such as social stress, smoking, chemical contaminants, and diet. These sensitive interactions are further aggravated by racial health disparities that ultimately impact cardiovascular disease outcomes through epigenetic interactions. Certainly, limiting our exposure to such causative events at younger ages seems our "golden opportunity" to tackle the incidence of coronary atherosclerosis and probably the answer to longevity.