Cause Of Events Research Articles

First Geoarchaeological and Archaeometric Investigation at the Lucanian (4th–3rd Century BCE) Site of Laurelli (Cilento, Vallo di Diano and Alburni UNESCO Global Geopark—Southern Italy)

The Lucanian site of Laurelli represents one of the largest, still poorly investigated, pre-Roman archaeological sites of the Cilento, Vallo di Diano and Alburni Geopark (southern Italy). The site lies on a large, low-relief surface in the upper portion of the Serrapotamo River basin, a right tributary of the Bussento River. Geomorphological, stratigraphical, archaeobotanical and archaeometric analyses were carried out to understand the following: (1) the choice to settle at the site of the town of Laurelli; (2) the origin of the raw material used to build the town; (3) the vegetation present during the lifetime of the town; and (4) the causes of its abandonment. The results indicate that the site was chosen because it was almost stable from a geomorphological point of view, not being affected by deep landslides. Further investigations are needed to unravel the possible occurrence of flooding as a causative event for the abandonment of Laurelli. Carpinus was widespread and used as a fuel for iron production, but the areas from which the iron was extracted have not yet been discovered. The multidisciplinary approach adopted is suitable for application in other archaeological areas worldwide.

Cause‐Specific Secular Trends and Prevention Measures of Post‐Neonatally Acquired Cerebral Palsy in Victoria and Western Australia 1975–2014: A Population‐Based Observational Study

ABSTRACTAimTo describe the timing and causes of post‐neonatally acquired cerebral palsy (PNN‐CP) and map the implementation of relevant preventive strategies against cause‐specific temporal trends in prevalence.MethodsData for a 1975–2014 birth cohort of children with PNN‐CP (brain injury between 28 days and 2 years of age) were drawn from the Victorian and Western Australian CP Registers. Descriptive statistics were used to report causal events and timing. Poisson regression models were used to investigate the strength of evidence for cause‐specific temporal trends in prevalence. Preventive strategies were mapped alongside cause‐specific trends.ResultsAmongst 512 children, causes of PNN‐CP included infections (31%, n = 157), head injuries (24%, n = 121) and cerebrovascular accidents (CVAs) (23%, n = 119). Infections were the only main causal group of PNN‐CP that declined significantly (p = 0.014). Fifty two percent (n = 267) of the PNN‐CP cohort acquired their brain injury before 6 months of age, the majority having an infectious cause (57%, n = 90). Improved clinical care and a range of preventive strategies, including childhood vaccination programs, occurred during this period.ConclusionInfants under 6 months are a priority group for preventive strategies for PNN‐CP. Declining temporal trends were observed for PNN‐CP caused by infection, and the causal subgroup of CVAs associated with surgery. Interventions aimed at further reducing the risk of head injury, CVAs and infections, are needed to reduce the prevalence of PNN‐CP.

Evolution and response of flood disaster network public opinion based on an event logic graph: A case study of rainstorms in Henan, China

Social media takes the center stage as the primary information and opinion-sharing platform for the public during disaster events. Related topics quickly spread, and multiple public opinion events exhibit clear coupling phenomena, leading to the formation of network public opinion. By creating a network public opinion event logic graph, our study illustrates the development and evolution of network public opinion during disaster events and provides the government with a crucial foundation for action. For this study, we selected tweets on Sina Weibo regarding the "Henan rainstorm" between July 19, 2021, and August 2, 2021. To construct a network public opinion event logic graph, pattern matching was used to discover event relationships and extract causal event pairs and sequential event pairs. Moreover, the term frequency-inverse document frequency+K-means clustering algorithm (TF-IDF+K-Means) clustering algorithm was used to group event pairs with high similarity into one category. The critical nodes and evolution pathways in the event logic graph were then thoroughly examined. Our findings demonstrated that the topic evolution of network public opinion was multidirectional in nature, covering topics such as disaster events, emergency rescue, public sentiment, media opinion, and livelihood-related events. The two main components of the evolutionary pathway of network public opinion are the evolution of the disaster itself and its evolution in the social sphere. Our study provides a technique for determining the dynamic evolution of network public opinion and provides useful consultation for the government's upcoming response to network public opinion during disaster events.

Semantic aware enhanced event causality identification

Semantic aware enhanced event causality identification

The Potential of Artificial Intelligence and Machine Learning in Pharmacovigilance: An Update

The integration of artificial intelligence (AI) and machine learning (ML) into pharmacovigilance (PV) marks a transformative step towards enhancing drug safety and patient outcomes. With their unparalleled ability to analyze vast and complex datasets, AI/ML technologies offer unprecedented opportunities to revolutionize adverse drug event (ADE) monitoring, signal detection, and causality assessment. This editorial explores the state-of-the-art applications, key challenges, and actionable strategies to unlock the full potential of AI in PV.

Knowledge interaction graph guided prompting for event causality identification

Event causality identification (ECI) aims to identify causality between event pairs in a text, and is commonly approached as a supervised classification task using pre-trained language models (PLMs). However, limitations in implicit causality identification and insufficient event-knowledge interaction pose significant challenges to ECI. To address these issues, we propose a novel Knowledge Interaction Graph guided Prompt Tuning (KIGP), which leverages prompt tuning and knowledge interaction to fully exploit the potential of PLMs for ECI by integrating external knowledge. Specifically, to accurately capture implicit causality, we design the guidance mechanism and construct event-knowledge interaction graphs that enable external knowledge to enhance event representations through deep interaction between events and knowledge. Experimental results on two benchmark datasets demonstrate that our model outperforms existing approaches significantly.Graphical abstract

Event-level supervised contrastive learning with back-translation augmentation for event causality identification

Event-level supervised contrastive learning with back-translation augmentation for event causality identification

“Crocus Flower”: Voriconazole-Induced Hallucinations and Visual Disturbances in a Patient with Recurrent Severe Vulvovaginitis—A Case Report on Irrational Drug Use

Background and Clinical Significance: Voriconazole is a commonly prescribed second-generation azole used for the prevention and treatment of fungal infections. This report seeks to elucidate the relationship between certain intravenous infusion parameters and the causality and severity of potential visual adverse events associated with voriconazole administration, despite existing reports of visual disturbances such as hallucinations and altered visual perception, the underlying causes of which remain inadequately understood. Case Presentation: This case report describes a 32-year-old female patient who experienced sudden hallucinations and visual impairments after receiving an initial dose of intravenous voriconazole for the treatment of recurrent severe vulvovaginitis caused by Candida glabrata. The symptoms quickly disappeared when the dosage and infusion rate were reduced as per the recommendations of the clinical pharmacist. Conclusions: This example emphasizes the possible negative drug responses linked to voriconazole, especially those provoked by its irrational use described as an inappropriate infusion rate, and the crucial role of clinical pharmacists in recognizing and handling these reactions.

EiGC: An Event-Induced Graph with Constraints for Event Causality Identification

EiGC: An Event-Induced Graph with Constraints for Event Causality Identification

Comparison Between Different Source Localization and Connectivity Metrics of Spiky and Oscillatory MEG Activities

Epilepsy is considered the second neurological disease in a coma after stroke. Famous markers of epilepsy are repetitive seizures, their origin is stroma and cortical deformation. A neurologist would be assisted by identifying Epileptogenic Zones EZ when diagnosing epilepsy.. Source localization is utilized to identify regions known as EZ, which are of excessive discharges. It consists of both forward and inverse problems. The forward problem models the head through analytical and numerical methods. The inverse problem can be resolved using several techniques to locate the cerebral abnormal sources, via the electrophysiological recording biomarkers. In our study, we will investigate four distributed inverse problem methods: minimum norm estimation MNE, standardized low-resolution brain electromagnetic tomography sLORETA, maximum entropy on the mean MEM, Dynamic statistical parametric maps dSPM, to define epileptic networks connectivity of spiky and oscillatory events. We will examine the epileptic network connectivity using Phase Locking Value (PLV), Phase Transfert Entropy (PTE) for oscillatory events, cross-correlation (CC), and Granger Causality (GC) for spiky events applied on 5 pharmaco resistant subjects. We suggest rating the effectiveness of these networks in locating EZ through a phase of confrontation within iEEG transitory and oscillatory networks connectivity by exploring concordant nodes, their distance, propagation delays connection strength, and their cooperation in recognition of seizure onset zone. All studied techniques of the inverse problem, connection metrics, for both biomarkers of the 5 patients succeed in detecting at least one part of SOZ. sLORETA provides the highest concordant nodes and the closed one for spiky events using CC and GC. sLORETA also depicts the lowest propagation delay for oscillatory events using PTE. Through the 5 patients, MEM, dSPM, and MNE using CC, CG for spiky events, and PTE, PLV for oscillatory activities provide about 72 % of concordant nodes between MEG and iEEG.

Modeling correlated causal-effect structure with a hypergraph for document-level event causality identification

Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Existing approaches for document-level event causality identification detect the causal relation for each pair of event mentions independently, while ignoring latent correlated cause–effect structure in a document, i.e., one cause (effect) with multiple effects (causes). We argue that identifying the causal relation of one event pair may facilitate the causality identification for other event pairs. In light of such considerations, we propose to model the correlated causal-effect structure by a hypergraph and jointly identify multiple causal relations with the same cause (effect). In particular, we propose an event-hypergraph neural encoding model, called EHNEM, for document-level event causality identification. In EHNEM, we first initialize event mentions’ embeddings via a pre-trained language model and obtain potential causal relation of each event pair via a multilayer perceptron. To capture causal correlations, we construct a hypergraph by integrating potential causal relations for the same event as a hyperedge. On the constructed event-hypergraph, we use a hypergraph convolutional network to learn the representation of each event node for final causality identification. Experiments on both EventStoryLine corpus and English-MECI corpus show that our EHNEM model significantly outperforms the state-of-the-art algorithms.

CRITICAL CAUSAL EVENTS IN SYSTEMS BASED ON CQRS WITH EVENT SOURCING ARCHITECTURE

Context. The article addresses the problem of causal events asynchrony which appears in the service-oriented information systems that does not guarantee that the events will be delivered in the order they were published. It may cause intermittent faults occurring at intervals, usually irregular, in a system that functions normally at other times. Objective. The goal of the work is the comparison and assessment of several existing approaches and providing a new approach for solving the causal events synchronization issue in application to the systems developed using Command Query Responsibility Segregation (CQRS) with Event Sourcing (ES) architecture approach. Methods. Firstly, the method of estimation of the likelihood of causal events occurring within the systems as the foundation for choosing the solution is suggested. Based on the results of the analysis of several projects based on CQRS with ES architecture it shows that the likelihood of critical causal events depends on the relationships among entities and the use-cases connected with the entities. Secondly, the Container of Events method, which represents a variation of event with full causality history, adapted to the needs of CQRS with ES architecture systems, was proposed in this work. The variants of its practical implementation have also been discussed. Also, the different solutions, such as Synchronous Event Queues and variation of Causal Barrier method were formalized and assessed. Thirdly, the methods described have been discussed and evaluated using performance and modification complexity criteria. To make the complexity-performance comparative assessment more descriptive the integrated assessment formula was also proposed. Results. The evaluation results show that the most effective solution of the issue is to use the Container of Events method. To implement the solution, it is proposed to make the modifications of the Event Delivery Subsystem and event handling infrastructure. Conclusions. The work is focused on the solution of the critical causal events issue for the systems based on CQRS with ES architecture. The method of estimation of the likelihood of critical causal events has been provided and different solutions of the problem have been formalized and evaluated. The most effective solution based on Container of Events method was suggested.

PTEKC: pre-training with event knowledge of ConceptNet for cross-lingual event causality identification

PTEKC: pre-training with event knowledge of ConceptNet for cross-lingual event causality identification

Diabetes and Early Development: Epigenetics, Biological Stress, and Aging.

Pregestational diabetes, either type 1 or type 2 diabetes, induces structural birth defects including neural tube defects and congenital heart defects in human fetuses. Rodent models of type 1 and type 2 diabetic embryopathy have been established and faithfully mimic human conditions. Hyperglycemia of maternal diabetes triggers oxidative stress in the developing neuroepithelium and the embryonic heart leading to the activation of proapoptotic kinases and excessive cell death. Oxidative stress also activates the unfolded protein response and endoplasmic reticulum stress. Hyperglycemia alters epigenetic landscapes by suppressing histone deacetylation, perturbing microRNA (miRNA) expression, and increasing DNA methylation. At cellular levels, besides the induction of cell apoptosis, hyperglycemia suppresses cell proliferation and induces premature senescence. Stress signaling elicited by maternal diabetes disrupts cellular organelle homeostasis leading to mitochondrial dysfunction, mitochondrial dynamic alteration, and autophagy impairment. Blocking oxidative stress, kinase activation, and cellular senescence ameliorates diabetic embryopathy. Deleting the mir200c gene or restoring mir322 expression abolishes maternal diabetes hyperglycemia-induced senescence and cellular stress, respectively. Both the autophagy activator trehalose and the senomorphic rapamycin can alleviate diabetic embryopathy. Thus, targeting cellular stress, miRNAs, senescence, or restoring autophagy or mitochondrial fusion is a promising approach to prevent poorly controlled maternal diabetes-induced structural birth defects. In this review, we summarize the causal events in diabetic embryopathy and propose preventions for this pathological condition. KEY POINTS: · Maternal diabetes induces structural birth defects.. · Kinase signaling and cellular organelle stress are critically involved in neural tube defects.. · Maternal diabetes increases DNA methylation and suppresses developmental gene expression.. · Cellular apoptosis and senescence are induced by maternal diabetes in the neuroepithelium.. · microRNAs disrupt mitochondrial fusion leading to congenital heart diseases in diabetic pregnancy..

A multi-omics study of brain tissue transcription and DNA methylation revealing the genetic pathogenesis of ADHD.

Attention-deficit/hyperactivity disorder (ADHD) is a chronic psychiatric disease that often affects a patient's whole life. Research has found that genetics plays an important role in the development of ADHD. However, there is still a lack of knowledge about the tissue-specific causal effects of biological processes beyond gene expression, such as alternative splicing (AS) and DNA methylation (DNAm), on ADHD. In this paper, a multi-omics study was conducted to investigate the causal effects of the transcription and the DNAm on ADHD, by integrating ADHD genome-wide association data with quantitative trait loci data of gene expression, AS, and DNAm across 14 different brain tissues. The causal effects were estimated using four different two-sample Mendelian randomization methods. Finally, we also prioritized the expression of 866 genes showing significant causal effects, including COMMD5, ENSG00000271904, HYAL3, etc., within at least one brain tissue. We prioritized 966 unique genes that have statistically significant causal AS events, within at least one of the 14 different brain tissues. These genes include PPP1R16A, GGT7, TREM2, etc. Furthermore, through mediation analysis, 106 regulatory pathways were inferred where DNAm influences ADHD through gene expression or AS processes. Our research findings provide guidance for future experimental studies on the molecular mechanisms of ADHD development, and also put forward valuable knowledge for the prevention, diagnosis, and treatment of ADHD.

CUG repeat RNA-dependent proteasomal degradation of MBNL1 in a cellular model of myotonic dystrophy type 1

Myotonic dystrophy type 1 (DM1) is caused by the expansion of a non-coding CTG repeat in DMPK. CUG-repeat-containing transcripts sequester the splicing regulator MBNL1 into nuclear RNA foci, causing aberrant splicing of many genes. Although the mislocalization of MBNL1 represents a causal event in DM1 pathogenesis, the effect of CUG repeat RNA on the protein level of MBNL1 remains unclear. Using a DM1 model cell line, we found that CUG repeat RNA caused a significant decrease in the protein, but not mRNA levels, of MBNL1. As CUG repeats did not decrease MBNL1 translation, we investigated protein degradation pathways. Although autophagy-related reagents induced little change, proteasome inhibitors partially recovered MBNL1 protein expression levels under conditions of CUG repeat expression and induced a slight, but significant, reversal of splicing dysregulation. MBNL1 was detected in the polyubiquitinated protein fraction, but MBNL1 polyubiquitination was not detected. Moreover, inhibition of the ubiquitin-activating enzyme E1 did not increase MBNL1 levels, suggesting that MBNL1 is a substrate of polyubiquitin-independent proteasomal degradation. These results suggest that CUG-repeat-induced proteasomal degradation partially contributes to the functional decline of MBNL1.

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

Exploring indoor thermal comfort and its causes and consequences amid heatwaves in a Southern European city—An unsupervised learning approach

This study investigates indoor thermal comfort during heatwaves in dwellings of the Southern European city of Pamplona, Spain. Utilizing K-means and Hierarchical clustering, it explores clustering patterns from occupants' survey responses (n = 189) to thermal comfort-related questions (i.e. day and night thermal sensation, thermal satisfaction and thermal preference) as well as causal links (i.e. indoor temperatures, building/occupant features) and consequences (i.e. sleep quality, heat-related symptoms) of such clusterings. Both unsupervised learning techniques coherently revealed two groups: the comfortable and uncomfortable clusters. Uncomfortable occupants coherently experience more sensation to heat, greater preference for cooler temperatures, and more thermal dissatisfaction, especially during daytime hours. Dwellings of comfortable occupants experience median indoor temperatures ranging 25.7–26 °C; dwellings of uncomfortable occupants 27.4 °C, with median temperatures above 28 °C during 15:00–23:00 and 23:00–07:00 periods. Discomfort or overheating—coherently expressed by the thermally uncomfortable cluster—is alleviated by multiple factors related to the presence of active cooling technologies in all rooms, and use of passive and low-energy cooling measures (e,g. fans); exacerbated by heatwave conditions. As coherently expressed by the uncomfortable cluster heat worsens the sleep quality of occupants (3 to 6-fold) and increases the likelihood of occupants to experience heat-related symptoms (10–19-fold). This study is particularly important to policymakers, as it sheds light, from dwellers’ first-hand experience in a Southern Europe city, on relevant factors that should be taken in consideration to allow them to cope better with heatwaves without compromising their comfort and health.

Infants evaluate informativeness of evidence and predict causal events as revealed in theta oscillations and predictive looking

This study investigates 16-month-old infants’ sensitivity to the informativeness of evidence and its potential link to infants’ ability to draw accurate causal inferences and predict unfolding events. Employing concurrent EEG and eye tracking, data from 66 infants revealed significantly increased theta oscillatory activity when infants expected to see causally unconfounded evidence compared to confounded evidence, suggesting heightened cognitive engagement in anticipation of informative evidence. Crucially, this difference was more pronounced in the subset of infants who later made correct predictions, suggesting that they had correctly inferred the causal structure based on the evidence presented. This research sheds light on infants’ motivation to seek explanatory causal information, suggesting that even at 16 months, infants can strategically direct attention to situations conducive to acquiring informative evidence, potentially laying the groundwork for the impressive abilities of humans to rapidly acquire knowledge and develop causal theories of the world.

A new approach to determine abnormality of radioactive discharges from pressurized water reactors and to derive abnormality indicators correlated with a specific causal event

Abstract A novel methodology to analyze the trend of radioactive effluent data from nuclear power plants (NPPs) has been proposed to estimate the possibility of abnormal releases of radioactive materials, and to identify sensitive radionuclides with high correlation to specific events and abnormal releases. Actual discharge data including abnormal releases for 21 years (1,610 reactor-years) from 62 US and 22 Korean pressurized water reactors were collected. A release correlation factor (RCF) was newly proposed as a quantitative indicator of the abnormality of radioactive discharges based on annual discharge data from each NPP. The probability of correct estimation of abnormality using the RCF for a test case ranges 27–36 %, and such a modest estimation power was ascribed to the relatively low contribution (i.e., about 12 %) of a specific causal event to the annual radioactivity release. Through this, the feasibility of the RCF for estimating the abnormality of radioactive discharges was demonstrated. The RCF was derived for three types of causal events, and subsequently the sensitive radionuclides to each specific event were derived as 88Kr, 137Xe, and 138Xe for leakage from gas decay tank, 3H for leakage from steam generator’s power operated relief valve, and 85Kr, 88Kr, 133Xe, 133mXe, 3H and 14C for leakage from defected spent fuel during handling. The new approach proposed in this study is to be used to suppose the significant unreported abnormality in radioactive effluent data and the potential causal event, which may contribute to improving the safety and performance of radioactive effluent control as necessary.