Simple Events Research Articles

Argument ordering in simple sentences is affected by age of first language acquisition: Evidence from late first language signers of ASL.

Research on the language acquisition of deaf individuals who are exposed to accessible linguistic input at a variety of ages has provided evidence for a sensitive period of first language acquisition. Recent studies have shown that deaf individuals who first learn language after early childhood, late first-language learners (LL1), do not comprehend reversible Subject-Verb-Object (SVO) sentences. The present study analyzed 478 signed productions elicited with pictures depicting simple events with one or two arguments by 28 signers. The argument order patterns of native signers converged with one another and the word order patterns of American Sign Language (ASL). By contrast, the ordering patterns of the LL1 signers did not converge with one another or with the patterns of the native signers. This indicates that early childhood is a period of heightened sensitivity to basic word order and may help explain why complex structures are difficult for LL1 signers to learn.

Home Blood Pressure Variability Risk Prediction Score for Cardiovascular Disease Using Data From the J-HOP Study.

Home blood pressure (BP) is more closely associated with cardiovascular event risk than office BP, but cardiovascular risk prediction based on home BP variability is lacking. This study developed a simple cardiovascular event prediction score, including home BP variability data, from the J-HOP study (Japan Morning Surge-Home Blood Pressure). The J-HOP study extended follow-up from December 2017 to May 2018 generated the study data set (4231 patients). Cardiovascular events included fatal/nonfatal stroke (n=94), coronary heart disease (n=124), heart failure (n=42), and aortic dissection (n=8). Cox proportional hazards models were used to predict overall cardiovascular risk. Potential covariates included age, sex, body mass index, smoking, history of diabetes, statin use, history of cardiovascular disease, total cholesterol:high-density lipoprotein cholesterol ratio, office systolic BP (SBP), mean of morning-evening average (MEave), home SBP, and average real variability of MEave home SBP. A risk score and models were constructed, and model performance was assessed. Model performance was best when average real variability of MEave SBP was included (C statistic, 0.760). The risk score assigns points for age (5-year bands), sex, cardiovascular disease history, high-density lipoprotein cholesterol, mean MEave home SBP, and average real variability of MEave home SBP. Estimated 10-year cardiovascular risk ranged from ≤0.6% (score ≤0) to >32% (score ≥26). Calibration 2 statistics values for the model (2.66) and risk score (5.29) indicated excellent goodness of fit. This simple cardiovascular disease prediction algorithm, including day-by-day home BP variability, could be used as part of a home BP-centered approach to hypertension management in clinical practice.

Dissociating Cerebellar Regions Involved in Formulating and Articulating Words and Sentences.

We investigated which parts of the cerebellum are involved in formulating and articulating sentences using (i) a sentence production task that involved describing simple events in pictures; (ii) an auditory sentence repetition task involving the same sentence articulation but not sentence formulation; and (iii) an auditory sentence-to-picture matching task that involved the same pictorial events and no overt articulation. Activation for each of these tasks was compared to the equivalent word processing tasks: noun production, verb production, auditory noun repetition, and auditory noun-to-picture matching. We associate activation in bilateral cerebellum lobule VIIb with sequencing words into sentences because it increased for sentence production compared to all other conditions and was also activated by word production compared to word matching. We associate a paravermal part of right cerebellar lobule VIIIb with overt motor execution of speech, because activation was higher during (i) production and repetition of sentences compared to the corresponding noun conditions and (ii) noun and verb production compared to all matching tasks, with no activation relative to fixation during any silent (nonspeaking) matching task. We associate activation within right cerebellar Crus II with covert articulatory activity because it activated for (i) all speech production more than matching tasks and (ii) sentences compared to nouns during silent (nonspeaking) matching as well as sentence production and sentence repetition. Our study serendipitously segregated, for the first time, three distinct functional roles for the cerebellum in generic speech production, and it demonstrated how sentence production enhanced the demands on these cerebellar regions.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Predicting the occurrence of minor adverse events in cardiac rehabilitation using physical variables

The safety of exercise practice by individuals with cardiovascular disease is due to a good clinical evaluation to identify patients with a higher risk of adverse events, thus the study of simple variables capable of predicting the rate of events during exercise is relevant and may provide better screening tools to be applied in the clinical practice. The study aimed to evaluate if clinical and physical parameters can predict the rate of adverse events during exercise-based cardiac rehabilitation. This was a cohort study that followed 73 patients during 24 sessions of exercise. Were registered 217 simple adverse events, at a rate of 2.5 events per hour of exercise. We found that higher adiposity, worse pulmonary function, lower functional capacity, and reduced heart rate variability were significant and acceptable predictors of adverse events during exercise, according to the ROC curve analysis. We state that these simple physical parameters may be useful for cardiac risk stratification in cardiac rehabilitation programs with low resources, contributing to its safety.

Lck Function and Modulation: Immune Cytotoxic Response and Tumor Treatment More Than a Simple Event.

Lck, a member of the Src kinase family, is a non-receptor tyrosine kinase involved in immune cell activation, antigen recognition, tumor growth, and cytotoxic response. The enzyme has usually been linked to T lymphocyte activation upon antigen recognition. Lck activation is central to CD4, CD8, and NK activation. However, recently, it has become clearer that activating the enzyme in CD8 cells can be independent of antigen presentation and enhance the cytotoxic response. The role of Lck in NK cytotoxic function has been controversial in a similar fashion as the role of the enzyme in CAR T cells. Inhibiting tyrosine kinases has been a highly successful approach to treating hematologic malignancies. The inhibitors may be useful in treating other tumor types, and they may be useful to prevent cell exhaustion. New, more selective inhibitors have been documented, and they have shown interesting activities not only in tumor growth but in the treatment of autoimmune diseases, asthma, and graft vs. host disease. Drug repurposing and bioinformatics can aid in solving several unsolved issues about the role of Lck in cancer. In summary, the role of Lck in immune response and tumor growth is not a simple event and requires more research.

Mathematics E-learning Packets for Technical and Livelihood Education (TLE) Automotive Servicing

This study aimed to develop and evaluate Mathematics e-learning packets tailored for Technical and Livelihood Education (TLE) Automotive Servicing students at a public national high school, in the City Schools Division of Marikina for the School Year 2023-2024. The impetus for the study was based on the increasing number of learners who are having difficulty solving basic mathematical problems. The research addressed the following questions: (1) identifying Mathematics 9 topics suitable for e-learning packets based on literature review, (2) evaluating the developed packets by automotive and mathematics teachers according to DepEd guidelines concerning content quality, instructional quality, technical quality, assessment, visuals, and plan layout, (3) assessing any significant differences between the evaluations of the two groups of respondents, and (4) gathering comments and suggestions for improvement. Utilizing a descriptive research method, the study involved evaluations from 15 mathematics teachers and 15 automotive teachers from two public national high schools in Marikina City, Philippines. Data were analyzed using Weighted Mean and the Mann-Whitney U Test. Key findings include (1) prioritized topics under the Matatag Curriculum: perpendicular and parallel lines, relations and functions, quadratic functions and equations, direct and inverse variations, trigonometric ratios, and probability of simple events; (2) both groups rated the e-learning packets highly acceptable, with grand weighted means of 3.50 and 3.60, respectively; (3) no significant difference was found between the evaluations of the two groups; (4) respondents praised the packets' adequacy, organization, relevance, and interactivity, offering suggestions to incorporate more visuals, expand topics and activities, add video demonstrations, and involve industry professionals. In conclusion, the developed e-learning packets were well-received and deemed effective, with recommended enhancements aimed at further enriching the learning experience and bridging theoretical knowledge with practical application.

How Transcription Factor Clusters Shape the Transcriptional Landscape.

In eukaryotic cells, gene transcription typically occurs in discrete periods of promoter activity, interspersed with intervals of inactivity. This pattern deviates from simple stochastic events and warrants a closer examination of the molecular interactions that activate the promoter. Recent studies have identified transcription factor (TF) clusters as key precursors to transcriptional bursting. Often, these TF clusters form at chromatin segments that are physically distant from the promoter, making changes in chromatin conformation crucial for promoter-TF cluster interactions. In this review, I explore the formation and constituents of TF clusters, examining how the dynamic interplay between chromatin architecture and TF clustering influences transcriptional bursting. Additionally, I discuss techniques for visualizing TF clusters and provide an outlook on understanding the remaining gaps in this field.

JunctionCounts: comprehensive alternative splicing analysis and prediction of isoform-level impacts to the coding sequence.

Alternative splicing (AS) is emerging as an important regulatory process for complex biological processes. Transcriptomic studies therefore commonly involve the identification and quantification of alternative processing events, but the need for predicting the functional consequences of changes to the relative inclusion of alternative events remains largely unaddressed. Many tools exist for the former task, albeit each constrained to its own event type definitions. Few tools exist for the latter task; each with significant limitations. To address these issues we developed junctionCounts, which captures both simple and complex pairwise AS events and quantifies them with straightforward exon-exon and exon-intron junction reads in RNA-seq data, performing competitively among similar tools in terms of sensitivity, false discovery rate and quantification accuracy. Its partner utility, cdsInsertion, identifies transcript coding sequence (CDS) information via in silico translation from annotated start codons, including the presence of premature termination codons. Finally, findSwitchEvents connects AS events with CDS information to predict the impact of individual events to the isoform-level CDS. We used junctionCounts to characterize splicing dynamics and NMD regulation during neuronal differentiation across four primates, demonstrating junctionCounts' capacity to robustly characterize AS in a variety of organisms and to predict its effect on mRNA isoform fate.

Exact solution of the propagation of ON-OFF signals by dispersive waves

The propagation of ON-OFF signals with dispersive waves is examined in this study. An integral-form exact solution for a simple ON-OFF switching event is derived, which holds for any dispersion relation. The integral can be exactly calculated for two types of dispersion relations. Further, the analysis of these solutions shows that the ON-OFF signal propagates with the group velocity and that the boundary thickness of the signal increases with time, typically at a rate proportional to the square root of time, owing to dispersion. Additionally, an approximate solution for a general dispersion relation is derived, and for a higher-complexity ON-OFF switching pattern is constructed.

Intelligent Cell Profiling and Precision Release: Multimolecular Marker-Activated Transmembrane DNA Computing Nanosystem.

Accurate classification of tumor cells is of importance for cancer diagnosis and further therapy. In this study, we develop multimolecular marker-activated transmembrane DNA computing systems (MTD). Employing the cell membrane as a native gate, the MTD system enables direct signal output following simple spatial events of "transmembrane" and "in-cell target encounter", bypassing the need of multistep signal conversion. The MTD system comprises two intelligent nanorobots capable of independently sensing three molecular markers (MUC1, EpCAM, and miR-21), resulting in comprehensive analysis. Our AND-AND logic-gated system (MTDAND-AND) demonstrates exceptional specificity, allowing targeted release of drug-DNA specifically in MCF-7 cells. Furthermore, the transformed OR-AND logic-gated system (MTDOR-AND) exhibits broader adaptability, facilitating the release of drug-DNA in three positive cancer cell lines (MCF-7, HeLa, and HepG2). Importantly, MTDAND-AND and MTDOR-AND, while possessing distinct personalized therapeutic potential, share the ability of outputting three imaging signals without any intermediate conversion steps. This feature ensures precise classification cross diverse cells (MCF-7, HeLa, HepG2, and MCF-10A), even in mixed populations. This study provides a straightforward yet effective solution to augment the versatility and precision of DNA computing systems, advancing their potential applications in biomedical diagnostic and therapeutic research.

Estimating event probabilities via signal temporal logic and first occurrence distributions

Abstract Estimating the probability of events is a significant challenge in many fields, often requiring a probabilistic model or additional labels and tasks for accurate prediction. However, those methods have limited scalability or unnecessary computational resource consumption due to predicting unrelated values. To address these issues, we propose a novel approach that estimates event probabilities based on the distributions of their first occurrence in the time domain. By using Signal Temporal Logic formulas to describe events and applying an algorithm that estimates complex events’ probabilities through simple event occurrence distributions, this study presents an efficient approach that does not depend on high-precision prediction. We evaluate the performance of our method on simulated scenarios of unmanned aerial vehicle motion and autonomous driving.

Determination of association rules for mathematical skills

The aim of this study is to classify the subjects and skills of middle school mathematics course in the context of MATH Taxonomy and to determine their relations. For this purpose, the questions and answers related to the mathematics subtest of a national exam were analyzed over the answers of 20154 students. The study continued with the analysis of the data set containing the answer patterns of the students according to the rules of association of data mining methods by using RapidMiner program and FP-Growth algorithm. The results show that students with any level of skill in MATH taxonomy related to mathematical skills could perform skills in subcategories at a high rate. Students who have attainment in the learning areas provided later in the mathematics program had a high rate of knowledge in the previously provided learning areas. However, this result is not valid for Probability of Simple Events.

Changes in serum immunoglobulin G glycosylation patterns for antiphospholipid syndrome patients with lectin microarray.

Antiphospholipid syndrome is a rare autoimmune disease characterized by persistent antiphospholipid antibodies. Immunoglobulin G plays a vital role in disease progression, with its structure and function affected by glycosylation. We aimed to investigate the changes in the serum immunoglobulin G glycosylation pattern in antiphospholipid syndrome patients. We applied lectin microarray on samples from 178 antiphospholipid syndrome patients, 135 disease controls (including Takayasu arteritis, rheumatoid arthritis and cardiovascular disease) and 100 healthy controls. Lectin blots were performed for validation of significant differences. Here, we show an increased immunoglobulin G-binding level of soybean agglutinin (p = 0.047, preferring N-acetylgalactosamine) in antiphospholipid syndrome patients compared with healthy and disease controls. Additionally, the immunoglobulin G from antiphospholipid syndrome patients diagnosed with pregnancy events had lower levels of fucosylation (p = 0.001, recognized by Lotus tetragonolobus) and sialylation (p = 0.030, recognized by Sambucus nigra I) than those with simple thrombotic events. These results suggest the unique serum immunoglobulin G glycosylation profile of antiphospholipid syndrome patients, which may inform future studies to design biomarkers for more accurate diagnosis of antiphospholipid syndrome and even for the prediction of clinical symptoms in patients.

A Hybrid Simulation Platform for quality-aware evaluation of complex events in an IoT environment

Complex Event Processing (CEP) is a successful method to transform simple IoT events created by sensors into meaningful complex business events. To enhance availability, an event fabrication mechanism is integrated within the CEP model, generating synthetic events to offset missing data, resulting in a quality-aware CEP model. In this model, generated complex events are characterized by quality properties, namely completeness and timeliness. To empirically assess the quality of complex events through experimentation, we have developed a hybrid simulation platform. The platform’s dual nature stems from its distinctive approach of simulating sensor behaviors while concurrently running the quality-aware CEP IoT platform. Users can conduct experiments that closely mimic actual operational scenarios and have, in real-time, full visibility and control over all involved aspects, including composite transformations, quality assessment, event fabrication and its effectiveness, and aggregated reports. A representative experiment in an IoT-enabled greenhouse with missing events is presented to demonstrate the usefulness of the platform. The contribution of the hybrid simulation platform is twofold: provide (a) quality assessment of complex events, using two established quality properties for IoT environments with specific computation formulas and (b) a comprehensive testbed covering all aspects of a typical IoT setup for realistic experimentation. Together, these elements provide significant cost–benefit advantages by enabling researchers and practitioners to pre-optimize operational efficiency and decision-making in IoT systems.

Causal relationship recognition algorithm based on external semantic and contextual structural features

Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.

Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network.

Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction. To avoid redundant information, we design dynamic path planning strategy for argument detection. To take full use of the information between the trigger identification and argument detection subtasks, and reduce the cascading errors, we build a joint event extraction model. Experimental results demonstrate our approach achieves the best F-score on the biomedical benchmark MLEE dataset and outperforms the recent state-of-the-art methods.

Video-PSG: An Intelligent Contactless Monitoring System for Sleep Staging.

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use for long-term sleep monitoring. As a complementary part of PSG, the video cameras are not utilized to their full potential, only for manual check of simple sleep events, thereby ignoring the potential for physiological and semantic measurement. This leads to a pivotal research question: Can camera be used for sleep staging, and to what extent? We developed a camera-based contactless sleep staging system in the Institute of Respiratory Diseases and created a clinical video dataset of 20 adults. The camera-based feature set, derived from both physiological signals (pulse and breath) and motions all measured from a video, was evaluated for 4-class sleep staging (Wake-REM-Light-Deep). Three optimization strategies were proposed to enhance the sleep staging accuracy: using motion metrics to prune measurement outliers, creating a more personalized model based on the baseline calibration of waking-stage physiological signals, and deriving a specialized feature for REM detection. It achieved the best accuracy of 73.1% (kappa = 0.62, F1-score = 0.75) in the benchmark of five sleep-staging classifiers. Notably, the system exhibited high accuracy in predicting the overall sleep structure and subtle changes between different sleep stages. The study demonstrates that camera-based contactless sleep staging is a new value stream for sleep medicine, which also provides clinical and technical insights for future optimization and implementation.

Methodology for construction-standard-production-rate-based simulation modeling and production-rate data generation

ABSTRACT While implementing a construction project, production-rate assessment needs to be conducted throughout the different stages of the project because this assessment can determine the success or failure of the project. In South Korea, the construction-standard-production-rate (CSPR) is used for determining the production-rate of individual construction equipment. However, CSPR cannot provide the production-rate of activities considering a combination of more than two different types of equipment (eg, excavator and dump truck used for excavation-load-haul activities). Having ready-made data on such production-rates of different equipment combinations would be beneficial for determining and optimal fleet management plan. Furthermore, such data can be beneficial for implementing optimized scheduling methods using supervised-learning methods. Accordingly, simple discrete event simulation approaches were carried out for data generation and regression analysis for testing the feasibility of the usage of such data. The time inputs used in this study were calculated based on the equations and information given in the CSPR.

Algorithms to mimic human interpretation of turbidity events from drinking water distribution systems

Abstract Deriving insight from the increasing volume of water quality time series data from drinking water distribution systems is complex and is usually situation- and individual-specific. This research used crowd-sourcing exercises involving groups of domain experts to identify features of interest within turbidity time series data from operational systems. The resulting labels provide insight and a novel benchmark against which algorithmic approaches to mimic the human interpretation could be evaluated. Reflection of the results of the labelling exercises resulted in the proposal of a turbidity event scale consisting of advisory <2 NTU, alert 2 < NTU < 4, and alarm >4 NTU levels to inform utility response. Automation, for scale up, was designed to enable event detection within these categories, with the <2NTU category being the most challenging. A time-based averaging approach, based on data at the same time of day, was found to be most effective for identifying these advisory events. The automation of event detection and categorisation presented here provides the opportunity to gain actionable insight to safeguard drinking water quality from ageing infrastructure.