Time-related Events Research Articles

Unraveling T-cell dynamics using fluorescent timer: Insights from the Tocky system.

Understanding the temporal dynamics of T-cell transcription is crucial for insights into immune cell function and development. In this study, we show the features of the Timer-of-Cell-Kinetics-and-Activity (Tocky) system, which enables analysis of temporal dynamics of cell activities and differentiation, leveraging Fluorescent Timer protein, which spontaneously changes its emission spectrum from blue to red fluorescence in known kinetics, as reporters. The current study examines the properties of the Tocky system, highlighting the Timer-Angle approach, which is a core algorithm of Tocky analysis and converts Timer Blue and Red fluorescence into Timer Angle and Intensity by trigonometric transformation. Importantly, Tocky analyzes time-related events within individual cells by the two phases of measurements, distinguishing between (1) the temporal sequence of cellular activities and differentiation within the time domain, and (2) the transcription frequency within the frequency domain. The transition from time measurement to frequency analysis, particularly at the Persistent locus that bridges these domains, highlights that system's unique property in what is measured and analyzed by Tocky. Intriguingly, the sustained transcriptional activities observed in cells at the Persistent locus may have unique biological features as demonstrated in activated regulatory T-cells (Treg) and pathogenic T-cells, respectively, using Foxp3-Tocky and Nr4a3-Tocky models. In conclusion, the Tocky system can provide crucial data for advancing our understanding of T-cell dynamics and function.

Extended Kumaraswamy Exponential Distribution with Application to COVID-19 Data set

There are many probability models describing the time related events data. In this study, the exponential distribution is modified by adding one more parameter to get more flexible probability model called Extended Kumaraswamy Exponential (EKwE) distribution using the New Kw-G family (NKwG) of distributions. We have studied some of the statistical characteristics of the model, such as its reliability function, hazard rate function, and quantile function. For testing the applicability of the model, a real data set based on COVID-19 data is taken. The Cramer-von Mises (CVM) approach, Least Square Estimation (LSE), and Maximum Likelihood Estimation (MLE) are used to estimate the model’s parameters. Validity of the model is checked by using P-P plot and Q-Q plot. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC) and Hannan-Quinn Information Criterion (HQIC) are also used for model comparison. Goodness of fit of the proposed model is tested using Kolmogrov-Smirnov (KS), Cramer-Von Mises (CVM) and Anderson-Darling (An) test statistics along with respective p-values. All the analysis of the study is performed by using R programming.

Time-Related Events

Time-Related Events

A novel machine learning approach to analyzing geospatial vessel patterns using AIS data

ABSTRACT In the maritime environment, the Automatic Identification System (AIS) contains information related to vessel trajectories that can be used to detect unusual maritime occurrences and maritime traffic patterns. To detect such occurrences with supervised learning methods the AIS messages must be manually annotated, which can be a demanding process. Therefore, unsupervised methods are used to identify anomalous traffic patterns based on vessel trajectories. Typically, dense regions of maritime activity are studied to capture common traffic patterns which help identify trajectories that do not follow the norm. However, these approaches cannot detect anomalous behaviors along common pathways or incorporate time-related events into the analysis. Such challenges motivate the approach taken in this work by using auto-regressive techniques to model vessel trajectories and clustering analyses to explore behavior patterns of vessels. Results confirm that the Auto-regressive Integrated Moving Average (ARIMA) and Ornstein-Uhlenbeck (OU) processes are able to model the trajectories and can be used with density-based spatial clustering of applications with noise (DBSCAN), hierarchical clustering (HC), and spectral clustering (SC) to identify different behavioral patterns.

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Time-Related Events

Abstract WP31: Really, Did A Code Stroke Simulation Improve Compliance In Patient Care Requirements?

Purpose: Enhance the care of ischemic stroke patients by improving compliance with implementation of appropriate order sets, following protocols addressing assessment of vital signs, neuro checks, and lowering door to needle times. Method: ED nurses were required to attend one of five simulation skills fair offered over a nine month period. A stroke simulation scenario was provided in the simulation lab. Groups of nurses performed each step in the care of a code stroke patient. An ED nurse facilitated each step to assure the staff had an understanding of time related events such as calling code stroke, identifying the exact time of last seen well, obtaining CT, vital signs, labs, neuro checks, scoring the patient, and administration of Alteplace, if applicable. Results/Outcomes: Metrics for fiscal year were compared to the statistics collected for the last quarter of fiscal year. Implementation of the stroke order set rose 56.4%, from 43.6% to 100%. Compliance with the protocol, neuro checks improved 40% to 90% and documentation of vital signs per protocol rose from 90% to 97%. Door to needle time improved by 11 minutes. Implications: The simulation educational intervention proved to show a significant improvement in compliance in performing vital signs, neuro checks, using stroke order sets and especially door to needle times. The ED leadership team has recognized the significant impact to our patient care outcomes and compliance resulting from providing this simulation education. The simulation also identified that the majority of the staff did not understand how to correctly perform neuro checks and were not placing the correct code stroke order sets that included the orders to do vital signs and neuro checks per protocol.

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Association between immune-related adverse events and long-term survival outcomes in patients treated with immune checkpoint inhibitors

Background: The impact of immune-related adverse events (irAE) on survival outcomes after single-agent immune checkpoint inhibitors (ICIs) remains unclear. We aimed to evaluate the association between irAEs and ICI efficacy in various malignancies.Methods: All patients treated with a single-agent ICI for any advanced cancer were included in this retrospective multicentric series. The primary objective was to assess the impact of all type grade ≥II irAEs on progression-free survival (PFS) and overall survival (OS). IrAEs were first considered as a fixed covariate and included in Cox-regression models. In addition, as irAEs are time-related events and can occur at any point during follow-up, we analysed the occurrence of irAEs as a time-varying covariate.Results: In this cohort of 410 patients, the majority of patients (70%) were treated for non-small cell lung cancer. The ICI was an anti-PD(L)1 for 356 patients (82%) and an anti-CTLA4 for 79 patients (18%). In total 126 (29%) of the patients presented at least one grade ≥II irAEs. The first occurrence of a grade ≥II irAE had a positive impact on PFS and OS when considered as a fixed or as a time-varying covariate (hazard ratio [HR] for PFS = 0.63, 95% confidence interval [CI] 0.50–0.81; P = 0.00022; HR for OS = 0.57, 95% CI 0.43–0.74, P < 0.0001). This overall finding was confirmed in patients treated with an anti-PD(L)1 and among patients with lung cancer.Conclusion: In this pooled multi-institutional cohort, the incidence of irAEs was associated with better long-term survival across different malignancies treated with ICI monotherapy.

More Than 25 Years of Experience With the Ross Procedure in Children: A Single-Center Experience

Aortic valve replacement in children represents an important challenge. Concerns regarding pulmonary autograft and homograft longevity requiring reoperations are well recognized. Very long-term outcomes after the Ross procedure are still unknown. We reviewed our experience with the Ross procedure, aiming to define very long-term survival rate and freedom from reintervention. This was a single-center retrospective cohort including 63 consecutive children who underwent the Ross procedure. Median follow-up duration was 20.5 years. Time-related events were assessed using Kaplan-Meier estimator. There were 51 (81%) boys, mean age 10.1 ± 5.8 years. Isolated aortic stenosis was the most common diagnosis (n= 29, 46%) and 34 (54%) patients previously underwent cardiac surgery. There was 1 (1.6%) in-hospital death. Overall survival at 5, 15, and 25 years was 96.7%, 94.4%, and 94.4%, respectively. Freedom from any autograft-related reintervention was 98.1%, 86.4%, and 61.2% at 5, 15, and 25 years, respectively. Fifteen (24%) patients underwent autograft reoperations. Among them, 10 (67%) patients underwent valve-sparing autograft reoperation. Freedom from any pulmonary conduit reintervention was 93.2%, 58.2%, and 28.3% at 5, 15, and 25 years, respectively. Thirty (46.6%) patients underwent conduit reintervention (8 percutaneous, 22 surgical replacements). The pediatric Ross procedure is associated with excellent long-term survival. Ross-related reinterventions are more than twice as common on the pulmonary homograft than on the autograft.

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Standardized Approach to Intervention for Intestinal Malrotation in Single Ventricle Patients with Heterotaxy Syndrome: Impact on Interstage Attrition and Time to Superior Cavopulmonary Connection.

Heterotaxy syndrome (HS) is a significant determinant of outcome in single ventricle (SV) physiology. Attrition rate and time-related events associated with intestinal malrotation (IM) are, yet, to be determined. We sought to evaluate hospital and interstage outcomes in relation with operative intervention for IM (IMO). Twelve SV/HS patients, who underwent IMO, from January 2004 to December 2016, were studied. Early shunt failure, time to superior cavopulmonary connection (SCPC) and interstage attrition were assessed. Since September 2014, based on a comprehensive standardized protocol, IMO was performed at the time of hospitalization for stage-I palliation (S1P) irrespective of clinical manifestations. Patients were assigned to Group A (n = 8): expectant /symptoms-driven versus Group B (n = 4): protocol-driven. At S1P 7 had systemic-to-pulmonary shunt (SPS), 1 SPS with anomalous pulmonary venous return (APVR) repair (Group A) compared to 2 SPS, 1 SPS with APVR repair and 1 Norwood operation (Group B). Median duration from S1P to IMO was 82days (range 57-336; Group A) compared to 14days (range 11-31; Group B); p < 0.05. Median age at IMO was 87days (range 8-345) [Group A: 99days (range 68-345) vs Group B: 25days (range 8-39)] (p < 0.05). Early SPS failure occurred in 25% (2 of 8) for Group A compared to none in Group B (p < 0.05). Hospital mortality following IMO was 25% [Group A: 37.5% (3 of 8) vs Group B: 0; p < 0.05]. Interstage survival was 67% [Group A: 50% (4 of 8) vs Group B: 100%; p < 0.05]. Time to SCPC following S1P was 186days (range 169-218) for Group A compared to 118days (range 97-161) (Group B); p < 0.05. Operative intervention for IM in SV/HS is associated with significant interstage attrition and might impact the time to SCPC. SPS is at risk for early failure after IMO. A comprehensive standardized concept can mitigate detrimental implications.

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Readmission for Acute Decompensated Heart Failure among Patients Successfully Treated with Transcatheter Aortic Valve Replacement: A PARTNER-1 Substudy

ABSTRACTBackground: Readmission for acute decompensated heart failure (ADHF) following transcatheter aortic valve replacement (TAVR) is common and linked with mortality. However, time course of ADHF readmissions, their risk factors, and their association with long-term outcomes are not well understood. Our objectives were to identify the temporal pattern of readmission for ADHF following TAVR, which patients are at high risk, and the association of readmission for ADHF and subsequent mortality.Methods: We evaluated 2467 patients successfully treated and discharged in the PARTNER-1 trial, and followed for 5 years. Baseline and discharge clinical, pharmacologic, procedure, and echocardiographic characteristics were considered. The likelihood and temporal pattern of ADHF readmission after accounting for the competing risk of death were assessed using nested competing-risk methodology for repeating time-related events. Risk factors were identified by unsupervised bootstrap-aggregation machine learning.Results: Admission for ADHF following TAVR was associated with an increased risk of subsequent readmission. Accounting for baseline, procedure, and discharge characteristics, we identified greater severity of post-procedure mitral and aortic regurgitation, both lower- and higher-gradient aortic stenosis, oxygen-dependent COPD, baseline calcium channel blocker and long-acting nitrate use, and longer index hospitalization length of stay as risk factors for readmission for ADHF.Conclusion: Early readmission for ADHF following TAVR was associated with clinical, pharmacologic, and echocardiographic characteristics. The resulting models may be used to identify patients at high risk for early readmission following TAVR and to suggest future prospective trials, particularly to treat combined mitral regurgitation associated with aortic stenosis.

Measuring users-windows interactions in buildings: behavioural models for the summer season

Including the human component in simulation software is of primary importance to enhance the building’s design and management. In particular, the user-window interaction impacts significantly energy and comfort profiles. Through an environmental and behavioural monitoring, this research investigated windows’ opening and closing actions during the summer season in three offices. The analyses highlighted that the behaviours are influenced by both environmental factors and time-related events. Using logistic regression techniques, stochastic behavioural models have been developed to predict windows status.

Time-Related Events

Time-Related Events

Experimental study on occupants' interaction with windows and lights in Mediterranean offices during the non-heating season

The modelling of human behaviour is an important challenge for the building sector, as the actions of users have significant impacts on both energy consumption and comfort assessment. In the search for a comprehensive understanding of the behaviour of occupants, many researchers have directed their efforts towards determining typical patterns and developing models to predict human-building interactions. This study investigates the behaviour of building users during the summer season in offices in Mediterranean climate. Studies focusing on this area are still lacking, despite their importance for cooling loads. A survey is conducted using a dedicated sensor network to monitor environmental variables, and to determine the presence of people and their interactions with windows and lights in three offices. The driving factors for the actions of users are assessed and behavioural models are proposed. The results indicate that interactions with windows and lights are driven by both time-related events and environmental factors, confirming previous findings. A comparison of the proposed models with others developed for different climate zones suggests that interactions with windows are affected by the geographic area, while light switching behaviour seems to be very similar for the different case studies. A simplified approach for the consideration of different user-device interactions is also proposed. This novel method, developed to evaluate the interactivity between users and building systems, is based on a coefficient of interactivity, CI. Both the behavioural models and the simplified approach could be introduced into future simulations to improve predictions of energy use in buildings.