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177 results for “Early warnings”
Data from: Relation between stability and resilience determines the performance of early warning signals under different environmental drivers
Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as "indicators for loss of resilience." We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.
Data from: Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter
Campylobacter is the commonest bacterial cause of gastrointestinal infection in humans and chicken meat is the major source of infection throughout the world. Strict and expensive on-farm biosecurity measures have been largely unsuccessful in controlling infection and are hampered by the time needed to analyze faecal samples with the result that Campylobacter status is often known only after a flock has been processed. Our data demonstrate an alternative approach that monitors the behaviour of live chickens with cameras and analyses the 'optical flow' patterns made by flock movements. Campylobacter-free chicken flocks have higher mean and lower kurtosis of optical flow than those testing positive for Campylobacter by microbiological methods. We show that by monitoring behaviour in this way, flocks likely to become positive can be identified within the first 7-10 days of life, much earlier than conventional on-farm microbiological methods. This early warning has the potential to lead to a more targeted approach to Campylobacter control and also provides new insights into possible sources of infection that could transform the control of this globally important foodborne pathogen.
Data from: Implementation of an automated early warning scoring system in a surgical ward: practical use and effects on patient outcomes
Introduction: Early warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients. The aim of this study was to evaluate the clinical use of an automated modified EWS (MEWS) for patients after surgery. Methods: This study conducted retrospective before-and-after comparative analysis of non-automated and automated MEWS for patients admitted to the surgical high-dependency unit in a tertiary hospital. Operational outcomes included number of recorded assessments of the individual MEWS elements, number of complete MEWS assessments, as well as adherence rate to related protocols. Clinical outcomes included hospital length of stay, in-hospital and 28-day mortality, and ICU readmission rate. Results: Recordings in the electronic medical record from the control period contained 7929 assessments of MEWS elements and were performed in 320 patients. Recordings from the intervention period contained 8781 assessments of MEWS elements in 273 patients, of which 3418 were performed with the automated EWS system. During the control period, 199 (2.5%) complete MEWS were recorded versus 3991 (45.5%) during intervention period. With the automated MEWS systems, the percentage of missing assessments and the time until the next assessment for patients with a MEWS of ≥2 decreased significantly. The protocol adherence improved from 1.1% during the control period to 25.4% when the automated MEWS system was involved. There were no significant differences in clinical outcomes. Conclusion: Implementation of an automated EWS system on a surgical high dependency unit improves the number of complete MEWS assessments, registered vital signs, and adherence to the EWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability.
EARLY WARNING OF ATRIAL FIBRILLATION USING DEEP LEARNING
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Data and scripts for: An analysis of the dynamic range of Distributed Acoustic Sensing for Earthquake Early Warning
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Fig. 1 in Dynamis borassi (Coleoptera: Curculionidae), a new potential pest to the palms (Arecaceae): an early warning for the palm producers
Fig. 1. Geographical location of the 36 peach palm production sites (circles) visited during the survey for palm weevil damage and pheromone trapping.
Fig. 5 in Dynamis borassi (Coleoptera: Curculionidae), a new potential pest to the palms (Arecaceae): an early warning for the palm producers
Fig. 5. Relationship between damaged inflorescence average with respect to the Dynamis borassi abundance (A) and proportion of d with rain (B); the proportion of healthy and main daily rainfall (C) damaged inflorescences; proportion of damaged inflorescence and maximum rainfall (D); the D. borassi abundance with respect main daily rainfall (E) and proportion of d with rain (F). Data was obtained in Sabaletas (A, B, D–F) and Bajo Calima (C), Valle del Cauca, Colombia.
Lahar spectrograms from the paper "Lahar Early Warning at Volcano Santiaguito, Guatemala: a Standard and a Deep Learning Approach"
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Data from: Dynamical state transitions into addictive behaviour and their early-warning signals
The theory of critical transitions in complex systems (ecosystems, climate, etc.), and especially its ability to predict abrupt changes by early-warning signals based on analysis of fluctuations close to tipping points, is seen as a promising avenue to study disease dynamics. However, the biomedical field still lacks a clear demonstration of this concept. Here, we used a well-established animal model in which initial alcohol exposure followed by deprivation and subsequent reintroduction of alcohol induces excessive alcohol drinking as an example of disease onset. Intensive longitudinal data (ILD) of rat drinking behaviour and locomotor activity were acquired by a fully automated drinkometer device over 14 weeks. Dynamical characteristics of ILD were extracted using a multi-scale computational approach. Our analysis shows a transition into addictive behaviour preceded by early-warning signals such as instability of drinking patterns and locomotor circadian rhythms, and a resultant increase in low frequency, ultradian rhythms during the first week of deprivation. We find evidence that during prolonged deprivation, a critical transition takes place pushing the system to excessive alcohol consumption. This study provides an adaptable framework for processing ILD from clinical studies and for examining disease dynamics and early-warning signals in the biomedical field.
Implementation and Evaluation of an Electronic Early Warning Score (e-EWS) System
ClinicalTrials.gov study NCT04425694. IPD Sharing: NO. Countries: 1. Publications: 0.
Early Warning System
ClinicalTrials.gov study NCT01741480. IPD Sharing: Not stated. Countries: 1. Publications: 0.
RECOVERY ROOM EARLY WARNING SCORE
ClinicalTrials.gov study NCT07051421. IPD Sharing: NO. Countries: 0. Publications: 2.
Feasibility and Acceptability of a Smartphone App to Assess Early Warning Signs of Psychosis Relapse
ClinicalTrials.gov study NCT03558529. IPD Sharing: UNDECIDED. Countries: 0. Publications: 17.
CHroniSense National Early Warning Score Study
ClinicalTrials.gov study NCT03448861. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Deep Learning Based Early Warning Score in Rapid Response Team Activation
ClinicalTrials.gov study NCT04951973. IPD Sharing: NO. Countries: 0. Publications: 3.
Research on Risk Assessment and Early Warning Models for Adverse Clinical Outcomes in Critically Ill Patients
ClinicalTrials.gov study NCT07317817. IPD Sharing: NO. Countries: 1. Publications: 0.
Construction and Evaluation of Tumor Immunotherapy and Organ Damage Early Warning System Based on Multi-omics
ClinicalTrials.gov study NCT07131007. IPD Sharing: NO. Countries: 0. Publications: 0.
Study on the Construction and Application of Early Warning Model of Sepsis in Critically Ill Patients
ClinicalTrials.gov study NCT06904001. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Implementation of an automated early warning scoring system in a surgical ward: practical use and effects on patient outcomes
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Data from: Early warning signals and the prosecutor's fallacy
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.