Hypothermia Outcome Prediction after Extracorporeal Life Support for Hypothermic Cardiac Arrest Patients: Assessing the Performance of the HOPE Score in Case Reports from the Literature

Aims: The hypothermia outcome prediction after extracorporeal life support (ECLS) score, or HOPE score, provides an estimate of the survival probability in hypothermic cardiac arrest patients undergoing ECLS rewarming. The aim of this study was to assess the performance of the HOPE score in case reports from the literature. Methods: Cases were identified through a systematic review of the literature. We included cases of hypothermic cardiac arrest patients rewarmed with ECLS and not included in the HOPE derivation and validation studies. We calculated the survival probability of each patient according to the HOPE score. Results: A total of 70 patients were included. Most of them (62/70 = 89%) survived. The discrimination using the HOPE score was good (Area Under the Receiver Operating Characteristic Curve = 0.78). The calibration was poor, with HOPE survival probabilities averaging 54%. Using a HOPE survival probability threshold of at least 10% as a decision criterion for rewarming a patient would have resulted in only five false positives and a single false negative, i.e., 64 (or 91%) correct decisions. Conclusions: In this highly selected sample, the HOPE score still had a good practical performance. The selection bias most likely explains the poor calibration found in the present study, with survivors being more often described in the literature than non-survivors. Our finding underscores the importance of working with a representative sample of patients when deriving and validating a score, as was the case in the HOPE studies that included only consecutive patients in order to minimize the risk of publication bias and lower the risk of overly optimistic outcomes.

Does a Starting Positive End-Expiratory Pressure of 8 cmH2O Decrease the Probability of a Ventilator-Associated Event?

Introduction: Ventilator-associated events (VAEs) are objective measures as defined by the Centers for Disease Control and Prevention (CDC). To reduce VAEs, some hospitals have started patients on higher baseline positive end-expiratory pressure (PEEP) to avoid triggering VAE criteria due to respiratory fluctuations.Methods: At our institution, VAEs were gathered from January 2014 through December 2019. Using the CDC-defined classifications, VAEs were split into two groups to separate patients with hypoxemia only (VAC) and those with hypoxemia and evidence of inflammation or infection (IVAC-plus). We used the geometric distribution to calculate the daily event probability before and after the protocol implementation. A probability threshold was used to determine if the days between events was exceeded during the post-protocol period.Results: A total of 306 VAEs were collected over the study period. Of those, 155 were VACs and 107 were IVAC-plus events during the pre-protocol period. After implementing the protocol, 24 VACs and 20 IVAC-plus events were reported. There was a non-significant decrease in daily event probabilities in both the VAC and IVAC-plus groups (0.083 vs. 0.068 and 0.057 vs. 0.039, respectively).Conclusion: We concluded a starting PEEP of 8 cmH2O is unlikely to be an effective intervention at reducing the probability of a VAE. Until specific guidelines by the CDC are established, hospitals should consider alternative methods to reduce VAEs.

Risk Analysis of Instability Failure of Earth–Rock Dams Based on the Fuzzy Set Theory

Determining the anti-sliding instability risk of earth–rock dams involves the analysis of complex uncertain factors, which are mostly regarded as random variables in traditional analysis methods. In fact, fuzziness and randomness are two inseparable uncertainty factors influencing the stability of earth–rock dams. Most previous research only focused on the randomness or the fuzziness of individual variables. Moreover, dam systems present a fuzzy transition from a stable state into a failure state. Therefore, both fuzziness and randomness of the influencing factors should be considered in the same framework, where the instability of an earth–rock dam is regarded as a mixed process. In this paper, a fuzzy risk model of instability of earth–rock dams is established by considering the randomness and fuzziness of parameters and the failure criteria comprehensively. We obtained the probability threshold of instability risk of earth–rock dams by Monte-Carlo simulation after the fuzzy parameters were transformed into interval numbers by cut set levels. By applying the proposed model to the instability analysis of the Longxingsi Reservoir, the calculation results showed that the lower limits of risk probability under different cut set levels exceeded the instability risk standard of grade C for earth–rock dams. Compared with the traditional risk determination value, the risk interval obtained with the proposed methods reflects different degrees of dam instability risk and can provide reference for dam structure safety assessment and management.

Bayesian Quadrature Optimization for Probability Threshold Robustness Measure

In many product development problems, the performance of the product is governed by two types of parameters: design parameters and environmental parameters. While the former is fully controllable, the latter varies depending on the environment in which the product is used. The challenge of such a problem is to find the design parameter that maximizes the probability that the performance of the product will meet the desired requisite level given the variation of the environmental parameter. In this letter, we formulate this practical problem as active learning (AL) problems and propose efficient algorithms with theoretically guaranteed performance. Our basic idea is to use a gaussian process (GP) model as the surrogate model of the product development process and then to formulate our AL problems as Bayesian quadrature optimization problems for probabilistic threshold robustness (PTR) measure. We derive credible intervals for the PTR measure and propose AL algorithms for the optimization and level set estimation of the PTR measure. We clarify the theoretical properties of the proposed algorithms and demonstrate their efficiency in both synthetic and real-world product development problems.

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.

Burdens of Proof

This chapter provides a brief introduction to the scholarly conversation concerning burdens of persuasion. An adequate account of burdens must first explain what case-related facts the burden draws upon to produce outcomes. I review a variety of answers to this question, including probability threshold, likelihood ratio, belief function, weight-of-evidence, explanatory, and story-based approaches. I then identify several key questions that theories must answer with respect to inputs and show that the best answer on any given question must depend on whether the theory is advanced as a psychological, doctrinal, or normative account. The remainder of the chapter considers varying methods of transforming these inputs into case outcomes, including fixed thresholds, variable thresholds, multi-stepped, and continuous approaches. With respect to these choices, the problem of describing current practices is much easier, but the normative debates are harder to resolve.

Position Optimization with Spectrum Sensing Decision for Unmanned Aerial Vehicles

In the emergency scenario of Unmanned Aerial Vehicle (UAV) relay, link interruption caused by shortage of communication resources often occurs. Using spectrum sensing, UAV can expand communication bandwidth and ensure communication quality. However, spectrum sensing of UAV will increase energy consumption and reduce UAV dwell time. Aiming at the contradiction of energy consumption and communication quality, this paper proposes a relay sensing decision algorithm. Firstly, the spectrum sensing model and algorithm are established. Then, the outage probability is taken as the threshold value adjusting three-dimensional coordinates in the UAV relay sensing decision algorithm. Finally, we construct a UAV relay sensing decision algorithm, which adds the limit of unless outage probability threshold to the firefly algorithm to adjust the position of UAV. By adjusting the three-dimensional coordinates of UAV, the unless outage probability is guaranteed for UAV relay link. Through the simulation of building emergency fire scene, it is verified that the UAV relay sensing decision algorithm can automatically find the optimal UAV sensing position and ensure communication quality.

Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden’s test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden’s index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10–7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.

Using Machine Learning for Remote Behaviour Classification—Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.

Generalized Kings and Single-Elimination Winners in Random Tournaments

Tournaments can be used to model a variety of practical scenarios including sports competitions and elections. A natural notion of strength of alternatives in a tournament is a generalized king: an alternative is said to be a k-king if it can reach every other alternative in the tournament via a directed path of length at most k. In this paper, we provide an almost complete characterization of the probability threshold such that all, a large number, or a small number of alternatives are k-kings with high probability in two random models. We show that, perhaps surprisingly, all changes in the threshold occur in the regime of constant k, with the biggest change being between k = 2 and k = 3. In addition, we establish an asymptotically tight bound on the probability threshold for which all alternatives are likely able to win a single-elimination tournament under some bracket.