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While the use of networks to understand how complex systems respond to perturbations is pervasive across scientific disciplines, the uncertainty associated with estimates of pairwise interaction strengths (edge weights) remains rarely considered. Mischaracterizations of interaction strength can lead to qualitatively incorrect predictions regarding system responses as perturbations propagate through often counteracting direct and indirect effects. Here, we introduce PressPurt, a computational package for identifying the interactions whose strengths must be estimated most accurately in order to produce robust predictions of a network's response to press perturbations. The package provides methods for calculating and visualizing these edge-specific sensitivities (tolerances) when uncertainty is associated to one or more edges according to a variety of different error distributions. The software requires the network to be represented as a numerical (quantitative or qualitative) Jacobian matrix evaluated at stable equilibrium. PressPurt is open source under the MIT license and is available as both a Python package and an R package hosted at https://github.com/dkoslicki/PressPurt and on the CRAN repository https://CRAN.R-project.org/package=PressPurt.

While it may be predictable that plasma norepinephrine (NE) concentration changes with efferent sympathetic nerve activity (SNA) in response to baroreceptor pressure inputs, an exact relationship between SNA and plasma NE concentration remains to be quantified in heart failure. We examined acute baroreflex‐mediated changes in plasma NE and epinephrine (Epi) concentrations in normal control (NC) rats and rats with myocardial infarction (MI) (n = 6 each). Plasma NE concentration correlated linearly with SNA in the NC group (slope: 2.17 ± 0.26 pg mL−1 %−1, intercept: 20.0 ± 18.2 pg mL−1) and also in the MI group (slope: 19.20 ± 6.45 pg mL−1 %−1, intercept: −239.6 ± 200.0 pg mL−1). The slope was approximately nine times higher in the MI than in the NC group (P < 0.01). Plasma Epi concentration positively correlated with SNA in the NC group (slope: 1.65 ± 0.79 pg mL−1 %−1, intercept: 115.0 ± 69.5 pg mL−1) and also in the MI group (slope: 7.74 ± 2.20 pg mL−1 %−1, intercept: 24.7 ± 120.1 pg mL−1). The slope was approximately 4.5 times higher in the MI than in the NC group (P < 0.05). Intravenous administration of desipramine (1 mg kg−1) significantly increased plasma NE concentration but decreased plasma Epi concentration in both groups, suggesting that neuronal NE uptake had contributed to the reduction in plasma NE concentration. These results indicate that high levels of plasma catecholamine in MI rats were still under the influence of baroreflex‐mediated changes in SNA, and may provide additional rationale for applying baroreflex activation therapy in patients with chronic heart failure. Plasma catecholamine concentrations are significantly elevated in a chronic rat model of myocardial infarction. However, the catecholamine levels are still under the influence of carotid sinus baroreflex‐mediated changes in sympathetic nerve activity, which may provide additional rationale for sympathetic suppression via the baroreflex activation therapy in patients with chronic heart failure.

Background Patient–ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure–volume (PV) loop. Methods Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors. Results The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment. Conclusions The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0–12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93–0.95 if maximum ΔPEEP ≤ 6cmH2O. Predicted dynamic functional residual capacity (Vfrc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model. •Nonlinear patient-specific pulmonary distension is accurately predicted by proposed physiologically relevant basis functions.•Peak inspiratory pressure and dynamic functional residual capacity are both accurately predicted.•The approach provides a quantified, harmless, and accurate prediction of risk in positive end expiratory pressure changing.•It presents the capability of physiologically relevant basis functions in offering an accurate lung mechanics prediction.

Evolution can alter the ecological dynamics of communities, but the effects depend on the magnitudes of standing genetic variation in the evolving species. Using an eco-coevolutionary predator–prey model, I identify how the magnitudes of prey and predator standing genetic variation determine when ecological, evolutionary, and eco-evolutionary feedbacks influence system stability and the phase lags in predator–prey cycles. Here, feedbacks are defined by subsystems, i.e., the dynamics of a subset of the components of the whole system when the other components are held fixed; ecological (evolutionary) feedbacks involve the direct and indirect effects between population densities (species traits) and eco-evolutionary feedbacks involve the direct and indirect effects between population densities and traits. When genetic variation is low in both species, ecological feedbacks and eco-evolutionary feedbacks involving either the predator or the prey trait have the strongest effects on system stability, when genetic variation is high in one species, evolutionary and eco-evolutionary feedbacks involving that species' trait have the strongest effects, and, when genetic variation is high in both species, evolutionary feedbacks involving one or both traits and eco-coevolutionary feedbacks involving both traits have the strongest effects. I present the biological conditions under which each feedback can destabilize the whole system and cause predator–prey cycles. Predator–prey cycles can also arise when all feedbacks are stabilizing. This counterintuitive outcome occurs when feedbacks involving many variables are more stabilizing than feedbacks involving fewer variables or vice versa. I also identify how the indirect effects of prey and predator density on the predator dynamics (mediated by evolutionary responses in one or both species) alter the phase lags in predator–prey cycles. I present conditions under which the trait-mediated indirect effects introduce delays that cause the lag between prey and predator peaks to increase. This work explains and unifies empirical and theoretical studies on how predator–prey coevolution alters the dynamics of predator–prey systems and how those effects depend on the magnitudes of prey and predator standing genetic variation.

The current paper examines how a commitment to a principle, adhered to by an individual agent, becomes an accepted standard of an epistemic community. Addressing this question requires three steps: first, to define the terms used throughout the paper, and especially the characteristics of commitments to a principle. The second step is to find a mechanism through which such epistemic commitments are introduced to an epistemic community and in certain cases are adopted as the standard by the community. While there could be several such mechanisms, the current paper focuses on the practice of model formulation. The third step is to demonstrate the analytical framework developed in the first two steps in a case study. The case study chosen for this paper is the unique approach to feedback analysis adopted by the ecologist and population geneticist Richard Levins. In what follows I will show that part of the features that made Levins’ approach unique was his Marxist commitments, and his attempt to embody those commitments in feedback analysis by formally representing them as modeling assumptions.

Loop analysis is introduced to demographic analysis as a tool to compare relative contributions of different life-history types to population growth rate. In 1998, G. M. Wardle brought in basic concepts of the graph theory to demographic loop analysis and proposed a methodology to determine the loops from any life-cycle graph based on these concepts. However, the mathematics behind Wardle's methodology cannot readily be used by most population ecologists. A new methodology that is also based on graph theory concepts but both makes ecological sense in its application and is simpler to implement is proposed. Three rules of thumb serve as the basis of the proposed methodology that brings a more systematic approach to loop selection: it identifies only those loops that are ecologically meaningful (i.e., loops that are forward-flowing and with positive elasticity values). Thus, it produces a loop set that is more amenable to answer questions on comparison of different life-history types. It is tested on several life-cycle graphs from the literature. Three of these are presented: Vouacapoua americana, Dipsacus sylvestris, and Alcyonium sp. In each case, the methodology successfully produced a loop set that makes sense in terms of the ecology of the species. The methodology is also implemented as a couple of open-source computer codes. It is hoped that the proposed methodology will lead to wider use of loop analysis in demographic population studies.

The widely used Bouc–Wen–Baber–Noori (BWBN) hysteresis model, although effective in simulating hysteresis behaviors, does not account for variations in the pinching region of hysteretic behaviors. This can negatively impact the accuracy of the BWBN model in simulating structural responses and damage mechanisms in structures such as reinforced concrete (RC) and timber, which exhibit highly pinched hysteresis behavior when damaged by earthquakes. This paper introduces a BWBN model with varying pinching region characteristics (BWBN-VP model) which can degrade pinching stiffness and increase pinching effects under seismic loads. Unlike the original BWBN model using constant pinching stiffness (kp), this modified new model, inspired by real-world structural damage, improves structural damage detection, identifiability, and analysis in real-world scenarios. Model validation uses experimental data from three RC column tests with different failure modes and hysteresis loop shapes, resulting in an ~0.98 correlation coefficient between the experimental and simulated responses. Further validation uses real-world seismic data from a six-story RC building and achieves an average correlation of ~0.97 with a minor 2.5% difference in the peak restoring forces compared to direct measurements. The proposed BWBN-VP model also accurately and realistically captures damage to both the elastic and pinching stiffness values of the building, with an average difference of ~4%. Results confirm that the BWBN-VP model, compared to the original, more accurately predicts hysteretic responses, especially in Shear Failure (SF) modes. Therefore, the BWBN-VP model, superior in simulating highly pinched behaviors in RC and timber structures, would be an advanced tool for resilient seismic design and Structural Health Monitoring (SHM).

Purpose>The building sector of the construction industry incorporates a precipitous and volatile nature with poor safety conditions being prevalent, owing to its inability to determine an appropriate trade-off between productivity and safety. This disproportionate trade-off produces production pressures, which contribute poorly to construction performance, by encouraging workers to prioritise their working productivity ahead of safety. The purpose of this study is to investigate the impacts of production pressures in the building sector and propose mitigation strategies accordingly.Design/methodology/approach>A systematic review of literature was conducted, and secondary data were extracted from peer-reviewed journal papers. The data was then analysed to achieve the objectives of this study.Findings>The main causes of production pressures are tight construction schedules, ineffective management and construction rework. Furthermore, the negative effects of production pressures are increased levels of stress in employees, reduced craftsmanship, encouraging accident-prone environments and decreasing employee’s safety behaviour. Effective mitigation strategies in relation to scheduling, leadership, communication and motivation were proposed. Finally, a causal loop diagram of production pressures in the building sector was developed.Originality/value>This research will assist in creating a safer working environment within the building sector, by providing useful information regarding the severity of production pressures and suggesting mitigation strategies that can be implemented in the construction projects.

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (∼∼25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.