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One‐dimensional (1D) cardiovascular models offer a non‐invasive method to answer medical questions, including predictions of wave‐reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient‐specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image‐derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three‐dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high‐fidelity patient‐specific haemodynamics models. Key points: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error.Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks.Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty.Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii. [ABSTRACT FROM AUTHOR]

Sensory drive predicts that the conditions under which signaling takes place have large effects on signals, sensory systems, and behavior. The coupling of an ecological genetics approach with sensory drive has been fruitful. An ecological genetics approach compares populations that experience different environments and asks whether population differences are adaptive and are the result of genetic and/or environmental variation. The multi-faceted effects of signaling environments are well-exemplified by the bluefin killifish. In this system, males with blue anal fins are abundant in tannin-stained swamps that lack UV/blue light but are absent in clear springs where UV/blue light is abundant. Past work indicates that lighting environments shape genetic and environmental variation in color patterns, visual systems, and behavior. Less is known about the selective forces creating the across population correlations between UV/blue light and the abundance of blue males. Here, we present three new experiments that investigate the roles of lighting environments on male competition, female mate choice, and predation. We found strong effects of lighting environments on male competition where blue males were more likely to emerge as dominant in tea-stained water than in clear water. Our preliminary study on predation indicated that blue males may be less susceptible to predation in tea-stained water than in clear water. However, there was little evidence for female preferences favoring blue males. The resulting pattern is one where the effects of lighting environments on genetic variation and phenotypic plasticity match the direction of selection and favor the expression of blue males in swamps. [ABSTRACT FROM AUTHOR]

Research is inconclusive regarding the association between aerobic fitness (objectively measured VO2max) and repeated sprint performance when the sprints are less than 40 meters. Soccer athletes must be able to repeat sprints without significant decreases in speed and strength and conditioning coaches need to better understand if aerobic fitness is related to repeated sprint ability (RSA). Twenty (10 male, 10 female) Division I soccer athletes first completed a graded maximal treadmill test to measure VO2max. Then on a separate day, athletes completed the RSA test. The RSA test consisted of 10, 30-meter sprints which athletes repeated every 30 seconds. There were significant negative correlations (r ≤ - 0.69, P < 0.001) between VO2max and all 10-sprint times and average sprint time. More aerobically fit Division I soccer athletes were faster at all time points during the RSA test. Aerobic fitness is associated with faster sprint times during a more anaerobic RSA test when sprint distances are less than 40 meters. [ABSTRACT FROM AUTHOR]

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance, and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in-vivo imaging introduces variability in network size and vessel dimensions, affecting hemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centerlines. Still, there is no exact way to generate vascular trees from the centerlines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labeled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D hemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore hemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analyzing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific hemodynamics models., Competing Interests: Competing Interests The authors declare that they have no competing interests.