Biology subjects

Ozturk, C.

Publications and source records attributed to Ozturk, C..

3 recordsLinked to original sources

bioRxiv2023

Soft robotics-enabled large animal model of HFpEF hemodynamics for device testing

Heart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for approximately 50% of all cases of heart failure. Due to the lack of effective therapies for this condition, the mortality associated with HFpEF remains higher than that of most cancers. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, we describe the development of a highly tunable porcine model of HFpEF hemodynamics using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. We demonstrate the feasibility of the proposed model in preclinical testing by evaluating the hemodynamic response of the model post-implantation of an interatrial shunt device, which was found to be consistent with findings from in silico studies and clinical trials. This work addresses several of the limitations associated with previous models of HFpEF, such as their limited hemodynamic fidelity, elevated costs, lengthy development time, and low throughput. By showcasing exceptional versatility and tunability, the proposed platform has the potential to revolutionize the current approach for HFpEF device development and selection, with the goal of improving the quality of life for the 32 million people affected by HFpEF worldwide.

bioengineering↗

bioRxiv2019

The effect of individual learning on collective foraging in honey bees in complex environments

The trade-off between exploiting known resources and exploring for new ones is a complex decision-making challenge, particularly when resource patches are variable in quality and heterogeneously distributed in the landscape. Social insect colonies navigate this challenge, in the absence of centralized control, by allocating different individuals to each of these tasks based on variation in individual behavior. To investigate how heritable differences in individual learning affect a colonys collective ability to locate and choose among different quality food resources, we develop an agent based model and test its predictions using two genetic lines of honey bees, selected for differences in their learning behavior. Here we show that, paradoxically, colonies containing individuals that are better at learning to ignore unrewarding stimuli are worse at choosing the highest quality resource at the collective level. This work highlights the importance of individual variation within groups on the emergence of collective behavior.

animal behavior and cognition↗

bioRxiv2019

Heritable Variation in Learning Phenotypes Drive Collective Cognition

Variation in cognition can influence how individuals respond to and communicate about their environment, which may scale to shape how a collective solves a cognitive task. However, few empirical examples of variation in collective cognition emerges from variation in individual cognition exist. Here, we show that interactions among individuals that differ in the performance of a cognitive task drives collective foraging behavior in honey bee colonies by utilizing a naturally variable and heritable learning behavior called latent inhibition (LI). We artificially selected two distinct phenotypes: high LI bees that are better at ignoring previously unrewarding familiar stimuli, and low LI bees that can learn previously unrewarding and novel stimuli equally well. We then provided colonies composed of these distinct phenotypes with a choice between a familiar feeder or a novel feeder. Colonies of high LI individuals preferred to visit familiar food locations, while low LI colonies visited novel and familiar food locations equally. However, in colonies of mixed learning phenotypes, the low LI bees showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low LI group. We show that the shift in feeder preference of low LI bees is driven by foragers of the high LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their interactions drive emergent collective outcomes. Significance StatementVariation in individual cognition affects how animals perceive their environment and which information they share with others. Here we provide empirical evidence that how individual honey bees learn contributes to collective cognition of a colony. By creating colonies of distinct learning phenotypes, we evaluated how bees make foraging choices in the field. Colonies containing individuals that learn to ignore unimportant information preferred familiar food locations, however colonies of individuals that are unable to ignore familiar information visit novel and familiar feeders equally. A 50/50 mix of these phenotypes prefer familiar food locations, because individuals who learn the familiar location recruit nestmates by dancing more intensely. Our results reveal that variation in individual cognition scales non-linearly to shape collective outcomes.

animal behavior and cognition↗