Mechanical Reliability of Implantable Polyimide-Based Magnetic Microactuators for Biofouling Removal

Hydrocephalus is a neurological disorder that typically requires a long-term implantation of a shunt system to manage its symptoms. These shunt systems are notorious for their extremely high failure rate. More than 40% of all implanted shunt systems fail within the first year of implantation. On average, 85% of all hydrocephalus patients with shunt systems undergo at least two shunt-revision surgeries within 10 years of implantation. A large portion of this high failure rate can be attributed to biofouling-related obstructions and infections. Previously, we developed flexible polyimide-based magnetic microactuators to remove obstructions formed on hydrocephalus shunts. To test the long-term reliability of these magnetic microactuators, here we evaluate the impact of actuation cycle on mechanical stability of these microdevices. Over 50 minutes, 8 devices were actuated at 100 Hz at 37 °C continuously in phosphate buffered solution. By measuring the primary resonant frequency of each device, we were able to quantify changes in the structural integrity of each actuator. On average, the devices showed a drop of 2.15% in resonant frequencies. Although additional evaluations are necessary to ascertain appropriate actuation duty cycles, preliminary results suggest that our polyimide-based devices have good mechanical reliability, which bodes well for our ultimate goal of improving quality of life and care for hydrocephalus using our MEMS-enabled self-clearing catheters

Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning

In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained under them. Specifically, we propose a new meta-learning framework for learning model-agnostic loss functions via a hybrid neuro-symbolic search approach. The framework first uses evolution-based methods to search the space of primitive mathematical operations to find a set of symbolic loss functions. Second, the set of learned loss functions are subsequently parameterized and optimized via an end-to-end gradient-based training procedure. The versatility of the proposed framework is empirically validated on a diverse set of supervised learning tasks. Results show that the meta-learned loss functions discovered by the newly proposed method outperform both the cross-entropy loss and state-of-the-art loss function learning methods on a diverse range of neural network architectures and datasets

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

© 2008 Wagner et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens