OpenFIBSEM: A universal API for FIBSEM control.

This paper introduces OpenFIBSEM, a universal API to control Focused Ion Beam Scanning Electron Microscopes (FIBSEM). OpenFIBSEM aims to improve the programmability and automation of electron microscopy workflows in structural biology research. The API is designed to be cross-platform, composable, and extendable: allowing users to use any portion of OpenFIBSEM to develop or integrate with other software tools. The package provides core functionality such as imaging, movement, milling, and manipulator control, as well as system calibration, alignment, and image analysis modules. Further, a library of reusable user interface components integrated with napari is provided, ensuring easy and efficient application development. OpenFIBSEM currently supports ThermoFisher and TESCAN hardware, with support for other manufacturers planned. To demonstrate the improved automation capabilities enabled by OpenFIBSEM, several example applications that are compatible with multiple hardware manufacturers are discussed. We argue that OpenFIBSEM provides the foundation for a cross-platform operating system and development ecosystem for FIBSEM systems. The API and applications are open-source and available on GitHub (https://github.com/DeMarcoLab/fibsem).

Ensemble Approach on Deep and Handcrafted Features for Neonatal Bowel Sound Detection.

For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis plan for early intervention. In this article, a novel technique is proposed for automated peristalsis sound detection from neonatal abdominal sound recordings and compared to various other machine learning approaches. It adopts an ensemble approach that utilises handcrafted as well as one and two dimensional deep features obtained from Mel Frequency Cepstral Coefficients (MFCCs). The results are then refined with the help of a hierarchical Hidden Semi-Markov Models (HSMM) strategy. We evaluate our method on abdominal sounds collected from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results of leave-one-patient-out cross validation show that our method provides an accuracy of 95.1% and an Area Under Curve (AUC) of 85.6%, outperforming both the baselines and the recent works significantly. These encouraging results show that our proposed Ensemble-based Deep Learning model is helpful for neonatologists to facilitate tele-health applications.