autoMEA: machine learning-based burst detection for multi-electrode array datasets.

Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity.

Modular, Scalable, and Customizable LC-HRMS for Exposomics.

In this chapter, we describe a multi-purpose, reversed-phase liquid chromatography-high-resolution mass spectrometry (LC-HRMS) workflow for acquiring high-quality, non-targeted exposomics data utilizing data-dependent acquisition (DDA) combined with the use of toxicant inclusion lists for semi-targeted analysis. In addition, we describe expected retention times for >160 highly diverse xenobiotics in human plasma and serum samples. The method described is intended to serve as a generic LC-HRMS exposomics workflow for research and educational purposes.

Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort.

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS.

Genetic and Metabolic Determinants of Atrial Fibrillation in a General Population Sample: The CHRIS Study.

Atrial fibrillation (AF) is a supraventricular arrhythmia deriving from uncoordinated electrical activation with considerable associated morbidity and mortality. To expand the limited understanding of AF biological mechanisms, we performed two screenings, investigating the genetic and metabolic determinants of AF in the Cooperative Health Research in South Tyrol study. We found 110 AF cases out of 10,509 general population individuals.

Core-softened water-alcohol mixtures: the solute-size effects.

Water is the most anomalous material on Earth, with a long list of thermodynamic, dynamic and structural behaviors that deviate from what is expected. Recent studies have indicated that these anomalies may be related to a competition between two liquids, which means that water has a potential liquid-liquid phase transition (LLPT) that ends at a liquid-liquid critical point (LLCP). In a recent study [J.

Metabolic Signature of Arrhythmogenic Cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM.

A review of blood sample handling and pre-processing for metabolomics studies.

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes.

Forensic Application of X-ray Fluorescence Spectroscopy for the Discrimination of Authentic and Counterfeit Revenue Stamps.

Energy-dispersive X-ray fluorescence (ED-XRF) spectroscopy with data treatment via chemometric tools was explored as an analytical protocol to discriminate between authentic and counterfeit revenue stamps. Untreated samples were directly analyzed, and the discrimination was based on the characterization of constituent elements present in the inks and paper. Authentic samples and samples that were suspected of being counterfeit were analyzed at three different areas on their surfaces: the ink-printed area, the non-printed area, and the holographic area.