Pediatric cerebrospinal fluid rhinorrhea caused by low flow vascular anomaly of the temporal bone: A case series.

Objective: Atraumatic cerebrospinal fluid (CSF) rhinorrhea is uncommon in children and necessitates a multi-disciplinary evaluation for an etiology. Underlying osseous abnormality due to extensive or multifocal low flow vascular anomaly should be considered as a potential cause of spontaneous CSF leak. Treatment of multifocal low flow vascular anomalies may include medical and surgical approaches.

An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons.

Full-scale morphologically and biophysically realistic model networks, aiming at modeling multiple brain areas, provide an invaluable tool to make significant scientific advances from in-silico experiments on cognitive functions to digital twin implementations. Due to the current technical limitations of supercomputer systems in terms of computational power and memory requirements, these networks must be implemented using (at least) simplified neurons. A class of models which achieve a reasonable compromise between accuracy and computational efficiency is given by generalized leaky integrate-and fire models complemented by suitable initial and update conditions.

CVD Growth of Hematite Thin Films for Photoelectrochemical Water Splitting: Effect of Precursor-Substrate Distance on Their Final Properties.

The development of photoelectrode materials for efficient water splitting using solar energy is a crucial research topic for green hydrogen production. These materials need to be abundant, fabricated on a large scale, and at low cost. In this context, hematite is a promising material that has been widely studied.

On the structural connectivity of large-scale models of brain networks at cellular level.

The brain's structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited.