Mathematical methods to analyze and interpret calcium signals of astrocytes

Astrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions. Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons. In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data. Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice.

Development and characterization of cutting-edge equipment for EMC measurements and advanced power line filter design

This Doctoral Thesis aims at studying, developing, and characterizing cutting edge equipment for EMC measurements and proposing innovative and advanced power line filter design techniques. This document summarizes a three-year work, is strictly industry oriented and relies on EMC standards and regulations. It contains the main results, findings, and effort with the purpose of bringing innovative contributions at the scientific community. Conducted emissions interferences are usually suppressed with power line filters. These filters are composed by common mode chokes, X capacitors and Y capacitors in order to mitigate both the differential mode and common mode noise, which compose the overall conducted emissions. However, even at present days, available power line filter design techniques show several disadvantages. First of all, filters are designed to be implemented in ideal 50 Ω systems, condition which is far away from reality. Then, the attenuation introduced by the filter for common or differential mode noise is analyzed independently, without considering the possible mode conversion that can be produced by impedance mismatches, or asymmetries in either the power line filter itself or the equipment under test. Ultimately, the instrumentation used to perform conducted emissions measurement is, in most cases, not adequate. All these factors lead to an inaccurate design, contributing at increasing the size of the filter, making it more expensive and less performant than it should be.