During the last decades, neuroscientists have increasingly exploited a variety of artificial,
synthesized materials with controlled nano-sized features. For instance, a renewed interest in the ...development of prostheses or neural interfaces was driven by the availability of novel nanomaterials that enabled the fabrication of implantable bioelectronics interfaces with reduced side effects and increased integration with the target biological tissue. The peculiar physical-chemical properties of nanomaterials have also contributed to the engineering of novel imaging devices toward sophisticated experimental settings, to smart fabricated scaffolds and microelectrodes, or other tools ultimately aimed at a better understanding of neural tissue functions. In this review, we focus on nanomaterials and specifically on carbon-based nanomaterials, such as carbon nanotubes (CNTs) and graphene. While these materials raise potential safety concerns, they represent a tremendous technological opportunity for the restoration of neuronal functions. We then describe nanotools such as nanowires and nano-modified MEA for high-performance electrophysiological recording and stimulation of neuronal electrical activity. We finally focus on the fabrication of three-dimensional synthetic nanostructures, used as substrates to interface biological cells and tissues
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This work presents an overview of the potential of microwave indices obtained from multi-frequency/polarization radiometry in detecting the characteristics of land surfaces, in particular soil ...covered by vegetation or snow and agricultural bare soils. Experimental results obtained with ground-based radiometers on different types of natural surfaces by the Microwave Remote Sensing Group of IFAC-CNR starting from ‘80s, are summarized and interpreted by means of theoretical models. It has been pointed out that, with respect to single frequency/polarization observations, microwave indices revealed a higher sensitivity to some significant parameters, which characterize the hydrological cycle, namely: soil moisture, vegetation biomass and snow depth or snow water equivalent. Electromagnetic models have then been used for simulating brightness temperature and microwave indices from land surfaces. As per vegetation covered soils, the well-known tau-omega (τ-ω) model based on the radiative transfer theory has been used, whereas terrestrial snow cover has been simulated using a multi-layer dense-medium radiative transfer model (DMRT). On the basis of these results, operational inversion algorithms for the retrieval of those hydrological quantities have been successfully implemented using multi-channel data from the microwave radiometric sensors operating from satellite.
The use of graphene-based materials to engineer sophisticated biosensing interfaces that can adapt to the central nervous system requires a detailed understanding of how such materials behave in a ...biological context. Graphene's peculiar properties can cause various cellular changes, but the underlying mechanisms remain unclear. Here, we show that single-layer graphene increases neuronal firing by altering membrane-associated functions in cultured cells. Graphene tunes the distribution of extracellular ions at the interface with neurons, a key regulator of neuronal excitability. The resulting biophysical changes in the membrane include stronger potassium ion currents, with a shift in the fraction of neuronal firing phenotypes from adapting to tonically firing. By using experimental and theoretical approaches, we hypothesize that the graphene-ion interactions that are maximized when single-layer graphene is deposited on electrically insulating substrates are crucial to these effects.
The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two ...sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle) at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target). Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB), while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM).
Abstract Carbon nanotube-based biomaterials critically contribute to the design of many prosthetic devices, with a particular impact in the development of bioelectronics components for novel neural ...interfaces. These nanomaterials combine excellent physical and chemical properties with peculiar nanostructured topography, thought to be crucial to their integration with neural tissue as long-term implants. The junction between carbon nanotubes and neural tissue can be particularly worthy of scientific attention and has been reported to significantly impact synapse construction in cultured neuronal networks. In this framework, the interaction of 2D carbon nanotube platforms with biological membranes is of paramount importance. Here we study carbon nanotube ability to interfere with lipid membrane structure and dynamics in cultured hippocampal neurons. While excluding that carbon nanotubes alter the homeostasis of neuronal membrane lipids, in particular cholesterol, we document in aged cultures an unprecedented functional integration between carbon nanotubes and the physiological maturation of the synaptic circuits.
Detailed and geo-referenced maps identifying the locations of saturated and dry levees can be produced using microwave radiometric measurements from a light aircraft or helicopter, and integrated ...with GPS for positioning and orientation. The development of synergetic remote sensing technology for raised groundwater and seepage detection by the joint use of microwave and optical data along with GIS databases is an effective and most contemporary way of supporting risk assessment and facilitating disaster prevention and management. In this paper we present a remote sensing microwave technology for monitoring and detection of areas of water seepage through irrigation constructions, levees and dykes as well as for revealing areas with dangerously high groundwater level. The possibility for emergency response mapping, integrated with GPS and GIS data, facilitates the risk assessment and management services. The passive microwave radiometry (PMR) is based on spectral measurements in the millimetre to decimetre range of wavelengths. Compared to other remote sensing techniques, such as colour and infrared photography, thermal images and lidar, PMR is the only technology taking measurements under the earth’s surface and therefore is very well suited for water seepage and underground water monitoring in a fast and reliable way.
Physicochemical modification of implantable electrode systems is recognized as a viable strategy to enhance tissue/electrode integration and electrode performance in situ. In this work, a bench‐top ...electrochemical process to formulate anodized indium tin oxide (ITO) films with altered roughness, conducting profiles, and thickness is explored. In addition, the influence of these anodized films on neural cell adhesion, proliferation, and function indicates that anodized ITO film cytocompatibility can be altered by varying the anodization current density. Furthermore, ITO‐anodized films formed with a current density of 0.4 mA cm−2 show important primary neural cell survival, modulation of glial scar formation, and promotion of neural network activity.
The anodization of indium tin oxide films by varying current densities is investigated as a facile method to modify the morphological, electrical, and cytocompatibility profiles of the resulting anodized films as neural electrodes. The systematic study elucidates that the current density of 0.4 mA cm−2 results in a well‐distributed surface morphology, minimum impedance, stability and support for cell viability, and neural network activity.
In this paper, we present an intercomparison of algorithms for retrieving soil moisture content (SMC) from ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar images. The algorithms ...taken into consideration were a feedforward artificial neural network (ANN) with two hidden layers, a statistical approach based on Bayes' theorem, and an iterative algorithm based on the nelder-mead direct-search method. The comparison was carried out by using both simulated and experimental data. Simulated data were obtained by means of the integral equation model (IEM). Experimental data were collected in an agricultural area in Northern Italy during 2003-2005; they included backscattering coefficient at HH and HV polarizations and at an incidence angle of thetas = 23 deg , as well as detailed ground truth measurements of SMC, surface roughness, and vegetation parameters. HH-polarized data were related to SMC, whereas the information of the cross-polarized channel was used to correct the backscatter for the effects of surface roughness. A comparison of the algorithms with experimental data showed that all the tested approaches produced SMC values that are very close to the measured ones. However, the predictions of the ANN were slightly more suitable than the other methods for generating maps in reasonable time. The production of moisture maps carried out at different dates using this algorithm pointed out the feasibility of separating up to six levels of spatial/temporal variations of SMC in the range of 10%-35%.
This study aims at relating the stickiness parameter (<inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>) of the dense media radiative transfer theory in quasi-crystalline ...approximation of Mie scattering of densely packed sticky spheres (DMRT-QMS), to the physical parameters of the layered snowpack. A relationship has been derived to express <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>, which modulates the attractive contact force between ice spheres, as a function of ice volume fraction (<inline-formula> <tex-math notation="LaTeX">\phi </tex-math></inline-formula>) and coordination number (<inline-formula> <tex-math notation="LaTeX">n_{c} </tex-math></inline-formula>). Since <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula> is not a measurable parameter, this is a step forward with respect to what is commonly made in the literature, where <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula> is assumed as an arbitrary parameter, generally ranging between 0.1 and 0.3, to fit simulated backscattering data with those measured. As a first validation, DMRT-QMS was integrated with the SNOWPACK model to simulate backscattering at X-band (9.6 GHz) driven by nivo-meteorological data acquired on a test area located in Monti Alti di Ornella, Italy. The simulations were compared with Synthetic Aperture Radar COSMO-SkyMed (CSK) satellite observations. The results show a significant agreement (<inline-formula> <tex-math notation="LaTeX">R^{2} =0.68 </tex-math></inline-formula>), although for a limited dataset of eight points in a unique winter season.
Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic ...aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient <inline-formula> <tex-math notation="LaTeX">R \simeq 0.77 </tex-math></inline-formula> between estimated and target SD, a root-mean-square error (RMSE) <inline-formula> <tex-math notation="LaTeX">\simeq 13 </tex-math></inline-formula> cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with <inline-formula> <tex-math notation="LaTeX">R =0.74 </tex-math></inline-formula>, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites.
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