Ligand-Based Regulation of Dynamics and Reactivity of Hemoproteins

Hemoproteins include several heme-binding proteins with distinct structure and function. The presence of the heme group confers specific reactivity and spectroscopic properties to hemoproteins. In this review, we provide an overview of five families of hemoproteins in terms of dynamics and reactivity. First, we describe how ligands modulate cooperativity and reactivity in globins, such as myoglobin and hemoglobin. Second, we move on to another family of hemoproteins devoted to electron transport, such as cytochromes. Later, we consider heme-based reactivity in hemopexin, the main heme-scavenging protein. Then, we focus on heme-albumin, a chronosteric hemoprotein with peculiar spectroscopic and enzymatic properties. Eventually, we analyze the reactivity and dynamics of the most recently discovered family of hemoproteins, i.e., nitrobindins

Looking at COVID-19 from a Systems Biology Perspective

The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus\u2013human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus\u2013host interactions, and in the development of disease classifiers and tools for drug repurposing

Assessing the Perspectives of Ground Penetrating Radar for Precision Farming

The United Nations 2030 Agenda for Sustainable Development highlighted the importance of adopting sustainable agricultural practices to mitigate the threat posed by climate change to food systems around the world, to provide wise water management and to restore degraded lands. At the same time, it suggested the benefits and advantages brought by the use of near-surface geophysical measurements to assist precision farming, in particular providing information on soil variability at both vertical and horizontal scales. Among such survey methodologies, Ground Penetrating Radar has demonstrated its effectiveness in soil characterisation as a consequence of its sensitivity to variations in soil electrical properties and of its additional capability of investigating subsurface stratification. The aim of this contribution is to provide a comprehensive review of the current use of the GPR technique within the domain of precision irrigation, and specifically of its capacity to provide detailed information on the within-field spatial variability of the textural, structural and hydrological soil properties, which are needed to optimize irrigation management, adopting a variable-rate approach to preserve water resources while maintaining or improving crop yields and their quality. For each soil property, the review analyses the commonly adopted operational and data processing approaches, highlighting advantages and limitations

Step-frequency ground penetrating radar for agricultural soil morphology characterisation

Soil morphology plays a fundamental role in the vertical and lateral movements of solutes and water transport, providing knowledge regarding spatial distribution of its textural properties and subsurface dynamics. In this framework, the measured values of electrical conductivity are able to reveal the heterogeneity of soil that is present in a particular agricultural field and they are affected by more than one important physical characteristic: soil texture, organic matter, moisture content, and the depth of the clay pan. In the microwave region, these dynamics are known to exhibit a frequency dependent behaviour. This study explores the application of a Step Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) to shed light on the practical impact that these dependencies have on the imaging results, not only regarding the electrical characterisation of the subsurface morphology, but also in its correct interpretation. This information is of notable importance for determining water-use efficiency and planning precision-agriculture programs. The results clearly show visible and significant fluctuations of the amplitude levels, depending on the considered central frequency, demonstrating that the frequency dependence of electromagnetic properties of heterogeneous soil are significant and cannot be ignored if the aim is to properly define the subsurface attributes. The measurements also suggest that correlating the delineated variations might help in the identification of extended features and the classification of areas that possess similar properties in order to increase the confidence in monitoring soil resource

Multi-azimuth ground penetrating radar surveys to improve the imaging of complex fractures

Ground Penetrating Radar (GPR) images are affected, to some degree, by the relative orientation of antennas and subsurface targets. This is particularly true not only for targets that show a significant directivity, but also for inclined planes, such as fractures and faults. Depending on the relative geometry between the antennas and the orientation of the target, radar waves can be preferentially scattered, which causes changes in the reflected signal amplitude. Therefore, traditional single polarization and single azimuth surveys may produce inadequate results. The work presented here examines the use of a multi-azimuth GPR survey to increase the imaging performance of inclined fractures, showing the shortcomings of single-profile surveying and highlighting the benefits that such a strategy has on detection and characterization

Sparse Ground Penetrating Radar Acquisition: Implication for Buried Landmine Localization and Reconstruction

The effectiveness of the ground penetrating radar (GPR) imaging process and its capability of correctly reconstructing buried objects is strictly bounded to a correct acquisition strategy, both in terms of data density and regularity. In some GPR applications, such as landmine detection, these requirements may not be fulfiled due to logistical limitations and environmental obstacles. In the light of autonomous platform, possibly driven by a positioning device, the knowledge of the maximum affordable grid irregularity is essential. This experimental work, employing a data set acquired at a landmine test site, provides a demonstration that the same information content could be maintained even with a sparser data grid, compared to the commonly adopted requirements, mitigating the pressing demand for a precise samples positioning

Landmine Detection from GPR Data Using Convolutional Neural Networks

The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles

Convolutional Autoencoder for Landmine Detection on GPR Scans

Buried unexploded landmines are a serious threat in many countries all over the World. As many landmines are nowadays mostly plastic made, the use of ground penetrating radar (GPR) systems for their detection is gaining the trend. However, despite several techniques have been proposed, a safe automatic solution is far from being at hand. In this paper, we propose a landmine detection method based on convolutional autoencoder applied to B-scans acquired with a GPR. The proposed system leverages an anomaly detection pipeline: the autoencoder learns a description of B-scans clear of landmines, and detects landmine traces as anomalies. In doing so, the autoencoder never uses data containing landmine traces at training time. This allows to avoid making strong assumptions on the kind of landmines to detect, thus paving the way to detection of novel landmine models