1,721,008 research outputs found
Statistical analysis of proteomics data: A review on feature selection
No full textThe spread of “-omics” strategies has strongly changed the way of thinking about the scientific method. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach with a data-driven inductive approach, so to generate mechanistical hypotheses from data. Data reduction is a crucial step in the process of proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). In this frame, the aim of the present review article is to give an overview of the methods available for proteomics data analysis, with a focus on biomedical translational research. Suggestions for the choice of the most appropriate standard statistical procedures are presented to perform data reduction by feature selection, cross-validation and functional analysis of proteomics profiles. Significance: The proteome, including all so-called “proteoforms” represents the highest level of complexity of biomolecules when compared to the other “-omes” (i.e., genome, transcriptome). For this reason, the use of proper data reduction strategies is mandatory for proteomics data analysis. However, the strategies to be employed for feature selection must be carefully chosen, since many different approaches exist based on both input data and desired output. So far, a well-established decision-making workflow for proteomics data analysis is lacking, opening up to misleading and incorrect data analysis and interpretation. In this review article many statistical approaches are described and compared for their application in the field of biomedical research, in order to suggest the reader the most suitable analysis pathway and to avoid mistakes
Evaluation of gpr frequency and polarisation for underground concrete tunnels assessment
No full textThis paper presents the results from a Ground Penetrating Radar experimental campaign aiming at characterising the thickness of principal concrete elements of a subway tunnel, following relevant water infiltration phenomena that might have affected their integrity and possibly have degraded their structural stability. Given the requirements of penetration and image quality, the main purpose of the study was to identify an optimal equipment design capable of accurately determine thickness variations of the subsurface structures by the joint analysis of different operational frequency, particularly a 200 MHz and a 600 MHz equipment, and antenna polarisation. While suitable depth performance has been achieved for both frequency bands, the image quality is notably higher when the interference from the metallic elements is minimised, and hence the 600 MHz equipment oriented perpendicular to the armour level has proven to be the best option. Obtained results allow a centimetric characterisation of the vertical extension of the investigated structures, and confirm their deterioration
Features Selection and Extraction in Statistical Analysis of Proteomics Datasets
No full text“Omics” techniques (e.g., proteomics, genomics, metabolomics), from which huge datasets can nowadays be obtained, require a different way of thinking about data analysis that can be summarized with the idea that, when data are enough, they can speak for themselves. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach (hypothesis-driven) with a data-driven hypothesis-generating inductive approach, so to generate mechanistical hypotheses from data. Data reduction is a crucial step in proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection/extraction methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). Despite big data generated almost daily by proteomics studies, a well-established statistical workflow for data analysis in proteomics is still lacking, opening up to misleading and incorrect data analysis and interpretation. This chapter will give an overview of the methods available for feature selection/extraction in proteomics datasets and how to choose the most appropriate one based on the type of dataset
The Impact of Spatial Sampling and Polarisation in GPR Survey for Utility Mapping
No full textThe presented analysis has quantitatively evaluated the performance of different GPR survey strategy for the detection and mapping of buried utilities. Results have shown that just the acquisition of a set of sparse 2D profiles is not a reliable strategy, as detection performance drops in presence of complex subsurface geometries, for which the profile spacing might be too wide to properly reconstruct the situation. A step ahead is represented by a 3D survey, in which the increased acquisition effort is balanced by the improvements achieved in terms of reconstruction quality. Targets can be tracked out of the 2D domain, feature that is highly relevant in areas with multiple intersecting objects. However, depolarisation phenomena, critical factor for linear and elongated targets, can be efficiently neutralised by the acquisition and combination of mutually orthogonal GPR volume
Testing of a GPR equipment to assess the integrity condition of a subway tunnel located under groundwater level
No full textDetermining the condition of underground concrete tunnels is fundamental for ensuring continuity and safety of operations, as well as for deploying efficient maintenance plans. The increased traffic volumes in early infrastructures, which are characterized by outdated construction techniques and potential approximated realization, as well as rising groundwater levels due to variations in urban industrialization patterns, are among the main causes of current concerns regarding their performance. Such uncertainties and constraints push towards the use of non-destructive techniques, rather than direct methods, to gather the required knowledge on the actual status of structures. This study presents the results of a ground penetrating radar (GPR) investigation carried out in a subway tunnel segment to characterize the thickness of its underlying concrete elements, namely the tunnel invert and the concrete filling, deemed necessary after relevant flooding events prompted the evaluation of its overall integrity condition. A preliminary step was performed to define the optimal frequency and polarimetric antenna configuration, given the resolution and penetration requirements and considering the high electromagnetic interference characterizing the site. The selected configuration was then assembled in a dedicated survey platform, and an entire 650 m long segment surveyed, producing a high resolution delineation of the concrete elements thickness. The accuracy of the estimation has been validated through nine core samples, demonstrating the reliability and consistency of the conceptualized GPR platform
Velocity and Absorption Analysis from Tomographic sonic Experiments on Ancient Stone Pillars
No full textLooking at COVID-19 from a Systems Biology Perspective
Full text linkThe 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–human 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–host interactions, and in the development of disease classifiers and tools for drug repurposing
Proteostasis and proteotoxicity in the network medicine era
No full textNeurodegenerative proteinopathies are complex diseases that share some pathogenetic processes. One of these is the failure of the proteostasis network (PN), which includes all components involved in the synthesis, folding, and degradation of proteins, thus leading to the aberrant accumulation of toxic protein aggregates in neurons. The single components that belong to the three main modules of the PN are highly interconnected and can be considered as part of a single giant network. Several pharmacological strategies have been proposed to ameliorate neurodegeneration by targeting PN components. Nevertheless, effective disease-modifying therapies are still lacking. In this review article, after a general description of the PN and its failure in proteinopathies, we will focus on the available pharmacological tools to target proteostasis. In this context, we will discuss the main advantages of systems-based pharmacology in contrast to the classical targeted approach, by focusing on network pharmacology as a strategy to innovate rational drug design
Identification and recognition of landmine internal structure scattering contribution from GPR data
No full textThe aim of the study was to quantify the potential increase in the information level produced by an increase in the data dimensionality, i.e. from analysing a 1D signature to the investigation of a 3D GPR volume. The experimental campaign was carried out employing two different neutralised landmines, characterised by a different internal structure and buried in controlled conditions. Obviously, the acquisition of a single monodimensional signature of the target has the advantage of being almost effortless, but shows significant limitations in achieving adequate performance, in particular for landmines showing an irregular internal structure. This is a consequence of the impossibility of effectively separating the different scattering contribution. As well, despite producing a clearer and more intuitive image of the target, a single 2D profile is not able to provide reliable performance, hence there is little benefit in acquiring a 2D profile as it still suffers from not producing unambiguous results. The analysis of a 3D volume, instead, allows for an accurate delineation of the internal structure of the target, providing a reliable solution to the complex target design critical issue
Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data
Full text linkBuried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this article, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad hoc data preprocessing to achieve accuracy higher than 93% on challenging data sets
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