Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2

Position Paper on Water, Energy, Food and Ecosystem (WEFE) Nexus and Sustainable development Goals (SDGs)

The EU and the international community is realising that the Water, Energy, Food and Ecosystem components are interlinked and require a joint planning in order to meet the daunting global challenges related to Water, Energy and Food security and maintaining the ecosystem health and in this way, reach the SDGs. If not dealt with, the world will not be able to meet the demand for water, energy and food in a not too far future and, in any case, in a not sustainable way. The strain on the ecosystems resulting from unsustainable single-sector planning will lead to increasing poverty, inequality and instability. The Nexus approach is fully aligned with and supportive of the EU Consensus on Development. Key elements of the Consensus will require collaborative efforts across sectors in ways that can be supported/implemented by a Nexus approach. In this way, transparent and accountable decision-making, involving the civil society is key and common to the European Consensus on Development and the Nexus approach. The Nexus approach will support the implementation of the SDG in particular SDG 2 (Food), SDG 6 (Water) and SDG 7 (Energy), but most SDGs have elements that link to food, water and energy in one or other way, and will benefit from a Nexus approach. The SDGs are designed to be cross-cutting and be implemented together, which is also reflected in a WEFE Nexus approach. A Nexus approach offers a sustainable way of addressing the effects of Climate Change and increase resilience. The WEFE Nexus has in it the main drivers of climate change (water, energy and food security) and the main affected sectors (water and the environment). Decisions around policy, infrastructure, … developed based on the WEFE Nexus assessments will be suitable as elements of climate change mitigation and adaptation. In fact, it is difficult to imagine solutions to the climate change issue that are not built on a form of Nexus approach. The Nexus approach is being implemented around the world, as examples in the literature demonstrate. These examples together with more examples from EU and member state development cooperation will help build experience that can be consolidated and become an important contribution to a Toolkit for WEFE Nexus Implementation. From the expert discussions, it appears that because of the novelty of the approach, a Toolkit will be an important element in getting the Nexus approach widely used. This should build on experiences from practical examples of NEXUS projects or similar inter-sectorial collaboration projects; and, there are already policy, regulation and practical experience to allow institutions and countries to start applying the Nexus concept.JRC.D.2-Water and Marine Resource

Blind Restoration of Astronomical Images

Image restoration is of considerable interest in numerous scientific applications. When the image formation system is space invariant and linear, the blurred noise-free image can be expressed as the convolution of the original image with a blurring function: if the latter is known, then the Fast Fourier Transform can be used to efficiently compute convolutions. The algorithm presented here tries to remove the blur, using a priori constraints, without the knowledge of the blurring function. This approach is often referred to as Blind Deconvolution and finds useful applications, in particular, in astronomical imaging, in which the atmosphere of the Earth and the instruments of observation constitute sources of distortion and aberration that cannot be quantified in advance

Multimodality, multiscale imaging systems for investigating physiological random processes

This paper presents new imaging systems for the estimation of physiological random processes in medical imaging. In this work, a physiological random process is a sequence of biochemical interactions taking place inside a living organism. These interactions involve things such as proteins and enzymes, that behave differently in response to external stimuli (such as nutrients or administered drugs). Understanding how these physiological processes interact and evolve is critical in the development of effective therapeutic approaches. The general setup of our imaging systems includes a fast detector for the measurement of visible light from which to estimate various parameters about the radiation emitted by the physiological process(es) of interest. Our setup is applicable to imaging with different kinds of radiation, including gamma rays (SPECT and PET), and charged particles, such as alpha and beta particles. Parameters we are interested in estimating for these photons/particles go beyond the 2D or 3D position typically measured in medical imaging applications, and include the direction of propagation and photon/particle energy. Recent work has shown the advantage of measuring direction of propagation and photon/particle energy, in addition to just position. It has been shown that if these additional photon/particle parameters are taken into account during reconstruction, the null space of the imaging system is strongly reduced or eliminated. This reduction in null space is critical to adequately characterize complicated physiological processes. © 2022 SPIE.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Constrained Iterations for Blind Deconvolution and Convexity Issues

The need for image restoration arises in many applications of various scientific disciplines, such as medicine and astronomy and, in general, whenever an unknown image must be recovered from blurred and noisy data. The algorithm studied in this work restores the image without the knowledge of the blur, using little a priori information and a 'blind inverse filter' iteration. It represents a variation of the methods proposed by Kundur and Hatzinakos (1998) and by Ng, Plemmons and Qiao (2000). The problem of interest here is an 'inverse' one, that cannot be solved by simple filtering since it is ill--posed. The imaging system is assumed to be linear and space--invariant: this allows a simplified relationship between unknown and observed images, described by a 'point spread function' modeling the distortion. The blurring, though, makes the restoration ill--conditioned: 'regularization' is therefore also needed, obtained by adding constraints to the formulation of the estimated solution. The problem is modeled as a constrained minimization: particular attention is given here to the analysis of the objective function and on establishing whether or not it is a convex function, whose minima can be located by classic optimization techniques and descent methods. Numerical examples are applied to simulated data and to real data derived from various applications. Comparison with the behavior of the two other methods, mentioned above, show the effectiveness of our variant

Stochastic models for objects and images in oncology and virology: Application to PI3K-Akt-mTOR signaling and COVID-19 disease

Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]