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This paper develops a computational framework for inverting Gompertz-Makeham mortality hazard rates, consistent with compensation laws of mortality for heterogeneous populations, to define a longevity-risk-adjusted global (L-RaG) age. To illustrate its salience and possible applications, the paper calibrates and presents L-RaG values using country data from the Human Mortality Database (HMD). Among other things, the author demonstrates that when properly benchmarked, the longevity-risk-adjusted global age of a 55-year-old Swedish male is 48, whereas a 55-year-old Russian male is closer in age to 67. The paper also discusses the connection between the proposed L-RaG age and the related concept of Biological age, from the medical and gerontology literature. Practically speaking, in a world of growing mortality heterogeneity, the L-RaG age could be used for pension and retirement policy. In the language of behavioral finance and economics, a salient metric that adjusts chronological age for longevity risk might help capture the public's attention, educate them about lifetime uncertainty and induce many of them to take action - such as working longer and/or retiring later.In this study, we synthesized a novel kind of cellulose-based microfibers for efficient adsorption of Enterovirus 71 (EV71), the leading causative agent of life-threatening hand, foot and mouth disease. The initial cellulose microfibers (CEL) were activated by (3-aminopropyl) triethoxysilane (APTES), and then covalently modified by polyglutamic acid (PGA) and mesoporous silica nanoparticles (MSN), obtaining the microfibers CEL-PGA-MSN. Owing to the electrostatic interaction between the negatively charged components (i.e., PGA and MSN) and positively charged amino acids of the epitope of EV71 capsid protein VP2 (VP2-ep), the obtained microfibers strongly adsorbed the epitope, and exhibited high EV71-adsorption capacity. This study sheds a novel light on development of cellulose-based materials for application in virus-capturing equipment.The earliest possible diagnosis and understanding of the infection mechanisms play a crucial role in the outcome of fighting viral diseases. Thus, we designed and developed for the first time, novel bioconjugates made of Ag-In-S@ZnS (ZAIS) fluorescent quantum dots coupled with ZIKA virus via covalent amide bond with carboxymethylcellulose (CMC) biopolymer for labeling and bioimaging the virus-host cell interactions mechanisms through confocal laser scanning microscopy. This work offers relevant insights regarding the profile of the ZIKA virus-nanoparticle conjugates interactions with VERO cells, which can be applied as a nanoplatform to elucidate the infection mechanisms caused by this viral disease.Due to the popularity of blockchain, there have been many proposed applications of blockchain in the healthcare sector, such as electronic health record (EHR) systems. Therefore, in this paper we perform a systematic literature review of blockchain approaches designed for EHR systems, focusing only on the security and privacy aspects. As part of the review, we introduce relevant background knowledge relating to both EHR systems and blockchain, prior to investigating the (potential) applications of blockchain in EHR systems. We also identify a number of research challenges and opportunities.The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Gaeta (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and São Paulo state, Brazil, showing different aspects of the epidemic. In both cases we see strong evidence that the adoption of social distancing measures contributed to a slower increase in the number of deceased individuals when compared to the baseline of no reduction in the infection rate. Both for Lombardy and São Paulo we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic.Calibration of a SIR (Susceptibles-Infected-Recovered) model with official international data for the COVID-19 pandemics provides a good example of the difficulties inherent in the solution of inverse problems. Inverse modeling is set up in a framework of discrete inverse problems, which explicitly considers the role and the relevance of data. Together with a physical vision of the model, the present work addresses numerically the issue of parameters calibration in SIR models, it discusses the uncertainties in the data provided by international authorities, how they influence the reliability of calibrated model parameters and, ultimately, of model predictions.Any epidemiological compartmental model with constant population is shown to be a Hamiltonian dynamical system in which the total population plays the role of the Hamiltonian function. Moreover, some particular cases within this large class of models are shown to be bi-Hamiltonian. this website New interacting compartmental models among different populations, which are endowed with a Hamiltonian structure, are introduced. The Poisson structures underlying the Hamiltonian description of all these dynamical systems are explicitly presented, and their associated Casimir functions are shown to provide an efficient tool in order to find exact analytical solutions for epidemiological models, such as the ones describing the dynamics of the COVID-19 pandemic.
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