Untangling the immune basis of disease susceptibility

© 2020 Copyright Ribeiro and Graca. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are creditedInteractions between immune cell receptors and proteins that determine disease susceptibility shed light on how different arms of the immune system are involved in three viral infections and Crohn's disease.info:eu-repo/semantics/publishedVersio

T-cell immunology : the maths of memory

© Copyright Borghans and Ribeiro. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Mathematical modeling reveals that long-term immunological memory is maintained in a manner that is even more dynamic than previously thought.info:eu-repo/semantics/publishedVersio

Introduction to modeling viral infections and immunity

Copyright © 2018 John Wiley & Sons, Inc. All rights reserved.Infectious agents, such as HIV, hepatitis B virus (HBV), hepatitis C virus (HCV), malaria, and influenza remain significant public health threats, with ~41 million people chronically infected by HIV, ~331 million infected by HBV, ~148 million infected by HCV, and ~351 million cases of malaria, according to the Global Burden of Disease 2013 study. In addition, threats of new influenza pandemics or emerging viruses, such as Ebola and Zika, have created alarm in the United States and in many parts of the world. Despite intensive research efforts by public and private institutions, there are still no vaccines for HIV, HCV, malaria, Ebola, Zika, and many other pathogens. Even though there has been enormous progress with antiviral therapies for chronic infections, we are still unable to cure HIV and HBV, and life‐long treatment is needed.info:eu-repo/semantics/publishedVersio

The role of infected cell proliferation in the clearance of acute HBV Infection in humans

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Around 90–95% of hepatitis B virus (HBV) infected adults do not progress to the chronic phase and, instead, recover naturally. The strengths of the cytolytic and non-cytolytic immune responses are key players that decide the fate of acute HBV infection. In addition, it has been hypothesized that proliferation of infected cells resulting in uninfected progeny and/or cytokine-mediated degradation of covalently closed circular DNA (cccDNA) leading to the cure of infected cells are two major mechanisms assisting the adaptive immune response in the clearance of acute HBV infection in humans. We employed fitting of mathematical models to human acute infection data together with physiological constraints to investigate the role of these hypothesized mechanisms in the clearance of infection. Results suggest that cellular proliferation of infected cells resulting in two uninfected cells is required to minimize the destruction of the liver during the clearance of acute HBV infection. In contrast, we find that a cytokine-mediated cure of infected cells alone is insufficient to clear acute HBV infection. In conclusion, our modeling indicates that HBV clearance without lethal loss of liver mass is associated with the production of two uninfected cells upon proliferation of an infected cell.This work was funded by National Institutes of Health grants R01-AI116868 (RMR), R01-AI028433 (ASP) and R01-OD011095 (ASP). Portions of this work were performed under the auspices of the U.S. Department of Energy under contract DE-AC52-06NA25396.info:eu-repo/semantics/publishedVersio

Noise is not error : detecting parametric heterogeneity between epidemiologic time series

© Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.This work was funded by NIH grants R01-AI087520 and R01-AI104373; grants FIS2013-47949-C2-2-P and FIS2016-78883-C2-2-P and PRX 16/00287 (Spain); and PIRSES-GA-2012-317893 (7th FP, EU).info:eu-repo/semantics/publishedVersio

Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series

Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa

Drying shrinkage behavior of metakaolin-based and bamboo fiber reinforced geopolymers

This Brazil-USA collaborative research uses bamboo cultivated in the Amazon region and metakaolin attained from calcined Amazonian kaolin. The durability of sustainable geopolymer materials is studied by means of the drying shrinkage aging behavior. Scanning electron microscopy and energy dispersive x-ray fluorescence were used to investigate the microstructure of the composite materials. X-ray diffraction was used to confirm the formation of geopolymer. The water treated geopolymer matrix (GP) samples dried at room conditions for the periods of 3-7-14-21-28-56-112 days showed very close and increasing weight and length changes. The GP reinforced with bamboo fiber (GPBF) treated samples weight and length changes increased from the 3-day sample up to the 21-day, then it dropped down to the 112-day. The GP water treated samples dried at room conditions for the aging periods showed increasing flexural strength (MOR) and modulus of elasticity (E). The GPBF treated samples MOR were higher and very close to each other

Strength and elastic behavior of metakaolin-based and bamboo fiber reinforced geopolymers

Amazonian metakaolin-based and bamboo fiber reinforced geopolymers were studied by means of the strength and elastic aging behaviors for construction materials applications. Scanning electron microscopy and energy dispersive x-ray fluorescence were used to investigate the microstructure of the composite materials. X-ray diffraction was used to confirm the reliability of the samples as being geopolymers. The geopolymer matrix (GP) and the GP reinforced with bamboo fiber (GPBF) samples were aged-dried at room conditions for the periods of 1-7-28 days. The GP and GPBF ultimate compressive stress increased with age from 1-day to 28-day, while elastic modulus decreased with age. The GPBF samples ultimate compressive stresses and elastic moduli were lower than the GP samples values, but still can be suitable as sustainable construction materials

Probabilistic control of HIV latency and transactivation by the Tat gene circuit

Copyright © 2020 National Academy of Sciences.The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The “shock-and-kill” strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV-infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat–transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the “block and lock” strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks.info:eu-repo/semantics/publishedVersio

Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios