Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system’s solutions, offering substantial computational efficiency for this application.

Measles is a highly contagious viral disease that poses a substantial public health risk. Vaccination is the primary strategy for measles prevention, requiring a 95% global coverage with two doses of the MMR (measles, mumps, and rubella) vaccine. Although the vaccine is 98% effective, the spread of anti-vaccine sentiment and misinformation about immunization mechanisms has significantly reduced vaccination rates, allowing the virus to persist. In 2016, there were 132,490 reported cases of measles globally; this number increased to 837,922 in 2019. This work presents a computational model, based on a previously established model from the literature, that simulates the immune response to measles vaccination. The objectives are to: (1) create an effective simulation tool that can model diverse vaccination scenarios, thereby reducing dependence on extensive clinical trials; (2) assess the immunological response to the measles vaccine through computational simulations, conserving both time and resources; and (3) conduct sensitivity analysis of the model’s parameters to identify key immune components and their interactions, leading to the most effective immune response.

Brucellosis is a highly contagious zoonotic disease caused by bacteria from the genus Brucella. It affects various animal species, with significant economic consequences for live-stock producers. The disease causes reproductive losses, such as abortions and reduced fertility, leading to decreased live-stock production. Additionally, infected animals may experience reduced milk production and weight gain, further impacting the economic viability of livestock operations. Furthermore, the potential for zoonotic transmission to humans poses risks to public health. In this study, we propose a new mathematical model that describes the innate immune response to brucellosis, focusing on the interactions between antigens and the innate immune system. The model is formulated as a system of five partial differential equations that represent healthy tissue, bacteria, cytokines, resting and mature antigen-presenting cells, and is solved using the finite difference method. To validate our model, we compare the initial numerical results with experimental data from the literature, achieving an absolute error of 0.0017.

About seven months after the start of the COVID-19 pandemic, the first vaccine against the disease was approved for emergency use. Since then, twenty-three more vaccines have been approved, while more than three hundred are in development. Despite being one of the fastest vaccines ever created, several questions about it remain open. Computational models can be useful to answer some of these questions. This paper aims to evaluate whether a computer model previously used to reproduce the effects of the yellow fever vaccine in the body is also capable of reproducing the effects of a distinct vaccine: ChAdOx1 nCoV-19. Preliminary results show that the model is a promising tool to achieve this goal since it was able to reproduce the antibody curves observed in individuals vaccinated with ChAdOx1 nCoV-19.

The human immune system (HIS) is responsible for the defense of the organism against pathogens. Mathematical models can be a useful tool to study different aspects of HIS through in silico trials. In this work, we investigate both primary and secondary responses to the Yellow Fever virus (YFV) using mathematical models. Uncertainty quantification and sensitivity analysis are performed via Monte Carlo and polynomial chaos expansion methods. It was possible to reproduce key aspects of antibody dynamics in a secondary response to the YFV and quantity the influence of model parameters in relevant phases of the immune response.

By November 2020, the Coronavirus disease 2019 (COVID-19) has infected more than 50 million people worldwide, causing more than 1.2 million deaths. This new contagious disease is not well understood, and the scientific community is trying to comprehend better the interactions of the causative agent of the disease, SAR2-CoV-2, and the immune response to identify its weak points to develop new therapies to impair its lethal effects. Mathematical and computational tools can help in this task: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. In this work, we present a simple system of five ordinary differential equations that can be used to model the immune response to SARS-CoV-2. The model parameters and initial conditions were adjusted to cohort studies that collected viremia and antibody data. The results have shown that the model was able to reproduce both viremia and antibodies dynamics successfully.

BACKGROUND: An effective yellow fever (YF) vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for a booster dose, the optimal immunisation schedule for immunocompetent, immunosuppressed, and pediatric populations, among other issues. This work aims to demonstrate that computational tools can be used to simulate different scenarios regarding YF vaccination and the immune response of individuals to this vaccine, thus assisting the response of some of these open questions. RESULTS: This work presents the computational results obtained by a mathematical model of the human immune response to vaccination against YF. Five scenarios were simulated: primovaccination in adults and children, booster dose in adult individuals, vaccination of individuals with autoimmune diseases under immunomodulatory therapy, and the immune response to different vaccine doses. Where data were available, the model was able to quantitatively replicate the levels of antibodies obtained experimentally. In addition, for those scenarios where data were not available, it was possible to qualitatively reproduce the immune response behaviours described in the literature. CONCLUSIONS: Our simulations show that the minimum dose to confer immunity against YF is half of the reference dose. The results also suggest that immunological immaturity in children limits the induction and persistence of long-lived plasma cells are related to the antibody decay observed experimentally. Finally, the decay observed in the antibody level after ten years suggests that a booster dose is necessary to keep immunity against YF.

An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents the results of a computational model of the human immune response to vaccination against yellow fever. The model takes into account essential cells and molecules of the human immune system, such as antigen-presenting cells, B and T lymphocytes, memory cells, and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data.

Viral infections cause a large number of deaths worldwide. During the inflammatory process secondary to a viral infection, edema may appear, that is, fluid may accumulate in the interstitial tissue. Computational modeling is a tool that can be used to understand this phenomenon better. This paper proposes a first-step model to describe the formation of viral cardiac edema. In particular, this paper presents a model that describes the increase in interstitial pressure during viral infections. Preliminary computational results show that the model can qualitatively reproduce results described in the literature.

In recent years, Petri nets are being used for modeling complex biological systems. Examples include the modeling of biochemical reactions, protein interaction networks, gene regulation networks and the immune system. In this work, Petri nets are used to model the innate immune response to a bacterial infection. The objective of this work is to study how changes in macrophage phenotype impact the regulation and resolution of the immune response. From simulations performed, we have observed that the conversion between a M1 proinflammatory phenotype to a M2 regulatory phenotype during an inflammatory response is an important process to avoid a state of persistent inflammation.

BACKGROUND: Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot's poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis. RESULTS: A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation. CONCLUSIONS: This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.

Summary This work presents a resource allocation algorithm that considers the characteristics of regular applications to choose the subset of processors, accelerators, and networks that minimize their parallel execution time in a small heterogeneous cluster environment. The resource allocation algorithm uses Heterogeneous Cluster Model (HCM) to estimate the execution time of the parallel applications. The experimental results have shown that, for applications with distinct behaviors, the resource allocation algorithm has successfully chosen the set of resources that minimizes their execution time.

An effective yellow fever vaccine has been available since 1937. However, some issues regarding its use remain open, such as the minimum dose that can provide immunity against the disease. Mathematical-computational tools can be useful to assist the search for answers to some of these open issues. In this context, this study presents a simplified mathematical-computational model of the human immune response to the vaccination against yellow fever that takes into account important cells of the immune system. The model was able to qualitatively reproduce some experimental results reported in the literature, such as the amount of antibodies and viremia along time, as well as to reproduce distinct behaviors of the immune response reported in the literature. This is the first step towards an ideal scenario where it will be possible to simulate distinct situations related to the use of the yellow fever vaccine, such as its application in immunodeficient individuals, different vaccination strategies, duration of immunity and the need for a booster dose.

The Human Immune System (HIS) plays a fundamental role in the defense of the body against diseases. However, the multi-scale interactions among several of its components make the complete understanding of the mechanisms involved in the body defense a complex task. Mathematical and computational tools can help towards this goal. Lots of works use differential equations, agent-based models and cellular automaton to describe the behavior of the HIS. In this work, we use the Gillespie stochastic simulation algorithm to model the stochastic nature of the HIS response to Yellow Fever vaccine.

In recent years, there has been an increasing interest in the mathematical and computational modeling of the human immune system (HIS). Computational models of HIS dynamics may contribute to a better understanding of the relationship between complex phenomena and immune response; in addition, computational models will support the development of new drugs and therapies for different diseases. However, modeling the HIS is an extremely difficult task that demands a huge amount of work to be performed by multidisciplinary teams. In this study, our objective is to model the spatio-temporal dynamics of representative cells and molecules of the HIS during an immune response after the injection of lipopolysaccharide (LPS) into a section of tissue. LPS constitutes the cellular wall of Gram-negative bacteria, and it is a highly immunogenic molecule, which means that it has a remarkable capacity to elicit strong immune responses. We present a descriptive, mechanistic and deterministic model that is based on partial differential equations (PDE). Therefore, this model enables the understanding of how the different complex phenomena interact with structures and elements during an immune response. In addition, the model's parameters reflect physiological features of the system, which makes the model appropriate for general use.

Medical imaging is becoming a key technique to visualize the internal structure of the body. Magnetic resonance imaging (MRI) is currently used to take different spatial images of organs, such as the heart. The output of such an analysis is a set of images representing different views of the body or of an organ. There exist many algorithms to pre-process and analyze medical images such as the well known Snake segmentation algorithm. An issue in medical imaging is the large size of images, that require large and efficient data stores, and the high computational power needed to process them. For these reasons, the Grid is being more and more used as an ideal environment for medical image processing. This paper presents a first experience in porting the snake algorithm on a Globus-based Grid.

Abstract Distributed Java virtual machine (dJVM) systems enable concurrent Java applications to transparently run on clusters of commodity computers. This is achieved by supporting Java's shared‐memory model over multiple JVMs distributed across the cluster's computer nodes. In this work, we describe and evaluate selective dynamicdiffingand lazy home allocation, two new runtime techniques that enable dJVMs to efficiently support memory sharing across the cluster. Specifically, the two proposed techniques can contribute to reduce the overheads due to message traffic, extra memory space, and high latency of remote memory accesses that such dJVM systems require for implementing their memory‐coherence protocol either in isolation or in combination. In order to evaluate the performance‐related benefits of dynamicdiffingand lazy home allocation, we implemented both techniques in Cooperative JVM (CoJVM), a basic dJVM system we developed in previous work. In subsequent work, we carried out performance comparisons between the basic CoJVM and modified CoJVM versions for five representative concurrent Java applications (matrix multiply, LU, Radix, fast Fourier transform, and SOR) using our proposed techniques. Our experimental results showed that dynamicdiffingand lazy home allocation significantly reduced memory sharing overheads. The reduction resulted in considerable gains in CoJVM system's performance, ranging from 9% up to 20%, in four out of the five applications, with resulting speedups varying from 6.5 up to 8.1 for an 8‐node cluster of computers. Copyright © 2007 John Wiley & Sons, Ltd.

We propose a new distributed run-time environment, which we called cJava, that enables multithread Java applications to execute in clusters transparently. Our implementation of cJava supports the distributed shared memory (DSM) which required significant extensions to the original Java virtual machine (JVM). First, a distributed object manager was incorporated to the JVM's memory management subsystem for creating a global object space. Second, synchronized accesses to the global object space were extended so that they could use the lock() and unlock() primitives that cJava's DSM supports. Third, cJava adapted the thread subsystem to enable remote creation and global monitors. Last, a subsystem/or remote signaling was added to the original JVM. The main advantage of cJava is that it can execute existing multithread Java applications straightaway. Most importantly, our results of cJava's performance across several benchmarks show that cJava offers an efficient run-time system for executing transparently multithread Java applications in clusters.

Abstract There has been an increasing research interest in extending the use of Java towards high‐performance demanding applications such as scalable Web servers, distributed multimedia applications, and large‐scale scientific applications. However, extending Java to a multicomputer environment and improving the low performance of current Java implementations pose great challenges to both the systems developer and application designer. In this survey, we describe and classify 14 relevant proposals and environments that tackle Java's performance bottlenecks in order to make the language an effective option for high‐performance network‐based computing. We further survey significant performance issues while exposing the potential benefits and limitations of current solutions in such a way that a framework for future research efforts can be established. Most of the proposed solutions can be classified according to some combination of three basic parameters: the model adopted for inter‐process communication, language extensions, and the implementation strategy. In addition, where appropriate to each individual proposal, we examine other relevant issues, such as interoperability, portability, and garbage collection. Copyright © 2002 John Wiley & Sons, Ltd.