SARS-CoV-2 pandemic dynamics and infection tracing in Denmark

We model and simulate the COVID-19 infection and healthcare dynamics in Denmark from the onset till March 5, 2021. The simulation is matched and calibrated to hospital and death data as well as antibody population measurement. In this work we focus on comparing the time evolution of the estimated infection level with the daily identified infected individuals based on the national testing and contact tracing program. We find that the national testing program on average identifies 1/3 of the infected individuals July 1, 2020 - March 5, 2021. Our investigations indicate the current program does not have a proper balance between random probing, focused contact tracing, and testing prioritization. Too much of the program operates as a semi-random daily sampling of part of the population. We propose a policy with a focus on local infection tracing and interventions.

Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers

Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modeling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.

Quantitative modeling of the effect of antigen dosage on B-cell affinity distributions in maturating germinal centers

Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis, and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modelling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.

The Lupus Anticoagulant Paradox

Lupus anticoagulant (LA) represents the most enigmatic antibody population in patients with antiphospholipid syndrome and represents a paradox that is still unsolved. This class of antiphospholipid antibody causes a phospholipid-dependent prolongation of the clotting time but is associated with an increased risk of thrombosis and pregnancy morbidity. In this review, we will provide an overview of the different antibodies that have been associated with LA activity, their importance based on clinical studies, and address the question why this prolongation of the clotting time is associated with thrombosis rather than a bleeding tendency.

Optimization of virtual machine placement based on constrained immune memory and immunodominance clone in IaaS cloud mode equipment training

In infrastructure as a service (IaaS) cloud mode equipment simulated training, to keep the resource utilization ratio in a rational high level, improve the training effect and reduce the system running cost, the problem of training virtual machine (TVM) placement needs to be resolved first. We make analysis to the problem and give the mathematical formulation to the problem. Then, we figure out the principle and target of the TVM placement. Based on above analysis, we propose a constrained immune memory and immunodominance clone (CIMIC) TVM placement optimization algorithm. By reverse optimization of the initial antibody population, the searching range is reduced. The common antibody population and the immunodominance antibody population evolve simultaneously, which realizes the simultaneous progressing of global searching and local searching of solutions. Further, local optimal is avoided by this means. Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained. The constraint information of the problem is utilized to improve the optimization effect. Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement, reduces the server number and improves the resource utilization and system stability.

A Multi-Objective Coordinative Optimization Model and its Cultural Immune Algorithm

To improve E-Commerce recommendation efficiency for multi-consumer population, coordinative optimization problem for multi-consumer purchase is studied. Multi-objective coordinative optimization model for multi-consumer purchase in E-commerce (MCOMMPE) is established, the optimization objectives consist of maximizing purchase probability, profit, green product orientation; minimizing lost consumer number; keeping equalization among varied purchase schemes. To solve easily, MCOMMPE is transformed into single objective optimization model. Cultural Immune Algorithm (CIA) is studied, affinity degree calculating, clone selecting, antibody population space and belief space are studied in detail, and the basic steps of CIA are designed. The validity of MCOMMPE and its CIA are validated.