The human antibody repertoire is one of the most important defenses against infectious disease, and the development of vaccines has enabled the conferral of targeted protection to specific pathogens. However, there are many challenges to measuring and analyzing the immunoglobulin sequence repertoire, such as the fact that each B cell contains a distinct antibody sequence encoded in its genome, that the antibody repertoire is not constant but changes over time, and the high similarity between antibody sequences. We have addressed this challenge by using high-throughput long read sequencing to perform immunogenomic characterization of expressed human antibody repertoires in the context of influenza vaccination. Informatic analysis of 5 million antibody heavy chain sequences from healthy individuals allowed us to perform global characterizations of isotype distributions, determine the lineage structure of the repertoire and measure age and antigen related mutational activity. Our analysis of the clonal structure and mutational distribution of individuals’ repertoires shows that elderly subjects have a decreased number of lineages but an increased pre-vaccination mutation load in their repertoire and that some of these subjects have an oligoclonal character to their repertoire in which the diversity of the lineages is greatly reduced relative to younger subjects. We have thus shown that global analysis of the immune system’s clonal structure provides direct insight into the effects of vaccination and provides a detailed molecular portrait of age-related effects.

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We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to recessionary shocks and recovers more slowly from them now than it did 40 years ago, due to structural changes in the world trade network induced by globalization. We also show that recession-induced change to the world trade network leads to an increased hierarchical structure of the global trade network for a few years after the recession.

Piezocatalysts have attracted much attention due to their excellent degradation ability for organics. In this work, three types of BaTiO 3 (BTO) nanostructures, including hydrothermally synthesized nanocubes (NCs), sol−gel calcined nanoparticles (NPs), and electrospun nanofibers (NFs), are prepared for catalyzing the dye degradation. Compared with the NCs and NPs, the NFs exhibit a higher piezocatalytic degradation performance due to the large specific surface area, fine crystal size, and easy deformation structure. Moreover, the kinetic factors, including initial dye concentration, ionic strength, ultrasonic power, and applied action, influencing the degradation performance of the BTO NFs are analyzed deeply. A high degradation rate constant of 0.0736 min −1 is achieved for rhodamine B, which is superior compared with the previous reports. The excellent stability of BTO NFs is demonstrated by the cycling tests, where a high degradation efficiency of 97.6% within 110 min is still obtained after the third cycle. Furthermore, the mechanism of piezocatalysis revealed that the hydroxyl and superoxide radicals are the main reactive species in the degradation process. This work is of importance for the development of high-performance piezocatalysts and highlights the potential of piezocatalysis for water remediation.

We investigate the selective forces that promote the emergence of modularity in nature. We demonstrate the spontaneous emergence of modularity in a population of individuals that evolve in a changing environment. We show that the level of modularity correlates with the rapidity and severity of environmental change. The modularity arises as a synergistic response to the noise in the environment in the presence of horizontal gene transfer. We suggest that the hierarchical structure observed in the natural world may be a broken symmetry state, which generically results from evolution in a changing environment. To support our results, we analyze experimental protein interaction data and show that protein interaction networks became increasingly modular as evolution proceeded over the last four billion years. We also discuss a method to determine the divergence time of a protein.