Differential word expression analyses highlight plague dynamics during the second pandemic

Research on the second plague pandemic that swept over Europe from the fourteenth to nineteenth centuries mainly relies on the exegesis of contemporary texts and is prone to interpretive bias. By leveraging certain bioinformatic tools routinely used in biology, we developed a quantitative lexicography of 32 texts describing two major plague outbreaks, using contemporary plague-unrelated texts as negative controls. Nested, network and category analyses of a 207-word pan-lexicome, comprising overrepresented terms in plague-related texts, indicated that ‘buboes' and ‘carbuncles' are words that were significantly associated with the plague and signalled an ectoparasite-borne plague. Moreover, plague-related words were associated with the terms ‘merchandise’, ‘movable’, ‘tatters', ‘bed’ and ‘clothes'. Analysing ancient texts using the method reported in this paper can certify plague-related historical records and indicate the particularities of each plague outbreak, which can inform on the potential sources for the causative Yersinia pestis .

Intraspecific antagonism through viral toxin encoded by chronic Sulfolobus spindle-shaped virus

Virus–host interactions evolve along a symbiosis continuum from antagonism to mutualism. Long-term associations between virus and host, such as those in chronic infection, will select for traits that drive the interaction towards mutualism, especially when susceptible hosts are rare in the population. Virus–host mutualism has been demonstrated in thermophilic archaeal populations where Sulfolobus spindle-shaped viruses (SSVs) provide a competitive advantage to their host Sulfolobus islandicus by producing a toxin that kills uninfected strains. Here, we determine the genetic basis of this killing phenotype by identifying highly transcribed genes in cells that are chronically infected with a diversity of SSVs. We demonstrate that these genes alone confer growth inhibition by being expressed in uninfected cells via a Sulfolobus expression plasmid. Challenge of chronically infected strains with vector-expressed toxins revealed a nested network of cross-toxicity among divergent SSVs, with both broad and specific toxin efficacies. This suggests that competition between viruses and/or their hosts could maintain toxin diversity. We propose that competitive interactions among chronic viruses to promote their host fitness form the basis of virus–host mutualism. This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.

A Ring-Based Cybersecurity Architecture for Critical Infrastructure

A defense-in-depth (DID) approach for securing critical information infrastructure has been a common method used in cybersecurity. However, holistic design guidelines are lacking which precludes organizations from adopting them. Therefore, this paper sets out to outline and detail a holistic framework using ring-based nested network zone architecture for the design and implementation of highly secured networked environments. The proposed cybersecurity architecture framework offers a structural design for holistically designed N-tier system architectures. Several implementation options, including zoning perimeters, are suggested as being capable of offering different security capability levels by trading off amongst various security aspects. Also, the proposed architecture allows adaptability in implementations for various real-world networks. This paper also proposes an attack-hops verification approach to evaluate the architectural design.

Safety evaluation of truss structures using nested discrete Bayesian networks

Truss structures have been widely adopted for civil structures such as long-span buildings and bridges. An actual truss system is usually statically indeterminate having numerous members and high redundancy. It is practically difficult to evaluate the truss safety through traditional reliability-based approaches in view of complex failure modes and uncertainties. Moreover, monitoring data are generally insufficient in reality due to limited sensors under cost consideration. Therefore, a nested discrete Bayesian network has been developed for safety evaluation of truss structures. A concept of member risk coefficient is first proposed based on the mechanical relationship between load effects and member resistance. According to the coefficients of all members, member risk sequences are found as the basis for establishing the topology of a member-level Bayesian network. Each network node represents a truss member and a nodal variable having three states: elasticity, plasticity, and failure. Two relevant member nodes are connected by a directed edge whose causality strength is expressed by a conditional probability table. Meanwhile, a system-level network topology is established to reflect the effects of member states on the truss system. The system is assigned with a node having two states: safety and failure. The directed edge of each member node directly points to the system node. Then, the two networks are combined to form a nested network topology. By this means, direct topology learning is avoided in order to find rational and concise topologies satisfying the mechanical characteristics of civil structures. After that, the conditional probability tables for the nested network are obtained through parameter learning on complete numerical observation data. The data acquirement procedure takes into account uncertainties by defining the randomness of cross-sectional areas and external loads. With the conditional probability tables, the nested network is ready for use. When new evidence from limited monitored members is input into the nested network, the state probabilities of the other members, as well as the system, are simultaneously updated using exact inference algorithms. The inference ability using insufficient information well accords with the demand of engineering practice. Finally, the proposed method has been successfully verified against both numerical and experimental truss structures. It was found that the network estimations could be further confirmed with more evidence.