Genome context influences evolutionary flexibility of nearly identical type III effectors in two phytopathogenic Pseudomonads

Integrative Conjugative Elements (ICEs) are replicons that can insert and excise from chromosomal locations in a site specific manner, can conjugate across strains, and which often carry a variety of genes useful for bacterial growth and survival under specific conditions. Although ICEs have been identified and vetted within certain clades of the agricultural pathogen Pseudomonas syringae, the impact of ICE carriage and transfer across the entire P. syringae species complex remains underexplored. Here we identify and vet an ICE (PmaICE-DQ) from P. syringae pv. maculicola ES4326, a strain commonly used for laboratory virulence experiments, demonstrate that this element can excise and conjugate across strains, and contains loci encoding multiple type III effector proteins. Moreover, genome context suggests that another ICE (PmaICE-AOAB) is highly similar in comparison with and found immediately adjacent to PmaICE-DQ within the chromosome of strain ES4326, and also contains multiple type III effectors. Lastly, we present passage data from in planta experiments that suggests that genomic plasticity associated with ICEs may enable strains to more rapidly lose type III effectors that trigger R-gene mediated resistance in comparison to strains where nearly isogenic effectors are not present in ICEs. Taken together, our study sheds light on a set of ICE elements from P. syringae pv. maculicola ES4326 and highlights how genomic context may lead to different evolutionary dynamics for shared virulence genes between strains.

Multimodal Provenance-based Analysis of Collaboration in Business Processes

Modeling business processes as a set of activities to accomplish goals naturally makes them be executed several times. Usually, such executions produce a large portion of provenance data in different formats such as text, audio, and video. Such a multiple-type nature gives origin to multimodal provenance data. Analyzing multimodal provenance data in an integrated form may be complex and error-prone when manually performed as it requires extracting information from free-text, audio, and video files. However, such an analysis may generate valuable insights into the business process. The present article presents MINERVA (Multimodal busINEss pRoVenance Analysis). This approach focuses on identifying improvements that can be implemented in business processes, as well as in collaboration analysis using multimodal provenance data. MINERVA was evaluated through a feasibility study that used data from a consulting company.

SEIMR/R-S. General Epidemic Simulation Model Multi-Infected States - Multi Sociodemographic Segments - Multi-Region Mobility. Theory

SEIMR/R-S corresponds to a generalized mathematical model of pandemics that enhances traditional, aggregated simulation models when considering inter-regional impacts in a macro region (conurbed); SEIMR/R-S also considers the impact of modeling the population divided into sociodemographic segments based on age and economic stratum (it is possible to include other dimensions, for example: ethnics, gender, … ). SEIMR/R-S is the core of the SEIMR/R-S/OPT epidemic management optimization model that determines optimal policies (mitigation and confinement) considering the spatial distribution of the population, segmented sociodemographically and multiple type of vaccines. The formulation of SEIMR/R-S/OPT is presented by Velasquez-Bermudez (2021a) that includes the modeling of the vaccination process. SEIMR/R-S can be understood and used by any epidemiologist, and/or physician, working with SIR, SEIR or similar simulation models, and by professionals working on the issue of public policies for epidemic control. Following the theory presented in this document, ITCM (Instituto Tecnologico de Ciudad Madero, México) implemented the SEIMR/R-S epidemic model in a JAVA program (Velasquez-Bermudez et. al, 2021). This program may be used by the organizations that considers the SEIMR/R-S will be useful for management the COVID-19 pandemic, it is presented by VelasquezBermudez et al. (2021).