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Mycobacterium abscessus, a rapidly growing nontuberculous mycobacterium, are increasingly present in soft tissue infections and chronic lung diseases, including cystic fibrosis, and infections are characterized by growth in neutrophil-rich environments. M. abscessus is observed as two distinct smooth and rough morphotypes. The environmental smooth morphotype initiates infection and has a relatively limited ability to activate neutrophils. The rough morphotype has increased virulence and immunogenicity. However, the neutrophil response to the rough morphotype has not been explored. Killing of the smooth and rough strains, including cystic fibrosis clinical isolates, was equivalent. Neutrophil uptake of M. abscessus was similar between morphotypes. Mechanistically, both rough and smooth morphotypes enhanced neutrophil reactive oxygen species generation but inhibition of NADPH oxidase activity did not affect M. abscessus viability. However, inhibition of phagocytosis and extracellular traps reduced killing of the smooth morphotype with lesser effects against the rough morphotype. Neutrophils treated with M. abscessus released a heat-labile mycobactericidal activity against the rough morphotype, but the activity was heat-tolerant against the smooth morphotype. Overall, M. abscessus stimulates ineffective neutrophil reactive oxygen species generation, and key mechanisms differ in killing of the smooth (phagocytosis-dependent, extracellular traps, and heat-tolerant secreted factor) and rough (extracellular traps and a heat-labile secreted factor) morphotypes. These studies represent an essential advancement in understanding the host response to M. abscessus, and help explain the recalcitrance of infection.

Process models are used by people for many different purposes. Depending on that purpose, users may look into process models in different ways. However, the current stream of research into process model comprehension does not explicitly consider the type of information that a user is seeking for. By failing to do so, attempts to improve the readability of process models may be lopsided at best. To overcome this situation, we propose a list of 17 so-called process model comprehension use cases. These capture the different types of information-seeking behavior of the users. We validated the list through interview and focus group studies, which included 24 participants from 8 organizations. Based on our findings, we present implications for researchers to re-investigate the comprehension topic. The use cases may also be beneficial for the development of modeling tools and process modelers to better support the user needs.

Certifying that a process model is aligned with the real process executions is perhaps the most desired feature a process model may have: aligned process models are crucial for organizations, since strategic decisions can be made easier on models instead of on plain data. In spite of its importance, the current algorithmic support for computing alignments is limited: either techniques that explicitly explore the model behavior (which may be worst-case exponential with respect to the model size), or heuristic approaches that cannot guarantee a solution, are the only alternatives. In this paper we propose a solution that sits right in the middle in the complexity spectrum of alignment techniques; it can always guarantee a solution, whose quality depends on the exploration depth used and local decisions taken at each step. We use linear algebraic techniques in combination with an iterative search which focuses on progressing towards a solution. The experiments show a clear reduction in the time required for reaching a solution, without sacrificing significantly the quality of the alignment obtained.