Background: Mathematical and computational models are widely used for examining transmission, pathogenicity and propagation of infectious diseases. Software implementation of such models and their accompanied modelling tools do not adhere to well established software engineering principles. These principles include both modularity and clear separation of concerns that can promote reproducibility and reusability by other researchers. On the contrary the software written for epidemiology is monolithic, highly coupled and severely heterogeneous. This reality ultimately makes these computational models hard to study and to reuse, both because of the programming competence required and because of the incompatibility of the different approaches involved. Our goal with Kendrick is to simplify the creation of epidemiological models through a unified Domain-Specific Language for epidemiology that can support a variety of modelling and simulation approaches classically used in the field. This goal can be achieved by promoting reproducibility and reuse with modular modelling abstractions.Results: We show through several examples how our modular abstractions and tools can reproduce uniformly complex mathematical and computational models of epidemics, despite being simulated by different methods. This is achieved without requiring sophisticated programming skills from the part of the user. We then successfully validate each kind of simulation through statistical analysis between the time series generated and the known theoretical expectations.Conclusions: Kendrick is one of the few DSLs for epidemiology that does not burden its users with implementation details or expecting sophisticated programming skills. It is also currently the only language for epidemiology that supports modularity through clear separation of concerns that promote reproducibility and reuse. Kendrick's wider adoption and further development from the epidemiological community could boost research productivity in epidemiology by allowing researchers to easily reproduce and reuse each other's software models and simulations. The tool can also be used by people who are not necessarily epidemiology modelers.

The selenium-binding protein 1 (SELENBP1) has been reported to be up-regulated in the prefrontal cortex (PFC) of schizophrenia patients in postmortem reports. However, no causative link between SELENBP1 and schizophrenia has yet been established. Here, we provide evidence linking the upregulation of SELENBP1 in the PFC of mice with the negative symptoms of schizophrenia. We verified the levels of SELENBP1 transcripts in postmortem PFC brain tissues from patients with schizophrenia and matched healthy controls. We also generated transgenic mice expressing human SELENBP1 (hSELENBP1 Tg) and examined their neuropathological features, intrinsic firing properties of PFC 2/3-layer pyramidal neurons, and frontal cortex (FC) electroencephalographic (EEG) responses to auditory stimuli. Schizophrenia-like behaviors in hSELENBP1 Tg mice and mice expressing Selenbp1 in the FC were assessed. SELENBP1 transcript levels were higher in the brains of patients with schizophrenia than in those of matched healthy controls. The hSELENBP1 Tg mice displayed negative endophenotype behaviors, including heterotopias- and ectopias-like anatomical deformities in upper-layer cortical neurons and social withdrawal, deficits in nesting, and anhedonia-like behavior. Additionally, hSELENBP1 Tg mice exhibited reduced excitabilities of PFC 2/3-layer pyramidal neurons and abnormalities in EEG biomarkers observed in schizophrenia. Furthermore, mice overexpressing Selenbp1 in FC showed deficits in sociability. These results suggest that upregulation of SELENBP1 in the PFC causes asociality, a negative symptom of schizophrenia.