We consider parameter-driven models of time series of counts, where the observations are assumed to arise from a Poisson distribution with a mean changing over time according to a latent process. Estimation of these models is carried out within a Bayesian framework using data augmentation and Markov chain Monte Carlo methods. We suggest a new auxiliary mixture sampler, which possesses a Gibbsian transition kernel, where we draw from full conditional distributions belonging to standard distribution families only. Emphasis lies on application to state space modelling of time series of counts, but we show that auxiliary mixture sampling may be applied to a wider range of parameter-driven models, including random-effects models and panel data models based on the Poisson distribution.

Developmental processes, such as angiogenesis, are associated with a constant remodeling of the actin cytoskeleton in response to different mechanical stimuli. The mechanosensitive transcription factors MRTF-A (MKL1) and YAP (also known as YAP1) are important mediators of this challenging adaptation process. However, it is as yet unknown whether both pathways respond in an identical or in a divergent manner to a given microenvironmental guidance cue. Here, we use a micropatterning approach to dissect single aspects of cellular behavior in a spatiotemporally controllable setting. Using the exemplary process of angiogenesis, we show that cell–cell contacts and adhesive surface area are shared regulatory parameters of MRTF and YAP on rigid 2D surfaces. By analyzing MRTF and YAP under laminar flow conditions and during cell migration on dumbbell-shaped microstructures, we demonstrate that they exhibit different translocation kinetics. In conclusion, our work promotes the application of micropatterning techniques as a cell biological tool to study mechanosensitive signaling in the context of angiogenesis.

Rapid and reliable fungal identification is crucial to delineate infectious diseases, and to establish appropriate treatment for onychomycosis. Compared to conventional diagnostic methods, molecular techniques are faster and feature higher accuracy in fungal identification. However, in current clinical practice, molecular mycology is not widely available, and its practical applicability is still under discussion. This study summarizes the results of 16,094 consecutive nail specimens with clinical suspicion of onychomycosis. We performed PCR/sequencing on all primary nail specimens for which conventional mycological diagnostics remained inconclusive. In specimens with a positive direct microscopy but negative or contaminated culture, molecular mycology proved superior and specified a fungal agent in 65% (587/898). In 75% (443/587), the identified pathogen was a dermatophyte. Positive cultures for dermatophytes, yeasts and non-dermatophyte molds (NDMs) were concordant with primary-specimen-DNA PCR/sequencing in 83% (10/12), 34% (22/65) and 45% (76/169), respectively. Among NDMs, agreement was high for Fusarium spp. (32/40; 80%), but low for Penicillium spp. (5/25; 20%) and Alternaria spp. (1/20; 5%). This study underlines the improvement in diagnostic yield by fungal primary-specimen-DNA PCR/sequencing in the event of a negative or contaminated culture, as well as its significance for the diagnosis of dermatophyte and non-dermatophyte onychomycosis. Molecular mycology methods like PCR and DNA sequencing should complement conventional diagnostics in cases of equivocal findings, suspected NDM onychomycosis or treatment-resistant nail pathologies.