Background: Comprehensive studies on caregiver burden (CB) of persons caring for dementia patients differ methodologically and show variable results. Objective: Analysis of known and hypothesized factors of CB in home care of dementia patients. Metho

We describe a novel method of heavy tails estimation based on transformed score (t-score). Based on a new score moment method we derive the t-Hill estimator, which estimates the extreme value index of a distribution function with regularly varying tail. t-Hill estimator is distribution sensitive, thus it differs in e.g. Pareto and log-gamma case. Here, we study both forms of the estimator, i.e. t-Hill and t-lgHill. For both estimators we prove weak consistency in moving average settings as well as the asymptotic normality of t-lgHill estimator in iid setting. In cases of contamination with heavier tails than the tail of original sample, t-Hill outperforms several robust tail estimators, especially in small samples. A simulation study emphasizes the fact that the level of contamination is playing a crucial role. The larger the contamination, the better are the t-score moment estimates. The reason for this is the bounded t-score of heavy-tailed distributions (and, consequently, bounded influence functions of the estimators). We illustrate the developed methodology on a small sample data set of stake measurements from Guanaco glacier in Chile.

As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change point estimator. Our method also permits demography to be taken into account. It uses several summary statistics within a regression model fitted on suitable scenarios. Our proposed method is accurate, computationally fast, and provides a parsimonious solution by ensuring a type I error control against too many changes in the recombination rate. An application to human genome data suggests a good congruence between our estimated and experimentally identified hotspots. Our method is implemented in the R‐package LDJump, which is freely available at https://github.com/PhHermann/LDJump.

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.