Case Report: Multidrug Resistant Raoultella ornithinolytica in a Septicemic Calf

Sepsis is a frequent life-threatening condition in young calves, requiring rapid broad spectrum and bactericidal therapy to maximize survival chances. Few studies have identified and characterized bacteria involved in sepsis in calves. This report demonstrates the involvement of a multidrug resistant , an emerging pathogen in human medicine, in a calf with suspected sepsis. was identified by MALDI-TOF MS from blood cultures of a critically ill calf. Susceptibility testing showed phenotypic resistance against ampicillin, gentamicin, potentiated sulphonamides, streptomycin, tetracyclines and intermediate susceptibility for enrofloxacin. Whole genome sequencing confirmed identification as and the multidrug resistant character of the isolate. Antimicrobial resistance genes acting against aminoglycosides, beta-lactam antibiotics, fosfomycin, quinolones, sulphonamides, trimethoprim and tetracyclines were found. The calf recovered after empirical parenteral therapy with enrofloxacin and sodium penicillin for seven days. Ancillary therapy consisted of fluid therapy, ketoprofen and doxapram hydrochloride. To the authors’ knowledge, this is the first report characterizing a multidrug resistant isolate from blood culture in cattle. It is currently unknown whether animals and farms may act as reservoirs for multidrug resistant&nbsp;strains.</p

A Framework of Map Comparison Methods to Evaluate Geosimulation Models from a Geospatial Perspective

Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in the model results. Such evaluation requires a geospatial perspective; it is not sufficient if the micro-level interactions are realistic. Importantly the interactions should be such that the meso and macro level patterns that emerge from the model are realistic. In recent years, a host of map comparison methods have been developed that address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess the breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to the whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press