Outcomes of displaced mid-shaft radius/ulna fractures in children treated with the Rang method: a pilot study.

Pediatric forearm fractures of the radius and ulna are common. Previous literature suggests that conservatively managed mid-shaft radius and ulna forearm fractures have a remanipulation rate of 10-70%. The purpose of this study is to compare the re-displacement rate of closed displaced mid-shaft both-bone forearm fractures (BBFF) in children treated with closed reduction and casting with a loop and sling applied proximal to the fracture site (Rang method) compared with a standard sling.

The Data Portal "Chromosome Numbers of the Flora of Germany": Progress After Five Years, Recent Developments, and Future Strategies.

Chromosome numbers and information on ploidy level are of great importance for better understanding of plant evolution and taxonomy but also for species identification. Technical developments in flow cytometry dramatically improved the measurement of genome size and triggered a renewed interest in chromosome numbers. Web-based portals make these kind of data accessible for a wide audience in both academia and citizen science.

Community engagement in tuberculosis research: the EU-Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) experience.

Objectives: Community representatives are key to ensuring that tuberculosis (TB) research is relevant, culturally sensitive, and appropriate. For all trials (new drugs or treatment regimens, diagnostics, or vaccines) this can result in improvement of recruitment, retention, and adherence to the trial schedule. The early engagement of the community will, later in time, support the process of implementation of new policies designed for successful products.

Detection and annotation of plant organs from digitised herbarium scans using deep learning.

As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN.