[Delphi-Study: Evaluation of the new job profile "Chief Digital Officer" in German hospitals].

Introduction: The main objective of the present study was to analyze the new job profile "Chief Digital Officer (CDO) in German hospitals". Here, best practices for the introduction of the job profile should be determined and the need for a CDO position to execute the digital transformation should be evaluated.

Methods: A standardized three-stage online Delphi process (expert consensus finding) followed by a roundtable discussion was performed, including 16 experts.

Synthetic minichromosomes in plants: past, present, and promise.

The status of engineered mini-chromosomes/artificial chromosomes/synthetic chromosomes in plants is summarized. Their promise is that they provide a means to accumulate foreign genes on an independent entity other than the normal chromosomes, which would facilitate stacking of novel traits in a way that would not be linked to endogenous genes and that would facilitate transfer between lines. Centromeres in plants are epigenetic, and therefore the isolation of DNA underlying centromeres and reintroduction into plant cells will not establish a functional kinetochore, which obviates this approach for in vitro assembly of plant artificial chromosomes.

Measuring autism with the ADOS-2 using a bifactor model.

The measurement of autism characteristics can be challenging due to variability of social impairments and restricted and repetitive behaviors or interests (RRBs). Psychometrically strong measures such as the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) can improve our capacity for thorough autism assessment. The conceptualization of the ADOS-2 has been shaped by research exploring the structure of its items, which evaluate autism traits associated with social affect and RRBs.

Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.

We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group-tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution.