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Abstract We have investigated the impact of recognized biogeographic barriers on genetic differentiation of grey box (Eucalyptus moluccana), a common and widespread tree species of the family Myrtaceae in eastern Australian woodlands, and its previously proposed four subspecies moluccana, pedicellata, queenslandica, and crassifolia. A range of phylogeographic analyses were conducted to examine the population genetic differentiation and subspecies genetic structure in E. moluccana in relation to biogeographic barriers. Slow evolving markers uncovering long term processes (chloroplast DNA) were used to generate a haplotype network and infer phylogeographic barriers. Additionally, fast evolving, hypervariable markers (microsatellites) were used to estimate demographic processes and genetic structure among five geographic regions (29 populations) across the entire distribution of E. moluccana. Morphological features of seedlings, such as leaf and stem traits, were assessed to evaluate population clusters and test differentiation of the putative subspecies. Haplotype network analysis revealed twenty chloroplast haplotypes with a main haplotype in a central position shared by individuals belonging to the regions containing the four putative subspecies. Microsatellite analysis detected the genetic structure between Queensland (QLD) and New South Wales (NSW) populations, consistent with the McPherson Range barrier, an east‐west spur of the Great Dividing Range. The substructure was detected within QLD and NSW in line with other barriers in eastern Australia. The morphological analyses supported differentiation between QLD and NSW populations, with no difference within QLD, yet some differentiation within NSW populations. Our molecular and morphological analyses provide evidence that several geographic barriers in eastern Australia, including the Burdekin Gap and the McPherson Range have contributed to the genetic structure of E. moluccana. Genetic differentiation among E. moluccana populations supports the recognition of some but not all the four previously proposed subspecies, with crassifolia being the most differentiated.

Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.

The prospect of including artificial intelligence (AI) in clinical decision-making is an exciting next step for some areas of healthcare. This article provides an analysis of the available kinds of AI systems, focusing on macro-level characteristics. This includes examining the strengths and weaknesses of opaque systems and fully explainable systems. Ultimately, the article argues that “grey box” systems, which include some combination of opacity and transparency, ought to be used in healthcare settings.