Impact of Cultivation Parameters on Cell Physiology of Limosilactobacillus reuteri

Optimisation of cultivation conditions in the industrial production of probiotics is crucial to reach a high-quality product with high probiotic functionality. The production process includes a fermentation step to produce biomass, accompanied by centrifugation to concentrate the cells. Subsequently, the cells are treated with stabilising solutions (lyoprotectants) before they are subjected to freezing. The frozen cell pellets can be subjected to freeze-drying to yield a dried final product. The probiotic product needs to withstand adverse environmental conditions both during production and after consumption (gastro-intestinal tract). The objective of this study was to elucidate the cellular response to various production process parameters and evaluate their influence on freeze-drying tolerance. In addition, the stability and probiotic activity of freeze-drying product was studied. Parameters such as temperature, pH, oxygen, media components during fermentation, and the pre-formulation hold time prior to freeze-drying were in focus. Furthermore, flow cytometry-based descriptors of bacterial morphology were evaluated for their potential correlation with process-relevant output parameters and physiological fitness during cultivation to avoid suboptimal growth. Additionally, a pipeline was developed for online flow cytometry combined with automated data processing using the kmeans clustering algorithm, which is a promising process analytical technology tool. The effects of temperature, initial pH, and oxygen levels on cell growth and cell size distributions of Limosilactobacillus reuteri DSM 17938 were investigated using multivariate flow cytometry. Morphological heterogeneities were observed under non-optimal growth conditions, with low temperature, high initial pH, and high oxygen levels triggering changes in morphology towards cell chain formation. High-growth pattern characterised by smaller cell sizes and decreased population heterogeneity was observed using the pulse width distribution parameter. This parameter can be used to distinguish larger cells from smaller cells and to separate singlets from doublets (i.e., single cells from aggregated cells). Although, oxygen is known to inhibit growth in L. reuteri, controlled oxygen supply resulted in noticeable effect on the cell metabolism, in a higher degree of unsaturated fatty acids in the cell, and improved freeze-drying stress tolerance. Another important component that was examined was the addition of exogeneous fatty acid source in the form of Tween 80. A chemically defined minimal medium was developed, with 14 amino acids identified as essential for growth. The addition of Tween 80 to the medium improved biomass yield, growth rate, and shortened cultivation time. L. reuteri DSM 17938 may not be able to efficiently synthesise unsaturated fatty acid without an exogenous fatty acid source, but this requires further investigation. Lastly, the pre-formulation hold time during the manufacture of probiotics was found to significantly affect long-term stability, with direct freeze samples showing better freeze-drying stability compared to those subjected to rest for 3 h incubation at room temperature. These findings suggest that an optimised production process and formulation of agents can lead to the successful production of high-quality probiotics with excellent stability

Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach

The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datasets. In this paper, we aim to alleviate these issues by learning generalizable inductive biases from the nodes' local subgraph and transfer them for faster learning over new subgraphs with a disjoint set of nodes, edges, and labels in a few-shot setting. We introduce a novel method, Hyperbolic GRAph Meta Learner (H-GRAM), that, for the tasks of node classification and link prediction, learns transferable information from a set of support local subgraphs in the form of hyperbolic meta gradients and label hyperbolic protonets to enable faster learning over a query set of new tasks dealing with disjoint subgraphs. Furthermore, we show that an extension of our meta-learning framework also mitigates the scalability challenges seen in HNNs faced by existing approaches. Our comparative analysis shows that H-GRAM effectively learns and transfers information in multiple challenging few-shot settings compared to other state-of-the-art baselines. Additionally, we demonstrate that, unlike standard HNNs, our approach is able to scale over large graph datasets and improve performance over its Euclidean counterparts.Comment: Accepted to NeurIPS 2023. 14 pages of main paper, 5 pages of supplementar