Bayesian cell therapy process optimization

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Data driven prediction of dairy cattle lifetime production and its use as a guideline to select surplus youngstock

The lifetime production of dairy cows is a complex trait influenced not only by genetics, but also by the environment in which a cow lives and the management practices of the farmer. Moreover, these influential factors show complex interactions with each other, making it difficult to reliably predict the lifetime production of individual animals at birth. However, since well managed dairy farms often have a surplus of youngstock, reliable lifetime production predictions would offer the opportunity to make more substantiated decisions when selecting calves or heifers to sell. Therefore, using data from Dutch herds, we constructed a data set capturing information on genetics, environment and management practices to develop multiple machine learning models capable of predicting the lifetime production of dairy cattle soon after birth. We found that a coupling of trends observed at the country level with farm-specific models largely outperforms off-the-shelf approaches. At birth, our best model could explain up to 47% of the variance in lifetime production, a considerable improvement in comparison with linear regression on the breeding values supplemented with the average lifetime production at farm level, which could only explain 21.7% of the variance in lifetime production. Moreover, we demonstrated surplus youngstock selection according to our model could more than double the surplus animal selection effect in comparison with the benchmark methodology, offering opportunities to increase the average (future) potential lifetime production of the retained heifers significantly. Assuming a static 20% surplus liveborn heifer scenario and random surplus animal selection as the default, our best model for surplus animal selection resulted in a 9.4% greater lifetime production in the retained animals compared with the current Dutch average lifetime production.

Quantifying the hydrodynamic stress for bioprocesses

Hydrodynamic stress is an influential physical parameter for various bioprocesses, affecting the performance and viability of the living organisms. However, different approaches are in use in various computational and experimental studies to calculate this parameter (including its normal and shear subcomponents) from velocity fields without a consensus on which one is the most representative of its effect on living cells. In this letter, we investigate these different methods with clear definitions and provide our suggested approach which relies on the principal stress values providing a maximal distinction between the shear and normal components. Furthermore, a numerical comparison is presented using the computational fluid dynamics simulation of a stirred and sparged bioreactor. It is demonstrated that for this specific bioreactor, some of these methods exhibit quite similar patterns throughout the bioreactor-therefore can be considered equivalent-whereas some of them differ significantly.

Quantifying agonistic interactions between group-housed animals to derive social hierarchies using computer vision: a case study with commercially group-housed rabbits

In recent years, computer vision has contributed significantly to the study of farm animal behavior. In complex environments such as commercial farms, however, the automated detection of social behavior and specific interactions between animals can be improved. The present study addresses the automated detection of agonistic interactions between caged animals in a complex environment, relying solely on computer vision. An automated pipeline including group-level temporal action segmentation, object detection, object tracking and rule-based action classification for the detection of agonistic interactions was developed and extensively validated at a level unique in the field. Comparing with observations made by human observers, our pipeline reaches 77% precision and 85% recall using a 5-min tolerance interval for the detection of agonistic interactions. Results obtained using this pipeline allow to construct time-dependent socio-matrices of a group of animals and derive metrics on the dominance hierarchy in a semi-automated manner. Group-housed breeding rabbits (does) with their litters in commercial farms are the main use-case in this work, but the idea is probably also applicable to other social farm animals.

SmartWoodID-an image collection of large end-grain surfaces to support wood identification systems

Wood identification is a key step in the enforcement of laws and regulations aimed at combatting illegal timber trade. Robust wood identification tools, capable of distinguishing a large number of timbers, depend on a solid database of reference material. Reference material for wood identification is typically curated in botanical collections dedicated to wood consisting of samples of secondary xylem of lignified plants. Specimens from the Tervuren Wood Collection, one of the large institutional wood collections around the world, are used as a source of tree species data with potential application as timber. Here, we present SmartWoodID, a database of high-resolution optical scans of the end-grain surfaces enriched with expert wood anatomical descriptions of macroscopic features. These can serve as annotated training data to develop interactive identification keys and artificial intelligence for computer vision-based wood identification. The first edition of the database consists of images of 1190 taxa, with a focus on potential timber species from the Democratic Republic of the Congo with at least four different specimens per species included. Database URL https://hdl.handle.net/20.500.12624/SmartWoodID_first_edition.

Improved wood species identification based on multi-view imagery of the three anatomical planes

BACKGROUND

The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance.

RESULTS

We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy.

CONCLUSIONS

Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.

Uncovering New Insights and Misconceptions on the Effectiveness of Phosphate Solubilizing Rhizobacteria in Plants: A Meta-Analysis

As the awareness on the ecological impact of chemical phosphate fertilizers grows, research turns to sustainable alternatives such as the implementation of phosphate solubilizing bacteria (PSB), which make largely immobile phosphorous reserves in soils available for uptake by plants. In this review, we introduce the mechanisms by which plants facilitate P-uptake and illustrate how PSB improve the bioavailability of this nutrient. Next, the effectiveness of PSB on increasing plant biomass and P-uptake is assessed using a meta-analysis approach. Our review demonstrates that improved P-uptake does not always translate in improved plant height and biomass. We show that the effect of PSB on plants does not provide an added benefit when using bacterial consortia compared to single strains. Moreover, the commonly reported species for P-solubilization, Bacillus spp. and Pseudomonas spp., are outperformed by the scarcely implemented Burkholderia spp. Despite the similar responses to PSB in monocots and eudicots, species responsiveness to PSB varies within both clades. Remarkably, the meta-analysis challenges the common belief that PSB are less effective under field conditions compared to greenhouse conditions. This review provides innovative insights and identifies key questions for future research on PSB to promote their implementation in agriculture.

Shifts in the rhizobiome during consecutive in planta enrichment for phosphate-solubilizing bacteria differentially affect maize P status

Phosphorus (P) is despite its omnipresence in soils often unavailable for plants. Rhizobacteria able to solubilize P are therefore crucial to avoid P deficiency. Selection for phosphate-solubilizing bacteria (PSB) is frequently done in vitro; however, rhizosphere competence is herein overlooked. Therefore, we developed an in planta enrichment concept enabling simultaneous microbial selection for P-solubilization and rhizosphere competence. We used an ecologically relevant combination of iron- and aluminium phosphate to select for PSB in maize (Zea mays L.). In each consecutive enrichment, plant roots were inoculated with rhizobacterial suspensions from plants that had grown in substrate with insoluble P. To assess the plants' P statuses, non-destructive multispectral imaging was used for quantifying anthocyanins, a proxy for maize's P status. After the third consecutive enrichment, plants supplied with insoluble P and inoculated with rhizobacterial suspensions showed a P status similar to plants supplied with soluble P. A parallel metabarcoding approach uncovered that the improved P status in the third enrichment coincided with a shift in the rhizobiome towards bacteria with plant growth-promoting and P-solubilizing capacities. Finally, further consecutive enrichment led to a functional relapse hallmarked by plants with a low P status and a second shift in the rhizobiome at the level of Azospirillaceae and Rhizobiaceae.

Respiratory CO2 Combined With a Blend of Volatiles Emitted by Endophytic Serendipita Strains Strongly Stimulate Growth of Arabidopsis Implicating Auxin and Cytokinin Signaling

Rhizospheric microorganisms can alter plant physiology and morphology in many different ways including through the emission of volatile organic compounds (VOCs). Here we demonstrate that VOCs from beneficial root endophytic Serendipita spp. are able to improve the performance of in vitro grown Arabidopsis seedlings, with an up to 9.3-fold increase in plant biomass. Additional changes in VOC-exposed plants comprised petiole elongation, epidermal cell and leaf area expansion, extension of the lateral root system, enhanced maximum quantum efficiency of photosystem II (Fv/Fm), and accumulation of high levels of anthocyanin. Notwithstanding that the magnitude of the effects was highly dependent on the test system and cultivation medium, the volatile blends of each of the examined strains, including the references S. indica and S. williamsii, exhibited comparable plant growth-promoting activities. By combining different approaches, we provide strong evidence that not only fungal respiratory CO2 accumulating in the headspace, but also other volatile compounds contribute to the observed plant responses. Volatile profiling identified methyl benzoate as the most abundant fungal VOC, released especially by Serendipita cultures that elicit plant growth promotion. However, under our experimental conditions, application of methyl benzoate as a sole volatile did not affect plant performance, suggesting that other compounds are involved or that the mixture of VOCs, rather than single molecules, accounts for the strong plant responses. Using Arabidopsis mutant and reporter lines in some of the major plant hormone signal transduction pathways further revealed the involvement of auxin and cytokinin signaling in Serendipita VOC-induced plant growth modulation. Although we are still far from translating the current knowledge into the implementation of Serendipita VOCs as biofertilizers and phytostimulants, volatile production is a novel mechanism by which sebacinoid fungi can trigger and control biological processes in plants, which might offer opportunities to address agricultural and environmental problems in the future.

A Combined RNA Preservation and Extraction Protocol for Gene Expression Studies in Cacao Beans

Despite the high economic importance of cacao beans, few RNA-based studies have been conducted on this plant material and hence no optimal RNA-extraction has been reported. Moreover, extraction of high-quality RNA from recalcitrant cacao bean tissue has shown many difficulties and requires optimization. Furthermore, cacao beans are mostly found at remote and under-resourced locations, which pressures the outsourcing of such analysis and thereby demands RNA-stable preservation and transportation of cacao beans. This study aims to select an appropriate RNA extraction and preservation/transportation method for cacao beans. For this purpose, three sample homogenization and five extraction protocols on cacao beans were compared. In addition, 13 preservation conditions-differing in tissue crushing degree, preservation method, duration, and temperature-were compared and evaluated. A comparative analysis revealed that CTAB-based homogenization and extraction outcompeted all tested commercial protocols in RNA yield and integrity, respectively. Preservation at -80°C affected RNA quality the least, whereas freeze-drying was most suitable for transportation at room temperature for maximum 1 week. The cacao bean RNA obtained from the selected methods were compatible for downstream applications. The results of this study will facilitate on-field sampling and transportation of genetically sensitive cacao material prior to cacao bean transcriptomic studies. In addition, valuable insights on sample homogenization, extraction, preservation, and transportation have been provided, which is of interest to every plant geneticist.

Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing

Black carbon is often used as an indicator for combustion-related air pollution. In urban environments, on-road black carbon concentrations have a large spatial variability, suggesting that the personal exposure of a cyclist to black carbon can heavily depend on the route that is chosen to reach a destination. In this paper, we describe the development of a cyclist routing procedure that minimizes personal exposure to black carbon. Firstly, a land use regression model for predicting black carbon concentrations in an urban environment is developed using mobile monitoring data, collected by cyclists. The optimal model is selected and validated using a spatially stratified cross-validation scheme. The resulting model is integrated in a dedicated routing procedure that minimizes personal exposure to black carbon during cycling. The best model obtains a coefficient of multiple correlation of R=0.520. Simulations with the black carbon exposure minimizing routing procedure indicate that the inhaled amount of black carbon is reduced by 1.58% on average as compared to the shortest-path route, with extreme cases where a reduction of up to 13.35% is obtained. Moreover, we observed that the average exposure to black carbon and the exposure to local peak concentrations on a route are competing objectives, and propose a parametrized cost function for the routing problem that allows for a gradual transition from routes that minimize average exposure to routes that minimize peak exposure.

Green leaf volatile production by plants: a meta-analysis

666 I. Introduction 667 II. Biosynthesis 667 III. Meta-analysis 669 IV. The type of stress influences the total amount of GLVs released 669 V. Herbivores can modulate the wound-induced release of GLVs 669 VI. Fungal infection greatly induces GLV production 672 VII. Monocots and eudicots respond differentially to different types of stress 673 VIII. The type of stress does not influence the proportion of GLVs per chemical class 673 IX. The type of stress does influence the isomeric ratio within each chemical class 674 X. GLVs: from signal perception to signal transduction 676 XI. GLVs influence the C/N metabolism 677 XII. Interaction with plant hormones 678 XIII. General conclusions and unanswered questions 678 Acknowledgements 679 References 679 SUMMARY: Plants respond to stress by releasing biogenic volatile organic compounds (BVOCs). Green leaf volatiles (GLVs), which are abundantly produced across the plant kingdom, comprise an important group within the BVOCs. They can repel or attract herbivores and their natural enemies; and they can induce plant defences or prime plants for enhanced defence against herbivores and pathogens and can have direct toxic effects on bacteria and fungi. Unlike other volatiles, GLVs are released almost instantly upon mechanical damage and (a)biotic stress and could thus function as an immediate and informative signal for many organisms in the plant's environment. We used a meta-analysis approach in which data from the literature on GLV production during biotic stress responses were compiled and interpreted. We identified that different types of attackers and feeding styles add a degree of complexity to the amount of emitted GLVs, compared with wounding alone. This meta-analysis illustrates that there is less variation in the GLV profile than we presumed, that pathogens induce more GLVs than insects and wounding, and that there are clear differences in GLV emission between monocots and dicots. Besides the meta-analysis, this review provides an update on recent insights into the perception and signalling of GLVs in plants.

Congolese Rhizospheric Soils as a Rich Source of New Plant Growth-Promoting Endophytic Piriformospora Isolates

In the last decade, there has been an increasing focus on the implementation of plant growth-promoting (PGP) organisms as a sustainable option to compensate for poor soil fertility conditions in developing countries. Trap systems were used in an effort to isolate PGP fungi from rhizospheric soil samples collected in the region around Kisangani in the Democratic Republic of Congo. With sudangrass as a host, a highly conducive environment was created for sebacinalean chlamydospore formation inside the plant roots resulting in a collection of 51 axenically cultured isolates of the elusive genus Piriformospora (recently transferred to the genus Serendipita). Based on morphological data, ISSR fingerprinting profiles and marker gene sequences, we propose that these isolates together with Piriformospora williamsii constitute a species complex designated Piriformospora (= Serendipita) 'williamsii.' A selection of isolates strongly promoted plant growth of in vitro inoculated Arabidopsis seedlings, which was evidenced by an increase in shoot fresh weight and a strong stimulation of lateral root formation. This isolate collection provides unprecedented opportunities for fundamental as well as translational research on the Serendipitaceae, a family of fungal endophytes in full expansion.

Milk fatty acids as possible biomarkers to diagnose hyperketonemia in early lactation

The aim of this study was to assess the potential of milk fatty acids as diagnostic tool for hyperketonemia of 93 dairy cows in a 3×2 factorial arrangement. Cows were fed a glucogenic or lipogenic diet and originally were intended to be subjected to a 0-, 30-, or 60-d dry period. Nevertheless, some of the cows, which were intended for inclusion in the 0-d dry period group, dried off spontaneously. Milk was collected in wk 2, 3, 4, and 8 of lactation for milk fat analysis. Blood was sampled from wk 2 to 8 after parturition for β-hydroxybutyrate (BHBA) analysis. Cases were classified into 2 groups: hyperketonemia (BHBA ≥1.2mmol/L) and nonhyperketonemia (BHBA <1.2mmol/L). Concentrations of 45 milk fatty acids and ratios of anteiso C15:0-to-anteiso C17:0 and C18:1 cis-9-to-C15:0 were subjected to a logistic regression analysis (stepwise forward method). The milk fat C18:1 cis-9-to-C15:0 ratio revealed the most discriminating factor for diagnosis of hyperketonemia. Ninety percent of nonhyperketonemia cases showed a milk fat C18:1 cis-9-to-C15:0 ratio of 40 or lower, whereas 70% of cows suffering from hyperketonemia showed milk fat C18:1 cis-9-to-C15:0 ratios exceeding 40. Additionally, cows with a milk fat ratio C18:1 cis-9-to-C15:0 of at least 45 in wk 2 after parturition had about 50% chance to encounter blood plasma BHBA values of 1.2mmol/L or more during the first 8 wk of lactation. Of the cows not suffering from hyperketonemia during the first 2 mo of lactation, only 9% exceeded this wk 2 threshold. Practical implementation requires routine analysis of both milk fatty acids, which currently is lacking for C15:0. The inclusion of other variables, such as test-day information and a more frequent sampling protocol should be considered to further improve diagnostic performance of this biomarker.