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The expanding field of synthetic biology requires diversification of microbial chassis to expedite the transition from a fossil fuel-dependent economy to a sustainable bioeconomy. Relying exclusively on established model organisms such as Escherichia coli and Saccharomyces cerevisiae may not suffice to drive the profound advancements needed in biotechnology. In this context, Cupriavidus necator, an extraordinarily versatile microorganism, has emerged as a potential catalyst for transformative breakthroughs in industrial biomanufacturing. This comprehensive book chapter offers an in-depth review of the remarkable technological progress achieved by C. necator in the past decade, with a specific focus on the fields of molecular biology tools, metabolic engineering, and innovative fermentation strategies. Through this exploration, we aim to shed light on the pivotal role of C. necator in shaping the future of sustainable bioprocessing and bioproduct development., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Fruit texture is a priority trait that guarantees the long-term economic sustainability of the cranberry industry through value-added products such as sweetened dried cranberries (SDCs). To develop a standard methodology to measure texture, we conducted a comparative analysis of 22 textural traits using five different methods under both harvest and postharvest conditions in 10 representative cranberry cultivars. A set of textural traits from the 10%-strain compression and puncture methods were identified that differentiate between cultivars primarily based on hardness/stiffness and elasticity properties. The complementary use of both methodologies allowed for a detailed evaluation by capturing the effect of key texture-determining factors such as structure, flesh, and skin. Furthermore, the high effectiveness of this approach in different conditions and its ability to capture high phenotypic variation in cultivars highlights its great potential for applicability in various areas of the value chain and research. Therefore, this study provides an informed reference for unifying future efforts to enhance cranberry fruit texture and quality., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Journal of Texture Studiespublished by Wiley Periodicals LLC.)

Mangroves are impacted by multiple environmental stressors, including sea level rise, erosion, and plastic pollution. Thus, mangrove soil may be an excellent source of as yet unknown plastic-transforming microorganisms. Here, we assess the impact of polyethylene terephthalate (PET) particles and seawater intrusion on the mangrove soil microbiome and report an enrichment culture experiment to artificially select PET-transforming microbial consortia. The analysis of metagenome-assembled genomes of two bacterial consortia revealed that PET catabolism can be performed by multiple taxa, of which particular species harbored putative novel PET-active hydrolases. A key member of these consortia (Mangrovimarina plasticivorans gen. nov., sp. nov.) was found to contain two genes encoding monohydroxyethyl terephthalate hydrolases. This study provides insights into the development of strategies for harnessing soil microbiomes, thereby advancing our understanding of the ecology and enzymology involved in microbial-mediated PET transformations in marine-associated systems., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: For nearly two centuries, cranberry ( Vaccinium macrocarpon Ait.) breeders have improved fruit quality and yield by selecting traits on fruiting stems, termed "reproductive uprights." Crop improvement is accelerating rapidly in contemporary breeding programs due to modern genetic tools and high-throughput phenotyping methods, improving selection efficiency and accuracy., Methods: We conducted genotypic evaluation on 29 primary traits encompassing fruit quality, yield, and chemical composition in two full-sib cranberry breeding populations- CNJ02 ( n = 168) and CNJ04 ( n = 67)-over 3 years. Genetic characterization was further performed on 11 secondary traits derived from these primary traits., Results: For CNJ02 , 170 major quantitative trait loci (QTL; R 2 ≥ 0.10) were found with interval mapping, 150 major QTL were found with model mapping, and 9 QTL were found to be stable across multiple years. In CNJ04 , 69 major QTL were found with interval mapping, 81 major QTL were found with model mapping, and 4 QTL were found to be stable across multiple years. Meta-QTL represent stable genomic regions consistent across multiple years, populations, studies, or traits. Seven multi-trait meta-QTL were found in CNJ02 , one in CNJ04 , and one in the combined analysis of both populations. A total of 22 meta-QTL were identified in cross-study, cross-population analysis using digital traits for berry shape and size (8 meta-QTL), digital images for berry color (2 meta-QTL), and three-study cross-analysis (12 meta-QTL)., Discussion: Together, these meta-QTL anchor high-throughput fruit quality phenotyping techniques to traditional phenotyping methods, validating state-of-the-art methods in cranberry phenotyping that will improve breeding accuracy, efficiency, and genetic gain in this globally significant fruit crop., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Maule, Loarca, Diaz-Garcia, Lopez-Moreno, Johnson-Cicalese, Vorsa, Iorizzo, Neyhart and Zalapa.)

Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson's r 2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Efrain Torres-Lomas et al.)