The two faces of microorganisms in traditional brewing and the implications for no- and low-alcohol beers

The production of alcoholic beverages is intrinsically linked to microbial activity. This is because microbes such as yeast are associated with the production of ethanol and key sensorial compounds that produce desirable qualities in fermented products. However, the brewing industry and other related sectors face a step-change in practice, primarily due to the growth in sales of no- and low-alcohol (NoLo) alternatives to traditional alcoholic products. Here we review the involvement of microbes across the brewing process, including both their positive contributions and their negative (spoilage) effects. We also discuss the opportunities for exploiting microbes for NoLo beer production, as well as the spoilage risks associated with these products. For the latter, we highlight differences in composition and process conditions between traditional and NoLo beers and discuss how these may impact the microbial ecosystem of each product stream in relation to microbiological stability and final beer quality

Body composition in young female eating-disorder patients with severe weight loss and controls: evidence from the four-component model and evaluation of DXA

BACKGROUND/OBJECTIVES: Whether fat-free mass (FFM) and its components are depleted in eating-disorder (ED) patients is uncertain. Dual energy X-ray absorptiometry (DXA) is widely used to assess body composition in pediatric ED patients; however, its accuracy in underweight populations remains unknown. We aimed (1) to assess body composition of young females with ED involving substantial weight loss, relative to healthy controls using the four-component (4C) model, and (2) to explore the validity of DXA body composition assessment in ED patients. SUBJECTS/METHODS: Body composition of 13 females with ED and 117 controls, aged 10-18 years, was investigated using the 4C model. Accuracy of DXA for estimation of FFM and fat mass (FM) was tested using the approach of Bland and Altman. RESULTS: Adjusting for age, height and pubertal stage, ED patients had significantly lower whole-body FM, FFM, protein mass (PM) and mineral mass (MM) compared with controls. Trunk and limb FM and limb lean soft tissue were significantly lower in ED patients. However, no significant difference in the hydration of FFM was detected. Compared with the 4C model, DXA overestimated FM by 5 +/- 36% and underestimated FFM by 1 +/- 9% in ED patients. CONCLUSION: Our study confirms that ED patients are depleted not only in FM but also in FFM, PM and MM. DXA has limitations for estimating body composition in individual young female ED patients

Inside Australian Culture Legacies of Enlightenment Values

Structure and Style of the Book Getting inside Australian culture is a difficult intellectual undertaking and not always easy. Given the enormous complexity of how values have been installed through institutional processes and the narratives of ..

Artificial intelligence as supporting reader in breast screening: a novel workflow to preserve quality and reduce workload

Objective To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. Methods Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. Results Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). Conclusion The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree