Microbial community succession on developing lesions on human enamel

Dental caries is one of the most common diseases in the world. However, our understanding of how the microbial community composition changes in vivo as caries develops is lacking.An in vivo model was used in a longitudinal cohort study to investigate shifts in the microbial community composition associated with the development of enamel caries.White spot lesions were generated in vivo on human teeth predetermined to be extracted for orthodontic reasons. The bacterial microbiota on sound enamel and on developing carious lesions were identified using the Human Oral Microbe Identification Microarray (HOMIM), which permits the detection of about 300 of the approximate 600 predominant bacterial species in the oral cavity.After only seven weeks, 75% of targeted teeth developed white spot lesions (8 individuals, 16 teeth). The microbial community composition of the plaque over white spot lesions differed significantly as compared to sound enamel. Twenty-five bacterial taxa, including Streptococcus mutans, Atopobium parvulum, Dialister invisus, and species of Prevotella and Scardovia, were significantly associated with initial enamel lesions. In contrast, 14 bacterial taxa, including species of Fusobacterium, Campylobacter, Kingella, and Capnocytophaga, were significantly associated with sound enamel.The bacterial community composition associated with the progression of enamel lesions is specific and much more complex than previously believed. This investigation represents one of the first longitudinally-derived studies for caries progression and supports microbial data from previous cross-sectional studies on the development of the disease. Thus, the in vivo experiments of generating lesions on teeth destined for extraction in conjunction with HOMIM analyses represent a valid model to study succession of supragingival microbial communities associated with caries development and to study efficacy of prophylactic and restorative treatments

Mitigating Snow on Rooftop PV Systems for Higher Energy Yield and Safer Roofs

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Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems

Mitigating Snow on Rooftop PV Systems for Higher Energy Yield and Safer Roofs

publishedVersio

Selective and marked decrease of complement receptor C5aR2 in human thoracic aortic aneurysms: A dysregulation with potential inflammatory effects

Objective - The aetiology of thoracic aortic aneurysm (TAA) is largely unknown, but inflammation is likely to play a central role in the pathogenesis. In this present study, we aim to investigate the complement receptors in TAA. Methods - Aortic tissue and blood from 31 patients with non-syndromic TAA undergoing thoracic aortic repair surgery were collected. Aortic tissue and blood from 36 patients with atherosclerosis undergoing coronary artery bypass surgery or aortic valve replacement were collected and served as control material. The expression of the complement anaphylatoxin receptors C3aR1, C5aR1 and C5aR2 in aortic tissue were examined by quantitative RT-PCR and C5aR2 protein by immunohistochemistry. Colocalisation of C5aR2 to different cell types was analysed by immunofluorescence. Complement activation products C3bc and sC5b-9 were measured in plasma. Results - Compared with controls, TAA patients had substantial (73%) downregulated gene expression of C5aR2 as seen both at the mRNA (p=0.005) level and protein (p=0.03) level. In contrast, there were no differences in the expression of C3aR1 and C5aR1 between the two groups. Immunofluorescence examination showed that C5aR2 was colocalised to macrophages and T cells in the aortic media. There were no differences in the degree of systemic complement activation between the two groups. Conclusion - Our findings suggest downregulation of the C5aR2, regarded to act mainly anti-inflammatory, in electively operated TAA as compared with non-aneurysmatic aortas of patients with aortic stenosis and/or coronary artery disease. This may tip the balance towards a relative increase in the inflammatory responses induced by C5aR1 and thus enhance the inflammatory processes in TAA