The Factor H gene cluster and complement homeostasis in the retina

A common polymorphism in Complement factor H (FH) that switches a tyrosine at position 402 to a histidine is linked to susceptibility to developing age-related macular degeneration (AMD), and other less common variants of FH are associated with higher penetrance early onset forms of the disease. Deletion of the genes encoding complement Factor H related protein 1 and 3 (CFHR1 and CFHR3) decreases AMD risk but almost nothing is known about the regulation of these genes in the mouse retina, or the effects of retinal stress on their expression. This study addresses the role of the CFH gene cluster in complement homeostasis in the retina. Initial experiments confirmed that inflammatory cytokines affect the expression of complement genes in ARPE-19 cells. The role of hypoxia in gene expression of complement genes was assessed in ARPE-19 cells and co-culture experiments revealed the effect of C3 and C5 on the capacity of microglia cells to phagocytose ARPE-19 cells. The main focus of this study was to characterise the retinal phenotype of RPE-CFH knock-out mice. In vivo studies showed changes in the retinal phenotype of RPE-CFH knock-out mice. Initial studies of Cfh-/- Cfhr-/- mice showed significant downregulation in the expression of complement genes in the RPE, and increased C3b deposition in the RPE and the retinal vessels. Gene expression analysis in the RPE of Cfh+/+ Cfhr-/- mice showed that CFH mRNA levels were decreased 8-fold. Analysis of the retinal phenotype of aged Abca4-/- Rdh8-/- mice, which accumulate lipofuscin in the retina, showed excessive stress in the RPE, evidence of complement activation and changes in the expression of CRPs. Subcellular re-localisation of the C3aR in the RPE suggests that C3a/C3aR signaling plays an important role in complement homeostasis in the RPE. This study highlights the importance of RPE derived FH in maintaining retinal health and provides further evidence of complement dysregulation in AMD pathogenesis

Disparate habitual physical activity and dietary intake profiles of elderly men with low and elevated systemic inflammation

The development of chronic, low-grade systemic inflammation in the elderly (inflammaging) has been associated with increased incidence of chronic diseases, geriatric syndromes, and functional impairments. The aim of this study was to examine differences in habitual physical activity (PA), dietary intake patterns, and musculoskeletal performance among community-dwelling elderly men with low and elevated systemic inflammation. Nonsarcopenic older men free of chronic diseases were grouped as ‘low’ (LSI: n = 17; 68.2 ± 2.6 years; hs-CRP: 1 mg/L) systemic inflammation according to their serum levels of high-sensitivity CRP (hs-CRP). All participants were assessed for body composition via Dual Emission X-ray Absorptiometry (DEXA), physical performance using the Short Physical Performance Battery (SPPB) and handgrip strength, daily PA using accelerometry, and daily macro- and micronutrient intake. ESI was characterized by a 2-fold greater hs-CRP value than LSI (p < 0.01). The two groups were comparable in terms of body composition, but LSI displayed higher physical performance (p < 0.05), daily PA (step count/day and time at moderate-to-vigorous PA (MVPA) were greater by 30% and 42%, respectively, p < 0.05), and daily intake of the antioxidant vitamins A (6590.7 vs. 4701.8 IU/day, p < 0.05), C (120.0 vs. 77.3 mg/day, p < 0.05), and E (10.0 vs. 7.5 mg/day, p < 0.05) compared to ESI. Moreover, daily intake of vitamin A was inversely correlated with levels of hs-CRP (r = −0.39, p = 0.035). These results provide evidence that elderly men characterized by low levels of systemic inflammation are more physically active, spend more time in MVPA, and receive higher amounts of antioxidant vitamins compared to those with increased systemic inflammation

Use of Satellite Remote Sensing in Hydro-Ecological Research

Precipitation and soil moisture are two key hydrologic variables in the global water, energy, and carbon cycles that control land-atmosphere interactions. Accurate quantification of both of these parameters at continental scale is of paramount importance in order to better characterize climate patterns and understand climate change. Although there has been significant improvement on the current satellite rainfall retrieval techniques, much can be done to minimize uncertainty, which becomes more apparent over complex terrain and during heavy precipitation events (HPEs). To this end, an evaluation of remote sensing rainfall estimates, derived from different satellite algorithms, is conducted over the high-heterogeneity terrain of Europe and for different seasons. Moreover, a detailed error analysis of different quasi-global high-resolution satellite products for major HPEs of different precipitation types (stratiform versus convective) over mountainous areas provides quantitative information about the error structure of satellite rainfall products during these major precipitation events. Furthermore, obtaining high-sensitivity soil moisture measurements at the regional scale is a very difficult problem that satellite retrievals are aiming to address. In this study, a first step towards the achievement of an improved soil moisture-retrieval algorithm is described, which demonstrates how combining the advantages of active (radar-derived) and passive (radiometer-derived) measurements constitutes a promising way of achieving estimates with unprecedented resolution and sensitivity. Taking into account that climate change is one of the major factors driving biodiversity patterns, it becomes evident that precipitation and soil moisture can be deemed as two fundamental environmental parameters that determine life. The last part of this study bridges the two remote sensing techniques with ecology, contributing to a more effective conservation planning. Specifically, the hydro-geomorphologic drivers of biodiversity patterns over Madagascar are assessed using satellite remote sensing data-based investigations of the different hydrologic properties of the watersheds of the island

On distinguishing snowfall from rainfall using near-surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

The accurate estimation of precipitation phase has broad applications. In this study, we compared the skill of using various atmospheric variables and their combinations as predictors in accurately identifying surface precipitation phase, determined uncertainties associated with commonly used fixed temperature thresholds, and explored the sensitivity of hydrologic model output to uncertainty in precipitation phase using two case-studies. The results suggest that among all single predictors, wet-bulb temperature yields the highest skill score for determining precipitation phase and can reduce uncertainties due to regional differences, especially compared to the commonly used near-surface air temperature. However, addition of good-quality near-surface wind speed measurement to dew-point temperature and pressure showed slightly higher skill than wet-bulb temperature. We showed that the scale mismatch between temperature from stations and gridded products can cause large uncertainties in determining precipitation phase, especially in regions with rugged topography. Such uncertainties need to be considered when the relationships developed based on station data are applied to remote-sensing observations and model-generated data to separate rain from snowfall. The sensitivity of hydrologic model outputs to uncertainty in precipitation phase delineation was also assessed over two major basins in California by modifying default near-surface temperatures used in the Variable Infiltration Capacity (VIC) model. It was found that regional and scaling uncertainties in determining temperature thresholds can largely influence the accuracy of simulated downstream runoff and snow water equivalent (SWE) (e.g. up to 40% change in SWE for 2 degrees C shift in temperature threshold). Therefore, to reduce simulation uncertainties, it is important to improve rain-snow partitioning methods, consider regional variabilities in determining temperature thresholds, and perform the analysis at the highest possible resolutions to mitigate scale-related uncertainties.National Aeronautics and Space Administration (NASA) GRACE [NNH15ZDA001N-GRACE]; NASA Energy and Water Cycle Study (NEWS) [NNH13ZDA001N-NEWS]; US Department of Agriculture/National Institute of Food Agriculture; National Science Foundation; Water Sustainability & Climate Program [1360506/1360507]; NASA Weather [NNH13ZDA001N-WEATHER]; NASA MIRO [NNX15AQ06A]12 month embargo; published on: 17 August 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation.

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications