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  • 1
    In: Current Diabetes Reviews, Bentham Science Publishers Ltd., Vol. 17, No. 1 ( 2020-12-04), p. 91-100
    Abstract: Type 2 diabetes (T2DM) has been associated with deficiencies in serum magnesium level, decreasing insulin sensitivity and glucose metabolism. Glycosylated hemoglobin (Hb1Ac) is a biomarker of glucose values within the half-life of the erythrocyte, that is, 3 months. Low circulating and intracellular magnesium levels can modify glucose metabolism and insulin sensitivity. Renal solute management is a parameter little used to estimate circulating and excreted concentrations of elements such as magnesium. Objective: The purpose of this study was to assess and associated fractional excretion of magnesium (FEMg) and serum magnesium with metabolic parameters, especially Hb1Ac percent, in a group of well characterized subjects with T2DM and non-diabetics subjects (ND). Methods: According to Hb1Ac, two groups were compared and associated with existing biochemical parameters, included Hb1Ac, fasting glucose, lipid profile, serum creatinine, serum magnesium and urinary creatinine for FEMg. Results: HbA1c levels were explained by serum magnesium in 25%. Serum magnesium levels in the ND group were higher than in the T2DM group and this was a statistically significant difference. Serum magnesium ≤1.8 is a risk factor (OD 16.1; P=0.021) for an HbA1c ≥ 6.5%. Conclusion: In this study, hypomagnesemia was a parameter strongly associated with the diagnosis and progression of T2DM, while FEMg showed no significant association.
    Type of Medium: Online Resource
    ISSN: 1573-3998
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2020
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  • 2
    In: Coral Reefs, Springer Science and Business Media LLC, Vol. 41, No. 4 ( 2022-08), p. 1031-1043
    Abstract: Decades of research have revealed relationships between the abundance of coral reef taxa and local conditions, especially at small scales. However, a rigorous test of covariation requires a robust dataset collected across wide environmental or experimental gradients. Here, we surveyed spatial variability in the densities of major coral reef functional groups at 122 sites along a 70 km expanse of the leeward, forereef habitat of Curaçao in the southern Caribbean. These data were used to test the degree to which spatial variability in community composition could be predicted based on assumed functional relationships and site-specific anthropogenic, physical, and ecological conditions. In general, models revealed less power to describe the spatial variability of fish biomass than cover of reef builders (R 2 of best-fit models: 0.25 [fish] and 0.64 [reef builders] ). The variability in total benthic cover of reef builders was best described by physical (wave exposure and reef relief) and ecological (turf algal height and coral recruit density) predictors. No metric of anthropogenic pressure was related to spatial variation in reef builder cover. In contrast, total fish biomass showed a consistent (albeit weak) association with anthropogenic predictors (fishing and diving pressure). As is typical of most environmental gradients, the spatial patterns of both fish biomass density and reef builder cover were spatially autocorrelated. Residuals from the best-fit model for fish biomass retained a signature of spatial autocorrelation while the best-fit model for reef builder cover removed spatial autocorrelation, thus reinforcing our finding that environmental predictors were better able to describe the spatial variability of reef builders than that of fish biomass. As we seek to understand spatial variability of coral reef communities at the scale of most management units (i.e., at kilometer- to island-scales), distinct and scale-dependent perspectives will be needed when considering different functional groups.
    Type of Medium: Online Resource
    ISSN: 0722-4028 , 1432-0975
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 9047-5
    detail.hit.zdb_id: 1472576-9
    SSG: 12
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  • 3
    In: Frontiers in Robotics and AI, Frontiers Media SA, Vol. 9 ( 2022-5-27)
    Abstract: Enabled by advancing technology, coral reef researchers increasingly prefer use of image-based surveys over approaches depending solely upon in situ observations, interpretations, and recordings of divers. The images collected, and derivative products such as orthographic projections and 3D models, allow researchers to study a comprehensive digital twin of their field sites. Spatio-temporally located twins can be compared and annotated, enabling researchers to virtually return to sites long after they have left them. While these new data expand the variety and specificity of biological investigation that can be pursued, they have introduced the much-discussed Big Data Problem: research labs lack the human and computational resources required to process and analyze imagery at the rate it can be collected. The rapid development of unmanned underwater vehicles suggests researchers will soon have access to an even greater volume of imagery and other sensor measurements than can be collected by diver-piloted platforms, further exacerbating data handling limitations. Thoroughly segmenting (tracing the extent of and taxonomically identifying) organisms enables researchers to extract the information image products contain, but is very time-consuming. Analytic techniques driven by neural networks offer the possibility that the segmentation process can be greatly accelerated through automation. In this study, we examine the efficacy of automated segmentation on three different image-derived data products: 3D models, and 2D and 2.5D orthographic projections thereof; we also contrast their relative accessibility and utility to different avenues of biological inquiry. The variety of network architectures and parameters tested performed similarly, ∼80% IoU for the genus Porites , suggesting that the primary limitations to an automated workflow are 1) the current capabilities of neural network technology, and 2) consistency and quality control in image product collection and human training/testing dataset generation.
    Type of Medium: Online Resource
    ISSN: 2296-9144
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2781824-X
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