166 research outputs found

    Nutrient Content and Stoichiometry of Pelagic \u3ci\u3eSargassum\u3c/i\u3e Reflects Increasing Nitrogen Availability In the Atlantic Basin

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    The pelagic brown macroalgae Sargassum spp. have grown for centuries in oligotrophic waters of the North Atlantic Ocean supported by natural nutrient sources, such as excretions from associated fishes and invertebrates, upwelling, and N2 fixation. Using a unique historical baseline, we show that since the 1980s the tissue %N of Sargassum spp. has increased by 35%, while %P has decreased by 44%, resulting in a 111% increase in the N:P ratio (13:1 to 28:1) and increased P limitation. The highest %N and δ15N values occurred in coastal waters influenced by N-rich terrestrial runoff, while lower C:N and C:P ratios occurred in winter and spring during peak river discharges. These findings suggest that increased N availability is supporting blooms of Sargassum and turning a critical nursery habitat into harmful algal blooms with catastrophic impacts on coastal ecosystems, economies, and human health

    A modeling study of the seasonal oxygen budget of the global ocean

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    Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): C05017, doi:10.1029/2006JC003731.An ecosystem model embedded in a global ocean general circulation model is used to quantify roles of biological and physical processes on seasonal oxygen variations. We find that the thermally induced seasonal net outgassing (SNO) of oxygen is overestimated by about 30% if gas phase equilibrium is assumed, and we find that seasonal variations in thermocline oxygen due to biology are approximated well using the oxygen anomaly. Outside the tropics and the north Indian Ocean, biological SNO is, on average, 56% of net community production (defined as net oxygen production above 76 m) during the outgassing period and 35% of annual net community production. In the same region the seasonal drawdown of the oxygen anomaly within the upper thermocline (76–500 m) is 76% of the remineralization during the drawdown and 48% of annual remineralization. Applying model-derived relationships to observed O2 climatologies and using independent estimates for tropical and monsoonal systems, we estimate global net community production to be 14.9 ± 2.5 Pg C yr−1.R.N., X.J., and F.L. were supported from the following grants: NOAA NA16GP2987, NASA NAG5-6451, and NSF OCE-9711937

    SerpinA3N is a novel hypothalamic gene upregulated by a high-fat diet and leptin in mice

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    Background: Energy homeostasis is regulated by the hypothalamus but fails when animals are fed a high-fat diet (HFD), and leptin insensitivity and obesity develops. To elucidate the possible mechanisms underlying these effects, a microarray-based transcriptomics approach was used to identify novel genes regulated by HFD and leptin in the mouse hypothalamus. Results: Mouse global array data identified serpinA3N as a novel gene highly upregulated by both a HFD and leptin challenge. In situ hybridisation showed serpinA3N expression upregulation by HFD and leptin in all major hypothalamic nuclei in agreement with transcriptomic gene expression data. Immunohistochemistry and studies in the hypothalamic clonal neuronal cell line, mHypoE-N42 (N42), confirmed that alpha 1-antichymotrypsin (α1AC), the protein encoded by serpinA3, is localised to neurons and revealed that it is secreted into the media. SerpinA3N expression in N42 neurons is upregulated by palmitic acid and by leptin, together with IL-6 and TNFα, and all three genes are downregulated by the anti-inflammatory monounsaturated fat, oleic acid. Additionally, palmitate upregulation of serpinA3 in N42 neurons is blocked by the NFκB inhibitor, BAY11, and the upregulation of serpinA3N expression in the hypothalamus by HFD is blunted in IL-1 receptor 1 knockout (IL-1R1−/−) mice. Conclusions: These data demonstrate that serpinA3 expression is implicated in nutritionally mediated hypothalamic inflammation

    Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

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    Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability
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