Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan

<p>Abstract</p> <p>Background</p> <p>Large spatial, seasonal and annual variability of major drivers of the carbon cycle (precipitation, temperature, fire regime and nutrient availability) are common in the Sahel region. This causes large variability in net ecosystem exchange and in vegetation productivity, the subsistence basis for a major part of the rural population in Sahel. This study compares the 2005 dry and wet season fluxes of CO₂for a grass land/sparse savanna site in semi arid Sudan and relates these fluxes to water availability and incoming photosynthetic photon flux density (PPFD). Data from this site could complement the current sparse observation network in Africa, a continent where climatic change could significantly impact the future and which constitute a weak link in our understanding of the global carbon cycle.</p> <p>Results</p> <p>The dry season (represented by Julian day 35–46, February 2005) was characterized by low soil moisture availability, low evapotranspiration and a high vapor pressure deficit. The mean daily NEE (net ecosystem exchange, Eq. 1) was -14.7 mmol d^-1for the 12 day period (negative numbers denote sinks, i.e. flux from the atmosphere to the biosphere). The water use efficiency (WUE) was 1.6 mmol CO₂mol H₂O^-1and the light use efficiency (LUE) was 0.95 mmol CO₂mol PPFD^-1. Photosynthesis is a weak, but linear function of PPFD. The wet season (represented by Julian day 266–273, September 2005) was, compared to the dry season, characterized by slightly higher soil moisture availability, higher evapotranspiration and a slightly lower vapor pressure deficit. The mean daily NEE was -152 mmol d^-1for the 8 day period. The WUE was lower, 0.97 mmol CO₂mol H₂O^-1and the LUE was higher, 7.2 <it>μ</it>mol CO₂mmol PPFD^-1during the wet season compared to the dry season. During the wet season photosynthesis increases with PPFD to about 1600 <it>μ</it>mol m^-2s^-1and then levels off.</p> <p>Conclusion</p> <p>Based on data collected during two short periods, the studied ecosystem was a sink of carbon both during the dry and wet season 2005. The small sink during the dry season is surprising and similar dry season sinks have not to our knowledge been reported from other similar savanna ecosystems and could have potential management implications for agroforestry. A strong response of NEE versus small changes in plant available soil water content was found. Collection and analysis of flux data for several consecutive years including variations in precipitation, available soil moisture and labile soil carbon are needed for understanding the year to year variation of the carbon budget of this grass land/sparse savanna site in semi arid Sudan.</p

Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

AIMS: The tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction. METHODS: Based on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random "training" (75%) and "test" (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients. RESULTS: The logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ(2) = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ(2) = 3.2673, p = 0.07). CONCLUSION: In this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for use across multiple ethnic groups or if population-specific models are required

The Insulin-Mediated Modulation of Visually Evoked Magnetic Fields Is Reduced in Obese Subjects

BACKGROUND: Insulin is an anorexigenic hormone that contributes to the termination of food intake in the postprandial state. An alteration in insulin action in the brain, named "cerebral insulin resistance", is responsible for overeating and the development of obesity. METHODOLOGY/PRINCIPAL FINDINGS: To analyze the direct effect of insulin on food-related neuronal activity we tested 10 lean and 10 obese subjects. We conducted a magnetencephalography study during a visual working memory task in both the basal state and after applying insulin or placebo spray intranasally to bypass the blood brain barrier. Food and non-food pictures were presented and subjects had to determine whether or not two consecutive pictures belonged to the same category. Intranasal insulin displayed no effect on blood glucose, insulin or C-peptide concentrations in the periphery; however, it led to an increase in the components of evoked fields related to identification and categorization of pictures (at around 170 ms post stimuli in the visual ventral stream) in lean subjects when food pictures were presented. In contrast, insulin did not modulate food-related brain activity in obese subjects. CONCLUSIONS/SIGNIFICANCE: We demonstrated that intranasal insulin increases the cerebral processing of food pictures in lean whereas this was absent in obese subjects. This study further substantiates the presence of a "cerebral insulin resistance" in obese subjects and might be relevant in the pathogenesis of obesity

Characterizing Large Scale Land Acquisitions Through Network Analysis

Large Scale Land Acquisitions (LSLAs) by private companies or states have seen a sudden increase in recent years, mainly due to combined and increasing demands for biofuel (i.e., caused by the increase in oil prices) and food (i.e., caused by the increase in world population and changes in dietary habits). These highly controversial phenomena raise many questions about production models, people’s rights, resource governance, and are often at the root of conflicts with local populations. A valuable source of open access information about LSLAs, which fosters the study of such phenomena, is the database collected by the Land Matrix initiative. The database lists land deals of at least 200 ha and details for example, their nature (e.g. agriculture, infrastructure, mining), their current status (e.g. ongoing, abandoned, pending), and the investing companies. The information about land deals collected in the Land Matrix database comes from heterogeneous sources such as press articles, government data, individual contributions and scientific publications. In this work, we focus on a land trade network built upon the Land Matrix data about top companies and target countries related to each deal in the database. Modeling the information about LSLAs in a land trade network allows us to leverage on network analysis techniques, which will help to characterize land acquisition deals from an original point of view. In order to take a first step in this direction, we provide: (i) a centrality based analysis of the land trade network, including an analysis based on the correlation of centrality measures with different country development indicators, and (ii) an analysis based on network motifs (i.e., recurring, statistically significant subgraphs), which provides an insight into higher order correlations between countries, thus providing fresh knowledge about recurring patterns in transnational land trade deals