Growth of bifidobacteria in mammalian milk

Microbial colonization of the mammalian intestine begins at birth, when from a sterile state a newborn infant is exposed to an external environment rich in various bacterial species. An important group of intestinal bacteria comprises bifidobacteria. Bifidobacteria represent major intestinal microbiota during the breast-feeding period. Animal milk contains all crucial nutrients for babies’ intestinal microflora. The aim of our work was to test the influence of different mammalian milk on the growth of bifidobacteria. The growth of seven strains of bifidobacteria in human milk, the colostrum of swine, cow’s milk, sheep’s milk, and rabbit’s milk was tested. Good growth accompanied by the production of lactic acid was observed not only in human milk, but also in the other kinds of milk in all three strains of Bifidobacterium bifidum of different origin. Human milk selectively supported the production of lactic acid of human bifidobacterial isolates, especially the Bifidobacterium bifidum species. The promotion of bifidobacteria by milk is species-specific. Human milk contains a key factor for the growth of specific species or strains of human-origin bifidobacteria compared to other kinds of milk. In contrast, some components (maybe lysozyme) of human milk inhibited the growth of Bifidobacterium animalis. Animal-origin strains of bifidobacteria were not able to significantly grow even in milk of animal origin, with the exception of B. animalis subsp. lactis 1,2, which slightly grew in sheep’s milk

Towards systematic evaluation of crop model outputs for global land-use models

Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include iterative improvement of parameter assumptions and evaluation of implications of GGCM performances for intended use in the IIASA EPIC-GLOBIOM model cluster. Our approach helps targeting future efforts at improving GGCM accuracy and would achieve highest efficiency if combined with traditional field-scale evaluation and sensitivity analysis

Predicting death over 8 years in a prospective cohort of HIV-infected women: the Women's Interagency HIV Study.

ObjectivesPredicting mortality in middle-aged HIV-infected (HIV+) women on antiretroviral therapies (ART) is important for understanding the impact of HIV infection. Several health indices have been used to predict mortality in women with HIV infection. We evaluated: (1) an HIV biological index, Veterans Aging Cohort Study (VACS); (2) a physical index, Fried Frailty Index (FFI); and (3) a mental health index, Center for Epidemiologic Studies-Depression (CES-D). Proportional hazards regression analyses were used to predict death and included relevant covariates.DesignProspective, observational cohort.SettingMulticentre, across six sites in the USA.Participants1385 multirace/ethnic ART-experienced HIV+ women in 2005.Primary and secondary outcomesAll deaths, AIDS deaths and non-AIDS deaths up to ~8 years from baseline.ResultsIncluded together in one model, VACS Index was the dominant, significant independent predictor of all deaths within 3 years (HR=2.20, 95% CI 1.83, 2.65, χ2=69.04, p<0.0001), and later than 3 years (HR=1.55, 95% CI 1.30, 1.84, χ2=23.88, p<0.0001); followed by FFI within 3 years (HR=2.06, 95% CI 1.19, 3.57, χ2=6.73, p=0.01) and later than 3 years (HR=2.43, 95% CI 1.58, 3.75, χ2=16.18, p=0.0001). CES-D score was not independently associated with mortality.Conclusions and relevanceThis is the first simultaneous evaluation of three common health indices in HIV+ adults. Indices reflecting physical and biological ageing were associated with death

Uncertainties in global land cover data and its implications for climate change mitigation policies assessment

Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how high will be competition for land between food and bioenergy in the future as well as how much land is there available for afforestation projects? These questions can only be answered if reliable maps of land cover exist. We put this research in the framework of GEOSS, examine how modeling tools can be used for benefit assessment and design an assessment framework. We illustrate the importance of good quality global land cover maps by using cropland extend from the currently best global maps of land cover namely GLC-2000, MODIS, GlobCover and CropLikelyhood as input for the EPIC model (to model crop yields) and global economic land use model GLOBIOM. We use all of the 4 maps and create a maximum crop extend and map. Based on a baseline map and the maximum crop extend map e model effects of climate policies (e.g. the potentials of substitution of fossil fuels with biofuels)

Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014). We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results

The Belarus Economy: The Challenges of Stalled Reforms. wiiw Research Report No.413

Twenty-five years after the dissolution of the Soviet Union, Belarus stands out as a special case in transition blending, on the one hand, signs of relative prosperity, socially oriented policies and sprouts of entrepreneurships and, on the other hand, remnants of the communist past. The core of the Belarusian economic model throughout most of this period was a combination of external rents and soft budget constraints on the state-owned part of the economy backed by a strong system of administrative control. In periods of favourable external conditions this mix provided for relatively high rates of economic growth and allowed the authorities to maintain a ‘social contract’ with the population targeting close to full employment. But this model also led to the persistent accumulation of a quasi-fiscal deficit which time and again came to the surface, and its subsequent monetisation provoked macroeconomic and currency turmoil. At present, Belarus’ economic model has run up against its limits and policy changes seem inevitable

Growth of bifidobacteria in mammalian milk

Microbial colonization of the mammalian intestine begins at birth, when from a sterile state a newborn infant is exposed to an external environment rich in various bacterial species. An important group of intestinal bacteria comprises bifidobacteria. Bifidobacteria represent major intestinal microbiota during the breast-feeding period. Animal milk contains all crucial nutrients for babies’ intestinal microflora. The aim of our work was to test the influence of different mammalian milk on the growth of bifidobacteria. The growth of seven strains of bifidobacteria in human milk, the colostrum of swine, cow’s milk, sheep’s milk, and rabbit’s milk was tested. Good growth accompanied by the production of lactic acid was observed not only in human milk, but also in the other kinds of milk in all three strains of Bifidobacterium bifidum of different origin. Human milk selectively supported the production of lactic acid of human bifidobacterial isolates, especially the Bifidobacterium bifidum species. The promotion of bifidobacteria by milk is species-specific. Human milk contains a key factor for the growth of specific species or strains of human-origin bifidobacteria compared to other kinds of milk. In contrast, some components (maybe lysozyme) of human milk inhibited the growth of Bifidobacterium animalis. Animal-origin strains of bifidobacteria were not able to significantly grow even in milk of animal origin, with the exception of B. animalis subsp. lactis 1,2, which slightly grew in sheep’s milk

Evaluating the effects of climate change on US agricultural systems: sensitivity to regional impact and trade expansion scenarios

Agriculture is one of the sectors that is expected to be most significantly impacted by climate change. There has been considerable interest in assessing these impacts and many recent studies investigating agricultural impacts for individual countries and regions using an array of models. However, the great majority of existing studies explore impacts on a country or region of interest without explicitly accounting for impacts on the rest of the world. This approach can bias the results of impact assessments for agriculture given the importance of global trade in this sector. Due to potential impacts on relative competitiveness, international trade, global supply, and prices, the net impacts of climate change on the agricultural sector in each region depend not only on productivity impacts within that region, but on how climate change impacts agricultural productivity throughout the world. In this study, we apply a global model of agriculture and forestry to evaluate climate change impacts on US agriculture with and without accounting for climate change impacts in the rest of the world. In addition, we examine scenarios where trade is expanded to explore the implications for regional allocation of production, trade volumes, and prices. To our knowledge, this is one of the only attempts to explicitly quantify the relative importance of accounting for global climate change when conducting regional assessments of climate change impacts. The results of our analyses reveal substantial differences in estimated impacts on the US agricultural sector when accounting for global impacts vs. US-only impacts, particularly for commodities where the United States has a smaller share of global production. In addition, we find that freer trade can play an important role in helping to buffer regional productivity shocks