Age × stage-classified demographic analysis: a comprehensive approach

This paper presents a comprehensive theory for the demographic analysis of populations in which individuals are classified by both age and stage. The earliest demographic models were age classified. Ecologists adopted methods developed by human demographers and used life tables to quantify survivorship and fertility of cohorts and the growth rates and structures of populations. Later, motivated by studies of plants and insects, matrix population models structured by size or stage were developed. The theory of these models has been extended to cover all the aspects of age‐classified demography and more. It is a natural development to consider populations classified by both age and stage. A steady trickle of results has appeared since the 1960s, analyzing one or another aspect of age × stage‐classified populations, in both ecology and human demography. Here, we use the vec‐permutation formulation of multistate matrix population models to incorporate age‐ and stage‐specific vital rates into demographic analysis. We present cohort results for the life table functions (survivorship, mortality, and fertility), the dynamics of intra‐cohort selection, the statistics of longevity, the joint distribution of age and stage at death, and the statistics of life disparity. Combining transitions and fertility yields a complete set of population dynamic results, including population growth rates and structures, net reproductive rate, the statistics of lifetime reproduction, and measures of generation time. We present a complete analysis of a hypothetical model species, inspired by poecilogonous marine invertebrates that produce two kinds of larval offspring. Given the joint effects of age and stage, many familiar demographic results become multidimensional, so calculations of marginal and mixture distributions are an important tool. From an age‐classified point of view, stage structure is a form of unobserved heterogeneity. From a stage‐classified point of view, age structure is unobserved heterogeneity. In an age × stage‐classified model, variance in demographic outcomes can be partitioned into contributions from both sources. Because these models are formulated as matrices, they are amenable to a complete sensitivity analysis. As more detailed and longer longitudinal studies are developed, age × stage‐classified demography will become more common and more important

Early intestinal Bacteroides fragilis colonisation and development of asthma

<p>Abstract</p> <p>Background</p> <p>The 'hygiene hypothesis' suggests that early exposure to microbes can be protective against atopic disease. The intestinal microbial flora could operate as an important postnatal regulator of the Th1/Th2 balance. The aim of the study was to investigate the association between early intestinal colonisation and the development of asthma in the first 3 years of life.</p> <p>Methods</p> <p>In a prospective birth cohort, 117 children were classified according to the Asthma Predictive Index. A positive index included wheezing during the first three years of life combined with eczema in the child in the first years of life or with a parental history of asthma. A faecal sample was taken at the age of 3 weeks and cultured on selective media.</p> <p>Results</p> <p>Asthma Predictive Index was positive in 26/117 (22%) of the children. The prevalence of colonisation with <it>Bacteroides fragilis </it>was higher at 3 weeks in index+ compared to index- children (64% vs. 34% p < 0,05). <it>Bacteroides fragilis </it>and <it>Total Anaerobes </it>counts at 3 weeks were significantly higher in children with a positive index as compared with those without. After adjusting for confounders a positive association was found between <it>Bacteroides fragilis </it>colonisation and Asthma Predictive Index (odds ratio: 4,4; confidence interval: 1,7 – 11,8).</p> <p>Conclusion</p> <p><it>Bacteroides fragilis </it>colonisation at age 3 weeks is an early indicator of possible asthma later in life. This study could provide the means for more accurate targeting of treatment and prevention and thus more effective and better controlled modulation of the microbial milieu.</p

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

BACKGROUND. Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.Italian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshi

GlobalSoilMap.net: a new digital soil map of the world.

Knowledge of the world soil resources is fragmented and dated. There is a need for accurate, up-to-date and spatially referenced soil information as frequently expressed by the modelling community, farmers and land users, and policy and decision makers. This need coincides with an enormous leap in technologies that allow for accurately collecting and predicting soil properties. We are working on a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties. Our aim is to map the global land surface in 5 years ? the resulting maps will depict the primary functional soil properties at a grid resolution of 90×90 m. They will be freely available, web-accessible and widely distributed and used.The maps will be produced by a global consortium with centres in each of the continents: NRCS for North America, Embrapa for Latin America, JRC for Europe, TSBF-CIAT for Africa, ISSAS for parts of Asia and CSIRO for Oceania. This new global soil map will be supplemented by interpretation and functionality options that aim to assist better decisions in a range of global issues like food production and hunger eradication, climate change, and environmental degradation. In November 2008, a grant of US$18 million was obtained from the Bill & Melinda Gates Foundation to map most parts of Sub-Saharan Africa and make the underlying data available. From this grant there are funds for coordinating efforts in the global consortium.bitstream/item/66584/1/GlobalSoilMap.net-A-New-Digital-Soil-Map-of-the-World-Guest-Article-MEA-Bulletin-Issue-No.pd

Invasive pulmonary aspergillosis in patients with decompensated cirrhosis: case series

BACKGROUND: Opportunistic invasive fungal infections are increasingly frequent in intensive care patients. Their clinical spectrum goes beyond the patients with malignancies, and for example invasive pulmonary aspergillosis has recently been described in critically ill patients without such condition. Liver failure has been suspected to be a risk factor for aspergillosis. CASE PRESENTATION: We describe three cases of adult respiratory distress syndrome with sepsis, shock and multiple organ failure in patients with severe liver failure among whom two had positive Aspergillus antigenemia and one had a positive Aspergillus serology. In all cases bronchoalveolar lavage fluid was positive for Aspergillus fumigatus. Outcome was fatal in all cases despite treatment with voriconazole and agressive symptomatic treatment. CONCLUSION: Invasive aspergillosis should be among rapidly raised hypothesis in cirrhotic patients developing acute respiratory symptoms and alveolar opacities

Effect of various normalization methods on Applied Biosystems expression array system data

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan(® )assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan(® )assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions