61 research outputs found

    When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects

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    In science, it is a common experience to discover that although the investigated effect is very clear in some individuals, statistical tests are not significant because the effect is null or even opposite in other individuals. Indeed, t-tests, Anovas and linear regressions compare the average effect with respect to its inter-individual variability, so that they can fail to evidence a factor that has a high effect in many individuals (with respect to the intra-individual variability). In such paradoxical situations, statistical tools are at odds with the researcher’s aim to uncover any factor that affects individual behavior, and not only those with stereotypical effects. In order to go beyond the reductive and sometimes illusory description of the average behavior, we propose a simple statistical method: applying a Kolmogorov-Smirnov test to assess whether the distribution of p-values provided by individual tests is significantly biased towards zero. Using Monte-Carlo studies, we assess the power of this two-step procedure with respect to RM Anova and multilevel mixed-effect analyses, and probe its robustness when individual data violate the assumption of normality and homoscedasticity. We find that the method is powerful and robust even with small sample sizes for which multilevel methods reach their limits. In contrast to existing methods for combining p-values, the Kolmogorov-Smirnov test has unique resistance to outlier individuals: it cannot yield significance based on a high effect in one or two exceptional individuals, which allows drawing valid population inferences. The simplicity and ease of use of our method facilitates the identification of factors that would otherwise be overlooked because they affect individual behavior in significant but variable ways, and its power and reliability with small sample sizes (<30–50 individuals) suggest it as a tool of choice in exploratory studies

    Yield and Economic Performance of Organic and Conventional Cotton-Based Farming Systems – Results from a Field Trial in India

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    The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost importance. However, information concerning the performance of farming systems under organic and conventional management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the conversion phase (2007–2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton) management was investigated. We observed a significant yield gap between organic and conventional farming systems in the 1st crop cycle (cycle 1: 2007–2008) for cotton (229%) and wheat (227%), whereas in the 2nd crop cycle (cycle 2: 2009–2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (21% in cycle 1, 211% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system (+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact of the different farming systems

    The Pathway Coexpression Network: Revealing pathway relationships.

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    A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/

    Episodic Occurrence of Favourable Weather Constrains Recovery of a Cold Desert Shrubland After Fire

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    Key to the long-term resilience of dryland ecosystems is the recovery of foundation plant species following disturbance. In ecosystems with high interannual weather variability, understanding the influence of short-term environmental conditions on establishment of foundation species is essential for identifying vulnerable landscapes and developing restoration strategies. We asked how annual environmental conditions affect post-fire establishment of Artemisia tridentata, a shrub species that dominates landscapes across much of the western United States, and evaluated the influence of episodic establishment on population recovery. We collected A. tridentata stem samples from 33 plots in 12 prescribed fire sites that burned 8–11 years before sampling. We determined individual establishment years using annual growth rings. We measured seasonal soil environmental conditions at the study sites and asked if these conditions predicted annual establishment density. We then evaluated whether establishment patterns could be predicted by site-level climate or dominant subspecies. Finally, we tested the effect of the magnitude and frequency of post-fire establishment episodes on long-term population recovery. Annual post-fire recruitment of A. tridentata was driven by the episodic availability of spring soil moisture. Annual establishment was highest with wetter spring soils (relative influence [RI] = 19.4%) and later seasonal dry-down (RI = 11.8%) in the year of establishment. Establishment density declined greatly 4 to 5 years after fire (RI = 17.1%). Post-fire establishment patterns were poorly predicted by site-level mean climate (marginal R2 ≀ 0.18) and dominant subspecies (marginal R2 ≀ 0.43). Population recovery reflected the magnitude, but not the frequency, of early post-fire establishment pulses. Post-fire A. tridentata density and cover (measured 8–11 years after fire) were more strongly related to the magnitude of the largest establishment pulse than to establishment frequency, suggesting that population recovery may occur with a single favourable establishment year. Synthesis and applications. This study demonstrates the importance of episodic periods of favourable weather for long-term plant population recovery following disturbance. Management strategies that increase opportunities for seed availability to coincide with favourable weather conditions, such as retaining unburned patches or repeated seeding treatments, can improve restoration outcomes in high-priority areas

    Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources

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    <p>Abstract</p> <p>Background</p> <p>Gene expression profiling studies of mastitis in ruminants have provided key but fragmented knowledge for the understanding of the disease. A systematic combination of different expression profiling studies via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of infections with mammary gland pathogens, including samples from cattle challenged <it>in vivo </it>with <it>S. aureus</it>, <it>E. coli</it>, and <it>S. uberis</it>, samples from goats challenged <it>in vivo </it>with <it>S. aureus</it>, as well as cattle macrophages and ovine dendritic cells infected <it>in vitro </it>with <it>S. aureus</it>. We combined different time points from those studies, testing different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific.</p> <p>Results</p> <p>Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the general disease response. In the overall response, pathways related to immune response and inflammation, as well as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk fat content depression commonly observed during mastitis infection. Complementarities between early and late stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by few genes, including <it>XBP1 </it>and <it>SREBF1</it>.</p> <p>The cattle-specific response was characterized by alteration of the immune response and by modification of lipid metabolism. Comparison of <it>E. coli </it>and <it>S. aureus </it>infections in cattle <it>in vivo </it>revealed that affected genes showing opposite regulation had the same altered biological functions and provided evidence that <it>E. coli </it>caused a stronger host response.</p> <p>Conclusions</p> <p>This meta-analysis approach reinforces previous findings but also reveals several novel themes, including the involvement of genes, biological functions, and pathways that were not identified in individual studies. As such, it provides an interesting proof of principle for future studies combining information from diverse heterogeneous sources.</p

    Lipid metabolism and Type VII secretion systems dominate the genome scale virulence profile of Mycobacterium tuberculosis in human dendritic cells

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