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Sustainability assessment methodology oriented to soil-associated agricultural experiments
- Oscar Iván Monsalve Camacho, Oscar Gonzalo Castillo-Romero, Carlos Ricardo Bojacá Aldana, Martha Cecilia Henao Toro
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- Journal:
- Experimental Agriculture / Volume 59 / 2023
- Published online by Cambridge University Press:
- 12 September 2023, e18
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- Article
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A variety of established tools are available for agricultural sustainability assessment at global, regional, and farm geographical scales. However, no assessment has been reported in research literature to indicate their ability to provide insights about the most sustainable cropping system at plot level or experimental unit. Despite the environmental and social importance of soil in agricultural systems, many of the sustainability assessments use few or no indicators related to soil properties or processes. Hence, we propose a sustainability assessment methodology oriented to soil-associated agricultural experiments (SMAES) by defining its parameters through simulations and testing the methodology with real data from a fertilization tomato experiment with five treatments: chemical control (CR); organic control (OR); and organic:chemical ratios (OR) of 25:75, 50:50, and 75:25. The distance from the maximum, principal component analysis, and product of weighted indicator techniques were chosen for normalization, weighting, and aggregation in a single index process, respectively. Applying the SMAES methodology, the sustainability level of the treatments followed this sequence: CR (0.95) > O25:C75 (0.73) > O50:C50 (0.60) > O75:C25 (0.55) > OR (0.45). The proposed SMAES methodology allows soil researchers to define the best treatment through the interaction of the environmental, social, and economic dimensions of agricultural systems.
13 - Inference of gene networks associated with the host response to infectious disease
- from Part IV - Big data over biological networks
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- By Zhe Gan, Duke University, USA, Xin Yuan, Duke University, USA, Ricardo Henao, Duke University, USA, Ephraim L. Tsalik, Duke University Medical Center, USA, Lawrence Carin, Duke University, USA
- Edited by Shuguang Cui, Texas A & M University, Alfred O. Hero, III, University of Michigan, Ann Arbor, Zhi-Quan Luo, University of Minnesota, José M. F. Moura, Carnegie Mellon University, Pennsylvania
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- Book:
- Big Data over Networks
- Published online:
- 18 December 2015
- Print publication:
- 14 January 2016, pp 365-390
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Summary
Inspired by the problem of inferring gene networks associated with the host response to infectious diseases, a new framework for discriminative factor models is developed. Bayesian shrinkage priors are employed to impose (near) sparsity on the factor loadings, while non-parametric techniques are utilized to infer the number of factors needed to represent the data. Two discriminative Bayesian loss functions are investigated, i.e. the logistic log-loss and the max-margin hinge loss. Efficient mean-field variational Bayesian inference and Gibbs sampling are implemented. To address large-scale datasets, an online version of variational Bayes is also developed. Experimental results on two real world microarray-based gene expression datasets show that the proposed framework achieves comparatively superior classification performance, with model interpretation delivered via pathway association analysis.
Background
From a statistical-modeling perspective, gene expression analysis can be roughly divided into two phases: exploration and prediction. In the former, the practitioner attempts to get a general understanding of a dataset by modeling its variability in an interpretable way, such that the inferred model can serve as a feature extractor and hypotheses generating mechanism of the underlying biological processes. Factor models are among the most widely employed techniques for exploratory gene expression analysis [1, 2], with principal component analysis a popular special case [3]. Predictive modeling, on the other hand, is concerned with finding a relationship between gene expression and phenotypes, that can be generalized to unseen samples. Examples of predictive models include classification methods like logistic regression and support vector machines [4, 5].
Factor models infer a latent covariance structure among the genes or biomarkers, with data modeled as generated from a noisy low-rank matrix factorization, manifested in terms of a loadings matrix and a factor scores matrix. Different specifications for these matrices give rise to special cases of factor models, such as principal components analysis [6], nonnegative matrix factorization [7], independent component analysis [8], and sparse factor models [1]. Factor models employing a sparse factor loadings matrix are of significant interest in gene-expression analysis, as the nonzero elements in the loadings matrix may be interpreted as correlated gene networks [1, 2, 9].