Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection

We propose a method for detecting significant interactions in very large multivariate spatial point patterns. This methodology develops high dimensional data understanding in the point process setting. The method is based on modelling the patterns using a flexible Gibbs point process model to directly characterise point-to-point interactions at different spatial scales. By using the Gibbs framework significant interactions can also be captured at small scales. Subsequently, the Gibbs point process is fitted using a pseudo-likelihood approximation, and we select significant interactions automatically using the group lasso penalty with this likelihood approximation. Thus we estimate the multivariate interactions stably even in this setting. We demonstrate the feasibility of the method with a simulation study and show its power by applying it to a large and complex rainforest plant population data set of 83 species

Organic source of nitrogen trials & miscellaneous trials.

1. Nitrogen sources Badgingarra 85BA3 Organic Source of Nitrogen 8SBA4 Interaction of Organic and Inorganic Nitrogen sources 85BAS Miscellaneous Organic Nitrogen Sources 85BA39 Times and Levels of Inorganic Nitrogen Application Lancelin 85M02 Organic sources of nitrogen 85M03 Interaction of Organic and Inorganic Nitrogen Sources 85M04 Miscellaneous Organic Nitrogen Sources 85MOS3 Times and Levels of Inorganic Nitrogen Application Meckering 85N01 Organic Sources of Nitrogen 85N03 Miscellaneous Organic Nitrogen Sources 85NOS9 Times and Levels of Inorganic Nitrogen Application 2. Miscellaneous trials 84M6 Rates of Nitrogen on Wheat - CSIRO Megatrial (E. Harvey) 85M4 Nitrogen and Water on Wheat (with M. Mason) 83WH29 Residual Effects of Crop and Pasture Species on Second Wheat Crop 82WH2 Residual Value of Ripping (4th Year

Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-{\em i.i.d} nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past $\tau$ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.Comment: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 201

Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock

Combinatorial quorum sensing allows bacteria to resolve their social and physical environment

Quorum sensing (QS) is a cell–cell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. Although the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories. Here, we address this challenge by showing that bacteria can use multiple QS signals to infer both their social (density) and physical (mass-transfer) environment. Analytical and evolutionary simulation models show that the detection of, and response to, complex social/physical contrasts requires multiple signals with distinct half-lives and combinatorial (nonadditive) responses to signal concentrations. We test these predictions using the opportunistic pathogen Pseudomonas aeruginosa and demonstrate significant differences in signal decay betweeallyn its two primary signal molecules, as well as diverse combinatorial responses to dual-signal inputs. QS is associated with the control of secreted factors, and we show that secretome genes are preferentially controlled by synergistic “AND-gate” responses to multiple signal inputs, ensuring the effective expression of secreted factors in high-density and low mass-transfer environments. Our results support a new functional hypothesis for the use of multiple signals and, more generally, show that bacteria are capable of combinatorial communication

Non-equilibrium dynamics and floral trait interactions shape extant angiosperm diversity.

Why are some traits and trait combinations exceptionally common across the tree of life, whereas others are vanishingly rare? The distribution of trait diversity across a clade at any time depends on the ancestral state of the clade, the rate at which new phenotypes evolve, the differences in speciation and extinction rates across lineages, and whether an equilibrium has been reached. Here we examine the role of transition rates, differential diversification (speciation minus extinction) and non-equilibrium dynamics on the evolutionary history of angiosperms, a clade well known for the abundance of some trait combinations and the rarity of others. Our analysis reveals that three character states (corolla present, bilateral symmetry, reduced stamen number) act synergistically as a key innovation, doubling diversification rates for lineages in which this combination occurs. However, this combination is currently less common than predicted at equilibrium because the individual characters evolve infrequently. Simulations suggest that angiosperms will remain far from the equilibrium frequencies of character states well into the future. Such non-equilibrium dynamics may be common when major innovations evolve rarely, allowing lineages with ancestral forms to persist, and even outnumber those with diversification-enhancing states, for tens of millions of years

CD4 testing at clinics to assess eligibility for Antiretroviral therapy

BackgroundIn 2011, the Ministry of Health raised the CD4 threshold for antiretroviral therapy (ART) eligibility from <250 cells/μl and <350 cells/μl, but at the same time only 8.8% of facilities in Malawi with HIV services provided CD4 testing. We conducted a record review at 10 rural clinics in Thyolo District to assess the impact of introducing CD4 testing on identifying patients eligible for ART.Methods:We abstracted CD4 counts of all ART-naïve, HIV-infected patients with WHO clinical stages 1 and 2 and an initial CD4 test between May 2008 and June 2009. At four clinics, we also abstracted CD4 counts of patients not initially eligible for ART who were retested before April 2010.ResultsOf 1,113 patients tested, the initial CD4 was “≤250 cells/μl” and “≤350 cells/μl” in 534 (48.0%). Of 203 patients with follow-up results, the most recent CD4 was ≤250 cells/μl in 34 (24.5%), and ≤350 cells/μl in 64 (46.0%).ConclusionsCD4 testing in rural clinics is feasible and identifies many patients eligible for ART who would not be identified without CD4 testing. CD4 testing needs to be scaled-up to identify patients eligible for ART. ART services need to be scaled-up concurrently to meet the resulting increased demand

Spatial vegetation patterns and neighborhood competition among woody plants in an East African savanna

The majority of research on savanna vegetation dynamics has focused on the coexistence of woody and herbaceous vegetation. Interactions among woody plants in savannas are relatively poorly understood. We present data from a 10-year longitudinal study of spatially explicit growth patterns of woody vegetation in an East African savanna following exclusion of large herbivores and in the absence of fire. We examined plant spatial patterns and quantified the degree of competition among woody individuals. Woody plants in this semi-arid savanna exhibit strongly clumped spatial distributions at scales of 1 - 5 m. However, analysis of woody plant growth rates relative to their conspecific and heterospecific neighbors revealed evidence for strong competitive interactions at neighborhood scales of up to 5 m for most woody plant species. Thus, woody plants were aggregated in clumps despite significantly decreased growth rates in close proximity to neighbors, indicating that the spatial distribution of woody plants in this region depends on dispersal and establishment processes rather than on competitive, density-dependent mortality. However, our documentation of suppressive effects of woody plants on neighbors also suggests a potentially important role for tree-tree competition in controlling vegetation structure and indicates that the balanced-competition hypothesis may contribute to well-known patterns in maximum tree cover across rainfall gradients in Africa

Contrast estimation for parametric stationary determinantal point processes

We study minimum contrast estimation for parametric stationary determi-nantal point processes. These processes form a useful class of models for repulsive (or regular, or inhibitive) point patterns and are already applied in numerous statistical applications. Our main focus is on minimum contrast methods based on the Ripley's K-function or on the pair correlation function. Strong consistency and asymptotic normality of theses procedures are proved under general conditions that only concern the existence of the process and its regularity with respect to the parameters. A key ingredient of the proofs is the recently established Brillinger mixing property of stationary determinantal point processes. This work may be viewed as a complement to the study of Y. Guan and M. Sherman who establish the same kind of asymptotic properties for a large class of Cox processes, which in turn are models for clustering (or aggregation)