Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Pseudomonas aeruginosa exhibits frequent recombination, but only a limited association between genotype and ecological setting

Pseudomonas aeruginosa is an opportunistic pathogen and an important cause of infection, particularly amongst cystic fibrosis (CF) patients. While specific strains capable of patient-to-patient transmission are known, many infections appear to be caused by unique and unrelated strains. There is a need to understand the relationship between strains capable of colonising the CF lung and the broader set of P. aeruginosa isolates found in natural environments. Here we report the results of a multilocus sequence typing (MLST)-based study designed to understand the genetic diversity and population structure of an extensive regional sample of P. aeruginosa isolates from South East Queensland, Australia. The analysis is based on 501 P. aeruginosa isolates obtained from environmental, animal and human (CF and non-CF) sources with particular emphasis on isolates from the Lower Brisbane River and isolates from CF patients obtained from the same geographical region. Overall, MLST identified 274 different sequence types, of which 53 were shared between one or more ecological settings. Our analysis revealed a limited association between genotype and environment and evidence of frequent recombination. We also found that genetic diversity of P. aeruginosa in Queensland, Australia was indistinguishable from that of the global P. aeruginosa population. Several CF strains were encountered frequently in multiple ecological settings; however, the most frequently encountered CF strains were confined to CF patients. Overall, our data confirm a non-clonal epidemic structure and indicate that most CF strains are a random sample of the broader P. aeruginosa population. The increased abundance of some CF strains in different geographical regions is a likely product of chance colonisation events followed by adaptation to the CF lung and horizontal transmission among patients.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

The imprint of the geographical, evolutionary and ecological context on species-area relationships

Species-area relationships (SAR) are fundamental in the understanding of biodiversity patterns and of critical importance for predicting species extinction risk worldwide. Despite the enormous attention given to SAR in the form of many individual analyses, little attempt has been made to synthesize these studies. We conducted a quantitative meta-analysis of 794 SAR, comprising a wide span of organisms, habitats and locations. We identified factors reflecting both pattern-based and dynamic approaches to SAR and tested whether these factors leave significant imprints on the slope and strength of SAR. Our analysis revealed that SAR are significantly affected by variables characterizing the sampling scheme, the spatial scale, and the types of organisms or habitats involved. We found that steeper SAR are generated at lower latitudes and by larger organisms. SAR varied significantly between nested and independent sampling schemes and between major ecosystem types, but not generally between the terrestrial and the aquatic realm. Both the fit and the slope of the SAR were scale-dependent. We conclude that factors dynamically regulating species richness at different spatial scales strongly affect the shape of SAR. We highlight important consequences of this systematic variation in SAR for ecological theory, conservation management and extinction risk predictions.

Ecological dynamics of extinct species in empty habitat networks. 1. The role of habitat pattern and quantity, stochasticity and dispersal

We examined a remnant host plant (Primula veris L.) habitat network that was last inhabited by the rare butterfly Hamearis lucina L. in north Wales in 1943, to assess the relative contribution of several spatial parameters to its regional extinction. We first examined relationships between P. veris characteristics and H. lucina eggs in surviving H. lucina populations, and used these to predict the suitability and potential carrying capacity of the habitat network in north Wales. This resulted in an estimate of roughly 4500 eggs (ca 227 adults). We developed a discrete space, discrete time metapopulation model to evaluate the relative contribution of dispersal distance, habitat and environmental stochasticity as possible causes of extinction. We simulated the potential persistence of the butterfly in the current network as well as in three artificial (historical and present) habitat networks that differed in the quantity (current and X3) and fragmentation of the habitat (current and aggregated). We identified that reduced habitat quantity and increased isolation would have increased the probability of regional extinction, in conjunction with environmental stochasticity and H. lucina's dispersal distance. This general trend did not change in a qualitative manner when we modified the ability of dispersing females to stay in, and find suitable habitats (by changing the size of the grid cells used in the model). Contrary to most metapopulation model predictions, system persistence declined with increasing migration rate, suggesting that the mortality of migrating individuals in fragmented landscapes may pose significant risks to system-wide persistence. Based on model predictions for the present landscape we argue that a major programme of habitat restoration would be required for a re-established metapopulation to persist for > 100 years.

Ecological dynamics of extinct species in empty habitat networks. 2. The role of host plant dynamics

This paper explores the relative effects of host plant dynamics and butterfly-related parameters on butterfly persistence. It considers an empty habitat network where a rare butterfly (Cupido minimus) became extinct in 1939 in part of its historical range in north Wales, UK. Surviving populations of the butterfly in southern Britain were visited to assess use of its host plant (Anthyllis vulneraria) in order to calibrate habitat suitability and carrying capacity in the empty network in north Wales. These data were used to deduce that only a portion ( similar to 19%) of the host plant network from north Wales was likely to be highly suitable for oviposition. Nonetheless, roughly 65,460 eggs (3273 adult equivalents) could be expected to be laid in north Wales, were the empty network to be populated at the same levels as observed on comparable plants in surviving populations elsewhere. Simulated metapopulations of C. minimus in the empty network revealed that time to extinction and patch occupancy were significantly influenced by carrying capacity, butterfly mean dispersal distance and environmental stochasticity, although for most reasonable parameter values, the model system persisted. Simulation outputs differed greatly when host plant dynamics was incorporated into the modelled butterfly dynamics. Cupido minimus usually went extinct when host plant were at low densities. In these simulations host plant dynamics appeared to be the most important determinant of the butterfly's regional extirpation. Modelling the outcome of a reintroduction programme to C. minimus variation at high quality locations, revealed that 65% of systems survived at least 100 years. Given the current amount of resources of the north Wales landscape, the persistence of C. minimus under a realistic reintroduction programme has a good chance of being successful, if carried out in conjunction with a host plant management programme.

Differential ecological impacts of an invader and a native under environmental change are revealed by comparative functional responses.

Predicting the ecological impacts of damaging invasive species under relevant environmental contexts is a major challenge, for which comparative functional responses (the relationship between resource availability and consumer uptake rate) have great potential. Here, the functional responses of Gammarus pulex, an ecologically damaging invader in freshwaters in Ireland and other islands, were compared with those of a native trophic equivalent Gammarus duebeni celticus. Experiments were conducted at two dissolved oxygen concentrations (80 and 50 % saturation), representative of anthropogenic water quality changes, using two larval prey, blackfly (Simuliidae spp.) and mayfly (Baetis rhodani). Overall, G. pulex had higher Type II functional responses and hence predatory impacts than G. d. celticus and the functional responses of both predators were reduced by lowered oxygen concentration. However, this reduction was of lower magnitude for the invader as compared to the native. Further, the invader functional response at low oxygen was comparable to that of the native at high oxygen. Attack rates of the two predators were similar, with low oxygen reducing these attack rates, but this effect occurred more strongly for blackfly than mayfly prey. Handling times were significantly lower for the invader compared with the native, and significantly higher at low oxygen, however, the effect of lowered oxygen on handling times was minimal for the invader and pronounced for the native. Maximum feeding rates were significantly greater for the invader compared with the native, and significantly reduced at low oxygen, with this effect again lesser for the invader as compared to the native. The greater functional responses of the invader corroborate with its impacts on recipient macroinvertebrate communities when it replaces the native. Further, our experiments predict that the impact of the invader will be less affected than the native under altered oxygen regimes driven by anthropogenic influences.

Use of an earthworm lysosomal biomarker for the ecological assessment of pollution from an industrial plastics fire

A low-cost field technique employing retention of the dye neutral-red by lysosomes in coelomocyte cells taken from earthworms (Lumbricus castaneus), was used as a means of assessing the ecological effects (if any) of an industrial accident. Earthworms and soil samples were collected at the site of a large industrial plastics fire in Thetford, UK along a 200 m transect leading from the factory perimeter fence, over a layer of molten plastic impregnated soil and into the surrounding forest. Coelomic fluid extracted from the earthworms was dye-loaded with neutral-red and lysosomal leaking observed. Metal residues in soil and earthworms were found to be highly elevated close to the factory perimeter and to rapidly drop to background levels within the first 50 m of the transect. Coelomocyte cells taken from earthworms adjacent to the factory perimeter showed the shortest period of neutral-red retention (2 min); cells taken from worms further into the surrounding forest had a longer retention time (12 min), whilst cells taken from worms from a control site showed even greater retention times (25 min). Thus, the neutral-red retention times correlated negatively with measured residues of heavy metals in the earthworms, the higher the body metal concentration the shorter the retention time. This field trial has demonstrated the validity of using an in vitro cellular biomarker technique for use in biological impact assessment along gradients of contamination.

Ecological communities are vulnerable to realistic extinction sequences

Loss of species will directly change the structure and potentially the dynamics of ecological communities, which in turn may lead to additional species loss (secondary extinctions) due to direct and/or indirect effects (e.g. loss of resources or altered population dynamics). Furthermore, the vulnerability of food webs to repeated species loss is expected to be affected by food web topology, species interactions, as well as the order in which species go extinct. Species traits such as body size, abundance and connectivity might determine a species' vulnerability to extinction and, thus, the order in which species go primarily extinct. Yet, the sequence of primary extinctions, and their effects on the vulnerability of food webs to secondary extinctions, when species abundances are allowed to respond dynamically, has only recently become the focus of attention. Here, we analyse and compare topological and dynamical robustness to secondary extinctions of model food webs, in the face of 34 extinction sequences based on species traits. Although secondary extinctions are frequent in the dynamical approach and rare in the topological approach, topological and dynamical robustness tends to be correlated for many bottom-up directed, but not for top-down directed deletion sequences. Furthermore, removing species based on traits that are strongly positively correlated to the trophic position of species (such as large body size, low abundance, high net effect) is, under the dynamical approach, found to be as destructive as removing primary producers. Such top-down oriented removal of species are often considered to correspond to realistic extinction scenarios, but earlier studies, based on topological approaches, have found such extinction sequences to have only moderate effects on the remaining community. Thus, our result suggests that the structure of ecological communities, and therefore the integrity of important ecosystem processes could be more vulnerable to realistic extinction sequences than previously believed.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.