Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Molecular species delimitation of a symbiotic fig-pollinating wasp species complex reveals extreme deviation from reciprocal partner specificity

Background: Symbiotic relationships have contributed to major evolutionary innovations, the maintenance of fundamental ecosystem functions, and the generation and maintenance of biodiversity. However, the exact nature of host/symbiont associations, which has important consequences for their dynamics, is often poorly known due to limited understanding of symbiont taxonomy and species diversity. Among classical symbioses, figs and their pollinating wasps constitute a highly diverse keystone resource in tropical forest and savannah environments. Historically, they were considered to exemplify extreme reciprocal partner specificity (one-to-one host-symbiont species relationships), but recent work has revealed several more complex cases. However, there is a striking lack of studies with the specific aims of assessing symbiont diversity and how this varies across the geographic range of the host. Results: Here, we use molecular methods to investigate cryptic diversity in the pollinating wasps of a widespread Australian fig species. Standard barcoding genes and methods were not conclusive, but incorporation of phylogenetic analyses and a recently developed nuclear barcoding gene (ITS2), gave strong support for five pollinator species. Each pollinator species was most common in a different geographic region, emphasising the importance of wide geographic sampling to uncover diversity, and the scope for divergence in coevolutionary trajectories across the host plant range. In addition, most regions had multiple coexisting pollinators, raising the question of how they coexist in apparently similar or identical resource niches. Conclusion: Our study offers a striking example of extreme deviation from reciprocal partner specificity over the full geographical range of a fig-wasp system. It also suggests that superficially identical species may be able to co-exist in a mutualistic setting albeit at different frequencies in relation to their fig host’s range. We show that comprehensive sampling and molecular taxonomic techniques may be required to uncover the true structure of cryptic biodiversity underpinning intimate ecological interactions.

Soil conditioning and plant-soil feedbacks in a modified forest ecosystem are soil-context dependent

Aims There is potential for altered plant-soil feedback (PSF) to develop in human-modified ecosystems but empirical data to test this idea are limited. Here, we compared the PSF operating in jarrah forest soil restored after bauxite mining in Western Australia with that operating in unmined soil. Methods Native seedlings of jarrah (Eucalyptus marginata), acacia (Acacia pulchella), and bossiaea (Bossiaea ornata) were grown in unmined and restored soils to measure conditioning of chemical and biological properties as compared with unplanted control soils. Subsequently, acacia and bossiaea were grown in soils conditioned by their own or by jarrah seedlings to determine the net PSF. Results In unmined soil, the three plant species conditioned the chemical properties but had little effect on the biological properties. In comparison, jarrah and bossiaea conditioned different properties of restored soil while acacia did not condition this soil. In unmined soil, neutral PSF was observed, whereas in restored soil, negative PSF was associated with acacia and bossiaea. Conclusions Soil conditioning was influenced by soil context and plant species. The net PSF was influenced by soil context, not by plant species and it was different in restored and unmined soils. The results have practical implications for ecosystem restoration after human activities.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Sampling and analyzing metals in soils for archaeological prospection: a critique

This paper presents a critique of current methods of sampling and analyzing soils for metals in archaeological prospection. Commonly used methodologies in soil science are shown to be suitable for archaeological investigations, with a concomitant improvement in their resolution. Understanding the soil-fraction location, concentration range, and spatial distribution of autochthonous (native) soil metals is shown to be a vital precursor to archaeological-site investigations, as this is the background upon which anthropogenic deposition takes place. Nested sampling is suggested as the most cost-effective method of investigating the spatial variability in the autochthonous metal concentrations. The use of the appropriate soil horizon (or sampling depth) and point sampling are critical in the preparation of a sampling regime. Simultaneous extraction is proposed as the most efficient method of identifying the location and eventual fate of autochthonous and anthropogenic metals, respectively.

Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05 degrees from satellite observations to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05-0.1 degrees, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04 K in Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.