Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Exploiting Kinetic Features of ORAC Assay for Evaluation of Radical Scavenging Capacity

The analysis and interpretation of data retrieved from Oxygen Radical Absorbance Capacity (ORAC) assays represent a challenging task. ORAC indexes originate from different mathematical approaches often lacking correct elucidation of kinetic features concerning radical scavenging reactions by antioxidant compounds. In this work, the expression of ORAC values as area under fluorescein (FL) decay curves (AUC) and lag time are critically compared. This multi-parametric analysis showed the extension of radical scavenging reactions beyond the lag time period for caffeic acid, gallic acid, reduced glutathione and quercetin, extending their antioxidant protection of FL. Ethanol delayed the reaction of both FL and antioxidant compounds with free radical species generated from 2,2′-azobis(2-amidinopropane) dihydrochloride thermolysis. Trolox equivalent values, commonly used to express ORAC values, were more affected by the differences in radical scavenging kinetics between the reference and the tested antioxidant compounds when calculated from AUC than from lag time. These findings stressed the importance of choosing calibrator compounds presenting ORAC kinetics similar to samples to prevent biased estimation of the antioxidant capacity. Additionally, the framework proposed here provides a sustainable analytical method for the evaluation of antioxidant capacity, with an AGREE score of 0.73

Correction: Community structure informs species geographic distributions.

[This corrects the article DOI: 10.1371/journal.pone.0197877.]

Community structure informs species geographic distributions

Understanding what determines species' geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. `community structure') reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could bebecause species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species' large-scale distributions, and this information can improve the predictions of species distributions

Expected covariance between species involved in biotic interactions and environmental filtering.

<p>The combination of 3-d shapes and colors represent four different species. Species with similar requirements (syndromes) are represented by the same shape (pyramids: Tertiary (T), cubes: Quaternary(Q)), but distinct colors. Environmental filters are represented as grey ellipses in which only species with certain traits can survive (e.g. moist and shaded environments on north facing slopes where species with a tertiary syndrome can survive, or sunny environments on south facing slopes where quaternary species can survive: the 3-d shapes must match the shape of the ellipse). In the case of negative abundance covariance, competition is expected to be more intense between species with similar traits and ecological requirements resulting in spatial segregation between species with similar requirements and traits, while environmental filtering will result in spatial segregation between species with dissimilar requirements and traits. In the case of positive abundance covariance, facilitation promotes the co-occurrence between species with dissimilar requirements and traits, while habitat filtering results in the co-occurrence of species with similar requirements and traits.</p

Correlation between the Bray-Curtis similarity index using “Env” and “Env + Bio models”.

<p>Bray-Curtis similarity index between the observed community structure and the community structure predicted by “Env+Bio” vs. the similarity index between the observed community structure and that predicted by “Env” models, for the 524 validation plots. Values of Bray-Curtis similarity index closer to 1 imply that community structure is predicted more accurately and values closer to 0 indicate less accurate predictions. Black points above the line represent plots with higher similarity between the observed values and those predicted using the “Env+Bio” rather than the “Env” predictors. The opposite is true for white points below the line.</p

Network structure learned using Bayesian network inference.

<p>Only significant links are presented, and grey lines indicating links with no sign was detected. Grey and black circles represent species with a Quaternary and Tertiary syndrome respectively. White circles are either environmental variables (mean temperature in the warmest quarter of the year (Twarm), annual precipitation (anualP), soil types (soil), land use (landuse), orientation (orientation), dominant form (dom_form) and spatial location (spac)) or species with no syndrome associated. Continuous and dashed lines represent negative and positive associations respectively. Complete names for species are provided in the appendix and environmental variable categories in the methods section.</p

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved