The InterPro protein families database: the classification resource after 15 years.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

Research recommendations for selected IARC-classified agents.

OBJECTIVES: There are some common occupational agents and exposure circumstances where evidence of carcinogenicity is substantial but not yet conclusive for humans. The objectives are to identify research gaps and needs for twenty agents prioritized for review based on evidence of widespread human exposures and potential carcinogenicity in animals or humans. DATA SOURCES: A systematic review was conducted of new data published since the most recent pertinent IARC monograph meeting. DATA EXTRACTION: Reviewers were charged with identifying data gaps and general and specific approaches to address them, focusing on research that would be important in resolving classification uncertainties. An expert meeting brought reviewers together to discuss each agent and the identified data gaps and approaches. DATA SYNTHESIS: Several overarching issues were identified that pertained to multiple agents; these included the importance of recognizing that carcinogenic agents can act through multiple toxicity pathways and mechanisms, including epigenetic mechanisms, oxidative stress and immuno- and hormonal modulation. CONCLUSIONS: Studies in occupational populations provide important opportunities to understand the mechanisms through which exogenous agents cause cancer and intervene to prevent human exposure and/or prevent or detect cancer among those already exposed. Scientific developments are likely to increase the challenges and complexities of carcinogen testing and evaluation in the future, and epidemiologic studies will be particularly critical to inform carcinogen classification and risk assessment processes.[Authors]

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

The biogeography of mitochondrial and nuclear discordance in animals.

Combining nuclear (nuDNA) and mitochondrial DNA (mtDNA) markers has improved the power of molecular data to test phylogenetic and phylogeographic hypotheses and has highlighted the limitations of studies using only mtDNA markers. In fact, in the past decade, many conflicting geographic patterns between mitochondrial and nuclear genetic markers have been identified (i.e. mito-nuclear discordance). Our goals in this synthesis are to: (i) review known cases of mito-nuclear discordance in animal systems, (ii) to summarize the biogeographic patterns in each instance and (iii) to identify common drivers of discordance in various groups. In total, we identified 126 cases in animal systems with strong evidence of discordance between the biogeographic patterns obtained from mitochondrial DNA and those observed in the nuclear genome. In most cases, these patterns are attributed to adaptive introgression of mtDNA, demographic disparities and sex-biased asymmetries, with some studies also implicating hybrid zone movement, human introductions and Wolbachia infection in insects. We also discuss situations where divergent mtDNA clades seem to have arisen in the absence of geographic isolation. For those cases where foreign mtDNA haplotypes are found deep within the range of a second taxon, data suggest that those mtDNA haplotypes are more likely to be at a high frequency and are commonly driven by sex-biased asymmetries and/or adaptive introgression. In addition, we discuss the problems with inferring the processes causing discordance from biogeographic patterns that are common in many studies. In many cases, authors presented more than one explanation for discordant patterns in a given system, which indicates that likely more data are required. Ideally, to resolve this issue, we see important future work shifting focus from documenting the prevalence of mito-nuclear discordance towards testing hypotheses regarding the drivers of discordance. Indeed, there is great potential for certain cases of mitochondrial introgression to become important natural systems within which to test the effect of different mitochondrial genotypes on whole-animal phenotypes.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Phylogeographical structure, postglacial recolonization and barriers to gene flow in the distinctive Valais chromosome race of the common shrew (Sorex araneus).

Using one male-inherited and eight biparentally inherited microsatellite markers, we investigate the population genetic structure of the Valais chromosome race of the common shrew (Sorex araneus) in the Central Alps of Europe. Unexpectedly, the Y-chromosome microsatellite suggests nearly complete absence of male gene flow among populations from the St-Bernard and Simplon regions (Switzerland). Autosomal markers also show significant genetic structuring among these two geographical areas. Isolation by distance is significant and possible barriers to gene flow exist in the study area. Two different approaches are used to better understand the geographical patterns and the causes of this structuring. Using a principal component analysis for which testing procedure exists, and partial Mantel tests, we show that the St-Bernard pass does not represent a significant barrier to gene flow although it culminates at 2469 m, close to the highest altitudinal record for this species. Similar results are found for the Simplon pass, indicating that both passes represented potential postglacial recolonization routes into Switzerland from Italian refugia after the last Pleistocene glaciations. In contrast with the weak effect of these mountain passes, the Rhône valley lowlands significantly reduce gene flow in this species. Natural obstacles (the large Rhône river) and unsuitable habitats (dry slopes) are both present in the valley. Moreover, anthropogenic changes to landscape structures are likely to have strongly reduced available habitats for this shrew in the lowlands, thereby promoting genetic differentiation of populations found on opposite sides of the Rhône valley.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

Assessment of a new MRI classification of spinal stenosis based on cross-sectional morphology of the dural sac : FM2

Introduction: Quantitative measures of degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal or dural sac cross sectional area vary widely and do not correlate with clinical symptoms or results of surgical decompression. In an effort to improve quantification of stenosis we have developed a grading system based on the morphology of the dural sac and its contents as seen on T2 axial images. The grading comprises seven categories ranging form normal to the most severe stenosis and takes into account the ratio of rootlet/CSF content. Material and methods: Fifty T2 axial MRI images taken at disc level from twenty seven symptomatic lumbar spinal stenosis patients who underwent decompressive surgery were classified into seven categories by five observers and reclassified 2 weeks later by the same investigators. Intra- and inter-observer reliability of the classification were assessed using Cohen's and Fleiss' kappa statistics, respectively. Results: Generally, the morphology grading system itself was well adopted by the observers. Its success in application is strongly influenced by the identification of the dural sac. The average intraobserver Cohen's kappa was 0.53 ± 0.2. The inter-observer Fleiss' kappa was 0.38 ± 0.02 in the first rating and 0.3 ± 0.03 in the second rating repeated after two weeks. Discussion: In this attempt, the teaching of the observers was limited to an introduction to the general idea of the morphology grading system and one example MRI image per category. The identification of the dimension of the dural sac may be a difficult issue in absence of complete T1 T2 MRI image series as it was the case here. The similarity of the CSF to possibly present fat on T2 images was the main reason of mismatch in the assignment of the cases to a category. The Fleiss correlation factors of the five observers are fair and the proposed morphology grading system is promising.

Peroxisome proliferator-activated receptors: finding the orphan a home.

The peroxisome proliferator-activated receptor (PPAR) is a member of the steroid hormone receptor superfamily and is activated by a variety of fibrate hypolipidaemic drugs and non-genotoxic rodent hepatocarcinogens that are collectively termed peroxisome proliferators. A key marker of peroxisome proliferator action is the peroxisomal enzyme acyl CoA oxidase, which is elevated about ten fold in the livers of treated rodents. Additional peroxisome proliferator responsive genes include other peroxisomal beta-oxidation enzymes and members of the cytochrome P450 IVA family. A peroxisome proliferator response element (PPRE), consisting of an almost perfect direct repeat of the sequence TGACCT spaced by a single base pair, has been identified in the upstream regulatory sequences of each of these genes. The retinoid X receptor (RXR) forms a heterodimer with PPAR and binds to the PPRE. Furthermore, the RXR ligand, 9-cis retinoic acid, enhances PPAR action. Retinoids may therefore modulate the action of peroxisome proliferators and PPAR may interfere with retinoid action, perhaps providing one mechanism to explain the toxicity of peroxisome proliferators. Interestingly, a variety of fatty acids can activate PPAR supporting the suggestion that fatty acids, or their acyl CoA derivatives, may be the natural ligands of PPAR and that the physiological role of PPAR is to regulate fatty acid homeostasis. Taken together, the discovery of PPAR has opened up new opportunities in understanding how lipid homeostasis is regulated, how the fibrate hypolipidaemic drugs may act and should lead to improvements in the assessment of human risk from peroxisome proliferators based upon a better understanding of their mechanism of action.

Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009

Different types of cell death are often defined by morphological criteria, without a clear reference to precise biochemical mechanisms. The Nomenclature Committee on Cell Death (NCCD) proposes unified criteria for the definition of cell death and of its different morphologies, while formulating several caveats against the misuse of words and concepts that slow down progress in the area of cell death research. Authors, reviewers and editors of scientific periodicals are invited to abandon expressions like 'percentage apoptosis' and to replace them with more accurate descriptions of the biochemical and cellular parameters that are actually measured. Moreover, at the present stage, it should be accepted that caspase-independent mechanisms can cooperate with (or substitute for) caspases in the execution of lethal signaling pathways and that 'autophagic cell death' is a type of cell death occurring together with (but not necessarily by) autophagic vacuolization. This study details the 2009 recommendations of the NCCD on the use of cell death-related terminology including 'entosis', 'mitotic catastrophe', 'necrosis', 'necroptosis' and 'pyroptosis'.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Inactivation of Notch 1 in immature thymocytes does not perturb CD4 or CD8T cell development.

Notch proteins influence cell-fate decisions in many developing systems. Several gain-of-function studies have suggested a critical role for Notch 1 signaling in CD4-CD8 lineage commitment, maturation and survival in the thymus. However, we show here that tissue-specific inactivation of the gene encoding Notch 1 in immature (CD25+CD44-)T cell precursors does not affect subsequent thymocyte development. Neither steady-state numbers nor the rate of production of CD4+ and CD8+ mature thymocytes is perturbed in the absence of Notch 1. In addition, Notch 1-deficient thymocytes are normally sensitive to spontaneous or glucocorticoid-induced apoptosis. In contrast to earlier reports, these data formally exclude an essential role for Notch 1 in CD4-CD8 lineage commitment, maturation or survival.

In susceptible mice, Leishmania major induce very rapid interleukin-4 production by CD4+ T cells which are NK1.1-.

Susceptibility of BALB/c mice to infection with Leishmania major is associated with a T helper type 2 (Th2) response. Since interleukin-4 (IL-4) is critically required early for Th2 cell development, the kinetics of IL-4 mRNA expression was compared in susceptible and resistant mice during the first days of infection. In contrast to resistant mice, susceptible mice exhibited a peak of IL-4 mRNA in their spleens 90 min after i.v. injection of parasites and in lymph nodes 16 h after s.c. injection. IL-12 and interferon-gamma (IFN-gamma) down-regulated this early peak of IL-4 mRNA; the effect of IL-12 was IFN-gamma dependent. Treatment of resistant C57BL/6 mice with anti-IFN-gamma allowed the expression of this early IL-4 response to L. major. The increased IL-4 mRNA expression occurred in V beta 8, 7, 2- CD4+ cells in BALB/c mice and NK1.1- CD4+ cells in anti-IFN-gamma treated C57BL/6 mice. These results show that the NK1.1+ CD4+ cells, responsible for the rapid burst of IL-4 production after i.v. injection of anti-CD3, do not contribute to the early IL-4 response to L. major.