Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

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.

Morphometric shape analysis using learning vector quantization neural networks - an example distinguishing two microtine vole species

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Sphingomonas wittichii RW1 gene reporters interrogating the dibenzofuran metabolic network highlight conditions for early successful development in contaminated microcosms.

In order to better understand the fate and activity of bacteria introduced into contaminated material for the purpose of enhancing biodegradation rates, we constructed Sphingomonas wittichii RW1 variants with gene reporters interrogating dibenzofuran metabolic activity. Three potential promoters from the dibenzofuran metabolic network were selected and fused to the gene for enhanced green fluorescent protein (EGFP). The stability of the resulting genetic constructions in RW1 was examined, with plasmids based on the broad-host range vector pME6012 being the most reliable. One of the selected promoters, upstream of the gene Swit_4925 for a putative 2-hydroxy-2,4-pentadienoate hydratase, was inducible by growth on dibenzofuran. Sphingomonas wittichii RW1 equipped with the Swit_4925 promoter egfp fusion grew in a variety of non-sterile sandy microcosms contaminated with dibenzofuran and material from a former gasification site. The strain also grew in microcosms without added dibenzofuran but to a very limited extent, and EGFP expression indicated the formation of consistent small subpopulations of cells with an active inferred dibenzofuran metabolic network. Evidence was obtained for competition for dibenzofuran metabolites scavenged by resident bacteria in the gasification site material, which resulted in a more rapid decline of the RW1 population. Our results show the importance of low inoculation densities in order to observe the population development of the introduced bacteria and further illustrate that the limited availability of unique carbon substrate may be the most important factor impinging growth.

Interleukin-1- and type I interferon-dependent enhanced immunogenicity of an NYVAC-HIV-1 Env-Gag-Pol-Nef vaccine vector with dual deletions of type I and type II interferon-binding proteins.

UNLABELLED: NYVAC, a highly attenuated, replication-restricted poxvirus, is a safe and immunogenic vaccine vector. Deletion of immune evasion genes from the poxvirus genome is an attractive strategy for improving the immunogenic properties of poxviruses. Using systems biology approaches, we describe herein the enhanced immunological profile of NYVAC vectors expressing the HIV-1 clade C env, gag, pol, and nef genes (NYVAC-C) with single or double deletions of genes encoding type I (ΔB19R) or type II (ΔB8R) interferon (IFN)-binding proteins. Transcriptomic analyses of human monocytes infected with NYVAC-C, NYVAC-C with the B19R deletion (NYVAC-C-ΔB19R), or NYVAC-C with B8R and B19R deletions (NYVAC-C-ΔB8RB19R) revealed a concerted upregulation of innate immune pathways (IFN-stimulated genes [ISGs]) of increasing magnitude with NYVAC-C-ΔB19R and NYVAC-C-ΔB8RB19R than with NYVAC-C. Deletion of B8R and B19R resulted in an enhanced activation of IRF3, IRF7, and STAT1 and the robust production of type I IFNs and of ISGs, whose expression was inhibited by anti-type I IFN antibodies. Interestingly, NYVAC-C-ΔB8RB19R induced the production of much higher levels of proinflammatory cytokines (tumor necrosis factor [TNF], interleukin-6 [IL-6], and IL-8) than NYVAC-C or NYVAC-C-ΔB19R as well as a strong inflammasome response (caspase-1 and IL-1β) in infected monocytes. Top network analyses showed that this broad response mediated by the deletion of B8R and B19R was organized around two upregulated gene expression nodes (TNF and IRF7). Consistent with these findings, monocytes infected with NYVAC-C-ΔB8RB19R induced a stronger type I IFN-dependent and IL-1-dependent allogeneic CD4(+) T cell response than monocytes infected with NYVAC-C or NYVAC-C-ΔB19R. Dual deletion of type I and type II IFN immune evasion genes in NYVAC markedly enhanced its immunogenic properties via its induction of the increased expression of type I IFNs and IL-1β and make it an attractive candidate HIV vaccine vector. IMPORTANCE: NYVAC is a replication-deficient poxvirus developed as a vaccine vector against HIV. NYVAC expresses several genes known to impair the host immune defenses by interfering with innate immune receptors, cytokines, or interferons. Given the crucial role played by interferons against viruses, we postulated that targeting the type I and type II decoy receptors used by poxvirus to subvert the host innate immune response would be an attractive approach to improve the immunogenicity of NYVAC vectors. Using systems biology approaches, we report that deletion of type I and type II IFN immune evasion genes in NYVAC poxvirus resulted in the robust expression of type I IFNs and interferon-stimulated genes (ISGs), a strong activation of the inflammasome, and upregulated expression of IL-1β and proinflammatory cytokines. Dual deletion of type I and type II IFN immune evasion genes in NYVAC poxvirus improves its immunogenic profile and makes it an attractive candidate HIV vaccine vector.

Trapping of naive lymphocytes triggers rapid growth and remodeling of the fibroblast network in reactive murine lymph nodes.

Adaptive immunity is initiated in T-cell zones of secondary lymphoid organs. These zones are organized in a rigid 3D network of fibroblastic reticular cells (FRCs) that are a rich cytokine source. In response to lymph-borne antigens, draining lymph nodes (LNs) expand several folds in size, but the fate and role of the FRC network during immune response is not fully understood. Here we show that T-cell responses are accompanied by the rapid activation and growth of FRCs, leading to an expanded but similarly organized network of T-zone FRCs that maintains its vital function for lymphocyte trafficking and survival. In addition, new FRC-rich environments were observed in the expanded medullary cords. FRCs are activated within hours after the onset of inflammation in the periphery. Surprisingly, FRC expansion depends mainly on trapping of naïve lymphocytes that is induced by both migratory and resident dendritic cells. Inflammatory signals are not required as homeostatic T-cell proliferation was sufficient to trigger FRC expansion. Activated lymphocytes are also dispensable for this process, but can enhance the later growth phase. Thus, this study documents the surprising plasticity as well as the complex regulation of FRC networks allowing the rapid LN hyperplasia that is critical for mounting efficient adaptive immunity.

Expression of Staphylococcus aureus clumping factor A in Lactococcus lactis subsp. cremoris using a new shuttle vector.

Staphylococcus aureus harbors redundant adhesins mediating tissue colonization and infection. To evaluate their intrinsic role outside of the staphylococcal background, a system was designed to express them in Lactococcus lactis subsp. cremoris 1363. This bacterium is devoid of virulence factors and has a known genetic background. A new Escherichia coli-L. lactis shuttle and expression vector was constructed for this purpose. First, the high-copy-number lactococcal plasmid pIL253 was equipped with the oriColE1 origin, generating pOri253 that could replicate in E. coli. Second, the lactococcal promoters P23 or P59 were inserted at one end of the pOri253 multicloning site. Gene expression was assessed by a luciferase reporter system. The plasmid carrying P23 (named pOri23) expressed luciferase constitutively at a level 10,000 times greater than did the P59-containing plasmid. Transcription was absent in E. coli. The staphylococcal clumping factor A (clfA) gene was cloned into pOri23 and used as a model system. Lactococci carrying pOri23-clfA produced an unaltered and functional 130-kDa ClfA protein attached to their cell walls. This was indicated both by the presence of the protein in Western blots of solubilized cell walls and by the ability of ClfA-positive lactococci to clump in the presence of plasma. ClfA-positive lactococci had clumping titers (titer of 4,112) similar to those of S. aureus Newman in soluble fibrinogen and bound equally well to solid-phase fibrinogen. These experiments provide a new way to study individual staphylococcal pathogenic factors and might complement both classical knockout mutagenesis and modern in vivo expression technology and signature tag mutagenesis.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Primary pineal tumors: outcome and prognostic factors-a study from the Rare Cancer Network (RCN).

PURPOSE: To better define outcome and prognostic factors in primary pineal tumors. MATERIALS AND METHODS: Thirty-five consecutive patients from seven academic centers of the Rare Cancer Network diagnosed between 1988 and 2006 were included. Median age was 36 years. Surgical resection consisted of biopsy in 12 cases and resection in 21 (2 cases with unknown resection). All patients underwent radiotherapy and 12 patients received also chemotherapy. RESULTS: Histological subtypes were pineoblastoma (PNB) in 21 patients, pineocytoma (PC) in 8 patients and pineocytoma with intermediate differentiation in 6 patients. Six patients with PNB had evidence of spinal seeding. Fifteen patients relapsed (14 PNB and 1 PC) with PNB cases at higher risk (p = 0.031). Median survival time was not reached. Median disease-free survival was 82 months (CI 50 % 28-275). In univariate analysis, age younger than 36 years was an unfavorable prognostic factor (p = 0.003). Patients with metastases at diagnosis had poorer survival (p = 0.048). Late side effects related to radiotherapy were dementia, leukoencephalopathy or memory loss in seven cases, occipital ischemia in one, and grade 3 seizures in two cases. Side effects related to chemotherapy were grade 3-4 leucopenia in five cases, grade 4 thrombocytopenia in three cases, grade 2 anemia in two cases, grade 4 pancytopenia in one case, grade 4 vomiting in one case and renal failure in one case. CONCLUSIONS: Age and dissemination at diagnosis influenced survival in our series. The prevalence of chronic toxicity suggests that new adjuvant strategies are advisable.