MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

One-year prevalence of low back pain in two Swiss regions: estimates from the population participating in the 1992-1993 MONICA project.

STUDY DESIGN: A cross-sectional survey was performed. OBJECTIVE: To estimate the extent of low back pain as a public health problem. SUMMARY OF BACKGROUND DATA: Health surveys converge on very high estimates of low back pain in general populations, but few studies have included severity criteria in their definition and conclusions. Because it is unlikely that interventions will influence the prevalence of minimal and infrequent symptoms, greater attention should be paid to characteristics of low back pain that indicate some impact on the life of survey respondents. METHODS: Two regions participated in the MONICA (MONitoring of trends and determinants in CArdiovascular disease) project in Switzerland. Participants randomly selected from the general population completed a standard self-administered questionnaire on cardiovascular risk factors. A special section on low back pain was added in the third (1992-1993) MONICA survey and completed by 3227 participants. RESULTS: A regional difference found in the 12-month prevalence rate disappeared with the inclusion of severity criteria. Low back pain over more than seven cumulated days was reported among men by 20.2% (age range, 25-34 years) to 28.5% (age range, 65-74 years), respectively, among women by 31.1% to 38.5%. Similar rates of reduction in activity (professional, housekeeping, and leisure time) and medical consultation (conventional and nonconventional) motivated by low back pain characterized the two participating regions. The cumulative duration of pain was related to all the indicators showing the impact of low back pain on everyday life. CONCLUSIONS: Determining the cumulative duration of low back pain over the preceding year is a straightforward task, and a cutoff at 1 week seems appropriate for distinguishing between low- and high-impact low back pain.

Linear random knots and their scaling behavior

We present here a nonbiased probabilistic method that allows us to consistently analyze knottedness of linear random walks with up to several hundred noncorrelated steps. The method consists of analyzing the spectrum of knots formed by multiple closures of the same open walk through random points on a sphere enclosing the walk. Knottedness of individual "frozen" configurations of linear chains is therefore defined by a characteristic spectrum of realizable knots. We show that in the great majority of cases this method clearly defines the dominant knot type of a walk, i.e., the strongest component of the spectrum. In such cases, direct end-to-end closure creates a knot that usually coincides with the knot type that dominates the random closure spectrum. Interestingly, in a very small proportion of linear random walks, the knot type is not clearly defined. Such walks can be considered as residing in a border zone of the configuration space of two or more knot types. We also characterize the scaling behavior of linear random knots.

The C-terminal domain of Plasmodium falciparum merozoite surface protein 3 self-assembles into alpha-helical coiled coil tetramer.

Proteins located on the surface of the pathogenic malaria parasite Plasmodium falciparum are objects of intensive studies due to their important role in the invasion of human cells and the accessibility to host antibodies thus making these proteins attractive vaccine candidates. One of these proteins, merozoite surface protein 3 (MSP3) represents a leading component among vaccine candidates; however, little is known about its structure and function. Our biophysical studies suggest that the 40 residue C-terminal domain of MSP3 protein self-assembles into a four-stranded alpha-helical coiled coil structure where alpha-helices are packed "side-by-side". A bioinformatics analysis provides an extended list of known and putative proteins from different species of Plasmodium which have such MSP3-like C-terminal domains. This finding allowed us to extend some conclusions of our studies to a larger group of the malaria surface proteins. Possible structural and functional roles of these highly conserved oligomerization domains in the intact merozoite surface proteins are discussed.

Measuring brain perfusion with intravoxel incoherent motion (IVIM): Initial clinical experience.

PURPOSE: To evaluate the feasibility of intravoxel incoherent motion (IVIM) perfusion measurements in the brain with currently available imaging systems. MATERIALS AND METHODS: We acquired high in-plane resolution (1.2 × 1.2 mm(2) ) diffusion-weighted images with 16 different values of b ranging from 0 to 900 s/mm(2) , in three orthogonal directions, on 3T systems with a 32-multichannel receiver head coil. IVIM perfusion maps were extracted by fitting a double exponential model of signal amplitude decay. Regions of interest were drawn in pathological and control regions, where IVIM perfusion parameters were compared to the corresponding dynamic susceptibility contrast (DSC) parameters. RESULTS: Hyperperfusion was found in the nonnecrotic or cystic part of two histologically proven glioblastoma multiforme and in two histologically proven glioma WHO grade III, as well as in a brain metastasis of lung adenocarcinoma, in a large meningioma, and in a case of ictal hyperperfusion. A monoexponential decay was found in a territory of acute ischemia, as well as in the necrotic part of a glioblastoma. The IVIM perfusion fraction f correlated well with DSC CBV. CONCLUSION: Our initial report suggests that high-resolution brain perfusion imaging is feasible with IVIM in the current clinical setting. J. Magn. Reson. Imaging 2014;39:624-632. © 2013 Wiley Periodicals, Inc.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Pseudogenes in the ENCODE regions: consensus annotation, analysis of transcription, and evolution.

Arising from either retrotransposition or genomic duplication of functional genes, pseudogenes are "genomic fossils" valuable for exploring the dynamics and evolution of genes and genomes. Pseudogene identification is an important problem in computational genomics, and is also critical for obtaining an accurate picture of a genome's structure and function. However, no consensus computational scheme for defining and detecting pseudogenes has been developed thus far. As part of the ENCyclopedia Of DNA Elements (ENCODE) project, we have compared several distinct pseudogene annotation strategies and found that different approaches and parameters often resulted in rather distinct sets of pseudogenes. We subsequently developed a consensus approach for annotating pseudogenes (derived from protein coding genes) in the ENCODE regions, resulting in 201 pseudogenes, two-thirds of which originated from retrotransposition. A survey of orthologs for these pseudogenes in 28 vertebrate genomes showed that a significant fraction ( approximately 80%) of the processed pseudogenes are primate-specific sequences, highlighting the increasing retrotransposition activity in primates. Analysis of sequence conservation and variation also demonstrated that most pseudogenes evolve neutrally, and processed pseudogenes appear to have lost their coding potential immediately or soon after their emergence. In order to explore the functional implication of pseudogene prevalence, we have extensively examined the transcriptional activity of the ENCODE pseudogenes. We performed systematic series of pseudogene-specific RACE analyses. These, together with complementary evidence derived from tiling microarrays and high throughput sequencing, demonstrated that at least a fifth of the 201 pseudogenes are transcribed in one or more cell lines or tissues.

Diffusion tensor echo planar imaging using surface coil transceiver with a semiadiabatic RF pulse sequence at 14.1T.

Diffusion magnetic resonance studies of the brain are typically performed using volume coils. Although in human brain this leads to a near optimal filling factor, studies of rodent brain must contend with the fact that only a fraction of the head volume can be ascribed to the brain. The use of surface coil as transceiver increases Signal-to-Noise Ratio (SNR), reduces radiofrequency power requirements and opens the possibility of parallel transmit schemes, likely to allow efficient acquisition schemes, of critical importance for reducing the long scan times implicated in diffusion tensor imaging. This study demonstrates the implementation of a semiadiabatic echo planar imaging sequence (echo time=40 ms, four interleaves) at 14.1T using a quadrature surface coil as transceiver. It resulted in artifact free images with excellent SNR throughout the brain. Diffusion tensor derived parameters obtained within the rat brain were in excellent agreement with reported values.

The co-evolution of culturally inherited altruistic helping and cultural transmission under random group formation.

Limited migration results in kin selective pressure on helping behaviors under a wide range of ecological, demographic and life-history situations. However, such genetically determined altruistic helping can evolve only when migration is not too strong and group size is not too large. Cultural inheritance of helping behaviors may allow altruistic helping to evolve in groups of larger size because cultural transmission has the potential to markedly decrease the variance within groups and augment the variance between groups. Here, we study the co-evolution of culturally inherited altruistic helping behaviors and two alternative cultural transmission rules for such behaviors. We find that conformist transmission, where individuals within groups tend to copy prevalent cultural variants (e.g., beliefs or values), has a strong adverse effect on the evolution of culturally inherited helping traits. This finding is at variance with the commonly held view that conformist transmission is a crucial factor favoring the evolution of altruistic helping in humans. By contrast, we find that under one-to-many transmission, where individuals within groups tend to copy a "leader" (or teacher), altruistic helping can evolve in groups of any size, although the cultural transmission rule itself hitchhikes rather weakly with a selected helping trait. Our results suggest that culturally determined helping behaviors are more likely to be driven by "leaders" than by popularity, but the emergence and stability of the cultural transmission rules themselves should be driven by some extrinsic factors.