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PubMed2025

Association of AGER genetic variants with chronic obstructive pulmonary disease susceptibility in Southern Chinese Han populations.

OBJECTIVE: Chronic obstructive pulmonary disease (COPD) remains a leading cause of disability and mortality among elderly populations. Studies indicate that AGER plays a critical regulatory role in the pathogenesis of respiratory disorders. However, the genetic variations in AGER to COPD susceptibility remain incompletely understood. This study employs a case-control design to investigate associations between AGER genetic variants and COPD risk in the Southern Chinese Han population. METHODS: This study enrolled 270 COPD patients and 271 healthy controls. AGER single-nucleotide polymorphisms (SNPs) were analysed using the MassARRAY iPLEX platform. Logistic regression models evaluated associations between AGER polymorphisms and COPD susceptibility, with false discovery rate (FDR) correction applied to mitigate multiple testing errors. SNP-SNP interactions were investigated through multifactor dimensionality reduction (MDR) analysis. Expression quantitative trait locus (eQTL) data from the GTEx database were further analysed to assess regulatory relationships between SNPs and AGER gene expression levels. RESULTS: This study showed that rs3134941 (G allele, OR = 0.21, 95% CI = 0.10-0.41, p (FDR) = 0.001) and rs3131300 (G allele, OR = 0.32, 95% CI = 0.20-0.49, p (FDR) = 0.0001) were significantly associated with a reduced susceptibility to COPD. MDR indicated that rs3131300 was the optimal predictive model for COPD risk. Additionally, initial mechanistic investigations utilizing the GTEx database identify rs3134941 (C > G) and rs3131300 (A > G) as significant expression quantitative trait loci for AGER mRNA in cell-cultured fibroblasts and whole blood. CONCLUSION: Our study demonstrated that AGER genetic variants might play a protective role in the progression of COPD.

Aged↗

PubMed2005

Emissions of organic compounds and trace metals in fine particulate matter from motor vehicles: a tunnel study in Houston, Texas.

Fine particulate matter (PM) samples collected in a highway tunnel in Houston, TX, were analyzed to quantify the concentrations of 14 n-alkanes, 12 polycyclic aromatic hydrocarbons, and nine petroleum biomarkers, as well as 21 metals, with the ultimate aim of identifying appropriate tracers for diesel engines. First, an exploratory multivariate dimensionality reduction technique called principal component analysis (PCA) was employed to identify all potential candidates for tracers. Next, emission indices were calculated to interpret PCA results physically. Emission indices of n-heneicosane, n-docosane, n-tricosane, n-tetracosane, n-pentacosane, fluoranthene, and pyrene were correlated highly and increased strongly with percentage carbon present in the tunnel emanating from diesel vehicles. This suggests that these organic compounds are useful molecular markers to separate emissions from diesel and gasoline engines. Additionally, the results are the first quantification of the metal composition of PM with aerodynamic diameters smaller than 2.5 microm (PM2.5) emissions from mobile sources in Houston. PCA of trace metal concentrations followed by emission index calculations revealed that barium in fine airborne particles can be linked quantitatively to diesel engine emissions, demonstrating its role as an elemental tracer for heavy-duty trucks.

Air Pollutants↗

PubMed2005

Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism.

Autism is a common neurodevelopmental disorder with a significant genetic component. Existing research suggests that multiple genes contribute to autism and that epigenetic effects or gene-gene interactions are likely contributors to autism risk. However, these effects have not yet been identified. Gamma-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the adult brain, has been implicated in autism etiology. Fourteen known autosomal GABA receptor subunit genes were studied to look for the genes associated with autism and their possible interactions. Single-nucleotide polymorphisms (SNPs) were screened in the following genes: GABRG1, GABRA2, GABRA4, and GABRB1 on chromosome 4p12; GABRB2, GABRA6, GABRA1, GABRG2, and GABRP on 5q34-q35.1; GABRR1 and GABRR2 on 6q15; and GABRA5, GABRB3, and GABRG3 on 15q12. Intronic and/or silent mutation SNPs within each gene were analyzed in 470 white families with autism. Initially, SNPs were used in a family-based study for allelic association analysis--with the pedigree disequilibrium test and the family-based association test--and for genotypic and haplotypic association analysis--with the genotype-pedigree disequilibrium test (geno-PDT), the association in the presence of linkage (APL) test, and the haplotype family-based association test. Next, with the use of five refined independent marker sets, extended multifactor-dimensionality reduction (EMDR) analysis was employed to identify the models with locus joint effects, and interaction was further verified by conditional logistic regression. Significant allelic association was found for markers RS1912960 (in GABRA4; P = .01) and HCV9866022 (in GABRR2; P = .04). The geno-PDT found significant genotypic association for HCV8262334 (in GABRA2), RS1912960 and RS2280073 (in GABRA4), and RS2617503 and RS12187676 (in GABRB2). Consistent with the allelic and genotypic association results, EMDR confirmed the main effect at RS1912960 (in GABRA4). EMDR also identified a significant two-locus gene-gene effect model involving RS1912960 in GABRA4 and RS2351299 in GABRB1. Further support for this two-locus model came from both the multilocus geno-PDT and the APL test, which indicated a common genotype and haplotype combination positively associated with disease. Finally, these results were also consistent with the results from the conditional logistic regression, which confirmed the interaction between GABRA4 and GABRB1 (odds ratio = 2.9 for interaction term; P = .002). Through the convergence of all analyses, we conclude that GABRA4 is involved in the etiology of autism and potentially increases autism risk through interaction with GABRB1. These results support the hypothesis that GABA receptor subunit genes are involved in autism, most likely via complex gene-gene interactions.

Autistic Disorder↗

PubMed2006

Immunogenetics of CD4 lymphocyte count recovery during antiretroviral therapy: An AIDS Clinical Trials Group study.

During antiretroviral therapy, CD4 lymphocyte count increases are modest in some patients despite virologic control. We explored whether polymorphisms in genes important for T cell expansion, survival, and apoptosis are associated with the magnitude of CD4 lymphocyte count recovery during antiretroviral therapy. We studied treatment-naive individuals who achieved sustained control of plasma viremia (<400 HIV-1 RNA copies/mL) for at least 48 weeks after initiation of antiretroviral therapy and compared genotypes among individuals who had an increase of either <200 or > or =200 CD4 cells/mm3 from baseline. A total of 137 single-nucleotide polymorphisms across 17 genes were characterized in 873 study participants. In multivariate analyses that controlled for clinical variables, polymorphisms in genes encoding tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), TNF- alpha , Bcl-2-interacting molecule (Bim), interleukin (IL)-15, and IL-15 receptor alpha chain (IL-15R alpha ) were associated with the magnitude of the increase in CD4 lymphocyte count, as were haplotypes in genes encoding interferon- alpha , IL-2, and IL-15R alpha (P < .05, for each). Multifactor dimensionality reduction identified a gene-gene interaction between IL-2/IL-15 receptor common beta chain and IL-2/IL-7/IL-15 receptor common gamma chain. Immune recovery during antiretroviral therapy is a complex phenotype that is influenced by multiple genetic variants. Future studies should validate these tentative associations and define underlying mechanisms.

Adult↗

PubMed1999

Linear transformations of data space in MEG.

Magnetoencephalography (MEG) is a method which allows the non-invasive measurement of the minute magnetic field which is generated by ion currents in the brain. Due to the complex sensitivity profile of the sensors, the measured data are a non-trivial representation of the currents where information specific to local generators is distributed across many channels and each channel contains a mixture of contributions from many such generators. We propose a framework which generates a new representation of the data through a linear transformation which is designed so that some desired property is optimized in one or more new virtual channel(s). First figures of merit are suggested to describe the relation between the measured data and the underlying currents. Within this context the new framework is established by first showing how the transformation matrix itself is designed and then by its application to real and simulated data. The results demonstrate that the proposed linear transformations of data space provide a computationally efficient tool for analysis and a very much needed dimensional reduction of the data.

Brain↗

PubMed2025

Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.

MOTIVATION: Advances in bacterial promoter predictors based on machine learning have greatly improved identification metrics. However, existing models overlooked the impact of negative datasets, previously identified in GC-content discrepancies between positive and negative datasets in single-species models. This study aims to investigate whether multiple-species models for promoter classification are inherently biased due to the selection criteria of negative datasets. We further explore whether the generation of synthetic random sequences (SRS) that mimic GC-content distribution of promoters can partly reduce this bias. RESULTS: Multiple-species predictors exhibited GC-content bias when using CDS as a negative dataset, suggested by specificity and sensibility metrics in a species-specific manner, and investigated by dimensionality reduction. We demonstrated a reduction in this bias by using the SRS dataset, with less detection of background noise in real genomic data. In both scenarios DNABERT showed the best metrics. These findings suggest that GC-balanced datasets can enhance the generalizability of promoter predictors across Bacteria. AVAILABILITY AND IMPLEMENTATION: The source code of the experiments is freely available at https://github.com/maigonzalezh/MultispeciesPromoterClassifier.

Machine Learning↗

PubMed2004

Approximate geodesic distances reveal biologically relevant structures in microarray data.

MOTIVATION: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. RESULTS: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.

Algorithms↗

PubMed2005

Classification of microarray data with factor mixture models.

MOTIVATION: The classification of few tissue samples on a very large number of genes represents a non-standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an 'absolute call', which represents a qualitative indication of whether or not a transcript is detected within a sample. The 'absolute call' is generally not taken in consideration in analyses. RESULTS: In contrast to frequently used cluster analysis methods to analyze gene expression data, we consider a problem of classification of tissues and of the variables selection. We adopted methodologies formulated by Ghahramani and Hinton and Rocci and Vichi for simultaneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated 'markers') that are able to distinguish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al. by advising to estimate the distribution of log LR statistic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric bootstrap approach. We compare conditional (on 'absolute call') and unconditional analyses performed on dataset described in Golub et al. We show that the proposed techniques improve the results of classification of tissue samples with respect to known results on the same benchmark dataset. AVAILABILITY: The software of Ghahramani and Hinton is written in Matlab and available in 'Mixture of Factor Analyzers' on http://www.gatsby.ucl.ac.uk/~zoubin/software.html while the software of Rocci and Vichi is available upon request from the authors.

Algorithms↗

PubMed2005

Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility.

Study results of single nucleotide polymorphisms (SNPs) and cancer susceptibility are often conflicting, possibly because of the analytic challenges of testing for multiple genetic and environmental risk factors using traditional analytic tools. We investigated the relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility in 355 cases and 559 controls enrolled in a population-based study of bladder cancer in the US. Our multifaceted analytical approach included logistic regression, multifactor dimensionality reduction, and hierarchical interaction graphs for the analysis of gene-gene and gene-environment interactions followed by linkage disequilibrium and haplotype analysis. Overall, we did not find an association between any single DNA repair gene SNP and bladder cancer risk. We did find a marginally significant elevated risk of the XPD codon 751 homozygote variant among never smokers [adjusted odds ratio (OR) 2.5, 95% confidence interval (CI) 1.0-6.2]. In addition, the XRCC1 194 variant allele was associated with a reduced bladder cancer risk among heavy smokers [adjusted OR 0.4, 95% CI 0.2-0.9)]. The best predictors of bladder cancer included the XPD codon 751 and 312 SNPs along with smoking. Interpretation of this multifactor model revealed that the relationship between the XPD SNPs and bladder cancer is mostly non-additive while the effect of smoking is mostly additive. Since the two XPD SNPs are in significant linkage disequilibrium (D' = 0.52, P = 0.0001), we estimated XPD haplotypes. Individuals with variant XPD haplotypes were more susceptible to bladder cancer [e.g. adjusted OR 2.5, 95% CI 1.7-3.6] and the effect was magnified when smoking was considered. These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and emphasize the need for a multifaceted statistical approach to identify gene-gene and gene-environment interactions.

Adult↗

PubMed2005

Susceptibility and modifier genes in Portuguese transthyretin V30M amyloid polyneuropathy: complexity in a single-gene disease.

Familial amyloid polyneuropathy type I is an autosomal dominant disorder caused by mutations in the transthyretin (TTR) gene; however, carriers of the same mutation exhibit variability in penetrance and clinical expression. We analyzed alleles of candidate genes encoding non-fibrillar components of TTR amyloid deposits and a molecule metabolically interacting with TTR [retinol-binding protein (RBP)], for possible associations with age of disease onset and/or susceptibility in a Portuguese population sample with the TTR V30M mutation and unrelated controls. We show that the V30M carriers represent a distinct subset of the Portuguese population. Estimates of genetic distance indicated that the controls and the classical-onset group were furthest apart, whereas the late-onset group appeared to differ from both. Importantly, the data also indicate that genetic interactions among the multiple loci evaluated, rather than single-locus effects, are more likely to determine differences in the age of disease onset. Multifactor dimensionality reduction indicated that the best genetic model for classical onset group versus controls involved the APCS gene, whereas for late-onset cases, one APCS variant (APCSv1) and two RBP variants (RBPv1 and RBPv2) are involved. Thus, although the TTR V30M mutation is required for the disease in Portuguese patients, different genetic factors may govern the age of onset, as well as the occurrence of anticipation.

Adult↗

PubMed2006

Analysis of epistasis for diabetic nephropathy among type 2 diabetic patients.

Diabetic nephropathy (DN) is one of the most serious complications of diabetes, accounting for the majority of patients with end-stage renal disease. The molecular pathogenesis of DN involves multiple pathways in a complex, partially resolved manner. The paper presents an exploratory epistatic study for DN. Association analysis were performed on 231 SNP loci in a cohort of 264 type 2 diabetes patients, followed by the epistasis analysis using the multifactor dimensionality reduction and the genetic algorithm with Boolean algebra. A two-locus epistatic effect of EGFR and RXRG was identified, with a cross-validation consistency of 91.7%.

Aged↗

PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗

PubMed2026

Genomic hallmarks of depot medroxyprogesterone acetate-associated meningiomas.

BACKGROUND: Population-based studies have linked progestin exposure to increased meningioma risk. However, the molecular basis of meningiomas associated with depot medroxyprogesterone acetate (DMPA)-a common injectable contraceptive-remains undefined. METHODS: We performed an integrated clinicopathologic and genomic analysis of meningiomas from 10 women with long-term DMPA exposure. Tumors underwent histopathological analysis, targeted sequencing, and DNA methylation profiling. Data were integrated with reference cohorts (Baylor and Heidelberg) and analyzed through classifier assignment, consensus clustering, copy number analysis, differential methylation testing, and dimensionality reduction. RESULTS: Depot medroxyprogesterone acetate-associated meningiomas were all newly diagnosed, World Health Organization grade 1 tumors with a predilection for the anterior and central skull base (n = 6). Nine patients harbored multiple meningiomas. Four experienced regression of untreated meningiomas following DMPA cessation, while 5 demonstrated stabilization. Histopathology demonstrated relative overrepresentation of metaplastic morphology, an uncommon meningioma subtype. All DMPA-associated meningiomas mapped to benign molecular groups, and most exhibited low copy number alteration burden. Targeted sequencing revealed enrichment for TRAF7 mutations (n = 5), with no NF2 mutations detected. Eight tumors shared consensus cluster identity, with cohesive grouping on principal component analysis and t-distributed stochastic neighbor embedding. No differential methylation was identified at the progesterone receptor locus. CONCLUSIONS: Depot medroxyprogesterone acetate-associated meningiomas represent a recognizable phenotype within the broader NF2-wildtype/TRAF7-enriched spectrum of benign meningiomas, characterized by chromosomal stability, a shared methylation profile, tumor multiplicity, and regression or stabilization following DMPA cessation. While derived from a small single-institution cohort, these findings provide a molecular framework for understanding progestin-associated meningioma biology, reinterpreting epidemiologic literature, and informing population-level risk stratification.

Humans↗

PubMed2006

Interaction among 5,10 methylenetetrahydrofolate reductase, plasminogen activator inhibitor and endothelial nitric oxide synthase gene polymorphisms predicts the severity of coronary artery disease in Turkish patients.

BACKGROUND: Genetic factors play a role in the onset of coronary artery disease. The objective of our study is to evaluate the single locus and combined effects of three different genetic polymorphisms (methylenetetrahydrofolate reductase C677T polymorphism, plasminogen activator inhibitor 4G/5G polymorphism, and endothelial nitric oxide synthase 3-27 base pairs repeat polymorphism) on the presence and extent of coronary artery disease in patients with early-onset coronary artery disease. MATERIALS AND METHODS: DNA samples were obtained from 102 consecutive patients with symptoms resulting from early-onset coronary artery disease documented by coronary angiography. The severity of coronary artery disease in patients was stratified into three groups as one, two, or three-vessel coronary artery disease. The control group was selected from older subjects with a recent and negative cardiac work-up. Information on standard risk factors was collected. Multifactor dimensionality reduction analysis was performed to seek a model of coronary artery disease based on these three genetic polymorphisms. RESULTS: Single-locus effects of the three polymorphisms were not significantly related to the presence or severity of coronary artery disease. When gene-gene interactions were studied, however, the severity of disease was related to the frequency of high-risk alleles, yet advanced analysis did not detect a significant genetic model for coronary artery disease in these patients based on these three genetic polymorphisms. CONCLUSION: These three genetic polymorphisms are susceptibility loci and genotypes of these genes are neither necessary nor sufficient for the coronary artery disease to occur, but coexistence of high-risk alleles may increase the severity of coronary artery disease.

Alleles↗

PubMed2006

Multilocus genetic interactions and response to efavirenz-containing regimens: an adult AIDS clinical trials group study.

OBJECTIVE: For the HIV-1 reverse transcriptase inhibitor efavirenz, variant drug transporter gene ABCB1 may predict virologic response but not plasma efavirenz exposure. Conversely, variant drug metabolizing enzyme gene CYP2B6 predicts greater plasma efavirenz exposure but not virologic response. We examined whether long-term responses to efavirenz, and/or plasma efavirenz exposure, are better predicted by multilocus genetic interactions than by individual polymorphisms. MATERIALS AND METHODS: We studied antiretroviral-naïve study participants randomized to receive efavirenz (with or without nelfinavir) plus two nucleoside analogues in study ACTG 384, and who had DNA available for analysis. Participants were followed up for up to 3 years. Nine single nucleotide polymorphisms in ABCB1, CYP2B6, CYP3A4, CYP3A5 and CYP2C19 were identified. Gene-gene interactions were identified using multifactor dimensionality reduction. RESULTS: Among 340 efavirenz recipients, higher efavirenz AUC24 h values were associated with a single locus model involving CYP2B6 516G>T (73% accuracy; P<0.001). This was also the best model among blacks (69% accuracy; P<0.001), whereas among whites the best model involved a gene-gene interaction between CYP2B6 516G>T and ABCB1 2677G>T (82% accuracy, P<0.001). Among 155 participants who received efavirenz without nelfinavir, virologic failure was associated with a two-locus interaction between ABCB1 2677G>T and CYP2B6 516G>T (65% accuracy, P<0.001). Toxicity failure was best predicted by an interaction between ABCB1 2677G>T and ABCB1 3435C>T (71% accuracy, P<0.001). CONCLUSIONS: Multilocus genetic interactions between variant drug metabolism and transporter genes may predict efavirenz pharmacokinetics and treatment responses. This finding may have implications for better individualizing antiretroviral therapy.

Acquired Immunodeficiency Syndrome↗

PubMed2006

SNP selection at the NAT2 locus for an accurate prediction of the acetylation phenotype.

PURPOSE: Genetic polymorphisms in the N-acetyltransferase 2 gene determine the individual acetylator status, which influences both the toxicity and efficacy profile of acetylated drugs. Determination of an individual's acetylation phenotype prior to initiation of therapy, through DNA-based tests, should permit to improve therapy response and reduce adverse events. However, due to extensive linkage disequilibrium between markers within NAT2, the genotyping of closely spaced markers yields highly redundant data: testing them all is expensive and often unnecessary. The objective of this study is to establish the optimal strategy to define, in the genetic context of a given ethnic group, the most informative set of single-nucleotide polymorphisms that best enables accurate prediction of acetylation phenotype. METHODS: Three classification methods have been investigated (classification trees, artificial neural networks and multifactor dimensionality reduction method) in order to find the optimal set of single-nucleotide polymorphisms enabling the most efficient classification of individuals in rapid and slow acetylators. RESULTS: Our results show that, in almost all population samples, only one or two single-nucleotide polymorphisms would be enough to obtain a good predictive capacity with no or only a modest reduction in power relative to direct assays of all common markers. In contrast, in Black African populations, where lower levels of linkage disequilibrium are observed at NAT2, a larger number of single-nucleotide polymorphisms are required to predict acetylation phenotype. CONCLUSION: The results of this study will be helpful for the design of time- and cost-effective pharmacogenetic tests (adapted to specific populations) that could be used as routine tools in clinical practice.

Acetylation↗

PubMed2004

Functional renormalization group and the field theory of disordered elastic systems.

We study elastic systems, such as interfaces or lattices, pinned by quenched disorder. To escape triviality as a result of "dimensional reduction," we use the functional renormalization group. Difficulties arise in the calculation of the renormalization group functions beyond one-loop order. Even worse, observables such as the two-point correlation function exhibit the same problem already at one-loop order. These difficulties are due to the nonanalyticity of the renormalized disorder correlator at zero temperature, which is inherent to the physics beyond the Larkin length, characterized by many metastable states. As a result, two-loop diagrams, which involve derivatives of the disorder correlator at the nonanalytic point, are naively "ambiguous." We examine several routes out of this dilemma, which lead to a unique renormalizable field theory at two-loop order. It is also the only theory consistent with the potentiality of the problem. The beta function differs from previous work and the one at depinning by novel "anomalous terms." For interfaces and random-bond disorder we find a roughness exponent zeta=0.208 298 04epsilon+0.006 858epsilon(2), epsilon=4-d. For random-field disorder we find zeta=epsilon/3 and compute universal amplitudes to order O(epsilon(2)). For periodic systems we evaluate the universal amplitude of the two-point function. We also clarify the dependence of universal amplitudes on the boundary conditions at large scale. All predictions are in good agreement with numerical and exact results and are an improvement over one loop. Finally we calculate higher correlation functions, which turn out to be equivalent to those at depinning to leading order in epsilon.

Journal Article↗

PubMed2003

Black holes in Gödel universes and pp waves.

We find exact solutions for rotating and nonrotating neutral black holes in the Gödel universe of five-dimensional minimal supergravity theory. We also describe the embedding of this solution in M-theory. After dimensional reduction and T-duality, we obtain a supergravity solution corresponding to placing a black string in a pp-wave background.

Journal Article↗