UL13 Protein Kinase of Herpes Simplex Virus 1 Complexes with Glycoprotein E and Mediates the Phosphorylation of the Viral Fc Receptor: Glycoproteins E and I

AbstractHerpes simplex virus 1 encodes a Fc receptor consisting of glycoproteins E (gE) and I (gI) and two protein kinases specified by UL13 and US3, respectively. We report the following: (i) Antibody to UL13 formed immune complexes containing gE and gI in addition to UL13 protein. Immune complexes formed by monoclonal antibody to gE, but not those formed by monoclonal antibody to gI, also contained the UL13 protein. This association may reflect direct interaction between gE and UL13 inasmuch as IgG in preimmune rabbit serum and an antiserum made against another viral protein which does not react with the UL13 protein directly also bound gE and UL13. (ii) In cells infected with the wild-type virus, gE formed two sharp bands and a diffuse, slower migrating band. The slower sharp band was undetectable, and the diffuse slower migrating forms of gE were diminished in lysates of cells infected with a mutant virus lacking the UL13 gene (ΔUL13). (iii) Both gE and gI were labeled with32Pi in cells infected with wild-type or the ΔUL13 virus, but the labeling was significantly stronger in cells infected with the wild-type virus than in those infected with the ΔUL13 virus. (iv) In anin vitroprotein kinase assay, UL13 immunoprecipitated from cells infected with wild-type virus labeled gE in the presence of [γ-32P]ATP. This activity was absent in precipitates from cells infected with ΔUL13 virus. The labeled gE comigrated with the slower, sharp band of gE. (v) gI present in the UL13 immune complex was also phosphorylated in thein vitrokinase assay. (vi) The cytoplasmic domain of gE contains recognition sequences for phosphorylation by casein kinase II (CKII). Exogenous CKII phosphorylated gE in immune complexes from lysates of cells infected with the ΔUL13 mutant or in immune complexes from lysates of cells infected with wild-type virus that had been heated to inactivate all endogenous kinase activity including that of UL13. In both instances, CKII phosphorylated gE in both the slow and fast migrating sharp bands. We conclude that UL13 physically associates with gE and mediates the phosphorylation of gE and gI. UL13 may also be a determinant in posttranslational processing of gE

Implications of Inconsistencies between fMRI and dMRI on Multimodal Connectivity Estimation

International audienceThere is a recent trend towards integrating resting state functional magnetic resonance imaging (RS-fMRI) and diffusion MRI (dMRI) for brain connectivity estimation, as motivated by how estimates from these modalities are presumably two views reflecting the same underlying brain circuitry. In this paper, we show on a cohort of 60 subjects that conventional functional connectivity (FC) estimates based on Pearson's correlation and anatomical connectivity (AC) estimates based on fiber counts are actually not that highly correlated for typical RS-fMRI (~7 min) and dMRI (~32 gradient directions) data. The FC-AC correlation can be significantly increased by considering sparse partial correlation and modeling fiber endpoint uncertainty, but the resulting FC-AC correlation is still rather low in absolute terms. We further exemplify the inconsistencies between FC and AC estimates by integrating them as priors into activation detection and demonstrating significant differences in their detection sensitivity. Importantly, we illustrate that these inconsistencies can be useful in fMRI-dMRI integration for improving brain connectivity estimation

Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations

International audienceDespite that diagnosis of neurological disorders commonly involves a collection of behavioral assessments, most neuroimaging studies investigating the associations between brain and behavior largely analyze each behavioral measure in isolation. To jointly model multiple behavioral scores, sparse mul-tiresponse regression (SMR) is often used. However, directly applying SMR without statistically controlling for false positives could result in many spurious findings. For models, such as SMR, where the distribution of the model parameters is unknown, permutation test and stability selection are typically used to control for false positives. In this paper, we present another technique for inferring statistically significant features from models with unknown parameter distribution. We refer to this technique as bootstrapped permutation test (BPT), which uses Studentized statistics to exploit the intuition that the variability in parameter estimates associated with relevant features would likely be higher with responses permuted. On synthetic data, we show that BPT provides higher sensitivity in identifying relevant features from the SMR model than permutation test and stability selection, while retaining strong control on the false positive rate. We further apply BPT to study the associations between brain connec-tivity estimated from pseudo-rest fMRI data of 1139 fourteen year olds and be-havioral measures related to ADHD. Significant connections are found between brain networks known to be implicated in the behavioral tasks involved. Moreover , we validate the identified connections by fitting a regression model on pseudo-rest data with only those connections and applying this model on resting state fMRI data of 337 left out subjects to predict their behavioral scores. The predicted scores are shown to significantly correlate with the actual scores of the subjects, hence verifying the behavioral relevance of the found connections

A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation

International audienceThe estimation of intra-subject functional connectivity is greatly complicated by the small sample size and complex noise structure in functional magnetic resonance imaging (fMRI) data. Pooling samples across subjects improves the conditioning of the estimation, but loses subject-specific connectivity information. In this paper, we propose a new sparse group Gaussian graphical model (SGGGM) that facilitates joint estimation of intra-subject and group-level connectivity. This is achieved by casting functional connectivity estimation as a regularized consensus optimization problem, in which information across subjects is aggregated in learning group-level connectivity and group information is propagated back in estimating intra-subject connectivity. On synthetic data, we show that incorporating group information using SGGGM significantly enhances intra-subject connectivity estimation over existing techniques. More accurate group-level connectivity is also obtained. On real data from a cohort of 60 subjects, we show that integrating intra-subject connectivity estimated with SGGGM significantly improves brain activation detection over connectivity priors derived from other graphical modeling approaches

Surface-Atmosphere CO2 Effluxes from urban Turfgrass Areas, Singapore

Master'sMASTER OF SOCIAL SCIENCE

1-Acetyl-4-phenyl-5a,6,7,8,9,9a-hexa­hydro-5H-1,5-benzodiazepin-2(1H)-one

The seven-membered ring of the title compound, C17H20N2O2, adopts an approximate boat conformation while the cyclo­hexyl ring adopts a chair conformation. In the crystal, adjacent mol­ecules are linked by N—H⋯O hydrogen bonds into a zigzag chain running along the c axis of the monoclinic unit cell

1-Benzyl-3-phenyl­quinoxalin-2(1H)-one

The ten-membered fused ring system in the title compound, C21H16N2O2, is planar (r.m.s. deviation = 0.03 Å). The phenyl substituent is aligned at 15.1 (1)° with respect to the mean plane through this system, whereas the phenyl ring of the benzyl substitutent is aligned at 84.4 (1)°

Connectivity-informed Sparse Classifiers for fMRI Brain Decoding

International audienceIn recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance imaging (fMRI) based brain decoding problems. Enforcing sparsity alone, however, neglects the interactions between connected brain areas. Methods that additionally impose spatial smoothness would account for local but not long-range interactions. In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be jointly modeled. On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy and more interpretable classifier weight patterns than standard classifiers. Our results thus illustrate the benefits of adding neurologically-relevant priors in fMRI brain decoding

1,5-Dibenzyl-3-propargyl-1,5-benzo­diazepine-2,4-dione

The title compound, C26H22N2O2, features a benzene ring fused with a seven-membered diazepine ring; the latter ring adopts a boat conformation (with the propargylallyl-bearing C atom as the prow and the fused-ring C atoms as the stern). The phenyl ring of one of the two benzyl substituents is disordered over two positions in a 0.812 (11):0.188 (11) ratio

Algebraic construction of quantum integrable models including inhomogeneous models

Exploiting the quantum integrability condition we construct an ancestor model associated with a new underlying quadratic algebra. This ancestor model represents an exactly integrable quantum lattice inhomogeneous anisotropic model and at its various realizations and limits can generate a wide range of integrable models. They cover quantum lattice as well as field models associated with the quantum $R$-matrix of trigonometric type or at the undeformed $q \to 1$ limit similar models belonging to the rational class. The classical limit likewise yields the corresponding classical discrete and field models. Thus along with the generation of known integrable models in a unifying way a new class of inhomogeneous models including variable mass sine-Gordon model, inhomogeneous Toda chain, impure spin chains etc. are constructed.Comment: Latex, 14pages, To be published in the Rev. Math. Phys annual conf.ROMP99 Proceedings (Tarun, Poland, 1999