Type II Protein Arginine Methyltransferase 5 (PRMT5) Is Required for Circadian Pperiod Determination in Arabidopsis Thaliana

Posttranslational modification is an important element in circadian clock function from cyanobacteria through plants and mammals. For example, a number of key clock components are phosphorylated and thereby marked for subsequent ubiquitination and degradation. Through forward genetic analysis we demonstrate that protein arginine methyltransferase 5 (PRMT5; At4g31120) is a critical determinant of circadian period in Arabidopsis. PRMT5 is coregulated with a set of 1,253 genes that shows alterations in phase of expression in response to entrainment to thermocycles versus photocycles in constant temperature. PRMT5 encodes a type II protein arginine methyltransferase that catalyzes the symmetric dimethylation of arginine residues (Rsme2). Rsme2 modification has been observed in many taxa, and targets include histones, components of the transcription complex, and components of the spliceosome. Neither arginine methylation nor PRMT5 has been implicated previously in circadian clock function, but the period lengthening associated with mutational disruption of prmt5 indicates that Rsme2 is a decoration important for the Arabidopsis clock and possibly for clocks in general

Preoperative Nomograms for Predicting Extracapsular Extension in Korean Men with Localized Prostate Cancer: A Multi-institutional Clinicopathologic Study

We developed a nomogram to predict the probability of extracapsular extension (ECE) in localized prostate cancer and to determine when the neurovascular bundle (NVB) may be spared. Total 1,471 Korean men who underwent radical prostatectomy for prostate cancer between 1995 and 2008 were included. We drew nonrandom samples of 1,031 for nomogram development, leaving 440 samples for nomogram validation. With multivariate logistic regression analyses, we made a nomogram to predicts the ECE probability at radical prostatectomy. Receiver operating characteristic (ROC) analyses were also performed to assess the predictive value of each variable alone and in combination. The internal validation was performed from 200 bootstrap re-samples and the external validation was also performed from the another cohort. Overall, 314 patients (30.5%) had ECE. Age, Prostate specific antigen (PSA), biopsy Gleason score, positive core ratio, and maximum percentage of biopsy tumor were independent predictors of the presence of ECE (all P values <0.05). The nomogram predicted ECE with good discrimination (an area under the ROC curve of 0.777). Our nomogram allows for the preoperative identification of patients with an ECE and may prove useful in selecting patients to receive nerve sparing radical prostatectomy

Nomogram to Predict Insignificant Prostate Cancer at Radical Prostatectomy in Korean Men: A Multi-Center Study

PURPOSE: Due to the availability of serum prostate specific antigen (PSA) testing, the detection rate of insignificant prostate cancer (IPC) is increasing. To ensure better treatment decisions, we developed a nomogram to predict the probability of IPC. MATERIALS AND METHODS: The study population consisted of 1,471 patients who were treated at multiple institutions by radical prostatectomy without neoadjuvant therapy from 1995 to 2008. We obtained nonrandom samples of n = 1,031 for nomogram development, leaving n = 440 for nomogram validation. IPC was defined as pathologic organ-confined disease and a tumor volume of 0.5 cc or less without Gleason grade 4 or 5. Multivariate logistic regression model (MLRM) coefficients were used to construct a nomogram to predict IPC from five variables, including serum prostate specific antigen, clinical stage, biopsy Gleason score, positive cores ratio and maximum % of tumor in any core. The performance characteristics were internally validated from 200 bootstrap resamples to reduce overfit bias. External validation was also performed in another cohort. RESULTS: Overall, 67 (6.5%) patients had a so-called "insignificant" tumor in nomogram development cohort. PSA, clinical stage, biopsy Gleason score, positive core ratio and maximum % of biopsy tumor represented significant predictors of the presence of IPC. The resulting nomogram had excellent discrimination accuracy, with a bootstrapped concordance index of 0.827. CONCLUSION: Our current nomogram provides sufficiently accurate information in clinical practice that may be useful to patients and clinicians when various treatment options for screen-detected prostate cancer are consideredope

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

We evaluated geographic variation of Type 1 and Type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States

A comparative study of Gaussian geostatistical and Gaussian Markov random field models 1

Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GM-RFs) are two distinct approaches commonly used in modeling point referenced and areal data, respectively. In this work the relations between GMRFs and GGMs are explored based on approximations of GMRFs by GGMs, and vice versa. The pro-posed framework for the comparison of GGMS and GMRFs is based on minimizing the distance between the corresponding spectral density functions. In particular, the Kullback-Leibler discrepancy of spectral densities and the chi-squared distance be-tween spectral densities are used as the metrics for the approximation. The proposed methodology is illustrated using simulation studies. We also apply the methods to a air pollution dataset in California to study the relation between GMRFs and GGMs

A comparative study of Gaussian geostatistical models and Gaussian Markov random field models

Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two distinct approaches commonly used in spatial models for modeling point-referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed : (i) the Kullback-Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method.91B76 86A32 62H11 91D72 60J20