A Test for Homogeneity of Odds Ratios in Ordered 2 × 2 Tables

Consider K ordered 2 × 2 contingency tables. A new test of the null hypothesis that the odds ratios of these tables are equal vs the alternative hypothesis that the odds ratios are nondecreasing, is recommended. The test is exact (non‐asymptotic), is easily carried out (software is available), and has other favorable properties. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

United States interests in the Arctic region

Abstract

The Arctic is a vast, forbidding and relatively unknown region. It covers about 14 million square kilometers of which 5.2 square kilometers is ice covered in summer and 11.7 million square kilometers in winter. It is a highly strategic region, and is the shortest distance between the two superpowers. It also contains vast resources, including oil, gas, and coal. Over 830,000 native peoples inhabit the Arctic Rim and have a culture that goes back 4500 years. The United Nations Convention on the Law of the Sea is generally applicable to the Arctic Ocean and has a special provision for ice‐covered areas. However, there are several unresolved jurisdictional and navigational problems between the United States and other Arctic Rim States concerning the Arctic's waters, including the Chukchi Sea, the Beaufort Sea and the Northwest Passage. Although the United States has paid some attention to the Arctic region in recent years, the Arctic still remains a relatively low priority interest for national policy and operational programs.     

Impact of serial thermotherapy on sugarcane mosaic virus titre and regeneration in sugarcane: Auswirkung serieller thermotherapie auf zuckerrohr-mosaikvirustiter und regeneration bei zuckerrohr

Serial thermotherapy with hot water involving different treatment series was followed in two sugarcane cultivars namely Co 740 and CoC 671 for assessing its effect on sugarcane mosaic virus (SCMV) titre in the regenerated plants. Observations on virus titre and sett regeneration were recorded at different intervals after planting the treated setts. The direct antigen coating (DAC) ELISA method was followed with the polyclonal antisera raised against the widespread local SCMV strain. All the thermo treatments affected the sett regeneration in both the cultivars. Although none of the treatment series produced virus free plants, the SCMV titre was comparatively reduced in the regenerated plants at an early phase (4 weeks after planting) in severe hot water treatment series but drastic reduction in sett germination was observed. However, the SCMV titre picked up later (8 weeks after planting) in all the treatment series. The early reduction in SCMV titre could be utilized for obtaining more virus free plants in meristem culture.     

Optimization of 3D Poisson–Nernst‐Planck model for fast evaluation of diverse protein channels

We show the accuracy and applicability of our fast algorithmic implementation of a three‐dimensional Poisson–Nernst–Planck (3D‐PNP) flow model for characterizing different protein channels. Due to its high computational efficiency, our model can predict the full current‐voltage characteristics of a channel within minutes, based on the experimental 3D structure of the channel or its computational model structure. Compared with other methods, such as Brownian dynamics, which currently needs a few weeks of the computational time, or even much more demanding molecular dynamics modeling, 3D‐PNP is the only available method for a function‐based evaluation of very numerous tentative structural channel models. Flow model tests of our algorithm and its optimal parametrization are provided for five native channels whose experimental structures are available in the protein data bank (PDB) in an open conductive state, and whose experimental current‐voltage characteristics have been published. The channels represent very different geometric and structural properties, which makes it the widest test to date of the accuracy of 3D‐PNP on real channels. We test whether the channel conductance, rectification, and charge selectivity obtained from the flow model, could be sufficiently sensitive to single‐point mutations, related to unsignificant changes in the channel structure. Our results show that the classical 3D‐PNP model, under proper parametrization, is able to achieve a qualitative agreement with experimental data for a majority of the tested characteristics and channels, including channels with narrow and irregular conductivity pores. We propose that although the standard PNP model cannot provide insight into complex physical phenomena due to its intrinsic limitations, its semiquantitative agreement is achievable for rectification and selectivity at a level sufficient for the bioinformatical purpose of selecting the best structural models with a great advantage of a very short computational time. Proteins 2013; 81:1802–1822. © 2013 Wiley Periodicals, Inc.     

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification accuracy of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.     

Automatic diagnosis of mammographic abnormalities based on hybrid features with learning classifier

Breast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer-aided detection (CAD) systems can serve as a double reader to improve radiologist performance. In this paper, we have applied a novel approach to segmentation of suspicious region by mammogram and classification based on hybrid features with learning classifier. We formulated differentiation of lesion from normal tissue as a supervised learning problem, and applied this learning method to develop the classification algorithm. The algorithm has been verified with 164 mammograms in the mini Mammographic Image Analysis Society database. The experimental results show that the detection method has a sensitivity of 94.5% at 0.26 false positives per image. The efficiency of algorithm is measured using free receiver operating characteristics curve and the results are highlighted. We conclude that CAD technology with learning classifier has the potential to help radiologists with the task of discriminating between lesion and normal tissues.     

Computed structures of point deletion mutants and their enzymatic activities

Point deletions in enzymes can vary in effect from negligible to complete loss of activity; however, these effects are not generally predictable. Deletions are widely observed in nature and often result in diseases such as cancer, cystic fibrosis, or osteogenesis imperfecta. Here, we have developed an algorithm to model the perturbed structures of deletion mutants with the ultimate goal of predicting their activities. The algorithm works by deleting the specified residue from the wild‐type structure, creating a gap that is closed using a combination of local and global moves that change the backbone torsion angles of the protein structure. On a set of five proteins for which both wild‐type and deletion mutant x‐ray crystal structures are available, the algorithm produces deep, narrow energy funnels within 1.5 Å of the crystal structure for the deletion mutants. To assess the ability of our algorithm to predict activity from the predicted structures, we tested the correlation of experimental activity with several measures of the predicted structure ensemble using a set of 45 point deletions from ricin. Estimates incorporating likely prevalence of active and inactive deletion sites suggest that activity can be predicted correctly over 60% of the time from the active site root‐mean squared deviation of the lowest energy predicted structures. The predictions are stronger than simple sequence organization measures, but more fundamental work is required in structure prediction and enzyme activity determination to allow consistent prediction of activity. Proteins 2011; © 2011 Wiley‐Liss, Inc.     

Analysis of Longitudinal Data of Epileptic Seizure Counts – A Two‐State Hidden Markov Regression Approach

This paper discusses a two‐state hidden Markov Poisson regression (MPR) model for analyzing longitudinal data of epileptic seizure counts, which allows for the rate of the Poisson process to depend on covariates through an exponential link function and to change according to the states of a two‐state Markov chain with its transition probabilities associated with covariates through a logit link function. This paper also considers a two‐state hidden Markov negative binomial regression (MNBR) model, as an alternative, by using the negative binomial instead of Poisson distribution in the proposed MPR model when there exists extra‐Poisson variation conditional on the states of the Markov chain. The two proposed models in this paper relax the stationary requirement of the Markov chain, allow for overdispersion relative to the usual Poisson regression model and for correlation between repeated observations. The proposed methodology provides a plausible analysis for the longitudinal data of epileptic seizure counts, and the MNBR model fits the data much better than the MPR model. Maximum likelihood estimation using the EM and quasi‐Newton algorithms is discussed. A Monte Carlo study for the proposed MPR model investigates the reliability of the estimation method, the choice of probabilities for the initial states of the Markov chain, and some finite sample behaviors of the maximum likelihood estimates, suggesting that (1) the estimation method is accurate and reliable as long as the total number of observations is reasonably large, and (2) the choice of probabilities for the initial states of the Markov process has little impact on the parameter estimates.