Determinants of Health-Promoting Lifestyle among Nurses in Taiwan

This two-phase study was undertaken to: (1) determine the relationships of self-efficacy, perceived health status, perceived social support, age, marital status, education, work shift, work setting, and years employed as a registered nurse (RN) to the practice of a health-promoting lifestyle; (2) determine the combination of predictor variables explaining the variance in the practice of a health-promoting lifestyle; and (3) investigate other personal and environmental cues and characteristics related to health-promoting lifestyles among nurses (N = 218) in Taiwan. Findings of the quantitative approach in Phase One indicated that a health-promoting lifestyle was significantly related to self-efficacy, perceived health status, perceived social support, age, and years employed as an RN. Four predictor variables, namely, self-efficacy, perceived health status, perceived social support, and working the evening shift, explained 40.4% of the variance in health-promoting lifestyle in this sample. Responses to open-ended questions revealed other factors that contribute to health-promoting lifestyle among the nurses. In Phase Two, nine subjects who scored very high and 10 who scored very low on the Health-Promoting Lifestyle Profile (HPLP) were interviewed regarding health beliefs, behaviors and factors influencing the practice of a health-promoting lifestyle. The interview data partly validate the findings from Phase One. In many situations, the subjects in Phase Two of this study cited predictor variables investigated such as social supports and rotating shift. Subjects also stressed the importance of energy, perseverance and partners in initiating and maintaining a health-promoting lifestyle. The interview data revealed other personal and environmental cues and characteristics of a health-promoting lifestyle. The high group had initiated less lifestyle changes, but maintained the changes longer than subjects in the low group. They also identified more enabling characteristics than did the low group. Subjects in the low group used more stress management techniques and identified more hindrances for lifestyle changes than the high HPLP group. A revised model was developed for testing in future studies

Rapid identification of the medicinal plant Taraxacum formosanum and distinguishing of this plant from its adulterants by ribosomal DNA internal transcribed spacer (ITS) based DNA barcode

Original identification of medicinal plants is essential for quality control. In this study, the internal transcribed spacer 2 (ITS2) nuclear ribosomal DNA served as a DNA barcode and was amplified by allele-specific PCR. This approach was exploited to differentiate Taraxacum formosanum from five related adulterants. Using a set of designed PCR primers, a highly specific 223 bp PCR product of T. formosanum was successfully amplified by PCR. However, no similar DNA fragment was amplified from any of the other adulterants. This indicates that, our allele specific primers have high specificity and can accurately discriminate T. formosanum from its adulterant plants.Key words: Medicinal plant, polymerase chain reaction (PCR), authentication, Taraxacum formosanum, traditional Chinese medicinal, internal transcribed spacers 2 (ITS2)

A new diagrammatic representation for correlation functions in the in-in formalism

In this paper we provide an alternative method to compute correlation functions in the in-in formalism, with a modified set of Feynman rules to compute loop corrections. The diagrammatic expansion is based on an iterative solution of the equation of motion for the quantum operators with only retarded propagators, which makes each diagram intrinsically local (whereas in the standard case locality is the result of several cancellations) and endowed with a straightforward physical interpretation. While the final result is strictly equivalent, as a bonus the formulation presented here also contains less graphs than other diagrammatic approaches to in-in correlation functions. Our method is particularly suitable for applications to cosmology.Comment: 14 pages, matches the published version. includes a modified version of axodraw.sty that works with the Revtex4 clas

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

BACKGROUND: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins. RESULTS: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction. CONCLUSION: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

Methionine Adenosyltransferase I/III Deficiency in Portugal: High Frequency of a Dominantly Inherited Form in a Small Area of Douro High Lands

Methionine adenosyltransferase deficienc(MAT I/III deficiency) is an inborn error of metabolism resulting in isolated hypermethioninemia, and usually inherited as an autosomal recessive trait, although a dominant form has been reported in several families. During the last 6 years, approximately 520,000 newborns were screened in the Portuguese Newborn Screening Laboratory by MS/MS, and 21 cases of persistent hypermethioninemia were found. One case was confirmed to be a deficiency of cystathionine b-synthase and 20 cases were confirmed by MAT1A gene analysis to have an elevation of methionine due to MAT I/III deficiency, which indicates an incidence for this condition of 1/26,000. Twelve of the MAT I/III deficient newborns, belonging to 11 families, were identified in the northern region of Portugal and sent to the same treatment center, where they are under follow-up. Clinical, biochemical, and genetic characteristics of individuals from these 11 families are presented. Plasma methionine and homocysteine concentrations were found to be moderately increased in all newborns, and molecular analysis revealed that they all were heterozygous for R264H mutation. Normal growth,development, and neurological examination were observed in all cases, and cerebral MRI performed in six cases revealed myelination abnormalities in one case. Plasma methionine concentration for all 12 cases was always below 300 mM, and they are all on a normal diet for their age

Predicting Transcriptional Activity of Multiple Site p53 Mutants Based on Hybrid Properties

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism

Identification of Potent EGFR Inhibitors from TCM Database@Taiwan

Overexpression of epidermal growth factor receptor (EGFR) has been associated with cancer. Targeted inhibition of the EGFR pathway has been shown to limit proliferation of cancerous cells. Hence, we employed Traditional Chinese Medicine Database (TCM Database@Taiwan) (http://tcm.cmu.edu.tw) to identify potential EGFR inhibitor. Multiple Linear Regression (MLR), Support Vector Machine (SVM), Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarities Indices Analysis (CoMSIA) models were generated using a training set of EGFR ligands of known inhibitory activities. The top four TCM candidates based on DockScore were 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid, and all had higher binding affinities than the control Iressa®. The TCM candidates had interactions with Asp855, Lys716, and Lys728, all which are residues of the protein kinase binding site. Validated MLR (r² = 0.7858) and SVM (r² = 0.8754) models predicted good bioactivity for the TCM candidates. In addition, the TCM candidates contoured well to the 3D-Quantitative Structure-Activity Relationship (3D-QSAR) map derived from the CoMFA (q² = 0.721, r² = 0.986) and CoMSIA (q² = 0.662, r² = 0.988) models. The steric field, hydrophobic field, and H-bond of the 3D-QSAR map were well matched by each TCM candidate. Molecular docking indicated that all TCM candidates formed H-bonds within the EGFR protein kinase domain. Based on the different structures, H-bonds were formed at either Asp855 or Lys716/Lys728. The compounds remained stable throughout molecular dynamics (MD) simulation. Based on the results of this study, 2-O-caffeoyl tartaric acid, Emitine, Rosmaricine, and 2-O-feruloyl tartaric acid are suggested to be potential EGFR inhibitors.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University (CMU98-TCM)China Medical University (CMU99-TCM)China Medical University (CMU99-S-02)China Medical University (CMU99-ASIA-25)China Medical University (CMU99-ASIA-26)China Medical University (CMU99-ASIA-27)China Medical University (CMU99-ASIA-28)Asia UniversityTaiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005