Validity and normative data for thirty-second chair stand test in elderly community-dwelling Hong Kong Chinese

It is important to establish valid field measures of lower body strength in the elderly, and to provide representative normative values that are culturally specific in order to help health professionals in the risk assessment of this group. A sample of 1,038 elderly Hong Kong Chinese undertook a 30-sec chair stand test (30CST), with a subsample of 143 completing isometric measures of maximal hip flexion and knee extension, plus a habitual physical activity questionnaire. The 30CST was significantly, yet only weakly, correlated with the isometric strength measures (r ∼ 0.3-0.4), but accurately discriminated between levels of habitual physical activity and across ages in decades. The normative values generated provide useful data for health screening in this elderly Hong Kong population, but do not compare well with their healthier US counterparts. © 2006 Wiley-Liss, Inc.postprin

Patterns of physical exercise and contributing factors among Hong Kong older adults

Key Messages 1. Studies suggest that regular physical activity and exercise offer a significant opportunity to enhance years of active independent life for older individuals. However, the majority of local respondents have a relatively sedentary lifestyle and were not getting adequate physical activity and exercise, though many recognised the health benefits of being active. 2. Compared with their relatively active American counterparts, in terms of strength, flexibility and agility, the respondents of this study fared unfavourably, with mean values in the 20-30 percentile score or lower. 3. More than 80% of elderly adults regularly participated in sports activities, the majority of whom engaged in only a low-to-moderate level of physical exercise mainly due to limited choices and time allocation for such activity. Nearly 17% of the respondents engaged in walking as a leisure activity; a substantial proportion included completion of their household activities (shopping, visiting friends) in what they termed ‘walking’. As walking confers many health benefits, its promotion in the immediate environments of the elderly is practical and undoubtedly health enhancing. 4. Findings suggested that cognitive-perceptual factors, ie perceived ‘pros’ and ‘cons’ of doing exercise, selfefficacy of exercise, stages of change and perceived barriers to exercise were all influential variables contributing to engagement in sports activity. Longitudinal studies are needed to understand the causal and temporal relations between these variables. 5. Health promotion programmes to educate older adults about the benefits of engaging in physical activity with respect to their self-efficacy are crucial. Education can enhance individual’s knowledge and/or awareness concerning the health benefits of physical activity and exercise. Those who are deprived and less educated should receive more attention from policymakers and service providers.published_or_final_versio

Mechanism for controlling the monomer-dimer conversion of SARS coronavirus main protease

[[incitationindex]]SCI[[booktype]]紙

Orbital and Spin Parameter Variations of Partial Eclipsing Low Mass X-ray Binary X 1822-371

We report our measurements for orbital and spin parameters of X 1822-371 using its X-ray partial eclipsing profile and pulsar timing from data collected by the Rossi X-ray Timing Explorer (RXTE). Four more X-ray eclipse times obtained by the RXTE 2011 observations were combined with historical records to trace evolution of orbital period. We found that a cubic ephemeris likely better describes evolution of the X-ray eclipse times during a time span of about 34 years with a marginal second order derivative of $ddot{P}_{orb}=(-1.05 pm 0.59) imes 10^{-19}$ s$^{-1}$. Using the pulse arrival time delay technique, the orbital and spin parameters were obtained from RXTE observations from 1998 to 2011. The detected pulse periods show that the neutron star in X 1822-371 is continuously spun-up with a rate of $dot{P}_{s}=(-2.6288 pm 0.0095) imes 10^{-12}$ s s$^{-1}$. Evolution of the epoch of the mean longitude $l=pi /2$ (i.e. $T_{pi / 2}$) gives an orbital period derivative value consistent with that obtained from the quadratic ephemeris evaluated by the X-ray eclipse but the detected $T_{pi / 2}$ values are significantly and systematically earlier than the corresponding expected X-ray eclipse times by $90 pm 11$ s. This deviation is probably caused by asymmetric X-ray emissions. We also attempted to constrain the mass and radius of the neutron star using the spin period change rate and concluded that the intrinsic luminosity of X 1822-371 is likely more than $10^{38}$ ergs s$^{-1}$.postprin

Schedule-dependent response of neuroblastoma cell lines to combinations of etoposide and cisplatin

The growth inhibitory effects of cisplatin and etoposide on neuroblastoma cell lines were investigated in several scheduled combinations. Results were analyzed using median effect and combination index analyses. In all schedules in which cisplatin was administered prior to etoposide a synergistic effect was observed. Conversely, an antagonistic effect was seen in all schedules where etoposide was administered before cisplatin

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

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

A huge Omental Lymphangioma with extention into Labia Majorae: A case report

BACKGROUND: Abdominal cystic lymphangiomas are uncommon congenital benign tumors. CASE PRESENTATION: We present a case of a 4 year old female child with a cystic lymphangioma arising from greater omentum and occupying whole of the abdomen and protruding through labia mejora. Ultrasonography and CT scan confirmed the diagnosis. Complete excision of the cyst along with omentectomy done with no clinical or radiological evidence of recurrence till 6 months. CONCLUSION: Due to variable presentation of abdominal lymphangiomas, extensive imaging studies are necessary for evaluation and diagnosis. Complete surgical resection is a treatment of choice

A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins

Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins

Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property

Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) “Metabolism”, (ii) “Genetic Information Processing”, (iii) “Environmental Information Processing”, (iv) “Cellular Processes”, (v) “Organismal Systems”, and (vi) “Human Diseases”. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area