Pedometry and 'peer support' in older Chinese adults: a 12-month cluster randomised controlled trial

Session - Exercise/RehabilitationResearch Dissemination ReportsThere is a need to increase physical activity to attenuate age-related morbidity. This 12-month factorial design cluster trial randomized 399 volunteers from 24 centres to buddy peer support, pedometry, or control group. Data were anaysed using last-observation carried-forward and intention-to-treat methods. Compared to the controls, participants in the pedometry group increased their levels of physical activity energy expenditure significantly, as did those in the buddy group. As recorded by the International Physical Activity Questionnaire [IPAQ], the respective increases amounted to 1820 (95% confidence interval [CI], 1360-2290) and 1260 (95% CI, 780-17 460) metabolic equivalent of task (MET).min.wk-1. The buddy group also had significantly improved aerobic fitness after adjustment for body weight (12%; 95% CI, 4-21%), but this did not attain significance in the pedometry group (7%; 95% CI, -1 to 15%). Our results suggest that recourse to pedometers and the buddy peer support system is simple means of increasing physical activity in older subjects.published_or_final_versio

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age

Novel Inhibitor Design for Hemagglutinin against H1N1 Influenza Virus by Core Hopping Method

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area

Design Novel Dual Agonists for Treating Type-2 Diabetes by Targeting Peroxisome Proliferator-Activated Receptors with Core Hopping Approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses

A genetic approach for building different alphabets for peptide and protein classification

<p>Abstract</p> <p>Background</p> <p>In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.</p> <p>Results</p> <p>The new approach has been tested in three peptide classification problems: HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens. The tests demonstrate that the idea of training a pool classifiers by reduced alphabets, created using a Genetic Algorithm, allows an improvement over other state-of-the-art feature extraction methods.</p> <p>Conclusion</p> <p>The validity of the novel strategy for creating reduced alphabets is demonstrated by the performance improvement obtained by the proposed approach with respect to other reduced alphabets-based methods in the tested problems.</p

Heralded quantum entanglement between two crystals

Quantum networks require the crucial ability to entangle quantum nodes. A prominent example is the quantum repeater which allows overcoming the distance barrier of direct transmission of single photons, provided remote quantum memories can be entangled in a heralded fashion. Here we report the observation of heralded entanglement between two ensembles of rare-earth-ions doped into separate crystals. A heralded single photon is sent through a 50/50 beamsplitter, creating a single-photon entangled state delocalized between two spatial modes. The quantum state of each mode is subsequently mapped onto a crystal, leading to an entangled state consisting of a single collective excitation delocalized between two crystals. This entanglement is revealed by mapping it back to optical modes and by estimating the concurrence of the retrieved light state. Our results highlight the potential of rare-earth-ions doped crystals for entangled quantum nodes and bring quantum networks based on solid-state resources one step closer.Comment: 10 pages, 5 figure

Correcting pervasive errors in RNA crystallography through enumerative structure prediction

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average Rfree factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

A solid state light-matter interface at the single photon level

Coherent and reversible mapping of quantum information between light and matter is an important experimental challenge in quantum information science. In particular, it is a decisive milestone for the implementation of quantum networks and quantum repeaters. So far, quantum interfaces between light and atoms have been demonstrated with atomic gases, and with single trapped atoms in cavities. Here we demonstrate the coherent and reversible mapping of a light field with less than one photon per pulse onto an ensemble of 10 millions atoms naturally trapped in a solid. This is achieved by coherently absorbing the light field in a suitably prepared solid state atomic medium. The state of the light is mapped onto collective atomic excitations on an optical transition and stored for a pre-programmed time up of to 1 mu s before being released in a well defined spatio-temporal mode as a result of a collective interference. The coherence of the process is verified by performing an interference experiment with two stored weak pulses with a variable phase relation. Visibilities of more than 95% are obtained, which demonstrates the high coherence of the mapping process at the single photon level. In addition, we show experimentally that our interface allows one to store and retrieve light fields in multiple temporal modes. Our results represent the first observation of collective enhancement at the single photon level in a solid and open the way to multimode solid state quantum memories as a promising alternative to atomic gases.Comment: 5 pages, 5 figures, version submitted on June 27 200

Healthcare costs associated with progressive diabetic retinopathy among National Health Insurance enrollees in Taiwan, 2000-2004

<p>Abstract</p> <p>Background</p> <p>Diabetic retinopathy is one of the most common microvascular complications of diabetes and one of the major causes of adult visual impairment in national surveys in Taiwan. This study aimed to identify the healthcare costs of Taiwan's National Health Insurance program on behalf of diabetic patients with stable or progressive retinopathy.</p> <p>Methods</p> <p>A retrospective cohort study was conducted with 4,988 medication-using diabetic retinopathy subjects ≥ 40 years of age under National Health Insurance Program coverage between 2000 and 2004. Study cohort subjects were recorded as having diabetic retinopathy according to ICD-9-CM codes. States of diabetic retinopathy were strategically divided into stable and progressive categories according to subjects' conditions at follow-up in 2004. Expenditures were calculated and compared for the years 2000 and 2004.</p> <p>Results</p> <p>During the 4-year follow-up (2000 through 2004), 4,116 subjects (82.5%) of 4,988 diabetic subjects were in the stable category, and 872 (17.5%) were in the progressive category. Average costs of those in the normal category increased by US $48 from US$1921 in 2000 to US $1969 in 2004 (p = 0.594), whereas costs for those progressing from normal to non-proliferative diabetic retinopathy (NPDR) or proliferative diabetic retinopathy (PDR) increased by US$1760, from US $1566 in 2000 to US$3326 in 2004 (p < 0.001). The PDR category had the highest average costs at US $3632 in 2000. The NPDR-to-PDR category experienced the greatest increase in costs at US$3482, from US $2723 in 2000 to US$6204 in 2004 (p = 0.042), and the greatest percentage of increase at 2.3% (2.2% when adjusted by comparing to normal category).</p> <p>Conclusions</p> <p>This large-scale longitudinal study provides evidence that increased healthcare costs are associated with progressive diabetic retinopathy among diabetic NHI enrollees in Taiwan.</p