An Evaluation of Prediction Equations for the 6 Minute Walk Test in Healthy European Adults Aged 50-85 Years

This study compared actual 6 minute walk test (6MWT) performance with predicted 6MWT using previously validated equations and then determined whether allometric modelling offers a sounder alternative to estimating 6MWT in adults aged 50-80 years.We compared actual 6MWT performance against predicted 6MWT in 125 adults aged 50-85 years (62 male, 63 female). In a second sample of 246 adults aged 50-85 years (74 male, 172 female), a new prediction equation for 6MWT performance was developed using allometric modelling. This equation was then cross validated using the same sample that the other prediction equations were compared with.Significant relationships were evident between 6MWT actual and 6MWT predicted using all of the commonly available prediction equations (all P<0.05 or better) with the exception of the Alameri et al prediction equation (P>0.05). A series of paired t-tests indicated significant differences between 6MWT actual and 6MWT predicted for all available prediction equations (all P<0.05 or better) with the exception of the Iwama et al equation (P = .540). The Iwama et al equation also had similar bias (79.8m) and a coefficient of variation of over 15%. Using sample 2, a log-linear model significantly predicted 6MWT from the log of body mass and height and age (P = 0.001, adjusted R2 = .526), predicting 52.6% of the variance in actual 6MWT. When this allometric equation was applied to the original sample, the relationship between 6MWT actual and 6MWT predicted was in excess of values reported for the other previously validated prediction equations (r = .706, P = 0.001). There was a significant difference between actual 6MWT and 6MWT predicted using this new equation (P = 0.001) but the bias, standard deviation of differences and coefficient of variation were all less than for the other equations.Where actual assessment of the 6MWT is not possible, the allometrically derived equation presented in the current study, offers a viable alternative which has been cross validated and has the least SD of differences and smallest coefficient of variation compared to any of the previously validated equations for the 6MWT

Firm-specific, country-specific and region-specific competitive advantages: the case of emerging economy MNEs - Thailand

Increasing levels of regional economic integration have created a new source of international competitiveness for MNEs from an emerging economy, Thailand, in the context of ASEAN economic integration. Building on the theoretical framework of firm-specific advantages (FSAs) and country-specific advantages (CSAs) grounded in internalization theory, we introduce region-specific advantages (RSAs) and advance a novel regional dual-double-diamond model to analyse regional competitiveness. Using both primary and secondary data we find that most Thai firms derive their international competitiveness from CSAs rather than FSAs, and will benefit from ASEAN RSAs. Our study significantly advances the literature on international competitiveness of emerging-economy MNEs

Predictive significance of the six-minute walk distance for long-term survival in chronic hypercapnic respiratory failure

Background: The 6-min walk distance ( 6-MWD) is a global marker of functional capacity and prognosis in chronic obstructive pulmonary disease ( COPD), but less explored in other chronic respiratory diseases. Objective: To study the role of 6-MWD in chronic hypercapnic respiratory failure ( CHRF). Methods: In 424 stable patients with CHRF and non-invasive ventilation ( NIV) comprising COPD ( n = 197), restrictive diseases ( RD; n = 112) and obesity-hypoventilation- syndrome ( OHS; n = 115), the prognostic value of 6-MWD for long- term survival was assessed in relation to that of body mass index (BMI), lung function, respiratory muscle function and laboratory parameters. Results: 6-MWD was reduced in patients with COPD ( median 280 m; quartiles 204/350 m) and RD ( 290 m; 204/362 m) compared to OHS ( 360 m; 275/440 m; p <0.001 each). Overall mortality during 24.9 (13.1/40.5) months was 22.9%. In the 424 patients with CHRF, 6-MWD independently predicted mortality in addition to BMI, leukocytes and forced expiratory volume in 1 s ( p <0.05 each). In COPD, 6-MWD was strongly associated with mortality using the median {[} p <0.001, hazard ratio ( HR) = 3.75, 95% confidence interval (CI): 2.24-6.38] or quartiles as cutoff levels. In contrast, 6-MWD was only significantly associated with impaired survival in RD patients when it was reduced to 204 m or less (1st quartile; p = 0.003, HR = 3.31, 95% CI: 1.73-14.10), while in OHS 6-MWD had not any prognostic value. Conclusions: In patients with CHRF and NIV, 6-MWD was predictive for long- term survival particularly in COPD. In RD only severely reduced 6-MWD predicted mortality, while in OHS 6-MWD was relatively high and had no prognostic value. These results support a disease-specific use of 6-MWD in the routine assessment of patients with CHRF. Copyright (C) 2007 S. Karger AG, Basel

Inspiratory muscle training reduces blood lactate concentration during volitional hyperpnoea

Although reduced blood lactate concentrations ([lac−]B) have been observed during whole-body exercise following inspiratory muscle training (IMT), it remains unknown whether the inspiratory muscles are the source of at least part of this reduction. To investigate this, we tested the hypothesis that IMT would attenuate the increase in [lac−]B caused by mimicking, at rest, the breathing pattern observed during high-intensity exercise. Twenty-two physically active males were matched for 85% maximal exercise minute ventilation (V˙Emax) and divided equally into an IMT or a control group. Prior to and following a 6 week intervention, participants performed 10 min of volitional hyperpnoea at the breathing pattern commensurate with 85% V˙Emax

Evolutionary potential and adaptation of Banksia attenuata (Proteaceae) to climate and fire regime in southwestern Australia, a global biodiversity hotspot

Substantial climate changes are evident across Australia, with declining rainfall and rising temperature in conjunction with frequent fires. Considerable species loss and range contractions have been predicted; however, our understanding of how genetic variation may promote adaptation in response to climate change remains uncertain. Here we characterized candidate genes associated with rainfall gradients, temperatures, and fire intervals through environmental association analysis. We found that overall population adaptive genetic variation was significantly affected by shortened fire intervals, whereas declining rainfall and rising temperature did not have a detectable influence. Candidate SNPs associated with rainfall and high temperature were diverse, whereas SNPs associated with specific fire intervals were mainly fixed in one allele. Gene annotation further revealed four genes with functions in stress tolerance, the regulation of stomatal opening and closure, energy use, and morphogenesis with adaptation to climate and fire intervals. B. attenuata may tolerate further changes in rainfall and temperature through evolutionary adaptations based on their adaptive genetic variation. However, the capacity to survive future climate change may be compromised by changes in the fire regime

Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk

Most breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P≤5×10-8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.Funding Agency Portuguese Foundation for Science and Technology CRESC ALGARVE 2020 European Union (EU) 303745 Maratona da Saude Award DL 57/2016/CP1361/CT0042 SFRH/BPD/99502/2014 CBMR-UID/BIM/04773/2013 POCI-01-0145-FEDER-022184info:eu-repo/semantics/publishedVersio

Electrostatic charging of jumping droplets

With the broad interest in and development of superhydrophobic surfaces for self-cleaning, condensation heat transfer enhancement and anti-icing applications, more detailed insights on droplet interactions on these surfaces have emerged. Specifically, when two droplets coalesce, they can spontaneously jump away from a superhydrophobic surface due to the release of excess surface energy. Here we show that jumping droplets gain a net positive charge that causes them to repel each other mid-flight. We used electric fields to quantify the charge on the droplets and identified the mechanism for the charge accumulation, which is associated with the formation of the electric double layer at the droplet–surface interface. The observation of droplet charge accumulation provides insight into jumping droplet physics as well as processes involving charged liquid droplets. Furthermore, this work is a starting point for more advanced approaches for enhancing jumping droplet surface performance by using external electric fields to control droplet jumping.United States. Dept. of Energy. Office of Basic Energy Sciences (Solid-State Solar-Thermal Energy Conversion Center Award DE-FG02-09ER46577)United States. Office of Naval ResearchNational Science Foundation (U.S.) (Major Research Instrumentation Grant for Rapid Response Research (MRI- RAPID))National Science Foundation (U.S.) (Award ECS-0335765)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374

Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices

BACKGROUND: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes. RESULTS: We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represented as super phylogenetic vectors, whose interactions are known. The performance, measured as ROC score in cross validation experiments, shows significant improvement of our method (ROC score 0.8446) over that of using Pearson correlations (0.6587). CONCLUSION: We have shown that the phylogenetic tree can be used as a guide to extract intra-matrix correlations in the distance matrices of orthologous proteins, where these correlations are represented as intermediate distance matrices of the ancestral orthologous proteins. Both the unsupervised and supervised learning paradigms benefit from the explicit inclusion of these intermediate distance matrices, and particularly so in the latter case, which offers a better balance between sensitivity and specificity in the prediction of protein-protein interactions