uptake efficiency
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IntroductionCardiorespiratory fitness is not limited by pulmonary mechanical reasons in the majority of adults. However, the degree to which lung function contributes to exercise response patterns among ostensibly healthy individuals remains unclear.MethodsWe examined 2314 Framingham Heart Study participants who underwent cardiopulmonary exercise testing (CPET) and pulmonary function testing. We investigated the association of FEV1, FVC, FEV1/FVC and DLCO with the primary outcome of peak VO2, along with other CPET parameters using multivariable linear regression. Finally, we investigated the association of total and peripheral pulmonary blood vessel volume with peak VO2.ResultsWe found lower FEV1, FVC and DLCO were associated with lower peak VO2. For example, a one-liter lower FEV1 and FVC were associated with 7.1% (95% CI: 5.1%, 9.1%) and 6.0% (95% CI: 4.3%, 7.7%) lower peak VO2, respectively. By contrast, FEV1/FVC ratio was not associated with peak VO2. Lower lung function was associated with lower oxygen uptake efficiency slope oxygen pulse slope, VO2 at AT, VE at AT and breathing reserve. In addition, lower total and peripheral pulmonary blood vessel volume were associated with a lower peak VO2.ConclusionIn a large, community-based cohort of adults, we found lower FEV1, FVC and DLCO were associated with lower exercise capacity, as well as oxygen uptake efficiency slope and ventilatory efficiency. In addition, lower total and peripheral pulmonary blood vessel volume were associated with lower peak VO2. These findings underscore the importance of lung function and blood vessel volume as contributors to overall exercise capacity.

Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules of varying sizes across the cell membrane into the cytoplasm. Correctly identifying CPPs is the basis for studying their functions and mechanisms. Here, we propose a novel CPP predictor that is able to predict CPPs and their uptake efficiency. In our method, five feature descriptors are applied to encode the sequence and compose a hybrid feature vector. Afterward, the wrapper + random forest algorithm is employed, which combines feature selection with the prediction process to find features that are crucial for identifying CPPs. The jackknife cross validation result shows that our predictor is comparable to state-of-the-art CPP predictors, and our method reduces the feature dimension, which improves computational efficiency and avoids overfitting, allowing our predictor to be adopted to identify large-scale CPP data.

The root system architectures (RSAs) largely decide the phosphorus use efficiency (PUE) of plants by influencing the phosphorus uptake. Very limited information is available on wheat’s RSAs and their deciding factors affecting phosphorus uptake efficiency (PupE) due to difficulties in adopting scoring values used for evaluating root traits. Based on our earlier research experience on nitrogen uptake efficiency screening under, hydroponics and soil-filled pot conditions, a comprehensive study on 182 Indian bread wheat genotypes was carried out under hydroponics with limited P (LP) and non-limiting P (NLP) conditions. The findings revealed a significant genetic variation, root traits correlation, and moderate to high heritability for RSAs traits namely primary root length (PRL), total root length (TRL), total root surface area (TSA), root average diameter (RAD), total root volume (TRV), total root tips (TRT) and total root forks (TRF). In LP, the expressions of TRL, TRV, TSA, TRT and TRF were enhanced while PRL and RAD were diminished. An almost similar pattern of correlations among the RSAs was also observed in both conditions except for RAD. RAD exhibited significant negative correlations with PRL, TRL, TSA, TRT and TRF under LP (r = -0.45, r = -0.35, r = -0.16, r = -0.30, and r = -0.28 respectively). The subclass of TRL, TSA, TRV and TRT representing the 0–0.5 mm diameter had a higher root distribution percentage in LP than NLP. Comparatively wide range of H’ value i.e. 0.43 to 0.97 in LP than NLP indicates that expression pattern of these traits are highly influenced by the level of P. In which, RAD (0.43) expression was reduced in LP, and expressions of TRF (0.91) and TSA (0.97) were significantly enhanced. The principal component analysis for grouping of traits and genotypes over LP and NLP revealed a high PC1 score indicating the presence of non-crossover interactions. Based on the comprehensive P response index value (CPRI value), the top five highly P efficient wheat genotypes namely BW 181, BW 103, BW 104, BW 143 and BW 66, were identified. Considering the future need for developing resource-efficient wheat varieties, these genotypes would serve as valuable genetic sources for improving P efficiency in wheat cultivars. This set of genotypes would also help in understanding the genetic architecture of a complex trait like P use efficiency.