Shrub type dominates the vertical distribution of leaf C : N : P stoichiometry across an extensive altitudinal gradient

Understanding leaf stoichiometric patterns is crucial for improving predictions of plant responses to environmental changes. Leaf stoichiometry of terrestrial ecosystems has been widely investigated along latitudinal and longitudinal gradients. However, very little is known about the vertical distribution of leaf C :N: P and the relative effects of environmental parameters, especially for shrubs. Here, we analyzed the shrub leaf C, N and P patterns in 125 mountainous sites over an extensive altitudinal gradient (523-4685 m) on the Tibetan Plateau. Results showed that the shrub leaf C and C :N were 7.3-47.5% higher than those of other regional and global flora, whereas the leaf N and N: P were 10.2-75.8% lower. Leaf C increased with rising altitude and decreasing temperature, supporting the physiological acclimation mechanism that high leaf C (e.g., alpine or evergreen shrub) could balance the cell osmotic pressure and resist freezing. The largest leaf N and high leaf P occurred in valley region (altitude 1500 m), likely due to the large nutrient leaching from higher elevations, faster litter decomposition and nutrient resorption ability of deciduous broadleaf shrub. Leaf N: P ratio further indicated increasing N limitation at higher altitudes. Interestingly, drought severity was the only climatic factor positively correlated with leaf N and P, which was more appropriate for evaluating the impact of water status than precipitation. Among the shrub ecosystem and functional types (alpine, subalpine, montane, valley, evergreen, deciduous, broadleaf, and conifer), their leaf element contents and responses to environments were remarkably different. Shrub type was the largest contributor to the total variations in leaf stoichiometry, while climate indirectly affected the leaf C :N: P via its interactive effects on shrub type or soil. Collectively, the large heterogeneity in shrub type was the most important factor explaining the overall leaf C :N: P variations, despite the broad climate gradient on the plateau. Temperature and drought induced shifts in shrub type distribution will influence the nutrient accumulation in mountainous shrubs. © Author(s) 2018

Mining anti-hypertensive peptides in animal food through deep learning: a case study of gastrointestinal digestive products of royal jelly

To shorten the complex and time-consuming process of the identification method of the traditional food angiotensin-I-converting enzyme (ACE-I) inhibitory peptides, we propose AHTPeptideFusion based on a segmented fusion with the protein language model and deep learning. The statistical analysis found that hydrophobic amino acids, N-terminal valine is a dominant amino acid in the activity of ACE-I inhibitory peptides. In 12 machine learning (ML) algorithms, the transformer outperformed the other 11 models, with the best performance in predicting short and medium peptides. In the external dataset, AHTPeptideFusion fused by transformer and random forest (RF) showed excellent performance (accuracy > 0.9) in predicting ACE-I inhibitory peptides with lengths ranging from 2 to 15 amino acid residues and different activity distributions, and the reliability and accuracy of AHTPeptideFusion was demonstrated by synthetic peptide and ACE-I inhibition experiments. In addition, hydrogen bonding and electrostatic interaction between 4 synthetic peptides and active residues of ACE-I were found by molecular docking. To further explore the ACE-I inhibitory peptides from animal-derived foods, we established an automated pipeline consisting of the trinity of proteomics, virtual enzymatic digestion and AHTPeptideFusion, and tapped the ACE-I inhibitory peptide released from royal jelly after digestion in the gastrointestinal tract. In conclusion, this computational pipeline will become a powerful screening tool for active peptides from animal-derived foods, which can help food scientists accelerate the mining and design of active peptides from animal-derived foods. Overall, AHTPeptideFusion will be a powerful ACE-I inhibitor peptide prediction tool, it can help food scientists accelerate the mining and design of ACE-I inhibitory peptides

Inhalation of Hydrogen Attenuates Progression of Chronic Heart Failure via Suppression of Oxidative Stress and P53 Related to Apoptosis Pathway in Rats

Background: Continuous damage from oxidative stress and apoptosis are the important mechanisms that facilitate chronic heart failure (CHF). Molecular hydrogen (H2) has potentiality in the aspects of anti-oxidation. The objectives of this study were to investigate the possible mechanism of H2 inhalation in delaying the progress of CHF.Methods and Results: A total of 60 Sprague-Dawley (SD) rats were randomly divided into four groups: Sham, Sham treated with H2, CHF and CHF treated with H2. Rats from CHF and CHF treated with H2 groups were injected isoprenaline subcutaneously to establish the rat CHF model. One month later, the rat with CHF was identified by the echocardiography. After inhalation of H2, cardiac function was improved vs. CHF (p < 0.05), whereas oxidative stress damage and apoptosis were significantly attenuated (p < 0.05). In this study, the mild oxidative stress was induced in primary cardiomyocytes of rats, and H2 treatments significantly reduced oxidative stress damage and apoptosis in cardiomyocytes (p < 0.05 or p < 0.01). Finally, as a pivotal transcription factor in reactive oxygen species (ROS)-apoptosis signaling pathway, the expression and phosphorylation of p53 were significantly reduced by H2 treatment in this rat model and H9c2 cells (p < 0.05 or p < 0.01).Conclusion: As a safe antioxidant, molecular hydrogen mitigates the progression of CHF via inhibiting apoptosis modulated by p53. Therefore, from the translational point of view and speculation, H2 is equipped with potential therapeutic application as a novel antioxidant in protecting CHF in the future

Plasmin Plays an Essential Role in Amplification of Psoriasiform Skin Inflammation in Mice

BACKGROUND: Although increased levels of plasminogen activators have been found in psoriatic lesions, the role of plasmin converted from plasminogen by plasminogen activators in pathogenesis of psoriasis has not been investigated. METHODOLOGY/PRINCIPAL FINDINGS: Here we examined the contribution of plasmin to amplification of inflammation in patients with psoriasis. We found that plasminogen was diminished, but that the amount and activity of its converted product plasmin were markedly increased in psoriasis. Moreover, annexin II, a receptor for plasmin was dramatically increased in both dermis and epidermis in psoriasis. Plasmin at sites of inflammation was pro-inflammatory, eliciting production of inflammatory factors, including CC chemokine ligand 20 (CCL20) and interleukin-23 (IL-23), that was mediated by the nuclear factor-kappaB (NF-κB) signaling pathway and that had an essential role in the recruitment and activation of pathogenic C-C chemokine receptor type 6 (CCR6)+ T cells. Moreover, intradermal injection of plasmin or plasmin together with recombinant monocyte/macrophage chemotactic protein-1 (MCP-1) resulted in induction of psoriasiform skin inflammation around the injection sites with several aspects of human psoriasis in mice. CONCLUSIONS/SIGNIFICANCE: Plasmin converted from plasminogen by plasminogen activators plays an essential role in amplification of psoriasiform skin inflammation in mice, and targeting plasmin receptor--annexin II--may harbor therapeutic potential for the treatment of human psoriasis

An Improved Splitting Method of Liquid Production With in Thick Reservoir

Reasonable and accurate splitting method of liquid production is key to improve producing status of thick reservoir, but common methods at this stage are all kinds of problems such as limited accuracy, tedious process and workload issues, an improved splitting method of liquid production suitable for thick reservoir is necessary. Adjustment coefficient can be obtained from the regression and relevance between the fluid amount and parameters combined with reservoir monitoring data, core data and flooding information based on the splitting of liquid production in production and injection wells in order to split the fluid amount in different parts of thick reservoir. The improved splitting method of liquid production is proved to be accurate, reasonable, reliable and effective to improve the accuracy of the results of numerical simulation and guidance fine tapping of thick reservoir

TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients

Experimental study on seepage flow patterns in heterogeneous low-permeability reservoirs

Abstract Heterogeneous artificial core plate models with low permeability are designed, made and evaluated based on similarity theory of heterogeneous reservoir with low-permeability physical simulation by artificial core plate model; then, simulative experiments for seepage flow patterns can be carried out. Pressure data are obtained by pressure transducers symmetrically arranged in artificial core plate models to study on the seepage flow patterns in heterogeneous reservoirs with low permeability. Experimental results show that the pressure gradient around injection and production points is high, and the pressure gradient of diagonal corner is very low. The distribution of pressure gradient changes as plane heterogeneity of artificial core plate models changes. The higher permeability increases the spread range of pressure, but the enhancement of heterogeneity has a negative effect on pressure transmission at the same time. The effect of permeability is greater than the negative impact of heterogeneous when the overall permeability of plate model is at a very low level. Non-seepage flow section becomes smaller with the increase in permeability, and the proportion of quasi-linear seepage flow section which is more conducive to fluid flow raise as seepage flow section becomes larger

SRP‐AKAZE: an improved accelerated KAZE algorithm based on sparse random projection

The AKAZE algorithm is a typical image registration algorithm that has the advantage of high computational efficiency based on non‐linear diffusion. However, it is weaker than the scale‐invariant feature transformation (SIFT) algorithm in terms of robustness and stability. We propose a new and improved version of the AKAZE algorithm by using the SIFT descriptor based on sparse random projection (SRP). The proposed method not only retains the advantage of high efficiency of the AKAZE algorithm in feature detection but also has the stability of the SIFT descriptor. Moreover, the computational complexity due to the high dimension of the SIFT descriptor, which limits the speed of feature matching, is drastically reduced by the SRP strategy. Experiments on several benchmark image datasets demonstrate that the proposed algorithm can significantly improve the stability of the AKAZE algorithm, and the results suggest the better matching performance and robustness of the feature descriptor