Intraspecific trait variation influences physiological performance and fitness in the South Africa shrub genus Protea (Proteaceae)

Background and AimsGlobal plant trait datasets commonly identify trait relationships that are interpreted to reflect fundamental trade-offs associated with plant strategies, but often these trait relationships are not identified when evaluating them at smaller taxonomic and spatial scales. In this study we evaluate trait relationships measured on individual plants for five widespread Protea species in South Africa to determine whether broad-scale patterns of structural trait (e.g. leaf area) and physiological trait (e.g. photosynthetic rates) relationships can be detected within natural populations, and if these traits are themselves related to plant fitness.MethodsWe evaluated the variance structure (i.e. the proportional intraspecific trait variation relative to among-species variation) for nine structural traits and six physiological traits measured in wild populations. We used a multivariate path model to evaluate the relationships between structural traits and physiological traits, and the relationship between these traits and plant size and reproductive effort.Key ResultsWhile intraspecific trait variation is relatively low for structural traits, it accounts for between 50 and 100 % of the variation in physiological traits. Furthermore, we identified few trait associations between any one structural trait and physiological trait, but multivariate regressions revealed clear associations between combinations of structural traits and physiological performance (R² = 0.37–0.64), and almost all traits had detectable associations with plant fitness.ConclusionsIntraspecific variation in structural traits leads to predictable differences in individual-level physiological performance in a multivariate framework, even though the relationship of any particular structural trait to physiological performance may be weak or undetectable. Furthermore, intraspecific variation in both structural and physiological traits leads to differences in plant size and fitness. These results demonstrate the importance of considering measurements of multivariate phenotypes on individual plants when evaluating trait relationships and how trait variation influences predictions of ecological and evolutionary outcomes.     

Toward insights on antimicrobial selectivity of host defense peptides via machine learning model interpretation

Host defense peptides are promising candidates for the development of novel antibiotics. To realize their therapeutic potential, high levels of target selectivity is essential. This study aims to identify factors governing selectivity via the use of the random forest algorithm for correlating peptide sequence information with their bioactivity data. Satisfactory predictive models were achieved from out-of-bag prediction that yielded accuracies and Matthew's correlation coefficients in excess of 0.80 and 0.57, respectively. Model interpretation through the use of variable importance metrics and partial dependence plots indicated that the selectivity was heavily influenced by the composition and distribution patterns of molecular charge and solubility related parameters. Furthermore, the three investigated bacterial target species (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) likely had a significant influence on how selectivity was realized as there appears to be a similar underlying selectivity mechanism on the basis of charge-solubility properties (i.e. but which is tailored according to the target in question).     

Inconsistent detection of extinction debts using different methods

The extinction debt, delayed species extinctions following landscape degradation, is a widely discussed concept. But a consensus about the prevalence of extinctions debts is hindered by a multiplicity of methods and a lack of comparisons among habitats. We applied three contrasting species–area relationship methods to test for plant community extinction debts in three habitats which had different degradation histories over the last century: calcareous grassland, heathland and woodland. These methods differ in their data requirements, with the first two using information on past and current habitat area alongside current species richness, whilst the last method also requires data on past species richness. The most data‐intensive, and hence arguably most reliable method, identified extinction debts across all habitats for specialist species, whilst the other methods did not. All methods detected an extinction debt in calcareous grassland, which had undergone the most severe degradation. We conclude that some methods failed to detect an extinction debt, particularly in habitats that have undergone moderate degradation. Data on past species numbers are required for the most reliable method; as such data are rare, extinction debts may be under‐reported.     

动物对孢子植物的传播模式及进化意义

孢子植物物种多样性丰富, 是自然生态系统的重要组成部分。孢子植物的传播通常被认为主要依靠风、水、弹力等非生物媒介, 而动物的作用往往被忽略。本文主要概述了: (1)孢子植物对动物传播的适应: 一方面孢子植物可为动物提供食物、庇护所、繁殖场所等, 另一方面孢子植物也可产生视觉、嗅觉等方面的线索来吸引动物, 从而促进动物传播其繁殖体。(2)动物对孢子植物的传播模式: 包括体内传播(消化道和组织寄生)和体外传播两种, 这些模式都能对孢子植物繁殖体进行有效传播。由于动物间形态或生活习性的不同, 以致传播距离存在差异, 最短距离为0.1 cm, 最长距离可从北半球至南半球。(3)动物对孢子植物传播的生态与进化意义; 由于某些孢子植物繁殖体的结构特点或萌发的需求, 以致其繁殖体只能通过动物的传播才能得以定殖, 因此动物与孢子植物之间存在密不可分的关系。目前, 动物对孢子植物的传播研究主要是描述性的内容以及研究单方面的传播途径, 建议在今后的研究中考虑动物对孢子植物传播的有效性以及多途径同时传播对孢子植物定殖的影响, 同时应更加关注孢子植物和动物互惠关系的形成、维持机制及将来的进化趋势。     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

A time course study on dose-response relationship between alcohol exposure and its effects on lipid profile and biomarkers of tissue damage

This present research investigated variations in lipid profiles and important biomarkers of tissue damage in response to graded concentrations of alcohol administration in male Wistar rats. Group A (control) received distilled water while group B, C and D received 30%, 40% and 50% (v/v) alcohol respectively. Five rats each from groups A-D were sacrificed after day(s) 1, 7, 14, 21 and 28 of administration. A significant increase was observed at day 28 for serum cholesterol by 79% (group B), 78% (group C) and 47% (group D) together with serum phospholipid 58% (group B), 50% (group C) and 92% (group D). Serum triacylglycerol increased by 71% (group B), 43% (group C) and 16% (group D) at day 21, while concentration of serum albumin decreased at day 28 by 40.9% (group B), 50.2% (group C), 53.3% (group D) respectively when compared with control (group A). Serum aminotransferases and alkaline phosphatase specific activities, as well as creatinine and uric acid concentration increased in a concentration-dependent manner, following alcohol administration. Though most of these effects induced by alcohol were time- and concentration-dependent, 40% alcohol appear to be more stable, giving results consistent with alcohol-induced damages, with minimal mortality. This study therefore further validated dyslipidemia and imbalance in clinical biomarkers as hallmarks of tissue damage induced by excessive alcohol consumption with an insight on the time- and concentration-response relationship between alcohol consumption and its toxicity.     

Structure-activity relationships of bergenin derivatives effect on α-glucosidase inhibition

The α-glucosidase inhibitory activities of bergenin derivatives were evaluated. Bergenin derivatives were synthesized from bergenin which is a characteristic compound of B. ligulata. A new bergenin derivative, 11-O-(3′,4′-dimethoxybenzoyl)-bergenin showed the highest potent inhibitory activity among those of bergenin derivatives. The presence of substituents at 3′,4′-position in bergenin derivatives altered the α-glucosidase inhibitory activity. 11-O-(3′,4′-dimethoxybenzoyl)-bergenin was noncompetitive inhibitor for α-glucosidase. The present study reveals that bergenin derivatives could be classified as a new group of α-glucosidase inhibitors.     

Synthesis and characterization of 5,7-dihydroxyflavanone derivatives as novel protein tyrosine phosphatase 1B inhibitors

A series of 5,7-dihydroxyflavanone derivatives were synthesized and identified as reversible and competitive protein tyrosine phosphatase (PTP) 1B inhibitors with IC₅₀ values in the micromolar range. Compound 4k had the most potent in vitro inhibition activity against PTP1B (IC₅₀ = 2.37?±?0.37 μM) and the greatest selectivity (3.7-fold) for PTP1B relative to T-cell protein tyrosine phosphatase. Cell-based studies revealed that 4k was membrane-permeable and enhanced insulin receptor tyrosine phosphorylation in CHO/hIR cells.     

Enantio-Selectivity of Human Nucleoside Monophosphate Kinases

Over recent years, there has been a renewed interest in the development of l-nucleosides as safe and efficacious drugs for the treatment of viral infections. Biological activity of these compounds requires phosphorylation to their triphosphate form, involving nucleoside monophosphate kinases in the second step. In order to characterize the activation pathway of l-nucleosides of the pyrimidine series, we studied the enantio-selectivity of human uridylate-cytidylate and thymidylate kinases. The results showed that these enzymes are only weakly enantio-selective and are thus probably involved in the activation of l-nucleosides in vivo. An activation pathway for telbivudine (l-dT) was therefore proposed.