Partitioning of plant functional trait variation into phenotypic plasticity and neutral components reveals functional differences among neotropical tree species

Background: Tropical plant communities exhibit extraordinary species richness and functional diversity in highly heterogeneous environments. Albeit the fact that such environmental filtering shapes local species composition and associated plant functional traits, it remains elusive to what extend tropical vegetation might be able to acclimate to environmental changes via phenotypic plasticity, which could be a critical determinant affecting the resistance and resilience of tropical vegetation to projected climate change. Methods: Based on a dataset compiled from 345 individuals and comprising 34 tropical tree species we here investigated the role of phenotypic plasticity versus non-plastic variation among key plant functional traits, i.e. wood density, maximum height, leaf thickness, leaf area, specific leaf area, leaf dry mass, nitrogen and phosphorus content. We hypothesized that trait variation due to plasticity is driven by environmental variability independently of spatial effects due to geographic distance between forest stands, whereas non-plastic variation increases with geographic distance due to adaption of the plant community to the local environment. Based on these hypotheses we partitioned total observed trait variation into phenotypic plasticity and neutral components and quantified respective amount of variation related to environmental filtering and neutral community assembly. Results: We found that trait variation was strongly related to spatial factors, thus often masking phenotypic plasticity in response to environmental cues. However, respective environmental factors differed among plant functional traits, such that leaf traits varied in association with light regime and soil nutrient content, whereas wood traits were related to topography and soil water content. Our results further suggest that phenotypic plasticity increased with the range size of congeneric tree species, indicating less plasticity within range restricted endemics compared to their widespread congeners. Conclusions: Differences in phenotypic trait plasticity affect stress tolerance and range size of tropical tree species, therefore endemic species could be especially prone to projected climate change

A simple approximation for the bit-interleaved coded modulation capacity

Abstract-The generalized mutual information (GMI) is an achievable rate for bit-interleaved coded modulation (BICM) and is highly dependent on the binary labeling of the constellation. The BICM-GMI, sometimes called the BICM capacity, can be evaluated numerically. This approach, however, becomes impractical when the number of constellation points and/or the constellation dimensionality grows, or when many different labelings are considered. A simple approximation for the BICM-GMI based on the area theorem of the demapper's extrinsic information transfer (EXIT) function is proposed. Numerical results show the proposed approximation gives good estimates of the BICM-GMI for labelings with close to linear EXIT functions, which includes labelings of common interest, such as the natural binary code, binary reflected Gray code, etc. This approximation is used to optimize the binary labeling of the 32-APSK constellation defined in the DVB-S2 standard. Gains of approximately 0.15 dB are obtained

The role of phenotypic plasticity for plant functional traits in tropical forests

Tropical tree communities comprise high species richness and functional diversity in highly heterogeneous environments. Phenotypic plasticity is the main mechanism by which trees adjust their functional traits in response to environmental variation and climate change. However, the degree of plasticity is not well known for most plant functional traits. We compiled a dataset based on 345 individuals from 35 tropical tree species investigating the role of phenotypic plasticity versus non-plastic variation among key plant functional traits, (i.e. wood density, total height, SLA, leaf N content). We hypothesized that trait variation due to plastic components are driven by environmental variability independently of geographic distance, whereas the non-plastic component increases with geographic distance due to local adaption of the population to different environments. Based on this hypothesis we partitioned total observed trait variation into phenotypic plasticity and non-plastic components and quantified how functional trait variation is related to environmental heterogeneity and geographic distance among tropical forest stands. We found that overall trait variation was strongly related to spatial factors, thus often masking phenotypic plasticity in response to environmental cues. However, respective environmental controlling factors differed among functional traits, such that leaf traits varied in strong association with edaphic factors, whereas wood traits were more significantly affected by topography and light regime. We further show that the identified pattern of phenotypic plasticity versus non-plastic trait variation increased with the range size of congeneric tree species. Hence, this might indicate less plastic responses of endemic tree species compared to their widespread congeners, which thus could be more vulnerable to environmental changes under future scenarios

Fluid Mechanics in Dentinal Microtubules Provides Mechanistic Insights into the Difference between Hot and Cold Dental Pain

Dental thermal pain is a significant health problem in daily life and dentistry. There is a long-standing question regarding the phenomenon that cold stimulation evokes sharper and more shooting pain sensations than hot stimulation. This phenomenon, however, outlives the well-known hydrodynamic theory used to explain dental thermal pain mechanism. Here, we present a mathematical model based on the hypothesis that hot or cold stimulation-induced different directions of dentinal fluid flow and the corresponding odontoblast movements in dentinal microtubules contribute to different dental pain responses. We coupled a computational fluid dynamics model, describing the fluid mechanics in dentinal microtubules, with a modified Hodgkin-Huxley model, describing the discharge behavior of intradental neuron. The simulated results agreed well with existing experimental measurements. We thence demonstrated theoretically that intradental mechano-sensitive nociceptors are not “equally sensitive” to inward (into the pulp) and outward (away from the pulp) fluid flows, providing mechanistic insights into the difference between hot and cold dental pain. The model developed here could enable better diagnosis in endodontics which requires an understanding of pulpal histology, neurology and physiology, as well as their dynamic response to the thermal stimulation used in dental practices

Predicting eco-evolutionary adaptations of plants to drought and rainfall variability

The future Earth is projected to experience elevated rainfall variability, with more frequent and intense droughts, as well as high-rainfall events. Increasing CO2 concentrations are expected to raise terrestrial gross primary productivity (GPP), whereas water stress is expected to lower GPP. Plant responses to water stress vary strongly with timescale, and plants adapted to different environmental conditions differ in their functional responses. Here, we embed a unified optimality-based theory of stomatal conductance and biochemical acclimation of leaves we have recently developed [Joshi, J. et al. (2020) Towards a unified theory of plant photosynthesis and hydraulics. bioRxiv 2020.12.17.423132] in an eco-evolutionary vegetation-modelling framework, with the goal to investigate emergent functional diversity and associated GPP impacts under different rainfall regimes. The model of photosynthesis used here simultaneously predicts the stomatal responses and biochemical acclimation of leaves to atmospheric and soil-moisture conditions. Using three hydraulic traits and two cost parameters, it successfully predicts the simultaneous declines in CO2 assimilation rate, stomatal conductance, and leaf photosynthetic capacity caused by drying soil. It also correctly predicts the responses of CO2 assimilation rate, stomatal conductance, leaf water potential, and leaf photosynthetic capacity to vapour pressure deficit, temperature, ambient CO2, light intensity, and elevation. Our model therefore captures the synergistic effects of atmospheric and soil drought, as well as of atmospheric CO2 changes, on plant photosynthesis and transpiration. We embed this model of photosynthesis and transpiration in a trait-height-patch structured eco-evolutionary vegetation model. This model accounts for allometric carbon allocation, height-structured competition for light, patch-structured successional dynamics, and coevolution of plant functional traits. It predicts functional species mixtures and emergent ecosystem properties under different environmental conditions. Using this model, we investigate the evolution of plant hydraulic strategies under different regimes of drought and rainfall variability. Our approach provides an eco-evolutionarily consistent framework to scale up the responses of plant communities from individual plants to ecosystems to provide ecosystem-level predictions of functional diversity, primary production, and plant water use, and could thus be used for reliable projections of the global carbon and water cycles under future climate scenarios

Brain tumour diagnostics using a DNA methylation-based classifier as a diagnostic support tool

Aims: Methylation profiling (MP) is increasingly incorporated in the diagnostic process of central nervous system (CNS) tumours at our centres in The Netherlands and Scandinavia. We aimed to identify the benefits and challenges of MP as a support tool for CNS tumour diagnostics. Methods: About 502 CNS tumour samples were analysed using (850 k) MP. Profiles were matched with the DKFZ/Heidelberg CNS Tumour Classifier. For each case, the final pathological diagnosis was compared to the diagnosis before MP. Results: In 54.4% (273/502) of all analysed cases, the suggested methylation class (calibrated score ≥0.9) corresponded with the initial pathological diagnosis. The diagnosis of 24.5% of these cases (67/273) was more refined after incorporation of the MP result. In 9.8% of cases (49/502), the MP result led to a new diagnosis, resulting in an altered WHO grade in 71.4% of these cases (35/49). In 1% of cases (5/502), the suggested class based on MP was initially disregarded/interpreted as misleading, but in retrospect, the MP result predicted the right diagnosis for three of these cases. In six cases, the suggested class was interpreted as ‘discrepant but noncontributory’. The remaining 33.7% of cases (169/502) had a calibrated score <0.9, including 7.8% (39/502) for which no class indication was given at all (calibrated score <0.3). Conclusions: MP is a powerful tool to confirm and fine-tune the pathological diagnosis of CNS tumours, and to avoid misdiagnoses. However, it is crucial to interpret the results in the context of clinical, radiological, histopathological and other molecular information

Participatory learning and action cycles with women s groups to prevent neonatal death in low-resource settings: A multi-country comparison of cost-effectiveness and affordability.

WHO recommends participatory learning and action cycles with women's groups as a cost-effective strategy to reduce neonatal deaths. Coverage is a determinant of intervention effectiveness, but little is known about why cost-effectiveness estimates vary significantly. This article reanalyses primary cost data from six trials in India, Nepal, Bangladesh and Malawi to describe resource use, explore reasons for differences in costs and cost-effectiveness ratios, and model the cost of scale-up. Primary cost data were collated, and costing methods harmonized. Effectiveness was extracted from a meta-analysis and converted to neonatal life-years saved. Cost-effectiveness ratios were calculated from the provider perspective compared with current practice. Associations between unit costs and cost-effectiveness ratios with coverage, scale and intensity were explored. Scale-up costs and outcomes were modelled using local unit costs and the meta-analysis effect estimate for neonatal mortality. Results were expressed in 2016 international dollars. The average cost was $203 (range:$61-$537) per live birth. Start-up costs were large, and spending on staff was the main cost component. The cost per neonatal life-year saved ranged from$135 to $1627. The intervention was highly cost-effective when using income-based thresholds. Variation in cost-effectiveness across trials was strongly correlated with costs. Removing discounting of costs and life-years substantially reduced all cost-effectiveness ratios. The cost of rolling out the intervention to rural populations ranges from 1.2% to 6.3% of government health expenditure in the four countries. Our analyses demonstrate the challenges faced by economic evaluations of community-based interventions evaluated using a cluster randomized controlled trial design. Our results confirm that women's groups are a cost-effective and potentially affordable strategy for improving birth outcomes among rural populations

Competition for light can drive adverse species-composition shifts in the Amazon Forest under elevated CO2

The resilience of biodiverse forests to climate change depends on an interplay of adaptive processes operating at multiple temporal and organizational scales. These include short-term acclimation of physiological processes like photosynthesis and respiration, mid-term changes in forest structure due to competition, and long-term changes in community composition arising from competitive exclusion and genetic trait evolution. To investigate the roles of diversity and adaptation for forest resilience, we present Plant-FATE, a parsimonious eco-evolutionary vegetation model. Tested with data from a hyperdiverse Amazonian terra-firme forest, our model accurately predicts multiple emergent ecosystem properties characterizing forest structure and function. Under elevated CO2 conditions, we predict an increase in productivity, leaf area, and aboveground biomass, with the magnitude of this increase declining in nutrient-deprived soils if trees allocate more carbon to the rhizosphere to overcome nutrient limitation. Furthermore, increased aboveground productivity leads to greater competition for light and drives a shift in community composition towards fast-growing but short-lived species characterized by lower wood densities. Such a transition reduces the carbon residence time of woody biomass, dampening carbon-sink strength and potentially rendering the Amazon Forest more vulnerable to future climatic extreme events

Predicting the adaptive responses of biodiverse plant communities using functional trait evolution

Climate change consists of synergistic changes in a wide range of environmental conditions, characterized by elevated CO2, higher mean temperatures, and higher climate variability. While elevated CO2 concentrations may potentially increase the productivity of some ecosystems, it has been argued that nutrient limitation, increased respiration, and increased mortality may dampen or even negate these productivity gains. The capacity of global forests to adjust to such synergistic environmental changes depends on their functional diversity and the ecosystem’s adaptive capacity. The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperature, and water limitation. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities. First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we show that CO2 and nutrient fertilization both drive changes in community composition towards fast life-histories, and that competition drives the system in a direction opposite to what is optimal for individual plants. Our novel eco-evolutionary vegetation modelling framework combines optimality-based modelling for simulating biophysical acclimation, demographic modelling for community composition changes, and evolutionary dynamics for long-term adaptation. It thus opens a new path for predicting multi-timescale ecosystem dynamics and their responses to global change

The rise of dentine hypersensitivity and tooth wear in an ageing population

Our understanding of the aetiology of dentine hypersensitivity (DH) has changed dramatically over the past few decades. It is no longer an enigma, but other problems exist. The prevalence of DH in the world and in particular in the UK is increasing, predominately due to increases in tooth wear and the erosive dietary intake in the younger population. DH is increasingly reported in all age groups and is shown to provide clinical indication of an active erosive tooth wear. As the population ages and possibly retain teeth for longer, the likelihood of tooth wear and DH could increase. This paper describes the prevalence, aetiology, diagnosis and management of DH in relation to tooth wear, which work together through a surface phenomenon. The aim is to raise awareness of the conditions and to help inform a prevention strategy in an ageing population, which starts from younger age groups to reduce disease into older age