Interactions between two sheets of a bilayer membrane and its internal lateral pressure

We have modelled a phospholipid bilayer as two monolayer sheets which interact with each other by a coupling which depends upon the states of the lipid hydrocarbon chains in each sheet. We make use of a model (Georgallas and Pink 1982a) and its parameters, already used to study monolayer phase changes at the LC-LE transition, in order to study the lipid main transition. Although the monolayer coexistence curve can be calculated exactly, we have made use of high-temperature series expansions to calculate the critical point of the bilayer. We also present the results of computer simulations on triangular lattices for the pressure-area isotherms. We find: (i) the interaction between the sheets of a DPPC bilayer is about 1.5–2% of the maximum interaction within the plane of each sheet; (ii) the internal lateral pressure of a DPPC bilayer is about 30.5 dyne/cm; (iii) the bilayer transition enthalpy depends sensitively upon the coupling between the sheets. Should this coupling vary from sample to sample (due, possibly, to its preparation) then very different values of transition enthalpy may be measured. (iv) We present a rough rule-of-thumb for estimating the internal lateral pressure of a bilayer from a knowledge of the corresponding monolayer pressure-area isotherms.Abbreviations LC-LE liquid condensed — liquid expanded - DPPC dipalmitoylphosphatidylcholine - Q transition enthalpy Work supported in part by the Natural Sciences and Engineering Research Council of Canada     

基于源-库互反馈的温室青椒坐果时空动态模拟

基于源-库互反馈的温室青椒坐果时空动态模拟     

Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody Plants in Hawaii

Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees, but species‐ and site‐specific models are more accurate. We developed species‐specific models to predict aboveground biomass in two of the most ubiquitous natives in Hawaiian forests and shrublands, Metrosideros polymorpha and Dodonaea viscosa. The utility of the M. polymorpha allometry for predicting biomass across a range of sites was explored by comparing size structure (diameter at breast height vs. tree height) of the trees used to develop the models against trees from four M. polymorpha‐dominated forests along a precipitation gradient (1630–2380 mm). We also compared individual tree biomass estimated with the M. polymorpha model against existing generalized equations, and the D. viscosa model with an existing species‐specific model. Our models were highly significant and displayed minimal bias. Metrosideros polymorpha size structures from the three highest precipitation sites fell well within the 95% confidence intervals for the harvested trees, indicating that the models are applicable at these sites. However, size structure in the area with the lowest precipitation differed from those in the higher rainfall sites, emphasizing that care should be taken in applying the models too widely. Existing generalized allometry differed from the M. polymorpha model by up to 88 percent, particularly at the extremes of the data range examined, underestimating biomass in small trees and overestimating in large trees. The existing D. viscosa model underestimated biomass across all sizes by a mean of 43 percent compared to our model. The species‐specific models presented here should enable more accurate estimates of biomass and carbon sequestration in Hawaiian forests and shrublands.     

Proteomic profiling of the hypothalamus in two mouse models of narcolepsy

Narcolepsy is a disabling neurological disorder of sleepiness linked to the loss of neurons producing orexin neuropeptides in the hypothalamus. Two well‐characterized phenotypic mouse models of narcolepsy, loss‐of‐function (orexin‐knockout), and progressive loss of orexin (orexin/ataxin‐3) exist. The open question is whether the proteomics signatures of the hypothalamus would be different between the two models. To address this gap, we utilized a label‐free proteomics approach and conducted a hypothalamic proteome analysis by comparing each disease model to that of wild type. Following data processing and statistical analysis, 14 484 peptides mapping to 2282 nonredundant proteins were identified, of which 39 proteins showed significant differences in protein expression across groups. Altered proteins in both models showed commonalties in pathways for mitochondrial dysfunction and neuronal degeneration, as well as altered proteins related to inflammatory demyelination, insulin resistance, metabolic responses, and the dopaminergic and monoaminergic systems. Model‐specific alterations in insulin degraded enzyme (IDE) and synaptosomal‐associated protein‐25 were unique to orexin‐KO and orexin/ataxin‐3, respectively. For both models, proteomics not only identified clinically suspected consequences of orexin loss on energy homeostasis and neurotransmitter systems, but also identified commonalities in inflammation and degeneration despite the entirely different genetic basis of the two mouse models.     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.     

The mechanistic basis for higher‐order interactions and non‐additivity in competitive communities

Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so‐called higher‐order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs – defined as non‐additive effects of density on per capita growth – are best interpreted as emergent properties of phenomenological models (e.g. Lotka–Volterra competition) rather than as distinct ‘ecological processes’ in their own right. Using simulations of consumer‐resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non‐additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single‐species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non‐additive effects in both theoretical and empirical research.     

MALDI–MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix‐assisted laser desorption ionization–mass spectrometry (MALDI–MS) profiling has emerged within this decade as a powerful tool to identify in routine micro‐organisms and is currently used in real‐time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram‐positive and ‐negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees’ systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high‐throughput strategy that addresses honey bee health on an individual scale is introduced.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.