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A balance to death
(2018)
Leaf senescence plays a crucial role in nutrient recovery in late-stage plant development and requires vast transcriptional reprogramming by transcription factors such as ORESARA1 (ORE1). A proteolytic mechanism is now found to control ORE1 degradation, and thus senescence, during nitrogen starvation.
Ecological communities are complex adaptive systems that exhibit remarkable feedbacks between their biomass and trait dynamics. Trait-based aggregate models cope with this complexity by focusing on the temporal development of the community’s aggregate properties such as its total biomass, mean trait and trait variance. They are based on particular assumptions about the shape of the underlying trait distribution, which is commonly assumed to be normal. However, ecologically important traits are usually restricted to a finite range, and empirical trait distributions are often skewed or multimodal. As a result, normal distribution-based aggregate models may fail to adequately represent the biomass and trait dynamics of natural communities. We resolve this mismatch by developing a new moment closure approach assuming the trait values to be beta-distributed. We show that the beta distribution captures important shape properties of both observed and simulated trait distributions, which cannot be captured by a Gaussian. We further demonstrate that a beta distribution-based moment closure can strongly enhance the reliability of trait-based aggregate models. We compare the biomass, mean trait and variance dynamics of a full trait distribution (FD) model to the ones of beta (BA) and normal (NA) distribution-based aggregate models, under different selection regimes. This way, we demonstrate under which general conditions (stabilizing, fluctuating or disruptive selection) different aggregate models are reliable tools. All three models predicted very similar biomass and trait dynamics under stabilizing selection yielding unimodal trait distributions with small standing trait variation. We also obtained an almost perfect match between the results of the FD and BA models under fluctuating selection, promoting skewed trait distributions and ongoing oscillations in the biomass and trait dynamics. In contrast, the NA model showed unrealistic trait dynamics and exhibited different alternative stable states, and thus a high sensitivity to initial conditions under fluctuating selection. Under disruptive selection, both aggregate models failed to reproduce the results of the FD model with the mean trait values remaining within their ecologically feasible ranges in the BA model but not in the NA model. Overall, a beta distribution-based moment closure strongly improved the realism of trait-based aggregate models.
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
Pneumonia is one of the most common and potentially lethal infectious conditions worldwide. Streptococcus pneumoniae is the pathogen most frequently associated with bacterial community-acquired pneumonia, while Legionella pneumophila is the major cause for local outbreaks of legionellosis. Both pathogens can be difficult to diagnose since signs and symptoms are nonspecific and do not differ from other causes of pneumonia. Therefore, a rapid diagnosis within a clinically relevant time is essential for a fast onset of the proper treatment. Although methods based on polymerase chain reaction significantly improved the identification of pathogens, they are difficult to conduct and need specialized equipment. We describe a rapid and sensitive test using isothermal recombinase polymerase amplification and detection on a disposable test strip. This method does not require any special instrumentation and can be performed in less than 20 min. The analytical sensitivity in the multiplex assay amplifying specific regions of S. pneumoniae and L. pneumophila simultaneously was 10 CFUs of genomic DNA per reaction. In cross detection studies with closely related strains and other bacterial agents the specificity of the RPA was confirmed. The presented method is applicable for near patient and field testing with a rather simple routine and the possibility for a read out with the naked eye.
Allometric trophic network (ATN) models offer high flexibility and scalability while minimizing the number of parameters and have been successfully applied to investigate complex food web dynamics and their influence on food web diversity and stability. However, the realism of ATN model energetics has never been assessed in detail, despite their critical influence on dynamic biomass and production patterns. Here, we compare the energetics of the currently established original ATN model, considering only biomass-dependent basal respiration, to an extended ATN model version, considering both basal and assimilation-dependent activity respiration. The latter is crucial in particular for unicellular and invertebrate organisms which dominate the metabolism of pelagic and soil food webs. Based on metabolic scaling laws, we show that the extended ATN version reflects the energy transfer through a chain of four trophic levels of unicellular and invertebrate organisms more realistically than the original ATN version. Depending on the strength of top-down control, the original ATN model yields trophic transfer efficiencies up to 71% at either the third or the fourth trophic level, which considerably exceeds any realistic values. In contrast, the extended ATN version yields realistic trophic transfer efficiencies 30% at all trophic levels, in accordance with both physiological considerations and empirical evidence from pelagic systems. Our results imply that accounting for activity respiration is essential for consistently implementing the metabolic theory of ecology in ATN models and for improving their quantitative predictions, which makes them more powerful tools for investigating the dynamics of complex natural communities.
Carbon nanomaterials doped with some other lightweight elements were recently described as powerful, heterogeneous, metal-free organocatalysts, adding to their high performance in electrocatalysis. Here, recent observations in traditional catalysis are reviewed, and the underlying reaction mechanisms of the catalyzed organic transformations are explored. In some cases, these are due to specific active functional sites, but more generally the catalytic activity relates to collective properties of the conjugated nanocarbon frameworks and the electron transfer from and to the catalytic centers and substrates. It is shown that the !earnings are tightly related to those of electrocatalysis; i.e., the search for better electrocatalysts also improves chemocatalysis, and vice versa. Carbon-carbon heterojunction effects and some perspectives on future possibilities are discussed at the end.
Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
Because of political conflicts and climate change, migration will be increased worldwide and integration in host societies is a challenge also for migrants. We hypothesize that migrants, who take up the challenge in a new social environment are taller than migrants who do not pose this challenge. We analyze by a questionnaire possible social, nutritional and ethnic influencing factors to body height (BH) of adult offspring of Turkish migrants (n = 82, 39 males) aged from 18 to 34 years (mean age 24.6 years). The results of multiple regression (downward selection) show that the more a male adult offspring of Turkish migrants feels like belonging to the Turkish culture, the smaller he is (95% CI, -3.79, -0.323). Further, the more a male adult offspring of Turkish migrants feels like belonging to the German culture, the taller he is (95% CI, -0.152, 1.738). We discussed it comparable to primates taking up their challenge in dominance, where as a result their body size increase is associated with higher IGF-1 level. IGF-1 is associated with emotional belonging and has a fundamental role in the regulation of metabolism and growth of the human body. With all pilot characteristics of our study results show that the successful challenge of integration in a new society is strongly associated with the emotional integration and identification in the sense of a personal sense of belonging to society. We discuss taller BH as a signal of social growth adjustment. In this sense, a secular trend of BH adaptation of migrants to hosts is a sign of integration.
Background: Flooding during seasonal monsoons affects millions of hectares of rice-cultivated areas across Asia. Submerged rice plants die within a week due to lack of oxygen, light and excessive elongation growth to escape the water. Submergence tolerance was first reported in an aus-type rice landrace, FR13A, and the ethylene-responsive transcription factor (TF) gene SUB1A-1 was identified as the major tolerance gene. Intolerant rice varieties generally lack the SUB1A gene but some intermediate tolerant varieties, such as IR64, carry the allelic variant SUB1A-2. Differential effects of the two alleles have so far not been addressed. As a first step, we have therefore quantified and compared the expression of nearly 2500 rice TF genes between IR64 and its derived tolerant near isogenic line IR64-Sub1, which carries the SUB1A-1 allele. Gene expression was studied in internodes, where the main difference in expression between the two alleles was previously shown. Results: Nineteen and twenty-six TF genes were identified that responded to submergence in IR64 and IR64-Sub1, respectively. Only one gene was found to be submergence-responsive in both, suggesting different regulatory pathways under submergence in the two genotypes. These differentially expressed genes (DEGs) mainly included MYB, NAC, TIFY and Zn-finger TFs, and most genes were downregulated upon submergence. In IR64, but not in IR64-Sub1, SUB1B and SUB1C, which are also present in the Sub1 locus, were identified as submergence responsive. Four TFs were not submergence responsive but exhibited constitutive, genotype-specific differential expression. Most of the identified submergence responsive DEGs are associated with regulatory hormonal pathways, i.e. gibberellins (GA), abscisic acid (ABA), and jasmonic acid (JA), apart from ethylene. An in-silico promoter analysis of the two genotypes revealed the presence of allele-specific single nucleotide polymorphisms, giving rise to ABRE, DRE/CRT, CARE and Site II cis-elements, which can partly explain the observed differential TF gene expression. Conclusion: This study identified new gene targets with the potential to further enhance submergence tolerance in rice and provides insights into novel aspects of SUB1A-mediated tolerance.