Factors of global change affecting plants act at different levels of the ecological hierarchy.

Plants and ecosystems worldwide are exposed to a wide range of chemical, physical, and biological factors of global change, many of which act concurrently. As bringing order to the array of factors is required in order to generate an enhanced understanding of simultaneous impacts, classification schemes have been developed. One such classification scheme is dedicated to capturing the different targets of global change factors along the ecological hierarchy. We build on this pioneering work, and refine the conceptual framework in several ways, focusing on plants and terrestrial systems: (i) we more strictly define the target level of the hierarchy, such that every factor typically has just one target level, and not many; (ii) we include effects above the level of the community, that is, there are effects also at the ecosystem scale that cannot be reduced to any level below this; (iii) we introduce the level of the landscape to capture certain land use change effects while abandoning the level below the individual. We discuss how effects can propagate along the levels of the ecological hierarchy, upwards and downwards, presenting opportunities for explaining non-additivity of effects of multiple factors. We hope that this updated conceptual framework will help inform the next generation of plant-focused global change experiments, specifically aimed at non-additivity of effects at the confluence of many factors.

Combined transcriptome and metabolome analysis reveals the effects of light quality on maize hybrids.

BACKGROUND: Heterosis, or hybrid vigor, refers to the phenotypic superiority of an F1 hybrid relative to its parents in terms of growth rate, biomass production, grain yield, and stress tolerance. Light is an energy source and main environmental cue with marked impacts on heterosis in plants. Research into the production applications and mechanism of heterosis has been conducted for over a century and a half, but little is known about the effect of light on plant heterosis. RESULTS: In this study, an integrated transcriptome and metabolome analysis was performed using maize (Zea mays L.) inbred parents, B73 and Mo17, and their hybrids, B73 × Mo17 (BM) and Mo17 × B73 (MB), grown in darkness or under far-red, red, or blue light. Most differentially expressed genes (73.72-92.50%) and differentially accumulated metabolites (84.74-94.32%) exhibited non-additive effects in BM and MB hybrids. Gene Ontology analysis revealed that differential genes and metabolites were involved in glutathione transfer, carbohydrate transport, terpenoid biosynthesis, and photosynthesis. The darkness, far-red, red, and blue light treatments were all associated with phenylpropanoid-flavonoid biosynthesis by Weighted Gene Co-expression Network Analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Five genes and seven metabolites related to phenylpropanoid-flavonoid biosynthesis pathway were identified as potential contributors to the interactions between maize heterosis and light conditions. Consistent with the strong mid-parent heterosis observed for metabolites, significant increases in both fresh and dry weights were found in the MB and BM hybrids compared with their inbred parents. Unexpectedly, increasing light intensity resulted in higher biomass heterosis in MB, but lower biomass heterosis in BM. CONCLUSIONS: The transcriptomic and metabolomic results provide unique insights into the effects of light quality on gene expression patterns and genotype-environment interactions, and have implications for gene mining of heterotic loci to improve maize production.

A kin-selection model of fairness in heterogeneous populations.

Humans and other primates exhibit pro-social preferences for fairness. These preferences are thought to be reinforced by strong reciprocity, a policy that rewards fair actors and punishes unfair ones. Theories of fairness based on strong reciprocity have been criticized for overlooking the importance of individual differences in socially heterogeneous populations. Here, we explore the evolution of fairness in a heterogeneous population. We analyse the Ultimatum Game in cases where players' roles in the game are determined by their status. Importantly, our model allows for non-random pairing of players, and so we also explore the role played by kin selection in shaping fairness. Our kin-selection model shows that, when individuals condition their behaviour on their role in the game, fairness can be understood as either altruistic or spiteful. Altruistic fairness directs resources from less valuable members of a genetic lineage to more valuable members of the same lineage, whereas spiteful fairness keeps resources away from the competitors of the actor's high-value relatives. When individuals express fairness unconditionally it can be understood as altruistic or selfish. When it is altruistic, unconditional fairness again serves to direct resources to high-value members of genetic lineages. When it is selfish, unconditional fairness simply improves an individual's own standing. Overall, we expand kin-selection based explanations for fairness to include motivations other than spite. We show, therefore, that one need not invoke strong reciprocity to explain the advantage of fairness in heterogeneous populations.

Genomic dominance variance analysis of health and milk production traits in German Holstein cattle.

Genomic analyses commonly explore the additive genetic variance of traits. The non-additive variance, however, is usually small but often significant in dairy cattle. This study aimed at dissecting the genetic variance of eight health traits that recently entered the total merit index in Germany and the somatic cell score (SCS), as well as four milk production traits by analysing additive and dominance variance components. The heritabilities were low for all health traits (between 0.033 for mastitis and 0.099 for SCS), and moderate for the milk production traits (between 0.261 for milk energy yield and 0.351 for milk yield). For all traits, the contribution of dominance variance to the phenotypic variance was low, varying between 0.018 for ovarian cysts and 0.078 for milk yield. Inbreeding depression, inferred from the SNP-based observed homozygosity, was significant only for the milk production traits. The contribution of dominance variance to the genetic variance was larger for the health traits, ranging from 0.233 for ovarian cysts to 0.551 for mastitis, encouraging further studies that aim at discovering QTLs based on their additive and dominance effects.

Searching for a mechanistic description of pairwise epistasis in protein systems.

When two or more amino acid mutations occur in protein systems, they can interact in a nonadditive fashion termed epistasis. One way to quantify epistasis between mutation pairs in protein systems is by using free energy differences: ϵ = ΔΔG1,2  - (ΔΔG1  + ΔΔG2 ) where ΔΔG refers to the change in the Gibbs free energy, subscripts 1 and 2 refer to single mutations in arbitrary order and 1,2 refers to the double mutant. In this study, we explore possible biophysical mechanisms that drive pairwise epistasis in both protein-protein binding affinity and protein folding stability. Using the largest available datasets containing experimental protein structures and free energy data, we derived statistical models for both binding and folding epistasis (ϵ) with similar explanatory power (R2 ) of .299 and .258, respectively. These models contain terms and interactions that are consistent with intuition. For example, increasing the Cartesian separation between mutation sites leads to a decrease in observed epistasis for both folding and binding. Our results provide insight into factors that contribute to pairwise epistasis in protein systems and their importance in explaining epistasis. However, the low explanatory power indicates that more study is needed to fully understand this phenomenon.

Deep distortion.

Conventional false memories recount events that either did not happen (item errors) or that happened in a different context (source errors). Fuzzy-trace theory predicts deeper anomalies that lie behind conventional false memories. These deep distortions are structural irregularities in the ways that specific recountings are related to each other or to some objective standard (e.g., the 0 and 1 limits of probability). I discuss five deep distortions for which substantial data have accumulated: overdistribution, non-additivity, conjunction illusions, non-compensation, and super-overdistribution. Together, these phenomena violate the disjunction and additivity axioms of probability, as well as the law of the excluded middle. The theoretical problem they pose is to explain how valid representations of our experience produce memory regularities that violate our experience in the most fundamental ways.

The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register.

Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the 'Mendel' software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4-9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.

Deep memory distortions.

Deep distortions are a new family of memory biases that comprise one of the two basic varieties of false memory. The first and older variety, surface distortions, are specific item or source memories that are erroneous because the events did not happen. The new variety, deep distortions, are emergent properties of multiple specific memories. They are relations among such memories that are false because they violate objective logical rules that real-world events must obey. I discuss four deep distortions for which substantial data have accumulated: overdistribution, super-overdistribution, non-additivity, and impossible conjunctions. These phenomena violate four axioms of classical probability (numerical bound, universal event, additivity, and countable additivity) and two rules that follow from them (empty set and monotonicity). Their psychological significance lies in four facts about them: (a) They demonstrate that although events in the real world are compensatory, our memories of them are not; (b) they establish that we persistently over remember experience; (c) they reveal that surface distortions are by-products of deep distortions; and (d) they pose the theoretical conundrum of how the structure of memory could so thoroughly misrepresent the objective structure of the events we are remembering.

Robust non-syntenic gene expression patterns in diverse maize hybrids during root development.

Distantly related maize (Zea mays L.) inbred lines exhibit an exceptional degree of structural genomic diversity, which is probably unique among plants. This study systematically investigated the developmental and genotype-dependent regulation of the primary root transcriptomes of a genetically diverse panel of maize F1-hybrids and their parental inbred lines. While we observed substantial transcriptomic changes during primary root development, we demonstrated that hybrid-associated gene expression patterns, including differential, non-additive, and allele-specific transcriptome profiles, are particularly robust to these developmental fluctuations. For instance, differentially expressed genes with preferential expression in hybrids were highly conserved during development in comparison to their parental counterparts. Similarly, in hybrids a major proportion of non-additively expressed genes with expression levels between the parental values were particularly conserved during development. Importantly, in these expression patterns non-syntenic genes that evolved after the separation of the maize and sorghum lineages were systemically enriched. Furthermore, non-syntenic genes were substantially linked to the conservation of all surveyed gene expression patterns during primary root development. Among all F1-hybrids, between ~40% of the non-syntenic genes with unexpected allelic expression ratios and ~60% of the non-syntenic differentially and non-additively expressed genes were conserved and therefore robust to developmental changes. Hence, the enrichment of non-syntenic genes during primary root development might be involved in the developmental adaptation of maize roots and thus the superior performance of hybrids.

Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information.

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows' additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.

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.

An Extended Twin-Pedigree Study of Neuroticism in the Netherlands Twin Register.

For the participants in the Netherlands Twin Register (NTR) we constructed the extended pedigrees which specify all relations among nuclear and larger twin families in the register. A total of 253,015 subjects from 58,645 families were linked to each other, to the degree that we had information on the relations among participants. We describe the algorithm that was applied to construct the pedigrees. For > 30,000 adolescent and adult NTR participants data were available on harmonized neuroticism scores. We analyzed these data in the Mendel software package (Lange et al., Bioinformatics 29(12):1568-1570, 2013) to estimate the contributions of additive and non-additive genetic factors. In contrast to much of the earlier work based on twin data rather than on extended pedigrees, we could also estimate the contribution of shared household effects in the presence of non-additive genetic factors. The estimated broad-sense heritability of neuroticism was 47%, with almost equal contributions of additive and non-additive (dominance) genetic factors. A shared household effect explained 13% and unique environmental factors explained the remaining 40% of the variance in neuroticism.

Differential Many-Body Cooperativity in Electronic Spectra of Oligonuclear Transition-Metal Complexes.

In computational chemistry, non-additive and cooperative effects can be defined in terms of a (differential) many-body expansion of the energy or any other physical property of the molecular system of interest. One-body terms describe energies or properties of the subsystems, two-body terms describe non-additive but pairwise contributions and three-body as well as higher-order terms can be interpreted as a measure for cooperativity. In the present article, this concept is applied to the analysis of ultraviolet/visible (UV/Vis) spectra of homotrinuclear transition-metal complexes by means of a many-body expansion of the change in the spectrum induced by replacing each of the three transition-metal ions by another transition-metal ion to yield a different homotrinuclear transition-metal complex. Computed spectra for the triangulo-complexes [M3 {Si(mt(Me) )3}2] (M=Pd/Pt, mt(Me) =methimazole) and tritopic triphenylene-based N-heterocyclic carbene Rh/Ir complexes illustrate the concept, showing large and small differential three-body cooperativity, respectively.

Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting.

While there has been extensive research developing gene-environment interaction (GEI) methods in case-control studies, little attention has been given to sparse and efficient modeling of GEI in longitudinal studies. In a two-way table for GEI with rows and columns as categorical variables, a conventional saturated interaction model involves estimation of a specific parameter for each cell, with constraints ensuring identifiability. The estimates are unbiased but are potentially inefficient because the number of parameters to be estimated can grow quickly with increasing categories of row/column factors. On the other hand, Tukey's one-degree-of-freedom model for non-additivity treats the interaction term as a scaled product of row and column main effects. Because of the parsimonious form of interaction, the interaction estimate leads to enhanced efficiency, and the corresponding test could lead to increased power. Unfortunately, Tukey's model gives biased estimates and low power if the model is misspecified. When screening multiple GEIs where each genetic and environmental marker may exhibit a distinct interaction pattern, a robust estimator for interaction is important for GEI detection. We propose a shrinkage estimator for interaction effects that combines estimates from both Tukey's and saturated interaction models and use the corresponding Wald test for testing interaction in a longitudinal setting. The proposed estimator is robust to misspecification of interaction structure. We illustrate the proposed methods using two longitudinal studies-the Normative Aging Study and the Multi-ethnic Study of Atherosclerosis.

Non-additive activity modulation during a decision making task involving tactic selection.

Human brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey's successful tactic selection and decision making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09702-0.

Supramolecular Approach to Tuning the Photophysical Properties of Quadrupolar Squaraines.

In the present study, the influence of the hydrogen bonding for the one- and two-photon absorption of the prototypical squaraine dye is investigated with quantum chemistry tools. The central squaraine unit is bound by strong hydrogen bonds with 4-substituted N,N'-diphenylurea and, alternatively, N,N'-diphenylthiourea molecules, which affects to a high extend the properties of the squaraine electron accepting moiety, thus shifting its maximum absorption wavelength and enhancing the TPA cross section. The replacement of oxygen by sulfur atoms in the squaraine central ring, known to affect its photophysical behavior, is considered here as the way of modifying the strength and nature of the intermolecular contacts. Additionally, the influence of the oxygen-by-sulfur replacement is also considered in the N,N'-diphenylurea moiety, as the factor affecting the acidity of the N-H protons. The introduction of the sequence of the substituents of varying electron-donating or electron-withdrawing characters in the position 4 of N,N'-diphenyl(thio)urea subsystems allows to finely tune the hydrogen bonding with the central squaraine unit by further modification of the N-H bond characteristics. All of these structural modifications lead to the controlled adjustment of the electron density distribution, and thus, the properties affected such as transition moments and absorption intensity. Ab initio calculations provide strong support for this way of tailoring of one- or two-photon absorption due to the obtained strong hypsochromic shift of the maximum one-photon absorption wavelength observed particularly for thiosquaraine complexes and an increase in the TPA wavelength together with the increase in the TPA cross section. Moreover, the source of the strong modification of the thiosquaraine OPA in contrast to the pristine oxosquaraine upon N,N'-diphenyl(thio)urea substitution is determined. Furthermore, for the first time, the linear dependence of the non-additivity in the interaction energy on the Hammett substituent constant is reported. The stronger the electron-donating character of the substituent, the larger the three-body non-additive components and the larger their percentage to the total interaction energy.

The Pitfalls of Heterosis Coefficients.

Heterosis (hybrid vigour) is a universal phenomenon of crucial agro-economic and evolutionary importance. We show that the most common heterosis coefficients do not properly measure deviation from additivity because they include both a component accounting for "real" heterosis and a term that is not related to heterosis, since it is derived solely from parental values. Therefore, these coefficients are inadequate whenever the aim of the study is to compare heterosis levels between different traits, environments, genetic backgrounds, or developmental stages, as these factors may affect not only the level of non-additivity, but also parental values. The only relevant coefficient for such comparisons is the so-called "potence ratio". Because most heterosis studies consider several traits/stages/environmental conditions, our observations support the use of the potence ratio, at least in non-agronomic contexts, because it is the only non-ambiguous heterosis coefficient.

Distributed Lag Interaction Models with Two Pollutants.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on a health outcome of interest such as mortality and morbidity. Most previous DLM approaches only consider one pollutant at a time. In this article, we propose distributed lag interaction model (DLIM) to characterize the joint lagged effect of two pollutants. One natural way to model the interaction surface is by assuming that the underlying basis functions are tensor products of the basis functions that generate the main-effect distributed lag functions. We extend Tukey's one-degree-of-freedom interaction structure to the two-dimensional DLM context. We also consider shrinkage versions of the two to allow departure from the specified Tukey's interaction structure and achieve bias-variance tradeoff. We derive the marginal lag effects of one pollutant when the other pollutant is fixed at certain quantiles. In a simulation study, we show that the shrinkage methods have better average performance in terms of mean squared error (MSE) across different scenarios. We illustrate the proposed methods by using the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data to model the joint effects of PM10 and O3 on mortality count in Chicago, Illinois, from 1987 to 2000.

Convex Regression with Interpretable Sharp Partitions.

We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.

Binding cooperativity between a ligand carbonyl group and a hydrophobic side chain can be enhanced by additional H-bonds in a distance dependent manner: A case study with thrombin inhibitors.

One of the underappreciated non-covalent binding factors, which can significantly affect ligand-protein binding affinity, is the cooperativity between ligand functional groups. Using four different series of thrombin inhibitors, we reveal a strong positive cooperativity between an H-bond accepting carbonyl functionality and the adjacent P3 hydrophobic side chain. Adding an H-bond donating amine adjacent to the P3 hydrophobic side chain further increases this positive cooperativity thereby improving the Ki by as much as 546-fold. In contrast, adding an amidine multiple H-bond/salt bridge group in the distal S1 pocket does not affect this cooperativity. An analysis of the crystallographic B-factors of the ligand groups inside the binding site indicates that the strong cooperativity is mainly due to a significant mutual reduction in the residual mobility of the hydrophobic side chain and the H-bonding functionalities that is absent when the separation distance is large. This type of cooperativity is important to encode in binding affinity prediction software, and to consider in SAR studies.