Sodium acetate increases the productivity of HEK293 cells expressing the ECD-Her1 protein in batch cultures: experimental results and metabolic flux analysis

Human Embryonic Kidney cells (HEK293) are a popular host for recombinant protein expression and production in the biotechnological industry. This has driven within both, the scientific and the engineering communities, the search for strategies to increase their protein productivity. The present work is inserted into this search exploring the impact of adding sodium acetate (NaAc) into a batch culture of HEK293 cells. We monitored, as a function of time, the cell density, many external metabolites, and the supernatant concentration of the heterologous extra-cellular domain ECD-Her1 protein, a protein used to produce a candidate prostate cancer vaccine. We observed that by adding different concentrations of NaAc (0, 4, 6 and 8 mM), the production of ECD-Her1 protein increases consistently with increasing concentration, whereas the carrying capacity of the medium decreases. To understand these results we exploited a combination of experimental and computational techniques. Metabolic Flux Analysis (MFA) was used to infer intracellular metabolic fluxes from the concentration of external metabolites. Moreover, we measured independently the extracellular acidification rate and oxygen consumption rate of the cells. Both approaches support the idea that the addition of NaAc to the culture has a significant impact on the metabolism of the HEK293 cells and that, if properly tuned, enhances the productivity of the heterologous ECD-Her1 protein.

Statistical indices of masculinity-femininity: A theoretical and practical framework

Statistical indices of masculinity-femininity (M-F) summarize multivariate profiles of sex-related traits as positions on a single continuum of individual differences, from masculine to feminine. This approach goes back to the early days of sex differences research; however, a systematic discussion of alternative M-F indices (including their meaning, their mutual relations, and their psychometric properties) has been lacking. In this paper I present an integrative theoretical framework for the statistical assessment of masculinity-femininity, and provide practical guidance to researchers who wish to apply these methods to their data. I describe four basic types of M-F indices: sex-directionality, sex-typicality, sex-probability, and sex-centrality. I examine their similarities and differences in detail, and consider alternative ways of computing them. Next, I discuss the impact of measurement error on the validity of these indices, and outline some potential remedies. Finally, I illustrate the concepts presented in the paper with a selection of real-world datasets on body morphology, brain morphology, and personality. An R function is available to easily calculate multiple M-F indices from empirical data (with or without correction for measurement error) and draw summary plots of their individual and joint distributions.

Prosocial motives underlie scientific censorship by scientists: A perspective and research agenda

Science is among humanity's greatest achievements, yet scientific censorship is rarely studied empirically. We explore the social, psychological, and institutional causes and consequences of scientific censorship (defined as actions aimed at obstructing particular scientific ideas from reaching an audience for reasons other than low scientific quality). Popular narratives suggest that scientific censorship is driven by authoritarian officials with dark motives, such as dogmatism and intolerance. Our analysis suggests that scientific censorship is often driven by scientists, who are primarily motivated by self-protection, benevolence toward peer scholars, and prosocial concerns for the well-being of human social groups. This perspective helps explain both recent findings on scientific censorship and recent changes to scientific institutions, such as the use of harm-based criteria to evaluate research. We discuss unknowns surrounding the consequences of censorship and provide recommendations for improving transparency and accountability in scientific decision-making to enable the exploration of these unknowns. The benefits of censorship may sometimes outweigh costs. However, until costs and benefits are examined empirically, scholars on opposing sides of ongoing debates are left to quarrel based on competing values, assumptions, and intuitions.

Relative density clouds: Visualizing and exploring multivariate patterns of group differences

This paper introduces relative density clouds, a simple but powerful method to visualize the relative density of two groups in multivariate space. Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. An easy-to-use R function is provided to make this visualization method widely accessible to researchers.

A general motivational architecture for human and animal personality

To achieve integration in the study of personality, researchers need to model the motivational processes that give rise to stable individual differences in behavior, cognition, and emotion. The missing link in current approaches is a motivational architecture-a description of the core set of mechanisms that underlie motivation, plus a functional account of their operating logic and inter-relations. This paper presents the initial version of such an architecture, the General Architecture of Motivation (GAM). The GAM offers a common language for individual differences in humans and other animals, and a conceptual toolkit for building species-specific models of personality. The paper describes the main components of the GAM and their interplay, and examines the contribution of these components to the emergence of individual differences. The final section discusses how the GAM can be used to construct explicit functional models of personality, and presents a roadmap for future research.

FOXA1 regulates alternative splicing in prostate cancer

Dysregulation of alternative splicing in prostate cancer is linked to transcriptional programs activated by AR, ERG, FOXA1, and MYC. Here, we show that FOXA1 functions as the primary orchestrator of alternative splicing dysregulation across 500 primary and metastatic prostate cancer transcriptomes. We demonstrate that FOXA1 binds to the regulatory regions of splicing-related genes, including HNRNPK and SRSF1. By controlling trans-acting factor expression, FOXA1 exploits an "exon definition" mechanism calibrating alternative splicing toward dominant isoform production. This regulation especially impacts splicing factors themselves and leads to a reduction of nonsense-mediated decay (NMD)-targeted isoforms. Inclusion of the NMD-determinant FLNA exon 30 by FOXA1-controlled oncogene SRSF1 promotes cell growth in vitro and predicts disease recurrence. Overall, we report a role for FOXA1 in rewiring the alternative splicing landscape in prostate cancer through a cascade of events from chromatin access, to splicing factor regulation, and, finally, to alternative splicing of exons influencing patient survival.

Hormone ratios suffer from striking lack of robustness to measurement error

Hormone ratios are often used to capture the joint effect (or "balance") of two hormones with opposing or mutually suppressive effects. Despite some statistical and interpretative problems, hormone ratios are being increasingly used to examine associations of testosterone/cortisol, estradiol/progesterone, testosterone/estradiol, and other hormone pairs. Here we discuss a methodological problem that has not been previously recognized, namely, the striking lack of robustness of raw hormone ratios in the face of measurement error. Hormone levels are measured with error, both due to inability of assays to perfectly assess concentrations "in the tube" and due to discrepancies between levels at the time of sample collection and effective levels that produce the physiological and/or behavioral effect of interest. Noise in measured hormone levels can be substantially exaggerated by ratios, especially when the distribution of the hormone at the denominator is positively skewed, as is frequently observed. To evaluate the extent of this problem and explore the conditions that exacerbate it, we present two sets of simulations, one using idealized distributions and one using empirically observed distributions from studies of estrogen and progesterone. Results show that the validity of raw hormone ratios-the correlation between measured levels and underlying effective levels-drops rapidly in the presence of realistic levels of measurement error. Log-ratios are much more robust to measurement error, and their validity is more stable across samples; under some conditions (e.g., moderate amounts of noise with positively correlated hormone levels), they may provide a more valid measurement of the underlying raw ratio than the measured raw ratio itself. These findings have important implications for research that uses hormone ratios as predictors.

Effective Dimensionality: A Tutorial

The topic of this tutorial is the effective dimensionality (ED) of a dataset, that is, the equivalent number of orthogonal dimensions that would produce the same overall pattern of covariation. The ED quantifies the total dimensionality of a set of variables, with no assumptions about their underlying structure. The ED of a dataset has important implications for the "curse of dimensionality"; it can be used to inform decisions about data analysis and answer meaningful empirical questions. The tutorial offers an accessible introduction to ED, distinguishes it from the related but distinct concept of intrinsic dimensionality, critically reviews various ED estimators, and gives indications for practical use with examples from personality research. An R function is provided to implement the techniques described in the tutorial.

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

Growing points in attachment disorganization: looking back to advance forward

In this special issue paper we reflect on the next generation of attachment research with a focus on disorganization, a central but still poorly understood topic in this area. We suggest that progress will be facilitated by a return to attachment theory's evolutionary roots, and to the emphasis on biological function that inspired Bowlby's original thinking. Increased interdisciplinary cross-fertilization and collaborations would enable novel and generative research on some of the long-standing questions surrounding attachment disorganization. Accordingly, we present an agenda for future research that encompasses contributions of modern ethology and neurobiology, novel hypotheses based on the concept of adaptive decanalization, connections with neurodevelopmental vulnerability and risk for mental disorders such as schizophrenia, and the possibility of sex differences in the behavioral manifestations of attachment disorganization. We believe that these avenues of theory and research offer exciting potential for innovative work in attachment disorganization in the years ahead.

Initial cell density encodes proliferative potential in cancer cell populations

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

Distinct retrograde microtubule motor sets drive early and late endosome transport

Although subcellular positioning of endosomes significantly impacts on their functions, the molecular mechanisms governing the different steady-state distribution of early endosomes (EEs) and late endosomes (LEs)/lysosomes (LYs) in peripheral and perinuclear eukaryotic cell areas, respectively, are still unsolved. We unveil that such differences arise because, while LE retrograde transport depends on the dynein microtubule (MT) motor only, the one of EEs requires the cooperative antagonism of dynein and kinesin-14 KIFC1, a MT minus end-directed motor involved in cancer progression. Mechanistically, the Ser-x-Ile-Pro (SxIP) motif-mediated interaction of the endoplasmic reticulum transmembrane protein stromal interaction molecule 1 (STIM1) with the MT plus end-binding protein 1 (EB1) promotes its association with the p150Glued subunit of the dynein activator complex dynactin and the distinct location of EEs and LEs/LYs. The peripheral distribution of EEs requires their p150Glued-mediated simultaneous engagement with dynein and SxIP motif-containing KIFC1, via HOOK1 and HOOK3 adaptors, respectively. In sum, we provide evidence that distinct minus end-directed MT motor systems drive the differential transport and subcellular distribution of EEs and LEs in mammalian cells.

Distinct retrograde microtubule motor sets drive early and late endosome transport

Although subcellular positioning of endosomes significantly impacts on their functions, the molecular mechanisms governing the different steady‐state distribution of early endosomes (EEs) and late endosomes (LEs)/lysosomes (LYs) in peripheral and perinuclear eukaryotic cell areas, respectively, are still unsolved. We unveil that such differences arise because, while LE retrograde transport depends on the dynein microtubule (MT) motor only, the one of EEs requires the cooperative antagonism of dynein and kinesin‐14 KIFC1, a MT minus end‐directed motor involved in cancer progression. Mechanistically, the Ser‐x‐Ile‐Pro (SxIP) motif‐mediated interaction of the endoplasmic reticulum transmembrane protein stromal interaction molecule 1 (STIM1) with the MT plus end‐binding protein 1 (EB1) promotes its association with the p150Glued subunit of the dynein activator complex dynactin and the distinct location of EEs and LEs/LYs. The peripheral distribution of EEs requires their p150Glued‐mediated simultaneous engagement with dynein and SxIP motif‐containing KIFC1, via HOOK1 and HOOK3 adaptors, respectively. In sum, we provide evidence that distinct minus end‐directed MT motor systems drive the differential transport and subcellular distribution of EEs and LEs in mammalian cells.

Global sex differences in personality: Replication with an open online dataset

OBJECTIVE

Sex differences in personality are a matter of continuing debate. In a study on the United States standardization sample of Cattell's 16PF (fifth edition), Del Giudice and colleagues (2012; PLoS ONE, 7, e29265) estimated global sex differences in personality with multigroup covariance and mean structure analysis. The study found a surprisingly large multivariate effect, D = 2.71. Here we replicated the original analysis with an open online dataset employing an equivalent version of the 16PF.

METHOD

We closely replicated the original MG-MCSA analysis on N = 21,567 U.S. participants (63% females, age 16-90); for robustness, we also analyzed N = 31,637 participants across English-speaking countries (61% females, age 16-90).

RESULTS

The size of global sex differences was D = 2.06 in the United States and D = 2.10 across English-speaking countries. Parcel-allocation variability analysis showed that results were robust to changes in parceling (U.S.: median D = 2.09, IQR [1.89, 2.37]; English-speaking countries: median D = 2.17, IQR [1.98, 2.47]).

CONCLUSIONS

Our results corroborate the original study (with a comparable if somewhat smaller effect size) and provide new information on the impact of parcel allocation. We discuss the implications of these and similar findings for the psychology of sex differences.

Identification of altered biological processes in heterogeneous RNA-sequencing data by discretization of expression profiles

Heterogeneity is a fundamental feature of complex phenotypes. So far, genomic screenings have profiled thousands of samples providing insights into the transcriptome of the cell. However, disentangling the heterogeneity of these transcriptomic Big Data to identify defective biological processes remains challenging. Here we present GSECA, a method exploiting the bimodal behavior of RNA-sequencing gene expression profiles to identify altered gene sets in heterogeneous patient cohorts. Using simulated and experimental RNA-sequencing data sets, we show that GSECA provides higher performances than other available algorithms in detecting truly altered biological processes in large cohorts. Applied to 5941 samples from 14 different cancer types, GSECA correctly identified the alteration of the PI3K/AKT signaling pathway driven by the somatic loss of PTEN and verified the emerging role of PTEN in modulating immune-related processes. In particular, we showed that, in prostate cancer, PTEN loss appears to establish an immunosuppressive tumor microenvironment through the activation of STAT3, and low PTEN expression levels have a detrimental impact on patient disease-free survival. GSECA is available at https://github.com/matteocereda/GSECA.

How Well Do Bibliometric Indicators Correlate With Scientific Eminence? A Comment on Simonton (2016)

Citing an earlier study on eminence in psychology, Simonton (2016) argued that associations between measures of scholars' reputation, scientific productivity, and citation counts are only small to moderate [Simonton, D. K. (2016). Giving credit where credit's due: Why it's so hard to do in psychological science. Perspectives on Psychological Science, 11, 888-892]. However, this reading is based on partial regression coefficients, which underestimate the joint explanatory power of correlated variables. A reanalysis of the original data showed that a composite bibliometric index was substantially associated with reputation (β = 0.70; 46% explained variance). Very similar results were obtained with a newly calculated h index (β = 0.67; 42% explained variance). Although both Simonton's original analysis and the current reanalysis are inherently limited, the data suggest that the reputation of psychologists tracks their scientific contribution more closely than has been acknowledged in the recent literature.

What Is Stress? A Systems Perspective

The term "stress" is used to describe important phenomena at multiple levels of biological organization, but finding a general and rigorous definition of the concept has proven challenging. Current models in the behavioral literature emphasize the cognitive aspects of stress, which is said to occur when threats to the organism are perceived as uncontrollable and/or unpredictable. Here we adopt the perspective of systems biology and take a step toward a general definition of stress by unpacking the concept in light of control theory. Our goal is to clarify the concept so as to facilitate integrative research and formal analysis. We argue that stress occurs when a biological control system detects a failure to control a fitness-critical variable, which may be either internal or external to the organism. Biological control systems typically include both feedback (reactive, compensatory) and feedforward (predictive, anticipatory) components; their interplay accounts for the complex phenomenology of stress in living organisms. The simple and abstract definition we propose applies to animals, plants, and single cells, highlighting connections across levels of organization. In the final section of the paper we explore some extensions of our approach and suggest directions for future research. Specifically, we discuss the classic concepts of conditioning and hormesis and review relevant work on cellular stress responses; show how control theory suggests the existence of fundamental trade-offs in the design of stress responses; and point to potential insights into the effects of novel environmental conditions, including those resulting from anthropogenic change.

Testosterone, cortisol, and status-striving personality features: A review and empirical evaluation of the Dual Hormone hypothesis

Decades of research in behavioral endocrinology has implicated the gonadal hormone testosterone in the regulation of mating effort, often expressed in primates in the form of aggressive and/or status-striving behavior. Based on the idea that neuroendocrine axes influence each other, recent work among humans has proposed that links between testosterone and indices of status-striving are rendered conditional by the effects of glucocorticoids. The Dual Hormone hypothesis is one particular instance of this argument, predicting that cortisol blocks the effects of testosterone on dominance, aggression, and risk-taking in humans. Support for the Dual Hormone hypothesis is wide-ranging, but considerations of theoretical ambiguity, null findings, and low statistical power pose problems for interpreting the published literature. Here, we contribute to the development of the Dual Hormone hypothesis by (1) critically reviewing the extant literature-including p-curve analyses of published findings; and, (2) "opening the file drawer" and examining relationships between testosterone, cortisol, and status-striving personality features in seven previously published studies from our laboratories (total N = 718; median N per feature = 318) that examined unrelated predictions. Results from p-curve suggest that published studies have only 16% power to detect effects, while our own data show no robust interactions between testosterone and cortisol in predicting status-striving personality features. We discuss the implications of these results for the Dual Hormone hypothesis, limitations of our analyses, and the development of future research.

Developmental Adaptation to Stress: An Evolutionary Perspective

The assumption that early stress leads to dysregulation and impairment is widespread in developmental science and informs prevailing models (e.g., toxic stress). An alternative evolutionary–developmental approach, which complements the standard emphasis on dysregulation, proposes that early stress may prompt the development of costly but adaptive strategies that promote survival and reproduction under adverse conditions. In this review, we survey this growing theoretical and empirical literature, highlighting recent developments and outstanding questions. We review concepts of adaptive plasticity and conditional adaptation, introduce the life history framework and the adaptive calibration model, and consider how physiological stress response systems and related neuroendocrine processes may function as plasticity mechanisms. We then address the evolution of individual differences in susceptibility to the environment, which engenders systematic person–environment interactions in the effects of stress on development. Finally, we discuss stress-mediated regulation of pubertal development as a case study of how an evolutionary–developmental approach can foster theoretical integration.

Stochastic sequestration dynamics: a minimal model with extrinsic noise for bimodal distributions and competitors correlation

Many biological processes are known to be based on molecular sequestration. This kind of dynamics involves two types of molecular species, namely targets and sequestrants, that bind to form a complex. In the simple framework of mass-action law, key features of these systems appear to be threshold-like profiles of the amounts of free molecules as a function of the parameters determining their possible maximum abundance. However, biochemical processes are probabilistic and take place in stochastically fluctuating environments. How these different sources of noise affect the final outcome of the network is not completely characterised yet. In this paper we specifically investigate the effects induced by a source of extrinsic noise onto a minimal stochastic model of molecular sequestration. We analytically show how bimodal distributions of the targets can appear and characterise them as a result of noise filtering mediated by the threshold response. We then address the correlations between target species induced by the sequestrant and discuss how extrinsic noise can turn the negative correlation caused by competition into a positive one. Finally, we consider the more complex scenario of competitive inhibition for enzymatic kinetics and discuss the relevance of our findings with respect to applications.

Addendum to: Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size

In a previous paper (Del Giudice, 2017 [Heterogeneity coefficients for Mahalanobis' D as a multivariate effect size. Multivariate Behavioral Research, 52, 216-221]), I proposed two heterogeneity coefficients for Mahalanobis' D based on the Gini coefficient, labeled H and EPV. In this addendum I discuss the limitations of the original approach and note that the proposed indices may overestimate heterogeneity under certain conditions. I then describe two revised indices H₂ and EPV₂, and illustrate the difference between the original and revised indices with some real-world data sets.

Rethinking IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters

Behavioral researchers have increasingly become interested in the idea that chronic, low-grade inflammation is a pathway through which social and behavioral variables exert long-term effects on health. Much research in the area employs putative inflammatory biomarkers to infer an underlying state of inflammation. Interleukin 6 (IL-6) and C-reactive protein (CRP, whose production is stimulated by IL-6) are arguably the two most commonly assayed biomarkers. Yet, in contrast with near-universal assumptions in the field, discoveries in immunology over the past two decades show that neither IL-6 nor CRP are unambiguous inflammatory markers. IL-6 operates through two distinct signaling pathways, only one of which is specifically upregulated during inflammation; both pathways have a complex range of effects and influence multiple physiological processes even in absence of inflammation. Similarly, CRP has two isoforms, one of which is produced locally in inflamed or damaged tissues. The other isoform is routinely produced in absence of inflammation and may have net anti-inflammatory effects. We propose a functional framework to account for the multiple actions of IL-6 and CRP. Specifically, we argue that both molecules participate in somatic maintenance efforts; hence elevated levels indicate that an organism is investing in protection, preservation, and/or repair of somatic tissue. Depending on the state of the organism, maintenance may be channeled into resistance against pathogens (including inflammation), pathogen tolerance and harm reduction, or tissue repair. The findings and framework we present have a range of potential implications for the interpretation of empirical findings in this area-a point we illustrate with alternative interpretations of research on socioeconomic status, stress, and depression.

On the role of extrinsic noise in microRNA-mediated bimodal gene expression

Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.

Individual differences in developmental plasticity: A role for early androgens?

Developmental plasticity is a widespread property of living organisms, but different individuals in the same species can vary greatly in how susceptible they are to environmental influences. In humans, research has sought to link variation in plasticity to physiological traits such as stress reactivity, exposure to prenatal stress-related hormones such as cortisol, and specific genes involved in major neurobiological pathways. However, the determinants of individual differences in plasticity are still poorly understood. Here we present the novel hypothesis that, in both sexes, higher exposure to androgens during prenatal and early postnatal life should lead to increased plasticity in traits that display greater male variability (i.e., a majority of physical and behavioral traits). First, we review evidence of greater phenotypic variation and higher susceptibility to environmental factors in males; we then consider evolutionary models that explain greater male variability and plasticity as a result of sexual selection. These empirical and theoretical strands converge on the hypothesis that androgens may promote developmental plasticity, at least for traits that show greater male variability. We discuss a number of potential mechanisms that may mediate this effect (including upregulation of neural plasticity), and address the question of whether androgen-induced plasticity is likely to be adaptive or maladaptive. We conclude by offering suggestions for future studies in this area, and considering some research designs that could be used to empirically test our hypothesis.

Sex differences in attachment styles

Sex differences in attachment styles have been found in adulthood, emerge as early as middle childhood, and can be sizable when described at the appropriate level of analysis. However, they have received relatively little attention in mainstream attachment research. Here I review the evidence of sex differences in attachment, including what is currently known about developmental patterns and cross-cultural variation. I summarize existing evolutionary models of sex differences, and discuss evidence for a role of prenatal and postnatal sex hormones. I highlight current theoretical and empirical gaps in the literature, and call for more integrative research on this fascinating topic.

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.

The Evolution of Interaction Shape in Differential Susceptibility

Expectations about the shape of statistical interactions play a crucial role in the study of differential susceptibility and other types of person-environment interplay. These expectations shape methodological guidelines and inform the interpretation of empirical findings; however, their logic has never been explicitly examined. This study is the first systematic exploration of the evolution of interaction shape in differential susceptibility. The model introduced here yields a number of novel insights; for example, interactions in differential susceptibility should usually be asymmetric and likely to be biased toward the prototypical shape of diathesis-stress models. This article also presents an exploratory analysis of interaction shape in recent empirical studies and ends with a discussion of the theoretical and methodological implications of the present findings.

Differential susceptibility to the environment: Are developmental models compatible with the evidence from twin studies?

According to models of differential susceptibility, the same neurobiological and temperamental traits that determine increased sensitivity to stress and adversity also confer enhanced responsivity to the positive aspects of the environment. Differential susceptibility models have expanded to include complex developmental processes in which genetic variation interacts with exposure to early environmental factors, such as prenatal stress hormones and family conflict. In this study I employed a simulation approach to explore whether, and under what conditions, developmental models of differential susceptibility are compatible with the cumulative findings from twin studies of personality and behavior, which consistently show sizable effects of genetic and nonshared environmental factors and small to negligible effects of the shared environment. Simulation results showed that, to a first approximation, current alternative models of differential susceptibility are all equally compatible with the evidence from twin research; that sizable interaction effects involving individual differences in plasticity are plausible, but only if direct environmental effects are correspondingly weak; and that a major role of shared environmental factors is plausible in early development (consistent with the developmental mechanisms postulated in the differential susceptibility literature) but not in later development. These results support the general plausibility of differential susceptibility models and suggest some realistic constraints on their assumptions. (PsycINFO Database Record

Plasticity as a developing trait: exploring the implications

Individual differences in plasticity have been classically framed as genotype-by-environment interactions, with different genotypes showing different reaction norms in response to environmental conditions. However, research has shown that early experience can be a critical factor in shaping an individual's plasticity to later environmental factors. In other words, plasticity itself can be investigated as a developing trait that reflects the combined action of an individual's genes and previous interactions with the environment. In this paper I explore some implications of the idea that the early environment modulates long-term plasticity, with an emphasis on plasticity in behavioral traits. I begin by focusing on the mechanisms that mediate plasticity at the proximate level, and discussing the possibility that some traits may work as generalized mediators of plasticity by affecting the sensitivity of multiple phenol types across developmental contexts. I then tackle the complex problem of the evolution of reaction norms for plasticity. Next, I consider a number of potential implications for research on parental effects and phenotypic matching, and conclude by discussing how plasticity may become a target of evolutionary conflict between parents and offspring. In total, I aim to show how the idea of plasticity as a developing trait offers a rich source of questions and insights that may inform future research in this area.