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.

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.

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.

An integrative evolutionary framework for psychopathology.

The field of psychopathology is in a transformative phase, and is witnessing a renewed surge of interest in theoretical models of mental disorders. While many interesting proposals are competing for attention in the literature, they tend to focus narrowly on the proximate level of analysis and lack a broader understanding of biological function. In this paper, we present an integrative framework for mental disorders built on concepts from life history theory, and describe a taxonomy of mental disorders based on its principles, the fast-slow-defense model (FSD). The FSD integrates psychopathology with normative individual differences in personality and behavior, and allows researchers to draw principled distinctions between broad clusters of disorders, as well as identify functional subtypes within current diagnostic categories. Simulation work demonstrates that the model can explain the large-scale structure of comorbidity, including the apparent emergence of a general "p factor" of psychopathology. A life history approach also provides novel integrative insights into the role of environmental risk/protective factors and the developmental trajectories of various disorders.

MicroRNAs organize intrinsic variation into stem cell states.

Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in part from cooption of fluctuations in the gene regulatory network. Here we find ESCs exhibit intrinsic heterogeneity in the absence of external gradients by forming interconverting cell states. States vary in developmental gene expression programs and display distinct activity of microRNAs (miRNAs). Notably, miRNAs act on neighborhoods of pluripotency genes to increase variation of target genes and cell states. Loss of miRNAs that vary across states reduces target variation and delays state transitions, suggesting variable miRNAs organize and propagate variation to promote state transitions. Together these findings provide insight into how a gene regulatory network can coopt variation intrinsic to cell systems to form robust gene expression states. Interactions between intrinsic heterogeneity and environmental signals may help achieve developmental outcomes.

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.

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.

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.

Are complex causal models less likely to be true than simple ones? A critical comment on Trafimow (2017).

Trafimow (2017) used probabilistic reasoning to argue that more complex causal models are less likely to be true than simpler ones, and that researchers should be skeptical of causal models involving more than a handful of variables (or even a single correlation coefficient) [Trafimow, D. (2017). The probability of simple versus complex causal models in causal analyses. Behavior Research Methods, 49, 739-746]. In this comment, I point out that Trafimow's argument is misleading, and reduces to the observation that more informative models (that make definite statements about certain causal relations) are less likely to be true than less informative models (that remain silent about those relations, by omitting some variables from consideration). This correct but trivial statement does not deliver the epistemological leverage promised in the paper. When complexity is evaluated with reasonable criteria (such as the number of nonzero effects in alternative models involving the same variables), more complex models can be more, less, or equally likely to be true compared with simpler ones. I also discuss Trafimow's claim that, if a model is unlikely to be true a priori, researchers will seldom be able to gather evidence of sufficient quality to support it; in practice, even low-probability models can receive strong support without the need for extraordinary evidence. Researchers should evaluate the plausibility of causal models on a case-by-case basis, and be skeptical of overblown claims about the dangers of complex theories.

Rab11 activity and PtdIns(3)P turnover removes recycling cargo from endosomes.

Directional transport of recycling cargo from early endosomes (EE) to the endocytic recycling compartment (ERC) relies on phosphatidylinositol 3-phosphate (PtdIns(3)P) hydrolysis and activation of the small GTPase Rab11. However, how these events are coordinated is yet unclear. By using a novel genetically-encoded FRET biosensor for Rab11, we report that generation of endosomal PtdIns(3)P by the clathrin-binding phosphoinositide 3-kinase class 2 alpha (PI3K-C2α) controls the activation of Rab11. Active Rab11, in turn, prompts the recruitment of the phosphatidylinositol 3-phosphatase myotubularin 1 (MTM1), eventually enabling the release of recycling cargo from the EE and its delivery toward the ERC. Our findings thus define that delivery of recycling cargo toward the ERC requires spatial and sequential coupling of Rab11 activity with PtdIns(3)P turnover.

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.

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.

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.

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.

Cognitive gadgets: A provocative but flawed manifesto.

The argument against innatism at the heart of Cognitive Gadgets is provocative but premature, and is vitiated by dichotomous thinking, interpretive double standards, and evidence cherry-picking. I illustrate my criticism by addressing the heritability of imitation and mindreading, the relevance of twin studies, and the meaning of cross-cultural differences in theory of mind development. Reaching an integrative understanding of genetic inheritance, plasticity, and learning is a formidable task that demands a more nuanced evolutionary approach.

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.

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.

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.

Statistical tests of differential susceptibility: Performance, limitations, and improvements.

Statistical tests of differential susceptibility have become standard in the empirical literature, and are routinely used to adjudicate between alternative developmental hypotheses. However, their performance and limitations have never been systematically investigated. In this paper I employ Monte Carlo simulations to explore the functioning of three commonly used tests proposed by Roisman et al. (2012). Simulations showed that critical tests of differential susceptibility require considerably larger samples than standard power calculations would suggest. The results also showed that existing criteria for differential susceptibility based on the proportion of interaction index (i.e., values between .40 and .60) are especially likely to produce false negatives and highly sensitive to assumptions about interaction symmetry. As an initial response to these problems, I propose a revised test based on a broader window of proportion of interaction index values (between .20 and .80). Additional simulations showed that the revised test outperforms existing tests of differential susceptibility, considerably improving detection with little effect on the rate of false positives. I conclude by noting the limitations of a purely statistical approach to differential susceptibility, and discussing the implications of the present results for the interpretation of published findings and the design of future studies in this area.