From the Cover: Functional changes of the reward system underlie blunted response to social gaze in cocaine users

Social interaction deficits in drug users likely impede treatment, increase the burden of the affected families, and consequently contribute to the high costs for society associated with addiction. Despite its significance, the neural basis of altered social interaction in drug users is currently unknown. Therefore, we investigated basal social gaze behavior in cocaine users by applying behavioral, psychophysiological, and functional brain-imaging methods. In study I, 80 regular cocaine users and 63 healthy controls completed an interactive paradigm in which the participants’ gaze was recorded by an eye-tracking device that controlled the gaze of an anthropomorphic virtual character. Valence ratings of different eye-contact conditions revealed that cocaine users show diminished emotional engagement in social interaction, which was also supported by reduced pupil responses. Study II investigated the neural underpinnings of changes in social reward processing observed in study I. Sixteen cocaine users and 16 controls completed a similar interaction paradigm as used in study I while undergoing functional magnetic resonance imaging. In response to social interaction, cocaine users displayed decreased activation of the medial orbitofrontal cortex, a key region of reward processing. Moreover, blunted activation of the medial orbitofrontal cortex was significantly correlated with a decreased social network size, reflecting problems in real-life social behavior because of reduced social reward. In conclusion, basic social interaction deficits in cocaine users as observed here may arise from altered social reward processing. Consequently, these results point to the importance of reinstatement of social reward in the treatment of stimulant addiction.Cocaine dependence is a chronically relapsing disorder defined by uncontrolled and compulsive drug use (1). Despite severe negative consequences including disrupted social relationships, loss of employment, and somatic and psychiatric illnesses, an addicted person’s life is often centered around the drug of choice and activities related to it (2). Therefore, drug use is classified as a major social, legal, and public health problem (3). After cannabis, cocaine is the second most prevalent illegal drug in the United States and Europe (4, 5), with a lifetime prevalence among young adults of 6.3% in Europe (15- to 34-y-olds) (4) and 13.3% in the United States (18- to 25-y-olds) (5).Social cognition and social support for drug users are of great clinical relevance, as they have been reported to influence onset of drug use and development of substance use disorders, and treatment success in patients with substance use disorders (6, 7). Impairments in social cognition may augment the risk of social isolation, aggression, and depression, likely supporting the vicious circle of drug use (8). Additionally, impaired social cognition may contribute to the decay of social relationships in addicted patients (9) with negative consequences for treatment success given that higher social support predicted longer abstinence duration (10). Furthermore, no efficient pharmacological treatment for cocaine addiction is currently available (11), and treatment approaches such as cognitive behavioral therapy rely, at least in part, on the emotional responsiveness and social abilities of drug users (12). Previous results suggest that cocaine users (CUs) show impairments in different facets of social cognition, particularly in emotional empathy, mental perspective taking, and emotion recognition in prosody, which are related to deficits in real-life social behavior such as fewer social contacts and more criminal offenses (13, 14). Furthermore, in money distribution games, CUs act more self-servingly and less altruistically than stimulant-naïve controls (15). Volkow et al. (9) postulated that neuroadaptations in the reward systems of drug users (e.g., ventral striatum and orbitofrontal cortex) alter reward processing such that the value of the abused drug is enhanced and concurrently the value of nondrug rewards, including social interaction, is reduced. Consequently, general social competence might become impaired and promote antisocial and criminal behavior. This may explain why social consequences of drug use (e.g., imprisonment or familial problems) do not prompt drug-addicted people to quit using the drug as well as how they contribute to increased drug use and transition from recreational drug use to addiction (9). However, whereas altered processing of monetary rewards has been reported in CUs (16), social reward processing has not been studied yet, neither on the psychological nor the neural level. Therefore, it remains elusive whether CUs (i) show behavioral differences to reward stemming from social interactions and, if so, (ii) which neural adaptations within reward circuitry underlie these potential changes in social interaction behavior.An essential part of social interaction is the phenomenon of “social gaze,” which has two aspects: Gaze can be used by the gazing person as a deictic cue to manipulate the attention of others, and can be read out by observers as a hint toward attentional focus of the gazing person (17). Both aspects can converge in joint attention (JA), which is a central element of social interaction (18) and is established when a person follows the direction of another person’s gaze so that both attend to the same object (19). Engagement in JA is considered to reflect our understanding of another person’s point of view (20). The capacity of JA emerges at 8–12 mo of age (21) and is predictive for later language learning (22) and the development of more advanced social skills such as mental perspective taking (e.g., the attribution of intentions and goals to others, also known as theory of mind) (23). Impaired JA is a core symptom of autism spectrum disorders (24).To test for social gaze differences between CUs and healthy controls (HCs), we applied a paradigm designed to capture the reciprocal and interactive nature of JA (25) (Fig. S1), where participants engage in an online interaction with an anthropomorphic virtual character in real time. Compared with self-initiated nonjoint attention (NJA; i.e., if the counterpart does not follow one’s gaze but rather pays attention to another object), self-initiated JA (i.e., if the counterpart follows one’s own gaze) is perceived as more pleasurable and associated with stronger activation of reward-related brain areas in healthy controls (25). This rewarding nature of JA might underlie the human motivation to engage in the sharing of experiences that emerges in early childhood (22, 25).It has been suggested that changes in social reward processing might underlie alterations in social behavior and cognition in CUs (9). Here we conducted two studies assessing JA processing, which constitutes an elegant approach to investigate basic social interaction patterns related to social reward processing (25), in CUs and stimulant-naïve HCs by means of behavioral, psychophysiological, and functional brain-imaging methods. In study I, a large sample of relatively pure CUs with few psychiatric comorbidities (n = 80) and stimulant-naïve HCs (n = 63) completed an interactive JA task (25) while valence and arousal ratings, error scores, reaction time, and pupil size were obtained. Pupil dilation provides an objective index of affective processing (26, 27). Based on the observations obtained in study I, we further investigated the neural correlates of the blunted emotional response to social gaze in subsamples of 16 CUs and 16 HCs using functional magnetic resonance imaging (fMRI) during an abridged version of the paradigm (study II). We hypothesized that altered emotional responses to JA are accompanied by less pronounced activation in reward-related brain areas of CUs.     

From the Cover: Long-term social dynamics drive loss of function in pathogenic bacteria

Laboratory experiments show that social interactions between bacterial cells can drive evolutionary change at the population level, but significant challenges limit attempts to assess the relevance of these findings to natural populations, where selection pressures are unknown. We have increasingly sophisticated methods for monitoring phenotypic and genotypic dynamics in bacteria causing infectious disease, but in contrast, we lack evidence-based adaptive explanations for those changes. Evolutionary change during infection is often interpreted as host adaptation, but this assumption neglects to consider social dynamics shown to drive evolutionary change in vitro. We provide evidence to show that long-term behavioral dynamics observed in a pathogen are driven by selection to outcompete neighboring conspecific cells through social interactions. We find that Pseudomonas aeruginosa bacteria, causing lung infections in patients with cystic fibrosis, lose cooperative iron acquisition by siderophore production during infection. This loss could be caused by changes in iron availability in the lung, but surprisingly, we find that cells retain the ability to take up siderophores produced by conspecifics, even after they have lost the ability to synthesize siderophores. Only when cooperative producers are lost from the population is the receptor for uptake lost. This finding highlights the potential pitfalls of interpreting loss of function in pathogenic bacterial populations as evidence for trait redundancy in the host environment. More generally, we provide an example of how sequence analysis can be used to generate testable hypotheses about selection driving long-term phenotypic changes of pathogenic bacteria in situ.Some of the most important bacterial pathogens are opportunistic in the sense that they infect a compromised human host from the surrounding environment. In cases where such infections become persistent, the evolutionary changes accompanying the transition from the environment to the human body have been the subject of intensive research, and we now have some information on what distinguishes clinical from environmental isolates (1). Surprisingly, our understanding of how this process is driven by selection often remains speculative. To study bacterial cells, we must remove them from the host environment into the laboratory, which may release them from the selection pressures that we wish to understand.In parallel, progress has been made in understanding how bacterial populations respond to selection through in vitro experimental evolution. These studies show that phenotypic dynamics result not only in response to the environment but also, to social interactions as bacteria cooperate and compete with one another (2). Selection to outcompete neighbors can even lead to loss of traits that increase survival in the environment but are costly to produce (3–5). Such loss has been shown for a range of traits, such as extracellular enzymes, signaling molecules, and iron chelators (2). These exoproducts act as “public goods”: products that are beneficial to the group but vulnerable to exploitation by cheats that reap the benefit without paying the cost (6). Understanding selection on public goods is clinically relevant, because many are virulence factors (7, 8), and social interactions have also been shown experimentally to affect infection dynamics in vivo (9, 10).We investigate the importance of social interactions in infectious populations of Pseudomonas aeruginosa, which is both a model organism of social evolution research and the primary cause of chronic lung infection in patients with the genetic disorder cystic fibrosis (CF). CF patients usually acquire their first P. aeruginosa infection in childhood, and these infections can persist for years, despite antibiotic treatment (11). P. aeruginosa produces an iron-scavenging molecule, pyoverdine, that acts as a cooperative public good in vitro (12). Iron is essential for growth but bound to transferrin, heme, and hemoglobin in the human host (13). P. aeruginosa circumvents this by releasing pyoverdine, which binds to iron and is taken up by a specific receptor. Detection of pyoverdine and expression of pyoverdine genes in sputum samples confirm that the pathway is active, and likely beneficial, in the CF lung environment (14, 15). However, cells that are deficient in production (i.e., potential cheaters) have also repeatedly been isolated from patients (16, 17). The pyoverdine metabolism is, therefore, an ideal system for testing whether social dynamics observed in the laboratory also occur in human hosts.Our aim is to identify selection pressures driving any changes that we observe in pyoverdine production in the lung. Pyoverdine production may be an adaptive response to acquire a limited nutrient. It may be lost, therefore, in response to availability of other iron sources (18–20). Alternatively, production may be lost from the population even if iron is limiting as a result of cooperator–cheat dynamics. Crucially, patterns of evolution of the pyoverdine system differ depending on whether adaptation to the human lung or social interactions drive selection (Fig. 1). If pyoverdine does not provide a growth benefit in the lung, the entire system will be redundant, including receptor function. In contrast, if pyoverdine production is lost because of cheating, receptor function will remain beneficial as long as extrinsic pyoverdine is available. Only when cheating is not possible does the receptor also become redundant. We can, therefore, distinguish between the two selection pressures by determining if and when receptor function is maintained in bacteria that have lost the ability to produce pyoverdine.Open in a separate windowFig. 1.The pyoverdine system. (Upper) The pyoverdine receptor FpvA spans the cell wall. In the absence of bound pyoverdine, the anti-σ factor FpvR inhibits the expression of σ-factors FpvI and PvdS. Pyoverdine acquires iron from transferrin. When ferripyoverdine binds to the receptor, FpvR releases FpvI and PvdS. Release of FpvI initiates synthesis of the receptor FpvA, and PvdS initiates synthesis of pyoverdine (illustrated by arrows). (Lower Left) If pyoverdine production is lost as an adaptation to the lung, receptor function also becomes redundant, irrespective of whether pyoverdine produced by neighbors is available. (Lower Right) However, if pyoverdine production is lost because of cheating, we expect to see retention of receptor function in the presence of pyoverdine produced by others and function only lost in the absence of pyoverdine.Two Danish collections of genome-sequenced P. aeruginosa isolates provide the opportunity to study selection on pyoverdine metabolism in CF patients (Dataset S1). The first collection gives a detailed insight into changes occurring during the first 10 y of infection across 36 young CF patients with 54 different clone types (21), representing the transition from initial colonization to chronic infection. With frequent and extensive sampling from each patient (451 isolates; on average, 13 per patient), we can estimate the point of colonization of each clone type and thereby, the time period over which a given isolate has evolved. The second collection provides insight into the long-term dynamics of two clone types causing chronic infections, with samples from 24 adult patients (85 isolates) infected with the two Danish transmissible clone types DK1 and DK2 (22–24). The two transmissible clone types established and spread in the Danish CF patient group from 1973 and all of the older patients (who got chronically infected up to the beginning of the 1990s) harbor one or both of these. Afterward, segregation of patients in the clinic has largely eliminated transmission of these clone types. The DK1 and DK2 isolates, thus, typically come from now older CF patients who have each been sampled a few times, providing insight into the long-term dynamics but not on a fine scale at the early infection stage. For some of the analyses, as specified below, only the isolates from the young patients have been used.     

From the Cover: Exploitative leaders incite intergroup warfare in a social mammal

Collective conflicts among humans are widespread, although often highly destructive. A classic explanation for the prevalence of such warfare in some human societies is leadership by self-serving individuals that reap the benefits of conflict while other members of society pay the costs. Here, we show that leadership of this kind can also explain the evolution of collective violence in certain animal societies. We first extend the classic hawk−dove model of the evolution of animal aggression to consider cases in which a subset of individuals within each group may initiate fights in which all group members become involved. We show that leadership of this kind, when combined with inequalities in the payoffs of fighting, can lead to the evolution of severe intergroup aggression, with negative consequences for population mean fitness. We test our model using long-term data from wild banded mongooses, a species characterized by frequent intergroup conflicts that have very different fitness consequences for male and female group members. The data show that aggressive encounters between groups are initiated by females, who gain fitness benefits from mating with extragroup males in the midst of battle, whereas the costs of fighting are borne chiefly by males. In line with the model predictions, the result is unusually severe levels of intergroup violence. Our findings suggest that the decoupling of leaders from the costs that they incite amplifies the destructive nature of intergroup conflict.

Humans are capable of astonishing feats of altruism and cooperation (1–3), but, at the same time, of violent and destructive conflicts (4–8). A key factor contributing to the latter may be that wars are often waged at the behest of leaders who do not share fully in the immediate risks of conflict, and stand to gain benefits in terms of resources and status that are not enjoyed by the majority of combatants (4, 9–11). Could such “warmongering” be a feature of animal conflicts too? Only recently have models of animal aggression begun to explore the impact of inequalities among combatants in collective conflict (12, 13), and the usual assumption of existing theory is that individuals who initiate intergroup conflicts also contribute most to group conflict effort and thereby confer fitness benefits on the rest of their group (a positive or “heroic” model of leadership) (14–17). Here, we explore the more sinister possibility that those who initiate conflict may actually harm their fellows in pursuit of their own interests by exposing them to the risks of conflict while contributing little to fighting themselves (a negative or “exploitative” model of leadership).     

From the Cover: Measuring social equity in urban energy use and interventions using fine-scale data

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.

Cities have become a key action arena for carbon mitigation, with the US Mayors’ Climate Protection Agreement launched in 2005 and the inclusion of cities in the 2011 United Nations Climate Change Conference. More recently, cities have also started to address equity in their carbon mitigation plans (1, 2). For example, in the United States, New York City and Boston have begun to evaluate social inequality in energy use to inform more equitable distribution of energy-related investments (e.g., efficiency rebates) (3–7). To inform equity, cities are seeking methods and metrics that advance a more nuanced and fine-scale understanding of inequality in energy use and in investments, addressing both race and income.Here, we distinguish between social inequality and inequity. Social inequality metrics quantify differences in any parameter of interest based on social stratification (8). Some inequality metrics, such as those representing income inequality, assess inequality across the whole population (or surveys representing the population), using the Gini coefficient (9–11) and interquantile income ratios (e.g., P80/P20). Other inequality metrics, such as those used in public health, compute health risk disparity ratios by race, gender, or income (12). Social equity goes beyond inequality to evaluate the fairness in allocating resources (e.g., energy assistance or health care investments) among different groups to reduce social inequality with a focus on reducing disparities for the most disadvantaged strata in society (8, 13, 14). Thus, the analysis of inequality guides the distributional aspect of social equity (13, 15), that is, the distribution of burdens and benefits across social strata. Social equity also includes a procedural dimension related to the agency and participation of disadvantaged groups in decisions that impact them (16).This paper focuses on distributional equity. Cities face four main challenges, described below, in quantifying social inequality in energy use to inform more equitable investments in energy conservation and efficiency.

• 1.Lack of fine-scale empirical data across a whole city: There have been relatively few analyses of energy use inequality and investments using empirical data (i.e., data provided by utilities) at fine spatial scales within cities to inform equity. A few efforts that explore inequality in intraurban energy use have primarily relied on modeled energy use (17, 18) using the US Energy Information Administration’s Residential Energy Consumption Survey (RECS). RECS only surveys some thousands of homes nationwide (e.g., ∼5,700 in 2015 and ∼12,000 in 2012), resulting in scattered coverage within 10 census divisions, each covering five states on average (19). Consequently, modeled intraurban energy use derived from RECS reports a goodness of fit of ∼60% (17, 18), which can mask social inequality at the fine spatial scale within cities where social stratification by race and income manifests spatially. Thus, empirical data from utilities are much needed, at least at the block group level, which is the finest scale at which sociodemographic data on race and income are reported by the Census Bureau. Furthermore, to inform equity, data on energy conservation and efficiency program participation and investment across the whole city are needed to evaluate the allocation of investments across all neighborhoods in a city. However, only a few cities such as Los Angeles (20) have obtained fine-scale energy use data from utilities to evaluate inequality in energy use covering the whole city. Likewise, only a few studies have explored social inequality in investment in energy conservation and efficiency at the intraurban scale (21, 22). No previous study has evaluated both inequality in energy use and inequity in energy investments at intraurban scales.
• 2.Lack of analysis of disparities in energy use and intensity by both race and income: No previous studies have explored disparities in energy use and intensity by both race and income using real intraurban consumption data. For example, Los Angeles (20) evaluated energy inequality at a fine spatial scale by income but not by race (23, 24). Only two previous studies addressed intraurban racial disparities in heating energy use intensity (EUI) using modeled data from RECS (17, 18); however, there is high uncertainty in RECS-derived models given the small survey sample sizes noted earlier.
• 3.The challenge of spatial scale of data aggregation: When utilities provide intraurban energy usage data to cities, these data are aggregated at different spatial scales, with unknown impacts on energy inequality metrics such as disparity ratios and Gini coefficients. Spatial scales of data aggregation range from the most disaggregated premise level to census blocks (∼76 person on average in the United States), census tracts (1,200 to 8,000 persons), and ZIP (Zone Improvement Plan) code (with an average of ∼8,000 people). For example, several municipal utilities analyze premise-level data for their city policymakers [e.g., Tallahassee (25), Los Angeles (20), and others (26)]; St. Paul, Minnesota has census tract–level data provided by the local utility (27), while California cities have ZIP code–level data (28) complying with state-level regulations on data privacy provided by utilities. The spatial scale of data aggregation can impact the analysis of dispersion, recognized in geography and public health as the modifiable areal unit problem (29, 30). However, this modifiable unit area problem has not been systematically analyzed for energy use inequality due to the lack of both energy use data and sociodemographic data at fine spatial scales. Assessing how the spatial scale of data aggregation impacts energy inequality measures is important, given that different utilities are spatially aggregating data at different scales for subsequent analysis by cities.
• 4.Suitable energy use metrics and analysis procedures to inform equity: Even when city-wide fine-scale energy use data are available, there are few analysis protocols and metrics to evaluate intraurban equitable distribution of investments in conservation and efficiency. While metrics to assess inequalities in energy access, energy burden (i.e., percentage of income spent on energy services), and energy use are well developed (1, 2, 31–36), energy use metrics that best represent the impact of energy conservation and efficiency investments are still evolving. Energy use metrics, such as household energy use (kilowatt hour/household a year), energy use per capita [kilowatt hour/person a year (36)], and household energy use intensity by floor area [kilowatt hour/square feet a year (17, 18)] have been used, but all have challenges. Studies have shown that high-income households have a higher energy consumption primarily due to having larger homes (37). These high-income households are also found to be more “efficient,” showing lower EUI (18, 23). Thus, only tracking total energy use per household will primarily represent floor area effects but not the efficiency of building stock. EUI has more potential to reflect the condition of the building stock and the efficiency of heating and cooling appliances; however, low-income homes may conserve energy by sacrificing thermal comfort, experiencing energy insufficiency (36). Thus, a lower EUI does not necessarily represent more efficient provision of thermal comfort for low-income homes. Housing stock occupancy can also influence EUI, which can be normalized by household size (38), that is, EUI/capita, to capture the impact of occupancy. A better understanding of floor area, along with total household energy use, housing occupancy, and thermal comfort in conjunction with EUI, is needed to develop suitable inequality metrics for energy use. In addition to exploring appropriate energy inequality metrics, there are no analysis protocols to apply those metrics to inform conservation and efficiency investments toward the triple goals of community-wide carbon mitigation, improving energy affordability (reducing burden), and reducing social inequality in energy use and intensity.

To address the above challenges, our paper makes three key contributions. First, we develop a unique intraurban fine-scale dataset, combining sociodemographic data with energy use, occupancy, program participation, and investment data covering all homes/neighborhoods across two cities. Second, using the empirical fine-scale data (suitable to unpack race and income effects), we explore metrics for cities to quantify social inequality in energy use by both income and race and apply those to inform social equity in energy sector investments in conservation and efficiency (ESICE), for example, efficiency rebates, loans, etc. Our study brings together inequality both in energy use and in efficiency investments at the intraurban scale. Third, with the availability of fine-scale data, we provide an assessment on how energy use inequality metrics are impacted by the spatial scale of data aggregation. Overall, this work informs how cities and utilities can gather and analyze information on energy inequality to guide ESICE to advance social equity and carbon mitigation. The analytical tools demonstrated in two cities in this paper are potentially translatable to other cities and utilities.Fine-scale data (block group or finer) on both residential energy use and ESICE across the entire city for Tallahassee, Florida, and St. Paul, Minnesota, are obtained through partnerships with electric utilities under nondisclosure agreements to preserve data privacy, consistent with state and federal regulations. Data were provided at the premise level for Tallahassee’s ∼90,000 households with 1 y monthly energy use and 5 y investment data and at the block level for St. Paul’s ∼110,000 households with 1 y monthly energy use and investment data. The energy investment data include various efficiency programs (e.g., efficiency rebates, home energy use analysis, etc.; SI Appendix, Table S1). Investments in household-scale renewable energy programs, for example, rooftop solar panels, are not within the scope of this study, which focuses on ESICE.The overall method is shown in SI Appendix, Fig. S1, wherein the fine-scale database incorporates social, ecological, infrastructural, and urban form variables, consistent with urban systems frameworks (39).The inequality metrics used in this study include Gini coefficients and disparity ratios. The Gini coefficient provides a general measure of dispersion for a given parameter, without considering social stratification by income or race, with the coefficient ranging from 0 (perfectly equal distribution) to 1 (extremely unequal). We also adapt the concepts of quintile ratios and disparity ratios used in public health (8) to energy use. Energy use disparity ratios by income are computed as the ratio of the average energy attribute (e.g., EUI) reported in the lowest-income quintile block groups (20% lowest) versus that reported in the highest-income quintile. EUI disparity ratios by race are computed as the ratio of EUI in the top 20% most racially diverse block groups (>80th percentile of non-White population percentage) versus the 20% least racially diverse block groups. Disparity ratios are closely related to differences across social groups, for example, a disparity ratio of 2.5 between the lowest- and highest-income groups indicates a 150% difference with respect to the highest-income group.     

From the Cover: Inferring friendship network structure by using mobile phone data

Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.     

From the Cover: Mapping social exclusion in STEM to men’s implicit bias and women’s career costs

Why are women socially excluded in fields dominated by men? Beyond the barriers associated with any minority group’s mere numerical underrepresentation, we theorized that gender stereotypes exacerbate the social exclusion of women in science, technology, engineering, and math (STEM) workplaces, with career consequences. Although widely discussed, clear evidence of these relationships remains elusive. In a sample of 1,247 STEM professionals who work in teams, we tested preregistered hypotheses that acts of gendered social exclusion are systematically associated with both men’s gender stereotypes (Part 1) and negative workplace outcomes for women (Part 2). Combining social network metrics of inclusion and reaction time measures of implicit stereotypes (the tendency to “think STEM, think men”), this study provides unique empirical evidence of the chilly climate women often report experiencing in STEM. Men with stronger implicit gender stereotypes had fewer social ties to female teammates. In turn, women (but not men) with fewer incoming cross-gender social ties reported worse career fit and engagement. Moderated mediation revealed that for women (but not men), cross-gender social exclusion was linked to more negative workplace outcomes via lower social fit. Effects of social exclusion were distinct from respect. We discuss the possible benefits of fostering positive cross-gender social relationships to promote women’s professional success in STEM.

Women continue making inroads into fields traditionally dominated by men, such as those in science, technology, engineering, and math (STEM), but often report a “chilly” climate once there. According to women, men often gatekeep social activities and avoid seeking friendships with women (1–4). Likewise, men report selectively socializing in cliques of mostly men (5–7). This paper investigates how women’s social inclusion in workplaces dominated by men is linked both to men’s gender stereotypes and women’s workplace outcomes. Does women’s social exclusion arise merely from generic preferences to affiliate with similar others, or is it symptomatic of women’s devaluation in STEM? Integrating network metrics with implicit measures of gender stereotypes, we examined this question among 1,247 STEM professionals. Our findings provide empirical evidence of how cross-gender social exclusion contributes to the chilly climate experienced by many women in STEM.     

From the Cover: Ramification of stream networks

The geometric complexity of stream networks has been a source of fascination for centuries. However, a comprehensive understanding of ramification—the mechanism of branching by which such networks grow—remains elusive. Here we show that streams incised by groundwater seepage branch at a characteristic angle of 2π/5 = 72°. Our theory represents streams as a collection of paths growing and bifurcating in a diffusing field. Our observations of nearly 5,000 bifurcated streams growing in a 100 km² groundwater field on the Florida Panhandle yield a mean bifurcation angle of 71.9° ± 0.8°. This good accord between theory and observation suggests that the network geometry is determined by the external flow field but not, as classical theories imply, by the flow within the streams themselves.     

From the Cover: Anthropogenic lead pervasive in Canadian Arctic seawater

Anthropogenic Pb is widespread in the environment including remote places. However, its presence in Canadian Arctic seawater is thought to be negligible based on low dissolved Pb (dPb) concentrations and proxy data. Here, we measured dPb isotopes in Arctic seawater with very low dPb concentrations (average ∼5 pmol ⋅ kg⁻¹) and show that anthropogenic Pb is pervasive and often dominant in the western Arctic Ocean. Pb isotopes further reveal that historic aerosol Pb from Europe and Russia (Eurasia) deposited to the Arctic during the 20th century, and subsequently remobilized, is a significant source of dPb, particularly in water layers with relatively higher dPb concentrations (up to 16 pmol ⋅ kg⁻¹). The 20th century Eurasian Pb is present predominantly in the upper 1,000 m near the shelf but is also detected in older deep water (2,000 to 2,500 m). These findings highlight the importance of the remobilization of anthropogenic Pb associated with previously deposited aerosols, especially those that were emitted during the peak of Pb emissions in the 20th century. This remobilization might be further enhanced because of accelerated melting of permafrost and ice along with increased coastal erosion in the Arctic. Additionally, the detection of 20th century Eurasian Pb in deep water helps constrain ventilation ages. Overall, this study shows that Pb isotopes in Arctic seawater are useful as a gauge of changing particulate and contaminant sources, such as those resulting from increased remobilization (e.g., coastal erosion) and potentially also those associated with increased human activities (e.g., mining and shipping).

Human activities have significantly altered the geochemical cycle of Pb with anthropogenic sources (e.g., leaded gasoline in the 20th century, coal combustion, and smelting) overwhelming natural sources (e.g., crustal particles and volcanic eruptions) (1–3). Anthropogenic Pb is also far reaching because Pb emissions from high-temperature processes can attach, nucleate, and condense to fine aerosol particles, allowing its dispersion over vast distances and deposition in remote places (4, 5). In the ocean, aerosol Pb can enter the water column through a variety of pathways and is redistributed laterally and vertically via ocean circulation. Biogenic (e.g., plankton and organic matter) and authigenic particles also influence the distribution by scavenging the dissolved Pb (dPb), with the estimated dPb residence time in surface waters rich with these particles being shorter (<1 y) (6) than in the deep water (∼100 y) (7, 8) with fewer particles.Although the Arctic is very remote, anthropogenic Pb from the midlatitude regions reaches the Arctic via atmospheric transport, as evidenced in aerosols (9, 10), snow (11, 12), and ice cores (11, 13, 14). This influx is particularly high during the Arctic haze period (winter and spring) when pollution from Europe and Russia (Eurasia) reaches the Arctic atmosphere (15). Anthropogenic Pb from aerosols can become incorporated in the Arctic Ocean through wet and dry deposition of aerosols (16), resuspension from coasts and shelves, river input, and sea ice melting. However, to date, the atmospheric pathway, which is dominated by anthropogenic Pb (1), is not considered a major source of dPb to the western Arctic Ocean (refer to Fig. 1A for the Arctic map). Based on a few proxy measurements from Arctic lake sediments (17), abyssal sediments (18), and Fe–Mn crusts (19) in the western Arctic, major contributions of anthropogenic Pb were not found, and therefore, the contribution of dPb from atmospheric deposition was not considered to be a major source. Abyssal sediments and Fe–Mn crusts in the ocean along with lake sediments are used as proxies to assess the presence of dPb in the water column, because Pb is particle reactive and is readily incorporated in these materials (18, 20). In particular, a study (18) on Pb in abyssal sediments in the Arctic Ocean found evidence of anthropogenic Pb in the abyssal sediments of the eastern but not the western Arctic Ocean. The authors attributed this difference to how Pb is scavenged from Atlantic waters entering the Arctic Ocean. Waters reaching the western Arctic Ocean mainly pass through the highly productive and particle-rich Barents Sea, allowing dPb to be largely scavenged, while waters reaching the eastern Arctic Ocean pass through the Fram Strait and undergo less scavenging (18). With what is assumed to be a limited contribution from anthropogenic Pb, it is thought that dPb in the western Arctic seawater would likely be predominantly natural Pb from coastal and riverine inputs. However, to date, no study has clearly assessed the relative importance of anthropogenic and natural Pb sources directly in Arctic Ocean seawater.Open in a separate windowFig. 1.Maps of the Arctic and the sampling stations (CB1 to CB4 and CAA8). (A) The study area (red ellipse) and relevant geographic features in the Arctic based on Rudels (60). The Arctic Ocean has four basins separated by submarine ridges (red text). The CB and Makarov Basin (MB) make up the Western Arctic Ocean, and the Amundsen Basin (AB) and Nansen Basin (NB) make up the Eastern Arctic Ocean. The Pacific-derived water (PW, green arrow) enters Bering Strait (BeS), goes to the CB and MB, and makes up the upper halocline layer. The Atlantic-derived water (AW, blue arrows) enters Fram Strait (FS), and Barents Sea (BaS) travels along the shelves and slopes and reaches all the basins. In the CB, the AW makes up the lower halocline layer, Atlantic water layer, and deep water. The PW from the CB goes to the Canadian Arctic Archipelago (CAA), Baffin Bay (BB), Labrador Sea (LS), and eventually the Atlantic Ocean. (B) The sampling locations in the CB are stations CAA8 and CB1 to CB4.The 2015 Canadian Arctic Geotraces Cruise (GN03) provided an opportunity to measure both dPb and Pb isotopes in seawater samples from the Canada Basin (CB, a part of the western Arctic Ocean; Fig. 1A). The dPb data (21) showed extraordinarily low dPb concentrations in the CB, with higher levels of dPb observed in the Pacific-derived water layer, suggesting that some of this dPb came from the Pacific inflow. However, given the very low-Pb concentrations, it was hard to determine the provenance of this Pb from concentrations alone. Thus, we present here Pb isotopes from the same samples (CB1 to CB4 and CAA8 stations collectively referred to as CB; Fig. 1B) in order to assess the different sources of Pb in the water column.Our results show that anthropogenic Pb is pervasive in the Arctic, even at water column depths with low dPb concentrations. We also identified 20th century Eurasian Pb, which is present in different water layers and at depths with relatively higher dPb, as an important component of the dPb. This 20th century Eurasian Pb has a distinctively low ²⁰⁶Pb/²⁰⁷Pb ratios, offering insights into the pathway and distribution of anthropogenic Pb in the water column. Our results indicate that remobilization of previously deposited aerosols, especially from the high-Pb emission period (e.g., peak of leaded gasoline in the 20th century), contributes significantly to the dPb budget of the CB.     

From the Cover: Genomic patterns of pleiotropy and the evolution of complexity

Pleiotropy refers to the phenomenon of a single mutation or gene affecting multiple distinct phenotypic traits and has broad implications in many areas of biology. Due to its central importance, pleiotropy has also been extensively modeled, albeit with virtually no empirical basis. Analyzing phenotypes of large numbers of yeast, nematode, and mouse mutants, we here describe the genomic patterns of pleiotropy. We show that the fraction of traits altered appreciably by the deletion of a gene is minute for most genes and the gene–trait relationship is highly modular. The standardized size of the phenotypic effect of a gene on a trait is approximately normally distributed with variable SDs for different genes, which gives rise to the surprising observation of a larger per-trait effect for genes affecting more traits. This scaling property counteracts the pleiotropy-associated reduction in adaptation rate (i.e., the “cost of complexity”) in a nonlinear fashion, resulting in the highest adaptation rate for organisms of intermediate complexity rather than low complexity. Intriguingly, the observed scaling exponent falls in a narrow range that maximizes the optimal complexity. Together, the genome-wide observations of overall low pleiotropy, high modularity, and larger per-trait effects from genes of higher pleiotropy necessitate major revisions of theoretical models of pleiotropy and suggest that pleiotropy has not only allowed but also promoted the evolution of complexity.     

From the Cover: Father's brain is sensitive to childcare experiences

Although contemporary socio-cultural changes dramatically increased fathers'' involvement in childrearing, little is known about the brain basis of human fatherhood, its comparability with the maternal brain, and its sensitivity to caregiving experiences. We measured parental brain response to infant stimuli using functional MRI, oxytocin, and parenting behavior in three groups of parents (n = 89) raising their firstborn infant: heterosexual primary-caregiving mothers (PC-Mothers), heterosexual secondary-caregiving fathers (SC-Fathers), and primary-caregiving homosexual fathers (PC-Fathers) rearing infants without maternal involvement. Results revealed that parenting implemented a global “parental caregiving” neural network, mainly consistent across parents, which integrated functioning of two systems: the emotional processing network including subcortical and paralimbic structures associated with vigilance, salience, reward, and motivation, and mentalizing network involving frontopolar-medial-prefrontal and temporo-parietal circuits implicated in social understanding and cognitive empathy. These networks work in concert to imbue infant care with emotional salience, attune with the infant state, and plan adequate parenting. PC-Mothers showed greater activation in emotion processing structures, correlated with oxytocin and parent-infant synchrony, whereas SC-Fathers displayed greater activation in cortical circuits, associated with oxytocin and parenting. PC-Fathers exhibited high amygdala activation similar to PC-Mothers, alongside high activation of superior temporal sulcus (STS) comparable to SC-Fathers, and functional connectivity between amygdala and STS. Among all fathers, time spent in direct childcare was linked with the degree of amygdala-STS connectivity. Findings underscore the common neural basis of maternal and paternal care, chart brain–hormone–behavior pathways that support parenthood, and specify mechanisms of brain malleability with caregiving experiences in human fathers.Throughout human history and across cultures, women have typically assumed primary caregiving responsibility for infants (1, 2). Although humans are among the few mammalian species where some male parental caregiving is relatively common, father involvement varies considerably within and across cultures, adapting to ecological conditions (1, 3). Involved fathering has been linked with children''s long-term physiological and social development and with increases in mothers'' caregiving-related hormones such as oxytocin and prolactin (3–6). In addition, animal studies demonstrated structural brain alterations in caregiving fathers (7, 8). It has been suggested that, although maternal caregiving is triggered by neurobiological processes related to pregnancy and labor, the human father''s brain, similar to other biparental mammals, adapts to the parental role through active involvement in childcare (1–3). Despite growing childcare involvement of fathers (3, 5, 6), mechanisms for human fathers'' brain adaptation to caregiving experiences remain largely unknown, and no study to our knowledge has examined the brain basis of human fatherhood when fathers assume primary responsibility for infant care.For social species with lengthy periods of dependence, parental caregiving is key to survival and relies on brain structures that maximize survival (2, 9). Animal studies have demonstrated that mammalian mothering is supported by evolutionarily ancient structures implicated in emotional processing, vigilance, motivation, and reward, which are rich in oxytocin receptors, including the amygdala, hypothalamus, nucleus accumbens, and ventral tegmental area (VTA), and that these regions are sensitive to caregiving behavior (9, 10). Imaging studies of human mothers found activation in similar areas, combined with paralimbic insula-cingulate structures that imbue infants with affective salience, ground experience in the present moment and enable maternal simulation of infant states (11–13). These structures implicate a phylogenetically ancient network of emotional processing that rapidly detects motivationally salient and survival-related cues (14) and enables parents to automatically identify and immediately respond to infant distress, thereby maximizing survival. In humans, this emotional processing network is complemented by a cortical mentalizing network of frontopolar-medial-prefrontal-temporo-parietal structures involved in social understanding, theory of mind, and cognitive empathy, including the medial prefrontal cortex (mPFC), frontopolar cortex, superior temporal sulcus (STS), and temporal poles (15). The mentalizing network plays an important role in individuals'' ability to infer mental states from behavior, is already activated during the parents'' first weeks of parenting, and enables parents to cognitively represent infant states, predict infant needs, and plan future caregiving (11–13).The few studies examining the human father''s brain showed activation in similar areas, including the STS, lateral and medial frontal regions, VTA, inferior frontal gyrus (IFG), and orbitofrontal cortex (OFC) (16, 17). Only one study compared maternal and paternal brain response to infant cues, reporting mothers'' greater amygdala activation, fathers'' greater superior-temporal and medial-frontal activation, and maternal and paternal oxytocin''s different associations with amygdala vs. cortical activation (18). Oxytocin, a nine-amino acid neuropeptide that underpins the formation of affiliative bonds (19), supports the development of human parental caregiving (20). Research has shown that maternal and paternal oxytocin levels are associated with parent–infant synchrony, which is the parent''s careful adaptation of caregiving behavior to infant''s social signals (21). However, although oxytocin levels are similar in mothers and fathers, oxytocin is differentially linked with the parent-specific repertoire, for instance, with affectionate contact in mothers and stimulatory play in fathers (5, 20).Ethological perspectives emphasize the importance of studying the neurobiology of parenting in its natural habitat and of using a behavior-based approach to test parents'' brain adaptation to ecological pressures (22). Consistent with findings in other mammals (10), studies on brain–behavior associations in human mothers describe links between mother–infant synchrony and brain activation in the mother''s subcortical regions, including the amygdala, nucleus accumebens, and hippocampus (11, 13). In contrast, the one study testing human fathers'' brain–behavior associations showed correlations with cortical activation (17). Overall, these findings suggest that distinct brain–hormone–behavior pathways may underpin maternal and paternal care; therefore, oxytocin and parenting behavior may be associated with the emotional processing network in mothers but with the socio-cognitive circuit in fathers. Furthermore, animal studies indicate that active caregiving in biparental fathers leads to greater integration of multiple brain networks involved in nurturance, learning, and motivation (7). Hence, active involvement in caregiving may possibly facilitate integration of both parenting-related networks in human fathers, particularly among those who undertake the primary caregiver role.The present study sought to examine the brain basis of human fatherhood by using a “natural experiment,” afforded for the first time in human history, to our knowledge, by contemporary socio-cultural changes. Throughout history, infants without mothers were cared for by other women (2). Current social changes enable the formation of two-father families raising children with no maternal involvement since birth (3). Such a context provides a unique setting to assess changes in the paternal brain on assuming the traditionally maternal role. Moreover, understanding mechanisms of brain adaptation to caregiving experiences in primary-caregiving fathers may shed further light on processes that refine all fathers'' responses to childcare activities.We visited the homes of two-parent families rearing their firstborn child: heterosexual mother-father couples comprising primary-caregiving mothers (PC-Mothers) and secondary-caregiving fathers (SC-Fathers) and homosexual couples comprising two primary-caregiving fathers (PC-Fathers) (SI Materials and Methods). We videotaped parent–infant interaction in the natural habitat, measured parental oxytocin, and used the videotaped parent–child interactions as stimuli for functional MRI (fMRI) to test parental brain response to infant-related cues. Five hypotheses were proposed. First, we expected activation in both subcortical areas involved in vigilance and reward and cortical circuits implicated in social understanding in all parents raising a young infant. Second, we expected greater subcortical activation in mothers, particularly in the amygdala, which has been repeatedly linked with mammalian mothering (23, 24), and greater activation in cortical socio-cognitive circuits in fathers. Third, the brain–hormone–behavior constellation underpinning maternal care was expected to center around the emotional-processing network, whereas the brain–hormone–behavior links in fathers were expected to coalesce with the socio-cognitive network. Fourth, consistent with the context-specific evolution of human fathering (1), we expected greater variability in fathers'' brain response as mediated by actual caregiving experiences. Such variability would be particularly noted among the primary-caregiving fathers raising infants without mothers and may involve functional integration of the subcortical and cortical networks subserving parenting. Finally, we expected that the pathways leading from the parent''s primary caregiving role to greater parent–infant synchrony would be mediated by parental brain activation and oxytocin levels.     

Aggression involving alcohol: relationship to drinking patterns and social context

Aims The present study examines the relationships between: (1) alcohol involvement/perceived intoxication level of participants and aggression severity; (2) respondent drinking patterns and involvement in alcohol‐related aggression; and (3) social context and alcohol‐related aggression. Design Random digit dialing (RDD) with computer assisted telephone interviewing (CATI) was used to obtain a random sample of Ontario adults aged 18–60 (response rate of 67%). Participants Respondents who reported that they had been involved personally in physical aggression in the past 12 months were the focus of the present study. Measurements Questions were asked regarding the most recent incident of physical aggression, including whether the respondent and opponent drank alcohol prior to aggression, perceived intoxication levels at the time, number of participants, relationship to opponent, social context of aggression, time of day and day of week. Three items were used to assess aggression severity: injury to respondent, use of threats by respondent or opponent and police involvement. Findings (1) Injury to respondent and threats by respondent were not associated with alcohol involvement per se, but were significantly related to perceived level of alcohol intoxication; (2) drinking pattern of respondent was significantly associated with alcohol‐related aggression but unrelated to aggression that did not involve alcohol; and (3) a number of contextual factors (e.g. gender, number of participants, time of day) were found to be associated with alcohol involvement in aggression. Conclusions The results suggest that both drinking pattern and contextual factors are important in distinguishing between alcohol‐related aggression and non‐alcohol‐related aggression. As well, alcohol intoxication may be an important predictor of aggression severity.     

From the Cover: Neural evidence that three dimensions organize mental state representation: Rationality,social impact,and valence

How do people understand the minds of others? Existing psychological theories have suggested a number of dimensions that perceivers could use to make sense of others’ internal mental states. However, it remains unclear which of these dimensions, if any, the brain spontaneously uses when we think about others. The present study used multivoxel pattern analysis (MVPA) of neuroimaging data to identify the primary organizing principles of social cognition. We derived four unique dimensions of mental state representation from existing psychological theories and used functional magnetic resonance imaging to test whether these dimensions organize the neural encoding of others’ mental states. MVPA revealed that three such dimensions could predict neural patterns within the medial prefrontal and parietal cortices, temporoparietal junction, and anterior temporal lobes during social thought: rationality, social impact, and valence. These results suggest that these dimensions serve as organizing principles for our understanding of other people.The human mind plays host to a panoply of thoughts, feelings, intentions, and impressions. External observers can never directly perceive these mental states—one can never see “nostalgia” nor touch “awe.” Nevertheless, humans are quite adept at representing other people’s internal states. Our ability to perceive and distinguish among the rich set of others’ mental states serves as the bedrock of human social life. We understand the fine differences between pure joy and schadenfreude and judge a friend’s glee accordingly. Our ability to distinguish a partner’s sympathy from sarcasm can make a world of difference to a relationship. Legal decisions frequently hinge on nuanced mental distinctions such as that between inattention and intentional neglect. How do people navigate such complexities in others’ internal mental worlds?One crucial tool for any navigator is a compass: a set of dimensions that help organize the contents of the world. By attending to the position of others’ mental states on key dimensions, humans might reduce the complexity of others’ minds to just a few essential elements—coordinates on a map. Might navigators of the world of mental states make use of such an intuitive compass? Research in other domains of cognition suggests such organization might be possible: The brain has a demonstrated capacity for extracting and capitalizing on useful regularities in the world. For example, our object representation system makes use of dimensions such as size and animacy to organize its processing tracts (1). Here, we explore the possibility that similar principles may organize our representations of other people’s minds.Decades of research in social cognitive neuroscience, primarily using functional magnetic resonance imaging (fMRI), have already implicated a well-defined set of brain regions in the process of thinking about mental states: Thinking about the lives and minds of others reliably engages a network including the medial prefrontal cortex (MPFC), medial parietal cortex (MPC), temporoparietal junction (TPJ), superior temporal sulcus (STS), and the anterior temporal lobe (ATL) (for a review, see refs. 2 and 3). However, this relatively young field has yet to explain how the social brain’s hardware processes the richness and complexity of others’ mental states. Fortunately, research in psychology supplies a set of theories regarding how people might organize their knowledge of mental states. The dimensions of these theories include valence and arousal (4, 5), warmth and competence (6, 7), agency and experience (8), emotion and reason, mind and body (9), social and nonsocial (2, 10, 11), and uniquely human and shared with animals (12). Any of these dimensions might plausibly play a role in organizing our understanding of mental states. But which, if any, do we spontaneously use during mentalizing? If a dimension actually matters to the way people typically think about others’ mental states, we should see evidence that the brain organizes its activity around that dimension. However, merely locating where in the brain mental state processing occurs—as social neuroscience has done so well already—cannot tell us how these regions represent mental states.Fortunately, new analytic techniques in functional neuroimaging, under the umbrella of multivariate or multivoxel pattern analysis (MVPA), enable us to bridge these levels of analysis. MVPA examines activity in distributed sets of voxels, allowing for discrimination between stimuli by their associated patterns of activity even when absolute magnitudes of activity remain constant. In this study, we use the form of MVPA known as representational similarity analysis (13) to test which psychological dimensions organize people’s understanding of mental states. These analyses work by measuring the extent to which neural patterns of activity can be predicted from theories of representational organization. To illustrate, the dimension “arousal” would predict that “ecstasy” and “rage” are represented very similarly in the brain because both are similarly intense mental states. In contrast, the dimension “valence” would predict that “ecstasy” and “rage” are represented very differently in the brain because one state is very positive, whereas the other is very negative. Both predictions can be tested by measuring the extent to which patterns of neural activity elicited by thinking about a person in ecstasy are similar to those elicited by thinking about a person in a fit of rage. Each dimension makes thousands of predictions about the similarity of each mental state compared with each other mental state; representational similarity analysis allows us to assess the accuracy of all of these predictions simultaneously. Thus, we can test which psychological dimensions capture the way the brain encodes others’ mental states.     

From the Cover: Development of socio-emotional competence in bonobos

Social and emotional skills are tightly interlinked in human development, and both are negatively impacted by disrupted social development. The same interplay between social and emotional skills, including expressions of empathy, has received scant attention in other primates however, despite the growing interest in caring, friendships, and the fitness benefits of social skills. Here we examine the development of socio-emotional competence in juvenile bonobos (Pan paniscus) at a sanctuary in the Democratic Republic of the Congo, focusing on the interplay between various skills, including empathy-related responding. Most subjects were rehabilitated orphans, but some were born at the sanctuary and mother-reared there. We observed how juveniles with different rearing backgrounds responded to stressful events, both when the stress affected themselves (e.g., a lost fight) or others (e.g., witnessing the distress of others). The main dependent variable was the consolation of distressed parties by means of calming body contact. As in children, consolation was predicted by overall social competence and effective emotion regulation, as reflected in the speed of recovery from self-distress and behavioral measures of anxiety. Juveniles more effective at self-regulation were more likely to console others in distress, and such behavior was more typical of mother-reared juveniles than orphans. These results highlight the interplay between the development of social and emotional skills in our ape relatives and the importance of the mother–offspring bond in shaping socio-emotional competence.Socio-emotional competence encompasses an array of skills, such as successfully forming and maintaining social relationships, behaving appropriately in social situations, being sensitive to the emotions of others, and effectively managing one’s own emotions (1). Emotion regulation (ER) is an essential part of socio-emotional competence and is defined as the process of modifying, inhibiting, evaluating, and monitoring internal states and reactions to enable an individual to adaptively respond to arousing situations so as to achieve individual goals (1, 2). Throughout development, social and emotional skills are tightly interconnected and “people who are unable to modulate the intensity and duration of their internal emotional responses and emotionally driven behavior are likely to be physiologically over-aroused and to behave in ways that do not foster constructive social interactions” (3).Studies of child development show that effectively managing one’s own emotions allows for greater empathy with others, including caring responses known as sympathetic concern (4–6). Sympathetic concern interacts with other social skills that emerge across development, including perspective-taking (5–9). It is reliably predicted by ER, with low-regulating individuals more likely to become emotionally overwhelmed when exposed to another’s distress, resulting in a more self-centered personal distress (6, 8, 9). This connection develops at an early age: infants with signs of better ER show less personal distress in response to peer cries than those with poorer ER (10). Overall, socially competent behavior, which includes expressions of sympathy and prosocial behavior, as well as socially appropriate responses and popularity, reliably relates to better ER in children and adults (1–3, 7–11).This socio-emotional framework is rarely applied to other species, however. This is curious, because if it is critically important for humans one would expect it to also apply to some degree to our closest relatives, the anthropoid apes. To explore this issue, we measured purported markers of socio-emotional competence in young bonobos along with consolation behavior, which previous research has suggested to be a marker of sympathetic concern (12–14). Consolation is defined as spontaneous contact comfort aimed at distressed parties by means of touching, stroking, embracing, and kissing (15) (Fig. 1), a behavior well-known of both children and apes (4–6, 12–16). In human children, consolation behavior appears already in the first year of life (10, 17), suggesting that although the cognitive component of empathy increases across development, it is no prerequisite for expressions of concern. ER seems to be critical: infants without effective ER do not orient to others because they cannot overcome their own personal distress in the face of another’s distress (10, 18).Open in a separate windowFig. 1.One juvenile bonobo embraces a distressed companion during postconflict consolation. Photograph by Zanna Clay at the Lola ya Bonobo Sanctuary in the Democratic Republic of the Congo.Whereas few animal studies have explicitly addressed ER, experimental research has revealed relevant similarities. For example, primates and other animals show human-like skin conductance and heart rate responses to emotionally arousing or calming stimuli (19–23). Chimpanzees (Pan troglodytes) spontaneously match images of positive vs. negative facial expressions to videos depicting pleasant vs. aversive situations, suggesting awareness of the emotional connotations of their species’ facial displays (19). Studies addressing emotional control typically adopt a deferred gratification paradigm. Apes, and to a lesser extent monkeys and nonprimates (24–26), are able to control the urge to reach for a reward if holding back increases the chance of a better reward later on. Moreover, like children, apes seek to distract themselves in an apparent attempt to control the temptation of immediate gratification (26). Such research suggests the importance of ER for ape behavior.An area with significant overlap in human and nonhuman developmental research is that of social deprivation. Socially deprived children demonstrate poor emotional and social competence, including lower sympathetic concern, increased risk for psychiatric disorders, and enlarged amygdala volumes, indicative of high anxiety (27–30). Although adoptive care can mitigate these negative effects, orphaned children typically show lasting socio-emotional disruptions (30). Similar effects of early deprivation have been documented in chimpanzees, rhesus monkeys (Macaca mulatta), and other primates, including increased anxiety, an inability to develop social relationships, lack of recognition of social signals, and stereotypical behaviors (31–36). A study of socially deprived monkeys found a deficiency in species-typical reconciliation with opponents after conflict, which is an essential social skill (34, 36). Although some negative effects of social deprivation can be overcome, this mostly applies to deprivation later in development, such as after weaning, or after having received substitutive maternal care (37, 38).Bonobos at a forested African sanctuary offered an opportunity to measure many of these variables in both mother-raised and orphaned juveniles, thus allowing a test of predictions derived from the above studies. Most study subjects were wild-caught orphans rescued from the illegal bush-meat and pet trades and subsequently rehabilitated with the help of human mother substitutes. The bonobo is a species of particular interest given its close genetic similarity to our own (39) and its reputation of social tolerance, peacefulness, and reduced levels of violence compared with its congener, the chimpanzee (40). Bonobos also seem to have high empathy levels (41) and are equipped with the neural substrate to support these tendencies (42). Consistent with empathy-based predictions (43), our previous study found consolation to be typical of closely bonded individuals, both kin and nonkin. Both reconciliation between former opponents and consolation of distressed parties occurred across all age classes, highlighting successful social rehabilitation within the sanctuary environment. Nevertheless, mother-reared juveniles were significantly more likely to offer consolation to others than orphans of any age (44).A year after our first study, we observed the same bonobo juveniles in greater detail to investigate the interplay between socio-emotional competence at baseline while experiencing self-distress (i.e., as a victim of a fight), and in response to the distress of others (i.e., as a bystander to conflict). Our main dependent variable was spontaneously offered consolation after naturally occurring aggressive and/or stressful episodes. We predicted that juvenile bonobos scoring higher on measures of overall sociality and socio-emotional competence, including ER, would be more likely to console distressed parties. We evaluated these effects using a generalized linear mixed model (GLMM), which helps determine independent contributions, controlled for age, sex, and other factors.     

From the Cover: Spiral precipitation patterns in confined chemical gardens

Chemical gardens are mineral aggregates that grow in three dimensions with plant-like forms and share properties with self-assembled structures like nanoscale tubes, brinicles, or chimneys at hydrothermal vents. The analysis of their shapes remains a challenge, as their growth is influenced by osmosis, buoyancy, and reaction–diffusion processes. Here we show that chemical gardens grown by injection of one reactant into the other in confined conditions feature a wealth of new patterns including spirals, flowers, and filaments. The confinement decreases the influence of buoyancy, reduces the spatial degrees of freedom, and allows analysis of the patterns by tools classically used to analyze 2D patterns. Injection moreover allows the study in controlled conditions of the effects of variable concentrations on the selected morphology. We illustrate these innovative aspects by characterizing quantitatively, with a simple geometrical model, a new class of self-similar logarithmic spirals observed in a large zone of the parameter space.Chemical gardens, discovered more than three centuries ago (1), are attracting nowadays increasing interest in disciplines as varied as chemistry, physics, nonlinear dynamics, and materials science. Indeed, they exhibit rich chemical, magnetic, and electrical properties due to the steep pH and electrochemical gradients established across their walls during their growth process (2). Moreover, they share common properties with structures ranging from nanoscale tubes in cement (3), corrosion filaments (4) to larger-scale brinicles (5), or chimneys at hydrothermal vents (6). This explains their success as prototypes to grow complex compartmentalized or layered self-organized materials, as chemical motors, as fuel cells, in microfluidics, as catalysts, and to study the origin of life (7–18). However, despite numerous experimental studies, understanding the properties of the wide variety of possible spatial structures and developing theoretical models of their growth remains a challenge.In 3D systems, only a qualitative basic picture for the formation of these structures is known. Precipitates are typically produced when a solid metal salt seed dissolves in a solution containing anions such as silicate. Initially, a semipermeable membrane forms, across which water is pumped by osmosis from the outer solution into the metal salt solution, further dissolving the salt. Above some internal pressure, the membrane breaks, and a buoyant jet of the generally less dense inner solution then rises and further precipitates in the outer solution, producing a collection of mineral shapes that resembles a garden. The growth of chemical gardens is thus driven in 3D by a complex coupling between osmotic, buoyancy, and reaction–diffusion processes (19, 20).Studies have attempted to generate reproducible micro- and nanotubes by reducing the erratic nature of the 3D growth of chemical gardens (10, 11, 13, 15, 21). They have for instance been studied in microgravity to suppress buoyancy (22, 23), or by injecting aqueous solutions of metallic salts directly into silicate solutions in 3D to dominate osmotic processes by controlled flows (10, 11). Analysis of their microstructure has also been done for different metallic salts, showing a difference of chemical composition on the inner and the outer tube surfaces (24, 25). The experimental characterization and modeling of the dynamics remains however dauntingly complex in 3D, which explains why progress in quantitative analysis remains so scarce.We show here that growing chemical gardens in a confined quasi-2D geometry by injecting one reagent solution into the other provides an innovative path to discover numerous original patterns, characterize quantitatively their properties, and explain their growth mechanism. A large variety of structures including spirals, filaments, worms, and flowers is obtained in a horizontal confined geometry when varying the reagent concentrations at a fixed flow rate. The patterns differ from those in 3D as the growth methodology decouples the different effects involved in the formation of classical chemical gardens. The buoyancy force is reduced by the vertical confinement, whereas injection decreases the influence of osmotic effects.     

From the Cover: Biophotonic probing of macromolecular transformations during apoptosis

We introduce here multiplex nonlinear optical imaging as a powerful tool for studying the molecular organization and its transformation in cellular processes, with the specific example of apoptosis. Apoptosis is a process of self-initiated cell death, critically important for physiological regulation and elimination of genetic disorders. Nonlinear optical microscopy, combining the coherent anti-Stokes Raman scattering (CARS) microscopy and two-photon excited fluorescence (TPEF), has been used for analysis of spatial distribution of major types of biomolecules: proteins, lipids, and nucleic acids in the cells while monitoring their changes during apoptosis. CARS imaging revealed that in the nuclei of proliferating cells, the proteins are distributed nearly uniformly, with local accumulations in several nuclear structures. We have found that this distribution is abruptly disrupted at the onset of apoptosis and is transformed to a progressively irregular pattern. Fluorescence recovery after photobleaching (FRAP) studies indicate that pronounced aggregation of proteins in the nucleoplasm of apoptotic cells coincides with a gradual reduction in their mobility.     

From the Cover: Model of genetic variation in human social networks

Social networks exhibit strikingly systematic patterns across a wide range of human contexts. Although genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here, we show that 3 of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a “mirror network” method to test extant network models and show that none account for observed genetic variation in human social networks. We propose an alternative “Attract and Introduce” model with two simple forms of heterogeneity that generates significant heritability and other important network features. We show that the model is well suited to real social networks in humans. These results suggest that natural selection may have played a role in the evolution of social networks. They also suggest that modeling intrinsic variation in network attributes may be important for understanding the way genes affect human behaviors and the way these behaviors spread from person to person.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Applications of social network analysis to obesity: a systematic review

People's health behaviours and outcomes can be profoundly shaped by the social networks they are embedded in. Based on graph theory, social network analysis is a research framework for the study of social interactions and the structure of these interactions among social actors. A literature search was conducted in PubMed and Web of Science for articles published until August 2017 that applied social network analysis to examine obesity and social networks. Eight studies (three cross‐sectional and five longitudinal) conducted in the US (n = 6) and Australia (n = 2) were identified. Seven focused on adolescents' and one on adults' friendship networks. They examined structural features of these networks that were associated with obesity, including degree distribution, popularity, modularity maximization and K‐clique percolation. All three cross‐sectional studies that used exponential random graph models found individuals with similar body weight status and/or weight‐related behaviour were more likely to share a network tie than individuals with dissimilar traits. Three longitudinal studies using stochastic actor‐based models found friendship network characteristics influenced change in individuals' body weight status and/or weight‐related behaviour over time. Future research should focus on diverse populations and types of social networks and identifying the mechanisms by which social networks influence obesity to inform network‐based interventions.