Motivations to reciprocate cooperation and punish defection are calibrated by estimates of how easily others can switch partners

Evolutionary models of dyadic cooperation demonstrate that selection favors different strategies for reciprocity depending on opportunities to choose alternative partners. We propose that selection has favored mechanisms that estimate the extent to which others can switch partners and calibrate motivations to reciprocate and punish accordingly. These estimates should reflect default assumptions about relational mobility: the probability that individuals in one’s social world will have the opportunity to form relationships with new partners. This prior probability can be updated by cues present in the immediate situation one is facing. The resulting estimate of a partner’s outside options should serve as input to motivational systems regulating reciprocity: Higher estimates should down-regulate the use of sanctions to prevent defection by a current partner, and up-regulate efforts to attract better cooperative partners by curating one’s own reputation and monitoring that of others. We tested this hypothesis using a Trust Game with Punishment (TGP), which provides continuous measures of reciprocity, defection, and punishment in response to defection. We measured each participant’s perception of relational mobility in their real-world social ecology and experimentally varied a cue to partner switching. Moreover, the study was conducted in the US (n = 519) and Japan (n = 520): societies that are high versus low in relational mobility. Across conditions and societies, higher perceptions of relational mobility were associated with increased reciprocity and decreased punishment: i.e., those who thought that others have many opportunities to find new partners reciprocated more and punished less. The situational cue to partner switching was detected, but relational mobility in one’s real social world regulated motivations to reciprocate and punish, even in the experimental setting. The current research provides evidence that motivational systems are designed to estimate varying degrees of partner choice in one’s social ecology and regulate reciprocal behaviors accordingly.

Determination and risk assessment of heavy metals in air dust fall particles

Background: Urban and industrial development has increased the concentration of heavy metals in various environments, and also, increased their amount in dust fall particles. The aim of this study was to determine and assess the risk of heavy metals in air dust fall particles. Methods: Sampling of air dust fall particles was performed using the British model Dust Fall Jar devices. Heavy metals concentrations in the samples were determined using an atomic absorption spectrometer device. Then, the risk assessment of heavy metals in air dust fall was calculated by three indicators including enrichment factor (EF), geo-accumulation index (Igeo), and integrated pollution index (IPI). Data were analyzed using descriptive statistics and Excel 2016 software. Results: Zn was the most abundant heavy metal. The results of EF index showed that the highest degree of enrichment of dust fall particles with heavy metals is related to Ag and the lowest one is related to Cr. Also, the changes of Mean of both Igeo and IP indicators were as Ag > Pb > Zn > Cd > Cu > Co > Cr. Conclusion: According to the results, it can be concluded that Cr metal is originated from the earth and other metals are of man-made origin and are mainly due to the emissions of vehicles and industries. Also, on average, the samples had very low pollution in relation to all metals. Although the amount of pollution caused by heavy metals has not exceeded the allowable limits, but considering industrial development programs in the region, continuous measures to control air pollution caused by industries, are absolutely necessary.

Hierarchical organization of rhesus macaque behavior

Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, fully delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of fifteen body landmarks from two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over many hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques' behavior is characterized by 49 distinct identifiable postures, lasting an average of 0.6 seconds each. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to intuitively identifiably actions; these actions are in turn organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior and provide a method for the automated "ethogramming" of primate behavior.

Dynamic Fit Index Cutoffs for One-Factor Models

Assessing unidimensionality of a scale is a frequent interest in behavioral research. Often, this is done with approximate model fit indices in a factor analysis framework such as RMSEA, CFI, or SRMR. These fit indices are continuous measures, so values indicating acceptable fit are up to interpretation. Cutoffs suggested by Hu and Bentler (1999) are a common guideline used in empirical research. However, these cutoffs were derived with intent to detect omitted cross-loadings or omitted factor covariances in three-factor models. These types of misspecifications cannot exist in one-factor models, so the appropriateness of using these guidelines in one-factor models is uncertain. This paper uses a simulation study to address whether traditional fit index cutoffs are sensitive to the types of misspecifications that can occur in one-factor models. The results showed that traditional cutoffs have very poor sensitivity to misspecification in one-factor models and that the traditional cutoffs generalize poorly to one-factor contexts. As an alternative, we investigate the accuracy and stability of the recently introduced dynamic fit cutoff approach for creating fit index cutoffs for one-factor models. Simulation results indicated excellent performance of dynamic fit index cutoffs to classify correct or misspecified one-factor models and that dynamic fit index cutoffs are a promising approach for more accurate assessment of unidimensionality.

Problems using ordinal traits with continuous measures of functional diversity

Continuous indices of functional diversity are popular in studies examining community structure and ecosystem function across a wide range of subfields from paleontology to range management. These indices were designed to replace the use of more arbitrary, discrete functional groups or guilds; however, the effect of typical methodological decisions on these new continuous measures has not been fully investigated. To test the effect of using ordinal traits in functional diversity analysis, I first calculated relative functional diversity index values in real plant communities with real continuous trait data and Euclidean distances. I then compared these original values to "treatment" functional diversity index values obtained by discretizing the trait data and using Gower's distance. Agreement between original and treatment values was highly unpredictable and often abysmal. Small methodological choices, such as whether to treat a functional trait as continuous (mm) or ordinal ("small", "medium", "large"), could completely change a perceived functional diversity relationship along an environmental gradient. Some parameter combinations returned results that were no better than random noise. Because simple methodological choices can have such a large impact on continuous functional diversity indices, it is ambiguous whether analyses using ordinal traits are actually measuring an underlying functional diversity relationship between communities or just reflecting the arbitrary parameter choices of researchers.

Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study

<strong>OBJECTIVE:</strong> Comprehensive assessment of alterations in lipid species preceding T2D is largely unknown. We aimed to identify plasma molecular lipids associated with risk of T2D in American Indians. <p><strong>RESEARCH DESIGN AND METHODS</strong>: Using an untargeted LC-MS, we repeatedly measured 3,907 fasting plasma samples from 1,958 participants who attended two exams (~5.5 year apart) and were followed up to 16 years in the Strong Heart Study. Mixed-effect logistic regression was used to identify lipids associated with risk of T2D adjusting for traditional risk factors. Repeated measurement analysis was performed to examine the association between change in lipidome and change in continuous measures of T2D adjusting for baseline lipids. Multiple testing was controlled by false discovery rate at 0.05.</p> <p><strong>RESULTS:</strong> Higher baseline level of 33 lipid species, including TAGs, DAGs, PEs, and PCs, was significantly associated with increased risk of T2D (odds ratio [OR] per SD increase in log2-transformed baseline lipids: 1.50-2.85) at 5-year follow-up. Of these, 21 lipids were also associated with risk of T2D at 16-year follow-up. Aberrant lipid profiles were also observed in prediabetes (OR per SD increase in log2-transformed baseline lipids: 1.30-2.19 for risk lipids; 0.70-0.78 for protective lipids). Longitudinal changes in 568 lipids were significantly associated with changes in continuous measures of T2D. Multivariate analysis identified distinct lipidomic signatures differentiating high from low risk groups. </p> <p><strong>CONCLUSIONS:</strong> Lipid dysregulation occurs many years preceding T2D, and novel molecular lipids (both baseline level and longitudinal change over time) are significantly associated with risk of T2D beyond traditional risk factors. Our findings shed light on the mechanisms linking dyslipidemia to T2D and may yield novel therapeutic targets for early intervention tailored to American Indians.</p>

Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study

<strong>OBJECTIVE:</strong> Comprehensive assessment of alterations in lipid species preceding T2D is largely unknown. We aimed to identify plasma molecular lipids associated with risk of T2D in American Indians. <p><strong>RESEARCH DESIGN AND METHODS</strong>: Using an untargeted LC-MS, we repeatedly measured 3,907 fasting plasma samples from 1,958 participants who attended two exams (~5.5 year apart) and were followed up to 16 years in the Strong Heart Study. Mixed-effect logistic regression was used to identify lipids associated with risk of T2D adjusting for traditional risk factors. Repeated measurement analysis was performed to examine the association between change in lipidome and change in continuous measures of T2D adjusting for baseline lipids. Multiple testing was controlled by false discovery rate at 0.05.</p> <p><strong>RESULTS:</strong> Higher baseline level of 33 lipid species, including TAGs, DAGs, PEs, and PCs, was significantly associated with increased risk of T2D (odds ratio [OR] per SD increase in log2-transformed baseline lipids: 1.50-2.85) at 5-year follow-up. Of these, 21 lipids were also associated with risk of T2D at 16-year follow-up. Aberrant lipid profiles were also observed in prediabetes (OR per SD increase in log2-transformed baseline lipids: 1.30-2.19 for risk lipids; 0.70-0.78 for protective lipids). Longitudinal changes in 568 lipids were significantly associated with changes in continuous measures of T2D. Multivariate analysis identified distinct lipidomic signatures differentiating high from low risk groups. </p> <p><strong>CONCLUSIONS:</strong> Lipid dysregulation occurs many years preceding T2D, and novel molecular lipids (both baseline level and longitudinal change over time) are significantly associated with risk of T2D beyond traditional risk factors. Our findings shed light on the mechanisms linking dyslipidemia to T2D and may yield novel therapeutic targets for early intervention tailored to American Indians.</p>

Person-centered coping profiles moderate the links between racial discrimination and mental health in Black Americans

Objectives: Racial discrimination is consistently linked to negative mental health outcomes. However, less is known about how unique patterns of coping in Black Americans experiencing high discrimination stress may buffer the impact of discrimination stress on mental health. The present study uses person-centered methods to identify and describe latent profiles of coping in Black Americans, understand how these coping profiles are linked to mental health, and examine whether latent coping profiles moderate the links between discrimination and mental health. Methods: Participants included only Black American individuals (N=289; Mage = 41.81; 54% female) from the MIDUS Milwaukee Refresher. Latent profiles were estimated based on seven continuous measures of coping responses. After performing model selection, individuals were classified to profiles based on posterior probabilities and outcome analyses were performed using an approach adjusting for measurement error. Results: Four profiles of coping responses were identified: passive responders (29% of the sample), evasive responders (15%), diverse responders (17%), and engaged responders (39%). Engaged responders had the lowest prevalence of mental health symptoms. Further, membership in the engaged responders profile moderated associations between discrimination and mental health: as discrimination stress increased, engaged responders had lower odds of depression and lower negative affect compared to members of the other profiles. Conclusions: Person-centered methods uncovered meaningful subgroups characterized by unique coping patterns and pointed to engaged responders as being most resilient to the effects of discrimination. Future research should test these associations longitudinally and examine whether more adaptive coping profiles can be fostered through intervention.

Characteristics Associated with Homeless Pregnant Women in Columbus, Ohio

Introduction The effects of homelessness on pregnant women are substantial. We aim to identify key characteristics of a group of women identified as homeless and pregnant in order to understand their history of housing, family composition, health, and demographics as a first step for future intervention. Methods We present cross-sectional survey data on a sample of 100 women reporting homelessness and pregnancy in the prior year in Columbus, Ohio, identified through social service and housing not for profit agencies. Our analysis uses data collected from a survey of health behaviors, housing, employment status, and demographics. Continuous measures are described with means and standard deviations, and categorical variables are described with percentages. Results The majority (81%) of the women identified as African American. Over 95% of the women were single, and 74 women reported a prior pregnancy. Almost half of the women reported being behind on rent at least one time in the last 6 months, and 43% indicated that they had lived in more than three places in the last year. Approximately 34% of the sample reported cigarette use during pregnancy, while 12% and 30% reported alcohol and illicit drug use, respectively. Discussion Women who were pregnant and experiencing homelessness in our study reported a multitude of complex and severe problems ranging from high rates of substance use, longstanding housing insecurity and financial stress. Programs hoping to successfully support women will need to address a variety of service needs while recognizing the resilience of many women.