Specialized Networks for Social Cognition in the Primate Brain.

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Gene-environment interaction analysis via deep learning.

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the "main effects, interactions" variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.

In Silico Investigation of Palladium-Catalyzed Chemoselective Monoalkoxycarbonylation of 1,3-diynes for Conjugated Enynes Synthesis.

The palladium-catalyzed monoalkoxycarbonylation of 1,3-diynes provides a chemoselective method for the construction of synthetically useful conjugated enynes. Here, in silico unraveling the detailed mechanism of this reaction and the origin of chemoselectivity were conducted. It is shown that the alkoxycarbonylation reaction preferably proceeds by a NH-Pd pathway, which including three substeps: hydropalladation, CO migratory insertion and methanolysis. The effectiveness of the NH-Pd catalytic system is attributed to the alkynyl-palladium π-back-bonding interaction, C-H⋅⋅⋅π interaction in reactant moiety and d-pπ conjugation between the Pd center and alkenyl group. The hydropalladation step was identified as the rate- and chemoselectivity-determining step, and the first alkoxycarbonylation requires a much lower energy barrier in comparison with the second alkoxycarbonylation, in line with the experimental outcomes that the monoalkoxycarbonylation product was obtained in high yield. Distortion-interaction analysis indicates the more favorable monoalkoxycarbonylation (compared to double alkoxycarbonylation) is caused by steric effect.

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

Personal exposure to airborne organic pollutants and lung function changes among healthy older adults.

Epidemiological evidence on the impact of airborne organic pollutants on lung function among the elderly is limited, and their underlying biological mechanisms remain largely unexplored. Herein, a longitudinal panel study was conducted in Jinan, Shandong Province, China, involving 76 healthy older adults monitored over a span of five months repetitively. We systematically evaluated personal exposure to a diverse range of airborne organic pollutants using a wearable passive sampler and their effects on lung function. Participants' pulmonary function indicators were assessed, complemented by comprehensive multi-omics analyses of blood and urine samples. Leveraging the power of interaction analysis, causal inference test (CIT), and integrative pathway analysis (IPA), we explored intricate relationships between specific organic pollutants, biomolecules, and lung function deterioration, elucidating the biological mechanisms underpinning the adverse impacts of these pollutants. We observed that bis (2-chloro-1-methylethyl) ether (BCIE) was significantly associated with negative changes in the forced vital capacity (FVC), with glycerolipids mitigating this adverse effect. Additionally, 31 canonical pathways [e.g., high mobility group box 1 (HMGB1) signaling, phosphatidylinositol 3-kinase (PI3K)/AKT pathway, epithelial mesenchymal transition, and heme and nicotinamide adenine dinucleotide (NAD) biosynthesis] were identified as potential mechanisms. These findings may hold significant implications for developing effective strategies to prevent and mitigate respiratory health risks arising from exposure to such airborne pollutants. However, due to certain limitations of the study, our results should be interpreted with caution.

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, which includes a set of low-dimensional covariates, a set of high-dimensional covariates, and their interactions. This model has been motivated by gene-environment (G-E) interaction analysis, where the E variables have a low dimension, and the G variables have a high dimension. For such a model, there has been extensive research on estimation and variable selection. Comparatively, inference studies with a valid false discovery rate (FDR) control have been very limited. The existing high-dimensional inference tools cannot be directly applied to interaction models, as interactions and main effects are not "equal". In this article, for high-dimensional survival analysis with interactions, we model survival using the Accelerated Failure Time (AFT) model and adopt a "weighted least squares + debiased Lasso" approach for estimation and selection. A hierarchical FDR control approach is developed for inference and respect of the "main effects, interactions" hierarchy. The asymptotic distribution properties of the debiased Lasso estimators are rigorously established. Simulation demonstrates the satisfactory performance of the proposed approach, and the analysis of a breast cancer dataset further establishes its practical utility.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

The synergistic effect of the triglyceride-glucose index and serum uric acid on the prediction of major adverse cardiovascular events after coronary artery bypass grafting: a multicenter retrospective cohort study.

BACKGROUND: Elevated serum uric acid (SUA) is regarded as a risk factor for the development of cardiovascular diseases. Triglyceride-glucose (TyG) index, a novel surrogate for insulin resistance (IR), has been proven to be an independent predictor for adverse cardiac events. However, no study has specifically focused on the interaction between the two metabolic risk factors. Whether combining the TyG index and SUA could achieve more accurate prognostic prediction in patients undergoing coronary artery bypass grafting (CABG) remains unknown. METHODS: This was a multicenter, retrospective cohort study. A total of 1225 patients who underwent CABG were included in the final analysis. The patients were grouped based on the cut-off value of the TyG index and the sex-specific criteria of hyperuricemia (HUA). Cox regression analysis was conducted. The interaction between the TyG index and SUA was estimated using relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). The improvement of model performance yielded by the inclusion of the TyG index and SUA was examined by C-statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The goodness-of-fit of models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC) and χ2 likelihood ratio test. RESULTS: During follow-up, 263 patients developed major adverse cardiovascular events (MACE). The independent and joint associations of the TyG index and SUA with adverse events were significant. Patients with higher TyG index and HUA were at higher risk of MACE (Kaplan-Meier analysis: log-rank P < 0.001; Cox regression: HR = 4.10; 95% CI 2.80-6.00, P < 0.001). A significant synergistic interaction was found between the TyG index and SUA [RERI (95% CI): 1.83 (0.32-3.34), P = 0.017; AP (95% CI): 0.41 (0.17-0.66), P = 0.001; SI (95% CI): 2.13 (1.13-4.00), P = 0.019]. The addition of the TyG index and SUA yielded a significant improvement in prognostic prediction and model fit [change in C-statistic: 0.038, P < 0.001; continuous NRI (95% CI): 0.336 (0.201-0.471), P < 0.001; IDI (95% CI): 0.031 (0.019-0.044), P < 0.001; AIC: 3534.29; BIC: 3616.45; likelihood ratio test: P < 0.001). CONCLUSIONS: The TyG index interacts synergistically with SUA to increase the risk of MACE in patients undergoing CABG, which emphasizes the need to use both measures concurrently when assessing cardiovascular risk.

Double Hook Perylene Diimide as a New Receptor for PAHs: An Experimental and Theoretical Study.

In a one-step reaction, we prepared a dibenzylamine perylene diimide derivative (PDI). Its double hook structure allows for self-association with a constant of Kd ∼108  M-1 determined by fluorescence. We confirmed its ability to bind PAHs using UV/Vis, fluorescence, and 1 H NMR titrations in CHCl3 . The complex formation signature in UV/vis is a new band at 567 nm. The calculated binding constants (Ka ∼104  M-1 ) follow the trend pyrene>perylene>phenanthrene>naphthalene>anthracene. Theoretical modeling of these systems using DFT ωB97X-D/6-311G(d,p) proved helpful in rationalizing the complex formation and the observed association trend. The distinctive signal in UV/vis is due to a charge transfer in the complex from orbitals in the guest to the host. SAPT(DFT) confirmed that the driving forces in the complex formation are exchange and dispersion (π-π interactions). Still, the recognition ability depends on the electrostatic component of the interaction, a minor fraction.

Two-level Bayesian interaction analysis for survival data incorporating pathway information.

Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower-level gene-gene interactions and higher-level pathway-pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two-level selection as well as censoring. Its computational efficiency is much desirable for high-dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

Pathological imaging-assisted cancer gene-environment interaction analysis.

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance and stability.

Surface plasmon resonance assays for the therapeutic drug monitoring of infliximab indicate clinical relevance of anti-infliximab antibody binding properties.

OBJECTIVES: The therapeutic antibody infliximab (IFX) has improved the life quality of numerous autoinflammatory disease patients. However, IFX can trigger the generation of anti-drug antibodies (ADA), whose optimal evaluation and management are currently subject of controversial discussions. We present two novel surface plasmon resonance (SPR) biosensor assays for therapeutic drug monitoring of IFX and characterization of ADA and investigated the diagnostic value of ADA binding properties. METHODS: IFX and ADA were quantified via developed SPR biosensor assays (IFXmon and ADAmon, respectively) and diagnostics-approved ELISA in sera from inflammatory bowel disease patients. Pre-analytic ADA enrichment with magnetic beads enabled analytical drug tolerance of the ADAmon assay. The dissociation ratio (DissR) as an index for ADA:IFX binding stability was calculated from the SPR sensorgrams of ADA quantification runs. RESULTS: IFX levels determined by IFXmon assay and ELISA showed high agreement, whereas ADA quantification concordance between ADAmon assay and ELISA was poor. In patients, DissR was predominantly constant over time and differed significantly between therapy outcomes. A DissR cut-off of 1.524 indicated undetectable IFX levels with 71.4% sensitivity and 88.9% specificity. Additionally, the SPR reference surface was exploited as serum-individual negative control to check result plausibility within multi-sample run sequences. CONCLUSIONS: Overall, both SPR biosensor assays exhibited reliable quantitative performance with accuracies superior to their ELISA counterparts and precision inferior to ELISA only for ADAmon. DissR presented itself as promising ADA binding parameter and could contribute to both earlier and more tailored therapeutic decisions.

In silico characterization of ligand-receptor interactions for U-47700, N,N-didesmethyl-U-47700, U-50488 at mu- and kappa-opioid receptors.

The increasing misuse of novel synthetic opioids (NSOs) represents a serious public health concern. In this regard, U-47700 (trans-3,4-dichloro-N-[2-(dimethylamino)cyclohexyl]-N-methylbenzamide) and related "U-compounds" emerged on recreational drug markets as synthetic substitutes for illicit heroin and constituents of counterfeit pain medications. While the pharmacology of U-compounds has been investigated using in vitro and in vivo methods, there is still a lack of understanding about the details of ligand-receptor interactions at the molecular level. To this end, we have developed a molecular modeling protocol based on docking and molecular dynamics simulations to assess the nature of ligand-receptor interactions for U-47700, N,N-didesmethyl U-47700, and U-50488 at the mu-opioid receptor (MOR) and kappa-opioid receptor (KOR). The evaluation of ligand-receptor and ligand-receptor-membrane interaction energies enabled the identification of subtle conformational shifts in the receptors induced by ligand binding. Interestingly, the removal of two key methyl groups from U-47700, to form N,N-didesmethyl U-47700, caused a loss of hydrogen bond contact with tryptophan (Trp)229, which may underlie the lower interaction energy and reduced MOR affinity for the compound. Taken together, our results are consistent with the reported biological findings for U-compounds and provide a molecular basis for the MOR selectivity of U-47700 and KOR selectivity of U-50488.

Unified model-free interaction screening via CV-entropy filter.

For many practical high-dimensional problems, interactions have been increasingly found to play important roles beyond main effects. A representative example is gene-gene interaction. Joint analysis, which analyzes all interactions and main effects in a single model, can be seriously challenged by high dimensionality. For high-dimensional data analysis in general, marginal screening has been established as effective for reducing computational cost, increasing stability, and improving estimation/selection performance. Most of the existing marginal screening methods are designed for the analysis of main effects only. The existing screening methods for interaction analysis are often limited by making stringent model assumptions, lacking robustness, and/or requiring predictors to be continuous (and hence lacking flexibility). A unified marginal screening approach tailored to interaction analysis is developed, which can be applied to regression, classification, and survival analysis. Predictors are allowed to be continuous and discrete. The proposed approach is built on Coefficient of Variation (CV) filters based on information entropy. Statistical properties are rigorously established. It is shown that the CV filters are almost insensitive to the distribution tails of predictors, correlation structure among predictors, and sparsity level of signals. An efficient two-stage algorithm is developed to make the proposed approach scalable to ultrahigh-dimensional data. Simulations and the analysis of TCGA LUAD data further establish the practical superiority of the proposed approach.

Correlative Method for Diagnosing Gas-Turbine Tribological Systems.

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

Exploration of ß-glucosidase-producing microorganisms community structure and key communities driving cellulose degradation during composting of pure corn straw by multi-interaction analysis.

Poor management of crop residues leads to environmental pollution and composting is a sustainable practice for addressing the challenge. However, knowledge about composting with pure crop straw is still limited, which is a novel and feasible composting strategy. In this study, pure corn straw was in-situ composted for better management. Community structure of ß-glucosidase-producing microorganisms during composting was deciphered using high-throughput sequencing. Results showed that the compost was mature with organic matter content of 37.83% and pH value of 7.36 and pure corn straw could be composted successfully. Cooling phase was major period for cellulose degradation with the highest ß-glucosidase activity (476.25 µmol·p-Nitr/kg·dw·min) and microbial diversity (Shannon index, 3.63; Chao1 index, 500.81). Significant compositional succession was observed in the functional communities during composting with Streptomyces (14.32%), Trichoderma (13.85%) and Agromyces (11.68%) as dominant genera. ß-Glucosidase-producing bacteria and fungi worked synergistically as a network to degrade cellulose with Streptomyces (0.3045**) as the key community revealed by multi-interaction analysis. Organic matter (-0.415***) and temperature (-0.327***) were key environmental parameters regulating cellulose degradation via influencing ß-glucosidase-producing communities, and ß-glucosidase played a key role in mediating this process. The above results indicated that responses of ß-glucosidase-producing microorganisms to cellulose degradation were reflected at both network and individual levels and multi-interaction analysis could better explain the relationship between variables concerning composting cellulose degradation. The work is of significance for understanding cellulose degradation microbial communities and process during composting of pure corn straw.

["Is Anything HappeningThere? And if so,What?" Video-Diagnostic Assessment of Parental Interaction Qualities in a Preschool Psychiatric Sample]. / "Tut sich da denn was? Und wenn ja, was?" Videodiagnostische Beurteilung elterlicher Interaktionsqualitäten in einer vorschulpsychiatrischen Stichprobe.

Standardized video diagnostic instruments, such as the Play-PAB used in this study, are suitable for an objective and multidimensional assessment of early parent-child interactions. Furthermore, the present results support the assumption that early parent-child interaction is a multivariate construct.The correlation analyses show that various influencing factors, such as parental stress, are related to specific parental interaction qualities, such as intrusiveness, and as a consequence, affect the relationship building process with the child.Therefore, video diagnosticmethods are suitable for both objective therapy evaluation and individualizing therapy in preschool psychiatric settings.

Comparative structural analysis of the human and Schistosoma glutathione transferase dimer interface using selective binding of bromosulfophthalein.

The binding channel of Schistosoma glutathione transferase (SGST) has been identified to possess a non-substrate site implicated in enzyme inhibition. This binding channel is formed by the interface of the GST dimer. We produced a comparative characterization of the SGST dimer interface with respect to that of human GST (hGST) analogues using the selective binding of bromosulfophthalein (BSP). First, two SGST and three hGST structures were used as search queries to assemble a data set of 48 empirical GST structures. Sequence alignment to generate a universal residue indexing scheme was then performed, followed by local superposition of the dimer interface. Principal component analysis revealed appreciable variation of the dimer interface, suggesting the potential for selective inhibition of SGST. BSP was found to dock invariably in the dimer interface core pocket, placing it in proximity to the GST catalytic domains, through which it may exert its inhibitory behavior. Binding poses across the GST forms were distinguished with ligand interaction profiling, where SGST complexes showed stabilization of ligand aromatic- and sulfonate moieties, which altogether anchor the ligand and produce a tight association. In comparison, missing aromatic stabilization in the hGST complexes impart large bonding distances, causing mobile poses likely to dissociate. Altogether, this study illustrates the potential for selective inhibition of SGST, rationalizes the selective behavior of the BSP inhibitor, and produces a reliable metric for construction and validation of pharmacophore models of the SGST binding channel.

Racial Inequities in Self-Rated Health Across Brazilian Cities: Does Residential Segregation Play a Role?

Racial health inequities may be partially explained by area-level factors such as residential segregation. In this cross-sectional study, using a large, multiracial, representative sample of Brazilian adults (n = 37,009 individuals in the 27 state capitals; National Health Survey (Pesquisa Nacional de Saúde), 2013), we investigated 1) whether individual-level self-rated health (SRH) (fair or poor vs. good or better) varies by race (self-declared White, Brown, or Black) and 2) whether city-level economic or racial residential segregation (using dissimilarity index values in tertiles: low, medium, and high) interacts with race, increasing racial inequities in SRH. Prevalence of fair or poor SRH was 31.5% (Black, Brown, and White people: 36.4%, 34.0%, and 27.3%, respectively). Marginal standardization based on multilevel logistic regression models, adjusted for age, gender, and education, showed that Black and Brown people had, respectively, 20% and 10% higher prevalence of fair or poor SRH than did White people. Furthermore, residential segregation interacted with race such that the more segregated a city, the greater the racial gap among Black, Brown, and White people in fair or poor SRH for both income and race segregation. Policies to reduce racial inequities may need to address residential segregation and its consequences for health.