Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

Ideology selectively shapes attention to inequality.

Contemporary debates about addressing inequality require a common, accurate understanding of the scope of the issue at hand. Yet little is known about who notices inequality in the world around them and when. Across five studies (N = 8,779) employing various paradigms, we consider the role of ideological beliefs about the desirability of social equality in shaping individuals' attention to-and accuracy in detecting-inequality across the class, gender, and racial domains. In Study 1, individuals higher (versus lower) on social egalitarianism were more likely to naturalistically remark on inequality when shown photographs of urban scenes. In Study 2, social egalitarians were more accurate at differentiating between equal versus unequal distributions of resources between men and women on a basic cognitive task. In Study 3, social egalitarians were faster to notice inequality-relevant changes in images in a change detection paradigm indexing basic attentional processes. In Studies 4 and 5, we varied whether unequal treatment adversely affected groups at the top or bottom of society. In Study 4, social egalitarians were, on an incentivized task, more accurate at detecting inequality in speaking time in a panel discussion that disadvantaged women but not when inequality disadvantaged men. In Study 5, social egalitarians were more likely to naturalistically point out bias in a pattern detection hiring task when the employer was biased against minorities but not when majority group members faced equivalent bias. Our results reveal the nuances in how our ideological beliefs shape whether we accurately notice inequality, with implications for prospects for addressing it.

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computational methods have been proposed for identifying potential adverse DDIs, there is still room for improvement. Existing methods are not explicitly based on the knowledge that DDIs are fundamentally caused by chemical substructure interactions instead of whole drugs' chemical structures. Furthermore, most of existing methods rely on manually engineered molecular representation, which is limited by the domain expert's knowledge.We propose substructure-substructure interaction-drug-drug interaction (SSI-DDI), a deep learning framework, which operates directly on the raw molecular graph representations of drugs for richer feature extraction; and, most importantly, breaks the DDI prediction task between two drugs down to identifying pairwise interactions between their respective substructures. SSI-DDI is evaluated on real-world data and improves DDI prediction performance compared to state-of-the-art methods. Source code is freely available at https://github.com/kanz76/SSI-DDI.

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways. RESULTS: To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/dubingxue/MLGL-MP.

Political ideology shapes the amplification of the accomplishments of disadvantaged vs. advantaged group members.

Recent years have witnessed an increased public outcry in certain quarters about a perceived lack of attention given to successful members of disadvantaged groups relative to equally meritorious members of advantaged groups, exemplified by social media campaigns centered around hashtags, such as #OscarsSoWhite and #WomenAlsoKnowStuff. Focusing on political ideology, we investigate here whether individuals differentially amplify successful targets depending on whether these targets belong to disadvantaged or advantaged groups, behavior that could help alleviate or entrench group-based disparities. Study 1 examines over 500,000 tweets from over 160,000 Twitter users about 46 unambiguously successful targets varying in race (white, black) and gender (male, female): American gold medalists from the 2016 Olympics. Leveraging advances in computational social science, we identify tweeters' political ideology, race, and gender. Tweets from political liberals were much more likely than those from conservatives to be about successful black (vs. white) and female (vs. male) gold medalists (and especially black females), controlling for tweeters' own race and gender, and even when tweeters themselves were white or male (i.e., advantaged group members). Studies 2 and 3 provided experimental evidence that liberals are more likely than conservatives to differentially amplify successful members of disadvantaged (vs. advantaged) groups and suggested that this is driven by liberals' heightened concern with social equality. Addressing theorizing about ideological asymmetries, we observed that political liberals are more responsible than conservatives for differential amplification. Our results highlight ideology's polarizing power to shape even whose accomplishments we promote, and extend theorizing about behavioral manifestations of egalitarian motives.

Beyond Black and White: Conceptualizing and essentializing Black-White identity.

OBJECTIVE: Psychological research suggests that Black-White individuals are often conceptualized as Black and White, and that essentialist beliefs about race are negatively associated with conceptualizing Black-White individuals as such. The present research examined what people think it means to be Black and White (e.g., a mixture of Black and White vs. completely Black and completely White) and whether essentialism is indeed negatively associated with such concepts. METHOD: We used multiple methodologies (e.g., surveys, open-ended explanations, experimental manipulations) to examine how Black, White, and Black-White perceivers conceptualized Black-White individuals (Studies 1-3) and the extent to which essentialist beliefs, both dispositional (Studies 2-3) and experimentally induced (Study 4), predicted those concepts. RESULTS: We find that U.S. Black-White individuals most often conceptualized "Black and White" to mean a mixture of Black and White (Study 1), as did U.S. White individuals and U.S. Black individuals (Studies 2 and 3), and that racial essentialism-both dispositional (Studies 2 and 3) and experimentally manipulated (Study 4)-was positively associated with this conception. CONCLUSION: Our data shed new light on the complexity of race concepts and essentialism and advance the psychological understanding of Black-White identity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Should Individuals Think Like Their Group? A Descriptive-to-Prescriptive Tendency Toward Group-Based Beliefs.

Across three pre-registered studies with children (ages 4-9) and adults (N = 303), we examined whether how a group is predicted evaluations of how group members should be (i.e., a descriptive-to-prescriptive tendency), under conditions in which the descriptive group norms entailed beliefs that were fact-based (Study 1), opinion-based (Study 2), and ideology-based (Study 3). Overall, participants tended to disapprove of individuals with beliefs that differed from their group, but the extent of this tendency varied across development and as a function of the belief under consideration (e.g., younger children did not show a descriptive-to-prescriptive tendency in the context of facts and ideologies, suggesting that they prioritized truth over group norms). Implications for normative reasoning and ideological polarization are discussed.

Hyperfine Averaging by Dynamic Decoupling in a Multi-Ion Lutetium Clock.

We propose and experimentally demonstrate a scheme that realizes hyperfine averaging during a Ramsey interrogation of a clock transition. The method eliminates the need to average over multiple optical transitions, reduces the sensitivity of the clock to its environment, and reduces inhomogeneous broadening in a multi-ion clock. The method is compatible with autobalanced Ramsey spectroscopy, which facilitates the elimination of residual shifts due to imperfect implementation and ac stark shifts from the optical probe. We demonstrate the scheme using correlation spectroscopy of the ^{1}S_{0}↔^{3}D_{1} clock transition in a three-ion Lu^{+} clock. From the demonstration we are able to provide a measurement of the ^{3}D_{1} quadrupole moment, Θ(^{3}D_{1})=0.634(9)ea_{0}^{2}.

Precision Measurements of the

Measurement of the ^{138}Ba^{+} ^{2}S_{1/2}-^{2}D_{5/2} clock transition frequency and D_{5/2} Landé g_{J} factor are reported. The clock transition frequency ν_{Ba^{+}}=170126432449333.31±(0.39)_{stat}±(0.29)_{sys} Hz, is obtained with accuracy limited by the frequency calibration of the maser used as a reference oscillator. The Landé g_{J} factor for the ^{2}D_{5/2} level is determined to be g_{D}=1.20036739(24), which is a 30-fold improvement on previous measurements. The g-factor measurements are corrected for an ac-magnetic field from trap-drive-induced currents in the electrodes, and data taken over a range of magnetic fields underscores the importance of accounting for this systematic.

Internal Delensing of Cosmic Microwave Background Polarization B-Modes with the POLARBEAR Experiment.

Using only cosmic microwave background polarization data from the polarbear experiment, we measure B-mode polarization delensing on subdegree scales at more than 5σ significance. We achieve a 14% B-mode power variance reduction, the highest to date for internal delensing, and improve this result to 22% by applying for the first time an iterative maximum a posteriori delensing method. Our analysis demonstrates the capability of internal delensing as a means of improving constraints on inflationary models, paving the way for the optimal analysis of next-generation primordial B-mode experiments.

Introducing the Sociopolitical Motive × Intergroup Threat Model to Understand How Monoracial Perceivers' Sociopolitical Motives Influence Their Categorization of Multiracial People.

Researchers have used social dominance, system justification, authoritarianism, and social identity theories to understand how monoracial perceivers' sociopolitical motives influence their categorization of multiracial people. The result has been a growing understanding of how particular sociopolitical motives and contexts affect categorization, without a unifying perspective to integrate these insights. We review evidence supporting each theory's predictions concerning how monoracial perceivers categorize multiracial people who combine their ingroup with an outgroup, with attention to the moderating role of perceiver group status. We find most studies cannot arbitrate between theories of categorization and reveal additional gaps in the literature. To advance this research area, we introduce the sociopolitical motive × intergroup threat model of racial categorization that (a) clarifies which sociopolitical motives interact with which intergroup threats to predict categorization and (b) highlights the role of perceiver group status. Furthermore, we consider how our model can help understand phenomena beyond multiracial categorization.

The Roles of Group Status and Group Membership in the Practice of Hypodescent.

Hypodescent emerged in U.S. history to reinforce racial hierarchy. Research suggests that among contemporary U.S. adults, hypodescent continues to shape social perception. Among U.S. children, however, hypodescent is less likely to be endorsed. Here, we tested for hypodescent by introducing U.S. children (ages 4-9) and adults (N = 273) to hierarchically ordered novel groups (one was high status and another was low status) and then to a child who had one parent from each group. In Study 1, we presented the groups in a third-party context. In Study 2, we randomly assigned participants to the high-status or the low-status group. Across both studies, participants did not reliably endorse hypodescent, raising questions as to what elicits this practice.

Democratizing health system data to impact social and environmental health contexts: a novel collaborative community data-sharing model.

BACKGROUND: Community health data are infrequently viewed in the context of social and environmental health determinants. We developed a novel data-sharing model to democratize health system data and to facilitate community and population health improvement. METHODS: Durham County, the City of Durham in North Carolina, Durham health systems and other stakeholders have developed a data-sharing model to inform local community health efforts. Aggregated health system data obtained through clinical encounters are shared publicly, providing data on the prevalence of health conditions of interest to the community. RESULTS: A community-owned web platform called the Durham Neighborhood Compass provides aggregate health data (e.g. on diabetes, heart disease, stroke and other conditions of interest) in the context of neighborhood social (e.g. income distribution, education level, demographics) and environmental (e.g. housing prices, crime rates, travel routes, school quality, grocery store proximity) contexts. Health data are aggregated annually to help community stakeholders track changes in health and health contexts over time. CONCLUSIONS: The Durham Neighborhood Compass is among the first collaborative public efforts to democratize health system data in the context of social and environmental health determinants. This model could be adapted elsewhere to support local community and population health improvement initiatives.

Suppressing Inhomogeneous Broadening in a Lutetium Multi-ion Optical Clock.

We demonstrate precision measurement and control of inhomogeneous broadening in a multi-ion clock consisting of three ^{176}Lu^{+} ions. Microwave spectroscopy between hyperfine states in the ^{3}D_{1} level is used to characterize differential systematic shifts between ions, most notably those associated with the electric quadrupole moment. By appropriate alignment of the magnetic field, we demonstrate suppression of these effects to the ∼10^{-17} level relative to the ^{1}S_{0}↔^{3}D_{1} optical transition frequency. Correlation spectroscopy on the optical transition demonstrates the feasibility of a 10-s Ramsey interrogation in the three ion configuration with a corresponding projection noise limited stability of σ(τ)=8.2×10^{-17}/sqrt[τ].

Children's descriptive-to-prescriptive tendency replicates (and varies) cross-culturally: Evidence from China.

Research with U.S. samples found that children use descriptive group regularities (characteristics shared by individuals within a group) to generate prescriptive judgments (characteristics that should be shared by individuals within a group). Here, we assessed this descriptive-to-prescriptive tendency in a sample of children (ages 4-13years) and adults (ages 18-40years) from mainland China. Participants were introduced to novel groups (i.e., Hibbles and Glerks) who engaged in contrasting morally neutral behaviors (e.g., listening to different kinds of music) and then to conforming and non-conforming individuals (e.g., a Hibble who listened to music more typical of Glerks). Like U.S. children, Chinese children disapproved of non-conformity and rates of disapproval declined with age. However, compared with U.S. children, younger Chinese children (ages 4-6years) rated non-conformity more disapprovingly, and unlike U.S. adults, Chinese adults rated non-conformity more negatively than conformity. Moreover, compared with U.S. participants, Chinese participants across all age groups appealed more often to norm-based explanations when justifying their disapproval. These data provide a cross-cultural replication of children's descriptive-to-prescriptive tendency but also reveal cross-cultural variation, and they have implications for understanding the mechanisms that underlie stereotyping and normative reasoning.

A Synoptic Analysis of the Temporal and Spatial Aspects of Grapevine Leafroll Disease in a Historic Napa Vineyard and Experimental Vine Blocks.

Five Grapevine leafroll-associated virus 3 (GLRaV-3) epidemics were analyzed utilizing a standardized approach to robustly characterize the temporal and spatial parameters. Published data included in the analysis are from Spain, New Zealand, and Napa Valley, CA together with new data from a historic vineyard in Napa Valley, CA. Linear regression analyses of logit-transformed incidence data indicated a maximum average increase of 11% per year in disease incidence, with considerable variation among locations. Spatial analyses, including distribution fitting, examination of the effective sample size, and evaluation of the parameters of the binary power law fitted to variance data for disease incidence, indicated a high degree of consistency among the data sets. In all cases, except at very low disease incidence, a high degree of spatial aggregation was noted, with evidence that the degree of aggregation varied as a function of mean disease incidence. The polyetic dynamics of disease follow a logistic-like pattern over multiple seasons, consistent with limitation by inoculum availability (infected vines) at low incidence and limitation by disease-free vines at high incidence.

Group presence, category labels, and generic statements influence children to treat descriptive group regularities as prescriptive.

Children use descriptive regularities of social groups (what is) to generate prescriptive judgments (what should be). We examined whether this tendency held when the regularities were introduced through group presence, category labels, or generic statements. Children (ages 4-9years, N=203) were randomly assigned to one of four conditions that manipulated how descriptive group regularities were presented: group presence (e.g., "These ones [a group of three individuals] eat this kind of berry"), category labels (e.g., "This [individual] Hibble eats this kind of berry"), generic statements (e.g., [showing an individual] "Hibbles eat this kind of berry"), or control (e.g., "This one [individual] eats this kind of berry"). Then, children saw conforming and non-conforming individuals and were asked to evaluate their behavior. As predicted, children evaluated non-conformity negatively in all conditions except the control condition. Together, these results suggest that minimal perceptual and linguistic cues provoke children to treat social groups as having normative force.

Suppression of Clock Shifts at Magnetic-Field-Insensitive Transitions.

We show that it is possible to significantly reduce rank 2 tensor shifts of a clock transition by operating at a judiciously chosen magnetic-field-insensitive point. In some cases shifts are almost completely eliminated making the transition an effective J=0 to J=0 candidate. This significantly improves the feasibility of a recent proposal for clock operation with large ion crystals. For such multi-ion clocks, geometric constraints and selection rules naturally divide clock operation into two categories based on the orientation of the magnetic field. We discuss the limitations imposed on each type and how calibrations might be carried out for clock operation.

Essentialism and Racial Bias Jointly Contribute to the Categorization of Multiracial Individuals.

Categorizations of multiracial individuals provide insight into the psychological mechanisms driving social stratification, but few studies have explored the interplay of cognitive and motivational underpinnings of these categorizations. In the present study, we integrated research on racial essentialism (i.e., the belief that race demarcates unobservable and immutable properties) and negativity bias (i.e., the tendency to weigh negative entities more heavily than positive entities) to explain why people might exhibit biases in the categorization of multiracial individuals. As theorized, racial essentialism, both dispositional (Study 1) and experimentally induced (Study 2), led to the categorization of Black-White multiracial individuals as Black, but only among individuals evaluating Black people more negatively than White people. These findings demonstrate how fundamental cognitive and motivational biases interact to influence the categorization of multiracial individuals.

Evidence for gravitational lensing of the cosmic microwave background polarization from cross-correlation with the cosmic infrared background.

We reconstruct the gravitational lensing convergence signal from cosmic microwave background (CMB) polarization data taken by the Polarbear experiment and cross-correlate it with cosmic infrared background maps from the Herschel satellite. From the cross spectra, we obtain evidence for gravitational lensing of the CMB polarization at a statistical significance of 4.0σ and indication of the presence of a lensing B-mode signal at a significance of 2.3σ. We demonstrate that our results are not biased by instrumental and astrophysical systematic errors by performing null tests, checks with simulated and real data, and analytical calculations. This measurement of polarization lensing, made via the robust cross-correlation channel, not only reinforces POLARBEAR auto-correlation measurements, but also represents one of the early steps towards establishing CMB polarization lensing as a powerful new probe of cosmology and astrophysics.