Publications in Proceedings of the National Academy of Sciences of the United States of America

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual experience. Although such mappings are well defined in vision and audition, they remain elusive in olfaction, particularly for complex odor mixtures. Here, we show that perceptual distances between odor mixtures can be predicted with high fidelity and are unexpectedly well captured by a compact semantic space derived from single-molecule representations. In the Dialogue for Reverse Engineering Assessment and Methods Olfactory Mixtures Prediction Challenge, we assembled a unified dataset of odor-mixture pairs, benchmarked predictions on a hidden test set of 46 pairs, and integrated the top-performing models into a postchallenge ensemble. This model outperformed existing state-of-the-art approaches on the hidden test set, reducing RMSE by about 33% to 0.08 and increasing Pearson correlation by 53% to 0.57, and maintained strong performance on an independent validation set of 50 newly designed mixture pairs. An ensemble, retaining only olfactory semantic features for each model included, further improved predictions, raising the Pearson correlation by 7% to 0.61 on the test set and by 15% to 0.54 on the validation set. Given that semantic features were extracted from pure molecules, it suggests that mixture perception may not require fundamentally different representational principles from single-molecule olfaction. Together, these results establish a reproducible quantitative framework for olfactory mixture perception and advance efforts to measure, model, and engineer smell.

The DNA damage response (DDR) is critical for pancreatic ductal adenocarcinoma (PDAC) development and therapeutic responses, including to genotoxic agents. While epigenetic modulators have been shown to contribute to the DDR, how chromatin regulation dictates responses to DNA damage in PDAC remains incompletely understood. Here, we identify Class I histone deacetylases (HDACs) as critical regulators of the DDR. HDAC1/2 direct the genomic distribution of H3K27ac, ensuring sufficient BRD4 and RNA polymerase II (Pol II) occupancy at DDR gene promoters. HDAC inhibition by entinostat shifts the balance of H3K27 acetylation preferentially toward intergenic regions, diverting BRD4 and Pol II from promoters, thereby suppressing DDR gene expression. In line with this, HDAC inhibition heightens DNA damage and sensitizes PDAC to diverse DNA-damaging and DDR-targeting agents. Since the clinical development of HDAC inhibitors has been limited by systemic toxicity, we developed bottlebrush prodrug (BPD) nanoparticles for tumor-selective entinostat delivery. Entinostat-BPD achieved tumor-specific HDAC inhibition while displaying potent efficacy and reduced systemic toxicity. These findings reveal an HDAC-dependent DDR vulnerability and offer combinational and precision targeting strategies to facilitate clinical translation and improve PDAC patient outcomes.

In the 1970s, physicist Richard Feynman turned lunch with a friend into a math problem—how to optimize dish selection over multiple meals—but his handwritten notes remained a mystery for decades. Here we present the fully deciphered problem and solution, prove its optimality, generalize it to related problems, and compare the results to human behavior. The optimal policy specifies decreasing thresholds for switching from exploring new dishes to exploiting the best, with thresholds varying based on the distribution of the quality of dishes. We connect these results to the existing psychological literature on optimal stopping problems, which has explored close variants on Feynman’s problem, and use our generalization of the solution to explore how the underlying distribution of the quality of the options influences people’s choices. A preregistered experiment with 2,520 participants shows that people adopt thresholds that decrease linearly with the proportion of trials remaining, consistent with the observation of linear thresholds in other optimal stopping problems. However, we show that people tend to explore more than predicted by linear thresholds, and that different distributions of quality result in thresholds with the same slope but different intercepts. These results indicate that people adapt linear thresholds used in optimal stopping tasks in a way that is sensitive to the underlying distribution—a simple strategy that we show is nearly as effective as Feynman’s solution.

Political emotions are widely acknowledged as key drivers of political participation and polarization. Yet while it is well established that political emotions matter, far less is known about how they are felt and represented in the body. Across a preregistered, nationally representative study (N = 992), we introduce an embodied approach to political emotion using the validated emBODY-tool, which allows participants to map where in the body they experience sensations when feeling canonical emotions (e.g., anger) and their political counterparts (e.g., “political anger”). Specifically, we address three questions: 1) how political emotions are embodied and differ from their nonpolitical counterparts, 2) whether political dispositions influence how these political emotions are embodied, and 3) how their embodied experience interacts with political dispositions in explaining political attitudes and behavior. Pixelwise bodily sensation maps and aggregated “embodied impact” metrics show that political anger, anxiety, depression, disgust, and hope do not merely mirror their canonical forms, but take on distinct bodily patterns. Political ideology, but not political sophistication, modulates these bodily experiences, with Democrat-leaning participants reporting more intense sensations for negative political emotions, suggesting the presence of “ideological bodies.” Crucially, political participation is not explained by how intensely people report feeling emotions, but is instead closely linked to how strongly these emotions are embodied in the body. Together, our findings underscore the body’s central role in democratic engagement by showing how political contexts shape embodied emotional experience and how these embodied experiences shape politics and democracy.

Epigenetic and transcriptional dysregulation plays a fundamental role in tumor lineage plasticity (LP). However, the underlying mechanisms, especially for the initial events of LP development, are still poorly understood. Here, we report that in progression of prostate cancer from adenocarcinoma to treatment-induced neuroendocrine prostate cancer (t-NEPC), anti-androgen receptor (AR) signaling inhibitors (ARSIs) reprogram the function of circadian regulator/nuclear receptor REV-ERBα by switching its target gene programs from kinase signaling and metabolic programs to programs of LP, which includes neurogenesis, stem cell, and epithelial–mesenchymal transition as well as over fifteen LP drivers including POU3F2/BRN2, ASCL1, FOXA2, ONECUT2, and MYCN. Unexpectedly, REV-ERBα facilitates the chromatin occupancy of BRN2, ASCL1, and FOXA1 in their activation of LP programs, thus functioning as a master regulator of ARSI-induced LP driver network. Mechanistically, REV-ERBα induces chromatin accessibility and H3K27ac modification at promoters of LP genes through its recruitment of BRD4 and p300. Overexpression of REV-ERBα alone is sufficient to induce LP and neuroendocrine phenotype and confers resistance to ARSI in adenocarcinoma cells. Loss of REV-ERBα potently inhibits NEPC cell growth and abolishes the expression of LP drivers and gene programs. Pharmacological inhibition of REV-ERBα exhibits high potency in blocking the growth of NEPC tumors including patient-derived xenografts. Our findings reveal that therapy-induced LP development entails a coordinated induction of a network of LP drivers and that REV-ERBα is an unexpected master regulator of the network and a promising therapeutic target for treatment of advanced prostate cancer such as NEPC.

We find evidence of belief in belief—intuitive preferences for religious belief over atheism, even among atheist participants—across eight comparatively secular countries. Religion is a cross-cultural human universal, yet explicit markers of religiosity have rapidly waned in large parts of the world in recent decades. We explored whether intuitive religious influence lingers, even among nonbelievers in largely secular societies. We adapted a classic experimental philosophy task to test for this intuitive belief in belief among people in eight comparatively nonreligious countries: Canada, China, Czechia, Japan, the Netherlands, Sweden, the United Kingdom, and Vietnam (total N = 3,804). Our analyses revealed strong evidence that 1) people intuitively favor religious belief over atheism and that 2) this pattern was not moderated by participants’ own self-reported atheism. Indeed, 3) even atheists in relatively secular societies intuitively prefer belief to atheism. These inferences were robust across different analytic strategies and across other measures of individual differences in religiosity and religious instruction. Although explicit religious belief has rapidly declined in these countries, it is possible that belief in belief may still persist. These results speak to the complex psychological and cultural dynamics of secularization.

Negative or antagonistic relationships are common in human social networks, but they are less often studied than positive or friendly relationships. The existence of a capacity to have and to track antagonistic ties raises the possibility that they may serve a useful function in human groups. Here, we analyze empirical data gathered from 24,770 and 22,513 individuals in 176 rural villages in Honduras in two survey waves 2.5 y apart in order to evaluate the possible relevance of antagonistic relationships for broader network phenomena. We find that the small-world effect is more significant in a positive world with negative ties compared to an otherwise similar hypothetical positive world without them. Additionally, we observe that nodes with more negative ties tend to be located near network bridges, with lower clustering coefficients, higher betweenness centralities, and shorter average distances to other nodes in the network. Positive connections tend to have a more localized distribution, while negative connections are more globally dispersed within the networks. Analysis of the possible impact of such negative ties on dynamic processes reveals that, remarkably, negative connections can facilitate the dissemination of information (including novel information experimentally introduced into these villages) to the same degree as positive connections, and that they can also play a role in mitigating idea polarization within village networks. Antagonistic ties hold considerable importance in shaping the structure and function of social networks.