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The Large Hadron Collider (LHC) is sensitive to signals of beyond the Standard Model physics through a variety of channels including missing energy and resonance searches. In most searches, the new physics and the Standard Model backgrounds are assumed to be invariant in time, up to systematic effects from the experiment. However, new physics with a time variation would provide an additional handle to separate signal from background. Such a time variation may come from ultralight dark matter coupling to an oscillating background field. In this paper, we consider an interaction of dark matter with quarks and an additional heavy particle, and show that the sensitivity of a search that uses timing information at the LHC can be up to a factor of two stronger compared to one that does not use time information.
Abstract Honey bees can swim on the water surface toward dark regions, a behavior known as scototaxis that may facilitate escape from water. Although this behavior has been reported in both honey bees and solitary bees, variation among honey bee species remains poorly understood. We compared scototaxis during swimming in four honey bee species representing two nesting types: open-nesting ( Apis florea and A. dorsata ) and cavity-nesting ( A. cerana and A. mellifera ). Individual bees were released into a water-filled arena containing a dark sector, and their landing angles were recorded. All species exhibited significant orientation toward the dark sector. However, open-nesting species showed significantly stronger orientation than cavity-nesting species. No significant differences were detected between replicate colonies within species or between species within the same nesting type, whereas differences between nesting types were highly significant. Hierarchical clustering based on orientation strength placed Osmia , a solitary cavity-nesting bee from our previous study, in the same behavioral cluster as the two open-nesting Apis species rather than the cavity-nesting honey bees. We also measured swimming duration, distance, and velocity, but found no consistent differences between nesting types. These results demonstrate substantial interspecific variation in swimming scototaxis. The behavioral clustering is consistent with the hypothesis that strong scototaxis represents an ancestral trait that has been reduced in the derived A. cerana / A. mellifera lineage.
Background California's Inland Empire (IE) is a federally designated Health Professional Shortage Area (HPSA) with high burdens of cardiovascular disease (CVD). Given limited regional health surveillance, this study assesses spatial clustering of hypertension and high cholesterol and associations between these outcomes and neighborhood-level demographic, socioeconomic, and health characteristics in spatially adjusted models. Methods This cross-sectional ecological study uses data from the 2025 CDC Population Level Analysis and Community Estimates (PLACES) dataset and 2019-2023 American Community Survey (ACS), representing 132 ZIP Code Tabulation Areas (ZCTAs) in Riverside and San Bernardino counties. With respect to data analysis, a spatial econometric workflow, variance inflation factors (VIF), diagnostic ordinary least squares (OLS) regression, Moran's I for geospatial clustering, and spatial error models (SEM) were applied. All results in this study are presented as hypothesis-generating. Results The mean modeled prevalence across ZCTAs was 34.3% for hypertension and 36.5% for high cholesterol, with significant geospatial clustering being present for hypertension and cholesterol (I = 0.293, p < 0.001; I = 0.162, p = 0.002). Additionally, in SEM-adjusted models, both outcomes were associated with obesity (β = 0.584, p < 0.001; β = 0.308, p < 0.001) and recent checkups (β = 1.395, p < 0.001; β = 1.215, p < 0.001), and negatively associated with median income (β = -0.465, p < 0.001; β = -0.180, p < 0.001). Conclusions Modeled hypertension and high cholesterol prevalence in the IE varied by ZCTA and were spatially clustered, while obesity, income, and healthcare engagement were associated with neighborhood-level cardiovascular risk. These results support the use of spatial surveillance and may inform future public health research and practice in these medically underserved regions.
