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The differential progression of ten chronic overlapping pain conditions (COPC) and four comorbid mental disorders across demographic groups have rarely been reported in the literature. To fill in this gap, we conducted retrospective cohort analyses using All of Us Research Program data from 1970 to 2023. Separate cohorts were created to assess the differential patterns across sex, race, and ethnicity. Logistic regression models, controlling for demographic variables and household income level, were employed to identify significant sociodemographic factors associated with the differential progression from one COPC or mental condition to another. Among the 139 frequent disease pairs, we identified group-specific patterns in 15 progression pathways. Black or African Americans with a COPC condition had a significantly increased association in progression to other COPCs (CLBP- > IBS, CLBP- > MHA, or IBS- > MHA, OR≥1.25, adj.p ≤ 4.0x10^-3) or mental disorders (CLBP- > anxiety, CLBP- > depression, MHA- > anxiety, MHA- > depression, OR≥1.25, adj.p ≤ 1.9x10^-2) after developing a COPC. Females had an increased likelihood of chronic low back pain after anxiety and depression (OR≥1.12, adj.p ≤ 1.5x10^-2). Additionally, the lowest income bracket was associated with an increased risk of developing another COPC from a COPC (CLBP- > MHA, IBS- > MHA, MHA- > CLBP, or MHA- > IBS, OR≥1.44, adj.p ≤ 2.6x10^-2) or from a mental disorder (depression- > MHA, depression- > CLBP, anxiety- > CLBP, or anxiety- > IBS, OR≥1.50, adj.p ≤ 2.0x10^-2), as well as developing a mental disorder after a COPC (CLBP- > depression, CBLP- > anxiety, MHA- > anxiety, OR≥1.37,adj.p ≤ 1.6x10⁻²). To our knowledge, this is the first study that unveils the sociodemographic influence on COPC progression. These findings suggest the importance of considering sociodemographic factors to achieve optimal prognostication and preemptive management of COPCs. 

Abstract Chronic overlapping pain conditions (COPCs) affect a wide population and incur a substantial disease burden including comorbid mental disorders. Although these comorbidities have been extensively documented, the longitudinal trajectories of their onset, particularly for those with three or more conditions, have been rarely studied. We conducted retrospective cohort studies to identify statistically significant COPCs and mental health trajectories in the All of Us Research Program (AoU v8, 338,170 persons) and the Mayo Data Warehouse (3,957,444 individuals). For each trajectory, cases were matched with controls by demographic factors. Logistic regression was then applied to assess the increased likelihood of the final condition given the prior conditions in that sequence. The significant trajectories were visualized as a “pain forest.” More than 88% of trajectories identified from AoU (Male: 78, Female: 361) were reproducible in the Mayo Data Warehouse data (Male: 361, Female: 1286), with female trajectories encompassing more than 96% of the male trajectories. The findings indicate that chronic low back pain generally occurs earlier than other conditions, while fibromyalgia tends to follow other COPCs. Endometriosis seems to be associated with an increased prevalence of COPCs in women. In addition, longer trajectories are associated with a greater risk of developing additional COPC or mental condition. The results offer new insights into the progression of COPCs and associated mental conditions, which may inform healthcare providers and patients in managing and preventing exacerbation of these conditions. 

Developing prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem over hyper-parameters. This exhaustive evaluation can be time-consuming and costly. In this paper, we propose an adaptive approach to explore this space. We are exploiting the fact that often only few samples are needed to identify clearly superior or inferior settings, and that many evaluation tests are highly correlated. We lean on multi-armed bandits to sequentially identify the next (method, validation sample)-pair to evaluate and utilize low-rank matrix factorization to fill in missing evaluations. We carefully assess the efficacy of our approach on several competitive benchmark problems and show that it can identify the top-performing method using only 5-15% of the typical resources—resulting in 85-95% LLM cost savings. Our code is available at https://github.com/kilian-group/banditeval. 

Background:Achieving optimal glycemic control for persons with diabetes remains difficult. Real-world continuous glucose monitoring (CGM) data can illuminate previously underrecognized glycemic fluctuations. We aimed to characterize glucose trajectories in individuals with Type 1 and Type 2 diabetes, and to examine how baseline glycemic control, CGM usage frequency, and regional differences shape these patterns. Methods:We linked Dexcom CGM data (2015–2020) with Veterans Health Administration electronic health records, identifying 892 Type 1 and 1716 Type 2 diabetes patients. Analyses focused on the first three years of CGM use, encompassing over 2.1 million glucose readings. We explored temporal trends in average daily glucose and time-in-range values. Results:Both Type 1 and Type 2 cohorts exhibited a gradual rise in mean daily glucose over time, although higher CGM usage frequency was associated with lower overall glucose or attenuated increases. Notable weekly patterns emerged: Sundays consistently showed the highest glucose values, whereas Wednesdays tended to have the lowest. Seasonally, glycemic control deteriorated from October to February and rebounded from April to August, with more pronounced fluctuations in the Northeast compared to the Southwest U.S. Conclusions:Our findings underscore the importance of recognizing day-of-week and seasonal glycemic variations in diabetes management. Tailoring interventions to account for these real-world fluctuations may enhance patient engagement, optimize glycemic control, and ultimately improve health outcomes. 

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon ( $e$-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study $e$-ph interactions and polarons in a prototypical parent (undoped) cuprate, ${\mathrm{La}}_2{\mathrm{CuO}}_4$(LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of $e$-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong $e$-ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong $e$-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight ( $Z\approx 0.25$) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the $e$-ph coupling distribution function not explained by existing models. These results show that the universal strong $e$-ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin. 

Molecular constructs define the elementary units in porous materials for efficient CO2 capture. The design of appropriate interpore and intermolecular space is crucial to stabilize CO2 molecules and maximize the capacity. While the molecular construct usually has a fixed dimension, whether its intermolecular space could be self-adjustable during CO2 capture and release, behaving as a balloon, has captured imagination. Here we report a flexible intermolecular space of the double chain structure of self-assembled 1,4-phenylene diisocyanide (PDI) molecules on Ag(110) surface, which dynamically broadens and recovers during the CO2 capture and release. The incipient PDI double chains organize along the [001] direction of Ag(110), in which individual PDI molecules stand up in a zigzag order with the interchain width defined by twice the Ag lattice distance along [11¯0] direction (2α[11¯0]). When CO2 molecules are introduced, they assemble to occupy the interchain spaces, expanding the interchain width to 3α[11¯0], 4α[11¯0] and 5α[11¯0]. Warming up the sample leads to the thermally-driven CO2 desorption that recovers the original interchain space. High-resolution scanning tunneling microscopy (STM) jointly with density functional theory (DFT) calculations determine the structural and electronic interactions of CO2 molecules with the dynamical PDI structures, providing a molecular-level perspective for the design of a self-adjustable metal-organic construct for reversible gas capture and release. 

High energy physics experiments produce petabytes of data annually that must be reduced to gain insight into the laws of nature. Early-stage reduction executes long-running high-throughput workflows across thousands of nodes spanning multiple facilities to produce shared datasets. Later stages are typically written by individuals or small groups and must be refined and re-run many times for correctness. Reducing iteration times of later stages is key to accelerating discovery. We demonstrate our experience reshaping late-stage analysis applications on thousands of nodes. It is not enough merely to increase scale: it is necessary to make changes throughout the stack, including storage systems, data management, task scheduling, and application design. We demonstrate these changes when applied to two analysis applications built on open source data analysis frameworks (Coffea, Dask, TaskVine). We evaluate the performance of the applications on opportunistic campus clusters, showing effective scaling up to 7200 cores, thus producing significant speedup.