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Electricity price forecasting with ensemble meta-models and SHAP explainers: a PCA-driven approach
Investigating the effects of cannabinoids for the reduction of inflammation and sickle cell disease pain (CRISP); A protocol for a randomized double-blind placebo-controlled study
Sickle Cell Disease (SCD) is a hemoglobinopathy affecting millions of people globally. Pain, both acute and chronic, affects over half of those living with SCD, but treatment of chronic pain is an ongoing challenge. While opioid treatments are widely used for chronic pain, it’s efficacy is limited, so alternatives must be explored. This protocol outlines a procedure for investigation of dronabinol, an FDA-approved synthetic tetrahydrocannabinol (THC), for the treatment of pain in patients living with SCD and chronic pain. The study is an 8-week, randomized, double-blind placebo-controlled study which aims to assess both the efficacy and safety of this opioid alternative to pain treatment. The study will also track biomarkers of inflammation as THC has demonstrated anti-inflammatory properties, and inflammation is a driver of SCD pain and disease severity. Results from this study have the potential to further clinical understanding of cannabinoids for pain management in Sickle Cell Disease treatment and spark new questions for research.
Temperature-dependence of charge and exciton transport in one-dimensional systems subject to static and dynamic disorder
The temperature dependence of dynamical properties (e.g., the asymptotic diffusion coefficient and the subdiffusive exponent) is calculated for charges and excitons in one-dimensional systems subject to static and dynamic disorder. These properties are determined by three complementary methods. One approach is based on the time integration of the velocity autocorrelation function. The second approach is based on the mean-squared displacement of thermal wave packets subject to stochastic collapse via Lindblad jump operators. These two methods are applicable in the high-temperature regime, where the noise is temporally uncorrelated. In this regime, the noise causes particle localization, and the transport is diffusive. The third approach—applicable in the low-temperature regime—is weak-coupling Redfield theory. Here, static disorder causes Anderson localization. When the dynamics is diffusive, the diffusion coefficient is a nonmonotonic function of temperature, increasing with temperature in the low-temperature Environment-Assisted Quantum Transport (ENAQT) regime and decreasing with temperature in the high-temperature Quantum-Zeno (QZ) regime. For any temperature, static disorder decreases the diffusion coefficient. Increasing the dephasing factor increases the diffusion coefficient in the ENAQT regime, whereas the diffusion coefficient decreases in the QZ regime. The dynamics is nondiffusive for thermal energies deep within the manifold of local ground states, where the subdiffusive exponent decreases with increasing disorder and decreasing temperature.
Linkage-Editing of β-Glucosylceramide and β-Glucosylcholesterol: Development of β-Selective <i>C</i> -Glucosylation and Potent Mincle Ligands
Rubber-like DNA hydrogel enabled by fast-shrinking-induced entanglement
Determinants of cervical cancer screening among women living with HIV in Lesotho using nationally representative 2023/24 DHS data
Abstract Women living with HIV are more prone to develop cervical cancer since they have a compromised immune system; hence, they need to be screened continuously in an attempt to identify and prevent it. Despite Lesotho’s high HIV prevalence (25.6%), cervical cancer screening coverage and its determinants among women living with HIV remain insufficiently characterized. This study aimed to quantify the rate and determinants of cervical cancer screening among women living with HIV using the 2023/24 Lesotho DHS data. Cross-sectional analysis was performed using the Lesotho DHS Individual Women’s Recode file. A weighted sample of 611 HIV-positive women aged 25 years and older participated in the study, as this age group is eligible for cervical cancer screening. Individual and community-level factors were determined using multilevel mixed-effects logistic regression. The level of significance was determined by the 95% confidence interval and a p value less than 0.05 for associations. The total prevalence of cervical cancer screening among women living with HIV was 85.4%. Women aged 40–44 years (adjusted odds ratio [AOR] 4.14; 95% confidence interval [CI] 1.53–11.18) and those who had a clinical breast exam (AOR 5.53; 95% CI 2.54–12.05) were more likely to undergo cervical cancer screening, whereas low parity (AOR 0.19; 95% CI 0.05–0.78) and rural residence (AOR 0.50; 95% CI 0.25–0.99) were associated with lower odds of screening. Adoption of cervical cancer screening among women living with HIV in Lesotho is high, with most screened women receiving normal results. Screening uptake varied by demographics, being higher among older women and those who had breast examinations, while lower among women with low parity and rural residents. Integration of breast and cervical cancer screening, rural outreach targeting, and health education for low-parity women can increase coverage and equity.
Lifestyle and environmental risk factors associated with cancer: A case-control study in Bangladesh
Cancer remains the second leading cause of death worldwide, with cases rising at an alarming rate. While the causes of cancer are complex and varied, certain risk factors - such as exposure to environmental pollutants and specific lifestyle choices - are modifiable and can be addressed. A case-control study was conducted in Bangladesh from 25 August 2023 to 18 April 2024 to examine the association between cancer risk and a range of lifestyle and environmental factors. The study specifically focused on six common cancer types: breast, hematological, oral, cervical, colorectal, and lung cancer. This study identified several lifestyle and environmental factors positively associated with cancer risk. Individuals using wood or kerosene for cooking had higher odds of cancer compared to those using supplied gas (AOR = 3.886). Consumption of overcooked or poorly cooked food was associated with an increased risk of cancer compared to the consumption of well-cooked food (AOR = 2.478). Oral hygiene also showed a relationship, with participants brushing their teeth only 2-3 times a week having a higher chance of cancer compared to those who brush regularly (AOR = 3.103). In addition, frequent exposure to mosquito repellent was positively associated with cancer risk (AOR = 1.569), and exposure to inorganic dust showed a similar association (AOR = 1.673). These findings highlight modifiable lifestyle and environmental factors that could inform future cancer prevention strategies in Bangladesh.
Excited states in auxiliary field quantum Monte Carlo
We systematically investigate the calculation of excited states in quantum chemistry using auxiliary field quantum Monte Carlo (AFQMC). Symmetry allows targeting of the lowest triplet excited states in AFQMC based on restricted open-shell determinants, effectively as a ground-state calculation. For open-shell singlet states, excited-state calculations can be stabilized with the appropriate trial states, but their quality can have a larger effect on the accuracy in AFQMC. We find that active space-based configuration interaction trial states are often not sufficient to obtain accurate results. We instead use truncated equation of motion coupled cluster with single and double excitations (EOM-CCSD) as trial states. We benchmark the performance of these calculations on a set of small and medium molecules and polyacenes, focusing on predominantly single excitations. We find that the AFQMC results, obtained at a per-sample cost scaling of O(N6), are systematically more accurate than those obtained using EOM-CCSD, reducing excitation energy errors by approximately half for open-shell singlets. In regimes where EOM-CC triples are impractical, these results position AFQMC as a scalable, higher-accuracy complement for low-lying excited states.
Stability and Degradation-based Proteome Profiling Reveals Cannabidiol as a Promising CDC123-eIF2γ Inhibitor for Colorectal Cancer Therapy
Cost-effectiveness of a smart pillbox intervention for adherence to oral HIV pre-exposure prophylaxis
Application of hierarchical self-supervised contrastive learning in domain adaptation matching of multimodal remote sensing image
Correction: Does papillary muscle free strain has predictive value in risk stratification of patients with hypertrophic cardiomyopathy?
A physics-informed long-range polarizable potential based on deep learning
Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic correlations, which are ubiquitous in polar and biomolecular systems. We present a physics-informed machine-learning interatomic potential that incorporates long-range electrostatic interactions through a polarizable framework. Our model combines two equivariant message-passing neural networks: one for short-range interactions and the other for environment-dependent atomic dipoles. The model is trained not only on energies and forces but also on Born effective-charge tensors, enabling accurate predictions of field-induced properties such as infrared absorption spectra and LO–TO phonon splittings. We validate the method on ionic solids (NaCl), liquid water, and halide perovskites (MAPbI3), demonstrating improved modeling of long-range polarization effects while maintaining competitive accuracy in energy and force predictions. Our results highlight the necessity of explicit long-range electrostatics for capturing collective phenomena in insulating and polar materials.
Polarization-modulated programmable photovoltaic performance of a designed ferroelectric heterojunction
Cognitive models facilitate real-time inference of latent motives
Abstract The ability to continuously make inferences about another person’s latent states from their behavior is integral to how people behave in social situations, yet is lacking from most artificial intelligence (AI) systems. The present study tests the capacity of cognitive models to assess latent motives in real time by evaluating different deep neural networks trained to infer a human player’s intent during a continuous control task. These networks were trained by (a) directly using observable information or (b) selecting important features by estimating the parameters of a generative model of movement behavior inspired by approach-avoidance theory. Comparisons of classifier accuracy suggest that latent model parameters predict a participant’s intent at a level exceeding human performance. Furthermore, classifier performance was best when model-based inferences were combined with summary statistics about behavior, yielding faster and more stable network training compared to networks that had no manual feature extraction. Equipping AI with cognitive models is a promising avenue for developing explainable, accurate, and trustworthy systems.
Correction: Toxicity of tributyltin to the European flat oyster Ostrea edulis: Metabolomic responses indicate impacts to energy metabolism, biochemical composition and reproductive maturation
Gauge invariance and hyperforce correlation theory for equilibrium fluid mixtures
We formulate gauge invariance for the equilibrium statistical mechanics of classical multi-component systems. Species-resolved phase space shifting constitutes a gauge transformation, which we analyze using Noether’s theorem and shifting differential operators that encapsulate the gauge invariance. The approach yields exact equilibrium sum rules for general mixtures. Species-resolved gauge correlation functions for the force–force and force–gradient pair correlation structure emerge on the two-body level. Exact 3g-sum rules relate these correlation functions to the spatial Hessian of the partial pair distribution functions. General observables are associated with hyperforce densities that measure the covariance of the given observable with the interparticle, external, and diffusive partial force density observables. Exact hyperforce and Lie algebra sum rules interrelate these correlation functions with each other. The practical accessibility of the framework is demonstrated for binary Lennard-Jones mixtures using both adaptive Brownian dynamics and grand canonical Monte Carlo simulations. In particular, we investigate the force–force pair correlation structure of the Kob–Andersen bulk liquid and we show results for representative hyperforce correlation functions in the symmetrical mixture of Wilding et al. confined between two asymmetric planar parallel walls.
Torsional Flexibility Tuning of Hexa-Carboxylate Ligands to Unlock Distinct Topological Access to Zirconium Metal–Organic Frameworks
Lipid Nanoparticle Database towards structure-function modeling and data-driven design for nucleic acid delivery
Abstract Lipid nanoparticles (LNPs) are the leading nonviral nucleic acid delivery technology, but LNP structure-function data remains fragmented and nonstandardized. Unlike protein engineering which is anchored by the centralized Protein Data Bank, the LNP field lacks a unified repository for systematic analysis. To address this, we develop Lipid Nanoparticle Database (LNPDB) ( https://lnpdb.molcube.com ), an integrated database and web tool that consolidates structural and functional data for 19,528 LNPs. LNPDB standardizes LNP featurization by encoding lipid composition, experimental methods, and functional results, and generates CHARMM force field files for constituent lipids to enable molecular dynamics simulations. LNPDB also supports future data contributions for continued growth. We examine the utility of LNPDB through two applications: advancing our deep learning model for predicting LNP delivery performance, and simulating bilayer dynamics to identify structural features – bilayer stability and critical packing parameter – that correlate with LNP delivery performance. Altogether, LNPDB provides the digital framework for LNP modeling and data-driven rational design.