To investigate the effect of R-R interval (RRI) normalization using different scaling factors ([Formula: see text]) on frequency-domain heart rate variability (HRV) parameters during sleep, and determine whether normalization improves the differentiation of sleep stages and enhances the accuracy of slow-wave sleep (SWS) detection. We analyzed the overnight electrocardiogram data of 130 participants. RRIs were normalized using a proposed z-score method with scaling factors (n = 1-4 in [Formula: see text]). Frequency-domain HRV parameters were extracted using raw and normalized RRIs within a 5-min time window shifted by 30 s. HRV parameters were statistically compared across sleep stages (wakefulness, light sleep, SWS, and rapid eye movement sleep), and the SWS detection performance was evaluated using receiver operating characteristic analysis. RRI normalization significantly magnified differences in HRV parameters across sleep stages. HF, which reflects parasympathetic activity, showed a markedly clearer separation between SWS and other stages with normalization (p < 0.05) than with the raw RRI. Consequently, the SWS detection performance was significantly improved. The area under the curve for HF-based SWS detection increased from 0.50 (raw RRI) to a maximum of 0.86 (with [Formula: see text] normalization). HRV parameters derived from normalized RRI reveal the hierarchical organization of autonomic nervous activity across sleep stages, particularly parasympathetic dominance in SWS. This method significantly improves SWS detection accuracy and provides a robust foundation for refined HRV-based sleep monitoring systems. It can be utilized in real-time health monitoring systems to accurately assess sleep stages, stress, and fatigue levels.

Precise prediction of cancer therapy response remains challenging because conventional biomarkers capture molecular features but overlook the physical state of tumors. We developed a multimodal deep-learning framework integrating ultrasound shear wave elastography (SWE) images with quantitative stiffness measurements (elastic modulus, kPa) to predict treatment outcomes in preclinical murine tumors. Each image-stiffness pair is tokenized within a transformer that learns interactions between local elastographic texture and global rigidity. A lightweight convolutional encoder extracts image features, the modulus is embedded as a numeric token, and self-attention fuses both modalities for classification. Trained on 1578 baseline SWE images from five syngeneic tumor models, the model classified tumors as responders, stable, or non-responders. Across five random seeds, it achieved 92.4% ± 1.3% accuracy, macro-F1 0.92, and ROC-AUC 0.99 on a held-out test set, with well-calibrated probabilities. Matched-split ablations-image-only, stiffness-token-removed, stiffness-shuffled, late-fusion, and stiffness-only-showed that performance reflected genuine cross-modal learning rather than scalar stiffness alone; shuffling image-stiffness pairings significantly reduced accuracy (all corrected p < 0.01). Leave-one-tumor-model-out analysis demonstrated generalization to unseen tumor types, with 95.5% ± 1.5% accuracy (range 93.9-97.9%) across five held-out models. Lower baseline stiffness correlated with better response, supporting the hypothesis that mechanically normalized tumors respond more effectively. These preclinical proof-of-concept findings establish tumor mechanics as candidate predictive biomarkers and transformer-based multimodal learning as a scalable approach to biomechanically informed response prediction, while requiring validation in human cohorts.

Cellular respiration depends on the rapid, lateral flow of protons along the inner mitochondrial membrane to drive ATP synthesis. The precise nanoscale thermodynamic forces confining protons to this "local circuit" remain highly debated. Previous attempts to model macroscopic interfacial proton diffusion have been hindered by parameter equifinality and geometric artifacts, preventing the deconvolution of structural water networks from lipid electrostatics. Here, we address this ambiguity using a constrained, high-resolution two-dimensional continuum model. By incorporating experimentally validated buffer proton consumption rates as strict biological priors, we break mathematical degeneracy and isolate the specific effective spatial components of planar lipid bilayers. Calibrating our model against time-resolved DOPG fluorescence kinetics, we decouple a universal structural water barrier (5.7kBT) from the specific -1e electrostatic trap (4.3kBT). Extrapolating these first principles, we predict the confinement architecture of cardiolipin, the signature -2e dimeric lipid of mitochondria. Our simulations reveal a deep 14.3kBT phenomenological potential well. Crucially, this massive barrier confines protons within 1 to 2 nanometres of the membrane surface, virtually abolishing vertical leakage into the bulk aqueous phase of the inter-membrane space. Our simulations suggest that this spatial confinement triggers "dimensional squeezing", preserving a robust lateral concentration gradient that actively accelerates radial proton wave propagation. Biologically, these findings reveal that cardiolipin does not merely prevent proton dissipation, we predict it functions as a highly efficient, quasi-two-dimensional nanoscale antenna that captures and rapidly channels protons directly to ATP synthase, ensuring the kinetic viability of eukaryotic energy production.

Chronic pain care involves interactions with multiple healthcare providers, yet how individuals with chronic pain perceive empathy across provider types, and whether associations between these perceptions and patient outcomes differ by patient gender, remain poorly understood. This prospective observational study examined perceived empathy across provider types, its associations with treatment satisfaction and psychosocial outcomes, and the moderating role of patient gender. A secondary analysis was conducted on 2,309 provider ratings from 231 individuals with chronic pain (77.9% women; Mage = 49.5 years; all cisgender) across four waves administered approximately three months apart. At each wave, participants rated all providers consulted in the preceding three months. Multilevel models decomposed perceived empathy into between- and within-person components. Perceived empathy differed significantly across provider types, with psychologists rated highest and orthopedists and rheumatologists rated lowest. No significant gender difference in mean perceived empathy was observed. Both between-person and within-person empathy were positively associated with treatment satisfaction. A crossover interaction indicated that at lower empathy levels, men reported greater reductions in treatment satisfaction than women. These findings highlight the importance of consistent empathic engagement across multidisciplinary chronic pain teams and suggest that gender-sensitive communication approaches may be warranted. PERSPECTIVE: This study shows that perceived empathy varies meaningfully across healthcare provider types in chronic pain care, with psychologists rated highest, and that lower empathy is associated with reduced treatment satisfaction, particularly among men. Consistent empathic engagement across multidisciplinary teams may support more equitable, patient-centered chronic pain care.

We put forward the characteristic equation of Stoneley waves along wavy interfaces, and calculate the dispersion curves, wave structures and cutoff frequencies. After confirming the existence of Stoneley waves at flat interfaces, by incorporating wavy boundary conditions, we derive the governing equation and reveal unique propagation behaviors, specifically a velocity reduction followed by an increase at high frequencies. Furthermore, the cutoff frequency is directly proportional to the Stoneley wave velocity and inversely proportional to the wavy period. The results indicate that the stable Stoneley wave propagation is confined to frequencies below the cutoff limit and to interfaces with a relatively low steepness. Finite element simulations are performed, and four different experimental samples are employed to verify the existence and characteristics of Stoneley waves such as dispersion curves and cutoff frequencies. The findings significantly advance the understanding of Stoneley wave propagation characteristics in wavy interfaces and establish a robust theoretical foundation for nondestructive evaluation in structures with wavy interfaces.

Algorithmic control (AC) embedded in food-delivery platforms is conceptualized as a persistent, digitally mediated job demand associated with greater algorithmic stress (AS) and psychological detachment failure (PDF), thereby contributing to work-life conflict (WLC) among food-delivery workers. The study employed a two-wave time-lagged longitudinal design involving 315 employees from prominent Indian cities. It examined the relationships among AC, AS, PDF and WLC. Confirmatory factor analysis and structural equation modeling were used to assess the measurement model and the proposed structural relationships among the constructs. The findings indicate that AC is positively associated with heightened stress and reduced ability to disengage from work during non-work hours. The specific sequential indirect effect of AC on WLC through AS and PDF was significant, while the direct association between AC and WLC remained significant, indicating partial sequential mediation. The findings highlight the importance of reducing uncertainty surrounding algorithmic evaluations, ratings and incentives that can keep work-related concerns active after working hours and hinder psychological detachment.

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent mental health disorder diagnosed in childhood in the United States, and evidence on its long-term economic consequences has largely been established using diagnosis-based measures. This paper examines how a childhood ADHD diagnosis affects adult labor market outcomes, focusing on disentangling employment effects from earnings effects and addressing sources of bias in self-reported diagnoses. Using data from Wave IV (2008-2009) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), comprising 8570 participants, we implement a framework that simultaneously accounts for non-random selection into employment and measurement error arising from self-reported ADHD. Our results show that childhood ADHD substantially reduces employment probabilities in early adulthood. In contrast, once both endogeneity and selection are properly addressed, the estimated effect of ADHD on earnings conditional on employment is substantially attenuated and precisely estimated near zero. Moreover, the employment penalties in early adulthood appear less pronounced later in the life course, suggesting that the economic costs of ADHD are concentrated at labor market entry rather than persisting throughout adulthood. These findings highlight the importance of accurately measuring mental health conditions when estimating their long-term economic impact. They further suggest that policies aimed at early and systematic detection, access to care, and targeted labor-market support may yield substantial economic benefits by mitigating employment disadvantages that emerge at the transition from education to work.

Background: Depressive symptoms may differ in both severity and configuration. This study identified depressive symptom profiles in adults aged 50 years and older and examined their external correlates and prospective cognitive and functional associations.Methods: We analyzed Health and Retirement Study person-wave observations from 2006, 2010, 2014, and 2018 linked to outcomes four years later. Eight binary indicators from the Center for Epidemiologic Studies Depression Scale (CES-D) were used to estimate latent class models. External correlates were examined using corrected three-step models, and longitudinal outcomes using posterior-probability weighting and Bolck-Croon-Hagenaars (BCH) correction.Results: The sample included 68,854 person-wave observations from 28,042 individuals. A six-class solution was retained: low depressive symptoms, somatic-motivational, sadness-loneliness-sleep, somatic-affective, depressed-sadness, and generalized high depressive symptoms. All symptomatic profiles were associated with lower future cognition and greater activities of daily living (ADL) and instrumental activities of daily living (IADL) limitations. After adjustment for total CES-D severity, global class membership remained associated with all three outcomes, with stronger evidence for functional outcomes.Conclusion: Depressive symptom configuration may provide information complementary to overall symptom burden, although the profiles should be interpreted as probabilistic descriptive patterns rather than diagnostic categories.

The photochemical formation of carbenes from 3-phenyl-3-(trifluoromethyl)-3H-diazirine (1) and its diazo isomer, phenyl-trifluoromethyl-diazomethane (2), was investigated using high-level multiconfigurational wave function methods (MS-CASPT2) and density functional theory (DFT). Vertical excitation energies and the vibronic absorption spectrum of 1 were computed, confirming that the experimental signals reported in the literature originate from a single conformer. Our results demonstrate that S 1 excitation in both isomers leads to ultrafast nonradiative deactivation through a shared S 1/S 0 conical intersection (CI1). In the condensed phase, this relaxation triggers a branching mechanism: the system can either revert to the precursor, undergo diazirine-diazo isomerization, or fragment into N 2 and a closed-shell singlet carbene. Notably, the topology of the ground-state potential energy surface suggests that CI1 is a primary connection between the two isomers, relegating a conventional transition-state-mediated pathway to a less likely alternative for isomerization. Furthermore, a comparative assessment shows that both MS-CASPT2 and DFT provide consistent descriptions of the critical S 1/S 0 and S 0/T 0 crossing geometries. These findings provide a comprehensive mechanistic framework for the photolysis of aryl-trifluoromethyl diazirines.

Background: Rising temperatures and the frequency of heat waves are challenges that have an impact on the health and mortality of populations. A high level of greenness is emerging as a strategy to mitigate the effects of extreme heat. Objective: To assess whether higher levels of greenness are associated with reduced mortality compared with lower greenness during periods of extreme heat. Methods: In this systematic review and meta-analysis, we searched MEDLINE, SCOPUS, WEB OF SCIENCE, CINHAL, Environment Complete, and GreenFILE. Observational studies were included. Pooled risk ratios (RRs) with 95% confidence intervals (CIs) were calculated via a random-effects model. Results: The systematic review and meta-analysis included 11 articles. These cover data from China, France, Germany, Jordan, Switzerland, and the USA, as well as a global analysis covering 24 countries. The meta-analysis suggested that heat waves significantly increased mortality in areas with the lowest greenness (RR = 1.33; 95% CI: 1.15-1.55; I 2 = 99.4%) and in areas with the highest greenness (RR = 1.18; 95% CI: 1.03-1.36; I 2 = 97.9%). The pooled ratio of RR (RoR) indicated that the association was significantly stronger in low-greenness areas than in high-greenness areas (RoR = 1.11; 95% CI: 1.06-1.15; I 2 = 90.5%), indicating that the heat-mortality association was 11% stronger in low-greenness areas. The pooled associations varied descriptively across subgroup analyses, although no statistically significant differences were observed between geographical regions or study settings. Conclusion: Higher temperatures were associated with increased mortality in both low- and high-greenness areas; however, the heat-mortality association was 11% stronger in low-greenness areas. These findings suggest that greater greenness may attenuate vulnerability to extreme heat, although the limited evidence base and considerable between-study heterogeneity warrant cautious interpretation.