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Phenotypic age (PhenoAge) is a biological aging clock that estimates an individual’s biological age. However, the effect of PhenoAge acceleration (PhenoAgeAccel) on cancer is unclear. This study investigates the relationship between PhenoAgeAccel and cancer survivors. Data for this cohort study were sourced from the U.S. National Health and Nutrition Examination Survey (NHANES) spanning 1999–2018. The relationship between PhenoAgeAccel and cancer prevalence was evaluated using weighted multivariate logistic regression. Kaplan–Meier analyses and weighted multivariate-adjusted Cox analyses were conducted to examine the association between PhenoAgeAccel and all-cause as well as cancer-specific mortality in cancer survivors. Restricted cubic spline (RCS) analysis was utilized to assess nonlinear associations. Subgroup and sensitivity analyses were also performed to confirm the robustness of the findings. A total of 34,246 participants were included in our study, of which 3067 were cancer survivors (8.95% prevalence). With a median follow-up of 117 months (interquartile range: 50–155 months), there were 1161 deaths, including 351 from cancer. Weighted multivariate regression analysis revealed a significant positive association between higher PhenoAgeAccel and cancer prevalence (P for trend < 0.001). Multivariable-adjusted Cox regression analyses showed that elevated PhenoAgeAccel was significantly associated with increased all-cause and cancer-specific mortality among cancer survivors (P for trend < 0.001). RCS regression indicated no nonlinear relationship between PhenoAgeAccel and mortality outcomes (P for nonlinear relationship > 0.05). Kaplan-Meier analyses indicated a poorer prognosis with higher PhenoAgeAccel. Subgroup analyses based on tumor classification highlighted the differential prognostic impact of PhenoAgeAccel across various tumor types. Our findings reveal a significant linear correlation between PhenoAgeAccel and both all-cause and cancer-specific mortality in cancer survivors.

Traditional permanent-magnet synchronous motor (PMSM) models assume constant inductance parameters in the dq frame, attributing torque ripple solely to local non-sinusoidal disturbances while neglecting nonlinear effects like iron saturation, flux linkage spatial harmonics, and inter-axis mutual coupling. These simplifications limit such models to predicting average torque but fail to capture harmonic components. To overcome these limitations, this study develops a nonlinear PMSM model using differential inductance theory and constructs a physics-informed neural network (PINN) surrogate trained on finite-element data. The proposed hybrid framework demonstrates high-fidelity torque prediction, validated against finite-element simulations, and provides insights into harmonic generation mechanisms under saturation and spatial field distortions.

Although there has been considerable debate about whether paternal mitochondrial DNA (mtDNA) transmission may coexist with maternal transmission of mtDNA, it is generally believed that mitochondria and mtDNA are exclusively maternally inherited in humans. Here, we identified three unrelated multigeneration families with a high level of mtDNA heteroplasmy (ranging from 24 to 76%) in a total of 17 individuals. Heteroplasmy of mtDNA was independently examined by high-depth whole mtDNA sequencing analysis in our research laboratory and in two Clinical Laboratory Improvement Amendments and College of American Pathologists-accredited laboratories using multiple approaches. A comprehensive exploration of mtDNA segregation in these families shows biparental mtDNA transmission with an autosomal dominantlike inheritance mode. Our results suggest that, although the central dogma of maternal inheritance of mtDNA remains valid, there are some exceptional cases where paternal mtDNA could be passed to the offspring. Elucidating the molecular mechanism for this unusual mode of inheritance will provide new insights into how mtDNA is passed on from parent to offspring and may even lead to the development of new avenues for the therapeutic treatment for pathogenic mtDNA transmission.

The subsidence caused by coal mining could cause the destruction of roads and houses, and even the failure of infrastructures. Understanding of the mechanism of coal mining subsidence may provide early protecting to infrastructures on coming failure, but dynamic analysis of subsidence due to coal mining is currently needed. In this study we apply particle image velocimetry (PIV) method to reveal strata movement and subsidence according to the prototype and indoor physical model similarity experiment of Henan. Our result shows magnitude of the subsidence of overlying strata during the coal mining at different excavation thickness, that more coal mining thickness may produce more subsidence, and that shallower coal may cause more significant subsidence. Our result suggests that further PIV test combined with field monitoring data may be an effective measure to study subsidence mechanism and pattern helping to predict disaster caused by subsidence.

To address the insufficient accuracy of conventional permanent magnet synchronous motor (PMSM) models caused by neglecting magnetic saturation nonlinearity and periodic parameter disturbances, a nonlinear motor system model integrating a Physics-Informed Neural Network (PINN) is developed. By exploiting the differential relationships among incremental inductance, flux linkage, and magnetic energy, the voltage and torque equations considering rotor position variation are derived, and analytical expressions for the derivatives of incremental inductances are obtained. To reduce the computational burden of PINN in system-level simulations, linear and nonlinear approximation strategies based on incremental inductances and their derivatives are proposed, which significantly reduce the frequency of PINN calls while maintaining model accuracy. CPU/GPU collaborative computation and cross-frequency-domain scheduling are further implemented to improve simulation efficiency. Considering the influence of the test bench mechanical dynamics, an electromechanical–magnetic coupled simulation model is established. The accuracy of the proposed nonlinear motor model is validated through two-phase short-circuit tests as well as simulations and test bench experiments under sinusoidal and non-sinusoidal excitations. The results demonstrate that the proposed model accurately captures the nonlinear electromagnetic characteristics of PMSMs while significantly improving system simulation efficiency.

In this paper, Nostoc commune crude polysaccharide was extracted by heating and Ultrasonic-assisted methods separately, homogeneous polysaccharide HNCP3 and UNCP4 were obtained after purified by DEAE-52 cellulose column chromatography and Sephacryl G-100 gel column chromatography. The structures of HNCP3 and UNCP4 were characterized by molecular weight determination, infrared spectroscopy, DSC detection, sodium periodate oxidation, smith degradation reaction and methylation analysis. The conformation of the solution was studied by SEM and AFM. The results showed that the Ultrasonic-assisted extraction had effects on the molecular weight, monosaccharide composition, molar ratio and configuration of Nostoc commune . The main chain of HNCP3 and UNCP4 was → 6)-D-Glcp(1→ and → 2, 6)-D-Glcp, but UNCP4 contained 1, 2, 6-galactose and 2, 3-Me2-D-Ara branches, while HNCP3 did not. The results of the monosaccharides composition of indicated that mannose was presented in both HNCP3 and UNCP4. SEM and AFM showed that the structure of UNCP4 was helical, and the solution conformations of HNCP3 and UNCP4 were different in different solution environments. Studies on DPPH radicals, superoxide anions, and hydroxyl radicals scavenging abilities showed that UNCP4 had higher antioxidant activity, while studies on the antidiabetic activities showed that the hypoglycemic effect of UNCP4 was stronger than that of HNCP3. Therefore, Ultrasonic-assisted extraction (UAE) increases the bioactivity of Nostoc commune polysaccharide (NCP) as well as the extraction rate.

The Advanced Lung Cancer Inflammation Index (ALI) represents both the inflammatory and nutritional status of the host, but its link with mortality in asthma patients is uncertain. The purpose of this study was to look at the relationship between ALI levels and all-cause and respiratory disease mortality in asthmatic patients. We conducted our research using cohort data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The National Death Index was used to calculate mortality until December 31, 2019. The study employed multivariate logistic regression to look into the relationship between ALI levels and asthma prevalence. Weighted Kaplan–Meier and multivariate-adjusted Cox analyses were utilized for investigating the relationship between ALI levels and all-cause and respiratory disease mortality in individuals with asthma. A restricted cubic spline (RCS) analysis was used to assess their nonlinear relationship. Subgroup and sensitivity analyses were also performed to evaluate the robustness of the results that were obtained. We enrolled 40,497 people in our study, and 5,469 of them had asthma, representing a 14% prevalence. A median follow-up of 11.19 (9.38, 14.29) years revealed 109 fatalities from respiratory diseases and 724 deaths from all causes. After correcting for several covariates, there was no longer any link (P-trend = 0.2) between ALI levels and the prevalence of asthma. When compared to the lowest quartile, the highest quartile of ALI levels was substantially linked to a lower risk of mortality from respiratory diseases and all causes (all P-trend < 0.001). In the RCS regression model, the relationship between ALI level and both all-cause and respiratory disease mortality in asthmatic participants was nonlinear, with P for nonlinearity of 0.006 and 0.015, respectively. We also discovered that the probability of mortality from respiratory disease decreased progressively to a nadir at an ALI level of 109.13 and then increased as the ALI level increased. Multiple subgroup and sensitivity analyses revealed that ALI was consistently related to lower all-cause and respiratory disease mortality in asthma patients. Our findings suggest that ALI is associated with a reduced risk of all-cause and respiratory disease mortality in asthma patients.

Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we propose a framework for estimating the temperature of a permanent magnet synchronous motor by combining neural networks with the differentiable physical thermal model, as well as utilizing the simulation results. In detail, we first implement a differentiable thermal model based on a lumped parameter thermal network within an automatic differentiation framework. Subsequently, we add a neural network to predict thermal resistances, capacitances, and losses in real time and utilize the thermal parameters’ optimized empirical values as the initial output values of the network to improve the accuracy and robustness of the final temperature estimation. We validate the conceivable advantages of the proposed method through extensive experiments based on both synthetic data and real-world data and then provide some further potential applications.

Human activity is the major factor driving the wetland degradation in shallow lakes. Human exploitation of lake wetlands alters the habitats of wintering waterbirds, and, in turn, waterbird diversity in the shallow lakes. In the present study, we surveyed species composition, abundance, and habitat characteristics of waterbirds in three types of wetland habitats (natural lakeside wetlands, paddy fields, and aquaculture ponds) at Caizi Lake, a shallow lake in the middle and lower Yangtze River during the wintering period, and investigated the effects of habitat change driven by human activity on the diversity of wintering waterbirds. There were significant differences in species composition and abundance among the three wetland habitats (natural lakeside wetlands, aquaculture ponds, and artificial paddy fields); however, there were no significant differences among the habitats with respect to the number of waterbirds. The numbers of overwintering waterbird species and waterbird individuals in aquaculture ponds and lakeside wetlands were significantly higher than the numbers in the paddy fields, indicating that wintering waterbirds prefer natural lake wetlands and aquaculture ponds. Principal component analysis of the three wetland habitat types revealed that factors influencing waterbird diversity include wetland area, vegetation cover, water level, and degree of human interference. Therefore, minimizing human interference and ensuring suitable habitats at specific periods could facilitate the maintenance of waterbird diversity.

The 2030 Agenda for Sustainable Development issued by the United Nations is an important foundation for countries to achieve common economic, social and environmental development. Important progress has been made in the evaluation of the Sustainable Development Goals (SDGs) in Hainan Island; nevertheless, there is still a lack of understanding around the trade-offs and synergies between the SDGs. Studying the trade-offs and synergies between Hainan Island’s sustainable development goals is of great significance for the coordinated development of these goals and the promotion of the construction of free trade ports. Therefore, based on the United Nations Sustainable Development Assessment System and the existing SDG indicator system on Hainan Island, this paper identifies and quantifies the trade-offs and synergies within and between SDGs and targets on the county scale. Based on the different impacts of different spatial, dimensional and geographical directions, the results show the following: (1) Hainan Province made good progress on multiple SDGs between 2010 and 2021. (2) The most significant synergies between SDGs exist between SDG1 (No Poverty) and SDG10 (Reduce Inequalities), while the most significant trade-offs exist between SDG2 (Zero Hunger) and SDG4 (Quality Education). (3) Obvious spatial characteristics in trade-offs and synergies exist, with the highest level of synergy being in the Haikou and Sanya Economic Circles and their surrounding areas, and in the central region of Hainan Island which has a higher level of trade-offs. (4) The synergistic effect between the SDG targets and indicators in Hainan is much greater than the trade-off effect: the four aspects of people’s livelihood improvement, economic development, resource utilization and environmental quality all show synergistic effects in different regions.