Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Single-cell transcriptome analysis reveals tumoral microenvironment heterogenicity and hypervascularization in human carotid body tumor.

Carotid body tumor (CBT) is a rare neck tumor located at the adventitia of the common carotid artery bifurcation. The prominent pathological features of CBT are high vascularization and abnormal proliferation. However, single-cell transcriptome analysis of the microenvironment composition and molecular complexity in CBT has yet to be performed. In this study, we performed single-cell RNA sequencing (scRNA-seq) analysis on human CBT to define the cells that contribute to hypervascularization and chronic hyperplasia. Unbiased clustering analysis of transcriptional profiles identified 16 distinct cell populations including endothelial cells (ECs), smooth muscle cells (SMCs), neuron cells, macrophage cells, neutrophil cells, and T cells. Within the ECs population, we defined subsets with angiogenic capacity plus clear signs of later endothelial progenitor cells (EPCs) to normal ECs. Two populations of macrophages were detectable in CBT, macrophage1 showed enrichment in hypoxia-inducible factor-1 (HIF-1) and as well as an early EPCs cell-like population expressing CD14 and vascular endothelial growth factor. In addition to HIF-1-related transcriptional protein expression, macrophages1 also display a neovasculogenesis-promoting phenotype. SMCs included three populations showing platelet-derived growth factor receptor beta and vimentin expression, indicative of a cancer-associated fibroblast phenotype. Finally, we identified three types of neuronal cells, including chief cells and sustentacular cells, and elucidated their distinct roles in the pathogenesis of CBT and abnormal proliferation of tumors. Overall, our study provided the first comprehensive characterization of the transcriptional landscape of CBT at scRNA-seq profiles, providing novel insights into the mechanisms underlying its formation.

Highly diverse sputum microbiota correlates with the disease severity in patients with community-acquired pneumonia: a longitudinal cohort study.

BACKGROUND: Community-acquired pneumonia (CAP) is a common and serious condition that can be caused by a variety of pathogens. However, much remains unknown about how these pathogens interact with the lower respiratory commensals, and whether any correlation exists between the dysbiosis of the lower respiratory microbiota and disease severity and prognosis. METHODS: We conducted a retrospective cohort study to investigate the composition and dynamics of sputum microbiota in patients diagnosed with CAP. In total, 917 sputum specimens were collected consecutively from 350 CAP inpatients enrolled in six hospitals following admission. The V3-V4 region of the 16 S rRNA gene was then sequenced. RESULTS: The sputum microbiota in 71% of the samples were predominately composed of respiratory commensals. Conversely, 15% of the samples demonstrated dominance by five opportunistic pathogens. Additionally, 5% of the samples exhibited sterility, resembling the composition of negative controls. Compared to non-severe CAP patients, severe cases exhibited a more disrupted sputum microbiota, characterized by the highly dominant presence of potential pathogens, greater deviation from a healthy state, more significant alterations during hospitalization, and sparser bacterial interactions. The sputum microbiota on admission demonstrated a moderate prediction of disease severity (AUC = 0.74). Furthermore, different pathogenic infections were associated with specific microbiota alterations. Acinetobacter and Pseudomonas were more abundant in influenza A infections, with Acinetobacter was also enriched in Klebsiella pneumoniae infections. CONCLUSION: Collectively, our study demonstrated that pneumonia may not consistently correlate with severe dysbiosis of the respiratory microbiota. Instead, the degree of microbiota dysbiosis was correlated with disease severity in CAP patients.

Interactions between inhalable aged microplastics and lung surfactant: Potential pulmonary health risks.

The relationship between microplastics (MPs) and human respiratory health has garnered significant attention since inhalation constitutes the primary pathway for atmospheric MP exposure. While recent studies have revealed respiratory risks associated with MPs, virgin MPs used as plastic surrogates in these experiments did not represent the MPs that occur naturally and that undergo aging effects. Thus, the effects of aged MPs on respiratory health remain unknown. We herein analyzed the interaction between inhalable aged MPs with lung surfactant (LS) extracted from porcine lungs vis-à-vis interfacial chemistry employing in-vitro experiments, and explored oxidative damage induced by aged MPs in simulated lung fluid (SLF) and the underlying mechanisms of action. Our results showed that aged MPs significantly increased the surface tension of the LS, accompanied by a diminution in its foaming ability. The stronger adsorptive capacity of the aged MPs toward the phospholipids of LS appeared to produce increased surface tension, while the change in foaming ability might have resulted from a variation in the protein secondary structure and the adsorption of proteins onto MPs. The adsorption of phospholipid and protein components then led to the aggregation of MPs in SLF, where the aged MPs exhibited smaller hydrodynamic diameters in comparison with the unaged MPs, likely interacting with biomolecules in bodily fluids to exacerbate health hazards. Persistent free radicals were also formed on aged MPs, inducing the formation of reactive oxygen species such as superoxide radicals (O2•-), hydrogen peroxide (HOOH), and hydroxyl radicals (•OH); this would lead to LS lipid peroxidation and protein damage and increase the risk of respiratory disease. Our investigation was the first-ever to reveal a potential toxic effect of aged MPs and their actions on the human respiratory system, of great significance in understanding the risk of inhaled MPs on lung health.

Imaging and Controlling Ultrafast Electron Pulses Emitted from Plasmonic Nanostructures.

We experimentally study photoemission from gold nanodisk arrays using space-, time-, and energy-resolved photoemission electron microscopy. When excited by a plasmonic resonant infrared (IR) laser pulse, plasmonic hotspots are generated owing to local surface plasmon resonance. Photoelectrons emitted from each plasmonic hotspot form a nanoscale and ultrashort electron pulse. When the system is excited by an extreme ultraviolet (EUV) laser pulse, a uniformly distributed photoelectron cloud is formed across the sample surface. When excited by the IR and EUV laser pulses together, both the photoemission image and kinetic energy vary significantly for the IR laser-generated electrons depending on the time delay between the two laser pulses. These observations are well explained by the Coulomb interaction with the EUV laser-generated electron cloud. Our study offers a feasible approach to manipulate the energy of electron pulse emitted from a plasmonic nanostructure on an ultrafast time scale.

Workplace-based primary prevention intervention reduces incidence of hypertension: a post hoc analysis of cluster randomized controlled study.

BACKGROUND: A workplace-based primary prevention intervention be an effective approach to reducing the incidence of hypertension (HTN). However, few studies to date have addressed the effect among the Chinese working population. We assessed the effect of a workplace-based multicomponent prevention interventions program for cardiovascular disease on reducing the occurrence of HTN through encouraging employees to adopt a healthy lifestyle. METHODS: In this post hoc analysis of cluster randomized controlled study, 60 workplaces across 20 urban regions in China were randomized to either the intervention group (n = 40) or control group (n = 20). All employees in each workplace were asked to complete a baseline survey after randomization for obtaining sociodemographic information, health status, lifestyle, etc. Employees in the intervention group were given a 2-year workplace-based primary prevention intervention program for improving their cardiovascular health, including (1) cardiovascular health education, (2) a reasonable diet, (3) tobacco cessation, (4) physical environment promotion, (5) physical activity, (6) stress management, and (7) health screening. The primary outcome was the incidence of HTN, and the secondary outcomes were improvements of blood pressure (BP) levels and lifestyle factors from baseline to 24 months. A mix effect model was used to assess the intervention effect at the end of the intervention in the two groups. RESULTS: Overall, 24,396 participants (18,170 in the intervention group and 6,226 in the control group) were included (mean [standard deviation] age, 39.3 [9.1] years; 14,727 men [60.4%]). After 24 months of the intervention, the incidence of HTN was 8.0% in the intervention groups and 9.6% in the control groups [relative risk (RR) = 0.66, 95% CI, 0.58 ~ 0.76, P < 0.001]. The intervention effect was significant on systolic BP (SBP) level (ß = - 0.7 mm Hg, 95% CI, - 1.06 ~ - 0.35; P < 0.001) and on diastolic BP (DBP) level (ß = - 1.0 mm Hg, 95% CI, - 1.31 ~ - 0.76; P < 0.001). Moreover, greater improvements were reported in the rates of regular exercise [odd ratio (OR) = 1.39, 95% CI, 1.28 ~ 1.50; P < 0.001], excessive intake of fatty food (OR = 0.54, 95% CI, 0.50 ~ 0.59; P < 0.001), and restrictive use of salt (OR = 1.22, 95% CI, 1.09 ~ 1.36; P = 0.001) in intervention groups. People with a deteriorating lifestyle had higher rates of developing HTN than those with the same or improved lifestyle. Subgroup analysis showed that the intervention effect of BP on employees with educational attainment of high school above (SBP: ß = - 1.38/ - 0.76 mm Hg, P < 0.05; DBP: ß = - 2.26/ - 0.75 mm Hg, P < 0.001), manual labor workers and administrative worker (SBP: ß = - 1.04/ - 1.66 mm Hg, P < 0.05; DBP: ß = - 1.85/ - 0.40 mm Hg, P < 0.05), and employees from a workplace with an affiliated hospital (SBP: ß = - 2.63 mm Hg, P < 0.001; DBP: ß = - 1.93 mm Hg, P < 0.001) were significantly in the intervention group. CONCLUSIONS: This post hoc analysis found that workplace-based primary prevention interventions program for cardiovascular disease were effective in promoting healthy lifestyle and reducing the incidence of HTN among employees. TRIAL REGISTRATION: Chinese Clinical Trial Registry No. ChiCTR-ECS-14004641.

Trajectory-controlled high-order harmonic generation in ZnO crystals.

We experimentally and theoretically study high-order harmonic generation in zinc oxide crystals irradiated by mid-infrared lasers. The trajectories are mapped to the far field spatial distribution of harmonics. The divergence angles of on-axis and off-axis parts exhibit different dependences on the order of the harmonics. This observation can be theoretically reproduced by the coherent interference between the short and long trajectories with dephasing time longer than 0.5 optical cycle. Further, the relative contribution of the short and long trajectories is demonstrated to be accurately controlled by a one-color or two-color laser on the attosecond time scale. This work provides a reliable method to determine the electron dephasing time and demonstrates a versatile control of trajectory interference in the solid high-order harmonic generation.

Entangled X-Ray Photon Pair Generation by Free-Electron Lasers.

We investigate entangled x-ray photon pair emissions in a free-electron laser (FEL) and establish a quantum electrodynamical theory for coherently amplified entangled photon pair emission from microbunched electron pulses in the undulator. We provide a scheme to generate highly entangled x-ray photon pairs and numerically demonstrate the properties of entangled emission, which is of great importance in x-ray quantum optics. Our work shows a unique advantage of FELs in entangled x-ray photon pair generation.

Deep Variational Free Energy Approach to Dense Hydrogen.

We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Boltzmann distribution and a fermionic neural network to model the electron wave function at given proton positions. By jointly optimizing the two neural networks we reached a comparable variational free energy to the previous coupled electron-ion Monte Carlo calculation. The predicted equation of state of dense hydrogen under planetary conditions is denser than the findings of ab initio molecular dynamics calculation and empirical chemical model. Moreover, direct access to the entropy and free energy of dense hydrogen opens new opportunities in planetary modeling and high-pressure physics research.

Localized Elevation of Wall Shear Stress Is Linked to Recent Symptoms in Patients with Carotid Stenosis.

BACKGROUND: Estimation of symptomatic and asymptomatic carotid atherosclerosis differences can be the basis for prevention and management of carotid artery stenosis disease. In clinical practice, carotid plaque vulnerability is assessed only on the basis of luminal stenosis. However, the evolution of carotid plaque from an asymptomatic state to a symptomatic one is a complex process and the underlying hemodynamic mechanisms are unknown. We aimed to investigate the differences in hemodynamic parameters between patients with recently symptomatic carotid stenosis and asymptomatic ones. METHODS: Hemodynamic simulations were performed on 26 carotid plaques from 25 patients with carotid artery stenosis ≥50%, 16 of whom had recent cerebrovascular ischemic events. Using human-specific flow parameters and 3D reconstruction of carotid computed tomography angiography images, we assessed hemodynamic characteristics such as wall shear stress (WSS), time-averaged WSS (TAWSS), oscillatory shear index, and relative residence time (RRT) during the cardiac cycle in patients with and without symptoms. RESULTS: We found that symptomatic carotid stenosis patients had greater local TAWSS (99.59 ± 26.29 vs. 60.40 ± 20.46 dyn/cm2, p = 0.0007) and maximal WSS (116.65 ± 39.11 vs. 68.28 ± 23.67 dyn/cm2, p = 0.003), but lower RRT (0.019 ± 0.006 vs. 0.013 ± 0.069 s, p = 0.049), than asymptomatic patients, but this hemodynamic difference was not associated with carotid stenosis severity (p = 0.70). Patients with transient ischemic attack (TIA) or stroke had higher local TAWSS and WSSmax than patients with asymptomatic stenosis (p < 0.05). Subgroup analysis showed that there was no statistical difference in local hemodynamic variables between TIA and stroke patients with carotid artery stenosis. CONCLUSIONS: Patients with carotid artery stenosis are more likely to experience acute ischemic cerebrovascular accidents if they have higher WSS. Simultaneous assessment with hemodynamic parameters like WSS along with stenosis severity may aid risk stratification in patients with asymptomatic carotid artery stenosis.

Geometric Phase Effect in Attosecond Stimulated X-ray Raman Spectroscopy.

Conical intersections (CIs) are diabolical points in the potential energy surfaces generally caused by point-wise degeneracy of different electronic states, and give rise to the geometric phases (GPs) of molecular wave functions. Here we theoretically propose and demonstrate that the transient redistribution of ultrafast electronic coherence in attosecond Raman signal (TRUECARS) spectroscopy is capable of detecting the GP effect in excited state molecules by applying two probe pulses including an attosecond and a femtosecond X-ray pulse. The mechanism is based on a set of symmetry selection rules in the presence of nontrivial GPs. The model of this work can be realized for probing the geometric phase effect in the excited state dynamics of complex molecules with appropriate symmetries, using attosecond light sources such as free-electron X-ray lasers.

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids.

A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component polymeric fluid systems based on the recently developed deep coarse-grained potential [Zhang et al., J. Chem. Phys. 149, 034101 (2018)] scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Prognostic significance of CircRNAs expression in oral squamous cell carcinoma.

This systematic review was aimed to comprehensively evaluate the clinicopathological and prognostic value of dysregulated expression of circRNAs in OSCC. The research was carried out by searching mainstream electronic databases including PubMed, Embase, Web of Science, Scopus, LILACS, and Cochrane Library to collect relevant studies on prognostic role of circRNAs in OSCC. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the association between circRNAs expression, overall survival (OS), disease/recurrence/progression survival (DFS/RFS/PFS), and clinical parameters. This research included 1813 patients from 26 selected articles. The pooled HR values (95% CIs) in OS were 2.38 (1.92-2.93) for oncogenic circRNAs and 0.43 (0.28-0.66) for tumor-suppressor circRNAs, respectively, in DFS/RFS/PFS were 2.34 (1.73-3.17). The meta-analysis on clinicopathology features showed higher level of oncogenic circRNAs is related to advanced TNM stage, tumor stage, worse histological differentiation, positive lymph node and distant metastasis, while enforced expression of tumor-suppressor circRNAs is related to inferior TNM stage, tumor stage and lymphatic metastasis. In conclusion, our meta-analysis implies that circRNAs may be candidate biomarkers for the prognosis and clinicopathology of OSCC.

Air pollution is associated with abnormal left ventricular diastolic function: a nationwide population-based study.

BACKGROUND: Air pollution is a growing public health concern of global significance. Till date, few studies have explored the associations between air pollutants and cardiac imaging phenotypes. In this study, we aim to explore the association of ambient air pollution and abnormal left ventricular diastolic function (ALVDF) among a large-scale free-living population. METHODS: The participants were from a national representative large-scale cross-sectional study, i.e., the China Hypertension Survey (CHS), 2012-15. After exclusion, 25,983 participants from 14 provinces and 30 districts in China were included for the final analysis. The annual average ambient PM2.5, PM10 and NO2 concentrations were obtained from the chemical data assimilation system (ChemDAS). The clinical evaluation of left ventricular function was conducted in the survey field which was based on echocardiography. Grading diastolic dysfunction was based on Recommendations for the evaluation of left ventricular diastolic function by echocardiography (2009). RESULTS: The mean age of 25,983 participants was 56.8 years, 46.5% were male, and the crude prevalence of GradeI-III ALVDF were 48.1%, 1.6% and 1.1%, respectively. The ORs (95% CI) for ALVDF in the fully adjusted model were 1.31 (1.11-1.56), 1.11 (1.01-1.21) and 1.18 (0.90-1.54) for an increase of 10 µg/m3 of PM2.5, PM10 and NO2, respectively. And for different grades of ALVDF, elevated concentration of PM2.5 and PM10 exposures significantly increased the risk of gradeIinstead of gradeII ~ III ALVDF. There was a positive linear and "J" shape concentration-response association between annual average ambient PM2.5 and NO2 and the ALVDF risk assessed by the restricted cubic spline. The exposure level of most participants to PM10 was less than 130 µg/m3, and the risk of ALVDF increased significantly with the concentration rise. CONCLUSIONS: This large-scale nationwide population study demonstrated a significantly positive association between ambient PM2.5, PM10 and NO2 with ALVDF, especially for mild ALVDF. The functional abnormality may partially explain the enhanced cardiovascular morbidity and mortality associated with air pollution, which highlights the importance of appropriate interventions to reduce ambient air pollution in China.

Cardiovascular disease and all-cause mortality associated with individual and combined cardiometabolic risk factors.

BACKGROUND: Previous studies have investigated the association between cardiometabolic risk factors and cardiovascular disease (CVD), but evidence of the attributable burden of individual and combined cardiometabolic risk factors for CVD and mortality is limited. We aimed to investigate and quantify the associations and population attributable fraction (PAF) of cardiometabolic risk factors on CVD and all-cause mortality, and calculate the loss of CVD-free years and years of life lost in relation to the presence of cardiometabolic risk factors. METHODS: Twenty-two thousand five hundred ninety-six participants aged ≥ 35 without CVD at baseline were included between October 2012 and December 2015. The outcomes were the composite of fatal and nonfatal CVD events and all-cause mortality, which were followed up in 2018 and 2019 and ascertained by hospital records and death certificates. Cox regression was applied to evaluate the association of individual and combined cardiometabolic risk factors (including hypertension, diabetes and high low-density lipoprotein cholesterol (LDL-C)) with CVD risk and all-cause mortality. We also described the PAF for CVD and reductions in CVD-free years and life expectancy associated with different combination of cardiometabolic conditions. RESULTS: During the 4.92 years of follow-up, we detected 991 CVD events and 1126 deaths. Hazard ratio were 1.59 (95% confidential interval (CI) 1.37-1.85), 1.82 (95%CI 1.49-2.24) and 2.97 (95%CI 1.85-4.75) for CVD and 1.38 (95%CI 1.20-1.58), 1.66 (95%CI 1.37-2.02) and 2.97 (95%CI 1.88-4.69) for all-cause mortality, respectively, in participants with one, two or three cardiometabolic risk factors compared with participants without diabetes, hypertension, and high LDL-C. 21.48% of CVD and 15.38% of all-cause mortality were attributable to the combined effect of diabetes and hypertension. Participants aged between 40 and 60 years old, with three cardiometabolic disorders, had approximately 4.3-year reductions life expectancy compared with participants without any abnormalities of cardiometabolic disorders. CONCLUSIONS: Cardiometabolic risk factors were associated with a multiplicative risk of CVD incidence and all-cause mortality, highlighting the importance of comprehensive management for hypertension, diabetes and dyslipidemia in the prevention of CVD.

Signatures of a liquid-liquid transition in an ab initio deep neural network model for water.

The possible existence of a metastable liquid-liquid transition (LLT) and a corresponding liquid-liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal-multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP's location. These combined results-obtained from a purely first-principles approach with no empirical parameters-are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.

Air pollution attenuated the benefits of physical activity on blood pressure: Evidence from a nationwide cross-sectional study.

INTRODUCTION: Although physical activity (PA) has multiple health benefits, the inhaled dose of fine particulate matter (PM2.5) during PA may increase. The trade-off between harmful effects of PM2.5 exposure and protective effects of PA remain unclear. Our study aims to examine the joint effects of PA and PM2.5 exposure on blood pressure (BP) in Chinese adults. METHODS: A total of 203,108 adults aged ≥ 18 years from the China Hypertension Survey study (2012-2015) were included. Individual-level PA was assessed as minutes of metabolic equivalent tasks per week (MET-min/week). The average weekly PM2.5 exposures were estimated by using a spatial resolution of 10 km, integrating multiple data sources, including monitoring values, satellite measurements and model simulations. BP was measured with a professional portable BP monitor. Generalized linear regressions were used to estimate joint associations and to further explore two-dimensional nonlinear associations. RESULTS: The median PA and 4-week PM2.5 average exposures were 3213.0 MET-min/week and 47.8 µg/m3, respectively. PA was negatively associated with BP, while PM2.5 exposure was positively with BP. The associations between PA and systolic BP were significantly modified by PM2.5 exposure (Pinteraction < 0.001). Compared with inactive participants under low PM2.5 exposure, those with highest level of PA under low PM2.5 exposure had a 0.90 (95 % CI: 0.53, 1.26) mmHg decrease in systolic BP, whereas they had a 0.48 (95 % CI: 0.07, 0.89) mmHg increase under high PM2.5 exposure. When PM2.5 exposure was approximately > 25 µg/m3, the joint exposure to total PA and PM2.5 was associated with an increase in systolic BP. CONCLUSIONS: The benefits of PA on BP were counteracted by high PM2.5 levels.

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.