Dynamics Trajectories Research Articles

Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.

Despite the power of path sampling strategies in enabling simulations of rare events, such strategies have not reached their full potential. A common challenge that remains is the identification of a progress coordinate that captures the slow relevant motions of a rare event. Here we have developed a weighted ensemble (WE) path sampling strategy that exploits reinforcement learning to automatically identify an effective progress coordinate among a set of potential coordinates during a simulation. We apply our WE strategy with reinforcement learning to three benchmark systems: (i) an egg carton-shaped toy potential, (ii) an S-shaped toy potential, and (iii) a dimer of the HIV-1 capsid protein (C-terminal domain). To enable rapid testing of the latter system at the atomic level, we employed discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model that was based on extensive conventional simulations. Our results demonstrate that using concepts from reinforcement learning with a weighted ensemble of trajectories automatically identifies relevant progress coordinates among multiple candidates at a given time during a simulation. Due to the rigorous weighting of trajectories, the simulations maintain rigorous kinetics.

Computational insights into dispiro-oxindolopyrrolizidines as aldose reductase inhibitors: Molecular docking, ADME analysis, molecular dynamic simulation, and MM-PBSA analysis

Chronic diabetes problems arise from the activation of aldose reductase (AR), the primary enzyme of the polyol pathway, under hyperglycemic circumstances. The spirooxindole ring system comes out to be a fascinating class of naturally occurring compounds with pharmaceutical interest. In this work, we have chosen three dispirooxindolopyrrolizidines (DSOIP-H, DSOIP-Me, and DSOIP-OMe) to investigate their ability to inhibit AR. Through molecular docking studies, the order of binding energy was calculated as DSOIP-H (-8.23 kcal/mol) < DSOIP-Me (-8.58 kcal/mol) < DSOIP-OMe (-8.72 kcal/mol). The three ligands showed effective binding with AR's catalytic active site through hydrophobic interactions. The ADME analysis revealed valuable insights into the ligands' pharmacokinetic properties. Through protein-ligand interactions, DSOIP-OMe consistently interacted with the amino acid residues of the AR enzyme's active site areas, as demonstrated by the molecular dynamic (MD) trajectory analysis. The exceptional inhibitory efficacy of DSOIP-OMe has been established based on its binding energy values of -92.69 kJ/mol as determined by MM-PBSA. The PHE122 residue of AR is recognized as a crucial residue that made a substantial contribution to the binding energy of MM-PBSA through hydrophobic interactions.

Molecular Thumbprints: Biological Signatures That Measure Loss of Identity.

Each life is challenged to adapt to an ever-changing environment with integrity-simply put, to maintain identity. We hypothesize that this mission statement of adaptive homeostasis is particularly poignant in an adaptive response, like inflammation. A maladaptive response of unresolved inflammation can seed chronic disease over a lifetime. We propose the concept of a molecular thumbprint: a biological signature of loss of identity as a measure of incomplete return to homeostasis after an inflammatory response. Over time, personal molecular thumbprints can measure dynamic and precise trajectories to chronic inflammatory diseases and further loss of self to cancer. Why is this important? Because the phenotypes and molecular signatures of established complex inflammatory diseases are a far cry from the root of the complex problem, let alone the initial seed. Understanding the science behind key germinating seeds of disease helps to identify molecular factors of susceptibility, resilience, and early dietary or drug intervention. We pilot this hypothesis in a rat colitis model that is well-established for understanding molecular mechanisms of colonic health, disease, and transition of colitis to cancer.

How to simulate dissociative chemisorption of methane on metal surfaces.

The dissociation of methane is not only an important reaction step in catalytic processes, but also of fundamental interest. Dynamical effects during the dissociative chemisorption of methane on metal surfaces cause significant differences in computed reaction rates, compared to what is predicted by typical transition state theory (TST) models. It is clear that for a good understanding of the catalytic activation of methane dynamical simulations are required. In this paper, a general blueprint is provided for performing dynamical simulations of the dissociative chemisorption of methane on metal surfaces, by employing either the quasi-classical trajectory or ring polymer molecular dynamics approach. If the computational setup is constructed with great care-since results can be affected considerably by the setup - chemically accurate predictions are achievable. Although this paper concerns methane dissociation, the provided blueprint is, so far, applicable to the dissociative chemisorption of most molecules.

CIRG-SL: Commonsense Inductive Relation Graph framework with Soft Labels for Empathetic Response Generation

Empathy, the ability to understand and respond to others’ emotions, is critical in dialogue systems, particularly for applications such as psychological counselling and casual conversation. However, existing approaches often struggle to accurately capture emotional transitions in user utterances and dialogue contexts across multiple levels of granularity. Additionally, the orthogonality of one-hot emotion representations overlooks the subtle relationships between emotion categories. To overcome these limitations, we propose a novel framework: the commonsense inductive relation graph with fine emotional soft labels for empathetic response generation. This framework refines the representation of user utterances by incorporating commonsense knowledge to derive external emotional cues, which are integrated into an adaptive inductive relation graph. This graph explicitly captures and models dynamic emotional trajectories throughout the dialogue. Additionally, we propose a fine-grained soft label construction method that more accurately reflects the nuanced distribution of emotions, replacing the rigid one-hot encoding with a continuous representation that better captures inter-emotion relationships. Our approach achieves strong performance on the benchmark EmpatheticDialogues dataset, with a perplexity score of 27.85, Dist-1 of 1.81, Dist-2 of 6.58, and a dialogue emotion accuracy of 55.65%. These results highlight the effectiveness of our framework in capturing subtle emotions and generating contextually rich, empathetic responses.

Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation.

Graph neural networks (GNNs) have emerged as powerful tools for quantum chemical property prediction, leveraging the inherent graph structure of molecular systems. GNNs depend on an edge-to-node aggregation mechanism for combining edge representations into node representations. Unfortunately, existing learnable edge-to-node aggregation methods substantially increase the number of parameters and, thus, the computational cost relative to simple sum aggregation. Worse, as we report here, they often fail to improve predictive accuracy. We therefore propose a novel learnable edge-to-node aggregation mechanism that aims to improve the accuracy and parameter efficiency of GNNs in predicting molecular properties. The new mechanism, called "patch aggregation", is inspired by the Multi-Head Attention and Mixture of Experts machine learning techniques. We have incorporated the patch aggregation method into the specialized, state-of-the-art GNN models SchNet, DimeNet++, SphereNet, TensorNet, and VisNet and show that patch aggregation consistently outperforms existing learnable and nonlearnable aggregation techniques (sum, multilayer perceptron, softmax, and set transformer aggregation) in the prediction of molecular properties such as QM9 thermodynamic properties and MD17 molecular dynamics trajectory energies and forces. We also find that patch aggregation not only improves prediction accuracy but also is parameter-efficient, making it an attractive option for practical applications for which computational resources are limited. Further, we show that Patch aggregation can be applied across different GNN models. Overall, Patch aggregation is a powerful edge-to-node aggregation mechanism that improves the accuracy of molecular property predictions by GNNs.

A physics-constraint neural network for CO2 storage in deep saline aquifers during injection and post-injection periods

CO2 capture and storage is a viable solution in the effort to mitigate global climate change. Deep saline aquifers, in particular, have emerged as promising storage options, owing to their vast capacity and widespread distribution. However, the task of proficiently monitoring and simulating CO2 behavior within these formations poses significant challenges. To address this, we introduce the physics-constraint neural network for CO2 storage (CO2PCNet), a model specifically designed for simulating and monitoring CO2 storage in deep saline aquifers during injection and post-injection periods. Recognizing the significant challenges in accurately modeling the distribution and movement of CO2 under varying permeability conditions, the CO2PCNet integrates the principles of physics with the robustness of deep learning, serving as a powerful surrogate model. The architecture of CO2PCNet starts with an encoder that adeptly processes spatial features from overall mole fraction (zCO2) and pressure fields (Pl), capturing the complex dynamics of a CO2 trajectory. By incorporating permeability information through a conditioning step, the network ensures a faithful representation of the influences on CO2 behavior in subsurface conditions. A ConvLSTM module subsequently discerns temporal evolutions, reflecting the real-world progression of CO2 plumes within the reservoir. Lastly, the decoder precisely reconstructs the predictive spatial profile of CO2 distribution. CO2PCNet, with its integration of convolutional layers, recurrent mechanisms, and physics-informed constraints, offers a refined approach to CO2 storage simulation. This model offers the potential of utilizing advanced computational methods in advancing CCS practices.

Phytobioinformatics screening of ayurvedic plants for potential α-glucosidase inhibitors in diabetes management

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Trajectories of disease severity and their clinical outcome in real-world patients with systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is characterised by variability of disease activity patterns over time. This study aimed to investigate the trajectories of SLE disease severity patterns, identify clinical and demographic variables, and assess the association between trajectories of SLE disease severity and all-cause mortality. MethodsA retrospective cohort of newly diagnosed patients with SLE was established using the Korean nationwide healthcare claims information database between 1st January 2008 and 31st December 2016. Using group-based trajectory modelling (GBTM), they were clustered based on the trajectory of SLE disease severity patterns during a two-year follow-up from the cohort entry date. We performed Cox proportional hazards models to compare the mortality between trajectories of SLE disease severity patterns. ResultsA total of 8901 patients with SLE were included in the analysis from 2008 to 2016. Five distinct SLE disease severity trajectories were identified as optimal: consistently severe (4.6 %), mild-then-moderate (11.6 %), moderate-then-mild (15.1 %), consistently moderate (30.4 %), and consistently mild (38.3 %). Patients with consistently mild disease severity were more likely to be older; those with consistently severe disease severity were more likely to be male with more comorbidities than other groups. Compared to the consistently mild disease severity, the other trajectory groups showed a higher risk of all-cause mortality. ConclusionThrough GBTM, dynamic two-year severity trajectories of newly diagnosed SLE were identified. Patients’ demographics and comorbidities attributed to changes in SLE severity trajectories, which may inform evidence for clinical decision-making.

Dynamic properties of the magnetic skyrmion driven by electromagnetic waves with spin angular momentum and orbital angular momentum

Abstract We theoretically studied the dynamic properties of the skyrmion driven by electromagnetic (EM) waves with spin angular momentum (SAM) and orbital angular momentum (OAM) using micromagnetic simulations. First, the guiding centers of the skyrmion driven by EM waves with SAM, i.e., left-handed and right-handed circularly polarized EM waves, present circular trajectories, while present elliptical trajectories under linear EM waves driving due to the superposition of oppositely polarized wave components. Second, the trajectories of the skyrmion driven by EM waves with OAM demonstrate similar behavior to that driven by linearly polarized EM waves. Because the wave vector intensity varies with the phase for both linearly polarized EM waves and EM waves with OAM, the angular momentum is transferred to the skyrmion non-uniformly, while the angular momentum is transferred to the skyrmion uniformly for left-handed and right-handed circularly polarized EM driving. Third, the dynamic properties of the skyrmion driven by EM waves with both SAM and OAM are investigated. It is found that the dynamic trajectories exhibit more complex behavior due to the contributions or competition of SAM and OAM. We investigate the characteristics of intrinsic gyration modes and frequency-dependent trajectories. Our research may provide insight into the dynamic properties of skyrmion manipulated by EM waves with SAM or OAM and provide a method for controlling skyrmion in spintronic devices.

The triglyceride-glucose index dynamic trajectory reveals the association between the clinical subphenotype of insulin resistance and mortality in patients with sepsis

BackgroundThe relationship between the dynamic changes in insulin resistance (IR) and the prognosis of septic patients remains unclear. This study aims to investigate the correlation between the clinical subphenotype of IR represented by the triglyceride-glucose (TyG) index trajectory and the mortality rate among patients with sepsis.MethodsIn this retrospective cohort study, we utilized data from septic patients within the Medical Information Mart for Intensive Care (MIMIC)-IV database version 2.0 to construct trajectories of the TyG index over 72 h. Subsequently, we computed the similarity among various TyG index trajectories with the dynamic time warping (DTW) algorithm and utilized the hierarchical clustering (HC) algorithm to demarcate distinct cluster and identified subphenotypes according to the trajectory trend. Subsequently, we assessed the mortality risk between different subphenotypes using analyses such as survival analysis and validated the robustness of the results through propensity score matching (PSM) and various models.ResultsA total of 2350 patients were included in the study. Two trajectory trends: TyG index decreasing (n = 926) and TyG index increasing (n = 1424) were identified, which indicated corresponding to the clinical subphenotype of increased and alleviative IR respectively. The 28-day and in-hospital mortality for the increased IR group was 28.51% and 25.49% respectively. In comparison, patients in the alleviative IR group with a 28-day mortality of 23.54% and an in-hospital mortality of 21.60%. These subphenotypes exhibited distinct prognosis, time dependent Cox model showed the increased IR group with a higher 28-day mortality [hazard ratio (HR): 1.07, 95% confidence interval (CI): 1.02–1.12, P = 0.01] and in-hospital mortality [HR: 1.05, 95% CI: 1.00–1.11, P = 0.045] compared to the alleviative IR group. Sensitivity analyses with various models further validated the robustness of our findings.ConclusionDynamic increase in the TyG index trajectory is associated with elevated mortality risk among patients with sepsis, which suggests that dynamic increased IR exacerbates the risk of poor outcomes in patients.

Method for Extracting Inland Unmanned Vessel Routes Based on AIS Data

This study introduces a novel approach to enhance inland navigation safety and optimize unmanned vessel routes using AIS data. The trajectory partition algorithm categorizes Yangtze River trajectory data to formulate round-trip routes. A turning point identification algorithm contributes to recognizing crucial turning points, followed by clustering using the HDBSCAN algorithm. Cluster centroids extracted from these clusters serve as essential waypoints for route planning. The Akima interpolation polynomial is applied judiciously to interpolate waypoints, culminating in meticulous route generation. Validation employs a dataset of 5,480,049 dynamic trajectory points from the Yangtze River, demonstrating the method's efficacy. Results indicate mean squared errors of 0.77% and 6.21%, symmetrical mean absolute percentage errors of 5.3% and 7.3%, and correlation coefficients of 99.62% and 97.14% with actual routes, respectively. This method adeptly extracts inland vessel round-trip routes, aligning seamlessly with stringent criteria for accuracy, effectiveness, and applicability.

Dynamic acoustic vowel distances within and across dialects.

Vowels vary in their acoustic similarity across regional dialects of American English, such that some vowels are more similar to one another in some dialects than others. Acoustic vowel distance measures typically evaluate vowel similarity at a discrete time point, resulting in distance estimates that may not fully capture vowel similarity in formant trajectory dynamics. In the current study, language and accent distance measures, which evaluate acoustic distances between talkers over time, were applied to the evaluation of vowel category similarity within talkers. These vowel category distances were then compared across dialects, and their utility in capturing predicted patterns of regional dialect variation in American English was examined. Dynamic time warping of mel-frequency cepstral coefficients was used to assess acoustic distance across the frequency spectrum and captured predicted Southern American English vowel similarity. Root-mean-square distance and generalized additive mixed models were used to assess acoustic distance for selected formant trajectories and captured predicted Southern, New England, and Northern American English vowel similarity. Generalized additive mixed models captured the most predicted variation, but, unlike the other measures, do not return a single acoustic distance value. All three measures are potentially useful for understanding variation in vowel category similarity across dialects.

Precession, nutation, and dynamic trajectory of magnetic rod in axisymmetric magnetic field

Precession and nutation in dynamical systems have been of long-standing research interest. Manifesting in systems as diverse as spinning tops to celestial bodies like planets, understanding these motions offers a window into the intricate dance of forces governing such systems. One example of a previously unexplored system exhibiting precession and nutation is that of a magnetic rod in an axisymmetric magnetic field. This project aims to study this system through qualitative, quantitative, and experimental means. A theoretical model was formulated using Newton's Second Law and the torque equation, accounting for energy dissipation. One unique aspect of this system compared to other systems exhibiting precession and nutation is the presence of the non-linear magnetic force and torque, which were modelled by considering both magnets as current-carrying cylinders. Experimentally, the dynamic trajectory and relevant degrees of freedom of the rod were measured using image binarization and ellipse fitting. This was used for a systematic investigation into how the nutation, precession and petaloid-like trajectories are affected by the number of magnets, initial angular velocity and other relevant parameters. Theoretical predictions and experimental results show a good agreement. This work has various engineering applications such as centrifuge design.

Study on micro electrochemical milling with programmable dynamic eccentric rotating electrode

Micro electrochemical machining (ECM) has the advantages of non-contact machining, no tool loss and no residual stress, and has great development potential in the field of microstructure manufacturing. However, the micron-scale machining gap is difficult to renew and discharge the electrolyte and electrolytic products in time. In this paper, a novel micro ECM with programmable dynamic eccentric rotating electrode (DER-ECM) is proposed, which can effectively improve the discharge of electrolytic products in the machining area. According to the process characteristics, a disc flexure hinge structure with one-stage amplification was designed, which was driven by a piezo-ceramic actuator, and the programmable eccentricity ranging from 0 to 20 μm. The multi-physics model of flow field and electric field of DER-ECM, micro ECM with eccentric rotating electrode (ER-ECM) and micro ECM with rotating electrode (R-ECM) were established. The theoretical simulation results showed that the flow rate generated by DER-ECM at the bottom of the electrode is 97 times that of ER-ECM. The current density generated by DER-ECM showed periodic pulsation, and the pulsation period was determined by the driving frequency of the piezo-ceramic actuator. The experimental results showed that DER-ECM could effectively eliminate the surface spike of the workpiece in the bottom machining area and effectively improve the machining accuracy. The micro-groove widths obtained under DER-ECM, ER-ECM and R-ECM were 418 μm, 446 μm and 468 μm, respectively. In addition, the influence of three types of dynamic eccentric rotation electrode trajectories on DER-ECM was studied. The experimental results showed that the sawtooth dynamic eccentric rotation electrode had better machining accuracy. Theoretical simulation and experimental results showed that DER-ECM could improve the flow field and achieve higher machining efficiency and machining localization.

Serum tumor markers and outcomes in lung cancer patients with brain metastases: a retrospective longitudinal cohort study.

Serum tumor markers (STMs) are recommended for cancer diagnosis and surveillance. However, their role in lung cancer with brain metastases (BM) is not yet clear. We aim to analyze the roles of baseline levels of STMs or ongoing STM surveillance on survival. This retrospective longitudinal cohort study included 1,169 lung cancer patients with BM. The STM data during disease course were collected. Distinct trajectory groups were identified using the latent class growth mixed model (LCGMM). The roles of STMs on survival were further analyzed using Kaplan-Meier analysis and Cox proportional hazard models. Serum levels of cytokeratin-19 fragment (CYFRA21-1) (P<0.001), carcinoembryonic antigen (CEA) (P=0.005) and neuron-specific enolase (NSE) (P<0.001) at baseline exhibited significant correlation with overall survival (OS) of patients with BM, serving as independent prognostic factors. Further analysis indicated that baseline CYFRA21-1, CEA, NSE as well as status of key driver genes were independent prognostic factors in non-small cell lung cancer (NSCLC) patients with BM, while for small cell lung cancer (SCLC) patients with BM, baseline NSE and receiving chemotherapy show independent correlations with survival. Furthermore, we delineated the dynamic trajectories of STMs based on changes in disease course. More specifically, compared to those showing a baseline-high trend in CEA levels, the survival of patients with either persistently-rising or consistently normal levels seemed to be more promising. For CYFRA21-1, both early-rising and later-rising trends were observed, indicating a prognosis inferior to that of individuals with normal-level trajectory. Likewise, for NSE, patients with persistently-rising or persistently-descending trends showed no significantly survival difference. However, in comparison with the status of driver genes, receiving radiotherapy and targeted therapy, the dynamic changes in STM levels lacked independent prognostic significance. Further analysis indicated that among BM patients lacking key driver genes, NSE trajectory (P<0.05), CYFRA21-1 trajectory (P<0.05) and receiving chemotherapy (P<0.001) were independent prognostic factors. Baseline levels of serum CYFRA21-1, CEA and NSE, as well as status of key driver genes are recommended for evaluating BM patients' outcome. Dynamic changes of STMs during disease course were not significantly associated with the final outcome of BM patients.

Формування траєкторії керованих літальних апаратів

Currently, there is an increased interest in the creation of flight control systems for unmanned aerial vehicles, which, taking into account real conditions, would ensure its trajectory and thereby determine the effectiveness of the use of the aerial vehicle. Aircraft navigation, in particular ballistic cruise missiles, requires the formation of high-precision control in order to achieve both final results and local optimization tasks at each of the flight stages: take-off, departure to set trajectories, maneuvering, etc. The development of admissible control and its optimization by computer-mathematical methods of modeling and optimization is a time-consuming process and requires significant expenditures of various types of resources. An element of the effectiveness of such a mathematical apparatus for guaranteeing and increasing the reliability and effectiveness in achieving the set goal is the speed of calculations, which, in turn, requires the simplification of the mathematical model by obtaining functional dependencies for calculating the flight trajectory, avoiding complex mathematical calculations. The complete working model for calculating the desired trajectory of the aircraft will be determined by the specified control functions and will be provided by a system of partial differential equations with time, and the necessary parameters for calculating all aerodynamic forces and moments are tabular data in most cases. A mathematical model for solving the task of forecasting navigation for such complex controlled systems is proposed to be carried out using two coordinate systems: starting and speed. Moreover, all calculations of the velocity vector, which depends on the net effect of three aerodynamic forces acting on the aircraft, should be carried out in the speed coordinate system, and coordinate control of the flight along the entire dynamic trajectory should be determined in the starting system

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

Healthy lifestyle associated with dynamic progression of type 2 diabetes: A multi-state analysis of a prospective cohort.

Although the association of a healthy lifestyle with type 2 diabetes (T2D) has been extensively studied, its impact on the dynamic trajectory, including progression, onset and prognosis, of T2D has not been investigated. Using data from the UK Biobank, 461 168 participants without diabetes or diabetes-related events were included. We incorporated four lifestyle factors to construct the healthy lifestyle score (HLS). We employed a multi-state model to examine the relationship between a healthy lifestyle and transition in T2D progression, including transitions from baseline to diabetes, complications, and further to death. The cumulative probability of above transitions based on the health lifestyle score was calculated. The results indicated that adhering to 3-4 healthy lifestyles had an inverse association with the risk of transition from baseline to diabetes (hazard ratio (HR) = 0.966; 95% confidence interval (CI) = 0.935-0.998, P = 0.038), diabetes to complications (HR = 0.869; 95% CI = 0.818-0.923, P = 5.2 × 10-6), baseline to death (HR = 0.528; 95% CI = 0.502-0.553, P < 2 × 10-16, and diabetes to death (HR = 0.765; 95% CI = 0.591-0.990, P = 0.041) compared with maintaining 0-1 healthy lifestyles. In addition, the transition probability of the above transitions can be lower with maintaining 3-4 healthy lifestyles. Healthy lifestyles are negatively associated with the risk of multiple outcomes during the dynamic progression of T2D. Adherence to 3-4 healthy lifestyle behaviours before diabetes onset can lower the risk of developing T2D, further reducing the risk of diabetes complications and death in patients with T2D.

A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm

A Novel Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Improved APF and RRT Algorithm