Dynamics Trajectories Research Articles

Longitudinal assessment of ventricular volume trajectories in early-stage schizophrenia: evidence of both enlargement and shrinkage

BackgroundLateral ventricular enlargement represents a canonical morphometric finding in chronic patients with schizophrenia; however, longitudinal studies elucidating complex dynamic trajectories of ventricular volume change during critical early disease stages are sparse.MethodsWe measured lateral ventricular volumes in 113 first-episode schizophrenia patients (FES) at baseline visit (11.7 months after illness onset, SD = 12.3) and 128 age- and sex-matched healthy controls (HC) using 3T MRI. MRI was then repeated in both FES and HC one year later.ResultsCompared to controls, ventricular enlargement was identified in 18.6% of patients with FES (14.1% annual ventricular volume (VV) increase; 95%CI: 5.4; 33.1). The ventricular expansion correlated with the severity of PANSS-negative symptoms at one-year follow-up (p = 0.0078). Nevertheless, 16.8% of FES showed an opposite pattern of statistically significant ventricular shrinkage during ≈ one-year follow-up (-9.5% annual VV decrease; 95%CI: -23.7; -2.4). There were no differences in sex, illness duration, age of onset, duration of untreated psychosis, body mass index, the incidence of Schneiderian symptoms, or cumulative antipsychotic dose among the patient groups exhibiting ventricular enlargement, shrinkage, or no change in VV.ConclusionBoth enlargement and ventricular shrinkage are equally present in the early stages of schizophrenia. The newly discovered early reduction of VV in a subgroup of patients emphasizes the need for further research to understand its mechanisms.

Exploring the dynamic evolutionary trajectories for urban residential building carbon emissions toward 2060: Evidence from the HSCW zone in China

Revealing the long-term development trajectory and peak scenarios of the urban residential building sector is crucial for formulating targeted emission reduction strategies and contributing to achieving the “Dual carbon” target. Given that, significant variables affecting carbon emissions from urban residential buildings (URBCE) at both macro and end-use building levels are identified in this study. Furthermore, an innovative dynamic simulation model is developed by integrating the Monte Carlo simulation model with the Kaya identity model, taking into account the uncertainties of the variables. Subsequently, the long-term evolution trajectories, potential emission peaks, and peak time of the URBCE close to 2060 in China's hot summer and cold winter (HSCW) zone are simulated. Results indicate that URBCE is expected to reach its peak of 272.43 Mt CO2 in 2040 under the baseline scenario. Through the dynamic simulation, the probable peak time of energy consumption in the HSCW zone is 2044 (±1.85), with a peak of 145.42 (±6.91) Mtce, and the possible peak time of carbon emissions is probably 2039 (±1.60), with a peak of 271.93 (±13.40) Mt CO2. According to dynamic sensitivity analysis, carbon emission factor, per capita residential space, and heating energy intensity can promote the peak size and timing. These findings lay a robust foundation for the formulation of regionally specific carbon reduction targets and initiatives.

Binding selectivity analysis of new delhi metallo-beta-lactamase-1 inhibitors using molecular dynamics simulations: Exploring possibilities for decoding antimicrobial drug resistance

BackgroundNew Delhi metallo-beta-lactamase-1 (NDM1) confers resistance to several bacterial species against a broad range of beta-lactam antibiotics and turning them into superbugs that pose a significant threat to healthcare systems worldwide. As such, it is a potentially relevant biological target for counteracting bacterial infections. Given the lack of effective treatment options against NDM1 producing bacteria, finding a reliable inhibitor for the NDM1 enzyme is crucial. MethodsUsing molecular dynamics simulations, the binding selectivities and affinities of three ligands, viz. PNK, 3S0, and N1G were investigated against NDM1. ResultsThe results indicate that N1G binds with more affinity to NDM1 than PNK and 3S0. The binding energy decomposition analysis revealed that residues I35, W93, H189, K211, and N220 showed significant binding energies with PNK, 3S0, and N1G, and hence are crucially involved in the binding of the ligands to NDM1. Molecular dynamics trajectory analysis further elicited that the ligands influence dynamic flexibility of NDM1 morphology, which contributes to the partial selectivities of PNK, 3S0, and N1G. ConclusionsThis in silico study offers a vital information for developing potential NDM1 inhibitors with high selectivity. Nevertheless, in vitro and in vivo experimental validation is mandated to extend the possible applications of these ligands as NDM1 inhibitors that succor in combating antimicrobial resistance.

Environmentally Driven Symmetry Breaking Quenches Dual Fluorescence in Proflavine.

Nonadiabatic couplings between several electronic excited states are ubiquitous in many organic chromophores and can significantly influence optical properties. A recent experimental study demonstrated that the proflavine molecule exhibits surprising dual fluorescence in the gas phase, which is suppressed in polar solvent environments. Here, we uncover the origin of this phenomenon by parametrizing a linear-vibronic coupling Hamiltonian from spectral densities of system-bath coupling constructed along molecular dynamics trajectories, fully accounting for interactions with the condensed-phase environment. The finite-temperature absorption, steady-state emission, and time-resolved emission spectra are then computed using powerful, numerically exact tensor network approaches. We find that the dual fluorescence in vacuum is driven by a single well-defined coupling mode but is quenched in solution due to dynamic solvent-driven symmetry breaking that mixes the two low-lying electronic states. We expect the computational framework developed here to be widely applicable to the study of non-Condon effects in complex condensed-phase environments.

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

Exploring the Anticancer Potential of Semisynthetic Derivatives of 7α-Acetoxy-6β-hydroxyroyleanone from Plectranthus sp.: An In Silico Approach.

The diterpene 7α-acetoxy-6β-hydroxyroyleanone isolated from Plectranthus grandidentatus demonstrates promising antibacterial, anti-inflammatory and anticancer properties. However, its bioactivity may be enhanced via strategic structural modifications of such natural products through semisynthesis. The anticancer potential of 7α-acetoxy-6β-hydroxyroyleanone and five derivatives was analyzed in silico via the prediction of chemicals absorption, distribution, metabolism, excretion, and toxicity (ADMET), quantum mechanical calculations, molecular docking and molecular dynamic simulation. The protein targets included regulators of apoptosis and cell proliferation. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Derivatives 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone achieved high predicted binding affinities towards their respective protein panels, with stable molecular dynamics trajectories. Both compounds demonstrated favorable ADMET parameters and toxicity profiles. Their stability and reactivity were confirmed via geometry optimization. Network analysis revealed their involvement in cancer-related pathways. Our findings justify the inclusion of 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone in in vitro analyses as prospective anticancer agents. Our binding mode analysis and stability simulations indicate their potential as selective inhibitors. The data will guide studies into their structure optimization, enhancing efficacy and drug-likeness.

A multi-view heterogeneous knowledge learning method for personalized POI recommendation

Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method.

Soil organic matter dynamics and stability: Climate vs. time

Climate and time are among the most important factors driving soil organic carbon (SOC) stability and accrual in mineral soils; however, their relative importance on SOC dynamics is still unclear. Therefore, understanding how these factors covary over a range of soil developmental stages is crucial to improve our knowledge of climate change impact on SOC accumulation and persistence. Two chronosequences located along a climate gradient were investigated to determine the main interactions among time (age) and climate (precipitation and temperature) on SOC stability and stock with depth. Considering a common depth (0–15 or 0–30 cm), in the drier chronosequence, the older soil showed the highest SOC stock, while the younger exhibited the lowest carbon accumulation. Considering the whole profile, the SOC stock increased with age. In the wetter chronosequence, the younger soil showed the highest SOC stock considering a common depth, whereas, when the entire profile is taken into account, the older one accumulated 2–3 times more SOC than the others. In both chronosequences, significant stocks of SOC (∼42 %) were accumulated below 30 cm. Soil organic matter stability, assessed by thermal analysis and heterotrophic respiration, increases with depth and age only in the drier chronosequence. Soils from the wetter chronosequence were instead characterized by a greater quantity of labile and/or not-stabilized SOC; here, the amorphous Fe/Al-rich secondary mineral weathering products showed an essential predictor function of SOC storage, although they do not seem to be involved in SOC stabilization mechanisms. Otherwise, the interaction of SOC with fine particles, short-range order minerals, and organo-metal complexes represent the significant stabilization mechanisms in soils from drier climate. The results highlighted how the age factor plays an unassuming role in geochemical processes influencing SOC dynamics; however, climate determines different trajectories of soil development and SOC dynamics for a given soil age. Thus, soil age shows a key role in SOC stabilization especially in drier climatic conditions, while wetter conditions determine an accumulation of a higher yet more labile amount of SOC.

Modeling single cell trajectory using forward-backward stochastic differential equations.

Recent advances in single-cell sequencing technology have provided opportunities for mathematical modeling of dynamic developmental processes at the single-cell level, such as inferring developmental trajectories. Optimal transport has emerged as a promising theoretical framework for this task by computing pairings between cells from different time points. However, optimal transport methods have limitations in capturing nonlinear trajectories, as they are static and can only infer linear paths between endpoints. In contrast, stochastic differential equations (SDEs) offer a dynamic and flexible approach that can model non-linear trajectories, including the shape of the path. Nevertheless, existing SDE methods often rely on numerical approximations that can lead to inaccurate inferences, deviating from true trajectories. To address this challenge, we propose a novel approach combining forward-backward stochastic differential equations (FBSDE) with a refined approximation procedure. Our FBSDE model integrates the forward and backward movements of two SDEs in time, aiming to capture the underlying dynamics of single-cell developmental trajectories. Through comprehensive benchmarking on multiple scRNA-seq datasets, we demonstrate the superior performance of FBSDE compared to other methods, highlighting its efficacy in accurately inferring developmental trajectories.

Port-Hamiltonian modeling and jumping trajectory tracking control for a bio-inspired quadruped robot

Applying jumping locomotion into autonomous mobile robot is an effective way for improving abilities to overcome barriers and pass through complex terrains. In this paper, based on the designed structural framework for bio-inspired quadruped jumping robot, dynamic model is established by utilizing port-Hamiltonian with dissipation (pHd) method, and a passivity-based control strategy for the robot joints trajectory tracking is presented. First, morphology and biomimetics knowledges of frogs (a kind of animals with excellent jumping skill) motivate us to complete a bio-inspired jumping robot framework based on frog’s motion mechanism and body structure with torsional springs as energy storage device. Then, combining system passivity and dissipation, port-Hamiltonian method is utilized to build a dynamic model for expressing the relationship of energy and force in the designed robot. Jumping process analysis of different stages is also designed for ensuring the robot to complete taking-off and landing stages successfully. Next, with the definition of extending feasible robotic joint trajectory by La Salle invariant set principle, interconnection and damping assignment passivity-based control (IDA-PBC) method is exerted to obtain a trajectory controller for realizing smoothly and stably trajectory tracking in joint space. At last, simulation results show the reasonableness of the designed framework. By comparing our method with state-feedback and sliding mode control, effectiveness of the dynamic model and trajectory controller is also verified.

Dwell Times, Wavepacket Dynamics, and Quantum Trajectories for Particles with Spin 1/2.

The theoretical connections between quantum trajectories and quantum dwell times, previously explored in the context of 1D time-independent stationary scattering applications, are here generalized for multidimensional time-dependent wavepacket applications for particles with spin 1/2. In addition to dwell times, trajectory-based dwell time distributions are also developed, and compared with previous distributions based on the dwell time operator and the flux-flux correlation function. Dwell time distributions are of interest, in part because they may be of experimental relevance. In addition to standard unipolar quantum trajectories, bipolar quantum trajectories are also considered, and found to relate more directly to the dwell time (and other quantum time) quantities of greatest relevance for scattering applications. Detailed calculations are performed for a benchmark 3D spin-1/2 particle application, considered previously in the context of computing quantum arrival times.

On the Upper Bound of Near Potential Differential Games

This letter presents an extended analysis and a novel upper bound of the subclass of Linear Quadratic Near Potential Differential Games (LQ NPDG). LQ NPDGs are a subclass of potential differential games, for which there is a distance between an LQ exact potential differential game and the LQ NPDG. LQ NPDGs exhibit a unique characteristic: The smaller the distance from an LQ exact potential differential game, the more closer their dynamic trajectories. This letter introduces a novel upper bound for this distance. Moreover, a linear relation between this distance and the resulting trajectory errors is established, opening the possibility for further application of LQ NPDGs.

From complex data to clear insights: visualizing molecular dynamics trajectories.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories

This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), time convolutions (TCs), self-attention (SA), graph neural networks (GNNs), neural ordinary differential equation (ODENets), and an example of physics-informed machine learning (PIML) model—assessing their effectiveness and limitations in understanding and replicating the underlying physics of particle systems. Through this analysis, this paper introduces a comprehensive set of criteria designed to evaluate the capability of ANNs in this context. These criteria include the minimization of losses, the permutability of particle indices, the ability to predict trajectories recursively, the conservation of particles, the model’s handling of boundary conditions, and its scalability. Each network type is systematically examined to determine its strengths and weaknesses in adhering to these criteria. While, predictably, none of the networks fully meets all criteria, this study extends beyond the simple conclusion that only by integrating physics-based models into ANNs is it possible to fully replicate complex particle trajectories. Instead, it probes and delineates the extent to which various neural networks can “understand” and interpret aspects of the underlying physics, with each criterion targeting a distinct aspect of this understanding.

Using Pose Estimation and 3D Rendered Models to Study Leg-Mediated Self-righting by Lanternflies.

The ability to upright quickly and efficiently when overturned on the ground (terrestrial self-righting) is crucial for living organisms and robots. Previous studies have mapped the diverse behaviors used by various animals to self-right on different substrates, and proposed physical models to explain how body morphology can favor specific self-righting methods. However, to our knowledge, no studies have quantified and modeled all of an animal's limb motions during these complicated behaviors. Here, we studied terrestrial self-righting by immature invasive spotted lanternflies (Lycorma delicatula), an insect species that must frequently recover from being overturned after jumping and falling in its native habitat. These nymphs self-righted successfully in 92-100% of trials on three substrates with different friction and roughness, with no significant difference in the time or number of attempts required. They accomplished this using three stereotypic sequences of movements. To understand these motions, we combined 3D poses tracked on multi-view high-speed video with articulated 3D models created using photogrammetry and Blender rendering software. The results were used to calculate the mechanical properties (e.g., potential and kinetic energy, angular speed, stability margin, torque, force, etc.) of these insects during righting trials. We used an inverted physical pendulum model (a "template") to estimate the kinetic energy available in comparison to the increase in potential energy required to flip over. While these insects began righting using primarily quasistatic motions, they also used dynamic leg motions to achieve final tip-over. However, this template did not describe important features of the insect's center of mass trajectory and rotational dynamics, necessitating the use of an "anchor" model comprising the 3D rendered body model and six articulated two-segment legs to model the body's internal degrees of freedom and capture the role of the legs' contribution to inertial reorientation. This anchor elucidated the sequence of highly coordinated leg movements these insects used for propulsion, adhesion, and inertial reorientation during righting, and how they frequently pivot about a body contact point on the ground to flip upright. In the most frequently used method, diagonal rotation, these motions allowed nymphs to spin their bodies to upright with lower force with a greater stability margin compared to the other less frequently used methods. We provide a concise overview of necessary background on 3D orientation and rotational dynamics, and the resources required to apply these low-cost modeling methods to other problems in biomechanics.

HICL: Hierarchical Intent Contrastive Learning for sequential recommendation

Users’ diverse intentions drive the subsequent interaction based on users’ dynamic behavior trajectories. Despite the impressive progress of existing work on capturing the user’s intent, these methods still have the following challenges: (1) How can the intent effectively be exploited in sparse and long-distance interacted items? (2) How to improve the performance of a recommendation system by fusing spatial and temporal information? To further exploit the intent from local and global contexts, we present a novel recommendation framework, namely hierarchical intent Contrastive Learning (HICL) for SR. First, a graph encoder is employed to enhance item embedding by incorporating global context information. And then, the sequential encoder is leveraged as a prediction baseline from a historical perspective. Moreover, diverse intent Contrastive Learning branches are integrated to model user latent by utilizing a new loss function. Specifically, sequence contrastive learning (CL) employs sequence augment to model the users’ local intent, while graph CL employs graph augment and feature clustering to model the users’ global latent from semantic and structural graph views. Extensive experiments are conducted on four real-world datasets, including highly sparse datasets, and the experiment results demonstrate the superiority of our model over the state-of-the-art.

A three-way trajectory privacy-preserving model based on multi-feature fusion

The prevalent method in trajectory privacy protection through publishing k-1 similar trajectories alongside a target trajectory, often relies on a single feature, which can compromise the balance between privacy, data utility, and processing efficiency. Addressing this, our study introduces a nuanced three-way decision model that integrates multiple trajectory features: staying areas, average velocity, and distance. This model begins by filtering candidate trajectories through staying areas and average velocity. Subsequently, it employs a three-way decision-making process based on a novel dynamic trajectory warping (DTrW) distance metric, which obviates the need for trajectory pre-alignment and mitigates information loss. This process classifies trajectories into accepted, rejected, or pending categories, with the final set compiled through a combined metric of average speed and staying areas. Comparative experiments on a classic real trajectory dataset validate the model’s superiority, demonstrating enhanced privacy protection, improved data utility, and increased efficiency over single-feature-based approaches and other k-anonymity models.

Enhancing quantum annealing in digital–analog quantum computing

Digital–analog quantum computing (DAQC) offers a promising approach to addressing the challenges of building a practical quantum computer. By efficiently allocating resources between digital and analog quantum circuits, DAQC paves the way for achieving optimal performance. We propose an algorithm designed to enhance the performance of quantum annealing. This method employs a quantum gate to estimate the goodness of the final annealing state and find the ground state of combinatorial optimization problems. We explore two strategies for integrating the quantum annealing circuit into the DAQC framework: (1) state preparation, and (2) embedding within the quantum gate. While the former strategy does not yield performance improvements, we discover that the latter enhances performance within a specific range of annealing time. Algorithms demonstrating enhanced performance utilize the imaginary part of the inner product of two states from different quantum annealing settings. This measure reflects not only the energy of the classical cost function but also the trajectory of the quantum dynamics. This study provides an example of how processing quantum data using a quantum circuit can outperform classical data processing, which discards quantum information.

Using a Drug Repurposing Strategy to Virtually Screen Potential HIV-1 Entry Inhibitors That Block the NHR Domain of the Viral Envelope Protein gp41

Using a drug repurposing strategy, virtual screening of potential inhibitors of the NHR domain of the HIV-1 gp41 protein, a conserved region critical for the virus-cell membrane fusion and viral infectivity, was carried out. The used computational approach included: (1) molecular docking of this functionally significant region of the HIV-1 envelope with compounds from a library of bioactive molecules containing clinically approved drugs, experimental drugs, and investigational drug candidates; (2) assessing the binding affinity of these compounds to the therapeutic target; (3) molecular dynamics simulations of ligand/NHR-gp41 complexes; (4) calculations of the binding free energy followed by the analysis of molecular dynamics trajectories and selection of compounds promising to test for anti-HIV-1 activity. As a result, six compounds that exhibited the high binding affinity to the NHR domain of the HIV-1 gp41 protein and showed acceptable pharmacological properties were identified. The predicted compounds are assumed to form a promising basis for the development of new, effective and safe broad-spectrum antiviral agents able to inhibit the HIV-1 entry into the host cell.

Trajectory dynamics of particles in accelerated toroidal pipe: A computational study using CFD-DPM simulations

In this study, we investigate the behavior of solid particles of varying sizes within an accelerated toroidal filled with fluid using computational fluid dynamics- discrete phase model (CFD-DPM) simulations. A comprehensive grid independence study and validation were conducted to ensure the accuracy of our simulations. We focus on particles with 10-, 50-, and 100-microns diameters. Our simulations indicate that the primary flow (relative tangential velocity) of both the fluid and the particles follow the input acceleration. We also examined the secondary flow within the toroidal. Smaller particles (10 microns) closely follow the fluid flow with minimal lag, while larger particles (50 and 100 microns) exhibit a noticeable lag due to the inertia effect. Furthermore, the displacement from the initial position is also dependent on the particle size, with smaller particles traveling further distances. However, despite the ongoing acceleration of the fluid and the toroidal, the largest particles (100 microns) eventually settle and stop due to their high inertia (mass), and their velocity begins to decrease during the acceleration of the toroidal. These findings highlight the significant role of particle size in determining particle behavior within a toroidal, paving the way for future research and Particle Imaging Velocimetry Gyroscope (PIVG) application.