Dynamic State Research Articles

Identification of Potential Inhibitors of TGFβR1 for the Treatment of Cancer through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

Globally, cancer is one of the leading causes of death. Resistance to conventional medications, such as chemotherapy and radiation, continues to be a significant challenge in the treatment of cancer despite the availability of numerous medicines. Therefore, the highest priority is to hunt for new therapeutic agents. Transforming growth factor-beta is a pivotal regulatory cytokine that exerts significant influence over cellular processes, particularly emphasizing its role in facilitating and modulating cell proliferation. TGF-β receptor 1, identified as the most promising active site of the TGF-β signaling, is a potent drug target site that has garnered wide attention for developing new anticancer agents. The present investigation investigates the potential natural products as TGFβR1 inhibitors. The SB431542 complexed TGFβR1 protein model was used to screen the natural product database to obtain a compound with high binding potential. NPC247629 has emerged as the best-scored compound among all the screened compounds, demonstrating the highest affinity towards the TGFβR1 regarding docking score -17.54 kcal/mol. The all-atoms MD simulation study indicated that all proposed hits are retained inside the receptor in dynamic states. Additionally, principal component and free energy landscape analysis were performed to explore the binding mechanism of top-hit natural products. The best-screened hits, NPC247629 and NPC60735, have excellent binding affinity and hold a massive potential for TGFβR1 inhibition, paving the way for promising future investigations in cancer treatment.

Computing hematopoiesis plasticity in response to genetic mutations and environmental stimulations.

Cell plasticity (CP), describing a dynamic cell state, plays a crucial role in maintaining homeostasis during organ morphogenesis, regeneration, and trauma-to-repair biological process. Single-cell-omics datasets provide an unprecedented resource to empower CP analysis. Hematopoiesis offers fertile opportunities to develop quantitative methods for understanding CP. In this study, we generated high-quality lineage-negative single-cell RNA-sequencing datasets under various conditions and introduced a working pipeline named scPlasticity to interrogate naïve and disturbed plasticity of hematopoietic stem and progenitor cells with mutational or environmental challenges. Using embedding methods UMAP or FA, a continuum of hematopoietic development is visually observed in wild type where the pipeline confirms a low proportion of hybrid cells ( P hc , with bias range: 0.4∼0.6) on a transition trajectory. Upon Tet2 mutation, a driver of leukemia, or treatment of DSS, an inducer of colitis, P hc is increased and plasticity of hematopoietic stem and progenitor cells was enhanced. We prioritized several transcription factors and signaling pathways, which are responsible for P hc alterations. In silico perturbation suggests knocking out EGR regulons or pathways of IL-1R1 and β-adrenoreceptor partially reverses P hc promoted by Tet2 mutation and inflammation.

In-Silico discovery of 17alpha-hydroxywithanolide-D as potential neuroprotective allosteric modulator of NMDA receptor targeting Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline, memory impairment, and behavioral alterations. The N-methyl-D-aspartate (NMDA) receptor has emerged as a promising target for AD pharmacotherapy due to its role in the disease's pathogenesis. This study leverages advanced computational methods to screen 80 active constituents of Withania somnifera (Ashwagandha), a traditional herb known for its neuroprotective effects, against the NMDA receptor, using FDA-approved Ifenprodil as a reference. Our blind virtual screening results demonstrated that all tested compounds could bind to various domains of the NMDA receptor, with binding energies ranging from - 4.1 to -11.9kcal/mol, compared to Ifenprodil's -7.8kcal/mol. Binding preference analysis revealed 7 compounds bound to the A-chain, 37 to the B-chain, 7 to the C-chain, and 29 to the D-chain of the receptor. Notable binding was observed predominantly at the Amino Terminal Domain (ATD) core site, some at the ATD-Ligand Binding Domain (LBD) interface, and a few at the Transmembrane Domain (TMD). Particularly, 17alpha-hydroxywithanolide D, with a binding energy of -11.9kcal/mol, emerged as a prime candidate for further investigation. Molecular dynamics simulations of this compound revealed key interactions, including direct hydrogen bonding with residues ASP165, ARG431, THR433, LYS466, and TYR476 on the D-chain, as well as additional hydrophobic and water-bridging interactions. These simulations highlighted the compound's influence on dynamic conformational states of the GluN1b-GluN2B receptor complex, modulating interactions between GluN1b Lys178 and GluN2B Asn184. Furthermore, the compound affected the distance between LBD heterodimers and the tension within the LBD-M30 linker, demonstrating its potential to modulate NMDA receptor activity. This comprehensive study not only underscores the therapeutic promise of Withania somnifera derivatives for AD but also provides a detailed molecular basis for their efficacy, offering valuable insights for targeted drug development and innovative therapeutic strategies against Alzheimer's disease.

Modelling the dynamic poroelastic state of saturated–unsaturated soil considering non-local interactions

Modelling the dynamic poroelastic state of saturated–unsaturated soil considering non-local interactions

Innovative hip-knee health evaluation: Leveraging pose estimation for single-leg stance postural stability assessment

The analysis of the Hip and Knee (HK) joint angles during single-leg stance (SLS) activity contributes to a great understanding of the bio-mechanical mechanisms and balance maintenance across different age groups. Comprehending how these joints operate in the dynamic state is critical for identifying age-related changes in joint control and stability which can contribute to reducing the risk of falls and improving mobility among people. The possibility of revealing the health of HK joints without resorting to an MRI scan is the significance of this work. By obtaining the data on joint angles during the SLS test, we succeeded in identifying those with a higher risk of HK issues and thus the possibility of early intervention and treatment. Our proposed work is going to use a pose estimation technique that will track the trajectories of HK angles followed by association with instabilities or compromised balance in this way, we can detect the affected joint and evaluate the severity of the issue. The study found stable mean and standard deviation values for HK joints in a young participant, both hips (107.14 ± 5, 96.42 ± 7) and both knees (36.76 ± 7, 44.30 ± 4), these values align with the expected norm (110–120° for the hip and 45–65° for the knee) which indicates stable results. while elderly participants showed high variability and low mean values for both hips (65.42 ± 77, 85 ± 76.67) and both knees (4.15 ± 10.8, 7 ± 18) indicating concerns about joint health and stability. The evaluation of HK joint angles through SLS activity offers new insights.

Uphill dynamics: A spring-mass model analysis of sloped walking

This study explores the biomechanics of uphill running by adjusting and analyzing the spring- mass model. Modifications specific to slope conditions were made to the equations of motion. Despite facing challenges in code execution, an in-depth investigation of the models physical characteristics was conducted through both static and dynamic state analyses. The findings aid in the theoretical understanding of motion on sloped surfaces and provide valuable experience for subsequent research.

Charge Dynamics and Defect States under “Spot‐Light”: Spectroscopic Insights into Halide Perovskite Solar Cells

Halide perovskite solar cells (PSCs) have shown remarkable power conversion efficiencies. However, the inherent defect issues of perovskite materials still limit their performance and long‐term stability, resulting in lifespans far from commercial standards. With their noninvasive approach to probing the electronic and vibrational properties of materials, spectroscopic techniques have become crucial tools for uncovering and understanding defect states and complex charge carrier dynamics in halide perovskites. This review explores the application of various advanced spectroscopic techniques in PSCs to elucidate the complex behaviors of charge carriers within PSCs. These techniques reveal detailed temporal and spatial distributions of charge carriers, enabling precise analysis of defect impacts and interfacial charge transfer processes. By integrating spectroscopic data, it is possible to more accurately identify and mitigate defect‐induced nonradiative recombination and charge transfer, thereby enhancing the stability and efficiency of PSCs. This comprehensive spectroscopic understanding is crucial for developing innovative technologies to accelerate the commercial viability of PSCs.

Exploring heterogeneous cell population dynamics in different microenvironments by novel analytical strategy based on images.

Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments.

Preparation of adaptive bifunctional reconfigurable polymers and their sand carrying and drag reduction behaviour

AbstractIn order to solve the problem of drag reduction at the front end of shale fractures and sand carrying at the tail end with increased viscosity, the molecular dynamics simulation (MD) method was used to design polymer molecules and simulate the steric resistance, interaction potential energy, mean square displacement, and radial distribution function of the polymer. The polymer AM‐AMPS‐LMA‐DiC12AM (ASLC12) with better solubility, diffusion, and resistance reduction potential was obtained and synthesized. By scanning electron microscope (SEM) and viscoelastic analysis, ASLC12 has a stable mesh structure, good viscoelasticity, and shear resistance, and the mesh structure formed by it is in a dynamic equilibrium state of fracture‐reorganization under shear. We then analyzed the drag reduction, sand carrying, and salt resistance of ASLC12. When the concentration of ASLC12 is 0.09%, the sand‐carrying requirement is satisfied. When the concentration is 0.05%, the drag reduction rate can reach 74.1%, and the resistance reduction rate of ASLC12 in salt ion solution can still reach more than 62%. This shows that the polymer ASLC12 has better sand carrying, drag reduction, and salt resistance.

Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states.

Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array

Capturing forceful interaction with deformable objects during manipulation benefits applications like virtual reality, telemedicine, and robotics. Replicating full hand-object states with complete geometry is challenging because of the occluded object deformations. Here, we report a visual-tactile recording and tracking system for manipulation featuring a stretchable tactile glove with 1152 force-sensing channels and a visual-tactile joint learning framework to estimate dynamic hand-object states during manipulation. To overcome the strain interference caused by contact with deformable objects, an active suppression method based on symmetric response detection and adaptive calibration is proposed and achieves 97.6% accuracy in force measurement, contributing to an improvement of 45.3%. The learning framework processes the visual-tactile sequence and reconstructs hand-object states. We experiment on 24 objects from 6 categories including both deformable and rigid ones with an average reconstruction error of 1.8 cm for all sequences, demonstrating a universal ability to replicate human knowledge in manipulating objects with varying degrees of deformability.

The Milky Way satellite galaxy Leo T: A perturbed cored dwarf

The impact of the dynamical state of gas-rich satellite galaxies at the early moments of their infall into their host systems and the relation to their quenching process are not completely understood at the low-mass regime. Two such nearby systems are the infalling Milky Way (MW) dwarfs Leo T and Phoenix located near the MW virial radius at $414 both of which present intriguing offsets between their gaseous and stellar distributions. Here we present hydrodynamic simulations with ramses to reproduce the observed dynamics of Leo T: its $80 stellar- offset and the 35 offset between its older ($ $) and younger ($ stellar population. We considered internal and environmental properties such as stellar winds, two components, cored and cuspy dark matter profiles, and different satellite orbits considering the MW circumgalactic medium. We find that the models that best match the observed morphology of the gas and stars include mild stellar winds that interact with the generating the observed offset, and dark matter profiles with extended cores. The latter allow long oscillations of the off-centred younger stellar component, due to long mixing timescales ($ and the slow precession of near-closed orbits in the cored potentials; instead, cuspy and compact cored dark matter models result in the rapid mixing of the material ($ These models predict that non-equilibrium substructures, such as spatial and kinematic offsets, are likely to persist in cored low-mass dwarfs and to remain detectable on long timescales in systems with recent star formation.

Energy equipartition in globular clusters through the eyes of dynamical models

Context. Following their birth, globular clusters (GCs) experience a very peculiar dynamical evolution. Gravitational encounters drive these systems toward energy equipartition, mass segregation, and evaporation, which alter structural, spatial, and kinematic features. Aims. We determine the dynamical state of a few GCs by means of a multi-mass King-like dynamical model. Our work focuses on the prediction of the energy equipartition degree and its relationship with model parameters. Methods. We adjusted the dynamical model parameters in order to reproduce the observed velocity dispersion – as derived from Hubble Space Telescope proper motion data – as a function of the stellar mass. By doing so, we estimated Φ0, a measure of the gravitational potential well. We repeated the same fit by means of the Bianchini relation, a function obtained by interpolating on N-body simulation results. We studied the relationship between Φ0 and the Bianchini equipartition mass meq and discuss the structural properties, such as concentration c, the number of core relaxation timescales Ncore, and core radius rc. To obtain an independent estimate of Φ0, we also fitted observed surface brightness profiles using the predicted surface density and a mass-luminosity relation from isochrones. Results. The quality of the fits of the velocity dispersion–mass relationship obtained by means of our dynamical model is comparable to those obtained with the Bianchini function. Nonetheless, when the Bianchini function is used to fit the projected velocity dispersion, the resulting degree of equipartition is underestimated. On the contrary, our approach provides the equipartition degree at any radial or projected distance by means of Φ0. As a result, a cluster in a more advanced dynamical state shows a larger Φ0, as well as larger Ncore and c, while rc decreases. We find the estimates of Φ0 obtained by fitting surface brightness profiles to be compatible at 2σ confidence level with those from internal kinematics, although further investigation of statistical and systematic errors is required. Conclusions. Our work illustrates the predicting power of dynamical models to determine the energy equipartition degree of GCs. These models are a unique tool for determining structural and kinematic properties, and can be used where observational data are poor, as is the case for the most crowded regions of a cluster, where stars are barely resolved.

Robust solar sail trajectories using proximal policy optimization

Reinforcement learning is used to design minimum-time trajectories of solar sails subject to the typical sources of uncertainty associated with such a propulsion system, i.e., inaccurate knowledge of the sail’s optical properties and the presence of wrinkles on the sail membrane. A proximal policy optimization (PPO) algorithm is used to train the agent and derive the control policy that associates the optimal sail attitude with each dynamic state. First, the agent is trained assuming deterministic unperturbed dynamics, and the results are compared with optimal solutions found by an indirect optimization method, thus demonstrating the effectiveness of this approach. Next, two stochastic scenarios are analyzed. In the first, the optical coefficients of the sail are assumed to be random variables with Gaussian distribution, which leads to random variations in the sail characteristic acceleration. In the second scenario, wrinkles on the sail membrane are taken into account, resulting in a misalignment of the thrust vector with respect to a perfectly smooth surface. Both phenomena are modeled based on experimental measurements available in the literature in order to perform realistic analyses. In the stochastic scenarios, Monte Carlo simulations are performed using the trained policies, demonstrating that the reinforcement learning approach is capable of finding near time-optimal solutions, while also being robust to the sources of uncertainty considered.

Goal Shifts Structure Memories and Prioritize Event-defining Information in Memory.

Every day, we encounter far more information than we could possibly remember. Thus, our memory systems must organize and prioritize the details from an experience that can adaptively guide the storage and retrieval of specific episodic events. Prior work has shown that shifts in internal goal states can function as event boundaries, chunking experiences into distinct and memorable episodes. In addition, at short delays, memory for contextual information at boundaries has been shown to be enhanced compared with items within each event. However, it remains unclear if these memory enhancements are limited to features that signal a meaningful transition between events. To determine how changes in dynamic goal states influence the organization and content of long-term memory, we designed a 2-day experiment in which participants viewed a series of black-and-white objects surrounded by a color border on a two-by-two grid. The location of the object on the grid determined which of two tasks participants performed on a given trial. To examine if distinct types of goal shifts modulate the effects of event segmentation, we changed the border color, the task, or both after every four items in a sequence. We found that goal shifts influenced temporal memory in a manner consistent with the formation of distinct events. However, for subjective memory representations in particular, these effects differed by the type of event boundary. Furthermore, to examine if goal shifts lead to the prioritization of goal-relevant features in longer lasting memories, we tested source memory for each object's color and grid location both immediately and after a 24-hr delay. On the immediate test, boundaries enhanced the memory for all concurrent source features compared with nonboundary items, but only if those boundaries involved a goal shift. In contrast, after a delay, the source memory was selectively enhanced for the feature relevant to the goal shift. These findings suggest that goals can adaptively structure memories by prioritizing contextual features that define a unique episode in memory.

Performance Evaluation of Solar PV Integrated with Custom Power Device under Various Load Conditions

The non-linear properties and rapid switching of power electronic equipment are the primary causes of power quality issues in power systems, particularly in the power distribution systems. The widespread use of delicate equipment, which continuously pollutes the environment, is making power quality problems worse. The increasing integration of renewable energy sources into the generation mix and the decarburization of the economy have created new challenges for smart grid technology, requiring creative solutions such as energy storage systems and smart transformers. This article describes a solar photovoltaic integrated unified power quality conditioner (UPQC) that uses a deep learning method based on neural networks and a novel compensating technique. Here, two Deep Neural Network algorithms are used, one in the solar PV system to obtain the maximum power under various irradiance situations, and the other to manage the UPQC under various load conditions. When compared to the conventional UPQC based on PQ Theory, DNN-UPQC produces superior results in terms of reducing total harmonic distortion. This iterative strategy, which is focused on soft computing, provides faster convergence to the target condition while maintaining the updating weight within a predetermined limit. PV-based UPQC has been connected individually to reduce voltage sag, swell, and unbalance in variable load conditions. The system's dynamic and steady state performance are assessed by modelling it with a MATLAB-Simulink in different load condition.

A Dynamic and Static Combined State Recovery Method Against FDI Attacks in Power Grids

A Dynamic and Static Combined State Recovery Method Against FDI Attacks in Power Grids

Effect of Ce co-doping on radiation resistance behavior and laser performance of Er/Yb/Ce co-doped high-phosphorous polarization-maintaining silica fiber

Er/Yb/Ce co-doped high-phosphorus silica fiber core glasses with varying Ce2O3 concentrations were prepared and systematically studied. Based on this research, optimized Er/Yb/Ce co-doped high-phosphorus polarization-maintaining silica fibers were subsequently fabricated using the modified chemical vapor deposition (MCVD) technique and hole drilling method. The study confirmed that there is an optimal range of Ce doping concentrations for spectral performance, Ce doping effectively suppressing the formation of P-related defects during X-ray irradiation, and the dynamic valence state transformation of Ce3+/Ce4+ ions during irradiation enhanced the radiation-resistant properties, as demonstrated through radiation-induced absorption (RIA), electron paramagnetic resonance (EPR), and L3-edge X-ray absorption near edge structure (XANES) spectra. The homemade fiber, with optimized element concentrations, exhibited high laser efficiency comparable to commercial high-power fibers. Furthermore, the radiation-induced background loss of the fiber was 0.16 dB/m under 60 krad γ-rays at a low dose rate of 100 rad/h, and the radiation-induced gain variation (RIGV) was 0.019 dB/krad.

Influence of a dynamic gas film on underwater drag reduction

The gas film at the liquid–solid interface, induced by hydrophobic microstructure, can achieve a high-efficiency underwater drag reduction. However, previous studies have rarely considered the impact of changes in gas structure morphology on drag performance, especially under turbulent conditions. We conducted numerical simulations to examine the dynamic process of gas on a hydrophobic spanwise grooved surface under turbulent conditions. Our findings indicate that the morphology of the gas phase structure at the liquid–solid interface undergoes continuous alterations due to fluid action, resulting in a dynamic state of drag performance. In addition, the gas morphology that completely covers the groove surface will reduce the turbulent kinetic energy on the groove surface, resulting in a better drag reduction effect. In the flow velocity range of 10–20 m/s, the drag reduction effect of the superhydrophobic grooved surface increases with the flow velocity. Finally, we conducted experiments to validate the effectiveness of this result. A mechanism for underwater drag reduction was proposed based on these simulation results. This study offers a novel perspective on the phenomenon of underwater gas drag reduction, which could significantly influence its practical applications, especially under real working conditions.

Unveiling the shape: A multi-wavelength analysis of the galaxy clusters Abell 76 and Abell 1307

We analyse the dynamical state of the galaxy clusters Abell 76 and Abell 1307 from the optical point of view, presenting a coherent scenario that responds to the X-ray emissions observed in these structures. Our study is based on 231 and 164 spectroscopic redshifts, for the clusters A76 and A1307, respectively, obtained mostly with the Telescopio Nazionale Galileo, and complemented with others collected from the SDSS DR16 spectroscopic database and the literature. We find that A76 and A1307 are two galaxy clusters at z = 0.0390 and 0.0815, respectively, with a velocity dispersion of 650 ± 56 km s−1 and 863 ± 85 km s−1, and they show velocity distributions following, in practice, Gaussian profiles. From our dynamical analysis, X-ray studies and SZ-Planck emission, we obtain a mean total mass M500 = 1.7 ± 0.6 ⋅ 1014 M⊙ and 3.5 ± 1.3 ⋅ 1014 M⊙ for A76 and A1307, respectively. Using the SDSS DR16 photometric database, we find that the spatial distribution of likely cluster members in the case of A76 is very anisotropic, while A1307 shows a compact distribution of galaxies, but it is double peaked and elongated in the south-north direction. Using XMM-Newton X-ray data, we compared the surface brightness maps with galaxy distributions and noticed that both distributions are correlated. We reconstructed the total mass profile and velocity anisotropy of both clusters by analysing the full projected phase space, through the MG-MAMPOSSt code. Our study reveals a slight indication of radial orbits for A76, while A1307 seems to prefer more isotropic orbits in the whole cluster range. In summary, A76 represents a typical young cluster, in an early stage of formation, with a very low X-ray surface brightness but a high temperature showing a very anisotropic galaxy distribution. A1307 is however more consolidated and massive showing in-homogeneous galaxy distribution and an asymmetric X-ray emission, which suggest a scenario characterised by recent minor mergers.