Dynamic Modulation Research Articles

Brucella NpeA is a secreted Type IV effector containing an N-WASP-binding short linear motif that promotes niche formation.

The modulation of actin polymerization is a common theme among microbial pathogens. Even though microorganisms show a wide repertoire of strategies to subvert the activity of actin, most of them converge in the ones that activate nucleating factors, such as the Arp2/3 complex. Brucella spp. are intracellular pathogens capable of establishing chronic infections in their hosts. The ability to subvert the host cell response is dependent on the capacity of the bacterium to attach, invade, avoid degradation in the phagocytic compartment, replicate in an endoplasmic reticulum-derived compartment and egress. Even though a significant number of mechanisms deployed by Brucella in these different phases have been identified and characterized, none of them have been described to target actin as a cellular component. In this manuscript, we describe the identification of a novel virulence factor (NpeA) that promotes niche formation. NpeA harbors a short linear motif (SLiM) present within an amphipathic alpha helix that has been described to bind the GTPase-binding domain (GBD) of N-WASP and stabilizes the autoinhibited state. Our results show that NpeA is secreted in a Type IV secretion system-dependent manner and that deletion of the gene diminishes the intracellular replication capacity of the bacterium. In vitro and ex vivo experiments demonstrate that NpeA binds N-WASP and that the short linear motif is required for the biological activity of the protein.IMPORTANCEThe modulation of actin-binding effectors that regulate the activity of this fundamental cellular protein is a common theme among bacterial pathogens. The neural Wiskott-Aldrich syndrome protein (N-WASP) is a protein that several pathogens target to hijack actin dynamics. The highly adapted intracellular bacterium Brucella has evolved a wide repertoire of virulence factors that modulate many activities of the host cell to establish successful intracellular replication niches, but, to date, no effector proteins have been implicated in the modulation of actin dynamics. We present here the identification of a virulence factor that harbors a short linear motif (SLiM) present within an amphipathic alpha helix that has been described to bind the GTPase-binding domain (GBD) of N-WASP stabilizing its autoinhibited state. We demonstrate that this protein is a Type IV secretion effector that targets N-WASP-promoting intracellular survival and niche formation.

Semantic segmentation for large-scale point clouds based on hybrid attention and dynamic fusion

This paper investigates the semantic segmentation problem for large-scale point clouds. Recent segmentation methods usually employ an encoder–decoder architecture. However, these methods may not effectively extract neighboring information in the encoder. Additionally, they typically use nearest neighbor interpolation and skip connections in the decoder, overlooking the semantic gap between encoder and decoder features. To resolve these issues, we propose HADF-Net, which consists of a Hybrid Attention Encoder (HAE), an Edge Dynamic Fusion module (EDF), and a Dynamic Cross-attention Decoder (DCD). HAE leverages the distinctive properties of geometric and semantic relations to aggregate local features at different stages. EDF aims to alleviate information loss during decoder upsampling by dynamically integrating the neighboring information. DCD employs an enhanced fusion mechanism with spatial-wise cross-attention to bridge the semantic gap between encoder and decoder features. Experimental results on 4 datasets demonstrate that our HADF-Net achieves superior performance.

Molecularization of Metasurfaces for Multifunctional Ultrafast All‐Optical Terahertz Waves

Hybrid metasurfaces incorporated by active materials hold great promise for state‐of‐the‐art terahertz functional devices. However, it is still a major challenge to achieve ultrafast, dynamic, and multifunctional effective control of THz waves via hybrid metasurfaces. Herein, a modulator consisting of split rings and cut‐wires is first demonstrated, with an amplitude of −35.6 dB at 0.524 THz. By embedding semiconductor silicon into specified locations to form a hybrid metasurface, the ultrastrong connectivity of the silicon bridges leads to rapid optical molecularization. Under photoexcitation, the frequency tuning range is 26.7%, the phase shifting reaches 357.5°, and the maximal modulation depth is 94.54%. Taking advantage of the rapid relaxation of photocarriers in the silicon bridges, the ultrafast frequency switching is within 1400 ps. More interestingly, by changing the positions of the silicon bridges, the frequency tuning range is further promoted to 60%, the phase shifting is 353.5°, the modulation depth of 100% is achieved, and the full recovery time is 1600 ps. Furthermore, the underlying mechanism of the ultrafast tuning process is elucidated. This work demonstrates the feasibility of all‐optical‐controlled hybrid metasurface to achieve multifunctional dynamic modulation of THz waves, which has tremendous potential for applications in optical switching, signal processing, and frequency conversion.

Electron Dynamics Associated With Advection and Diffusion in Self‐Consistent Wave‐Particle Interactions With Oblique Chorus Waves

AbstractChorus waves are intense electromagnetic emissions critical in modulating electron dynamics. In this study, we perform two‐dimensional particle‐in‐cell simulations to investigate self‐consistent wave‐particle interactions with oblique chorus waves. We first analyze the electron dynamics sampled from cyclotron and Landau resonances with waves, and then quantify the advection and diffusion coefficients through statistical studies. It is found that phase‐trapped cyclotron resonant electrons satisfy the second‐order resonance condition and gain energy from waves. While phase‐bunched cyclotron resonant electrons cannot remain in resonance for long periods. They transfer energy to waves and are scattered to smaller pitch angles. Landau resonant electrons are primarily energized by waves. For both types of resonances, advection coefficients are greater than diffusion coefficients when the wave amplitude is large. Our study highlights the important role of advection in electron dynamics modulation resulting from nonlinear wave‐particle interactions.

Modulating Carrier Dynamics in PdSe2: The Role of Pressure in Electronic and Phononic Interactions.

PdSe2 is a puckered transition metal dichalcogenide that has been reported to undergo a two-dimensional to three-dimensional structural transition under pressure. Here, we investigated the electronic and phononic evolution of PdSe2 under high pressure using pump-probe spectroscopy. We observed the electronic intraband and interband transitions occurring in the d orbitals of Pd, revealing the disappearance of the Jahn-Teller effect under high pressure. Furthermore, we found that the decay rates of interband recombination and intraband relaxation lifetimes change at 3 and 7 GPa, respectively. First-principles calculations suggest that the bandgap closure slows the decay rate of interband recombination after 3 GPa, while the saturation of phonon-phonon scattering is the main reason for the relatively constant intraband relaxation lifetime. Our work provides a novel perspective for understanding the evolution of the electron and modulation of the carrier dynamics by phonons under pressure.

Symmetry-Engineered Dual Plasmon-Induced Transparency via Triple Bright Modes in Graphene Metasurfaces

Dynamical manipulation of plasmon-induced transparency (PIT) in graphene metasurfaces is promising for optoelectronic devices such as optical switching and modulating; however, previous design approaches are limited within one or two bright/dark modes, and the realization of dual PIT windows through triple bright modes in graphene metasurfaces is seldom mentioned. Here, we demonstrate that dual PIT can be realized through a symmetry-engineered graphene metasurface, which consists of the graphene central cross (GCC) and graphene rectangular ring (GRR) arrays. The GCC supports a bright mode from electric dipole (ED), the GRR supports two nondegenerate bright modes from ED and electric quadrupole (EQ) due to the C2v symmetry breaking, and the resonant coupling of these three bright modes induces the dual PIT windows. A triple coupled-oscillator model (TCM) is proposed to evaluate the transmission performances of the dual PIT phenomenon, and the results are in good agreement with the finite-difference time-domain (FDTD) method. In addition, the dual PIT windows are robust to the variation of the structural parameters of the graphene metasurface except for the y-directioned length of the GRR. By changing the carrier mobility of graphene, the amplitudes of the two PIT windows can be effectively tuned. The alteration of the Fermi level of graphene enables the dynamic modulation of the dual PIT with good performances for both modulation degree (MD) and insertion loss (IL).

Simulation Analysis of Non-concentric Squeeze Film Damper

Squeeze film dampers are components that provide additional damping for rotating motors. In general, in order to achieve the best results of the squeeze film damper, it is necessary to appropriately increase the damping value, reduce the stiffness and set a reasonable eccentricity. In the process of designing the squeeze film damper, we can apply the rotor dynamics module of COMSOL software to model and simulate it, so as to achieve better simulation results and lay a foundation for the subsequent development and design of the product. In COMSOL software, in order to simplify the modeling of rotor components, we usually model squeeze film dampers based on the damping coefficient. The model calculates the damping coefficient for a short squeeze film damper and compares the results with the analytically calculated values. In addition, the damping coefficient depends on the position of the journal in the damper, i.e., the magnitude of the eccentricity. In this paper, by analyzing the dimensionless damping coefficient, bearing stiffness, journal displacement, and fluid load at different eccentricity, and plotting the fluid pressure distribution and the average oil film flow rate of the squeeze film damper, we can find the most suitable range of eccentricity and the damage-prone part of the damper, which can provide an important reference for the actual design of the squeeze film damper.

Preliminary architecture design for human-in-the-loop control of robotic equipment in remote handling tasks: Case study on the NEFERTARI project

In this work, we present a general control architecture for robotic systems dedicated to the remote handling of in-vessel components in fusion machines. This control architecture will be tested for the inspection and maintenance of the first wall components of the RFX-mod2 experiment, in the scope of the New Equipment For the Experimental Research and Technological Advancement for the Rfx Infrastructure (NEFERTARI) project. The architecture is split into low-level and high-level layers. The former is used for the low-level control of the robotics systems and to manage safety; the latter is used for the high-level planning and implements several sub-modules, such as a Virtual Reality (VR) environment for the visualisation of the robot’s digital twin. Moreover, an Application Programming Interface (API) is intended to connect the two layers, and the communication between modules and layers is provided by the ROS2 framework. A typical usage of such a framework involves a human operator who is teleoperating the real robot, data from motor encoders are used as inputs for the dynamic model module. This module is used to compute the forward dynamics in real-time, providing an accurate simulation of the robot (digital twin) in the virtual environment.

SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking.

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements.

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Abstract To solve the problems of easy miss and false detection on rail surface defects caused by small size, dense target, and high similarity between features and background, this paper proposed an improved detection algorithm in complex background. First, the conventional convolution of YOLOv5 backbone network is replaced with omni-dimensional dynamic convolution (ODConv), which improves the feature extraction capability of the network without increasing the computational cost; second, to improve the model's performance in detecting tiny objects, a two-layer context augmentation module(CAM) is introduced into the path aggregation network(PAN) structure; finally, the traditional non-maximum suppression(NMS) algorithm is replaced by the Soft-NMS algorithm in the network post-processing to reduce the false-alarm and miss-rate. The experimental results on the Railway Track Fault Detection public dataset show that the OD-YOLO (OD stands for ODConv) and C-PAN(CAM module is introduced into PAN) structures could achieve better performance in the same type of improved algorithms; compared with the baseline algorithm YOLOv5, the ODCS-YOLO (OD stands for ODConv, C stands for CAM, and S stands for Soft-NMS) algorithm improves the precision by 12.4%, the recall by 3.6%, the map50 by 8.6% and the GFLOPs is reduced by 0.6. Compared with seven classical object detection algorithms, the ODCS-YOLO algorithm achieves the highest detection accuracy, which makes it able to meet the real-time detection requirements of rail surface defects in real working conditions. The ODCS-YOLO model provides certain technical support for the defects detection and a new method for the detection of dense small objects.

Mitochondrial dynamics in pulmonary disease: Implications for the potential therapeutics.

Mitochondria are dynamic organelles that continuously undergo fusion/fission to maintain normal cell physiological activities and energy metabolism. When mitochondrial dynamics is unbalanced, mitochondrial homeostasis is broken, thus damaging mitochondrial function. Accumulating evidence demonstrates that impairment in mitochondrial dynamics leads to lung tissue injury and pulmonary disease progression in a variety of disease models, including inflammatory responses, apoptosis, and barrier breakdown, and that the role of mitochondrial dynamics varies among pulmonary diseases. These findings suggest that modulation of mitochondrial dynamics may be considered as a valid therapeutic strategy in pulmonary diseases. In this review, we discuss the current evidence on the role of mitochondrial dynamics in pulmonary diseases, with a particular focus on its underlying mechanisms in the development of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis (PF), pulmonary arterial hypertension (PAH), lung cancer and bronchopulmonary dysplasia (BPD), and outline effective drugs targeting mitochondrial dynamics-related proteins, highlighting the great potential of targeting mitochondrial dynamics in the treatment of pulmonary disease.

Application of graphene-based metasurface plasma-induced transparency in terahertz band optical switches

In this paper, a monolayer graphene-based metasurface that can achieve plasma-induced transparency (PIT) is proposed. This metasurface consists of a horizontal strip and a pair of vertical ribbons. The simulation results of Finite-Difference Time-Domain (FDTD) method perfectly match with the theoretical results based on Coupled-mode Theory (CMT). Based on the dynamic modulation characteristics of graphene, an optical switch is studied with a vibration amplitude of 94.7%, 89.59% and 89.58%, corresponding to frequencies of 3.34 THz, 3.86 THz and 4.36 THz, respectively. The proposed graphene-based metasurface has potential applications in mid-infrared photoconducting devices, this work has a certain theoretical significance for the development of terahertz dynamic integrated photonic devices.

Assembly/disassembly control of gold nanorods with uniform orientation on anionic polymer brush substrates

Abstract Sophisticated control of the spatial arrangement of gold nanorods provides significant advantages in the design of plasmonic systems. However, dynamic modulation of the gold nanorod spatial arrangements remains challenging. Here, we present a novel strategy for dynamic control of thermo-responsive gold nanorods with uniform alignment on a solid substrate using polymer brushes. In this system, cationic and thermo-responsive gold nanorods were immobilized into anionic polymer brushes via moderate electrostatic interactions, providing vertically aligned gold nanorod arrays. Upon heating, the gold nanorods were assembled while maintaining their vertical orientation within the polymer brushes. They returned to the original state upon cooling, indicating reversible assembly/disassembly. It is noticeable that this system exhibits rapid changes in nanostructure arrangement even when immobilized in the polymer brush substrate on a solid substrate rather than those dispersed in solution. Importantly, the gold nanorods showed good adhesion stability in polymer brushes without any significant detachment during washing and thermal cycling processes but performed assembly formation even at largely separated conditions, indicating the traveling of considerable distances similar to the lateral diffusion of membrane proteins in cell membranes. In addition to providing unprecedented control over gold nanorod spatial configurations, our approach introduces a versatile platform for developing advanced plasmonic devices.

A fine-grained grading network for natural products based on dynamic association inference

Fine-grained grading of natural products, based on internal defects, plays an important role in food processing. Although X-ray imaging is critical for revealing internal defects due to its capacity to penetrate surfaces, the inherent grayscale and lower contrast make it challenging to extract fine-grained features. To address this, we propose the Fine-Grained Grading Network (FGGN) for classifying the quality grades of multiple natural products from X-ray images. Leveraging a ResNet18 backbone, FGGN enhances feature extraction by employing a hybrid attention module that emphasizes crucial local details by analyzing joint dependencies within tensor dimensions. Moreover, by combining cross-dimensional context encoding and dynamic graph convolution, a dynamic correlation inference module is proposed to construct dynamic graphs in a gated inference pattern, thereby integrating fine-grained information across abstraction levels. Finally, the cross-entropy loss is combined with graph constraint loss to train the network end-to-end. Extensive evaluation of three natural product datasets shows that the Fine-Grained Grading Network achieves state-of-the-art recognition performance, which demonstrates its grading applicability to multiple natural products.

Breast Cancer Cells Exhibit Mesenchymal-Epithelial Plasticity Following Dynamic Modulation of Matrix Stiffness.

Mesenchymal-epithelial transition (MET) is essential for tissue and organ development and is thought to contribute to cancer by enabling the establishment of metastatic lesions. Despite its importance in both health and disease, there is a lack of in vitro platforms to study MET and little is known about the regulation of MET by mechanical cues. Here, hyaluronic acid-based hydrogels with dynamic and tunable stiffnesses mimicking that of normal and tumorigenic mammary tissue are synthesized. The platform is then utilized to examine the response of mammary epithelial cells and breast cancer cells to dynamic modulation of matrix stiffness. Gradual softening of the hydrogels reduces proliferation and increases apoptosis of breast cancer cells. Moreover, breast cancer cells exhibit temporal changes in cell morphology, cytoskeletal organization, and gene expression that are consistent with mesenchymal-epithelial plasticity as the stiffness of the matrix is reduced. A reduction in matrix stiffness attenuates the expression of integrin-linked kinase, and inhibition of integrin-linked kinase impacts proliferation, apoptosis, and gene expression in cells cultured on stiff and dynamic hydrogels. Overall, these findings reveal intermediate epithelial/mesenchymal states as cells move along a matrix stiffness-mediated MET trajectory and suggest an important role for matrix mechanics in regulating mesenchymal-epithelial plasticity.

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows

In real industrial environments, acquiring vibration data from bearings is often challenging due to noise, resulting in network models that excel when trained on datasets with sufficient samples but struggle with accurate fault identification in real-world scenarios, inevitably threatening the reliability of fault diagnosis. To address this problem, this paper proposes an end-to-end fault diagnosis framework (DRSwin-ST) based on sparse transformer with a shift window and dynamic threshold noise reduction. The Swin-Transformer serves as the backbone, leveraging a multi-head self-attention mechanism with a shift window to capture global information. The 1.5-Entmax replaces Softmax in the self-attention mechanism, sparsifying irrelevant information and allowing the model to focus on essential details. The self-attention mechanism, combined with a multi-scale structure, forms a forward feedback network to obtain rich fault feature information. In addition, the paper integrates a large convolutional kernel and a dynamic soft-threshold noise reduction module to construct a convolutional network in front of the transformer structure. This configuration extracts fault feature information and removes the noise, enhancing the fault recognition accuracy of the model. Experimental results on three diverse datasets demonstrate that DRSwin-ST exhibits robustness and high accuracy even in scenarios with limited samples and high noise, validating its exceptional performance.

Contact angle hysteresis due to electric inhomogeneity of topographical patterning of dielectric layer in electrowetting

Despite the crucial role of contact angle hysteresis (CAH) in various industrial processes, there is a lack of approaches to the dynamic modulation of CAH. Herein, dielectric layers in electrowetting systems are fabricated with long-range and low-magnitude topographical patterning of the dielectric layer to generate an inhomogeneous electric field at the solid-liquid interface. Reversible and dynamic modulation of CAH is implemented while avoiding possible liquid pinning. The outcome of this work enables the programming of CAH and expands its application in the modulation of solid-liquid interactions.

Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling.

Dopaminergic neurons play a crucial role in associative learning, but their capacity to regulate behavior on subsecond timescales remains debated. It is thought that dopaminergic neurons drive certain behaviors by rapidly modulating striatal spiking activity; however, a view has emerged that only artificially high (that is, supra-physiological) dopamine signals alter behavior on fast timescales. This raises the possibility that moment-to-moment striatal spiking activity is not strongly shaped by dopamine signals in the physiological range. To test this, we transiently altered dopamine levels while monitoring spiking responses in the ventral striatum of behaving mice. These manipulations led to only weak changes in striatal activity, except when dopamine release exceeded reward-matched levels. These findings suggest that dopaminergic neurons normally play a minor role in the subsecond modulation of striatal dynamics in relation to other inputs and demonstrate the importance of discerning dopaminergic neuron contributions to brain function under physiological and potentially nonphysiological conditions.

Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects.