Dynamic Mapping Research Articles

Efficient Distortion-Free Neural Projector Deblurring in Dynamic Projection Mapping.

Dynamic Projection Mapping (DPM) necessitates geometric compensation of the projection image based on the position and orientation of moving objects. Additionally, the projector's shallow depth of field results in pronounced defocus blur even with minimal object movement. Achieving delay-free DPM with high image quality requires real-time implementation of geometric compensation and projector deblurring. To meet this demand, we propose a framework comprising two neural components: one for geometric compensation and another for projector deblurring. The former component warps the image by detecting the optical flow of each pixel in both the projection and captured images. The latter component performs real-time sharpening as needed. Ideally, our network's parameters should be trained on data acquired in an actual environment. However, training the network from scratch while executing DPM, which demands real-time image generation, is impractical. Therefore, the network must undergo pre-training. Unfortunately, there are no publicly available large real datasets for DPM due to the diverse image quality degradation patterns. To address this challenge, we propose a realistic synthetic data generation method that numerically models geometric distortion and defocus blur in real-world DPM. Through exhaustive experiments, we have confirmed that the model trained on the proposed dataset achieves projector deblurring in the presence of geometric distortions with a quality comparable to state-of-the-art methods.

Application of Image-Based Phenotyping for QTL Identification of Tiller Angle in Rice (Oryza sativa L.)

Rice tiller angle is a key agronomic trait that regulates plant architecture and plays a critical role in determining rice yield. Given that tiller angle is regulated by multiple genes, it is important to identify quantitative trait loci (QTL) associated with tiller angle. Recently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations. In this study, we extracted tiller angle and various image-based parameters from Red-Green-Blue (RGB) images of a recombinant inbred line (RIL) population derived from a cross between Milyang23 (Indica) and Giho (Japonica). Correlations among the obtained data were analyzed, and through dynamic QTL mapping, five major QTLs (qTA1, qTA1-1, qTA2, qTA2-1, and qTA9) related to tiller angle were detected on chromosomes 1, 2, and 9. Among them, 26 candidate genes related to auxin signaling and plant growth, including the TAC1 (Tiller Angle Control 1) gene, were identified in qTA9 (RM257-STS09048). These results demonstrate the potential of image-based phenotyping to overcome the limitations of traditional manual measurements in crop structure research. Furthermore, the identification of key QTLs and candidate genes related to tiller angle provides valuable genetic insights for the development of high-yielding varieties through crop morphology control.

Improved Visual SLAM Algorithm Based on Dynamic Scenes

This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm’s ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.

Comparison Between Different Source Localization and Connectivity Metrics of Spiky and Oscillatory MEG Activities

Epilepsy is considered the second neurological disease in a coma after stroke. Famous markers of epilepsy are repetitive seizures, their origin is stroma and cortical deformation. A neurologist would be assisted by identifying Epileptogenic Zones EZ when diagnosing epilepsy.. Source localization is utilized to identify regions known as EZ, which are of excessive discharges. It consists of both forward and inverse problems. The forward problem models the head through analytical and numerical methods. The inverse problem can be resolved using several techniques to locate the cerebral abnormal sources, via the electrophysiological recording biomarkers. In our study, we will investigate four distributed inverse problem methods: minimum norm estimation MNE, standardized low-resolution brain electromagnetic tomography sLORETA, maximum entropy on the mean MEM, Dynamic statistical parametric maps dSPM, to define epileptic networks connectivity of spiky and oscillatory events. We will examine the epileptic network connectivity using Phase Locking Value (PLV), Phase Transfert Entropy (PTE) for oscillatory events, cross-correlation (CC), and Granger Causality (GC) for spiky events applied on 5 pharmaco resistant subjects. We suggest rating the effectiveness of these networks in locating EZ through a phase of confrontation within iEEG transitory and oscillatory networks connectivity by exploring concordant nodes, their distance, propagation delays connection strength, and their cooperation in recognition of seizure onset zone. All studied techniques of the inverse problem, connection metrics, for both biomarkers of the 5 patients succeed in detecting at least one part of SOZ. sLORETA provides the highest concordant nodes and the closed one for spiky events using CC and GC. sLORETA also depicts the lowest propagation delay for oscillatory events using PTE. Through the 5 patients, MEM, dSPM, and MNE using CC, CG for spiky events, and PTE, PLV for oscillatory activities provide about 72 % of concordant nodes between MEG and iEEG.

Abstract 4134729: Quantitative, time-efficient viability cardiac magnetic resonance with delayed-phase dynamic contrast enhancement model: a pilot study in dogs with myocardial infarction

Introduction: Late gadolinium enhancement (LGE) is the gold standard for myocardial viability imaging, recommended by all major cardiology societies. In this study, we aimed to develop a time-efficient viability imaging technique utilizing a dynamic contrast enhancement(dDCE) model to shorten the LGE wait time, provide quantitative measures of the contrast washout procedure, and potentially enable a fast and quantitative mean for scar imaging. Methods: A canine study(n=10) was conducted on reperfused myocardial infarction(MI). A dDCE model using dynamic post-contrast T1 maps was adopted to depict the contrast washout process. dDCE maps were reconstructed using the whole dataset(dDCE 30min ) and a 5-minute subset of the T1 maps(dDCE 5min ). To test the dDCE models for shortening the LGE wait time, LGE dDCE images were synthesized using the dDCE 5min maps and compared to the clinical LGE images. Remote and MI dDCE parameters on dDCE 30min and dDCE 5min maps were compared to test the model's ability to extract physiological features. Results: dDCE 30min and dDCE 5min map showed a good visuospatial difference between remote and MI(Fig. 1A) and no quantitative difference (Fig.1B). v e and PS showed a significant elevation in MI than in remote (61.12±13.65% vs 13.43±5.00%, P =0.018; 44.62±21.40mL/g/min vs 0.42±0.55mL/g/min, P =0.018, respectively). v p and F p were significantly decreased in MI than in remote (4.17±2.06% vs 10.85±3.77%, P =0.02; 1.11±0.32mL/g/min vs 1.51±0.42mL/g/min, P =0.04, respectively). Representative LGE and LGE dDCE images showed high agreement in the presence of MI (Fig. 2A). Bland-Altman analysis showed good agreement between LGE and LGE dDCE images for the measurement of infarct area (bias, -1.74 ± 6.60 %, Fig. 2B) and transmuraltiy (bias, 1.86 ± 2.73 %, Fig. 2C). The simple liner regression showed strong correlations between LGE and LGE dDCE images for the infarct area (R 2 , 0.95; slope, 0.93, P <0.01; Fig. 2D) and transmurality (R 2 , 0.97; slope, 0.93, P <0.01; Fig. 2E). ROC analysis showed that the AUC was 0.97 (95% CI: 0.94 to 1.00) (Fig. 2F). Increased lesion-blood pool contrast by 10 folds on dDCE images improved subendocardial MI detection (Fig.3). Conclusions: The developed dDCE model provides comparable viability assessment ability to the standard LGE images without the prolonged wait time and reflects MI pathophysiological changes. It shows the potential for a rapid and quantitative myocardial viability imaging protocol using cardiac magnetic resonance.

CONECTANDO CONCEITOS: O IMPACTO DOS MAPAS MENTAIS DIGITAIS DINÂMICOS NA APRENDIZAGEM DE SISTEMAS LINEARES

Traditional teaching methods tend to emphasize verbal and linear learning, focusing on isolated information rather than identifying relationships between concepts. However, building meaningful understanding requires visualizing these conceptual interconnections. The shift from a linear approach to integrated network models, such as mind maps, facilitates this knowledge organization, allowing students to develop a deeper understanding of concepts. With the advent of distance learning, additional challenges arise in the production of teaching materials, requiring critical reflection on the potentialities and challenges of digital technologies in pedagogical practice. Therefore, this research investigates the effectiveness of using dynamic digital mind maps (DDMM) in learning Electrical Engineering, focusing on the Linear Systems course. The study aims to evaluate, through a data collection instrument, how mind maps can assist students in understanding mathematical and abstract concepts, highlighting two perspectives: how students learn, and their perceptions of meaningful learning based on Digital Educational Resources. The experience emphasized a lesson plan for knowledge construction in the face of the challenges of 21st-century engineering education, developing competencies and skills for solving real and interdisciplinary problems.

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

Using spatial video and deep learning for automated mapping of ground-level context in relief camps

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Playback method for dynamic star map simulation by fusing cosmic background radiation information

In this study, a playback method for dynamic star map simulation fusing cosmic background radiation information was proposed to improve the playback speed of star maps. The image distortion effect before and after the fusion of star maps and cosmic background radiation was evaluated using peak signal-to-noise ratios. The performance of the proposed and the global catalog traversal generation algorithms without cosmic background radiation simulation capability, as well as the indicators, were experimentally compared. The results showed that the average refresh rates of the view cone filtering star map fast-generation algorithm and the fusion playback algorithm of the star map and cosmic background radiation were 74.2 and 73.7 Hz, respectively. Compared with the entire catalog traversal generation algorithm, the instantaneous stability of the single-frame star map was improved by 3.045 and 3.305 times, respectively, and the consumed time consistency was improved by 4.646 and 5.068 times, respectively.

Identification of novel SARS-CoV-2 3CLpro inhibitors by molecular docking, in vitro assays, molecular dynamics simulations and DFT analyses.

SARS-CoV-2 pandemic has presented a significant threat to global health and the economy, necessitating urgent efforts to develop effective antiviral drugs. The main protease (3CLpro) of SARS-CoV-2 is a critical target for antiviral therapy due to its essential role in viral replication. In order to find new structural types of 3CLpro inhibitors to facilitate the solution to the problem of new virus resistance. Six potential pharmacologically bioactive compounds were identified by utilizing structure-based virtual screening and in vitro assays from the Topscience database containing 10 million compounds. Among these, compounds 34 and 36 exhibited potent inhibitory activity with IC50 values of 6.12 ± 0.42μM and 4.47 ± 0.39μM, respectively. To elucidate their binding mechanisms with 3CLpro, all-atom molecular dynamics (MD) simulations were conducted. Principal component analysis (PCA), free energy landscapes (FEL) and dynamic cross-correlation maps (DCCM) revealed that the binding of compounds 34 and 36 to 3CLpro significantly enhanced the structural stability of 3CLpro, reducing conformational flexibility and internal motions. The results of protein-ligand interaction showed that compounds 34 and 36 formed strong and stable interactions to key residues at active site of 3CLpro with different binding modes from S-217622. And HOMO-LUMO gap and molecular electrostatic potential distribution revealed the quantum chemical properties of compounds 34 and 36. These findings suggested that compounds 34 and 36 can be as novel SARS-CoV-2 3CLpro inhibitors and promising lead-like drug candidates for developing COVID-19 treatments.

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

Oligotrophic state reduces the time dependence of the observed survival fraction for heavy ion beam-irradiated Saccharomyces cerevisiae and provides new insights into DNA repair.

Heavy ion beam (HIB) irradiation is widely utilized in studies of cosmic rays-induced cellular effects and microbial breeding. Establishing an accurate dose-survival relationship is crucial for selecting the optimal irradiation dose. Typically, after irradiating logarithmic-phase cell suspensions with HIB, the survival fraction (SF) is determined by the ratio of clonal-forming units in irradiated versus control groups. However, our findings indicated that SF measurements were time sensitive. For the Saccharomyces cerevisiae model, the observed SF initially declined and subsequently increased in a eutrophic state; conversely, in an oligotrophic state, it remained relatively stable within 120 minutes. This time effect of SF observations in the eutrophic state can be ascribed to HIB-exposed cells experiencing cell cycle arrest, whereas the control proliferated rapidly, resulting in an over-time disproportionate change in viable cell count. Therefore, an alternative involves irradiating oligotrophic cells, determining SF thereafter, and transferring cells to the eutrophic state to facilitate DNA repair-mutation. Transcriptomic comparisons under these two trophic states yield valuable insights into the DNA damage response. Although DNA repair was postponed in an oligotrophic state, cells proactively mobilized specific repair pathways to advance this process. Effective nutritional supplementation should occur within 120 minutes, beyond this window, a decline in SF indicates an irreversible loss of repair capability. Upon transition to the eutrophic state, S. cerevisiae swiftly adapted and completed the repair. This study helps to minimize time-dependent variability in SF observations and to ensure effective damage repair and mutation in microbial breeding using HIB or other mutagens. It also promotes the understanding of microbial responses to complex environments.IMPORTANCEMutation breeding is a vital means of developing excellent microbial resources. Consequently, understanding the mechanisms through which microorganisms respond to complex environments characterized by mutagens and specific physiological-biochemical states holds significant theoretical and practical values. This study utilized Saccharomyces cerevisiae as a microbial model and highly efficient heavy ion beam (HIB) radiation as a mutagen, it revealed the time dependence of observations of survival fractions (SF) in response to HIB radiation and proposed an alternative to avoid the indeterminacy that this variable brings. Meanwhile, by incorporating an oligotrophic state into the alternative, this study constructed a dynamic map of gene expression during the fast-repair and slow-repair stages. It also highlighted the influence of trophic states on DNA repair. The findings apply to the survival-damage repair-mutation effects of single-celled microorganisms in response to various mutagens and contribute to elucidating the biological mechanisms underlying microbial survival in complex environments.

Geoelectric fields and geomagnetically induced currents during the April 23–24, 2023 geomagnetic storm

We present a study on the dynamical variations of geoelectric fields E during the intense geomagnetic storm of April 23–24, 2023. The storm is caused by the interplanetary counterpart of a coronal mass ejection erupted from the Sun in association with an M1.7 X-ray flare. The GeoElectric Dynamic Mapping (GEDMap) code is applied to develop spatial E maps using magnetometer data from 29 observatories distributed across northern Europe. The code has been applied during local nighttime and morningtime intense substorms which occurred during the storm main phase. The maps show intensifications and strong variability of the E-fields during the substorm events. In particular, the morningtime event is associated with a significant variability in the E-field direction. Using the computed E-fields, we determined geomagnetically induced currents (GICs) at the sub-auroral station Mäntsälä. The modeled GIC values exhibit significant correlation (correlation coefficients r=0.73−0.76\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r=0.73-0.76$$\\end{document}) with the observed values. The performed E-field mapping provides a basis for the further determination of GICs in selected locations not covered by magnetometers and can be useful in understanding the GIC variability during magnetic storms and substorms.

Three-Dimensional Space Chaotic Scrolls Generated by Memristive Hopfield Neural Network and Its Application in Robot Path Planning

Abstract In this paper, a novel three-dimensional space-scroll chaotic attractor is constructed by introducing piece-wise linear memristors into a three-neurons-based Hopfield neural network to simulate the electromagnetic influence on neural dynamics. The dynamical behaviors of the novel memristive Hopfield neural network are discussed by means of numerical methods including the Lyapunov exponent spectrum, bifurcation diagrams, phase plots and dynamic maps. Furthermore, we also investigated different neurons subject to the memristive electromagnetic influence, where the two-dimensional grid-scroll chaotic attractors can be generated by one neuron subjects to the electromagnetic influence and three-dimensional space-scroll chaotic attractors can be generated by all of the neurons subject to the memristive electromagnetic influence. Moreover, we present a three-dimensional space-scroll scroll attractor-based chaotic mobile robot system. Superiority of the proposed space-scroll-based chaotic mobile robot system compared to existing methods has been demonstrated through numerical simulations.

Lean process mining: adopting process mining in lean manufacturing for dynamic process mapping and avoiding waste occurrence in real time

Purpose This paper aims to recommend a method entitled “lean process mining (LPM)” for mapping, analyzing and improving the material/information flows in the value stream of manufacturing processes. Design/methodology/approach The method is developed based on literature review and in-depth explorative research in value stream mapping and process mining approaches. The proposed LPM framework consists of three phases including as-realized process state, improvement strategies and reengineered process state. Hence, firstly, extracts the as-realized model, measures the identified wastes and identifies the sources of wastes. Secondly, implements prediction-recommendation-prevention strategies. Thirdly, reengineers the process model and measures the improved wastes. Findings It presents the applicability of the proposed approach in (1) online observation of manufacturing process behavior and tracing the process deviations dynamically in real time to identify the sources of waste; (2) avoiding defective products occurring during the production and eliminating the relevant derived wastes including wasted material, wasted energy, waste of labor, excess inventory, increased production lead time and wasted operational costs. Originality/value The practical application of LPM is illustrated through implementing it in a real-life manufacturing case. The outcomes prove the remarkable applicability of this method in lean manufacturing to avoid waste occurrence in the value stream.

Dynamic Voltage Mapping of the Post-infarct Ventricular Tachycardia Substrate: A Practical Technique to Help Differentiate Scar from Borderzone Tissue.

During catheter ablation of post-infarct ventricular tachycardia (VT), substrate mapping is used when VT is non-inducible or poorly tolerated. Substrate mapping aims to identify regions of slowly conducting myocardium (borderzone) within and surrounding myocardial scar for ablation. Historically, these tissue types have been identified using bipolar voltage mapping, with areas of low bipolar voltage (<0.50 mV) defined as scar, and areas with voltages between 0.50 mV and 1.50 mV as borderzone. In the era of high-density mapping, studies have demonstrated slow conduction within areas of bipolar voltage <0.50 mV, suggesting that this historical cut-off is outdated. While electrophysiologists often adapt voltage cut-offs to account for this, the optimal scar-borderzone threshold is not known. In this review, we discuss dynamic voltage mapping, a novel substrate mapping technique we have developed, which superimposes data from both activation and voltage maps, to help delineate the post-infarct VT circuit through identification of the optimal scar-borderzone voltage threshold.

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

RGB Mapping: A Dynamic Approach for Flow Pattern Identification and Classification

Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies. As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows. In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection. By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.