Spatial Temporal Resolution Research Articles

Left Insula and Right Middle Temporal Gyrus Dominate Cortical Network Discriminating Arousal-dependent Emotions.

Emotion processing is an integral part of everyone's life. The basic neural circuits involved in emotion perception are becoming clear, though the emotion's cognitive processing remains under investigation. Utilizing the stereo-electroencephalograph with high temporal-spatial resolution, this study aims to decipher the neural pathway responsible for discriminating low-arousal and high-arousal emotions. This study involves 19 patients with pharmacologically resistant epilepsy who participate in a delayed match/mismatch sample task designed to separately assess their ability to discriminate between low-arousal and high-arousal emotions. Three groups of 11 brain subregions, with dominant lateralization, compose a network, which is identified as responsible for discriminating arousal-dependent emotions. The connection of these subregions, leading by the left insula and right middle temporal gyrus, defines the pathways for discriminating emotions with different arousals. Further, the separated network patterns related to emotional discrimination are face-independent. Overall, the left insula and the right middle temporal gyrus emerge as core components in the network, which plays key roles in the dynamic course for discriminating low- and high-arousal emotions in the human brain.

Gypsum heterogenous nucleation pathways regulated by surface functional groups and hydrophobicity

Gypsum (CaSO4·2H2O) plays a critical role in numerous natural and industrial processes. Nevertheless, the underlying mechanisms governing the formation of gypsum crystals on surfaces with diverse chemical properties remain poorly understood due to a lack of sufficient temporal-spatial resolution. Herein, we use in situ microscopy to investigate the real-time gypsum nucleation on self-assembled monolayers (SAMs) terminated with −CH3, −hybrid (a combination of NH2 and COOH), −COOH, −SO3, −NH3, and −OH functional groups. We report that the rate of gypsum formation is regulated by the surface functional groups and hydrophobicity, in the order of −CH3 > −hybrid > −COOH > −SO3 ≈ − NH3 > − OH. Results based on classical nucleation theory and molecular dynamics simulations reveal that nucleation pathways for hydrophilic surfaces involve surface-induced nucleation, with ion adsorption sites (i.e., functional groups) serving as anchors to facilitate the growth of vertically oriented clusters. Conversely, hydrophobic surfaces involve bulk nucleation with ions near the surface that coalesce into larger horizontal clusters. These findings provide new insights into the spatial and temporal characteristics of gypsum formation on various surfaces and highlight the significance of surface functional groups and hydrophobicity in governing gypsum formation mechanisms, while also acknowledging the possibility of alternative nucleation pathways due to the limitations of experimental techniques.

Neuronal Imaging at 8-Bit Depth to Combine High Spatial and High Temporal Resolution With Acquisition Rates Up To 40 kHz.

A challenge in neuroimaging is acquiring frame sequences at high temporal resolution from the largest possible number of pixels. Measuring 1%-10% fluorescence changes normally requires 12-bit or higher bit depth, constraining the frame size allowing imaging in the kHz range. We resolved Ca2+ or membrane potential signals from cell populations or single neurons in brain slices by acquiring fluorescence at 8-bit depth and by binning pixels offline, achieving unprecedented frame sizes at kHz rates. In hippocampal slices stained with the Ca2+ indicator Fluo-4 AM, we resolved transients at 2 kHz from large frames. Along the apical dendrite of a layer-5 pyramidal neuron, we measured Ca2+ signals associated with a back-propagating action potential at 10 kHz. Finally, in the axon initial segment of the same cell type, we recorded an action potential at 40 kHz by voltage-sensitive dye imaging. This approach unlocks the potential for a range of imaging measurements.

Flexible Electrode Arrays Based on a Wide Bandgap Semiconductors for Chronic Implantable Multiplexed Sensing and Heart Pacemakers.

Implantable systems with chronic stability, high sensing performance, and extensive spatial-temporal resolution are a growing focus for monitoring and treating several diseases such as epilepsy, Parkinson's disease, chronic pain, and cardiac arrhythmias. These systems demand exceptional bendability, scalable size, durable electrode materials, and well-encapsulated metal interconnects. However, existing chronic implantable bioelectronic systems largely rely on materials prone to corrosion in biofluids, such as silicon nanomembranes or metals. This study introduces a multielectrode array featuring a wide bandgap (WBG) material as electrodes, demonstrating its suitability for chronic implantable applications. Our devices exhibit excellent flexibility and longevity, taking advantage of the low bending stiffness and chemical inertness in WBG nanomembranes and multimodalities for physical health monitoring, including temperature, strain, and impedance sensing. Our top-down manufacturing process enables the formation of distributed electrode arrays that can be seamlessly integrated onto the curvilinear surfaces of skins. As proof of concept for chronic cardiac pacing applications, we demonstrate the effective pacing functionality of our devices on rabbit hearts through a set of ex vivo experiments. The engineering approach proposed in this study overcomes the drawbacks of prior WBG material fabrication techniques, resulting in an implantable system with high bendability, effective pacing, and high-performance sensing.

Tracking Single Kinesin in Live Cells Using MINFLUX.

MINFLUX is a super-resolution fluorescence microscopy technique that enables single-molecule tracking in live cells at a single-nanometer spatial and sub-millisecond temporal resolution. This chapter describes a method for tracking fluorescently labeled human kinesin-1 in live cells using MINFLUX and analyzing kinesin stepping dynamics.

Advancements in organic fluorescent materials: unveiling the potential of peripheral group modification in dithienyl-diketopyrrolopyrrole derivatives for one- and two-photon bioimaging.

The quest for novel organic fluorescent materials capable of two-photon absorption (2PA) has intensified in recent years due to their promising applications in biological imaging. Two-photon fluorescence microscopy (2PFM) offers high spatial-temporal resolution, reduced photodamage, and deeper tissue penetration compared to conventional techniques. However, the development of bright two-photon molecular markers remains a challenge, necessitating compounds with high fluorescence quantum yield and 2PA cross-section (σ2PA). Strategies such as increasing π-conjugation have shown promise but are hindered by synthesis complexities and limited biocompatibility. Alternatively, incorporating electron-donating (ED) or electron-withdrawing (EW) peripheral groups in a main structure has emerged as a viable approach, leading to significant enhancements in σ2PA. This study highlights the advantages and challenges of these strategies, emphasizing the importance of exploring new organic compounds and evaluating the efficacy of peripheral groups for advanced two-photon bioimaging applications.

Cross-Attention-Based High Spatial-Temporal Resolution Fusion of Sentinel-2 and Sentinel-3 Data for Ocean Water Quality Assessment

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications.

The clinical applications of dual-layer spectral detector CT in digestive system diseases.

Dual-layer spectral detector CT (DLCT) has several advantages in clinical practice, this study aims to reveal the clinical applications of DLCT in digestive system diseases. We searched PubMed and Cochrane Reviews for articles published from January 1, 2010 to May 31, 2024, using the terms "dual-layer spectral detector CT" or "dual-layer CT" combined with "hepatic fat" or "hepatic fibrosis" "hepatocellular carcinoma" or "pancreatic ductal adenocarcinoma" or "pancreatic neuroendocrine tumors" or "gastric cancer" or "colorectal cancer" or "Crohn's disease" or "bowel ischemia" or "acute abdominal conditions". DLCT consists of a top layer sensitive to lower-energy photons and a bottom layer sensitive to higher-energy photons. This configuration enables simultaneous acquisition of two energy spectra from a single X-ray beam ensuring consistent spatial alignment and temporal resolution. Spectral raw images allow image post-processing to improve image quality, reduce radiation doses and contrast media doses, and generate multiple quantitative parameters. It has broad potential for early detection, accurate staging, efficacy assessment, and prognosis prediction of liver, pancreatic, and gastrointestinal diseases, as well as for the assessment of digestive system vasculature. DLCT not only provides valuable information for the clinical diagnosis and therapeutic effect evaluation of digestive system diseases but also may play a more important role in the overall management of digestive diseases and in the decision-making of individualized medicine. Question What are the advantages of DLCT compared to traditional single-energy CT in the early detection, staging, and therapeutic evaluation of digestive system diseases? Findings DLCT enhances image quality, improves tissue characterization, and allows for multi-parametric analysis, making it superior in detecting and evaluating liver, pancreatic, and gastrointestinal diseases. Clinical relevance DLCT provides high-quality, multi-parametric imaging that improves the accuracy of diagnosing digestive diseases, facilitates more precise treatment planning, and enhances monitoring of treatment response, ultimately contributing to better patient management and prognosis.

The South Island Seismology at the Speed of Light Experiment (SISSLE): Distributed Acoustic Sensing Across and Along the Alpine Fault, South Westland, New Zealand

Abstract Distributed acoustic sensing (DAS) is positioned to revolutionize observational seismology by providing dense spatial sampling and temporal resolution. DAS repurposes long sections (1–10s of kilometers) of fiber-optic cable into thousands of individual sensors at meter spacing and uses light to measure the ground motion as seismic waves pass through the fiber. The South Island Seismology at the Speed of Light experiment involved the acquisition of DAS data from two dark telecommunication fibers along the highway near Haast, South Westland, New Zealand that run perpendicularly across the Alpine Fault and subparallel to the Alpine Fault and Tasman Sea coastline. The DAS acquisition was supplemented by the deployment of 24 nodal seismometers within 1 km of the surface trace of the Alpine Fault. We introduce the experimental setup, share initial processing steps, and preliminary observations from the DAS array deployed in two phases for nearly five months between late-February and mid-May 2023 and again in October–November 2023.

Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry

Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.

Spatial–Temporal Differentiation and Driving Factors of Vegetation Landscape Pattern in Beijing–Tianjin–Hebei Region Based on the ESTARFM Model

Urbanization and industrialization have led to obvious changes in the ecological environment and landscape pattern in the Beijing–Tianjin–Hebei region. Therefore, it is crucial to clarify the spatial–temporal changes in vegetation cover and its landscape pattern and conduct its analysis with the driving factors for ecological preservation in the Beijing–Tianjin–Hebei region. This study combined AVHRR GIMMS NDVI and MODIS NDVI data based on the ESTARFM model to obtain a high spatial–temporal resolution for vegetation cover; it then analyzed the vegetation cover changes at the type and landscape scales using a landscape index and explored the driving factors of the landscape pattern through principal component analysis. The results show that (1) the vegetation is mainly of medium and higher coverage and is distributed in the northeast, the western part of the Taihang Mountains and the central plains in the study area. From 1985 to 2022, there was no statistically significant difference in the overall change in its coverage. (2) From 1985 to 2022, at the landscape level, the vegetation cover landscape exhibited the following characteristics: increased fragmentation, an increase in the complexity of the landscape shape, a decrease in connectivity, a discrete landscape and a decrease in species diversity. At the type level, the medium vegetation demonstrated the most significant degree of fragmentation. The high-vegetation-cover areas exhibited a more concentrated distribution. Additionally, the low, lower and higher vegetation types displayed an increase in complexity, shape, discreteness and heterogeneity within the landscape. (3) Meanwhile, the principal component analysis showed that the changes in the landscape pattern of vegetation cover were mainly the result of the combined effects of climatic and anthropogenic factors in the Beijing–Tianjin–Hebei region. The human factor played the dominant role; this was followed by larger contributions from climatic factors. In addition to offering pertinent scientific insights for the maximization of the ecological environment and the fostering of regional ecological and sustainable development in the Beijing–Tianjin–Hebei region, the aforementioned analysis and research could serve as the foundation for the sustainable management and planning of vegetation cover.

A temporally insensitive spatio-temporal fusion method for remote sensing imagery via semantic prior regularization

Spatio-temporal fusion has become a popular technology for generating remote sensing images with high spatial and high temporal resolutions, thus providing valuable data support for remote sensing monitoring applications, such as environmental monitoring and city planning. Currently, deep learning-based methods have garnered a significant amount of attention, and they mostly employ the fine image at the neighboring date as an auxiliary image. However, capturing usable neighboring fine images may be challenging due to the adverse effects of weather conditions on optical images. Moreover, the fusion performance drops sharply when the temporal interval is long (i.e., there are significant differences in images). In this paper, we proposed a bidirectional pyramid fusion network with semantic prior regularization (BPFN-SPR), which exhibits remarkable flexibility and robustness to temporal intervals.Specifically, the proposed BPFN-SPR contains dual-path operations (i.e., Semantic Extraction path and Image Reconstruction path). The semantic extraction path has two modes: parameter learning mode and parameter freezing mode. The parameter learning mode aims to learn the information representation of the auxiliary fine image, while the parameter freezing mode aims to perceive the accurate semantic information of the target fine image. The image reconstruction path progressively reconstructs spatial details of fine images from coarse images, which jointly optimizes the target fine image and the auxiliary fine image, reducing the temporal sensitivity of the reconstruction branch, and thereby improving its generalization ability. Experimental results show that the proposed method has competitive performance, especially for areas with land cover changes. In addition, extensive experiments using images at multi-temporal intervals as auxiliary images have also demonstrated the significant advantages of the proposed method. The mean PSNR value attains 31.0713, while the average spectral index SAM measures 0.1640 on the LGC test set. Meanwhile, for the CIA test set, the average PSNR is recorded at 29.5332, accompanied by an average spectral index SAM of 0.1865. Therefore, the proposed BPFN-SPR has considerable potential in monitoring Earth's surface dynamics.

Abstract 4122134: Relationships between aortic valve area planimetry on four-dimensional cardiac computed tomography and survival following transcatheter aortic valve replacement

Background: Aortic valve area planimetry (AVA CT ) is feasible on four-dimensional computed tomography (4D-CT) for grading aortic stenosis severity, with superior spatial but inferior temporal resolution to echocardiography, however its prognostic utility is not well-established. We evaluated the prognostic value of AVA CT in patients with significant aortic valve stenosis undergoing transcatheter aortic valve replacement (TAVR). Methods: We studied 1035 native aortic valve stenosis patients (age 79.0±9.0 years, 429 (41.5%) women) undergoing TAVR during 2019-2020. AVA CT and indexed to body surface area (AVAi CT ) were retrospectively measured on contrast-enhanced 4D-CT at peak systole, and standard statistical analyses performed to evaluate its associations with all-cause mortality during follow-up. Results: Mean AVA CT and AVAi CT were 1.02±0.21 cm 2 and 0.53±0.12 cm 2 /m 2 respectively, with 240 (23.2%) deaths during mean follow-up of 2.1±0.9 years. Areas under curves and optimal thresholds for identifying severe aortic stenosis (defined as AVA<1.0 cm 2 on echocardiography) were 0.78 and <1.10 cm 2 for AVA CT and 0.83 and <0.65 cm 2 /m 2 for AVAi CT . AVA CT and AVAi CT below these thresholds representing severe aortic stenosis were independently associated all-cause mortality during follow-up, with hazards ratios (95% confidence intervals) of 1.84 (1.34-2.53) and 2.38 (1.48-3.81) respectively in multivariable analyses. The Figure illustrates Kaplan-Meier survival curves for the cohort based on A) AVA CT and B) AVAi CT thresholds. Conclusion: Optimal thresholds of AVA CT (<1.10 cm 2 ) and AVAi CT (<0.65 cm 2 /m 2 ) were independently associated with worse survival in patients undergoing TAVR, indicating that AVA CT and AVAi CT as potentially useful additional imaging biomarkers in the diagnostic and prognostic evaluation of aortic stenosis patients.

Abstract 4136692: Spatial Transcriptomics Identifies Novel Clinical Markers to Classify the Etiology of Rare and Aggressive Cardiac Sarcomas

Background: Cardiac sarcomas, such as leiomyosarcoma (LMS), synovial sarcoma (SS), and undifferentiated pleomorphic sarcoma (UPS), lack sufficient diagnostic markers for early diagnosis and tailored therapeutic interventions. Understanding their molecular signatures with spatial-temporal resolution compared to normal heart tissue is crucial. Hypothesis: Spatial transcriptomics will reveal a comprehensive set of novel clinical markers, including changes in cellular composition, stemness, and metabolic profiles unique for each cardiac sarcoma subtypes. Aims: Identify novel molecular markers for the accurate diagnosis of cardiac sarcoma subtypes. Methods: Using 10X Visium spatial transcriptomics, we analyzed three cardiac sarcoma subtypes (LMS, SS, UPS) and compared them with normal heart tissue. Differential expression analysis identified novel target genes suitable for clinical disease stratification. Subsequently, machine learning models reveal an unbiased transcriptomic profile to distinguish normal from sarcoma tissues. Finally, immunohistochemistry analysis validated the differential marker gene expression. Results: We identify several novel clinical markers, such as IGKC, PCOLCE, NET1, TLE2, MDFI, BGN, TNNC1, and CNN3, that classify normal heart tissue from sarcoma subtypes. Machine learning models and immunostaining analysis confirm the transcriptomic signature in each sarcoma subtype. Pathway enrichment analysis show LMS enriched in antigen processing and B cell-mediated immunity, SS in protein folding and response to incorrect proteins, and UPS in axon development and Wnt signaling regulation. Trajectory Reconstruction Analysis indicate higher stemness scores in all sarcomas, with SS highest, followed by UPS and LMS. Additional cell cycling analysis show LMS cells in G2M phase, SS in G1, and UPS in S phase. SS express a high number of cancer stem markers (EZH2, BMI1, KDM5B, LGR5, HIF1A), while UPS express KDM5B and CTNNB1, and LMS show fewer stem-related marker genes (EZH2, BMI1, KDM5B, CTNNB1). Both SS and LMS express CD44. Glucose and oxidative pathways are also differentially regulated in each sarcoma subtype. Conclusion: Spatial transcriptomics uncovers distinct clinical markers, stemness characteristics, and metabolic activity profiles in cardiac sarcomas, offering insights for fast, accurate and improved diagnostics and therapeutics.

Deterministic Sea Wave Reconstruction and Prediction Based on Coherent S-Band Radar Using Condition Number Regularized Least Squares

Coherent S-band radar is a remote sensing observation device with high spatial-temporal resolution and can be used to achieve deterministic sea wave reconstruction and prediction (DSWRP) technology. However, coherent S-band radar can observe nonlinear details of the sea surface due to its high resolution, which makes the propagation operator matrix an ill-conditioned overdetermined matrix. To solve this problem, this paper proposes a DSWRP scheme using condition number regularized least squares (CN-RLS) for coherent S-band radar. First, the space-time velocity information was obtained from the radar echo. Second, the CN-RLS method solved the phase-resolved model coefficients. Finally, the deterministic wave field was predicted according to the solved model coefficients. The proposed scheme was verified by simulation data and the real radar dataset observed by the coherent S-band wave-measuring radar onboard the ship XIANGYANGHONG-18 in the East China Sea in April 2024. The predicted wave elevation of the proposed method was compared with the wave elevation observed based on the X-band wave-measuring radar, and the root mean square error (RMSE) and correlation coefficient (CC) were 0.22 m and 0.76, respectively, which show that the proposed method could effectively implement the DSWRP technology.

Analisa dan Pemetaan Kepadatan Hutan Mangrove di Wilayah Pesisir Pantai Gresik

The mangrove forest ecosystem in Ujung Pangkah sub-district plays a very important role in the lives of living creatures around it. The management and use of mangrove ecosystems by people in this sub-district tends to lead to the conversion of land into ponds, whether in the form of forest or emerging land. Cutting down trees in mangrove gforests is one of the causes of abrasion in several areas in Indonesia. Apart from abrasion, there is also accretion or addition of land around the Bengawan Solo estuary which provides access to the Pangkahwetan and Pangkahkulon coasts. The aim is to analyzezchanges in the area of mangrove vegetationgin Ujungpangkah. This research uses a remote sensing method known as the resolution concept, namely spatial resolution, temporal resolution, spectral resolution, radiometric resolution and layer resolution. Quantitative observations were made of changes in mangrove forest area using Landsat -8 imagery. The location of the Ujungpangkah ground is based on the conditions of the study location, in accordance with the results of initial image processing. Changes in the area of mangrove vegetation when viewed from remote sensing results in 2023 and 2024 experience visible differences, the distribution of mangroves will increase in 2024, namely there will be an increase in the area of vegetation. The distribution area of mangroves always changes every year. Either in the form of addition or reduction of area. Things that can affect the extent of mangrove distribution include 2 types of damage to the mangrove ecosystem in Ujung Pangkah District, namely abrasion and deforestation. The efforts made to overcome this problem include starting mangrove planting activities in the Banyuurip Mangrove Center (BMC) area. The results of data processing from the 2023 RBI map in Ujungpangkah District recorded a total area of ​​11904.93052 ha. The area is divided into several land uses, namely Mangrove covering an area of ​​1253.24609 ha and Non-Mangrove covering an area of ​​1525.32660 ha. Meanwhile, processing data from Lansat-8 in 2024 in Ujungpangkah District, the total area was recorded at 11904.93052 ha. Mangrove land use was recorded at 10651.68443 ha and non-mangrove land at 10379.60392 ha.

A rapid method for composition tracking in hydrogen-blended pipeline using Fourier neural operator

Blending hydrogen into natural gas for transportation is a crucial approach for achieving the widespread utilization of hydrogen. Tracking the concentration of the hydrogen within the pipeline is important for monitoring gas quality and managing pipeline operations. This study develops a rapid computational model to predict the hydrogen and natural gas concentrations within the pipeline during transportation based on the Fourier Neural Operator (FNO), an operator neural network capable of learning the differential operator in the partial differential equation. In the proposed model, the numerical method is employed to generate datasets, with the spline interpolation used to enhance data smoothness. The initial and boundary conditions are taken as the inputs to accommodate varying transportation scenarios. Comparison results indicate that the proposed model can notably reduce the time needed to predict the hydrogen and natural gas concentrations while maintaining prediction accuracy. The accuracy of the proposed model is validated by comparing its calculated results with the analytical solution and the concentrations of hydrogen and natural gas within the pipeline under two transportation scenarios, with relative errors of 0.49%, 0.31%, and 0.45%, respectively. Notably, the trained model demonstrates strong grid invariance, a type of model generalization. Trained on data generated from a coarse grid of 101 × 41 spatial-temporal resolution, the proposed model can accurately predict results on a fine grid of 401 × 81 spatial-temporal resolution with a relative error of only 0.38%. Regarding the prediction efficiency, the proposed model achieves an average 17.7-fold speedup compared to the numerical method. The positive results indicate that the proposed model can serve as a rapid and accurate solver for the composition transport equation.

Comparative analysis of multiple observation data during two typhoons affecting Hainan Island

Based on different observation data, the wind and rain processes of two typhoons that continuously affected Hainan Island in October 2021 were compared and analyzed. The analysis shows that the two typhoons are similar in weather conditions, water vapor transport conditions, cold air intrusion, sea surface temperature providing energy, and so on. The spatial distribution of rainfall and wind field exhibits an evidently asymmetrical structure. Before the typhoon landed, the gale area was located mainly in the northeast of the typhoon center. Furthermore, different detection data have advantages and disadvantages, which should be verified and used each other. For example, there are also differences between radar echo reflectivity, wind profile radar data and MTCSWA data. By means of analysis and research, the ability to analyze and articulate the typhoon impact process with high spatial-temporal resolution data is further enhanced, thereby aiding in the forecast and service work during the typhoon period.

Identification of left ventricular flow features in healthy and pathological conditions

Abstract Background In the presence of mitral valve diseases or following surgical interventions, the hemodynamics in the left ventricle (LV) are significantly altered [1]. These maladaptive intraventricular hemodynamic alterations can stimulate a cascade of events leading to progressive adverse LV remodeling and eventual heart failure [2]. Qualitative and quantitative evaluation of LV flow features may provide a potential for sensitive risk stratification of clinical cardiac function [3]. Purpose The purpose of this study is to identifying LV flow features using in vitro experiments under both normal and pathological conditions, including mitral regurgitation (MR), mitral valve replacement with bioprosthetic/mechanical valve, and transcatheter mitral valve edge-to-edge repair (TEER). Methods We utilized a biohybrid approach to construct the in vitro experimental platform, where the native mitral valve (including chordae tendineae and papillary muscles) and aortic valve from porcine were combined with a 3D-printed silicon LV (figure 1). To ensure comparability across different conditions, we maintained consistency by utilizing the same native valve and 3D-printed LV. For pathological conditions, we cut the chordae tendineae in A2 zone to induce the severe degenerative MR, and TEER was performed on this prolapsed valve by placing two clips in A2-P2 zone. Then, the anterior leaflet of valve was cut, and a bioprosthetic valve and mechanical valve were sutured to mitral annulus to simulate surgical valve replacement, respectively. Left ventricular flow fields were measured using four-dimensional particle image velocimetry (4D-PIV) technique, with vector interval of 0.9 mm in all the spatial directions and temporal resolution of 10 ms. Results Significant differences in mean flow field were observed among healthy and pathological conditions (figure 2). Firstly, the vortex orientation was clockwise in healthy, MR, bioprosthetic valve replacement, and TEER conditions, which could accompany the redirection of blood flows towards the aortic valve. However, the replacement with a mechanical valve resulted in an alteration in vortex orientation from clockwise to counterclockwise. Secondly, the position of the center of the vortex changed in pathological conditions, with the center of the vortex closest to the apex and the left ventricle outflow tract in the healthy condition. Thirdly, the maximum Reynolds shear stress (RSS), which may be responsible for thrombus formation, significantly increased after TEER procedure and bioprosthetic valve replacement. Conclusions This study demonstrates significant changes in LV hemodynamics across different conditions. The flow features, including vortex orientations, vortex positions and the RSS, may potentially become crucial considerations for the optimization of artificial valves and mitral valve repair devices.Figure 1 Figure 2

Implementation of a high-frequency phosphor thermometry technique to study the heat transfer of a single droplet impingement

Contributing to a better understanding of spray cooling systems, the heat transfer process underlying the event of a droplet impinging onto a uniformly heated stainless steel surface (SS304) was experimentally investigated. Since the heat transfer process is linked to the droplet’s hydrodynamics, high-speed videos were recorded to measure the deformation of the droplet. A series of isothermal and non-isothermal impacts were performed for Weber numbers (We) within the range 17.7≤We≤58.2. A strong relationship between the maximum spreading ratio reached by the droplet and its initial kinetic energy was found. The surface temperature directly affects the droplet hydrodynamic during the impact by promoting an oscillatory behavior of the droplet after the maximum spreading is reached. Given the spatial–temporal resolution of the heat transfer process, a high-frequency phosphor thermometry technique was implemented, finding that the temperature drop upon droplet impact was independent of impact velocity. The sharp temperature drop results in an intense thermal interaction that occurred during the first 10 ms of the impact. The maximum average heat flux registered was 98.56 W/cm2 with a cooling effectiveness of 3.5%.