Characteristics Of Vessels Research Articles

Heat pipe type double-containment vessel as passive safety system for small modular reactors

This paper proposes a new concept of a passive safety system for small modular reactors as a heat pipe type double-containment vessel. The proposed system configures an additional vessel outside the existing one and functions as a heat pipe that utilizes phase change heat transfer to reduce the requirement of a large pool design. The double-containment vessel features a design consisting solely of the evaporation and condensation sections for heat pipe function with compactness. The ultimate heat sink only serves as a condenser part for the double-containment vessel, therefore significantly reducing the volume of stored fluid. The study numerically evaluates several postulated accidental cases to compare the performance of the double-containment vessel using the thermal hydraulic system code, MARS-KS. Even for serious conditions such as multiple failure accidents without safety systems operation, the double-containment can secure extra golden time to core damage by more than 2,090 sec compared to the original design. This study highlights the feasibility and potential of the heat pipe-based double-containment vessel as a passive safety system, enhancing the safety and reliability of small modular reactors. This kind of design can provide the advantages of safety and economics in the construction of small modular reactors.

Lightweight multiobject ship tracking algorithm based on trajectory association and improved YOLOv7tiny

In response to various challenges, such as high computational complexity, large parameter counts, and frequent vessel ID switching in ship tracking models, we propose a trajectory association and improved YOLOv7tiny-based vessel tracking algorithm, named the vessel tracking model with YOLOv7tiny-Vessel and StrongSORT-Vessel (VTM-YS). The algorithm consists of two stages: vessel detection and vessel tracking. In the vessel detection stage, we propose a lightweight Fast-PP-LCNet backbone network for efficient feature extraction of ships. Then, a novel Slim Bidirectional Pyramid Aggregation Network (Slim-BiPANet) is constructed to effectively fuse multiscale vessel features. Furthermore, introducing a coordinate attention (CA) module enhances the focus on vessel detection regions. In the vessel tracking stage, we introduce the OSNet reidentification network and the NSA Kalman filter algorithm into the StrongSORT model to reduce the frequency of tracked vessel ID switches. The results indicate that, in terms of vessel detection, the improved YOLOv7tiny achieves a 1.5% increase in mAP50 and a 13.9% reduction in parameters compared to the baseline YOLOv7tiny. For vessel tracking, the StrongSORT model achieves a 3.5% improvement in HOTA, a 7.0% improvement in MOTA, and a 73% reduction in ID switches, meeting the requirements for lightweight design and tracking accuracy.

Ecological and economic predictors of métiers in a mixed fishery

Abstract Marine ecosystem-based management requires the understanding of species interactions and what species are harvested together. This study combines two major questions: the first regarding what drives the probability that a métier (species assemblages, with spatial distribution and seasonality) will be observed as catch, and the second regarding the level of control fishers have over this catch mix. To address these questions, we analysed highly resolved logbook records of an Arctic and sub-Arctic industrial demersal fishery operating in Icelandic waters. The study employs a multi-class random forest model to identify predictors of métier occurrence and consistency of predictions using a dataset of >100 000 hauls over 4 years (2016–2019). The overall accuracy of the random forest model is 69–70%, indicating moderate predictability of catch mix based on known environmental, vessel, and company characteristics. We find that habitat-related variables (depth and temperature) are most important to predict catch mix. Still, company, trip, and vessel characteristics are also very important (e.g. vessel and trip length, distance to port). Beyond these more traditional bio-economic variables, important predictors include variables related to harvesting strategies, such as quota diversity and a vessel’s mobility. These findings contribute to a fuller picture of fisher decision-making in mixed fisheries.

The Framework of Quantifying Biomarkers of OCT and OCTA Images in Retinal Diseases.

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions.

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang-Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Investigation on Calm Water Resistance of Wind Turbine Installation Vessels with a Type of T-BOW

Given the typical characteristics of self-propulsion and jack-up wind turbine installation vessels (WTIVs), including their full and blunt hull form and complex appendages, this paper combines the model test method with the RANS-based CFD numerical prediction method to experimentally and numerically study the resistance of the optimized hull at different spudcan retraction positions. The calm water resistance components and their mechanisms of WTIVs based on T-BOW were obtained. Furthermore, using the multivariate nonlinear least squares method, an empirical formula for rapid resistance estimation based on the Holtrop method was derived, and its prediction accuracy and applicability were validated with a full-scale ship case. This study indicates that the primary resistance components of such low-speed vessels are viscous pressure resistance, followed by frictional resistance and wave-making resistance. Notably, the spudcan retraction well area, as a unique appendage of WTIVs, exhibits a significant “moonpool additional resistance” effect. Different spudcan retraction positions affect the total calm water resistance by approximately 20% to 30%. Therefore, in the resistance optimization design of WTIVs, special attention should be paid to the matching design of the spudcan structure and the hull shell plate lines in the spudcan retraction well area.

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific haemodynamics models. KEY POINTS: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error. Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks. Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty. Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.

An Empirical Assessment of Drug Trafficking Trends from Latin America into the United States: The Role of Fishing Vessels

ABSTRACT Drug trafficking is one of the most serious crimes of our century. The market reach is estimated to be over $500 billion annually, with a particularly robust user base found in the Americas and Europe. The United States, in particular, is one of the strongest consumer markets for drugs originating from Latin America. While such serious drugs as cocaine have been trafficked into the United States using various means, one method that has been gaining attention is the use of fishing vessels. Nevertheless, studies on the role of fishing vessels in trafficking drugs from Latin America into the United States are rare. To fill this gap, this research uses fishing vessel interception data obtained through Freedom of Information Act request from the United States Coast Guard for the years 2015–2021. Informed by crime science theory, this research examines the geospatial and temporal trends associated with drug trafficking by fishing vessels, as well as such vessel characteristics as flags they carried at the time of interception, and vessel type. Policy implications are discussed in light of the main research findings.

VEPO-S2S: A VEssel Portrait Oriented Trajectory Prediction Model Based on S2S Framework

The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models.

Xylem Vessel Size Is Related to Grapevine Susceptibility to Phaeomoniella chlamydospora

Grapevine trunk diseases are a threat to table- and grape-wine cultivation worldwide. Phaeomoniella chlamydospora (Pch) is a vascular fungus recognized as one of the most important pathogens associated with grapevine trunk diseases. The relationships between xylem vessel features and Pch susceptibility of 10 table- and 17 wine-grape genotypes, as well as 3 rootstocks, were investigated by image analysis of 50 µm cross-sections and artificial Pch inoculation on one-year-old vine cuttings. Vessels were grouped in the diameter classes 1–30, 31–60, 61–90, 91–120, and >120 µm. Among the table-grape varieties, ‘Sable’, ‘Timco’, and ‘Red Globe’ showed higher densities of large vessels (>120 μm) than ‘Italia’, ‘Sugar Crisp’, and ‘Sugraone’. Among the wine-grape varieties, ‘Minutolo’, ‘Montepulciano’, ‘Primitivo’ CDTa19, and ‘Verdeca’ showed higher densities of large vessels than ‘Aglianico’, ‘Nero di Troia’, ‘Sangiovese’, and ‘Susumaniello’. In the rootstocks, the vessel diameters were 50.8, 54.0, and 60.9 μm for ‘34 E.M.’, ‘140 Ruggieri’, and ‘1103 Paulsen’, in that order. For table-grape varieties, Pch was re-isolated from 13.3% for ‘Sugar Crisp’ and ‘Sugraone’ cuttings up to 93.3% for ‘Timco’. For wine-grape varieties, Pch re-isolation ranged from 51.1% (‘Bombino nero’, ‘Negroamaro’ D15, and ‘Sangiovese’) to 81.1% (‘Montepulciano’), while for the rootstocks, the values were from 33 to 51%. A principal component analysis (PCA) revealed a positive correlation between the frequencies of large vessels and Pch re-isolation. In addition, in wine grapes and rootstocks, higher Pch re-isolation frequencies in the lateral parts of cuttings were correlated (r = 0.79) to a higher frequency of large vessels. The results highlight relationships between grapevine xylem vessel sizes and susceptibility to P. chlamydospora that are worthy of further research.

Miniaturizing Nanotoxicity Assays in Daphnids.

The rapid progress of the modern world has resulted in new materials and products created at an accelerating pace. As such, nanoparticles have widespread applications and often find their way into the aquatic ecosystem. In the case of freshwater ecosystems, one of the commonly used bioindicators species used for pollution assessment is Daphnid magna. The Organization for Economic Co-operation and Development (OECD), and other organizations such as the European Chemicals Agency (ECHA) and Environmental Protection Agency (EPA), have set guidelines for acute toxicity testing in daphnids that are severely lacking in terms of information on the characteristics of the exposure vessel when studying the adverse effects of nanoparticles (NPs). Understanding the toxicity mechanisms of nanomaterials is imperative given the scarcity of information on their adverse effects. Furthermore, miniaturization of nanotoxicity assays can reduce the number of daphnids used, as well as the cost and nanomaterial waste, and provide results even at the individual animal level with enhanced reproducibility of testing. In this study, the impact of the exposure vessel on the observed physiological changes of daphnids was investigated for a silver nano ink. Exposures in eleven commercially available vessels; nine made of plastic and two made of glass were compared for 24 h. The effect of surface to volume ratio of the exposure vessel and the animal number or "crowding" during exposure was investigated in the context of miniaturizing biomarker assays as alternatives to traditional experimental setups in Daphnid magna. Toxicity curves showed differences depending on the vessel used, while a novel feeding rate assay and the activity of key enzymes were assessed as physiology endpoints.

A novel deep network with triangular-star spatial–spectral fusion encoding and entropy-aware double decoding for coronary artery segmentation

Coronary artery segmentation is a crucial prerequisite for computer-aided diagnosis of coronary artery disease (CAD). However, this task remains challenging due to the intricate anatomical structures and morphologies of coronary arteries (CAs), which are characterized by tortuous and numerous slender branches, large inter-subject variations, and low contrast with adjacent tissues. To address these challenges, we propose a novel deep network with triangular-star spatial–spectral fusion encoding and an entropy-aware double decoding process to comprehensively explore CA features from diverse perspectives. Specifically, to enhance the encoder’s ability to exploit spectral characteristics, we incorporate a tri-stage attention-mediated Fourier (tri-AMF) structure. This structure dynamically modulates the global features of blood vessels in the frequency domain with superior resilience against spectral noises. Simultaneously, we introduce a triangular-star cross-domain feature fusion (▽-Star fusion) module, integrating features from a pair of closely intertwined encoders dedicated to spatial and spectral domains, along with features transmitted to the decoder. This module, facilitated by richly connected pairwise interaction pathways, is designed to learn to segment coronary arteries through cross-domain deep analysis. Furthermore, our network’s decoder employs a novel local entropy-aware double decoding (LEAD2) process to adaptively fuse feature maps across all scales with the local entropy associated with each scale, explicitly modeling the network’s derivation of the final segmentation outcome. Extensive experiments on two in-house and three publicly available datasets consistently demonstrate that the proposed method has superior performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. The code is available at https://github.com/Cassie-CV/CASeg.

Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET.

The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD). Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression. Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1. By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality. NCT01245933.

Optical coherence tomography-measured blood vessel characteristics of port-wine birthmarks by depth: A cross-sectional study

BackgroundPort-Wine Birthmarks (PWB) are congenital capillary malformations requiring multiple treatments. Optical coherence tomography (OCT), a non-invasive imaging technique, characterizes vessels in cutaneous vascular lesions including PWBs. ObjectiveTo assess variability in blood vessel characteristics within and between individual PWBs. MethodsOCT was used to measure blood vessel density (%) and modal vessel diameter (micrometres) at increments of 0.05 mm from skin surface to a depth of 0.50 mm at several adjacent spots of single PWBs in this cross-sectional study. Average ratios of vessel density and diameter in affected to control skin were obtained for each PWB by averaging data for all spots within a lesion. Statistical analysis was performed with linear mixed effects model using SPSS (IBM, Corp.). ResultsThere was great variability in vessel density and diameter within and between PWBs. Depths where average ratios of vessel density were consistently greater in affected to control skin were shallow, between 0.15 - 0.2 mm deep from the skin surface. LimitationsSmall sample size and device inability to measure diameters smaller than 20 micrometres. ConclusionThere is variability in vessel density and diameter within and between PWBs. Individualized treatment planning guided by OCT mapping should be studied further.

ARSA-UNet: Atrous residual network based on Structure-Adaptive model for retinal vessel segmentation

Retinal vessel segmentation remains a challenging task due to the myriad morphological characteristics of the retinal vessel and the complexity of the retinal background. To address the challenge, a novel ARSA-UNet network is proposed in this paper. Firstly, a structure adaptive layer is proposed as a substitute for the traditional convolutional layer to enhance the convolution performance. It aims to adapt different feature mappings through model structure adjustment and improve the learning efficiency of the network. Then, the proposed atrous residual path is incorporated in the hopping layer to dynamically adjust the receptive field to obtain deeper multi-scale semantic information and richer spatial information. Finally, a feature-screening fused module is added to the decoder to suppress redundant features, achieving a more effective fusion of upper- and lower-layer information. In addition, to mitigate the network degradation caused by the deepening of layers, a multi-scale deep supervised mechanism is introduced so that the deep network can also be adequately trained. Extensive experiments have been conducted on the mainstream datasets DRIVE, CHASE, and STARE, with results suggesting that the Sen value of the proposed method achieves the optimal results by the metrics set out in all three datasets. Meanwhile, F1, Acc and Spe fare well in most of the datasets. Overall, ARSA-UNet outperforms other state-of-the-art methods for the procedure. Source code is available at https://github.com/LaboratoryofCSBC/SADS-UNet.

Evaluation of an Active Disturbance Rejection Controller for Ophthalmic Robots with Piezo-Driven Injector.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precise enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel make it difficult to achieve the desired contact force without harming the retina. The paper utilizes a viscoelastic contact model to explain the mechanical characteristics of retinal blood vessels to address this issue. The uncertainty in the viscoelastic properties is considered an internal disturbance of the contact model, and an active disturbance rejection controller is then proposed to precisely control the contact force. The experimental results show that this method can precisely adjust the contact force at the millinewton level even when the viscoelastic parameters vary significantly (up to 403.8%). The root mean square (RMS) and maximum value of steady-state error are 0.32 mN and 0.41 mN. The response time is below 2.51 s with no obvious overshoot.

Transformer Connections: Improving Segmentation in Blurred Near‐Infrared Blood Vessel Image in Different Depth

AbstractHigh‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Role of the vessel morphology on the lenticulostriate arteries hemodynamics during atrial fibrillation: A CFD-based multivariate regression analysis

Background and objectiveAtrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations, reduced exercise tolerance, and chest discomfort - there is growing evidence that an alteration of deep cerebral hemodynamics due to AF increases the risk of vascular dementia and cognitive impairment, even in the absence of clinical strokes. The alteration of deep cerebral circulation in AF represents one of the least investigated among the possible mechanisms. Lenticulostriate arteries (LSAs) are small perforating arteries mainly departing from the middle cerebral artery (MCA) and susceptible to small vessel disease, which is one of the mechanisms of subcortical vascular dementia development. The purpose of this study is to investigate the impact of different LSAs morphologies on the cerebral hemodynamics during AF. MethodsBy combining a computational fluid dynamics (CFD) analysis of LSAs with 7T high-resolution magnetic resonance imaging (MRI), we performed different CFD-based multivariate regression analyses to detect which geometrical and morphological vessel features mostly affect AF hemodynamics in terms of wall shear stress. We exploited 17 cerebral 7T-MRI derived LSA vascular geometries extracted from 10 subjects and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow conditions. ResultsOur results revealed that few geometrical variables - namely the size of the MCA and the bifurcation angles between MCA and LSA - are able to satisfactorily predict the AF impact. In particular, the present study indicates that LSA morphologies exhibiting markedly obtuse LSA-MCA inlet angles and small MCA size downstream of the LSA-MCA bifurcation may be more prone to vascular damage induced by AF. ConclusionsThe present MRI-based computational study has been able for the first time to: (i) investigate the net impact of LSAs vascular morphologies on cerebral hemodynamics during AF events; (ii) detect which combination of morphological features worsens the hemodynamic response in the presence of AF. Awaiting necessary clinical confirmation, our analysis suggests that the local hemodynamics of LSAs is affected by their geometrical features and some LSA morphologies undergo greater hemodynamic alterations in the presence of AF.

Variability in airborne noise emissions of container ships approaching ports

Container ships emit airborne noise that can vary significantly due to diverse vessel characteristics and operating conditions. This study aims to investigate the factors influencing noise emission from container ships moving in ports to enhance our understanding and improve noise prediction models. Using a dataset comprising long-term sound pressure level measurements, video recordings, and weather station data, a multiple regression analysis was conducted to assess the impact of static (ship-specific) and dynamic (pass-by specific) variables on noise emission. Static variables included ship dimensions and age, while dynamic variables encompassed distance from the microphone, speed, and draught. A k-means unsupervised clustering analysis was performed using 1/3rd octave band spectra to identify subcategories of container carriers based on sound emission characteristics. Significant correlations between emissions and both static and dynamic variables were found. The k-means clustering analysis yielded distinct subcategories of container carriers based on their sound emission profiles. This study highlights the importance of considering various factors, including ship characteristics and operating conditions, when assessing noise emission from container ships. By better understanding the factors contributing to noise emission, we can effectively mitigate noise pollution and minimize the impact on surrounding communities.

Similarities and differences between brain and skin GNAQ p.R183Q driven capillary malformations.

Capillary malformations (CM) are congenital vascular irregularities of capillary and venous blood vessels that appear in the skin, leptomeninges of the brain, and the choroid of the eye in the disorder known as Sturge Weber Syndrome (SWS). More common are non-syndromic CM found only in the skin, without brain or ocular involvement. A somatic activating mutation in GNAQ (p.R183Q) is found in ~90% of syndromic and non-syndromic CM specimens and is present in CD31pos endothelial cells isolated from brain and skin CM specimens. Endothelial expression of the GNAQ p.R183Q variant is sufficient to form CM-like vessels in mice. Given the distinct features and functions of blood vessels in the brain versus the skin, we examined the features of CM vessels in both tissues to gain insights into the pathogenesis of CM. Herein, we present morphologic characteristics of CM observed in specimen from brain and skin. The GNAQ p.R183Q variant allelic frequency in each specimen was determined by droplet digital PCR. Sections were stained for endothelial cells, tight junctions, mural cells, and macrophages to assess the endothelium as well as perivascular constituents. CM blood vessels in brain and skin were enlarged, exhibited fibrin leakage and reduced zona occludin-1, and were surrounded by MRC1pos/LYVE1pos macrophages. In contrast, the CMs from brain and skin differ in endothelial sprouting activity and localization of mural cells. These characteristics might be helpful in the development of targeted and/or tissue specific therapies to prevent or reverse non-syndromic and syndromic CM.