Dense Network Research Articles

Estimating risk to prevent damage: predicting and preventing coypu (Myocastor coypus) damage to transport infrastructure.

A major impact of the invasive Myocastor coypus in the introduction range is the collapse of riverbanks and nearby infrastructure, such as railway lines, due to the species' burrowing activities. As the ubiquitous implementation of preventive measures along watercourses is unfeasible, identifying susceptible areas is key to guide targeted management actions. This study used species-habitat models to (i) identify the local environmental features of the railway line/watercourse intersections (RLWIs) that make them particularly susceptible to coypu damage, and (ii) predict species occurrence probability in a wide lowland-hilly area of northern Italy (Lombardy) to identify priority areas for monitoring. The local scale models stressed that the RLWIs most susceptible to burrowing were those surrounded by arable lands with interspersed hedgerows locally characterized by high herbaceous vegetation and clay soil. In urbanized and intensive agricultural areas coypu dens were generally located significantly closer to the railway, increasing the collapse risk. The landscape-scale species distribution model showed that lowland areas located along major rivers and lake shores, but also agricultural areas with a dense minor hydrographic network especially in the southeast of the study area, are more likely occupied by the species. The local scale models shown that specific environmental characteristics increase the risk of burrowing near RLWIs. The landscape scale model allowed predicting which areas require thorough monitoring of RLWIs in search of such local characteristics to implement preventive management measures. The proposed model-based framework can be applied to any geographical context to predict and prevent coypu damages. This article is protected by copyright. All rights reserved.

Spectral Domain and Angiography Optical Coherence Tomography in Parkinson’s Disease: Structural And Vascular Changes in the Retina Correlate with Disease Severity and Progression

Background. Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by bradykinesia, resting tremor, and muscle rigidity. Visual disturbances have been also described among non-motor features. Objective. We aimed to investigate the structural and vascular changes in the retinal and choroidal vascular networks, and to assess any relationship with motor and non-motor symptoms (NMS) in PD patients. Methods. Ganglion cell complex (GCC), retinal nerve fiber layer (RNFL), and subfoveal choroidal thickness (SFCT) were examined using spectral domain-optical coherence tomography (SD-OCT). The vessel density (VD) of retinal and choriocapillary vascular networks in macular area and the foveal avascular zone (FAZ) area were evaluated by OCT angiography (OCTA). All patients underwent clinical evaluation using motor section of the Unified PD Rating Scale (UPDRS-III) and the Hoehn and Yahr (HY) scale. Results. A total of 48 eyes from 24 PD patients and 50 eyes from 25 controls were assessed. At SD-OCT, GCC and RNFL were more significantly thin in patients compared to controls. At OCTA exam, PD subjects showed lower values in VD of superficial capillary plexus (SCP) and radial peripapillary capillary plexus in comparison to controls, whereas FAZ area resulted in a significant increase in the patient group. We found a negative correlation between the age at onset and VD of SCP, and between HY score and RNFL thickness and FAZ. UPDRS-III score was negatively correlated with VD of deep capillary plexus. Discussion. The impairment of retinal structure and microvasculature seems to correlate with disease severity and progression in PD. Retinal anomalies can be considered as non-motor manifestations that could occur already in the early stage of the disease.

The Role of Technology Use in Moderating the Influence of Social Capital and Work Environment on Teacher Performance

Many things are indicated as the driving force in improving performance, and the success of a graduate at the high school level cannot be separated from an educator (teacher). So this research aims to determine the moderating influence of the use of technology in encouraging performance between social capital and the work environment on teacher performance. This research uses a quantitative descriptive approach with primary and secondary data sources. The research population was all teachers in high schools in Sekotong sub-district, West Lombok, namely 91 teachers. Determination of samples using purposive sampling so that 91 teacher informants were obtained. The research instrument used a questionnaire distributed using Google Form. Data analysis method using path analysis and with SmartPLS 4.0 as a processing tool. The results of this research are that social capital is proven to have no significant effect on teacher performance, meaning that a wide and dense network of relationships does not have an effect on increasing teacher performance for high school/vocational school level teachers in Cendi Manik Village, Sekotong District, West Lombok Regency, West Nusa Tenggara. Meanwhile, the work environment has been proven to have a significant positive effect on teacher performance. This means that the better the work environment, the better the teacher performance will be at the High School teachers in Cendi Manik Village, Sekotong District, West Lombok Regency, West Nusa Tenggara. Then technology is not proven to moderate social capital and the work environment on teacher performance. This means that technology must be balanced with other things to support it, for example balancing knowledge with training and updating knowledge.

Changes in Economic Network Patterns and Influencing Factors in the Urban Agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area: A Comprehensive Study

The aim of this study is to comprehensively evaluate the economic network patterns and their influencing factors of the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) in China, considering population scale, economic development level, and land-resource endowment. By employing a modified gravity model and a social-network method, we quantitatively analyzed urban agglomeration integrity indices, such as network density, edge–core structure, cohesive-subgroup index, and urban individual index (e.g., centrality degree) of this region, encompassing nine cities in Guangdong Province and two special administrative regions. The results revealed significant changes in the economic network patterns within the GHMGBA over time. Furthermore, the quadratic assignment procedure correlation analysis index was used to identify the various factors affecting the strength of the economic interaction. The findings demonstrated an annual increase in the strength of economic interaction between cities and regions within the GHMGBA over the past 20 years, along with the emergence of a polycentric economic development pattern. The results also suggest that the spatial location and level of economic development are key determinants influencing the strength of economic linkages in this area. This study supports the conclusion that deepening exchanges and cooperation among core cities, bolstering economic development in sub-core cities, and facilitating the construction of an integrated regional transportation network will expedite the process of economic integration.

Construction of an Intelligent Identification Model for Drugs in Near Infrared Spectroscopy and Research on Drog Classification based on Improved Deep Algorithm

Near-infrared spectroscopy has important applications in drug and food identification. Combining machine learning with near-infrared spectroscopy to achieve intelligent identification of drugs has become a research hotspot in recent years. To solve the problem of machine learning’s inefficiency in classifying small-scale data, a drug identification model based on near-infrared spectroscopy combined with a random fading depth belief network is proposed. Aiming at the problem that the training time of the machine learning algorithm is too long, the extreme learning machine is used to replace the back propagation algorithm to optimize the stack sparse auto-encoder network. Additionally, the stack sparse auto-encoder algorithm based on extreme learning machine algorithm is constructed. The study found that the precision of the Dropout Deep Belief Network model was 99.12%, which was higher than the other three models. Additionally, the area under the curve value of the Dropout Deep Belief Network model was 0.87, which was 0.04 higher than the binary whale optimization algorithm model, 0.26 higher than the factor decomposition machine and depth neural network model, and 0.05 higher than the random forest network model. The sparse auto-encoder algorithm based on the extreme learning machine algorithm achieved a precision of 99.72%. The study proposes two algorithm models that can effectively identify drugs using near-infrared spectroscopy. This has a positive impact on the medical industry and the safety of patients' lives and health.

Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area’s total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county’s geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses’ strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.

Impact of the Russia–Ukraine Conflict on Global Marine Network Based on Massive Vessel Trajectories

Maritime transportation plays a vital role in global trade, and studying the resilience of the global maritime network is crucial for ensuring its sustainable development. Currently, the ongoing conflict between Russia and Ukraine has garnered significant global attention. However, there is a lack of specific research on the impact of the conflict on maritime shipping, particularly the resilience of the global maritime network. This paper proposes a resilience assessment framework under the influence of significant events by combining complex network metrics and network performance indicators from the resilience triangle model. It quantitatively evaluates the resilience changes in the global maritime network before and after the outbreak of the Russia–Ukraine conflict. The experiment utilizes real automatic identification system (AIS) maritime trajectory data to quantify and visualize the changes in global maritime traffic during a 20-day period before and after the conflict, constructing the global maritime network for resilience calculations. The research findings indicate the following changes occurred after the Russia–Ukraine conflict. Firstly, the global maritime industry experienced overall growth, with increased ship transportation between ports. Transportation in certain regions was negatively affected, with a significant decrease in ship activities in the Black Sea and Adriatic Sea areas. The positions of Russia and Ukraine in the world maritime industry noticeably declined. Secondly, the network connectivity, network size, and network density of the global maritime network significantly increased, indicating an enhanced network resilience. According to our quantitative results, from a topological perspective, we observed the following changes: network connectivity increased by 27.2%, network scale increased by 36.6%, network density increased by 32.4%, and network resilience increased by 18.6%. Thirdly, the global maritime network is characterized by a high degree of heterogeneity, and the impact of conflicts on the heterogeneity of the shipping network is not significant. Finally, the network exhibited a slower performance decline under random attacks, while deliberate attacks led to a sharp decline. Due to the adaptive nature of the maritime network, the resilience of the network improves in terms of its topology following the outbreak of conflicts. After conflict incidents, the rate of performance decline during simulated attacks is lower compared to the pre-conflict period.

Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain

Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain

Statistically significant difference between earthquake size distributions of independent and triggered seismicity

The Alto Tiberina Fault system, located in Central Italy, is an active structure about 60 km long composed of a principal low-angle normal fault and several minor synthetic and antithetic splays. The system is monitored by a dense seismic network, giving us the opportunity to construct high-definition seismic catalogs with a low completeness magnitude. We analyze the clustering properties of the 2010-2015 seismicity by using a 3D stochastic declustering algorithm that also includes the earthquakes’ depth. We demonstrate that the earthquake size distribution is strongly correlated with the clustering of seismic events and their depth; in particular, the principal fault and secondary faults show an opposite behavior both in terms of earthquake size distribution and clustering properties.

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

SM-GRSNet: sparse mapping-based graph representation segmentation network for honeycomb lung lesion

Objective. Honeycomb lung is a rare but severe disease characterized by honeycomb-like imaging features and distinct radiological characteristics. Therefore, this study aims to develop a deep-learning model capable of segmenting honeycomb lung lesions from Computed Tomography (CT) scans to address the efficacy issue of honeycomb lung segmentation. Methods. This study proposes a sparse mapping-based graph representation segmentation network (SM-GRSNet). SM-GRSNet integrates an attention affinity mechanism to effectively filter redundant features at a coarse-grained region level. The attention encoder generated by this mechanism specifically focuses on the lesion area. Additionally, we introduce a graph representation module based on sparse links in SM-GRSNet. Subsequently, graph representation operations are performed on the sparse graph, yielding detailed lesion segmentation results. Finally, we construct a pyramid-structured cascaded decoder in SM-GRSNet, which combines features from the sparse link-based graph representation modules and attention encoders to generate the final segmentation mask. Results. Experimental results demonstrate that the proposed SM-GRSNet achieves state-of-the-art performance on a dataset comprising 7170 honeycomb lung CT images. Our model attains the highest IOU (87.62%), Dice(93.41%). Furthermore, our model also achieves the lowest HD95 (6.95) and ASD (2.47). Significance. The SM-GRSNet method proposed in this paper can be used for automatic segmentation of honeycomb lung CT images, which enhances the segmentation performance of Honeycomb lung lesions under small sample datasets. It will help doctors with early screening, accurate diagnosis, and customized treatment. This method maintains a high correlation and consistency between the automatic segmentation results and the expert manual segmentation results. Accurate automatic segmentation of the honeycomb lung lesion area is clinically important.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-  = 0.51 (with LCS). We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Gelling properties of acid-induced tofu (soybean curd): Effects of acid type and nano-fish bone.

The effects of different types of acid coagulants and nano fish bone (NFB) additives on the characteristics of tofu were investigated using texture analyzers, SEM, FT-IR, and other techniques. The breaking force and penetration distance, in descending order, were found in the tofu induced by glucono-d-lactone (GDL) (180.27g and 0.75cm), citric acid (152.90g and 0.74cm), lactic acid (123.33g and 0.73cm), and acetic acid (69.84g and 0.58cm), respectively. The syneresis of these tofu samples was in the reverse order (35.00, 35.66, 39.66, and 44.50%). Lightness and whiteness were not significantly different among the different samples. Regardless of the acid type, the soluble calcium content in the soybean milk was significantly increased after adding NFB. As a result, the breaking force and penetration distance of all tofu samples increased significantly, but the syneresis decreased. Compared with tofu coagulated by other acids, GDL tofu formed a more uniform and dense gel network maintained by the highest intermolecular forces (especially hydrophobic interactions). Regarding the secondary structure, the lowest percentage of α-helix (22.72%) and, correspondingly, the highest β-sheet (48.32%) and random coil (18.81%) were noticed in the GDL tofu. The effects of NFB on the tofu characteristics can be explained by the changes in the gel network, intermolecular forces, and secondary structure, which were in line with the acid type. The characteristics of acid-induced tofu can be most synergistically improved by coagulation with GDL and NFB.

Numerical simulation of multi-field coupling in geothermal reservoir heat extraction of enhanced geothermal systems

AbstractThe coupled analysis of multi-field heat and mass transfer in geothermal reservoirs is a pivotal concern within the realm of geothermal rock exploitation. It holds significant implications for the assessment of thermal energy capacity and the formulation of reservoir optimization strategies in the context of geothermal rock resources. Parameters governing production, along with fracture network characteristics (such as injection well temperature, injection well pressure, fracture width, and fracture network density), exert an influence on enhanced geothermal systems (EGS) heat production. In this study, aiming to comprehend the dynamic heat generation of EGS during prolonged exploitation, a coupling of various fields including permeation within the rock formations of geothermal reservoirs and the deformation of these rocks was achieved. In this study, we formulated the governing equations for the temperature field, stress field, and permeability field within the geothermal reservoir rock. Subsequently, we conducted numerical simulations to investigate the heat transfer process in an enhanced geothermal system. We analyzed the effects of injection well temperature, injection well pressure, primary fracture width, and secondary fracture density on the temperature distribution within the reservoir and the thermal power output of the production well. The research findings underscore that ill-conceived exploitation schemes markedly accelerate the thermal breakthrough rate of production wells, resulting in a diminished rate of geothermal resource extraction from the geothermal reservoir rock. Variations in influent well temperature and secondary fracture density exhibit an approximately linear impact on the output from production wells. Crucially, injection well pressure and primary fracture width emerge as pivotal factors influencing reservoir output response, with excessive widening of primary fractures leading to premature thermal breakthrough in production wells.

Disaster scenario optimised link state routing protocol and message prioritisation

AbstractNatural and artificial (human‐made) disasters have been steadily increasing all over the world, signifying the importance of providing reliable and energy friendly communication network to survivors in the aftermath of a disaster. On the other hand, low‐battery devices running optimised link state routing (OLSR) protocol often experience quick power failure which restricts their ability to communicate for a necessary period during rescue operations. To extend the lifespans and prioritise message delivery on low‐battery devices, the authors examine disaster scenario optimised link state routing (DS‐OLSR) protocol ALERT message and propose an innovative solution to prioritise messages based on the device battery energy level, leading to more energy conservation, packet delivery as well as better emotional state of survivors. An ALERT message is a novel message type added to mobile ad‐hoc network's (MANET) popular OLSR protocol for energy efficiency. The proposed DS‐OLSR Protocol and Message Prioritisation (DS‐OLSRMP) as an extension of DS‐OLSR modifies the multipoint relay mechanism and uses a prioritisation technique which classify nodes into four priority groups: Critical, High, Medium, and Low priorities. These priority groups help in prioritising both message delivery and message status notifications for devices with low battery energy. The DS‐OLSRMP was implemented in a Network Simulator, version 3.29 and compared with DS‐OLSR, OLSRv1 and OLSRv2. The simulation results show that DS‐OLSRMP performs better than DS‐OLSR, OLSRv1 and OLSRv2 in terms of energy conservation and packets delivery in the simulation of both sparse and dense network scenarios.

ACTOR: Adaptive Control of Transmission Power in RPL.

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

Cucurbit[8]uril Mediated Supramolecular and Photocrosslinked Interpenetrating Network Hydrogel Matrices for 3D-Bioprinting.

Printing of biologically functional constructs is significant for applications in tissue engineering and regenerative medicine. Designing bioinks remains remarkably challenging due to the multifaceted requirements in terms of the physical, chemical, and biochemical properties of the three-dimensional matrix, such as cytocompatibility, printability, and shape fidelity. In order to promote matrix and materials stiffness, while not sacrificing stress relaxation mechanisms which support cell spreading, migration, and differentiation, this work reports an interpenetrating network (IPN) bioink design. The approach makes use of a chemically defined network, combining physical and chemical crosslinking units with a tunable composition and network density, as well as spatiotemporal control over post-assembly material stiffening. To this end, star-shaped poly(ethylene glycol)s functionalized with Phe-Gly-Gly tripeptide or photoactive stilbazolium are synthesized, and used to prepare three-dimensional networks with cucurbit[8]uril (CB[8]) through supramolecular host-guest complexation. The hydrogel obtained shows fast relaxation and thus supports the proliferation and differentiation of cells. Upon irradiation, the mechanical properties of the hydrogel can be rapidly adapted via selective photochemical dimerization of stilbazolium within CB[8], leading to IPNs with increased form stability while retaining the dynamic nature of the hydrogels. This modular approach opens new design opportunities for extrudable and cell-friendly dynamic biomaterials for applications in 3D-bioprinting.

Characterization of Atherosclerotic Mice Reveals a Sex-Dependent Susceptibility to Plaque Calcification but No Major Changes in the Lymphatics in the Arterial Wall.

Lymphatics participate in reverse cholesterol transport, and their presence in the arterial wall of the great vessels and prior experimental results suggest their possible role in the development of atherosclerosis. The aim of this study was to characterize the lymphatic vasculature of the arterial wall in atherosclerosis. Tissue sections and tissue-cleared aortas of wild-type mice unveiled significant differences in the density of the arterial lymphatic network throughout the arterial tree. Male and female Ldlr-/- and ApoE-/- mice on a Western diet showed sex-dependent differences in plaque formation and calcification. Female mice on a Western diet developed more calcification of atherosclerotic plaques than males. The lymphatic vessels within the aortic wall of these mice showed no major changes regarding the number of lymphatic junctions and end points or the lymphatic area. However, female mice on a Western diet showed moderate dilation of lymphatic vessels in the abdominal aorta and exhibited indications of increased peripheral lymphatic function, findings that require further studies to understand the role of lymphatics in the arterial wall during the development of atherosclerosis.