Multiple Patches Research Articles

Mesh Simplification for Unfolding*

AbstractWe present a computational approach for unfolding 3D shapes isometrically into the plane as a single patch without overlapping triangles. This is a hard, sometimes impossible, problem, which existing methods are forced to soften by allowing for map distortions or multiple patches. Instead, we propose a geometric relaxation of the problem: We modify the input shape until it admits an overlap‐free unfolding. We achieve this by locally displacing vertices and collapsing edges, guided by the unfolding process. We validate our algorithm quantitatively and qualitatively on a large dataset of complex shapes and show its proficiency by fabricating real shapes from paper.

Risky hybrid foraging: The impact of risk, reward value, and prevalence on foraging behavior in hybrid visual search.

In hybrid foraging, foragers search for multiple targets in multiple patches throughout the foraging session, mimicking a range of real-world scenarios. This research examines outcome uncertainty, the prevalence of different target types, and the reward value of targets in human hybrid foraging. Our empirical findings show a consistent tendency toward risk-averse behavior in hybrid foraging. That is, people display a preference for certainty and actively avoid taking risks. While altering the prevalence or reward value of the risky targets does influence people's aversion to risk, the overall effect of risk remains dominant. Additionally, simulation results suggest that the observed risk-averse strategy is suboptimal in the sense that it prevents foragers from maximizing their overall returns. These results underscore the crucial role of outcome uncertainty in shaping hybrid foraging behavior and shed light on potential theoretical developments bridging theories in decision making and hybrid foraging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Stability of a Predator–Prey Model with Cross-Dispersal in a Multi-Patch Environment

This paper investigates the stability of predator–prey models within multi-patch environments, with a particular focus on the influence of cross-dispersion across patches. We apply Kirchhoff’s matrix tree theorem and Liapunov’s method to derive criteria related to the cross-dispersion topology, thus solving the challenge of determining global asymptotic stability conditions. The method incorporates realistic ecological interactions and spatial heterogeneity, offering a framework for stability analysis. Our findings demonstrate that an appropriate level of cross-dispersion can effectively mitigate oscillations and foster convergence toward equilibrium. Two numerical examples validate these theoretical results and demonstrate the feasibility and effectiveness of the model across multiple patches.

Correction for highly spatially varying aberrations over multiple isoplanatic patches by exploiting their spatial correlation

Correction for highly spatially varying aberrations over multiple isoplanatic patches by exploiting their spatial correlation

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Nonlinear Radiative Response to Patterned Global Warming due to Convection Aggregation and Nonlinear Tropical Dynamics

Abstract Climate responses to global warming exhibit large dependence on the spatial pattern of sea surface temperature (SST) changes. A Green’s function (GF) approach—predicting the global climate response to a complex SST pattern change as the linear superposition of the response to finite-area SST perturbations evaluated in isolation—has been used to systematically evaluate and understand the top of atmosphere (TOA) radiation response to patterned warming. However, in general, the linear GF approach fails for TOA radiation reconstruction under future global warming scenarios in coupled models. Here, we show that the linear superposition of global mean TOA radiation responses to large SST warming perturbations at multiple patches (the GF approach prediction) overestimates the actual response to simultaneous multipatch perturbation. This is because the linear superposition overestimates tropical large-scale convection aggregation strengthening upon localized heating that enhances longwave radiative cooling, which is further explained by the overestimation of circulation response and associated horizontal water vapor transport. The nonadditivity of TOA radiation response is caused by the nonadditivity of convection aggregation, ultimately rooted in nonlinear tropical dynamics. We also demonstrate that the prediction error of the GF approach grows with decreasing patch size (equivalently, increasing patch number). We conclude that using the GF approach to predict future climate change could overestimate longwave radiative cooling and underestimate (effective) climate sensitivity. Numerical experiments may be used to identify specific perturbation patterns where the errors are smaller than the signal. Our research also highlights that an increase in the degree of large-scale convection aggregation has a nonlinear and negative feedback for mean warming.

Skin Barrier Dysfunction in Cutaneous T-Cell Lymphoma: From Pathogenic Mechanism of Barrier Damage to Treatment.

Cutaneous T-cell lymphoma (CTCL) is a group of non-Hodgkin lymphomas characterized by multiple erythematous patches, plaques, or even nodules on the skin. As the disease progresses, patients develop widespread pruritic skin lesions, leading to skin barrier dysfunction, which significantly impacts their quality of life, appearance, and social adaptation. The pathogenesis of CTCL is not fully understood. Recent studies have recognized the important role of skin barrier dysfunction in the development and progression of CTCL, yet a comprehensive review on this topic is lacking. This review summarizes recent findings on skin barrier dysfunction in CTCL, focusing on physical barrier dysfunction, chronic inflammation, and immune dysregulation. We also discuss current and potential therapies aimed at restoring barrier function in CTCL. By emphasizing the integration of barrier-centric approaches into CTCL management, this review provides valuable insights for improving treatment outcomes.

Efficient layerwise multiphysics spectral element model for delaminated composite strips with PWAS transducers

Efficient structural element-based models are essential for fast simulations of wave propagation in composite structures for model-based and physics-informed data-driven structural health monitoring. This article introduces the first efficient multiphysics time-domain spectral structural element for wave propagation analysis of beam and panel-type composite structures with piezoelectric transducers (patch or full layers) containing delaminations. A general framework is presented to model multiple delaminations and transducer patches located arbitrarily. The intact host and patch transducer-bonded laminates and sub-laminates between delaminations are modelled separately using an electromechanically coupled efficient layerwise zigzag theory (ZIGT) for kinematics and a piecewise quadratic variation for the electric potential across piezoelectric layers. The high-order spectral element (SE) features a virtual electric node to model equipotential surfaces of piezoelectric transducers apart from the usual physical nodes having mechanical and internal electric degrees of freedom. A hybrid point-least squares continuity approach is employed to maintain continuity at the intersections of delaminated sub-laminates or the patch-bonded laminate with the host laminate. The model’s performance in capturing electroelastic waves’ interaction with delamination is examined with reference to the conventional finite element (FE) solution based on the ZIGT, continuum-based FE solutions, and the SE solution based on a non-layerwise version of the laminate theory. Finally, the model is used to examine the impact of interfacial location and size of delaminations on wave propagation behaviour.

Predicting 1-year atrial fibrillation risk from a patch-based ambulatory ECG monitoring without atrial fibrillation

Abstract Introduction Early identification of atrial fibrillation (AF) can enable interventions that reduce cardiovascular events. However, AF remains undiagnosed in about 10-20% of affected individuals, and paroxysmal AF often remains undetected despite the use of in-clinic ECGs, consumer smartwatch ECGs, or ambulatory ECG monitoring. We therefore investigated whether ambulatory ECG patch-based monitoring without AF can predict AF in the subsequent 1-year post monitoring. Purpose This study aims to validate a machine-learning model for AF prediction within 1-year post-wear, based on the observation of non-AF single-lead ECG recordings. Methods We considered a clinically monitored cohort of 67,296 individuals wearing multiple single-lead ECG multi-day ambulatory monitoring patches as part of real-world care. For all selected individuals, the first patch recorded no occurrences of AF and was worn for a minimum of 6.5 days. We assess the risk of future AF based on the raw ECG, heartrate variability, frequency of ectopic beats, age and gender (ECG model), which is then compared with the risk assessment by a model based on age and gender (Baseline model). We then observe AF incidence on subsequent patches worn within one year from the first patch. We evaluated the model's accuracy by observing the number of AF cases identified (or missed) among the individuals with highest risk according to the model. Results In the clinically monitored cohort, a total of 3463 individuals experienced AF within the next year (incidence 5.1%) after a first AF-free monitoring period of a minimum 6.5 days. The receiver operating characteristic (ROC) curve shows an area under the curve (AUC) for the ECG model of 0.75 (95% CI: [0.74, 0.76]), and for the Baseline model of 0.66 (CI: [0.65, 0.67]). When selecting the top 20% (13,459) participants with the highest risk and verifying who developed AF in the following 1-year, we obtain positive prediction value (PPV) or precision of 12% and sensitivity (recall) of 46% for the ECG model (which correctly identifies 1,594 individuals with incident AF), and of 8% and 29%, respectively, for the Baseline model (p-value<0.001). (Figure) Conclusion A machine learning model using as input an AF-free single-lead ECG can quantify the risk of observing AF in a population including individuals with low burdens of paroxysmal or new onset of AF within 1 year. Further research and validation are needed to enhance the interpretability and clinical applicability of the model, while prediction may be further improved with clinical AF diagnoses ascertained from other data sources. The findings of this study have important implications for the early detection and management of AF, potentially enabling earlier intervention and improved outcomes in individuals with undiagnosed AF.

Multimodal shunt damping of monolithic bladed drums using multiple digital vibration absorbers

Abstract Based on a digital realization of piezoelectric shunt circuits, this works presents a proof-of-concept damping approach tailored to address the complex dynamics of monolithic bladed drums (BluMs) and attenuate their vibrations. This approach comprises the simultaneous use of multiple digital vibration absorbers (DVAs) together with a mean shunt strategy, taking advantage of the fact that multiple absorbers act simultaneously on the structure and that the blade modes of BluMs appear closely-spaced. In order to target multiple groups of modes, this strategy is incorporated in a multi-staged shunt circuit. The damping concept is numerically and experimentally demonstrated on a BluM with multiple piezoelectric patches, proving its ability to achieve excellent damping performance on multiple groups of modes. This performance is shown to be relatively insensitive to the spatial distribution of the shunted patches. Based on a robustness study, it was also found that the shunts are robust to changes in the host structure which could, e.g., be due to mistuning. Owing their digital nature, DVAs are easily adjustable making them highly attractive in practical applications.

The Assessment of Different Calibration Strategies on the Luminance Measurement Using High Dynamic Range Images

ABSTRACT High Dynamic Range Imaging (HDRI) is an increasingly popular technique for studying the lighting environment through luminance maps. However, a thorough calibration process is necessary to retrieve accurate photometric measurements when generating HDR images using commercially affordable equipment like cameras. This paper aims to provide an overview of the most commonly used luminance calibration strategies and evaluate their effectiveness in luminance measurements. Seven strategies were examined in two experiments consisting of four scenes with varying levels of lighting disparity on the training patches and the testing patches. The LDR images were taken by two professional digital single-lens reflex cameras fitted with the standard zoom lens and merged into HDR images using the software Photomatix. The results indicate the much simpler sRG B D 65 method using a reliable standard target as the reference could deliver a reasonable luminance measurement accuracy which is within 10% even for scenes with large luminance difference on the training and testing patches. Although the more complex camera-specific CTM methods can enhance colorimetric accuracy, they do not significantly improve photometric accuracy. Moreover, it was demonstrated that a linear regression model with an intercept, utilizing pixel luminance extracted from multiple grayscale patches in the HDR image, can serve as a potential solution for checking the significance of the scattering effect and providing a reference for the selection of appropriate reference standards. For lighting researchers seeking an appropriate strategy to calibrate HDR luminance maps, this study can serve as a valuable reference.

Treatment Options in Bilateral Diffuse Uveal Melanocytic Proliferation (BDUMP): Case Presentation and Review of the Literature

ABSTRACT Purpose To outline the therapeutic approach for a rare case of Bilateral Diffuse Uveal Melanocytic Proliferation (BDUMP) and examine the current management recommendations of this uncommon condition. Methods-Results Literature review on the current treatment options in BDUMP cases. An 82-year-old woman was referred to our clinic due to bilateral visual loss. She was treated elsewhere with anti-vascular endothelial growth factors (anti-VEGF) in both eyes for presumed choroidal neovascularization (CNV) without improvement. Her past medical history (PMH) entailed colon cancer, treated with surgical resection and adjuvant chemotherapy 15 years ago. The patient presented with low visual acuity in both eyes, multiple oval orange patches in the fundus with striking hyperfluorescent pattern in fluorescein angiography (FA), giraffe pattern in fundus autofluorescence (FAF) and rapidly progressive cataracts. Intravitreal dexamethasone implants were administered with mild improvement and subretinal fluid absorption. Conclusions The management strategy in BDUMP should focus on the systemic, often occult malignancy. There is no standard treatment protocol for BDUMP; however, plasmapheresis in combination with primary malignancy treatment seems to yield promising results in current literature reports. Anti-VEGF injections failed to control BDUMP sequelae, however intravitreal dexamethasone implants may offer temporary relief.

Habitat area more consistently affects seagrass faunal communities than fragmentation per se

AbstractSeminal ecological theories, island biogeography and the single large or several small (SLOSS) reserve debate, examine whether large contiguous habitats conserve biodiversity better than multiple smaller patches. Today, delineating the ecological effects of habitat area versus configuration in a fragmentation context remains difficult, and often confounds efforts to understand proximate and ultimate drivers of community change in response to habitat alteration. We examined how the major components of fragmentation, habitat division versus area loss, independently influence faunal communities using landscapes constructed from artificial seagrass at scales relevant for juvenile estuarine nekton. We deployed 25 unique, 234‐m2 landscapes designed along orthogonal axes: habitat percent cover (i.e., area) and fragmentation per se (i.e., patchiness) to examine their effects on faunal density, community composition, and probability of bait‐assay consumption. Faunal sampling occurred in both artificial seagrass and interspaced sandflat matrix. We also examined whether larval‐settler density drove faunal density patterns across landscapes. Further, we assessed the relative importance of landscape‐scale parameters versus fine‐scale complexity–canopy height and epiphyte biomass–in determining faunal densities. We most consistently observed increasing epibenthic fish and macroinvertebrate density with increasing seagrass percent cover. Fragmentation per se only negatively affected epibenthic faunal density within the matrix at low seagrass coverage. Bait consumption increased with seagrass cover, suggesting larger habitats are relative foraging hotspots. Alternatively, benthopelagic fish density was unaffected by habitat parameters, reflecting lower seagrass reliance, or increased matrix tolerance. Community compositions did not vary across landscapes, suggesting that abundant species used landscapes indiscriminately. Finally, the relative importance of habitat parameters shifted across faunal guilds and life stages. Landscape percent cover most affected epibenthic faunal density, but not benthopelagic fish density, and neither pattern was related to settler density. Further, only fine‐scale complexity influenced settler densities. Collectively, our results indicate habitat area is a primary, positive driver of faunal densities and generalist consumption, and therefore should be prioritized in seagrass conservation. However, sampling across spatial scales and habitat types revealed nuances in habitat use patterns among faunal guilds and life stages that were not solely area‐dependent, illustrating that a variety of landscape configurations support essential nursery functions.

Ensemble memory of a scene interacts with current perception regardless of attentional requirements.

How do we maintain a rich and stable perceptual experience across the entire visual scene, even when we are focusing on a subset of visual inputs? The current study explored this question by investigating whether the visual system processes summary statistics of multiple features regardless of task relevance, and how they interact with subsequent perception. To test the processing of multifeature summary statistics under different attentional requirements, we presented multiple Gabor patches with heterogeneous orientations/colors and asked participants to attend to a single feature dimension (Experiments 1 and 3) or a single item (Experiment 2) for the memory task. During the memory maintenance period (before memory response), we asked the participants to perform a discrimination task (Experiments 1 and 2) or a boundary localization task (Experiment 3) to test how the memory of the ensemble representation alters the subsequent perceptual experience. We found evidence for obligatory processing of scene summary statistics presented for the memory task, which interacted with the subsequent perceptual sensitivity. Specifically, not only summary statistics relevant but also those of task-irrelevant feature (Experiments 1 and 3) and outside the focus of attention (Experiment 2) were encoded and bidirectionally interacted with subsequent perception. These results suggest obligatory processing of summary statistics of a scene, which may allow rich and stable visual experience by integrating temporally adjacent visual inputs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient extraction of text information from the background in degraded color document images is an important challenge in the preservation of ancient manuscripts. The imperfect preservation of ancient manuscripts has led to different types of degradation over time, such as page yellowing, staining, and ink bleeding, seriously affecting the results of document image binarization. This work proposes an effective three-stage network method to image enhancement and binarization of degraded documents using generative adversarial networks (GANs). Specifically, in Stage-1, we first split the input images into multiple patches, and then split these patches into four single-channel patch images (gray, red, green, and blue). Then, three single-channel patch images (red, green, and blue) are processed by the discrete wavelet transform (DWT) with normalization. In Stage-2, we use four independent generators to separately train GAN models based on the four channels on the processed patch images to extract color foreground information. Finally, in Stage-3, we train two independent GAN models on the outputs of Stage-2 and the resized original input images (512 × 512) as the local and global predictions to obtain the final outputs. The experimental results show that the Avg-Score metrics of the proposed method are 77.64, 77.95, 79.05, 76.38, 75.34, and 77.00 on the (H)-DIBCO 2011, 2013, 2014, 2016, 2017, and 2018 datasets, which are at the state-of-the-art level. The implementation code for this work is available at https://github.com/abcpp12383/ThreeStageBinarization.

An efficient isogeometric buckling optimization framework for multi-patch laminated shells: Leveraging 3D solid elements, lamination parameters and penalty method

Buckling is a common failure mode of laminated shell structures during service, and isogeometric analysis methods are highly suitable for the buckling analysis due to their accurate geometric modeling capability and high-order continuity. However, the isogeometric buckling analysis for a multi-patch structure and its optimal design still face challenges in computational efficiency. To address this issue, this paper proposes an integrated framework for the buckling optimization of multi-patch laminated shells, which leverages 3D solid elements, lamination parameters, and the penalty coupling method. Solid-shell elements have advantages in shell analysis due to their simplicity, absence of rotational degrees of freedom, and adaptive layering in the thickness direction. Lamination parameters simplify the optimization of fiber orientations in laminated shells by condensing the design variable space and improving the convexity of the optimization problem. The penalty method can handle the coupling in multiple patches at a relatively low cost. Nevertheless, lamination parameters are rarely applied to the case of solid-shell elements. In addition, despite using lamination parameters, the number of design variables for multi-patch structures remains significantly large. To tackle these issues, this study derives the solid-shell formulation in lamination parameters and the sensitivity analysis formulae in the reverse mode. Experiment validation is conducted to assess the accuracy of the proposed method based on lamination parameters with solid elements, the feasibility of the multi-patch optimization model, and the computational efficiency of optimization sensitivity based on the reverse mode compared to the forward counterpart common in the literature.

A time patch dynamic attention transformer for enhanced well production forecasting in complex oilfield operations

In the field of oil production, accurately predicting well production is of great significance for optimizing production processes and enhancing economic benefits. However, oil production data generally exhibit characteristics such as high noise, complex distribution, and severe temporal fluctuations, which pose significant challenges to traditional prediction models, thereby limiting their accuracy. To address this issue, this study proposes an innovative Time Patch Dynamic Attention Transformer (TPDAT) model. This model segments the time series data into multiple time patches, extracts local features, and integrates a dynamically fused causal temporal attention mechanism to enhance the recognition of transient events and short-term fluctuations. The TPDAT model comprises the Temporal Patch Feature Extraction (TPF) module and the Dynamic Fusion Causal Temporal Attention (DFCTA) mechanism, effectively capturing multi-scale temporal features and enhancing the modeling of causal relationships during oilfield production. Experiments on the North Sea Volve oilfield dataset show that the TPDAT model significantly outperforms traditional deep learning models in well production prediction, achieves the best performance in model evaluation metrics, and demonstrates strong adaptability to complex data. The results confirm that the TPDAT model improves the accuracy and stability of well production prediction in complex oilfield environments, providing reliable support for oilfield production optimization.

RAPID: Robust multi-pAtch masker using channel-wise Pooled varIance with two-stage patch Detection

Recently, adversarial patches have become frequently used in adversarial attacks in real-world settings, evolving into various shapes and numbers. However, existing defense methods often exhibit limitations in addressing specific attacks, datasets, or conditions. This underscores the demand for versatile and robust defenses capable of operating across diverse scenarios. In this paper, we propose the RAPID (Robust multi-pAtch masker using channel-wise Pooled varIance with two-stage patch Detection) framework, a stable solution to restore detection efficacy in the presence of multiple patches. The RAPID framework excels in defending against attacks regardless of patch number or shape, offering a versatile defense adaptable to diverse adversarial scenarios. RAPID employs a two-stage strategy to identify and mask coordinates associated with patch attacks. In the first stage, we propose the ‘channel-wise pooled variance’ to detect candidate patch regions. In the second step, upon detecting these regions, we identify dense areas as patches and mask them accordingly. This framework easily integrates into the preprocessing stage of any object detection model due to its independent structure, requiring no modifications to the model itself. Evaluation indicates that RAPID enhances robustness by up to 60% compared to other defenses. RAPID achieves mAP50 and mAP@50-95 values of 0.696 and 0.479, respectively.

Mixed-mode thermo-mechanical fracture: An adaptive multi-patch isogeometric phase-field cohesive zone model

This work presents an adaptive phase-field cohesive zone model (PF-CZM) for simulating mixed-mode crack nucleation and growth in isotropic rock-like materials subjected to thermo-mechanical interactions. The proposed approach combines an adaptive multi-patch isogeometric analysis (MP-IGA) and length-scale insensitive PF-CZM. The formulation captures the distinct critical energy release rates for Mode-I and Mode-II fractures, which is crucial for predicting mixed-mode thermo-mechanical fracture behavior in isotropic rock-like materials. The PF-CZM governing equations are solved with isogeometric analysis based on locally refined non-uniform rational B-splines (LR NURBS), and the complex structural geometry is exactly described with multiple LR NURBS patches. The field variables, such as displacement, phase-field, and temperature at the interface of adjacent patches, are coupled using Nitsche’s method. To enhance the computational efficiency while maintaining accuracy, a refinement-correction adaptive scheme combined with the structured mesh refinement strategy is developed. The proposed framework is validated against recent numerical and experimental results in the literature, particularly in the context of capturing complex behavior of mixed-mode crack propagation in isotropic rock-like materials subjected to thermo-mechanical loading.

Multiple RONS-Loaded Plasma-Activated Ice Microneedle Patches for Transdermal Treatment of Psoriasis.

Cold atmospheric plasma (CAP) is a fledgling therapeutic technique for psoriasis treatment with noninvasiveness, but clinical adoption has been stifled by the insufficient production and delivery of plasma-generated reactive oxygen and nitrogen species (RONS). Herein, patches of air-discharge plasma-activated ice microneedles (PA-IMNs) loaded with multiple RONS are designed for local transdermal delivery to treat psoriasis as an alternative to direct CAP irradiation treatment. By mixing two RONS generated by the air-discharge plasma in the NOx mode and O3 mode, abundant high-valence RONS are produced and incorporated into PA-IMNs via complex gas-gas and gas-liquid reactions. The PA-IMNs abrogate keratinocyte overproliferation by inducing reactive oxygen species (ROS)-mediated loss of the mitochondrial membrane potential and apoptosis of keratinocytes. The in vivo transdermal treatment confirms that PA-IMNs produce significant anti-inflammatory and therapeutic actions for imiquimod (IMQ)-induced psoriasis-like dermatitis in mice by inhibiting the release of associated inflammatory factors while showing no evident systemic toxicity. Therefore, PA-IMNs have a large potential in transdermal delivery platforms as they overcome the limitations of using CAP directly in the clinical treatment of psoriasis.