Explore the vast range of titles available.

Since its initial release in 2022, ggClusterNet has become a vital tool for microbiome research, enabling microbial co‐occurrence network analysis and visualization in over 300 studies. To address emerging challenges, including multi‐factor experimental designs, multi‐treatment conditions, and multi‐omics data, we present a comprehensive upgrade with four key components: (1) A microbial co‐occurrence network pipeline integrating network computation (Pearson/Spearman/SparCC correlations), visualization, topological characterization of network and node properties, multi‐network comparison with statistical testing, network stability (robustness) analysis, and module identification and analysis; (2) Network mining functions for multi‐factor, multi‐treatment, and spatiotemporal‐scale analysis, including Facet.Network() and module.compare.m.ts(); (3) Transkingdom network construction using microbiota, multi‐omics, and other relevant data, with diverse visualization layouts such as MatCorPlot2() and cor_link3(); and (4) Transkingdom and multi‐omics network analysis, including corBionetwork.st() and visualization algorithms tailored for complex network exploration, including model_maptree2(), model_Gephi.3(), and cir.squ(). The updates in ggClusterNet 2 enable researchers to explore complex network interactions, offering a robust, efficient, user‐friendly, reproducible, and visually versatile tool for microbial co‐occurrence networks and indicator correlation patterns. The ggClusterNet 2R package is open‐source and available on GitHub (https://github.com/taowenmicro/ggClusterNet). ggClusterNet 2 drives the evolution of network analysis, offering researchers an accurate, efficient, convenient, reproducible, and visually compelling tool. Highlights The ggClusterNet 2 introduces a comprehensive microbial co‐occurrence network analysis pipeline. Enhanced network analysis workflow tailored for complex experimental designs and diverse data types. Enhanced visualization of microbiomes and their correlated environmental or host‐associated indicators. Introduced various visualization algorithms for transkingdom and multi‐omics interaction networks.

Background Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. Objectives Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. Methods We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi‐network fusion object detection framework based on the object detection technique Faster R‐CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi‐network fusion object detection framework was compared with that between dermatologists. Results A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi‐network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. Conclusions Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi‐network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

Subcutaneous injection of monoclonal antibodies (mAbs) has attracted much attention in the pharmaceutical industry. During the injection, the drug is delivered into the tissue producing strong fluid flow and tissue deformation. While data indicate that the drug is initially uptaken by the lymphatic system due to the large size of mAbs, many of the critical absorption processes that occur at the injection site remain poorly understood. Here, we propose the MPET 2 approach, a multi-network poroelastic and transport model to predict the absorption of mAbs during and after subcutaneous injection. Our model is based on physical principles of tissue biomechanics and fluid dynamics. The subcutaneous tissue is modeled as a mixture of three compartments, i.e., interstitial tissue, blood vessels, and lymphatic vessels, with each compartment modeled as a porous medium. The proposed biomechanical model describes tissue deformation, fluid flow in each compartment, the fluid exchanges between compartments, the absorption of mAbs in blood vessels and lymphatic vessels, as well as the transport of mAbs in each compartment. We used our model to perform a high-fidelity simulation of an injection of mAbs in subcutaneous tissue and evaluated the long-term drug absorption. Our model results show good agreement with experimental data in depot clearance tests.

Tough elastomers and gels have garnered broad research interest due to their wide-ranging potential applications. However, during the loading and unloading cycles, a clear stress softening behavior can be observed in many material systems, which is also named as the Mullins effect. In this work, we aim to provide a complete review of the Mullins effect in soft yet tough materials, specifically focusing on nanocomposite gels, double-network hydrogels, and multi-network elastomers. We first revisit the experimental observations for these soft materials. We then discuss the recent developments of constitutive models, emphasizing novel developments in the damage mechanisms or network representations. Some phenomenological models will also be briefly introduced. Particular attention is then placed on the anisotropic and multiaxial modeling aspects. It is demonstrated that most of the existing models fail to accurately predict the multiaxial data, posing a significant challenge for developing future anisotropic models tailored for tough gels and elastomers.

[This corrects the article DOI: 10.3389/fbioe.2022.1070566.].

PurposeIn the context of open innovation, more and more enterprises are leveraging innovation networks to drive disruptive innovation performance, but there is no consensus on the relationship between network embeddedness and enterprise disruptive innovation performance. This paper aims to systematically explore the relationship between them.Design/methodology/approachThis paper constructs a multi-level network embeddedness model and uses 58 independent studies as samples to explore the relationship between multi-level network embeddedness and enterprise disruptive innovation performance by meta-analysis.FindingsFirst, network embeddedness at the enterprise and regional levels will promote the improvement of disruptive innovation performance. Although industrial relationship embeddedness will promote the improvement of disruptive innovation performance, its structural embeddedness will bring negative effects. Second, in terms of mediating effect, policy-oriented support will promote the relationship between network embeddedness and disruptive innovation performance at the enterprise and industry levels. Compared with large enterprises, small- and medium-sized enterprises will have more advantages in the performance of multi-level network embedding and disruptive innovation performance. Under the subjective performance measurement method, the promotion effect of multi-level network embedding is more prominent.Research limitations/implicationsThis study enriches the theoretical research of network embeddedness and disruptive innovation and provides management enlightenment for the network embeddedness strategy of enterprise disruptive innovation. Limited by data samples and article length, future research can further expand literature samples to test the stability of variable relationships and test the moderating effects of more internal and external factors.Originality/valueFirst, it constructs a theoretical analysis model of “point-line-surface” multi-level network embedding and disruptive innovation performance of enterprises and expands the theoretical analysis framework of network embedding and disruptive innovation performance. The second is to explore the influence mechanism of multi-level network embeddedness and enterprise disruptive innovation performance. Third, it deepens the theoretical understanding of the moderating variables of the impact of network embeddedness and enterprise disruptive innovation performance.

This paper introduces a novel approach for managing fault propagation in interconnected energy networks comprising electric, gas, and heating systems. As energy infrastructures become increasingly integrated, the risk of cascading failures across these networks grows, making it critical to develop robust models for predicting and mitigating fault propagation. To tackle the complexity of fault propagation in interconnected energy systems, we develop a novel AI-based management architecture that couples adversarial learning mechanisms with graph-structured predictive models. Specifically, a generative network is employed to synthesize plausible fault evolution patterns from historical records, while a graph-based neural architecture captures the spatiotemporal correlations among the subsystems. Furthermore, a robust optimization scheme under distributional uncertainty is incorporated to devise adaptive recovery strategies, enhancing the resilience and reliability of system restoration processes. The proposed model is tested using a synthetic case study based on the IEEE 123-bus electric network, the Belgian gas transmission system, and a standard heating network. The results demonstrate the effectiveness of the model in accurately predicting fault propagation and optimizing recovery strategies, significantly reducing recovery time and minimizing the impact of cascading failures. The primary innovations presented in this study are the development of an integrated fault propagation framework spanning multiple energy networks, the pioneering use of adversarial and graph-based learning techniques for fault trajectory prediction, and the incorporation of distributionally robust optimization to strengthen recovery planning. Collectively, these advancements contribute to a deeper understanding of fault dynamics in interconnected infrastructures and propose a scalable pathway for enhancing system resilience in modern energy networks.

•Both HFS and LFS improve gait, with HFS also enhancing motor symptoms.•DBS restores functional connectivity, normalizing brain network activity in PD.•HFS and LFS modulate distinct brain networks, improving gait performance.•HFS improves gait by enhancing motor control network activity.•LFS boosts gait via increased executive-related cortical activity. Subthalamic nucleus (STN) deep brain stimulation (DBS) is used to treat Parkinson’s disease (PD), yet neither high-frequency stimulation (HFS) nor low frequency stimulation (LFS) fully resolves gait issues. Previous studies indicate that STN-DBS modulates motor-related brain networks. Given that PD patients with gait disturbances exhibit cognitive deficits—and considering the extensive projections between the STN and cerebral cortex—we hypothesized that varying STN stimulation frequencies may improve gait by modulating distinct brain networks. We collected gait data, cortical electrophysiological signals, and resting-state fMRI from 44 PD patients and 32 healthy controls. Multi-network cortical activity and functional connectivity were c ompared under three conditions: DBS OFF, HFS, and LFS. Additionally, the connectivity values were correlated to the gait behaviors and clinical assessment scores. We found that: (1) HFS improved both motor and gait performance, while LFS enhanced gait but may not be optimal for long-term use; (2) STN-DBS induced widespread modulation across sensorimotor, frontoparietal, salience, dorsal attention, and default mode networks. HFS improved motor and gait functions via network modulation related to motor control, whereas LFS may enhance gait by boosting executive-related cortical activities and connections; (3) Relative to healthy controls, PD exhibited widespread reductions in functional connectivity, with DBS modulation trending toward normalization. These results reveal distinct brain network responses to different STN-DBS frequencies in PD, offering a theoretical basis for optimizing DBS treatment for gait impairments. These findings provide critical insights for tailoring DBS parameters to maximize both motor and cognitive benefits in PD patients.

In this study, a novel calcium phosphate/polyacrylamide copolymer/α-type hemihydrate gypsum (CPO/PAM/α-HHG) composite material was prepared by polymerising a stable inorganic CPO precursor, end-capped with triethylamine (TEA), with an organic polyacrylamide (PAM) hydrogel to form a CPO/PAM precursor solution. Subsequently, this precursor solution was mixed with inorganic α-hemihydrate gypsum. The effects of CPO/PAM precursor addition and CPO addition on the slurry flowability, initial setting time, and mechanical properties of hardened specimens of the CPO/PAM/α-HHG composite were investigated. The structural characteristics of the composites were analysed by XRD, FE-SEM, and TGA. The results show that the initial setting time of the CPO/PAM/α-HHG composites was 26.7 min, which was 140.5% longer than that of the pure water α-HHG system and 3.9% longer than that of the PAM/α-HHG system; additionally, the oven-dried specimens had a flexural strength of 27.59 MPa and a compressive strength of 68.48 MPa, which were 77.2% and 102.0% higher than those of the pure water α-HHG system and 38.8% and 14.1% higher than those of the PAM/α-HHG system, respectively. The wet compressive strength of the CPO/PAM/α-HHG composites was improved by 11.8% compared to that of the PAM/α-HHG system. A structural analysis showed that CPO promoted the gelation process of PAM and allowed the hydration reaction process of α-HHG to be fully carried out by slowing down the gelation process of the organic network, which led to the full development of both organic and inorganic networks, ultimately forming an interspersed inorganic/organic dual-network structure, which enhanced the comprehensive mechanical properties of the composites. This study provides a new idea for the modification of α-type hemihydrate gypsum and a new method for the preparation of high-utilisation and high-performance gypsum-based composites.

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.