Multiple metastable network states in urban traffic.

While abrupt regime shifts between different metastable states have occurred in natural systems from many areas including ecology, biology, and climate, evidence for this phenomenon in transportation systems has been rarely observed so far. This limitation might be rooted in the fact that we lack methods to identify and analyze possible multiple states that could emerge at scales of the entire traffic network. Here, using percolation approaches, we observe such a metastable regime in traffic systems. In particular, we find multiple metastable network states, corresponding to varying levels of traffic performance, which recur over different days. Based on high-resolution global positioning system (GPS) datasets of urban traffic in the megacities of Beijing and Shanghai (each with over 50,000 road segments), we find evidence supporting the existence of tipping points separating three regimes: a global functional regime and a metastable hysteresis-like regime, followed by a global collapsed regime. We can determine the intrinsic critical points where the metastable hysteresis-like regime begins and ends and show that these critical points are very similar across different days. Our findings provide a better understanding of traffic resilience patterns and could be useful for designing early warning signals for traffic resilience management and, potentially, other complex systems.

Switch between critical percolation modes in city traffic dynamics.

Percolation transition is widely observed in networks ranging from biology to engineering. While much attention has been paid to network topologies, studies rarely focus on critical percolation phenomena driven by network dynamics. Using extensive real data, we study the critical percolation properties in city traffic dynamics. Our results suggest that two modes of different critical percolation behaviors are switching in the same network topology under different traffic dynamics. One mode of city traffic (during nonrush hours or days off) has similar critical percolation characteristics as small world networks, while the other mode (during rush hours on working days) tends to behave as a 2D lattice. This switching behavior can be understood by the fact that the high-speed urban roads during nonrush hours or days off (that are congested during rush hours) represent effective long-range connections, like in small world networks. Our results might be useful for understanding and improving traffic resilience.

Carbide Formation in Refractory Mo15Nb20Re15Ta30W20 Alloy under a Combined High-Pressure and High-Temperature Condition.

In this work, the formation of carbide with the concertation of carbon at 0.1 at.% in refractory high-entropy alloy (RHEA) Mo15Nb20Re15Ta30W20 was studied under both ambient and high-pressure high-temperature conditions. The x-ray diffraction of dilute carbon (C)-doped RHEA under ambient pressure showed that the phases and lattice constant of RHEA were not influenced by the addition of 0.1 at.% C. In contrast, C-doped RHEA showed unexpected phase formation and transformation under combined high-pressure and high-temperature conditions by resistively employing the heated diamond anvil cell (DAC) technique. The new FCC_L12 phase appeared at 6 GPa and 809 °C and preserved the ambient temperature and pressure. High-pressure and high-temperature promoted the formation of carbides Ta3C and Nb3C, which are stable and may further improve the mechanical performance of the dilute C-doped alloy Mo15Nb20Re15Ta30W20.

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis.

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA-associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease-related networks based on 21756 gene expression correlation coefficients, hub-genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits-related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA-associated genes. Moreover, 310 OA-associated genes were found, and 34 of them were among hub-genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)-receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'-kinase (PI3K)-Akt signaling pathway (PI3K-AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.

Quadriceps combined with hip abductor strengthening versus quadriceps strengthening in treating knee osteoarthritis: a study protocol for a randomized controlled trial.

BACKGROUND: Lower limb strengthening, especially the quadriceps training, is of much necessity for patients with knee osteoarthritis (KOA). Previous studies suggest that strengthening of the hip muscles, especially the hip abductor, can potentially relieve the KOA-associated symptoms. Nevertheless, the effects of quadriceps combined with hip abductor strengthening remain unclear. Therefore, the current randomized controlled trial is designed aiming to observe whether quadriceps in combination with hip abductor strengthening can better improve the function and reduce pain in KOA patients than quadriceps training alone. METHODS: A total of 80 subjects with symptomatic KOA will be recruited from the communities and hospital outpatient, and will be randomly assigned to the experiment group (Quadriceps-plus-hip-abductor-strengthening) or the control group (Quadriceps-strengthening). Specifically, participants in the experiment group will complete 4 exercises to train the quadriceps and hip abductor twice a day for 6 weeks at home, while those in the control group will only perform 2 exercises to strengthen the quadriceps. Besides, all patients will also receive usual care management, including health education and physical agent therapy when necessary. Knee pain will be measured using the Visual Analogue Scale (VAS) at baseline, in every week during the course of treatment, as well as 8 and 12 weeks after randomization. Furthermore, knee function will be measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale, and the quality of life will be measured using the MOS Item Short-form Health Survey (SF-36). In this study, several simple tests will be applied to assess the objective function. All the assessments except for VAS will be carried out at baseline, and in the 6th, 8th and 12th weeks respectively. DISCUSSION: Our findings will provide more evidence for the effects of hip abductor strengthening on relieving pain and improving function in KOA patients. Hip abductor strengthening can be added into the muscle training program for KOA patients as a supplementary content if it is proved to be effective. TRIAL REGISTRATION: The current study has been registered with the Chinese Clinical Trials Registry (the registration number is ChiCTR-IOC-15007590 , 3rd December, 2015).

Artificial intelligence in ischemic stroke images: current applications and future directions.

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

Cerium oxide nanoparticles in wound care: a review of mechanisms and therapeutic applications.

Skin wound healing is a complex and tightly regulated process. The frequent occurrence and reoccurrence of acute and chronic wounds cause significant skin damage to patients and impose socioeconomic burdens. Therefore, there is an urgent requirement to promote interdisciplinary development in the fields of material science and medicine to investigate novel mechanisms for wound healing. Cerium oxide nanoparticles (CeO2 NPs) are a type of nanomaterials that possess distinct properties and have broad application prospects. They are recognized for their capabilities in enhancing wound closure, minimizing scarring, mitigating inflammation, and exerting antibacterial effects, which has led to their prominence in wound care research. In this paper, the distinctive physicochemical properties of CeO2 NPs and their most recent synthesis approaches are discussed. It further investigates the therapeutic mechanisms of CeO2 NPs in the process of wound healing. Following that, this review critically examines previous studies focusing on the effects of CeO2 NPs on wound healing. Finally, it suggests the potential application of cerium oxide as an innovative nanomaterial in diverse fields and discusses its prospects for future advancements.

Improving Lower Limb Function and Frailty in Frail Older Patients with Acute Myocardial Infarction After Percutaneous Coronary Intervention: A Randomized Controlled Study of Neuromuscular Electrical Stimulation.

Background: A global public health problem, frailty is closely associated with poor prognosis after percutaneous coronary intervention (PCI) in older patients with acute myocardial infarction (AMI). Although exercise intervention is the most commonly used method to reverse and alleviate frailty, its application is restricted in patients with acute myocardial infarction following PCI due to cardiovascular instability and autonomic imbalance. Consequently, there is a need for a new practical intervention to address frailty syndrome in these patients. Purpose: This study aimed to investigate the effect of neuromuscular electrical stimulation in frail older AMI patients post-PCI. Patients and Methods: A single-blind, randomized controlled trial was carried out in the Department of Cardiovascular Medicine from March to October 2023. A total of 100 eligible participants were randomly divided into two groups: experimental (n = 50) and control (n = 50) groups, respectively. Both groups received usual care. The experimental group underwent neuromuscular electrical stimulation (NMES) on bilateral quadriceps and gastrocnemius muscles for 30 minutes daily from day 1 to day 7 after surgery. The primary outcomes measured included the frailty score, lower limb muscle strength, and lower limb muscle quality. Secondary outcomes included the activities of daily living score, inflammatory markers, and length of hospital stay. All participants were included in an intention-to-treat analysis after the study ended. Results: The frailty scores of the two groups exhibited a gradual decrease over time, and the scores of the experimental group were lower than those of the control group at 4 and 7 days after surgery (P<0.001). Concurrently, the lower limb muscle strength showed an increasing trend over the time in the experimental group and a decreasing trend in the control group, and the scores of the experimental group surpassed those of the control group (p<0.001). Moreover, a statistical difference was observed in the lower limb muscle mass across the groups after 7 days postoperatively compared with baseline on both sides (p<0.05). Conclusion: Neuromuscular electrical stimulation has the potential to enhance lower limb function and alleviate frailty in elderly patients with acute myocardial infarction after PCI. These findings introduce a novel intervention approach for frailty management in the elderly population.

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study.

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization. Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm. Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97-0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model. Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients' clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-ß) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life.

Hardness Distribution of Al2050 Parts Fabricated Using Additive Friction Stir Deposition.

The solid-state additive friction stir deposition (AFSD) process is a layer-by-layer metal 3D-printing technology. In this study, AFSD is used to fabricate Al-Cu-Li 2050 alloy parts. The hardness values for various regions of the as-deposited built parts are measured, and the results are contrasted with those of the feedstock material. The as-fabricated Al2050 parts are found to have a unique hardness distribution due to the location-specific variations in the processing temperature profile. The XRD results indicate the presence of the secondary phases in the deposited parts, and EDS mapping confirms the formation of detectable alloying particles in the as-deposited Al2050 matrix. The AFSD thermal-mechanical process causes the unique hardness distribution and the reduced microhardness level in the AFSD components, in contrast to those of the feedstock material.

Effects of Printing Layer Orientation on the High-Frequency Bending-Fatigue Life and Tensile Strength of Additively Manufactured 17-4 PH Stainless Steel.

In this paper, small blocks of 17-4 PH stainless steel were manufactured via extrusion-based bound powder extrusion (BPE)/atomic diffusion additive manufacturing (ADAM) technology in two different orientations. Ultrasonic bending-fatigue and uniaxial tensile tests were carried out on the test specimens prepared from the AM blocks. Specifically, a recently-introduced small-size specimen design is employed to carry out time-efficient fatigue tests. Based on the results of the testing, the stress-life (S-N) curves were created in the very high-cycle fatigue (VHCF) regime. The effects of the printing orientation on the fatigue life and tensile strength were discussed, supported by fractography taken from the specimens' fracture surfaces. The findings of the tensile test and the fatigue test revealed that vertically-oriented test specimens had lower ductility and a shorter fatigue life than their horizontally-oriented counterparts. The resulting S-N curves were also compared against existing data in the open literature. It is concluded that the large-sized pores (which originated from the extrusion process) along the track boundaries strongly affect the fatigue life and elongation of the AM parts.

The top 100 most cited articles on triple-negative breast cancer: a bibliometric analysis.

Breast cancer is the most frequent cause of death from cancer in 12 regions of the world. Among the breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive, leading to an adverse prognosis. Thus, we aimed to identify the top 100 most cited articles regarding TNBC through bibliometric analyses, to explore their current impact and publication status. We searched TNBC-related articles from the Web of Science core database on September 19, 2021, and ranked them by citation in descending order. Information extracted from the top 100 most cited articles, included title, author, institution, country, year, publication, Journal Cited Rank, citation, Web of Science category, were extracted by two researchers independently. VOSviewer was used to analyze data, such as co-authoring institutions, and author cooperation. Descriptive statistical analysis of data was performed using Excel. Citations from the top 100 most cited articles ranged from 225 to 2753, and all were published after 2007. The top 100 most cited articles were published in 38 journals, with the Journal of Clinical Oncology published the most (n = 15), followed by Clinical Cancer Research (n = 11), Lancet Oncology(n = 6). Eric P. Winer from USA, who participated 12 articles, was the most frequently published author. The USA (n = 71) was the country with the most contributions to TNBC. The most prevalent topics included: clinical features, molecular subtypes, relevant influencing factors, exploration of treatment options and prognosis of TNBC. The leading institution was the Dana Farber Cancer Institute (n = 20) from the USA. This is the first bibliometric analysis on the TNBC 100 most cited article. We provided insights into the most cited articles on TNBC and listed a detailed description of their characteristics and trend, which provides ideas for further study.

High expression of NF-κB inducing kinase in the bulge region of hair follicle induces tumor.

The bulge region, a reservoir of multipotent stem cells, is possibly responsible for tumorigenesis. NF-κB-inducing kinase (NIK) is a kinase involved in the activation of the noncanonical NF-κB pathway and exhibits positive staining in tumor cells. However, whether high expression of NIK can result in tumorigenesis has not been reported in published papers. By establishing Nik-coe (Nik-stopF/F crossed with Chat-cre) and Nik-soe (Nik-stopF/F crossed with Sox9-cre) mice, we found that overexpression of Nik in the bulge region of hair follicles induced hair follicle loss and tumorigenesis. Furthermore, RNA sequencing, proteomic and phosphopeptide analyses revealed that multiple cancer pathways are involved in tumor formation. Taken together, these findings indicate that constitutive activation of Nik in the bulge region induces tumorigenesis.

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach.

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

Developing Fused Deposition Modeling Additive Manufacturing Processing Strategies for Aluminum Alloy 7075: Sample Preparation and Metallographic Characterization.

Currently, no commercial aluminum 7000 series filaments are available for making aluminum parts using fused deposition modeling (FDM)-based additive manufacturing (AM). The key technical challenge associated with the FDM of aluminum alloy parts is consolidating the loosely packed alloy powders in the brown-body, separated by thin layers of surface oxides and polymer binders, into a dense structure. Classical pressing and sintering-based powder metallurgy (P/M) technologies are employed in this study to assist the development of FDM processing strategies for making strong Al7075 AM parts. Relevant FDM processing strategies, including green-body/brown-body formation and the sintering processes, are examined. The microstructures of the P/M-prepared, FDM-like Al7075 specimens are analyzed and compared with commercially available FDM 17-4 steel specimens. We explored the polymer removal and sintering strategies to minimize the pores of FDM-Al7075-sintered parts. Furthermore, the mechanisms that govern the sintering process are discussed.

Microstructure Evolution of Al6061 Alloy Made by Additive Friction Stir Deposition.

In this paper, the phase structure, composition distribution, grain morphology, and hardness of Al6061 alloy samples made with additive friction stir deposition (AFS-D) were examined. A nearly symmetrical layer-by-layer structure was observed in the cross section (vertical with respect to the fabrication-tool traversing direction) of the as-deposited Al6061 alloy samples made with a back-and-forth AFS-D strategy. Equiaxed grains were observed in the region underneath the fabrication tool, while elongated grains were seen in the "flash region" along the mass flow direction. No clear grain size variance was discovered along the AFS-D build direction except for the last deposited layer. Grains were significantly refined from the feedstock (~163.5 µm) to as-deposited Al6061 alloy parts (~8.5 µm). The hardness of the as-fabricated Al6061 alloy was lower than those of the feedstock and their heat-treated counterparts, which was ascribed to the decreased precipitate content and enlarged precipitate size.

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts.

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

Improvement of osseointegration by recruiting stem cells to titanium implants fabricated with 3D printing.

Slow and incomplete osseointegration and loss of osseointegration are major problems in dental and bone implants. We designed implants with interconnected 3D-tubulous structures and hypothesized that such interconnecting 3D (I3D) structures would serve as a repository for chemoattractants to recruit stem cells to promote osseointegration. A concept Laser Mlab-cusing-R laser-powder-bed-fusion (LPBF) 3D printing system was used to produce titanium implants with designed features. The implants were loaded (coated) with stromal cell-derived factor-1 alpha (SDF-1α), and subjected to stem cell recruitment. Implants were then surgically transplanted into the rabbit skull bone. After 12 weeks, osseointegration was analyzed by reverse-torque test and the implants were examined for calcium deposition by Alizarin Red staining. The I3D implants attracted significantly more stem cells than solid implants when coated (loaded) with SDF-1α. Greater torque force was needed to extract the I3D implants with 200 and 300 µm I3D structures than to extract solid implants from the skull. Generally, more calcium deposition was observed on the I3D implants than on the solid counterparts. LPBF 3D printing can be used to fabricate implants with complex structures. I3D-tubulous structures of implants can retain chemoattractant for recruitment of stem cells to enhance osseointegration.

Bibliometric and Visualized Analysis of Stem Cells Therapy for Spinal Cord Injury Based on Web of Science and CiteSpace in the Last 20 Years.

OBJECTIVE: To provide an analysis of Web of Science (WoS) indexed literature related to stem cells therapy in spinal cord injury published between 1999 and 2018. METHODS: Data were obtained from the WoS Core Collection on March 30, 2019. Qualitative and quantitative analysis was conducted based on WoS. Co-citation analysis, collaboration analysis, and co-words analysis of keywords was conducted by using CiteSpace. RESULTS: A total of 4188 references were obtained. The number of publications continually increased over the investigated period. Articles were the most frequently document type. Cell Transplantation (127) was the most productive journal. Experimental Neurology (2180) was the most frequently co-cited journal. H. Okano was the most productive and influential author, with 98 publications and 4860 cited counts. The most productive country and institution were the United States and University of Toronto, respectively. Researchers and institutions from Canada, the United States, Japan, and China were the core research forces. There was a broad and close cooperation worldwide. The Lu et al.'s (2012) article (co-citation counts, 177) was the most representative and symbolic reference. Transplantation, functional recovery, marrow-derived mesenchymal stem cell treatment, and progenitor cells were the hot spots. Inflammation, glial scar, nerve regeneration, neurite outgrowth, and bone marrow stromal cell were research frontiers. CONCLUSIONS: Research on stem cells for spinal cord injury is a well-developed and promising research field. There is broad global scientific research cooperation. More cooperation among top authors, institutions, and countries is needed. Our results may be helpful for researchers in identifying further potential perspectives on collaborators, research frontiers, and hot topics.