Bidirectional Interactions Research Articles

Modeling and detection of false data injection attacks in cyber-physical distribution system with load aggregator interaction

With the increasing number of users and multi-type loads accessing the cyber–physical distribution system (CPDS), the bidirectional interaction between the system becomes more and more frequent. The Load aggregator (LA) also plays an increasingly important role in the interaction process. However, the LA’s high dependence on information and lack of security measures make it vulnerable to attacks, so this paper analyzes the interactive security of the LAs from two points. Considering the game process between the LAs and users from the attacker’s point of view, this paper puts forward an attack strategy aiming cost function of the LAs, establishes a bi-level multi-objective programming attack model of false data injection attacks(FDIAs), and proposes an attack detection method based on the multi-state matching method and improved similar daily data preprocessing generative adversarial network (P-GAN) from the defender’s point of view to defend against the above attack strategy. Furthermore, a hybrid detection mechanism combining event triggering and periodic detection is proposed to ensure response speed and adaptability of detection. The effectiveness of the proposed attack model and the detection method is verified by simulation analysis.

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificial intelligence-driven analysis. However, existing segmentation methods are limited by ambiguous boundaries, shape variations and texture complexities between PZ and TZ. Moreover, they suffer from inadequate modeling capabilities and limited receptive fields. To address these challenges, we propose a Enhanced MixFormer, which integrates window-based multi-head self-attention (W-MSA) and depth-wise convolution with parallel design and cross-branch bidirectional interaction. We further introduce MixUNETR, which use multiple Enhanced MixFormers as encoder to extract features from both PZ and TZ in prostate MRI. This augmentation effectively enlarges the receptive field and enhances the modeling capability of W-MSA, ultimately improving the extraction of both global and local feature information from PZ and TZ, thereby addressing mis-segmentation and challenges in delineating boundaries between them. Extensive experiments were conducted, comparing MixUNETR with several state-of-the-art methods on the Prostate158, ProstateX public datasets and private dataset. The results consistently demonstrate the accuracy and robustness of MixUNETR in MRI prostate segmentation. Our code of methods is available at https://github.com/skyous779/MixUNETR.git.

Dynamic analysis of whipping behavior induced by the circumferential fracture of high energy pipes considering fluid-structure interaction effect

To analyze the dynamic characteristics of whipping behavior induced by the circumferential fracture of high energy pipes, a numerical model of the piping system was presented based on the bidirectional fluid-structure interaction method. The reliability of this model was validated against experimental results. Furthermore, the dynamic characteristics of whipping behavior were studied, as well as the effects of some main parameters. The numerical results suggest that the entire whipping behavior can be divided into four stages: free-whipping stage Ⅰ, free-whipping stage Ⅱ, collision stage and periodic stage. The dynamic response of whipping behavior is different in the four stages and is dominated by a single mode with a frequency of 29.35 Hz under the combined action of the thrust force and U-bolt restraints. The increase of inlet flow rate causes the enhancement of thrust force and thus makes its dynamic response more complicated. The parameter analysis shows that the whipping behavior can be suppressed by choosing the small initial clearance, setting the suitable gap and increasing the straight segment length of U-bolt restraints. The above results are helpful to understand the dynamic characteristics of whipping behavior and provide valuable suggestions for the optimization of the protection measures.

CoMaSa:Context Multi-aware Self-attention for emotional response generation

Dialogue generation is an important research direction in natural language generation, and generating utterances with appropriate contextual emotion is a challenging task. The previous work incorporates commonsense knowledge as an auxiliary information source by simply concatenating or adding it to the embeddings of the dialogue context. They did not consider updating the commonsense knowledge from the perspective of context semantic information or selecting context from the perspective of commonsense knowledge semantic information. At the same time, they did not extract emotional information from dialogue context and commonsense knowledge to enhance the embeddings of the dialogue text. These defects may lead to the dialogue model’s inability to judge the semantic consistency between context and generated utterance, and result in the generation of utterance unassociated with the dialogue context and the corresponding context emotion being untrue. Our goal is to build an emotional dialogue generation model that can deeply integrate commonsense knowledge with dialogue context and achieve bidirectional semantic interaction and enhancement. Therefore, in this paper, we propose a context multi-aware self-attention emotion dialogue generation model that models from multiple aspects such as the semantic information, emotional information of context and external knowledge, it effectively alleviates the problems of generating utterance unrelated to the context and disordered emotional expressions in utterance. First, we adopt the transformer encoder as the basic framework and carefully construct a context-aware encoder capable of deeply extracting semantic information. Specifically, we ingeniously integrate different levels of positional information to simulate the natural flow of conversation between speakers. Then, we effectively integrate these positional information with the basic word embedding vectors, and enrich the dialogue’s embedding representation. Lastly, by introducing the attention mechanism, the model can precisely capture and extract context information closely related to the current situation and achieve a deep understanding and accurate grasp of the dialogue content. Second, we consider that the relationship between context and commonsense knowledge is not just a unidirectional selection but a more intimate bidirectional interaction. Therefore, we designed a bidirectional interaction attention component for context and external knowledge. Specifically, we use the method of iterative similarity matrices to calculate the bidirectional similarity relationship between context and external knowledge. Then, using the attention algorithm, we calculate the connectivity from context to commonsense knowledge and from commonsense knowledge to context. Finally, by combining the semantic embeddings of both aspects, we achieve bidirectional updating of context and external knowledge. Third, we establish an emotion-aware decoder based on multi-task learning, which includes an emotion classifier and an utterance generator. In the emotion classifier, we establish an auxiliary task for emotion recognition to help the model understand the process of emotional transition. We use the output of the emotion recognition model as an additional supervisory signal to better control the generation of emotional responses. In the utterance generator, we integrate the output of the emotion classifier with the interaction vector from the bidirectional interaction attention component. This fusion allows us to generate utterance that aligns with the emotional context. Our experiments on DailyDialog and ESConv datasets show that CoMaSa outperforms the baselines in terms of Perplexity, Distinct-1, Distinct-2, and human evaluation. The experiment demonstrates the effectiveness of our Context Multi-aware Self-attention model in the task of emotion generation.

Bidirectional interaction between IL and 17A/IL-17RA pathway dysregulation and α-synuclein in the pathogenesis of Parkinson’s disease

Parkinson’s disease (PD) pathogenesis is characterized by α-synuclein (α-syn) pathology, which is influenced by various factors such as neuroinflammation and senescence. Increasing evidence has suggested a pivotal role for Interleukin-17A(IL-17A) and Interleukin-17 Receptor A (IL-17RA) in PD, yet the trigger and impact of IL-17A/IL-17RA activation in PD remains elusive. This study observed an age-related increase in IL-17A and IL-17RA in the human central nervous system, accompanied by increased α-syn and senescence biomarkers. Interestingly, both levels of IL-17A and IL-17RA in PD patients were significantly elevated compared to age-matched controls, wherein the IL-17A was mainly present in neurons. This abnormal neuronal IL-17A activation in the PD brain was recapitulated in α-syn mouse models. Correspondingly, administration of recombinant IL-17A exacerbated pathological α-syn in both neuron and mouse models. Furthermore, IL-17A/IL-17RA pathway interventions via blocking antibody or shRNA-mediated knockdown can mitigate the effects of pathological α-syn. This study reveals an interplay between dysregulation of the IL-17A/IL-17RA pathway and α-syn, suggesting that regulating the IL-17A/IL-17RA pathway could modify PD progression by disrupting the detrimental cycle.

Bidirectional Influence and Interaction for Multiagent Reinforcement Learning

Bidirectional Influence and Interaction for Multiagent Reinforcement Learning

Lightweight cross-modal transformer for RGB-D salient object detection

Recently, Transformer-based RGB-D salient object detection (SOD) models have pushed the performance to a new level. However, they come at the cost of consuming abundant resources, including memory and power, thus hindering their real-life applications. To remedy this situation, a novel lightweight cross-modal Transformer (LCT) for RGB-D SOD will be presented in this paper. Specifically, LCT will first reduce its parameters and computational costs by employing a middle-level feature fusion structure and taking a lightweight Transformer as the backbone. Then, with the aid of Transformers, it will compensate for performance degradation by effectively capturing the cross-modal and cross-level complementary information from the multi-modal input images. To this end, a cross-modal enhancement and fusion module (CEFM) with a lightweight channel-wise cross attention block (LCCAB) will be designed to capture the cross-modal complementary information effectively but with fewer costs. A bi-directional multi-level feature interaction module (Bi-MFIM) with a lightweight spatial-wise cross attention block (LSCAB) will be designed to capture the cross-level complementary context information. By virtue of CEFM and Bi-MFIM, the performance degradation caused by parameter reduction can be well compensated, thus boosting the performances. By doing so, our proposed model has only 2.8M parameters with 7.6G FLOPs and runs at 66 FPS. Furthermore, experimental results on several benchmark datasets show that our proposed model can achieve competitive or even better results than other models. Our code will be released on https://github.com/nexiakele/lightweight-cross-modal-Transformer-LCT-for-RGB-D-SOD.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Bidirectional Interaction of Sepsis and Sleep Disorders: The Underlying Mechanisms and Clinical Implications.

Sepsis is defined as life-threatening organ injury induced by infection, with high incidence and mortality. Sleep disorder is prevalent in septic patients and approximately 50% of patients with sepsis may develop atypical sleep patterns, but many of them may have been underdiagnosed by physicians. Sleep disorders and sepsis exhibit a close bidirectional relationship, with each condition significantly influencing the other. Conversely, sleep deprivation, sleep dysrhythmia and sleep fragmentation have been shown to impact the outcome of sepsis. This review endeavors to offer a comprehensive understanding of the intricate mechanisms that underpin the interplay between sepsis and sleep disorders, in addition to exploring potential clinical intervention strategies that could enhance outcomes for patients suffering from sepsis.

Taking relations as known conditions: A tagging based method for relational triple extraction

Relational triple extraction refers to extracting entities and relations from natural texts, which is a crucial task in the construction of knowledge graph. Recently, tagging based methods have received increasing attention because of their simple and effective structural form. Among them, the two-step extraction method is easy to cause the problem of category imbalance. To address this issue, we propose a novel two-step extraction method, which first extracts subjects, generates a fixed-size embedding for each relation, and then regards these relations as known conditions to extract the objects directly with the identified subjects. In order to eliminate the influence of irrelevant relations when predicting objects, we use a relation-special attention mechanism and a gate unit to select appropriate relations. In addition, most current models do not account for two-way interaction between tasks, so we design a feature interactive network to achieve bidirectional interaction between subject and object extraction tasks and enhance their connection. Experimental results on NYT, WebNLG, NYT⋆ and WebNLG⋆ datasets show that our model is competitive among joint extraction models.

Interplay among IL1R1, gut microbiota, and bile acids in metabolic dysfunction-associated steatotic liver disease: a comprehensive review.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent metabolic disorder characterized by hepatic steatosis associated with metabolic abnormalities. Recent research has shed light on the intricate interplay among interleukin-1 receptor 1 (IL1R1), gut microbiota, and bile acids in the pathogenesis of MASLD. This review aims to provide a comprehensive overview of the current understanding of the role of IL1R1, gut microbiota, and bile acids in MASLD, exploring their interrelationships and potential mechanisms. We summarize the evidence supporting the involvement of IL1R1 in inflammation, discuss the influence of gut microbiota on bile acid metabolism and its influence on liver health, and elucidate the bidirectional interactions among IL1R1 signaling, gut microbiota composition, and bile acid homeostasis in MASLD. Furthermore, we highlight emerging therapeutic strategies targeting these interrelated pathways for the management of MASLD.

Lymphoma cell-driven IL-16 is expressed in activated B-cell-like diffuse large Bcell lymphomas and regulates the pro-tumor microenvironment.

The activated B-cell-like subtype of diffuse large B-cell lymphoma (ABC-DLBCL) displays a worse outcome than the germinal center B-cell-like subtype (GCB-DLBCL). Currently, targeting tumor microenvironment (TME) is the promising approach to cure DLBCL with profound molecular heterogeneity, however, the factors affecting the tumor-promoting TME of ABCDLBCL are elusive. Here, cytokine interleukin-16 (IL-16) is expressed in tumor cells of ABCDLBCL and secreted by the cleavage of active caspase-3. The serum IL-16 levels are not only a sensitive marker of treatment response but also positively correlated with unfavorable prognosis in DLBCL patients. While IL-16 shows few direct promotional effects on tumor cell growth in vitro, its bioactive form significantly promotes tumor progression in vivo. Mechanically, IL-16 increases the infiltration of macrophages by the chemotaxis of CD4+ monocytes in the TME enhancing angiogenesis, and the expression of cytokine IL-6 and IL-10, as well as decreasing T cell infiltration to accelerate tumor progression. This study demonstrates that IL-16 exerts a novel role in coordinating the bidirectional interactions between tumor progression and the TME. IMM0306, a fusion protein of CD20 mAb with the CD47 binding domain of SIRPα, reverses the tumorpromoting effects of IL-16,which provides new insight into treatment strategy in ABC-DLBCL.

Gut Microbiota Disruption in Hematologic Cancer Therapy: Molecular Insights and Implications for Treatment Efficacy.

Hematologic malignancies (HMs), including leukemia, lymphoma, and multiple myeloma, involve the uncontrolled proliferation of abnormal blood cells, posing significant clinical challenges due to their heterogeneity and varied treatment responses. Despite recent advancements in therapies that have improved survival rates, particularly in chronic lymphocytic leukemia and acute lymphoblastic leukemia, treatments like chemotherapy and stem cell transplantation often disrupt gut microbiota, which can negatively impact treatment outcomes and increase infection risks. This review explores the complex, bidirectional interactions between gut microbiota and cancer treatments in patients with HMs. Gut microbiota can influence drug metabolism through mechanisms such as the production of enzymes like bacterial β-glucuronidases, which can alter drug efficacy and toxicity. Moreover, microbial metabolites like short-chain fatty acids can modulate the host immune response, enhancing treatment effectiveness. However, therapy often reduces the diversity of beneficial bacteria, such as Bifidobacterium and Faecalibacterium, while increasing pathogenic bacteria like Enterococcus and Escherichia coli. These findings highlight the critical need to preserve microbiota diversity during treatment. Future research should focus on personalized microbiome-based therapies, including probiotics, prebiotics, and fecal microbiota transplantation, to improve outcomes and quality of life for patients with hematologic malignancies.

The relationship between work-family conflict and job burnout among primary and secondary school teachers: the role of depression and cognitive reappraisal.

Primary and secondary school teachers are a high-risk group for job burnout, and how to alleviate their job burnout has become an increasingly urgent issue. Previous studies have paid less attention to the differential effects of the bidirectional interaction between work and family on the job burnout of the teachers. This study aim to explore the different impact of work-family conflict and family-work conflict on job burnout among primary and secondary school teachers, as well as its underlying mechanisms. This study selected 2,184 primary and secondary school teachers in China (Mage = 37.26; SD = 9.40) as participants using a random sampling method. Using the SPSS Process 4.0 macro plugin constructed a moderated mediation model, the study explored the relationships between two different forms of work-family conflict, depression, cognitive reappraisal, and job burnout. The study results indicated that both forms of work-family conflict were significantly positively related to the job burnout, and this relationship was influenced by the mediating role of depression. Furthermore, cognitive reappraisal moderated the relationship between depression and job burnout. This study revealed the potential pathways influencing job burnout among primary and secondary school teachers in the Chinese cultural context. Focusing on and alleviating work-family conflicts for primary and secondary school teachers is crucial for mitigating their occupational burnout. Additionally, teachers should also carefully and reasonably use cognitive reappraisal as an emotional regulation strategy to adjust the impact of depression on occupational burnout.

Focus on the role of calcium signaling in ferroptosis: a potential therapeutic strategy for sepsis-induced acute lung injury.

By engaging in redox processes, ferroptosis plays a crucial role in sepsis-induced acute lung injury (ALI). Although iron stimulates calcium signaling through the stimulation of redox-sensitive calcium pathways, the function of calcium signals in the physiological process of ferroptosis in septic ALI remains unidentified. Iron homeostasis disequilibrium in ferroptosis is frequently accompanied by aberrant calcium signaling. Intracellular calcium overflow can be a symptom of dysregulation of the cellular redox state, which is characterized by iron overload during the early phase of ferroptosis. This can lead to disruptions in calcium homeostasis and calcium signaling. The mechanisms controlling iron homeostasis and ferroptosis are reviewed here, along with their significance in sepsis-induced acute lung injury, and the potential role of calcium signaling in these processes is clarified. We propose that the development of septic acute lung injury is a combined process involving the bidirectional interaction between iron homeostasis and calcium signaling. Our goal is to raise awareness about the pathophysiology of sepsis-induced acute lung injury and investigate the relationship between these mechanisms and ferroptosis. We also aimed to develop calcium-antagonistic therapies that target ferroptosis in septic ALI and improve the quality of survival for patients suffering from acute lung injury.

Abstract C069: A conserved reepithelialization program driven by FOSL1 underlies malignancy in pancreatic ductal adenocarcinoma

Abstract Benign lesions named pancreatic intraepithelial neoplasia (PanIN) are frequent in the healthy population but are precursors to deadly pancreatic ductal adenocarcinoma (PDAC). Conversion of PanINs to PDAC is poorly understood. Here we harness high resolution spatial transcriptomics to identify a core transcriptional program associated with invasive growth across human PDAC and required for PanIN-to-PDAC progression in mice. The malignancy program is lineage restricted, and predominantly comprised of transcripts activated to drive reepithelialization during skin wound healing. This wound-like program appears in rare PanIN cells that also activate tumor suppressor genes (TSGs), which keep the incipient malignant cells in check. TSG KO in PanINs leads to rapid outgrowth of the wound-like population, unleashing invasive growth, driven by the wound-induced TF FOSL1. Bidirectional interactions between malignant epithelial cells and wound-like CTHRC1-expressing CAFs enforce the malignant state. Our results suggest that wound recapitulation drives the highly invasive properties of PDAC. Citation Format: Charles David, Melian Zhuo, Yong Li, Yifeng Zhang, Chenlu Yang, Ziqing Deng, Yupei Zhao, Wennming Wu, Junya Peng, Mo Chen. A conserved reepithelialization program driven by FOSL1 underlies malignancy in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C069.

Abstract A041: Deciphering the bidirectional influences of sympathetic neurons and cancer associated fibroblasts and the resulting contributions to PDAC progression

Abstract The pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME) is innervated by a variety of peripheral nerves that are emerging as facilitators of tumor initiation, progression and metastasis. However, the influences of sympathetic nerves on the TME and how these bi-directional interactions can exacerbate PDAC progression remains unclear. Sympathetic nerves innervate both human and murine PDAC tumors and are frequently surrounded by abundant and heterogeneous cancer-associated fibroblasts (CAFs), which are known to play various crucial roles in PDAC progression. In our studies, we aim to elucidate the bidirectional CAF and sympathetic-neuron crosstalk that may promote tumor growth using a variety of in vitro coculture systems to model sympathetic-CAF interactions, as well as in vivo modeling of nerves in murine PDAC tumors. Our nerve-CAF cocultures reveal that CAFs closely and frequently interact with sympathetic nerves, and also enhance sympathetic nerve outgrowth. We further sequenced these cocultured conditions to evaluate intrinsic transcriptional changes that may be contributing to the enhanced nerve outgrowth. We identified several genes that may contribute to upregulated CAF adhesion, ECM deposition and neuroactive ligand-receptor interactions, which may also be supportive of the axon outgrowth and tumor cell growth. We are also currently characterizing human and murine PDAC samples that contain and lack sympathetic innervation to evaluate spatial phenotypic shifts within the TME. Collectively, our results suggest that this crosstalk between sympathetic neurons and CAFs may both enhance tumor innervation and tumor-promoting phenotypes in the TME, such as axonogenesis and fibrosis in PDAC. Citation Format: Ariana Sattler, Parham Diba, Tetiana Korzun, Marigold Kuykendall, Kevin MacPherson-Hawthorne, Mara Sherman, Teresa Zimmers, Sebnem E Eksi. Deciphering the bidirectional influences of sympathetic neurons and cancer associated fibroblasts and the resulting contributions to PDAC progression [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A041.

Investigating the Impact of Nutrition and Oxidative Stress on Attention Deficit Hyperactivity Disorder.

Background/Objectives: Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, characterized by difficulty maintaining attention, impulsivity, and hyperactivity. While the cause of this disorder is still unclear, recent studies have stated that heredity is important in the development of ADHD. This is linked to a few comorbidities, including depression, criminal behavior, and anxiety. Although genetic factors influence ADHD symptoms, there are also non-genetic factors, one of which is oxidative stress (OS), which plays a role in the pathogenesis and symptoms of ADHD. This review aims to explore the role of OS in ADHD and its connection to antioxidant enzyme levels, as well as the gut-brain axis (GBA), focusing on diet and its influence on ADHD symptoms, particularly in adults with comorbid conditions. Methods: The literature search included the main available databases (e.g., Science Direct, PubMed, and Google Scholar). Articles in the English language were taken into consideration and our screening was conducted based on several words such as "ADHD", "oxidative stress", "diet", "gut-brain axis", and "gut microbiota." The review focused on studies examining the link between oxidative stress and ADHD, the role of the gut-brain axis, and the potential impact of dietary interventions. Results: Oxidative stress plays a critical role in the development and manifestation of ADHD symptoms. Studies have shown that individuals with ADHD exhibit reduced levels of key antioxidant enzymes, including glutathione peroxidase (GPx), catalase (CAT), and superoxide dismutase (SOD), as well as a diminished total antioxidant status (TOS) compared to healthy controls. Additionally, there is evidence of a close bidirectional interaction between the nervous system and gut microbiota, mediated by the gut-brain axis. This relationship suggests that dietary interventions targeting gut health may influence ADHD symptoms and related comorbidities. Conclusions: Oxidative stress and the gut-brain axis are key factors in the pathogenesis of ADHD, particularly in adults with comorbid conditions. A better understanding of these mechanisms could lead to more targeted treatments, including dietary interventions, to mitigate ADHD symptoms. Further research is required to explore the therapeutic potential of modulating oxidative stress and gut microbiota in the management of ADHD.

Similar Pathophysiological Mechanisms Between Osteoarthritis and Vascular Disease

Osteoarthritis (OA) is a prevalent, chronic joint disorder affecting millions of people worldwide, characterized by articular cartilage degradation, subchondral bone remodeling, synovial cytokine secretion, and osteophyte formation. OA primarily affects the hips, knees, hands, and spine. Patients with OA exhibit a higher prevalence of cardiovascular comorbidities and potentially important associations between OA and cardiovascular diseases have prompted investigations into potentially similar pathophysiological associations. This review explores the coexistence of atherosclerotic peripheral vascular disease (ASPVD) in OA patients, including evidence from a contemporary study suggesting associations between OA and arterial wall thickness and blood flow changes which are characteristic of early atherosclerosis, and which stimulate reactive pathology in endothelial cells. Observations from this study demonstrate elevated arterial flow volume and increased intima-media thickness in arteries ipsilateral to OA knees, suggesting a potential link between OA and arterial wall disease. We further explore the intricate relationship between the vascular system and skeletal health, highlighting bidirectional interactions among endothelial cells, inflammatory cells, and various bone cells. Mechanical endothelial cell dysfunction is discussed, emphasizing the impact of vessel wall material changes and endothelial cell responses to alterations in fluid shear stress. Inflammatory changes in OA and ASPVD are also explored, showcasing shared pathophysiological processes involving immune cell infiltration and pro-inflammatory cytokines. Additionally, the role of hypofibrinolysis in OA and ASPVD is discussed, highlighting similarities in elevations of the hypercoagulative and hypofibrinolytic factor, plasminogen activator inhibitor (PAI-1). The review suggests a provocative relationship among low-grade chronic inflammation, endothelial dysfunction, and hypofibrinolytic states in OA and ASPVD, warranting further investigation. In conclusion, this review provides an exploration of the possible associations between OA and ASPVD. While the ongoing study’s findings and other reports are observational, they suggest shared pathophysiological processes and emphasize the need for further research to elucidate additional potentially correlative linkages between these conditions. Understanding common molecular pathways may pave a way for targeted interventions that address both OA and ASPVD.

Analysis of pedestrian second crossing behavior based on physics-informed neural networks

Pedestrian two-stage crossings are common at large, busy signalized intersections with long crosswalks and high traffic volumes. This design aims to address pedestrian operation and safety by allowing navigation in two stages, negotiating each traffic direction separately. Understanding crosswalk behavior, especially during bidirectional interactions, is essential. This paper presents a two-stage pedestrian crossing model based on Physics-Informed Neural Networks (PINNs), incorporating fluid dynamics equations to determine characteristics such as speed, density, acceleration, and Reynolds number during crossings. The study shows that PINNs outperform traditional deep learning methods in calculating and predicting pedestrian fluid properties, achieving a mean squared error as low as 10–8. The model effectively captures dynamic pedestrian flow characteristics and provides insights into pedestrian behavior impacts. The results are significant for designing pedestrian facilities to ensure comfort and optimizing signal timing to enhance mobility and safety. Additionally, these findings can aid autonomous vehicles in better understanding pedestrian intentions in intelligent transportation systems.