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Abstract The inherent antigen cross-reactivity of the T cell receptor (TCR) is balanced by high specificity. Surprisingly, TCR specificity often manifests in ways not easily interpreted from static structures. Here we show that TCR discrimination between an HLA-A*03:01 (HLA-A3)-restricted public neoantigen and its wild-type (WT) counterpart emerges from distinct motions within the HLA-A3 peptide binding groove that vary with the identity of the peptide’s first primary anchor. These motions create a dynamic gate that, in the presence of the WT peptide, impedes a large conformational change required for TCR binding. The neoantigen is insusceptible to this limiting dynamic, and, with the gate open, upon TCR binding the central tryptophan can transit underneath the peptide backbone to the opposing side of the HLA-A3 peptide binding groove. Our findings thus reveal a novel mechanism driving TCR specificity for a cancer neoantigen that is rooted in the dynamic and allosteric nature of peptide/MHC-I binding grooves, with implications for resolving long-standing and often confounding questions about T cell specificity.

Abstract Background The study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making. Methods A retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared. Results KRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p

The loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances. The V-I trajectories of loads are at first classified with Resnet18. Then, load features with low redundancy is obtained through the Max-Relevance and Min-Redundancy (mRMR) feature selection algorithm from various operating states of loads that were not successfully classified. The SVM algorithm is developed for two-stage identification to achieve high accuracy of classification for identifying the multi-state appliances quickly. This proposed NILM method can significantly improve the accuracy of identification for multi-state loads. Finally, the Plaid dataset is acquired to validate the effectiveness and accuracy of the proposed method.

Abstract Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein–protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p

In an era marked by rapid pace and demanding work schedules, young individuals frequently experience mental exhaustion and emotional downturns, exacerbating mental health issues. Recognizing this, prior studies have underscored the significant role of pocket parks in fostering emotional rejuvenation. However, previous research has primarily suggested that green natural elements in pocket parks positively influence restorative effects, without thoroughly examining the restorative power of different natural elements. Furthermore, there has been a lack of systematic analysis to quantify the impact of both natural elements and activities in pocket parks on their restorative effects. This research adopts a quantitative approach to explore the interaction between psychological attributes and the restorative impacts of various natural elements within pocket parks. It also considers the influence of socio-demographic factors, ultimately proposing an evaluation model to assess these spaces’ psychological restoration capabilities. Focusing on Guiyang and Chongqing as study locations, the research utilized 20 photographs to represent typical elements within pocket parks. Analysis of these images led to the identification of nine critical elements. Participants provided insights into how these factors relate to psychological characteristics and their subsequent restorative effects by responding to five carefully designed questions. The analysis revealed that amenities for relaxation and sports play a pivotal role in enhancing restorative outcomes and influencing psychological traits. Additionally, the effect of natural elements on restoration varied; while trees and water bodies positively contributed to restoration, shrubs and lawns were found to have a less significant effect. The study concludes by introducing an evaluation model that identifies the primary built environment factors influencing restoration. The results of this study are intended to guide the redesign of pocket parks and inform policy-making processes.

Abstract In the field of deep learning, the attention mechanism, as a technology that mimics human perception and attention processes, has made remarkable achievements. The current methods combine a channel attention mechanism and a spatial attention mechanism in a parallel or cascaded manner to enhance the model representational competence, but they do not fully consider the interaction between spatial and channel information. This paper proposes a method in which a space embedded channel module and a channel embedded space module are cascaded to enhance the model’s representational competence. First, in the space embedded channel module, to enhance the representational competence of the region of interest in different spatial dimensions, the input tensor is split into horizontal and vertical branches according to spatial dimensions to alleviate the loss of position information when performing 2D pooling. To smoothly process the features and highlight the local features, four branches are obtained through global maximum and average pooling, and the features are aggregated by different pooling methods to obtain two feature tensors with different pooling methods. To enable the output horizontal and vertical feature tensors to focus on different pooling features simultaneously, the two feature tensors are segmented and dimensionally transposed according to spatial dimensions, and the features are later aggregated along the spatial direction. Then, in the channel embedded space module, for the problem of no cross-channel connection between groups in grouped convolution and for which the parameters are large, this paper uses adaptive grouped banded matrices. Based on the banded matrices utilizing the mapping relationship that exists between the number of channels and the size of the convolution kernels, the convolution kernel size is adaptively computed to achieve adaptive cross-channel interaction, enhancing the correlation between the channel dimensions while ensuring that the spatial dimensions remain unchanged. Finally, the output horizontal and vertical weights are used as attention weights. In the experiment, the attention mechanism module proposed in this paper is embedded into the MobileNetV2 and ResNet networks at different depths, and extensive experiments are conducted on the CIFAR-10, CIFAR-100 and STL-10 datasets. The results show that the method in this paper captures and utilizes the features of the input data more effectively than the other methods, significantly improving the classification accuracy. Despite the introduction of an additional computational burden (0.5 M), however, the overall performance of the model still achieves the best results when the computational overhead is comprehensively considered.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

IntroductionNatural plants are valuable resources for exploring new bioactive compounds. Artemisia vulgaris L. is a traditional Chinese medicinal herb that has been historically used for treating multiple diseases. Active compounds isolated and extracted from A. vulgaris L. typically possess immunomodulatory and anti-inflammatory properties. Artemvulactone E (AE) is a new sesquiterpene lactone isolated and extracted from A. vulgaris L. with unclear biological activities.MethodsThe immunoregulatory effects of AE on macrophages were assessed by ELISA, RT-qPCR, immunofluorescence, and western blot assay. The effect of AE on lipopolysaccharide (LPS) -relates signaling pathways was examined by western blot assay. In zebrafish models, the larvae were yolk-microinjected with LPS to establish inflammation model and the effect of AE was evaluated by determining the survival rate, heart rate, yolk sac edema size, neutrophils and macrophages infiltration of zebrafish. The interaction between AE and Toll-like receptor 4 (TLR4) was examined by molecular docking and dynamic stimulation.ResultsAE reduced the expression and secretion of pro-inflammatory cytokines (TNF-α and IL-6), inflammatory mediators iNOS and COX-2, as well as decreases the production of intracellular NO and ROS in LPS-stimulated macrophages. In addition, AE exerted its anti-inflammatory effect synergistically by inhibiting MAPK/JAK/STAT3-NF-κB signaling pathways. Furthermore, AE enhanced the survival rate and attenuated inflammatory response in zebrafish embryos treated with LPS. Finally, the molecular dynamics results indicate that AE forms stable complexes with LPS receptor TLR4 through the Ser127 residue, thus completely impairing the subsequent activation of MAPK-NF-κB signaling.ConclusionAE exhibits notable anti-inflammatory activity and represents as a potential agent for treating inflammation-associated diseases.

Bone defect disease seriously endangers human health and affects beauty and function. In the past five years, the three dimension (3D) printed radially graded triply periodic minimal surface (TPMS) porous scaffold has become a new solution for repairing bone defects. This review discusses 3D printing technologies and applications for TPMS scaffolds. To this end, the microstructural effects of 3D printed TPMS scaffolds on bone regeneration were reviewed and the structural characteristics of TPMS, which can promote bone regeneration, were introduced. Finally, the challenges and prospects of using TPMS scaffolds to treat bone defects were presented. This review is expected to stimulate the interest of bone tissue engineers in radially graded TPMS scaffolds and provide a reliable solution for the clinical treatment of personalised bone defects.

Abstract Zero-emission vehicle (ZEV) adoption is a key climate mitigation tool, but its environmental justice implications remain unclear. Here, we quantify ZEV adoption at the census tract level in California from 2015 to 2020 and project it to 2035 when all new passenger vehicles sold are expected to be ZEVs. We then apply an integrated traffic model together with a dispersion model to simulate air quality changes near roads in the Greater Los Angeles. We found that per capita ZEV ownership in non-disadvantaged communities (non-DACs) as defined by the state of California is 3.8 times of that in DACs. Racial and ethnic minorities owned fewer ZEVs regardless of DAC designation. While DAC residents receive 40% more pollutant reduction than non-DACs due to intercommunity ZEV trips in 2020, they remain disproportionately exposed to higher levels of traffic-related air pollution. With more ZEVs in 2035, the exposure disparity narrows. However, to further reduce disparities, the focus must include trucks, emphasizing the need for targeted ZEV policies that address persistent pollution burdens among DAC and racial and ethnic minority residents.

Diabetic neuropathy (DN) represents a common and debilitating complication of diabetes, affecting a significant proportion of patients. Despite available treatments focusing on symptom management, there remains an unmet need for therapies that address the underlying pathophysiology. In pursuit of novel interventions, this study evaluated the therapeutic effects of caffeic acid—a natural phenolic compound prevalent in various foods—on diabetic neuropathy using a mouse model, particularly examining its interaction with the Insulin-like Growth Factor 1 (IGF-1) signaling pathway. Caffeic acid was administered orally at two dosages (5 mg/kg and 10 mg/kg), and a comprehensive set of outcomes including fasting blood glucose levels, body weight, sensory behavior, spinal cord oxidative stress markers, inflammatory cytokines, and components of the IGF-1 signaling cascade were assessed. Additionally, to determine the specific contribution of IGF-1 signaling to the observed benefits, IGF1R inhibitor Picropodophyllin (PPP) was co-administered with caffeic acid. Our results demonstrated that caffeic acid, at both dosages, effectively reduced hyperglycemia and alleviated sensory behavioral deficits in diabetic mice. This was accompanied by a marked decrease in oxidative stress markers and an increase in antioxidant enzyme activities within the spinal cord. Significantly lowered microglial activation and inflammatory cytokine expression highlighted the potent antioxidative and anti-inflammatory effects of caffeic acid. Moreover, increases in both serum and spinal levels of IGF-1, along with elevated phosphorylated IGF1R, implicated the IGF-1 signaling pathway as a mediator of caffeic acid's neuroprotective actions. The partial reversal of caffeic acid's benefits by PPP substantiated the pivotal engagement of IGF-1 signaling in mediating its effects. Our findings delineate the capability of caffeic acid to mitigate DN symptoms, particularly through reducing spinal oxidative stress and inflammation, and pinpoint the integral role of IGF-1 signaling in these protective mechanisms. The insights gleaned from this study not only position caffeic acid as a promising dietary adjunct for managing diabetic neuropathy but also highlight the therapeutic potential of targeting spinal IGF-1 signaling as part of a strategic treatment approach.

IntroductionMagnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.MethodsA total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.ResultsAll of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P

BackgroundPrior investigations have indicated associations between Knee Osteoarthritis (KOA) and certain inflammatory cytokines, such as the interleukin series and tumor necrosis factor-alpha (TNFα). To further elaborate on these findings, our investigation utilizes Mendelian randomization to explore the causal relationships between KOA and 91 inflammatory cytokines.MethodsThis two-sample Mendelian randomization utilized genetic variations associated with KOA from a large, publicly accessible Genome-Wide Association Study (GWAS), comprising 2,227 cases and 454,121 controls of European descent. The genetic data for inflammatory cytokines were obtained from a GWAS summary involving 14,824 individuals of European ancestry. Causal relationships between exposures and outcomes were primarily investigated using the inverse variance weighted method. To enhance the robustness of the research results, other methods were combined to assist, such as weighted median, weighted model and so on. Multiple sensitivity analysis, including MR-Egger, MR-PRESSO and leave one out, was also carried out. These different analytical methods are used to enhance the validity and reliability of the final results.ResultsThe results of Mendelian randomization indicated that Adenosine Deaminase (ADA), Fibroblast Growth Factor 5(FGF5), and Hepatocyte growth factor (HFG) proteins are protective factors for KOA (IVWADA: OR = 0.862, 95% CI: 0.771–0.963, p = 0.008; IVWFGF5: OR = 0.850, 95% CI: 0.764–0.946, p = 0.003; IVWHFG: OR = 0.798, 95% CI: 0.642–0.991, p = 0.042), while Tumor necrosis factor (TNFα), Colony-stimulating factor 1(CSF1), and Tumor necrosis factor ligand superfamily member 12(TWEAK) proteins are risk factors for KOA. (IVWTNFα: OR = 1.319, 95% CI: 1.067–1.631, p = 0.011; IVWCSF1: OR = 1.389, 95% CI: 1.125–1.714, p = 0.002; IVWTWEAK: OR = 1.206, 95% CI: 1.016–1.431, p = 0.032).ConclusionThe six proteins identified in this study demonstrate a close association with the onset of KOA, offering valuable insights for future therapeutic interventions. These findings contribute to the growing understanding of KOA at the microscopic protein level, paving the way for potential targeted therapeutic approaches.

Optical flow estimation captures the motion information of objects in a scene through analyzing the displacement of pixels in an image over time. This technology provides a powerful tool for vision systems, allowing them to understand and perceive changes in dynamic environments. Optical flow estimation has a wide range of applications in fields such as military, medicine, traffic regulation, and intelligent robotics. This study systematically explores two key directions in the field of optical flow estimation—traditional methods and emerging strategies based on deep learning—aiming to provide a comprehensive and in-depth perspective to help scholars gain a deeper understanding of the development of the optical flow estimation field. First, the core principles and constraints of conventional optical flow estimation are briefly analyzed, focusing on reviewing the faced challenges and associated solutions based on differential, variational, and matching optical flow estimation principles. Then, we discuss the backbone networks and training strategies used in deep learning approaches in depth, with a particular focus on the current challenges faced under supervised and unsupervised conditions, as well as existing solutions. In addition, to evaluate the performance of these methods, existing datasets and evaluation indicators are analyzed and comprehensive comparisons on several publicly available datasets are conducted. Finally, we discuss prospects related to various application fields and future research directions in the field of optical flow estimation.