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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

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 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

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.

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.

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.

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.

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.

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

We present a new benchmark task for graph-based machine learning, aiming to predict future air quality (PM2.5 concentration) observed by a geographically distributed network of environmental sensors. While prior work has successfully applied Graph Neural Networks (GNNs) on a wide family of spatio-temporal prediction tasks, the new benchmark task introduced here brings a technical challenge that has been less studied in the context of graph-based spatio-temporal learning: distribution shift across a long period of time. An important goal of this paper is to understand the behavior of spatio-temporal GNNs under distribution shift. We conduct a comprehensive comparative study of both graph-based and non-graph-based machine learning models under two data split methods, one results in distribution shift and one does not. Our empirical results suggest that GNN models tend to suffer more from distribution shift compared to non-graph-based models, which calls for special attention when deploying spatio-temporal GNNs in practice.

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.

Objective Campylobacter species are the main causes of foodborne illness worldwide, posing significant threats to public health. This study aimed to investigate the antibiotic resistance and genomic characterization of C. jejuni/C.coli from retail chickens in Beijing. Methods Antimicrobial susceptibility testing was conducted on 126 C. jejuni/C. coli isolated from retail chickens in Beijing, following CLSI protocols. Whole genomes of all isolates were sequenced using the Illumina platform. Results More C. coli (83.82%) showed multi-drug resistance than C. jejuni (8.62%). Genomic analysis demonstrated 42 sequence types (STs) and 12 clonal complexes (CCs), from which CC828 and CC52 were dominant. cdtA, cdtB and cdtC encoding cytotoxic protein were present spontaneously in most C. jejuni but not found in any C. coli isolates. The abundances of antibiotic resistance genes (ARGs) and virulence genes (VGs) in C. jejuni and C. coli were significantly different, with ARGs numbered in C. coli and VGs in C. jejuni. Conclusions High prevalence of multi-drug resistance C. coli and C. jejuni isolated from Beijing chickens were challenging clinical antibiotic usages in the treatment of Campylobacter infection. The surveillance of particular C. jejuni and C. coli STs correlated with higher resistance and virulence needs to be strengthened in the future.

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.

As an effective non-thermal sterilization method, ultrasound remains at the level of passive bacterial death despite the initial understanding of its sterilization mechanism. Here, we present the perspective that bacteria can choose to actively enter an apoptosis-like death state in response to external ultrasonic stress. In this study, Vibrio parahaemolyticus exhibited apoptotic markers such as phosphatidylserine ectropion and activated caspases when subjected to ultrasound stress. Additionally, the accumulation of reactive oxygen species (ROS) and enhanced calcium signaling were observed. Further transcriptomic analysis was conducted to investigate the regulatory mechanism of the SOS response in Vibrio parahaemolyticus during an apoptosis-like state. The results showed that the genes encoding the citrate cycle were down-regulated in Vibrio parahaemolyticus cells adapted to ultrasonic stress, leading to an apoptosis-like state and a decrease in production capacity and ability to catabolize carbon dioxide. Furthermore, the level of oxidized glutathione increased, suggesting that the bacteria were engaged in various anti-oxidative stress responses, ultimately leading to apoptosis. Moreover, the ultrasound field activated the regulatory factor CsrA, which facilitates stress survival as cells transition from rapid growth to an apoptotic state through a stringent response and catabolic inhibition system. Parallel reaction monitoring (PRM) revealed that the expression of certain key SOS proteins in Vibrio parahaemolyticus was up-regulated following ultrasound treatment, resulting in a gradual adaptation of the cells to external stress and ultimately leading to active cell death. In conclusion, the biological lethal effect of ultrasound treatment is not solely a mechanical cell necrosis process as traditionally viewed, but also a programmed cell death process regulated by cellular adaptation. This enriched the biological effect pathway of ultrasound sterilization.

ObjectiveTo examine the status quo of information construction in centers for disease control and prevention (CDCs) of provincial level and in pilot regions across China. Methods An online questionnaire survey was conducted among 32 provincial-level CDCs and 31 prefecture-level and 29 county/district level CDCs in pilot regions during 2021 via China Disease Prevention and Control Information System. ResultsFor the CDCs surveyed, there were totally 341 full-time staff engaged in the information construction. Of all the CDCs, 56.04% established specific information departments; 62.64% had the information construction fund less than 1% of their total expenditure; 43.96% put the information construction into the scope of performance evaluation; and 72.83% paid special attention to the integration of new technologies and the information construction. During 2019 – 2021, the professionals of the investigated CDCs were involved in the formulation or revision of 16 standards on health information and the publication of 187 papers about informatization. The Immunization Program Information System was operated in all the provincial CDCs surveyed, together with infectious disease surveillance and early warning system and mental health management system established in 50% and 59.38% of the provincial CDCs. Cross-sectoral information sharing was carried out in 12 provincial CDCs and 3 prefecture-level CDCs in pilot regions. ConclusionThe pilot work of information construction for disease control has promoted the improvement of the national information system for disease control. However, there are still some problems such as lack of investment, regional imbalance, insufficient specific informatizition support, connectivity and management collaboration.

Polygonati rhizoma (known as Huangjing in Chinese), an edible medicinal herb with homology of medicine and food, was also commonly used as food additives in Asian countries to provide many health benefits. Currently, the amelioration of fatigue induced by excessive exercise has become a health concern. In the present study, we studied the anti-fatigue effect of P. rhizoma polysaccharides (PRP) and also explored its potential mechanism by gut flora regulation. The molecular weight, monosaccharide composition, morphology, and FT-IR analysis of PRP were performed. Following intragastric administration of PRP, the exhaustive swimming model mice demonstrated increased swimming time, creatine kinase and malondialdehyde level, decreased lactic acid and blood urine nitrogen levels. Furthermore, the abundance of Akkermansia, Lactobacillus, and Faecalibacterium was remarkably enhanced, while the abundance of Streptococcus and Bacteroides was decreased, compared to model group. In summary, our findings offer compelling evidence supporting the beneficial effects of PRP on exercise-induced fatigue.

Transportation electrification offers societal benefits like reduced emissions and decreased dependence on fossil fuels. Understanding the deployment of electric vehicles (EVs) and electric vehicle supply equipment (EVSE) has been a popular focus, however, achieving their equitable distribution in the transportation system remains a challenge for successful electrification. To address this issue, this paper proposes a multi-dimensional equity metric system that assesses the equity status in the impacts of EV and EVSE deployment across different socio-demographic groups. Four types of equity are considered in the equity metric system: a fair share of resources and external costs that are grouped into horizontal equity, as well as inclusivity and affordability that refer to vertical equity. This paper performs a case study to examine equity concerns regarding the adoption of EVs and EVSE in Los Angeles County in 2035 by leveraging the proposed equity metric system. The results reveal disparities in the adoption of EVs and public chargers, as well as variations in EV trips and economic status across different socio-demographic groups. These disparities underscore the urgency to address equity issues during electrification. Building upon the results, this study puts forth recommendations to tackle these equity challenges to provide valuable insights for local agencies.

Dear Authors and Readers, Welcome to the 2024 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). This marks my second year serving as the Editor-in-Chief (EiC) of OJ-ITS. First and foremost, I would like to express my gratitude to all the active associate editors and reviewers who have devoted their valuable time to OJ-ITS and enabled the journal’s rapid growth. I also want to thank the IEEE staff and the ITS society for their efforts in publishing each article and promoting the journal.

Aims: Altered lipid, energy metabolism and sleep disorders had been linked with coronary heart disease (CHD), however, the metabolic signatures and sleep rhythm in non-obstructive coronary atherosclerosis-CHD remain unclear. This pilot study aims to investigate the lipidome and central carbon metabolites profiles and associated sleep characteristics among CHD patients without traditional risk factors. Methods: From January to July 2021, 15 CHD patients and 15 healthy controls were randomly selected from the cardiology unit of Zhongshan Hospital, Shanghai. A total of 464 lipids and 45 central carbon metabolites (CCM) were quantified in blood plasma. Metabolic signatures were selected through orthogonal projections to latent structures discriminant analysis (OPLS-DA) and principal component analysis (PCA) was conducted to link the profiles of identified metabolites with CHD risk, sleep patterns, cardiometabolic traits and cardiac electrophysiologic parameters. Results: Using OPLS-DA, we identified 40 metabolites (variable influence on projection >1) that were altered in CHD patients, with 38 lipids, including 25 triacylglycerols (TAGs), 8 diacylglycerols (DAGs), being elevated and two CCM metabolites (i.e., succinic acid and glycolic acid) being reduced. Using PCA, four principal components (PCs) were identified and associated with increased risk of CHD. Specifically, one standard unit increasement in the PC that was characterized by high levels of DAG (18:1) and low succinic acid and the PC that was characterized by high levels of two sphingomyelins [SM (26:0) and SM (24:0)] was associated with 21% [odds ratio (OR) = 1.21, 95% CI: 1.02,1.43] and 14% (OR = 1.14,1.02,1.29) increased risk of CHD, respectively. Further regression analyses confirmed that the identified metabolites and the four PCs were positively associated with TG and ALT. Interestingly, glycolic acid was negatively associated with sleep quality and PSQI. Participants with night sleep mode tended to have a high level of the identified lipids, especially FFA (20:4). Conclusion: In the present pilot study, our findings provide clues on alterations of lipid and energy metabolism in CHD patients without traditional risk factors, with multiple triacylglycerols and diacylglycerols metabolites seemingly elevated and certain nonlipids metabolites (e.g., succinic acid and glycolic acid) decreased in cases. Considering the limit sample size, further studies are warranted to confirm our results.

The wide application of drip irrigation under mulch in semi-arid agricultural regions in China not only improves agricultural water efficiency, but also affects formation of groundwater and the mechanism of water infiltration to a certain extent. This paper takes the typical semi-arid agricultural region in China as the research object. The movement of soil water under the three types of underlying surface was simulated by the Hydrus-2D model for the quantitative analysis of groundwater recharge. The influence of drip irrigation under mulch on groundwater infiltration depth and cumulative infiltration amount under different level years was simulated. Taking a normal flow year as an example, the simulated results showed that the maximum infiltration depth of drip irrigation under mulch reached 250 cm, which was greater than that of border irrigation (138 cm) and bare area (158 cm). The cumulative infiltration amounts of drip irrigation under mulch at 80, 120, 140 and 200 cm were respectively 1,484.8 m3/hm2, 686.3 m3/hm2, 554.1 m3/hm2 and 238.1 m3/hm2, which were greater than that of border irrigation and bare land at the same depth. The results proved that drip irrigation under mulch could increase the infiltration depth and cumulative infiltration amount, which is beneficial to groundwater recharge in semi-arid agricultural regions of China. HIGHLIGHTS The cumulative infiltration and the maximum infiltration depth under different underlying surfaces were simulated.; The effect of drip irrigation under mulch on groundwater recharge under membrane was annualized.; The technology of drip irrigation under mulch was evaluated from the perspective of water resources.; A reference is provided for choosing water-saving irrigation in similar areas.;

Although nano-systems can promote the cellular internalization, polymeric nanoparticles (NPs) are easily to be destroyed or eliminated in the gastrointestinal tract, which leads to premature drug release or insufficient colonic accumulation. A novel oral nano-in-micro system for the efficient colonic delivery of curcumin (Cur) was developed in this study, in which the hyaluronic acid (HA)/zein complex NPs loading Cur were embedded in alginate/chitosan hydrogel microparticles (Cur@NMPs) with the electrospray technology. The Cur@NMPs showed uniform-sized sphere with an average size of 218.36 ± 10 μm, encapsulating Cur@HA/zein NPs with the average size of 148.64 ± 3.21 nm. Cur in NMPs showed the sustained drug-release profiles in simulated gastric fluid, whereas it showed the rapid release in simulated colonic fluid. Mediated by the HA-CD44 receptor recognition, Cur@HA/zein NPs had the increased cellular uptake efficiency in macrophages. Furthermore, NMPs had the significant colon-retention and bio-adhesiveness capacity in colon tissues. As expected, the oral administration of Cur@NMPs significantly mitigated colitis symptoms in DSS-induced UC mice by inhibiting TLR4/NF-κB pathway. The above results can provide a useful drug delivery strategy for Cur in the treatment of UC by retaining the advantages of nano- and micro-scaled carriers.

Although a natural anti-inflammatory ingredient, isoliquiritigenin (ISL), plays an effective role in ulcerative colitis (UC) treatment, a series of drawbacks still limit its clinical application, including the poor solubility, instability in gastrointestinal tract, and rapid elimination rate of ISL. Zein-based NPs display the benefits on drug loading and delivery, whereas with the poor stability. In this study, an edible nano-system composed by zein/caseinate complex was fabricated for the colon-targeting delivery of ISL, to improve its colon retention and anti-UC effects. The optimized ISL loaded zein/caseinate NPs (ISL@NPs) were prepared by single-factor design by anti-solvent precipitation method, and then characterized. The improved cellular uptake of ISL@NPs on NCM460 and RAW 264.7 cells was evaluated in vitro. The colon tissue permeability and retention capacity in vivo, and the anti-UC efficacy of ISL@NPs in DSS-induce UC were implemented. As a result, ISL@NPs with the high drug loading efficiency of 9.39% ± 0.26%, the average particle diameter of 137.32 ± 2.54 nm, exhibited the pH-sensitive stability in the different simulated gastrointestinal buffer. Compared with free ISL, ISL@NPs showed significantly higher cellular uptake ability in NCM460 and RAW 264.7 cells. Based on in vivo imaging system, zein/caseinate NPs showed the prolonged colonic retention and the enhanced penetration into the colonic epithelium. Finally, the oral administration of ISL@NPs could effectively alleviate the UC-related symptoms, down-regulate the production of pro-inflammatory factors, and reduce the infiltration of macrophages and neutrophils in colon tissues. In this study, an oral colon-specific nano-system, composed with the natural compound and edible materials, was developed as the promising alternatives in the prevention and treatment of UC.

Abstract Background The toxicity and inefficient delivery of triptolide (TPL) in tumor therapy have greatly limited the clinical application. Thus, we fabricated a CD44-targeting and tumor microenvironment pH/redox-sensitive nanosystem composed of hyaluronic acid-vitamin E succinate and poly (β-amino esters) (PBAEss) polymers to enhance the TPL-mediated suppression of breast cancer proliferation and lung metastasis. Results The generated TPL nanoparticles (NPs) had high drug loading efficiency (94.93% ± 2.1%) and a desirable average size (191 nm). Mediated by the PBAEss core, TPL/NPs displayed a pH/redox-dual-stimuli-responsive drug release profile in vitro. Based on the hyaluronic acid coating, TPL/NPs exhibited selective tumor cellular uptake and high tumor tissue accumulation capacity by targeting CD44. Consequently, TPL/NPs induced higher suppression of cell proliferation, blockage of proapoptotic and cell cycle activities, and strong inhibition of cell migration and invasion than that induced by free TPL in MCF-7 and MDA-MB-231 cells. Importantly, TPL/NPs also showed higher efficacy in shrinking tumor size and blocking lung metastasis with decreased systemic toxicity in a 4T1 breast cancer mouse model at an equivalent or lower TPL dosage compared with that of free TPL. Histological immunofluorescence and immunohistochemical analyses in tumor and lung tissue revealed that TPL/NPs induced a high level of apoptosis and suppressed expression of matrix metalloproteinases, which contributed to inhibiting tumor growth and pulmonary metastasis. Conclusion Collectively, our results demonstrate that TPL/NPs, which combine tumor active targeting and pH/redox-responsive drug release with proapoptotic and antimobility effects, represent a promising candidate in halting breast cancer progression and metastasis while minimizing systemic toxicity. Graphic Abstract

The production and consumption of pomegranates have always been increasing owing to their taste and nutrition. However, during fruit processing, a large number of by-products are produced, such as peels and seeds, which can lead to environmental pollution problems if not handled properly. The pomegranate peel takes up about 26–30% of the total weight, while it contains abundant bioactive substances. This paper carries out a mini review of the characterization and physiological functions of key bioactive compounds in pomegranate peel, comprehensively assessing their effects on human health. The overview summarizes the main phenolic substances in pomegranate peel, including tannins, flavonoids, and phenolic acids. Dietary fiber and other bioactive substances such as alkaloids, minerals, and vitamins are also mentioned. These components act as antioxidants by improving oxidative biomarkers and scavenging or neutralizing reactive oxygen species, further contributing to their extensive functions like anti-inflammatory, anti-cancer, antibacterial, and cardiovascular protection. Overall, it is envisaged that through the deeper understanding of bioactive compounds in pomegranate peel, the waste sources can be better reused for physiological applications.

Inflammatory bowel disease (IBD) is a non-specific colorectal disease caused by multifaceted triggers. Although conventional treatments are effective in the management of IBD, high cost and frequent side effects limit their applications and have turned sufferers toward alternative and complementary approaches. Salvia miltiorrhiza Bge (Danshen) is an herbal medicine that reportedly alleviates the symptoms of IBD. A large body of research, including clinical trials in which Danshen-based products or botanical compounds were used, has unmasked its multiple mechanisms of action, but no review has focused on its efficacy as a treatment for IBD. Here, we discussed triggers of IBD, collected relevant clinical trials and analyzed experimental reports, in which bioactive compounds of Danshen attenuated rodent colitis in the management of intestinal integrity, gut microflora, cell death, immune conditions, cytokines, and free radicals. A network pharmacology approach was applied to describe sophisticated mechanisms in a holistic view. The safety of Danshen was also discussed. This review of evidence will help to better understand the potential benefits of Danshen for IBD treatment and provide insights for the development of innovative applications of Danshen.

Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Dear Authors and Readers, Welcome to the 2023 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). It is my great pleasure and honor to serve as the second Editor-in-Chief (EiC) of OJ-ITS, starting from January 2023, taking over the position from the founding EiC Prof. Bart van Arem. I am really looking forward to working closely with Prof. Bilge Küçük Atasoy (newly appointed Associate Editor-in-Chief, AEiC), the editorial board, staff, reviewers, IEEE, and the ITS society in the coming years to continuously develop OJ-ITS.

Summary: Defining factors that govern CD8+ T cell immunodominance is critical for the rational design of vaccines for viral pathogens. Here, we assess the contribution of human leukocyte antigen (HLA) class-I-peptide stability for 186 optimal HIV epitopes across 18 HLA alleles using transporter associated with antigen processing (TAP)-deficient mono-allelic HLA-expressing cell lines. We find that immunodominant HIV epitopes increase surface stabilization of HLA class-I molecules in comparison to subdominant epitopes. HLA class-I-peptide stability is also strongly correlated with overall immunodominance hierarchies, particularly for epitopes from high-abundance proteins (e.g., Gag). Moreover, HLA alleles associated with HIV protection are preferentially stabilized by epitopes derived from topologically important viral regions at a greater frequency than neutral and risk alleles. These findings indicate that relative stabilization of HLA class-I is a key factor for CD8+ T cell epitope immunodominance hierarchies, with implications for HIV control and the design of T-cell-based vaccines.

Filterless narrowband photodetectors are preferred in imaging applications as they save cost and space. Here Li et al. develop photodetectors with tunable spectral responsivities based on two-dimensional perovskites and the excellent performance can be attributed to the self-trapped states.

Haptic feedback is appealing for achieving the realistic perception of environmental changes for human bodies in human–computer interaction fields. However, existing haptic actuators have some hurdles such as single mode, poor compatibility, or incomplete tactile information. In this study, we proposed a novel way to generate haptic feedback by designing a soft electro-hydraulic pneumatic actuator (SEHPA) with dual drive modes. The SEHPA was structured with silicone films, a silicone air chamber, flexible electrodes, and an insulating liquid dielectric for good human–machine compatibility. The SEHPA had the advantages of high output force (1.5 N at 10 kPa) and displacement (4.5 mm at 5 kPa), as well as various haptic notifications (0~400 Hz vibration). The electro-hydraulic drive method realized smooth output force changes at the millinewton level (0~40 mN) and output displacement changes at the micron level (0~800 μm), which further enriched the details of the tactile experience. In addition, the self-sensing capability of the SEHPA can be dedicated to monitoring and ensuring precise output. The SEHPAs can be potentially mounted on the fingertips to provide accurate tactile sensation once the manipulator touches an object through teleoperation. More invisible information can also be obtained by customizing various haptic notifications. The excellent response behavior and accurate tactile haptic feedback demonstrate the candidate for teleoperation fields.

Understanding the effect of thermal treatment on the physical and chemical properties of protein and its mechanisms has important theoretical implications in food science. Pea seed ferritin (PSF) is an iron storage protein naturally occurring in pea seeds, which represents a promising iron supplement. However, how thermal processing affects the structure and function of PSF remains unknown. In this work, during the production of pea seed milk, we investigated the effect of thermal treatments at boiling temperature for two different times (5 and 10 min), respectively, on the structure and function of PSF. The results demonstrated that thermal treatment resulted in a pronounced change in the primary, secondary, and tertiary structure, iron content, and iron oxidation activity of PSF. However, the shell-like structure of PSF can be kept during the processing of pea seed milk. Interestingly, upon thermal treatment, both thermal-treated samples exhibit larger higher iron absorption rate by Caco-2 than untreated PSF at the same protein concentration. Such an investigation provides a better understanding of the relationship between the structure and function of food protein, as affected by thermal treatment.

Most existing longitudinal control strategies for connected and automated vehicles (CAVs) have unclear adaptability without scientific analysis regarding the key parameters of the control algorithm. This paper presents an optimal longitudinal control strategy for a homogeneous CAV platoon. First of all, the CAV platoon models with constant time-headway gap strategy and constant spacing gap strategy were, respectively, established based on the third-order linear vehicle dynamics model. Then, a linear-quadratic optimal controller was designed considering the perspectives of driving safety, efficiency, and ride comfort with three performance indicators including vehicle gap error, relative speed, and desired acceleration. An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. Based on the Matlab/Simulink experimental simulation, the analysis results show that the proposed strategy can significantly reduce the gap error and relative speed and improve the flexibility and initiative of the platoon control strategy compared with the unoptimized strategies. Sensitivity analysis was provided for communication lag and actuator lag in order to prove the applicability and effectiveness of this proposed strategy, which will achieve better distribution of system performance.

Gaining direct tactile sensation is becoming increasingly important for humans in human–computer interaction fields such as space robot teleoperation and augmented reality (AR). In this study, a novel electro-hydraulic soft actuator was designed and manufactured. The proposed actuator is composed of polydimethylsiloxane (PDMS) films, flexible electrodes, and an insulating liquid dielectric. The influence of two different voltage loading methods on the output characteristics of the actuator was studied. The special voltage loading method (AC voltage) enables the actuator to respond rapidly (within 0.15 s), output a stable displacement in 3 s, and remain unchanged in the subsequent time. By adjusting the voltages and frequencies, a maximum output displacement of 1.1 mm and an output force of 1 N/cm2 can be rapidly achieved at a voltage of 12 kV (20 Hz). Finally, a haptic feedback system was built to control the robotic hand to perform gripping tasks in real time, and a more realistic tactile sensation could be realized, similar to that obtained when a human directly grabs objects. Therefore, the actuator has excellent portability, robustness, rapid response, and good compatibility with the human body for human–computer interaction.

Dielectric elastomer (DE) is a soft material that can deform to a large degree under the action of an electric field. In this paper, multilayer DE films were stacked in parallel to prepare a 20-layer dielectric elastomer actuator (DEA). This DEA could provide a peak output force of 30N, which significantly improves the driving performance of the DEA and provides conditions for large load driving of the DEA. As a new driving method, the DEA was applied to a jumping robot, and the heavy-weight robot accomplished jumping motion after several cycles of energy storage.

Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

Tourism experience has great impact on the tourist satisfaction, and therefore, tourists pay more attention to the tourism experience utility of the tour. The present problem is how to plan the tour route to maximize tourism experience utility considering tourists’ preference of attraction, time, and cost budgets. The utility function for the tourism experience, consisting of utilities of tourism activities and travel, was proposed. An optimization model for tour route planning was established with the objective function of the tourism experience utility. Then, the computational method to obtain the optimal solution was given, and the feasibility of the method was validated by an example of a tourism transportation network. Finally, sensitivity analyses were conducted by varying the parameters of the tourism experience utility. The results showed that the tourists’ preference of attraction, degree of attention to travel time, and travel cost had great influence on the tour route planning. The tourists with high value of time tend to choose transportation mode with shorter travel times, and the tourism experience utility of the tourists with high value of time was higher than that of the tourists with low value of time.

Objective To evaluate the performance of China’s infectious disease automated alert and response system in the detection of outbreaks of hand, foot and mouth (HFM) disease. Methods We estimated size, duration and delay in reporting HFM disease outbreaks from cases notified between 1 May 2008 and 30 April 2010 and between 1 May 2010 and 30 April 2012, before and after automatic alert and response included HFM disease. Sensitivity, specificity and timeliness of detection of aberrations in the incidence of HFM disease outbreaks were estimated by comparing automated detections to observations of public health staff. Findings The alert and response system recorded 106 005 aberrations in the incidence of HFM disease between 1 May 2010 and 30 April 2012 – a mean of 5.6 aberrations per 100 days in each county that reported HFM disease. The response system had a sensitivity of 92.7% and a specificity of 95.0%. The mean delay between the reporting of the first case of an outbreak and detection of that outbreak by the response system was 2.1 days. Between the first and second study periods, the mean size of an HFM disease outbreak decreased from 19.4 to 15.8 cases and the mean interval between the onset and initial reporting of such an outbreak to the public health emergency reporting system decreased from 10.0 to 9.1 days. Conclusion The automated alert and response system shows good sensitivity in the detection of HFM disease outbreaks and appears to be relatively rapid. Continued use of this system should allow more effective prevention and limitation of such outbreaks in China.

With ongoing changes in the age distribution of drivers in the United States, it is important to obtain insights on how to make the roadways equally safe for drivers across different age groups. In light of this, the objective of this study is to examine various crash characteristics and make recommendations on how to potentially improve roadway safety for all age groups. Using the Highway Safety Information System (HSIS) data, this study investigates the factors influencing motor-vehicle crash injury severity for young (aged 16−25), middle-aged (aged 26−64), and older drivers (above 64) in the state of California. A multinomial logit model was used to separately model crashes involving each age group and to evaluate the weight of different predictor variables on driver injury severity. The predictor variables were classified into four—driver, roadway, accident and environmental characteristics. Results suggest that there are close relationships between severity determinants for young and middle-aged drivers. However, older drivers tend to be most cautious among all age groups under all environmental and roadway conditions. Young drivers are more likely to explore their driving skills due to newness to driving. Middle-aged drivers are familiar with driving and tend to demonstrate less cautious behaviors, especially male drivers. Another insight obtained from this study is that older driver behavior is less dynamic compared to other age groups; their driving pattern is usually regular regardless of the surrounding conditions.

Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

BACKGROUND:An outbreak of dengue fever (DF) occurred in Guangdong Province, China in 2013 with the highest number of cases observed within the preceding ten years. DF cases were clustered in the Pearl River Delta economic zone (PRD) in Guangdong Province, which accounted for 99.6% of all cases in Guangdong province in 2013. The main vector in PRD was Aedes albopictus. We investigated the socioeconomic and environmental factors at the township level and explored how the independent variables jointly affect the DF epidemic in the PRD. METHODOLOGY/PRINCIPAL FINDINGS:Six factors associated with the incidence of DF were identified in this project, representing the urbanization, poverty, accessibility and vegetation, and were considered to be core contributors to the occurrence of DF from the perspective of the social economy and the environment. Analyses were performed with Generalized Additive Models (GAM) to fit parametric and non-parametric functions to the relationships between the response and predictors. We used a spline-smooth technique and plotted the predicted against the observed co-variable value. The distribution of DF cases was over-dispersed and fit the negative binomial function better. The effects of all six socioeconomic and environmental variables were found to be significant at the 0.001 level and the model explained 45.1% of the deviance by DF incidence. There was a higher risk of DF infection among people living at the prefectural boundary or in the urban areas than among those living in other areas in the PRD. The relative risk of living at the prefectural boundary was higher than that of living in the urban areas. The associations between the DF cases and population density, GDP per capita, road density, and NDVI were nonlinear. In general, higher "road density" or lower "GDP per capita" were considered to be consistent risk factors. Moreover, higher or lower values of "population density" and "NDVI" could result in an increase in DF cases. CONCLUSION:In this study, we presented an effect analysis of socioeconomic and environmental factors on DF occurrence at the smallest administrative unit (township level) for the first time in China. GAM was used to effectively detect the nonlinear impact of the predictors on the outcome. The results showed that the relative importance of different risk factors may vary across the PRD. This work improves our understanding of the differences and effects of socioeconomic and environmental factors on DF and supports effectively targeted prevention and control measures.