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Simple Summary: Immunosenescence is the decrease in the function of the immune system with age. The immune system plays a key role in the development and progression of tumors. Targeting immunosenescence is considered a promising therapeutic approach to improving tumor prognosis. However, few studies have been conducted to reveal immunosenescence biomarkers of lung cancer. Thus, our study aimed to fill this critical knowledge gap by developing an immunosenescence gene set to characterize tumor immune microenvironment and proposing an immunosenescence risk model to understand the roles of immunosenescence in tumor prognosis and immunotherapy. Background: Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach. Methods: We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model. Results: We found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy. Conclusions: Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery. [ABSTRACT FROM AUTHOR]

Advances in modal analysis have motivated finite element model updating for damage identification based on vibration monitoring. However, the model updating problem is usually ill-posed owing to limited and noisy measurement data, and the predominant sensitivity-based optimization approaches may suffer from large linearization errors. To address these challenges, this article proposes using the unscented Kalman inversion with $ {l_1} $ l 1 -norm regularization (UKI- $ {l_1} $ l 1 ) to solve the damage identification problem in a derivative-free manner. UKI- $ {l_1} $ l 1 implements an explicit transformation converting the $ {l_1} $ l 1 -norm regularized least-squares problem into the $ {l_2} $ l 2 -norm regularized problem, which can be readily solved by the UKI with measurement augmentation. The effectiveness of the proposed approach is evaluated through identifying damage of a continuous beam structure and a reinforced concrete frame structure. Numerical investigation confirms the robustness of UKI- $ {l_1} $ l 1 to measurement noise, and experimental study demonstrates that the damage identification results obtained by UKI- $ {l_1} $ l 1 match well with expectations. [ABSTRACT FROM AUTHOR]

Background: Poly-victimization (PV) not only threatens physical and mental health but also causes a range of social problems. Left-behind children in rural areas are more likely to experience PV problems. However, there have been fewer studies on PV among rural children, and even fewer intervention studies. Objective: The difference-in-differences method was employed to analyze the impact of intervention measures, based on the theory of planned behavior, on PV among left-behind children in rural areas. Methods: The study subjects were left-behind children from six middle schools in two cities in southern China, who completed the baseline survey from 2020 to 2021. They were divided into a control group and an intervention group, each consisting of 228 cases, based on their schools. Before and after the intervention, the Self-made victimization-related knowledge, attitude, and practice questionnaire, Poly-victimization scale, and Middle school students' coping style scale were used to evaluate the victimization-related KAP(knowledge, attitude, and practice), victimization occurrence, and coping styles of left-behind children, respectively. Stata 15.0 was used to establish a difference-in-differences regression model to analyze the impact of the intervention measures on poly-victimization and coping styles. Results: Mixed Anova revealed that after the intervention, the KAP scores of the intervention group were significantly higher than those of the control group (p < 0.05). After the intervention, the incidence of child victimization in the intervention group dropped to 9.60% (n = 22), lower than in the baseline survey, with a statistically significant difference (p < 0.01). The incidence of PV among children in the intervention group was lower than that in the control group, with the difference being statistically significant (p < 0.01). The net reduction in the incidence of PV among children was 21.20%. After the intervention, the protection rate for preventing PV among children was 73.33%, and the effect index was 3.75. The intervention improved children's coping styles, problem-solving, and help-seeking, while reducing negative coping styles such as avoidance and venting, with the differences being statistically significant (p < 0.05). Conclusion: Intervention measures based on the theory of planned behavior reduce the occurrence of PV among left-behind children, and the intervention effects on different types of victimization are also different. [ABSTRACT FROM AUTHOR]

The aim is to identify latent classes of poly-victimization among children in early adolescence, investigate transition probabilities between these latent categories, and examine the influencing factors. Methods: We used stratified cluster random sampling to select 2275 junior high students from five rural middle schools in Shantou and Jieyang, China, and surveyed them in two waves. Latent Class Analysis (LCA) and Latent Transition Analysis (LTA) identified latent classes of poly-victimization, and multi-factor logistic regression examined factors influencing the probability of students transitioning between these latent classes. Results: LCA identified three categories of poly-victimization: low poly-victimization, group, and high child maltreatment and peer and sibling victimization. The probabilities of remaining in the high child maltreatment and peer and sibling victimization group, transitioning to the transition group, or shifting to the low poly-victimization group were 37.00%, 29.20%, and 33.80%, respectively. Most transition group members remained in the same group, with a conversion probability of 77.10%, followed by transitioning to the low poly-victimization group with a probability of 15.80%. Physically healthy children, compared to those with disabilities or illnesses, were less likely to switch from the low poly-victimization group to the transition group (OR=0.034) or the high child maltreatment and peer and sibling victimization group (OR=0.14). Non-left-behind children, compared to left-behind children, have a higher probability of switching from the high child maltreatment and peer and sibling victimization group to the low poly-victimization group (OR=6.905). Conclusion: The high child maltreatment and peer and sibling victimization group had similar probabilities of transitioning into other categories. Physical illness or disability, as well as being left behind, are significant risk factors for children transitioning from the low-harm group to the high-harm group. [ABSTRACT FROM AUTHOR]

Background: Externalizing problems, internalizing problems, and obesity are among the greatest challenges to adolescent health. However, the moderating and mediating mechanisms that underlie this association remain predominantly unexplored. Objectives: In this study, we examined the association between body mass index (BMI) and externalizing and internalizing scores in adolescents, tested whether traditional bullying and cyberbullying mediated the association, and explored the moderated role of sex. Methods: The data came from 1486 adolescents from grade 7, 8, and 10 living in Shantou, China. Information on BMI, traditional bullying, and cyberbullying victimization was obtained through a self-administered questionnaire. The students' externalizing and internalizing scores were evaluated using the Strengths and Difficulties Questionnaire (SDQ). Furthermore, we built two parallel mediation models with sex as a moderating variable. Results: Compared to their peers with normal weight, adolescents with increased BMI reported higher externalizing and internalizing scores. Traditional bullying and cyberbullying were both significant mediators in the two relationships. Sex moderated the pathway from BMI to cyberbullying. But sex did not moderate the relationship between BMI and traditional bullying. Conclusions: The results highlight that it is imperative for educators to identify students who are subjected to weight-based bullying and provide them with recommendations for effective coping strategies. Meanwhile, both victims of traditional bullying and those affected by cyberbullying should be the focus of prevention and intervention efforts when developing a strategy to improve levels of internalizing and externalizing symptoms among adolescents with increased BMI. [ABSTRACT FROM AUTHOR]

Central Nervous System Germ Cell Tumors (CNS GCTs) represent a subtype of intracranial malignant tumors characterized by highly heterogeneous histology. Current diagnostic methods in clinical practice have notable limitations, and treatment strategies struggle to achieve personalized therapy based on patient risk stratification. Advances in molecular genetics, biology, epigenetics, and understanding of the tumor microenvironment suggest the diagnostic potential of associated molecular alterations, aiding risk subgroup identification at diagnosis. Furthermore, they suggest the existence of novel therapeutic approaches targeting chromosomal alterations, mutated genes and altered signaling pathways, methylation changes, microRNAs, and immune checkpoints. Moving forward, further research is imperative to explore the pathogenesis of CNS GCTs and unravel the intricate interactions among various molecular alterations. Additionally, these findings require validation in clinical cohorts to assess their role in the diagnosis, risk stratification, and treatment of patients. [ABSTRACT FROM AUTHOR]

Purpose: To authenticate the existence and principles of the adhesion recovery phenomenon under water pollution conditions, an innovative circumferential rail–wheel adhesion test rig was used. The study conducted extensive tests on the adhesion characteristics under large sliding conditions. Design/methodology/approach: Experiments were conducted to investigate the influence of speed, axle load and slip on adhesion recovery. Based on the experimental results, the adhesion recovery transition function was re-fitted. Findings: The study reveals that the adhesion recovery phenomenon truly exists under water conditions. The adhesion coefficient shows an increasing trend with the growth of the slip ratio. Moreover, at the current speed and axle load levels, the adhesion recovery is directly proportional to the square of the slip ratio and inversely proportional to the axle load. Originality/value: The phenomenon of adhesion recovery and the formulated equations in this study can serve as an experimental and theoretical foundation for the design of braking and anti-skid control algorithms for trains. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0379/ [ABSTRACT FROM AUTHOR]

Objectives: To evaluate various diet quality indices and to estimate their associations with major non-communicable diseases (NCD) (i.e. diabetes mellitus (DM) and myocardial infarction (MI)) and risk for overweight (OW). Design: Four dietary diversity indices (namely, count index (Count), dietary diversity score index, berry index (BI) and entropy index (EI)) and three Chinese dietary guideline-based indices (namely, China healthy diet index, Chinese food pagoda score and diet quality divergence index) were employed to evaluate Chinese diet quality. DM, MI and OW were used as diet-related health indicators. Logit regressions were employed to unveil the associations between diet quality indices and NCD and risk for OW. The relationships between diet quality indices and daily energy intakes were checked with ordinary least squares linear regressions. Setting: Four recent waves (2004, 2006, 2009, 2011) of longitudinal individual data from China Health and Nutrition Survey. Participants: Chinese adults (aged 18–64 years) from twelve provinces were included in the analysis (n 30 350). Results: Count, BI, and EI were positively associated with higher OW risk and daily energy intakes. As dietary guideline-based indices got better, people were exposed to lower DM and OW risks and got lower daily energy intakes. Finally, dietary guideline-based indices properly revealed the expected relationships that high-quality diets would reduce NCD and risk for OW, while high diversity indices were usually correlated with over-nutrition and high risks. Conclusions: Increasing diversity of the diet does not necessarily improve the nutrition and health. Dietary guideline-based indices are more robust than dietary diversity indices; thus, they should be highly recommended when evaluating diet quality. [ABSTRACT FROM AUTHOR]

In order to provide key precursors for humic-like substances formation via alkaline hydrothermal pretreatment (AHP), this study introduced an innovative approach by incorporating sodium lignosulfonate (SL) into the AHP to boost the production of humic-like acid (HLA) and fulvic-like acid (FLA) from biomass wastes. The SL-AHP conditions were optimized, and structural analysis of HLA was performed. The highest HLA yield reached 21.90 %, and FLA content reached 13.38 g/L after SL-AHP at 200 °C for 2 h with 15 g/L SL. The inclusion of SL led to a 19.67 % increase in HLA yield and a 5.25-fold enhancement in FLA content compared to that without SL. The probable reason underlying SL's facilitation of HLA and FLA formation involved the effective dissolution of lignin and hemicelluloses to produce more precursors, as well as the incorporation of SL structure into the HLA molecule. SL acted as both a surfactant and a precursor for the production of HLA and FLA. In addition, an increase in acidic groups improved the quality of HLA. [Display omitted] • Sodium lignosulfonate (SL) favored alkaline hydrothermal pretreatment (AHP). • SL-AHP increased the yield of humic-like acid (HLA) by 19.67 %. • SL-AHP significantly increased the content of fulvic-like acid (FLA) by 5.25-fold. • Chemical component analysis proved that SL promoted lignin dissolution. • Structure analysis showed that SL incorporated into the structure of HLA. [ABSTRACT FROM AUTHOR]

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, from the observation of heterophilous data, we notice that certain high-order information exhibits higher homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: (1) over-smoothing due to excessive model depth and propagation times; (2) high-order information is not fully utilized; (3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency. The source code will be available in https://github.com/Graph4Sec-Team/PathMLP. [ABSTRACT FROM AUTHOR]

The use of agrochemical inputs has significantly enhanced agricultural yields in China; however, their excessive utilization has also caused a range of environmental issues. This paper examines the costs associated with reducing agrochemicals by employing shadow prices, which represent the value of the marginal product of agrochemicals, to further develop cost-effective environmental policy measures for reducing their usage. To this end, the shadow prices of agrochemicals have been assessed by adopting a newly developed convex expectile regression approach and using statistical data from 31 provinces in China spanning from 2005 to 2020. Furthermore, the present study investigates the disparities between shadow prices and market prices for different agrochemicals across various regions in China. The findings suggest that the costs of reducing chemical fertilizers are higher than those of reducing pesticides and plastic films. Moreover, the results indicate that central China exhibits relatively high potential for decreasing agrochemical usage. Finally, these findings can inform the Chinese government's restructuring of producer support and environmental policy in a cost-effective way to mitigate agrochemicals use in the future. Additionally, the research method employed in this study holds potential for extension to other agrochemicals-dependent countries. • Point No.1: Shadow prices of different agrochemicals are estimated in a holistic way. • Point No.2: Convex expectile regression is used to address the impacts of inefficiency. • Point No.3: Different types of market distortion are identified. • Point No.4: Suggest suitable cost-effective environmental management measures. [ABSTRACT FROM AUTHOR]

NdFeB material has excellent comprehensive magnetic properties, which plays a crucial role in the field of rare earth magnetic materials, and is one of the important basic materials to support modern high-tech industries. In the production of NdFeB alloys, the control of rare earth elements (REEs) is directly related to the quality and resource utilization efficiency of NdFeB materials. Therefore, the prediction of REEs content in NdFeB alloys is of great research significance and application value for the quality control of products, the enhancement of production efficiency and reduction of energy consumption by the rare earth material industry. In this study, a combination of laser-induced breakdown spectroscopy (LIBS) and random forest (RF) was used to investigate the quantitative analysis of four REEs (Nd, Pr, Tb and Dy) in NdFeB alloys. Firstly, the original LIBS spectra were screened by principal component analysis-mahalanobis distance (PCA-MD). Then, the effects of different data processing methods on the screened LIBS spectra were explored, and next, the feature variables were extracted from the preprocessed spectral data by the variable importance measurement (VIM). In order to further verify the prediction performance of the model, the prediction results of the RF models based on the different methods were compared. Finally, a PCA-VIM-RF calibration model was established on the basis of the optimized spectra, selected feature variables and parameters. Leave-one-out cross validation (LOOCV) was used to optimize the parameters of PCA-MD method, spectral preprocessing method and variable importance thresholds during the construction of the calibration model. The results show that the PCA-VIM-RF model has better prediction performance than the RF calibration model based on the raw spectra. For the PCA-VIM-RF calibration method of Nd, Pr, Tb and Dy elements, the values of R2 CV are 0.9991, 0.9998, 0.9986, and 0.9984, respectively, the values of RMSE CV are 0.06296%, 0.02788%, 0.04647% and 0.05252%, respectively. The values of R2 P are 0.9508, 0.9975, 0.9691 and 0.9457, respectively, and the values of RMSE P are 0.6082%, 0.09205%, 0.5776% and 0.2631%, respectively.The above results indicate that LIBS combined with PCA-VIM-RF algorithm is a promising method for rapid quantitative analysis of REEs in NdFeB alloys without complicated sample preparation, which can provide some new ideas or strategies for the future research, development and quality control of rare earth materials. [Display omitted] • The feasibility of LIBS combined with RF for identification of REEs in NdFeB alloys is explored. • A RF calibration model based on the strategy of PCA-VIM is constructed. • The PCA-VIM-RF model reflects a better predictive performance than Raw-RF model. • LIBS combined with RF is expected to be applied in rare earth NdFeB material fields. [ABSTRACT FROM AUTHOR]

Poly-victimization is more detrimental to adolescents' physical and mental health than is a single type of victimization. However, there has been limited research on the trajectory of poly-victimization among Chinese adolescents. Identify the different developmental trajectories of poly-victimization among Chinese adolescents over time and examine the influencing factors of poly-victimization trajectories. Data from four surveys conducted between 2020 and 2022, encompassing a cohort of 319 adolescents who had experienced poly-victimization, were utilized to identify their developmental trajectories via group-based trajectory modeling. Potential influencing factors were screened and compared using ANOVA or chi-square tests, while factors affecting the developmental trajectories of poly-victimization were analyzed through multinomial logistic regression. We identified three poly-victimization trajectories among adolescents: increasing poly-victimization (n = 39, 12.2 %), relieved poly-victimization (n = 228, 71.5 %), and fluctuating poly-victimization (n = 52, 16.3 %). Our findings indicate that boys, and those with poor class grade ranking, a lower level of parental education, lower household economy, smoking, drinking, suicide attempts, and suicide ideation, constitute the primary focus for the prevention and treatment of poly-victimization. We identified three poly-victimization trajectories, highlighting a significant heterogeneity in poly-victimization development. Understanding the characteristics of these developmental trajectories is crucial for realizing the dynamics of different poly-victimization subgroups and informing effective interventions. [ABSTRACT FROM AUTHOR]

To evaluate the potential efficacy of Triptolide (TP) on cerebral ischemia/reperfusion injury (CIRI) and to uncover the underlying mechanism through which TP regulates CIRI. We constructed a middle cerebral artery occlusion/reperfusion (MCAO/R) mouse model to simulate CIRI, and established a lipopolysaccharide (LPS)-stimulated BV-2 cell model to mimic the inflammatory state during CIRI. The neurological deficits score (NS) of mice were measured for assessment of neurologic functions. Both the severity of cerebral infarction and the apoptosis level in mouse brain tissues or cells were respectively evaluated using corresponding techniques. The expression levels of Ionized calcium binding adapter molecule 1 (IBA-1), Inductible Nitric Oxide Synthase (iNOS), Arginase 1 (Arg-1), Tumor necrosis factor-α (TNF-α), Interleukin 1 β (IL-1 β), Cysteine histoproteinase S (CTSS), Fractalkine, chemokine C-X3-C motif receptor 1 (CX3CR1), BCL-2-associated X protein (BAX), and antiapoptotic proteins (Bcl-2) were detected using immunofluorescence, qRT-PCR as well as Western blot, respectively. Relative to the Sham group, treatment with TP attenuated the increased NS, infarct area and apoptosis levels observed in MCAO/R mice. Upregulated expression levels of IBA-1, iNOS, Arg-1, TNF-α and IL-1β were found in MCAO/R mice, while TP suppressed iNOS, TNF-α and IL-1β expression, and enhanced Arg-1 expression in both MCAO/R mice and LPS-stimulated BV-2 cells. Besides, TP inhibited the CTSS/Fractalkine/CX3CR1 pathway activation in both MCAO/R mice and LPS-induced BV-2 cells, while overexpression of CTSS reversed such effect. Co-culturing HT-22 cells with TP+LPS-treated BV-2 cells led to enhanced cell viability and decreased apoptosis levels. However, overexpression of CTSS further aggravated HT-22 cell injury. TP inhibits not only microglia polarization towards the M1 phenotype by suppressing the CTSS/Fractalkine/CX3CR1 pathway activation, but also HT-22 apoptosis by crosstalk with BV-2 cells, thereby ameliorating CIRI. These findings reveal a novel mechanism of TP in improving CIRI, and offer potential implications for addressing the preventive and therapeutic strategies of CIRI. • Triptolide regulates the Fractalkine/CX3CR1 signaling pathway. • A new mechanism of Triptolide in improving CIRI. • A potential new preventive and therapeutic strategy of CIRI. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the adhesion characteristics of the wheel–rail under water and large sliding conditions. This is carried out by conducting a series of tests on a full-scale roller rig. The measured data provides an experimental base for conducting further theoretical research. Design/methodology/approach: The influence of the slip ratio, rolling speed and the axle load on the adhesion coefficient between the wheel and the rail is analyzed under wet conditions using a full-scale roller rig. Findings: From the research, it is found that the adhesion coefficient–slip ratio curve varies from the traditional theoretical description under water and large sliding conditions. Moreover, it is also observed that after the adhesion coefficient reaches the saturation point, the adhesion coefficient does not decrease, but continues to increase as the slip ratio increases. Originality/value: The adhesion improvement phenomenon in this paper may provide new ideas for designing anti-skid control and braking system mechanisms for trains. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0236/ [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Methods Results Conclusions Osteoporosis is a crucial risk factor for screw loosening. Our studies indicate that the bone mineral density (BMD) in the screw trajectory is a better predictor of screw loosening than the BMD of the lumbar spine or the screw insertion position. Research has shown that anchorage on the screw tip is the most significant factor for screw anchorage ability, while others argue that decreased bony quality in the pedicle poses a significant risk for screw loosening. This study aimed to determine whether the bony quality of the screw tip, pedicle, or screw‐anchored vertebral body plays the most significant role in screw anchorage ability.A total of 73 patients who underwent single–segment bilateral pedicle screw fixation, along with posterior and transforaminal lumbar interbody fusion (PLIF and TLIF), from March 2019 to September 2020 were included in this retrospective study. The Hounsfield unit (HU) value of the fixed vertebral bodies, the entire screw trajectory, screw tip, screw–anchoraged vertebral body, and pedicles were measured separately. Data from patients with and without screw loosening were compared, and regression analyses were conducted to identify independent risk factors. Additionally, the area under the curve (AUC) values were computed to assess the predictive performance of different parameters. Furthermore, fixation strength was calculated in numerical models with varying bony densities in different regions.HU values were found to be significantly lower in the loosening group across most measuring methods (HU values in the pedicle, 148.79 ± 97.04, 33.06 ± 34.82, p < 0.001). Specifically, the AUC of screw loosening prediction was notably higher when using HU values of the pedicle compared to other methods (AUC in the pedicle > 0.9 and in the screw insertion position > 0.7). Additionally, computational results for fixation strength revealed a clear decline in screw anchorage ability in models with poor BMD in the pedicle region.Pedicle bone quality plays a more significant role in screw anchorage ability than that in other regions. The innovation of bony augmentation strategies should pay more attention to this region to optimize the screw anchorage ability effectively. [ABSTRACT FROM AUTHOR]

Background: School bullying, a serious problem for the physical and mental health of adolescents, is presently a significant issue in China. It is essential to recognize and comprehend potential risk factors and establish efficient preventive strategies. The purpose of this study was to examine the association between childhood maltreatment and school bullying in adolescents and to assess the mediating role of peer relationships. Methods: Between March and April 2024, a cross-sectional survey was conducted among 2119 adolescents aged between 12 and 18 years in Guangdong Province, China. Self-report questionnaires were employed to collect data on childhood maltreatment, school bullying, and peer relationships. Subgroup analyses and mediating effects modeling were employed to analyze the data. Results: The results indicated that adolescents who had experienced maltreatment were more at risk of bullying victimization (OR: 2.92, 95% CI: 2.34–3.64, P < 0.001), bullying perpetration (OR: 2.84, 95% CI: 1.99–4.05, P < 0.001), and bully-victimization (OR: 2.93, 95% CI: 1.95–4.41, P < 0.001), compared to adolescents who have not. Sexual abuse showed the most significant connection with all forms of bullying. The mediating effect of peer relationships was found to mediate the association between child maltreatment and bullying behaviour. The results indicated that worse peer relationships may exacerbate the adverse effects of maltreatment experiences and increase the risk of adolescents becoming bullies, either perpetrators or victims of bullying. Conclusions: Child maltreatment has been identified as one of the most significant influences on bullying behaviour in adolescents. The quality of peer relationships has been demonstrated to play an important role in preventing and reducing the occurrence of bullying. The results underscore the crucial role of early intervention in cases of child maltreatment and the fostering of positive peer relationships in schools. [ABSTRACT FROM AUTHOR]

Manufacturing services collaboration (MSC) is crucial to the industrial internet platform in determining the proper integration of multiple functionality unique services for a complex manufacturing process. As a critical enabler for resolving MSC, evolutionary algorithm (EA) plays an essential role in enhancing the efficiency of MSC optimization. However, EA solvers are generally executed from scratch and suffer from a high computational burden. Inspired by transfer learning, researchers have considered performing knowledge extraction across distinct problem instances to promote the problem-solving efficiency, giving rise to the evolutionary multi-task optimization (EMTO) paradigm. The appearance of EMTO brings an emerging knowledge-aware search paradigm that supports the online learning and exploitation of optimization experiences during the course of evolution process, thereby accelerating the search efficiency. In spite of competence in continuous problems, EMTO has received little visibility in the field of combinatorial optimization, particularly, MSC problem, let alone an experimental comparison of state-of-the-art EMTO approaches in the context of MSC. To fill in this void, this article explores the suitability of versatile EMTO solvers for addressing MSC and provides insights into the behavior of various knowledge transfer techniques in multi-task MSC environments. To our knowledge, this study is the first attempt that investigates the scalability of EMTO on MSC problems and the systematic evaluation of their performances. Our results unveil the intrinsic characteristics of distinct transfer techniques to MSC in different scenarios, along with a deep analysis of the abilities for EMTO alternatives on resolving MSC. [ABSTRACT FROM AUTHOR]

With the popularity of location-based social networks, personalized points-of-interest (POIs) recommendation has become an essential online service, providing a wide variety of user preferred check-in locations, namely POIs. However, the sparsity of user-POI matrix makes it difficult to recommend unvisited POIs to users. The availability of the user, social and geographical information offers an unprecedented opportunity to address this problem. In this paper, our goal is to accurately recommend POIs to users with the proper order. To handle this, we propose a practical POI recommendation framework UFC by incorporating User preference, Friend importance and Check-in correlation. UFC combines three fundamental factors and derives an overall prediction score of each user for any POIs. User preference is personalized based on collaboration filtering. As for the friend importance, we argue that intimate friends and distant friends share common influence and thus merge these two different factors to model friend importance. We study the geographical correlation of user check-in behavior in depth and employ a probabilistic model to characterize check-in correlation. Extensive experimental results exhibit a significant improvement in our proposed method compared to other state-of-art POI recommendation algorithms. [ABSTRACT FROM AUTHOR]

Regulatory T cells are a subgroup of T cells with immunomodulatory functions. Different from most cytotoxic T cells and helper T cells, they play a supporting role in the immune system. What's more, regulatory T cells often play an immunosuppressive role, which mainly plays a role in maintaining the stability of the immune system and regulating the immune response in the body. However, recent studies have shown that not only playing a role in autoimmune diseases, organ transplantation, and other aspects, regulatory T cells can also play a role in the immune escape of tumors in the body, through various mechanisms to help tumor cells escape from the demic immune system, weakening the anti-cancer effect in the body. For a better understanding of the role that regulatory T cells can play in cancer, and to be able to use regulatory T cells for tumor immunotherapy more quickly. This review focuses on the research progress of various mechanisms of regulatory T cells in the tumor environment, the related research of tumor cells acting on regulatory T cells, and the existing various therapeutic methods acting on regulatory T cells. • Unlike in the past, Treg cells have also been found to play a powerful role in cancer treatment. • Treg cells exert immunosuppressive effects through various ways, such as cell-cell contact, secretion of cytokines, regulation of DC function, binding to chemokine CCL22/CCL17, and so on, and acting in the tumor microenvironment. • Tumorigenesis is often accompanied by an increase in the number of Treg cells. • Many recent studies have found that metabolic pathways, receptors, and proteins on Treg cells, bile acids, and even apoptotic Treg cells can promote Treg cells to exert immune suppression, and then help tumors escape from the immune system. • More and more targeted therapy drugs that block the target sites on Treg cells and destroy the homeostasis between Th17 cells and Treg cells can be effective immunotherapy for tumors. [ABSTRACT FROM AUTHOR]

The aryl hydrocarbon receptor (AHR) is a cytosolic ligand-activated transcription factor involved in xenobiotic sensing, cell cycle regulation, and cell development. In humans, the activation of AHR by 2,3,7,8-Tetrachlorodibenzo- p -dioxin (TCDD), a high affinity AHR-ligand, impairs the secretion of immunoglobulin M (IgM) to suppress humoral immunity. However, the mechanisms bridging the activation of AHR and the impairment of IgM secretion by human primary B cells remain poorly understood. Recent transcriptomic analysis revealed upregulation of lymphocyte-specific protein tyrosine kinase (LCK) in AHR-activated human primary B cells. LCK is a well-characterized tyrosine kinase that phosphorylates critical signaling proteins involved in activation and cytokine production in T cells. Conversely, the role of LCK in human primary B cells is not well understood. In the current studies, we have verified the transcriptomic finding by detecting AHR-mediated upregulation of LCK protein in human primary B cells. We also confirmed the role of AHR in the upregulation of LCK by using a specific AHR antagonist, which abolished the AHR-mediated increase of LCK. Furthermore, we have confirmed the role of LCK in the AHR-mediated suppression of IgM by using LCK specific inhibitors, which restored the IgM secretion by human B cells in the presence of TCDD. Collectively, the current studies demonstrate a novel role of LCK in IgM response and provide new insights into the mechanism for AHR-mediated impairment of immunoglobulin secretion by human primary B cells. [ABSTRACT FROM AUTHOR]

Pesticide residues in market products pose a serious threat to consumers' health. This study aimed to analyze pesticide residues in agricultural products in the Chinese market. According to the data on agricultural products exceeding maximum residue levels (MRLs) obtained by the market supervision administration, 5387 batches of agricultural products (BAPs) in the Chinese market from 2011 to 2020 were extracted. The basic information of the data, time, region, crops and pesticides were discussed. The characteristics and trends of unqualified pesticide residues (UQPRs) in the Chinese market were statistically analyzed. The results showed that the BAPs withUQPRs increased over time, mainly occurring in spring and autumn. Most of the crops with UQPRs were leafy crops, among which Leek and Celery accounted for 28.3% and 16.9% of the total, respectively. Meanwhile, the regions with the most BAPs were Shandong, Henan, and Chongqing. Combining the statistical results and analyzing the reasons, the occurrence of UQPRs can be reduced from three aspects: government, market, and farmers. [Display omitted] • Establishing a database of unqualified agricultural products in China (2011–2020). • The region with the highest number of unqualified BAPs was Shandong; Leek and Procymidone were the most crops and pesticides in all unqualified BAPs. • May and September are the peak periods for the occurrence of excessive pesticide residues. [ABSTRACT FROM AUTHOR]

Evolutionary multitasking optimization (EMTO) is a newly emerging search paradigm for tackling multiple problems concurrently via integrating population-based meta-heuristic method and transfer learning technique, which has attracted considerable interest in computational intelligence community. Notably, effective knowledge transfer across tasks is the main challenge for intertask synergies in EMTO. Particularly, how to select proper auxiliary tasks for each constitutive task, how to adapt the intensity of intertask knowledge transfer and how to narrow the discrepancy between tasks are three key concerns in complicated many-task scenario. However, these issues are rarely studied jointly till date. Bearing this in mind, this article introduces a novel adaptive EMTO solver for many-task transfer optimization. Specifically, we develop an adaptive task selection mechanism to choose sources reasonably by the aid of maximum mean discrepancy. Besides, multi-armed bandit model is employed to control the intensity of knowledge transfer across tasks. In addition, restricted Boltzmann machine is utilized to extract latent features between tasks so that their discrepancy is reduced. Experiments are conducted on a series of numerical benchmarks to investigate the performance of our proposal, the comparison results reveal that our proposal manages to solve EMTO problems competitively when compared to several existing counterparts. • Propose a novel multi-task transfer optimization solver with online inter-task learning ability. • Design a bandit based method to control the inter-task knowledge transfer intensity. • Restricted Boltzmann Machine is utilized to narrow the discrepancy between tasks. • Experimental results confirm the competitiveness of the proposed solver. [ABSTRACT FROM AUTHOR]

Determining changes in gene expression by measuring mRNA levels is an important capability in biological research. Real-Time Quantitative PCR (RT-qPCR) is the most ubiquitous technique for measuring changes in mRNA transcript levels, but heterogeneity of cell populations and low cell number are serious technical limitations. Recent advances in flow cytometric analytical techniques have enabled the quantification of mRNA levels in individual cells. Here, we present examples demonstrating the strength and challenges of concurrently measuring mRNA using PrimeFlow™ with other endpoints in immunotoxicological studies. Specifically, we demonstrate how measuring gene specific mRNA levels on a per cell basis was used to study: 1) markers of activation and differentiation; 2) cell signaling by measuring intracellular proteins in mature and developing cell types; and 3) a cell type that constitutes a minor population in peripheral blood. We also discuss cell type-specific modifications to the parent technique, which facilitated optimal performance in these cells. While the examples provided are focused on immunotoxicological questions and endpoints, this new strategy can be applied to a wide variety of toxicological research problems. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing services on-demand. The service composition with optimal overall quality of service (QoS) is of great significance since it affects the efficiency of resource sharing and optimal allocation in CMfg systems. However, some limitations still exist in manufacturing service composition methods especially in the complicated cloud environment. This owes to two aspects: (1) current service composition approaches assume that the QoS of services is fixed and not influenced by other services, the existence of the service correlation context is not considered adequately, thus resulting in no accordance with practical applications; (2) with the growing number of candidate services in cloud resource pools, the traditional methods might be inefficient for addressing large scale service composition problems. To overcome such drawbacks, this study proposes a new hybrid teaching-learning-based optimization (HTLBO) algorithm for optimal service composition with the consideration of service correlations. Experiments are conducted to verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions; (2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications; and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments. [ABSTRACT FROM AUTHOR]

Industrial internet platform is regarded as an emerging infrastructure to increase the manufacturing efficiency via sharing resources located in multiple sites. Manufacturing cloud service allocation (MCSA) aims to assign available services to the interconnected subtasks of a complicated task such that some performance indices are optimized. Current studies on MCSA are single-task-oriented and fail to exploit the shared task-solving experiences to jointly optimize a group of tasks with enhanced solution quality and search speed. This work considers the joint optimization of multiple MCSA problems in a parallel fashion via cross-task transfer learning mechanism, and two novel transfer learning strategies are embedded into the framework of bee colony algorithm to make the best use of cross-task helpful knowledge when resolving multi-task MCSA. The first one is to design an individual-dependent transfer learning mechanism to govern the probability of whether a bee to perform intra-task self-evolution or cross-task knowledge transfer, which adaptively regulates the search behavior of each bee according to its state. The second one is to select the potential bees from foreign tasks for knowledge exchange with the aid of anomaly detection mechanism. The proposed optimizer is extensively examined on different scales of MCSA instances in multi-task scenario. Experimental results confirm the performance advantage of our proposal in comparison with other state-of-the-art peers. [ABSTRACT FROM AUTHOR]

With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to examine the impact of CA on DN and elucidate its underlying molecular mechanisms of inflammation. Methods: We fed C57BL/6 mice injected with streptozotocin to induce diabetes. In addition, we stimulated NRK-52E cells with 20 mmol/L d-glucose to mimic the diabetic condition. Results: Our findings demonstrated that CA effectively reduced blood glucose levels, and improved DN in mice models. Additionally, CA reduced kidney injury and inflammation in both mice models and in vitro models. CA decreased high glucose-induced ferroptosis of NRK-52E cells by inducing GSH/GPX4 axis. Conversely, the ferroptosis activator or the PI3K inhibitor reversed positive effects of CA on DN in both mice and in vitro models. CA suppressed PAQR3 expression in DN models to promote PI3K/AKT activity. The PAQR3 activator reduced the positive effects of CA on DN in vitro models. Moreover, CA directly targeted the PAQR3 protein to enhance the ubiquitination of the PAQR3 protein. Conclusion: Overall, our study has uncovered that CA promotes the ubiquitination of PAQR3, leading to the attenuation of ferroptosis in DN. This effect is achieved through the activation of the PI3K/AKT signaling pathways by disrupting the interaction between PAQR3 and the P110α pathway. These findings highlight the potential of CA as a viable therapeutic option for the prevention of DN and other forms of diabetes. [ABSTRACT FROM AUTHOR]

A new kind of hyperbranched semi-aromatic poly(ester-imide)s with hydroxyl terminated groups has been prepared from commercially available 1,2,4-benzenetricarboxylic anhydride (TMA) as the AA′-type aromatic monomer and 3-amino-1,2-propanediol (3APPD), 2-amino-1,3-propanediol (2APPD), N,N-bis(2-hydroxyethyl)ethylenediamine (BHED) as CB 2 -type aliphatic monomers via a simple one-pot melting polymerization method. The effects of synthesis conditions on the structures and properties of the products were investigated in detail. The combination of FTIR, 1H NMR, 2D NMR, MALDI-FTICR-MS and model reactions have been used to characterize the structures of the obtained polymers. As assessed using thermal measurements, the obtained hyperbranched semi-aromatic poly(ester-imide)s possess low glass transition temperature (≤120 °C) and high thermostability with an onset decomposition temperature ranging from 355 to 420 °C in nitrogen. The products possess abundant terminal hydroxyl functional groups. Hydroxyl values vary between 198 and 248 mg KOH/g depending on the particular precursors used to generate the polymers. The synthesis of hydroxyl terminated hyperbranched semi-aromatic poly(ester-imide)s. [Display omitted] • Hyperbranched semi-aromatic poly(ester-imide)s were prepared by one-pot melting polymerization. • HBPEIs possess low T g and high thermostability. • HBPEIs are terminated by abundant hydroxyl function groups. • This synthetic strategy exhibits good fault-tolerant ability for reaction condition. [ABSTRACT FROM AUTHOR]

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination ( R 2 ) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules. [ABSTRACT FROM AUTHOR]

[Display omitted] • A triarylboron-based fluorescence probe for F- detection was developed. • The chemosensor exhibited high selectivity and rapid response for F- (90 s). • This probe have high anti-interference capability. • This probe has high sensitivity with a detection limit of 0.63 μM. Fluoride ions (F-) play a significant role in life science and clinical medicine science. A fluorescence probe (TN-F) for detecting fluoride ions was synthesized using naphthalimide as the fluorophore and triarylboron as the recognition group. The selectivity and sensitivity to fluoride ions were determined by UV–visible absorption and fluorescence spectroscopy, which can meet the need for rapid detection (90 s) of F- in organic solution. Moreover, this probe exhibited high anti-interference capability and sensitivity with a detection limit of 0.63 μM, achieving efficient and rapid detection of fluoride ions. [ABSTRACT FROM AUTHOR]

BiFeO 3 -BaTiO 3 (BF-BT)-based lead-free ceramics have attracted widespread attention in high-temperature piezoelectric fields owing to its high Curie temperature (T C). Comparing with bismuth-layered high-temperature piezoelectric ceramics, better piezoelectric coefficient of BF-BT show advantages in boosting the sensitivity. Nevertheless, achieving high piezoelectric performance in BF-BT-based ceramics is restricted by ambiguous phase boundary and high leakage conduction. Herein, the phase structures, microstructures, piezoelectric properties together with electrical properties of the (1- x)Bi 1.02 FeO 3 - x BaTiO 3 (BF- x BT) ceramics were investigated and discussed systematically within a narrow compositional range. With the intention of seeking the optimized composition and properties near the morphotropic phase boundary since the sensitive functional properties to the x contents. The results of d 33 = 191 pC/N, T c = 485 °C along with high T d of ∼486 °C were obtained in BF-0.31BT ceramics, which proves BF-0.31BT piezoelectric ceramics a competitive high-temperature piezoelectric application material. [ABSTRACT FROM AUTHOR]

N -nitrosodimethylamine (NDMA), a probable carcinogenic disinfection by-product, can be formed with high molar yields following chloramination of ranitidine (RNTD), a histamine H 2 -receptor antagonist. Although RNTD and some of its transformation products (TPs) have been studied under chlorination and photo-irradiation, the relationship between RNTD TPs and NDMA formation potential (NDMA-FP) remaining after those processes is still unclear. This study investigated the effects of chlorination and/or photo-irradiation on NDMA-FP derived from RNTD, simulating an urban water environment receiving treated wastewater. After chlorination and/or photo-irradiation of RNTD, ten TPs including five new ones were identified by liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTof-MS). In addition, important RNTD TPs responsible for NDMA-FP (e.g., chlorinated and hydroxylated RNTD: TP-364) were also confirmed by the relationship between detected peak area and NDMA-FP. The results showed that NDMA-FP remained due to the presence of RNTD TPs, although RNTD itself was significantly removed by chlorination and/or photo-irradiation. TP-364 was only formed by chlorination of RNTD and could not be removed by photo-irradiation. TP-314 (a stereoisomer of RNTD), -299, and -286, which were mainly formed by photo-irradiation of RNTD but not by photo-irradiation after chlorination, had strong positive correlations with NDMA-FP (R 2 > 0.90; F -test, P < 0.01). Image 1 • Chlorination removed almost all RNTD and sunlight removed RNTD gradually. • Chlorination and/or sunlight could not completely remove NDMA-FP from RNTD. • Ten RNTD TPs (five new ones) were found after chlorination/photo-irradiation. • RNTD TPs formed by chlorination were stable under photo-irradiation. • RNTD TPs explained the NDMA-FP remaining after chlorination/photo-irradiation. [ABSTRACT FROM AUTHOR]

Link prediction, which aims to infer missing edges or predict future edges based on currently observed graph connections, has emerged as a powerful technique for diverse applications such as recommendation, relation completion, etc. While there is rich literature on link prediction based on node representation learning, direct link embedding is relatively less studied and less understood. One common practice in previous work characterizes a link by manipulate the embeddings of its incident node pairs, which is not capable of capturing effective link features. Moreover, common link prediction methods such as random walks and graph auto-encoder usually rely on full-graph training, suffering from poor scalability and high resource consumption on large-scale graphs. In this paper, we propose Inductive Subgraph Embedding for Link Prediciton (SE4LP) — an end-to-end scalable representation learning framework for link prediction, which utilizes the strong correlation between central links and their neighborhood subgraphs to characterize links. We sample the “link-centric induced subgraphs” as input, with a subgraph-level contrastive discrimination as pretext task, to learn the intrinsic and structural link features via subgraph classification. Extensive experiments on five datasets demonstrate that SE4LP has significant superiority in link prediction in terms of performance and scalability, when compared with state-of-the-art methods. Moreover, further analysis demonstrate that introducing self-supervision in link prediction can significantly reduce the dependence on training data and improve the generalization and scalability of model. [ABSTRACT FROM AUTHOR]

Background The immunopathological mechanisms underlying neurosyphilis remain incompletely elucidated, and the diagnosis of neurosyphilis presents challenges. Methods We used an antibody microarray to detect 640 proteins in cerebrospinal fluid (CSF) samples collected from 6 patients with non-neurosyphilis and 10 with neurosyphilis. The levels of CSF CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9 in 46 patients with non-neurosyphilis, 51 with untreated neurosyphilis, and 31 posttreatment for neurosyphilis were quantified using enzyme-linked immunosorbent assay. The associations between the levels of these proteins and clinical parameters in neurosyphilis were evaluated using Spearman analysis, and the diagnostic performance of these proteins in neurosyphilis was assessed using receiver operating characteristic curve. Results A total of 102 differentially expressed proteins between neurosyphilis and non-neurosyphilis were identified. The levels of significantly elevated neutrophil-associated proteins (CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9) in neurosyphilis positively correlated with white blood cell counts, rapid plasma regain (RPR) titer, and protein concentration in CSF. The combination of CSF CXCL8, MMP9, and LCN2 yielded an area under the curve of 0.92 for diagnosing neurosyphilis, surpassing that of CSF RPR. Conclusions CXCL1, CXCL8, G-CSF, LCN2, MMP8, and MMP9 could be associated with central nervous system damage of neurosyphilis. The combination of CSF CXCL8, MMP9, and LCN2 is a promising biomarker for diagnosing neurosyphilis. [ABSTRACT FROM AUTHOR]

• AHR activation suppresses STAT3 serine phosphorylation. • Decreased STAT3-S727 phosphorylation is due to increased PP2a activity. • IFNγ restores IgM secretion by enhancing STAT3-S727 phosphorylation. • AHR-induced SHP-1 activates PP2a. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant formed as a byproduct in organic synthesis and burning of organic materials. TCDD has potent immunotoxic effects in B lymphocytes resulting in decreased cellular activation and suppressed IgM secretion following activation with CD40 ligand. Previous work from our lab demonstrated that TCDD treatment of naïve human B cells resulted in significant increases in the levels of the tyrosine phosphatase SHP-1, which corresponded with suppression of IgM secretion. STAT3 is a critical B cell transcription factor for B cell activation and secretion of immunoglobulins (Ig). STAT3 dimerizes and translocates to the nucleus following phosphorylation as a result of cytokine receptor signaling. We hypothesized that TCDD-mediated increases in SHP-1 could result in decreased STAT3 tyrosine phosphorylation. Interestingly, only modest changes in the levels of STAT3 tyrosine phosphorylation were observed. By contrast, TCDD significantly reduced levels of STAT3 serine phosphorylation as early as 12 h post B cell activation. These results corresponded with decreased inhibitory phosphorylation of the serine specific phosphatase PP2a, which is regulated by SHP-1. Further, studies revealed that interferon gamma (IFNγ), which signals through the type II interferon receptor, can non-canonically induce STAT3 activation via Src kinase activity. Indeed, treatment of human B cells with IFNγ resulted in increased STAT3 serine phosphorylation and reversed TCDD-mediated suppression of the IgM response. Together, these data putatively identify a key event in the mechanism by which TCDD induces suppression of Ig secretion and demonstrate the potential of IFNγ as a means to reverse this effect in primary human B lymphocytes. [ABSTRACT FROM AUTHOR]

Thailand is the largest nature rubber exporter in the world. The rubber industry is particularly important for both the national economy and farmers. However, the increasing popularity of monoculture rubber plantation has posed challenges for the sustainable development of rubber industry due to the negative impacts of homogeneous cropping systems. Rubber crop diversity system is expected to alleviate the environment degradation and secure rubber farmers' income. Nevertheless, the rubber diverse cropping systems adoption is more complicated for most rubber farmers in Thailand. Using the data on rubber farmers which cover 3344 observations from Office of Agricultural Economics in Thailand throughout the period of 2012–2014, this paper investigated the influential factors of rubber crop diversity adoption. The results indicate that rubber farmers are usually lack of management skills, information, and market access in crop diversity adoption. Adequate rainfall and water, household labour, land right certificate, land degradation, the use of organic fertilizer, crop sales channels and irrigation access have positive impacts on the adoption of rubber crop diversity. On the other hand, in the southern region, the hiring of foreign labours, agricultural training, off-farm work, male household heads as well as their age and education level are negatively associated with crop diversity adoption. According to the empirical results, it is necessary for rubber institutions to pay more attention to training programmes and education on rubber farmers' diverse cropping system management skills. Moreover, rubber sustainable development strategies should offer rubber households additional incentives and financial support to relieve their financial constraints and improve the market accessibility. In addition, it is also necessary to focus on rubber households with poor natural resources and agricultural infrastructure and offer supports for infrastructure, such as irrigation systems, to increase rubber crop diversity adoption in Thailand. [ABSTRACT FROM AUTHOR]

Flavonoid compounds are considered important components of new sources of natural antioxidants. This study was performed to optimize the ultrasound-assisted extraction processes of flavonoids from date plum persimmon (DPP) fruits, along with the in vitro antioxidant capacities and identities of those components. First, the effects of liquid/solid ratio, ethanol concentration, extraction time, and extraction temperature on flavonoid yields were investigated by independent variables analysis. Then the response surface methodology was used to optimize the extraction process, and the maximum flavonoid content was 3.05 ± 0.03 mg/g DW under the conditions of liquid/solid ratio 27:1 (mL/g), 74 mL ethanol/100 mL, extraction time 27 min, and extraction temperature 80 °C. Moreover, by DPPH and ABTS assays, the crude extracts of flavonoids in DPP fruits exhibited robust scavenging capacities with IC 50 values of 1.76 and 0.82 μg/mL, respectively. In addition, 17 bioactive compounds of DPP were isolated and identified by HPLC-ESI-QTOF-MS/MS, among which two were phenolic acid derivatives, two were flavanones, eight were flavones, four were pentacyclic triterpenoids and one was xanthone. This study provided information regarding the phytochemical composition and functional properties of DPP fruits, supporting future research to facilitate the development and utilization of DPP as a functional food. • Effects of extract parameters on flavonoid yield were studied by single factor test. • The optimal conditions of flavonoid extraction were selected by RSM. • The flavonoid in DPP had robust abilities to scavenge DPPH and ABTS free radicals. • Bioactive compounds of flavonoids were identified by HPLC-ESI-QTOF-MS/MS. [ABSTRACT FROM AUTHOR]

Emerging evidence has indicated that long non-coding RNAs (lncRNAs) are closely associated with the pathogenesis of ischemic stroke. It has been reported that small nucleolar RNA host gene 12 (SNHG12) serves a critical role in ischemic stroke by acting as a competitive endogenous RNA (ceRNA). SNHG12 competes with various microRNAs (miRs) to regulate RNA transcription of specific targets. However, the effect of SNHG12 on oxygen-glucose deprivation (OGD)-induced neuronal apoptosis has rarely been reported. The present study demonstrated that SNHG12 expression was downregulated in OGD-injured SH-SY5Y cells. Furthermore, miR-181a-5p was reported as a target of SNHG12 and was negatively regulated by SNHG12. Moreover, NEGR1 was a target of miR-181a-5p, which functions as a negative regulator of NEGR1 in OGD-induced neuronal apoptosis. In summary, the results strongly confirmed the hypothesis that SNHG12 functions as a ceRNA for miR-181a-5p and regulates the expression of NEGR1 thus inhibiting OGD-induced apoptosis of SH-SY5Y cells. Neuronal apoptosis aggravates brain damage during ischemic stroke, indicating that the activation of SNHG12 and NEGR1 expression and inhibition of miR-181a-5p may be a novel strategy for the clinical treatment of ischemic stroke. [ABSTRACT FROM AUTHOR]

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (T T D). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality; (2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution; (3) A knowledge-based destruction and construction is designed to improve the exploration capability; (4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors. • This paper first investigates DAHFSSP-DRC with minimization TTD. • A MILP model and a KBIG are proposed for DAHFSSP-DRC. • The importance level of different knowledge is considered in KBIG. • Experimental results prove the effectiveness of MILP model and KBIG. [ABSTRACT FROM AUTHOR]