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Abstract Ensemble learning has the potential to enhance the efficacy of feeble classifiers significantly and is increasingly being utilized in Twitter bot detection. Previous methods have utilized stacking techniques to train the primary classifiers, implementing cross-validation to mitigate overfitting and enhance predictive accuracy. However, cross-validation substantially amplifies the computation time associated with stacking. To overcome this challenge, this paper presents a novel approach, the Simplified Stacking Graph Neural Network (SStackGNN), specifically designed for the detection of social bots. Our methodology leverages the power of Graph Neural Networks (GNNs) as base classifiers, enabling effective capturing of inter-account interactions. In addition, a Multilayer Perceptron (MLP) serves as a secondary classifier, amalgamating the outcomes of the foundational classifiers to generate final predictions, thereby enhancing the predictive performance of the GNNs. Instead of relying on cross-validation and distinct base classifier structures, we employ node-level, edge-level, and feature-level graph data augmentation techniques to acquire diverse foundational classifiers. This approach significantly alleviates the computational complexity while achieving superior performance. Experimental results demonstrate that our proposed SStackGNN outperforms other approaches.

Abstract Radiation enteritis is the most common complication of pelvic radiotherapy, but there is no effective prevention or treatment drug. Apoptotic T cells and their products play an important role in regulating inflammation and maintaining physiological immune homeostasis. Here it is shown that systemically infused T cell‐derived apoptotic extracellular vesicles (ApoEVs) can target mice irradiated intestines and alleviate radiation enteritis. Mechanistically, radiation elevates the synthesis of intestinal 2′3′ cyclic GMP‐AMP (cGAMP) and activates cyclic GMP‐AMP synthase (cGAS)‐stimulator of interferon genes (STING) proinflammatory pathway. After systemic infusion of ApoEVs, the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) enriches on the surface of ApoEVs hydrolyze extracellular cGAMP, resulting in inhibition of the cGAS‐STING pathway activated by irradiation. Furthermore, after ApoEVs are phagocytosed by phagocytes, ENPP1 on ApoEVs hydrolyzed intracellular cGAMP, which serves as an intracellular cGAMP hydrolyzation mode, thereby alleviating radiation enteritis. The findings shed light on the intracellular and extracellular hydrolysis capacity of ApoEVs and their role in inflammation regulation.

In the clinical diagnosis of pneumonia, particularly during the COVID-19 pandemic, individuals who progress to a critical stage requiring mechanical ventilation are classified as mechanically ventilated critically ill patients. Accurately predicting the discharge outcomes for this specific cohort, especially those with COVID-19, is of paramount clinical importance. Missing data, a common issue in medical research, can significantly impact the validity of analyses. In this work, we address this challenge by employing two missing data imputation techniques: multiple imputation and missForest, to enhance data completeness. Additionally, we utilize the smoothly clipped absolute deviation (SCAD) penalized logistic regression method to select significant features. Our real data analysis compares the predictive performances of extreme learning machines, random forests, support vector machines, and XGBoost using 10-fold cross-validation. The results consistently show that XGBoost outperforms the other methods in predicting discharge outcomes, making it a reliable tool for clinical decision-making in the treatment of severe pneumonia, including COVID-19 cases. Within this context, the random forest imputation method generally enhances performance, underscoring its effectiveness in managing missing data compared to multiple imputation.

This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics.

Background: Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. Methods: In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. Results: The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. Conclusion: This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common primary malignancies of the urinary tract, highly heterogeneous, and increasing in incidence worldwide. Anoikis is a specific type of programmed cell death in which solid tumor cells or normal epithelial cells that do not have metastatic properties lose adhesion to the extracellular matrix or undergo inappropriate cell adhesion-induced apoptosis. Anoikis is thought to play a critical role in tumorigenesis, maintenance, and treatment, according to an increasing amount of research. However, there is still some uncertainty regarding the general impact of anoikis-related genes (ARGs) on the prognostic importance, tumor microenvironment characteristics, and treatment reaction of ccRCC patients. For this study, we used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus datasets to access the RNA sequencing results and clinical information from ccRCC patients. 29 ARGs related to survival were found using differential analysis and univariate Cox regression analysis. The samples were then divided into two clusters that had different immune traits via unsupervised cluster analysis using 29 prognosis-associated differently expressed ARGs. Then, to build an ARGs signature, 7 genes (PLAU, EDA2R, AFP, PLG, TUBB3, APOBEC3G, and MALAT1) were found using Least Absolute Shrinkage and Selection Operator regression analysis. The new ARGs signature demonstrated outstanding prognostic capability for ccRCC patients' overall survival. In conclusion, for ccRCC patients, we created an ARGs signature that strongly connects to immunological traits and therapy response. Clinicians may find this ARGs signature helpful in developing more individualized and detailed treatment strategies for ccRCC patients.

As a severe inflammatory response syndrome, sepsis presents complex challenges in predicting patient outcomes due to its unclear pathogenesis and the unstable discharge status of affected individuals. In this study, we develop a machine learning-based method for predicting the discharge status of sepsis patients, aiming to improve treatment decisions. To enhance the robustness of our analysis against outliers, we incorporate robust statistical methods, specifically the minimum covariance determinant technique. We utilize the random forest imputation method to effectively manage and impute missing data. For feature selection, we employ Lasso penalized logistic regression, which efficiently identifies significant predictors and reduces model complexity, setting the stage for the application of more complex predictive methods. Our predictive analysis incorporates multiple machine learning methods, including random forest, support vector machine, and XGBoost. We compare the prediction performance of these methods with Lasso penalized logistic regression to identify the most effective approach. Each method’s performance is rigorously evaluated through ten iterations of 10-fold cross-validation to ensure robust and reliable results. Our comparative analysis reveals that XGBoost surpasses the other models, demonstrating its exceptional capability to navigate the complexities of sepsis data effectively.

BackgroundThe relationship between visceral adipose tissue and osteoarthritis is not clear. The purpose of our study was to explore the relationship between visceral adipose tissue and osteoarthritis.MethodsWe used a two-sample Mendelian randomization method to select single-nucleotide polymorphisms (SNPs) significantly associated with visceral adipose tissue as instrumental variables to explore the relationship between visceral adipose tissue and all osteoarthritis, hand osteoarthritis, hip osteoarthritis, knee osteoarthritis, and spine osteoarthritis. The reliability of the results was tested using sensitivity analysis.ResultsOur findings indicated that visceral adipose tissue was associated with all osteoarthritis, hip osteoarthritis, knee osteoarthritis, and spine osteoarthritis (all osteoarthritis: OR = 1.399, 95% CI: 1.335–1.467, p = 7.95e-44; hip osteoarthritis: OR = 1.399, 95% CI: 1.284–1.524, p = 1.41e-14; knee osteoarthritis: OR = 1.794, 95% CI: 1.662–1.937, p = 1.33e-50; and spine osteoarthritis: OR = 1.445, 95% CI: 1.314–1.589, p = 2.89e-14). Sensitivity analysis demonstrated the reliability of these results.ConclusionOur study suggests that genetically predicted visceral adipose tissue is associated with osteoarthritis. Reducing the accumulation of visceral adipose tissue could potentially have an impact on the incidence of osteoarthritis.

The Sitophilus zeamais (maize weevil) and Sitophilus oryzae (rice weevil) are two insect pests that have caused huge economic losses to stored grains worldwide. It is urgent to develop an environmentally friendly strategy for the control of these destructive pests. Here, the olfactory-mediated selection preference of the two weevil species to three stored grains was analyzed, which should help establish a pull–push system in managing them. Bioassays showed that maize weevil adults prefer to select maize, followed by paddy and wheat, while rice weevil adults mainly migrate towards wheat. Volatile analyses revealed that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene are the major components in volatiles from both maize and wheat, but the abundance of these chemicals is much lower in maize than that in wheat. The volatile limonene was only detected in paddy. Y-tube bioassays suggest that 2-ethylhexanol, piperitone, and (+)-Δ-cadiene were all attractive to both weevils, whereas limonene was attractive only to rice weevils. Overall, maize weevil appeared more sensitive to the tested volatiles based on having much lower effective concentrations of these volatiles needed to attract them. The differences in volatile profiles among the grains and the sensitivity of the two species towards these volatiles may explain the behavioral differences between maize and rice weevils in selecting host grains. The differences in sensitivity of maize and rice weevils towards host volatile components with abundance differences are likely determinants driving the two insect species to migrate towards different host grains.

Osteoarthritis, a prevalent long-term condition of the joints, primarily impacts older individuals, resulting in discomfort, restrictions in mobility, and a decrease in overall well-being. Although Osteoarthritis is widely spread, there is a lack of successful interventions to stop the advancement of the condition. Numerous signaling pathways have been emphasized in recent research on Osteoarthritis, yet the diagnostic significance of numerous genes has not been investigated. To identify genes that were expressed differently in osteoarthritis, we utilized the Gene Expression Omnibus database. To identify marker genes, we built machine learning models including Least Absolute Shrinkage and Selection Operator and Random Forest. We categorized Osteoarthritis samples and performed immune cell infiltration analysis based on the expression patterns of these characteristic genes. Both the Least Absolute Shrinkage and Selection Operator and Random Forest models selected six marker genes (TOX3, ARG1, CST7, RERGL, COL11A1, NCRNA00185) out of a total of 17 differentially expressed genes. The osteoarthritis samples were categorized into two groups, namely a high expression group and a low expression group, based on the median levels of TOX3 expression. Comparative analysis of these groups identified 85 differentially expressed genes, showing notable enrichment in pathways related to lipid metabolism in the group with high expression. Analysis of immune cell infiltration revealed noticeable differences in immune profiles among the two groups. The group with high expression of TOX3 showed a notable increase in Mast cells and Type II IFN Response, whereas B cells, Cytolytic activity, Inflammation-promoting cells, NK cells, pDCs, T cell co-inhibition, Th1 cells, and Th2 cells were significantly decreased. We constructed a ceRNA network for TOX3, revealing 57 lncRNAs and 18 miRNAs involved in 57 lncRNA-miRNA interactions, and 18 miRNA-mRNA interactions with TOX3. Validation of TOX3 expression was confirmed using an external dataset (GSE29746), revealing a notable increase in Osteoarthritis samples. In conclusion, our study presents a comprehensive analysis identifying TOX3 as a potential feature gene in Osteoarthritis. The distinct immune profiles and involvement in fat metabolism pathways associated with TOX3 expression suggest its significance in Osteoarthritis pathogenesis. The study establishes a basis for comprehending the intricate correlation between characteristic genes and Osteoarthritis, as well as for the formulation of individualized therapeutic approaches.

Abstract Background The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and evaluate a deep learning‐based diagnostic model for automated LDH detection and classification on lumbar axial T2‐weighted MR images. Methods A total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R‐CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning‐based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs). Results High detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%–88.86%) and 74.23% (95% CI: 71.83%–76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86–0.88, P

Cuproptosis is a novel form of cell death, mediated by protein lipid acylation and highly associated with mitochondrial metabolism, which is regulated in the cell. Ulcerative colitis (UC) is a chronic inflammatory bowel disease that recurs frequently, and its incidence is increasing worldwide every year. Currently, a growing number of studies have shown that cuproptosis-related genes (CRGs) play a crucial role in the development and progression of a variety of tumors. However, the regulatory role of CRGs in UC has not been fully elucidated. Firstly, we identified differentially expressed genes in UC, Likewise, CRGs expression profiles and immunological profiles were evaluated. Using 75 UC samples, we typed UC based on the expression profiles of CRGs, followed by correlative immune cell infiltration analysis. Using the weighted gene co-expression network analysis (WGCNA) methodology, the cluster’s differentially expressed genes (DEGs) were produced. Then, the performances of extreme gradient boosting models (XGB), support vector machine models (SVM), random forest models (RF), and generalized linear models (GLM) were constructed and predicted. Finally, the effectiveness of the best machine learning model was evaluated using five external datasets, receiver operating characteristic curve (ROC), the area under the curve of ROC (AUC), a calibration curve, a nomogram, and a decision curve analysis (DCA). A total of 13 CRGs were identified as significantly different in UC and control samples. Two subtypes were identified in UC based on CRGs expression profiles. Immune cell infiltration analysis of subtypes showed significant differences between immune cells of different subtypes. WGCNA results showed a total of 8 modules with significant differences between subtypes, with the turquoise module being the most specific. The machine learning results showed satisfactory performance of the XGB model (AUC = 0.981). Finally, the construction of the final 5-gene-based XGB model, validated by the calibration curve, nomogram, decision curve analysis, and five external datasets (GSE11223: AUC = 0.987; GSE38713: AUC = 0.815; GSE53306: AUC = 0.946; GSE94648: AUC = 0.809; GSE87466: AUC = 0.981), also proved to predict subtypes of UC with accuracy. Our research presents a trustworthy model that can predict the likelihood of developing UC and methodically outlines the complex relationship between CRGs and UC.

Knowledge about resistance mechanisms can provide ideas for pesticide resistance management. Although several studies have unveiled the positive or negative impacts of gut microbes on host pesticide resistance, minimal research is available regarding the association between gut microbes and host phosphine resistance. To explore the influence of gut bacteria on host phosphine susceptibility and its molecular basis, mortality, fitness, redox responses, and immune responses of adult Tribolium castaneum were determined when it was challenged by phosphine exposure and/or gut bacteria inoculation. Five cultivable gut bacteria were excised from a population of phosphine-resistant T. castaneum. Among them, only Enterococcus sp. inoculation significantly promoted host susceptibility to phosphine, while inoculation of any other gut bacteria had no significant effect on host phosphine susceptibility. Furthermore, when T. castaneum was exposed to phosphine, Enterococcus sp. inoculation decreased the female fecundity, promoted host oxidative stress, and suppressed the expression and activity of host superoxide dismutase, catalase, and peroxidase. In the absence of phosphine, Enterococcus sp. inoculation also elicited overactive immune responses in T. castaneum, including the immune deficiency and Toll signaling pathways and the dual oxidase–reactive oxygen species system. These results indicate that Enterococcus sp. likely promotes host phosphine susceptibility by aggravating oxidative stress and fitness costs.

Rapid urban expansion places unprecedented pressure on urban land management. Optimization of urban spatial layout has become a theoretical proposition and a practical problem associated with urban expansion. Thus, by using the social value measurement model, equivalent factor method, and marginal benefit measurement model, in this paper, we analyze the land resource allocation mechanism, efficiency loss, and reasonable interval. The study results are as follows: (1) Theoretical analysis shows that there are three ranges of land use for urban expansion in China—ideal, moderate and extreme—and it is reasonable to control the amount of land used for urban expansion within the moderate range. (2) Value measurement reveals that the marginal social value and marginal ecological value of cultivated land in Xuzhou show an overall upward trend. From the perspective of the constant price in 2018, the marginal social value increased from CNY 4.91 million/hm2 in 2005 to CNY 14.31 million/hm2 in 2018, while the marginal ecological value increased from CNY 9513.60/hm2 in 2005 to CNY 13,629.70/hm2 in 2018. (3) Empirical analysis shows that when the marginal value of land as cultivated land and construction land is equal, the actual scale of occupied agricultural land is 51,887.23 hm2, of which 25,686.67 hm2 of cultivated land is converted into construction land, sacrificing the social value of farmland, and 12,281.70 hm2 of cultivated land is converted into construction land, ignoring the non-market value of land. Based on the perspective of the marginal principle, in this paper, we construct a model of urban expansion land use allocation and define reasonable intervals for urban expansion land use so as to provide a quantitative reference and decision-making basis for urban expansion governance.

La teoría y la investigación sobre el apego se utilizan en muchos contextos aplicados, incluidos los juzgados de familia, pero hay malentendidos muy extendidos y que a veces dan lugar a aplicaciones erróneas. El objetivo de este documento de consenso es, por lo tanto, mejorar la comprensión, contrarrestar la desinformación y dirigir el uso de la teoría del apego por parte de los juzgados de familia en la dirección de un apoyo basado en la evidencia, especialmente en lo que respecta a la protección de niñas y niños y las decisiones sobre su custodia. El artículo se divide en dos partes. En la primera abordamos los problemas relacionados con el uso de la teoría y la investigación sobre apego en los juzgados de familia, analizando las causas de tales problemas. Para ello examinamos las aplicaciones de la teoría del apego en los juzgados de familia en el marco del concepto de interés superior de niños y niñas, analizamos los malentendidos relacionados con la teoría del apego e identificamos los factores que han obstaculizado su adecuada aplicación. En la segunda parte ofrecemos recomendaciones para la aplicación de la teoría y la investigación sobre apego. Para ello establecemos tres principios del apego: la necesidad de niñas y niños de cuidadoras y cuidadores habituales que no maltraten, el valor de la continuidad de cuidados de calidad suficientemente buena y los beneficios de las redes de relaciones de apego. También analizamos la idoneidad de las evaluaciones de los estilos de apego y del comportamiento de los cuidadores para fundamentar la toma de decisiones de los juzgados de familia. Llegamos a la conclusión de que las evaluaciones de quienes cuidan deberían ocupar un lugar central. Aunque no hay consenso entre nosotros sobre el uso de las evaluaciones del estilo de apego para fundamentar las decisiones sobre custodia y protección infantil, dichas evaluaciones son actualmente las más adecuadas para orientar y dirigir las intervenciones de apoyo. Por último, proporcionamos orientaciones para guiar la futura colaboración en la investigación interdisciplinaria.

ObjectivesThe present study examined parental sleep-supporting practices during toddlerhood in relation to temperament across 14 cultures. We hypothesized that passive sleep-supporting techniques (e.g., talking, cuddling), but not active techniques (e.g., walking, doing an activity together), would be associated with less challenging temperament profiles: higher Surgency (SUR) and Effortful Control (EC) and lower Negative Emotionality (NE), with fine-grained dimensions exhibiting relationships consistent with their overarching factors (e.g., parallel passive sleep-supporting approach effects for dimensions of NE).MethodsCaregivers (N = 841) across 14 cultures (M = 61 families per site) reported toddler (between 17 and 40 months of age; 52% male) temperament and sleep-supporting activities. Utilizing linear multilevel regression models and group-mean centering procedures, we assessed the role of between- and within-cultural variance in sleep-supporting practices in relation to temperament.ResultsBoth within-and between-culture differences in passive sleep-supporting techniques were associated with temperament attributes, (e.g., lower NE at the between-culture level; higher within-culture EC). For active techniques only within-culture effects were significant (e.g., demonstrating a positive association with NE). Adding sleep-supporting behaviors to the regression models accounted for significantly more between-culture temperament variance than child age and gender alone.ConclusionHypotheses were largely supported. Findings suggest parental sleep practices could be potential targets for interventions to mitigate risk posed by challenging temperament profiles (e.g., reducing active techniques that are associated with greater distress proneness and NE).

Bio‐inspired soft robots provide a promising solution for robots working in human‐centered scenarios and interacting with unstructured environments. However, the functional versatility and multimodal sensing of soft robots still need improvements. On one hand, the configuration of a soft robot is predefined during manufacturing; on the other hand, the multimodal perception of the deformable soft actuator is challenging. In this work, a reconfigurable and proprioceptive soft origami module is presented, where two kinds of basic actuation modes (i.e., extension and bending) are realized, and multimodal perception is enabled using a novel foldable self‐inductance sensor. As a result, the origami module can be reconfigured to assemble multifunctional robots that can measure interaction force, body configuration, and other environmental information. Dedicated experiments are performed to validate the performance of the proposed origami module. An intelligent gripper assembled using three origami modules is designed with the capabilities of grasping mode adjustment, grasping force measurement, and the grasping target's size measurement. An intelligent jellyfish is assembled using five origami modules, and equipped with buoyancy adjustment and underwater grasping capabilities. The proposed proprioceptive modular soft origami provides an effective solution for versatile and intelligent soft robot design.

Chemical communication is widespread among insects and exploited to adjust their behavior, such as food and habitat seeking and preferences, recruitment, defense, and mate attraction. Recently, many studies have revealed that microbial symbionts could regulate host chemical communication by affecting the synthesis and perception of insect semiochemicals. In this paper, we review recent studies of the influence of microbial symbionts on insect chemoreception. Microbial symbionts may influence insect sensitivity to semiochemicals by regulating the synthesis of odorant-binding proteins or chemosensory proteins and olfactory or gustatory receptors and regulating host neurotransmission, thereby adjusting insect behavior. The manipulation of insect chemosensory behavior by microbial symbionts is conducive to their proliferation and dispersal and provides the impetus for insects to change their feeding habits and aggregation and dispersal behavior, which contributes to population differentiation in insects. Future research is necessary to reveal the material and information exchange between both partners to improve our comprehension of the evolution of chemoreception in insects. Manipulating insect chemoreception physiology by inoculating them with microbes could be utilized as a potential approach to managing insect populations.

The symbiotic microbiome is critical in promoting insect resistance against colonization by exogenous microorganisms. The mechanisms by which symbionts contribute to the host’s immune capacity is referred to as colonization resistance. Symbionts can protect insects from exogenous pathogens through a variety of mechanisms, including upregulating the expression of host immune-related genes, producing antimicrobial substances, and competitively excluding pathogens. Concordantly, insects have evolved fine-tuned regulatory mechanisms to avoid overactive immune responses against symbionts or specialized cells to harbor symbionts. Alternatively, some symbionts have evolved special adaptations, such as the formation of biofilms to increase their tolerance to host immune responses. Here, we provide a review of the mechanisms about colonization resistance of symbionts in their insect hosts. Adaptations of symbionts and their insect hosts that may maintain such symbiotic relationships, and the significance of such relationships in the coevolution of symbiotic systems are also discussed to provide insights into the in-depth study of the contribution of symbionts to host physiology and behavior.

Objective: The aim of the research was to study the effect of azithromycin (AZM) in the treatment of MDR P. aeruginosa VAP combined with other antimicrobial therapies.Methods: The clinical outcomes were retrospectively collected and analyzed to elucidate the efficacy of different combinations involving azithromycin in the treatment of MDR-PA VAP. The minimal inhibitory concentration (MIC) of five drugs was measured by the agar dilution method against 27 isolates of MDR-PA, alone or in combination.Results: The incidence of VAP has increased approximately to 10.4% (961/9245) in 5 years and 18.4% (177/961) caused by P. aeruginosa ranking fourth. A total of 151 cases of MDR P. aeruginosa were included in the clinical retrospective study. Clinical efficacy results are as follows: meropenem + azithromycin (MEM + AZM) was 69.2% (9/13), cefoperazone/sulbactam + azithromycin (SCF + AZM) was 60% (6/10), and the combination of three drugs containing AZM was 69.2% (9/13). The curative effect of meropenem + amikacin (MEM + AMK) was better than that of the meropenem + levofloxacin (MEM + LEV) group, p = 0.029 (p < 0.05). The curative effect of cefoperazone/sulbactam + amikacin (SCF + AMK) was better than that of the cefoperazone/sulbactam + levofloxacin (SCF + LEV) group, p = 0.025 (p < 0.05). There was no significant difference between combinations of two or three drugs containing AZM, p > 0.05 (p = 0.806). From the MIC results, the AMK single drug was already very sensitive to the selected strains. When MEM or SCF was combined with AZM, the sensitivity of them to strains can be significantly increased. When combined with MEM and AZM, the MIC50 and MIC90 of MEM decreased to 1 and 2 ug/mL from 8 to 32 ug/mL. When combined with SCF + AZM, the MIC50 of SCF decreased to 16 ug/mL, and the curve shifted obviously. However, for the combination of SCF + LEV + AZM, MIC50 and MIC90 could not achieve substantive changes. From the FIC index results, the main actions of MEM + AZM were additive effects, accounting for 72%; for the combination of SCF + AZM, the additive effect was 40%. The combination of AMK or LEV with AZM mainly showed unrelated effects, and the combination of three drugs could not improve the positive correlation between LEV and AZM.Conclusion: AZM may increase the effect of MEM or SCF against MDR P. aeruginosa VAP. Based on MEM or SCF combined with AMK or AZM, we can achieve a good effect in the treatment of MDR P. aeruginosa VAP.

This paper defined heavily polluting enterprises as the treatment group based on relevant data on the listed companies in the 5 years from 2016 to 2020. Companies other than the heavily polluting enterprises were defined as the control group. An empirical analysis applying the double-difference technique was performed to assess the impact of China’s environmental tax on the economic performance and technological innovation input of industries that pollute heavily. The findings demonstrated an increased positive effects of environmental taxes on the economic performance and technology innovation input of heavily polluting industries since the inception of the environmental tax in China. As some enterprises in heavily polluting industries are under increased financial pressure owing to green upgrades and the low environmental tax rate in some regions, recommendations are presented to improve the environmental regulation system, adapt environmental taxes to local conditions, and strengthen the implementation and supervision of environmental taxes through big data technology.

While negative associations between behavioral inhibition/shyness and social competence are well established for children from Western cultures, the directions of these associations have been inconsistent for Chinese children, partly due to the ongoing social-cultural changes in China. Drawing from three samples of young Chinese children (born between 2009 and 2019), we aim at examining how inhibition/shyness predicts cooperative behaviors and prosocial behaviors throughout early childhood. In Study 1 (N = 700, children aged between 36 and 72 months), mother-reported inhibition/shyness was negatively associated with mother-reported cooperative and prosocial behaviors during the preschool years. In Study 2 (N = 251, at 6, 15, 25, and 37 months of children's ages), mother-reported inhibition/shyness in infancy was negatively associated with mother-reported cooperative behaviors but was not related to observed cooperative behaviors at the early preschool age. Infancy inhibition/shyness was negatively associated with mother-reported and observed prosocial behaviors. In Study 3 (N = 95, at 14, 25, 38, and 60 months of children's ages), the inhibition/shyness trait, assessed by both observation and maternal report, did not predict any indicators of cooperative behaviors. Early childhood inhibition/shyness, however, still predicted fewer observed and mother-reported prosocial behaviors. On balance, our research supports a negative association between early inhibition/shyness and later prosocial behaviors. The mixed findings concerning cooperative behaviors are interpreted in light of sociopolitical changes in China during the past two decades.

As a recently discovered waste removal system in the brain, cerebral lymphatic system is thought to play an important role in regulating the homeostasis of the central nervous system. Currently, more and more attention is being focused on the cerebral lymphatic system. Further understanding of the structural and functional characteristics of cerebral lymphatic system is essential to better understand the pathogenesis of diseases and to explore therapeutic approaches. In this review, we summarize the structural components and functional characteristics of cerebral lymphatic system. More importantly, it is closely associated with peripheral system diseases in the gastrointestinal tract, liver, and kidney. However, there is still a gap in the study of the cerebral lymphatic system. However, we believe that it is a critical mediator of the interactions between the central nervous system and the peripheral system. [ABSTRACT FROM AUTHOR]

Objectives: The present study examined parental sleep-supporting practices during toddlerhood in relation to temperament across 14 cultures. We hypothesized that passive sleep-supporting techniques (e.g., talking, cuddling), but not active techniques (e.g., walking, doing an activity together), would be associated with less challenging temperament profiles: higher Surgency (SUR) and Effortful Control (EC) and lower Negative Emotionality (NE), with fine-grained dimensions exhibiting relationships consistent with their overarching factors (e.g., parallel passive sleep-supporting approach effects for dimensions of NE). Methods: Caregivers (N = 841) across 14 cultures (M = 61 families per site) reported toddler (between 17 and 40 months of age; 52% male) temperament and sleep-supporting activities. Utilizing linear multilevel regression models and group-mean centering procedures, we assessed the role of between- and within-cultural variance in sleep-supporting practices in relation to temperament. Results: Both within-and between-culture differences in passive sleep-supporting techniques were associated with temperament attributes, (e.g., lower NE at the between-culture level; higher within-culture EC). For active techniques only within-culture effects were significant (e.g., demonstrating a positive association with NE). Adding sleep-supporting behaviors to the regression models accounted for significantly more between-culture temperament variance than child age and gender alone. Conclusion: Hypotheses were largely supported. Findings suggest parental sleep practices could be potential targets for interventions to mitigate risk posed by challenging temperament profiles (e.g., reducing active techniques that are associated with greater distress proneness and NE). [ABSTRACT FROM AUTHOR]