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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.

Abstract Background Imported falciparum malaria from Africa has become a key public health challenge in Guizhou Province since 2012. Understanding the polymorphisms of molecular markers of drug resistance can guide selection of antimalarial drugs for the treatment of malaria. This study was aimed to analyze the polymorphisms of pfcrt, pfmdr1, and K13-propeller among imported falciparum malaria cases in Guizhou Province, China. Method Fifty-five imported falciparum malaria cases in Guizhou Province during 2012–2016 were included in this study. Their demographic information and filter paper blood samples were collected. Genomic DNA of Plasmodium falciparum was extracted from the blood samples, and polymorphisms of pfcrt, pfmdr1, and K13-propeller were analyzed with nested PCR amplification followed by sequencing. Data were analyzed with the SPSS17.0 software. Results The prevalence of pfcrt K76T, pfmdr1 N86Y, and pfmdr1 Y184F mutation was 56.6, 22.2, and 72.2%, respectively, in imported falciparum malaria cases in Guizhou Province. We detected two mutant haplotypes of pfcrt, IET and MNT, with IET being more commonly found (54.7%), and five mutant haplotypes of pfmdr1, of which NFD was the most frequent (53.7%). There were totally 10 combined haplotypes of pfcrt and pfmdr1, of which the haplotype IETNFD possessed a predominance of 28.8%. In addition, three nonsynonymous mutations (S459T, C469F, and V692L) and two synonymous mutations (R471R and V589V) were detected in K13-propeller, all having prevalence less than 6.0%. In particular, a candidate K13 resistance mutation, C469F, was identified for the first time from Democratic Republic of the Congo with the prevalence of 2.0%. Conclusions The high prevalence of IET haplotype of pfcrt and NFD haplotype of pfmdr1 suggests the presence of chloroquine, artemether/lumefantrine, and dihydroartemisinin/piperaquine resistance in these cases. Therefore cautions should be made to artemisinin therapy for P. falciparum in Africa. Continuous monitoring of anti-malarial drug efficacy in imported malaria cases is helpful for optimizing antimalarial drug therapy in Guizhou Province, China.

Insect symbionts Wolbachia used for pest population control has focused on vector pest species and agricultural insects while rare reports in Chinese stored-product insect samples. In this paper, we surveyed the prevalence of Wolbachia using a PCR detection method in ten Tenebrionidae stored-product insects. Subsequently, the spatiotemporal Wolbachia infection dynamics in Tribolium confusum and Wolbachia elimination patterns using tetracycline treatment were investigated in detail by TaqMan® probe real-time quantitative PCR, and host reproductive fitness parameters were compared. T. confusum was the only Wolbachia infected species in all the surveyed species. Wolbachia infection density consistently increased with the development of T. confusum and plateaued at 3.7 × 107 wsp copies per individual insect at the young adult stage. Wolbachia densities in females were higher than that in males with a significant difference at the pupae stage and varied among different tissues and organs. Uninfected female beetles were completely incapable of producing mature progenies when crossed with Wolbachia infected males. Embryogenesis and egg hatch rate were specifically inhibited after Wolbachia elimination, while other traits, including the number of eggs, pupation rate and sex ratio, remained unaffected by tetracycline treatment. Our results show that the TaqMan® probe qPCR is a reliable detection method for quantifying the density of Wolbachia as compared to qualitative detection of wsp gene by PCR and relatively quantified by real-time qPCR. The fitness results indicated that Wolbachia infection was not an obligate symbiont and benefited the host confused flour beetle. Keywords: Wolbachia, Tenebrionidae, Tribolium spp., Stored product insects, Endosymbiont, Infection density, Tetracycline

Background: Catheter ablation of atrial fibrillation is an alternative treatment for patients with tachycardia-bradycardia syndrome (TBS) to avoid pacemaker implantation. The risk stratification for atrial fibrillation and outcomes between ablation and pacing has not been fully evaluated.Methods: This retrospective study involved 306 TBS patients, including 141 patients who received catheter ablation (Ablation group, age: 62.2 ± 9.0 months, mean longest pauses: 5.2 ± 2.2 s) and 165 patients who received pacemaker implement (Pacing group, age: 62.3 ± 9.1 months, mean longest pauses: 6.0 ± 2.3 s). The primary endpoint was a composite of call cause mortality, cardiovascular-related hospitalization or thrombosis events (stroke, or peripheral thrombosis). The second endpoint was progress of atrial fibrillation and heart failure.Results: After a median follow-up of 75.4 months, the primary endpoint occurred in significantly higher patients in the pacing group than in the ablation group (59.4 vs.15.6%, OR 6.05, 95% CI: 3.73–9.80, P < 0.001). None of deaths was occurred in ablation group, and 1 death occurred due to cancer. Cardiovascular-related hospitalization occurred in 50.9% of the pacing group compared with 14.2% in the ablation group (OR: 4.87, 95% CI: 2.99–7.95, P < 0.001). More thrombosis events occurred in the pacing group than in the ablation group (12.7 vs. 2.1%, OR 6.06, 95% CI: 1.81–20.35, P = 0.004). Significant more patients progressed to persistent atrial fibrillation in pacing group than in ablation group (23.6 vs. 2.1%, P < 0.001). The NYHA classification of the pacing group was significantly higher than that of the ablation group (2.11 ± 0.83 vs. 1.50 ± 0.74, P < 0.001). The proportion of antiarrhythmic drugs and anticoagulants used in the pacing group was significantly higher than that in the ablation group (41.2 vs. 7.1%, P < 0.001; 16.4 vs. 2.1%, P = 0.009).Conclusion: Catheter ablation for patients with TBS was associated with a significantly lower rate of a composite end point of cardiovascular related hospitalization and thromboembolic events. Furthermore, catheter ablation reduced the progression of atrial fibrillation and heart failure.

Patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) were treated with immediate or sequential withdrawal after 5 days of systemic glucocorticoids. The effects of the two withdrawal methods on the prognosis of patients were compared at 30, 90, 180, and 360 days after discharge. A multicenter, randomized, double-blind, parallel-controlled, open-label study was conducted in the respiratory department of tertiary hospitals in Central China. Patients met inclusion criteria for AECOPD and needed to use systemic glucocorticoids. They were randomly assigned to immediate and sequential withdrawal groups at a 1:1 ratio. The study was completed in August 2020 and is registered at the China Clinical Trials Registry (Chictr.org) (ChiCTR1800018894). According to general data and clinical characteristics, there were no statistically significant differences between the 329 patients in the immediate withdrawal group and the 310 patients in the sequential withdrawal group (P > 0.05). At the 30, 90, 180, and 360-days follow-up, the acute exacerbation frequency, rehospitalization rate, mortality, and intensive care unit (ICU) treatment rate were not significantly different between the immediate withdrawal group and sequential withdrawal group (P > 0.05). The modified Medical Research Council (mMRC) and COPD assessment test (CAT) scores were also not significantly different between the two groups. At the 180- and 360-day follow-up, forced expiratory volume in 1 s (FEV1%) and peak expiratory flow (PEF) were not significantly different between the two groups (P > 0.05). The time from discharge to first acute exacerbation was significantly lower in the immediate withdrawal group (46.12 days) than in sequential withdrawal group (49.02 days) (P < 0.05). The time of stay in the hospital for the first time after discharge was not significantly different between the two groups (P > 0.05). Adverse events were not significantly different between the immediate withdrawal group and sequential withdrawal group (P < 0.05). Subgroup analysis was performed according to age, degree of disease, and relevant indicators. At the 30-day follow-up, the acute exacerbation frequency of patients with advanced age, high global strategy for chronic obstructive lung disease (GOLD), and high fractional exhaled nitric oxide was significantly higher in the immediate withdrawal group than in the sequential withdrawal group (P < 0.05). In addition, according to receiver operating characteristic (ROC) curve analysis, the frequency of acute exacerbations at the 30-day follow-up was significantly higher in patients with age > 63.5 years or GOLD > 3 in the immediate withdrawal group than in the sequential withdrawal group, suggesting that the short-term efficacy was poor.

Bioluminescent imaging (BLI) has emerged as a popular in vivo tracking modality in bone regeneration studies stemming from its clear advantages: non-invasive, real-time, and inexpensive. We recently adopted bioluminescence resonance energy transfer (BRET) principle to improve BLI cell tracking and generated the brightest bioluminescent signal known to date, which thus enables more sensitive real-time cell tracking at deep tissue level. In the present study, we brought BRET-based cell tracking strategy into the field of bone tissue engineering for the first time. We labeled rat mesenchymal stem cells (rMSCs) with our in-house BRET-based GpNLuc reporter and evaluated the cell tracking efficacy both in vitro and in vivo. In scaffold-free spheroid 3D culture system, using BRET-based GpNLuc labeling resulted in significantly better correlation to cell numbers than a fluorescence based approach. In scaffold-based 3D culture system, GpNLuc-rMSCs displayed robust bioluminescence signals with minimal background noise. Furthermore, a tight correlation between BLI signal and cell number highlighted the robust reliability of using BRET-based BLI. In calvarial critical sized defect model, robust signal and the consistency in cell survival evaluation collectively supported BRET-based GpNLuc labeling as a reliable approach for non-invasively tracking MSC. In summary, BRET-based GpNLuc labeling is a robust, reliable, and inexpensive real-time cell tracking method, which offers a promising direction for the technological innovation of BLI and even non-invasive tracking systems, in the field of bone tissue engineering.

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.

Abstract Background Perimenopausal insomnia is one of the core symptoms of the menopausal transition. Acupuncture is considered to exert a positive effect on restoring the normal sleep–wake cycle. However, there is little intuitive evidence besides evaluation using clinical effectiveness scales. We therefore designed this study, aiming to use more intuitive and reliable detection techniques such as functional magnetic resonance imaging before and after applying acupuncture to provide neuroimaging evidence, as well as to verify the effectiveness with other curative effect indicators. Methods/Design This study is a randomized, assessor–statistician-blinded, positive medicine controlled trial involving 40 participants. A total of 40 eligible patients with perimenopausal insomnia will be randomly assigned to two groups in a 1:1 ratio as an intervention group using acupuncture and a control group taking estazolam. Participants in the intervention group will receive six acupuncture treatment sessions per week for 4 consecutive weeks, for a total of 24 sessions during the study. Meanwhile, the medicine control group will be prescribed estazolam 1–2 mg/day to be taken 30 minutes before sleep for 4 weeks. The primary outcome is the Pittsburgh Sleep Quality Index. Secondary outcomes are the micro-movement sensitive mattress-type sleep monitoring system, the Hamilton Depression Scale, and the Hamilton Anxiety Scale. All outcomes will be evaluated before and after treatment. The safety of interventions will be assessed at every visit. Discussion The results of this trial, which will be available in 2018, will investigate the impact of acupuncture treating perimenopausal insomnia from assessment of the sleep architecture, hormone level, and emotional-circuit neurological function, and will uncover the effective mechanism of acupuncture regulating the emotional center integrated effect. Trial registration Chinese Clinical Trials Register, ChCTR-IPC-16007832 . Registered on 26 January 2016.

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs that play essential regulatory roles in various developmental processes and stress responses. However, the functions of lncRNAs during the flowering period of tomato are largely unknown. To explore the lncRNA profiles and functions during flowering in tomato, we performed strand-specific paired-end RNA sequencing of tomato leaves, flowers and roots, with three biological replicates. We identified 10919 lncRNAs including 248 novel lncRNAs, of which 65 novel lncRNAs were significantly differentially expressed (DE) in the flowers, leaves, and roots. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out to identify the cis target gene of DE lncRNAs. The results showed that the lncRNAs might play an important role in the growth, development, and apoptosis of flowering tomato plant by regulating the formation of intima in flower tissues, binding to various molecules, influencing metabolic pathways, and inducing apoptosis. Moreover, we identified the interaction between 32, 78, and 397 kinds of miRNAs, lncRNAs, and mRNAs. The results suggest that the lncRNAs can regulate the expression of mRNA during flowering period in tomato by forming competitive endogenous RNA, and further regulate various biological metabolism pathways in tomato.

Background: Primary insomnia (PI) is one of the most common complaints among the general population. Both non-pharmacological and pharmacological therapies have proven effective in treating primary insomnia. However, the underlying mechanism of treatment remains unclear, and no studies have compared the underlying mechanisms of different treatments. Methods: In this study, we investigated gray matter volume (GMV) and resting-state functional connectivity (rsFC) changes following both pharmacological and non-pharmacological treatments in patients with PI. A total of 67 PI patients were randomized into benzodiazepine treatment, cupping treatment, or a wait-list control group for 4 weeks. The Pittsburgh Sleep Quality Index (PSQI), gray matter volume (GMV), and resting-state functional connectivity (rsFC) of the hippocampus were measured at the beginning and end of the experiment. Results: We found 1) significantly decreased PSQI scores in the cupping and benzodiazepine treatment groups compared to the control group with no significant differences between the two treatment groups; 2) significant GMV increases in the cupping group compared to the control group at the right hippocampus after 4 weeks of treatment; 3) significantly increased rsFC between the right hippocampus and left rostral anterior cingulate cortex/medial prefrontal cortex (rACC/mPFC) in the two treatment groups, which was significantly associated with PSQI score decreases. Discussion: Our findings suggest that benzodiazepine and cupping may share a common mechanism to relieve the symptoms of patients with PI. Keywords: Primary insomnia, Hippocampus, Non-pharmacological therapy, Pharmacological therapy, Gray matter volume, Resting-state functional connectivity

Insomnia and depression are highly comorbid symptoms in both primary insomnia (PI) and major depressive disorder (MDD). In the current study, we aimed at exploring both the homogeneous and heterogeneous brain structure alteration in PI and MDD patients. Sixty-five MDD patients and 67 matched PI patients were recruited and underwent a structural MRI scan. The subjects were sub-divided into four groups, namely MDD patients with higher or lower insomnia, and PI patients with higher or lower severe depression. A general linear model was employed to explore the changes in cortical thickness and volume as a result of depression or insomnia, and their interaction. In addition, partial correlation analysis was conducted to detect the clinical significance of the altered brain structural regions. A main effect of depression on cortical thickness was seen in the superior parietal lobe, middle cingulate cortex, and parahippocampal gyrus, while a main effect of insomnia on cortical thickness was found in the posterior cingulate cortex. Importantly, the interaction between depression and insomnia was associated with decreased gray matter volume in the right orbitofrontal cortex, i.e., patients with co-occurring depression and insomnia showed smaller brain volume in the right orbitofrontal cortex when compared to patients with lower insomnia/depression. These findings highlighted the role of the orbitofrontal cortex in the neuropathology of the comorbidity of insomnia and depression. Our findings provide new insights into the understanding of the brain mechanism underlying comorbidity of insomnia and depression.

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.

It is important to identify the developmental antecedents of externalizing behavioral problems in early childhood. The current study examined the main effects of maternal personality and its interactive effects with child temperamental reactivity in predicting child externalizing behavioral problems, indicated by impulsivity and aggression. This study was composed of 70 children (Mage = 17.6 months, SD = 3.73) and their mothers. The results showed that maternal agreeableness was negatively associated with child impulsivity. Child temperamental reactivity moderated the effect of maternal conscientiousness on child impulsivity in support of the differential susceptibility model. Specifically, for highly reactive children, maternal conscientiousness was negatively associated with child impulsivity whereas this association was non-significant for low reactive children. Child reactivity also moderated the contribution of maternal neuroticism to child impulsivity. That is, maternal neuroticism was negatively associated with impulsivity, only for highly reactive children.

Whereas the effects of maternal parenting quality during infants’ 2nd year on later executive function (EF) have been studied extensively, less is known about the impact of maternal parenting quality during the 1st year. The aim of this study was to examine whether maternal parenting during infants’ 1st year predicted EF performance at 2 and 3 years of age in a Chinese sample. Data were collected from 96 mother-infant dyads (42 males) when the infants were 6, 9, 25, and 38 months old. Cognitive development as a control variable was measured with the Bayley Scales of Infant Development II at 6 months. At 9 months, three aspects of maternal parenting quality (sensitivity, mind-mindedness, and encouragement of autonomy) were assessed with MBQS, mind-mindedness coding system, and encouragement of autonomy coding schema within a 15-min mother–infant interaction. Three aspects of EF (working memory, inhibitory control, and delay EF) were measured at 25 and 38 months with age-appropriate tasks. Hierarchical regression analysis showed that maternal mind-mindedness had a more important effect than did the encouragement of autonomy and maternal sensitivity during infants’ preverbal period. More precisely, maternal mind-mindedness at 9 months predicted inhibitory control at 2 and 3 years, and maternal encouragement of autonomy predicted performance on delay EF tasks at 3 years, maternal sensitivity had no observed effect on children’s EF. This study suggests that maternal parenting quality during the 1st year (maternal mind-mindedness and encouragement of autonomy, but not maternal sensitivity) impacts later EF development.

Determining whether an enterprise has target holdings and figuring out how to reasonably determine these cash holdings are common problems faced by all enterprises. This paper first establishes an uncertain optimal cash holdings model with a security area constraint, and then proves that the model is a typical bang−bang control model. The control variables in the model can be expressed as a symbolic function. Then, under the specific objective function, the optimal cash holdings are discussedfor two casesincluding whether to consider transaction costs or not. Finally, the applicability of the model is verified by specific examples, and the influences of factors, including risky asset returns and the transaction cost of unit securities on decision-making results are discussed.

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]