Government subsidy and benefit distribution mechanisms for transportation PPP projects: An evolutionary game perspective.

Public-private partnerships (PPP), as an important model for collaboration between the public and private sectors, is an urgent and critical topic due to the serious financial losses of governments involved in transportation PPP projects in recent years. Current research focuses on the government subsidy model, in which the effective implementation of government subsidies relies on the design of incentives for stakeholder behavior. Although the positive externalities are strong, they are prone to the problem of "free riding," which leads to low project performance and challenges in compensating for the government's financial losses. Therefore, this study proposes a novel dynamic subsidy mechanism that can be adjusted based on actual changes in transportation demand and that is linked to project performance. We use evolutionary game theory to construct a two-party evolutionary game model of the government and social capital, focusing on the stability and influencing factors of these interactions. Our research unveils that reaching specific thresholds in both the incentive coefficient and benefit distribution ratio induces an "positive management-negative management" shift in the behavior of involved parties, leading to enhanced project outcomes. Notably, fluctuations in operational quality substantially enhance the efficiency of the active management of private sector, with no discernible impact on the subsidy efficiency of the government. Therefore, our study provides a theoretical framework for improving the revenue allocation and government subsidy mechanism, which has theoretical and practical implications for enhancing the effect of government incentives and improving the quality of operational social capital.

PM2.5 can help adjust building's energy consumption.

Long- or short-term exposure to air pollution would distort human's cognitive function which has aroused widespread concern in interdisciplinary fields. It furtherly seems rational to assume that air pollution may affect energy use in public buildings. However, the overlooks of the potential impacts of air pollution on energy use would result in substantially higher energy saving cost. By matching the real-time energy consumption of public buildings to indoor and outdoor PM2.5, we construct a panel containing 193,226 data items. Based on this, we conduct the first preliminary exploration to try to reveal the impact of PM2.5 on energy use at the building-hourly level. Results show that the increase of energy intensity caused by PM2.5 is subtle, it indeed exists significantly. When indoor PM2.5 is 1 µg/m3, the marginal effect is minimum. After indoor PM2.5 exceeding 1 µg/m3, the marginal effect began to increase and the maximum is 0.3224 when PM2.5 is 1114 µg/m3. However, given the sorting and contrast effect, the practical relationship between indoor PM2.5 and energy use is possible inverted-U shaped. Furtherly, we find long term exposure to outdoor PM2.5 would not make people adapt to air pollution and instead cumulative the impact on energy use. Besides, centralized office could be an economical and feasible measure to achieve energy saving goal. Finally, we propose that it is promising for achieving the synergy between air pollution control and energy consumption reduction.

Clinical significance of circulating tumour cells and Ki-67 in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is a common malignant tumour of the genitourinary system. We aimed to analyse the potential value of metastasis-related biomarkers, circulating tumour cells (CTCs) and the proliferative marker Ki-67 in the diagnosis of RCC. METHODS: Data from 24 laparoscopic radical nephrectomies (RNs) and 17 laparoscopic partial nephrectomies (PNs) were collected in 2018. The numbers and positive rates of CTCs and circulating tumour microemboli (CTM) in the peripheral blood were obtained at three different time points: just before surgery, immediately after surgery and 1 week after surgery. Ki-67 protein expression was evaluated in the RCC tissue by immunohistochemistry. RESULTS: Except for the statistically significant association between the preoperative CTC counts and tumour size, no association between the number and positive rate of perioperative CTCs and clinicopathological features was found. The CTC counts gradually decreased during the perioperative period, and at 1 week after surgery, they were significantly lower than those before surgery. High Ki-67 expression was significantly positively correlated with preoperative CTC counts. In addition, Ki-67 expression was higher in the high CTC group (≥ 5 CTCs). CONCLUSION: Our results suggest that surgical nephrectomy is associated with a decrease in CTC counts in RCC patients. CTCs can act as a potential biomarker for the diagnosis and prognosis of RCC. A careful and sufficient long-term follow-up is needed for patients with high preoperative CTC counts.

A rapid and sensitive UHPLC-MS/MS method for the determination of celosin A in rat plasma with application to pharmacokinetic study.

Celosin A (CA), a natural compound isolated from Celosia argentea L., has been shown significant hepatoprotective effect on AHNP-induced liver injury. This study described a rapid and sensitive ultra-high-pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay for determination of CA in rat plasma. Methanol-mediated precipitation was used for sample pretreatment. Chromatographic separation was achieved on a T3 column with gradient elution using water and acetonitrile as mobile phase. Determination was obtained using an electrospray ionization source in negative selected reaction monitoring mode at the transitions of m/z 793.3 → m/z 661.2 and m/z 955.6 → m/z 793.2 for CA and IS, respectively. The assay was linear over the concentration range 0.25-2500 ng/mL (r > 0.995) with a lowest limit of quantification (LLOQ) of 0.25 ng/mL. The intra- and inter-day precisions (RSD) were 1.65-9.84 and 2.46-13.49%, respectively, while accuracy (RR) ranged from 96.21 to 99.45%, respectively. The recovery ranged from 95.09 to 102.22% and the matrix effect from 98.29 to 100.13%. The analyte was stable under the tested storage conditions. The method has been successfully applied to a preclinical pharmacokinetic study in rats after a single intravenous (2 mg/kg) or oral (50 mg/kg) administration. The oral bioavailability of CA was ~1.94%; in addition, there was no difference between male and female rats. This is the first time of the use of an UHPLC-MS/MS method for determination of CA concentration in rat plasma and for evaluation of its pharmacokinetic behavior.

Using the DAS-ELISA Test to Establish an Effective Distance Between Bait Stations for Control of Linepithema humile (Hymenoptera: Formicidae) in Natural Areas.

Linepithema humile (Mayr), the Argentine ant, is an invasive pest that has spread throughout the United States and is a problem in natural and managed habitats in South Carolina. Foraging patterns and the effectiveness of liquid baits for control of this pest have been studied in urban areas. However, similar studies have not been conducted in natural areas such as parks, picnic grounds, or campsites. L. humile populations can be large and widespread, making them a major nuisance pest for visitors to these natural areas. The primary objective of this study was to determine an effective distance between bait stations for control of L. humile in a natural area. A double antibody-sandwich enzyme-linked immunosorbent assay (DAS-ELISA) procedure was used to detect individual ants that consumed rabbit immunoglobin (IgG) protein for marking and tracking. In both lab and field conditions, there was a significant difference in the detection of IgG in ants fed protein marker mixed with sugar water compared with ants only fed sugar water. Additional field studies revealed that an individual ant could retain detectable levels of protein marker for 3 d and that an ant feeding on IgG containing bait could be detected over 15 m from the original bait source. Overall, we found that using liquid ant baits, with a placement of 20 m between stations, was effective in reducing L. humile numbers between April to October, 2012 in a natural park area of Lake Greenwood State Park, SC.

A review regarding the article 'Electrocardiographic abnormalities in patients with metabolic dysfunction-associated Steatotic liver disease: A systematic review and meta-analysis.'

Metabolic-dysfunction-associated Steatotic liver disease (MASLD) is a high-risk condition for both liver fibrosis and cardiovascular disease (CVD). Therefore, therapeutic strategies to prevent both liver fibrosis and atherosclerotic CVD are required for the treatment of MASLD. Metabolic dysfunction-associated steatohepatitis (MASH) is the more severe form of MASLD, is defined histologically by the presence of lobular inflammation and hepatocyte ballooning and is associated with a greater risk of fibrosis progression. While CVD is the leading cause of mortality in patients with MASLD, those with more severe liver fibrosis are at increased risk of liver-related mortality, with the risk increasing exponentially with fibrosis stage. MASH has been found in 63% of patients with MASLD undergoing liver biopsy in an Asian multi-center cohort. Multiple complex pathways are involved in the association between MASLD and CVD. The visceral accumulation of fat around the liver and other organs, including the pericardium, leads to the release of fat-derived metabolites with the activation of several inflammatory pathways Cardiac rhythm abnormalities are prevalent in MASLD, such as prolongation of the QT interval, ventricular arrhythmias, and atrial fibrillation. Therapeutic interventions that improve cardiometabolic risk factors may be beneficial for an improvement in MASLD. The effects of such therapeutic interventions on lipid, lipoprotein and apoprotein accumulation in the liver and on hepatic steatosis and fibrosis still remain unelucidated. Which lipid factor is crucial for developing MASLD also remains largely unknown.

Effect of bioturbation of the mitten crab on distribution of tire wear particles and their combined effect on sediment ecosystem.

Tire wear particles (TWPs) are a major source of environmental microplastic pollution which gradually settle and accumulate in sediments after entering the aquatic environment, which can affect the behaviors of benthic organisms. Bioturbation of benthic species could affect the fate, impacts and potential risks of TWPs by altering the properties and structure of sediments. Therefore, in this study, the effect of TWPs on the burrowing activity of Chinese mitten crab (Eriocheir sinensis) was investigated. In addition, the effects of crab bioturbation on the distribution of TWPs and their additives were studied. The combined effects of TWPs and crab bioturbation on the microbial communities in the sediments were also explored. The results of this study showed that both TWPs and the leachate significantly inhibited the burrowing activity of crabs. TWPs in the surface layer of sediments were re-distributed by crab bioturbation and enriched mainly in the sediments near the burrow walls. Meanwhile, the heavy metals (i.e., Zn, Ca, Mg, Ba and Al) used as additives during the tire production in the burrow walls significantly increased as the accumulation of TWPs near burrow walls. In this study, TWP exposure decreased the bacterial diversity and abundance, as well as the functional genes related to carbon and nitrogen cycling process, but crab bioturbation increased them in the sediments of burrow walls by constructing a unique habitat. However, after TWPs entering into burrows, they were significantly decreased in the sediments near the burrow walls like the effects of TWPs, suggesting the negative effects of TWPs could play a dominant role in this combined system. Overall, this study is important for evaluating the distribution and effects of TWP pollution in the sediment ecosystem under biological factors such as bioturbation.

Reverse subsidy dilemma caused by the transformation from individual heating to central heating.

With the increase in household income enhancing people's expectations for life comfort, the demand for central heating in hot summer and cold winter areas (HSCWs) has increased. This study aims to explore whether it is appropriate to promote central heating in HSCWs from the perspectives of inequality and reverse subsidies. Reverse subsidy dilemma caused by the transformation from individual heating to central heating was proposed by the analysis relying on utility theory. This paper presents data suggesting that individual heating could provide more options for different household income groups than could central heating. Furthermore, the heating inequality among different income groups is assessed, and reverse subsidies from the poor to the rich are discussed. We find that the implementation of central heating leads to few adverse effects and high utility for the rich and increased expenditures and low utility satisfaction for the poor at the same price level.

Improving the greenness of enterprise supply chains by designing government subsidy mechanisms: based on prospect theory and evolutionary games.

Fostering sustainable development through green supply chains is of paramount significance. Government subsidies emerge as a successful strategy for motivating businesses to actively participate in such eco-friendly practices. This study employs prospect theory and an evolutionary game model to analyze the transition toward carbon peaking and neutrality while promoting the expansion of highly sustainable businesses. By exploring the decision-making processes of businesses and governments regarding sustainability, we develop an evolutionary game-based decision model to assess the impact of government subsidies on businesses engaged in green supply chains. Through numerical simulation obtained via MATLAB, we examine various factors influencing the evolution of the game system between green supply chain businesses and the government. Additionally, we investigate how government incentives impact the decision-making behavior of green supply chain businesses. Our findings indicate that governmental fines can effectively encourage the adoption of green supply chains. Furthermore, moderate government subsidies incentivize enterprises to opt for sustainable supply chains, benefiting both the government and businesses. However, providing hefty government subsidies not only fails to encourage the adoption of green supply chains but also incurs costs for the government, without yielding any positive change in the businesses' approach. By incorporating evolutionary game theory and prospect theory, this study contributes to the body of knowledge on government-supported green supply chains, offering incentive programs tailored to the real-world conditions faced by businesses while demonstrating practical application values.

Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma.

BACKGROUND: Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment. METHODS: A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis. RESULTS: This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups. CONCLUSION: According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.

Dynamic event-triggered resilient state estimation for time-varying complex networks with Markovian switching topologies.

This paper addresses a resilient state estimation problem for an array of nonlinear complex networks with switching topologies under the dynamic event-triggered mechanism (ETM). To reduce the unnecessary data delivery, the dynamic ETM is introduced to schedule the data delivery from sensors to estimators. The model of the switched complex networks is established by adopting a Markov chain which is better to reflect the characteristics of practical complex networks. A set of novel estimators is obtained by using the properties of Kronecker product combining with the Lyapunov-Krasovskii method, and some easy-to-check conditions are derived such that the dynamics of state estimation error satisfies the prescribed H∞ performance index. In addition, the parameters of the designed resilient state estimators can be acquired by solving a series of convex optimization problems. In the end, a simulation example is given to demonstrate the validity of the proposed theoretical results in this paper.

Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19.

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

Toxicity of tire wear particles and the leachates to microorganisms in marine sediments.

Tire wear particles (TWPs), which are among the microplastic pollutants in the environment, can inevitably accumulate in coastal sediments. The present study comprehensively investigated the effect of pristine TWPs on bacterial community structure in coastal sediments and compared the effect of pristine TWPs and aged TWPs on nine strains of bacteria in sediments. In addition, the effect of the TWP leachate was studied with all the nine bacterial strains and the toxicity-causing substances in the leachate was investigated using Bacillus subtilis. Exposure to TWPs could lead to a shift in bacteria community and affect nitrogen metabolism in marine sediments. Aged TWPs were more toxic than pristine TWPs due to changes in particle surface characteristics. The leachate exhibited greater toxicity than TWPs as well, and Zn was identified to be the major toxicity-causing substance. The overall results of this study are important for understanding the effects of TWPs and the leachates on microorganisms in marine sediments.

Identification of a Prognostic Signature Associated With the Homeobox Gene Family for Bladder Cancer.

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA. Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the "limma" and robust rank aggregation (RRA) methods. The mutation data were obtained with the "TCGAmutation" package and visualized with the "maftools" package. Kaplan-Meier curves were plotted with the "survminer" package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using "clusterProfiler." Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses. Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not. Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient's prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer.

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed. Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package "limma," and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package "clusterProfiler." Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves. Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations. Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

Polymer-assisted synthesis and applications of hydroxyapatite (HAp) anchored nitrogen-doped 3D graphene foam-based nanostructured ceramic framework.

In the present work, a hydroxyapatite anchored nitrogen-doped three-dimensional graphene (HAp-N3DG) skeletal network (foam) based nanostructured ceramic framework (CF) was developed through a polymer-assisted solvothermal route. Field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) studies reveal that the nano sized 0D HAp particles are anchored on the N3DG skeletal network with an average size of less than 50 nm. EDX and X-ray photoelectron spectroscopy (XPS) analysis confirmed the presence of Ca, P, O, N, and C. In addition, XPS analysis reveals the existence of N-C bonds in the prepared sample. The X-ray diffraction (XRD) patterns indicate the presence of hexagonal phase hydroxyapatite and the calculated average crystallite size was found to be 12 nm. The developed HAp-N3DG foam based nanostructured CF was found to have a mesoporous structure and the measured specific surface area (SSA) and the mean pore diameter were found to be 64.73 m2 g-1 and 23.6 nm, respectively. Electrochemical analysis shows that HAp anchored on nitrogen-doped 3D graphene foam based nanostructured CF has moderate electrochemical activity towards lithium ion charge/discharge. In addition, the prepared material showed adsorption activity values of 204.89 mg g-1 and 243.89 mg g-1 for the volatile organic compounds (VOCs) benzene and toluene, respectively. The present findings suggest that the newly developed HAp anchored nitrogen-doped 3DG (HAp-N3DG) skeletal network (foam) based nanostructured CF material can be used in energy devices and in the removal of volatile organic compounds. Moreover, the present study initiates a new kind of approach in energy device (lithium ion battery-LIB) research and in the removal of VOCs.

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3-D CNN) based method for fall detection is developed, which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2-D CNN could only encode spatial information, and the employed 3-D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a long short-term memory (LSTM) based spatial visual attention scheme is incorporated. Sports dataset Sports-1 M with no fall examples is employed to train the 3-D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.

Climbing ability of teneral and sclerotized adult bed bugs and assessment of adhesive properties of the exoskeletal fluid using atomic force microscopy.

We observed that teneral adults (<1 h post-molt) of Cimex lectularius L. appeared more adept at climbing a smooth surface compared to sclerotized adults. Differences in climbing ability on a smooth surface based on sclerotization status were quantified by measuring the height to which bed bugs climbed when confined within a glass vial. The average maximum height climbed by teneral (T) bed bugs (n = 30, height climbed = 4.69 cm) differed significantly (P< 0.01) from recently sclerotized (RS) bed bugs (n = 30, height climbed = 1.73 cm at ~48 h post molt), sclerotized group 1 (S1) bed bugs (n = 30, S1 = 2.42 cm at >72 h), and sclerotized group 2 (S2) bed bugs (n = 30, height climbed = 2.64 cm at >72 h post molt). When heights from all climbing events were summed, teneral bed bugs (650.8 cm climbed) differed significantly (P< 0.01) from recently sclerotized (82 cm climbed) and sclerotized (group 1 = 104.6 cm climbed, group 2 = 107.8 cm climbed) bed bugs. These findings suggested that the external surface of teneral bed bug exoskeletons possess an adhesive property. Using atomic force microscopy (AFM), we found that adhesion force of an exoskeletal (presumably molting) fluid decreased almost five-fold from 88 to 17 nN within an hour of molting. Our findings may have implications for laboratory safety and the effectiveness of bed bug traps, barriers, and biomimetic-based adhesives.