Search

Cellulose fibers from ramie is a kind of functional biological material with multipurpose in the textile industry, which called as “China grass.” The degumming of ramie fibers performed with Bacillus subtilis were regulated by response regulator CitT from CitS/CitT two-component system through specifically functioning on the degradation of component pectin. In this study, comparative proteomic analysis was executed to obtain insights into the sensitivity by which the metabolic network induced by the absence of CitT protein, and to further explore the regulatory mechanism during bio-degumming process of ramie fibers. Results showed that 29 differentially expressed proteins were detected from original strain and mutant strain, which were mainly involved in transmembrane transport system, two-component system, and amino acid metabolism. This study demonstrated that the lack of CitT protein could result in the down-regulation of enzymes in histidine biosynthesis pathway, and the up-regulation of enzyme in arginine degradation pathway. This study is the first time to reveal comprehensive information about the regulatory function of CitT protein in bio-degumming of ramie fibers, and may provide important scientific and technological basis for targeted constructing engineering strains in degumming of ramie fibers.

The aging population has brought increased attention to the urgent need to address social isolation and health risks among the elderly. While previous research has established the positive effects of parks in promoting social interaction and health among older adults, further investigation is required to understand the complex relationships between perceptions of the park environment, social interaction, and elderly health. In this study, structural equation modeling (SEM) was employed to examine these relationships, using nine parks in Wuhan as a case study. The findings indicate that social interaction serves as a complete mediator between perceptions of the park environment and elderly health (path coefficients: park environment on social interaction = 0.45, social interaction on health = 0.46, and indirect effect = 0.182). Furthermore, the results of the multi-group SEM analysis revealed that the mediating effect was moderated by the pattern of social interaction (the difference test: the friend companionship group vs. the family companionship group (Z = 1.965 > 1.96)). Notably, family companionship had a significantly stronger positive impact on the health of older adults compared to friend companionship. These findings contribute to our understanding of the mechanisms through which urban parks support the physical and mental well-being of the elderly and provide a scientific foundation for optimizing urban park environments.

Urban wetlands represent a significant ecosystem type within urban landscapes. The quantitative assessment of their ecological service value holds great significance in guiding and improving the urban habitat. However, due to the insufficient spatial resolution of traditional low-to-medium resolution remote sensing imagery for surface monitoring, previous studies have conducted relatively limited research on the ecosystem services of urban wetlands. In this paper, based on multi-source data including multi-scale remote sensing data, a spatial-temporal fusion model and multiple ecological parameter inversion models were employed to invert three key ecological parameters at high spatial resolution, thereby assessing the ecosystem service values (ESVs) of urban wetlands. Taking the East Lake Scenic Area (ELSA) in Wuhan as an example, the dynamics of its ecosystem services’ value components were comparatively analyzed. The results indicate that, while the total value of ecosystem services declined slightly in 2015 compared to 2011, there was a notable increase in their value to CNY 3.219 billion by 2019, which represents a doubling of the total value relative to 2011. This trend could be primarily attributed to a significant rise in cultural services within the region. Specifically, the value of tourism services reached CNY 2.090 billion in 2019, representing a threefold increase compared to 2011. This demonstrates that ecosystem services in the ELSA have been significantly optimized and enhanced through associated ecological projects. Further research should investigate the mechanisms by which urbanization affects these crucial ecosystem services, particularly the characterization of cultural services in urban wetlands, and develop more effective strategies to enhance urban resilience and sustainable development.

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.

Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan as a case study and focuses on the spatial agglomeration distribution of urban elements. Point of Interest (POI) data from 2017 to 2021 were collected, and the Block2Vec model was employed to extract the comprehensive geographic information from various elements within the traffic analysis zones (TAZs). Subsequently, identification and division were carried out to access the level of urban spatial element agglomeration. Finally, the spatial–temporal evolution characteristics of urban aggregated elements in the Wuhan metropolitan development area over five years were compared and analyzed. The results indicate the following: (1) urban elements present an obvious circle structure in their spatial agglomeration, with distinct differences observed among different element types; (2) from 2017 to 2021, the Wuhan urban development zone experienced obvious expansion in urban space; (3) increased agglomeration of spatial elements mainly occurred in the surrounding areas of the city, while some areas in the city center displayed weaker element agglomeration and a reduction in various service facilities. The results demonstrate that the method used in this study could effectively identify the spatial agglomeration distribution of urban elements, as well as accurately distinguishing regions with distinct development characteristics. This approach could provide robust support for optimizing land use and urban spatial planning.

In recent years, with the current access in techniques, studies have significantly advanced the knowledge on meningeal immunity, revealing that the central nervous system (CNS) border acts as an immune landscape. The latest concept of meningeal immune system is a tertiary structure, which is a comprehensive overview of the meningeal immune system from macro to micro. We comprehensively reviewed recent advances in meningeal immunity, particularly the new understanding of the dural sinus and meningeal lymphatics. Moreover, based on the clues from the meningeal immunity, new insights were proposed into the dural arteriovenous fistula (DAVF) pathology, aiming to provide novel ideas for DAVF understanding.

Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China’s technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises’ websites in 2022. It explores the network’s inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure.

Cerebral cavernous malformation (CCM) is a type of vascular anomaly that arises due to the dyshomeostasis of brain capillary networks. In the past two decades, many advances have been made in this research field. Notably, as a more reasonable current view, the CCM lesions should be attributed to the results of a great number of additional events related to the homeostasis disorder of the endothelial cell. Indeed, one of the most fascinating concerns in the research field is the inflammatory perturbation in the immune microenvironment, which would affect the disease progression as well as the patients’ outcomes. In this work, we focused on this topic, and underlined the immune-related factors’ contribution to the CCM pathologic progression.

Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the impact of the built environment and fail to consider the equal importance of poverty indicators. The emerging multi-source big data provide new opportunities for accurately measuring and monitoring urban poverty. This study aims to map urban poverty spatial at a fine scale by using multi-source big data, including social sensing and remote sensing data. The urban core of Zhengzhou is selected as the study area. The characteristics of the community’s living environment are quantified by accessibility, block vitality, per unit rent, public service infrastructure, and socio-economic factors. The urban poverty spatial index (SI) model is constructed by using the multiplier index of the factors. The SOM clustering method is employed to identify urban poverty space based on the developed SI. The performance of the proposed SI model is evaluated at the neighborhood scale. The results show that the urban poverty spatial measurement method based on multi-source big data can capture spatial patterns of typical urban poverty with relatively high accuracy. Compared with the urban poverty space measured based on remote sensing data, it considers the built environment and socio-economic factors in the identification of the inner city poverty space, and avoids being affected by the texture information of the physical surface of the residential area and the external structure of the buildings. Overall, this study can provide a comprehensive, cost-effective, and efficient method for the refined management of urban poverty space and the improvement of built environment quality.

The micro-movement feature is recognized as one of the practical features of space target recognition in academic circles. The separation of the micro-Doppler curve of the scattering center is the key to feature extraction and parameter estimation, which depends on the time–frequency analysis method. The existing techniques have low separation accuracy and adaptability when there are overlap and noise in the time–frequency domain. This paper proposes a micro-Doppler feature extraction algorithm of a space target based on the modified synchro-reassigning transform (MSRT) and ridge segment linking. The MSRT can eliminate repeated assignment problems, has more accurate micro-Doppler frequency estimates than the synchro-reassigning transform, and has lower computational complexity than second-order synchronous compression and synchronous extraction transforms. The re-linking of the ridge realizes the correct connection of the micro-Doppler curves of each scattering center. The simulation data and the electromagnetic calculation data verify the method’s effectiveness.

The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing.

The COVID-19 pandemic has changed and influenced people’s attitudes and behaviors toward visiting green spaces. This paper aims to explore the association between residents’ health and urban green spaces (UGS) through an in-depth study of changes in residents’ use of UGS under the influence of the COVID-19 pandemic. The Wuhan East Lake Greenway Park was selected as the location for the field survey and in-depth interviews. At the same time, an online survey was also conducted (total number = 302) regarding participants’ physical and mental health and their attitude and behavior toward the UGS. A paired sample t-test and binary logistic regression were performed to investigate the association between participants’ health and UGS during COVID-19. The results show that: (1) the COVID-19 pandemic has primarily changed the leisure patterns of parks, with potential impacts on the physical and mental health of participants; (2) the purpose, frequency, timing, and preferred areas of participants’ park visits have changed to varying degrees after the pandemic, highlighting the important role and benefits of UGSs; (3) the physical and mental health of participants and urban development issues reflected by UGS use are prominent. This study reveals that awareness of the construction and protection of UGSs is an important prerequisite for ensuring the health of urban residents.

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house prices is not yet clear. This study takes transportation analysis zone of Wuhan city as the spatial unit, measure the public facilities accessibility of schools, hospitals, green space, and public transit stations with four kinds of accessibility models such as the nearest distance, real time travel cost, kernel density, and two step floating catchment area (2SFCA), and explores the multiscale effect of public services accessibility on house prices with multiscale geographically weighted regression model. The results show that the differentiated scale effect not only exists among different public facility accessibilities, but also exists in different accessibility models of the same sort of facility. The article also suggests that different facilities should adopt its appropriate accessibility model. This study provides insights into spatial heterogeneity of urban public service facilities accessibility, which will benefit decision making in equal accessibility planning and policy formulation for the layout of urban service facilities.

The efficient use of public space in affordable housing is of great significance to the physical and mental health of low-income and aging residents. Previous studies have evaluated the layout and quality of public space in residential areas based on residents’ subjective satisfaction, however, there still lack studies exploring residents’ behavior patterns and the use of public spaces based on objective measurement standards. Therefore, this paper selected the public space in the large affordable housing areas in the suburbs as the research object and used social network analysis (SNA) to objectively evaluate the network density, clustering coefficient and small-world value of the public space in affordable housing from the perspective of the physical spatial network of the built public space. Based on the network structure characteristics of existing public spaces, this paper further explores the relationship between the frequency of public space use in and the characteristics of nodes’ social networks and their own attributes, and the influence of public space layout structure on the behavioral patterns of affordable housing residents. This paper puts forward proposals for the renovation and optimization of public space according to the behavioral preferences of affordable housing residents, so as to complete the network of public space, promote the interaction and communication of residents in the residential area, enhance the residents’ experience of using public space and improve the living standard of residents in the residential area.

Much affordable housing has poor accessibility to external urban public space facilities because of its suburb location, which makes the residents’ daily life and social activities mainly depend on the internal public space of the community. Such affordable housing needs urgent upgrading of the internal public space based on the thorough understanding of the low-income residents’ demand ranking. The internal spaces’ transformation will significantly improve the living environment and the quality of residents’ life, and it also provides a way to promote social equity and sustainable urban development. By using the multi-source data and the two-step floating catchment area method, this paper selects typical affordable housing, which has poor accessibility to external urban public space, as our case study. After establishing the evaluation index system, IPA (Importance and Performance Analysis) is used to calculate the quadrant value of each index so as to clarify the upgrading urgency indexes from the residents’ demand for internal public space of affordable housing. Studies have shown that tables, chairs and pavilions, pedestrian systems, retail commercial facilities, medical and health facilities, and recreational space have the strongest urgency for upgrading; fitness facilities, exercise space, barrier-free access, guidance signs and parking lot design are the next most urgent indexes; Recreational facilities, entrances/exits of the residential area, green space in front of t residential buildings, and cultural facilities all have general urgency for upgrading.

Companionship is the most important social support factor in physical activities, but the influence of companionship on the daily physical activities of Chinese people in the square is not clear. The ordered logistic regression was conducted to identify the companionship and physical activities associated with the physical and mental health of residents (n = 196). The results show that companionship has direct and indirect effects on mental health, and companionship acts on physical health through physical activity in public squares. Our research understands the use of public open space (POS) from the perspective of companionship and provides a new perspective for improving the sociality of POS design.

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Urban functional regions are essential information in parsing urban spatial structure. The rapid and accurate identification of urban functional regions is important for improving urban planning and management. Thanks to its low cost and fast data update characteristics, the Point of Interest (POI) is one of the most common types of open access data. It mainly identifies urban functional regions by analyzing the potential correlation between POI data and the regions. Even though this is an important manifestation of the functional region, the spatial correlation between regions is rarely considered in previous studies. In order to extract the spatial semantic information among regions, a new model, called the Block2vec, is proposed by using the idea of the Skip-gram framework. The Block2vec model maps the spatial correlation between the POIs, as well as the regions, to a high-dimensional vector, in which classification of urban functional regions can be better performed. The results from cluster analysis showed that the high-dimensional vector extracted can well distinguish the regions with different functions. The random forests classification result (Overall accuracy = 0.7186, Kappa = 0.6429) illustrated the effectiveness of the proposed method. This study also verified the potential of the sentence embedding model in the semantic information extraction of POIs.

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly.

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

The natural city, which is essential to understand urban physical scale and identify urban sprawling in urban studies, represents the urban functional boundaries of the city defined by human activities rather than the administrative boundaries. Most studies tend to utilize physical environment data such as street networks and remote sensing data to delimitate the natural city, however, such data may not match the real distribution of human activity density in the new cities or even ghost cities in China. This paper suggests aggregating the natural city boundary from the service area polygons of points of interest based on Reilly’s Law of Retail Gravitation and the maximum entropy method, since most points of interests provide services for surrounding communities, reflecting the vitality in a bottom-up way. The results indicate that the natural city defined by points of interests shows a high resolution and accuracy, providing a method to define the natural city with POIs.

Imatinib mesylate (Gleevec) inhibits Abl1, c-Kit, and related protein tyrosine kinases (PTKs) and serves as a therapeutic for chronic myelogenous leukemia and gastrointestinal stromal tumors. Imatinib also has efficacy against various pathogens, including pathogenic mycobacteria, where it decreases bacterial load in mice, albeit at doses below those used for treating cancer. We report that imatinib at such low doses unexpectedly induces differentiation of hematopoietic stem cells and progenitors in the bone marrow, augments myelopoiesis but not lymphopoiesis, and increases numbers of myeloid cells in blood and spleen. Whereas progenitor differentiation relies on partial inhibition of c-Kit by imatinib, lineage commitment depends upon inhibition of other PTKs. Thus, imatinib mimics "emergency hematopoiesis," a physiological innate immune response to infection. Increasing neutrophil numbers by adoptive transfer sufficed to reduce mycobacterial load, and imatinib reduced bacterial load of Franciscella spp., which do not utilize imatinib-sensitive PTKs for pathogenesis. Thus, potentiation of the immune response by imatinib at low doses may facilitate clearance of diverse microbial pathogens.

AZD6244 and MK2206 are targeted small-molecule drugs that inhibit MEK and AKT respectively. The efficacy of this combination in lung cancer is unknown. Our previous work showed the importance of activated AKT in mediating resistance of non-small cell lung cancer (NSCLC) to AZD6244. Thus we hypothesized that dual inhibition of both downstream MEK and AKT pathways would induce synergistic antitumor activity. In this study, we evaluated the efficacy of AZD6244 and MK2206 individually on a large panel of lung cancer cell lines. Then, we treated 28 human lung cancer cell lines with a combination of AZD6244 and MK2206 at clinically applicable drug molar ratios. The AZD6244-MK2206 combination therapy resulted in a synergistic effect on inhibition of lung cancer cell growth compared to the results of single drug treatment alone. MK2206 enhanced AZD6244-induced Bim overexpression and apoptosis in A549 and H157 cells. When we tested the combination of AZD6244 and MK2206 at ratios of 8∶1, 4∶1, 2∶1, and 1∶8, we found that the synergistic effect of the combination therapy was ratio-dependent. At ratios of 8∶1, 4∶1, and 2∶1, the drug combination consistently demonstrated synergy, whereas decreasing the ratio to 1∶8 resulted in a loss of synergy and produced an additive or antagonistic effect in most cell lines. Furthermore, the AZD6244-MK2206 combination therapy showed synergy in the suppression of A549 and H157 xenograft tumor growth and increased mean animal survival time. The AZD6244-MK2206 combination therapy resulted in effective inhibition of both p-ERK and p-AKT expression in tumor tissue. In addition, a significant increase of apoptosis was detected in tumor tissue from mice treated with AZD6244-MK2206 compared with that from the single agent treated mice. Our study suggests that the combination of AZD6244 and MK2206 has a significant synergistic effect on tumor growth in vitro and in vivo and leads to increased survival rates in mice bearing highly aggressive human lung tumors.