Conformational Stability Analyses of Alpha Subunit I Domain of LFA-1 and Mac-1

β2 integrin of lymphocyte function-associated antigen-1 (LFA-1) or macrophage-1 antigen (Mac-1) binds to their common ligand of intercellular adhesion molecule-1 (ICAM-1) and mediates leukocyte-endothelial cell (EC) adhesions in inflammation cascade. Although the two integrins are known to have distinct functions, the corresponding micro-structural bases remain unclear. Here (steered-)molecular dynamics simulations were employed to elucidate the conformational stability of α subunit I domains of LFA-1 and Mac-1 in different affinity states and relevant I domain-ICAM-1 interaction features. Compared with low affinity (LA) Mac-1, the LA LFA-1 I domain was unstable in the presence or absence of ICAM-1 ligand, stemming from diverse orientations of its α7-helix with different motifs of zipper-like hydrophobic junction between α1- and α7-helices. Meanwhile, spontaneous transition of LFA-1 I domain from LA state to intermediate affinity (IA) state was first visualized. All the LA, IA, and high affinity (HA) states of LFA-1 I domain and HA Mac-1 I domain were able to bind to ICAM-1 ligand effectively, while LA Mac-1 I domain was unfavorable for binding ligand presumably due to the specific orientation of S144 side-chain that capped the MIDAS ion. These results furthered our understanding in correlating the structural bases with their functions of LFA-1 and Mac-1 integrins from the viewpoint of I domain conformational stability and of the characteristics of I domain-ICAM-1 interactions

Mechanically Regulated Outside-In Activation of an I-Domain-Containing Integrin

Integrins are heterodimeric transmembrane proteins that mediate cellular adhesion and bidirectional mechanotransductions through their conformational allostery. The allosteric pathway of an I-domain-containing integrin remains unclear because of its complexity and lack of effective experiments. For a typical I-domain-containing integrin alpha(x)beta(2), molecular dynamics simulations were employed here to investigate the conformational dynamics in the first two steps of outside-in activation, the bindings of both the external and internal ligands. Results showed that the internal ligand binding is a prerequisite to the allosteric transmission from the alpha- to beta-subunits and the exertion of external force to integrin-ligand complex. The opening state of alpha l domain with downward movement and lower half unfolding of alpha(7)-helix ensures the stable intersubunit conformational transmission through external ligand binding first and internal ligand binding later. Reverse binding order induces a, to our knowledge, novel but unstable swingout of beta-subunit Hybrid domain with the retained close states of both alpha l and beta l domains. Prebinding of external ligand greatly facilitates the following internal ligand binding and vice versa. These simulations furthered the under-standing in the outside-in activation of I-domain-containing integrins from the viewpoint of internal allosteric pathways

Non-Linear Response of PM2.5 Pollution to Land Use Change in China

Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 μg/m3 to 31.23 μg/m3 during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m3 in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use

Contribution of the CR domain to P-selectin lectin domain allostery by regulating the orientation of the EGF domain.

The allostery of P-selectin has been studied extensively with a focus on the Lec and EGF domains, whereas the contribution of the CR domain remains unclear. Here, molecular dynamics simulations (MDS) combined with homology modeling were preformed to investigate the impact of the CR domain on P-selectin allostery. The results indicated that the CR domain plays a role in the allosteric dynamics of P-selectin in two ways. First, the CR1 domain tends to stabilize the low affinity of P-selectin during the equilibration processes with the transition inhibition from the S1 to S1' state by restraining the extension of the bent EGF orientation, or with the relaxation acceleration of the S2 state by promoting the bending of the extended EGF orientation. Second, the existence of CR domain increases intramolecular extension prior to complex separation, increasing the time available for the allosteric shift during forced dissociation with a prolonged bond duration. These findings further our understanding of the structure-function relationship of P-selectin with the enriched micro-structural bases of the CR domain

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition

Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies