Molecular recognition of trehalose and trehalose analogues by Mycobacterium tuberculosis LpqY-SugABC.

Trehalose plays a crucial role in the survival and virulence of the deadly human pathogen Mycobacterium tuberculosis (Mtb). The type I ATP-binding cassette (ABC) transporter LpqY-SugABC is the sole pathway for trehalose to enter Mtb. The substrate-binding protein, LpqY, which forms a stable complex with the translocator SugABC, recognizes and captures trehalose and its analogues in the periplasmic space, but the precise molecular mechanism for this process is still not well understood. This study reports a 3.02-Å cryoelectron microscopy structure of trehalose-bound Mtb LpqY-SugABC in the pretranslocation state, a crystal structure of Mtb LpqY in a closed form with trehalose bound and five crystal structures of Mtb LpqY in complex with different trehalose analogues. These structures, accompanied by substrate-stimulated ATPase activity data, reveal how LpqY recognizes and binds trehalose and its analogues, and highlight the flexibility in the substrate binding pocket of LpqY. These data provide critical insights into the design of trehalose analogues that could serve as potential molecular probe tools or as anti-TB drugs.

Microwave-based soil moisture improves estimates of vegetation response to drought in China.

The increased frequency and severity of drought has heightened concerns over the risk of hydraulic vegetative stress and the premature mortality of ecosystems globally. Unfortunately, most land surface models (LSMs) continue to underestimate ecosystem resilience to drought - which degrades the credibility of model-predicted ecohydrological responses to climate change. This study investigates the response of vegetation gross productivity to water-stress conditions using microwave-based vegetation optical depth (VOD) and soil moisture retrievals. Based on the estimated isohydric/anisohydric spectrum, we find that vegetation at isohydric state exhibits a larger decrease in gross primary productivity and higher water use efficiency than anisohydric vegetation due to their more rigorous stomatal control and higher tolerance of carbon starvation risk. In addition, the introduction of microwave soil moisture improves the accuracy of isohydricity/anisohydricity estimates compared to those obtained using microwave VOD alone (i.e., increases their Spearman rank correlation versus the benchmark of Global Biodiversity Information Facility dataset from 0.12 to 0.63). Results of this study provide clear justification for the use of microwave-based soil moisture retrievals to enhance stomatal conductance parameterization within LSMs.

Two-dimensional semiconductor materials with high stability and electron mobility in group-11 chalcogenide compounds: MNX (M = Cu, Ag, Au; N = Cu, Ag, Au; X = S, Se, Te; M ≠ N).

It is still an urgent task to find new two-dimensional (2D) semiconductor materials with a suitable band gap, high stability and high mobility for the applications of next generation electronic devices. Based on first-principles calculations, we report a new class of 2D group-11-chalcogenide trielement monolayers (MNX, where M = Cu, Ag, Au; N = Cu, Ag, Au; X = S, Se, Te; M ≠ N) with a wide band gap, excellent stability (dynamic stability, thermodynamic stability and environmental stability) and high mobility. At the mixed density functional level, the energy band gap extends from 0.61 eV to 2.65 eV, covering the ultraviolet-A and visible light regions, which is critical for a broadband optical response. For δ-MNX monolayers, the carrier mobility is as high as 104 cm2 V-1 s-1 at room temperature. In particular, the mobility of δ-AgAuS is as high as 6.94 × 104 cm2 V-1 s-1, which is of great research significance for the application of electronic devices in the future. Based on the above advantages, group-11 chalcogenide MNX monomolecular films have broad prospects in the field of nanoelectronics and optoelectronics in the future.

First-Principles Study of Hydrogen Storage of Sc-Modified Semiconductor Covalent Organic Framework-1.

At present, the development of new carbon-based nanoporous materials with semiconductor properties and high hydrogen storage capacity has become a research hotspot in the field of hydrogen storage and hydrogen supply. Here, we pioneered the study of the hydrogen storage capacity of a scandium (Sc) atom-modified semiconductor covalent organic framework-1 (COF-1) layer. It was found that the hydrogen storage capacity of the COF-1 structure was significantly enhanced after the modification of the Sc atom. We found that each Sc atom of the modified COF-1 structure can stably adsorb up to four H2 molecules, and the average adsorption energy of the four hydrogen molecules is -0.284 eV/H2. Six Sc atoms are stably adsorbed most bilaterally on the cell of the COF-1 unit, which can adsorb 24 H2 molecules in total. In addition, we have further studied the adsorption and desorption behaviors of H2 molecules on the 6Sc-COF-1 surface at 300 and 400 K, respectively. It can be found that each Sc atom of the COF-1 unit cell can stably adsorb three H2 molecules with a hydrogen storage performance of 5.23 wt % at 300 K, which is higher than those of lithium-modified phosphorene (4.4 wt %) and lithium-substituted BHNH sheets (3.16 wt %). At 400 K, all of the adsorbed H2 molecules are released. This confirms the excellent reversibility of 6Sc-COF-1 in hydrogen storage performance. This research has great significance in the application of fuel cells, surpassing traditional hydrogen storage materials.

Synthetic Homogeneous Glycoforms of the SARS-CoV-2 Spike Receptor-Binding Domain Reveals Different Binding Profiles of Monoclonal Antibodies.

SARS-CoV-2 attaches to its host receptor, angiotensin-converting enzyme 2 (ACE2), via the receptor-binding domain (RBD) of the spike protein. The RBD glycoprotein is a critical target for the development of neutralizing antibodies and vaccines against SARS-CoV-2. However, the high heterogeneity of RBD glycoforms may lead to an incomplete neutralization effect and impact the immunogenic integrity of RBD-based vaccines. Investigating the role of different carbohydrate domains is of paramount importance. Unfortunately, there is no viable method for preparing RBD glycoproteins with structurally defined glycans. Herein we describe a highly efficient and scalable strategy for the preparation of six glycosylated RBDs bearing defined structure glycoforms at T323, N331, and N343. A combination of modern oligosaccharide, peptide synthesis and recombinant protein engineering provides a robust route to decipher carbohydrate structure-function relationships.

Depth-to-bedrock map of China at a spatial resolution of 100 meters.

Depth to bedrock influences or controls many of the Earth's physical and chemical processes. It plays important roles in soil science, geology, hydrology, land surface processes, civil engineering, and other related fields. However, information about depth to bedrock in China is very deficient, and there is no independent map of depth to bedrock in China currently. This paper describes the materials and methods to produce high-resolution (100 m) depth-to-bedrock maps of China. For different research and application needs, two sets of data are provided for users. One is the prediction by the ensemble of the random forests and gradient boosting tree models, and the other is the prediction and the uncertainty of prediction based on quantile regression forests model. In comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy and more spatial details. These data sets can provide more accurate information for Earth system research compared with previous depth-to-bedrock maps.

Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios.

To enhance the reality of Connected and Autonomous Vehicles (CAVs) kinematic simulation scenarios and to guarantee the accuracy and reliability of the verification, a four-layer CAVs kinematic simulation framework, which is composed with road network layer, vehicle operating layer, uncertainties modelling layer and demonstrating layer, is proposed in this paper. Properties of the intersections are defined to describe the road network. A target position based vehicle position updating method is designed to simulate such vehicle behaviors as lane changing and turning. Vehicle kinematic models are implemented to maintain the status of the vehicles when they are moving towards the target position. Priorities for individual vehicle control are authorized for different layers. Operation mechanisms of CAVs uncertainties, which are defined as position error and communication delay in this paper, are implemented in the simulation to enhance the reality of the simulation. A simulation platform is developed based on the proposed methodology. A comparison of simulated and theoretical vehicle delay has been analyzed to prove the validity and the creditability of the platform. The scenario of rear-end collision avoidance is conducted to verify the uncertainties operating mechanisms, and a slot-based intersections (SIs) control strategy is realized and verified in the simulation platform to show the supports of the platform to CAVs kinematic simulation and verification.

SoilGrids250m: Global gridded soil information based on machine learning.

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Soil diversity as affected by land use in China: consequences for soil protection.

Rapid land-use change in recent decades in China and its impact on terrestrial biodiversity have been widely studied, particularly at local and regional scales. However, the effect of land-use change on the diversity of soils that support the terrestrial biological system has rarely been studied. Here, we report the first effort to assess the impact of land-use change on soil diversity for the entire nation of China. Soil diversity and land-use effects were analyzed spatially in grids and provinces. The land-use effects on different soils were uneven. Anthropogenic soils occupied approximately 12% of the total soil area, which had already replaced the original natural soils. About 7.5% of the natural soil classes in China were in danger of substantial loss, due to the disturbance of agriculture and construction. More than 80% of the endangered soils were unprotected due to the overlook of soil diversity. The protection of soil diversity should be integrated into future conservation activities.

Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

[Influence factor for prediction of air-dry density of Eucalyptus pellita by near infrared spectroscopy].

Near infrared spectroscopy(NIR)technique was applied to compare the influence factors of Eucalyptus pellita's air-dry density. Air-dry density of eucalypt wood was tested by direct measurement After collecting the near infrared reflectance spectra of samples in different section and with different thickness, moisture content and roughness, the NIR spectra were preprocessed with the second-derivative and the regression models were built in certain spectra. The calibration models were established using 50-140 samples with the partial least squares method and validated with external validation method. The results showed that the predicted results were influenced by sample's section, thickness, roughness and moisture content. The best near infrared spectroscopy prediction model was built under the condition of transverse section, 2-5 mm thickness, 12% moisture content and meticulous roughness of wood.