Predicting residual friction angle of lunar regolith based on Chang'e-5 lunar samples.

With the rapid development of human lunar exploration projects, the lunar base establishment and resource utilization are on the way, and hence it is urgent and significant to reasonably predict engineering properties of the lunar regolith, which remains to be unclear due to limited lunar samples currently accessible for geotechnical tests. In this contribution, we aim to address this outstanding challenge from the perspective of granular material mechanics. To this end, the 3D multi-aspect geometrical characteristics and mechanical properties of Chang'e-5 lunar samples are for the first time evaluated with a series of non-destructive microscopic tests. Based on the measured particle surface roughness and Young's modulus, the interparticle friction coefficients of lunar regolith particles are well predicted through an experimental fitting approach using previously published data on terrestrial geomaterials or engineering materials. Then the residual friction angle of the lunar regolith under low confining pressure is predicted as 53° to 56° according to the particle overall regularity and interparticle friction coefficients of Chang'e-5 lunar samples. The presented results provide a novel cross-scale method to predict engineering properties of the lunar regolith from particle scale information to serve for the future lunar surface engineering construction.

Regulation intensity, environmental disclosure, and cost of capital: evidence from listed firms in China.

While theoretical deductions propose that environmental disclosure helps reduce cost of capitals, this conclusion is not always supported in empirical investigations and many factors moderate this connection. In this paper, we re-examine the issue against a unique background, say in the new development stage of China that pursues a balance between economic growth and environmental protection. Specifically, we build an environmental disclosure index and relate it with the costs of equity and debt capitals. We find the disclosure does not significantly contribute to the variation in either cost, when the relationship is estimated in pooled samples. However, when regulation intensity is involved, the puzzle is resolve. In the debt market, we find institutional investors generally devalues environmental disclosure but appreciate disclosure by essentially polluting firms. In contrast, retail investors in the stock market generally respond to environment disclosure in a positive way, but the disclosure by traditionally recognized polluting firms leads to increase in the cost of equity capital. Our work features important heterogeneity between debt and equity markets, and also the role of regulation intensity in moderating the connection.

Mechanism analysis of efficient degradation of carbamazepine by chalcopyrite-activated persulfate.

In this study, natural chalcopyrite (NCP) was used to activate peroxymonosulfate (PMS) to degrade carbamazepine (CBZ) oxidatively. Before and after the NCP reaction, the physical and chemical properties were characterized by SEM-EDS, XRD, XPS, XRF, and VSM. The effects of the amount of NCP and PMS, the initial pH value, and the reaction temperature on the catalytic performance of NCP were systematically studied. The research results show that the degradation efficiency of the NCP/PMS system for CBZ can reach 82.34% under the optimal reaction conditions, and the degradation process follows a pseudo-second-order kinetic model. The results of the radical quenching experiment and EPR analysis show that the active species in the system are OH·, SO4-·, and 1O2, of which SO4-· is the main active species. In addition, this study shows that the NCP/PMS system can degrade CBZ with high efficiency of 90.73% only with the assistance of 0.15 g/L Fe0. This study determined the optimal reaction conditions for natural chalcopyrite to activate PMS to degrade CBZ and clarified the activation mechanism, which broadened the application of natural ores in the field of water treatment.

TPD: a web tool for tipping-point detection based on dynamic network biomarker.

Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify 'dark' genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network.

BACKGROUND: Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. METHODS: By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study. RESULTS: The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.

Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker.

BACKGROUND: The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. RESULTS: By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits. CONCLUSIONS: The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons.

1H NMR study of the magnetic properties and electronic structure of the hydroxide complex of substrate-bound heme oxygenase from Neisseria meningitidis: influence of the axial water deprotonation on the distal H-bond network.

The substrate and active site residues of the low-spin hydroxide complex of the protohemin complex of Neisseria meningitidis heme oxygenase (NmHO) have been assigned by saturation transfer between the hydroxide and previously characterized aquo complex. The available dipolar shifts allowed the quantitation of both the orientation and anisotropy of the paramagnetic susceptibility tensor. The resulting positive sign, and reduced magnitude of the axial anisotropy relative to the cyanide complex, dictate that the orbital ground state is the conventional "d(pi)" (d(2)(xy)(d(xz), d(yz))(3)); and not the unusual "d(xy)" (d(2)(xz)d(2)(yz)d(xy)) orbital ground state reported for the hydroxide complex of the homologous heme oxygenase (HO) from Pseudomonas aeruginosa (Caignan, G.; Deshmukh, R.; Zeng, Y.; Wilks, A.; Bunce, R. A.; Rivera, M. J. Am. Chem. Soc. 2003, 125, 11842-11852) and proposed as a signature of the HO distal cavity. The conservation of slow labile proton exchange with solvent from pH 7.0 to 10.8 confirms the extraordinary dynamic stability of NmHO complexes. Comparison of the diamagnetic contribution to the labile proton chemical shifts in the aquo and hydroxide complexes reveals strongly conserved bond strengths in the distal H-bond network, with the exception of the distal His53 N(epsilon)(1)H. The iron-ligated water is linked to His53 primarily by a pair of nonligated, ordered water molecules that transmit the conversion of the ligated H-bond donor (H(2)O) to a H-bond acceptor (OH(-)), thereby increasing the H-bond donor strength of the His53 side chain.