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Severe regional haze pollution events occurred in eastern and central China in January 2013, which had adverse effects on the environment and public health. Extremely high levels of particulate matter with aerodynamic diameter of 2.5 μm or less (PM2.5) with dominant components of sulfate and nitrate are responsible for the haze pollution. Although heterogeneous chemistry is thought to play an important role in the production of sulfate and nitrate during haze episodes, few studies have comprehensively evaluated the effect of heterogeneous chemistry on haze formation in China by using the 3-D models due to of a lack of treatments for heterogeneous reactions in most climate and chemical transport models. In this work, the WRF-CMAQ model with newly added heterogeneous reactions is applied to East Asia to evaluate the impacts of heterogeneous chemistry and the meteorological anomaly during January 2013 on regional haze formation. As the parameterization of heterogeneous reactions on different types of particles is not well established yet, we arbitrarily selected the uptake coefficients from reactions on dust particles and then conducted several sensitivity runs to find the value that can best match observations. The revised CMAQ with heterogeneous chemistry not only captures the magnitude and temporal variation of sulfate and nitrate, but also reproduces the enhancement of relative contribution of sulfate and nitrate to PM2.5 mass from clean days to polluted haze days. These results indicate the significant role of heterogeneous chemistry in regional haze formation and improve the understanding of the haze formation mechanisms during the January 2013 episode.

Extreme haze episodes repeatedly shrouded Beijing during the winter of 2012–2013, causing major environmental and health problems. To better understand these extreme events, we performed a model-assisted analysis of the hourly observation data of PM2.5 and its major chemical compositions. The synthetic analysis shows that (1) the severe winter haze was driven by stable synoptic meteorological conditions over northeastern China, and not by an abrupt increase in anthropogenic emissions. (2) Secondary species, including organics, sulfate, nitrate, and ammonium, were the major constituents of PM2.5 during this period. (3) Due to the dimming effect of high loading of aerosol particles, gaseous oxidant concentrations decreased significantly, suggesting a reduced production of secondary aerosols through gas-phase reactions. Surprisingly, the observational data reveals an enhanced production rate of secondary aerosols, suggesting an important contribution from other formation pathways, most likely heterogeneous reactions. These reactions appeared to be more efficient in producing secondary inorganics aerosols than organic aerosols resulting in a strongly elevated fraction of inorganics during heavily polluted periods. (4) Moreover, we found that high aerosol concentration was a regional phenomenon. The accumulation process of aerosol particles occurred successively from cities southeast of Beijing. The apparent sharp increase in PM2.5 concentration of up to several hundred μg m−3 per hour recorded in Beijing represented rapid recovery from an interruption to the continuous pollution accumulation over the region, rather than purely local chemical production. This suggests that regional transport of pollutants played an important role during these severe pollution events.

Assessing carbon footprint (CF) of crop production in a whole crop life-cycle could provide insights into the contribution of crop production to climate change and help to identify possible greenhouse gas (GHG) mitigation options. In the current study, data for the major crops of China were collected from the national statistical archive on cultivation area, yield, application rates of fertilizer, pesticide, diesel, plastic film, irrigated water, etc. The CF of direct and indirect carbon emissions associated with or caused by these agricultural inputs was quantified with published emission factors. In general, paddy rice, wheat, maize and soybean of China had mean CFs of 2472, 794, 781 and 222 kg carbon equivalent (CE)/ha, and 0·37, 0·14, 0·12 and 0·10 kg CE/kg product, respectively. For dry crops (i.e. those grown without flooding the fields: wheat, maize and soybean), 0·78 of the total CFs was contributed by nitrogen (N) fertilizer use, including both direct soil nitrous oxide (N2O) emission and indirect emissions from N fertilizer manufacture. Meanwhile, direct methane (CH4) emissions contributed 0·69 on average to the total CFs of flooded paddy rice. Moreover, the difference in N fertilizer application rates explained 0·86–0·93 of the provincial variations of dry crop CFs while that in CH4 emissions could explain 0·85 of the provincial variation of paddy rice CFs. When a 30% reduction in N fertilization was considered, a potential reduction in GHGs of 60 megatonne (Mt) carbon dioxide equivalent from production of these crops was projected. The current work highlights opportunities to gain GHG emission reduction in production of crops associated with good management practices in China.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli 's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery. Synopsis Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery. AlphaFold2‐based molecular docking predictions for 296 Escherichia coli proteins, 218 active antibacterial compounds and 100 inactive compounds predict widespread promiscuity and similar distributions of binding affinities between active and inactive compounds. Quantitative enzymatic inhibition assays for 12 essential E. coli proteins treated with each of the 218 antibacterial compounds confirm extensive promiscuity. The enzymatic inhibition dataset reveals that the performance of the molecular docking model is weak. Rescoring of docking poses using machine learning‐based scoring functions improves model performance. Graphical Abstract Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery.

Traditional structural uncertainty analysis is mainly based on probability models and requires the establishment of accurate parametric probability distribution functions using large numbers of experimental samples. In many actual engineering problems, the probability distributions of some parameters can be established due to sufficient samples available, whereas for some parameters, due to the lack or poor quality of samples, only their variation intervals can be obtained, or their probability distribution types can be determined based on the existing data while some of the distribution parameters such as mean and standard deviation can only be given interval estimations. This thus will constitute an important type of probability-interval hybrid uncertain problem, in which the aleatory and epistemic uncertainties both exist. The probability-interval hybrid uncertainty analysis provides an important mean for reliability analysis and design of many complex structures, and has become one of the research focuses in the field of structural uncertainty analysis over the past decades. This paper reviews the four main research directions in this area, i.e., uncertainty modeling, uncertainty propagation analysis, structural reliability analysis, and reliability-based design optimization. It summarizes the main scientific problems, technical difficulties, and current research status of each direction. Based on the review, this paper also provides an outlook for future research in probability-interval hybrid uncertainty analysis.

Twenty‐three published proxy temperature series over China spanning the last 2000 years were selected for an uncertainty analysis in five climate regions. Results indicated that, although large uncertainties are found for the period prior to the 16th century, high level of consistency were identified in all regions during the recent 500‐years, highlighted by the two cold periods 1620s–1710s and 1800s–1860s, and the warming during the 20th century. The latter started in Tibet, Northwest and Northeast, and migrated to Central East and Southeast. The analysis also indicates that the warming during the 10–14th centuries in some regions might be comparable in magnitude to the warming of the last few decades of the 20th century which was unprecedented within the past 500 years.

Angiogenesis is one of the critical biological elements affecting the development and progression of cancer. Long non-coding RNAs (lncRNAs) are important regulators and aberrantly expressed in various types of human cancer. Our previous studies indicated that lncRNA taurine upregulated 1 (TUG1) implicated in the regulation of blood-tumor barrier permeability; however, its role in glioblastoma angiogenesis still unclear. Here we demonstrated that TUG1 was up-expressed in human glioblastoma tissues and glioblastoma cell lines. Knockdown of TUG1 remarkably suppressed tumor-induced endothelial cell proliferation, migration and tube formation as well as reducing spheroid-based angiogenesis ability in vitro , which are the critical steps for tumor angiogenesis. Besides, knockdown of TUG1 significantly increased the expression of mircroRNA-299 (miR-299), which was down-expressed in glioblastoma tissues and glioblastoma cell lines. Bioinformatics analysis and luciferase reporter assay revealed that TUG1 influenced tumor angiogenesis via directly binding to the miR-299 and there was a reciprocal repression between TUG1 and miR-299 in the same RNA-induced silencing complex. Moreover, knockdown of TUG1 reduced the expression of vascular endothelial growth factor A (VEGFA), which was defined as a functional downstream target of miR-299. In addition, knockdown of TUG1, shown in the in vivo studies, has effects on suppressing tumor growth, reducing tumor microvessel density and decreasing the VEGFA expression by upregulating miR-299 in xenograft glioblastoma model. Overall, the results demonstrated that TUG1 enhances tumor-induced angiogenesis and VEGF expression through inhibiting miR-299. Also, the inhibition of TUG1 could provide a novel therapeutic target for glioblastoma treatment.

Tungsten ditelluride (WTe 2 ) is an atomically layered transition metal dichalcogenide whose physical properties change systematically from monolayer to bilayer and few-layer versions. In this report, we use apertureless scattering-type near-field optical microscopy operating at Terahertz (THz) frequencies and cryogenic temperatures to study the distinct THz range electromagnetic responses of mono-, bi- and trilayer WTe 2 in the same multi-terraced micro-crystal. THz nano-images of monolayer terraces uncovered weakly insulating behavior that is consistent with transport measurements. The near-field signal on bilayer regions shows moderate metallicity with negligible temperature dependence. Subdiffractional THz imaging data together with theoretical calculations involving thermally activated carriers favor the semimetal scenario with Δ ≈ − 10 meV over the semiconductor scenario for bilayer WTe 2 . Also, we observed clear metallic behavior of the near-field signal on trilayer regions. Our data are consistent with the existence of surface plasmon polaritons in the THz range confined to trilayer terraces in our specimens. Finally, data for microcrystals up to 12 layers thick reveal how the response of a few-layer WTe 2 asymptotically approaches the bulk limit. The behaviour of Tungsten ditelluride (WTe2) in few-layer form is not yet fully characterized. Here the authors use a near-field terahertz microscopy technique to observe the electromagnetic responses of WTe2 flakes from one to several layers and to study their semimetallic/ semiconducting behavior.

Simultaneous measurements of particle number size distribution, particle hygroscopic properties, and size-resolved chemical composition were made during the summer of 2014 in Beijing, China. During the measurement period, the mean hygroscopicity parameters (κs) of 50, 100, 150, 200, and 250 nm particles were respectively 0.16    0.07, 0.19    0.06, 0.22    0.06, 0.26    0.07, and 0.28    0.10, showing an increasing trend with increasing particle size. Such size dependency of particle hygroscopicity was similar to that of the inorganic mass fraction in PM1. The hydrophilic mode (hygroscopic growth factor, HGF  >  1.2) was more prominent in growth factor probability density distributions and its dominance of hydrophilic mode became more pronounced with increasing particle size. When PM2.5 mass concentration was greater than 50 μg m−3, the fractions of the hydrophilic mode for 150, 250, and 350 nm particles increased towards 1 as PM2.5 mass concentration increased. This indicates that aged particles dominated during severe pollution periods in the atmosphere of Beijing. Particle hygroscopic growth can be well predicted using high-time-resolution size-resolved chemical composition derived from aerosol mass spectrometer (AMS) measurements using the Zdanovskii–Stokes–Robinson (ZSR) mixing rule. The organic hygroscopicity parameter (κorg) showed a positive correlation with the oxygen to carbon ratio. During the new particle formation event associated with strongly active photochemistry, the hygroscopic growth factor or κ of newly formed particles is greater than for particles with the same sizes not during new particle formation (NPF) periods. A quick transformation from external mixture to internal mixture for pre-existing particles (for example, 250 nm particles) was observed. Such transformations may modify the state of the mixture of pre-existing particles and thus modify properties such as the light absorption coefficient and cloud condensation nuclei activation.

We use principal component regression and partial least squares regression to separately reconstruct a composite series of temperature variations in China, and associated uncertainties, at a decadal resolution over the past 2000 yr. The reconstruction is developed using proxy temperature data with relatively high confidence levels from five regions across China, and using a temperature series from observations by the Chinese Meteorological Administration, covering the period from 1871 to 2000. Relative to the 1851–1950 climatology, our two reconstructions show four warm intervals during AD 1–AD 200, AD 551–AD 760, AD 951–AD 1320, and after AD 1921, and four cold intervals during AD 201–AD 350, AD 441–AD 530, AD 781–AD 950, and AD 1321–AD 1920. The temperatures during AD 981–AD 1100 and AD 1201–AD 1270 are comparable to those of the Present Warm Period, but have an uncertainty of ±0.28 °C to ±0.42 °C at the 95% confidence interval. Temperature variations over China are typically in phase with those of the Northern Hemisphere (NH) after 1000, a period which covers the Medieval Climate Anomaly, the Little Ice Age, and the Present Warm Period. In contrast, a warm period in China during AD 541–AD 740 is not obviously seen in the NH.