On Monday, May 12, 2008, a devastating mega-earthquake of magnitude 8.0 struck the Wenchuan area, northwestern Sichuan Province, China. The focal mechanism of the earthquake was successive massive rock fracturing 15 km in depth at Yingxiu. Seismic analysis confirms that the major shock occurred on the Beichuan-Yingxiu Fault and that aftershocks rapidly extended in a straight northeast-southeast direction along the Longmenshan Fault zone. Fatalities approaching a total of 15,000 occurred, with a significant number resulting from four types of seismically triggered geohazards-rock avalanches and landslides, landslide-dammed lakes (''earthquake lakes''), and debris flows. China Geological Survey has identified 4,970 potentially risky sites, 1,701 landslides, 1,844 rock avalanches, 515 debris flows, and 1,093 unstable slopes. Rock avalanches and landslides caused many fatalities directly and disrupted the transportation system, extensively disrupting rescue efforts and thereby causing additional fatalities. Landslide-dammed lakes not only flooded human habitats in upstream areas but also posed threats to potentially inundated downstream areas with large populations. Debris flows become the most remarkable geohazards featured by increasing number, high frequency, and low triggering rainfall. Earthquake-triggered geohazards sequentially induced and transformed to additional hazards. For example, debris flows occurred on rock avalanches and landslides, followed by landslide-dammed lakes, and then by additional debris flows and breakouts of the landslide-dammed lakes and downstream flooding. Earthquake-induced geohazards occurred mainly along the fault zone and decreased sharply with distance from the fault. It can be anticipated that post-earthquake geohazards, particularly for debris flows, will continue for 5-10 years and even for as long as 20 years. An integrated strategy of continuing emergency response and economic reconstruction is required. The lesson from Wenchuan Earthquake is that the resulted geohazards may appear in large number in active fault regions. A plan for geohazard prevention in the earthquake-active mountainous areas is needed in advance.

Objectives Although myeloid-derived suppressive cells (MDSCs) have been linked to T-cell tolerance, their role in autoimmune rheumatoid arthritis (RA) remains elusive. Here we investigate the potential association of MDSCs with the disease pathogenesis using a preclinical model of RA and specimen collected from RA patients. Methods The frequency of MDSCs in blood, lymphoid tissues, inflamed paws, or synovial fluid and their association with disease severity, tissue inflammation, and the levels of pathogenic T-helper (Th) 17 cells was examined in arthritic mice or in patients with RA (n=35) and osteoarthritis (OA, n=15). The MDSCs in arthritic mice were also characterized for their phenotype, inflammation status, T-cell suppressive activity, and their capacity of pro-Th17 cell differentiation. The involvement of MDSCs in the disease pathology and a Th17 response was examined by adoptive transfer or antibody depletion of MDSCs in arthritic mice or by co-culturing mouse or human MDSCs with naïve CD4+ T cells under Th17-polarizing conditions. Results MDSCs significantly expanded in arthritic mice and in RA patients, which correlated positively with disease severity and an inflammatory Th17 response. While displaying T-cell suppressive activity, MDSCs from arthritic mice produced high levels of inflammatory cytokines (e.g., IL-1β, TNF-α). Both mouse and human MDSCs promoted Th17 cell polarization ex vivo. Transfer of MDSCs facilitated disease progression, whereas their elimination in arthritic mice ameliorates disease symptoms concomitant with reduction of IL-17A/Th17 cells. Conclusions Our studies suggest that proinflammatory MDSCs with their capacity to drive Th17 cell differentiation may be a critical pathogenic factor in autoimmune arthritis.

Characterization of Nanocrystals: UV-vis absorption spectra were recorded on a Shimadzu UV-2450 double-beam recording spectrophotometer using 1 cm quartz cells. Fluorescence spectra were obtained on a Shimadzu RF-5301 with a resolution of 1.0 nm. Transmission electron microscopy (TEM) examination was carried out using a JEOL-1011 electron microscope using an accelerating voltage of 100 kV. Samples for TEM observation were prepared by dropping 10 lL of a dilute toluene solution onto 400-mesh carbon-coated copper grids. The particle size and size-distribution diagrams were obtained from measuring more than two hundred individual TiO 2 nanoparticles carefully. The X-ray powder diffraction (XRD) investigation was performed on a Rigaku D/MAX-2500 using Cu Ka radiation at 50 kV and 250 mA in the range of 20±80 by step scanning with a step size of 0.02. The crystallite size was estimated using Scherrer's method. The Raman spectra were measured at room temperature with a Renishaw 2000 Raman spectrometer equipped with a laser beam of 514 nm and a charge-coupled device (CCD) detector. XRD and Raman samples were prepared by the evaporation of one drop of a concentrated nanocrystal solution on a glass plate.

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In the present paper, we successfully prepared PbS microcrystals with a flower-shaped structure in a simple aqueous system using microwave irradiation and systematically researched various factors affecting the growth of the flower-shaped PbS crystals. Experimental results indicated that the change of the molar ratio of Pb 2+ / S 2 O 3 2could significantly influence the morphology of the product while keeping the other experimental conditions constant: with the decrease of the molar ratio of Pb 2+ /S 2 O 3 2from 1:1 to 1:4, the shapes of the products varied from rod to cuboid to flower. Changing the irradiation power in a small range (10-40%) had little effect on the shape of the product. When thiourea and thioacetamide were used as sulfur ion sources instead of Na 2 S 2 O 3 , the shapes of the as-obtained PbS crystals were dendritic and platelike, respectively. The above results further confirmed that Na 2 S 2 O 3 played an important role in the formation process of PbS crystals with the flower-shaped structure. At the same time, transmission electron spectroscopy observations showed that the counterions for Pb 2+ could influence the shape of the product: when Pb(NO 3 ) 2 and PbCl 2 were used as the lead ion sources, cuboid PbS crystals were produced; while when PbSO 4 was used as the lead ion source, the flower-shaped PbS crystals were obtained. A growth mechanism of the flower-shaped PbS crystals was proposed.

the intrinsic characteristics of nano materials, nanozymes have potential widespread applications within the fields of biosensing, [2] antibacterials, [3] environ mental pollution, [4] and disease therapy. [5] Since our discovery of ferromagnetic nanoparticles with intrinsic peroxidase like activity in 2007, [6] there have been thousands of publications that reported on enzymemimicking activities of nanoma terials, which involve at least six classes of enzymemimicry. [7] According to the litera ture, different nanomaterials can intrinsi cally possess the same enzymemimicking activities, [8] and certain types of nano materials tend to exhibit differential enzymelike catalytic activities. [9] The het erogeneous results reveal the complexity and diversity of nanozymes in terms of catalytic capacity. [10] Indeed, the particle property relationship of nanozymes is complicated, with a current lack of fun damental understanding. Furthermore, the synthesis of nanozymes with desired characteristics are generally determined by trial and error, and based on intui tion and experience, which are timeconsuming, laborious and resourceintensive.As a branch of artificial intelligence, machine learning aims to develop computational algorithms to infer mathematical An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R 2 (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202201736.