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Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition. [ABSTRACT FROM AUTHOR]

High-locality landslides are located on slopes at high elevations and are characterized by long sliding distances, large gravitational potential energy, high movement velocities, tremendous kinetic energy, and sudden onset. Thus, they often cause catastrophic damage to human lives and engineering facilities. It is of great significance to identify active high-locality landslides in their early deformational stages and to reveal their deformational rules for effective disaster mitigation. Due to alpine-canyon landforms, Mao County is a representative source of high-locality landslides. This work employs multisource data (geological, terrain, meteorological, ground sensor, and remote sensing data) and time-series InSAR technology to recognize active high-locality landslides in Mao County and to reveal their laws of development. Some new viewpoints are suggested. (1) Nineteen active high-locality landslides are identified by the time-series InSAR technique, of which 7 are newly discovered in this work. All these high-locality landslides possessed good concealment during their early deformational stages. The newly discovered HL-16 landslide featured a large scale and a great slope height, posing a large threat to the surrounding buildings and residents. (2) The high-locality landslides in Mao County were mainly triggered by three factors: earthquakes, precipitation, and road construction. (3) Three typical high-locality landslides that were triggered by different factors are highlighted with their deformational rules under the functions of steep terrain, shattered rocks, fissure-water penetration, precipitation, and road construction. This work may provide clues to the prevention and control of high-locality landslides and can be applied to the determination of active high-locality landslides in other hard-hit areas. [ABSTRACT FROM AUTHOR]

The abundance and distribution of oxides and Mg# (Mg/(Mg ​+ ​Fe) in mole percent) values are important for revealing the petrologic characteristics and evolutionary history of the Moon. In this work, new lunar surface chemistry maps, including Al 2 O 3 , CaO, FeO, MgO, TiO 2 , and Mg# maps, are generated from KAGUYA multiband imager (MI) spectra. These new maps are produced by the machine learning algorithm of the particle swarm optimization-support vector machine (PSO-SVM), and are characterized by relatively high accuracies and a high spatial resolution of 59 ​m/pixel, which are free to some degree from the topographic shadow effect. Moreover, Mg# values across the Moon exhibit an obvious chemical trichotomy. According to this Mg# feature, a new geological partition is suggested, and the lunar surface is divided into three geological units: the PM, SPAM, and FH units. The PM unit features low Mg# values, with an average of 0.55. The PM unit consists of Oceanus Procellarum, Grimaldi Crater, Tsiolkovskiy Crater, and some maria on the nearside and farside (e.g., the Frigoris, Imbrium, Tranquillitatis, Smythii, Crisium, Marginis, Orientale, and Moscoviense Maria). The FH unit possesses the highest Mg# values across the Moon, with an average of 0.70. The FH unit is composed of the Feldspathic Highlands Terrane (FHT), except for some maria (e.g., Orientale, Moscoviense, Australe, Marginis, and Smythii) and some craters (e.g., Tsiolkovskiy and Schickard). The SPAM unit is typical of medium Mg# values with an average of 0.65, and is constituted by the South Pole-Aitken Terrane (SPAT), Mare Australe, and some craters (e.g., the Schickard, Phocylides, Nasmyth, Schiller, and Inghirami Craters). The significant difference in Mg# values among the three geological units highlights the asymmetric magmatic evolution, crustal growth, and mare volcanism in the three units. • New abundance maps of five major oxides across the Moon are generated. • A new Mg# map is generated and exhibits an obvious trichotomy feature. • A new partition of geological units across the Moon is suggested. [ABSTRACT FROM AUTHOR]

Objective To observe the changes in blood glucose in patients with liver cirrhosis and investigate the relationship between glu-cose metabolism and hepatic encephalopathy (HE). Methods Sixty patients with liver cirrhosis (cirrhosis group) and 30 patients without liver cirhosis ( control group ), who were hospitalized in Department of Gastroenterology, Jiading District Central Hospital from August 2008 to August 2010, were included in the study. The general data were collected ; the consciousness of patients was observed ; the physical exam-ination was performed for detecting flapping tremors ; blood samples were collected for measuring blood glucose. The cirhosis group was fur-ther divided into flapping tremor subgroup and non - flapping tremor subgroup. The cirrhosis group and control group were compared in terms of the incidence rate of abnormal glucose metabolism. The patients were divided into normal glucose metabolism group and abnormal glucose metabolism group, and the two groups were compared in terms of the incidence rates of flapping tremors and HE. Results Of the 60 pa-tients in cirrhosis group, 28 had no flapping tremors, and 32 had flapping tremors. The incidence rate of abnormal glucose metabolism was significantly lower in the control group than in the cirrhosis group ( 13. 33% vs 26. 67% ; ?² =2. 058, ? < 0. 05 ). The incidence rates of flapping tremors and HE were 50. 00% and 34. 09% in the normal glucose metabolism group, versus 62. 50% and 37. 50% in the abnormal glucose metabolism group (?>0. 05). Conclusion The patients with liver cirrhosis have abnormal glucose metabolism, but which is not necessarily associated with HE. It suggests that the patients with HE may have impaired glucose utilization in the brain. [ABSTRACT FROM AUTHOR]

Krüppel‐like factor 4 (KLF4) has a tumor suppressor role in the progression of gastric cancer (GC), and inhibition or loss of KLF4 expression was identified in GC. The aim of this study was to explore the new molecular mechanism of KLF4 inactivation in gastric cancer. Herein, we report that Helicobacter pylori infection or Cag pathogenicity island protein A (CagA) gene transduction resulted in KLF4 expression downregulation and promoted gastric epithelial cell and gastric cancel cell proliferation, migration, and colony formation. Mechanistically, we found that CagA gene transduction led to DNA methylation of the KLF4 promoter, an effect that was relevant to the significant downregulation of TET1 expression. Causally, knockdown of TET1 expression decreased KLF4 expression, whereas overexpression of TET1 had the opposite effect. Clinically, we found that KLF4 expression and the 5‐hmC levels were lower in GC cells with H pylori infection than in GC cells without H pylori infection. Thus, our study not only sheds new light on how H pylori infection promotes the progression of GC but also elucidates a novel mechanism of KLF4 inactivation in GC pathogenesis. During pathogenesis, an alteration in the H pylori/CagA‐TET1‐KLF4 signaling pathway plays a critical role, suggesting that this pathway may be a prospective target for gastric carcinoma intervention and therapy. [ABSTRACT FROM AUTHOR]