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The Beiya skarn gold deposit is located in the eastern Tethyan orogenic belt in western Yunnan province, China. It is one of the largest gold deposits in China, with significant amounts of silver and base metals. To the end of 2014, the estimated resources are 125 million tonnes (Mt) of ore, grading 2.42 g/t Au, 0.48 wt % Cu, 25.5 wt % Fe, 38.85 g/t Ag, 1.24 wt % Pb, and 0.53 wt % Zn. Skarn alteration and mineralization are related to shoshonitic quartz monzonite porphyries that were emplaced in Triassic carbonates (Beiya Formation). Re-Os dating on molybdenite from a skarn orebody indicates an ore-forming age of 36.82 ± 0.48 Ma, which is consistent with previous dating results of the quartz monzonite porphyries. At least two paragenetic stages of skarn minerals and associated sulfides were recognized, with the early stage typified by garnet ± pyroxene, magnetite, and calcite, and the late stage characterized by epidote, amphibole, chlorite, quartz, and calcite, containing up to 70% sulfides (pyrite, chalcopyrite, and minor pyrrhotite). The early skarn is dominated by anhydrous minerals, which were replaced by hydrous minerals formed during the late stage. The garnet in the Beiya deposit is andradite rich (Ad36-97 Gr3-61), and pyroxene is relatively diopside rich (Di8-91 Hd7-89 ). This mineral assemblage indicates an oxidized skarn system, similar to other Au-Cu, Fe-bearing skarn deposits around the world. Fluid inclusions from pyroxene indicate precipitation from high-temperature and high- to moderate-salinity fluids (420°-530°C, 11.1-43.3 wt % NaCl equiv), which probably results from boiling of a moderately saline magmatic fluid. Cooler (180°-365°C) and moderate- to low-salinity fluids (1.6-16.5 wt % NaCl equiv) were trapped in garnet and quartz and are interpreted to be responsible for gold deposition. Chlorite chemistry indicates ore-forming temperatures between 300° and 340°C, in agreement with fluid inclusion data. It appears that gold was transported as chloride complexes under oxidized conditions and was deposited at temperatures of about 300°C, when transport of chloride complexes as gold carriers was less efficient. [ABSTRACT FROM AUTHOR]

The aesthetic perceptions of local residents towards residential landscapes are significant to the protection and sustainable development of regional landscapes, especially in underdeveloped areas with landscape resources of higher aesthetic value. Using Nujiang Lisu Autonomous Prefecture in Yunnan Province as a case study, we conducted questionnaires with 620 local residents and a logistic regression model to determine aesthetic perceptions and factors affecting these perceptions. We found that local residents in Nujiang hold a relatively high perception of their residential landscapes. A majority of respondents, 75.81%, believe their residential landscapes are beautiful. Eight factors were found to influence the aesthetic perception. In direct influential factors, administrative village located in the protected areas and the standard deviation of the elevation around the administrative village have positive influence; distance from administrative village to county town, the ecological environment has changed in recent years, whether tourism development has a negative impact and wish to live in the city have negative influence. In indirect influential factors, both residential house type and have a television at home have positive influence. Based on these results, we discussed relevant policy suggestions to maintain the aesthetic perceptions held by local residents of their residential landscapes and promote the protection and sustainable development of this valuable landscape in Nujiang. [ABSTRACT FROM AUTHOR]

Lung cancer is the most prevalent and observed type of cancer in Xuanwei County, Yunnan, South China. Lung cancer in this area is called Xuanwei lung cancer. However, its pathogenesis remains largely unknown. To date, a number of studies have shown that microRNA (miR)-218 functions as a tumor suppressor in multiple types of cancer. However, the role of miR-218 and its regulatory gene network in Xuanwei lung cancer have yet to be investigated. The current study identified that the expression levels of miR-218 in XWLC-05 cells were markedly lower compared with those in immortalized lung epithelial BEAS-2B cells. The present study also demonstrated that overexpression of miR-218 could decrease cell proliferation, invasion, viability and migration in Xuanwei lung cancer cell line XWLC-05 and NSCLC cell line NCI-H157. Additionally, the results revealed that overexpression of miR-218 could induce XWLC-05 and NCI-H157 cell apoptosis by arresting the cell cycle at G2/M phase. Finally, the present study demonstrated that overexpression of miR-218 could lead to a significant increase in phosphatase and tensin homolog (PTEN) and YY1 transcription factor (YY1), and a decrease in B-cell lymphoma 2 (BCL-2) and BMI1 proto-oncogene, polycomb ring finger (BMI-1) at the mRNA and protein level in XWLC-05 and NCI-H157 cell lines. However, we did not observe any remarkable difference in the roles of miR-218 and miR-218-mediated regulation of BCL-2, BMI-1, PTEN and YY1 expression in the progression of Xuanwei lung cancer. In conclusion, miR-218 could simultaneously suppress cell proliferation and tumor invasiveness and induce cell apoptosis by increasing PTEN and YY1 expression, while decreasing BCL-2 and BMI-1 in Xuanwei lung cancer. The results demonstrated that miR-218 might serve a vital role in tumorigenesis and progression of Xuanwei lung cancer and overexpression of miR-218 may be a novel approach for the treatment of Xuanwei lung cancer. [ABSTRACT FROM AUTHOR]

• We construct a novel lightweight network named IDC Net to realize 3-classification tasks, BI-RADS 4 sub-categories 4a, 4b, and 4c. • The IDC Net has two paths, CNN and CapsNet, which combines the advantages of both. CNN is used to extract rich and deep local semantic features, and CapsNet is used to extract the position and posture information of the image. • For the CNN path, we design a network named ID-Net, which is composed of two sub-paths: one is constructed by superimposed ID block to extract rich semantic information from bad artifact images, and the other is constructed by superimposed DD block to better extract boundary information. • To realize the lightweight of the network, we use a special operation when fusing the information extracted by the two paths, which can control the growth of parameters caused by the combination of CNN and CapsNet. In the diagnosis of breast cancer, the 3 sub-categories 4a-4c of BI-RADS 4 are of great significance to doctors. However, low resolution of ultrasound image and high similarity between different category images pose great challenges to this task, which requires the network to be more capable of extracting image features. Therefore, in response to the efficient classification of BI-RADS 4a-4c in breast ultrasound images, we developed a lightweight classification network IDC Net, a neural network model combining the advantages of convolutional neural network(CNN) and CapsNet. In this model: Firstly, we proposed ID-Net based on CNN architecture and mainly constructed by ID block and DD block, which ensure the ID-Net deep and wide enough to extract sufficient local semantic information of image, and at the same time being lightweight. Secondly, we use the CapsNet to learn the position and posture information between the global features of the image, which makes up for the defects of CNN. Finally, two parallel paths of IDC Net and CapsNet are fused to enhance IDC Net's capability of feature extraction. To verify our method, experiments have been conducted on the breast ultrasound dataset of Yunnan cancer hospital and two public datasets. The classification results of our method have been compared with those obtained by five existing approaches. The experimental results show that the proposed method IDC Net has the highest Accuracy (98.54 %), Precision (98.54 %) and F1 score (98.54 %). [ABSTRACT FROM AUTHOR]