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Li-Na Guo,1,2 Shu-Ying Yu,1– 3 Meng Xiao,1,2 Chun-Xia Yang,4 Chun-Mei Bao,5 Yan-Hua Yu,6 Li-Yan Ye,7 Yang Yang,1,2 Ge Zhang,1,2 Jie Liu,8 Guo-Wei Liang,9 Rong Min,10 Yu Zhu,11 Hong Lei,12 Yu-Lei Liu,13 Lin-Juan Liu,14 Yun-Jian Hu,15 Po-Ren Hsueh,16 Ying-Chun Xu1,2 1Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences; 2Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases (BZ0447); 3Graduate School, Peking Union Medical College, Chinese Academy of Medical Sciences; 4Department of Infectious Diseases & Clinical Microbiology, Beijing Chao-Yang Hospital, Capital Medical University; 5Clinical Laboratory Medical Center, The Fifth Medical Center of Chinese PLA General Hospital; 6Department of Clinical Laboratory, You’an Hospital, Capital Medical University; 7Center for Clinical Laboratory Medicine, Chinese PLA General Hospital; 8Department of Clinical Laboratory, Seventh Medical Center, PLA General Hospital; 9Department of Clinical Laboratory, Aerospace Center Hospital; 10Department of Clinical Laboratory, Xuanwu Hospital, Capital Medical University; 11Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College; 12Department of Clinical Laboratory, The 8th Medical Center of Chinese PLA General Hospital; 13Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University; 14Department of Clinical Laboratory, Peking University Cancer Hospital and Institute; 15Department of Clinical Laboratory, Beijing Hospital, Beijing, People’s Republic of China; 16Departments of Laboratory Medicine & Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, TaiwanCorrespondence Ying-Chun XuDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1, Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, People’s Republic of ChinaTel/ Fax +86 106 915 9766Email xycpumch@139.comPo-Ren HsuehDepartments of Laboratory Medicine & Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, TaiwanTel +886-2-23224262 ext. 65355Fax +886-2-23955072Email hsporen@ntu.edu.twObjective: Invasive candidiasis (IC), a life-threatening fungal infection prevalent among hospitalized patients, has highly variable regional epidemiology. We conducted a multicenter surveillance study to investigate recent trends in species distribution and antifungal susceptibility patterns among IC-associated Candida spp. in Beijing, China, from 2016 to 2017.Materials and Methods: A total of 1496 non-duplicate Candida isolates, recovered from blood and other sterile body fluids of IC patients, were identified using matrix-assisted laser desorption/ionization time of flight mass spectrometry combined with ribosomal DNA internal transcribed spacer (ITS) region sequencing. Broth microdilution-based susceptibility testing using six antifungal agents was also conducted.Results: Candida albicans was the most frequently isolated species (49.9%), followed by Candida tropicalis (15.5%), Candida glabrata (14.7%) and Candida parapsilosis (14.2%). No significant differences in species distribution were observed when compared with a 2012– 2013 dataset. Overall, the rates of susceptibility to fluconazole and voriconazole were high among C. albicans (98% and 97.2%, respectively) and C. parapsilosis species complex (91.1% and 92%, respectively) isolates but low among C. tropicalis (81.5% and 81.1%, respectively) isolates. In addition, the rate of azole resistance among C. tropicalis isolates increased significantly (1.8-fold, P< 0.05) compared with that observed in 2012– 2013, while micafungin resistance rates were < 5% for all tested Candida species.Conclusion: Our results suggest that species distribution has remained stable among IC-associated Candida isolates in Beijing. Resistance to micafungin was rare, but increased azole resistance among C. tropicalis isolates was noted. Our study provides information on local epidemiology that will be important for the selection of empirical antifungal agents and contributes to global assessments of antifungal resistance.Keywords: invasive fungal infections, Candida, species distribution, antifungal resistance, surveillance, Beijing

Additional file 5: Table S4. Primers used in Bind-n-seq procedure.

Additional file 4: Table S3. Barcode assignment for Bind-n-seq.

Additional file 3: Table S2. Fragment used for EMSA experiment.

Additional file 7: Table S6. Binding reaction conditions.

Additional file 4: Table S3. Barcode assignment for Bind-n-seq.

Additional file 9. Analysis Bind-n-seq data using macOS operating system.

Pi-Jiang Sun,1 Yan-Hua Yu,2 Xi-Jun Cui1 1Department of Hepatobiliary Surgery, Weihai Central Hospital, Weihai 264400, Shandong Province, People’s Republic of China; 2Department of Dermatology, Weihai Central Hospital, Weihai 264400, Shandong Province, People’s Republic of China Abstract: Primary clear cell carcinoma of pancreas is extremely rare. We present a case of a 64-year-old male with a mass in the distal body and tail of the pancreas. He underwent a distal pancreatectomy. The histopathology of tumor cells showed features with abundant clear cytoplasm and prominent cell boundaries. Immunohistochemical analysis of neoplastic cells showed reactions to antibodies against cytokeratin-7 and showed no reactions to antibodies against hepatocyte nuclear factor-1β, carbonic anhydrase 9, synaptophysin, and chromogranin A. The patient was subsequently diagnosed with a primary clear cell carcinoma of the pancreas. This is the first time we have encountered it. We report this rare case and update the current literature of this tumor. Keywords: clear cell carcinoma, pancreas, rare tumor

The proposes of this paper are to investigate the properties and methods of fuzzy time series. It starts off with introducing the fuzzy time series analysis proposed by Q. Song and B.S. Chissom. Then, the theory structure, calculation, forecasting procedure and application of fuzzy time series are characterized from a comparison between traditional time series and fuzzy time series.

The selection of hyper-parameters is a crucial point in support vector machine modeling. Different from previous method of choosing an optimal model by using basic statistics of residuals in, the new approach selects hyper-parameters by checking whether there is redundant information in residual sequence. Furthermore, omni-directional correlation function (ODCF) is used to test redundancy in residual, and the accuracy of the method is proved by theoretical analysis and numerical simulation. Experiments conducted on benchmark time series, annual sunspot number and Mackey-Glass time series, indicating that the proposed method has better performance than the recorded in the literature.

Additional file 7: Table S6. Binding reaction conditions.

Additional file 6: Table S5. Composition for KCl salt solutions.

Additional file 6: Table S5. Composition for KCl salt solutions.

Additional file 2: Table S1. RAST-genome-scale DNA-pattern search result.

Additional file 5: Table S4. Primers used in Bind-n-seq procedure.