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Environmental change and human activity can be recorded in sediment cores in aquatic systems such as lakes. Information from such records may be useful for environmental governance in the future. Six sediment cores were collected from Lake Qinghai, China and its sublakes during 2012 and 2013. Measurements of sediment grain-size fractions indicate that sedimentation in the north and southwest of Lake Qinghai is dominated by river input, whereas that in Lake Gahai and Lake Erhai is dominated by dunes. The sedimentation rates in Lake Qinghai were calculated to be 0.101-0.159 cm/y, similar to the rates in other lakes on the Qinghai-Tibetan Plateau. Using these data and sedimentation rates from the literature, we compiled the spatial distribution of sedimentation rates. Higher values were obtained in the three main areas of Lake Qinghai: two in river estuaries and one close to sand dunes. Lower values were measured in the center and south of the lake. Measurements of total organic carbon (TOC), total nitrogen (TN), phosphorus concentrations, and TOC/TN ratios in three cores (QH01, QH02, and Z04) revealed four horizons corresponding to times of increased human activity. These anthropogenic events were (1) the development of large areas of cropland in the Lake Qinghai watershed in 1960, (2) the beginning of nationwide fertilizer use and increases in cropland area in the lake watershed after 1970, (3) the implementation of the national program 'Grain to Green,' and (4) the rapid increase in the tourism industry from 2000. Profiles of Rb, Sr concentrations, the Rb/Sr ratio, and grain-size fraction in core Z04 indicate that the climate has become drier over the past 100 years. Therefore, we suggest that lake sediments such as those in Lake Qinghai are useful media for high-resolution studies of regional environmental change and human activity. [ABSTRACT FROM AUTHOR]

Background/aims: There are no satisfactory biomarkers for hepatocellular carcinoma (HCC). The surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique has been used to identify biomarkers for cancer., Methodology: Four hundred thirty five serum samples were tested by SELDI-TOF-MS matching on a gold chip. Samples were assigned to a training set and a testing set according to collection order. The training set was used to identify statistically significant peaks and to develop the artificial neural network (ANN) model for diagnosing HCC. The testing set was used in a blind test to validate the diagnostic efficiency of the ANN model., Results: A total of 75 proteins that differed between patients and controls were identified (p<0.05). Seven of these proteins (p<0.01; m/z at 4207Da, 6604Da, 7734Da, 8106Da, 8545Da, 8599Da, 8894Da) were chosen to develop the ANN model. The model was subjected to a blind test using the testing set for HCC diagnosis. Sensitivity and specificity were 84.00% and 81.25%, respectively, and the accuracy was 81.90%., Conclusions: These results suggest that patients with HCC may have serum proteins that differ from healthy controls. The ANN is a new method for diagnosing and identifying HCC.