A Method to Evaluate Spectral Analysis by Spectroscopy.

Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method.

Analysis and Model Comparison of Carbon and Nitrogen Concentrations in Sediments of the Yellow Sea and Bohai Sea by Visible-Near Infrared Spectroscopy.

Visible-near infrared spectroscopy is considered an effective method for rapidly determining total carbon (TC) and total nitrogen (TN) in terrestrial soils. However, reports on measuring them by VNIR in marine sediments are limited. This article provides an analysis and spectral model comparison of TC and TN in marine sediments using VNIR. The best TC and TN spectral models were established when using the least square support vector machine algorithm with a wavelength, which extended from 226 nm to 975 nm. The prediction results of TN have a high coefficient of determination and residual predictive deviation, providing accurate quantitative predictions. The TC spectral model comes with a disadvantage might due to its usual high concentrations of organic carbon. Characteristic wavelength extraction may lead to the loss of identification information for the characteristics of TC and TN, and full wavelength spectrum contains more information helps more to the quantification.

Electrochemical degradation of DCF by boron-doped diamond anode: degradation mechanism, pathways and influencing factors.

Nonsteroidal anti-inflammatory drugs (NSAIDs) have been widely detected in wastewater and surface water, indicating that the removal of NSAIDs by wastewater treatment plants was not efficient. Electrochemical advanced oxidation technology is considered to be an effective process. This study presents an investigation of the kinetics, mechanism, and influencing factors of diclofenac (DCF) degradation by an electrochemical process with boron-doped diamond anodes. Relative operating parameters and water quality parameters are examined. It appears that the degradation follows the pseudo-first-order degradation kinetics. DCF degradation was accelerated with the increase of pH from 6 to 10. The degradation was promoted by the addition of electrolyte concentrations and current density. Humic acid and bicarbonate significantly inhibited the degradation, whereas chloride accelerated it. According to the quenching tests, hydroxyl radicals (•OH) and sulfate radicals contributed 76.5% and 6.5%, respectively, to the degradation. Sodium sulfate remains a more effective electrolyte, compared to sodium nitrate and sodium phosphate, suggesting the quenching effect of nitrate and phosphate on •OH. Major DCF transformation products were identified. According to the degradation products detected by liquid chromatography-mass spectrometry, hydroxylation and decarboxylation are the main pathways of DCF degradation; while dechlorination, chlorination, and nitro substitution are also included in this electrochemical degradation process.

Lactobacillus supplementation prevents cisplatin-induced cardiotoxicity possibly by inflammation inhibition.

PURPOSE: Cardiotoxicity of chemotherapy exerts as the main hurdle for prognosis, while whether cisplatin causes severe cardiotoxicity remains largely unknown. Accumulating evidence reveals that intestinal microbiota functions importantly in nutrient metabolism and cardiovascular function. In this study, we observed the possible cardiotoxicity of cisplatin and explored the possible role of microbiota in the mouse model. METHODS: C57Bl6 mice were treated with 0, 3 or 6 mg/kg cisplatin via i.p. injection, together with or without Lactobacillus supplementation. Cardiac function was analyzed by echocardiography. Gut microbiota was analyzed by 16S RNA sequencing. Gene expression was analyzed by qPCR. The data differences were compared with Graphpad Prism 7.0. RESULTS: In comparison with the control group, 6 mg/kg per week cisplatin treatment for 3 weeks significantly decreased the body weight by about 33% (18.1 ± 2.1 vs 27.2 ± 0.9) and decreased the left ventricular ejection fraction by about 15% (0.57 ± 0.07 vs 0.67 ± 0.04). Together, the gut microbiota was found dramatically changed, manifested as 27% decrease of Firmicutes and increased pathological bacteria. Antibiotics treatment had no obvious beneficial effects on the body weight and cardiac function caused by cisplatin. However, Lactobacillus supplementation significantly increased the body weight and restored cardiac function, together with lower inflammation gene expression. CONCLUSIONS: The study here has established a possible role of microbiota dysbiosis in cisplatin-associated toxic effects, while delivery of Lactobacillus would be beneficial for the cardiac function prevention possibly via inflammation control.

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems.

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.