Model studies on the dynamics of hydrophobic organic compounds in shallow lake ecosystems

The twentieth century witnesses the widespread eutrophication and intensive organic contaminations in earth surface water systems located in highly populated areas, resulting in severe deterioration of water quality in freshwater ecosystems around the globe. This is particularly the case for many freshwater shallow lakes in China. The interaction between excess nutrient loading and enormous organic contaminants discharge has raised increasing attention from both scientists and lake managers, whereas accurate prediction for both substances cannot be properly predicted based on knowledge from either field alone. However, efforts in the related scientific research, particularly the development of relevant modeling tools, remains scarce. To this end, the aim of this thesis is to develop an integrated ecological and chemical modeling tool, which is composed of contaminant fate module (CF), food web accumulation module (FW) and ecological module (EM), in the hope to fulfill the research gap above. We collected three groups of HOCs, namely hexachlorocyclohexanes (HCHs), polycyclic aromatic hydrocarbons (PAHs) and Per- and polyfluoroalkyl substances (PFASs), in multiple compartments from two Chinese shallow lakes that are currently in distinct ecological states, i.e., Lake Small Baiyangdian (in clear state) and Lake Chaohu (in turbid state). In particular, paleo-records of PAHs residual levels in Lake Chaohu in two sediment cores covering the time span of over 60 years were obtained. We elaborated to explicitly investigate the fate, transport and transformation of these contaminants in these two shallow lakes using the developed modeling tool, with either steady state or dynamic simulations (in time scales of both short-term intra-annual (1-2 years) and long-term inter-annual (60 years)). The following issues were addressed: 1) fate of the chemicals in lake environment and the dominant processes; 2) seasonal patterns of chemicals in lakes and the driving factors; 3) long-term dynamics of chemicals in lakes and the driving factors; and 4) impact of abrupt changes in ecosystems on the distribution of contaminations in shallow lakes. For modeling techniques, we implemented uncertainty analysis on the model using both classic Monde Carlo and more advanced Bayesian Markov Chain Monte Carlo (MCMC) algorithm. We recommend to apply MCMC to contaminant modeling approach to make calibration possible and to remove the overestimated uncertainty in predictions. Furthermore, we compared the advantages and disadvantages of our model to other models with similar objectives, and we further proposed a more comprehensive modeling framework that incorporates hydrodynamic models to address spatial variations of contamination, which embraces the fruitful outcomes in aquatic ecosystem modeling. Finally, we advocate to add modeling approach as the third dimension for the ‘contemporary & paleo-observations’ strategy, which together contribute to the ‘golden triangle’ framework. New insights and discoveries may emerge for the evaluation on the organic contaminants in shallow lake systems, which may contribute to ecological and human health risk assessment. This ‘golden triangle’ may serve as the multidiscipline framework for limnologic research in the future.</p

Analysis of the control strategy of range extender system on the vehicle NVH performance

With focus on NVH performance, this paper studies the range extender system control strategy such as the initial start speed, operating points, speed up and down control method between operating points of the range extender, etc. At the same time, the confirmation of the operating points of the range extender based on the full vehicle frequency distribution and vibration and noise level of key points (seat rail, driver’s inner ear) was performed. Finally, we conducted objective test and compared the test data with benchmark vehicles

Association of MTHFR A1298C polymorphism with breast cancer and/or ovarian cancer risk: an updated meta-analysis

Background: Recent years have witnessed the discovery of similar gene variations between breast cancer and ovarian cancer, inherited breast and ovarian cancer in particular. A large number of case-control studies have been conducted to explore the association of Methylenetetrahydrofolate Reductase (MTHFR) A1298C polymorphism with breast cancer and/or ovarian cancer risk. However, the results are still inconsistent and inconclusive. Consequently, we performed a meta-analysis to evaluate the association between MTHFR A1298C polymorphism and breast, ovarian cancer risk.Materials and Methods: A comprehensive retrieval was conducted in the electronic database of PubMed, Web of Science and Chinese National Knowledge Infrastructure (CNKI) until June 2015 to identify eligible studies. A total of 35 studies which examined the association of MTHFR A1298C polymorphism with breast cancer and/or ovarian cancer were identified. The pooled odds ratios (ORs) with 95 % confidence intervals (CIs) were used to assess the effect of gene polymorphism. And allele model, homozygous model, co-dominant model, dominant model, recessive model were applied.Result: In the overall analysis, significantly increased breast cancer and/or ovarian cancer risk was found (for allele model A VS C OR = 1.05, CI: 1.02-1.08, P = 4×10-3; for homozygous model AA VS CC OR = 1.11, CI: 1.03-1.19, P = 5×10-3; for recessive model (AC +AA) VS CC: OR = 1.10, CI: 1.03-1.18, P = 7×10-3).Conclusion: In the subgroup analysis, significantly increased breast cancer risk was identified among Caucasians. MTHFR A1298C polymorphism might contribute to an increased risk of breast cancer and/or ovarian cancer susceptibility. In addition, MTHFR A1298C polymorphism had a significant association with breast cancer in Caucasians.Keywords: Breast cancer, Ovarian cancer, MTHFR A1298C, Polymorphism, Meta analysi

Expression Profiles of microRNAs in Drug-Resistant Non-Small Cell Lung Cancer Cell Lines Using microRNA Sequencing

Background/Aims: Drug resistance remains a main obstacle to the treatment of non- small cell lung cancer (NSCLC). The aim of this study was to identify the expression profiles of microRNAs (miRNAs) in drug-resistant NSCLC cell lines. Methods: The expression profiles of miRNAs in drug-resistant NSCLC cell lines were examined using miRNA sequencing, and the common dysregulated miRNAs in these cell lines were identified and analyzed by bioinformatics methods. Results: A total of 29 upregulated miRNAs and 36 downregulated miRNAs were found in the drug-resistant NSCLC cell lines, of which 26 upregulated and 36 downregulated miRNAs were found to be involved in the Ras signaling pathway. The expression levels, survival analysis, and receiver operating characteristic curve of the dysregulated miRNAs based on The Cancer Genome Atlas database for lung adenocarcinoma showed that hsa-mir-192, hsa-mir-1293, hsa-mir-194, hsa-mir-561, hsa-mir-205, hsa-mir-30a, and hsa-mir-30c were related to lung cancer, whereas only hsa-mir-1293 and hsa-mir-561 were not involved in drug resistance. Conclusion: The results of this study may provide novel biomarkers for drug resistance in NSCLC and potential therapies for overcoming drug resistance, and may also reveal the potential mechanisms underlying drug resistance in this disease

Perspectives and challenges of applying the water-food-energy nexus approach to lake eutrophication modelling

Embargo until August 4, 2023The water-food-energy (WFE) nexus is about balancing competing interests to secure the sustainability of services provided by interconnected sectors. Ignoring the interconnections could cause serious consequences. For example, eutrophication caused by overemphasizing on food production maximization could threaten water security. Worldwide eutrophication intensification is one of the most important causes of the lake water quality deteriorations. Water quality models are usually important decision making tools for policy makers. This study attempts to explore the possibilities of applying the WFE nexus concept into water quality models. We propose the most significant challenge is lack of a common modelling framework to streamline connections between up- and downstream models. As the most important water quality issue, eutrophication modeling should increase its visibility in the United Nations Sustainable Develop Goals.acceptedVersio

Characterizing 19 thousand Chinese lakes, ponds and reservoirs by morphometric, climate and sediment characteristics

Chinese lakes, including ponds and reservoirs, are increasingly threatened by algal blooms. Yet, each lake is unique, leading to large inter-lake variation in lake vulnerability to algal blooms. Here, we aim to assess the effects of unique lake characteristics on lake vulnerability to algal blooms. To this end, we built a novel and comprehensive database of lake morphometric, climate and sediment characteristics of 19,536 Chinese lakes, including ponds and reservoirs (>0.1 km2). We assessed lake characteristics for nine stratification classes and show that lakes, including ponds and reservoirs, in eastern China typically have a warm stratification class (Tavg>4 °C) and are slightly deeper than those in western China. Model results for representative lakes suggest that the most vulnerable lakes to algal blooms are in eastern China where pollution levels are also highest. Our characterization provides an important baseline to inform policymakers in what regions lakes are potentially most vulnerable to algal blooms