ABSTRACT
The water body's suspended concentration reflects many coastal environmental indicators, which is important for predicting ecological hazards. The modeling of any concentration in water requires solving the settling-diffusion equation (SDE), and the values of several key input parameters therein (settling velocity ws, eddy diffusivity Ds, and erosion rates p(t)) directly determine the prediction performance. The time-consuming large-scale simulations would benefit if the parameter values could be estimated through available observations in the target sea area. The present work proposes a new optimization method for synchronously estimating the three parameters from limited concentration observations. First, an analytical solution to the one-dimensional vertical (1DV) SDE for suspended concentrations in an unsteady scenario is derived. Second, the near bottom suspended sediment concentration (SSC) profiles are measured with high-resolution observation. Third, the key parameters are optimized through the best fit of the measured SSC profiles and those modeled with the unsteady solution. Nonlinear least square fitting (NLSF) is introduced to judge the best fits automatically. The high-resolution concentration measurements in a specially-designed cylindrical tank experiment using the Yellow River Delta sediments test the proposed method. The method performs well in the initial period of turbulence generation when sediment resuspension is significant. It optimizes p(t), ws, and Ds with reasonable values and uniqueness of their combination. The proposed theory is a practical tool for quickly estimating key substance transport parameters from limited observations; it also has the potential to construct local parametric models to benefit the 3D modeling of coastal substance transport. Although the present work takes SSC as an example, it can be extended to any suspended particulate concentration in the water.
Subject(s)
Geologic Sediments , Water , Rivers , Water Movements , Environmental Monitoring/methodsABSTRACT
BACKGROUND AND PURPOSE: In major depressive disorder (MDD), exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment and predicting the treatment response. Currently, tRNA-derived small ribonucleic acids (tsRNAs) have been established as promising non-invasive biomarker candidates that may enable a more reliable diagnosis or monitoring of various diseases. Herein, we aimed to explore tsRNA expression together with functional activities in MDD development. EXPERIMENTAL APPROACH: Serum samples were obtained from patients with MDD and healthy controls, and small RNA sequencing (RNA-Seq) was used to profile tsRNA expression. Dysregulated tsRNAs in MDD were validated by quantitative real-time polymerase chain reaction (qRT-PCR). The diagnostic utility of specific tsRNAs and the expression of these tsRNAs after antidepressant treatment were analysed. KEY RESULTS: In total, 38 tsRNAs were significantly differentially expressed in MDD samples relative to healthy individuals (34 up-regulated and 4 down-regulated). qRT-PCR was used to validate the expression of six tsRNAs that were up-regulated in MDD (tiRNA-1:20-chrM.Ser-GCT, tiRNA-1:33-Gly-GCC-1, tRF-1:22-chrM.Ser-GCT, tRF-1:31-Ala-AGC-4-M6, tRF-1:31-Pro-TGG-2 and tRF-1:32-chrM.Gln-TTG). Interestingly, serum tiRNA-Gly-GCC-001 levels exhibited an area under the ROC curve of 0.844. Moreover, tiRNA-Gly-GCC-001 is predicted to suppress brain-derived neurotrophic factor (BDNF) expression. Furthermore, significant tiRNA-Gly-GCC-001 down-regulation was evident following an 8-week treatment course and served as a promising baseline predictor of patient response to antidepressant therapy. CONCLUSION AND IMPLICATIONS: Our current work reports for the first time that tiRNA-Gly-GCC-001 is a promising MDD biomarker candidate that can predict patient responses to antidepressant therapy.