Explicit Topology Optimization of Conforming Voronoi Foams

Topology optimization is able to maximally leverage the high DOFs and mechanical potentiality of porous foams but faces three fundamental challenges: conforming to free-form outer shapes, maintaining geometric connectivity between adjacent cells, and achieving high simulation accuracy. To resolve the issues, borrowing the concept from Voronoi tessellation, we propose to use the site (or seed) positions and radii of the beams as the DOFs for open-cell foam design. Such DOFs cover extensive design space and have clear geometrical meaning, which makes it easy to provide explicit controls (e.g. granularity). During the gradient-based optimization, the foam topology can change freely, and some seeds may even be pushed out of the shape, which greatly alleviates the challenges of prescribing a fixed underlying grid. The mechanical property of our foam is computed from its highly heterogeneous density field counterpart discretized on a background mesh, with a much improved accuracy via a new material-aware numerical coarsening method. We also explore the differentiability of the open-cell Voronoi foams w.r.t. its seed locations, and propose a local finite difference method to estimate the derivatives efficiently. We do not only show the improved foam performance of our Voronoi foam in comparison with classical topology optimization approaches, but also demonstrate its advantages in various settings, especially when the target volume fraction is extremely low

Identification and characterization of an efficient acyl-CoA: diacylglycerol acyltransferase 1 (DGAT1) gene from the microalga Chlorella ellipsoidea

RT-PCR detection of DGAT1 genes in transgenic yeast (INVSc1). The yeast actin was used as an internal control. 1, The yeast transformed with pYES2.0; 2–5, the yeast expressing AtDGAT1, GmDGAT1, BnDGAT1 and CeDGAT1, respectively. (DOCX 55 kb

Identification and Characterization of an Efficient acyl-CoA:Diacylglycerol Acyltransferase 1 (\u3cem\u3eDGAT1\u3c/em\u3e) Gene from the Microalga \u3cem\u3eChlorella ellipsoidea\u3c/em\u3e

Background: Oil in the form of triacylglycerols (TAGs) is quantitatively the most important storage form of energy for eukaryotic cells. Diacylglycerol acyltransferase (DGAT) is considered the rate-limiting enzyme for TAG accumulation. Chlorella, a unicellular eukaryotic green alga, has attracted much attention as a potential feedstock for renewable energy production. However, the function of DGAT1 in Chlorella has not been reported. Results: A full-length cDNA encoding a putative diacylglycerol acyltransferase 1 (DGAT1, EC 2.3.1.20) was obtained from Chlorella ellipsoidea. The 2,142 bp open reading frame of this cDNA, designated CeDGAT1, encodes a protein of 713 amino acids showing no more than 40% identity with DGAT1s of higher plants. Transcript analysis showed that the expression level of CeDGAT1 markedly increased under nitrogen starvation, which led to significant triacylglycerol (TAG) accumulation. CeDGAT1 activity was confirmed in the yeast quadruple mutant strain H1246 by restoring its ability to produce TAG. Upon expression of CeDGAT1, the total fatty acid content in wild-type yeast (INVSc1) increased by 142%, significantly higher than that transformed with DGAT1s from higher plants, including even the oil crop soybean. The over-expression of CeDGAT1 under the NOS promoter in wild-type Arabidopsis thaliana and Brassica napus var. Westar significantly increased the oil content by 8–37% and 12–18% and the average 1,000-seed weight by 9–15% and 6–29%, respectively, but did not alter the fatty acid composition of the seed oil. The net increase in the 1,000-seed total lipid content was up to 25–50% in both transgenic Arabidopsis and B. napus. Conclusions: We identified a gene encoding DGAT1 in C. ellipsoidea and confirmed that it plays an important role in TAG accumulation. This is the first functional analysis of DGAT1 in Chlorella. This information is important for understanding lipid synthesis and accumulation in Chlorella and for genetic engineering to enhance oil production in microalgae and oil plants

Water level management of lakes connected to regulated rivers: An integrated modeling and analytical methodology

Reservoir operations significantly alter the hydrological regime of the downstream river and river-connected lake, which has far-reaching impacts on the lake ecosystem. To facilitate the management of lakes connected to regulated rivers, the following information must be provided: (1) the response of lake water levels to reservoir operation schedules in the near future and (2) the importance of different rivers in terms of affecting the water levels in different lake regions of interest. We develop an integrated modeling and analytical methodology for the water level management of such lakes. The data-driven method is used to model the lake level as it has the potential of producing quick and accurate predictions. A new genetic algorithm-based synchronized search is proposed to optimize input variable time lags and data-driven model parameters simultaneously. The methodology also involves the orthogonal design and range analysis for extracting the influence of an individual river from that of all the rivers. The integrated methodology is applied to the second largest freshwater lake in China, the Dongting Lake. The results show that: (1) the antecedent lake levels are of crucial importance for the current lake level prediction; (2) the selected river discharge time lags reflect the spatial heterogeneity of the rivers’ impacts on lake level changes; (3) the predicted lake levels are in very good agreement with the observed data (RMSE ≤ 0.091 m; R2 ≥ 0.9986). This study demonstrates the practical potential of the integrated methodology, which can provide both the lake level responses to future dam releases and the relative contributions of different rivers to lake level changes

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

An algal bloom is a complex hydro-biological phenomenon driven by multi-attribute environmental processes and thus is still difficult to predict. In this paper, a comprehensive modelling framework for forecasting algal bloom risks in shallow lakes is presented, which is based on long-term field observation and modelling of eutrophic shallow lakes. In the procedure, the major factors and their suitable ranges are investigated, and the individual influence of various driving factors is evaluated quantitatively, using an integrated approach of orthogonal design and regression analysis. By analysing the possible combined effects of the major driving factors and the relationship between algal bloom risk and major bloom-driving factors, a cost-effective environmentally driven risk assessment model is developed to forecast the likelihood of algal bloom occurrence, through a parameter optimization and prediction comparison routine. The risk model has been calibrated and validated against long-term field observations of algal blooms in Taihu Lake, with the prediction accuracy higher than 70%, which only requires readily available meteorological and water quality data. It is noted that for the closed shallow lake, the influence of hydrodynamics can be indirectly reflected by the variation of wind speed; and, total phosphorus, water temperature, photosynthetically active radiation, and average wind speed could be used as major bloom-driving factors in Taihu Lake generally. This study provides a practical framework for the development of algal bloom early warning schemes for shallow lakes and helps to understand the combined function of complex bloom-driving factors

Newly Identified Essential Amino Acids Affecting <i>Chlorella ellipsoidea</i> DGAT1 Function Revealed by Site-Directed Mutagenesis

Diacylglycerol acyltransferase (DGAT) is a rate-limiting enzyme in the synthesis of triacylglycerol (TAG), the most important form of energy storage in plants. Some residues have previously been proven to be crucial for DGAT1 activity. In this study, we used site-directed mutagenesis of the CeDGAT1 gene from Chlorella ellipsoidea to alter 16 amino acids to investigate effects on DGAT1 function. Of the 16 residues (L482R, E542R, Y553A, G577R, R579D, Y582R, R596D, H603D, H609D, A624R, F629R, S632A, W650R, A651R, Q658H, and P660R), we newly identified 5 (L482, R579, H603, A651, and P660) as being essential for DGAT1 function and 7 (E542, G577, R596, H609, A624, S632, and Q658) that significantly affect DGAT1 function to different degrees, as revealed by heterologous expression of the mutants in yeast strain INVSc1. Importantly, compared with CeDGAT1, expression of the mutant CeDGAT1Y553A significantly increased the total fatty acid and TAG contents of INVSc1. Comparison among CeDGAT1Y553A, GmDGAT1Y341A, AtDGAT1Y364A, BnDGAT1Y347A, and BoDGAT1Y352A, in which tyrosine at the position corresponding to the 553rd residue in CeDGAT1 is changed into alanine, indicated that the impact of changing Y to A at position 553 is specific for CeDGAT1. Overall, the results provide novel insight into the structure and function of DGAT1, as well as a mutant gene with high potential for lipid improvement in microalgae and plants

PM2.5 Pollution in Xingtai, China: Chemical Characteristics, Source Apportionment, and Emission Control Measures

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are one of the most polluted regions in China. Xingtai, as a heavy industrial city of BTH and its surrounding areas, has been experiencing a severe PM2.5 pollution in recent years, characterized by extremely high concentrations of PM2.5. In 2014, PM2.5 mass concentrations monitored by online instruments in urban areas of Xingtai were 116, 77, 128, and 200 &micro;g m&minus;3 in spring, summer, autumn and winter, respectively, with annually average concentrations of 130 &micro;g m&minus;3 exhibiting 3.7 times higher than National Ambient Air Quality Standard (NAAQS) value for PM2.5 (35 &micro;g m&minus;3). To identify PM2.5 emission sources, ambient PM2.5 samples were collected during both cold and warm periods in 2014 in urban areas of Xingtai. Organic carbon (OC), sulfate, nitrate, ammonium and elemental carbon (EC) were the dominant components of PM2.5, accounting for 13%, 11%, 12%, 11% and 8% in the cold period, respectively, and 11%, 12%, 9%, 6%, and 5% in the warm period, respectively. Source apportionment results indicated that coal combustion (24.4%) was the largest PM2.5 emission source, followed by secondary sulfate (22.2%), secondary nitrate (18.4%), vehicle exhaust dust (12.4%), fugitive dust (9.7%), construction dust (5.5%), soil dust (3.4%) and metallurgy dust (1.6%). Based on PM2.5 source apportionment results, some emission control measures, such as replacing bulk coal with clean energy sources, controlling coal consumption by coal-fired boiler upgrades, halting operations of unlicensed small polluters, and controlling fugitive and VOCs emission, were proposed to be implemented in order to improve Xingtai&rsquo;s ambient air quality

Improving Lake Level Prediction by Embedding Support Vector Regression in a Data Assimilation Framework

Data-driven models are widely used in the field of water level prediction due to their generalizability and predictive abilities. In long-series prediction, however, data-driven models degrade rapidly due to the uncertainty and constraints of model data and parameters. To address the problem of inaccurate continuous water level prediction, this study introduced a data assimilation technique, the unscented Kalman filter (UKF), and embedded support vector regression (SVR) into the framework and applied it to Dongting Lake, the second largest freshwater lake in China. The results demonstrated that the assimilation model is significantly better than the non-assimilation model in predicting water levels and is not affected by the characteristics of lake level changes, with the R2 increasing from 0.975–0.982 to 0.998–0.999 and the RMSE decreasing from 0.436–0.159 m to 0.105–0.042 m. The prediction lead time also increased with the increase of continuous assimilation data. Further analysis of the assimilation model showed that when there was an assimilation cycle, the prediction remained stable for successive sets of two or more assimilated data, and the prediction lead time increased with successive assimilated data, from 4–8 days (one successive assimilation data) to 9–12 days (five successive assimilation data). Overall, this study found that the data assimilation framework can improve the prediction ability of data-driven models, with assimilated models having a smaller fluctuation range and higher degree of concentration than non-assimilated models. The increase in assimilated data will improve model accuracy as well as the number of days of prediction lead time when an assimilation cycle exists

Genome-Wide Characterization of DGATs and Their Expression Diversity Analysis in Response to Abiotic Stresses in Brassica napus

Triacylglycerol (TAG) is the most important storage lipid for oil plant seeds. Diacylglycerol acyltransferases (DGATs) are a key group of rate-limiting enzymes in the pathway of TAG biosynthesis. In plants, there are three types of DGATs, namely, DGAT1, DGAT2 and DGAT3. Brassica napus, an allotetraploid plant, is one of the most important oil plants in the world. Previous studies of Brassica napus DGATs (BnaDGATs) have mainly focused on BnaDGAT1s. In this study, four DGAT1s, four DGAT2s and two DGAT3s were identified and cloned from B. napus ZS11. The analyses of sequence identity, chromosomal location and collinearity, phylogenetic tree, exon/intron gene structures, conserved domains and motifs, and transmembrane domain (TMD) revealed that BnaDGAT1, BnaDGAT2 and BnaDGAT3 were derived from three different ancestors and shared little similarity in gene and protein structures. Overexpressing BnaDGATs showed that only four BnaDGAT1s can restore TAG synthesis in yeast H1246 and promote the accumulation of fatty acids in yeast H1246 and INVSc1, suggesting that the three BnaDGAT subfamilies had greater differentiation in function. Transcriptional analysis showed that the expression levels of BnaDGAT1s, BnaDGAT2s and BnaDGAT3s were different during plant development and under different stresses. In addition, analysis of fatty acid contents in roots, stems and leaves under abiotic stresses revealed that P starvation can promote the accumulation of fatty acids, but no obvious relationship was shown between the accumulation of fatty acids with the expression of BnaDGATs under P starvation. This study provides an extensive evaluation of BnaDGATs and a useful foundation for dissecting the functions of BnaDGATs in biochemical and physiological processes

Hydrological Drivers for the Spatial Distribution of Wetland Herbaceous Communities in Poyang Lake

Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its wetland vegetation, leading to the degradation of the entire wetland ecosystem. We developed an integrated framework to quantitatively investigate the relationship between the spatial distribution of major wetland herbaceous communities and the hydrological regimes of Poyang Lake. First, the wetland herbaceous community classification was built using a support-vector machine and simultaneous parameter optimization, achieving an overall accuracy of over 98%. Secondly, based on the inundation conditions since 2000, four hydrological drivers of the spatial distribution of these communities were evaluated by canonical correspondence analysis. Finally, the hydrological niches of the communities were quantified by Gaussian regression and quantile methods. The results show that there were significant interspecific differences in terms of the hydrological niche. For example, Carex cinerascens Ass was the most adaptable to inundation, while Triarrhena lutarioriparia + Phragmites australis Ass was the least. Our integrated analytical framework can contribute to hydrological management to better maintain the wetland plant community structure in the Poyang Lake area