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The folding of two-dimensional epithelial sheets into specific three-dimensional structures is a fundamental tissue construction mechanism in animal development. A common mechanism that mediates epithelial folding is apical constriction, the active shrinking of cell apices driven by actomyosin contractions. It remains unclear whether cells outside of the constriction domain also contribute to folding. During Drosophila mesoderm invagination, ventrally localized mesoderm epithelium undergoes apical constriction and subsequently folds into a furrow. While the critical role of apical constriction in ventral furrow formation has been well demonstrated, it remains unclear whether, and if so, how the laterally localized ectodermal tissue adjacent to the mesoderm contributes to furrow invagination. In this study, we combine experimental and computational approaches to test the potential function of the ectoderm in mesoderm invagination. Through laser-mediated, targeted disruption of cell formation prior to gastrulation, we found that the presence of intact lateral ectoderm is important for the effective transition between apical constriction and furrow invagination in the mesoderm. In addition, using a laser-ablation approach widely used for probing tissue tension, we found that the lateral ectodermal tissues exhibit signatures of tissue compression when ablation was performed shortly before the onset of mesoderm invagination. These observations led to the hypothesis that in-plane compression from the surrounding ectoderm facilitates mesoderm invagination by triggering buckling of the mesoderm epithelium. In support of this notion, we show that the dynamics of tissue flow during mesoderm invagination displays characteristic of elastic buckling, and this tissue dynamics can be recapitulated by combining local apical constriction and global compression in a simulated elastic monolayer. We propose that Drosophila mesoderm invagination is achieved through epithelial buckling jointly mediated by apical constriction in the mesoderm and compression from the neighboring ectoderm.

Power grid construction projects are proceeded on the basis of the multi-department collaboration. Besides, the whole process of project management involves a huge amount of data, which is difficult to disclose its inside correlation. Currently, the higher standards for power grid construction project management poses challenges on data management. Thus, it is necessary to further explore the inside data relationship in the process of project management, so as to address the data inaccuracy issue. In this paper, a correlation analysis method is proposed based on a modified apriori algorithm to study the correlation among the key indicators in the process of power grid construction project management. Specifically, the modified apriori algorithm by deploying dichotomizing search, can successfully decrease high computation demand while guaranteeing the analyzing accuracy. By establishing a data mining tool to analyze the indexes from the power grid construction projects in recent years, the pre-control and in-process control methods are proposed for the project management. In this regard, the paper offers a brand-new way to for the data management in the power grid construction projects.

In the UWB indoor positioning system, the positioning anchor is extremely susceptible to interference, which affects the accuracy, stability and reliability of the positioning system. When the anchor is subject to strong interference, its data will fluctuate abnormally and accurate positioning cannot be completed. In order to solve the localization problem of anchor anomaly in UWB indoor positioning system, this paper proposes a localization model based on the least squares support vector machine optimized by particle swarm optimization. Firstly, the distance between the label and the anchor is collected using bilateral ranging. Then, the indoor positioning model is established using the least squares support vector machine. Finally, the particle swarm algorithm is introduced to optimize the penalty factor and kernel width of the least squares support vector machine. The experimental results show that the average positioning accuracy of the particle swarm optimization least squares support vector machine positioning model can reach 0.02m under normal anchor conditions, and the average positioning accuracy can reach 0.03m under abnormal anchor conditions.

We employ Bayesian method to estimate a time-varying coefficient version of the de facto currency basket model of Frankel and Wei (2007) for the RMB of China, using daily data from February 2005 to July 2011. We estimate jointly the implicit time-varying weights of all 11 currencies in the reference basket announced by the Chinese government. We find the dollar weight has been reduced and sometimes significantly smaller than one, but there is no evidence of systematic operation of a currency basket with discernable pattern of significant weights on other currencies. During specific periods, the reduced dollar weight has not been switched to other major international currencies, but to some East Asian currencies, which is hard to explain by trade importance to or trade competition with China. We examine currency baskets of these East Asian Economies, including major international currencies and the RMB in their baskets. We find an evident tendency of Malaysia and Singapore to increase the weights of RMB in their own currency baskets, and a steadily and significantly positive weight of RMB in the basket of Thailand. These evidences suggest that, the positive weights of some East Asian currencies in RMB currency basket during specific periods largely reflect the fact that these East Asia economies have been systematically placing greater weights on RMB under the new regime of RMB exchange rate.