Transcriptome profiling reveals the crucial biological pathways involved in cold response in Moso bamboo (Phyllostachys edulis).

Most bamboo species including Moso bamboo (Phyllostachys edulis) are tropical or subtropical plants that greatly contribute to human well-being. Low temperature is one of the main environmental factors restricting bamboo growth and geographic distribution. Our knowledge of the molecular changes during bamboo adaption to cold stress remains limited. Here, we provided a general overview of the cold-responsive transcriptional profiles in Moso bamboo by systematically analyzing its transcriptomic response under cold stress. Our results showed that low temperature induced strong morphological and biochemical alternations in Moso bamboo. To examine the global gene expression changes in response to cold, 12 libraries (non-treated, cold-treated 0.5, 1 and 24 h at -2 °C) were sequenced using an Illumina sequencing platform. Only a few differentially expressed genes (DEGs) were identified at early stage, while a large number of DEGs were identified at late stage in this study, suggesting that the majority of cold response genes in bamboo are late-responsive genes. A total of 222 transcription factors from 24 different families were differentially expressed during 24-h cold treatment, and the expressions of several well-known C-repeat/dehydration responsive element-binding factor negative regulators were significantly upregulated in response to cold, indicating the existence of special cold response networks. Our data also revealed that the expression of genes related to cell wall and the biosynthesis of fatty acids were altered in response to cold stress, indicating their potential roles in the acquisition of bamboo cold tolerance. In summary, our studies showed that both plant kingdom-conserved and species-specific cold response pathways exist in Moso bamboo, which lays the foundation for studying the regulatory mechanisms underlying bamboo cold stress response and provides useful gene resources for the construction of cold-tolerant bamboo through genetic engineering in the future

The K giant stars from the LAMOST survey data I: identification, metallicity, and distance

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about 2 million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with uncertainty of $0.13\sim0.29$\,dex based on the equivalent widths of Mg$_{\rm b}$ and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at $K=11$\,mag, which is the median brightness of the K giant sample, is about 0.6\,mag, corresponding to $\sim30$% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80\,kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.Comment: 24 pages, 20 figures, submitted to Ap

How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies

The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths

The numerical simulation study on the dynamic variation of residual oil with water drive velocity in water flooding reservoir

The results of core displacement experiments show that increasing the water drive velocity when it is bigger than the limit value can effectively reduce the residual oil saturation and improve the oil displacement efficiency under the same PV. However, the existing commercial simulators (Eclipse, CMG et al.) cannot simulate the effect of water velocity on the relative permeability curve in the process of numerical simulation.In this article, capillary number (Ca), defined as the dimensionless ratio of viscous force to capillary force, is used to characterize the relationship between water drive velocity and residual oil. Second, a new Boltzmann (BG) equation is proposed to match the nonlinear relationship between Ca and residual oil. The BG equation is a continuous function, which is very beneficial to the stability of numerical calculation. Finally, a new reservoir numerical simulator is established which captures the dynamic variation of residual oil saturation with water drive velocity in a water flooding reservoir based on the black oil model. The new simulator was verified by comparing it with the commercial reservoir simulator ECLIPSE and experimental data. The simulation results show that compared with the common model, the model considering the dynamic variation of residual oil saturation with water drive velocity reduced the residual oil saturation near the main flow line after enhanced injection rate. The oil phase flow capacity in the model is enhanced, the water cut is decreased, and the oil recovery rate is higher. The history matching of the S oilfield in Bohai Bay is achieved with the new simulator, and the history matching accuracy is obviously higher than that of Eclipse. The findings of this study can help with a better understanding of the distribution law and flow law of remaining oil in the high water cut stage of the reservoir and have good theory and application value for water flooding offshore oilfields