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The understanding of the molecular defensive mechanism of Echinacea purpurea (L.) Moench against polycyclic aromatic hydrocarbon (PAH) contamination plays a key role in the further improvement of phytoremediation efficiency. Here, the responses of E. purpurea to a defined mixture of phenanthrene (PHE) and pyrene (PYR) at different concentrations or a natural mixture from an oilfield site with a history of several decades were studied based on transcriptomics sequencing and widely targeted metabolomics approaches. The results showed that upon 60-day PAH exposure, the growth of E. purpurea in terms of biomass (p < 0.01) and leaf area per plant (p < 0.05) was negatively correlated with total PAH concentration and significantly reduced at high PAH level. The majority of genes were switched on and metabolites were accumulated after exposure to PHE + PYR, but a larger set of genes (3964) or metabolites (208) showed a response to a natural PAH mixture in E. purpurea. The expression of genes involved in the pathways, such as chlorophyll cycle and degradation, circadian rhythm, jasmonic acid signaling, and starch and sucrose metabolism, was remarkably regulated, enhancing the ability of E. purpurea to adapt to PAH exposure. Tightly associated with transcriptional regulation, metabolites mainly including sugars and secondary metabolites, especially those produced via the phenylpropanoid pathway, such as coumarins, flavonoids, and their derivatives, were increased to fortify the adaptation of E. purpurea to PAH contamination. These results suggest that E. purpurea has a positive defense mechanism against PAHs, which opens new avenues for the research of phytoremediation mechanism and improvement of phytoremediation efficiency via a mechanism-based strategy.

These days, with the increasingly widespread employment of sensors, particularly those attached to vehicles, the collection of spatial data is becoming easier and more accurate. As a result, many relevant areas, such as spatial crowdsourcing, are gaining ever more attention. A typical spatial crowdsourcing scenario involves an employer publishing a task and some workers helping to accomplish it. However, most of previous studies have only considered the spatial information of workers and tasks, while ignoring individual variations among workers. In this paper, we consider the Software Development Team Formation (SDTF) problem, which aims to assemble a team of workers whose abilities satisfy the requirements of the task. After showing that the problem is NP-hard, we propose three greedy algorithms and a multiple-phase algorithm to approximately solve the problem. Extensive experiments are conducted on synthetic and real datasets, and the results verify the effectiveness and efficiency of our algorithms.

With the rapid development of mobile internet and online to offline marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called Top-k team recommendation in spatial crowdsourcing (TopkTR) problem. We prove that the TopkTR problem is NP-hard and designs a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. In addition, we study a variant of the TopkTR problem, called TopkTRL, where a team leader is appointed among each recommended team of crowd workers in order to coordinate different crowd workers conveniently, and the aforementioned framework can be extended to address this variant. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.