The effect of NaCl on reproductive development was investigated in euhalophyte Suaeda salsa L. under controlled conditions. Results showed that NaCl promoted the reproductive growth of S. salsa and 200 mM NaCl was optimal. This was reflected in the increases of seed yield, seed number, flower number per plant and leaf axil, 1000 seed weight, as well as a decrease in flower abortion percentage with supply of NaCl. NaCl reduced the flower abortion percentage by increasing stigma receptivity instead of pollen viability. The Na+ and Cl– concentration in petals, stems and leaves were increased significantly but slightly in stamen and pistil. In contrast, the K+ concentration decreased markedly in leaves, stems and petals but a little in stamen and pistil. The Na+ and Cl– concentrations also increased significantly in seed from mother plants exposed to NaCl, whereas K+ decreased. However, seed quality was not influenced. Our results showed that high concentration of NaCl markedly increases the seed number and quality of S. salsa primarily via increasing flower number and fertility and S. salsa develops strategy to maintain ion homeostasis in reproductive organs for the generation. These factors play a pivotal role in setting up plant populations in saline environment.

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Modern digital cameras use sensor arrays that correspond to millions of pixels in the image. However, the single-pixel imaging (SPI) system captures images by a single-pixel detector without the need for a pixelated sensor to have spatial resolution. Research on SPI has attracted more and more attention from scholars. The SPI system provides a potential low-cost solution for sensing beyond the visible spectrum. Also, it is suitable for obtaining information in low light, high absorption, and backscattering conditions. This paper reviews the developments and performance of SPI, the research on static and dynamic objects, the methods of the real-time three-dimensional imaging and video. The potential applications of SPI are further explained in detail.

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Dendritic cells (DCs) are antigen presenting cells capable of inducing specific immune responses against microbial infections, transplant antigens, or tumors. DCs have been shown to possess a high plasticity showing different phenotypes in response to their microenvironment. For example, tumor-associated DCs can acquire an angiogenic phenotype thus promoting tumor growth. Further, DCs cultured in vitro under different conditions are able to upregulate the expression of endothelial markers and to express angiogenic factors. Indeed, it has been shown that soluble factors such as VEGF of PGE-2, that are present in the microenvironment of several tumors, affect the biology of these cells. We hypothesize that in addition to soluble factors the adhesion to different substrates will also define the phenotype and function of DCs. Herewith we demonstrate that murine myeloid(m) DCs upregulate endothelial markers such as VE-Cadherin, and to a lesser extent TIE-2, and decrease their immune capabilities when cultured on solid surfaces as compared with the same cells cultured on ultra-low binding (ULB) surfaces. On the other hand, the expression of angiogenic molecules at the level of RNA was not different among these cultures. In order to further investigate this phenomenon we used the murine ID8 model of ovarian cancer which can generate solid tumors when cancer cells are injected subcutaneously or a malignant ascites when they are injected intraperitoneally. This model gave us the unique opportunity to investigate DCs in suspension or attached to solid surfaces under the influence of the same tumor cells. We were able to determine that DCs present in solid tumors showed higher levels of expression of endothelial markers and angiogenic molecules but were not able to respond to inflammatory stimuli at the same extent as DCs recovered from ascites. Moreover, mDCs cultured on ULB surfaces in the presence of tumor factors do not expressed endothelial markers. Taking into account all these data we consider that tumor factors might be responsible for inducing angiogenic properties in DCs, but that in some settings the expression of endothelial markers such as VE-Cadherin and TIE-2 might be a function of attachment to solid surfaces and independent of the angiogenic properties of these cells.

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance.With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out. CCS Concepts: • Networks → Network algorithms; • Theory of computation → Design and analysis of algorithms; • Computing methodologies → Machine learning approaches; Knowledge representation and reasoning.

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