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A secondary structure in single-stranded DNA refers to its propensity to undergo self-folding, leading to functional inactivity and irreparable failures within DNA storage systems. Consequently, the property of secondary structure avoidance (SSA) becomes a crucial criterion in the design of single-stranded DNA sequences for DNA storage, as it prohibits the inclusion of reverse-complement subsequences that contribute to such structures. This work is specifically focused on addressing the avoidance of secondary structures in single-stranded DNA sequences. We propose a novel sequence replacement approach, which successfully resolves the SSA problem under conditions where the stem exceeds a length of 2 log 2 ⁡ n + 2 , and the loop is of length k ≥ 4 . These parameters have been carefully chosen to closely resemble the real-world scenarios encountered in biochemical processes, enhancing the practical relevance of our study., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

The buoyancy of Microcystis colonies determines the occurrence and dominance of bloom on the water surface. Besides the cell density regulation and the formation of larger size aggregates, increases in cell volume per colony (V cell ) and the colony's compactness (i.e., volume ratio of cells to the colony, VR) may promote Microcystis colony buoyancy. Yet only a few studies have studied the relationship between the internal structure variation of colonies and their buoyancy, and the co-regulatory role of V cell and VR of Microcystis colonies in the floating velocity (FV) remains largely unexplored. In the present study, we optimized a method for measuring the compactness of Microcystis colonies based on the linear relationship between total V cell and chlorophyll a. Different relationships between the VRs and FVs were observed with different colony size and V cell range groups. Both field and laboratory experiments showed that FV/(D 50 , median diameter) 2 had a significant linear relationship with VR, indicating that the cell density and extracellular polysaccharides were unchanged over a short time period and could be estimated via the slope and intercept of a fitted line. We also constructed a functional relationship between FV, VR, and V cell and found that high VR and V cell can promote Microcystis buoyancy. This means that increasing cell compactness or V cell may be an active regulation strategy for Microcystis colonies to promote buoyancy. Therefore, quantifying the internal structure of Microcystis colonies is strongly recommended for the assessment of Microcystis bloom development and their management. Graphical abstract.

Background: The blocking of the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis has been found to have an anticancer activity against various types of cancer by enhancing T cell immunity, while there are no studies linking the PD-1/PD-L1 axis to chemotherapy drugs in osteosarcoma (OS). The present study aimed to investigate the effects of blocking PD-1/PD-L1 axis on the cisplatin chemotherapy in OS in vitro and in vivo., Methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to detect PD-L1 mRNA in OS tissues. Cell proliferation and apoptosis were measured by Cell Counting Kit-8 (CCK-8) and flow cytometry assays, respectively. In vivo, the syngeneic mice were treated with cisplatin and anti-PD-1 antibody alone or jointly., Results: In this study, it revealed that PD-L1 mRNA was highly expressed in OS tissues. Further inhibitory evaluation showed that the K7M2-LV cells (PD-L1 overexpression) co-cultured with PD-1 + lymphocytes could promote K7M2 cell proliferation. Meanwhile, the combination of anti-PD-1 antibody and cisplatin significantly decreased the proliferation and increased the apoptosis of K7M2 cells in a co-culture system. In vivo, the combination of anti-PD-1 antibody and cisplatin significantly inhibited tumor growth, while the mechanisms did not involve regulatory T cells., Conclusion: The present data suggested that the blocking of PD-1/PD-L1 axis had a positive prognostic value, which can enhance the chemotherapeutic effect of cisplatin in OS. These findings provide a rationale for utilizing PD1/PD-L1 blocking antibodies as a single agent to cure refractory OS in patients receiving cisplatin treatment.