The involvement of estrogen and its receptors in the development of cancer has been known for years. However, the exact mechanism responsible is far from clear. The estrogen-mediated carcinogenic process is complicated by recent findings, which reveal that estrogens have multiple functions in cells, which can be either adverse or beneficial, and that the effects of estrogen may be cell-type or organ dependent. The estrogenic effect may be also greatly influenced by the state of two estrogen receptors, ERα and ERβ. This review will discuss the role and function of estrogens and its receptors in cancers of three categories: (1) Breast cancer and gynecologic cancers, (2) Cancers of endocrine organs, (3) Lung cancer and cancers of digestive system. We will also review some novel treatments aiming to interfere with relevant pathways mediated by estrogens and its receptors. © 2008 Wiley Periodicals, Inc. Med Res Rev, 28, No. 6, 954–974, 2008

Estrogen and its receptors in cancer

Estrogen and progesterone receptor isoform distribution through the menstrual cycle in uteri with and without adenomyosis

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.

Neural Multi-Task Recommendation from Multi-Behavior Data

Estrogen receptor-regulated microRNAs contribute to the BCL2/BAX imbalance in endometrial adenocarcinoma and precancerous lesions

P2Y2 receptor-mediated modulation of estrogen-induced proliferation of breast cancer cells

DeoxyriboNucleic Acid (DNA) encryption is a new encryption method that appeared along with the research of DNA nanotechnology in recent years. Due to the complexity of biology in DNA nanotechnology, DNA encryption brings in an additional difficulty in deciphering and, thus, can enhance information security. As a new approach in DNA nanotechnology, DNA strand displacement has particular advantages such as being enzyme free and self-assembly. However, the existing research on DNA-strand-displacement-based encryption has mostly stayed at a theoretical or simulation stage. To this end, this paper proposes a new DNA-strand-displacement-based encryption framework. This encryption framework involves three main strategies. The first strategy was a tri-phase conversion from plaintext to DNA sequences according to a Huffman-coding-based transformation rule, which enhances the concealment of the information. The second strategy was the development of DNA strand displacement molecular modules, which produce the initial key for information encryption. The third strategy was a cyclic-shift-based operation to extend the initial key long enough, and thus increase the deciphering difficulty. The results of simulation and biological experiments demonstrated the feasibility of our scheme for encryption. The approach was further validated in terms of the key sensitivity, key space, and statistic characteristic. Our encryption framework provides a potential way to realize DNA-strand-displacement-based encryption via biological experiments and promotes the research on DNA-strand-displacement-based encryption.

/pdf/an-operational-dna-strand-displacement-encryption-approach-rhtqme7x.pdf

An Operational DNA Strand Displacement Encryption Approach

The aim of this study was to explore how to modulate the expression of estrogen receptors (ER) α and β and to verify the role of ERα and β in relationship to estrogen and tamoxifen (TAM). A series of oligodeoxyribonucleotides (ODN) corresponding to regions of the ERα or β was tested in human endometrial cancer cell lines (HEC-1B). The change in HEC-1B proliferation in response to 17β-estradiol (E2) and TAM under the impact of antisense ODN was studied. The results of the study are as follows: 1) transfection with antisense ODN significantly inhibited ERα and ERβ protein production, 2) the cells lost the ability to proliferate in response to E2 after transfection with ERα antisense ODN especially at 24, 48, and 72 h. There was no obvious change in response to E2 in HEC-1B cell lines that were transfected with ERβ antisense ODN, and 3) after transfection with ERα antisense ODN, HEC-1B cells lost the ability to proliferate in response to TAM at 48 h. This inhibition was also observed after transfection with ERβ antisense ODN at 24 h. ERα may be the primary receptor in the proliferation of HEC-1B cells in response to E2. Both ERα and ERβ are involved in the agonist impact of TAM on endometrial cancer cells.

https://ijgc.bmj.com/content/ijgc/16/4/1656.full.pdf

Function of estrogen receptor isoforms α and β in endometrial carcinoma cells

Self-assembled DNA nanostructures significantly contribute to DNA nanotechnology. Algorithmic guiding of the assembly of DNA arrays remains a challenge in nanoarchitecture. Usually, the more sophisticated a DNA nanoarchitecture, the more DNA connections with specific sequences are required. This study aimed to investigate the feasibility of using the minimum pairs of DNA connection strands to implement algorithm-based self-assembly with finite DNA origamis. We found that the DNA origami linking complexity was markedly reduced. By rotating and turning the origami tile in different linking directions, we obtained 2 × 2 arrays of DNA origamis using a pair of DNA connections, 2 × 4 arrays using two pairs of DNA connections, and 4 × 4 arrays using three pairs of connection strands. We further analysed the effects of distortion on array formation. Overall, this study presents a hierarchical assembly strategy with minimal connections to generate multi-scale DNA arrays.

Multiform DNA origami arrays using minimal logic control

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user’s behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user’s preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user’s demand on the news diversity. We have three mainsteps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user’s interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

The graph-based behavior-aware recommendation for interactive news

DNA, as an excellent nano-engineering material, contributes to a new computing model, namely, DNA computing. This model is a type of biological computing, which takes advantage of the high density and high parallelism of molecules. One of the current methods of implementing DNA computing is to construct DNA circuits, among which the toehold-mediated DNA strand displacement technique is an important method. The hybridization of toehold domains provides the start position and accelerates the branch migration process. Toehold-based DNA combinatorial displacement is a practical method for designing and implementing DNA circuits. In this paper, we designed and simulated a multiplexer using the DNA combinatorial displacement mechanism to verify its practicability. Additionally, we improved and optimized the existing logic INHIBIT gate by leveraging the DNA combinatorial displacement mechanism so that the DNA strands in the entire chemical reaction network (CRN) system are capable of coexisting in large quantities. Moreover, we applied this improvement to the demultiplexer. Our method provides more capabilities to larger and more complicated DNA integrated circuits.

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Congzhou Chen

Papers

An Operational DNA Strand Displacement Encryption Approach

Function of estrogen receptor isoforms α and β in endometrial carcinoma cells

Multiform DNA origami arrays using minimal logic control

The graph-based behavior-aware recommendation for interactive news

Biochemical Logic Circuits Based on DNA Combinatorial Displacement