An impulse to exploit: the behavioral turn in data-driven marketing

Industry advocates argue that by tailoring services and commercial solicitations to match media users’ personal interests, data-driven marketing benefits both consumers and businesses. This article shows, however, that advertisers and marketers who are taking up ideas and techniques from behavioral economics tell their clients a very different story about the aims and use of digital marketing and consumer surveillance. Listening in on this discourse demonstrates that some digital marketers conceptualize their own practices as forms of social control, appropriating concepts from behavioral economics to identify consumers’ cognitive and affective biases and target their vulnerabilities. Behavioral economics recognizes that economic decisions are not simply dictated by rational self-interest; rather, such choices depend on cognitive heuristics and habits, and can be manipulated through the design of “choice architecture.” This article discusses implications of the behavioral turn in data-driven marketing for critical advertising scholars, public advocates, and regulators.     

Combination of Se-methylselenocysteine,D-α-tocopheryl succinate, β-carotene,and L-lysine can prevent cancer metastases using as an adjuvant therapy

ObjectivePrimary tumor treatment through surgical resection and adjuvant therapy has been extensively studied, but there is a lack of effective strategies and drugs for the treatment of tumor metastases. Here, we describe a functional product based on a combination of compounds, which can be used as an adjuvant therapy and has well-known mechanisms for inhibiting cancer metastases, improving anti-cancer treatment, and enhancing immunity and antioxidant capacity. Our designed combination, named MVBL, consists of four inexpensive compounds: L-selenium-methylselenocysteine (MSC), D-‍α‍-tocopheryl succinic acid (VES), β‍-carotene (β‍-Ca), and L-lysine (Lys).MethodsThe effects of MVBL on cell viability, cell cycle, cell apoptosis, cell migration, cell invasion, reactive oxygen species (ROS), and paclitaxel (PTX)-combined treatment were studied in vitro. The inhibition of tumor metastasis, antioxidation, and immune enhancement capacity of MVBL were determined in vivo.ResultsMVBL exhibited higher toxicity to tumor cells than to normal cells. It did not significantly affect the cell cycle of cancer cells, but increased their apoptosis. Wound healing, adhesion, and transwell assays showed that MVBL significantly inhibited tumor cell migration, adhesion, and invasion. MVBL sensitized MDA-MB-231 breast cancer cells to PTX, indicating that it can be used as an adjuvant to enhance the therapeutic effect of chemotherapy drugs. In mice, experimental data showed that MVBL inhibited tumor metastasis, prolonged their survival time, and enhanced their antioxidant capacity and immune function.ConclusionsThis study revealed the roles of MVBL in improving immunity and antioxidation, preventing tumor growth, and inhibiting metastasis in vitro and in vivo. MVBL may be used as an adjuvant drug in cancer therapy for improving the survival and quality of life of cancer patients.     

国产十种乌头的染色体研究

本文描述了我国产毛茛科乌头属的10个种的染色体数目和形态。  根据该属染色体基 数为8(x＝8)，可将这些种归为二倍体、四倍体、六倍体和八倍体种。  10种乌头的染色体在数目、大小、结构上表现出相关性。其中具根状茎而多年生的种多为二倍体，染色体形态较大，第3-7号多为近端着丝点(st)染色体, 具块茎而二年生的种多为多倍体，染色体形态较小，第3-7号多为近中着丝点(sm)染色体。染色体演化方向可能是二倍体→多倍体、大→ 小、st→sm。这进一步证明根状茎种较块茎种原始。根据染色体数目和形态，又可将这些种的染色体分为两类。这进一步支持了目前分类学上的牛扁亚属和乌头亚属的划分。本文还讨论了这些种的有关分类问题。     

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.