The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

Bisphenol A (BPA), a synthetic additive used to harden polycarbonate plastics and epoxy resin, is ubiquitous in our everyday environment. Many studies have indicated detrimental effects of BPA on the mammalian reproductive abilities. This study is aimed to test the potential effects of BPA on methylation of imprinted genes during oocyte growth and meiotic maturation in CD-1 mice. Our results demonstrated that BPA exposure resulted in hypomethylation of imprinted gene Igf2r and Peg3 during oocyte growth, and enhanced estrogen receptor (ER) expression at the levels of mRNA and protein. The relationship between ER expression and imprinted gene hypomethylation was substantiated using an ER inhibitor, ICI182780. In addition, BPA promoted the primordial to primary follicle transition, thereby speeding up the depletion of the primordial follicle pool, and suppressed the meiotic maturation of oocytes because of abnormal spindle assembling in meiosis I. In conclusion, neonatal exposure to BPA inhibits methylation of imprinted genes during oogenesis via the ER signaling pathway in CD-1 mice.

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this paper, an in-depth survey of the current status of parallel sequential pattern mining (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state of the art parallel SPM. We review the related work of parallel sequential pattern mining in detail, including partition-based algorithms for PSPM, Apriori-based PSPM, pattern growth based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative / weighted / utility sequential pattern mining, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.In the field of data mining, pattern mining has become an important task for a wide range of real-world applications. Pattern mining consists of discovering interesting, useful, and unexpected patterns in databases. This field of research has emerged in the 1990s with the Apriori algorithm [3] which was proposed by Agrawal

Bisphenol A (BPA) is an estrogenic environmental toxin widely used for the production of plastics. Frequent human exposure to this chemical has been proposed to be a potential public health risk. The objective of this study was to assess the effects of BPA on germ cell cyst breakdown and primordial follicle formation. Pregnant mice were treated with BPA at doses of 0, 0.02, 0.04, 0.08 mg/kg body weight/day from 12.5 day postcoitum. BPA was delivered orally to pregnant female mice. A dose-response relationship was observed with increased BPA exposure level associated with more oocytes in germ cell cyst and less primordial follicle at postnatal day 3 (P < 0.01). Progression to meiosis prophase I of oocytes was delayed in the 0.08 mg/kg bw/day treated group (P < 0.01). Decreased mRNA expression of specific meiotic genes including Stra8, Dmc1, Rec8 and Scp3 were observed. In conclusion, BPA exposure can affect the formation of primordial follicle by inhibiting meiotic progression of oocytes.

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