The relationship of scientific knowledge development to technological development is widely recognized as one of the most important and complex aspects of technological evolution. This paper adds to our understanding of the relationship through use of a more rigorous structure for differentiating among technologies based upon technological domains (defined as consisting of the artifacts over time that fulfill a specific generic function using a specific body of technical knowledge). The key findings of the work are:Firstly, a Pearson correlation of 0.564 is found between technological relatedness among technological domains based upon patents citing other patents and technological relatedness among technological domains based upon patents citing similar scientific papers (assessed through 176 scientific categories developed by ISI). This result indicates that a large portion (but not all) of technological relatedness is due to relatedness of the underlying scientific categories.Secondly, the overall structure of the links found between scientific categories and technological domains is many-to-many rather than focused indicating a sciencefostered mechanism for fairly broad "spillover": Specific technological domains cite a wide variety of scientific categories (in 2010 the average number of scientific categories cited by the 44 individual technological domains studied was 36); Some scientific categories are cited in a variety of domains (124 scientific categories are cited in 30% or more of the 44 domains over the period studied, 1976-2013).Thirdly, Some evidence is found supporting the co-evolution of science and technology but the evidence is not strong:The lack of clear evidence for co-evolution is interpreted as resulting from the documented complex many-to-many relationship of science categories and technological domains and is not considered evidence against co-evolution.

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SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) is a histone H3K9 methyltransferase that stimulates cell proliferation by methylating AKT, which contributes to drug resistance in multiple myeloma (MM). Lenalidomide is an immunomodulatory agent widely used in the treatment of MM. However, lenalidomide resistance occurs in patients with MM. Currently, the role of SETDB1 in lenalidomide resistance in MM remains unclear. Thus, the present study aimed to explore the functional association between SETDB1 and lenalidomide resistance in MM. The analysis of GEO datasets revealed that SETDB1 was upregulated in lenalidomide-resistant MM cells and that its expression was associated with poor prognosis of patients with MM. Apoptosis analysis revealed that overexpression of SETDB1 in MM cells significantly decreased apoptosis, while knockdown of SETDB1 increased apoptosis. Furthermore, the IC 50 value of lenalidomide in MM cells increased following SETDB1 overexpression and decreased following SETDB1 silencing. Additionally, SETDB1 mediated epithelial-mesenchymal transition (EMT) and activated the PI3K/AKT pathway. Mechanistic analysis revealed that inhibition of PI3K/AKT signaling in MM cells increased apoptosis, sensitized the cells to lenalidomide and inhibited EMT, whereas SETDB1 overexpression inhibited the effects of PI3K/AKT cascade inhibition. In conclusion, the findings of the present study indicated that SETDB1 promoted lenalidomide resistance in MM cells by promoting EMT and the PI3K/AKT signaling pathway. Thus, SETDB1 may be a potential therapeutic target for MM.