Composition of culture medium for mass production of Mycoplasma hyopneumoniae was optimized using a response surface methodology (RSM). Initially, the influence of glucose, thallium acetate, fresh yeast extract, horse serum, and porcine serum on the production of mycoplasmal protein was assessed using a 'one factor at a time' technique. Next, factors with a significant effect, including fresh yeast extract, and horse and porcine sera, were selected for further optimization using a central composite design (CCD) of RSM. The experimental results were fitted into a second order polynomial model equation. Estimated optimal condition of the factors for maximum production of mycoplasmal protein (i.e., triple-fold increase from 0.8 mg/L produced by basal mycoplasma media to 2.5 mg/L) was 10.9% fresh yeast extract, 15% horse serum, and 31.5% porcine serum (v/v). For the optimized conditions, a 2.96 mg/L experimental result was observed, similar to the estimated optimal conditions result of the CCD.
The trends of technical terms express the changes of particular subjects in a specific research field over time. However, the amount of academic literature and patent data is too large to be analyzed by human resources. In this paper, we propose a method that can detect and analyze the trends of terms by modeling the life cycle of the terms. The proposed method is composed of the following steps. First, the technical terms are extracted from academic literature data, and the TDVs(Term Dominance Values) of terms are computed on a periodic basis. Based on the TDVs, the life cycles of terms are modeled, and technical terms with similar temporal patterns of the life cycles are classified into the same trends class. The experiments shown in this paper is performed by exploiting the NDSL academic literature data maintained by KISTI.
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