Re: Free Download Awakening Of Sleeping Beauty
Vita Strait
Propionic acid is used primarily as a food preservative with smaller applications as a chemical building block for the production of many products including fabrics, cosmetics, drugs, and plastics. Biological production using propionibacteria would be competitive against chemical production through hydrocarboxylation of ethylene if native producers could be engineered to reach near-theoretical yield and good productivity. Unfortunately, engineering propionibacteria has proven very challenging. It has been suggested that activation of the sleeping beauty operon in Escherichia coli is sufficient to achieve propionic acid production. Optimising E. coli production should be much easier than engineering propionibacteria if tolerance issues can be addressed.
Propionic acid is produced in E. coli via the sleeping beauty mutase operon under anaerobic conditions in rich medium via amino acid degradation. We observed that the sbm operon enhances amino acids degradation to propionic acid and allows E. coli to degrade isoleucine. However, we show here that the operon lacks an epimerase reaction that enables propionic acid production in minimal medium containing glucose as the sole carbon source. Production from glucose can be restored by engineering the system with a methylmalonyl-CoA epimerase from Propionibacterium acidipropionici (0.23 0.02 mM). 1-Propanol production was also detected from the promiscuous activity of the native alcohol dehydrogenase (AdhE). We also show that aerobic conditions are favourable for propionic acid production. Finally, we increase titre 65 times using a combination of promoter engineering and process optimisation.
Escherichia coli is a robust microorganism. The heterologous production of organic acids and alcohols by E. coli has proven to be a suitable option as evidenced by the commercial success of succinate production [9] by Requette, Bioamber, Reverdia and Myriad [10, 11]; 1,3-propanediol (PDO) by DuPont Tate & Lyle [12,13,14]; and 1,4-butanediol (BDO) by Genomatica [15,16,17]. Amongst the many products that have been produced in E. coli, PA was first produced in E. coli by Kandasamy et al. [6] using the acrylate pathway. More recently, several investigations have reported PA production using the silent native operon known as the sleeping beauty mutase, sbm [18,19,20,21,22]. The sbm operon encodes four enzymes, three of them allegedly able to complete a succinate dissimilation cycle: methylmalonyl-CoA mutase (ScpA), biotin-independent methylmalonyl-CoA carboxylase (ScpB), and propionyl-CoA:succinate CoA transferase (ScpC) (Fig. 1) [23]. The fourth gene in the operon (argK) corresponds to a membrane-bound ATP kinase, and its role in the cycle has not yet been elucidated.
Then I saw a vision of the older women arising as well. They arose in a renewed strength, beauty and authority. A queens crown was placed upon their heads and as they stood up there was a sense of awe in all who saw them. These queens stood with great authority that demanded attention. It was like everything around them stood still in awe and waited with baited breath for what she would say.
I believe that this vision of the princesses speaks of the emerging of young women in ministry that will capture the attention of many, due to the way they represent the King; in a humble authority that reveals His beauty.
We investigate publications in medical research that have gone unnoticed for a number of years after being published and then suddenly become cited to a significant degree. Such publications are called Sleeping Beauties (SBs). This study focuses on SBs that are cited in patents. We find that the increasing trend of the relative number of SBs comes to an end around 1998. However, still a constant fraction of publications becomes an SB. Many SBs become highly cited publications, they even belong to the top-10 to 20% most cited publications in their field. We measured the scaling of the number of SBs in relation to the sleeping period length, during-sleep citation-intensity, and with awakening citation-intensity. We determined the Grand Sleeping Beauty Equation for these medical SBs which shows that the probability of awakening after a period of deep sleep is becoming rapidly smaller for longer sleeping periods and that the probability for higher awakening intensities decreases extremely rapidly. The exponents of the scaling functions show a time-dependent behavior which suggests a decreasing occurrence of SBs with longer sleeping periods. We demonstrate that the fraction of SBs cited by patents before scientific awakening exponentially increases. This finding shows that the technological time lag is becoming shorter than the sleeping time. Inventor-author self-citations may result in shorter technological time lags, but this effect is small. Finally, we discuss characteristics of an SBs that became one of the highest cited medical papers ever.
To make a comparison of the results in this study for the medical research fields with our previous results [6, 7, 8] for the natural sciences possible, we follow similar procedures for data collection and data analysis. We used the WOS categories presented in S1 Table to delineate the medical research fields. After discussing the identification of medical SBs on the basis of a set of measurement variables and the calculation of numbers and trends as a function of time, we analyze the scaling of the number of SBs with sleeping period, sleeping intensity and awakening citation intensity. We then identify those SBs that are cited in patents (SB-SNPRs) and analyze the time lag between publication year and year of the first patent citation. In this context we also focus on the role of inventor-author combinations. Finally, we discuss the characteristics of an extremely highly cited medical SB-SNPR in particular on the basis of co-citation and bibliographic coupling maps.
With a fast and efficient search algorithm written in SQL which can be applied to the CWTS enhanced Web of Science (WoS) database (starting year 1980) [6, 7, 8] we can tune four main variables: (1) length of the sleep in years after publication (sleeping period s); (2) depth of sleep in terms of the citation rate during the sleeping period (cs); (3) awakening period in years after the sleeping period (a); and (4) awakening citation-intensity in terms of the citation rate during the awakening period (ca). We define cs = 0 as a coma, cs between 0.1 and 0.5 as a very deep sleep, and cs between 0.6 and 1.0 as a deep sleep. In the algorithm we can apply a threshold value cs(max) for the citation rate during the sleeping period. For instance, if we take cs(max) = 1.0 we cover the range from cs = 0 (coma) to cs = 1.0 (deep sleep). In this study we use a five-years awakening period immediately after the sleeping period, i.e., a(max) = a(min) = 5. Furthermore, we require that the SBs must have an awakening intensity of at least, on average, 5 citations per year, i.e., ca(min) = 5.0. Thus, for a complete analysis of the SBs, we need a total measuring period equal to sleeping period plus awakening period of five years (a(max) = a(min) = 5). As a consequence, the longer s, the less publication years we have for our analysis. For instance, if s = 20, we need a time period 20+5 = 25 years, and given that 2016 is the last year of the citation measurements only SBs (with s = 20) published between 1980 and 1992 can be considered. On the other hand, for SBs with s = 5 we need 5+5 = 10 years and thus 2007 is in this case the last publication year of the measurement.
The above definition of sleeping and awakening period can be written as follows. Given that t1 is the year of publication and c(ti) is the number of citations (excluding self-citations) in any year ti then ifthe sleeping time is n years in time period [t1 , tn] and the subsequent time period
The annual numbers N(s) of SBs are determined with our SQL search algorithm. These numbers are given in S2 Table for all SBs with s = 5, 10, 15, 20, 25, and 30 and for five values of cs(max). These numbers are what they are but if we want to find out whether the number of SBs is increasing or not, we have to normalize the numbers relative to the total number of all medical papers, i.e., normalization on the basis of the growth factors (i.e., dividing N(s) by the growth factor of the relevant year), see Table 2. As an example, we show in S3 Table the results for SBs with s = 5. The effect of normalization is shown in Figs 2 and 3. In Fig 2 the trend of the real (not-normalized) numbers of all medical publications and of the SBs with sleeping time s = 5 is given. Notice that the number of SBs with s = 5 is about three orders of magnitudes lower than the total number of medical publications. We see that from the late 1990s the real number of SBs shows a sort of oscillating behavior rather than a clear increase whereas the total number of medical papers covered by the WoS still increases. This effect is even more clear if we normalize the numbers as discussed above, see Fig 3 where we also included the normalized numbers n(s) for SBs with s = 10 and 15.
The SBs with s = 5 have the shortest sleeping period and thus they can be analyzed for the most recent times, until 2007. The numbers of these SBs show considerable fluctuations during the measuring period. Looking at the normalized numbers, we find that the relative occurrence of SBs with s = 5 doubled since the early 1980s with an average increase of 4% up to about the late 1990s. But in the more recent years the increase of the relative occurrence of SBs with s = 5 comes to a halt. In fact, the relative occurrence of SBs with s = 5 in 2007 is about the same as twenty years before. It confirms our observations in the natural science research fields and supports our conjecture that the expanding worldwide facilities to access scientific publications seems to have stopped increasing trends in the occurrence of SBs. However, it does not prevent that, also similar to the natural sciences and engineering, a more a more or less constant fraction of publications still becomes an SB.
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