Origin of tetracycline efflux proteins: conclusions from nucleotide sequence analysis

The sequences of six tetracycline efflux proteins and three transport proteins which have some resemblance to them were compared. The tetracycline efflux proteins fall into three families: (i) those encoded by pBR322, RP1, and Tn10 (Escherichia coli); (ii) pT181 (Staphylococcus aureus) and pTHT15 (Bacillus subtilis); and (iii) tet347 (Streptomyces rimosus). There is global sequence homology within each of the first two families, but there is none between the families. The pT181/pTHT15 family shares close homology with the N-terminal half of the methylenomycin A efflux protein (Streptomyces coelicor), while tet347 resembles the C-terminal half. Portions of the N-terminal half of the Tn10-encoded protein show significant resemblance to portions in the N-terminal half of the pT181/pTHT15 family, but this sometimes occurs among transport proteins which do not have a common substrate. Tetracycline efflux proteins, therefore, appear to have arisen on at least two, or possibly three, separate occasions, probably from other transport proteins.     

Modulation of the uptake and retention of estradiol-17beta in the ovine corpus luteum by luteinizing hormone.

The uptake and retention of 3H-estradiol by the ovine corpus luteum (induced by superovulation) was maximal through days 8-12 of the cycle with little or no uptake and retention during luteinization or regression. LH, but not FSH or prolactin, inhibited the uptake and retention of estradiol via stimulation of progesterone synthesis.     

PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks

Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman’s original method which had evidently gone unnoticed since its publication over a decade ago.