Biomedical subjects

Qing Tao

Publications and source records attributed to Qing Tao.

3 recordsLinked to original sources

PubMed2024

Ammonia-oxidizing bacteria and archaea exhibit differential nitrogen source preferences.

Ammonia-oxidizing microorganisms (AOM) contribute to one of the largest nitrogen fluxes in the global nitrogen budget. Four distinct lineages of AOM: ammonia-oxidizing archaea (AOA), beta- and gamma-proteobacterial ammonia-oxidizing bacteria (β-AOB and γ-AOB) and complete ammonia oxidizers (comammox), are thought to compete for ammonia as their primary nitrogen substrate. In addition, many AOM species can utilize urea as an alternative energy and nitrogen source through hydrolysis to ammonia. How the coordination of ammonia and urea metabolism in AOM influences their ecology remains poorly understood. Here we use stable isotope tracing, kinetics and transcriptomics experiments to show that representatives of the AOM lineages employ distinct regulatory strategies for ammonia or urea utilization, thereby minimizing direct substrate competition. The tested AOA and comammox species preferentially used ammonia over urea, while β-AOB favoured urea utilization, repressed ammonia transport in the presence of urea and showed higher affinity for urea than for ammonia. Characterized γ-AOB co-utilized both substrates. These results reveal contrasting niche adaptation and coexistence patterns among the major AOM lineages.

Archaea↗

PubMed2005

Posterior probability support vector machines for unbalanced data.

This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter v is obtained which is similar to the v-SVM developed by Schölkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine v. The main advantage of an PPSVM classifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.

Algorithms↗

PubMed2002

A critical role for a tyrosine residue in the cannabinoid receptors for ligand recognition.

Previous mutation and modeling studies have identified an aromatic cluster in the transmembrane helix (TMH) 3-4-5 region as important for ligand binding at the CB(1) and CB(2) cannabinoid receptors. Through novel mixed mode Monte Carlo/Stochastic Dynamics (MC/SD) calculations, we tested the importance of aromaticity at position 5.39(275) in CB(1). MC/SD calculations were performed on wild-type (WT) CB(1) and two mutants, Y5.39(275)F and Y5.39(275)I. Results indicated that while the CB(1) Y5.39(275)F mutant is very similar to WT, the Y5.39(275)I mutant shows pronounced topology changes in the TMH 3-4-5 region. Site-directed mutagenesis studies of tyrosine 5.39 to phenylalanine (Y-->F) or isoleucine (Y-->I) in both CB(1) and CB(2) were performed to determine the functional role of this amino acid in each receptor subtype. HEK 293 cells transfected with mutant receptor cDNAs were evaluated in radioligand binding and cyclic AMP assays. The CB(1) mutant and WT receptors were also co-expressed with G-protein-coupled inwardly rectifying channels (GIRK1 and GIRK4) in Xenopus oocytes to assess functional coupling. The Y-->F mutation resulted in cannnabinoid receptors with subtle differences in WT binding and signal transduction. In contrast, the Y-->I mutations produced receptors that could not produce signal transduction or bind to multiple cannabinoid compounds. However, immunofluorescence data indicate that the Y-->I mutation was compartmentalized and expressed at a level similar to that of the WT cannabinoid receptor. These results underscore the importance of aromaticity at position CB(1) 5.39(275) and CB(2) 5.39(191) for ligand recognition in the cannabinoid receptors.

Amino Acid Sequence↗