Engineering acetyl-CoA supply and ERG9 repression to enhance mevalonate production in Saccharomyces cerevisiae.

Mevalonate is a key precursor in isoprenoid biosynthesis and a promising commodity chemical. Although mevalonate is a native metabolite in Saccharomyces cerevisiae, its production is challenged by the relatively low flux toward acetyl-CoA in this yeast. In this study we explore different approaches to increase acetyl-CoA supply in S.

Endoscopic sinonasal meningoencephalocele repair: a 13-year experience with stratification by defect and reconstruction type.

Objective: Sinonasal meningoencephalocele is a rare defect, with varying etiologies and treatment strategies. Here we present the largest published series from a single institution of patients with endoscopic repair. The primary goal is to examine rates of success with consideration to accompanying patient demographic data.

Minimizing morbidity in endoscopic pituitary surgery: outcomes of the novel nasoseptal rescue flap technique.

Objective: The novel nasoseptal rescue flap has been proven to provide complete coverage of dural defects that may be encountered during endoscopic pituitary surgery through cadaveric studies. In this case series, the authors report outcomes from the first cohort of patients who had a nasoseptal rescue flap raised prior to surgery.

Study Design: Case series with chart review.

Pediatric acute sinusitis: predictors of increased resource utilization.

Objective: To determine variations in resource utilization in the management of pediatric acute sinusitis.

Study Design: Retrospective analysis of a publicly available national dataset.

Methods: The Kids' Inpatient Database 2006 was analyzed using ICD-9 codes for acute sinusitis.

Ranking chemical structures for drug discovery: a new machine learning approach.

With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure-activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods - namely, ranking methods, which are designed to directly optimize ranking performance - have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications.

Predicting alternate structure attainment and amyloidogenesis: a nonlinear signal analysis approach.

Chain hydrophobicity values have been used in prediction of alternate structure attainment by a polypeptide. Nonlinear signal analysis on the hydrophobicity values gives important clues about the propensities of particular stretches of a protein to form local or nonlocal contacts. These contacts determine the folding behavior of a polypeptide and helps in predicting the final structure that can be attained.