New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

MiR-33 Contributes to the Regulation of Cholesterol Homeostasis

Abstract: Cholesterol metabolism is tightly regulated at the cellular level. Here we show that miR-33, an intronic microRNA (miRNA) located within the gene encoding sterol-regulatory element-binding factor-2 (SREBF-2), a transcriptional regulator of cholesterol synthesis, modulates the expression of genes involved in cellular cholesterol transport. In mouse and human cells, miR-33 inhibits the expression of the adenosine triphosphate-binding cassette (ABC) transporter, ABCA1, thereby attenuating cholesterol efflux to apolipoprotein A1. In mouse macrophages, miR-33 also targets ABCG1, reducing cholesterol efflux to nascent high-density lipoprotein (HDL). Lentiviral delivery of miR-33 to mice represses ABCA1 expression in the liver, reducing circulating HDL levels. Conversely, silencing of miR-33 in vivo increases hepatic expression of ABCA1 and plasma HDL levels. Thus, miR-33 appears to regulate both HDL biogenesis in the liver and cellular cholesterol efflux.

Robust Classification for Imprecise Environments

Abstract: In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.

Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Abstract: Summary. Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AICc, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AICc can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AICc avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other 'classical' approaches (such as generalized cross-validation or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AICc-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.

Addition of ifosfamide and etoposide to standard chemotherapy for Ewing's sarcoma and primitive neuroectodermal tumor of bone.

Abstract: Background Ewing's sarcoma and primitive neuroectodermal tumor of bone are closely related, highly malignant tumors of children, adolescents, and young adults. A new drug combination, ifosfamide and etoposide, was highly effective in patients with Ewing's sarcoma or primitive neuroectodermal tumor of bone who had a relapse after standard therapy. We designed a study to test whether the addition of these drugs to a standard regimen would improve the survival of patients with newly diagnosed disease. Methods Patients 30 years old or younger with Ewing's sarcoma, primitive neuroectodermal tumor of bone, or primitive sarcoma of bone were eligible. The patients were randomly assigned to receive 49 weeks of standard chemotherapy with doxorubicin, vincristine, cyclophosphamide, and dactinomycin or experimental therapy with these four drugs alternating with courses of ifosfamide and etoposide. Results A total of 518 patients met the eligibility requirements. Of 120 patients with metastatic disease, 62 were random...