Rutgers University

About: Rutgers University is a education organization based out in New Brunswick, New Jersey, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 68736 authors who have published 159418 publications receiving 6713860 citations. The organization is also known as: Rutgers, The State University of New Jersey & Rutgers.

Trial Design and Objectives for Castration-Resistant Prostate Cancer: Updated Recommendations From the Prostate Cancer Clinical Trials Working Group 3

Abstract: PurposeEvolving treatments, disease phenotypes, and biology, together with a changing drug development environment, have created the need to revise castration-resistant prostate cancer (CRPC) clinical trial recommendations to succeed those from prior Prostate Cancer Clinical Trials Working Groups.MethodsAn international expert committee of prostate cancer clinical investigators (the Prostate Cancer Clinical Trials Working Group 3 [PCWG3]) was reconvened and expanded and met in 2012-2015 to formulate updated criteria on the basis of emerging trial data and validation studies of the Prostate Cancer Clinical Trials Working Group 2 recommendations.ResultsPCWG3 recommends that baseline patient assessment include tumor histology, detailed records of prior systemic treatments and responses, and a detailed reporting of disease subtypes based on an anatomic pattern of metastatic spread. New recommendations for trial outcome measures include the time to event end point of symptomatic skeletal events, as well as tim...

Boundary S matrix and boundary state in two-dimensional integrable quantum field theory

Abstract: We study integrals of motion and factorizable S matrices in two-dimensional integrable field theory with boundary. We propose the "boundary cross-unitarity equation," which is the boundary analog of the crossing-symmetry condition of the "bulk" S matrix. We derive the boundary S matrices for the Ising field theory with boundary magnetic field and for the boundary sine–Gordon model.

Inhibition of carcinogenesis by tea.

Abstract: Tea has received a great deal of attention because tea polyphenols are strong antioxidants, and tea preparations have inhibitory activity against tumorigenesis. The bioavailability and biotransformation of tea polyphenols, however, are key factors limiting these activities in vivo. The inhibition of tumorigenesis by green or black tea preparations has been demonstrated in animal models on different organ sites such as skin, lung, oral cavity, esophagus, forestomach, stomach, small intestine, colon, pancreas, and mammary gland. Epidemiological studies, however, have not yielded clear conclusions concerning the protective effects of tea consumption against cancer formation in humans. The discrepancy between the results from humans and animal models could be due to 1) the much higher doses of tea used in animals in comparison to human consumption, 2) the differences in causative factors between the cancers in humans and animals, and 3) confounding factors limiting the power of epidemiological studies to detect an effect. It is possible that tea may be only effective against specific types of cancer caused by certain etiological factors. Many mechanisms have been proposed for the inhibition of carcinogenesis by tea, including the modulation of signal transduction pathways that leads to the inhibition of cell proliferation and transformation, induction of apoptosis of preneoplastic and neoplastic cells, as well as inhibition of tumor invasion and angiogenesis. These mechanisms need to be evaluated and verified in animal models or humans in order to gain more understanding on the effect of tea consumption on human cancer.

Automated learning of decision rules for text categorization

Abstract: We describe the results of extensive experiments using optimized rule-based induction methods on large document collections. The goal of these methods is to discover automatically classification patterns that can be used for general document categorization or personalized filtering of free text. Previous reports indicate that human-engineered rule-based systems, requiring many man-years of developmental efforts, have been successfully built to “read” documents and assign topics to them. We show that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation. In comparison with other machine-learning techniques, results on a key benchmark from the Reuters collection show a large gain in performance, from a previously reported 67% recall/precision breakeven point to 80.5%. In the context of a very high-dimensional feature space, several methodological alternatives are examined, including universal versus local dictionaries, and binary versus frequency-related features.