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.

The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data

Abstract: The worldwide Protein Data Bank (wwPDB) is the international collaboration that manages the deposition, processing and distribution of the PDB archive. The online PDB archive is a repository for the coordinates and related information for more than 38 000 structures, including proteins, nucleic acids and large macromolecular complexes that have been determined using X-ray crystallography, NMR and electron microscopy techniques. The founding members of the wwPDB are RCSB PDB (USA), MSD-EBI (Europe) and PDBj (Japan) [H.M. Berman, K. Henrick and H. Nakamura (2003) Nature Struct. Biol., 10, 980]. The BMRB group (USA) joined the wwPDB in 2006. The mission of the wwPDB is to maintain a single archive of macromolecular structural data that are freely and publicly available to the global community. Additionally, the wwPDB provides a variety of services to a broad community of users. The wwPDB website at http://www.wwpdb.org/ provides information about services provided by the individual member organizations and about projects undertaken by the wwPDB.

The AETG system: an approach to testing based on combinatorial design

Abstract: This paper describes a new approach to testing that uses combinatorial designs to generate tests that cover the pairwise, triple, or n-way combinations of a system's test parameters. These are the parameters that determine the system's test scenarios. Examples are system configuration parameters, user inputs and other external events. We implemented this new method in the AETG system. The AETG system uses new combinatorial algorithms to generate test sets that cover all valid n-way parameter combinations. The size of an AETG test set grows logarithmically in the number of test parameters. This allows testers to define test models with dozens of parameters. The AETG system is used in a variety of applications for unit, system, and interoperability testing. It has generated both high-level test plans and detailed test cases. In several applications, it greatly reduced the cost of test plan development.

Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies

Abstract: . Progress has been made over the past decade in predicting secondary organic aerosol (SOA) mass in the atmosphere using vapor pressure-driven partitioning, which implies that SOA compounds are formed in the gas phase and then partition to an organic phase (gasSOA). However, discrepancies in predicting organic aerosol oxidation state, size and product (molecular mass) distribution, relative humidity (RH) dependence, color, and vertical profile suggest that additional SOA sources and aging processes may be important. The formation of SOA in cloud and aerosol water (aqSOA) is not considered in these models even though water is an abundant medium for atmospheric chemistry and such chemistry can form dicarboxylic acids and "humic-like substances" (oligomers, high-molecular-weight compounds), i.e. compounds that do not have any gas phase sources but comprise a significant fraction of the total SOA mass. There is direct evidence from field observations and laboratory studies that organic aerosol is formed in cloud and aerosol water, contributing substantial mass to the droplet mode. This review summarizes the current knowledge on aqueous phase organic reactions and combines evidence that points to a significant role of aqSOA formation in the atmosphere. Model studies are discussed that explore the importance of aqSOA formation and suggestions for model improvements are made based on the comprehensive set of laboratory data presented here. A first comparison is made between aqSOA and gasSOA yields and mass predictions for selected conditions. These simulations suggest that aqSOA might contribute almost as much mass as gasSOA to the SOA budget, with highest contributions from biogenic emissions of volatile organic compounds (VOC) in the presence of anthropogenic pollutants (i.e. NOx) at high relative humidity and cloudiness. Gaps in the current understanding of aqSOA processes are discussed and further studies (laboratory, field, model) are outlined to complement current data sets.