University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Rapid, Ultrasensitive, and Quantitative Detection of SARS-CoV-2 Using Antisense Oligonucleotides Directed Electrochemical Biosensor Chip.

Abstract: A large-scale diagnosis of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is essential to downregulate its spread within as well as across communities and mitigate the current out...

Crystal structure of dimeric HIV-1 capsid protein.

Abstract: X-ray diffraction analysis of a human immunodeficiency virus (HIV-1) capsid (CA) protein shows that each monomer within the dimer consists of seven alpha-helices, five of which are arranged in a coiled coil-like structure. Sequence assignments were made for two of the helices, and tentative connectivity of the remainder of the protein was confirmed by the recent solution structure of a monomeric N-terminal fragment. The C-terminal third of the protein is mostly disordered in the crystal. The longest helices in the coiled coil-like structure are separated by a long, highly antigenic peptide that includes the binding site of an antibody fragment complexed with CA in the crystal. The site of binding of the Fab, the position of the antigenic loop and the site of cleavage between the matrix protein and CA establish the side of the dimer that would be on the exterior of the retroviral core.

Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements

Abstract: [1] We use radiance measurements and inversions of the Aerosol Robotic Network (AERONET) (Dubovik and King, 2000; Holben et al., 1998; Holben et al., 2001) to classify global atmospheric aerosols using the complete archive of the AERONET data set as of December 2002 and dating back to 1993 for some sites. More than 143,000 records of AERONET solar radiance measurements, derived aerosol size distributions, and complex refractive indices are used to generate the optical properties of the aerosol at more than 250 sites worldwide. Each record is used in a clustering algorithm as an object, with 26 variables comprising both microphysical and optical properties to obtain six significant clusters. Using the mean values of the optical and microphysical properties together with the geographic locations, we identified these clusters as desert dust, biomass burning, urban industrial pollution, rural background, polluted marine, and dirty pollution. When the records in each cluster are subdivided by optical depth class, the trends of the class size distributions show that the extensive properties (mode amplitude and total volume) vary by optical depth, while the intensive properties (mean radius and standard deviation) are relatively constant. Seasonal variations of aerosol types are consistent with observed trends. In particular, the periods of intense biomass burning activity and desert dust generation can be discerned from the data and the results of the analyses. Sensitivity and uncertainty analyses show that the clustering algorithm is quite robust. When subsets of the data set are randomly created and the clustering algorithm applied, we found that more than 94% of the records retain their classification. Adding 10% random noise to the microphysical properties and propagating this error through the scattering calculations, followed by the clustering algorithm, results in a misclassification rate of less than 9% when compared with the noise-free data.

A policy based approach to security for the semantic web

Abstract: Along with developing specifications for the description of meta-data and the extraction of information for the Semantic Web, it is important to maximize security in this environment, which is fundamentally dynamic, open and devoid of many of the clues human societies have relied on for security assessment. Our research investigates the marking up of web entities with a semantic policy language and the use of distributed policy management as an alternative to traditional authentication and access control schemes. The policy language allows policies to be described in terms of deontic concepts and models speech acts, which allows the dynamic modification of existing policies, decentralized security control and less exhaustive policies. We present a security framework, based on this policy language, which addresses security issues for web resources, agents and services in the Semantic Web.