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 & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

The impact of food allergy on the daily activities of children and their families.

Abstract: Background Food allergy affects a significant number of children, and its management requires considerable time and vigilance. Objective To determine the impact of food allergy on the daily activities of food allergic children and their families. Methods Caregivers of food allergic children from a university-based allergy practice completed a questionnaire that evaluated their perception of the impact of their child's food allergy on family activities. Results Of the 87 families who completed the study, more than 60% of caregivers reported that food allergy significantly affected meal preparation and 49% or more indicated that food allergy affected family social activities. Forty-one percent of parents reported a significant impact on their stress levels and 34% reported that food allergy had an impact on school attendance, with 10% choosing to home school their children because of food allergy. The number of food allergies had a significant impact on activity scores, but the existence of comorbid conditions such as asthma and atopic dermatitis did not significantly affect the results. Conclusions Food allergy has a significant effect on activities of families of food allergic children. Further study is needed to determine more detailed effects of food allergy on parent-child interactions and development.

Application of MJO Simulation Diagnostics to Climate Models

Abstract: The ability of eight climate models to simulate the Madden‐Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November‐April) behavior. Initially, maps of the mean state and variance and equatorial space‐time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space‐time spectral peak in the zonal wind compared to that of precipitation. Using the phase‐ space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (;20‐ 29 days) than observations (;31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30‐80-day band. Several key variables associated with the model’s MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/ Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.

Myeloid-Derived Suppressor Cells: Immune-Suppressive Cells That Impair Antitumor Immunity and Are Sculpted by Their Environment.

Abstract: Myeloid-derived suppressor cells (MDSC) are a diverse population of immature myeloid cells that have potent immune-suppressive activity. Studies in both mice and humans have demonstrated that MDSC accumulate in most individuals with cancer, where they promote tumor progression, inhibit antitumor immunity, and are an obstacle to many cancer immunotherapies. As a result, there has been intense interest in understanding the mechanisms and in situ conditions that regulate and sustain MDSC, and the mechanisms MDSC use to promote tumor progression. This article reviews the characterization of MDSC and how they are distinguished from neutrophils, describes the suppressive mechanisms used by MDSC to mediate their effects, and explains the role of proinflammatory mediators and the tumor microenvironment in driving MDSC accumulation, suppressive potency, and survival.

Constrained subpixel target detection for remotely sensed imagery

Abstract: Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared. One is a target abundance-constrained approach, referred to as nonnegatively constrained least squares (NCLS) method. It is a constrained least squares spectral mixture analysis method which implements a nonnegativity constraint on the abundance fractions of targets of interest. Another is a target signature-constrained approach, called constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. A quantitative study is conducted to analyze the advantages and disadvantages of both methods. Some suggestions are further proposed to mitigate their disadvantages.