University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Liposomal Nanocapsules in Food Science and Agriculture

Abstract: Liposomes, spherical bilayer vesicles from dispersion of polar lipids in aqueous solvents, have been widely studied for their ability to act as drug delivery vehicles by shielding reactive or sensitive compounds prior to release. Liposome entrapment has been shown to stabilize encapsulated, bioactive materials against a range of environmental and chemical changes, including enzymatic and chemical modification, as well as buffering against extreme pH, temperature, and ionic strength changes. Liposomes have been especially useful to researchers in studies of various physiological processes as models of biological membranes in both eukaryotes and prokaryotes. Industrial applications include encapsulation of pharmaceuticals and therapeutics, cosmetics, anti-cancer and gene therapy drugs. In the food industry, liposomes have been used to deliver food flavors and nutrients and more recently have been investigated for their ability to incorporate food antimicrobials that could aid in the protection of food products against growth of spoilage and pathogenic microorganisms. In this review we briefly introduce key physicochemical properties of liposomes and review competing methods for liposome production. A survey of non-agricultural and food applications of liposomes are given. Finally, a detailed up-to-date summary of the emerging usage of liposomes in the food industry as delivery vehicles of nutrients, nutraceuticals, food additives, and food antimicrobials is provided.

The role of perceived spatial separation in the unmasking of speech.

Abstract: Spatial separation of speech and noise in an anechoic space creates a release from masking that often improves speech intelligibility. However, the masking release is severely reduced in reverberant spaces. This study investigated whether the distinct and separate localization of speech and interference provides any perceptual advantage that, due to the precedence effect, is not degraded by reflections. Listeners’ identification of nonsense sentences spoken by a female talker was measured in the presence of either speech-spectrum noise or other sentences spoken by a second female talker. Target and interference stimuli were presented in an anechoic chamber from loudspeakers directly in front and 60 degrees to the right in single-source and precedence-effect (lead-lag) conditions. For speech-spectrum noise, the spatial separation advantage for speech recognition (8 dB) was predictable from articulation index computations based on measured release from masking for narrow-band stimuli. The spatial separation advantage was only 1 dB in the lead-lag condition, despite the fact that a large perceptual separation was produced by the precedence effect. For the female talker interference, a much larger advantage occurred, apparently because informational masking was reduced by differences in perceived locations of target and interference.

Collective multi-label classification

Abstract: Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random field (CRF)classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classification error by as much as 40%.