University of North Texas

About: University of North Texas is a education organization based out in Denton, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 11866 authors who have published 26984 publications receiving 705376 citations. The organization is also known as: Fight, North Texas & UNT.

Fluorous Metal–Organic Frameworks with Superior Adsorption and Hydrophobic Properties toward Oil Spill Cleanup and Hydrocarbon Storage

Abstract: We demonstrate that fluorous metal–organic frameworks (FMOFs) are highly hydrophobic porous materials with a high capacity and affinity to C6–C8 hydrocarbons of oil components. FMOF-1 exhibits reversible adsorption with a high capacity for n-hexane, cyclohexane, benzene, toluene, and p-xylene, with no detectable water adsorption even at near 100% relative humidity, drastically outperforming activated carbon and zeolite porous materials. FMOF-2, obtained from annealing FMOF-1, shows enlarged cages and channels with double toluene adsorption vs FMOF-1 based on crystal structures. The results suggest great promise for FMOFs in applications such as removal of organic pollutants from oil spills or ambient humid air, hydrocarbon storage and transportation, water purification, etc. under practical working conditions.

Identification and molecular characterization of csrA, a pleiotropic gene from Escherichia coli that affects glycogen biosynthesis, gluconeogenesis, cell size, and surface properties.

Abstract: Current evidence suggests that a few global regulatory factors mediate many of the extensive changes in gene expression that occur as Escherichia coli enters the stationary phase. One of the metabolic pathways that is transcriptionally activated in the stationary phase is the pathway for biosynthesis of glycogen. To identify factors that regulate glycogen biosynthesis in trans, a collection of transposon mutants was generated and screened for mutations which independently increase or decrease glycogen levels and the expression of a plasmid-encoded glgC'-lacZ fusion. The glycogen excess mutation TR1-5 was found to be pleiotropic. It led to increased expression of the genes glgC (ADPglucose pyrophosphorylase) and glgB (glycogen branching enzyme), which are representative of two glycogen synthesis operons, and the gluconeogenic gene pckA (phosphoenolpyruvate carboxykinase), and it exhibited effects on cell size and surface (adherence) properties. The mutated gene was designated csrA for carbon storage regulator. Its effect on glycogen biosynthesis was mediated independently of cyclic AMP (cAMP), the cAMP receptor protein, and guanosine 3'-bisphosphate 5'-bisphosphate (ppGpp), which are positive regulators of glgC expression. A plasmid clone of the native csrA gene strongly inhibited glycogen accumulation and affected the ability of cells to utilize certain carbon sources for growth. Nucleotide sequence analysis, complementation experiments, and in vitro expression studies indicated that csrA encodes a 61-amino-acid polypeptide that inhibits glycogen biosynthesis. Computer-assisted data base searches failed to identify genes or proteins that are homologous with csrA or its gene product.

Measuring the Semantic Similarity of Texts

Abstract: This paper presents a knowledge-based method for measuring the semantic-similarity of texts. While there is a large body of previous work focused on finding the semantic similarity of concepts and words, the application of these word-oriented methods to text similarity has not been yet explored. In this paper, we introduce a method that combines word-to-word similarity metrics into a text-to-text metric, and we show that this method outperforms the traditional text similarity metrics based on lexical matching.

Towards multimodal sentiment analysis: harvesting opinions from the web

Abstract: With more than 10,000 new videos posted online every day on social websites such as YouTube and Facebook, the internet is becoming an almost infinite source of information. One crucial challenge for the coming decade is to be able to harvest relevant information from this constant flow of multimodal data. This paper addresses the task of multimodal sentiment analysis, and conducts proof-of-concept experiments that demonstrate that a joint model that integrates visual, audio, and textual features can be effectively used to identify sentiment in Web videos. This paper makes three important contributions. First, it addresses for the first time the task of tri-modal sentiment analysis, and shows that it is a feasible task that can benefit from the joint exploitation of visual, audio and textual modalities. Second, it identifies a subset of audio-visual features relevant to sentiment analysis and present guidelines on how to integrate these features. Finally, it introduces a new dataset consisting of real online data, which will be useful for future research in this area.