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Institution

University of Alabama

EducationTuscaloosa, Alabama, United States
About: University of Alabama is a education organization based out in Tuscaloosa, Alabama, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 27323 authors who have published 48609 publications receiving 1565337 citations. The organization is also known as: Alabama & Bama.


Papers
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Journal ArticleDOI
TL;DR: There are multiple variables associated with survival in transplant patients with IA and understanding these prognostic factors may assist in the development of treatment algorithms and clinical trials.
Abstract: P ! .001 insufficiency, hepatic insufficiency, early-onset IA, proven IA, and methylprednisolone use. In contrast, white race was associated with decreased risk of death. Among SOT patients, hepatic insufficiency, malnutrition, and central nervous system disease were poor prognostic indicators, whereas prednisone use was associated with decreased risk of death. Among HSCT or SOT patients who received antifungal therapy, use of an amphotericin B preparation as part of initial therapy was associated with increased risk of death. Conclusions. There are multiple variables associated with survival in transplant patients with IA. Understanding these prognostic factors may assist in the development of treatment algorithms and clinical trials.

262 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a relationship customer typology based on specific consumer characteristics related to the maintenance of these relationships, and then profile the different relationship customer types in terms of demographics and also important retail consequences.

262 citations

Journal ArticleDOI
01 May 1994-System
TL;DR: Research on second language vocabulary instruction is described, with a focus on what motivates students, what they need, why knowing a word is a complex act and which factors influence vocabulary acquisition.

262 citations

Journal Article
TL;DR: The results are consistent with the suggestion that Ag-specific Th1 cells and their derived cytokines, IFN-gamma and IL-2, and Th2-derived IL-10 together with IL-6 produced by macrophages provide important signals for the development of mucosal IgA and serum IgG subclass responses in the absence of preferential expression of Th2 cytokines IL-4 andIL-5.
Abstract: We have assessed regulatory Th cell and cytokine responses in mice after oral immunization with recombinant Salmonella (BRD 847) expressing fragment C of tetanus toxoid, since little information is available to explain how these vectors induce mucosal IgA responses. A single dose of BRD 847 elicited serum IgG2a and mucosal IgA anti-tetanus toxoid Ab responses. To assess Th1-and Th2-type responses, CD4+ T cells from Peyer's patches and spleen were restimulated in vitro, and cytokine-specific ELISPOT, ELISA, and reverse transcriptase-PCR assays were used to assess cytokine patterns. CD4+ T cells produced IFN-gamma and IL-2 as well as IL-10, but not IL-4 or IL-5. Although IL-6 was elevated, further purification of cells from in vitro cultures into CD4+ Mac-1- T cells and Mac-1+ CD4- cells revealed that only the latter cell population had consistently elevated IL-6 gene expression, whereas both sorted populations exhibited increased IFN-gamma and IL-10 gene expression. Thus, orally administered recombinant Salmonella expressing fragment C of tetanus toxoid elicited dominant Ag-specific Th1-type responses together with Th2-type cells producing IL-10 in both mucosal and systemic tissues. Macrophages producing IL-6 were also evident. Our results are consistent with the suggestion that Ag-specific Th1 cells and their derived cytokines, IFN-gamma and IL-2, and Th2-derived IL-10 together with IL-6 produced by macrophages provide important signals for the development of mucosal IgA and serum IgG subclass responses in the absence of preferential expression of Th2 cytokines IL-4 and IL-5.

262 citations

Journal ArticleDOI
TL;DR: A three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent Convolutional layers, and is shown to be more effective than other deep learning architectures.
Abstract: Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar—17.3% improvement over SVM.

262 citations


Authors

Showing all 27508 results

NameH-indexPapersCitations
Jasvinder A. Singh1762382223370
Hongfang Liu1662356156290
Ian J. Deary1661795114161
Yongsun Kim1562588145619
Dong-Chul Son138137098686
Simon C. Watkins13595068358
Kenichi Hatakeyama1341731102438
Conor Henderson133138788725
Peter R Hobson133159094257
Tulika Bose132128588895
Helen F Heath132118589466
James Rohlf131121589436
Panos A Razis130128790704
David B. Allison12983669697
Eduardo Marbán12957949586
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202372
2022357
20212,703
20202,759
20192,602
20182,411