University of Louisville

About: University of Louisville is a education organization based out in Louisville, Kentucky, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 24600 authors who have published 49248 publications receiving 1573346 citations. The organization is also known as: UofL.

Snoring During Early Childhood and Academic Performance at Ages Thirteen to Fourteen Years

Abstract: Objectives. Obstructive sleep apnea syndrome in young children is associated with an adverse effect on learning. However, the long-term impact of sleep-disordered breathing (SDB) during early childhood on learning remains unknown. Methods. Questionnaires were mailed to seventh and eighth graders attending public schools whose class ranking was either in the top 25% (high performance [HP]) or bottom 25% of their class (low performance [LP]), and who were matched for age, gender, race, school, and street of residence. Snoring frequency and loudness at 2 to 6 years of age, tonsillectomy and adenoidectomy (T&A) for snoring or recurrent infection, school grades, and parental smoking and snoring were assessed. Results. The questionnaire response rate was 82.8%. Because of ongoing snoring, 13 responders were excluded, such that 1588 questionnaires could be analyzed (797 in LP and 791 in HP group). Frequent and loud snoring during early childhood was reported in 103 LP children (12.9%) compared with 40 HP children (5.1%; odds ratio: 2.79; confidence interval: 1.88–4.15). Furthermore, 24 LP and 7 HP children underwent TA confidence interval: 1.47–7.84), while 21 LP and 19 HP children required surgery for recurrent tonsillitis. Conclusions. Children with lower academic performance in middle school are more likely to have snored during early childhood and to require T&A for snoring compared with better performing schoolmates. These findings support the concept that SDB-associated neurocognitive morbidity may be only partially reversible or that a “learning debt” may develop with SDB during early childhood and hamper subsequent school performance.

The pleiotropic effects of the SDF-1-CXCR4 axis in organogenesis, regeneration and tumorigenesis.

Abstract: Proper response of normal stem cells (NSC) to motomorphogens and chemoattractants plays a pivotal role in organ development and renewal/regeneration of damaged tissues. Similar chemoattractants may also regulate metastasis of cancer stem cells (CSC). Growing experimental evidence indicates that both NSC and CSC express G-protein-coupled seven-transmembrane span receptor CXCR4 and respond to its specific ligand alpha-chemokine stromal derived factor-1 (SDF-1), which is expressed by stroma cells from different tissues. In addition, a population of very small embryonic-like (VSEL) stem cells that express CXCR4 and respond robustly to an SDF-1 gradient was recently identified in adult tissues. VSELs express several markers of embryonic and primordial germ cells. It is proposed that these cells are deposited early in the development as a dormant pool of embryonic/pluripotent NSC. Expression of both CXCR4 and SDF-1 is upregulated in response to tissue hypoxia and damage signal attracting circulating NSC and CSC. Thus, pharmacological modulation of the SDF-1-CXCR4 axis may lead to the development of new therapeutic strategies to enhance mobilization of CXCR4+ NSC and their homing to damaged organs as well as inhibition of the metastasis of CXCR4+ cancer cells.

Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art

Abstract: Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases.