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Institution

University of Louisville

EducationLouisville, Kentucky, United States
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.


Papers
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Journal ArticleDOI
TL;DR: The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
Abstract: Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

252 citations

Proceedings ArticleDOI
Yi Li1
22 Jun 2012
TL;DR: A novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HGR system is implemented that is able to detect the presence of gestures, to identify fingers, and to recognize the meanings of nine gestures in a pre-defined Popular Gesture scenario.
Abstract: Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this article, a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HGR system is implemented. The system is able to detect the presence of gestures, to identify fingers, and to recognize the meanings of nine gestures in a pre-defined Popular Gesture scenario. The accuracy of the HGR system is from 84% to 99% with single-hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions, signers' skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life.

252 citations

Journal ArticleDOI
TL;DR: In this paper, the authors study how internal and downstream integration and their interaction affect logistical and financial performance within a firm and find that the higher the internal integration, the stronger the relationship of downstream integration with logistical performance.
Abstract: The authors study how internal and downstream integration and their interaction affect logistical and financial performance within the firm. The results indicate that internal and downstream integration and their interaction affect logistical performance — that is, the higher the internal integration, the stronger the relationship of downstream integration with logistical performance. The results also suggest that logistical performance directly predicts financial performance. The results suggest that superior performance derives from the firm simultaneously integrating functions, decision-making, and processes both within the firm and across the supply chain.

251 citations

Journal ArticleDOI
TL;DR: Oxamate did not attenuate cellular lactate production as predicted by its LDH inhibitory activity, but did have an anti-metabolic effect that was similar to AAT inhibition with AOA, which concludes that AAT may be a valid molecular target for the development of anti-neoplastic agents.
Abstract: Introduction Glycolysis is increased in breast adenocarcinoma cells relative to adjacent normal cells in order to produce the ATP and anabolic precursors required for survival, growth and invasion. Glycolysis also serves as a key source of the reduced form of cytoplasmic nicotinamide adenine dinucleotide (NADH) necessary for the shuttling of electrons into mitochondria for electron transport. Lactate dehydrogenase (LDH) regulates glycolytic flux by converting pyruvate to lactate and has been found to be highly expressed in breast tumours. Aspartate aminotransferase (AAT) functions in tandem with malate dehydrogenase to transfer electrons from NADH across the inner mitochondrial membrane. Oxamate is an inhibitor of both LDH and AAT, and we hypothesised that oxamate may disrupt the metabolism and growth of breast adenocarcinoma cells. Methods We examined the effects of oxamate and the AAT inhibitor amino oxyacetate (AOA) on 13 C-glucose utilisation, oxygen consumption, NADH and ATP in MDA-MB-231 cells. We then determined the effects of oxamate and AOA on normal human mammary epithelial cells and MDA-MB-231 breast adenocarcinoma cell proliferation, and on the growth of MDAMB-231 cells as tumours in athymic BALB/c female mice. We ectopically expressed AAT in MDA-MB-231 cells and examined the consequences on the cytostatic effects of oxamate. Finally, we examined the effect of AAT-specific siRNA transfection on MDA-MB-231 cell proliferation.

251 citations

Journal ArticleDOI
TL;DR: Diagnostic criteria were created following a Delphi consensus exercise and validated against peer-reviewed established cases of pyoderma gangrenosum and mimickers using k-fold cross-validation with methods of multiple imputation to serve as a guideline for clinicians.
Abstract: Importance Pyoderma gangrenosum is a rare inflammatory skin condition that is difficult to diagnose. Currently, it is a “diagnosis of exclusion,” a definition not compatible with clinical decision making or inclusion for clinical trials. Objective To propose and validate diagnostic criteria for ulcerative pyoderma gangrenosum. Evidence Review Diagnostic criteria were created following a Delphi consensus exercise using the RAND/UCLA Appropriateness Method. The criteria were validated against peer-reviewed established cases of pyoderma gangrenosum and mimickers using k-fold cross-validation with methods of multiple imputation. Findings Delphi exercise yielded 1 major criterion—biopsy of ulcer edge demonstrating neutrophilic infiltrate—and 8 minor criteria: (1) exclusion of infection; (2) pathergy; (3) history of inflammatory bowel disease or inflammatory arthritis; (4) history of papule, pustule, or vesicle ulcerating within 4 days of appearing; (5) peripheral erythema, undermining border, and tenderness at ulceration site; (6) multiple ulcerations, at least 1 on an anterior lower leg; (7) cribriform or “wrinkled paper” scar(s) at healed ulcer sites; and (8) decreased ulcer size within 1 month of initiating immunosuppressive medication(s). Receiver operating characteristic analysis revealed that 4 of 8 minor criteria maximized discrimination, yielding sensitivity and specificity of 86% and 90%, respectively. Conclusions and Relevance This Delphi exercise produced 1 major criterion and 8 minor criteria for the diagnosis of ulcerative pyoderma gangrenosum. The criteria may serve as a guideline for clinicians, allowing for fewer misdiagnoses and improved patient selection for clinical trials.

251 citations


Authors

Showing all 24802 results

NameH-indexPapersCitations
Robert M. Califf1961561167961
Aaron R. Folsom1811118134044
Yang Gao1682047146301
Stephen J. O'Brien153106293025
James J. Collins15166989476
Anthony E. Lang149102895630
Sw. Banerjee1461906124364
Hermann Kolanoski145127996152
Ferenc A. Jolesz14363166198
Daniel S. Berman141136386136
Aaron T. Beck139536170816
Kevin J. Tracey13856182791
C. Dallapiccola1361717101947
Michael I. Posner134414104201
Alan Sher13248668128
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202373
2022249
20212,489
20202,234
20192,193
20182,153