Institution
University of Graz
Education•Graz, Steiermark, Austria•
About: University of Graz is a education organization based out in Graz, Steiermark, Austria. It is known for research contribution in the topics: Population & Context (language use). The organization has 17934 authors who have published 37489 publications receiving 1110980 citations. The organization is also known as: Carolo Franciscea Graecensis & Karl Franzens Universität.
Papers published on a yearly basis
Papers
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TL;DR: The biochemistry of relaxosomes is reviewed and some of the remaining questions about the nature of the signal that begins the process of bacterial conjugation are pondered.
Abstract: Bacterial conjugation in Gram-negative bacteria is triggered by a signal that connects the relaxosome to the coupling protein (T4CP) and transferosome, a type IV secretion system. The relaxosome, a nucleoprotein complex formed at the origin of transfer (oriT), consists of a relaxase, directed to the nic site by auxiliary DNA-binding proteins. The nic site undergoes cleavage and religation during vegetative growth, but this is converted to a cleavage and unwinding reaction when a competent mating pair has formed. Here, we review the biochemistry of relaxosomes and ponder some of the remaining questions about the nature of the signal that begins the process.
297 citations
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Medical University of Vienna1, IBM2, Ankara University3, University of Zurich4, Emory University5, Memorial Sloan Kettering Cancer Center6, Kitware7, University of Graz8, Aristotle University of Thessaloniki9, Karolinska University Hospital10, University of Modena and Reggio Emilia11, Carlos III Health Institute12, University of Barcelona13, University of Sydney14, University of Auckland15, University of Gothenburg16, University of Queensland17, Sheba Medical Center18, French Institute of Health and Medical Research19, University of Trieste20
TL;DR: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice.
Abstract: Summary Background Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. Methods For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. Findings Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p Interpretation State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. Funding None.
297 citations
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TL;DR: A regulatory role for β 2 -glycoprotein-I is suggested in the pathway of blood coagulation because it causes a reduction of the prothrombinase binding sites of these coagulations factors to platelets or phospholipid vesicles.
297 citations
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TL;DR: The distribution of nitric oxide synthase was investigated in rat kidney to provide the morphological basis for a possible role of NO as a mediator substance in signal transfer from distal tubular fluid to glomerular arterioles.
296 citations
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TL;DR: Using data simulation, the authors illustrate how the value of the chi-square test, the root-mean-square error of approximation, and the standardized root- mean-square residual are decreased when unique variances are increased although model misspecification is present.
Abstract: Fit indices are widely used in order to test the model fit for structural equation models. In a highly influential study, Hu and Bentler (1999) showed that certain cutoff values for these indices could be derived, which, over time, has led to the reification of these suggested thresholds as "golden rules" for establishing the fit or other aspects of structural equation models. The current study shows how differences in unique variances influence the value of the global chi-square model test and the most commonly used fit indices: Root-mean-square error of approximation, standardized root-mean-square residual, and the comparative fit index. Using data simulation, the authors illustrate how the value of the chi-square test, the root-mean-square error of approximation, and the standardized root-mean-square residual are decreased when unique variances are increased although model misspecification is present. For a broader understanding of the phenomenon, the authors used different sample sizes, number of observed variables per factor, and types of misspecification. A theoretical explanation is provided, and implications for the application of structural equation modeling are discussed.
296 citations
Authors
Showing all 18136 results
Name | H-index | Papers | Citations |
---|---|---|---|
David Haussler | 172 | 488 | 224960 |
Russel J. Reiter | 169 | 1646 | 121010 |
Frederik Barkhof | 154 | 1449 | 104982 |
Philip Scheltens | 140 | 1175 | 107312 |
Christopher D.M. Fletcher | 138 | 674 | 82484 |
Jennifer S. Haas | 128 | 840 | 71315 |
Jelena Krstic | 126 | 839 | 73457 |
Michael A. Kamm | 124 | 637 | 53606 |
Frances H. Arnold | 119 | 510 | 49651 |
Gert Pfurtscheller | 117 | 507 | 62873 |
Georg Kresse | 111 | 430 | 244729 |
Manfred T. Reetz | 110 | 959 | 42941 |
Alois Fürstner | 108 | 459 | 43085 |
David N. Herndon | 108 | 1227 | 54888 |
David J. Williams | 107 | 2060 | 62440 |