1,721,020 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Computational Synthesis of Histological Stains: A Step Toward Virtual Enhanced Digital Pathology
Full text linkHistological staining plays a crucial role in anatomic pathology for the analysis of biological tissues and the formulation of diagnostic reports. Traditional methods like hematoxylin and eosin (H&E) primarily offer morphological information but lack insight into functional details, such as the expression of biomarkers indicative of cellular activity. To overcome this limitation, we propose a computational approach to synthesize virtual immunohistochemical (IHC) stains from H&E input, transferring imaging features across staining domains. Our approach comprises two stages: (i) a multi-stage registration framework ensuring precise alignment of cellular and subcellular structures between the source H&E and target IHC stains, and (ii) a deep learning-based generative model which incorporates functional attributes from the target IHC stain by learning cell-to-cell mappings from paired training data. We evaluated our approach of virtual restaining H&E slides to simulate IHC staining for phospho-histone H3, on inguinal lymph node and bladder tissues. Blind pathologist assessments and quantitative metrics validated the diagnostic quality of the synthetic slides. Notably, mitotic counts derived from synthetic images exhibited a strong correlation with physical staining. Moreover, global and stain-specific metrics confirmed the high quality of the synthetic IHC images generated by our approach. This methodology represents an important advance in automated functional restaining, achieved through robust registration and a model trained on precisely paired H&E and IHC data to transfer functions cell-by-cell. Our approach forms the basis for multiparameter histology analysis and comprehensive cohort staining using only digitized H&E slides
Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification
Full text linkBackground
Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model.
Method
In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected.
Results
Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement).
Conclusion
We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS)
Automated assessment of glomerulosclerosis and tubular atrophy using deep learning
Full text linkIn kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity
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