1,720,954 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
Understanding omics data of lung cancer patients: Correlations between metabolomics and radiomics
No full textEur J Nucl Med Mol Imaging (2021) 48 (Suppl 1): S1-S648 predicting response and survival was obtained by combining clinical data with PET and CT texture parameters (AUC 0.87). Conclusion: PET/CT derived parameters demonstrated better performances-than the clinical parameters in predicting the response and overall survival confirming the interest in considering a radiomics based approach for the optimization of therapy management in patients with head and neck cancer. References: none Aim/Introduction: Qualitative and semi-quantitative parameters of PET and CT images are used to assist decision making for cancer treatment. In initial studies PET and CT radiomic features have shown promising results in disease prognostication and treatment outcome prediction in cancer. These features are specific to the outcome and different features show association with different outcomes. Hence finding the scalability of radiomic features from one modality to another can have promising impact. In our study we have tried to check the scalability of radiomic features across the modalities, PET and CT. We have performed a study to predict CT radiomic features using PET radiomic features and vice versa. Materials and Methods: This study was approved by the institutional ethics committee as retrospective study. 104 NSCLC patients who underwent pre-treatment PET-CT scan were included in this study. Primary lung tumor was delineated by SUV cutoff (42%) method on PET images and saved as RTStructure for PET and CT series. These Images and RTStructures were used for radiomic extraction using bin-width of 25 and 5 for CT and PET respectively using pyradiomic 2.1.0 software and in-house developed python script. Subsequently, concordance correlation coefficient (CCC) was calculated between PET and CT features and top 25 correlated features (excluding shape features) were selected to develop a prediction model. Entire set of data was split into training and validation sets (70:30). For each PET radiomic feature; a set of CT features were selected and vice versa using Recursive Feature Elimination(RFE). For individual feature prediction across modalities, a multivariate linear regression model was developed using selected features. Model performance was assessed based on accuracy of prediction (C-index) on validation set. Results: Around 54% and 46 % radiomic features show positive and negative correlation across PET and CT respectively. Only 91(8.33%), 69(6.3%) and 51(4.67%) features have 0.5<CCC<0.7, 0.7≤CCC<0.9 and CCC≥0.9 respectively. Top 25 selected radiomic features had CCC equal to or more than 0.99. The average C-Index and p-value in validation set for 25 PET radiomic features prediction was found to be 0.988(±0.019) and <0.0001 respectively. Similarly, average C-Index value and p-value in validation set for 25 CT radiomic features prediction was found to be 0.987(±0.016) and <0.0001 respectively. Conclusion: As per our findings, very few radiomic features have good correlation between PET and CT. These features show excellent capability to predict features across these modalities. References: none Aim/Introduction: Treatment of lung cancer remains challenging, partly due to the late-stage diagnosis of patients. With a strong focus on non-small cell lung cancer (NSCLC), this pilot study examines the diagnostic and prognostic potential of combining specific metabolic biomarkers from blood plasma (metabolomics) with features out of medical images (radiomics). This way, metabolomics and radiomics might be at the base of developing a more personalized treatment plan for lung cancer patients. This study aims to combine a metabolomics and radiomics dataset from lung cancer patients and to unravel the underlying correlations between the techniques. Materials and Methods: The initial patient cohort consisted of 32 patients, all diagnosed with early-stage NSCLC. All patients underwent a lobectomy as part of their standard-of-care treatment plan. The PET-CT images of all the patients were collected using 18 F-FDG (Biograph Horizon camera, Siemens). The PET-CT images were then segmented using a semi-automatic tool (ACCURATE), creating specific volumes of interest (VOIs) of the lung lesions for each patient. By loading the VOIs into the second tool (RADIOMICS), 486 radiomics parameters were extracted from each VOI (Both tools developed by R.B.) Simultaneously, 238 metabolic parameters representing 62 plasma metabolites were determined from the same patients using proton nuclear magnetic resonance (1 H-NMR) spectroscopy. A correlation coefficient test was used on the total omics-dataset to find a correlation between these two sets of parameters. Results: The correlation values found between the radiomics and metabolomics parameters showed R 2 values between 0.5 and 0.65 (positive correlation) or between-0.5 and-0.65 (negative correlation). The positive correlations found in the metabolomics dataset were mainly related to the concentration of plasma glucose. The radiomics S50
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
Understanding omics data of lung cancer patients: Correlations between metabolomics and radiomics
No full textEur J Nucl Med Mol Imaging (2021) 48 (Suppl 1): S1-S648 predicting response and survival was obtained by combining clinical data with PET and CT texture parameters (AUC 0.87). Conclusion: PET/CT derived parameters demonstrated better performances-than the clinical parameters in predicting the response and overall survival confirming the interest in considering a radiomics based approach for the optimization of therapy management in patients with head and neck cancer. References: none Aim/Introduction: Qualitative and semi-quantitative parameters of PET and CT images are used to assist decision making for cancer treatment. In initial studies PET and CT radiomic features have shown promising results in disease prognostication and treatment outcome prediction in cancer. These features are specific to the outcome and different features show association with different outcomes. Hence finding the scalability of radiomic features from one modality to another can have promising impact. In our study we have tried to check the scalability of radiomic features across the modalities, PET and CT. We have performed a study to predict CT radiomic features using PET radiomic features and vice versa. Materials and Methods: This study was approved by the institutional ethics committee as retrospective study. 104 NSCLC patients who underwent pre-treatment PET-CT scan were included in this study. Primary lung tumor was delineated by SUV cutoff (42%) method on PET images and saved as RTStructure for PET and CT series. These Images and RTStructures were used for radiomic extraction using bin-width of 25 and 5 for CT and PET respectively using pyradiomic 2.1.0 software and in-house developed python script. Subsequently, concordance correlation coefficient (CCC) was calculated between PET and CT features and top 25 correlated features (excluding shape features) were selected to develop a prediction model. Entire set of data was split into training and validation sets (70:30). For each PET radiomic feature; a set of CT features were selected and vice versa using Recursive Feature Elimination(RFE). For individual feature prediction across modalities, a multivariate linear regression model was developed using selected features. Model performance was assessed based on accuracy of prediction (C-index) on validation set. Results: Around 54% and 46 % radiomic features show positive and negative correlation across PET and CT respectively. Only 91(8.33%), 69(6.3%) and 51(4.67%) features have 0.5<CCC<0.7, 0.7≤CCC<0.9 and CCC≥0.9 respectively. Top 25 selected radiomic features had CCC equal to or more than 0.99. The average C-Index and p-value in validation set for 25 PET radiomic features prediction was found to be 0.988(±0.019) and <0.0001 respectively. Similarly, average C-Index value and p-value in validation set for 25 CT radiomic features prediction was found to be 0.987(±0.016) and <0.0001 respectively. Conclusion: As per our findings, very few radiomic features have good correlation between PET and CT. These features show excellent capability to predict features across these modalities. References: none Aim/Introduction: Treatment of lung cancer remains challenging, partly due to the late-stage diagnosis of patients. With a strong focus on non-small cell lung cancer (NSCLC), this pilot study examines the diagnostic and prognostic potential of combining specific metabolic biomarkers from blood plasma (metabolomics) with features out of medical images (radiomics). This way, metabolomics and radiomics might be at the base of developing a more personalized treatment plan for lung cancer patients. This study aims to combine a metabolomics and radiomics dataset from lung cancer patients and to unravel the underlying correlations between the techniques. Materials and Methods: The initial patient cohort consisted of 32 patients, all diagnosed with early-stage NSCLC. All patients underwent a lobectomy as part of their standard-of-care treatment plan. The PET-CT images of all the patients were collected using 18 F-FDG (Biograph Horizon camera, Siemens). The PET-CT images were then segmented using a semi-automatic tool (ACCURATE), creating specific volumes of interest (VOIs) of the lung lesions for each patient. By loading the VOIs into the second tool (RADIOMICS), 486 radiomics parameters were extracted from each VOI (Both tools developed by R.B.) Simultaneously, 238 metabolic parameters representing 62 plasma metabolites were determined from the same patients using proton nuclear magnetic resonance (1 H-NMR) spectroscopy. A correlation coefficient test was used on the total omics-dataset to find a correlation between these two sets of parameters. Results: The correlation values found between the radiomics and metabolomics parameters showed R 2 values between 0.5 and 0.65 (positive correlation) or between-0.5 and-0.65 (negative correlation). The positive correlations found in the metabolomics dataset were mainly related to the concentration of plasma glucose. The radiomics S50
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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