86,514 research outputs found
Variations on the Author
No full text“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
ABS0671 IgG4-RELATED DISEASE IN OVERLAP WITH IDIOPATHIC MULTICENTRIC CASTLEMAN DISEASE: CLINICAL-PATHOLOGICAL CHARACTERISTICS AND THERAPEUTIC IMPLICATIONS OF AN ORPHAN DISEASE
No full text[Newspaper Clipping: Author Claims Evidence of Second JFK Assassin #1]
No full textNewspaper article titled "Author Claims Evidence of Second JFK Assassin." The article states that author Richard J. Whalen concluded "that there is circumstantial evidence to support the theory of a second assassin in the shooting of President John F. Kennedy.
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
No full textThe desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters
John F. Kennedy telegram to Roosevelt
No full textJersey Homesteads (later the Borough of Roosevelt) was established in the 1930s as an agro-industrial cooperative community. It was established specifically for urban Jewish garment workers, many of whom had emigrated from Europe. President John F. Kennedy sent a telegram to the citizens of Roosevelt, New Jersey, apologizing for not being able to attend the memorial dedication in honor of former President Franklin Delano Roosevelt. (Jersey Homesteads became Roosevelt in 1945 in honor of the president.) President Kennedy expressed his gratitude to the people of Roosevelt for constructing the memorial, and commented that it will serve as a constant reminder of Roosevelt's good works
Logarithmic variance profiles and the corresponding f-1 spectra of temperature fluctuations in turbulent Rayleigh-Bénard convection
No full textWe report experimental results for the temperature variance 2(z) and the corresponding frequency spectra P(f) in turbulent Rayleigh-Bénard convection (RBC) in a cylindrical sample of aspect ratioT= D/L = 1:00 (D = 1:12 m is the diameter and L = 1:12 m the height). The measurements were conducted in the Rayleigh-number range 1011 < Ra < 1:35 1014 and Pr ' 0:8. For Ra = 1:35x1014, 2(z) could be described well by a logarithmic dependence on the vertical position z in a range of z 1 < z < z 2 with z 1 ' 70 and z 2 = 0:1L. Here L=(2Nu) is the thickness of a thin thermal sublayer adjacent to the horizontal plate where the heat flux (denoted by the Nusselt number Nu) is carried mostly by thermal diffusion. In the log layer, we found that the temperature spectra had a significant frequency range over which P(f) f with close to 1. As Ra decreased, increased so that the log layer became thinner. At Ra = 2:05 1011, z 2 < z 1 and therefore there was no range for a log layer. Correspondingly, the temperature spectrum near the horizontal plate did not have the f1 scaling form either
Maine author Franklin F. Gould recalls his first glimpse of the outside world
No full textMaine author Franklin F. Gould recalls his first glimpse of the outside world as he relates how, as a young farm boy in the late 1800\u27s, he drove his father\u27s horses on an errand to an icebound river
Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients
No full textPrimary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < (Formula presented.)). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice
Mapping the Discipline of the Olympic Games An Author-Cocitation Analysis
No full textThe authors conducted an author cocitation analysis on prominent authors writing about the Olympics during the 1990s. Author cocitation is an established bibliometric technique that can be used to measure the relative similarities of topics written about by the cited authors. This enables a visual representation of the “intellectual space” of the discipline, in this case the Olympics, to be created for the period under review. So core and peripheral research areas are identified, along with their major contributors. The representation appears as a two-dimensional cluster-enhanced map. Subject expertise was then applied to the results to place labels on the generated clusters of authors and their topics
Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma
No full textPurpose: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation. Methods: In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied. Results: We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)—area under curve = 0.81 (0.03) , accuracy = 0.87 (0.07) , precision = 0.88 (0.07) , recall = 0.88 (0.07) and F1-score = 0.87 (0.07) , while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome. Conclusions: All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model
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