1,720,959 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Assessing the role of ATF4 dependent signaling in limiting pancreatic cancer by tomatidine.
No full textHealth Sciences: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), comprising of 90% pancreatic cancer cases, is a highly aggressive cancer with a five-year survival rate of 10.8%. Current therapies include surgery and chemotherapy, which are mostly ineffective because of metastasis to different organs of the body and dysfunctional immune cell populations in the tumor microenvironment. Plant based metabolites have been used as chemotherapy agents to target cancer as a less toxic alternative therapeutic for several decades. Tomatidine, a natural metabolite present in the tomato plant, has anti-cancer and anti-inflammatory properties. Further, tomatidine is reported to inhibit Activating Transcription Factor 4 (ATF4) dependent signaling in a range of diseases such as skeletal muscle atrophy, dengue virus infection, neurons, and myoblasts. ATF4, a master regulator of cellular stress, has been implicated in different cancers including PDAC by promoting cancer cell survival, as well as by affecting anti-tumor immunity and inducing chemotherapy resistance.
HYPOTHESIS: Tomatidine can inhibit pancreatic cancer by regulating ATF4-dependent signaling.
METHODS: Miapaca-2 (human) and MT5 (mouse) pancreatic tumor cell lines were treated with tomatidine for 72 hours and assayed for cell growth. ATF4 and its downstream protein expressions were evaluated. Pancreatic cancer bearing immunocompetent mice and immunocompromised mice were treated with 5 mg/kg daily injections of tomatidine or vehicle control and monitored for tumor growth. RNA from the tumor bearing immunocompetent mice were assayed for ATF4 and related gene expression. ATF4 expression was silenced in human pancreatic cancer cells and assayed for cell growth. Further, healthy donor immune cells were treated with tomatidine in presence or absence of factors that help differentiate these cells into a dysfunctional immunosuppressive population called as myeloid derived suppressor cells (MDSCs). Quantity of MDSCs before and after treatment were compared along with assaying for ATF4 and related gene expression.
RESULTS: Tomatidine can inhibit pancreatic tumor growth in vitro (cell culture) and in vivo (in mice). However, there was little effect of tomatidine on tumor growth when pancreatic cancer cells were implanted into immunocompromised mice. Lack of an effect in an immunocompromised animal model suggests tomatidine may affect anti-tumor immunity. Treatment with tomatidine reduced expression of downstream protein p-4EBP1 (direct ATF4 target) in vitro. Tumor-bearing mice treated with tomatidine have reduced mRNA expression of ATF4 and downstream gene eIF4EBP1 compared to vehicle control treated animals. Further, reducing expression of ATF4 in MiaPaca-2 cells in vitro reduced cell viability. Tomatidine can affect the rate of MDSC differentiation without causing immune cell death and reduce expression of ATF4 and related gene expression in immune cells.
CONCLUSION: Tomatidine can inhibit pancreatic tumor growth and reduce ATF4 expression in PDAC and immune cells. Tomatidine can also affect the activity of immunosuppressive cells present in PDAC tumor microenvironment.
SIGNIFICANCE: Therefore, this study sheds light on a novel plant derived anti-cancer treatment strategy that targets an upregulated pathway (ATF4 dependent signaling) in pancreatic cancer and can be used to develop a new holistic therapeutic strategy for targeting both the pancreatic tumor and its microenvironment.A three-year embargo was granted for this item
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
Statistically-informed multimodal domain adaptation in industrial human-robot collaboration environments
No full textIncreased global competition has placed a great demand for manufacturers to be flexible with their products and services. This can be addressed with the introduction of robots which are very effective in carrying out repetitive, non-ergonomic tasks working in partnership with human operators who typically excel in precise tasks requiring dexterity, flexibility, and cognitive decision-making. This paradigm of humans and robots working together, forms the motivation behind the field of human-robot collaboration (HRC). This dissertation begins by introducing a novel taxonomy of HRC to better articulate the possible interactions between humans and robots based on levels of robot intelligence and autonomy.
Cohesive HRC can be achieved through communication between human and robot partners. The field of human-robot communication (HRCom) finds its roots in human communication aiming to achieve the “naturalness” inherent in the latter. This dissertation posits that the design aspects can take inspiration from human communication to create more intuitive systems that truly leverage the presence of the human as a collaborating agent so that the human's role is more meaningful than just a command centre. However, this goal must come at no additional effort to the human.
HRCom can be achieved through a robust robot perception system developed using machine learning. The challenge is the dearth of comprehensive, labelled datasets while standard, publicly available ones do not generalize well to domain and application specific scenarios. Furthermore, models also fail to generalize under domain shifts stemming from changes in the environment of the robot. Keeping in mind the aforementioned challenges and the complexities inherent in HRCom, a framework, SIMLea, is presented. Statistically-Informed Multimodal (Domain Adaptation by Transfer) Learning takes inspiration from human communication to use human feedback to auto-label for domain adaptation.
The strength of the contribution lies in the use of incommensurable multimodal decision-level inputs for personalizing with user-specific data leading to statistically-informed extension of datasets, greater safety, enhanced monitoring of the continuous learning of the model, and judicious use of resources. The framework is validated with facial expression and hand gesture recognition for involuntary and voluntary communications; but is also applicable to other combinations of multimodal inputs in HRC applications.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat
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