130,667 research outputs found
Interview with Charles "Chuck" Reiley, 1980
No full textRetired from the Air Force as dentist, Reiley went into private practice in San Antonio, where he also formed the Alamo City Jazz Band. He discusses fellow musicians and his professional experiences as a jazz trombonist
Role of Microcalcifications in Breast Cancer Bone Mimicry
No full textBreast microcalcifications (MCs) are insoluble calcium deposits, that are used as a diagnostic tool to detect breast cancer where up to 93% of cases of ductal carcinoma in situ (DCIS) are determined based on the presence of MCs. Breast MCs are categorized into type I (benign – calcium oxalate (OX)) and type II (malignant – calcium phosphate, hydroxyapatite (HA)) MCs. Breast lesions with the presence of malignant MCs show an increase in the expression of mesenchymal and bone markers, also termed as “bone mimicry”. Breast cancer cells with bone mimicry are known to exhibit propensity to metastasize to bone. Despite correlative evidence linking MCs to bone mimicry and bone mimicry to bone metastasis, the biological role of MCs in promoting invasive phenotype, bone mimicry and subsequent metastasis remains unknown. To address this, our laboratory has developed collagen-inspired extracellular matrices (“ECM-Mimics”) recapitulating benign or malignant MC composition observed in MC-positive DCIS patients. We hypothesize that the presence of malignant microcalcifications in the primary breast tumor can promote invasive and bone mimicry phenotypes. We also hypothesize that breast cancer cells pre-conditioned to malignant MCs create a premetastatic niche in the primary breast tumor where breast cancer cells secrete cytokines and chemokines which are important for remodeling the bone microenvironment.
In this MS thesis project, we show that the ECM-mimics are able to deposit and recapitulate the composition of benign (OX) and malignant (HA) microcalcifications found in breast biopsies of cancer patients. When seeded onto the ECM-mimics containing different microcalcifications, non-metastatic T47D breast cancer cells exhibit higher mRNA expression of mesenchymal and bone markers by qPCR analysis. The conditioned media collected from T47D cells exposed to malignant MCs show higher alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) enzyme activity, which are markers for osteoblast and osteoclast activity, respectively. Furthermore, ex vivo human bone explants exposed to conditioned media from the T47D cells seeded on malignant MCs also show significant increase in the bone metabolic activity, ALP enzyme activity, and TRAP enzyme activity of the bones compared to bones exposed to conditioned media from the T47D cells seeded on non-mineralized or benign MC containing ECM-mimics. These results suggest that malignant MCs may play a critical role in metastatic breast cancer progression
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
The R&D Tax Incentives
No full textThis article sets out some background information and reflections of the author on the R&D tax incentive schemes included in the Common Corporate Tax Base (CCTB) Proposal. In particular the author analyzes the stimulus to private R&D through ad hoc tax incentives included in the CCTB Proposal and dives into the actual provisions included in the Proposal highlighting the most relevant issues connected with their design and interpretation. Moreover, the author explores the interaction between the CCTB Proposal and the granting by Member States of domestic R&D tax incentives
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