A conceptual model for public relations in museums

Purpose: This paper aims to present a conceptual model for public relations specific to museums. Design/methodology/approach: Based on relevant literature, a contingency model is developed for the public relations practices of museums. Findings: The model offers the market orientation level of the management and the interest level of the publics as the major factors that influence the effectiveness of the public relations programs in museums. The interest level of the publics is offered as a moderating variable. Practical implications: The model suggests that the effectiveness of the public relations programs of museums depends on two major factors. Although the interest level of the publics may seem to be uncontrollable at first glance, its negative impact can be largely controllable by managers by changing their own market orientation level - by adapting the public relations strategy to the targeted public depending on the interest level of that public. Originality/value: The model is specifically designed for museums. It can be accepted as the first public relations model specifically offered for museums. The model here recognises the relationship between marketing and public relations. © Emerald Group Publishing Limited

Vision-driven Autocharacterization of Perovskite Semiconductors

In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty samples of unique composition from the formamidinium-methylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation and X-ray diffraction.Comment: Manuscript 8 pages; Supplemental 7 page

Flexible and expandable robot for tissue therapies - Modeling and design

Objective: Implantable technologies should be mechanically compliant with the tissue in order to maximize tissue quality and reduce inflammation during tissue reconstruction. We introduce the development of a flexible and expandable implantable robotic (FEIR) device for the regenerative elongation of tubular tissue by applying controlled and precise tension to the target tissue while minimizing the forces produced on the surrounding tissue. Methods: We introduce a theoretical framework based on iterative beam theory static analysis for the design of an expandable robot with a flexible rack. The model takes into account the geometry and mechanics of the rack to determine a trade-off between its stiffness and capability to deliver the required tissue tension force. We empirically validate this theory on the benchtop and with biological tissue. Results: We show that FEIR can apply the required therapeutical forces on the tissue while reducing the amount of force it applies to the surrounding tissues as well as reducing self-damage. Conclusion: The study demonstrates a method to develop robots that can change size and shape to fit their dynamic environment while maintaining the precision and delicacy necessary to manipulate tissue by traction. Significance: The method is relevant to designers of implantable technologies. The robot is a precursor medical device for the treatment of Long-Gap Esophageal Atresia and Short Bowel Syndrome

Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa

Background: Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32 × -48×). Results: We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency. Conclusion: This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey. © 2014 Alkan et al

Bilkent University at TRECVID 2005

We describe our second-time participation, that includes one high-level feature extraction run, and three manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edgebased low-level features and textual information consisted of the speech transcript provided in the collection. With the experience gained with our second-time participation, we are in the process of building a system for automatic classification and indexing of video archives

Activating mutations of STAT5B and STAT3 in lymphomas derived from γδ-T or NK cells

Lymphomas arising from NK or γδ-T cells are very aggressive diseases and little is known regarding their pathogenesis. Here we report frequent activating mutations of STAT3 and STAT5B in NK/T-cell lymphomas (n=51), γδ-T-cell lymphomas (n=43) and their cell lines (n=9) through next generation and/or Sanger sequencing. STAT5B N642H is particularly frequent in all forms of γδ-T-cell lymphomas. STAT3 and STAT5B mutations are associated with increased phosphorylated protein and a growth advantage to transduced cell lines or normal NK cells. Growth-promoting activity of the mutants can be partially inhibited by a JAK1/2 inhibitor. Molecular modelling and surface plasmon resonance measurements of the N642H mutant indicate a marked increase in binding affinity of the phosphotyrosine-Y699 with the mutant histidine. This is associated with the prolonged persistence of the mutant phosphoSTAT5B and marked increase of binding to target sites. Our findings suggest that JAK-STAT pathway inhibition may represent a therapeutic strategy. © 2015 Macmillan Publishers Limited. All rights reserved

Record linkage to obtain birth outcomes for the evaluation of screening biomarkers in pregnancy: a feasibility study

<p>Abstract</p> <p>Background</p> <p>Linking population health data to pathology data is a new approach for the evaluation of predictive tests that is potentially more efficient, feasible and efficacious than current methods. Studies evaluating the use of first trimester maternal serum levels as predictors of complications in pregnancy have mostly relied on resource intensive methods such as prospective data collection or retrospective chart review. The aim of this pilot study is to demonstrate that record-linkage between a pathology database and routinely collected population health data sets provides follow-up on patient outcomes that is as effective as more traditional and resource-intensive methods. As a specific example, we evaluate maternal serum levels of PAPP-A and free <it>β</it>-hCG as predictors of adverse pregnancy outcomes, and compare our results with those of prospective studies.</p> <p>Methods</p> <p>Maternal serum levels of PAPP-A and free <it>β</it>-hCG for 1882 women randomly selected from a pathology database in New South Wales (NSW) were linked to routinely collected birth and hospital databases. Crude relative risks were calculated to investigate the association between low levels (multiples of the median ≤ 5^thpercentile) of PAPP-A or free <it>β</it>-hCG and the outcomes of preterm delivery (<37 weeks), small for gestational age (<10^thpercentile), fetal loss and stillbirth.</p> <p>Results</p> <p>Using only full name, sex and date of birth for record linkage, pregnancy outcomes were available for 1681 (89.3%) of women included in the study. Low levels of PAPP-A had a stronger association with adverse pregnancy outcomes than a low level of free <it>β</it>-hCG which is consistent with results in published studies. The relative risk of having a preterm birth with a low maternal serum PAPP-A level was 3.44 (95% CI 1.96–6.10) and a low free <it>β</it>-hCG level was 1.31 (95% CI 0.55–6.16).</p> <p>Conclusion</p> <p>This study provides data to support the use of record linkage for outcome ascertainment in studies evaluating predictive tests. Linkage proportions are likely to increase if more personal identifiers are available. This method of follow-up is a cost-efficient technique and can now be applied to a larger cohort of women.</p

Bias associated with delayed verification in test accuracy studies: accuracy of tests for endometrial hyperplasia may be much higher than we think!

BACKGROUND: To empirically evaluate bias in estimation of accuracy associated with delay in verification of diagnosis among studies evaluating tests for predicting endometrial hyperplasia. METHODS: Systematic reviews of all published research on accuracy of miniature endometrial biopsy and endometr ial ultrasonography for diagnosing endometrial hyperplasia identified 27 test accuracy studies (2,982 subjects). Of these, 16 had immediate histological verification of diagnosis while 11 had verification delayed > 24 hrs after testing. The effect of delay in verification of diagnosis on estimates of accuracy was evaluated using meta-regression with diagnostic odds ratio (dOR) as the accuracy measure. This analysis was adjusted for study quality and type of test (miniature endometrial biopsy or endometrial ultrasound). RESULTS: Compared to studies with immediate verification of diagnosis (dOR 67.2, 95% CI 21.7–208.8), those with delayed verification (dOR 16.2, 95% CI 8.6–30.5) underestimated the diagnostic accuracy by 74% (95% CI 7%–99%; P value = 0.048). CONCLUSION: Among studies of miniature endometrial biopsy and endometrial ultrasound, diagnostic accuracy is considerably underestimated if there is a delay in histological verification of diagnosis

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors