by Markus Heinonen, Fabien Milliat, Mohamed Amine Benadjaoud, Agnès François, Valérie Buard, Georges Tarlet, Florence d’Alché-Buc, Olivier GuipaudThe vascular endothelium is considered as a key cell compartment for the response to ionizing radiation of normal tissues and tumors, and as a promising target to improve the differential effect of radiotherapy in the future. Following radiation exposure, the global endothelial cell response covers a wide range of gene, miRNA, protein and metabolite expression modifications. Changes occur at the transcriptional, translational and post-translational levels and impact cell phenotype as well as the microenvironment by the production and secretion of soluble factors such as reactive oxygen species, chemokines, cytokines and growth factors. These radiation-induced dynamic modifications of molecular networks may control the endothelial cell phenotype and govern recruitment of immune cells, stressing the importance of clearly understanding the mechanisms which underlie these temporal processes. A wide variety of time series data is commonly used in bioinformatics studies, including gene expression, protein concentrations and metabolomics data. The use of clustering of these data is still an unclear problem. Here, we introduce kernels between Gaussian processes modeling time series, and subsequently introduce a spectral clustering algorithm. We apply the methods to the study of human primary endothelial cells (HUVECs) exposed to a radiotherapy dose fraction (2 Gy). Time windows of differential expressions of 301 genes involved in key cellular processes such as angiogenesis, inflammation, apoptosis, immune response and protein kinase were determined from 12 hours to 3 weeks post-irradiation. Then, 43 temporal clusters corresponding to profiles of similar expressions, including 49 genes out of 301 initially measured, were generated according to the proposed method. Forty-seven transcription factors (TFs) responsible for the expression of clusters of genes were predicted from sequence regulatory elements using the MotifMap system. Their temporal profiles of occurrences were established and clustered. Dynamic network interactions and molecular pathways of TFs and differential genes were finally explored, revealing key node genes and putative important cellular processes involved in tissue infiltration by immune cells following exposure to a radiotherapy dose fraction.
To identify the key common components of knowledge transfer and exchange in existing models to facilitate practice developments in health services research.
There are over 60 models of knowledge transfer and exchange designed for various areas of health care. Many of them remain untested and lack guidelines for scaling‐up of successful implementation of research findings and of proven models ensuring that patients have access to optimal health care, guided by current research.
A scoping review was conducted in line with PRISMA guidelines. Key components of knowledge transfer and exchange were identified using thematic analysis and frequency counts.
Six electronic databases were searched for papers published before January 2015 containing four key terms/variants: knowledge, transfer, framework, health care.
Double screening, extraction and coding of the data using thematic analysis were employed to ensure rigour. As further validation stakeholders’ consultation of the findings was performed to ensure accessibility.
Of the 4,288 abstracts, 294 full‐text articles were screened, with 79 articles analysed. Six key components emerged: knowledge transfer and exchange message, Stakeholders and Process components often appeared together, while from two contextual components Inner Context and the wider Social, Cultural and Economic Context, with the wider context less frequently considered. Finally, there was little consideration of the Evaluation of knowledge transfer and exchange activities. In addition, specific operational elements of each component were identified.
The six components offer the basis for knowledge transfer and exchange activities, enabling researchers to more effectively share their work. Further research exploring the potential contribution of the interactions of the components is recommended.
To explore the social impact of, comfort with, and negative attitudes towards robots among young, middle‐aged, and older adults in the United States.
Descriptive, cross‐sectional. Conducted in 2014–2015 in an urban area of the western United States using a purposive sample of adults 18 years of age or older.
Respondents completed a survey that included the Negative Attitudes Toward Robots Scale (NARS) and two questions taken or modified from the European Commission's Autonomous System 2015 Report. Analyses were conducted to compare perceptions and demographic factors by age groups (young adults:18–44, middle‐aged adults: 45–64, and older adults: >65 years old).
Sample included 499 individuals (n = 322 age 18–44 years, n = 50 age 45–64 years, and n = 102 age 65–98 years). There were no significant differences between age groups for 9 of the 11 items regarding social impact of robots and comfort with robots. There were no significant differences by age groups for 9 of the 14 items in the NARS. Among those items with statistically significant differences, the mean scores indicate similar sentiments for each group.
Older, middle‐aged, and younger adults had similar attitudes regarding the social impact of and comfort with robots; they also had similar negative attitudes towards robots. Findings dispel current perceptions that older adults are not as receptive to robots as other adults. This has implications for nurses who integrate supportive robots in their practice.
Nurses working in clinical and community roles can use these findings when developing and implementing robotic solutions. Understanding attitudes towards robots can support how, where, and with whom robots can be used in nursing practice.