Measurement of collagen synthesis by cells grown under different mechanical stimuli

INTRODUCTION: The use of scaffolds in tissue engineering is essential to provide cells with a matrix for cell proliferation and differentiation resulting in tissue regeneration. Normally this process involves seeding cells onto an artificial biodegradable scaffold providing mechanical support for cells until there is sufficient extracellular matrix deposition (ECM) to replace the artificial scaffold. Collagen is the bulk protein found in the ECM and measurement of its synthesis is the most direct, absolute indicator of ECM production

Face Recognition using Multi-Modal Low-Rank Dictionary Learning

Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion

On becoming irrelevant:an analysis of charity workers’ untold epic stories

While the nature, character and function of stories are variously theorized in organizational storytelling literature, little research has tried to unpack how organizational narrative domain may transform over time. Attending to the contextual transformation of organizational story space can reveal how popular stories at one epoch could be reformulated, ignored, or forgotten all together during another epoch. Drawing on ethnographic data of a children’s charity in UK, which experienced a stage of rapid professionalization, specialization, and bureaucratization, I examine the influence of this restructuring initiative on the organizational narrative domain. It was shown that the professionalization of the charity starved the old stories of the oxygen of relevance. The memories of the old pioneers, from the days of stress and violence, became less welcome as the organization turned increasingly managerial in character. The notion of ‘irrelevancy’ is further developed drawing on the work of Maurice Halbwachs, and its implications are elaborated building on storytelling research

A Research Framework for Evaluating the Effectiveness of Implementations of Social Media in Higher Education

Following the lead of today’s hi-tech businesses and industries, many college campuses have begun using Web.2.0 social media technologies like Facebook, blogs, Twitter, and YouTube to facilitate information sharing and collaboration among administrators, faculty, and students. An examination of research on campus social media initiatives revealed that universities are beginning to provide support and infrastructure to support social media initiatives, and that social media tools are being used as part of course content and delivery, where students can use them for collaboration and group decision making on real-world projects. However, much of the research to date was found to be anecdotal, descriptive, and lacking objective evaluation. The paper argues that more rigorous, analytical research is needed to compare and contrast specific features of social media software, the way it is used and implemented, and the outcomes achieved, by students and/or by other stakeholders. To guide future research, the author proposes a research framework that identifies various factors that impact implementations of social media in higher education, as well as relevant outcome variables that should be measured

A robust AHP-DEA method for measuring the relative efficiency: An application of airport industry

Measuring the relative efficiency of similar units has been an important topic of research among many researchers. Data envelopment analysis has been one of the most important techniques for measuring the efficiency of different units. However, there are some limitations on using such technique and some people prefer to use other methods such as analytical hierarchy process to measure the relative efficiencies. Besides, uncertainty in the input data is another issue, which makes some misleading results. In this paper, we present an integrated robust DEA-AHP to measure the relative efficiency of similar units. The proposed model of this is believed to capable of presenting better results in terms of efficiency compared with exclusive usage of DEA or AHP. The implementation of the proposed model is demonstrated for a real-world case study of Airport industry and the results are analyzed

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf