Supplementary data for the article: Selaković, Z.; Soloveva, V.; Gharaibeh, D. N.; Wells, J.; Šegan, S.; Panchal, R. G.; Šolaja, B. A. Anti-Ebola Activity of Diazachrysene Small Molecules. ACS Infectious Diseases 2016, 1 (6), 264–271. https://doi.org/10.1021/acsinfecdis.5b00028

Supporting information for: [https://doi.org/10.1021/acsinfecdis.5b00028]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/2035

A role for cell sex in stem cell-mediated skeletal muscle regeneration: Female cells have higher muscle regeneration efficiency

We have shown that muscle-derived stem cells (MDSCs) transplanted into dystrophic (mdx) mice efficiently regenerate skeletal muscle. However, MDSC populations exhibit heterogeneity in marker profiles and variability in regeneration abilities. We show here that cell sex is a variable that considerably influences MDSCs' regeneration abilities. We found that the female MDSCs (F-MDSCs) regenerated skeletal muscle more efficiently. Despite using additional isolation techniques and cell cloning, we could not obtain a male subfraction with a regeneration capacity similar to that of their female counterparts. Rather than being directly hormonal or caused by host immune response, this difference in MDSCs' regeneration potential may arise from innate sex-related differences in the cells' stress responses. In comparison with F-MDSCs, male MDSCs have increased differentiation after exposure to oxidative stress induced by hydrogen peroxide, which may lead to in vivo donor cell depletion, and a proliferative advantage for F-MDSCs that eventually increases muscle regeneration. These findings should persuade researchers to report cell sex, which is a largely unexplored variable, and consider the implications of relying on cells of one sex. © The Rockefeller University Press

Effective use of personal health records to support emergency services

Smart City systems capture and exchange information with the aim to improve public services. Particularly, healthcare data could help emergency services to plan resources and make life-saving decisions. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens’ privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and a large amount of information available represent a significant barrier. In this paper, we focus on a case study involving the use of personal health records to support emergency services in the context of a fire building evacuation. We propose a methodology involving a knowledge engineering approach and a common-sense knowledge base to address the problem of deriving useful information from health records and, at the same time, preserve citizens’ privacy. We perform extensive experiments involving a synthetic dataset of health records and a curated gold standard to demonstrate how our approach allows us to identify vulnerable people and interpret their particular needs while avoiding the disclosure of personal information

The development of a radial based integrated network for the modelling of 3D fused deposition

Purpose The purpose of this research paper is to investigate and model the fused deposition modelling (FDM) process to predict the mechanical attributes of 3D printed specimens. Design/methodology/approach By exploiting the main effect plots, a Taguchi L18 orthogonal array is used to investigate the effects of such parameters on three mechanical attributes of the 3D printed specimens. A radial-based integrated network is then developed to map the eight FDM parameters to the three mechanical attributes for both PEEK and PEKK. Such an integrated network maps and predicts the mechanical attributes through two consecutive phases that consist of several radial basis functions (RBFs). Findings Validated on a set of further experiments, the integrated network was successful in predicting the mechanical attributes of the 3D printed specimens. It also outperformed the well-known RBF network with an overall improvement of 24% in the coefficient of determination. The integrated network is also further validated by predicting the mechanical attributes of a medical-surgical implant (i.e. the MidFace Rim) as an application. Originality/value The main aim of this paper is to accurately predict the mechanical properties of parts produced using the FDM process. Such an aim requires modelling a highly dimensional space to represent highly nonlinear relationships. Therefore, a radial-based integrated network based on the combination of composition and superposition of radial functions is developed to model FDM using a limited number of data points

Accurate Estimates of Microarray Target Concentration from a Simple Sequence-Independent Langmuir Model

Background: Microarray technology is a commonly used tool for assessing global gene expression. Many models for estimation of target concentration based on observed microarray signal have been proposed, but, in general, these models have been complex and platform-dependent. Principal Findings: We introduce a universal Langmuir model for estimation of absolute target concentration from microarray experiments. We find that this sequence-independent model, characterized by only three free parameters, yields excellent predictions for four microarray platforms, including Affymetrix, Agilent, Illumina and a custom-printed microarray. The model also accurately predicts concentration for the MAQC data sets. This approach significantly reduces the computational complexity of quantitative target concentration estimates. Conclusions: Using a simple form of the Langmuir isotherm model, with a minimum of parameters and assumptions, and without explicit modeling of individual probe properties, we were able to recover absolute transcript concentrations with high R 2 on four different array platforms. The results obtained here suggest that with a ‘‘spiked-in’ ’ concentration serie