Privacy In The Age Of Technology

The rapid developments in technology have brought increased convenience, but at the price of loss of privacy.  Consumers should be aware of the potential threat to their personal information and should learn the facts and take steps to protect themselves.

Soil microbial communities shift along an urban gradient in Berlin, Germany

The microbial communities inhabiting urban soils determine the functioning of these soils, in regards to their ability to cycle nutrients and support plant communities. In an increasingly urbanized world these properties are of the utmost importance, and the microbial communities responsible are worthy of exploration. We used 53 grassland sites spread across Berlin to describe and explain the impacts of urbanity and other environmental parameters upon the diversity and community composition of four microbial groups. These groups were (i) the Fungi, with a separate dataset for (ii) the Glomeromycota, (iii) the Bacteria, and (iv) the protist phylum Cercozoa. We found that urbanity had distinct impacts on fungal richness, which tended to increase. Geographic distance between sites and soil chemistry, in addition to urbanity, drove microbial community composition, with site connectivity being important for Glomeromycotan communities, potentially due to plant host communities. Our findings suggest that many microbial species are well adapted to urban soils, as supported by an increase in diversity being a far more common result of urbanity than the reverse. However, we also found distinctly separate distributions of operational taxonomic unit (OTU)s from the same species, shedding doubt of the reliability of indicator species, and the use of taxonomy to draw conclusion on functionality. Our observational study employed an extensive set of sites across an urbanity gradient, in the region of the German capital, to produce a rich microbial dataset; as such it can serve as a blueprint for other such investigations

Social innovation: worklessness, welfare and well-being

The UK Government has recently implemented large-scale public-sector funding cuts and substantial welfare reform. Groups within civil society are being encouraged to fill gaps in service provision, and ‘social innovation’ has been championed as a means of addressing social exclusion, such as that caused by worklessness, a major impediment to citizens being able to access money, power and resources, which are key social determinants of health. The aim of this article is to make the case for innovative ‘upstream’ approaches to addressing health inequalities, and we discuss three prominent social innovations gaining traction: microcredit for enterprise; social enterprise in the form of Work Integration Social Enterprises (WISEs); and Self Reliant Groups (SRGs). We find that while certain social innovations may have the potential to address health inequalities, large-scale research programmes that will yield the quality and range of empirical evidence to demonstrate impact, and, in particular, an understanding of the causal pathways and mechanisms of action, simply do not yet exist

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

PURPOSE: We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. METHODS: TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV RESULTS: Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV CONCLUSIONS: We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

BACKGROUND: Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial. METHODS: Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images. FINDINGS: Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects. INTERPRETATION: Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies