Complicating the Resilience Model: A Four-Country Study About Misinformation

The resilience model to disinformation (Humprecht et al., 2020, 2021) suggests that countries will differ in exposure and reactions to disinformation due to their distinct media, economic, and political environments. In this model, higher media trust and the use of public service broadcasters are expected to build resilience to disinformation, while social media use and political polarization undermine resilience. To further test and develop the resilience model, we draw on a four-country (the US, Canada, the UK, and France) survey conducted in February 2021. We focus on three individual-level indicators of a lack of resilience: awareness of, exposure to, and sharing of misinformation. We find that social media use is associated with higher levels of all three measures, which is consistent with the resilience model. Social media use decreases resilience to misinformation. Contrary to the expectations of the resilience model, trust in national news media does not build resilience. Finally, we consider the use of public broadcasting media (BBC, France Télévisions, and CBC). The use of these sources does not build resilience in the short term. Moving forward, we suggest that awareness of, exposure to, and reactions to misinformation are best understood in terms of social media use and left–right ideology. Furthermore, instead of focusing on the US as the exceptional case of low resilience, we should consider the UK as the exceptional case of high resilience to misinformation. Finally, we identify potential avenues to further develop frameworks to understand and measure resilience to misinformation

Mobile tools for Windows: user guide : report produced in the context of the Inventory Data Capture Tools Risk Global Component

The aim of this document is to provide guidelines for the use of the digital Windows Mobile Tools that have been designed and built to collect building inventory pre- and post-earthquake events. The guide instructs users how to install the software on a Windows device and provides step-by-step instructions for collecting and managing the data that has been collected. It is expected that the field staff are already experts in collecting building inventory, therefore this guide does not provides instructions how to recognise or understand building structural components. Appendices to this guide also include the following: A copy of the paper Forms that are used to collect data in the field if the digital Mobile Tools are unavailable The Photos-4-GEM Protocol that provides guidance for photography of structures in the context of the Inventory Data Capture Tools and the GEM Taxonomy

Prevention of Decubitus Ulcers in the Clinical Setting

https://scholarworks.moreheadstate.edu/student_scholarship_posters/1084/thumbnail.jp

Commit and Connect: VCU Goes Green

In alignment with Theme IV of VCU’s Quest for Distinction, this university volunteer project will help to commit and connect faculty, staff, students, and alumni with a community education partner to help launch a green or sustainable project while promoting, teaching and educating participants on the value of sustainable living

Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy for Primary Breast Cancer Comparing Interim Ultrasound, Shear Wave Elastography and MRI

Abstract Background Prediction of pathological complete response (pCR) of primary breast cancer to neoadjuvant chemotherapy (NACT) may influence planned surgical approaches in the breast and axilla. The aim of this project is to assess the value of interim shear wave elastography (SWE), ultrasound (US) and magnetic resonance imaging (MRI) after 3 cycles in predicting pCR. Methods 64 patients receiving NACT had baseline and interim US, SWE and MRI examinations. The mean lesion stiffness at SWE, US and MRI diameter was measured at both time points. We compared four parameters with pCR status: a) Interim mean stiffness ≤ or &gt; 50 kPa; b) Percentage stiffness reduction; c) Percentage US diameter reduction and d) Interim MRI response using RECIST criteria. The Chi square test was used to assess significance. Results Interim stiffness of ≤ or &gt; 50 kPa gave the best prediction of pCR with pCR seen in 10 of 14 (71 %) cancers with an interim stiffness of ≤ 50 kPa, compared to 7 of 50 (14 %) of cancers with an interim stiffness of &gt; 50 kPa, (p &lt; 0.0001) (sensitivity 59 %, specificity 91 %, PPV 71 %, NPV 86 % and diagnostic accuracy 83 %). Percentage reduction in stiffness was the next best parameter (sensitivity 59 %, specificity 85 %, p &lt; 0.0004) followed by reduction in MRI diameter of &gt; 30 % (sensitivity 50 % and specificity 79 %, p = 0.03) and % reduction in US diameter (sensitivity 47 %, specificity 81 %, p = 0.03). Similar results were obtained from ROC analysis. Conclusion SWE stiffness of breast cancers after 3 cycles of NACT and changes in stiffness from baseline are strongly associated with pCR after 6 cycles.</jats:p

Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo

PURPOSE: Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo. METHODS: Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time. RESULTS: QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc  = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect. CONCLUSION: We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization

Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings