Nanoethics, science communication, and a fourth model for public engagement

This paper develops a fourth model of public engagement with science, grounded in the principle of nurturing scientific agency through online participatory bioethics. It argues that social media is an effective device through which to enable such engagement, as it has the capacity to empower users and transforms audiences into co-producers of knowledge, rather than consumers of content, the value of which is recognised within the citizen science movement. Social media also fosters greater engagement with the political and legal implications of science, thus promoting the value of scientific citizenship through the acquisition of science capital. This argument is explored by considering the case of nanoscience and nanotechnology, as an exemplar for how emerging technologies may be handled by the scientific community and science policy makers, and as a technology that has defined a second era of science communication

A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography

PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans. METHODS: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves. RESULTS: The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. CONCLUSIONS: The models achieved high classification and segmentation performance, similar to human performance. TRANSLATIONAL RELEVANCE: Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented

Проектирование автоматизированной групповой замерной установки при разработке Макарьевского месторождения нефти на проточном-1 лицензионном участке недр Томской области

В данной дипломной работе произведено обоснование оптимальной конфигурации и проектирование автоматизированной группой замерной установки для системы сбора продукции эксплуатационных скважин в рамках прогноза эффективности разработки Макарьевского месторождения Томской области.In this graduation work the study of the optimal configuration and design of automated group measuring system for the collection system of production wells within the forecast development efficiency Makar deposits of Tomsk region