A Unified Deep Learning Approach for Prediction of Parkinson’s Disease

The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson’s disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson’s across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson’s, using different medical image sets from real environments

Machine learning for predictive modelling of ambulance calls

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom

Variation in thermal stress response in two populations of the brown seaweed, Fucus distichus, from the Arctic and subarctic intertidal

It is unclear whether intertidal organisms are ‘preadapted’ to cope with the increase of temperature and temperature variability or if they are currently at their thermal tolerance limits. To address the dichotomy, we focused on an important ecosystem engineer of the Arctic intertidal rocky shores, the seaweed Fucus distichus and investigated thermal stress responses of two populations from different temperature regimes (Svalbard and Kirkenes, Norway). Thermal stress responses at 20°C, 24°C and 28°C were assessed by measuring photosynthetic performance and expression of heat shock protein (HSP) genes (shsp, hsp90 and hsp70). We detected population-specific responses between the two populations of F. distichus, as the Svalbard population revealed a smaller decrease in photosynthesis performance but a greater activation of molecular defence mechanisms (indicated by a wider repertoire of HSP genes and their stronger upregulation) compared with the Kirkenes population. Although the temperatures used in our study exceed temperatures encountered by F. distichus at the study sites, we believe response to these temperatures may serve as a proxy for the species’ potential to respond to climate-related stresses

Machine Learning for Analysis of Real Nuclear Plant Data in the Frequency Domain

Machine Learning is used in this paper for detecting anomalies in nuclear plant reactor cores. The proposed approach first generates large amounts of simulated data with different types of perturbations occurring at various locations in the core. This is achieved using the CORE SIM+ modelling framework, which generates these data in the frequency domain. State-of-the-art machine and deep learning models are then extended and used to successfully perform semantic segmentation of the core, classification and localisation of perturbations. Actual plant data are then considered, provided by two different reactors, including no labels about perturbation existence. A domain adaptation methodology is then developed, which uses self-supervised, or unsupervised learning, so as to align the simulated data with the actual plant data and detect perturbations, whilst classifying their type and estimating their location. Experimental studies illustrate the successful performance of the developed approach and extensions are described that indicate a great potential for further research

Neutron Noise-based Anomaly Classification and Localization using Machine Learning

A methodology is proposed in this paper allowing the classification of anomalies and subsequently their possible localization in nuclear reactor cores during operation. The method relies on the monitoring of the neutron noise recorded by in-core neutron detectors located at very few discrete locations throughout the core. In order to unfold from the detectors readings the necessary information, a 3-dimensional Convolutional Neural Network is used, with the training and validation of the network based on simulated data. In the reported work, the approach was also tested on simulated data. The simulations were carried out in the frequency domain using the CORE SIM+ diffusion-based two-group core simulator. The different scenarios correspond to the following cases: a generic “absorber of variable strength”, axially travelling perturbations at the velocity of the coolant flow (due to e.g. fluctuations of the coolant temperature at the inlet of the core), fuel assembly vibrations, control rod vibrations, and core barrel vibrations. In all those cases, various frequencies were considered and, when relevant, different locations of the perturbations and different vibration modes were taken into account. The machine learning approach was able to correctly identify the different scenarios with a maximum error of 0.11%. Moreover, the error in localizing anomalies had a mean squared error of 0.3072 in mesh size, corresponding to less than 4 cm. The proposed methodology was also demonstrated to be insensitive to parasitic noise and will be tested on actual plant data in the near future

Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements

The research conducted has been made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 for the “CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX)” Horizon 2020 project, 2017-2021.Peer reviewedPublisher PD

Practical Recommendations for Optimal Thromboprophylaxis in Patients with COVID-19:A Consensus Statement Based on Available Clinical Trials

Coronavirus disease 2019 (COVID-19) has been shown to be strongly associated with increased risk for venous thromboembolism events (VTE) mainly in the inpatient but also in the outpatient setting. Pharmacologic thromboprophylaxis has been shown to offer significant benefits in terms of reducing not only VTE events but also mortality, especially in acutely ill patients with COVID-19. Although the main source of evidence is derived from observational studies with several limitations, thromboprophylaxis is currently recommended for all hospitalized patients with acceptable bleeding risk by all national and international guidelines. Recently, high quality data from randomized controlled trials (RCTs) further support the role of thromboprophylaxis and provide insights into the optimal thromboprophylaxis strategy. The aim of this statement is to systematically review all the available evidence derived from RCTs regarding thromboprophylaxis strategies in patients with COVID-19 in different settings (either inpatient or outpatient) and provide evidence-based guidance to practical questions in everyday clinical practice. Clinical questions accompanied by practical recommendations are provided based on data derived from 20 RCTs that were identified and included in the present study. Overall, the main conclusions are: (i) thromboprophylaxis should be administered in all hospitalized patients with COVID-19, (ii) an optimal dose of inpatient thromboprophylaxis is dependent upon the severity of COVID-19, (iii) thromboprophylaxis should be administered on an individualized basis in post-discharge patients with COVID-19 with high thrombotic risk, and (iv) thromboprophylaxis should not be routinely administered in outpatients. Changes regarding the dominant SARS-CoV-2 variants, the wide immunization status (increasing rates of vaccination and reinfections), and the availability of antiviral therapies and monoclonal antibodies might affect the characteristics of patients with COVID-19; thus, future studies will inform us about the thrombotic risk and the optimal therapeutic strategies for these patients

Driving chronicity in rheumatoid arthritis: perpetuating role of myeloid cells

Acute inflammation is a complex and tightly regulated homeostatic process that includes leukocyte migration from the vasculature into tissues to eliminate the pathogen/injury, followed by a pro-resolving response promoting tissue repair. However, if inflammation is uncontrolled as in chronic diseases such as Rheumatoid Arthritis (RA) it leads to tissue damage and disability. Synovial tissue inflammation in RA patients is maintained by sustained activation of multiple inflammatory positive-feedback regulatory pathways in a variety of cells including myeloid cells. In this review, we will highlight recent evidence uncovering biological mechanisms contributing to the aberrant activation of myeloid cells that contributes to perpetuation of inflammation in RA, and discuss emerging data on anti-inflammatory mediators contributing to sustained remission that may inform a novel category of therapeutic targets

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed

Efficacy of anthropometric measures for identifying cardiovascular disease risk in adolescents: review and meta-analysis.

INTRODUCTION: To compare the ability of Body Mass Index (BMI), waist circumference (WC) and waist to height ratio (WHtR) to estimate cardiovascular disease (CVD) risk levels in adolescents. EVIDENCE ACQUISITION: A systematic review and meta-analysis was performed after a database search for relevant literature (Cochrane, Centre for Review and Dissemination, PubMed, British Nursing Index, CINAHL, BIOSIS citation index, ChildData, metaRegister). EVIDENCE SYNTHESIS: The study included 117 records representing 96 studies with 994,595 participants were included in the systematic review, 14 of which (13 studies, N.=14,610) were eligible for the meta-analysis. The results of the meta-analysis showed that BMI was a strong indicator of systolic blood pressure, diastolic blood pressure, triglycerides, high-density lipoprotein cholesterol and insulin; but not total cholesterol, low-density lipoprotein or glucose. Few studies were eligible for inclusion in the meta-analysis considering WC or WHtR (N.≤2). The narrative synthesis found measures of central adiposity to be consistently valid indicators of the same risk factors as BMI. CONCLUSIONS: BMI was an indicator of CVD risk. WC and WHtR were efficacious for indicating the same risk factors BMI performed strongly for, though there was insufficient evidence to judge the relative strength of each measure possibly due to heterogeneity in the methods for measuring and classifying WC