9 research outputs found
Multiproxy quantitative paleoceanographic dataset from late Quaternary marine sediment archives in the western Ross Sea (Antarctica)
No full textThe past ice sheet dynamics and the timing of retreat events in the paleo-record in the Ross Sea is an issue still few understood. In order to contribute to this topic, we provide a multiproxy data from marine sediment archives (cores and box cores) collected in three sites in the Central Basin (Western Ross Sea, Antarctica). Each site recorded different environments, affected by different oceanographic conditions and sedimentary regime. This makes the three investigated sediment cores and box cores unique and useful for comparison with other studied cores collected in the same basin. The data set includes physical (paleomagnetism, grain size and petrography), chemical, micropaleontological (diatom, foraminifera and silicoflagellate assemblages) analyses and cryptotephra characterization increasing the information already reported in literature. The importance of this dataset is related to a multi-disciplinary approach in a site, the Central Basin, few investigated which represents a key area to connect the Southern Ocean and the Ross Sea. © 2024 The Author
A modelling study investigating short and medium-term challenges for COVID-19 vaccination: From prioritisation to the relaxation of measures
No full textBackground: The roll-out of COVID-19 vaccines is a multi-faceted challenge whose performance depends on pace of vaccination, vaccine characteristics and heterogeneities in individual risks.Methods: We developed a mathematical model accounting for the risk of severe disease by age and comorbidity, and transmission dynamics. We compared vaccine prioritisation strategies in the early roll-out stage and quantified the extent to which measures could be relaxed as a function of the vaccine coverage achieved in France.Findings: Prioritizing at-risk individuals reduces morbi-mortality the most if vaccines only reduce severity, but is of less importance if vaccines also substantially reduce infectivity or susceptibility. Age is the most important factor to consider for prioritization; additionally accounting for comorbidities increases the performance of the campaign in a context of scarce resources. Vaccinating 90% of >= 65 y.o. and 70% of 18-64 y.o. before autumn 2021 with a vaccine that reduces severity by 90% and susceptibility by 80%, we find that control measures reducing transmission rates by 15-27% should be maintained to remain below 1000 daily hospital admissions in France with a highly transmissible variant (basic reproduction number R-0 = 4). Assuming 90% of >= 65 y.o. are vaccinated, full relaxation of control measures might be achieved with a vaccine coverage of 89-100% in 18-64 y.o or 60-69% of 0-64 y.o.Interpretation: Age and comorbidity-based vaccine prioritization strategies could reduce the burden of the disease. Very high vaccination coverage may be required to completely relax control measures. Vaccination of children, if possible, could lower coverage targets necessary to achieve this objective. (C) 2021 The Author(s). Published by Elsevier Ltd
Entropy of Dynamical Social Networks
No full textPMCID: PMC3241622This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Influenza : a scientometric and density-equalizing analysis
No full textBackground: Novel influenza in 2009 caused by H1N1, as well as the seasonal influenza, still are a challenge for the public health sectors worldwide. An increasing number of publications referring to this infectious disease make it difficult to distinguish relevant research output. The current study used scientometric indices for a detailed investigation on influenza related research activity and the method of density equalizing mapping to make the differences of the overall research worldwide obvious. The aim of the study was to compare scientific effort over the time as well as geographical distribution including the cooperation on national and international level.
Methods: Therefore, publication data was retrieved from Web of Science (WoS) of Thomson Scientific. Subsequently the data was analysed in order to show geographical distributions and the development of the research output over the time.
The query retrieved 51,418 publications that are listed in WoS for the time interval from 1900 to 2009. There is a continuous increase in research output and general citation activity especially since 1990.
Results: The identified all in all 51,418 publications were published by researchers from 151 different countries. Scientists from the USA participate in more than 37 percent of all publications, followed by researchers from the UK and Germany with more than five percent. In addition, the USA is in the focus of international cooperation.
In terms of number of publications on influenza, the Journal of Virology ranks first, followed by Vaccine and Virology. The highest impact factor (IF 2009) in this selection can be established for The Lancet (30.75). Robert Webster seems to be the most prolific author contributing the most publications in the field of influenza.
Conclusions: This study reveals an increasing and wide research interest in influenza. Nevertheless, citation based-declaration of scientific quality should be considered critically due to distortion by self-citation and co-authorship
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
No full textAbstract Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. Methods A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. Results Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). Conclusion Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets
Structure and dynamics of evolving complex networks
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe analysis of large disordered complex networks has recently received enormous attention motivated by both academic and commercial interest. The most important results in this discipline have come from the analysis of stochastic models which mimic the growth and evolution of real networks as they change over time. The purpose of this thesis is to introduce various novel processes which dictate the development of a network on a small scale, and use techniques learned from statistical physics to derive the dynamical and structural properties of the network on the macroscopic scale. We introduce each model as a set of mechanisms determining how a network changes over a small period in time, from these rules we derive several topological
properties of the network after many iterations, most notably the degree distribution. 1. In the rst mechanism, nodes are introduced and linked to older nodes in the network in such a way as to create triangles and maintain a high level of clustering. The mechanism resembles the growth of a citation network and we demonstrate analytically that the mechanism introduced su ces to explain the power-law form commonly found in citation distributions. 2. The second mechanism involves edge rewiring processes - detaching one end of an edge and reattaching it, either to a random node anywhere in the network or to one selected locally. 3. We analyse a variety of processes based around a novel fragmentation mechanism. 4. The nal model concerns the problem of nding the electrical resistance across a network. The network grows as a random tree, as it grows the distribution of resistance converges towards a steady state solution. We nd an application of the relatively recent concept of a random Fibonacci sequence in deriving the rate of convergence of the mean.EPSR
Spatial aggregation choice in the era of digital and administrative surveillance data
No full textTraditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks. Author summary Administrative health records, social media streams like Twitter, and participatory surveillance systems like Influenzanet are increasingly available for infectious disease surveillance, but are often geographically aggregated to preserve data privacy and confidentiality. We explored how an arbitrary choice in the spatial aggregation of non-traditional disease data sources may influence estimates of disease burden and epidemiological understanding of an outbreak. Using influenza-like illness as measured through a medical claims database as our case study, we find that there is substantial variation in influenza season timing and magnitude across spatial scales due to which spatial aggregation could lead to misleading estimates of epidemiological quantities. In particular, we find that epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Non-traditional disease surveillance may have distinct advantages in reporting speed and volume, but care is required when aggregating this data for spatial epidemiological analysis
Additional file 1: of Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
No full textVolume Overlap and Surface Distance Equations. (DOCX 14 kb
