A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms

YesThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere

Solar Magnetic Feature Detection and Tracking for Space Weather Monitoring

We present an automated system for detecting, tracking, and cataloging emerging active regions throughout their evolution and decay using SOHO Michelson Doppler Interferometer (MDI) magnetograms. The SolarMonitor Active Region Tracking (SMART) algorithm relies on consecutive image differencing to remove both quiet-Sun and transient magnetic features, and region-growing techniques to group flux concentrations into classifiable features. We determine magnetic properties such as region size, total flux, flux imbalance, flux emergence rate, Schrijver's R-value, R* (a modified version of R), and Falconer's measurement of non-potentiality. A persistence algorithm is used to associate developed active regions with emerging flux regions in previous measurements, and to track regions beyond the limb through multiple solar rotations. We find that the total number and area of magnetic regions on disk vary with the sunspot cycle. While sunspot numbers are a proxy to the solar magnetic field, SMART offers a direct diagnostic of the surface magnetic field and its variation over timescale of hours to years. SMART will form the basis of the active region extraction and tracking algorithm for the Heliophysics Integrated Observatory (HELIO)

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi

BICCO-Net II. Final report to the Biological Impacts of Climate Change Observation Network (BICCO-Net) Steering Group

• BICCO-Net Phase II presents the most comprehensive single assessment of climate change impacts on UK biodiversity to date. • The results provide a valuable resource for the CCRA 2018, future LWEC report cards, the National Adaptation Programme and other policy-relevant initiatives linked to climate change impacts on biodiversity

Between Boston and Berlin: American MNCs and the shifting contours of industrial relations in Ireland

peer-reviewedDrawing on detailed qualitative case studies and utilizing a national business system lens, we explore a largely underrepresented debate in the literature, namely the nature of change in a specific but critical element of business systems, that is the industrial relations (IR) institutions of the State and the impact of MNCs thereon. Given the critical mass of US investment in Ireland, we examine how US MNCs manage IR in their Irish subsidiaries, how the policies and practices they pursue have impacted on the Irish IR system, and more broadly their role in shaping the host institutional environment. Overall, we conclude that there is some evidence of change in the IR system, change that we trace indirectly to the US MNC sector. Further, the US MNC sector displays evidence of elements of the management of IR that is clearly at odds with Irish traditions. Thus, in these firms we point to the emergence of a hybrid system of the management of IR and the establishment of new traditions more reflective of US business system.ACCEPTEDpeer-reviewe

Managing the risks and benefits of clinical research in response to a pandemic

Introduction: The coronavirus disease 2019 (COVID-19) created major disruptions at academic centers and healthcare systems globally. Clinical and Translational Science Awards (CTSA) fund hubs supported by the National Center for Advancing Translational Sciences provideinfrastructure and leadership for clinical and translational research at manysuch institutions. Methods: We surveyed CTSA hubs and received responses from 94% of them regarding the impact of the pandemic and the processes employed for the protection of research personnel and participants with respect to the conduct of research, specifically for studies unrelated to COVID-19. Results: In this report, we describe the results of the survey findings in the context of the current understanding of disease transmission and mitigation techniques. Conclusions: We reflect on common practices and provide recommendations regarding lessons learned that will be relevant to future pandemics, particularly with regards to staging the cessation and resumption of research activities with an aim to keep the workforce, research participants, and our communities safe in future pandemics

A new approach to modelling the relationship between annual population abundance indices and weather data

Weather has often been associated with fluctuations in population sizes of species; however, it can be difficult to estimate the effects satisfactorily because population size is naturally measured by annual abundance indices whilst weather varies on much shorter timescales. We describe a novel method for estimating the effects of a temporal sequence of a weather variable (such as mean temperatures from successive months) on annual species abundance indices. The model we use has a separate regression coefficient for each covariate in the temporal sequence, and over-fitting is avoided by constraining the regression coefficients to lie on a curve defined by a small number of parameters. The constrained curve is the product of a periodic function, reflecting assumptions that associations with weather will vary smoothly throughout the year and tend to be repetitive across years, and an exponentially decaying term, reflecting an assumption that the weather from the most recent year will tend to have the greatest effect on the current population and that the effect of weather in previous years tends to diminish as the time lag increases. We have used this approach to model 501 species abundance indices from Great Britain and present detailed results for two contrasting species alongside an overall impression of the results across all species. We believe this approach provides an important advance to the challenge of robustly modelling relationships between weather and species population size

Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations

BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. METHODOLOGY/PRINCIPAL FINDINGS: To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I2 inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I2 = 32-77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies. SIGNIFICANCE: Between-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations

Local Literature Bias in Genetic Epidemiology: An Empirical Evaluation of the Chinese Literature

BACKGROUND: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND FINDINGS: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14–35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2–21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se). CONCLUSION: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties