Consociational conflict transformation: Ethno-national identity in post-Good Friday Agreement Northern Ireland

The case of Northern Ireland is heralded by many as a consociational success story. Since the signing of the Good Friday (Belfast) Agreement in 1998, significant conflict transformation has taken place in the form of a considerable reduction in levels of violence and the establishment of power sharing between unionists and nationalists. This thesis, however, asks whether consociational arrangements are transforming conflict in a different way: through mitigating the salience of ethno-national identities. It argues that if this is taking place, it would be demonstrated in the focus of the election campaigns of Northern Ireland’s political parties, which would be almost exclusively based around socio-economic issues affecting the whole population, rather than narrow single identity concerns. Elections contested using Proportional Representation – Single Transferable Vote (PR-STV) offer the greatest potential to induce moderation, as this electoral system is preferential and allows inter-party lower order preference votes (or transfers) to be cast. It should, therefore, be in the interest of parties to maximise their support by moderating their campaigns with the intention of attracting inter-bloc transfers. If this is occurring, it would demonstrate a shift from traditional unionist versus nationalist politics to a more inclusive political system that is less concerned with ethno-national divisions. On the whole, however, this has not been realised. Although election campaigns are today less strident than they were in the pre-1998 era, it remains the case that they usually foreground single identity symbolism, as it is this that resonates with voters. Whilst consociational power sharing has been very successful in reducing levels of violent conflict and facilitating elite level cooperation between unionists and nationalists, it has been much less successful in reducing divisions within wider society. This indicates a dissonance between ‘high’ politics and society in Northern Ireland. The conclusions of this thesis also highlight a disparity between what political theory argues may happen and what actually does, as demonstrated by the practice of politics. It is the novel way in which this research tests its hypothesis that makes an original contribution to knowledge. This is achieved through the unique application of ethno-symbolism to analyse political party election campaigns

A new solar fuels reactor concept based on a liquid metal heat transfer fluid: Reactor design and efficiency estimation

Abstract A new reactor concept for two-step partial redox cycles is presented and evaluated by transient simulation that considers heat and mass transfer along with reaction kinetics. The major difference between the reactor described herein and previous designs is that the conversion from solar to chemical energy is divided into two steps: sunlight-to-thermal energy conversion accomplished with a liquid metal based receiver, and the thermal-to-chemical conversion accomplished with a separately optimized array of reaction chambers. To connect these two conversion steps, liquid metal is used as a high temperature heat transfer fluid that feeds the solar energy captured in the receiver to the reactor. The liquid metal also facilitates efficient heat recuperation ($80%) between the reaction chambers. The overall thermal-to-chemical efficiency from the thermal energy in the liquid metal to the chemical energy in the hydrogen fuel is estimated to be 19.8% when ceria is employed as the reactive oxygen storage material. This estimated efficiency is an order of magnitude higher than previous designs and the reactor concept discussed herein identifies important insights that apply to solar-fuel conversion in general

Primary Epstein-Barr virus infection with and without infectious mononucleosis

Background: Infectious mononucleosis (IM) is a common adverse presentation of primary infection with Epstein-Barr virus (EBV) in adolescence and later, but is rarely recognized in early childhood where primary EBV infection commonly occurs. It is not known what triggers IM, and also not why IM risk upon primary EBV infection (IM attack rate) seemingly varies between children and adolescents. IM symptoms may be severe and persist for a long time. IM also markedly elevates the risk of Hodgkin lymphoma and multiple sclerosis for unknown reasons. The way IM occurrence depends on age and sex is incompletely described and hard to interpret etiologically, because it depends on three quantities that are not readily observable: the prevalence of EBV-naϊve persons, the hazard rate of seroconverting and the attack rate, i.e. the fraction of primary EBV infections that is accompanied by IM. We therefore aimed to provide these quantities indirectly, to obtain epidemiologically interpretable measures of the dynamics of IM occurrence to provide etiological clues. Methods and findings: We used joint modeling of EBV prevalence and IM occurrence data to provide detailed sex- and age-specific EBV infection rates and IM attack rates and derivatives thereof for a target population of all Danes age 0–29 years in 2006–2011. We demonstrate for the first time that IM attack rates increase dramatically rather precisely in conjunction to typical ages of puberty onset. The shape of the seroconversion hazard rate for children and teenagers confirmed a priori expectations and underlined the importance of what happens at age 0–2 years. The cumulative risk of IM before age 30 years was 13.3% for males and 22.4% for females. IM is likely to become more common through delaying EBV infection in years to come. Conclusions: The change in attack rate at typical ages of puberty onset suggests that the immunologic response to EBV drastically changes over a relatively short age-span. We speculate that these changes are an integrated part of normal sexual maturation. Our findings may inform further etiologic research into EBV-related diseases and vaccine design. Our methodology is applicable to the epidemiological study of any infectious agent that establishes a persistent infection in the host and the sequelae thereof

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Houskeeper, H. F., Rosenthal, I. S., Cavanaugh, K. C., Pawlak, C., Trouille, L., Byrnes, J. E. K., Bell, T. W., & Cavanaugh, K. C. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). Plos One, 17(1), (2022): e0257933, https://doi.org/10.1371/journal.pone.0257933.Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.This work was funded by the National Aeronautics and Space Administration as part of the Citizen Science for Earth Systems Program (https://earthdata.nasa.gov/esds/competitive-programs/csesp) with grant #80NSSC18M0103 (awarded to JEKB), which also provided salary to HFH, and by the National Science Foundation through the Santa Barbara Coastal Long-Term Environmental Research (https://sbclter.msi.ucsb.edu) program with grants #OCE 0620276 and 1232779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop

O3‐06‐01: Nih Toolbox Cognition Tablet Battery In Identification Of Early Cognitive Impairment

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152934/1/alzjjalz2019064654.pd

Somatic genome editing with CRISPR/Cas9 generates and corrects a metabolic disease

Germline manipulation using CRISPR/Cas9 genome editing has dramatically accelerated the generation of new mouse models. Nonetheless, many metabolic disease models still depend upon laborious germline targeting, and are further complicated by the need to avoid developmental phenotypes. We sought to address these experimental limitations by generating somatic mutations in the adult liver using CRISPR/Cas9, as a new strategy to model metabolic disorders. As proof-of-principle, we targeted the low-density lipoprotein receptor (Ldlr), which when deleted, leads to severe hypercholesterolemia and atherosclerosis. Here we show that hepatic disruption of Ldlr with AAV-CRISPR results in severe hypercholesterolemia and atherosclerosis. We further demonstrate that co-disruption of Apob, whose germline loss is embryonically lethal, completely prevented disease through compensatory inhibition of hepatic LDL production. This new concept of metabolic disease modeling by somatic genome editing could be applied to many other systemic as well as liver-restricted disorders which are difficult to study by germline manipulation

The Structure of 2MASS Galaxy Clusters

We use a sample of galaxies from the Two Micron All Sky Survey (2MASS) Extended Source Catalog to refine a matched filter method of finding galaxy clusters that takes into account each galaxy's position, magnitude, and redshift if available. The matched filter postulates a radial density profile, luminosity function, and line-of-sight velocity distribution for cluster galaxies. We use this method to search for clusters in the galaxy catalog, which is complete to an extinction-corrected K-band magnitude of 13.25 and has spectroscopic redshifts for roughly 40% of the galaxies, including nearly all brighter than K = 11.25. We then use a stacking analysis to determine the average luminosity function, radial distribution, and velocity distribution of cluster galaxies in several richness classes, and use the results to update the parameters of the matched filter before repeating the cluster search. We also investigate the correlations between a cluster's richness and its velocity dispersion and core radius, using these relations to refine priors that are applied during the cluster search process. After the second cluster search iteration, we repeat the stacking analysis. We find a cluster galaxy luminosity function that fits a Schechter form, with parameters M_K* - 5 log h = -23.64\pm0.04 and \alpha = -1.07\pm0.03. We can achieve a slightly better fit to our luminosity function by adding a Gaussian component on the bright end to represent the brightest cluster galaxy (BCG) population. The radial number density profile of galaxies closely matches a projected Navarro-Frenk-White (NFW) profile at intermediate radii, with deviations at small radii due to well-known cluster centering issues and outside the virial radius due to correlated structure. The velocity distributions are Gaussian in shape, with velocity dispersions that correlate strongly with richness.Comment: 13 pages, 10 figures, 3 tables. Updated to match version published in Ap