FAIRMODE: A FORUM FOR AIR QUALITY MODELLING IN EUROPE

FAIRMODE (Forum for AIR quality MODelling in Europe) is an air quality modelling network that was established as a joint initiative of the European Environment Agency (EEA) and European Commission’s Joint Research Centre (JRC). In a common effort EEA and JRC aim at responding to the requirements of the new Air Quality Directive, with particular focus on the introduction of modelling as a necessary tool for air quality assessment and air quality management. The main aim of the modelling network is to bring together air quality modellers and model users in order to promote and support harmonised use of modelling for the assessment of air quality by EU and EEA member countries. The network will thus encourage synergy – at a local, national and European level - through the development and implementation of a common infrastructure based on best practices for reporting and storing information relevant to air quality modelling. A major objective of the FAIRMODE initiative is to provide guidance to present and future air quality model users in EEA’s EIONET partnership network. FAIRMODE also aims to enhance awareness of model usefulness, reliability and accuracy through model validation and intercomparison exercises at a national or European level. The JRC has taken on a leading role in the co-ordination of the latter activities gaining from its experience in leading the “Eurodelta” and “CityDelta” intercomparison exercises. A centralised web portal has been created in support of FAIRMODE, which is currently being used for internal communication purposes of the network participants, but will also provide the means for exchange of relevant material and experiences between all interested modellers and model users. The initial activities of the network will be organised by two main Work Groups, focusing on the preparation of a Guidance Document for model use and on model QA/QC procedures (input data, other uncertainties) respectively. The progress of the preparation of these documents as well as of the rest of the regular activities of the network will be reviewed and discussed within the frame of annual Plenary meetings and Steering Committee meetings

Trauma-Related Distress During the COVID-19 Pandemic In 59 Countries

First published online March 11, 2022The COVID-19 pandemic has upended life like few other events in modern history, with differential impacts on varying population groups. This study examined trauma-related distress among 6,882 adults ages 18 to 94 years old in 59 countries during April to May 2020. More than two-thirds of participants reported clinically significant trauma-related distress. Increased distress was associated with unemployment; identifying as transgender, nonbinary, or a cisgender woman; being from a higher income country; current symptoms and positive diagnosis of COVID-19; death of a loved one; restrictive government-imposed isolation; financial difficulties; and food insecurity. Other factors associated with distress included working with potentially infected individuals, care needs at home, a difficult transition to working from home, conflict in the home, separation from loved ones, and event restrictions. Latin American and Caribbean participants reported more trauma-related distress than participants from Europe and Central Asia. Findings inform treatment efforts and highlight the need to address trauma-related distress to avoid long-term mental health consequences.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the European Commission (H2020-MSCA-IF-2018-837228-ENGRAVING). Daniela Ramos-Usuga was supported by a predoctoral fellowship from the Basque Government (PRE_2019_1_0164)

14-3-3 transits to the nucleus and participates in dynamic nucleocytoplasmic transport

14-3-3 proteins regulate the cell cycle and prevent apoptosis by controlling the nuclear and cytoplasmic distribution of signaling molecules with which they interact. Although the majority of 14-3-3 molecules are present in the cytoplasm, we show here that in the absence of bound ligands 14-3-3 homes to the nucleus. We demonstrate that phosphorylation of one important 14-3-3 binding molecule, the transcription factor FKHRL1, at the 14-3-3 binding site occurs within the nucleus immediately before FKHRL1 relocalization to the cytoplasm. We show that the leucine-rich region within the COOH-terminal α-helix of 14-3-3, which had been proposed to function as a nuclear export signal (NES), instead functions globally in ligand binding and does not directly mediate nuclear transport. Efficient nuclear export of FKHRL1 requires both intrinsic NES sequences within FKHRL1 and phosphorylation/14-3-3 binding. Finally, we present evidence that phosphorylation/14-3-3 binding may also prevent FKHRL1 nuclear reimport. These results indicate that 14-3-3 can mediate the relocalization of nuclear ligands by several mechanisms that ensure complete sequestration of the bound 14-3-3 complex in the cytoplasm

Aging in a Long-Lived Clonal Tree

Using genetic estimates of clone age in trembling aspen, this study demonstrates a significant decline in male sexual fitness with increasing age, showing that long-lived clonal organisms are vulnerable to aging

COVID-19 Unmasked Global Collaboration Protocol: longitudinal cohort study examining mental health of young children and caregivers during the pandemic

Background: Early empirical data shows that school-aged children, adolescents and adults are experiencing elevated levels of anxiety and depression during the COVID-19 pandemic. Currently, there is very little research on mental health outcomes for young children. Objectives: To describe the formation of a global collaboration entitled, ‘COVID-19 Unmasked’. The collaborating researchers aim to (1) describe and compare the COVID-19 related experiences within and across countries; (2) examine mental health outcomes for young children (1 to 5 years) and caregivers over a 12-month period during the COVID-19 pandemic; (3) explore the trajectories/time course of psychological outcomes of the children and parents over this period and (4) identify the risk and protective factors for different mental health trajectories. Data will be combined from all participating countries into one large open access cross-cultural dataset to facilitate further international collaborations and joint publications. Methods: COVID-19 Unmasked is an online prospective longitudinal cohort study. An international steering committee was formed with the aim of starting a global collaboration. Currently, partnerships have been formed with 9 countries (Australia, Cyprus, Greece, the Netherlands, Poland, Spain, Turkey, the UK, and the United States of America). Research partners have started to start data collection with caregivers of young children aged 1–5 years old at baseline, 3-months, 6-months, and 12-months. Caregivers are invited to complete an online survey about COVID-19 related exposure and experiences, child’s wellbeing, their own mental health, and parenting. Data analysis: Primary study outcomes will be child mental health as assessed by scales from the Patient-Reported Outcomes Measurement Information System–Early Childhood (PROMIS-EC) and caregiver mental health as assessed by the Depression Anxiety Stress Scale (DASS-21). The trajectories/time course of mental health difficulties and the impact of risk and protective factors will be analysed using hierarchical linear models, accounting for nested effects (e.g. country) and repeated measures

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Navigating Novel Hostilities: A Story of Cooperation between Armed Political Groups and Their Perception of Threat

This research engages the ongoing conflict in the Middle East. In 2011, the region fell under the influence of a revolutionary wave known as the Arab Spring. Almost a decade later, effects of pro-democracy uprisings, which have challenged some of the long standing authoritarian regimes in the region, are still experienced. While some states have established their respective stability, in others, such as Syria, the conflict still persists. The instability influencing the region for so long has also created new opportunities for some armed political groups and reinforced the post-Cold War perception of threat associated with the rise of non-state actors. While the relative power that these groups have gained has led nation-states within and outside of the region to rethink their perception of threat, this has also altered incentives for cooperation and competition between these armed political groups in the region. These groups and their changing incentives for cooperation are the main focus of this analysis.This research offers a descriptive analysis of the changing incentives for cooperation between armed political groups faced by a new threat inflicted from another political actor(s). The study derives from the type of cooperation occurred between the Kurdistan Workers’ Party and Democratic Union Party in 2014. Before 2014, while a form of alliance limited to ideological support was present between the groups, why and how the groups’ strategies changed regarding cooperation, especially in the case of a new threat. It uses qualitative case study analysis to determine to what extent perceived threats play a role in changing the incentives for cooperation between previously independent armed political groups. It proposes a theoretical model derived from the cases. This research finds some support for the idea that incentives for cooperation are altered by the rise of a common threat when it provides new opportunities and benefits for the groups involved