Changing memories by interference: the effect of emotional dimensions in reconsolidation of episodic memories

Episodes with an emotional component preoccupy memory formation and this advantage facilitates their preservation and mitigates the impact of interfering episodes. The present study examined the relation of the emotional dimensions of original and interfering episodes to the memory outcome, using a reconsolidation paradigm. In a between-subjects design, 102 healthy young adults were presented with an emotional or neutral image and learned either an emotional or neutral story, respectively (day 1). On day 2, experimental groups were presented with an image of the opposite emotionality, reactivated the original story, and learned a story of the opposite emotionality. On day 3, experimental and control groups were tested for their memory on target and filler clues of the original story and rated both stories for arousal and valence. Overall, there was evidence of interference on the long-term retention of target clues only for the neutral story (i.e. when the interfering story was emotional), and of filler clues for both types of stories. Moreover, individual target clue retention rates correlated with the arousal ratings for both the original neutral story and the interfering emotional story, while they were not related to arousal ratings for the original emotional story or the interfering neutral one

Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling)

Science Gateways with Embedded Ontology-based E-learning Support

Science gateways are widely utilised in a range of scientific disciplines to provide user-friendly access to complex distributed computing infrastructures. The traditional approach in science gateway development is to concentrate on this simplified resource access and provide scientists with a graphical user interface to conduct their experiments and visualise the results. However, as user communities behind these gateways are growing and opening their doors to less experienced scientists or even to the general public as “citizen scientists”, there is an emerging need to extend these gateways with training and learning support capabilities. This paper describes a novel approach showing how science gateways can be extended with embedded e-learning support using an ontology-based learning environment called Knowledge Repository Exchange and Learning (KREL). The paper also presents a prototype implementation of a science gateway for analysing earthquake data and demonstrates how the KREL can extend this gateway with ontology-based embedded e-learning support

Sharing Data Collections and Models for Ionosphere, Thermosphere and Plasmasphere Research

PITHIA-NRF (Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities) is a project funded by the European Commission’s H2020 programme to build a distributed network of observing facilities, data processing tools and prediction models dedicated to ionosphere, thermosphere and plasmasphere research. One of the core components of PITHIA-NRF is the PITHIA e-Science Centre that supports access to distributed data resources and facilitates the execution of various models on local infrastructures and remote cloud computing resources. There are two major types of resources to be registered with the e-Science Centre: Data Collections and Models. Data Collections are either generated as direct outcome of an observation facility (e.g. radars, radio telescopes, meteor cameras, etc.) or can also be generated by various scientific Models. Models are scientific applications that take either raw or cleaned data from observation facilities and produce higher level datasets with predicted characteristics to facilitate further scientific research. Both Data Collections and Models are registered with the PITHIA e-Science Centre using a rich set of metadata that is based on the ISO 19156 standard on Observations and Measurements (O&M), and specifically augmented and tailored for the requirements of space physics. The metadata structure and the related ontology were originally developed in the FP7 ESPAS project [1] and are currently being modified for the specific requirements of PITHIA. PITHIA-NRF decided to describe and register data collections only, instead of the central registration of every individual data granule, as in previous projects such as ESPAS. Such simplification enables easier management of the e-Science Centre and can lead to longer term sustainability with feasible amount of maintenance effort required. On the other hand, local searchability of individual data pieces still remains, not restricting the scientists to access the required details at the necessary granularity. When it comes to the execution of models, the PITHIA e-Science Centre supports three types of model execution and access scenarios, all provided from a single entry-point. Models can be executed on local resources of the various PITHIA nodes (institutions sharing Data Collections and Models). Additionally, some Models can be deployed and executed on cloud computing resources on-demand. Finally, nodes can also offer Models to be downloaded and executed on the users’ own resources. Model providers can select the most suitable execution mechanism, based on the specific characteristics of the models and the resources (both human and computational resources) they have. The implementation of the PITHIA e-Science Centre is work in progress. This presentation will report on the current state of this development work. The ESPAS metadata structure and ontology, tailored for the specific requirements of the project, have already been demonstrated to the research community on the example of some Data Collections and Models. Based on this metadata structure, work is currently ongoing to enable the registration and the ontology-based search facility of both Models and Data Collections. Proof of concept implementations [2] of the various Model access and execution mechanisms have also been implemented and demonstrated to the research community. Acknowledgement This work was funded by the PITHIA-NRF - Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities (No. 101007599) EU H2020 project. Keywords – e-Science Centre, ontology, metadata, Data Collection, Model execution. REFERENCES [1] Anna Belehaki, Sarah James, Mike Hapgood, Spiros Ventouras, Ivan Galkin, Antonis Lembesis, Ioanna Tsagouri, Anna Charisi, Luca Spogli, Jens Berdermann, Ingemar Häggström, The ESPAS e-infrastructure: Access to data from near-Earth space, Advances in Space Research, Volume 58, Issue 7, 2016, Pages 1177-1200, ISSN 0273-1177, https://doi.org/10.1016/j.asr.2016.06.014. [2] Gabriele Pierantoni, Tamas Kiss, Alexander Bolotov, Dimitrios Kagialis, James DesLauriers, Amjad Ullah, Huankai Chen, David Chan You Fee, Hai-Van Dang, Jozsef Kovacs, Anna Belehaki, Themistocles Herekakis, Ioanna Tsagouri, Sandra Gesing: Towards a Reference Architecture based Science Gateway Framework with Embedded E-Learning Support, Concurrency and Computation, Practice and Experience, Wiley, 2022, https://doi.org/10.1002/cpe.687

Toward a reference architecture based science gateway framework with embedded e‐learning support

Science gateways have been widely utilised by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on-demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e-learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection and deployment of cloud-based Reference Architectures that can be automatically instantiated and executed even by non-technical users. The framework also incorporates a Knowledge Repository Exchange and Learning module that provides embedded e-learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere and thermosphere research communities

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion.

Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.AI Luppi is funded by a Gates Cambridge Scholarship (OPP 1144). EA Stamatakis is funded by the Stephen Erskine Fellowship, Queens’ College, Cambridge

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion.

Funder: Stephen Erskine FellowshipTraumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.AI Luppi is funded by a Gates Cambridge Scholarship (OPP 1144). EA Stamatakis is funded by the Stephen Erskine Fellowship, Queens’ College, Cambridge

Subjective well-being among psychotherapists during the coronavirus disease pandemic: A cross-cultural survey from 12 European countries

Objective The aim of this study to examine the amount of the total variance of the subjective well-being (SWB) of psychotherapists from 12 European countries explained by between-country vs. between-person differences regarding its cognitive (life satisfaction) and affective components (positive affect [PA] and negative affect [NA]). Second, we explored a link between the SWB and their personal (self-efficacy) and social resources (social support) after controlling for sociodemographics, work characteristics, and COVID-19-related distress. Methods In total, 2915 psychotherapists from 12 countries (Austria, Bulgaria, Cyprus, Finland, Great Britain, Serbia, Spain, Norway, Poland, Romania, Sweden, and Switzerland) participated in this study. The participants completed the Satisfaction with Life Scale (SWLS), the International Positive and Negative Affect Schedule Short Form (I-PANAS-SF), the General Self-Efficacy Scale, and the Multidimensional Scale of Perceived Social Support. Results Cognitive well-being (CWB; satisfaction with life) was a more country-dependent component of SWB than affective well-being (AWB). Consequently, at the individual level, significant correlates were found only for AWB but not for CWB. Higher AWB was linked to being female, older age, higher weekly workload, and lower COVID-19-related distress. Self-efficacy and social support explained AWB only, including their main effects and the moderating effect of self-efficacy. Conclusions The results highlight more individual characteristics of AWB compared to CWB, with a more critical role of low self-efficacy for the link between social support and PA rather than NA. This finding suggests the need for greater self-care among psychotherapists with regard to their AWB and the more complex conditions underlying their CWB