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Cancer-associated retinopathy (CAR) is a rare paraneoplastic disorder mediated by auto-antibodies that cross-react with retinal antigens leading to gradual visual defects. Early diagnosis and initiation of treatment is crucial to avoid permanent visual loss. Although most patients with CAR respond to intravenous steroids and intravenous immunoglobulin (IVIG), there are some cases refractory to the aforementioned treatment strategies. The present study describes a case of CAR in a patient with ovarian cancer that was initially resistant to most treatment regimens (chemotherapy, steroids, IVIG). Treatment with rituximab at 375 mg/m(2) and oral cyclophosphamide was administered and the patient showed marked improvement of visual acuity. Electroretinogram showed a 40 and 10% improvement in scotopic and photopic vision, respectively. Notably, at the most recent follow up, the patient was still in remission. In conclusion, treatment with intravenous rituximab and oral cyclophosphamide is a promising treatment option for those cases of CAR that do not respond to steroids, immunomodulatory agents and IVIG.

This document is the third and last software demonstrator deliverable of PolicyCLOUD at M34, October 2022, of the project and is intended for the reviewers of the software deliverables. This deliverable provides the description of the final software demonstrator for the components of the Integrated Data Acquisition and Analytics (DAA) Layer, which provides the analytical capabilities of the PolicyCLOUD platform. The components include the DAA API Gateway (responsible for the overall orchestration and the layer API), the built-in analytical tools such as the new Trend Analysis tool as well as the analytical tools already presented in D4.4 [6]: Data cleaning and interoperability, Situational Knowledge, Opinion Mining & Sentiment Analysis and Social Dynamics & Behavioural Data analysis, and the Operational Data Repository. The full integration of the DAA API Gateway (responsible for the overall orchestration and the layer API) was demonstrated during the review of June 2021 for two use cases. As of the publication of this deliverable, this has been extended in multiple ways: Additional ingest analytics capabilities such as Trend Analysis were added as detailed in section 2.1.3 and 2.1.4 The new integration of the Politika framework with PolicyCloud as detailed in section 2.2.5 of D4.5 [3] and as further detailed in sections 2.1.5.4 and 2.1.6 of this deliverable. The integrated Politika framework was itself used for two use cases, as detailed in section 2.2.6.2 The novel seamless architecture which was introduced in D4.4 [6] and which presents single logical datasets to users that can be explored with SQL had further been enhanced in the past year by performance enhancements of the SQL JOIN as detailed in section 4.4.4 of D4.5. The impact of using the seamless technology is minimal, as the users do not even know in what storage tier their datasets are stored. The only impact is a change of the SQL statement that now makes use of atable function that accepts standard SQL statements though, as detailed in section 2.2.8.3., This deliverable is submitted to the EC, not yet approved.

This document is the second architecture deliverable for WP4. It is an incremental update of PolicyCLOUD deliverable D4.1, and as such it repeats some of the content and adds new developments and work to do. It provides an update of the architecture of the Data Acquisition and Analytics Layer. As such, it details the included data analytic and transformation tools, the cloud platform and software tools that are planned at this point to be used for the implementation of the PolicyCLOUD platform towards the demonstration in D4.4 Reusable Model & Analytical Tools: Software Prototype 2. In essence, the Data Acquisition and Analytics Layer provides an extensible framework for data source and analytic tools, controlling the full data pass from the data sources through filtering, transformation and initial analytic, to hot storage and then to cold storage while enabling deeper analytics by the registered analytic tools on the ingested data at any time. We added for each sub-section (or when relevant for each section) an ���Update since D4.1��� sub-section which summarized the evolution from D4.1. In the following paragraph we give a summary of all the reported changes from D4.1. In Section 2, we updated the registration APIs for the sake of legal concerns. These changes are revisited and further commented in the new section 2.3: ���Security and Legal concerns���. In section 3, the main novelty for Enhanced Interoperability is the SemAI hybrid mechanism and its associated changes. Concerning the Cleaning technology, we report of additional rules which render this cleaning technology more general. In section 4 we present in D4.3 a major update of the ���Social Dynamics and Behavioral Data Analytics��� technology (4.3). In addition we added 4.4: ���Optimization & Reusability of Analytical Tools��� covering task T4.6. In section 5 which covers the cloud platform and the software tools for PolicyCLOUD we have no notable change which proves the soundness of our initial choices as reported in D4.1., This deliverable is submitted to the EC, not yet approved., {"references":["PolicyCLOUD D2.1 - STATE OF THE ART & REQUIREMENTS ANALYSIS. Pavlos Kranas. June 2020","PolicyCLOUD D2.2 - CONCEPTUAL MODEL & REFERENCE ARCHITECTURE. Panayiotis Michael. 2020","PolicyCLOUD D3.1 - Cloud Infrastructure Incentives Management and Data Governance Design and Open Specification 1","Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.","Seaborn: statistical data visualization, https://seaborn.pydata.org/.","Motta, G., Puccinelli, R., Reggiani, L., and Saccone, M., Extracting Value from Grey Literature: processes and technologies for aggregating and analyzing the hidden «big data» treasure of organizations. Grey Journal (TGJ), vol. 12, no. 1, 2016.","DCAT Application profile for data portals in Europe (DCAT-AP), https://op.europa.eu/en/web/eu- vocabularies/dcat-ap.","Hellmann S., Lehmann J., Auer S., and Brümmer M., Integrating NLP using Linked Data,12th International Semantic Web Conference, pp. 21-25, 2013.","JSON-LD - JSON for Linking Data, http://json-ld.org.","Xin J., Afrasiabi C., Lelong S., Adesara J., Tsueng G., Su A. I., and Wu C., Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration, BMC bioinformatics, 19(1), 30, https://doi.org/10.1186/s12859-018-2041-5, 2018.","T. Berners-Lee, Linked Data - Design Issues, www.w3.org, 2006.","D. Brickley, and R. V. Guha, Resource Description Framework (RDF) Schema Specification 1.0: W3C, 2000.","D. L. McGuinness, and F. Van Harmelen, OWL Web Ontology Language Overview. W3C recommendation, World Wide Web Consortium, 2004.","T. Berners-Lee, The semantic web, Scientific American, Vol. 284(5), pp. 35–43, 2001.","Groß A., Pruski C., and Rahm E, Evolution of biomedical ontologies and mappings: overview of recent approaches. Computational and structural biotechnology journal, Vol. 14, pp. 333-340, 2016.","H2020 BigDataStack deliverable D4.3 https://bigdatastack.eu/deliverables/d43-%E2%80%93- wp4-scientific-report-and-prototype-description-%E2%80%93-y3","C. F. Manski, Public Policy in an Uncertain World: Analysis and Decisions, Harvard University Press, 2013.","https://www.phoenixframework.org/","https://elixir-lang.org/","https://www.erlang.org/","https://github.com/elixir-ecto","W. N. Dunn, Public Policy Analysis: An Introduction, Routledge, 2017.","E. Bardach, E. Patashnik. A Practical Guide for Policy Analysis (6th ed.), Sage, 2020.","R. Meltzer, A. Schwartz, Policy Analysis as Problem Solving. Routledge, 2019.","M. Mintrom, Contemporary Policy Analysis. Oxford University Press, 2012.","P. Cairney, The Politics of Policy Analysis, Palgrave Macmillan, 2021.","Will, M., Groeneveld, J., Frank, K. & Müller, B. \"Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review,\" Socio-Environmental Systems Modelling, vol. 2, 2020.","S. Abar, G. K. Theodoropoulos, P. Lemarinier, G. M.P. O'Hare, \"Agent Based Modelling and Simulation tools: A review of the state-of-art software\", Computer Science Review, vol. 24, Elsevier, 2017.","https://ccl.northwestern.edu/netlogo/","https://repast.github.io/repast_hpc.html","https://gama-platform.github.io/","http://epinoetic.org/Politika/politikaDemo.mp4","S. O. Bhakti, Y. Levent, F. Klügl, T. Takao, C M. Macal, \"Credible Agent-Based Simulation – An Illusion or only a Step away?\", Proceedings of the 2019 Winter Simulation Conference, N. Mustafee, K.-H.G. Bae, S.Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, eds, 2019.","F. Taghikhah, T. Filatova, A. Voinov, \"Where Does Theory Have It Right? A Comparison of Theory- Driven and Empirical Agent Based Models\", Journal of Artificial Societies and Social Simulation 24 (2) 4, 2021.","http://epinoetic.org:4000","https://d3js.org/","PolicyCLOUD. D3.3 – PolicyCLOUD's Societal and Ethical Requirements & Guidelines. Audino Alice. 2020","HIGH-LEVEL EXPERT GROUP ON AI, Ethics Guidelines for Trustworthy AI, https://digital- strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai, retrieved 2021-07-22.","UK INFORMATION COMMISSIONER'S OFFICE, Trade-offs, https://ico.org.uk/about-the-ico/news- and-events/ai-blog-trade-offs/, retrieved 2021-07-22.","EUROPEAN UNION AGENCY FOR CYBERSECURITY, EUCS – Cloud Services Scheme, https://www.enisa.europa.eu/publications/eucs-cloud-service-scheme, retrieved 2021-07-22.","PolicyCLOUD D3.2 - CLOUD INFRASTRUCTURE INCENTIVES MANAGEMENT AND DATA GOVERNANCE SOFTWARE PROTOTYPE 1, Giannis Ledakis, November 2020","Harvey, Andrew. \"Trend analysis.\" Wiley StatsRef: Statistics Reference Online (2014): 1-21","Mehmood, Asad, Abdul S. Palli, and M. N. A. Khan. \"A study of sentiment and trend analysis techniques for social media content.\" International Journal of Modern Education and Computer Science 6.12 (2014): 47."]}

This document is the second software demonstrator deliverable of PolicyCLOUD at M22 (October 2021) of the project and is intended for the reviewers of the software deliverables. This deliverable provides a second and much upgraded description of the software demonstration for the components of the Integrated Data Acquisition and Analytics (DAA) Layer, which provides the analytical capabilities of the PolicyCLOUD platform. The components include the DAA API Gateway (responsible for the overall orchestration and the layer API), the built-in analytical tools - for Data cleaning and interoperability, Situational Knowledge, Opinion Mining & Sentiment Analysis and Social Dynamics & Behavioural Data analysis, and the Operational Data Repository. The DAA API Gateway (responsible for the overall orchestration and the layer API) as well as the built-in analytical tools for Data cleaning and interoperability, Situational Knowledge, Opinion Mining & Sentiment Analysis have been integrated with almost all the use cases at least in standalone mode. In addition, full integration for two use cases was demonstrated during the review of June 2021. The advanced Social Dynamics & Behavioural Data analysis component is fully operational in standalone mode and will be integrated with the PolicyCLOUD framework this coming year. In terms of used infrastructure, the Operational Data Repository has been enhanced thanks to the adoption of the ���seamless��� technology (section 4.4 of [34]) which offers a two-tier storage architecture based on the LeanXcale relational database: the first tier which was used till now and the Object Storage: the second tier. This novel architecture presents single logical datasets to users which can be explored with SQL., This deliverable is submitted to the EC, not yet approved., {"references":["PolicyCLOUD. D4.1 - Reusable Model & Analytical Tools: Design and Open Specification 1. Biran Ofer. 2020.","PolicyCLOUD. D2.2 CONCEPTUAL MODEL & REFERENCE ARCHITECTURE. 2020.","PolicyCLOUD. D6.3 - Use Case Scenarios Definition & Design. Sancho Javier. 2020.","Wikidata, https://www.wikidata.org/wiki/Wikidata:Main_Page","Xin J., Afrasiabi C., Lelong S., Adesara J., Tsueng G., Su A. I., and Wu C., Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration, BMC bioinformatics, 19(1), 30, 2018.","Kubernetes, https://kubernetes.io","Kafka, https://kafka.apache.org","OpenWhisk, https://OpenWhisk.apache.org","Python, https://www.python.org","spaCy, https://spacy.io/","SPARQL, https://www.w3.org/TR/rdf-sparql-query","Jupyter, https://jupyter.org/","Anaconda, https://www.anaconda.com/","PIP Installer https://pip.pypa.io/en/stable/","L. Zhao, L. Li, X. Zheng, and J. Zhang, \"A BERT based sentiment analysis and key entity detection approach for online financial texts\", in 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 1233-1238. IEEE, 2021.","C. Sun, L. Huang, and X. Qiu, \"Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence\", arXiv preprint arXiv:1903.09588, 2019.","T. Nasukawa, and J. Yi., \"Sentiment analysis: Capturing favorability using natural language processing\", in Proceedings of the 2nd international conference on Knowledge capture, pp. 70-77, October 2003.","K. Dave, S. Lawrence, and D.M. Pennock, \"Mining the peanut gallery: Opinion extraction and semantic classification of product reviews\", in Proceedings of the 12th international conference on World Wide Web, pp. 519-528, May 2003.","B. Pang, and L. Lee, \"Seeing Stars: Exploiting class relationships for sentiment categorization with respect to rating scales\", in Proceedings of the 43rd annual meeting on association for computational linguistics, Association for Computational Linguistics, pp. 115-124. 2005.","P.D. Turney, \"Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews\", in Proceedings of the 40th annual meeting on association for computational linguistics, Association for Computational Linguistics, pp. 417-424, July 2002.","B. Pang, L. Lee, and S. Vaithyanathan. \"Thumbs up? Sentiment classification using Machine Learning Techniques\", in Proceedings of the ACL-02 conference on empirical methods in natural language processing, Association for Computational Linguistics. pp. 79–86. July 2002.","R. Moraes, J.F. Valiati, and W.P. Neto, \"Document-level sentiment classification: an empirical comparison between SVM and ANN\". Expert Systems with Applications, vol. 40, no. 2, 2013.","W. C. F. Mariel, S. Mariyah, and S. Pramana, \"Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and Naϊve Bayes for Indonesian text\", in Journal of Physics: Conference Series, vol. 971, no. 1, p. 012049. IOP Publishing, March 2018.","R. Johnson, and T. Zhang, \"Effective use of word order for text categorization with convolutional neural networks\", in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2015), 2015.","A. Vaswani et al, \"Attention is all you need\", in Advances in neural information processing systems, pp. 5998-6008, 2017.","Z. Yang, D. Yang, C. Dyer, X. He, A.J. Smola, and E.H. Hovy. \"Hierarchical attention networks for document classification\", in Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp. 1480-1489, June 2016.","X. Li, L. Bing, W. Zhang, and W. Lam, \"Exploiting BERT for end-to-end aspect-based sentiment analysis\", arXiv preprint arXiv:1910.00883, 2019.","Pramanik, Jitendra & Samal, Abhaya Kumar & Sahoo, Kabita & Pani, Dr. Subhendu. Exploratory Data Analysis using Python. 8. 4727-4735, 2019.","Visualizing Categorical Distributions. 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D4.2 - Reusable Model & Analytical Tools: Software Prototype 1.","Pandas, https://pandas.pydata.org/","Docker, https://www.docker.com/","Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169- 15211, 2019.","Kim, S., Park, H., & Lee, J. Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis. Expert Systems with Applications, 152, 113401, 2020.","Shimosaka, M., Tsukiji, T., Tominaga, S., & Tsubouchi, K. Coupled hierarchical Dirichlet process mixtures for simultaneous clustering and topic modeling. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 230-246). Springer, 2016.","Syed, S., & Spruit, M. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. 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This document is an accompanying report providing information about the demonstrator of the second version of the Cloud Infrastructure, Incentives Management and Data Governance software prototype. It describes the cloud gateways and APIs, the cloud provisioning mechanisms, the implemented version of the algorithms as well as the data governance tools according to the D3.1 and D3.4 specifications. The second version of the Cloud Infrastructure, Incentives Management and Data Governance software prototype includes the cloud gateways and APIs, the cloud provisioning mechanisms, the implemented version of the algorithms as well as the data governance tools according to the D3.1[1]and D3.4[2] specifications and is built upon the first version of the prototype described in D3.2 [3]. The prototype���s cloud infrastructure is supported by RECAS-BARI and is utilized by EGI through cloud gateways. These gateways allow the prototype to gather data from heterogenous data sources, such as Twitter and the global terrorism database. This second version of the prototype also includes an ABAC based access control mechanism suitable for PolicyCLOUD. This is broken down to 4 key components that can be combined, along with a test client, in order to demonstrate the access control capabilities to the use cases and to proceed with the definition of an accurate data model, based on the provided feedback. Furthermore, an alternative way to authenticate to the prototype with academic and social credential is provided in order to increase the ease of access and the pool of potential adaptors of the whole platform. An updated version of this prototype will be delivered and documented at a later stage for deliverable D3.8 Cloud Infrastructure Incentives Management and Data Governance: Software Prototype 3, due in October 2022., This deliverable is submitted to the EC, not yet approved., {"references":["PolicyCLOUD D3.1, Cloud Infrastructure Incentives Management and Data Governance: Design and Open Specification 1, August 2020","PolicyCLOUD D3.4, Cloud Infrastructure Incentives Management and Data Governance: Design and Open Specification 2, August 2021","PolicyCLOUD D3.2, Cloud Infrastructure Incentives Management and Data Governance: Software Prototype 1, November 2020","PolicyCLOUD D6.6, Integration Plan, August 2021","PolicyCLOUD D6.10, Use Case Scenarios Definition & Design M16, April 2020","Swagger Stats, https://www.npmjs.com/package/swagger-stats","Prometheus, https://prometheus.io/docs/introduction/overview/.","Grafana, https://grafana.com/.","Twitter Search Tweets, https://developer.twitter.com/en/docs/twitter-api/tweets/search/api- reference/get-tweets-search-recent","Twitter Filtered Stream, https://developer.twitter.com/en/docs/twitter-api/tweets/filtered- stream/introduction","Twitter Rate Limits, https://developer.twitter.com/en/docs/twitter-api/rate-limits","SheetJS, https://docs.sheetjs.com/.","KeyCloak, https://keycloak.org","C. Flow, https://auth0.com/docs/flows/authorization-code-flow","Netflix ZUUL Proxy, https://github.com/Netflix/zuul","EGI Check-In, https://www.egi.eu/services/check-in/.","eduGAIN, https://edugain.org","ORCID, https://orcid.org/.","Keycloak Admin REST API, https://www.keycloak.org/docs-api/5.0/rest-api/index.html.","Balana, https://github.com/wso2/balana","W. I. Server, https://wso2.com/identity-and-access-management/.","Thrift, https://thrift.apache.org/.","ApacheDS, https://directory.apache.org/apacheds/.","O. 2.0, https://oauth.net/2/.","OpenIDConnect, https://openid.net/connect/."]}

The purpose of this deliverable is to describe the end to end (E2E) scenarios that will explore how users will effectively use the Policy Cloud platform. The given scenarios will describe the different steps required in order to perform the modelling of the policy. The scenarios will also include details of the necessary configurations and settings needed to determine the output of the visualization module. This document is the first of a series of deliverables that will detail the implementation and experimentation process. The E2E scenarios will be used to experiment the adaptability of the Policy Development Toolkit (PDT)in conjunction with several use case-based scenarios. This document will include a single scenario from each respective pilot and further details on alternative scenarios will be included in the upcoming D6.10 Use Case Scenarios Definition & Design M16in deliverable in April 2021. The experimentation process for software usually consists of four phases: definition, planning, operation and interpretation. This approach was adopted for the design of the E2E scenarios. Taking the four phases into consideration the E2E scenarios descriptions can be used to achieve the definition and planning stages. The E2E helps explore how well the PDT can be adapted for various situations. At the current stage of the project there needs to be a clear definition of the end users experience and expected results. In order to achieve this, the visualizations and analytical tools will help create the full E2E scenario. The contents within deliverable D2.2 Conceptual Model & Reference Architecture and D2.4 State of the Art & Requirements Analysis was used to specify the available analytical tools that were available for the user stories. This approach was necessary to avoid the E2E scenarios becoming unattainable in terms of implementation to the PDT. There was also input provided from D6.3 Use Case Scenarios Definition & Design as well in relation to the use case scenarios. The final result of these series of deliverables will be the implementation and experimentation of the use cases based on the E2Escenarios detailed within this report.

Undoubtedly, the development of COVID-19 vaccines displays a critical step towards ending this devastating pandemic, considering their protective benefits in the general population. Yet, data regarding their efficacy and safety in cancer patients are limited. Herein we provide the initial analysis of immune responses after the first dose of vaccination in 21 breast cancer patients receiving cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. The levels of neutralizing antibodies post vaccination were similar to the matched healthy controls, whereas no safety issues have been raised. Further exploration is needed to reduce the uncertainty of SARS-CoV-2 immunity among cancer patients under treatment. (c) 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Simple Summary MicroRNAs are endogenous non-coding 20-22 nucleotide long RNAs that play a fundamental role in the post-transcriptional control of gene expression. Consequently, microRNAs are involved in multiple biological processes of cancer and could be used as biomarkers with prognostic and predictive significance. Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have become a mainstay of treatment for patients with advanced hormone receptor-positive (HR) breast cancer. Despite the initial high response rates, approximately 10% of patients demonstrate primary resistance to CDK4/6 inhibitors while acquired resistance is almost inevitable. Considering the fundamental role of miRNAs in tumorigenesis, we aimed to explore the potential involvement of microRNAs in response to CDK4/6 inhibition in solid tumors. A number of microRNAs were shown to confer resistance or sensitivity to CDK4/6 inhibitors in preclinical studies, although this remains to be proved in human studies. Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have emerged as novel treatment options in the management of advanced or metastatic breast cancer. MicroRNAs are endogenous non-coding 19-22-nucleotide-long RNAs that regulate gene expression in development and tumorigenesis. Herein, we systematically review all microRNAs associated with response to CDK4/6 inhibitors in solid tumors and hematological malignancies. Eligible articles were identified by a search of the MEDLINE and ClinicalTrials.gov databases for the period up to1 January 2021; the algorithm consisted of a predefined combination of the words “microRNAs”, “cancer” and “CDK 4/6 inhibitors”. Overall, 15 studies were retrieved. Six microRNAs (miR-126, miR-326, miR3613-3p, miR-29b-3p, miR-497 and miR-17-92) were associated with sensitivity to CDK4/6 inhibitors. Conversely, six microRNAs (miR-193b, miR-432-5p, miR-200a, miR-223, Let-7a and miR-21) conferred resistance to treatment with CDK4/6 inhibitors. An additional number of microRNAs (miR-124a, miR9, miR200b and miR-106b) were shown to mediate cellular response to CDK4/6 inhibitors without affecting sensitivity to treatment. Collectively, our review provides evidence that microRNAs could serve as predictive biomarkers for treatment with CDK4/6 inhibitors. Moreover, microRNA-targeted therapy could potentially maximize sensitivity to CDK4/6 inhibition.