<< Objectives >>BAG-SIGN aims to strengthen metalinguistic awareness of sign languages among deaf, hard of hearing and hearing children. All over Europe, there is a lack of materials for teaching sign languages. Therefore, BAG-SIGN transforms linguistic findings into a pedagogical grammar for sign languages and qualifies teachers for grammar teaching. This improves the quality of sign language teaching, which in turn contributes to the formation of an inclusive society.<< Implementation >>Researchers and teachers from Germany, France, Italy, Austria and Switzerland cooperate in order to create a demand-oriented pedagogical grammar for five European sign languages. In addition, BAG-SIGN develops and offers classroom training and multilingual web-based tutorials for the self-study of teachers. The results are science-based and field-tested, then revised and made available to the public on an open-access website.<< Results >>The pedagogical grammar for sign languages is independent of textbooks, it is multilingual and combines sign language videos with visual and written material in order to meet heterogeneous learning conditions. The tutorials introduce a unified didactic terminology for sign language grammar and the promotion of metalinguistic awareness. Strengthening their metalinguistic awareness supports pupils in acquiring a sign language and helps them to learn other languages.

Acute Myeloid Leukemia (AML) is the most frequent cancer of the blood system, with >80% mortality within 5 years of diagnosis. Straightforward clinical decisions are complicated by the genetic complexity of AML. In particular, fusion proteins arising from chromosomal aberrations are recurrently found in AML and often act as strong driver oncogenes. In “Multi-Partner Translocation” (MPT) families, one specific gene is fused to many recipient loci. Due to this modular architecture, MPT families are of particular interest to comparative studies of oncogenic mechanisms. The three most common MPT families in AML represent translocations of the MLL, RUNX1 and NUP98 genes. Despite their clinical significance, the molecular mechanism of transformation remains unknown for the majority of fusion proteins and it is unclear if transforming mechanisms are conserved within and across different MPT families. We hypothesize that common oncogenic mechanisms of fusion proteins are encoded in physical and genetic cellular interaction networks that are specific to MPT families. We propose to delineate critical common effectors of oncogenic mechanisms in AML driven by MPT families through a comprehensive, comparative, functional analysis of 20 clinically representative MLL-, RUNX1- and NUP98-fusion proteins using a unique experimental pipeline. Characterization of protein interactomes and their effects on gene expression will identify common cellular denominators of MPT families, whose functional contribution will be assessed through pooled shRNA screens in clinically relevant model systems. High-confidence hits will be validated in mouse models and primary cells from AML patients. This project will generate large informative datasets and novel experimental systems that are of relevance for basic and clinical cancer research. It will contribute to improved understanding of oncogenic mechanisms, which may directly impact on diagnostic and therapeutic strategies in the management of AML.

The evidence base of Internet-based interventions in the prevention and treatment of mental health conditions has rapidly grown in the past decade. Yet many European countries (e.g., Germany, Austria, Switzerland, Great Britain, The Netherlands, Spain) have not implemented these promising approaches into health systems. Individuals with risk conditions or distinct mental health problems interested in using online interventions are often unable to access appropriate and evidence-based online interventions. The aim of this proposal is to establish a comprehensive model of health promotion, risk detection, disease prevention, and treatment facilitation for the most prevalent mental health problems and disorders (depression, anxiety, adjustment disorders, eating disorders/weight management and substance abuse) that assists individuals and mental health professionals in selecting and using evidence-based, online interventions. To reach this aim, the project partners bring together over 30 evidence-based, online interventions spanning the mental health intervention spectrum from universal and targeted prevention, self-help to treatment for the respective conditions applicable to children, adolescents and adults. Following a stakeholder needs survey, the model will be integrated into existing health care and other settings in Germany, Great Britain, Switzerland, Austria, The Netherlands, and Spain by 1. developing valid and economic, online screenings to allocate individuals to interventions, 2. developing technology for a common e-Health intervention platform, 3. developing implementation plans, 4. implementing evidence-based interventions into health care, and 5. evaluating and comparing their feasibility, acceptability, reach, efficacy and (cost)-effectiveness, adoption, and dissemination including moderators of interventions. Our proposal aims at the sustained implementation of the ICare model into health services and collaborations with health care providers across different EU countries.

IDERHA will address the key obstacles to achieving appropriate access, sharing, use and reuse of lung cancer data, and thus enable enhanced regulatory and HTA decision-making recommendations for integrated health research, with the aim to improve care and better meet the needs of patients and health care professionals. The IDERHA open platform for multi-modal health data will enhance innovation in EU health care systems and is directly scalable by means of connecting additional systems, data sources and additional services and tools. It will extend and elaborate standards in semantic interpretation, data quality, ethics and transferability to ensure the harmonisation of heterogeneous data sources and wider health data reuse. Use cases positioned along the lung cancer patient pathway will be implemented using retrospective data and in a remote patient care context. These practical implementations will demonstrate the added value of multi-modal data aggregation and analysis with impacts expected on public health, patient burden, health outcomes and cost. We will perform AI/ML based lung-cancer risk profiling using patient`s EHRs, and improved CT image- based AI/ML to provide risk prediction of potential lung cancer patients, and explore the possibilility of personal prognosis of disease progression. Using patient monitoring and engagement, including digital biomarker, PROMs, and connected devices, the IDERHA platform will enable remote patient monitoring, and provide data for joint patient-health professional decision making. Along with health care stakeholders, IDERHA will develop consensus policy recommendations for appropriate data sharing to enable multi-stakeholder research. Informed by Patient Advisory Groups, we will address critical obstacles and issues to heterogeneous health data primary and secondary use. Regulatory and HTA agencies will be engaged to create criteria for assessing the acceptability of heterogeneous health research results in regulatory and HTA decision-making.