Νέα καρδιακή βαλβίδα που μεγαλώνει μαζί με τα παιδιά εγκρίθηκε από τον FDA

The MDS International Congress is the premier annual event to advance the clinical and scientific discipline of Movement Disorders, including Parkinson’s disease.Convening thousands of leading clinicians, scientists, and other health professionals from around the globe, the 2026 International Congress of Parkinson’s Disease and Movement Disorders® will introduce nearly 2,000 novel scientific abstracts and provide a forum for education and collaboration on latest research findings and state-of-the-art treatment options.

Course overviewMetabarcoding and metagenomic sequencing unveil the compositional diversity of microbial communities across habitats, from ocean ecosystems to the human gut. While compositional shifts often correlate with environmental or health states, mechanistic insights into dynamic microbiota-environment interactions are often lacking. Integrative approaches can shed light on these mechanisms in native and synthetic communities, such as metabolic cross-feeding driving subpopulation emergence (Varahan et al. eLife 2019, 2020) and long-term community stability (Blasche, Kim et al. Nat Microbiol 2021), or metabolic cues affecting environmental and gut microbiota structure (Clerc et al. Nat Commun 2023, Wuyts et al. Mol Sys Bio 2023, Adreani-Gerard et al. Env. Microbiome 2025, Deng et al. Nature Microbiology 2025, Kuhlish et al. PNAS 2026).Metabarcoding and metagenomic sequencing unveil the compositional diversity of microbial communities across habitats, from ocean ecosystems to the human gut. While compositional shifts often correlate with environmental or health states, mechanistic insights into dynamic microbiota-environment interactions are often lacking. Integrative approaches can shed light on these mechanisms in native and synthetic communities, such as metabolic cross-feeding driving subpopulation emergence (Varahan et al. eLife 2019, 2020) and long-term community stability (Blasche, Kim et al. Nat Microbiol 2021), or metabolic cues affecting environmental and gut microbiota structure (Clerc et al. Nat Commun 2023, Wuyts et al. Mol Sys Bio 2023, Adreani-Gerard et al. Env. Microbiome 2025, Deng et al. Nature Microbiology 2025, Kuhlish et al. PNAS 2026).We will introduce the concepts of top-down and bottom-up approaches (e.g., large-scale metagenomic analysis of environmental samples and experimental design to mimic complex interactions in the lab using synthetic communities under controlled conditions), which are relevant to researchers investigating diverse microbial habitats.ModulesExperimental design, culture set-up, and sampling16S analysis of bacterial communities using Nanopore sequencing technologyMetabolite changes during community growth using MetabolomicsInteraction mapping plating experiment (optional)Computational analysis introduction using R-studio/Python16S and metabolomics data analysis (computational)Metabolic modelling of community interactions (computational)Learning outcomesThe main goal of the course is to equip participants with state-of-the-art concepts in microbial ecosystems and to introduce key tools for gaining mechanistic insights into community dynamics. The central learning objective is for participants to apply these concepts and tools to their own research. Further, we also aim at enabling scientific networking among the participants (including speakers and trainers) and fostering dialogue between scientists from different areas (microbiology, metabolomics, modelling, ecology).What past participants say about the course“The course was a great blend of theoretical knowledge and practical applications. It was very dynamic, allowing for engaging discussions and networking opportunities among participants. From trainers to speakers and organisers, everyone was so helpful and approachable, making it easy to ask questions and seek guidance. The diverse facets and perspectives presented created an ideal environment for learning, hands-on exploration, and collaboration.” – Humberto Castillo González, Université de Franche-Comté, France.

This course provides an introduction to causal inference and causal representation learning, offering both theoretical foundations and hands-on training. You will learn how to apply these methodologies to various biomedical data types, including clinical, genotype-phenotype, molecular, and multimodal data.Causal inference and causal representation learning are emerging fields in AI and biomedical data science, enabling a shift from associational to cause-and-effect reasoning. These approaches have significant applications in biomedicine, such as evaluating treatment effectiveness, understanding causal mechanisms, identifying genetic risk factors, and uncovering causal relationships in complex molecular datasets. For instance, they can help answer questions like how effective a treatment is in preventing disease, whether its effects are direct or mediated by intermediate variables, and which genetic variants causally increase disease risk and can be targeted by drugs.Gain practical experience using widely adopted tools and resources to apply causal techniques in real-world biomedical contexts. This EMBO Practical Course will also feature keynote talks that will provide insights into the role of causality in genomic, molecular, and computational biology, highlighting recent advancements and future directions in the field.