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The document discusses the history and development of artificial intelligence over the past 70 years. It outlines some of the key milestones in AI research from the early work in the 1950s to modern advances in machine learning using neural networks. While progress has been made, fully general human-level artificial intelligence remains an ongoing challenge that researchers are still working to achieve.

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Knowledge Graphs (KGs) have emerged as a valuable tool for supporting humanities scholars and cultural heritage organisations. In this resource paper, we present the Musical Meetups Knowledge Graph (MMKG), a collection of evidence of historical collaborations between personalities relevant to the music history domain. We illustrate how we built the KG with a hybrid methodology that, combining knowledge engineering with natural language processing, including the use of Large Language Models (LLM), machine learning, and other techniques, identifies the constituent elements of a historical meetup. MMKG is a network of historical meetups extracted from ∼33k biographies collected from Wikipedia focused on European musical culture between 1800 and 1945. We discuss how, by providing a structured representation of social interactions, MMKG supports digital humanities applications and music historians’ research, teaching, and learning.

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Slide 1: Title Slide Extrachromosomal Inheritance Slide 2: Introduction to Extrachromosomal Inheritance Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus. Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids. Slide 3: Mitochondrial Inheritance Mitochondria: Organelles responsible for energy production. Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria. Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring. Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy. Slide 4: Chloroplast Inheritance Chloroplasts: Organelles responsible for photosynthesis in plants. Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts. Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species. Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA. Slide 5: Plasmid Inheritance Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes. Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation. Significance: Important in biotechnology for gene cloning and genetic engineering. Slide 6: Mechanisms of Extrachromosomal Inheritance Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance. Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells. Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression. Slide 7: Examples of Extrachromosomal Inheritance Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells. Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration. Slide 8: Importance of Extrachromosomal Inheritance Evolution: Provides insight into the evolution of eukaryotic cells. Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases. Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification. Slide 9: Recent Research and Advances Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA. Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases. Slide 10: Conclusion Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology. Future Directions: Continued research and technological advancements hold promise for new treatments and applications. Slide 11: Questions and Discussion Invite Audience: Open the floor for any questions or further discussion on the topic.

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Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes. In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.

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Within the uncertainties of involved astronomical and biological parameters, the Drake Equation typically predicts that there should be many exoplanets in our galaxy hosting active, communicative civilizations (ACCs). These optimistic calculations are however not supported by evidence, which is often referred to as the Fermi Paradox. Here, we elaborate on this long-standing enigma by showing the importance of planetary tectonic style for biological evolution. We summarize growing evidence that a prolonged transition from Mesoproterozoic active single lid tectonics (1.6 to 1.0 Ga) to modern plate tectonics occurred in the Neoproterozoic Era (1.0 to 0.541 Ga), which dramatically accelerated emergence and evolution of complex species. We further suggest that both continents and oceans are required for ACCs because early evolution of simple life must happen in water but late evolution of advanced life capable of creating technology must happen on land. We resolve the Fermi Paradox (1) by adding two additional terms to the Drake Equation: foc (the fraction of habitable exoplanets with significant continents and oceans) and fpt (the fraction of habitable exoplanets with significant continents and oceans that have had plate tectonics operating for at least 0.5 Ga); and (2) by demonstrating that the product of foc and fpt is very small (< 0.00003–0.002). We propose that the lack of evidence for ACCs reflects the scarcity of long-lived plate tectonics and/or continents and oceans on exoplanets with primitive life.

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Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric composition and climates of temperate terrestrial planets. We report the discovery of an Earth-sized planet transiting the nearby (12 pc) inactive M3.0 dwarf Gliese 12 (TOI-6251) with an orbital period (Porb) of 12.76 days. The planet, Gliese 12 b, was initially identified as a candidate with an ambiguous Porb from TESS data. We confirmed the transit signal and Porb using ground-based photometry with MuSCAT2 and MuSCAT3, and validated the planetary nature of the signal using high-resolution images from Gemini/NIRI and Keck/NIRC2 as well as radial velocity (RV) measurements from the InfraRed Doppler instrument on the Subaru 8.2 m telescope and from CARMENES on the CAHA 3.5 m telescope. X-ray observations with XMM-Newton showed the host star is inactive, with an X-ray-to-bolometric luminosity ratio of log 5.7 L L X bol » - . Joint analysis of the light curves and RV measurements revealed that Gliese 12 b has a radius of 0.96 ± 0.05 R⊕,a3σ mass upper limit of 3.9 M⊕, and an equilibrium temperature of 315 ± 6 K assuming zero albedo. The transmission spectroscopy metric (TSM) value of Gliese 12 b is close to the TSM values of the TRAPPIST-1 planets, adding Gliese 12 b to the small list of potentially terrestrial, temperate planets amenable to atmospheric characterization with JWST.

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Below are the important points I note from the 2020 paper by Martin Grohe: - 1-WL distinguishes almost all graphs, in a probabilistic sense - Classical WL is two dimensional Weisfeiler-Leman - DeepWL is an unlimited version of WL graph that runs in polynomial time. - Knowledge graphs are essentially graphs with vertex/edge attributes ABSTRACT: Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

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