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Our Future in Imaging Comes Into Focus

Pushing the bounds of imaging isn’t new for the San Francisco Biohub and Imaging Institute. Both organizations have already taken down barriers to research by building imaging tools that don’t exist anywhere else, as well as creating pioneering cell atlases that have redefined how we understand health and disease.

One example is the San Francisco Biohub’s research on how zebrafish embryos develop over time. In order to capture video images of whole zebrafish embryos through various developmental stages, Biohub scientists built a custom microscope, along with novel software that can find and track the movement of each cell in the videos. The “Google Earth” of embryology resulting from this research is Zebrahub, which brings a new vision to developmental biology and helps us understand our own cellular origins.

Projects like Zebrahub require scientists from a host of different disciplines. Teams across the Biohub, along with interdisciplinary partners, worked to build the microscope, develop the cell tracking software and interpret the resulting images. The collaborative nature of this project isn’t unique to our research on zebrafish — it’s part of our philosophy, and we believe collaboration is critical to drive scientific advancement in all of our work.

Scientists just found a molecule that could stop Parkinson’s in its tracks

Researchers have designed a peptide that prevents the deadly misfolding of alpha-synuclein, the protein behind Parkinson’s and some dementias. In lab and animal tests, it stabilized the protein and improved motor function. The work demonstrates the power of rational drug design in tackling brain diseases that have long lacked effective treatments.

1 Gene, 1 Disease no More — Acknowledging The Full Complexity of Genetics Could Improve And Personalize Medicine

Your DNA contains millions of genetic variants that interact with each other in ways that affect whether diseases such as schizophrenia and heart disease develop, and with what severity.

Michael Freedman | The Poincaré Conjecture and Mathematical Discovery

Millennium Prize Problems Lecture 9/17/2025
Speaker: Michael Freedman, Harvard CMSA and Logical Intelligence.

Title: the poincaré conjecture and mathematical discovery.

Abstract: The AI age requires us to re-examine what mathematics is about. The Seven Millenium Problems provide an ideal lens for doing so. Five of the seven are core mathematical questions, two are meta-mathematical – asking about the scope of mathematics. The Poincare conjecture represents one of the core subjects, manifold topology. I’ll explain what it is about, its broader context, and why people cared so much about finding a solution, which ultimately arrived through the work of R. Hamilton and G. Perelman. Although stated in manifold topology, the proof requires vast developments in the theory of parabolic partial differential equations, some of which I will sketch. Like most powerful techniques, the methods survive their original objectives and are now deployed widely in both three-and four-dimensional manifold topology.

A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

In this section, the authors reveal the findings of this review. The findings are categorized based on data modalities, showcasing the effectiveness of AI models in terms of evaluation metrics. Figure 1 summarizes the extraction process, providing a clear representation of the progression from article identification to final selection of studies. The initial search yielded a substantial total of 1,175 articles. Based on the inclusion and exclusion criteria, the subsequent screening process excluded irrelevant articles. By meticulously filtering the literature, 25 studies were deemed suitable for inclusion into this review.

A chronology of research studies on the uses of AI in DS diagnosis is shown in Figure 2. This timeline highlights a considerable growth in academic interest over the course of the years. A single study was published per year between the years 2013 and 2017. Technical restrictions and the availability of datasets restricted the early attempts to integrate AI into DS diagnoses. Advancements in deep learning and machine learning technologies have been driven by continuous growth in research, representing a milestone in 2021. These developments are signs of increasing confidence in the ability of artificial intelligence to identify and resolve challenging diagnostic problems. The year 2021 reaches a high with four studies, indicating a surge of innovation. This may result from improved computing tools and a more extensive understanding of the usefulness of artificial intelligence in the medical field. However, the minor decline in 2022 and 2023, with three studies, may indicate difficulties in maintaining the rapid pace of research. These challenges may include restricted access to different datasets or limitations to clinical adoption.

In 2024, there was a significant increase in DS diagnostics approaches, achieving a total of seven studies. This increase is a result of developments in AI algorithms, collaborations across diverse fields, and the significant role of AI in medical diagnosis. It demonstrates the increased academic and multidisciplinary interest in developing effective AI-powered DS detection models. In addition, an increasing trajectory highlights the importance of maintaining research efforts in order to overcome current challenges in implementing AI applications in the healthcare sector.

Novel alloy withstands extreme conditions, could replace metals used in aircraft engines and gas turbines

A new material might contribute to a reduction of the fossil fuels consumed by aircraft engines and gas turbines in the future. A research team from Karlsruhe Institute of Technology (KIT) has developed a refractory metal-based alloy with properties unparalleled to date.

The novel combination of chromium, molybdenum, and silicon is ductile at . With its of about 2,000°C, it remains stable even at high temperatures and is at the same time oxidation resistant. These results are published in Nature.

High-temperature-resistant metallic materials are required for , , X-ray units, and many other technical applications. Refractory metals such as tungsten, molybdenum, and chromium, whose melting points are around or higher than 2,000°C, can be most resistant to high temperatures.

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