AI agents for oncology: Dresden research team develops system to support clinical decisions
Peer-Reviewed Publication
Updates every hour. Last Updated: 7-Nov-2025 10:11 ET (7-Nov-2025 15:11 GMT/UTC)
To enhance existing strategies for controlling the Aedes aegypti mosquito, geoinformation scientist Dr Steffen Knoblauch has created a high-resolution environmental suitability map for Rio de Janeiro (Brazil) that can help identify areas most conducive to breeding. It is based on advanced geospatial big data methods – leveraging openly available geodata such as satellite imagery, street view images, and climate data – that the researcher developed at Heidelberg University’s Interdisciplinary Center for Scientific Computing (IWR) and at HeiGIT (Heidelberg Institute for Geoinformation Technology).
On June 5 and 6, 2025, the University of Stuttgart hosted a high-profile event honoring the legacy of Frei Otto - architect, Pritzker Prize winner and master of lightweight construction. Under the title "Frei Otto 100 – The Spirit of Lightweight Construction," international guests from science, architecture, and society gathered at the Institute of Lightweight Structures and Conceptual Design (ILEK) to celebrate the visionary thinking of one of the 20th century’s most influential designers – at the very place where Otto once worked. Frei Otto is considered a pioneer of ecological and experimental building.
A new AI tool to predict the spread of infectious disease outperforms existing state-of-the-art forecasting methods.
The tool, created with federal support by researchers at Johns Hopkins and Duke universities, could revolutionize how public health officials predict, track and manage outbreaks of infectious diseases including flu and COVID-19.
Biological cells exhibit nearly transparent characteristics with weak absorption properties in the visible light spectrum, resulting in extremely low optical contrast between cells and the surrounding medium under traditional bright-field microscopy. To enhance imaging contrast, conventional methods rely on chemical staining or fluorescent labeling, introducing exogenous absorption/fluorescence probes to visualize cellular structures. However, these approaches suffer from drawbacks such as phototoxicity, photobleaching, and poor biocompatibility, severely limiting long-term dynamic observation of living cells. Quantitative phase imaging (QPI) utilizes the inherent physical property of cellular phase (thickness) as an endogenous “probe”, resolving cellular thickness, refractive index, and 3D topography with nanoscale accuracy. It provides a new avenue for dynamic observation of living cells and nanoscale biological studies.
As a significant branch of QPI technology, differential phase contrast (DPC) has attracted considerable attention due to its advantages of being non-interferometric and low-cost. However, its theoretical framework relies on the “weak object approximation”, linking intensity images to sample phase through a linear model. This simplified model introduces two fundamental limitations. First, the phase reconstruction result is highly dependent on the precise modeling of the phase transfer function (PTF) under an ideal pupil. In practical optical systems, however, wavefront aberrations couple with the sample phase, leading to significant reconstruction errors. Second, the conventional half-circle illumination suffers from the problem of PTF response cancellation, resulting in the loss of low-frequency phase information and making it difficult to accurately reconstruct the fine structure of weak phase objects. These limitations significantly compromise the robustness of DPC in non-ideal optical environments and restrict its practical applicability in frontier biological research, such as cellular morphology characterization and tracking of subcellular dynamic processes.