In pancreatic cancer, knowing if there is metastasis is key to deciding whether to operate or not. Nowadays, a significant number of patients undergo unnecessary invasive surgeries because their metastasis was not detected in time.

"Our algorithm accurately predicts metastasis using images that are already routinely obtained," says Malats.

The article is published in the journal 'GUT'.

The algorithm will be tested in hospitals thanks to a project that has received nearly 800,000 euros in funding from the Spanish Department for Digital Transformation.

Time division multiplexing (TDM) architecture is an important approach to creating sensor arrays for massive scale monitoring. But it is paradoxical for the TDM interferometric sensor array to keep a short delay fiber for high sensing resolution and meanwhile use low sampling rate for practical applications. In this paper, a phase matching sampling (PMS) paradigm is proposed to address the above contradiction.

Prof. Laura Gagliardi at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) and Chemistry Department has developed a powerful new tool that harnesses electronic structure theories and machine learning to simulate transition metal catalytic dynamics with both accuracy and speed.

In Chaos, researchers in Istanbul develop a more efficient and flexible algorithm to model traffic. The model, which they call the data-driven macroscopic mobility model, relies on simple observations that city planners routinely collect, like how packed the streets are. The researchers tested their model on both synthetic benchmarks and real-world traffic data, and the faster simulation speeds and easier data requirements mean city planners may have the tools to design better, smarter cities.

Researchers at Kyushu University in Japan have developed a mathematical model that recreates the muscle movements of the esophagus that occur during swallowing. The model, reported in Royal Society Open Science, also replicates muscle dynamics seen in various esophageal motility disorders, revealing insights into their underlying causes and opening up new avenues for treatment.

In August 2017, the National Natural Science Foundation of China (NSFC) launched the Major Research Plan “Dynamic Modifications and Chemical Interventions of Biomacromolecules” (implementation period 2017–2025). Through interdisciplinary research that integrates chemistry, life sciences, medicine, mathematics, materials science, and information science, its aim is to develop specific labeling methods and detection techniques for dynamic chemical modifications of biomacromolecules, elucidate the recognition mechanisms and biological functions of dynamic modifications in the regulation of cellular traits, and discover potential drug targets and corresponding lead compounds related to dynamic biomacromolecular modifications. Since its establishment, this Major Research Plan has achieved significant progress and original results in many aspects such as the dynamic properties of biomacromolecular chemical modifications, regulatory mechanisms, and chemical interventions. Recently, members of the expert group, management group, and secretariat of the program collaborated to systematically review representative research achievements obtained since the program’s implementation, and jointly published a review article in CCS Chemistry. This review provides important references for promoting development in related frontier fields, as well as for the future trend of integration between chemistry, life sciences, and medicine.