Cytology tests are widely used for cancer screening, but diagnosis can be challenging when tumor and non-tumor cells look very similar. Researchers from Japan developed a machine learning system that identifies tumor cells based on their white-light scattering spectra. The approach achieved high diagnostic accuracy for mesothelioma and showed potential for distinguishing several other tumor types, offering a promising approach for enhancing cytological diagnosis.

As brain-computer interfaces become increasingly precise in reading human emotions, a practical question arises: Will a model trained today still be able to recognize the emotions of the same person tomorrow, in a few weeks, or even months later? Recently, the team led by Shuang Liu from the Medical School of Tianjin University/Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration published a research paper titled "Toward Sustainable Cross-Time Affective Brain–Computer Interfaces: Explicit Modeling and Structural Calibration of Temporal Variability" in the National Science Review (NSR). This study offers a cross-time "EEG ruler" for affective brain-computer interfaces (aBCIs). To address temporal variability in cross-time emotion recognition, the researchers propose a Multi-level Dynamic Integrated Perception Network (MDIN), which explicitly models and structurally calibrates temporal variability on three levels: emotional neural representation, dynamic functional connectivity, and cross-time discriminative structures, offering a new technical pathway for long-term stable affective brain-computer interfaces. This work may further accelerate the transition of aBCIs from short-term experimental validation to long-term, adaptive use in real-world mental health monitoring and human-machine interaction.

Researchers have developed a high‑precision state of health prediction model that combines physical information constraints and multi‑dimensional features for lithium‑ion batteries under random variable load conditions. A neural network‑transformer framework based on physical information was constructed, converting the consistency laws of irreversible battery aging into regularization loss terms in the training process to ensure physical rationality. Multidimensional coupled features including voltage, current, and cycle count were extracted, and Bayesian optimization was applied to adaptively adjust key hyperparameters. Experimental results show that the model outperforms comparison models in terms of MAE, RMSE, and R² metrics, effectively suppressing non‑physical fluctuations and balancing high prediction accuracy with physical rationality.

Each year, the processing of Glycyrrhiza uralensis—one of the most widely used herbs in traditional Chinese medicine—generates over 6 million tons of residue. This waste is rich in cellulose and bioactive flavonoids, but the very compounds that give Glycyrrhiza uralensis its medicinal value also inhibit the microbes needed to break it down. Now, researchers have identified a fungal strain that not only tolerates these inhibitory compounds but actually adjusts its defense strategy depending on how much it faces. The discovery could unlock more efficient ways to convert Glycyrrhiza uralensis processing waste into valuable industrial products.Each year, the processing of Glycyrrhiza uralensis—one of the most widely used herbs in traditional Chinese medicine—generates over 6 million tons of residue. This waste is rich in cellulose and bioactive flavonoids, but the very compounds that give Glycyrrhiza uralensis its medicinal value also inhibit the microbes needed to break it down. Now, researchers have identified a fungal strain that not only tolerates these inhibitory compounds but actually adjusts its defense strategy depending on how much it faces. The discovery could unlock more efficient ways to convert Glycyrrhiza uralensis processing waste into valuable industrial products.Each year, the processing of Glycyrrhiza uralensis—one of the most widely used herbs in traditional Chinese medicine—generates over 6 million tons of residue. This waste is rich in cellulose and bioactive flavonoids, but the very compounds that give Glycyrrhiza uralensis its medicinal value also inhibit the microbes needed to break it down. Now, researchers have identified a fungal strain that not only tolerates these inhibitory compounds but actually adjusts its defense strategy depending on how much it faces. The discovery could unlock more efficient ways to convert Glycyrrhiza uralensis processing waste into valuable industrial products.Each year, the processing of Glycyrrhiza uralensis—one of the most widely used herbs in traditional Chinese medicine—generates over 6 million tons of residue. This waste is rich in cellulose and bioactive flavonoids, but the very compounds that give Glycyrrhiza uralensis its medicinal value also inhibit the microbes needed to break it down. Now, researchers have identified a fungal strain that not only tolerates these inhibitory compounds but actually adjusts its defense strategy depending on how much it faces. The discovery could unlock more efficient ways to convert Glycyrrhiza uralensis processing waste into valuable industrial products.