Pusan National University

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.

Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.

Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.

Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).

Using a threshold of nLw555 = 1.5 mW cm-2 µm-1 sr-1 from MODIS to separate the East China Sea into Clear Water (CW) and Turbid Water (TW), this study reconstructed surface pCO2 for 2003–2023 with a CatBoost model that treats water type as a categorical input alongside multi-spectral satellite variables, achieving independent-test accuracy of R2 = 0.86 and RMSE = 16.55 µatm and cutting TW RMSE by up to about 81% relative to conventional models; the 21-year climatology gives CW 361.2 µatm and TW 415.1 µatm, with TW acting as a weak net source (+0.50 Tg C yr-1) driven by positive non-thermal anomalies (+28.09 µatm), implying conventional models overestimate total ECS carbon uptake by roughly 46% (about 4.04 Tg C yr-1).