Lanzhou University

A team including City University of Hong Kong released TriStageHLA, which splits HLA class I epitope discovery into peptide-only screening, allele-specific binding prediction and immunogenicity prediction in sequence. On held-out immunogenicity data the immunogenicity module reached AUROC 81.4% and AUPRC 73.8%, above ImmuneApp at 70.7%/63.6% and BigMHC-IM at 60.1%/51.5%; the binding module reached AUROC 96.2% and AUPRC 95.2% on combined BA/EL evaluation. In a simulated screen over 300,000 human-proteome background peptides, the screening stage kept 82.4% of true candidate peptides while discarding 87% of background peptides.

A team including City University of Hong Kong released TriStageHLA, which splits HLA class I epitope discovery into peptide-only screening, allele-specific binding prediction and immunogenicity prediction in sequence. On held-out immunogenicity data the immunogenicity module reached AUROC 81.4% and AUPRC 73.8%, above ImmuneApp at 70.7%/63.6% and BigMHC-IM at 60.1%/51.5%; the binding module reached AUROC 96.2% and AUPRC 95.2% on combined BA/EL evaluation. In a simulated screen over 300,000 human-proteome background peptides, the screening stage kept 82.4% of true candidate peptides while discarding 87% of background peptides.

Retrieval-augmented generation does not improve every task, since irrelevant evidence can distract a generator, and measuring each candidate retriever's gain directly repeats generation for every configuration. EviCast splits a retriever's net utility into the generator's response to controlled evidence states and that retriever's offline distribution over those states; the response is measured once on a small calibration set and reused. On 14 HotpotQA retrieval configurations, one calibration used 96 generation calls, made 14 of 14 correct deployment decisions with a 2.75 F1-point forecast error, and cut diagnostic generation calls by 80% relative to per-configuration direct measurement.

Retrieval-augmented generation does not improve every task, since irrelevant evidence can distract a generator, and measuring each candidate retriever's gain directly repeats generation for every configuration. EviCast splits a retriever's net utility into the generator's response to controlled evidence states and that retriever's offline distribution over those states; the response is measured once on a small calibration set and reused. On 14 HotpotQA retrieval configurations, one calibration used 96 generation calls, made 14 of 14 correct deployment decisions with a 2.75 F1-point forecast error, and cut diagnostic generation calls by 80% relative to per-configuration direct measurement.

Holocene agriculture in China did not expand as one uniform farming frontier. Combining archaeological crop records with palaeoclimate, terrain and river-network data, a crop-specific dynamic non-local diffusion model estimates the relative probability of new crop records during 6000–2000 BP. Millet records were most associated with same-crop persistence and non-local diffusion, rice records most with humid-lowland environmental filtering, and wheat/barley records with corridor-like accessibility along the Hexi Corridor–Central Plains belts and higher frontier mobility.

Holocene agriculture in China did not expand as one uniform farming frontier. Combining archaeological crop records with palaeoclimate, terrain and river-network data, a crop-specific dynamic non-local diffusion model estimates the relative probability of new crop records during 6000–2000 BP. Millet records were most associated with same-crop persistence and non-local diffusion, rice records most with humid-lowland environmental filtering, and wheat/barley records with corridor-like accessibility along the Hexi Corridor–Central Plains belts and higher frontier mobility.