University of Wisconsin–Madison

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.

A single-nucleus RNA-sequencing atlas of the human dorsolateral prefrontal cortex built from nearly 1,500 donors and more than 6.3 million nuclei, together with the companion statistical tool Dreamlet, characterizes cell-type-specific transcriptional changes across eight brain disorders, reports shared cross-disease signatures, cell-composition shifts along Alzheimer's disease progression, and marked up-regulation of PTPRG in microglia.

A single-nucleus RNA-sequencing atlas of the human dorsolateral prefrontal cortex built from nearly 1,500 donors and more than 6.3 million nuclei, together with the companion statistical tool Dreamlet, characterizes cell-type-specific transcriptional changes across eight brain disorders, reports shared cross-disease signatures, cell-composition shifts along Alzheimer's disease progression, and marked up-regulation of PTPRG in microglia.

This work releases curated 2020-2021 Genomes to Fields imagery from 356 drone flights across 19 environments covering 1,180 maize hybrids, and uses functional principal components of vegetation index and weather trajectories together with genomic information for yield prediction and QTL mapping, finding that combined genomic and phenomic kernels raised held-out hybrid prediction to r = 0.501 when environments were represented in training and 0.408 when environments were also withheld, that accumulated growing degree days offered no consistent advantage over days after planting, that weather contributed modest task-dependent gains, and that NGRDI functional principal components repeatedly mapped to quantitative trait loci on chromosomes 3 and 7.

This work releases curated 2020-2021 Genomes to Fields imagery from 356 drone flights across 19 environments covering 1,180 maize hybrids, and uses functional principal components of vegetation index and weather trajectories together with genomic information for yield prediction and QTL mapping, finding that combined genomic and phenomic kernels raised held-out hybrid prediction to r = 0.501 when environments were represented in training and 0.408 when environments were also withheld, that accumulated growing degree days offered no consistent advantage over days after planting, that weather contributed modest task-dependent gains, and that NGRDI functional principal components repeatedly mapped to quantitative trait loci on chromosomes 3 and 7.