The PSG challenge: towards comprehensive scene understanding.

The Panoptic Scene Graph Generation (PSG) challenge evaluates computer vision models to identify relations in images beyond object classification and localization, enabling a deeper understanding of scenes for real-world AI applications.

Predicting Synthetic Lethality in Human Cancers via Multi-Graph Ensemble Neural Network.

Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.

Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies.

BACKGROUND: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. RESULTS: In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. CONCLUSIONS: After validating the performance of our method using simulation experiments, we further apply it to Alzheimer's disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer's disease.

Cross-section measurements for (n, 2n), (n, p) and (n, n'alpha) reactions on gallium isotopes in the neutron energy range of 13.5-14.6 MeV.

Cross-sections for (n, 2n), (n, p) and (n, n'alpha) reactions have been measured on gallium isotopes at the neutron energies of 13.5-14.6MeV using the activation technique. Data are reported for the following reactions: 69Ga(n, 2n) 68Ga, 69Ga(n, p) 69mZn, 71Ga(n, p) (71m)Zn, and 71Ga(n, n'alpha) 67Cu. The neutron fluences were determined using the monitor reaction 93Nb(n, 2n) 92mNb.