Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However, existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this, we construct TACO, an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views, precise hand-object 3D meshes, and action labels. To rapidly expand the data scale, we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO, we benchmark three generalizable hand-object-interaction tasks: compositional action recognition, generalizable hand-object motion forecasting, and cooperative grasp synthesis. Extensive experiments reveal new insights, challenges, and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://taco2024.github.io.
The identification of odorant-binding proteins (OBPs) involved in host location by Oides leucomelaena (O. leucomelaena Weise, 1922, Coleoptera, Galerucinae) is significant for its biological control. Tools in the NCBI database were used to compare and analyze the transcriptome sequences of O. leucomelaena with OBP and other chemosensory-related proteins of other Coleoptera insects. Subsequently, MEGA7 was utilized for OBP sequence alignment and the construction of a phylogenetic tree, combined with expression profiling to screen for candidate antennae-specific OBPs. In addition, fumigation experiments with star anise volatiles were conducted to assess the antennae specificity of the candidate OBPs. Finally, molecular docking was employed to speculate on the binding potential of antennae-specific OBPs with star anise volatiles. The study identified 42 candidate OBPs, 8 chemosensory proteins and 27 receptors. OleuOBP3, OleuOBP5, and OleuOBP6 were identified as classic OBP family members specific to the antennae, which was confirmed by volatile fumigation experiments. Molecular docking ultimately clarified that OleuOBP3, OleuOBP5, and OleuOBP6 all exhibit a high affinity for β-caryophyllene among the star anise volatiles. We successfully obtained three antennae-specific OBPs from O. leucomelaena and determined their high-affinity volatiles, providing a theoretical basis for the development of attractants in subsequent stages.
This study aimed to screen and identify common variants and long noncoding RNA (lncRNA) single nucleotide polymorphisms (SNPs) associated with gastric cancer risk, and construct prediction models based on polygenic risk score (PRS). The risk factors associated with gastric cancer were screened following meta-analysis and bioinformatics, verified by population-based case-control study. We constructed PRS and weighted genetic risk scores (wGRS) derived from the validation data set. Net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate model. The PRS was divided into 10 quantiles, with the 40–60% quantile as a reference. A risk gradient was revealed across quantile of the PRS, the risk of gastric cancer in the highest 10 quantile of PRS was 3.24-fold higher than that in control population (OR = 3.24, 95%CI: 2.07, 5.06). For NRI and IDI, PRS combinations were significantly improved compared to wGRS model combinations (P < 0.001). The model of PRS combined with lncRNA SNPs, smoking, drinking and Helicobacter pylori infection was the best-fitting model (AIC = 117.23, BIC = 122.31). The model based on PRS combined with lncRNA SNPs, H. pylori infection, smoking, and drinking had the optimal predictive ability for gastric cancer risk, which was helpful to distinguish high-risk groups.
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