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Abstract Data integration is a powerful tool for facilitating a comprehensive and generalizable understanding of microbial communities and their association with outcomes of interest. However, integrating data sets from different studies remains a challenging problem because of severe batch effects, unobserved confounding variables, and high heterogeneity across data sets. We propose a new data integration method called MetaDICT, which initially estimates the batch effects by weighting methods in causal inference literature and then refines the estimation via a novel shared dictionary learning. Compared with existing methods, MetaDICT can better avoid the overcorrection of batch effects and preserve biological variation when there exist unobserved confounding variables or data sets are highly heterogeneous across studies. Furthermore, MetaDICT can generate comparable embedding at both taxa and sample levels that can be used to unravel the hidden structure of the integrated data and improve the integrative analysis. Applications to synthetic and real microbiome data sets demonstrate the robustness and effectiveness of MetaDICT in integrative analysis. Using MetaDICT, we characterize microbial interaction, identify generalizable microbial signatures, and enhance the accuracy of disease prediction in an integrative analysis of colorectal cancer metagenomics studies. 

A genetically modified fungal strain generated a natural product library used to conduct various activity screens for Alzheimer's disease tau aggregation. The hit compound, penicillic acid, was optimized for the development of analogs. 

Deep neural networks (DNNs) have been widely deployed in real-world, mission-critical applications, necessitating effective approaches to protect deep learning models against malicious attacks. Motivated by the high stealthiness and potential harm of backdoor attacks, a series of backdoor defense methods for DNNs have been proposed. However, most existing approaches require access to clean training data, hindering their practical use. Additionally, state-of-the-art (SOTA) solutions cannot simultaneously enhance model robustness and compactness in a data-free manner, which is crucial in resource-constrained applications. To address these challenges, in this paper, we propose Clean & Compact (C&C), an efficient data-free backdoor defense mechanism that can bring both purification and compactness to the original infected DNNs. Built upon the intriguing rank-level sensitivity to trigger patterns, C&C co-explores and achieves high model cleanliness and efficiency without the need for training data, making this solution very attractive in many real-world, resource-limited scenarios. Extensive evaluations across different settings consistently demonstrate that our proposed approach outperforms SOTA backdoor defense methods.