With the rapid development of high-throughput sequencing technology, the amount of metagenomic data (including both 16S and whole-genome sequencing data) in public repositories is increasing exponentially. However, owing to the large and decentralized nature of the data, it is still difficult for users to mine, compare, and analyze the data. The animal metagenome database (AnimalMetagenome DB) integrates metagenomic sequencing data with host information, making it easier for users to find data of interest. The AnimalMetagenome DB is designed to contain all public metagenomic data from animals, and the data are divided into domestic and wild animal categories. Users can browse, search, and download animal metagenomic data of interest based on different attributes of the metadata such as animal species, sample site, study purpose, and DNA extraction method. The AnimalMetagenome DB version 1.0 includes metadata for 82,097 metagenomes from 4 domestic animals (pigs, bovines, horses, and sheep) and 540 wild animals. These metagenomes cover 15 years of experiments, 73 countries, 1,044 studies, 63,214 amplicon sequencing data, and 10,672 whole genome sequencing data. All data in the database are hosted and available in figshare 10.6084/m9.figshare.19728619.
CRAMdb (a database for composition and roles of animal microbiome) is a comprehensive resource of curated and consistently annotated metagenomes for non-human animals. It focuses on the composition and roles of the microbiome in various animal species. The main goal of the CRAMdb is to facilitate the reuse of animal metagenomic data, and enable cross-host and cross-phenotype comparisons. To this end, we consistently annotated microbiomes (including 16S, 18S, ITS and metagenomics sequencing data) of 516 animals from 475 projects spanning 43 phenotype pairs to construct the database that is equipped with 9430 bacteria, 278 archaea, 2216 fungi and 458 viruses. CRAMdb provides two main contents: microbiome composition data, illustrating the landscape of the microbiota (bacteria, archaea, fungi, and viruses) in various animal species, and microbiome association data, revealing the relationships between the microbiota and various phenotypes across different animal species. More importantly, users can quickly compare the composition of the microbiota of interest cross-host or body site and the associated taxa that differ between phenotype pairs cross-host or cross-phenotype. CRAMdb is freely available at (http://www.ehbio.com/CRAMdb).
Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.
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