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Background: Human gut microbiome has complex relation-ships with the host, contributing to metabolism, immunity, and carcinogenesis. Methods: Summary-level data for gut microbiota and metabo-lites were obtained from MiBioGen, FINRISK and human meta-bolome consortia. Summary-level data for colorectal cancer were derived from a genome-wide association study meta-analysis. In forward Mendelian randomization (MR), we employed genetic instrumental variables (IV) for 24 gut microbiota taxa and six bacterial metabolites to examine their causal relationship with colorectal cancer. We also used a lenient threshold for nine apriori gut microbiota taxa as secondary analyses. In reverse MR, we explored association between genetic liability to colorectal neoplasia and abundance of microbiota studied above using 95, 19, and 7 IVs for colorectal cancer, adenoma, and polyps, respectively. Results: Forward MR did not find evidence indicating causal relationship between any of the gut microbiota taxa or six bacterial metabolites tested and colorectal cancer risk. However, reverse MR supported genetic liability to colorectal adenomas was causally related with increased abundance of two taxa: Gammaproteobacteria (b = 0.027, which represents a 0.027 increase in log-transformed relative abundance values of Gam-maproteobacteria for per one-unit increase in log OR of adenoma risk; P = 7.06x10-8), Enterobacteriaceae (b = 0.023, P = 1.29x10-5). Conclusions: We find genetic liability to colorectal neoplasia may be associated with abundance of certain microbiota taxa. It is more likely that subset of colorectal cancer genetic liability variants changes gut biology by influencing both gut microbiota and colo-rectal cancer risk.Impact: This study highlights the need of future complemen-tary studies to explore causal mechanisms linking both host genetic variation with gut microbiome and colorectal cancer susceptibility.

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first paper of this series, based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) DR1 and Gaia EDR3. The optimal design of stellar-parameter sensitive $uv$ filters by SAGES has enabled us to determine photometric-metallicity estimates down to $-3.5$, similar to our previous results with the SkyMapper Southern Survey (SMSS), yielding a large sample of over five million metal-poor (MP; [Fe/H]$\le -1.0$) stars and nearly one million very metal-poor (VMP; [Fe/H]$\le -2.0$) stars. The typical precision is around $0.1$ dex for both dwarf and giant stars with [Fe/H]$>-1.0$, and 0.15-0.25/0.3-0.4 dex for dwarf/giant stars with [Fe/H]$<-1.0$. Using the precise parallax measurements and stellar colors from Gaia, effective temperature, luminosity classification, distance and stellar age are further derived for our sample stars. This huge data set in the Northern sky from SAGES, together with similar data in the Southern sky from SMSS, will greatly advance our understanding of the Milky Way, in particular its formation and evolution., Comment: 14 pages, 14 figures, 3 tables, accepted by ApJ. arXiv admin note: text overlap with arXiv:2104.14154

We report the discovery of a new stream (dubbed as Yangtze) detected in $Gaia$ Data Release 3. The stream is at a heliocentric distance of $\sim$ 9.12 kpc and spans nearly 27$\deg$ by 1.9$\deg$ on sky. The colour-magnitude diagram of Yangtze indicates a stellar population of Age $\sim$ 11 Gyr and [M/H] $\sim$ -0.7 dex. It has a number density of about 5.5 stars degree$^{-2}$ along with a surface brightness of $\Sigma_G \simeq$ 34.9 mag arcsec$^{-2}$. The dynamics and metallicity estimate suggest that Yangtze may be closely related to Palomar 1 and the Anticenter stream., Comment: 8 pages, 4 figures, accepted by ApJL. arXiv admin note: substantial text overlap with arXiv:2208.05176

In recent years, ultra-dense relay network (UDRN) has become a hot research topic. Mobile edge computing (MEC) can improve the performance of intelligent interactive applications in terms of time delay and energy consumption. In traditional MEC, MEC server in the base station (BS) is responsible for all the computing in the network. However, the MEC deployed in the BS may lead to huge computation burden on the BS and heavy traffic load through the network. Deploying MEC server in the relays of the network, which helps computing and caching for the users, will lower the burden of the BS, reduce the demand for traffic transmission through the network and improve the quality of service for users. This paper studies the problem of task caching, offloading and stable matching (SM) in UDRNs with MEC deployed in the relays, which is formulated as a multi-variable integer programming problem. To enable task caching and offloading, we propose an improved branch-and-bound algorithm and a low-complexity suboptimal algorithm to obtain the optimal and suboptimal schemes, respectively. Then, according to the principle of minimizing individual costs, the mutual preference lists between the users and relays are established. Based on the mutual preference lists, we propose an improved SM algorithm to obtain the user-relay SM results. Simulation results show that the proposed algorithms can bring considerable performance gains compared with conventional algorithms.