Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.

SUMMARY: We present HoPhage (Host of Phage) to identify the host of a given phage fragment from metavirome data at the genus level. HoPhage integrates two modules using a deep learning algorithm and a Markov chain model, respectively. HoPhage achieves 47.90% and 82.47% mean accuracy at the genus and phylum levels for ∼1-kb long artificial phage fragments when predicting host among 50 genera, representing 7.54-20.22% and 13.55-24.31% improvement, respectively. By testing on three real virome samples, HoPhage yields 81.11% mean accuracy at the genus level within a much broader candidate host range. AVAILABILITY AND IMPLEMENTATION: HoPhage is available at http://cqb.pku.edu.cn/ZhuLab/HoPhage/data/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short-read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short reads and presents more reliable results for real plasmid metagenomic data than traditional gene prediction tools designed primarily for chromosome-derived short reads. AVAILABILITY AND IMPLEMENTATION: The PlasGUN software is available at http://cqb.pku.edu.cn/ZhuLab/PlasGUN/ or https://github.com/zhenchengfang/PlasGUN/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

One of the central problems in object recognition is to develop appropriate representations for the objects in images. The authors present a novel approach for image representation that is based on graphs. In the proposed image graph, each node represents a patch and edges are added between neighbouring nodes. First, class‐specific match‐set graphs are generated by matching the image graphs that are in the same categories, and the multi‐image matching problem is solved by applying a seed‐expansion strategy. Then, the matches between the match‐set graphs and an image graph are considered to be the object patches in the image. Finally, the features extracted from these patches are used for the image representation. Extensive experiments are conducted to demonstrate that their approach can obtain state‐of‐the‐art results on several challenging datasets.

The paper proposes a new video-on-demand (VOD) broadcasting scheme for variable bit rate (VBR) encoded videos; it is called forward segmentation using equal bandwidth (FSEB). For all practical purposes, FSEB solves the elusive problem of minimizing the server bandwidth for delivering VBR videos with no loss, given an upper bound on the wait time, a uniform upper bound on the channel bandwidths, and the number of channels that a user can access simultaneously. FSEB takes a video as a frame sequence, divides the video into segments, and broadcasts each segment periodically on a separate logical channel. Experimental results with real videos reveal that FSEB requires less server bandwidth and user bandwidth than the only other lossless scheme for VBR videos currently known, which also can limit the number of channels that a user can access simultaneously.

Broadcast schemes scale well in transporting popular movies in video-on-demand (VOD) systems. A large number of broadcast schemes have been proposed, a small number of which deal with the variable bit rate (VBR) encoded videos used in practice. Among them, some take the frame sequence of a VBR video into consideration. Although a few of them can achieve optimal server bandwidth under certain conditions, they cannot ensure proper decoding and full display of each downloaded segment if there are frames, for example, B frames in MPEG, that require future reference frames for decoding. Here we present a novel lossless scheme, general frame level segmentation (GFLS), to solves the above two problems. GFLS uses a segment-end-flag (SEF) sequence based on the frame interdependency to ensure continuous display of the whole video. Its important feature is that, both frame sequences in transmission order and in display order of a video have the same SEF sequence. We provide an efficient algorithm that uses the SEF sequence to implement GFLS.