ALBERT

Description: Methane produced by methanogenic archaea has an important influence on Earth’s changing climate. Methanogenic archaea are phylogenetically diverse and widespread in anoxic environments. These microorganisms can be divided into two subgroups based on whether or not they use b-type cytochromes for energy conservation. Methanogens with b-type cytochromes have a wider substrate range and higher growth yields than those without them. To date, methanogens with b-type cytochromes were found exclusively in the phylum “Ca. Halobacteriota” (formerly part of the phylum Euryarchaeota). Here, we present the discovery of metagenome-assembled genomes harboring methyl-coenzyme M reductase genes reconstructed from mesophilic anoxic sediments, together with the previously reported thermophilic “Ca. Methylarchaeum tengchongensis”, representing a novel archaeal order, designated the “Ca. Methylarchaeales”, of the phylum Thermoproteota (formerly the TACK superphylum). These microorganisms contain genes required for methyl-reducing methanogenesis and the Wood-Ljundahl pathway. Importantly, the genus “Ca. Methanotowutia” of the “Ca. Methylarchaeales” encode a cytochrome b-containing heterodisulfide reductase (HdrDE) and methanophenazine-reducing hydrogenase complex that have similar gene arrangements to those found in methanogenic Methanosarcinales. Our results indicate that members of the “Ca. Methylarchaeales” are methanogens with cytochromes and can conserve energy via membrane-bound electron transport chains. Phylogenetic and amalgamated likelihood estimation analyses indicate that methanogens with cytochrome b-containing electron transfer complexes likely evolved before diversification of Thermoproteota or “Ca. Halobacteriota” in the early Archean Eon. Surveys of public sequence databases suggest that members of the lineage are globally distributed in anoxic sediments and may be important players in the methane cycle.

Description: Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.

Description: Deep learning-based methods have achieved remarkable performance in 3-D sensing since they perceive environments in a biologically inspired manner. Nevertheless, the existing approaches trained by monocular sequences are still prone to fail in dynamic environments. In this work, we mitigate the negative influence of dynamic environments on the joint estimation of depth and visual odometry (VO) through hybrid masks. Since both the VO estimation and view reconstruction process in the joint estimation framework is vulnerable to dynamic environments, we propose the cover mask and the filter mask to alleviate the adverse effects, respectively. As the depth and VO estimation are tightly coupled during training, the improved VO estimation promotes depth estimation as well. Besides, a depth-pose consistency loss is proposed to overcome the scale inconsistency between different training samples of monocular sequences. Experimental results show that both our depth prediction and globally consistent VO estimation are state of the art when evaluated on the KITTI benchmark. We evaluate our depth prediction model on the Make3D dataset to prove the transferability of our method as well.