Unraveling the phylogenomic diversity of Methanomassiliicoccales and implications for mitigating ruminant methane emissions.

BACKGROUND: Methanomassiliicoccales are a recently identified order of methanogens that are diverse across global environments particularly the gastrointestinal tracts of animals; however, their metabolic capacities are defined via a limited number of cultured strains. RESULTS: Here, we profile and analyze 243 Methanomassiliicoccales genomes assembled from cultured representatives and uncultured metagenomes recovered from various biomes, including the gastrointestinal tracts of different animal species. Our analyses reveal the presence of numerous undefined genera and genetic variability in metabolic capabilities within Methanomassiliicoccales lineages, which is essential for adaptation to their ecological niches. In particular, gastrointestinal tract Methanomassiliicoccales demonstrate the presence of co-diversified members with their hosts over evolutionary timescales and likely originated in the natural environment. We highlight the presence of diverse clades of vitamin transporter BtuC proteins that distinguish Methanomassiliicoccales from other archaeal orders and likely provide a competitive advantage in efficiently handling B12. Furthermore, genome-centric metatranscriptomic analysis of ruminants with varying methane yields reveal elevated expression of select Methanomassiliicoccales genera in low methane animals and suggest that B12 exchanges could enable them to occupy ecological niches that possibly alter the direction of H2 utilization. CONCLUSIONS: We provide a comprehensive and updated account of divergent Methanomassiliicoccales lineages, drawing from numerous uncultured genomes obtained from various habitats. We also highlight their unique metabolic capabilities involving B12, which could serve as promising targets for mitigating ruminant methane emissions by altering H2 flow.

Effects of Fumarate and Nitroglycerin on In Vitro Rumen Fermentation, Methane and Hydrogen Production, and on Microbiota.

This study aimed to investigate the effects of fumarate and nitroglycerin on rumen fermentation, methane and hydrogen production, and microbiota. In vitro rumen fermentation was used in this study with four treatment groups: control (CON), fumarate (FA), nitroglycerin (NG) and fumarate plus nitroglycerin (FN). Real-time PCR and 16S rRNA gene sequencing were used to analyze microbiota. The results showed that nitroglycerin completely inhibited methane production and that this resulted in hydrogen accumulation. Fumarate decreased the hydrogen accumulation and improved the rumen fermentation parameters. Fumarate increased the concentration of propionate and microbial crude protein, and decreased the ratio of acetate to propionate in FN. Fumarate, nitroglycerin and their combination did not affect the abundance of bacteria, protozoa and anaerobic fungi, but altered archaea. The PCoA showed that the bacterial (Anosim, R = 0.747, p = 0.001) and archaeal communities (Anosim, R = 0.410, p = 0.005) were different among the four treatments. Compared with CON, fumarate restored Bacteroidetes, Firmicutes, Spirochaetae, Actinobacteria, Unclassified Ruminococcaceae, Streptococcus, Treponema and Bifidobacterium in relative abundance in FN, but did not affect Succinivibrio, Ruminobacter and archaeal taxa. The results indicated that fumarate alleviated the depressed rumen fermentation caused by the inhibition of methanogenesis by nitroglycerin. This may potentially provide an alternative way to use these chemicals to mitigate methane emission in ruminants.

Fluorescence imaging of hypochlorous acid and peroxynitrite in vitro and in vivo with emission wavelength beyond 750 nm.

A series of near-infrared and photostable Si-oxazine fluorescent dyes was synthesized using a simple three-step procedure, and one of their reduced products, i.e. hydro-Si-oxazine HSiO3, has been utilized to sensitively detect hypochlorous acid and peroxynitrite generation by phagocytes in inflamed and pulmonary fibrosis diseased mice with emission wavelength beyond 750 nm.

Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency.

The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, while the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach is comparable to 11 state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS and 1,490.5 FPS on the GPU and CPU platforms, respectively.

Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation.

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the finetuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9; 433 faces per second on GPU.

[Comparison of toluene adsorption with two kinds of modified activated carbon].

Granular activated carbon (GAC) was modified by microwave irradiation and electrical heating. The characters and toluene adsorption capacity of modified GAC were studied. The results showed that for microwave heating, with the temperature increasing, the activated carbon's ability of toluene adsorption and content of the surface basic functional group were increased, specific surface area was decreased. After heated at 850 degrees C, GAC had the highest ability for toluene adsorption, at 650 degrees C and 450 degrees C, the ability for toluene adsorption of GAC were similar. For the electrical heating, the same results were obtained. But the GAC modified with electrical heating had lower capacity of toluene adsorption than microwave modified GAC. The SEM photos showed that thermal modification made the porous structure of modified GAC smoother than original GAC, but the structure of GAC shrink at high temperature. Finally, all the experimental data and SEM photos were analyzed; it indicated that due to the difference of heating mechanism and heat transmission direction, the modified GAC have different characters.