Effect of fire and post-fire management on soil microbial communities in a lower subtropical forest ecosystem after a mountain fire.

Wildfires and post-fire management exert profound effects on soil properties and microbial communities in forest ecosystems. Understanding microbial community recovery from fire and what the best post-fire management should be is very important but needs to be sufficiently studied. In light of these gaps in our understanding, this study aimed to assess the short-term effects of wildfire and post-fire management on both bacteria and fungi community composition, diversity, structure, and co-occurrence networks, and to identify the principal determinants of soil processes influencing the restoration of these communities.

The rapid start-up of CANON process through adding partial nitration sludge to ANAMMOX system.

This study aimed to start up the completely autotrophic nitrogen removal over nitrite (CANON) process after adding partial nitration (PN) sludge to the ANAMMOX reactor, so as to help the rapid start-up and stable operation of the CANON process in practical engineering applications. There were three steps in the research: cultivating the PN sludge, building a reliable ANAMMMOX system, and finally starting and running the CANON process. The PN sludge was successfully cultivated in less than 45 days with around 90% nitrite accumulation rate.

The nitrogen removal performance and microbial community on mixotrophic denitrification process.

Sulfur autotrophic denitrifiers and heterotrophic denitrifiers widely exist in aquatic ecosystem, however, the response of sulfide to the microbial community structure in mixotrophic denitrification ecosystem is unknown yet. In this study, the denitrification performance and microbial community were explored by changing the molar ratio of influent C/N/S. From the level of genus, the joint action of Thauera, Pacacoccus, Fusibacter Pseudoxanthomonas, Thiobacillus, Sulfurovum and Sulfurimonas brought about the efficient denitrification performance in the mixotrophic system.

A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation.

Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN.

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis.

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance.