Effect of bioaugmentation on gas production and microbial community during anaerobic digestion in a low-temperature fixed-bed reactor.

Low temperature is one of the limiting factors for anaerobic digestion in cold regions. To improve the efficiency of anaerobic digestion for methane production in stationary reactors under low-temperature conditions, and to improve the structure of the microbial community for anaerobic digestion at low temperatures. We investigated the effects of different concentrations of exogenous Methanomicrobium (10, 20, 30%) and different volumes of carbon fiber carriers (0, 10, 20%) on gas production and microbial communities to improve the performance of low-temperature anaerobic digestion systems. The results show that the addition of 30% exogenous microorganisms and a 10% volume of carbon fiber carrier led to the highest daily (128.15 mL/g VS) and cumulative (576.62 mL/g VS) methane production. This treatment effectively reduced the concentrations of COD and organic acid, in addition to stabilizing the pH of the system. High-throughput sequencing analysis revealed that the dominant bacteria under these conditions were Acidobacteria and Firmicutes and the dominant archaea were Candidatus_Udaeobacter and Methanobacterium. While the abundance of microorganisms that metabolize organic acids was reduced, the functional abundance of hydrogenophilic methanogenic microorganisms was increased. Therefore, the synergistic effect of Methanomicrobium bioaugmentation with carbon fiber carriers can significantly improve the performance and efficiency of low-temperature anaerobic fermentation systems.

NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection.

MOTIVATION: 5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios. RESULTS: Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies. AVAILABILITY AND IMPLEMENTATION: The project code is available at https://github.com/Challis-yin/NanoCon.

The effects of adding exogenous lignocellulose degrading bacteria during straw incorporation in cold regions on degradation characteristics and soil indigenous bacteria communities.

Low temperature is one of the bottleneck factors that limits the degradation of straw during rice straw incorporation. Determining strategies to promote the efficient degradation of straw in cold regions has become a highly active research area. This study was to investigate the effect of rice straw incorporation by adding exogenous lignocellulose decomposition microbial consortiums at different soil depths in cold regions. The results showed that the lignocellulose was degraded the most efficiently during straw incorporation, which was in deep soil with the full addition of a high-temperature bacterial system. The composite bacterial systems changed the indigenous soil microbial community structure and diminished the effect of straw incorporation on soil pH, it also significantly increased rice yield and effectively enhanced the functional abundance of soil microorganisms. The predominant bacteria SJA-15, Gemmatimonadaceae, and Bradyrhizobium promoted straw degradation. The concentration of bacterial system and the depth of soil had significantly positive correlations on lignocellulose degradation. These results provide new insights and a theoretical basis for the changes in the soil microbial community and the application of lignocellulose-degrading composite microbial systems with straw incorporation in cold regions.

Large-Scale Fabrication of Tunable Sandwich-Structured Silver Nanowires and Aramid Nanofiber Films for Exceptional Electromagnetic Interference (EMI) Shielding.

The recent advancements in communication technology have facilitated the widespread deployment of electronic communication equipment globally, resulting in the pervasive presence of electromagnetic pollution. Consequently, there is an urgent necessity to develop a thin, lightweight, efficient, and durable electromagnetic interference (EMI) shielding material capable of withstanding severe environmental conditions. In this paper, we propose an innovative and scalable method for preparing EMI shielding films with a tunable sandwich structure. The film possesses a nylon mesh (NM) backbone, with AgNWs serving as the shielding coating and aramid nanofibers (ANFs) acting as the cladding layer. The prepared film was thin and flexible, with a thickness of only 0.13 mm. AgNWs can easily form a conductive network structure, and when the minimum addition amount was 0.2 mg/cm2, the EMI SE value reached 28.7 dB, effectively shielding 99.884% of electromagnetic waves and thereby meeting the commercial shielding requirement of 20 dB. With an increase in dosage up to 1.0 mg/cm2, the EMI SE value further improved to reach 50.6 dB. The NAAANF film demonstrated remarkable robustness in the face of complex usage environments as a result of the outstanding thermal, acid, and alkali resistance properties of aramid fibers. Such a thin, efficient, and environmentally resistant EMI shielding film provided new ideas for the broad EMI shielding market.

Heterogeneous Information Network-Based Patient Similarity Search.

Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. In addition, most of existing methods cannot capture the time information in the EHR, which is not conducive to analyzing the influence of time factors on patient similarity search. To solve these problems, we propose a patient similarity search method based on a heterogeneous information network. On the one hand, the proposed method uses a heterogeneous information network to connect patients, diseases, and drugs, which solves the problem of vector representation of mixed information related to patients, diseases, and drugs. Meanwhile, our method measures the similarity between patients by calculating the similarity between nodes in the heterogeneous information network. In this way, the challenges caused by high-dimensional and sparse vectors can be addressed. On the other hand, the proposed method solves the problem of inaccurate patient similarity search caused by the lack of use of time information in the patient similarity measurement process by encoding time information into an annotated heterogeneous information network. Experiments show that our method is better than the compared baseline methods.