Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Atomic catalysts (AC) are emerging as a highly attractive research topic, especially in sustainable energy fields. Lack of a full picture of the hydrogen evolution reaction (HER) impedes the future development of potential electrocatalysts. In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy‐related areas.     

Comparison of bacterial community structure and function under different petroleum hydrocarbon degradation conditions

Bioprocess and Biosystems Engineering - Bioremediation methods have been successfully applied to the removal of organic pollutants for decades, but the responses of the microbial community to...     

An auto-inducible expression and high cell density fermentation of Beefy Meaty Peptide with Bacillus subtilis

Bioprocess and Biosystems Engineering - Currently, some cases about the expression of flavor peptides with microorganisms were reported owing to the obvious advantages of biological expression over...     

Establishment of Murine Infection Models with Biological Clones of Dengue Viruses Derived from a Single Clinical Viral Isolate

Virologica Sinica - Dengue virus (DENV) is a single-stranded RNA virus transmitted by mosquitoes in tropical and subtropical regions. It causes dengue fever, dengue hemorrhagic fever and dengue...     

A Prognostic Model to Predict Recovery of COVID-19 Patients Based on Longitudinal Laboratory Findings

Virologica Sinica - The temporal change patterns of laboratory data may provide insightful clues into the whole course of COVID-19. This study aimed to evaluate longitudinal change patterns of key...     

Serologic Response to SARS-CoV-2 in COVID-19 Patients with Different Severity

Virologica Sinica - The immense patient number caused by coronavirus disease 2019 (COVID-19) global pandemic brings the urge for more knowledge about its immunological features, including the...     

Distinct lung microbial community states in patients with pulmonary tuberculosis

An improved understanding of the lung microbiome may lead to better strategies to diagnose, treat, and prevent pulmonary tuberculosis(PTB). However, the characteristics of the lung microbiomes of patients with TB remain largely undefined. In this study, 163 bronchoalveolar lavage(BAL) samples were collected from 163 sputum-negative suspected PTB patients. Furthermore, 12 paired BAL samples were obtained from 12 Mycobacterium tuberculosis-positive(MTB+) patients before and after negative conversion following a two-month anti-TB treatment. The V3–V4 region of the 16 S ribosomal RNA(rRNA) gene was used to characterize the microbial composition of the lungs. The results showed that the prevalence of MTB in the BAL samples was 42.9%(70/163) among the sputum-negative patients. The α-diversity of lung microbiota was significantly less diverse in MTB+ patients compared with Mycobacterium tuberculosis-negative(MTB–) patients. There was a significant difference in β-diversity between MTB+ and MTB– patients. MTB+ patients were enriched with Anoxybacillus, while MTB– patients were enriched with Prevotella, Alloprevotella, Veillonella, and Gemella. There was no significant difference between the Anoxybacillus detection rates of MTB+ and MTB– patients. The paired comparison between the BAL samples from MTB+ patients and their negative conversion showed that BAL negative-conversion microbiota had a higher α-diversity. In conclusion, distinct features of airway microbiota could be identified between samples from patients with and without MTB. Our results imply links between lung microbiota and different clinical groups of active PTB.