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Magnetotactic bacteria (MTB) have the remarkable capability of producing intracellularly membrane-enveloped magnetic nanocrystals (i.e. magnetosomes) and swimming along geomagnetic field lines. Despite more than 50 years of research, bacterial diversity and magnetosome biomineralization within MTB are relatively less known in the Gammaproteobacteria class than other groups. This is incompatible with the status of Gammaproteobacteria as the most diverse class of gram-negative bacteria with a number of ecologically important bacteria. Here, we identify a novel MTB strain YYHR-1 affiliated with the Gammaproteobacteria class of the Pseudomonadota phylum from a freshwater lake. In YYHR-1, most magnetosome crystals are organized into a long chain aligned along the cell long axis; unusually, a few small superparamagnetic crystals are located at the side of the chain, off the main chain axis. Micromagnetic simulations indicate that magnetostatic interactions among adjacent crystals within a chain reduce the Gibbs energy to enhance chain stability. Genomic analysis suggests that duplication of magnetosome gene clusters may result in off-chain magnetosomes formation. By integrating available genomic data from Gammaproteobacteria , the phylogenetic position of MTB in this class is reassigned here. Our new findings expand knowledge about MTB diversity and magnetosome biomineralization, and deepen understanding of the phylogenetics of the Gammaproteobacteria. [ABSTRACT FROM AUTHOR]

With the rapid development of China's industrialization, the air pollution is becoming more and more serious. It is vital for us to predict the air quality for determining the further prevention measures of avoiding the brought disasters. In this paper, we are going to propose an approach of predicting the air quality based on the multiple data features through fusing the multiple machine learning models. The approach takes the meteorological data and air quality data for the past six days as one batch of input (the whole data set is for 46 days) and employs a multi-model fusion to provide an improved 24-hour prediction of PM2.5 pollutant concentration all over Beijing. During the above process, two focal feature groups are composed. The first focal feature group contains the historical meteorological data, while the second group includes the statistical information, the date information and the polynomial variations. Besides the two groups, we complement one million more data items by employing the time sliding means. Among the supplementary data, we select the most critical 500 features with Light Gradient Boosting Machine (LightGBM) model and send the features as the input to Gradient Boosting Decision Tree (GBDT) and LightGBM models. Meanwhile, we screen the most critical 300 features with eXtreme Gradient Boosting (XGBoost) model and send them as the input to the three prediction models. Referring to each of the models, we respectively gain the optimal parameters through grid search methods and then fuse the models' contribution with the linear weighting. The experiments indicate that the proposed approach based on the weighting fusion is better than that provided by a single modeling scheme, and the loss value is 0.4158 under the SMAPE index. • An approach of predicting air quality with fusing the multiple machine learning models. • The machine learning solutions to model PM2.5 pollutant concentration distribution feature. • The time sliding means for developing one million more data items. • The fusion of multiple models under linear weighting strategy. The experiments indicate that the approach based on the weighting fusion is better than that provided by the single modeling scheme, and the loss value is 0.4158 under the SMAPE index. [ABSTRACT FROM AUTHOR]