Identification of long non-coding RNAs in the chalkbrood disease pathogen Ascospheara apis.

Ascospheara apis is a widespread fungal pathogen that exclusively invades honeybee larvae. Thus far, non-coding RNA in A. apis has not yet been documented. In this study, we sequenced A. apis using strand specific cDNA library construction and Illumina RNA sequencing methods, and identified 379 lncRNAs, including antisense lncRNAs, lincRNAs, intronic lncRNAs and sense lncRNAs. Additionally, these lncRNAs were found to be shorter in length and have fewer exons and transcript isoforms than protein-coding genes, similar to those identified in mammals and plants. Furthermore, the existence of 15 predicted lncRNAs of A. apis was confirmed using RT-PCR and expression levels of 11 were lower than those of adjacent protein-coding genes. Our findings not only enlarge the lncRNA database for fungi, but also lay a foundation for further investigation of potential lncRNA-mediated regulation of genes in A. apis.

Dynamic prediction of displacement and deformation of any point on mining surface based on B-normal model.

The surface displacement and deformation of goaf caused by coal mining destroy the underground rock structure and surface ecological environment in the mining area and endanger the safety of human life and property. An accurate and efficient dynamic prediction system of mining subsidence is indispensable. Given the limited scope of the application of the probability integral model on the edge of the mobile basin, its poor prediction effect, and its low accuracy, a new mining subsidence prediction model based on the Boltzmann function is proposed. Combined with the transformed normal distribution time function, a B-normal prediction model that can predict the dynamic displacement and deformation of any point on the surface was constructed. The global optimal solution of the parameters of the dynamic prediction model was inversed by introducing particle swarm optimization shuffled frog leaping intelligent algorithm (PSO-SFLA), and then, the model was applied to the 8102 working face of the Guobei coal mine to dynamically predict the subsidence, inclination, curvature, horizontal displacement, and horizontal deformation of the goaf surface. The prediction results showed that on the strike and dip observation lines, the prediction accuracy of the dynamic subsidence and horizontal displacement of the surface could reach the centimeter level, the predicted root mean square error (RMSE) of dynamic tilt and horizontal deformation was less than 0.51 mm/m, and the predicted RMSE of dynamic curvature was within 0.020 mm/m2. The prediction results reflected the dynamic evolution law of surface displacement and deformation and verified the reliability of the B-normal dynamic prediction model, which can fully meet the needs of practical engineering applications.

Identification of soil heavy metal pollution by constructing 2D plane using hyperspectral index.

This study proposes a method for the rapid identification of elements in soil heavy metal pollution using spectral indices. Set up a simulation experiment of planting crops in soil polluted by multi-gradient Cu and Pb. The obtained polluted soil spectral data was initially pre-processed to obtain the original spectrum (OR), the continuum removed spectrum (CR), and the first-order differential spectrum (FOD). Then the preliminary model of soil heavy metal pollution index (SHMPI) was constructed. Using the correlation optimal algorithm, the maximum median distance algorithm, and the maximum average distance algorithm to select the optimal bands corresponding to the OR, CR, and FOD. The optimal bands selected by each algorithm were substituted into the SHMPI. Each algorithm obtains three indices, and two of them were selected as the x-axis and y-axis to form a two-dimensional pollution identification plane. Nine two-dimensional planes can be obtained by three algorithms and three combinations of OR, CR, and FOD. Support vector machine classifier was used to classify the Cu and Pb polluted samples in the planes, and nine classification models to distinguish Cu and Pb pollution in soil were constructed. The results show that using the correlation optimal algorithm to extract the optimal bands, and using OR and CR to construct SHMPI, the accuracy of the classification line model of Cu and Pb pollution obtained was 93% in the training group and 86% in the validation group. This method can stably and effectively identify the types of heavy metal pollution in soil, and can also effectively identify whether the soil is polluted by heavy metals, which is expected to guide the rapid and non-destructive identification of heavy metal pollution in polluted areas, and provide new ideas for the identification of other types of heavy metals in soil.