Evaluation of different computational methods on 5-methylcytosine sites identification.

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Predicting lamb carcase composition from tissue depth measured at a single point with an ultrawide-band microwave scanner.

This study evaluated the ability of portable ultra-wide band microwave system (MiS) to predict lamb carcase computed tomography (CT) determined composition % of fat, lean muscle and bone. Lamb carcases (n = 343) from 6 slaughter groups were MiS scanned at the C-site (45 mm from spine midline at the 12th /13th rib) prior to CT scanning to determine the proportion of fat, muscle and bone. A machine learning ensemble stacking technique was used to construct the MiS prediction equations. Predictions were pooled and divided in 5 groups stratified for each CT composition trait (fat, lean or bone%) and a k-fold cross validation (k = 5) technique was used to test the predictions. MiS predicted CT fat% with an average RMSEP of 2.385, R2 0.78, bias 0.156 and slope 0.095. The prediction of CT lean% had an average RMSEP of 2.146, R2 0.64, bias 0.172 and slope 0.117. CT bone% prediction had an average RMSEP of 0.990, R2 0.75, bias 0.051 and slope 0.090. Predictions for CT bone% met AUS-MEAT device accreditation error tolerances on the whole range of the dataset. Predictions for CT lean% and fat% met AUS-MEAT error tolerances on a constrained dataset.

Highly Efficient and Reversible Carbon Dioxide Capture by Carbanion-Functionalized Ionic Liquids.

Due to the active unstable nature of carbon anions, it is challenging to develop carbanion-functionalized ionic liquids (ILs) for efficient and reversible carbon dioxide (CO2) capture. Here, a series of carbanion-based ILs with large conjugated structures were designed and a promising system was achieved through tuning the nucleophilicity of carbanions and screening the cation. The ideal carbanion-functionalized IL trihexyl(tetradecyl)phosphonium N,N-diethycyanoacetoamide ([P66614][DECA]) showed equimolar chemisorption of CO2 ( up to 0.98 mol CO2/mol IL) under ambient pressure and excellent absorption rate. What's more, the combined CO2 can be released easily, leading to excellent reversibility due to high stability of anion conjugated structures. More importantly, the presence of water had negligible effect on the absorption capacity, which makes it potential to be applied to the CO2 capture in industrial flue gas. The chemisorption mechanism of the carbanion and CO2 was confirmed by spectroscopic investigations and DFT calculations, where carboxylic acid product was formed through proton transfer after the carbanions reacted with CO2. Considering that high capacity, quick rate as well as excellent reversibility, these carbanion-functionalized ILs should certainly represent competitive candidates for further scale up and practical application in CO2 capture.

EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.

Introduction: N4-acetylcytidine (ac4C) is a critical acetylation modification that has an essential function in protein translation and is associated with a number of human diseases. Methods: The process of identifying ac4C sites by biological experiments is too cumbersome and costly. And the performance of several existing computational models needs to be improved. Therefore, we propose a new deep learning tool EMDL-ac4C to predict ac4C sites, which uses a simple one-hot encoding for a unbalanced dataset using a downsampled ensemble deep learning network to extract important features to identify ac4C sites. The base learner of this ensemble model consists of a modified DenseNet and Squeeze-and-Excitation Networks. In addition, we innovatively add a convolutional residual structure in parallel with the dense block to achieve the effect of two-layer feature extraction. Results: The average accuracy (Acc), mathews correlation coefficient (MCC), and area under the curve Area under curve of EMDL-ac4C on ten independent testing sets are 80.84%, 61.77%, and 87.94%, respectively. Discussion: Multiple experimental comparisons indicate that EMDL-ac4C outperforms existing predictors and it greatly improved the predictive performance of the ac4C sites. At the same time, EMDL-ac4C could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDLac4C.

The degradation of printing and dyeing wastewater by manganese-based catalysts.

Printing and dyeing wastewater generally has high pH, high turbidity, poor biodegradability, complex composition, and high chroma, which make it one of the most difficult industrial wastewaters to treat. Herein, heterogeneous ozone oxidation technology is applied to oxidize and degrade printing and dyeing wastewater. A metal oxide catalyst supported on activated carbon (γ-MnO2/AC) was prepared by hydrothermal synthetic method and shown to enable synergistic catalysis involving MnO2 metal sites and N/C sites. A simulated methyl orange solution was used to determine the effects of various preparation and operation parameters. The results confirmed that the γ-MnO2/AC catalyst exhibited good chemical oxygen demand (COD) removal and reusability. Additionally, γ-MnO2/AC demonstrated excellent degradation of the secondary biochemical effluent of printing and dyeing wastewater (COD removal = 72.45% within 120 min). The γ-MnO2/AC catalyst was fully characterized, and the mechanism governing its catalytic ozone oxidation process was investigated experimentally.