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Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs (especially in mRNAs) still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.

To optimize the gas transfer, adsorption and photo-generated charge separation in the process of photocatalytic CO2 reduction by g-C3N4, the photocatalytic materials were designed from the aspects of foam pore structure and heterojunction construction. The typical g-C3N4 foam was first constructed using surfactant foaming method, and then Cu(OH)2 nanosheets were loaded to prepare the Cu(OH)2/CNF composites with projects of electroless copper plating and hydrogen oxidation treatment. The structure and photocatalytic properties of the as-prepared samples were investigated. The results show that g-C3N4 foam and Cu(OH)2/CNF all demonstrate developed structures with 3D micron pore frameworks, which is conducive to improving CO2 diffusion and adsorption at dynamics during gas-solid catalytic process. The adsorption amounts of CO2 for g-C3N4 foam and Cu(OH)2/CNF are respectively 3.97 cm3/g and 3.59 cm3/g, which are 2.96 times and 2.68 times respectively higher than that of pure g-C3N4 powder. Moreover, many Cu(OH)2 nanosheets are also formed in the Cu(OH)2/CNF samples which provide a way to simultaneously broaden light absorption and form heterojunction between g-C3N4 and Cu(OH)2. This heterojunction can accelerate the separation of photo-generated e--h+ and make photo-generated electrons transfer from g-C3N4 to Cu(OH)2. As a result, the Cu(OH)2/CNF has demonstrated optimal photocatalytic activity with CO production rate at 11.041 μmol·g-1·h-1, which is 2.76 times and 6.83 times respectively higher than that of g-C3N4 foam and g-C3N4 powder.

N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, various experimental techniques and computational methods have been developed to characterize the transcriptome-wide landscapes of m6A modification for understanding its underlying mechanisms and functions in mRNA regulation. However, the experimental techniques are generally costly and time-consuming, while the existing computational models are usually designed only for m6A site prediction in a single-species and have significant limitations in accuracy, interpretability and generalizability. Here, we propose a highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously. Extensive computational experiments demonstrate the superior performances of MASS when compared to the state-of-the-art prediction methods. Furthermore, the contextual sequence features of m6A captured by MASS can be explained by the known critical binding motifs of the related RNA-binding proteins, which also help elucidate the similarity and difference among m6A features across species. In addition, based on the predicted m6A profiles, we further delineate the relationships between m6A and various properties of gene regulation, including gene expression, RNA stability, translation, RNA structure and histone modification. In summary, MASS may serve as a useful tool for characterizing m6A modification and studying its regulatory code. The source code of MASS can be downloaded from https://github.com/mlcb-thu/MASS.

Intelligent distributed protection and control is a promising route towards flexible and safe operation of the medium-voltage distribution network with widespread access to distributed energy resources. A fundamental premise of distributed decision-making is that each smart terminal unit (STU) can identify the topology of the feeder and track its changes. A distributed topology identification algorithm with high fault tolerance based on peer-to-peer communication is proposed in this paper. The STU configured at the looped network node requires no pre-set static topology of the feeder group, but only relies on local measurement and information exchange with neighboring STUs. Through rule judgment, it can realize the dynamic tracking and monitoring of the complete topology and open-loop mode of the feeder group, which reduces the configuration requirements of the algorithm on the terminal. Meanwhile, the method only needs each STU to exchange the local measurement and judgment information, which reduces the requirement of communication. Test examples show that the proposed method is reasonable and effective, and it can significantly improve the tolerance of topological identification to measurement errors by combining local measurement mutual checking and contralateral connectivity predictive correction.

Abstract Background Correct torque of the incisors is beneficial in the assessment of the effects of orthodontic treatment. However, evaluating this process effectively remains a challenge. Improper anterior teeth torque angle can cause bone fenestrations and exposure of the root surface. Methods A three-dimensional finite element model of the maxillary incisor torque controlled by a homemade four-curvature auxiliary arch was established. The four-curvature auxiliary arch placed on the maxillary incisors was divided into four different state groups, among which 2 groups had tooth extraction space retracted traction force set to 1.15 N. Initial displacements and pressure stresses of the periodontal tissue in the maxillary incisors and molars were calculated after torque forces (0.5, 1, 1.5, and 2 N) were applied to the teeth at different stable states. Results The effect of using the four-curvature auxiliary arch on the incisors was significant but did not affect the position of the molars. Given the absence of tooth extraction space, when the four-curvature auxiliary arch was used in conjunction with absolute anchorage, the recommended force value was