BACKGROUND: Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence that increases the complexity of generation, while adding leaving groups only considers those in the training set, which leads to poor generalization.
RESULTS: In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Given that chemically meaningful motifs fall between the size of atoms and leaving groups, our model achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups. We evaluate our proposed model on a benchmark dataset and show that it significantly outperforms previous state-of-the-art models. Furthermore, we conduct ablation studies to investigate the contribution of each component of our proposed model to the overall performance on benchmark datasets. Experiment results demonstrate the effectiveness of our model in predicting retrosynthesis pathways and suggest its potential as a valuable tool in drug discovery.
METHODS: All code and data are available at https://github.com/szu-ljh2020/MARS.

Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA\'s structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations.

RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.

This paper introduces the market framing bias (MFB): a framing effect that affects the return-risk tradeoff under different frameworks of aggregate market losses and profits, which is measured by the absolute difference between betas in the rising and falling markets. The paper finds that the MFB can predict lower future stock return on the cross-section. Specifically, after controlling for various firm-specific characteristics, this predictive power of the FMB declines over time. Furthermore, the predictive power of the FMB is stable in the short term even after controlling for various pricing factors and firm-specific characteristics.

In order to realize the lightweight design of mobile pump truck, this paper takes the frame of a certain type of mobile pump truck as the research object. The response surface method is used to carry out lightweight design of the longitudinal beam structure of the frame, and the finite element method is used to establish the finite element model to compare and analyze the optimized and original designs. The results show that the height, width and thickness of the optimized longitudinal beam section are reduced by 10mm, 11mm, and 0.8mm respectively, and the weight of the whole frame is reduced by 35.8kg. Before and after optimization, the displacement and stress changes of the frame are small in four motion situations, which meet the lightweight requirements of optimization design.

Dysregulation of alternative splicing is a key molecular hallmark of cancer. However, the common features and underlying mechanisms remain unclear. Here, we report an intriguing length-dependent splicing regulation in cancers. By systematically analyzing the transcriptome of thousands of cancer patients, we found that short exons are more likely to be mis-spliced and preferentially excluded in cancers. Compared to other exons, cancer-associated short exons (CASEs) are more conserved and likely to encode in-frame low-complexity peptides, with functional enrichment in GTPase regulators and cell adhesion. We developed a CASE-based panel as reliable cancer stratification markers and strong predictors for survival, which is clinically useful because the detection of short exon splicing is practical. Mechanistically, mis-splicing of CASEs is regulated by elevated transcription and alteration of certain RNA binding proteins in cancers. Our findings uncover a common feature of cancer-specific splicing dysregulation with important clinical implications in cancer diagnosis and therapies.

The introduction of frameshifting non-3n indels enables the identification of gene-trait associations. However, it has been hypothesised that recovery of the original reading frame owing to usage of non-canonical splice forms could cause rescue. To date there is very little evidence for organism-level rescue by such a mechanism and it is unknown how commonly indels induce, or are otherwise associated with, frame-restoring splice forms. We perform CRISPR/Cas9 editing of randomly selected loci in rice to investigate these issues. We find that the majority of loci have a frame-restoring isoform. Importantly, three quarters of these isoforms are not seen in the absence of the indels, consistent with indels commonly inducing novel isoforms. This is supported by analysis in the context of NMD knockdowns. We consider in detail the two top rescue candidates, in wax deficient anther 1 (wda1) and brittle culm (bc10), finding that organismal-level rescue in both cases is strong but owing to different splice modification routes. More generally, however, as frame-restoring isoforms are low abundance and possibly too disruptive, such rescue we suggest to be the rare exception, not the rule. Nonetheless, assuming that indels commonly induce frame-restoring isoforms, these results emphasize the need to examine RNA level effects of non-3n indels and suggest that multiple non-3n indels in any given gene are advisable to probe a gene\'s trait associations.

Epidermal growth factor receptor (EGFR) mutations are associated with response of tyrosine kinase inhibitors (TKIs) for patients with advanced non-small cell lung cancer (NSCLC). However, the existing methods for detection of samples having rare mutations(i.e. ~0.01%) have limits in terms of specificity, time consumption or cost. In the current study, novel wild-type blocking (WTB) oligonucleotides modified with phosphorothioate or inverted dT at the 5\'-termini were designed to precisely detect 11 common deletion mutations in exon 19 of EGFR gene (E19del) using a WTB-PCR assay. And internal competitive leptin amplifications were further applied to enhance the specificity of the WTB-PCR system. Our results showed that WTB-PCR could completely block amplification of wild-type EGFR when 200 ng of DNA was used as template. Furthermore, the current WTB-PCR assay facilitated the detection of E19del mutations with a selectivity of 0.01% and sensitivity as low as a single copy. And, the results showed that the current WTB-PCR system exceeded detection limits afforded by the ARMS-PCR assay. In conclusion, the current WTB-PCR strategy represents a simple and cost-effective method to precisely detect various low-abundance deletion mutations.

Orphan genes that lack detectable homologues in other lineages could contribute to a variety of biological functions. However, their origination and function mechanisms remain largely unknown. Herein, through a comprehensive and systematic computational pipeline, we identified 893 orphan genes in the lineage of C. elegans, of which only a low fraction (0.9%) were derived from transposon elements. Six new protein-coding genes that de novo originated from non-coding DNA sequences in the genome of C. elegans were also identified. The authenticity and functionality of these orphan genes and de novo genes are supported by three lines of evidences, consisting of transcriptional data, and in silico proteomic data, and the fixation status data in wild populations. Orphan genes and de novo genes exhibited simple gene structures, such as, short in protein length, of fewer exons, and are frequently X-linked. RNA-seq data analysis showed these orphan genes are enriched with expression in embryo development and gonad, and their potential function in early development was further supported by gene ontology enrichment analysis results. Meanwhile, de novo genes were found to be with significant expression in gonad, and functional enrichment analysis of the co-expression genes of these de novo genes suggested they may be functionally involved in signaling transduction pathway and metabolism process. Our results presented the first systematic evidence on the evolution of orphan genes and de novo origin of genes in nematodes and their impacts on the functional and phenotypic evolution, and thus could shed new light on our appreciation of the importance of these new genes.