Effects of chlorantraniliprole on the life history traits of fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae)

Introduction:Spodoptera frugiperda is an important nomadic agricultural pest with a diverse host range and resistance against several insecticides. The current study investigated the life history traits of two strains of the field-collected population against chlorantraniliprole using an age-stage two-sex life table.Method: For this, we established the chlorantraniliprole-susceptible (Crp-SUS G12), and chlorantraniliprole-reduced susceptible (Crp-RES G12) strains derived from the sixth generation of the QJ-20 population having a resistance ratio (RR) of 10.39-fold, compared with the reported susceptible population.Results: The results showed that the chlorantraniliprole-reduced susceptible strain attained a 4.0-fold RR, while the chlorantraniliprole-susceptible strain attained an RR of 0.85-fold, having overlapped fiducial limits (FLs) with the referred susceptible baseline. Meanwhile, the present study revealed that the development time of the susceptible strain was significantly longer than that of the reduced susceptible strain. Similarly, the mean longevity, adult pre-oviposition period (APOP), and total pre-oviposition period (TPOP) of the female chlorantraniliprole-susceptible strain were considerably longer than those of the female chlorantraniliprole-reduced susceptible strain. Contrarily, the population parameters, including the intrinsic rate of increase (r), finite rate of increase (λ), and net reproductive rate (R), of the chlorantraniliprole-susceptible strain were considerably lower than those of the chlorantraniliprole-reduced susceptible strain, while the mean generation time (T) of the chlorantraniliprole-susceptible strain was substantially longer than the chlorantraniliprole-reduced susceptible strain. The age-stage characteristic survival rate (sxj) and age-stage characteristic life expectancy (exj) of the chlorantraniliprole-susceptible strain were longer than those of the chlorantraniliprole-reduced susceptible strain, but the age-stage-specific reproductive value (vxj) of the chlorantraniliprole-susceptible strain was shorter than that of the chlorantraniliprole-reduced susceptible strain. Moreover, the contents of vitellogenin (Vg) and VgR in the chlorantraniliprole-reduced susceptible strain were higher than those in the chlorantraniliprole-susceptible strain.Discussion: These findings showed that reducing susceptibility to chlorantraniliprole promoted population growth in S. frugiperda. Therefore, this study could provide conceptual support for the integrated pest management (IPM) approach to control S. frugiperda in the field

Comparative Analysis for the Performance of Variant Calling Pipelines on Detecting the de novo Mutations in Humans

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Source data for plots in manuscript Integrative Residue-intuitive Machine Learning and Molecular Dynamics Approach to Unveil Allosteric Site and Mechanism for G-protein Couple Receptors

Source data for plots in manuscript Integrative Residue-intuitive Machine Learning and Molecular Dynamics Approach to Unveil Allosteric Site and Mechanism for G-protein Couple Receptors</p

data-test.xlsx

测试数据</p

The complete mitogenome of Smith’s shrew (Chodsigoa smithii)

The Smith’s shrew (Chodsigoa smithii) belongs to subfamily Soricinae, which is an endemic shrew to China. In this study, we obtained the complete mitochondrial genome of the C. smithii. This mitogenome is a circular molecule with 17,108 bp in length, containing 13 protein-coding genes, 22 transfer RNA genes, two ribosome RNA genes, one light strand replication origin (OL), one non-coding region, and with a base composition of 32.5% A, 29.3% T, 24.8% C, and 13.4% G. The nucleotide sequence data of 13 protein-coding genes of C. smithii and other 19 Soricomorpha species were used for phylogenetic analyses. Phylogenetic tree shows that Soricinae includes two major phylogenetic lineages. Chodsigoa smithii is located as a basal position in tribe Nectogalini

Data-driven machine learning models for the quick and accurate prediction of thermal stability properties of OLED materials

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher-quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties genernally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than one thousand samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of MAE, RMSE, and R2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models were further tested by out-of-sample data, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based model. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties

A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening

Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.<br /

A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom&ndash;up and top&ndash;down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application