Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Progress and future prospects of decadal prediction and data assimilation:A review

年代际预测,也称为“近期气候预测”,旨在预测未来1-10年内的气候变化,是气候预测和气候变化研究领域的一个新关注点.它位于季节至年际预测和长期气候变化预测之间,结合了初值问题和外部强迫问题的两个方面.年代际预测的核心技术在于用于模式初始化的同化方法的准确性和效率,其目标是为模式提供准确的初始条件,其中包含观测到的气候系统内部变率,年代际预测的初始化通常涉及在耦合框架内同化海洋观测,其中观测到的信号通过耦合过程传递到其他分量,如大气和海冰.然而,最近的研究越来越关注在海洋-大气耦合模式中探索耦合数据同化(CDA),有人认为CDA有潜力显著提高年代际预测技巧.本文综合评述了该领域的三个方面的研究现状:初始化方法,年代际气候预测的可预测性和预测技巧,以及年代际预测的未来发展和挑战.

PREDICTION OF THE MIXING ENTHALPIES OF BINARY LIQUID ALLOYS BY MOLECULAR INTERACTION VOLUME MODEL

The mixing enthalpies of 23 binary liquid alloys are calculated by molecular interaction volume model （MIVM）, which is a two-parameter model with the partial molar infinite dilute mixing enthalpies. The predicted values are in agreement with the experimental data and then indicate that the model is reliable and convenient.

Quantitative prediction of the bitterness of atomoxetine hydrochloride and taste-masked using hydroxypropyl-β-cyclodextrin:A biosensor evaluation and interaction study

The bitterness of a drug is a major challenge for patient acceptability and compliance,especially for children.Due to the toxicity of medication,a human taste panel test has certain limitations.Atomoxetine hydrochloride(HCl),which is used for the treatment of attention deficit/hyperactivity disorder(ADHD),has an extremely bitter taste.The aim of this work is to quantitatively predict the bitterness of atomoxetine HCl by a biosensor system.Based on the mechanism of detection of the electronic tongue(Etongue),the bitterness of atomoxetine HCl was evaluated,and it was found that its bitterness was similar to that of quinine HCl.The bitterness threshold of atomoxetine HCl was 8.61μg/ml based on the Change of membrane Potential caused by Adsorption(CPA)value of the BT0 sensor.In this study,the taste-masking efficiency of 2-hydroxypropyl-β-cyclodextrin(HP-β-CyD)was assessed by Euclidean distances on a principle component analysis(PCA)map with the SA402B Taste Sensing System,and the host–guest interactions were investigated by differential scanning calorimetry(DSC),powder X-ray diffraction(XRD),nuclear magnetic resonance(NMR)spectroscopy and scanning electron microscopy(SEM).Biosensor evaluation and characterization of the inclusion complex indicated that atomoxetine HCl could actively react with 2-hydroxypropyl-β-cyclodextrin.

A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian trajectory prediction is rather challenging.Most existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many assumptions.In this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian trajectories.The RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling module.The temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different pedestrians.The spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current time.The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise.We conduct extensive experiments on several public datasets.The results demonstrate that our method outperforms many that are state-ofthe-art.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

A novel computational method for protein-protein interaction networks prediction of alpha-synuclein

Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.

Review of multimer protein–protein interaction complex topology and structure prediction

Protein–protein interactions (PPI) are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein–protein interactions. At the same time, understanding the complex structure of proteins helps to explore their function. And accurately predicting protein complexes from PPI networks helps us understand the relationship between proteins. In the past few decades, scholars have proposed many methods for predicting protein interactions and protein complex structures. In this review, we first briefly introduce the methods and servers for predicting protein interaction sites and interface residue pairs, and then introduce the protein complex structure prediction methods including template-based prediction and template-free prediction. Subsequently, this paper introduces the methods of predicting protein complexes from the PPI network and the method of predicting missing links in the PPI network. Finally, it briefly summarizes the application of machine/deep learning models in protein structure prediction and action site prediction.

A DAMAGE MODEL OF LIFE PREDICTION UNDER CREEP-FATIGUE INTERACTION

Based on the fundamental definition of damage and inelastic strain energy hypothesis, this paper presents an inelastic strain energy damage model under creepfatigue interaction condition, with the damage constitutional equations and life prediction formulae respectively described by strain and stress. Creepfatigue tests with notchedbar specimens were carried out at 550. The actual creepfatigue lives are in good agreement to the predicted lives according to inelastic strain energy damage model.

Fatigue-Creep Interaction Fracture Maps and Life Prediction under Combined Fatigue-Creep Stress Cycling

The purpose of this paper is to introduce sys- tematically the theory of fatigue-creep interaction fracture map and its application.

Click-Through Rate Prediction Network Based on User Behavior Sequences and Feature Interactions

In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.

Interacting mechanism and initiation prediction of multiple cracks

The maximum Mode Ⅰ and Mode Ⅱ stress intensity factors(SIFs), KI,kmax(θ) and KII,kmax(θ)(0°<θ<360°), of inclined parallel multi-crack varying with relative positions(including horizontal and vertical spacings) are calculated by the complex function and integration method to analyze their interacting mechanism and determine the strengthening and weakening zone of SIFs. The multi-crack initiation criterion is established based on the ratio of maximum tension-shear SIF to predict crack initiation angle, load, and mechanism. The results show that multi-crack always initiates in Mode Ⅰ and the vertical spacing is better not to be times of half crack-length for crack-arrest, which is in good agreement with test results of the red-sandstone cube specimens with three parallel cracks under uniaxial compression. This can prove the validity of the multi-crack initiation criterion.

Aircraft Trajectory Prediction Based on Modified Interacting Multiple Model Algorithm

In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

DeepCBA:A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.

Cancer Specific Non-Synonymous Single Nucleotide Polymorphism Prediction in the Context of Haplotype and Protein Interacting Sites

In this work, we study predicting the effect of non-synonymous SNPs on several cancers. We trained classifiers on both sequential and structural features extracted from the affected genes and assessed the predictions made by the trained classifiers using cross validation. Specifically, we investigated how the prediction performance can be improved by connecting SNPs in the context of haplotype and interacting sites of proteins encoded by affected genes. We found that accuracy was consistently enhanced by combining sequential and structural features, with increase ranging from a few percentage points up to more than 20 percentage points. The results for putting SNPs in the context of interacting sites were less consistent. Compared to individual SNPs, these that appear together in haplotype showed stronger correlation with one another and with the phenotype, and therefore led to significant improvement inprediction performance, with ROC score increased from 0.81 to 0.95. Although some similar effect has been expected for connecting SNPs to interacting sites in proteins, the performance actually got worse. This decrease in prediction accuracy may be caused by the small data set being used in the study, as many affected proteins in the study do not have known interacting sites.

Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits:Genetic Model for Diploid Plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction （NCI） as well as their genotype by environment （GE） interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction （AUP） method. Data on four quantitative traits （boll number, lint percentage, fiber length, and micronaire） in Upland cotton （Gossypium hirsutum L.） were analyzed as a worked example to show the effectiveness of the model.

Quantitative method to predict the energetics of helium-nanocavities interactions in metal systems based on electrophobic interaction

Energetics of helium-nanocavities interactions are crucial for unveiling underlying mechanisms of nanocavity evolution in nuclear materials.Nevertheless,it becomes intractable and even not feasible to obtain these energetics via atomic simulations with increasing nanocavity size and increasing helium content in nanocavities.Herein,a universal scaling law of helium-induced interaction energies in nanocavities in metal systems is proposed based on electrophobic interaction of helium.Based on this scaling law and ab-initio calculations,a predictive method for binding energies of helium and displacement defects to nanocavities of arbitrary sizes and with different helium/vacancy ratios is established for BCC iron as a representative and validated by atomic simulations.This predictive method reveals that the critical helium/vacancy ratio for helium-enhanced vacancy binding to nanocavities in-creases with increasing nanocavity size,and the helium/vacancy ratio giving the highest stability of nanocavities is about 1.6.The Ostwald ripening of nanocavities is delayed by helium to higher temper-atures due to reduced vacancy de-trapping rates from nanocavities.The proposed scaling law can be generalized to many metal systems studied in the nuclear materials community.Being readily coupled into mesoscale models of irradiation damages,this predictive method facilitates clarifying helium role in cavity swelling of metallic nuclear materials.

DDI-Transform:A neural network for predicting drug-drug interaction events

Drug-drug interaction(DDI)event prediction is a challenging problem,and accurate prediction of DDI events is critical to patient health and new drug development.Recently,many machine learning-based techniques have been proposed for predicting DDI events.However,most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information.To address these limitations,we propose a DDI-Transform neural network framework for DDI event prediction.In DDI-Transform,we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information.A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning,thus adaptively selecting the effective feature information for prediction.The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models.Results on different scale datasets confirm the robustness of the method.

QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut(Arachis hypogaea L.)

To dissect the genetic mechanism of multi-seed pod in peanut, we explored the QTL/gene controlling multi-seed pod and analyzed the interaction effect of QTL and environment. Two hundred and forty eight recombinant inbred lines(RIL) from cross Silihong × Jinonghei 3 were used as experimental materials planted in 8 environments from 2012 to 2017. Three methods of analysis were performed. These included individual environment analysis, joint analysis in multiple environments, and epistatic interaction analysis for multi-seed pod QTL. Phenotypic data and best linear unbiased prediction(BLUP) value of the ratio of multi-seed pods per plant(RMSP) were used for QTL mapping. Seven QTL detected by the individual environmental mapping analysis and were distributed on linkage groups 1, 6, 9, 14, 19(2), and 21. Each QTL explained 4.42%–11.51% of the phenotypic variation in multi-seed pod, and synergistic alleles of5 QTL were from the Silihong parent. One QTL, explaining 4.93% of the phenotypic variation was detected using BLUP data, and this QTL mapped in the same interval as q RMSP19.1 detected in the individual environment analysis. Seventeen additive QTL were identified by joint analysis across multiple environments. A total of 43 epistatic QTL were detected by ICIM-EPI mapping in the multiple environment trials(MET) module, and involved 57 loci. Two main-effect QTL related to multi-seed pod in peanut were filtered. We also found that RMSP had a highly significant positive correlation with pod yield per plant(PY), and epistatic effects were much more important than additive effects. These results provide theoretical guidance for the genetic improvement of germplasm resources and further fine mapping of related genes in peanut.