QTL IciMapping:Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations

QTL Ici Mapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci(QTL) in biparental populations. Eight functionalities are integrated in this software package:(1) BIN: binning of redundant markers;(2) MAP: construction of linkage maps in biparental populations;(3) CMP: consensus map construction from multiple linkage maps sharing common markers;(4) SDL: mapping of segregation distortion loci;(5) BIP: mapping of additive, dominant, and digenic epistasis genes;(6) MET: QTL-by-environment interaction analysis;(7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and(8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL,and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci,and to perform analysis of variance for multi-environmental trials.

Ordering of high-density markers by the k-Optimal algorithm for the traveling-salesman problem

Construction of accurate and high-density linkage maps is a key research area of genetics.We investigated the efficiency of genetic map construction(MAP)using modifications of the k-Optimal(k-Opt)algorithm for solving the traveling-salesman problem(TSP).For TSP,different initial routes resulted in different optimal solutions.The most optimal solution could be found only by use of as many initial routes as possible.But for MAP,a large number of initial routes resulted in one optimal order.k-Opt using open route length gave a slightly higher proportion of correct orders than the method of adding one virtual marker and using closed route length.Recombination frequency(REC)and logarithm of odds(LOD)score gave similar proportions of correct order,higher than that given by genetic distance.Both missing markers and genotyping error reduced ordering accuracy,but the best order was still achieved with high probability by comparison of the optimal orders from multiple initial routes.Computation time increased rapidly with marker number,and 2-Opt took much less time than 3-Opt.The 2-Opt algorithm was compared with ordering methods used in two other software packages.The best method was 2-Opt using open route length as the criterion to identify the optimal order and using REC or LOD as the measure of distance between markers.We describe a unified software interface for using k-Opt in high-density linkage map construction for a wide range of genetic populations.

Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis

Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm.Recently,a computer simulation tool called QuMARS has been developed,which allows the simulation and optimization of various recurrent selection strategies.Our major objective in this study was to use the QuMARS tool to compare phenotypic recurrent,marker-assisted recurrent,and genomic selections(abbreviated respectively as PS,MARS and GS)for both short-and long-termbreeding procedures.ForMARS,twomarker selection models were considered,i.e.,stepwise(Rstep)and forward regressions(Forward).For GS,three prediction models were considered,i.e.,genomic best linear unbiased predictors(GBLUP),ridge regression(Ridge),and regression by Moore-Penrose general inverse(InverseMP).To generate genotypes and phenotypes for a given individual during simulation,one additive and two epistasis genetic models were considered with three levels of heritability.Results demonstrated that selection responses from GBLUP-based GS and MARS(Forward)were consistently greater than those from PS under the additive model,particularly in early selection cycles.In contrast,selection response from PS was consistently superior over MARS and GS under epistatic models.For the two epistasis models,total genetic variance and the additive variance component were increased in some cases after selection.Through simulation,we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci(QTL).QuMARS provides an opportunity for breeders to compare,optimize and integrate new technology into their conventional breeding programs.

Hyperbolic metamaterials for high-efficiency generation of circularly polarized Airy beams

Metasurfaces have exhibited considerable capability for generating Airy beams.However,the available plasmonic/dielectric metasurfaces Airy-beam generators have low transmission efficiency and/or poor quality of generated beam because they lack the amplitude modulation.Hyperbolic metamaterials(HMMs)have recently provided an alternative strategy for building high-performance meta-devices that are capable of flexibly modulating the phase,amplitude and polarization state of light.Here we reveal that both the propagation phase and the Pancharatnam-Berry phase can contribute to the local transmission phase of circularly polarized electromagnetic waves by using HMMs.This thus provides us with great freedom to design HMM units with different cross-sections to independently control the transmission phase and amplitude.Here,we design circularly polarized Airy-beam generators in the microwave and near-infrared domains,which require binary phase and polynary amplitude,and validate the good performance in the microwave experiment.Our work can facilate the generation of a complicated light field that highly requires independent and complete control of the transmission phase and amplitude under circularly polarized incidence.

Multiple SiGe/Si layers epitaxy and SiGe selective etching for vertically stacked DRAM

Fifteen periods of Si/Si_(0.7)Ge_(0.3)multilayers(MLs)with various Si Ge thicknesses are grown on a 200 mm Si substrate using reduced pressure chemical vapor deposition(RPCVD).Several methods were utilized to characterize and analyze the ML structures.The high resolution transmission electron microscopy(HRTEM)results show that the ML structure with 20 nm Si_(0.7)Ge_(0.3)features the best crystal quality and no defects are observed.Stacked Si_(0.7)Ge_(0.3)ML structures etched by three different methods were carried out and compared,and the results show that they have different selectivities and morphologies.In this work,the fabrication process influences on Si/Si Ge MLs are studied and there are no significant effects on the Si layers,which are the channels in lateral gate all around field effect transistor(L-GAAFET)devices.For vertically-stacked dynamic random access memory(VS-DRAM),it is necessary to consider the dislocation caused by strain accumulation and stress release after the number of stacked layers exceeds the critical thickness.These results pave the way for the manufacture of high-performance multivertical-stacked Si nanowires,nanosheet L-GAAFETs,and DRAM devices.

新农科背景下大学化学课程融入思政的探索与实践

大学化学是一门具有哲学属性的课程,蕴含着丰富的思政元素。通过对教学内容的拓展、课程思政元素的挖掘和教学案例的撰写,有目的地将思政元素融入到课程教学的全过程,突出隐性思政的教学功能,做到时时处处事事育人,逐步实现知识传授、能力培养和价值塑造的教学目标。

新高考与大类招生背景下的基础化学实验教学改革思考与实践

基础化学实验是农林高校一门重要的公共基础课程,在培养学生专业知识技能、锻炼实践动手能力、提升科学素养和综合实践能力方面有着重要的意义。我校基础化学实验已开设20年,在新高考和大类招生的背景下,进一步总结实践教学中的经验有助于改进教学、提升教学质量和课程质量。同时,在新时代的背景下,加强基础化学实验课程建设对推进我校“双一流”和“新农科”建设,创新型人才的培养有着重要的现实意义。

Extraction and Purification Process Optimization and Antioxidant Activity in vitro of Flavonoids in Amaranthus caudatus L.

[Objectives]To explore the optimal extraction and purification process of the flavonoids in Amaranthus caudatus L.and to study the antioxidant activity in vitro of the flavonoids in A.caudatus.[Methods]Taking A.caudatus as the raw material,flavonoids were extracted by alcohol extraction method,and AB-8 macroporous adsorption resin was selected for purification.The hydroxyl radical scavenging ability,DPPH radical scavenging ability,and O^2-radical scavenging ability were used as evaluation indicators,to explore the antioxidant activity in vitro of the flavonoids in A.caudatus.[Results]The optimal extraction process conditions of flavonoids in A.caudatus are:liquid-to-material ratio 40:1,extraction temperature 60℃,ethanol concentration 60%,ultrasonic power 320 W,extraction time 50 min.Under these conditions,the extraction yield of flavonoids in A.caudatus is(1.35±0.01)%.The optimal purification process conditions of flavonoids in A.caudatus are 2.5 g AB-8 macroporous adsorption resin,sample volume 5 mL,mass concentration of adsorption solution 1.60 mg/mL,pH value of adsorption solution 3.0,sample flow rate 3 BV/h,ethanol concentration in desorption process is 70%and the desorption flow rate is 3 BV/h.Under these conditions,the recovery rate reaches 88.35%±0.68%.[Conclusions]A.caudatus has a high content of flavonoids and has excellent free radical scavenging ability in vitro.This study is intended to provide important technical support for the research of flavonoid activity of A.caudatus and the development of functional products.

Molecular phenotypes reveal heterogeneous engraftments of patient-derived hepatocellular carcinoma xenografts

Objective:Patient-derived xenograft(PDX)models provide a promising preclinical platform for hepatocellular carcinoma(HCC).However,the molecular features associated with successful engraftment of PDX models have not been revealed.Methods:HCC tumor samples from 76 patients were implanted in immunodeficient mice.The molecular expression was evaluated by immunohistochemistry.Patient and tumor characteristics as well as tumor molecular expressions were compared for PDX engraftment using the Chi-square test.The independent prediction parameters were identified by logistic regression analyses.Results:The engraftment rate for PDX models from patients with HCC was 39.47%(30/76).Tumors from younger patients and patients with elevated preoperative alpha-fetoprotein level had higher engraftment rates.Tumors with poor differentiation and vascular invasion were related to engraftment success.The positive expression of CK19,CD133,glypican-3(GPC3),and Ki67 in tumor samples was associated with engraftment success.Logistic regression analyses indicated that GPC3 and Ki67 were two of the strongest predictors of PDX engraftment.Tumors with GPC3/Ki67 phenotypes showed heterogeneous engraftment rates,with 71.9%in GPC3^(+)/Ki67^(+)tumors,30.8%in GPC3^(-)/Ki67^(+)tumors,15.0%in GPC3^(+)/Ki67^(-)tumors,and 0 in GPC3^(-)/Ki67^(-)tumors.Conclusions:Successful engraftment of HCC PDXs was significantly related to molecular features.Tumors with the GPC3+/Ki67+phenotype were the most likely to successfully establish HCC PDXs.

A non-two-dimensional van der Waals In Se semispherical array grown by vapor-liquid-solid method for hydrogen evolution

Two-dimensional(2D)layered materials with layer-number dependent properties are promising candidates for next-generation noble-metal-free electrocatalytic reaction.However,the main group metal chalcogenides(MMCs)used for this purpose are rarely explored.Herein,we report the controlled growth of indium selenide(In Se)with a novel morphology(semispherical array)on a silicon substrate and its application in hydrogen evolution reaction(HER).The formation of the spherical InSe is explained with a vapor-liquid-solid growth mechanism,in which the distribution and size of the spheres could be facilely tuned by the reaction parameters.The InSe semispherical array was demonstrated as more efficient catalyst for HER than the flake-like 2D InSe counterparts,originating from the fully exposed InSe spherical surface with abundant adsorbing sites and the high crystalline quality for electron transport.This work provides a controlled synthesis way of the layered In Se with a distinct spherical morphology used for the electrocatalysis applications and could be extended to other main group metal chalcogenides.

Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling

With the increasing number of sequenced species,phylogenetic profiling(PP)has become a powerful method to predict functional genes based on co-evolutionary information.However,its potential in plant genomics has not yet been fully explored.In this context,we combined the power of machine learning and PP to identify salt stress-related genes in a halophytic grass,Spartina alterniflora,using evolutionary information generated from 365 plant species.Our results showed that the genes highly co-evolved with known salt stress-related genes are enriched in biological processes of ion transport,detoxification and metabolic pathways.For ion transport,five identified genes coding two sodium and three potassium transporters were validated to be able to uptake Na?.In addition,we identified two orthologs of trichome-related AtR3-MYB genes,SaCPC1 and SaCPC2,which may be involved in salinity responses.Genes co-evolved with SaCPCs were enriched in functions related to the circadian rhythm and abiotic stress responses.Overall,this work demonstrates the feasibility of mining salt stress-related genes using evolutionary information,highlighting the potential of PP as a valuable tool for plant functional genomics.

Smart Breeding Platform:A web-based tool for high-throughput population genetics,phenomics,and genomic selection

Dear Editor,In the era of big data and artificial intelligence,"smart breeding"has become a broad conceptual framework encompassing the paradigm shift of crop breeding to relying on analysis of high-throughput population genetics and phenomics data to conduct genomic selection,allowing identification and optimal use of the genetic potential in crop species(Xiao et al.,2022;Xu et al.,2022;Wang et al.,2023).Most existing tools for analyzing high-throughput breeding data require extensive computational power,complex installation processes,and command-line expertise and are therefore challenging and inconvenient for the majority of researchers and breeders(Brandies and Hogg,2021).

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation,followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and,more recently,by incorporation of molecular marker genotypes.However,plant performance or phenotype(P)is determined by the combined effects of genotype(G),envirotype(E),and genotype by environment interaction(GEI).Phenotypes can be predicted more precisely by training a model using data collected from multiple sources,including spatiotemporal omics(genomics,phenomics,and enviromics across time and space).Integration of 3D information profiles(G-P-E),each with multidimensionality,provides predictive breeding with both tremendous opportunities and great challenges.Here,we first review innovative technologies for predictive breeding.We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy,particularly envirotypic data,which have largely been neglected in data collection and are nearly untouched in model construction.We propose a smart breeding scheme,integrated genomic-enviromic prediction(iGEP),as an extension of genomic prediction,using integrated multiomics information,big data technology,and artificial intelligence(mainly focused on machine and deep learning).We discuss how to implement iGEP,including spatiotemporal models,environmental indices,factorial and spatiotemporal structure of plant breeding data,and cross-species prediction.A strategy is then proposed for prediction-based crop redesign at both the macro(individual,population,and species)and micro(gene,metabolism,and network)scales.Finally,we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives.We call for coordinated efforts in smart breeding through iGEP,institutional partnerships,and innovative technological support.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms.

Immunopathogenesis and immunomodulatory therapy for myocarditis

Myocarditis is an inflammatory cardiac disease characterized by the destruction of myocardial cells, infiltration of interstitial inflammatory cells, and fibrosis, and is becoming a major public health concern. The aetiology of myocarditis continues to broaden as new pathogens and drugs emerge. The relationship between immune checkpoint inhibitors, severe acute respiratory syndrome coronavirus 2, vaccines against coronavirus disease-2019, and myocarditis has attracted increased attention. Immunopathological processes play an important role in the different phases of myocarditis, affecting disease occurrence, development, and prognosis. Excessive immune activation can induce severe myocardial injury and lead to fulminant myocarditis,whereas chronic inflammation can lead to cardiac remodelling and inflammatory dilated cardiomyopathy. The use of immunosuppressive treatments, particularly cytotoxic agents, for myocarditis, remains controversial. While reasonable and effective immunomodulatory therapy is the general trend. This review focuses on the current understanding of the aetiology and immunopathogenesis of myocarditis and offers new perspectives on immunomodulatory therapies.

内部控制源对创业意向的影响研究——计划行为理论的视角

创业意向的研究具有重要的现实意义,相关研究主要从个体因素和环境因素两个方面来分析创业意向的影响因素。本研究基于计划行为理论,以内部控制源为解释变量,构建了创业意向影响因素的实证模型。基于江苏某高校在校生问卷调查的数据,借助Mplus软件的数据分析结果显示:内部控制倾向对创业意向具有显著正向影响;内部控制源对创业态度、主观规范感知和自我效能感具有促进作用;创业态度、主观规范感知、自我效能感能够对提高创业意向产生积极影响,并在内部控制源与创业意向的关系中发挥中介作用;创业态度的中介作用显著大于主观规范感知和自我效能感的中介作用。

The p53/miR-34 axis in development and disease

肿瘤 suppressor p53 是在人的癌症的最经常变异的基因之一。MicroRNAs (miRNAs ) 是在 post-transcriptional 水平上调整基因表示的小 non-proteincoding RNA。最近， p53 调整几 miRNAs 的 theexpression，这被显示出，从而代表 p53 发信号的重要机制。几个独立人士作为最流行的导致 p53 的 miRNAs 学习 miR-34 家庭的 identifiedthe 成员。miR-34s 经常是在 tumorentities 的变化的 silenced，建议他们是重要肿瘤 suppressors。确实， miR-34s 的宫外的表示禁止增长，对各种各样的癌症房间实体的间充质的转变，移植，侵略，和转移上皮。而且， miR-34 的 deliveryor 重新表示在癌症老鼠模型导致肿瘤生长和转移的著名压抑，并且可以 thereforerepresent 为未来癌症治疗学的有效策略。除他们在癌症的关键函数以外， themiR-34 家庭的成员也在精子发生，干细胞区别， neuronal 开发，老化，和 cardiovascularfunctions 起重要作用。因而， miR-34 也在各种各样的非癌的疾病被含有，例如大脑混乱，骨质疏松症，和心血管的复杂并发症。

The Statistical Power of Inclusive Composite Interval Mapping in Detecting Digenic Epistasis Showing Common F_2 Segregation Ratios

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping （ICIM） is able to identify epistatic quantitative trait loci （QTLs） no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate （FDR） of ICIM epistatic mapping, by considering F2 and doubled haploid （DH） populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd （LOD） threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping （MIM） in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice （Oryza sativa L.）.

Lateral Hypothalamic Area Glutamatergic Neurons and Their Projections to the Lateral Habenula Modulate the Anesthetic Potency of Isoflurane in Mice

The lateral hypothalamic area(LHA)plays a pivotal role in regulating consciousness transition,in which orexinergic neurons,GABAergic neurons,and melanin-concentrating hormone neurons are involved.Glutamatergic neurons have a large population in the LHA,but their anesthesia-related effect has not been explored.Here,we found that genetic ablation of LHA glutamatergic neurons shortened the induction time and prolonged the recovery time of isoflurane anesthesia in mice.In contrast,chemogenetic activation of LHA glutamatergic neurons increased the time to anesthesia and decreased the time to recovery.Optogenetic activation of LHA glutamatergic neurons during the maintenance of anesthesia reduced the burst suppression pattern of the electroencephalogram(EEG)and shifted EEG features to an arousal pattern.Photostimulation of LHA glutamatergic projections to the lateral habenula(LHb)also facilitated the emergence from anesthesia and the transition of anesthesia depth to a lighter level.Collectively,LHA glutamatergic neurons and their projections to the LHb regulate anesthetic potency and EEG features.