Transcriptomic analysis reveals the defense mechanisms of citrus infested with Diaphorina citri

Huanglongbing(HLB) is a devastating disease that has led to an acute crisis for growers of citrus, one of the world's most important fruit crops. The phloem-feeding Asian citrus psyllid(ACP), Diaphorina citri, is the main pest at the new shoot stage and is the only natural vector of HLB pathogenic bacteria. Little is known about how plants perceive and defend themselves from this destructive pest. Here, we characterized changes in the expression of various genes in citrus plants that were continuously infested by D. citri for different durations(12, 24, and 48 h). A total of 5 219 differentially expressed genes(DEGs) and 643 common DEGs were identified across all time points. Several pathways related to defense were activated, such as peroxisome, alpha-linolenic acid metabolism, and phenylpropanoid and terpenoid biosynthesis, and some pathways related to growth and signal transduction were suppressed in response to D. citri infestation. The expression of genes including kinases(CML44, CIPK6, and XTH6), phytohormones(SAMT, LOX6, and NPR3), transcription factors(bHLH162, WRKY70, and WRKY40), and secondary metabolite synthesis-related genes(PAL, 4CL2, UGT74B1 and CYP82G1) was significantly altered in response to D. citri infestation. The findings of this study greatly enhance our understanding of the mechanisms underlying the defense response of citrus plants to D. citri infestation at the molecular level. Functional characterization of the candidate defense-related genes identified in this study will aid the molecular breeding of insect-resistant citrus varieties.

船舶厨房通风系统的设计及布置

在船舶厨房通风系统的计算、设备如何选型及规范对厨房通风系统设计的相关要求等方面介绍船舶厨房通风系统设计布置的方法。

A kinetic description of the impact of agent competence and psychological factors on investment decision-making

The kinetic theory is employed to analyze influence of agent competence and psychological factors on investment decision-making.We assume that the wealth held by agents in the financial market is non-negative,and agents set their own investment strategies.The herding behavior is considered when analyzing the impact of an agent's psychological factors on investment decision-making.A nonlinear Boltzmann model containing herding behavior,agent competence and irrational behavior is employed to investigate investment decision-making.To characterize the agent's irrational behavior,we utilize a value function which includes current and ideal-investment decisions to describe the agent's irrational behavior.Employing the asymptotic procedure,we obtain the Fokker-Planck equation from the Boltzmann equation.Numerical results and the stationary solution of the obtained Fokker-Planck equation illustrate how herding behavior,agent competence,psychological factors,and irrational behavior affect investment decision-making,i.e.,herding behavior has both advantages and disadvantages for investment decision-making,and the agent's competence to invest helps the agent to increase income and to reduce loss.

Equivalence of Uniqueness in Law and Joint Uniqueness in Law for SDEs Driven by Poisson Processes

We give an extension result of Watanabe’s characterization for 2-dimensional Poisson processes. By using this result, the equivalence of uniqueness in law and joint uniqueness in law is proved for one-dimensional stochastic differential equations driven by Poisson processes. After that, we give a simplified Engelbert theorem for the stochastic differential equations of this type.

Metabolic Profiling in Banana Pseudo-Stem Reveals a Diverse Set of Bioactive Compounds with Potential Nutritional and Industrial Applications

Banana(Musa spp.)is an ancient and popular fruit plant with highly nutritious fruit.The pseudo-stem of banana represents on average 75%of the total dry mass but its valorization as a nutritional and industrial by-product is limited.Recent advances in metabolomics have paved the way to understand and evaluate the presence of diverse sets of metabolites in different plant parts.This study aimed at exploring the diversity of primary and secondary metabolites in the banana pseudo-stem.Hereby,we identified and quantified 373 metabolites from a diverse range of classes including,alkaloids,flavonoids,lipids,phenolic acids,amino acids and its derivatives,nucleotide and its derivatives,organic acids,lignans and coumarins,tannins,and terpene using the widely-targeted metabolomics approach.Banana pseudo-stem is enriched in metabolites for utilization in the food industry(L-lysine and L-tryptophan,L-glutamic acid,Phenylalanine,Palmitoleic acid,α-Linolenic acid,and Lauric acid,and Adenine)and pharmaceutical industry(Guanosine and Cimidahurinine,Bergapten,Coumarins,Procyanidin A2,Procyanidin B1,Procyanidin B3,Procyanidin B2,and Procyanidin B4,Asiatic acid).The metabolome of banana pseudo-stem with integration across multiomics data may provide the opportunity to exploit the rich metabolome of banana pseudo-stem for industrial and nutritional applications.

Is There any Hexazirconium Cluster in the Compounds [HP(t-Bu)_2Ph_3]_3[Zr_6Cl_(18)H_5] and Zr_6Cl_(14)(PPr_3)_4H_4?

The [Zr6(μ2-Cl)12Cl6H4]3- ion with C4v Symmetry in the title compounds has been calculated by DV-Xα program. The calculated results show that ther is almost no available covalence bonding between zirconium atoms and significant Zr 4d AO compositions in the MOs. but there are much stronger Zr-Cl(bridging) bonding. All analysis results suggest that these compounds would be the cluster with Zr6Cl12 cage linked by Zr-Cl bridging bonding plus six Cl teminal ligands rather than the hexazirconium cluster in view of the chemical bonding.

KeyStone内存架构

随着全球大量的资料强烈冲击无线和有线网络，营运商面临着严峻的挑战，需要不断推出能满足当前与未来需求的网络。因此，通信基础建设设备制造商致力于降低每bit成本和功耗的同时，也不断寻求能够满足当前及未来需求的核心技术。TI最新推出的新型KeyStone多核心SoC架构即能满足这些挑战。

Two haplotype-resolved genome assemblies for AAB allotriploid bananas provide insights into banana subgenome asymmetric evolution and Fusarium wilt control

Bananas(Musa spp.)are one of the world’s most important fruit crops and play a vital role in food security for many developing countries.Most banana cultivars are triploids derived from inter-and intraspecific hybrid-izations between the wild diploid ancestor species Musa acuminate(AA)and M.balbisiana(BB).We report two haplotype-resolved genome assemblies of the representative AAB-cultivated types,Plantain and Silk,and precisely characterize ancestral contributions by examining ancestry mosaics across the genome.Widespread asymmetric evolution is observed in their subgenomes,which can be linked to frequent homol-ogous exchange events.We reveal the genetic makeup of triploid banana cultivars and verify that subge-nome B is a rich source of disease resistance genes.Only 58.5%and 59.4%of Plantain and Silk genes,respectively,are present in all three haplotypes,with>50%of genes being differentially expressed alleles in different subgenomes.We observed that the number of upregulated genes in Plantain is significantly higher than that in Silk at one-week post-inoculation with Fusarium wilt tropical race 4(Foc TR4),which con-firms that Plantain can initiate defense responses faster than Silk.Additionally,we compared genomic and transcriptomic differences among the genes related to carotenoid synthesis and starch metabolism between Plantain and Silk.Our study provides resources for better understanding the genomic architecture of culti-vated bananas and has important implications for Musa genetics and breeding.

Efficient and transgene‑free genome editing in banana using a REG‑2 promoter-driven gene‑deletion system

The Streptococcus-derived CRISPR/Cas9 system can introduce precise and predictable modifications into the plant genome to obtain the desired traits.As one of the most advanced tools for editing crop genomes,the CRISPR/Cas9 system has been expanding rapidly and has been widely applied to determine gene function and improve agronomic traits in horticultural crops such as fruits and vegetables(Ma et al.2023).

From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

Plant disease recognition is of vital importance to monitor plant development and predicting crop production.However,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory vs.field environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains.In this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training.Specifically,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty regularization.The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain.This effectively alleviates the problem of large interdomain discrepancy.Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation.Finally,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer.MSUN was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

Automatic detection of pecan fruits based on Faster RCNN with FPN in orchard

Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the background.In recent,there are few studies on pecan fruit detection and location based on machine vision.In this study,an accurate and efficient pecan fruit detection method was proposed based on machine vision under natural pecan orchards.In order to solve the illumination problem,a light compensation algorithm was first utilized to process the collected samples,and then an improved Faster Region Convolutional Neural Network(Faster RCNN)with the Feature Pyramid Networks(FPN)was established to train the samples.Finally,the pecan number counting method was introduced to count the number of pecan.A total of 241 pecan images were tested,and comparison experiments were carried out.The mean average precision(mAP)of the proposed detection method was 95.932%,compared with the result without uneven illumination correction(UIC),which was increased by 0.849%,while the mAP of the Single Shot Detector(SSD)+FPN was 92.991%.In addition,the number of clusters was counted using the proposed method with an accuracy rate of 93.539%compared with the actual clusters.The results demonstrate that the proposed network has good robustness for pecan fruit detection in different illumination and various unstructured environments,and the experimental achievement has great potential for robot-picking visual systems.

Phenotyping of poplar seedling leaves based on a 3D visualization method

In plant research,there is a demand for non-destructive and non-invasive trait measurement methods for phenotyping that can be used to accurately analyze various aspects of plants,such as stem length,leaf area,and leaf inclination.In this study,a method for measuring the leaf geometric characteristics of poplar seedlings based on 3D visualization via the use of time-of-flight(ToF)and digital cameras was proposed.Firstly,the average distance density function method was applied to process outliers of leaves.Secondly,to improve the accuracy of data fitting,a specific method using the angle of adjacent two-point normal vectors was introduced to filter redundant data,kept essential sample values as the control points,and then used the control points to fit the leaf surface based on non-uniform rational B-splines(NURBS).At the same time,NURBS was used to fit the trunk according to its control points and an iterative method to fit the other branches.Finally,3D visualization of poplar seedlings was achieved,and leaf traits,including leaf width,leaf length,leaf area,and leaf inclination angle,were calculated.To obtain accurate results,multiple experiments were conducted including assessments of poplar seedlings exhibiting normal growth and those grown under water shortage.The results of the proposed method were compared with the real values of the leaves.The RMSE for leaf width,leaf length,leaf area,and leaf angle were 0.18 cm,0.21 cm,1.14 cm^(2),and 1.97°,respectively.The results proved that this approach could be used to accurately measure the leaf characteristics of poplar seedlings via visualization.

High-quality genome assemblies for two Australimusa bananas(Musa spp.)and insights into regulatory mechanisms of superior fiber properties

Bananas(Musa spp.)are monocotyledonous plants with high genetic diversity in the Musaceae family that are cultivated mainly in tropical and subtropical countries.The fruits are a popular food,and the plants themselves have diverse uses.Four genetic groups(genomes)are thought to have contributed to current banana cultivars:Musa acuminata(A genome),Musa balbisiana(B genome),Musa schizocarpa(S genome),and species of the Australimusa section(T genome).However,the T genome has not been effectively explored.Here,we present the high-quality TT genomes of two representative accessions,Abaca(Musa textilis),with high-quality naturalfiber,and Utafun(Musa troglodytarum,Fe’i group),with abundant b-carotene.Both the Abaca and Utafun assemblies comprise 10 pseudochromosomes,and their total genome sizes are 613 Mb and 619 Mb,respectively.Comparative genome analysis revealed that the larger size of the T genome is likely attributable to rapid expansion and slow removal of trans-posons.Compared with those of Musa AA or BB accessions or sisal(Agava sisalana),Abacafibers exhibit superior mechanical properties,mainly because of their thicker cell walls with a higher content of cellulose,lignin,and hemicellulose.Expression of MusaCesA cellulose synthesis genes peaks earlier in Abaca than in AA or BB accessions during plant development,potentially leading to earlier cellulose accumulation during secondary cell wall formation.The Abaca-specific expressed gene MusaMYB26,which is directly regulated by MusaMYB61,may be an important regulator that promotes precocious expression of secondary cell wall MusaCesAs.Furthermore,MusaWRKY2 and MusaNAC68,which appear to be involved in regulating expression of MusaLAC and MusaCAD,may at least partially explain the high accumulation of lignin in Abaca.This work contributes to a better understanding of banana domestica-tion and the diverse genetic resources in the Musaceae family,thus providing resources for Musa genetic improvement.