SPP-extractor:Automatic phenotype extraction for densely grown soybean plants

Automatic collecting of phenotypic information from plants has become a trend in breeding and smart agriculture.Targeting mature soybean plants at the harvesting stage,which are dense and overlapping,we have proposed the SPP-extractor(soybean plant phenotype extractor)algorithm to acquire phenotypic traits.First,to address the mutual occultation of pods,we augmented the standard YOLOv5s model for target detection with an additional attention mechanism.The resulting model could accurately identify pods and stems and could count the entire pod set of a plant in a single scan.Second,considering that mature branches are usually bent and covered with pods,we designed a branch recognition and measurement module combining image processing,target detection,semantic segmentation,and heuristic search.Experimental results on real plants showed that SPP-extractor achieved respective R^(2) scores of 0.93–0.99 for four phenotypic traits,based on regression on manual measurements.

Genetic dissection of and genomic selection for seed weight, pod length,and pod width in soybean

A biparental soybean population of 364 recombinant inbred lines(RILs)derived from Zhongdou 41×ZYD02.878 was used to identify quantitative trait loci(QTL)associated with hundred-seed weight(100-SW),pod length(PL),and pod width(PW).100-SW,PL,and PW showed moderate correlations among one another,and 100-SW was correlated most strongly with PW(0.64–0.74).Respectively 74,70,75 and19 QTL accounting for 38.7%–78.8%of total phenotypic variance were identified by inclusive composite interval mapping,restricted two-stage multi-locus genome-wide association analysis,3 variancecomponent multi-locus random-SNP-effect mixed linear model analysis,and conditional genome-wide association analysis.Of these QTL,189 were novel,and 24 were detected by multiple methods.Six loci were associated with 100-SW,PL,and PW and may be pleiotropic loci.A total of 284 candidate genes were identified in colocalizing QTL regions,including the verified gene Seed thickness 1(ST1).Eleven genes with functions involved in pectin biosynthesis,phytohormone,ubiquitin-protein,and photosynthesis pathways were prioritized by examining single nucleotide polymorphism(SNP)variation,calculating genetic differentiation index,and inquiring gene expression.The prediction accuracies of genomic selection(GS)for 100-SW,PL,and PW based on single trait-associated markers reached 0.82,0.76,and 0.86 respectively,but selection index(SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy.These results shed light on the genetic basis of 100-SW,PL,and PW and provide GS models for these traits with potential application in breeding programs.

Maintenance Strategy of Multi-Component System Due to Degradation and Shock

The proposed maintenance strategy uses component grades and survival signature to describe and update system structure. A system dynamic stress-strength reliability(SSR) model was established to describe component failure processes because of internal degradation and external shock. Besides, a corrective maintenance rule and two-level preventive maintenance rule made up the proposed maintenance strategy. The former combined sequentially minimal repair and corrective replacement(SMRCR) based on system structure updating. The latter considered group importance measure based on the system dynamic SSR and preventive maintenance cost. Further, a cost model was developed by the proposed strategy, and the optimal decision variables were found by genetic algorithm. Finally, using a case system to illustrate the above strategy improved system and reduced maintenance costs.

Dry-type AMDT Fault Detection Based on Vibration Signal Analysis by Wavelet Packet

Amorphous metal distribution transformers(AMDT)are widely used in power grids due to their low no-load loss.Many scholars have carried out research on the fault detection of transformer windings,tap changers and the other parts.However,due to the high magnetostriction of the amorphous alloy,the vibration generated by AMDT during operation will cause various mechanical failures.This paper studies the vibration characteristics of SCBH 15-200/100 AMDT through no-load tests to find some mechanical failures of AMDT.The installation position of the vibration sensor in AMDT are determined according to finite element analysis(FEA)of the magnetic flux density distribution and modal analysis,and the vibration analyses are performed under different operating conditions of AMDT.The wavelet packet transform(WPT)is used to perform detailed analysis of the vibration signal in the time domain and frequency domain to obtain the energy characteristic value of each frequency band,and it includes the frequency spectrum and waveform data under normal and fault conditions.After obtaining the energy characteristic thresholds of different frequency bands under different conditions,the operating status can be detected by comparing test data with the thresholds.The operation condition including mechanical failures induced by magnetostrictive actions can be accurately determined by the energy characteristic value,such as loose nuts and stress,etc.

惯容式油气悬架力学建模和试验研究

为满足轮式装甲车辆对高机动越野性能的要求,提出了将滚珠丝杠惯容器与双筒式油气弹簧集成的技术方案,构建了惯容式油气悬架装置。通过剖析该悬架装置弹性力、阻尼力和惯性力的产生机理,揭示了各参数之间的耦合关系,提取出3个相互独立的关键结构参数,采用正交试验法,结合惯容式悬架系统非线性动力学仿真,确定了上述3个关键结构参数的最优值。力学特性试验和1/4悬架试验的结果表明,与传统油气悬架相比,采用惯容式悬架使车体振动加速度降低了13.89%,大幅提高了车辆的乘坐舒适性。

自动驾驶车辆与行人交互:基于手势的意图传达行为实验研究

目的探讨使用手势传达自动驾驶系统意图对行人的行为和态度影响。方法招募30名被试作为行人参与自动驾驶系统的交互,采用了2×3被试内设计(手势类型让道vs.占道)和手臂类型(人手vs.快速挥动的机械手vs.慢速挥动的机械手)进行视频实验室实验观察行人客观行为反应和主观态度变化。结果无论是让道还是占道,慢速挥动的机械手都带来较长的反应时和较低的反应正确率,且比人手和快速挥动的机械手有更低可理解度和舒适度。人手和快速挥动的机械手能带来相似程度的可理解性和舒适性,但两者在行为指标上各有优势,即人手条件下行人的反应快于快速挥动的机械手,而快速挥动的机械手比人手能带来更高的行人反应正确率。结论在非紧急的交通环境下,通过快速挥动的机械手来传递手势可能是自动驾驶车辆和行人进行交互的有效方式,制造商和车辆设计者可以参照设计。

Specific enrichment of urinary exosomes and exosomal glycopeptides by coefficient affinity of integrated L-cysteine and titania

Exosome and inclusive cargoes have manifested significant function in different biological events. In particular, glycopeptides in exosome are closely associated with occurrence and development of various diseases. Developing advanced tools is highly desired to enrich glycopeptides from exosomes, and enrich exosomes from complex biological samples as well. In this work, integration of L-cysteine and titania onto the surface of magnetic nanoparticles is designed to realize the coefficient affinity towards exosomes and inclusive glycopeptides. Benefiting from the synergistic affinity, we separate exosomes from human urine concentrate directly, which was proved by the detection of three typical antigen markers of exosomes. Furthermore, hardly any exosomes remained on materials after ultrasonication, which confirmed the good capture performance of Fe_(3)O_(4)@TiO_(2)@L-Cys and high release effect of direct lysis.Moreover, 146 glycopeptides corresponding to 77 glycoproteins were successfully identified from captured exosomes. These satisfactory results will inspire more efforts to be devoted to this field and will be extremely helpful to in-depth information excavation of biological markers, especially disease-related ones, through exosomes and inclusive glycopeptides.

Metal organic frameworks as advanced extraction adsorbents for separation and analysis in proteomics and environmental research

Human health is always under global spotlight, but now it suffers from severe environmental issue and various diseases.Developing highly selective and effective extraction technique for environmental pollutant removal and target protein/peptide molecules conduces to the solution of environmental contamination and early disease detection, which will promise human health to a great degree. Metal-organic frameworks(MOFs) are typical porous nanomaterials featuring rigid structure, large specific surface area, thermal and chemical stability, numerous active sites and easy functionalization. Moreover, multitudinous combination of metal centers and organic ligands often endows MOFs with structure and function diversity. Owing to such splendid merits, MOFs are able to act as advanced adsorbents for sample-pretreatment in proteomics and environmental field,which indicates great efficiency, reproducibility, convenience and cost-effectiveness. In this review, the novel design of MOFs and their applications in proteomics and environmental research are roughly summarized. We first introduce the representative researches of functional MOFs in protein post-translational modifications including glycosylation and phosphorylation, mainly focusing on hydrophilic interaction liquid chromatography(HILIC) or metal-oxide affinity chromatography(MOAC). The important role of MOFs in enzyme immobilization is also covered. Then MOF-based adsorptive removal of diverse environmental contaminants embracing plasticizers, pesticides and varied water pollutants(pharmaceutical and personal care products,organic dyes and heavy metals), in which multiple chemical and non-chemical interactions play a vital role, is emphatically discussed. Finally, we analyze the existing problems and challenges in the way of wider practical applications and put forward prospective anticipation.

Nanocellulose from bamboo shoots as perfect Pickering stabilizer:Effect of the emulsification process on the interfacial and emulsifying properties

In this work,the medium internal phase O/W Pickering emulsions stabilized with bamboo shoots nanocellulose(BSNC)was successfully fabricated.The nanocellulose extracted from bamboo shoots with an average width of 56.37 nm and height of 7.44 nm showed great potential as an emulsifier in the Pickering emulsions.The effects of BSNC content,oil-water ratio and emulsification process on the structure,stability and rheology of the resultant Pickering emulsions was explored.The obtained emulsion with the BSNC content of 0.5 wt%at the oil to water volume ratio of 5:5 had a lower particle size of around 25μm.With the increasing BSNC content,the emulsions presented increased droplet size,and even demulsification occurred.Interestingly,the physicochemical properties of the emulsions could be significantly improved through twice shearing,which effectively reduced droplet size.The surface coverage was above 100%for the Pickering emulsions stabilized with the BSNC content of 0.5%-1.1%.With the increasing BSNC content,the apparent viscosity was increased first and then decreased,and all emulsions showed elastic behaviors.This work would provide theoretical guidance for preparing medium or high internal phase Pickering emulsions stabilized with nanocellulose.

Predicting Student Rankings Based on the Dual-Student Performance Comparison Model

Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performance regression gives an absolute score value,and it cannot directly show the position of a student in the class.To overcome the above shortcomings,we will focus on a rare learning early warning method-ranking prediction.We propose a dual-student performance comparison model(DSPCM)to judge the ranking relationship between a pair of students.Then,we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713,respectively.We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students.The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter.This reflects the advantage of the DSPCM in ranking prediction.