Multi-omics analyses provide insights into the evolutionary history and the synthesis of medicinal components of the Chinese wingnut

Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In this study,we successfully assembled its chromosome-level genome and performed multiomics analyses to address its evolutionary history and synthesis of medicinal components.A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae.This notable increase may be attributed to their frequent exposure to flood-prone environments.After further differentiation between Chinese wingnut and Cyclocarya paliurus,significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut,enhancing its ability to cope with waterlogging stress.Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis,potentially endowing it with a higher capacity to purify nutrient-rich water bodies.Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles,potentially serving as an evolved defense mechanism against herbivorous insects.Through combined transcriptomic and metabolomic analysis,we identified the candidate genes involved in the synthesis of terpenoid volatiles.Our study offers essential genetic resources for Chinese wingnut,unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Meter-Scale Thin-Walled Structure with Lattice Infill for Fuel Tank Supporting Component of Satellite:Multiscale Design and Experimental Verification

Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.

Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

基于Visual Components的套筒类零件柔性制造单元的应用研究

为实现多品种中、小批量零件的智能化加工,以套筒类零件加工为研究对象,通过对零件工艺的分析,为满足单元加工、搬运、翻转等功能需求,对数控机床和工业机器人进行选型,实现柔性制造单元的设计。利用Visual Components软件进行单元搭建、仿真优化反馈以提高真实产线运行效率。利用Profinet实现制造单元真实搭建,完成单元安装调试。套筒类零件柔性制造单元的设计为生产线设计提供了新思路。

Umami taste components in chicken-spices blends and potential effect of aroma on umami taste intensity

In this study,umami taste intensity(UTI)and umami taste components in chicken breast(CB)and chickenspices blends were characterized using sensory and instrumental analysis.Our main objective was to assess the aroma-umami taste interactions in different food matrices and reconcile the aroma-taste perception to assist future product development.The impact of key aroma,including vegetable-note"2-pentylfuran",meaty"methional",green"hexanal",and spicy-note-estragole and caryophyllene"on UTI was evaluated in monosodium glutamate and chicken extract.We found that spices significantly decreased UTI and umami taste components in CB.Interestingly,the perceptually similar odorants and tastants exhibited the potential to enhance UTI in food matrices.Methional was able to increase the UTI,whereas spicy and green-note components could reduce the UTI significantly.This information would be valuable to food engineers and formulators in aroma selection to control the UTI perceived by consumers,thus,improving the quality and acceptability of the chicken products.

The bio-active components of the Mongolian medicine Horcha-6 and therapeutic mechanism in the rat migraine model

Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Leveraging React Components in Business Process Management (BPM) Applications

As organizations increasingly embrace digital transformation, the integration of modern web technologies like React.js with Business Process Management (BPM) applications has become essential. React components offer flexibility, reusability, and scalability, making them ideal for enhancing user interfaces and driving user engagement within BPM environments. This article explores the benefits, challenges, and best practices of leveraging React components in BPM applications, along with real-world examples of successful implementations.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

Effect of dissolved components of malachite and calcite on surface properties and flotation behavior

In general,malachite is recovered via sulfidization–xanthate flotation,although many unsatisfactory flotation indexes are frequently obtained as a result of the presence of associated calcite.This phenomenon occurs because the dissolved components of malachite and calcite affect the flotation behavior of both minerals.In this study,the effect of the dissolved components derived from malachite and calcite on the flotation behavior and surface characteristics of both minerals was investigated.Flotation tests indicated that malachite recovery decreased when the calcite supernatant was introduced,while the presence of the malachite supernatant increased the recovery of calcite.Dissolution and adsorption tests,along with zeta potential measurements,X-ray photoelectron spectroscopy,Fourier transform infrared spectrometry,and timeof-flight secondary ion mass spectrometry demonstrated that the Ca species in the calcite supernatant were adsorbed on the malachite surface,which hindered the interaction of Na2S with malachite,thereby resulting in the insufficient adsorption of sodium isoamyl xanthate(NaIX)on the surface of malachite.By contrast,the Cu species in the malachite supernatant were adsorbed on the calcite surface,and they provided active sites for the subsequent adsorption of Na_(2)S and NaIX.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Single-component organic solar cells

Bulk-heterojunction organic solar cells(OSCs)with physically mixed electron donor and acceptor as photoactive layer have received broad attention due to the advantages of light weight,flexibility,solution-processed fabrication,etc.[1−11].With the rapid development of non-fullerene acceptors,the power conversion efficiencies(PCEs)of OSCs already exceed 19%[12,13].However,the photoactive layer is thermodynamically unstable and tends to form self-aggregated structure,resulting in reduced thermal/light stability during long-term operation.This issue can be tackled via covalently linking donor and acceptor into one material as double-cable conjugated polymers to fabricate single-component organic solar cells(SCOSCs)[14−16].In 2019,Brabec et al.reported that SCOSCs exhibited excellent thermal stability and photostability due to the built-in microstructure[17].SCOSCs have some merits such as simplified fabrication and homogenous viscosity,which can be applied in large-area devices.

Recent research and development on forming for large magnesium alloy components with high mechanical properties

The lightweight of high-end equipment relies on high mechanical properties magnesium(Mg) alloy structural components, because it is the best way to improve equipment service performance and reduce energy consumption. This article summarizes the current progress and characteristics of large-scale high-performance Mg alloy components by analyzing the strengthening-toughening mechanisms, characteristics of plastic forming, and the preparation of large high mechanical properties forging blanks. Due to the lack of breakthroughs in the key technologies for forming large-scale Mg alloy components, their uniformity of mechanical properties and consistency are poor, the forming accuracy of components is low, and the production cost is high, which limit their engineering application and restrict the lightweight level of high-end equipment. In view of the above problems, the forming trends and research directions of large-scale and high mechanical properties Mg alloy components are proposed in this paper. It can provide help for the breakthrough of the key technology of large-scale Mg alloy components with high mechanical properties and expand the application of Mg alloy in high-end products.

Effects of Different Sterilization Conditions on Active Components and Flavor of Apple Vinegar

[Objectives]To explore the effects of different sterilization conditions on nutrition and flavor of apple vinegar.[Methods]Five kinds of high temperature short time(HTST)sterilization conditions were selected to treat apple vinegar,and the volatile aroma components and the content of active components in apple vinegar before and after sterilization were analyzed.[Results]Compared with the control,the contents of total acid and malic acid in the samples after sterilization changed little,but the contents of citric acid increased significantly(P<0.01),and the contents of total phenols,ascorbic acid and total flavonoids decreased.Ethyl acetate,isopentyl acetate,ethyl caprylate,phenethyl acetate,1-pentanol,phenylethyl alcohol,acetic acid,and sec-butyl ether were the characteristic aroma components which contributed to the flavor of apple vinegar.As sterilization temperature increased,the content of esters decreased,while the content of acids,alcohols and aldehydes increased.The contents of nutrition,active components and volatile aroma components in apple vinegar under 100℃and 30 s sterilization conditions had little change compared with other sterilization conditions,so 100℃and 30 s were the optimal sterilization conditions.[Conclusions]Under different sterilization conditions,the content of flavor components in apple vinegar will change greatly,which will affect the quality of apple vinegar.

Behavior Characteristics of Asphalt Components during the Process of Nano-cracks Temperature Self-healing

In order to investigate the behavior characteristics of asphalt components during the process of nano-cracks temperature self-healing(NTS),molecular simulation technology was used to simulate the temperature self-healing of asphalt.Based on the determination of asphalts(virgin asphalt and aged asphalt),the proportional changes of asphalt components were confirmed.The distribution characteristics of asphalt components were obtained by marking different components in the process of NTS.At the same time,the rationality of the micro simulation findings was confirmed by asphalt performance tests and infrared spectroscopy results.The results show that different asphalt components have different behavior characteristics in the process of NTS.The content of asphaltene and resin plays a key role in the process of NTS,while saturation and aromatics play an active role in the temperature self-healing before and after aging.The NTS is the result of the comprehensive action of different components,and the self-healing efficiency is proportional to the relative molecular mass.The effect of aging on the NTS is also realized by changing the proportion of asphalt components.

Natural variation of main biochemical components,morphological and yield traits among a panel of 87 tea [Camellia sinensis(L.) O. Kuntze] cultivars

Many attentions have been previously focused to identify the multiple biochemical components related to tea quality and health benefits,however, the natural variation of biochemical components present in tea germplasm has not been adequately evaluated. In this study, the main biochemical components, leaf morphological and yield characteristics were evaluated for four rounds of tea leaves in a panel of 87 elite tea cultivars suitable for black, green, or oolong tea. Significant variations were observed among the tea cultivars, as well as seasonal differences in the levels of the free amino acid(FAA), caffeine(CAF), tea polyphenols(TP), water extract(WE) and TP to FAA ratio(TP/FAA). Results showed that the average levels of FAA showed a seasonal change, with the highest level of 4.0% in the 1st spring tea in the cultivars suitable for green tea and the lowest of 3.2% in summer tea in the cultivars suitable for black tea. The average CAF content was highest 3.2% in the cultivars suitable for oolong tea in the 1st spring and the lowest 2.5% in the cultivars suitable for green tea in summer. Limited seasonal and varietal variations were noticed in the average levels of WE among the three categories of tea. In addition, significant natural variation of the morphological characteristics, bud length varying from 2.5 cm to 8.7 cm, bud density from 190.3 buds · m-2to 1 730.3 buds · m-2, mature leaves biomass from 128.4 kg · hm-2to 2 888.4 kg · hm-2, and yield component traits of 100 buds(one bud with two leaves) dry weight from 3.7 g to37.7 g, tea yield/round from 444.6 kg · hm-2to 905.3 kg · hm-2, were observed. The aim of our evaluation was not only to identify the advantages of seasonal and clonal variations but also to provide a new viewpoint for their further application. Representative accessions were selected from the germplasm to promote the establishment of an inherent biochemical constituent expressing the quality of black, green, and oolong tea. The findings might be utilized to establish early selection criteria to enhance the tea breeding and production program.