Microbial community structure and functional metabolic diversity are associated with organic carbon availability in an agricultural soil

Exploration of soil environmental characteristics governing soil microbial community structure and activity may improve our understanding of biogeochemical processes and soil quality. The impact of soil environmental characteristics especially organic carbon availability after 15-yr different organic and inorganic fertilizer inputs on soil bacterial community structure and functional metabolic diversity of soil microbial communities were evaluated in a 15-yr fertilizer experiment in Changping County, Beijing, China. The experiment was a wheat-maize rotation system which was established in 1991 including four different fertilizer treatments. These treatments included： a non-amended control（CK）, a commonly used application rate of inorganic fertilizer treatment（NPK）; a commonly used application rate of inorganic fertilizer with swine manure incorporated treatment（NPKM）, and a commonly used application rate of inorganic fertilizer with maize straw incorporated treatment（NPKS）. Denaturing gradient gel electrophoresis（DGGE） of the 16 S r RNA gene was used to determine the bacterial community structure and single carbon source utilization profiles were determined to characterize the microbial community functional metabolic diversity of different fertilizer treatments using Biolog Eco plates. The results indicated that long-term fertilized treatments significantly increased soil bacterial community structure compared to CK. The use of inorganic fertilizer with organic amendments incorporated for long term（NPKM, NPKS） significantly promoted soil bacterial structure than the application of inorganic fertilizer only（NPK）, and NPKM treatment was the most important driver for increases in the soil microbial community richness（S） and structural diversity（H）. Overall utilization of carbon sources by soil microbial communities（average well color development, AWCD） and microbial substrate utilization diversity and evenness indices（H＇ and E） indicated that long-term inorganic fertilizer with organic amendments incorporated（NPKM, NPKS） could significantly stimulate soil microbial metabolic activity and functional diversity relative to CK, while no differences of them were found between NPKS and NPK treatments. Principal component analysis（PCA） based on carbon source utilization profiles also showed significant separation of soil microbial community under long-term fertilization regimes and NPKM treatment was significantly separated from the other three treatments primarily according to the higher microbial utilization of carbohydrates, carboxylic acids, polymers, phenolic compounds, and amino acid, while higher utilization of amines/amides differed soil microbial community in NPKS treatment from those in the other three treatments. Redundancy analysis（RDA） indicated that soil organic carbon（SOC） availability, especially soil microbial biomass carbon（Cmic） and Cmic/SOC ratio are the key factors of soil environmental characteristics contributing to the increase of both soil microbial community structure and functional metabolic diversity in the long-term fertilization trial. Our results showed that long-term inorganic fertilizer and swine manure application could significantly improve soil bacterial community structure and soil microbial metabolic activity through the increases in SOC availability, which could provide insights into the sustainable management of China＇s soil resource.

Altered intrinsic functional connectivity of the primary visual cortex in youth patients with comitant exotropia: a resting state fMRI study

AIM： To evaluate the differences in the functional connectivity（FC） of the primary visual cortex（V1） between the youth comitant exotropia（CE） patients and health subjects using resting functional magnetic resonance imaging（f MRI） data.METHODS： Totally, 32 CEs（25 males and 7 females） and 32 healthy control subjects（HCs）（25 males and 7 females） were enrolled in the study and underwent the MRI scanning. Two-sample t-test was used to examine differences in FC maps between the CE patients and HCs. RESULTS： The CE patients showed significantly less FC between the left brodmann area（BA17） and left lingual gyrus/cerebellum posterior lobe, right middle occipital gyrus, left precentral gyrus/postcentral gyrus and right inferior parietal lobule/postcentral gyrus. Meanwhile, CE patients showed significantly less FC between right BA17 and right middle occipital gyrus（BA19, 37）.CONCLUSION： Our findings show that CE involves abnormal FC in primary visual cortex in many regions, which may underlie the pathologic mechanism of impaired fusion and stereoscopic vision in CEs.

Evidence of cortical reorganization of language networks after stroke with subacute Broca's aphasia:a blood oxygenation level dependent-functional magnetic resonance imaging study

Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood oxygenation level-dependent functional magnetic resonance imaging to evaluate the activation of bilateral cortices in patients with Broca＇s aphasia 1 to 3 months after stroke.Our results showed that language expression was associated with multiple brain regions in which the right hemisphere participated in the generation of language.The activation areas in the left hemisphere of aphasia patients were significantly smaller compared with those in healthy adults.The activation frequency,volumes,and intensity in the regions related to language,such as the left inferior frontal gyrus（Broca＇s area）,the left superior temporal gyrus,and the right inferior frontal gyrus（the mirror region of Broca＇s area）,were lower in patients compared with healthy adults.In contrast,activation in the right superior temporal gyrus,the bilateral superior parietal lobule,and the left inferior temporal gyrus was stronger in patients compared with healthy controls.These results suggest that the right inferior frontal gyrus plays a role in the recovery of language function in the subacute stage of stroke-related aphasia by increasing the engagement of related brain areas.

Botulinum toxin type A plus rehabilitative training for improving the motor function of the upper limbs and activities of daily life in patients with stroke and brain injury

BACKGROUND:Botulinum toxin type A(BTX-A)is mostly to be used to treat various diseases of motor disorders,whereas its effect on muscle spasm after stroke and brain injury needs further observation.OBJECTIVE:To observe the effect of BTX-A plus rehabilitative training on treating muscle spasm after stroke and brain injury.DESIGN:A randomized controlled observation.SETTINGS:Department of Rehabilitation,Department of Neurology and Department of Neurosurgery,the Second Hospital of Hebei Medical University.PARTICIPANTS:Sixty inpatients with brain injury and stroke were selected from the Department of Rehabilitation,Department of Neurology and Department of Neurosurgery,the Second Hospital of Hebei Medical University from January 2001 to August 2006.They were all confirmed by CT and MRI,and had obvious increase of spastic muscle strength in upper limbs,their Ashworth grades were grade 2 or above.The patients were randomly divided into treatment group(n=30)and control group(n=30).METHODS:①Patients in the treatment group undertook comprehensive rehabilitative trainings,and they were administrated with domestic BTX-A,which was provided by Lanzhou Institute of Biological Products,Ministry of Health(S10970037),and the muscles of flexion spasm were selected for upper limbs,20-25 IU for each site.②Patients in the treatment group were assessed before injection and at 1 and 2 weeks,1 and 3 months after injection respectively,and those in the control group were assessed at corresponding time points.The recovery of muscle spasm was assessed by modified Ashworth scale(MAS,grade 0-Ⅳ;Grade 0 for without increase of muscle strength;GradeⅣfor rigidity at passive flexion and extension);The recovery of motor function of the upper limbs was evaluated with Fugl-Meyer Assessment(FMA,total score was 226 points,including 100 for exercise,14 for balance,24 for sense,44 for joint motion,44 for pain and 66 for upper limb);The ADL were evaluated with Barthel index,the total score was 100 points,60 for mild dysfunction,60-41 for moderate dysfunction,<40 for severe dysfunction).MAIN OUTCOME MEASURES:Changes of MAS grade,FMA scores and Barthel index before and after BTX-A injection.RESULTS:All the 60 patients with brain injury and stroke were involved in the analysis of results.①FMA scores of upper limbs:The FMA score in the treatment group at 2 weeks after treatment was higher than that before treatment[(14.98±10.14),(13.10±9.28)points,P<0.05],whereas there was no significant difference at corresponding time point in the control group.The FMA scores at 1 and 3 months in the treatment group[(23.36±10.69),(35.36±11.36)points]were higher than those in the control group[(20.55±10.22),(30.33±10.96)points,P<0.01].②MAS grades of upper limbs:There were obviously fewer cases of gradeⅢin MAS at 2 weeks after treatment than before treatment in the treatment group(0,9 cases,P<0.05),whereas there was no obvious difference in the control group.There were obviously fewer cases of gradeⅢin MAS at 2 weeks and 1 month after treatment in the treatment group(0,0 case)than the control group(5,2 cases,P<0.01).③Barthel index of upper limbs:The Barthel index at 2 weeks after treatment was higher than that before treatment in the treatment group[(30.36±22.25),(28.22±26.21)points,P<0.05],whereas there was no significant difference in the control group.The Barthel indexes at 1 and 3 months after treatment in the treatment group were obviously higher than those in the control group[(20.55±10.22),(30.33±10.96)points,P<0.01].CONCLUSION:BTX-A has obvious efficacy on decreasing muscle tension after stroke and brain injury,and relieving muscle spasm;Meanwhile,the combination with rehabilitative training can effectively ameliorate the motor function of upper limbs and ADL of the patients.

Effect of Preparation Process of Functional Polymer Active Materials on Properties of Gadolinium Ion Selective Electrode

Recently we have studied the rare earth ion-selective electrodes with active materials of the func-tional polymers and found that the process chosen for the functional polymers had an effect on the propertiesof gadolinium ion selective electrode besides the effects of their structures.1.Effect of preparation process of the grafted polymers on the properties ofgadolinium ion selective electrodesThe electrode membranes which consist of functional polymers as active materials were prepared by re-action of gadolinium chloride with the radiation grafted clmer of acrlic acid and polystyrene of which

EFFECT OF STRUCTURE OF FUNCTIONAL POLYMER ACTIVE MATERIALS ON PROPERTIES OF GADOLINIUM ION SELECTIVE ELECTRODE

In this paper,the functional polymeric active materials were prepared by the grafting copolymerization and their structure and properties were studied.The results show that the structure and properties of these ac- tive materials have the relative large effects on the properties of gadolinium ion selective electrodes.

Characterization and Functionalisation of Activated Carbon for the Enhancement of Enzyme Catalyst Activity

Background and objective:Activated carbon is commonly used as an immobilisation matrix due to its large surface area,making it a highly desirable matrix for use in immobilising enzymes as preparation for use on the industrial scale.The objective of this research is to determine the effectiveness of different acids for functionalisation on immobilisation capacity and also to characterize the functionalized activated carbon for the functional groups present.Materials and methods:Activated carbon was functionalised with three acids(hydrochloric acid,nitric acid and sulphuric acid)along with a control sample washed with distilled water.Immobilisation capacity was calculated with hydrochloric acid functionalized activated carbon(HCl-FAC)giving the highest immobilization capacity(6.022 U/g).Characterisation of the functionalised activated carbon was conducted using FT-IR(Fourier Transform Infra-Red)spectroscopy analysis of the samples with the aim of analyzing the various functional groups present to determine the sample with distinct characteristics thus telling the degree of adsorption of lipase onto the activated carbon powder.Results:HNO3-FAC(functionalized activated carbon)showed a very distinct pattern as a larger number of surface functional groups emerged.The immobilisation on a matrix ensures thermal stability and increased reusability of the enzyme.Therefore,in this research,lipase sourced from Candida antarctica was immobilised on acid functionalised activated carbon.The best acid for functionalisation was found to be hydrochloric acid.Conclusion:Due to the very distinct patterns shown by the FT-IR spectrum of the HNO3-FAC after a fair comparison with others,it allows for a larger number of surface functional groups which will definitely enhance the stability of the enzyme lipase.

FUNCTIONALIZED CYCLODEXSTRINS BEARING AM INOALKYLIMINO GROUPS AS ACTIVE CENTERS TO CATALYZE ALDOL CONDENSATIONS

Efficient catalysis of functinnatized β-cyctodextrins bearing aninoatkytimino groups for atdot condensations of nitrobenzatdehydes and acetone has been effected and substantiated by preparative experiments

Functional magnetic resonance imaging evidence for activated functional brain areas following acupoint needling in the extremities

Totally three articles focusing on functional magnetic resonance imaging features of brain function in the activated brain regions of stroke patients undergoing acupuncture on the healthy limbs and healthy controls undergoing acupuncture on the lower extremities are published in three issues. We hope that our readers find these papers useful to their research.

Theoretical Study on the C-H Activation in Decarbonylation of Acetaldehyde by NiL_2(L=SO_3CH_3) Using Density Functional Theory

Density functional theory calculations were carried out to explore the potential energy surface（PES） associated with the gas-phase reaction of Ni L2（L=SO3CH3） with acetone. The geometries and energies of the reactants, intermediates, products and transition states of the triplet ground potential energy surfaces of [Ni, O, C2, H4] were obtained at the B3LYP/6-311＋＋G（d,p） levels in C,H,O atoms and B3LYP/ Lanl2 dz in Ni atom. It was found through our calculations that the decabonylation of acetaldehyde contains four steps including encounter complexation, C-C activation, aldehyde H-shift and nonreactive dissociation. The results revealed that C-C activation induced by Ni L2（L=SO3CH3） led to the decarbonylation of acetaldehyde.

What Information Gap Activities can Teach Us in TESOL

Starting from presenting and analyzing some information gap activities during the previous teaching experience, this article has inferred the major roles of information gap activities. Some strategies to implement the information gap activities are also recommended together with the functions of the instructors via these activities. What information gap activities can teach us in TESOL （teaching English for speakers of other languages） is that information gap activities contribute to setting up a climate of a mutual autonomous learning style both for the learners and the instructors, and these activities activate a diversity in the learning atmosphere.

Progress on the melanoidins produced by the Maillard reaction of fermented food and traditional Chinese medicine

FMs(Food-borne melanoidins)are brown high molecular weight polymers formed by the Mailiard reaction between carbohydrates and nitrogen-containing compounds during the processing of food or Traditional Chinese Medicine(TCM),and are widely found in food-borne products such as TCM concoctions,bakery,brewing,soy sauce,ferment and other food-borne products.FMs not only have a variety of biological activities,such as antioxidant,antibacterial,immunomodulation,regulation of intestinal flora,etc.,and can change the color,aroma and taste of food.The diversity of its components has become a research hotspot at home and abroad in recent years,with a wide range of application prospects.Therefore,this paper summarizes the existing information on FMs at home and abroad,mainly describes their preparation process,physicochemical properties,structural characteristics and functional activity research progress.Typical FMs,such as coffee,biscuits,wine and soy sauce in daily food,and Polygonatum,Perilla oil,Black ginseng,and Red jujube in T,were highlighted.Summarising the current status of research between the chemistry and pharmacodynamics of relevant FMs and presenting challenges and future recommendations for melanoidin research.In future research on FMs,one should pay more attention to basic research,especially isolation and purification and generation mechanisms,to further demonstrate the biological activity of FMs in vivo and in clinical trials.Thus,the potential value of its existence is deeply exploited to meet the needs of technology,production and health.

Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Large-scale self-normalizing neural networks

Self-normalizing neural networks(SNN)regulate the activation and gradient flows through activation functions with the self-normalization property.As SNNs do not rely on norms computed from minibatches,they are more friendly to data parallelism,kernel fusion,and emerging architectures such as ReRAM-based accelerators.However,existing SNNs have mainly demonstrated their effectiveness on toy datasets and fall short in accuracy when dealing with large-scale tasks like ImageNet.They lack the strong normalization,regularization,and expression power required for wider,deeper models and larger-scale tasks.To enhance the normalization strength,this paper introduces a comprehensive and practical definition of the self-normalization property in terms of the stability and attractiveness of the statistical fixed points.It is comprehensive as it jointly considers all the fixed points used by existing studies:the first and second moment of forward activation and the expected Frobenius norm of backward gradient.The practicality comes from the analytical equations provided by our paper to assess the stability and attractiveness of each fixed point,which are derived from theoretical analysis of the forward and backward signals.The proposed definition is applied to a meta activation function inspired by prior research,leading to a stronger self-normalizing activation function named‘‘bi-scaled exponential linear unit with backward standardized’’(bSELU-BSTD).We provide both theoretical and empirical evidence to show that it is superior to existing studies.To enhance the regularization and expression power,we further propose scaled-Mixup and channel-wise scale&shift.With these three techniques,our approach achieves 75.23%top-1 accuracy on the ImageNet with Conv MobileNet V1,surpassing the performance of existing self-normalizing activation functions.To the best of our knowledge,this is the first SNN that achieves comparable accuracy to batch normalization on ImageNet.

Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range.In most radiators that are used to cool an engine,water serves as a cooling fluid.The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water.Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids.The shape and size of nanoparticles also have a great impact on the performance of heat transfer.Many researchers are investigating the impact of nanoparticles on heat transfer.This study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion engine.The implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the nanofluid.Accuracies of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and temperatures.In artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training phase.The findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of accuracy.The best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

Electrochemically reduced graphene oxide with enhanced electrocatalytic activity toward tetracycline detection

An electrochemically reduced graphene oxide sample, ERGO_0.8v, was prepared by electrochemical reduction of graphene oxide （GO） at -0.8 V, which shows unique electrocatalytic activity toward tetracycline （TTC） detection compared to the ERGO-12v （GO applied to a negative potential of-1.2 V）, GO, chemically reduced GO （CRGO）-modified glassy carbon electrode （GC） and bare GC electrodes. The redox peaks of TTC on an ERGO-0.8v-modifled glass carbon electrode （GC/ERGO-0.8v） were within 0-0.5 V in a pH 3.0 buffer solution with the oxidation peak current correlating well with TTC concentration over a wide range from 0.1 to 160 mg/L Physical characterizations with Fourier transform infrared （FT-IR）, Raman, and X-ray photoelectron spectroscopies （XPS） demonstrated that the oxygen-containing functional groups on GO diminished after the electrochemical reduction at -0.8 V, yet still existed in large amounts, and the defect density changed as new sp2 domains were formed. These changes demonstrated that this adjustment in the number of oxygen-containing groups might be the main factor affecting the electrocatalytic behavior of ERGO. Additionally, the defect density and sp2 domains also exert a profound influence on this behavior. A possible mechanism for the TTC redox reaction at the GC/ERGO-0.8v electrode is also presented. This work suggests that the electrochemical reduction is an effective method to establish new catalytic activities of GO by setting appropriate parameters.

Chip-Based High-Dimensional Optical Neural Network

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.