Evolutionary history shapes variation of wood density of tree species across the world

The effect of evolutionary history on wood density variation may play an important role in shaping variation in wood density,but this has largely not been tested.Using a comprehensive global dataset including 27,297 measurements of wood density from 2621 tree species worldwide,we test the hypothesis that the legacy of evolutionary history plays an important role in driving the variation of wood density among tree species.We assessed phylogenetic signal in different taxonomic(e.g.,angiosperms and gymnosperms)and ecological(e.g.,tropical,temperate,and boreal)groups of tree species,explored the biogeographical and phylogenetic patterns of wood density,and quantified the relative importance of current environmental factors(e.g.,climatic and soil variables)and evolutionary history(i.e.,phylogenetic relatedness among species and lineages)in driving global wood density variation.We found that wood density displayed a significant phylogenetic signal.Wood density differed among different biomes and climatic zones,with higher mean values of wood density in relatively drier regions(highest in subtropical desert).Our study revealed that at a global scale,for angiosperms and gymnosperms combined,phylogeny and species(representing the variance explained by taxonomy and not direct explained by long-term evolution process)explained 84.3%and 7.7%of total wood density variation,respectively,whereas current environment explained 2.7%of total wood density variation when phylogeny and species were taken into account.When angiosperms and gymnosperms were considered separately,the three proportions of explained variation are,respectively,84.2%,7.5%and 6.7%for angiosperms,and 45.7%,21.3%and 18.6%for gymnosperms.Our study shows that evolutionary history outpaced current environmental factors in shaping global variation in wood density.

Flexible,high-density,laminated ECoG electrode array for high spatiotemporal resolution foci diagnostic localization of refractory epilepsy

High spatiotemporal resolution brain electrical signals are critical for basic neuroscience research and high-precision focus diagnostic localization,as the spatial scale of some pathologic signals is at the submillimeter or micrometer level.This entails connecting hundreds or thousands of electrode wires on a limited surface.This study reported a class of flexible,ultrathin,highdensity electrocorticogram(ECoG)electrode arrays.The challenge of a large number of wiring arrangements was overcome by a laminated structure design and processing technology improvement.The flexible,ultrathin,high-density ECoG electrode array was conformably attached to the cortex for reliable,high spatial resolution electrophysiologic recordings.The minimum spacing between electrodes was 15μm,comparable to the diameter of a single neuron.Eight hundred electrodes were prepared with an electrode density of 4444 mm^(-2).In focal epilepsy surgery,the flexible,high-density,laminated ECoG electrode array with 36 electrodes was applied to collect epileptic spike waves inrabbits,improving the positioning accuracy of epilepsy lesions from the centimeter to the submillimeter level.The flexible,high-density,laminated ECoG electrode array has potential clinical applications in intractable epilepsy and other neurologic diseases requiring high-precision electroencephalogram acquisition.

Calculation of microscopic nuclear level densities based on covariant density functional theory

In this study,a microscopic method for calculating the nuclear level density(NLD)based on the covariant density functional theory(CDFT)is developed.The particle-hole state density is calculated by a combinatorial method using single-particle level schemes obtained from the CDFT,and the level densities are then obtained by considering collective effects such as vibration and rotation.Our results are compared with those of other NLD models,including phenomenological,microstatisti-cal and nonrelativistic Hartree–Fock–Bogoliubov combinatorial models.This comparison suggests that the general trends among these models are essentially the same,except for some deviations among the different NLD models.In addition,the NLDs obtained using the CDFT combinatorial method with normalization are compared with experimental data,including the observed cumulative number of levels at low excitation energies and the measured NLDs.The CDFT combinatorial method yields results that are in reasonable agreement with the existing experimental data.

The effect of topographic density variations on the geoid and orthometric heights in Hong Kong

Utilizing the adopted average topographic density of 2670 kg/m^(3)in the reduction of gravity anomalies introduces errors attributed to topographic density variations,which consequently affect geoid modeling accuracy.Furthermore,the mean gravity along the plumbline within the topography in the definition of Helmert orthometric heights is computed approximately by applying the Poincar e-Prey gravity reduction where the topographic density variations are disregarded.The Helmert orthometric heights of benchmarks are then affected by errors.These errors could be random or systematic depending on the specific geological setting of the region where the leveling network is physically established and/or the geoid model is determined.An example of systematic errors in orthometric heights can be given for large regions characterized by sediment or volcanic deposits,the density of which is substantially lower than the adopted topographic density used in Helmert's definition of heights.The same applies to geoid modeling errors.In this study,we investigate these errors in the Hong Kong territory,where topographic density is about 20%lower than the density of 2670 kg/m^(3).We use the digital rock density model to estimate the effect of topographic density variations on the geoid and orthometric heights.Our results show that this effect on the geoid and Helmert orthometric heights reach maxima of about 2.1 and 0.5 cm,respectively.Both results provide clear evidence that rock density models are essential in physical geodesy applications involving gravimetric geoid modeling and orthometric height determination despite some criticism that could be raised regarding the reliability of these density models.However,in regions dominated by sedimentary and igneous rocks,the geological information is essential in these applications because topographic densities are substantially lower than the average density of 2670 kg/m^(3),thus introducing large systematic errors in geoid and orthometric heights.

Correlation between DKK-1 Level and Bone Density Status in Children on Maintenance Haemodialysis

Background: Renal osteodystrophy (ROD) is a bone disorder resulting from chronic kidney disease (CKD) and related metabolic diseases. Dickkopf-related protein-1 (DKK-1) is critical in regulating bone biology. This study aimed to evaluate the serum DKK-1 level as a bone marker in children with CKD who undergo regular hemodialysis (HD). Subjects and Methods: This case-control study involved 40 children with CKD on HD and 40 healthy children as controls. The study measured serum DKK-1 levels and performed a dual-energy X-ray absorptiometry scan (DEXA) in line with routine laboratory investigations. Results: There was a significant increase in the serum level of DKK-1 in the patient group compared to the control group. The DKK-1 levels were 2540.65 (2215.4 - 2909.2) pg/ml and 1110.45 (885.45 - 1527.65) pg/ml, respectively, with a p-value of less than 0.001. In the hemodialysis group, 25 patients (62.5%) had low bone mineral density (BMD) with a Z-score of under -2.0. Eighteen of these patients had low BMD in both the neck of the femur and lumbar spines. Additionally, there was a significant increase in serum DKK-1 level in patients with low BMD (2567.35 (2303.8 - 3108.1) pg/ml) compared to patients with normal BMD (2454 (1859 - 2820) pg/ml) (p = 0.041). There was also a significant positive correlation between DKK1 level and phosphorus, alkaline phosphatase, and Parathormone serum levels. In conclusion, the study indicates a clear correlation between DKK-1 and BMD in children undergoing maintenance hemodialysis. DKK1 is a promising biomarker for CKD-MBD.

Recent advances in fabricating high-performance triboelectric nanogenerators via modulating surface charge density

Triboelectric nanogenerators(TENGs),a type of promising micro/nano energy source,have been arousing tremendous research interest since their inception and have been the subject of many striking developments,including defining the fundamental physical mechanisms,expanding applications in mechanical to electric power conversion and self-powered sensors,etc.TENGs with a superior surface charge density at the interfaces of the electrodes and dielectrics are found to be crucial to the enhancement of the performance of the devices.Here,an overview of recent advances,including material optimization,circuit design,and strategy conjunction,in developing TENGs through surface charge enhancement is presented.In these topics,different strategies are retrospected in terms of charge transport and trapping mechanisms,technical merits,and limitations.Additionally,the current challenges in high-performance TENG research and the perspectives in this field are discussed.

Research on Preparation and Electrochemical Performance of the High Compacted Density Ni-Co-Mn Ternary Cathode Materials

The high compacted density LiNi0.5-xCo0.2Mn0.3MgxO2 cathode material for lithium-ion batteries was synthesized by high temperature solid-state method, taking the Mg element as a doping element and the spherical Ni0.5Co0.2Mn0.3 (OH)2, Li2CO3 as raw materials. The effects of calcination temperature on the structure and properties of the products were investigated. The structure and morphology of cathode materials powder were analyzed by X-ray diffraction spectroscopy (XRD) and scanning electronmicroscopy (SEM). The electrochemical properties of the cathode materials were studied by charge-discharge test and cyclic properties test. The results show that LiNi0.4985Co0.2Mn0.3 Mg0.0015O2 cathode material prepared at calcination temperature 930°C has a good layered structure, and the compacted density of the electrode sheet is above 3.68 g/cm3. The discharge capacity retention rate is more than 97.5% after 100 cycles at a charge-discharge rate of 1C, displaying a good cyclic performance.

Maternal dietary patterns associated with bone density in Chinese lactating women and infants at 6 months postpartum:a prospective study using data from 2018-2019

This cohort study was designed to explore the relationship between maternal dietary patterns(DPs)and bone health in Chinese lactating mothers and infants.We recruited 150 lactating women at 1-month postpartum.The estimated bone mineral density(eBMD)of subjects’calcanei and the information on dietary intake were collected.After 5-month follow-up,the eBMD of mothers and their infants were measured again.Factor analysis was applied to determine maternal DPs.General linear models were used to evaluate the association between maternal DPs and maternal eBMD loss or infants’eBMD.With all potential covariates adjusted,Factor 2(high intake of whole grains,tubers,mixed beans,soybeans and soybean products,seaweeds,and nuts)showed a positive association with the changes of maternal eBMD(β=0.16,95%CI:0.005,0.310).Factor 3(high intake of soft drinks,fried foods,and puffed foods)was inversely correlated with the changes of maternal eBMD(β=-0.22,95%CI:-0.44,0.00).The changes of maternal eBMD were positively associated with 6-month infants’eBMD(β=0.34,95%CI:0.017,0.652).In conclusion,Factor 2 might contribute to the maintenance of eBMD in lactating women,while Factor 3 could exacerbate maternal eBMD loss.Additionally,the changes of maternal eBMD presented a positive correlation with 6-month infants’eBMD.

Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

Robustness of the octupole collectivity in 144Ba within the cranking covariant density functional theory in 3D lattice

The octupole deformation and collectivity in octupole double-magic nucleus 144Ba are investigated using the Cranking covariant density functional theory in a three-dimensional lattice space.The reduced B(E3)transition probability is implemented for the first time in semiclassical approximation based on the microscopically calculated electric octupole moments.The available data,including the I-ωrelation and electric transitional probabilities B(E2)and B(E3)are well reproduced.Furthermore,it is shown that the ground state of 144Ba exhibits axial octupole and quadrupole deformations that persist up to high spins(I≈24h).

Sensitivity impacts owing to the variations in the type of zero-range pairing forces on the fission properties using the density functional theory

Using the Skyrme density functional theory,potential energy surfaces of^(240)Pu with constraints on the axial quadrupole and octupole deformations(q_(20)and q_(30))were calculated.The volume-like and surface-like pairing forces,as well as a combination of these two forces,were used for the Hartree–Fock–Bogoliubov approximation.Variations in the least-energy fission path,fission barrier,pairing energy,total kinetic energy,scission line,and mass distribution of the fission fragments based on the different forms of the pairing forces were analyzed and discussed.The fission dynamics were studied based on the timedependent generator coordinate method plus the Gaussian overlap approximation.The results demonstrated a sensitivity of the mass and charge distributions of the fission fragments on the form of the pairing force.Based on the investigation of the neutron-induced fission of^(239)Pu,among the volume,mixed,and surface pairing forces,the mixed pairing force presented a good reproduction of the experimental data.

Diagnosing ratio of electron density to collision frequency of plasma surrounding scaled model in a shock tube using low-frequency alternating magnetic field phase shift

A non-contact low-frequency(LF)method of diagnosing the plasma surrounding a scaled model in a shock tube is proposed.This method utilizes the phase shift occurring after the transmission of an LF alternating magnetic field through the plasma to directly measure the ratio of the plasma loop average electron density to collision frequency.An equivalent circuit model is used to analyze the relationship of the phase shift of the magnetic field component of LF electromagnetic waves with the plasma electron density and collision frequency.The applicable range of the LF method on a given plasma scale is analyzed.The upper diagnostic limit for the ratio of the electron density(unit:m^(-3))to collision frequency(unit:Hz)exceeds 1×10^(11),enabling an electron density to exceed 1×10^(20)m^(-3)and a collision frequency to be less than 1 GHz.In this work,the feasibility of using the LF phase shift to implement the plasma diagnosis is also assessed.Diagnosis experiments on shock tube equipment are conducted by using both the electrostatic probe method and LF method.By comparing the diagnostic results of the two methods,the inversion results are relatively consistent with each other,thereby preliminarily verifying the feasibility of the LF method.The ratio of the electron density to the collision frequency has a relatively uniform distribution during the plasma stabilization.The LF diagnostic path is a loop around the model,which is suitable for diagnosing the plasma that surrounds the model.Finally,the causes of diagnostic discrepancy between the two methods are analyzed.The proposed method provides a new avenue for diagnosing high-density enveloping plasma.

Prediction and Output Estimation of Pattern Moving in Non-Newtonian Mechanical Systems Based on Probability Density Evolution

A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.

Moments of inertia of triaxial nuclei in covariant density functional theory

The covariant density functional theory(CDFT)and five-dimensional collective Hamiltonian(5DCH)are used to analyze the experimental deformation parameters and moments of inertia(MoIs)of 12 triaxial nuclei as extracted by Allmond and Wood[J.M.Allmond and J.L.Wood,Phys.Lett.B 767,226(2017)].We find that the CDFT MoIs are generally smaller than the experimental values but exhibit qualitative consistency with the irrotational flow and experimental data for the relative MoIs,indicating that the intermediate axis exhibites the largest MoI.Additionally,it is found that the pairing interaction collapse could result in nuclei behaving as a rigid-body flow,as exhibited in the^(186-192)Os case.Furthermore,by incorporating enhanced CDFT MoIs(factor of f≈1.55)into the 5DCH,the experimental low-lying energy spectra and deformation parameters are reproduced successfully.Compared with both CDFT and the triaxial rotor model,the 5DCH demonstrates superior agreement with the experimental deformation parameters and low-lying energy spectra,respectively,emphasizing the importance of considering shape fluctuations.

Quantifying peripapillary vessel density and retinal nerve fibre layer in type 1 diabetic children without clinically detectable retinopathy using OCTA

AIM:To quantify changes in radial peripapillary capillary vessel density(ppVD)and the peripapillary retinal nerve fiber layer(pRNFL)in children with type 1 diabetes without clinical diabetic retinopathy by optical coherence tomography angiography(OCTA),providing a basis for early retinopathy in children with type 1 diabetes.METHODS:This was a retrospective study.A total of 30 patients(3–14y)with type 1 diabetes without clinical diabetic retinopathy(NDR group)were included.A total of 30 age-matched healthy subjects were included as the normal control group(CON group).The HbA1c level in the last 3mo was measured once in the NDR group.The pRNFL thickness and ppVD were automatically measured,and the mean pRNFL and ppVD were calculated in the nasal,inferior,temporal,and superior quadrants.The changes in ppVD and pRNFL in the two groups were analyzed.RESULTS:Compared with CON group,the nasal and superior ppVDs decreased in the NDR group(all P<0.01).The thickness of the nasal pRNFL decreased significantly(P<0.01),while the inferior,temporal and superior pRNFLs slightly decreased but not significant in the NDR group(all P>0.05).Person and Spearman correlation analysis of ppVD and pRNFL thickness in each quadrant of the NDR group showed a positive correlation between nasal and superior(all P<0.01),while inferior and temporal had no significant correlation(all P>0.05).There was no significant correlation between the HbA1c level and ppVD and pRNFL in any quadrant(all P>0.05).There was no significant correlation between the course of diabetes mellitus and ppVD and pRNFL in any quadrant(all P>0.05).CONCLUSION:ppVD and pRNFL decrease in eyes of children with type 1 diabetes before clinically detectable retinopathy and OCTA is helpful for early monitoring.

Associations between Geriatric Nutrition Risk Index,bone mineral density and body composition in type 2 diabetes patients

BACKGROUND Type 2 diabetes mellitus(T2DM),a fast-growing issue in public health,is one of the most common chronic metabolic disorders in older individuals.Osteoporosis and sarcopenia are highly prevalent in T2DM patients and may result in fractures and disabilities.In people with T2DM,the association between nutrition,sarcopenia,and osteoporosis has rarely been explored.AIM To evaluate the connections among nutrition,bone mineral density(BMD)and body composition in patients with T2DM.METHODS We enrolled 689 patients with T2DM for this cross-sectional study.All patients underwent dual energy X-ray absorptiometry(DXA)examination and were categorized according to baseline Geriatric Nutritional Risk Index(GNRI)values calculated from serum albumin levels and body weight.The GNRI was used to evaluate nutritional status,and DXA was used to investigate BMD and body composition.Multivariate forward linear regression analysis was used to identify the factors associated with BMD and skeletal muscle mass index.RESULTS Of the total patients,394 were men and 295 were women.Compared with patients in tertile 1,those in tertile 3 who had a high GNRI tended to be younger and had lower HbA1c,higher BMD at all bone sites,and higher appendicular skeletal muscle index(ASMI).These important trends persisted even when the patients were divided into younger and older subgroups.The GNRI was positively related to ASMI(men:r=0.644,P<0.001;women:r=0.649,P<0.001),total body fat(men:r=0.453,P<0.001;women:r=0.557,P<0.001),BMD at all bone sites,lumbar spine(L1-L4)BMD(men:r=0.110,P=0.029;women:r=0.256,P<0.001),FN-BMD(men:r=0.293,P<0.001;women:r=0.273,P<0.001),and hip BMD(men:r=0.358,P<0.001;women:r=0.377,P<0.001).After adjustment for other clinical parameters,the GNRI was still significantly associated with BMD at the lumbar spine and femoral neck.Additionally,a low lean mass index and higherβ-collagen special sequence were associated with low BMD at all bone sites.Age was negatively correlated with ASMI,whereas weight was positively correlated with ASMI.CONCLUSION Poor nutrition,as indicated by a low GNRI,was associated with low levels of ASMI and BMD at all bone sites in T2DM patients.Using the GNRI to evaluate nutritional status and using DXA to investigate body composition in patients with T2DM is of value in assessing bone health and physical performance.

Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.

不同无土栽培处理对东方百合‘Siberia’籽球生长的影响

东方百合籽球抗逆性差,田间土壤栽培易感染病虫害,种球年生长量小。为提高种球质量,缩短繁育周期,开展籽球无土栽培技术研究,建立东方百合种球无土栽培繁育技术体系。[方法]以东方百合‘Siberia’为试验材料,采用单因素随机区组设计,探讨不同栽培基质配方、栽培密度、水溶性肥对百合籽球生长的影响。[结果]东方百合籽球无土栽培最佳基质配方为进口草炭∶珍珠岩=3∶1,最适宜无土栽培密度为100粒/箱,生长期施花无缺水溶性肥2000 mg/L最有利于籽球的生长。[结论]筛选出东方百合籽球无土栽培最佳基质配方、栽培密度、肥料种类和浓度,为东方百合籽球规模化生产提供参考。