Biomedical subjects

Hai-Long Wu

Publications and source records attributed to Hai-Long Wu.

15 recordsLinked to original sources

PubMed2026

XsiAMT1.1a was identified as a novel ammonium uptake functional gene and its overexpression combined with GA4 application significantly increased yield in Arabidopsis thaliana.

Nitrogen (N) is a key limiting factor for plant yield. Ammonium is one of the main N forms absorbed by plants. Overexpression of ammonium uptake functional genes, such as ammonium transporter (AMT), can increase yield. However, the AMTs reported to enhance yield significantly is still limited. No researches have focused on the effect of overexpressing AMT combined with hormone application on yield improvement. In this study, we first investigated the role of XsiAMT1.1a, a potential ammonium uptake functional gene in an ammonium preference plant Xanthium sibiricum, in ammonium uptake by the analysis of bioinformatics, gene expression and subcellular localization, and the determination of ammonium uptake rate in endogenous silencing and heterologous overexpression plants. Subsequently, the effect of XsiAMT1.1a overexpression combined with hormone application on yield increase was further investigated in model plant Arabidopsis thaliana. Our results showed that XsiAMT1.1a shared the same conserved domains with AtAMT1 subfamily members and localized on the plasma membrane. XsiAMT1.1a was induced by N deficiency and highly expressed during the reproductive period. XsiAMT1.1a endogenous silencing and heterologous overexpression significantly decreased and increased ammonium uptake rates in X. sibiricum and A. thaliana, respectively. Overexpression of XsiAMT1.1a significantly improved total N accumulation, biomass and yield in A. thaliana, while XsiAMT1.1a overexpression combined with GA4 application had a stronger promoting effect on the above indicators. Our research identified a novel ammonium uptake functional gene, XsiAMT1.1a, and provided a new yield-increasing strategy which was verified in A. thaliana.

Arabidopsis↗

PubMed2007

Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

Algorithms↗

PubMed2006

Fluorescence determination of metoprolol in human plasma by trilinear decomposition-based calibration techniques.

In this study a new spectrofluorimetric method for the direct determination of metoprolol in human plasma is presented and discussed. It is based on the use of fluorescence excitation-emission matrices (EEMs) and second-order calibration performed with parallel factor analysis (PARAFAC) or alternating trilinear decomposition (ATLD). This methodology enables accurate and reliable discrimination of the analyte signal, even in the presence of unknown and uncalibrated fluorescent component(s), which is often referred to as the second-order advantage. No separation or sample pretreatment steps were required. Satisfactory results were obtained. Metoprolol recoveries in plasma were determined as 87+/-2% and 90+/-4% with PARAFAC and ATLD, respectively. All RSD values of intra- and interday assays were below 5%.

Calibration↗

PubMed2006

Simultaneous determination of cortisol and prednisolone in body fluids by using HPLC-DAD coupled with second-order calibration based on alternating trilinear decomposition.

A novel method for simultaneous determination of cortisol and prednisolone in body fluids has been developed in this paper. Three-way data recorded by high-performance liquid chromatography with a diode array detector (HPLC-DAD) have been analyzed by second-order calibration based on the alternating trilinear decomposition (ATLD) algorithm. The chemometric methodology selected exploits the second-order advantage of the three-way data arrays, which allows one to obtain concentrations of individual calibrated analytes even in the presence of interferences not present in the calibration samples (e.g. background in urine or plasma). It was applied to simultaneous determination of cortisol and prednisolone in both plasma and urine samples. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of the analytes could be obtained with HPLC-DAD coupled with second-order calibration based on ATLD. Sample preparation was based on solvent extraction (SE), and quantification can be carried out with simple mobile phase. The time required for the quantification process is shorter than other methods.

Algorithms↗

PubMed2006

Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.

In this paper, boosting has been coupled with SVR to develop a new method, boosting support vector regression (BSVR). BSVR is implemented by firstly constructing a series of SVR models on the various weighted versions of the original training set and then combining the predictions from the constructed SVR models to obtain integrative results by weighted median. The proposed BSVR algorithm has been used to predict toxicities of nitrobenzenes and inhibitory potency of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. As comparisons to this method, the multiple linear regression (MLR) and conventional support vector regression (SVR) have also been investigated. Experimental results have shown that the introduction of boosting drastically enhances the generalization performance of individual SVR model and BSVR is a well-performing technique in QSAR studies superior to multiple linear regression.

Algorithms↗

PubMed2006

Population structure and gene flow among wild populations of the black muntjac (Muntiacus crinifrons) based on mitochondrial DNA control region sequences.

The black muntjac (Muntiacus crinifrons) is a rare species endemic to China and its current distribution is confined to partial mountain ranges in eastern China. To assess the population structure and gene flow among the extant populations, we sequenced a portion of the mitochondrial control region (424 bp) for 47 samples collected from its current three large populations (Huangshan, Tiammushan and Suichang). A total of 18 unique haplotypes were defined based on 22 polymorphic sites. Of these haplotypes, 15 ones were population-specific and only one haplotype was shared among the three populations. Significant genetic differentiation was detected between Suichang and Huangshan populations (ø(ST)=0.1677, P<0.001) or between Suichang and Tianmushan populations (ø(ST)=0.2002, P<0.001), indicating that the Suichang population may be spatially structured from other two populations along matriline. The Mantel test revealed that this significant differentiation was not driven by geographic distance (P=0.14), implying that genetic divergence of Suichang population might result from recent human disturbances. Phylogenetic analyses suggested the mitochondrial control region haplotypes were split into two well divergent clades (Clade I and Clade II). Interestingly, the two distinct haplotype clades were found to coexist in Suichang area. The nested clade analysis revealed a significant phylogeographic structure among the black muntjac populations (total cladogram: chi2=18.68; P<0.001), which was inferred to result from past fragmentation followed by range expansion. The population expansion was supported by the analysis of mismatch distribution and the tests of neutrality. Therefore, we suggest that the coexistence of distinct haplotypes in Suichang population was induced by historical population expansion after fragmentation and that the current genetic differentiation should be attributed to the reduction of female-mediated gene flow due to recent habitat fragmentation and subsequent loss.

Animals↗

PubMed2006

Determination of daunomycin in human plasma and urine by using an interference-free analysis of excitation-emission matrix fluorescence data with second-order calibration.

Daunorubicin (DNR) is a significant antineoplastic antibiotic, which is usually applied to a chemotherapy of acute lymphatic and myelogenous leukaemia. Unfortunately, cardiotoxicity research in animals has indicated that DNR is cardiotoxic. Therefore, it is important to quantify DNR in biological fluids. A new algorithm, the alternating fitting residue (AFR) method, and the traditional parallel factor analysis (PARAFAC) have been utilized to directly determine DNR in human plasma and urine. These methodologies fully exploit the second-order advantage of the employed three-way fluorescence data, allowing the analyte concentrations to be quantified even in the presence of unknown fluorescent interferents. Furthermore, in contrast to PARAFAC, more satisfactory results were gained with AFR.

Algorithms↗

PubMed2005

Mitochondrial DNA genetic diversity of black muntjac (Muntiacus crinifrons), an endangered species endemic to China.

Genetic diversities based on the mtDNA control region were measured for both a wild population (n = 26) and a captive population (n = 18) of the black muntjac. In total, nine haplotypes were obtained from 44 samples. The wild population exhibited a low nucleotide diversity (pi = 0.00562), which suggests that the black muntjac had a small effective population size historically. In contrast to its low nucleotide diversity, haplotype diversity (h = 0.862) of the wild population was relatively high. Haplotype distribution among local samples shows a distinct difference. As anticipated because of the paucity of available founders, nucleotide diversity (pi = 0.00214) of the captive population was very low. Additionally, a high degree of haplotype identity and an obvious haplotype frequency bias was revealed in the captive population, which implies that the current breeding program should be readjusted to balance distributions of haplotypes, and some new founders should be introduced to the captive population to alleviate potential inbreeding depression.

Animals↗

PubMed2003

An improved trilinear decomposition algorithm based on a Lagrange operator.

An improved trilinear decomposition algorithm based on a Lagrange operator (LO) is developed in this paper, which introduces a Lagrange operator and penalty terms in the loss function to improve the performance of the algorithm. Compared to the traditional parallel factor (PARAFAC) algorithm, the algorithm not only may converge much faster, but also overcome the sensibility to estimate the number of components. A set of simulated and measured excitation/emission fluorescence data were treated by both the proposed and traditional PARAFAC algorithm to compare their efficiencies. The analytical results obtained with real chemical system containing aspirin and its metabolic products show that the trilinear decomposition methodology is a promising tool to obtain spectral and composition information from mixtures without chemical separation.

Journal Article↗

PubMed2003

An algorithm for three-way data analysis that alternatively minimizes coupled vector (COV) resolution error and PARAFAC error.

A novel algorithm, alternatively minimizing coupled vector (COV) resolution error and PARAFAC error algorithm, is proposed in this paper. This algorithm can overcome the problem of slow convergence and is insensitive to the estimation of component number, such problems are unavoidable while using the traditional parallel factors analysis (PARAFAC) algorithm. In other words, this algorithm is capable of improving the computing speed and providing accurate resolutions provided that the number of factors used in the computation is no less than that of the actual underlying ones. The characteristic performances were demonstrated with a novel fluorescence data array.

Journal Article↗

PubMed2002

Inner chromatogram projection (ICP) for resolution of GC-MS data with embedded chromatographic peaks.

The chromatographic peak located inside another peak in the time direction is called an embedded or inner peak in contradistinction with the embedding peak, which is called an outer peak. The chemical components corresponding to inner and outer peaks are called inner and outer components, respectively. This special case of co-eluting chromatograms was investigated using chemometric approaches taking GC-MS as an example. A novel method, named inner chromatogram projection (ICP), for resolution of GC-MS data with embedded chromatographic peaks is derived. Orthogonal projection resolution is first utilized to obtain the chromatographic profile of the inner component. Projection of the two-way data matrix columnwise-normalized along the time direction to the normalized profile of the inner component found is subsequently performed to find the selective m/z points, if they exist, which represent the chromatogram of the outer component by itself. With the profiles obtained, the mass spectra can easily be found by means of a least-squares procedure. The results for both simulated data and real samples demonstrate that the proposed method is capable of achieving satisfactory resolution performance not affected by the shapes of chromatograms and the relative positions of the components involved.

Journal Article↗

PubMed2002

Spectrofluorometric resolution of closely overlapping drug mixtures using chemometrics methods.

A modified parallel factors analysis (PARAFAC) algorithm with a penalty diagonalization error (PDE), newly proposed by the present authors, was utilized to simultaneously resolve drug mixtures of propranolol (PRO), dipyridamole (DIP) and amiloride (AMI) without any loss of sensitivity. The analyses were performed in aqueous solution. The experimental results demonstrated that the profiles of the spectra and the concentrations could be accurately resolved using the PDE algorithm with a high sensitivity and stable repeatability. That is to say, the closely overlapping problem of the spectra could be easily solved. Furthermore, simultaneous determinations of three kinds of tablets, which contain PRO, AMI and DIP, respectively, were successfully performed with satisfactory results.

Algorithms↗

PubMed2002

[Simultaneous determination of isomers of dihydroxybenzene using alternating trilinear decomposition algorithm combined with reversed-phase high performance liquid chromatography/diode array detection].

Alternating trilinear decomposition (ATLD) algorithm is a promising method for second-order calibration, which is based on an alternating least-squares principle to overcome some shortcomings of the traditional parallel factor analysis (PARAFAC) algorithms, such as the sensitivity to the estimated component numbers and slow convergence. It utilizes an iterative procedure involving Moore-Penrose generalized inverse computations based on singular value decomposition and can be applied to the simultaneous determination of several organic components even in the presence of unknown interfering components. The overlapped chromatogram and spectra system of dihydroxybenzenes were resolved by ATLD combined with reversed-phase high performance liquid chromatography (RP-HPLC)/ diode array detection (DAD). The elution time was set from 1.086 min to 1.399 min with an interval of 1/150 min and the ultraviolet wavelength from 268 nm to 298 nm with an interval of 1 nm. The resolved results were in agreement with the actual results excellently. Catechol (CAT), resorcinol (RES) and hydroquinone (HYD) in aqueous solution were determined simultaneously, whose recoveries were (100.1 +/- 1.0)%, (99.4 +/- 1.4)% and (100.5 +/- 1.7)%, respectively. The results showed this experiment can be easily performed without resorting to time-consuming and complicated procedures and quantified accurately. It successfully solved the resolving problem caused by chromatogram and spectra overlapping in the presence of interfering components. It also revealed the ATLD algorithm can be efficiently applied to solve the second-order calibration problem in HPLC-DAD.

English Abstract↗

PubMedDate not supplied

Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds.

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.

Angiotensin II↗

PubMedDate not supplied

Adaptive configuring of radial basis function network by hybrid particle swarm algorithm for QSAR studies of organic compounds.

The configuring of a radial basis function network (RBFN) consists of selecting the network parameters (centers and widths in RBF units and weights between the hidden and output layers) and network architecture. The issues of suboptimum and overfitting, however, often occur in RBFN configuring. This paper presented a hybrid particle swarm optimization (HPSO) algorithm to simultaneously search the optimal network structure and parameters involved in the RBFN (HPSORBFN) with an ellipsoidal Gaussian function as a basis function. The continuous version of PSO was used for parameter training, while the modified discrete PSO was employed to determine the appropriate network topology. The proposed HPSORBFN algorithm was applied to modeling the inhibitory activities of substituted bis[(acridine-4-carboxamide)propyl]methylamines to murine P388 leukemia cells and the bioactivities of COX-2 inhibitors. The results were compared with those obtained from RBFNs with the parameters optimized by continuous PSO and by conventionally RBFN training the algorithm for a fixed network topology, indicating that the HPSO was competent for RBFN configuring in that it converged quickly toward the optimal solution and avoided overfitting.

Algorithms↗