Biomedical subjects

Haifeng Li

Publications and source records attributed to Haifeng Li.

16 recordsLinked to original sources

PubMed2026

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Humans↗

PubMed2006

Analysis of Artemisia annua L. volatile oil by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

Artemisia annua L. is an annual herb native of Asia, it has been used for many centuries for the treatment of fever and malaria. In this paper, analysis of the volatile oil of Artemisia annua L. was performed by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC x GC-TOF MS). Three hundred and three components were tentatively identified and terpene compounds are the main components of Artemisia annua L. volatile oil. Artemisinic acid is tentatively qualified.

Artemisia annua↗

PubMed2006

Integrative missing value estimation for microarray data.

BACKGROUND: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. RESULTS: We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. CONCLUSION: We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

Algorithms↗

PubMed2006

Continuous-wave, 15.2 W diode-end-pumped Nd:YAG laser operating at 946 nm.

A high-power continuous-wave (cw) Nd:YAG laser operating at 946 nm by utilizing a quasi-three-level transition is reported. The laser consists of a composite Nd:YAG rod end pumped by a fiber-coupled diode laser and a simple plane-concave cavity. At an incident pump power of 40.2 W, a maximum cw output of 15.2 W at 946 nm is obtained, achieving a slope efficiency of 45%. To the best of our knowledge, this is the highest output at 946 nm ever generated by diode-pumped Nd:YAG lasers. In addition, at an incident pump power of 15.2 W, a 1.25 W blue output at 473 nm is achieved with a simple compact three-element cavity and a type-I lithium triborate (LiB(3)O(5)) crystal as a frequency doubler.

Journal Article↗

PubMed2006

Efficient and robust feature extraction by maximum margin criterion.

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem. In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.

Algorithms↗

PubMed2006

A general framework for biclustering gene expression data.

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.

Algorithms↗

PubMed2005

Resolution prediction and optimization of temperature programme in comprehensive two-dimensional gas chromatography.

A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine mixture.

Chromatography, Gas↗

PubMed2005

Effects of nano HAP on biological and structural properties of glass bone cement.

A novel type of glass-based nanoscale hydroxyapatite (HAP) bioactive bone cement (designed as GBNHAPC) was synthesized by adding nanoscale hydroxyapatite crystalline (20-40 nm), into the self-setting glass-based bone cement (GBC). The inhibition rate of nanoscale HAP and micron HAP on osteosarcoma U2-OS cells was examined. The effects of nanoscale HAP on the crystal phase, microstructure and compressive strength of GBNHAPC were studied, respectively. It was concluded that nanoscale HAP could inhibit the cell proliferation, whereas micron HAP could not, and that nanoscale HAP could be dispersed in the cement evenly and the morphology did not change significantly after a longer immersion time. XRD and FTIR results show nanoscale HAP did not affect the setting reaction of the cement. Furthermore, GBNHAPC had a higher compressive strength (92.6 +/- 3.8 MPa) than GBC (80.1 +/- 3.0 MPa). It was believed that GBNHAPC might be a desirable biomaterial that could not only fill bone defects but also inhibit cancer cell growth.

Bone Cements↗

PubMed2005

Slow release chelate enhancement of lead phytoextraction by corn (Zea mays L.) from contaminated soil--a preliminary study.

Short-term enhancement of lead (Pb) and zinc (Zn) uptake by corn (Zea mays L.) seedlings from a contaminated soil was compared using slow-release coated EDTA granules-a coated chelating agent (CCA), uncoated EDTA granules, and EDTA solution in a greenhouse experiment. Soil Pb and Zn fractions were determined using a sequential extraction scheme. Release of the metals in the soil was examined in a column leaching study. After only 7 days of seedling growth, shoot biomass was decreased by all EDTA treatments compared with the zero-EDTA control. The amount of shoot biomass produced was highest with uncoated EDTA, intermediate with CCA, and lowest with the EDTA solution. Shoot Pb contents were highest with solid EDTA, intermediate with CCA, and lowest with EDTA solution, and they were always higher with EDTA treatments than in controls. In contrast, shoot Zn contents following EDTA treatments were lower than in the control. Levels of soil dissolved organic carbon (DOC) in aqueous soil extracts were much lower after CCA application than following treatments with solid EDTA and EDTA solution. After 17 days of plant growth, when most of the chelating agent had been released from the CCA, soil organic carbon levels remained relatively constant and similar to those in the control, indicating that a relatively low chelating agent concentration can be maintained for the plants to take up the metals. The distribution of Pb in the sequential extraction procedure showed that the Pb level in the exchangeable+carbonate-bound fraction with CCA was significantly lower than that with solid EDTA or EDTA solution, further indicating that slow release of CCA improves the bioavailability of metals in the soil to match plant uptake of those metals. The results suggest that CCA can enhance shoot content of Pb but not of Zn from the contaminated soil in the short term, and may also reduce the risk of metal leaching from the soil.

Biomass↗

PubMed2005

Bcl6 regulates Th2 type cytokine productions by mast cells activated by FcepsilonRI/IgE cross-linking.

Bcl6-deficient (Bcl6-/-) mice displayed Th2 type inflammation, which caused by abnormality of non-lymphoid cells. However, initiators for the Th2 type inflammation were not clear. In order to elucidate the initiators, we investigated property and function of mast cells derived from Bcl6-/- mice. Mast cells were developed from bone marrow cells cultured with IL-3 (BMMCs). Although the development of BMMCs from Bcl6-/- mice was similar to that from wild-type mice, proliferation of Bcl6-/- BMMCs stimulated with IL-3 was slightly lower than that of wild-type BMMCs. When these BMMCs were stimulated by FcepsilonRI/IgE cross-linking, Bcl6-/- BMMCs produced Th2 cytokines more than wild-type BMMCs did. Thus, Bcl6-/- mast cells are one of the initiators for Th2 type inflammation in Bcl6-/- mice, and Bcl6 may be a molecular target for Th2 type allergic diseases.

Animals↗

PubMed2005

Robust and accurate cancer classification with gene expression profiling.

Robust and accurate cancer classification is critical in cancer treatment. Gene expression profiling is expected to enable us to diagnose tumors precisely and systematically. However, the classification task in this context is very challenging because of the curse of dimensionality and the small sample size problem. In this paper, we propose a novel method to solve these two problems. Our method is able to map gene expression data into a very low dimensional space and thus meets the recommended samples to features per class ratio. As a result, it can be used to classify new samples robustly with low and trustable (estimated) error rates. The method is based on linear discriminant analysis (LDA). However, the conventional LDA requires that the within-class scatter matrix S(w) be nonsingular. Unfortunately, Sw is always singular in the case of cancer classification due to the small sample size problem. To overcome this problem, we develop a generalized linear discriminant analysis (GLDA) that is a general, direct, and complete solution to optimize Fisher's criterion. GLDA is mathematically well-founded and coincides with the conventional LDA when S(w) is nonsingular. Different from the conventional LDA, GLDA does not assume the nonsingularity of S(w), and thus naturally solves the small sample size problem. To accommodate the high dimensionality of scatter matrices, a fast algorithm of GLDA is also developed. Our extensive experiments on seven public cancer datasets show that the method performs well. Especially on some difficult instances that have very small samples to genes per class ratios, our method achieves much higher accuracies than widely used classification methods such as support vector machines, random forests, etc.

Algorithms↗

PubMed2004

Wavelength-division multiplexed thin-film filters used in tilted incident angles of light.

The properties of wavelength-division multiplexed (WDM) narrowband filters used in tilted collimated light are described. The wavelength shift for s polarization in a low-index-spacer filter is larger than that for p polarization when the filter is tilted, but it is smaller in a high-index-spacer filter. Therefore the passbands of the tilted filter can be centered at the same wavelength for two polarization modes by use of high- and low-index materials, as appropriate, as spacers or by selection of a moderate-index material instead of a spacer. With such spacers, WDM filters used for incident angles of 20 degrees or even greater are constructed. Experimental results agree with the computed ones.

Journal Article↗

PubMed2004

Preparation and characterization of a novel bioactive bone cement: glass based nanoscale hydroxyapatite bone cement.

A novel type of glass-based nanoscale hydorxypatite (HAP) bioactive bone cement (designed as GBNHAPC) was synthesized by adding nanoscale hydroxyapatite (HAP) crystalline (20-40 nm), into the self-setting glass-based bone cement (GBC). The inhibition rate of nanoscale HAP and micron HAP on osteosarcoma U2-OS cells was examined. The effects of nanoscale HAP on the crystal phase, microstructure and compressive strength of GBNHAPC were studied respectively. It was concluded that nanoscale HAP could inhibit the cell proliferation, while micron HAP could not, and that nanoscale HAP could be dispersed in the cement evenly and the morphology did not change significantly after a longer immersion time. XRD and FTIR results show nanoscale HAP did not affect the setting reaction of the cement. Furthermore, GBNHAPC had a higher compressive strength (92 MPa) than GBC. It was believed that GBNHAPC might be a desirable biomaterial that could not only fill bone defects but also inhibit cancer cell growth.

Bone Cements↗

PubMed2004

Effect of elemental sulphur on solubility of soil heavy metals and their uptake by maize.

A pot experiment was conducted to study the influence of elemental sulphur (S) on solubility of soil Pb, Zn and Cd and uptake by maize (Zea mays L.). Two rates of elemental sulphur (S) applied at 0 (S0) and 200 (S200) mmol kg(-1) soil with three rates of each heavy metal at Pb, 0 (Pb0), 200 (Pb200), 400 (Pb400) mg kg(-1) soil, Zn, 0 (Zn0), 100 (Zn100), 200 (Zn200) mg kg(-1) soil and Cd, 0 (Cd0), 50 (Cd50), 100 (Cd100) mg kg(-1) soil, respectively. The result showed that with S application at 200 mmol S kg(-1), soil pH decreased about 0.3 unit and the solubility of the Zn and Cd was significantly increased, but the solubility of Pb had no significant influence. The concentration of Pb, Zn and Cd in maize shoots and roots were increased with increasing rates of heavy metals. However, the concentration of Zn and Cd in shoots and roots were higher with application of S rather than without S but no significant difference was found for Pb. The highest concentration of Zn in the shoots was 2.3 times higher with application of S rather than without at the same rate of Zn, 200 mg kg(-1). Plant biomass was also significantly affected by the application of S and of heavy metals. With heavy metal addition, the shoot and root biomass were decreased with the rates of those of heavy metals increased either with or without application of S. However, the shoot biomass was significantly decreased with S application at the same rate of heavy metals except that with Zn addition. The removal of Cd and Pb by maize uptake and accumulation with application of S had no significant increase compared to that without, but the removal Zn by maize uptake from the soil increased by application of S, 90.9 microg plant(-1) contrast to 25.7 microg plant(-1) at Zn200 within a growth period of only 40 days.

Biomass↗

PubMed2004

Minimum entropy clustering and applications to gene expression analysis.

Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First, we propose the minimum entropy (measured on a posteriori probabilities) criterion, which is the conditional entropy of clusters given the observations. Fano's inequality indicates that it could be a good criterion for clustering. We generalize the criterion by replacing Shannon's entropy with Havrda-Charvat's structural alpha-entropy. Interestingly, the minimum entropy criterion based on structural alpha-entropy is equal to the probability error of the nearest neighbor method when alpha = 2. This is another evidence that the proposed criterion is good for clustering. With a non-parametric approach for estimating a posteriori probabilities, an efficient iterative algorithm is then established to minimize the entropy. The experimental results show that the clustering algorithm performs significantly better than k-means/medians, hierarchical clustering, SOM, and EM in terms of adjusted Rand index. Particularly, our algorithm performs very well even when the correct number of clusters is unknown. In addition, most clustering algorithms produce poor partitions in presence of outliers while our method can correctly reveal the structure of data and effectively identify outliers simultaneously.

Algorithms↗

PubMedDate not supplied

A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs.

The prediction of translation initiation sites (TISs) in eukaryotic mRNAs has been a challenging problem in computational molecular biology. In this paper, we present a new algorithm to recognize TISs with a very high accuracy. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string editing, called edit kernels, for use with support vector machines (SVMs) in a discriminative approach to predict TISs. The edit kernels are simple and have significant biological and probabilistic interpretations. Although the edit kernels are not positive definite, it is easy to make the kernel matrix positive definite by adjusting the parameters. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code. The algorithm has been implemented and tested on previously published data. Our experimental results on real mRNA data show that both ideas improve the prediction accuracy greatly and that our method performs significantly better than those based on neural networks and SVMs with polynomial kernels or Salzberg kernels.

Algorithms↗