[Spectrum-effect relationship of the blood-activating efficacy of Notoginseng Radix et Rhizoma].

This study aims to establish the high-performance liquid chromatography(HPLC) fingerprints of different batches of Notoginseng Radix et Rhizoma, determine their pharmacodynamic indexes of promoting blood circulation, and explore the spectrum-effect relationship between the chemical components of Notoginseng Radix et Rhizoma and the efficacy of promoting blood circulation. Firstly, the HPLC fingerprints of different batches of Notoginseng Radix et Rhizoma were established. Then, the pharmacodynamic indexes were determined after the capillary coagulation experiment and the cerebral ischemia-reperfusion in rats, including capillary coagulation time, percentage of cerebral ischemic area, cerebral water loss rate, and brain-body index. Afterward, the partial least-squares method was used to explore the spectrum-effect relationship between the chemical components of Notoginseng Radix et Rhizoma and the pharmacodynamic indexes. The results showed that this study successfully established the HPLC fingerprints of different batches of Notoginseng Radix et Rhizoma, found 23 common peaks, and identified 12 of them, all of which were saponins. The method was proved stable and reliable. Both the capillary coagulation experiment and the middle cerebral artery occlusion(MCAO)-induced cerebral ischemia-reperfusion experiment on rats revealed that there were obvious differences in the pharmacodynamic indexes of different batches of Notoginseng Radix et Rhizoma. The relationships between 23 common components of Notoginseng Radix et Rhizoma in different batches and the pharmacodynamic indexes were discussed by means of spectrum-effect correlation analysis, of which 17 components had positive effects while 6 components had negative effects on the pharmacodynamic indexes. This study provides a certain reference basis for the clinical rational use and quality control of Notoginseng Radix et Rhizoma.

Discovery and proteomics analysis of effective compounds in Valeriana jatamansi jones for the treatment of anxiety.

ETHNOPHARMACOLOGICAL RELEVANCE: Zhizhu Xiang (ZZX for short) is the root and rhizome of Valeriana jatamansi Jones, which is a Traditional Chinese Medicine (TCM) used to treat various mood disorders for more than 2000 years, especially anxiety. However, there have been few investigations to clarify the compounds in ZZX for the treatment of anxiety. AIM OF THE STUDY: Our previous study has identified five anti-anxiety components, including hesperidin, isochlorogenic acid A, isochlorogenic acid B and isochlorogenic acid C and chlorogenic acid, from extract of ZZX. In order to find the optimal combination and the underlying mechanism of these five components in the treatment of anxiety disorder, researches were designed based on uniform design method and proteomic technology. MATERIALS AND METHODS: The samples with different proportion and content of the five active components were arranged by uniform design method. Then a mathematical model was formulated using partial least square method and stepwise regression analysis. Moreover, the empty bottle stress-induced anxiety rat model was established, and the anti-anxiety effect was recorded by the unconditioned reflex elevated maze test and the open field test. In addition, the isobaric tags for relative and absolute quantitation (iTRAQ) technique, along with the multidimensional liquid chromatography and high-resolution mass spectrometry were applied in proteomic study. At last, the result of proteomic analysis was further confirmed by Western blot. RESULTS: The optimal combination of the components from the extract of ZZX was 1.153 mg/kg hesperidin, 2.197 mg/kg Isochlorogenic acid A, 0.699 mg/kg Isochlorogenic acid B and 1.249 mg/kg Chlorogenic acid. Total 6818 proteins were identified using proteomic analysis and 80 differentially expressed proteins were used for further bioinformatic analysis. These proteins were involved in the neuroactive ligand-receptor interaction, protein digestion and absorption, cholesterol metabolism, Chagas disease, and AGE/RAGE signaling pathway. CONCLUSIONS: The composition and proportion of anti-anxiety components in extract of ZZX was disclosed, and there was an anti-anxiety effect for the combined components of flavonoids and phenolic acids. Through proteomic analysis and Western blot, it was found that the effective components of extract of ZZX can exert synergistic anti-anxiety effects via the regulation of multi-signaling pathways. These findings could provide a preliminary research basis for the development of new low-toxic, efficient, stable and controllable anti-anxiety drugs.

Anxiolytic Effect of Alcohol-Water Extracted Suanzaoren-Wuweizi Herb-Pair by Regulating ECS-BDNF-ERK Signaling Pathway Expression in Acute Restraint Stress Male Rats.

Herb-pairs are the basic units of composition in Chinese herbal formulae, where the bridge linking Chinese medicine and prescription consists of two Chinese medicine herbs. The Suanzaoren-Wuweizi herb-pair (SWHP) is commonly used as a sedative or tranquilizer. SWHP has been demonstrated to exert an antianxiety effect in animal models of anxiety. However, little information about its mechanism is available and the effects of SWHP have not been investigated. This study examined the effects of SWHP on ameliorating anxiety-like behaviors by regulating endocannabinoids system (ECS)-brain-derived neurotrophic factor (BDNF)-extracellular regulated protein kinases (ERK) signaling pathway expression, induced by restraint stress (RS) procedures. The antianxiety effects of SWHP on RS rats were then examined through the open-field test (OF) and the elevated plus maze test (EPM). The concentration of BNDF, ERK1/2, p-ERK1/2, cAMP-response element binding protein (CREB), and p-CREB expression in the prefrontal cortex and hippocampus of the rats was then measured by western blot. The number of positive cells of CB1 and CB2 in the rats' hippocampus CA1 region was measured by immunohistochemistry. These results gave compelling evidence that SWHP could modify anxiety-like behaviors of RS rats through regulation of the ECS-BDNF-ERK signaling pathway. Our study demonstrated that SWHP improved anxiety-like behaviors in RS rat models by regulating the ECS-BDNF-ERK signaling pathway. The findings indicate that SWHP may have a therapeutic application in the RS model of anxiety disorder, which proposes a potential new direction for research into anxiety disorders regarding mechanisms and the development of novel antianxiety drugs.

Identification of Bioactive Chemical Markers in Zhi zhu xiang Improving Anxiety in Rat by Fingerprint-Efficacy Study.

Zhi zhu xiang (ZZX for short) is the root and rhizome of Valeriana jatamansi Jones, which is a Traditional Chinese Medicine (TCM) used to treat various mood disorders for more than 2000 years, especially anxiety. The aim of the present work was to identify the bioactive chemical markers in Zhi zhu xiang improving anxiety in rats by a fingerprint-efficacy study. More specifically, the chemical fingerprint of ZZX samples collected from 10 different regions was determined by High Performance Liquid Chromatography (HPLC) and the similarity analyses were calculated based on 10 common characteristic peaks. The anti-anxiety effect of ZZX on empty bottle stimulated rats was examined through the Open Field Test (OFT) and the Elevated Plus Maze Test (EPM). Then we measured the concentration of CRF, ACTH, and CORT in rat's plasma by the enzyme-linked immune sorbent assay (ELISA) kit, while the concentration of monoamine and metabolites (NE, DA, DOPAC, HVA, 5-HT, 5-HIAA) in the rat's cerebral cortex and hippocampus was analysed by HPLC coupled with an Electrochemical Detector. At last, the fingerprint-efficacy study between chemical fingerprint and anti-anxiety effect of ZZX was accomplished by partial least squares regression (PLSR). As a result, we screened out four compounds (hesperidin, isochlorogenic acid A, isochlorogenic acid B and isochlorogenic acid C) as the bioactive chemical markers for the anti-anxiety effect of ZZX. The fingerprint-efficacy study we established might provide a feasible way and some elicitation for the identification of the bioactive chemical markers for TCM.

Gas/particle partitioning, particle-size distribution of atmospheric polybrominated diphenyl ethers in southeast Shanghai rural area and size-resolved predicting model.

A size-segregated gas/particle partitioning coefficient KPi was proposed and evaluated in the predicting models on the basis of atmospheric polybrominated diphenyl ether (PBDE) field data comparing with the bulk coefficient KP. Results revealed that the characteristics of atmospheric PBDEs in southeast Shanghai rural area were generally consistent with previous investigations, suggesting that this investigation was representative to the present pollution status of atmospheric PBDEs. KPi was generally greater than bulk KP, indicating an overestimate of TSP (the mass concentration of total suspended particles) in the expression of bulk KP. In predicting models, KPi led to a significant shift in regression lines as compared to KP, thus it should be more cautious to investigate sorption mechanisms using the regression lines. The differences between the performances of KPi and KP were helpful to explain some phenomenon in predicting investigations, such as PL0 and KOA models overestimate the particle fractions of PBDEs and the models work better at high temperature than at low temperature. Our findings are important because they enabled an insight into the influence of particle size on predicting models.

Occurrence and Source Effect of Novel Brominated Flame Retardants (NBFRs) in Soils from Five Asian Countries and Their Relationship with PBDEs.

This paper presents the first comprehensive survey of 19 novel brominated flame retardants (NBFRs) in soil samples collected among five Asian countries. High variability in concentrations of all NBFRs was found in soils with the geometric mean (GM) values ranging from 0.50 ng/g dry weight (dw) in Vietnam to 540 ng/g dw in the vicinity of a BFR manufacturer in China. In urban, rural, and background locations, the GM concentrations of ∑19NBFRs decreased in the order of Japan > South Korea > China > India > Vietnam. Correlations among different NBFR compounds were positive and statistically significant (p < 0.05), suggesting that they originate from similar sources. Evidence for simultaneous application between polybrominated diphenyl ethers (PBDEs) and NBFRs were also noted. Principal component analysis of NBFR concentrations revealed specific pollution sources for different NBFRs coming from urban, BFR-related industrial, and e-waste sites. For the first time, this study demonstrates a "point source fractionation effect" for NBFRs and PBDEs. The concentrations of all NBFRs and PBDEs were negatively and significantly correlated with the distance from BFR-related industrial and e-waste regions. Positive and significant correlation between population density and NBFR concentrations in soils was identified. Our study revealed that the primary sources effects were stronger than the secondary sources effects in controlling the levels and distribution of NBFRs and PBDEs in soils in these five Asian countries.

Temporal and spatial distributions of PBDEs in atmosphere at Shanghai rural regions, China.

Atmospheric samples were collected using polyurethane foam (PUF) passive air sampling device for every 3 months from June 2012 to May 2013 in Shanghai rural regions in order to investigate the concentrations, profiles, spatial distributions, and seasonal variations of polybrominated diphenyl ethers (PBDEs). Twelve PBDE congeners (BDE-17, BDE-28, BDE-47, BDE-49, BDE-66, BDE-85, BDE-99, BDE-100, BDE-138, BDE-153, BDE-154, and BDE-183) were measured and analyzed by GC-MS. The results showed that detectable PBDEs were examined in all air samples, which indicated that these pollutants are widespread in the research areas. The ∑12PBDE concentrations in Shanghai rural air ranged from 4.49 to 77.5 pg m-3, with mean value up to 26.7 pg m-3. The highest concentration was found at Jinshan sampling site in summer (from June to August in 2012). Furthermore, among the PBDE compounds investigated, the most frequently detected and the major congeners were BDE-17, BDE-28, BDE-47, and BDE-99. And the lower brominated diphenyl ethers (accounting for 75.0%) were the majority of the PBDE congeners. Finally, the result of principal component analysis (PCA) revealed that the lower and higher brominated diphenyl ethers in Shanghai rural regions were emitted from different pollutant sources.

New sonographic morphology score for the differentiation of malignant from benign ovarian tumors.

AIM: The aim of this study was to explore the efficacy of a new sonographic morphology score (SMS) to differentiate malignant from benign ovarian tumors. METHODS: Data on 84 ovarian tumors in patients hospitalized in our hospital between 2013 and 2014 were retrospectively analyzed. We established a new sonographic morphology score for ovarian tumors based on volume and structure scores. The efficacy of the new SMS was compared with that of Ueland's index (the old SMS). Receiver-operator curves (ROC) of the two SMS were constructed, and the areas under the curve were calculated and compared. The ROC of the new SMS was also compared with those for the patients' CA-125 and CA-72-4 levels. RESULTS: The area under the ROC of the new SMS for ovarian tumors was 0.836, while for the old SMS for ovarian tumors it was 0.709. By Z-test (Z = 2.452, P = 0.0384), there was a significant difference between the new SMS and the old SMS in the diagnosis of ovarian tumors. There was no significant difference in the area under the ROC between the new SMS and CA-125 and CA-72-4 in the diagnosis of ovarian tumors. With a cut-off value of 6, the sensitivity, specificity, positive prognostic value, and negative prognostic value of the new SMS were 0.797, 0.85, 0.944, and 0.567, respectively. CONCLUSION: The new SMS may be used to differentiate malignant ovarian carcinomas from benign tumors except in the case of ovarian thecoma.

Laboratory study of the particle-size distribution of Decabromodiphenyl ether (BDE-209) in ambient air.

Laboratory measurements for particle-size distribution of Decabromodiphenyl ether (BDE-209) were performed in a 0.5 m(3) sealed room at 25 °C. BDE-209 was manually bounded to ambient particles. An electrostatic field-sampler was employed to collect particles. The number of collected particles (n(i,j), i and j was the class of particle diameter and applied voltage on electrostatic field-sampler sampler, respectively) and the corresponding mass of BDE-209 in collected particles (m(∑i,j)) were determined in a series of 6 experiments. The particle-size distribution coefficient (ki) was calculated through equations related to n(i,j) and m(∑i,j), and the particle-size distribution of BDE-209 was determined by ki·n(i,j). Results revealed that BDE-209 distributed in particles of all size and were not affiliated with fine particles as in field measurements. The particle size-fraction should be taken into account when discussing the particle-size distribution of BDE-209 in ambient air due to the normalized coefficients (normalized to k1) and were approximately in the same order of magnitude for each diameter class. The method described in the present study was deemed feasible in determining the particle-size distribution of BDE-209 from vaporization sources and helpful to understanding the instinct rule of particle-size distribution of BDE-209, and potentially feasible for other SVOCs.

Polybrominated diphenyl ethers in air across China: levels, compositions, and gas-particle partitioning.

Air samples were concurrently collected using high volume air samplers for 24 h every week from September 2008 to August 2009 at 15 sites (11 urban, 1 suburban, and 3 background/rural) across China. Twelve polybrominated diphenyl ether (PBDE) congeners (BDE-17, -28, -47, -66, -85, -99, -100, -138, -153, -154, -183, and -209) were measured. Total PBDE concentrations (∑12PBDEs) in air (gas + particle phases) were in the range of 11.0-838 pg m(-3) with a mean of 232 ± 72 (mean ± SE) pg m(-3). The site with the highest concentration was Guangzhou (838 ± 126 pg m(-3)), followed by Beijing (781 ± 107 pg m(-3)). Significant positive correlations were found between PBDEs levels and urban population (R = 0.69, P < 0.05) and gross industrial output values (R = 0.87, P < 0.001) as well. BDE-209 was the dominating congener with the contribution of 64 ± 23% to ∑12PBDEs, followed by BDE-47(8 ± 8%) and -99(6 ± 5%) at all urban and suburban sites. At background/rural sites, however, BDE-47 was the dominating congener, followed by BDE-99, together accounting for 52 ± 21% of ∑12PBDEs, while BDE-209 was only 11 ± 2%. It was found that PBDEs at the 15 sites showed a primary distribution and fractionation pattern. This study produced more than 700 pairs of air samples in gaseous and particulate phases with a wide temperature range of ∼60 °C, providing a good opportunity to investigate gas-particle partitioning for individual PBDE congeners. The results of gas-particle partitioning analysis for PBDEs using both subcooled-liquid-vapor pressure (PL)-based and octanol-air partition coefficient (KOA)-based models indicated that PBDEs in air at all sampling sites had not reached equilibrium because the slope values (mO) in the KOA-based equation and the opposite slope values (mP) in the PL-based equation at all 15 sampling sites were less than 1. It also found that both mO and -mP were significantly and positively correlated with the annual average temperatures of sampling sites and also significantly and negatively correlated with the mole masses of PBDE congeners, indicating a general trend that the higher the temperature at the sampling site and the lower the mole mass of the PBDE congeners are, the closer to the equilibrium the congeners approach and vice versa. To our knowledge, this is the first study to report the correlations of the slope values for both the KOA-based and PL-based equations with temperatures at sampling sites and mole masses for individual PBDE congeners.

Eukaryotic evolutionary transitions are associated with extreme codon bias in functionally-related proteins.

Codon bias in the genome of an organism influences its phenome by changing the speed and efficiency of mRNA translation and hence protein abundance. We hypothesized that differences in codon bias, either between-species differences in orthologous genes, or within-species differences between genes, may play an evolutionary role. To explore this hypothesis, we compared the genome-wide codon bias in six species that occupy vital positions in the Eukaryotic Tree of Life. We acquired the entire protein coding sequences for these organisms, computed the codon bias for all genes in each organism and explored the output for relationships between codon bias and protein function, both within- and between-lineages. We discovered five notable coordinated patterns, with extreme codon bias most pronounced in traits considered highly characteristic of a given lineage. Firstly, the Homo sapiens genome had stronger codon bias for DNA-binding transcription factors than the Saccharomyces cerevisiae genome, whereas the opposite was true for ribosomal proteins--perhaps underscoring transcriptional regulation in the origin of complexity. Secondly, both mammalian species examined possessed extreme codon bias in genes relating to hair--a tissue unique to mammals. Thirdly, Arabidopsis thaliana showed extreme codon bias in genes implicated in cell wall formation and chloroplast function--which are unique to plants. Fourthly, Gallus gallus possessed strong codon bias in a subset of genes encoding mitochondrial proteins--perhaps reflecting the enhanced bioenergetic efficiency in birds that co-evolved with flight. And lastly, the G. gallus genome had extreme codon bias for the Ciliary Neurotrophic Factor--which may help to explain their spontaneous recovery from deafness. We propose that extreme codon bias in groups of genes that encode functionally related proteins has a pathway-level energetic explanation.

Identifying the hub proteins from complicated membrane protein network systems.

The so-called "hub proteins" are those proteins in a protein-protein interaction network system that have remarkably higher interaction relations (or degrees) than the others. Therefore, the information of hub proteins can provide very useful insights for selecting or prioritizing targets during drug development. In this paper, by combining the multi-agent-based method with the graphical spectrum analysis and immune-genetic algorithm, a novel simulator for identifying the hub proteins from membrane protein interaction networks is proposed. As a demonstration of using the simulator, two hub membrane proteins, YPL227C and YIL147C, were identified from a complicated network system consisting of 1500 membrane proteins. Meanwhile, along with the two identified hub proteins, their molecular functions, biological processes, and cellular components were also revealed. It is anticipated that the hub-protein-simulator may become a very useful tool for system biology and drug development, particularly in deciphering unknown protein functions, determining protein complexes, and in identifying the key targets from a complicated disease system.

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection.

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.

Using maximum entropy model to predict protein secondary structure with single sequence.

Prediction of protein secondary structure is somewhat reminiscent of the efforts by many previous investigators but yet still worthy of revisiting it owing to its importance in protein science. Several studies indicate that the knowledge of protein structural classes can provide useful information towards the determination of protein secondary structure. Particularly, the performance of prediction algorithms developed recently have been improved rapidly by incorporating homologous multiple sequences alignment information. Unfortunately, this kind of information is not available for a significant amount of proteins. In view of this, it is necessary to develop the method based on the query protein sequence alone, the so-called single-sequence method. Here, we propose a novel single-sequence approach which is featured by that various kinds of contextual information are taken into account, and that a maximum entropy model classifier is used as the prediction engine. As a demonstration, cross-validation tests have been performed by the new method on datasets containing proteins from different structural classes, and the results thus obtained are quite promising, indicating that the new method may become an useful tool in protein science or at least play a complementary role to the existing protein secondary structure prediction methods.

Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network.

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.

A novel fingerprint map for detecting SARS-CoV.

Spike (S) protein is the most important membrane protein on the surface of severe acute respiratory syndrome coronavirus (SARS-CoV). It associates with cellular receptors to mediate infection of their target cells. Inspired by such a mechanism, an in-depth investigation into the genome sequences of S protein of SARS-CoV and its receptor are conducted thru a mathematical transformation and graphic approach. As an outcome, a novel method for visualizing the characteristic of SARS-CoV is suggested. An extensive comparison among a large number of genome sequences has proved that the characteristic thus revealed is unique for SARS-CoV. As such, the characteristic can be regarded as the fingerprint map of SARS-CoV for diagnostic usage. Moreover, the conclusion has been further supported in a real case in Guangdong province of China. The fingerprint map proposed here has the merits of clear visibility and reliability that can serve as a complementary clinical tool for detecting SARS-CoV, particularly for the cases where the results obtained by the conventional methods are uncertain or conflicted with each other.

[Research of microbio-remediation of oil-contaminated wetland in Liaodong Bay].

To investigate the effect of microbes on oil degradation, we use the method of introducing selected bacteria to the soil samples. Experiment shows that the combined bacteria are preferable to the single ones because of their synergetic functions. The optimum condition for oil degradation was controlled at pH 8.0, 25 degrees C. Some surfactants are helpful to breed bacteria, thus improves the oil degradation. After adding TW-80 (300 mg x kg(-1)) for 8 days, the number of bacteria was amplified 6.22 times and the oil degradation rate was enlarged 20.1%. Adequate amount of H2O2 is beneficial for microbes to decompose oil, but its overdose may slay bacteria. 400 mg x L(-1) H2O2 each time is suitable dose. In 11 days after 7 000 mg x L(-1) H2O2 was added entirely, the oil degradation rate rose greatly from 39.81% (without H2O2) up to 66.79%.

Some factors affecting separation and detection of amino acids by high-performance anion-exchange chromatography with integrated pulsed amperometric detection.

Some factors influencing the separation and detection of amino acids by high-performance anion-exchange chromatography with integrated pulsed amperometric detection were investigated. These factors include eluent concentration, column temperature, and detection waveform. The selectivity changes in weakly retained amino acids are slight with changing sodium hydroxide eluent concentration. When sodium acetate eluent concentration is changed, the selectivity variations between strongly retained amino acids containing two carboxyl groups and containing only one carboxyl group are obviously different. Significant but slight selectivity changes in weakly retained amino acids can be achieved through changing the column temperature. Sodium hydroxide and sodium acetate eluent concentration affect the detection of amino acids. Detection sensitivity of amino acids can be improved by increasing the concentration of sodium hydroxide and sodium acetate in a certain concentration range. The detections of amino acids at two different detection waveforms were compared. The hydroxyl amino acids can be selectively detected by choosing a modified detection waveform. The optimized gradient elution condition and column temperature for analyzing 19 amino acids were obtained. The time for the gradient elution program was 60 min. The column temperature was 35 degrees C. Under the optimized conditions, detection limits for 19 amino acids were 0.15-4.52 pmol. The calibration graphs of peak area for all the analytes were linear for about three orders of magnitude. The RSDs (n=5) of peak area were 0.6-5.6%. The determination of trace amino acid impurities in valine product is shown as an application example.