[Phytoplankton pigment patterns and community structure in the Yangtze Estuary and its adjacent areas].

Three cruises were carried out in the Yangtze Estuary and its adjacent areas in May, November, June during 2009-2010. The spatial variations of phytoplankton community structure were investigated based on RP-HPLC analysis of pigments and CHEMTAX processing of the pigment data. 21 kinds of pigments were detected, among which chlorophyll a, peridinin, fucoxanthin, 19'-butanoyloxyfucoxanthin, 19'-hexanoyloxyfucoxanthin, chlorophyll b, diadinoxanthin, alloxanthin and zeaxanthin were the major pigments in the Yangtze Estuary and its adjacent areas. Chlorophyll a was the most abundant in all pigments, followed by fuxoxanthin. Other pigments generally contributed a minor proportion to the total pigments. High concentrations of fucoxanthin and peridinin were observed in May 2009 and June 2010, indicating blooms of diatoms and dinoflagellates. The results showed that the composition and distribution of phytoplankton pigments were influenced by environmental factors. The phytoplankton community, as determined by biomarker pigment concentration using HPLC and CHEMTAX, was composed mainly of diatoms, dinoflagellates, cryptophytes, chlorophytes, cyanobacteria, prymnesiophytes, chrysophytes and prasinophytes. The dominant algal groups were diatoms, dinoflagellates and chlorophytes in May 2009. The phytoplankton community was characterized by high contribution of diatoms in November 2009. Diatoms, dinoflagellates and cryptophytes accounted for 62.5% of chlorophyll a in June 2010, and the relative abundance of cyanobacteria was higher in this cruise. The spatial variations of phytoplankton community structure featured distinct regionality. Diatoms, chlorophytes and cryptophytes were the main groups in the inshore waters, and the abundances of prymnesiophytes, chrysophytes and cyanobacteria were increasing from inshore to the open sea.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

BACKGROUND: The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. RESULTS: In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method). This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS) and Progression Score (PS) in progression analysis, True Positive Rate (TPR) in gene pair analysis, and Pathway Enrichment Score (PES) in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From this research, several gene interaction networks inferred could provide clues for the mechanism of prostate cancer progression. CONCLUSION: The SIG method reliably identifies cancer progression correlated gene pairs, and performs well both in gene pair ontology analysis and in pathway enrichment analysis. This method provides an effective means of understanding the molecular mechanism of carcinogenesis by appropriately tracking down the process of cancer progression.

[Three-dimensional fluorescence characteristic of dissolved organic matter in marine mesocosm experiment in Jiaozhou Bay, China].

The three-dimensional fluorescence of dissolved organic matter in the mesocosm with different nutrients enrichment experiments in Jiaozhou Bay was determined by using excitation-emission matrix spectrum. The result indicates that phytoplankton can produce protein-like and humic-like fluorescent matter. The protein-like fluorescence is composed of tyrosine-like fluorescence and tryptophan-like fluorescence. The main position of protein-like fluorescent peak is Ex(max)/Em(max) = 270 nm/290 - 310 nm. The fluorescent intensity of the peak located in Ex(max/ Em(max) = 270 - 290 nm/320 - 350 nm is less. The centers of humic-like peaks disperse at Ex(max)/Em(max) = 250 - 260 nm/380 - 480 nm (Peak A), Ex(max)/Em(max) = 310 - 320 nm/380 - 420 nm(Peak C) and Ex(max)/Em(max) = 330 - 350 nm/420 - 480 nm(Peak M) in which peak A is the main peak. The fluorescent intensity of tyrosine-like matter is stronger than the intensity of humic-like matter. When the amount of phytoplankton decreased, the fluorescent intensity of tyrosine-like matter has negative relativity with the chlorophyll-a concentration. Tyrosine-like matter and tryptophan-like matter have similar origin. Dinoflagellate can produce more protein-like fluorescent matter than diatom. The composition ratios of humic-like mixture are different in different environment. And it has a small A/C value in dinoflagellate environment compared to diatom environment.