Mutagenicity testing of some commonly used dyes.

Seventeen commonly used dyes and 16 of their metabolites or derivatives were tested in the Salmonella-mammalian microsome mutagenicity test. Mutagens active with and without added Aroclor-induced rat liver microsome preparations (S9) were 3-aminopyrene, lithol red, methylene blue (USP), methyl yellow, neutral red, and phenol red. Those mutagenic only with S9 activation were 4-aminopyrazolone, 2,4-dimethylaniline, N,N-dimethyl-p-phenylenediamine, methyl red, and 4-phenyl-azo-1-naphthylamine. Orange II was mutagenic only without added S9. Nonmutagenic azo dyes were allura red, amaranth, ponceau R, ponceau SX, sunset yellow, and tartrazine. Miscellaneous dyes not mutagenic were methyl green, methyl violet 2B, and nigrosin. Metabolites of the azo dyes that were not mutagenic were 1-amino-2-naphthol hydrochloride, aniline, anthranilic acid, cresidine salt, pyrazolone T,R-amino salt (1-amino-2-naphthol-3,6-disulfonic disodium salt), R-salt, Schaeffer's salt (2-naphthol-6-sulfonic acid, sodium salt), sodium naphthionate, sulfanilamide, and sulfanilic acid. 4-Amino-1-naphthalenesulfonic acid sodium salt was also not mutagenic. Fusobacterium sp. 2 could reductively cleave methyl yellow to N,N-dimethyl-p-phenylenediamine which was then activated to a mutagen.     

LSimpute: accurate estimation of missing values in microarray data with least squares methods

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.     

Comparison of statistical methods commonly used in predictive modelling

Logistic Multiple Regression, Principal Component Regression and Classification and Regression Tree Analysis (CART), commonly used in ecological modelling using GIS, are compared with a relatively new statistical technique, Multivariate Adaptive Regression Splines (MARS), to test their accuracy, reliability, implementation within GIS and ease of use. All were applied to the same two data sets, covering a wide range of conditions common in predictive modelling, namely geographical range, scale, nature of the predictors and sampling method. We ran two series of analyses to verify if model validation by an independent data set was required or cross‐validation on a learning data set sufficed. Results show that validation by independent data sets is needed. Model accuracy was evaluated using the area under Receiver Operating Characteristics curve (AUC). This measure was used because it summarizes performance across all possible thresholds, and is independent of balance between classes. MARS and Regression Tree Analysis achieved the best prediction success, although the CART model was difficult to use for cartographic purposes due to the high model complexity.     

Reevaluation of colorimetric iron determination methods commonly used in geomicrobiology

The ferrozine and phenanthroline colorimetric assays are commonly applied for the determination of ferrous and total iron concentrations in geomicrobiological studies. However, accuracy of both methods depends on slight changes in their protocols, on the investigated iron species, and on geochemical variations in sample conditions. Therefore, we tested the performance of both methods using Fe(II)((aq)), Fe(III)((aq)), mixed valence solutions, synthetic goethite, ferrihydrite, and pyrite, as well as microbially-formed magnetite and a mixture of goethite and magnetite. The results were compared to concentrations determined with aqua regia dissolution and inductively coupled plasma atomic emission spectroscopy (ICP-AES). Iron dissolution prior to the photometric assays included dissolution in 1M or 6M HCl, at 21 or 60°C, and oxic or anoxic conditions. Results indicated a good reproducibility of quantitative total iron determinations by the ferrozine and phenanthroline assays for easily soluble iron forms such as Fe(II)((aq)), Fe(III)((aq)), mixed valence solutions, and ferrihydrite. The ferrozine test underestimated total iron contents of some of these samples after dissolution in 1M HCl by 10 to 13%, whereas phenanthroline matched the results determined by ICP-AES with a deviation of 5%. Total iron concentrations after dissolution in 1M HCl of highly crystalline oxides such as magnetite, a mixture of goethite and magnetite, and goethite were underestimated by up to 95% with both methods. When dissolving these minerals in 6M HCl at 60°C, the ferrozine method was more reliable for total iron content with an accuracy of ±5%, related to values determined with ICP-AES. Phenanthroline was more reliable for the determination of total pyritic iron as well as ferrous iron after incubation in 1M HCl at 21°C in the Fe(II)((aq)) sample with a recovery of 98%. Low ferrous iron concentrations of less than 0.5mM were overestimated in a Fe(III) background by up to 150% by both methods. Heating of mineral samples in 6M HCl increased their solubility and susceptibility for both photometric assays which is a need for total iron determination of highly crystalline minerals. However, heating also rendered a subsequent reliable determination of ferrous iron impossible due to fast abiotic oxidation. Due to the low solubility of highly crystalline samples, the determination of total iron is solely possible after dissolution in 6M HCl at 60°C which on the other hand makes determination of ferrous iron impossible. The recommended procedure for ferrous iron determination is therefore incubation at 21°C in 6M HCl, centrifugation, and subsequent measurement of ferrous iron in the supernatant. The different procedures were tested during growth of G. sulfurreducens on synthetic ferrihydrite. Here, the phenanthroline test was more accurate compared to the ferrozine test. However, the latter provided easy handling and seemed preferable for larger amounts of samples.     

Aligning amino acid sequences: Comparison of commonly used methods

Summary We examined two extensive families of protein sequences using four different alignment schemes that employ various degrees of weighting in order to determine which approach is most sensitive in establishing relationships. All alignments used a similarity approach based on a general algorithm devised by Needleman and Wunsch. The approaches included a simple program, UM (unitary matrix), whereby only identities are scored; a scheme in which the genetic code is used as a basis for weighting (GC); another that employs a matrix based on structural similarity of amino acids taken together with the genetic basis of mutation (SG); and a fourth that uses the empirical log-odds matrix (LOM) developed by Dayhoff on the basis of observed amino acid replacements. The two sequence families examined were (a) nine different globins and (b) nine different tyrosine kinase-like proteins. It was assumed a priori that all members of a family share common ancestry. In cases where two sequences were more than 30% identical, alignments by all four methods were almost always the same. In cases where the percentage identity was less than 20%, however, there were often significant differences in the alignments. On the average, the Dayhoff LOM approach was the most effective in verifying distant relationships, as judged by an empirical jumbling test. This was not universally the case, however, and in some instances the simple UM was actually as good or better. Trees constructed on the basis of the various alignments differed with regard to their limb lengths, but had essentially the same branching orders. We suggest some reasons for the different effectivenesses of the four approaches in the two different sequence settings, and offer some rules of thumb for assessing the significance of sequence relationships.     

Aligning amino acid sequences: comparison of commonly used methods

We examined two extensive families of protein sequences using four different alignment schemes that employ various degrees of "weighting" in order to determine which approach is most sensitive in establishing relationships. All alignments used a similarity approach based on a general algorithm devised by Needleman and Wunsch. The approaches included a simple program, UM (unitary matrix), whereby only identities are scored; a scheme in which the genetic code is used as a basis for weighting (GC); another that employs a matrix based on structural similarity of amino acids taken together with the genetic basis of mutation (SG); and a fourth that uses the empirical log-odds matrix (LOM) developed by Dayhoff on the basis of observed amino acid replacements. The two sequence families examined were (a) nine different globins and (b) nine different tyrosine kinase-like proteins. It was assumed a priori that all members of a family share common ancestry. In cases where two sequences were more than 30% identical, alignments by all four methods were almost always the same. In cases where the percentage identity was less than 20%, however, there were often significant differences in the alignments. On the average, the Dayhoff LOM approach was the most effective in verifying distant relationships, as judged by an empirical "jumbling test." This was not universally the case, however, and in some instances the simple UM was actually as good or better. Trees constructed on the basis of the various alignments differed with regard to their limb lengths, but had essentially the same branching orders. We suggest some reasons for the different effectivenesses of the four approaches in the two different sequence settings, and offer some rules of thumb for assessing the significance of sequence relationships.     

The selective loss of lysophospholipids in some commonly used lipid-extraction procedures

Different procedures for the extraction of tissue phosphatidic acid, lecithin, phosphatidylethanolamine, and phosphatidylserine, as well as their corresponding lyso derivatives, have been compared. Butanol extraction usually gives a complete recovery of the lysophospholipids, while heavy losses may be suffered when extraction with chloroform-methanol mixtures is used.     

基因芯片数据预处理方法(LnMR和RAln)的评估和比较

【目的】评估并比较两种基因芯片数据预处理方法(LnMR和RAln)。【方法】以西藏高寒草甸草原夏季放牧实验和中国东部农田土壤移栽与玉米种植交互作用实验的两套基因芯片数据为例,利用等级-丰度曲线、均匀度指数、单因素方差分析、Q-Q图、α多样性和响应比等统计方法评估预处理效果。【结果】两种方法均能有效缩小极值和信号差异,改善信号分布,减小随机误差,提高数据正态性,增强实验结果的趋势,使芯片数据更适于进一步统计分析。两种预处理方法各有特点,LnMR法更适合检测不同处理间微生物结构差异,RAln法可以一定程度上消除基因芯片测定的系统误差。【结论】LnMR法和RAln法是两种行之有效的基因芯片预处理方 法,在实际分析中,研究者应根据研究需要合理选择。     

Mu receptor binding of some commonly used opioids and their metabolites.

The binding affinity to the mu receptor of some opioids chemically related to morphine and some of their metabolites was examined in rat brain homogenates with 3H-DAMGO. The chemical group at position 6 of the molecule had little effect on binding (e.g. morphine-6-glucuronide Ki = 0.6 nM; morphine = 1.2 nM). Decreasing the length of the alkyl group at position 3 decreased the Ki values (morphine less than codeine less than ethylmorphine less than pholcodine). Analgesics with high clinical potency containing a methoxyl group at position 3 (e.g. hydrocodone, Ki = 19.8 nM) had relatively weak receptor binding, whilst their O-demethylated metabolites (e.g. hydromorphone, Ki = 0.6 nM) had much stronger binding. Many opioids may exert their pharmacological actions predominantly through metabolites.     

Nonspecificity of some commonly used inhibitors of DNA synthesis in cultures ofStreptococcus faecalis

The specificity of three commonly used inhibitors of DNA synthesis were tested in the batch culture ofStreptococcus faecalis ATCC 8043 in rich broth medium. It was shown that nalidixic acid, mitomycin C and 6-(4-hydroxyphenylazo)uracil inhibit the cell mass as much as they decrease net DNA synthesis. Hence the drugs tested are highly unspecific inhibitors of DNA synthesis inS. faecalis; i.e. they all interfere with other processes as well as with DNA synthesis.