Discrimination of enterobacterial repetitive intergenic consensus PCR types of Campylobacter coli and Campylobacter jejuni by Fourier transform infrared spectroscopy

Fourier transform infrared spectroscopy (FT-IR) has been used together with pattern recognition methodology to study isolates belonging to the species Campylobacter coli and Campylobacter jejuni and to compare FT-IR typing schemes with established genomic profiles based on enterobacterial repetitive intergenic consensus PCR (ERIC-PCR). Seventeen isolates were cultivated under standardized conditions for 2, 3, and 4 days to study variability and improve reproducibility. ERIC-PCR profiles and FT-IR spectra were obtained from strains belonging to the species Campylobacter coli and C. jejuni, normalized, and explored by hierarchical clustering and stepwise discriminant analysis. Strains could be differentiated by using mainly the first-derivative FT-IR spectral range, 1,200 to 900 cm(-1) (described as the carbohydrate region). The reproducibility index varied depending on the ages of the cultures and on the spectral ranges investigated. Classification obtained by FT-IR spectroscopy provided valuable taxonomic information and was mostly in agreement with data from the genotypic method, ERIC-PCR. The classification functions obtained from the discriminant analysis allowed the identification of 98.72% of isolates from the validation set. FT-IR can serve as a valuable tool in the classification, identification, and typing of thermophilic Campylobacter isolates, and a number of types can be differentiated by means of FT-IR spectroscopy.     

Analysis of 16S rRNA gene mutations in a subset of Aeromonas strains and their impact in species delineation

Characterization of 999 Aeromonas strains using a published 16S rDNA RFLP identification method showed that 8.1% of the strains produced unexpected (hereafter called "atypical") restriction patterns, making their identification uncertain. Atypical patterns were due to the presence of nucleotide polymorphisms among the rrn operons of the 16S rRNA gene (so-called microheterogeneities). Double sequencing signals at certain positions revealed the nucleotide composition was responsible for the microheterogeneities. Although the number of microheterogeneities was relatively low (0.06-0.66%), trees inferred from the 16S rRNA gene led either to a misidentification or to an inconclusive result for the majority of these strains. Strains with atypical patterns were, however, correctly identified using the rpoD gene sequences, as belonging to Aeromonas caviae, A. veronii, and A. media. All of them, but particularly the two former species, are associated with human disease. Microheterogeneities in 16S rRNA gene sequence were significantly (P 0.01) more prevalent in clinical than in environmental strains. This work also analyzed the effects of these microheterogeneities on the taxonomic position of the investigated strains. The results suggest the need for recording microheterogeneities in the 16S rRNA gene.     

Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.     

Fourier transform infrared spectroscopy as a new tool for characterization of mollicutes

Fourier transform infrared (FT-IR) spectroscopy is a convenient physico-chemical technique to investigate various cell materials. Bacteria of class Mollicutes, identified by conventional methods, as Mycoplasma, Acholeplasma and Ureaplasma genera were characterized using this method. A data set of 74 independent experiments corresponding to fourteen reference strains of Mollicutes was examined by FT-IR spectroscopy to attempt a spectral characterization based on the biomolecular structures. In addition to the separation of Mollicutes within the lipidic region into five main clusters corresponding to the three phylogenetic groups tested, FT-IR spectroscopy allowed a fine discrimination between strains belonging to the same species by using selective spectral windows, particularly in the 1200-900 cm(-1) saccharide range. The results obtained by FT-IR were in good agreement with both taxonomic and phylogenetic classifications of tested strains. Thus, this technique appears to be a useful tool and an accurate mean for a rapid characterization of Mollicutes observed in humans.     

FT-IR microspectroscopy: a promising method for the rapid identification of Listeria species

This work presents a pilot study to investigate the potential of fourier transform infrared (FT-IR) microspectroscopy for rapid identification of Listeria at the species level. Using this technique, FT-IR spectra were acquired from 30 strains from five Listeria species. The FT-IR spectra were analysed using stepwise canonical discriminant analysis and partial least-squares regression in a stepwise identification scheme. The results showed that 93% of all the samples were assigned to the correct species, and that 80% of the Listeria monocytogenes strains were correctly identified. In comparison, 100% of the samples, including the L. monocytogenes samples, were correctly identified using spectra acquired by FT-IR macrospectroscopy. The results show that FT-IR microspectroscopy has potential as a rapid screening method for Listeria, which is especially valuable for the food industry.     

Classification and identification of bacteria by Fourier-transform infrared spectroscopy.

This study describes a computer-based technique for classifying and identifying bacterial samples using Fourier-transform infrared spectroscopy (FT-IR) patterns. Classification schemes were tested for selected series of bacterial strains and species from a variety of different genera. Dissimilarities between bacterial IR spectra were calculated using modified correlation coefficients. Dissimilarity matrices were used for cluster analysis, which yielded dendrograms broadly equated with conventional taxonomic classification schemes. Analyses were performed with selected strains of the taxa Staphylococcus, Streptococcus, Clostridium, Legionella and Escherichia coli in particular, and with a database containing 139 bacterial reference spectra. The latter covered a wide range of Gram-negative and Gram-positive bacteria. Unknown specimens could be identified when included in an established cluster analysis. Thirty-six clinical isolates of Staphylococcus aureus and 24 of Streptococcus faecalis were tested and all were assigned to the correct species cluster. It is concluded that: (1) FT-IR patterns can be used to type bacteria; (2) FT-IR provides data which can be treated such that classifications are similar and/or complementary to conventional classification schemes; and (3) FT-IR can be used as an easy and safe method for the rapid identification of clinical isolates.     

Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment

Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO₂) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment.     

Characterization of Staphylococcus species by ribosomal RNA gene restriction patterns

The rRNA gene restriction pattern sof 110 strains belonging to 12 staphylococcal species have been determined. The strains, isolated from various sources, were epidemiologically unrelated. Total DNA was cleaved with restriction enzymes HindIII and EcoRI, electrophoretically separated and probed with radiolabelled 16S rDNA from Bacillus subtilis inserted in a plasmid vector, pBR322. Fourty-four distinct HindIII patterns and 44 distinct EcoRI patterns were observed. Strains belonging to different species had different patterns. Although distinct patterns were also observed with some species, a core of common bands could be discerned within each species or subspecies. Analysis of the patterns revealed two taxa in Staphylococcus xylosus which were not evident using phenotypic characteristics. Of 18 strains which were difficult to identify using phenotypic schemes, 15 showed patterns typical of known species. The three remaining atypical strains showed unusual patterns and may belong either to a known species, not included in the study, or to a new species. Since various patterns were observed within some species (e.g.S.aureus and S. epidermidis), rRNA gene restriction patterns may have epidemiological, as well as taxonomic interest.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 microm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets ("unknown spectra") were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Numerical analysis of electrophoretic protein patterns of Campylobacter laridis and allied thermophilic campylobacters from the natural environment

Twenty-one strains comprising Campylobacter laridis (nine), nalidixic acid sensitive campylobacters (NASC) (four), and urease-positive thermophilic campylobacters (UPTC) (eight) were characterized by one-dimensional SDS-PAGE of cellular proteins. The UPTC and NASC strains included six from river water, two from mussels and four from sea water. The type strains of three other Campylobacter species were included for reference. The protein patterns, which contained 45-50 discrete bands, were highly reproducible and were used as the basis for two numerical analyses. In the first, which included all the protein bands, the 21 strains formed nine clusters at the 80% similarity (S) level. The typical C. laridis strains were restricted to two phenons (2 and 5); the atypical strains being distributed among the remaining phenons. In the second analysis, which excluded the principal protein bands (40-48.5 kD range), the 21 strains formed five clusters at the 80% S level. The typical C. laridis strains were relatively homogeneous and fell into a single phenon (2) within which two subgroups were discernable. The atypical strains were more heterogeneous with respect to background protein pattern, with representatives appearing in all five phenons. An electropherotyping scheme comprising six electropherotypes, and based on both analyses is proposed. The high within-group S level and separation from reference strains of Campylobacter in the second analysis, suggested that UPTC and NASC strains belonged within C. laridis possibly as biovars.     

Numerical analysis of electrophoretic protein patterns of Campylobacter laridis and allied thermophilic campylobacters from the natural environment

Twenty-one strains comprising Campylobacter laridis (nine), nalidixic acid sensitive campylobacters (NASC) (four), and urease-positive thermophilic campylobacters (UPTC) (eight) were characterized by one-dimensional SDS-PAGE of cellular proteins. The UPTC and NASC strains included six from river water, two from mussels and four from sea water. The type strains of three other Campylobacter species were included for reference. The protein patterns, which contained 45–50 discrete bands, were highly reproducible and were used as the basis for two numerical analyses. In the first, which included all the protein bands, the 21 strains formed nine clusters at the 80% similarity (S) level. The typical C. laridis strains were restricted to two phenons (2 and 5); the atypical strains being distributed among the remaining phenons. In the second analysis, which excluded the principal protein bands (40–48.5 kD range), the 21 strains formed five clusters at the 80% S level. The typical C. laridis strains were relatively homogeneous and fell into a single phenon (2) within which two subgroups were discernable. The atypical strains were more heterogeneous with respect to background protein pattern, with representatives appearing in all five phenons. An electropherotyping scheme comprising six electropherotypes, and based on both analyses is proposed. The high within-group S level and separation from reference strains of Campylobacter in the second analysis, suggested that UPTC and NASC strains belonged within C. laridis possibly as biovars.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.     

Fourier-transform infrared microspectroscopy,a novel and rapid tool for identification of yeasts

Fourier-transform infrared (FT-IR) microspectroscopy was used in this study to identify yeasts. Cells were grown to microcolonies of 70 to 250 micro m in diameter and transferred from the agar plate by replica stamping to an IR-transparent ZnSe carrier. IR spectra of the replicas on the carrier were recorded using an IR microscope coupled to an IR spectrometer, and identification was performed by comparison to reference spectra. The method was tested by using small model libraries comprising reference spectra of 45 strains from 9 genera and 13 species, recorded with both FT-IR microspectroscopy and FT-IR macrospectroscopy. The results show that identification by FT-IR microspectroscopy is equivalent to that achieved by FT-IR macrospectroscopy but the time-consuming isolation of the organisms prior to identification is not necessary. Therefore, this method also provides a rapid tool to analyze mixed populations. Furthermore, identification of 21 Debaryomyces hansenii and 9 Saccharomyces cerevisiae strains resulted in 92% correct identification at the strain level for S. cerevisiae and 91% for D. hansenii, which demonstrates that the resolution power of FT-IR microspectroscopy may also be used for yeast typing at the strain level.     

Artificial neural networks for species identification by taxonomists

This paper is a study of the value of applying artificial neural networks (ANNs), specifically a multilayer perceptron (MLP), to identification of higher plants using morphological characters collected by conventional means. A practical methodology is thus demonstrated to enable botanical or zoological taxonomists to use ANNs as advisory tools for identification purposes. A comparison is made between the ability of the neural network and that of traditional methods for plant identification by means of a case study in the flowering plant genus Lithops N.E. Brown (Aizoaceae). In particular, a comparison is made with taxonomic keys generated by means of the DELTA system. The ANN is found to perform better than the DELTA key generator, for conditions where the available data is limited, and species relatively difficult to distinguish.     

傅立叶变换红外光谱技术对5种沙门氏菌的快速分类鉴定

[目的]建立沙门氏菌属内鼠伤寒沙门氏菌、肠炎沙门氏菌、猪霍乱沙门氏菌、亚利桑那沙门氏菌、波斯坦沙门氏菌5种菌的傅立叶变换红外(Fourier transform infrared,FT-IR)光谱数据库及FT-IR分类鉴定方法.[方法]应用FT-IR技术对5种沙门氏菌进行指纹图谱数据采集,应用化学计量学分析方法对光谱进行分析.[结果]建立了5种沙门氏菌的标准FT-IR光谱数据库,用于FT-IR技术对5种可疑目标沙门氏菌进行鉴定;建立了基于主成分分析(Principal component analysis,PCA)和分级聚类分析(Hierarchical cluster analysis,HCA)两种聚类分析模型,均可成功将5种沙门氏菌进行区分.[结论]傅立叶变换红外光谱分析方法简便、快速、易操作,结果重现性好,是一种区分5种沙门氏菌的有效方法.     

Nest microhabitats and tree size mediate shifts in ant community structure across elevation in tropical rainforest canopies

Declines or mid-elevation peaks in invertebrate diversity with elevation are often attributed to climate and geometric constraints. However, vegetation structure may also drive diversity patterns, especially for tree-dwelling species, via its effects on microhabitat use and competitive interactions. Here we investigate these effects on the diversity and community structure of tree-nesting ants over elevation. We exhaustively sampled ant nests in 1254 trees within continuous plots of primary rainforest at low (200 m a.s.l.), mid (900 m a.s.l.) and high (1800 m a.s.l.) elevation in Papua New Guinea. Ant diversity, nest abundance and tree occupancy peaked at mid-elevation. Although host tree diversity also peaked at mid-elevation, there was low specialisation of ant species to tree species at all elevations. Mid-elevation trees hosted more species, more nests and a greater diversity of nest types than trees of a similar size at low or high elevation. Tree size and nest microhabitat use were the strongest predictors of species composition, explaining twice as much of the variability in the communities than elevation. At mid to high elevation there were proportionally fewer large nests than in the lowlands, with an increase in smaller nests in live hollow twigs and epiphytes. There was high species turnover between elevations, and between trees within elevations. Species co-occurrence patterns within trees differed with tree size, and with elevation. In large trees species tended to co-occur at random at low and high elevation, but co-occurred more often than expected by chance at mid elevation, indicating an elevational shift in competitive interactions. We conclude that the more extreme diurnal temperatures at higher elevations, combined with increased epiphyte availability, drive ants to nest in more insulated microhabitats. This results in smaller colony sizes and a decrease in interspecific competition, thereby boosting species co-existence at mid elevation.     

Fourier-Transform Infrared Microspectroscopy, a Novel and Rapid Tool for Identification of Yeasts

Fourier-transform infrared (FT-IR) microspectroscopy was used in this study to identify yeasts. Cells were grown to microcolonies of 70 to 250 μm in diameter and transferred from the agar plate by replica stamping to an IR-transparent ZnSe carrier. IR spectra of the replicas on the carrier were recorded using an IR microscope coupled to an IR spectrometer, and identification was performed by comparison to reference spectra. The method was tested by using small model libraries comprising reference spectra of 45 strains from 9 genera and 13 species, recorded with both FT-IR microspectroscopy and FT-IR macrospectroscopy. The results show that identification by FT-IR microspectroscopy is equivalent to that achieved by FT-IR macrospectroscopy but the time-consuming isolation of the organisms prior to identification is not necessary. Therefore, this method also provides a rapid tool to analyze mixed populations. Furthermore, identification of 21 Debaryomyces hansenii and 9 Saccharomyces cerevisiae strains resulted in 92% correct identification at the strain level for S. cerevisiae and 91% for D. hansenii, which demonstrates that the resolution power of FT-IR microspectroscopy may also be used for yeast typing at the strain level.     

Antibiotic resistance and resistance mechanisms in Campylobacter jejuni and Campylobacter coli

Campylobacter jejuni and Campylobacter coli are recognized as the most common causative agents of bacterial gastroenteritis in the world and infections with these organisms occur more frequently than do infections due to Salmonella species, Shigella species, or Escherichia coli 0157:H7. The incidence of human Campylobacter infections has increased markedly in both developed and developing countries worldwide and, more significantly, so has the rapid emergence of antibiotic-resistant Campylobacter strains, with evidence suggesting that the use of antibiotics, in particular the fluoroquinolones, as growth promoters in food animals and the veterinary industry is accelerating this trend. In this minireview, the patterns of emerging resistance to the antimicrobial agents useful in treatment of the disease are presented and the mechanisms of resistance to these drugs in Campylobacter spp are discussed.     

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.