New approaches for automated data processing of annually laminated sediments

International audienceLaminated sediments, like evaporites and biogenic lake sediments, provide high-resolution paleo-climate records. Yet detection and counting of laminae causes still problems because sedimentary structures are often disturbed. In the past laminated rocks often were analysed manually - a tedious and subjective work. The present study describes four automated approaches for lamina detection based on 1d grey-scale vectors. Best results are obtained with a newly developed algorithm, called Adaptive Template Method (ATM) in combination with the Hilbert transform. ATM improves the signal to noise ratio of the grey-value signal. Its basic idea is to extract first a characteristic waveform, the template, which describes the typical grey-value variation transverse to the laminae. This is a kind of "template learning" process, which in practice is done by an appropriate averaging method. This template is in a second step used for processing the whole sample. One calculates the overlap of the template with the actual signal, the grey-value variation along the core, as function of position in core direction. This method generates a new signal with maxima at positions, where the template optimally matches the original signal. The new time-series is called AT-transform. It is smoother than the initial data sequence. High frequency noise and local trend effects are suppressed. Afterwards, the AT-transform can be analysed with the Hilbert transformation for extracting phase information. The data processing methods are tested both on artificial data sequences and on a seasonally laminated sedimentary record of the Oligocene Baruth Maar (Germany). Although ATM is no panacea for highly disturbed signals, our comparison with other approaches shows that it provides the best results. The combination of ATM and the Hilbert transform allows to detect clearly long-term oscillations in the sedimentation patterns and thus cycles in climatic variations

Bacterial 16S rRNA/rDNA Profiling in the Liquid Phase of Human Saliva

Human saliva can be separated by centrifugation into cell pellet and cell-free supernatant, which are called cellular phase and liquid phase in this study. While it is well documented that the cellular phase of saliva contains hundreds of oral bacteria species, little is known whether the liquid phase of saliva contains any information related to oral microbiota. In this study, we analyzed the bacterial nucleic acid contents of the liquid phase of saliva. Using primers universal to most eubacterial 16S rDNA, we detected large amounts of bacterial 16S rRNA and rDNA in the cell-free phase of saliva. Random sequencing analysis of forty PCR amplicons from the cell-free phase of saliva led to 15 operational taxonomic unit (OTU) groups. Furthermore, using denaturing gradient gel electrophoresis (DGGE), we compared 16S rRNA/rDNA profiles derived from liquid phases and cellular phases of saliva samples, and found positive correlations (Pearson Correlation=0.822, P<0.001) between these sample groups. These findings indicate that the liquid phase of saliva contains numerous bacterial 16S rRNA/rDNA molecules that have correlations with bacteria existing in the cellular phase

A New Method to Extract Dental Pulp DNA: Application to Universal Detection of Bacteria

BACKGROUND: Dental pulp is used for PCR-based detection of DNA derived from host and bacteremic microorganims. Current protocols require odontology expertise for proper recovery of the dental pulp. Dental pulp specimen exposed to laboratory environment yields contaminants detected using universal 16S rDNA-based detection of bacteria. METHODOLOGY/PRINCIPAL FINDINGS: We developed a new protocol by encasing decontaminated tooth into sterile resin, extracting DNA into the dental pulp chamber itself and decontaminating PCR reagents by filtration and double restriction enzyme digestion. Application to 16S rDNA-based detection of bacteria in 144 teeth collected in 86 healthy people yielded a unique sequence in only 14 teeth (9.7%) from 12 individuals (14%). Each individual yielded a unique 16S rDNA sequence in 1-2 teeth per individual. Negative controls remained negative. Bacterial identifications were all confirmed by amplification and sequencing of specific rpoB sequence. CONCLUSIONS/SIGNIFICANCE: The new protocol prevented laboratory contamination of the dental pulp. It allowed the detection of bacteria responsible for dental pulp colonization from blood and periodontal tissue. Only 10% such samples contained 16S rDNA. It provides a new tool for the retrospective diagnostic of bacteremia by allowing the universal detection of bacterial DNA in animal and human, contemporary or ancient tooth. It could be further applied to identification of host DNA in forensic medicine and anthropology

Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

BACKGROUND: Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. METHODOLOGY/PRINCIPAL FINDINGS: A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. CONCLUSIONS/SIGNIFICANCE: MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS