Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020; @Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title = {Hidden Markov Models and their Application for Predicting Failure Events}, howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}

The Microbial Communities in Male First Catch Urine Are Highly Similar to Those in Paired Urethral Swab Specimens

Urine is the CDC-recommended specimen for STI testing. It was unknown if the bacterial communities (microbiomes) in urine reflected those in the distal male urethra. We compared microbiomes of 32 paired urine and urethral swab specimens obtained from adult men attending an STD clinic, by 16S rRNA PCR and deep pyrosequencing. Microbiomes of urine and swabs were remarkably similar, regardless of STI status of the subjects. Thus, urine can be used to characterize urethral microbiomes when swabs are undesirable, such as in population-based studies of the urethral microbiome or where multiple sampling of participants is required

A Metagenomic Approach to Characterization of the Vaginal Microbiome Signature in Pregnancy

While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age

Use of 16S rRNA Gene Based Clone Libraries to Assess Microbial Communities Potentially Involved in Anaerobic Methane Oxidation in a Mediterranean Cold Seep

This study provides data on the diversities of bacterial and archaeal communities in an active methane seep at the Kazan mud volcano in the deep Eastern Mediterranean sea. Layers of varying depths in the Kazan sediments were investigated in terms of (1) chemical parameters and (2) DNA-based microbial population structures. The latter was accomplished by analyzing the sequences of directly amplified 16S rRNA genes, resulting in the phylogenetic analysis of the prokaryotic communities. Sequences of organisms potentially associated with processes such as anaerobic methane oxidation and sulfate reduction were thus identified. Overall, the sediment layers revealed the presence of sequences of quite diverse bacterial and archaeal communities, which varied considerably with depth. Dominant types revealed in these communities are known as key organisms involved in the following processes: (1) anaerobic methane oxidation and sulfate reduction, (2) sulfide oxidation, and (3) a range of (aerobic) heterotrophic processes. In the communities in the lowest sediment layer sampled (22–34 cm), sulfate-reducing bacteria and archaea of the ANME-2 cluster (likely involved in anaerobic methane oxidation) were prevalent, whereas heterotrophic organisms abounded in the top sediment layer (0–6 cm). Communities in the middle layer (6–22 cm) contained organisms that could be linked to either of the aforementioned processes. We discuss how these phylogeny (sequence)-based findings can support the ongoing molecular work aimed at unraveling both the functioning and the functional diversities of the communities under study

Preparation of name and address data for record linkage using hidden Markov models

BACKGROUND: Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs). METHODS: HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems. RESULTS: Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed. CONCLUSION: Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve

Longitudinal Study of the Dynamics of Vaginal Microflora during Two Consecutive Menstrual Cycles

Although the vaginal microflora (VMF) has been well studied, information on the fluctuation of the different bacterial species throughout the menstrual cycle and the information on events preceding the presence of disturbed VMF is still very limited. Documenting the dynamics of the VMF during the menstrual cycle might provide better insights. In this study, we assessed the presence of different Lactobacillus species in relation to the BV associated species during the menstrual cycle, assessed the influence of the menstrual cycle on the different categories of vaginal microflora and assessed possible causes, such as menstruation and sexual intercourse, of VMF disturbance. To our knowledge, this is the first longitudinal study in which swabs and Gram stains were available for each day of two consecutive menstrual cycles, whereby 8 grades of VMF were distinguished by Gram stain analysis, and whereby the swabs were cultured every 7(th) day and identification of the bacterial isolates was carried out with a molecular technique.status: publishe

Comparison of Storage Conditions for Human Vaginal Microbiome Studies

BACKGROUND: The effect of storage conditions on the microbiome and metabolite composition of human biological samples has not been thoroughly investigated as a potential source of bias. We evaluated the effect of two common storage conditions used in clinical trials on the bacterial and metabolite composition of the vaginal microbiota using pyrosequencing of barcoded 16S rRNA gene sequencing and (1)H-NMR analyses. METHODOLOGY/PRINCIPAL FINDINGS: Eight women were enrolled and four mid-vaginal swabs were collected by a physician from each woman. The samples were either processed immediately, stored at -80°C for 4 weeks or at -20°C for 1 week followed by transfer to -80°C for another 4 weeks prior to analysis. Statistical methods, including Kolmogorovo-Smirnov and Wilcoxon tests, were performed to evaluate the differences in vaginal bacterial community composition and metabolites between samples stored under different conditions. The results showed that there were no significant differences between samples processed immediately after collection or stored for varying durations. (1)H-NMR analysis of the small molecule metabolites in vaginal secretions indicated that high levels of lactic acid were associated with Lactobacillus-dominated communities. Relative abundance of lactic acid did not appear to correlate with relative abundance of individual Lactobacillus sp. in this limited sample, although lower levels of lactic acid were observed when L. gasseri was dominant, indicating differences in metabolic output of seemingly similar communities. CONCLUSIONS/SIGNIFICANCE: These findings benefit large-scale, field-based microbiome and metabolomic studies of the vaginal microbiota