Definition of a near real time microbiological monitor for space vehicles

Efforts to identify the ideal candidate to serve as the biological monitor on the space station Freedom are discussed. The literature review, the evaluation scheme, descriptions of candidate monitors, experimental studies, test beds, and culture techniques are discussed. Particular attention is given to descriptions of five candidate monitors or monitoring techniques: laser light scattering, primary fluorescence, secondary fluorescence, the volatile product detector, and the surface acoustic wave detector

A Method Of Detecting Viral Contamination In Parenteral Solutions.

The presence of contaminants in parenteral solutions is a constant nemesis against which pharmaceutical manufacturers, as well as medical, pharmacy , and nursing practitioners mus t vigilantly struggle to provide quality health care. At each level in the parenteral drug delivery system, contamination is possible before the patient actually receives the infusion. The implementation of better practices and procedures continues in the quest of contaminant-free parenterals, nevertheless, the literature is replete with articles documenting contamination of parenteral medication. Foreign body particulate matter has been found sequestered in the lungs of patients who have received intravenous therapy. The entrapment of foreign bodies can occur in other body organs besides the lungs. The hazardous effects of this particulate matter has been the subject of much concern. Other forms of parenteral contaminants have been reported in the literature. These include both bacterial and fungal contaminants. Contaminant detection in parenteral solutions has been accomplished by several methods. These have included: visual inspection, nephelometric methods, methods of membrane filtration with subsequent microscopic examination, and methods employing various electronic adaptations. No references have been published describing viral contamination of parenterals or methods for viral detection in parenteral solutions . Yet, viral contaminants infused directly into the blood of a patient may be of grave clinical significance. Thus, the objective of this project was to develop a method for detecting the presence of viruses in small and bulk parenteral solutions. Both small and large volumes of Sodium Chloride Injection U.S.P. and 5 percent Dextrose Injection U.S.P. were inoculated with 100 I.U . or 1 I.U. of Tobacco Mosaic Virus (TMV) per ml of solution . The contents of these parenterals were concentrated to a retentate volume using molecular filtration . The retentate volume was examined for viral content using transmission electron microscopy with negative staining techniques. Efficacy was determined by comparison of the results of the contaminated controls with the contaminated test groups . Statistically significant differences were observed between the control groups, which were not subjected to the test method, and the test groups for both small and large volume parenteral solutions. Efficiency, which denotes the viral contamination level at which viruses are detectable, was determined by comparing the control groups of uncontaminated parenteral solutions with contaminated test groups of the same solutions . Both groups were subjected to the test methodology. The control and the test groups showed statistically significant differences at the 100 and the 1 I.U. TMV contamination levels. The results showed that the defined method of viral detection is efficacious and efficient at the tested TMV contamination levels . This method could probably be applied to the detection of other viral contaminants of parenteral solutions as well as to biological viral analysis methods

Deep learning approach to bacterial colony classification

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Intelligent computational system for colony-forming-unit enumeration and differentiation

Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nível mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo às adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço

The Application of Microbial Source Tracking to aid in Site Prioritization for Remediation in Lower Michigan

Non-point source fecal pollution is a threat to both the environment and public health. Climate change, aging infrastructure, and intensified agricultural practices are predicted to accentuate this issue. In Michigan, due to the high instance of aging infrastructure and intensified agriculture, non-point source fecal pollution has caused many waterbodies to exceed the state standards posing a risk to recreational activities and source water. Due to this threat, there is an increased effort to identify and remediate these sources. My study focused on improving the identification of non-point source fecal pollution through a combination of culture-based and molecular fecal indicator bacteria (FIB) identification, combined with geospatial and statistical modeling approaches. In Chapter 2, I assessed associations between measured FIB and key watershed characteristics in two watersheds located in Ottawa County, Michigan: Bass River and Deer Creek. Results indicated several associations between watershed characteristics and monitored FIB, which should be considered in future non-point source monitoring efforts. In Chapter 3, I created a new tool to aid stakeholders in interpreting FIB monitoring results. This tool was applied to FIB data from the previous chapter as well as FIB data from five public beaches in Macomb County, Michigan. Results indicated that the framework could improve the interpretation of monitored data. Using this tool, stakeholders can better identify and remediate the most impaired areas first, maximizing their impact and minimizing costs. In Chapter 4, I assessed potential improvements to components of a commonly used geospatial model, the Agricultural Conservation Planning Framework (ACPF), and looked at the model’s ability to assess non-point source fecal pollution from runoff derived events. To determine this, I first updated the sediment delivery ratio (SDR) in runoff risk and compared the updated outputs to measured FIB to identify ACPF’s ability to assess FIB concentrations. Results indicated a significant difference between model outputs, but limitations in experimental design precluded an adequate assessment of my objective for this chapter. Recommendations on future studies to properly assess these objectives were offered

APPLICATIONS OF MACHINE LEARNING IN MICROBIAL FORENSICS

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity. Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as novel tools to detect geospatial location and object provenance (previous known origin). These contributions follow with a framework that allows extraction of interpretable metrics and actionable insights from microbiome-based machine learning models. The first part of this work provides an overview of machine learning in the context of microbial ecology, human microbiome studies and environmental monitoring – outlining common practice and shortcomings. The second part of this work demonstrates a field study to demonstrate how machine learning can be used to characterize patterns in microbial biogeography globally – using microbes from ports located around the world. The third part of this work studies the persistence and stability of natural microbial communities from the environment that have colonized objects (vessels) and stay attached as they travel through the water. Finally, the last part of this dissertation provides a robust framework for investigating the microbiome. This framework provides a reasonable understanding of the data being used in microbiome-based machine learning and allows researchers to better apprehend and interpret results. Together, these extensive experiments assist an understanding of how to carry an in-silico design that characterizes candidate microbial biomarkers from real world settings to a rapid, field deployable diagnostic assay. The work presented here provides evidence for the use of microbial forensics as a toolkit to expand our basic understanding of microbial biogeography, microbial community stability and persistence in complex systems, and the ability of machine learning to be applied to downstream molecular detection platforms for rapid and accurate detection

RNA CoMPASS: RNA Comprehensive Multi-Processor Analysis System for Sequencing

The main theme of this dissertation is to develop a distributed computational pipeline for processing next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate hundreds of millions of short reads for each DNA/RNA sample. There are many existing bioinformatics tools developed for the analysis and visualization of this data, but very large studies present computational and organizational challenges that are difficult to overcome manually. We designed a comprehensive pipeline for the analysis of RNA sequencing which leverages many existing tools and parallel computing technology to facilitate the analysis of extremely large studies. RNA CoMPASS provides a web-based graphical user interface and distributed computational pipeline including endogenous transcriptome quantification and additionally the investigation of exogenous sequences

Rapid detection of faecally contaminated drinking water with in-situ fluorescence spectroscopy

Two billion people consume drinking water contaminated with human and animal faeces. The resulting infections are a major source of disease globally, with an estimated 500,000 deaths per year from diarrhoea alone. This thesis explores the viability of in-situ fluorescence spectroscopy as a simpler, instantaneous, more temporally resilient alternative to faecal indicator organisms (FIOs) to indicate faecal contamination risk in drinking water sources across a range of hydrological and climatological settings. TLF was a significant predictor in logistic regression models of the presence-absence of FIOs and moderately to very strongly correlated with FIO enumeration, including in real-time data. HLF was also shown to be a similarly effective indicator to TLF of FIO presence-absence and enumeration. TLF/HLF were superior to other rapid approaches, such as turbidity, sanitary risk scores or total bacterial cell counts, to indicate FIOs. Seasonal sampling demonstrated TLF/HLF were more associated with FIOs during the wet season than dry season. The ranking of sources using TLF/HLF was more resilient with time than that using FIOs, with dry season TLF/HLF more related to wet season FIOs, when FIOs are elevated, then dry season FIOs. In groundwater, tryptophan-like and humic-like fluorophores were shown to be predominantly extracellular and hence will have different transport properties in comparison to FIOs and larger pathogens. Fluorophores were also demonstrated to accumulate in highly contaminated intergranular aquifers where TLF/HLF intensity related to other indicators of faecal contamination, such as on-site sanitation density, but not to FIO enumeration. In-situ fluorescence spectroscopy is a simple, instantaneous approach to screen water sources for faecal contamination risk. The technique offers global scope to reduce population exposure to enteric pathogens in drinking water: from providing real-time data across municipal supply infrastructure to assessing the relative risks between drinking water sources in low-income settings