Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

Threat management of water distribution systems is essential for protecting consumers. In a contamination event, different strategies may be implemented to protect public health, including flushing the system through opening hydrants or isolating the contaminant by manipulating valves. To select the most effective options for responding to a contamination threat, the location and loading profile of the source of the contaminant should be considered. These characteristics can be identified by utilizing water quality data from sensors that have been strategically placed in a water distribution system. A simulation-optimization approach is described here to solve the inverse problem of source characterization, by coupling an evolutionary computation-based search with a water distribution system model. The solution of this problem may reveal, however, that a set of non-unique sources exists, where sources with significantly different locations and loading patterns produce similar concentration profiles at sensors. The problem of non-uniqueness should be addressed to prevent the misidentification of a contaminant source and improve response planning. This paper aims to address the problem of non-uniqueness through the use of Niched Co-Evolution Strategies (NCES). NCES is an evolutionary algorithm designed to identify a specified number of alternative solutions that are maximally different in their decision vectors, which are source characteristics for the water distribution problem. NCES is applied to determine the extent of non-uniqueness in source characterization for a virtual city, Mesopolis, with a population of approximately 150,000 residents. Results indicate that NCES successfully identifies non-uniqueness in source characterization and provides alternative sources of contamination. The solutions found by NCES assist in making decisions about response actions. Once alternative sources are identified, each source can be modeled to determine where the vulnerable areas of the system are, indicating the areas where response actions should be implemented

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

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Simulation methods for reliability-based design optimization and model updating of civil engineering structures and systems

This thesis presents a collection of original contributions pertaining to the subjects of reliability-based design optimization (RBDO) and model updating of civil engineering structures and systems. In this regard, probability theory concepts and tools are instrumental in the formulation of the herein reported developments. Firstly, two approaches are devised for the RBDO of structural dynamical systems under stochastic excitation. Namely, a stochastic search technique is proposed for constrained and unconstrained RBDO problems involving continuous, discrete and mixed discrete-continuous design spaces, whereas an efficient sensitivity assessment framework for linear stochastic structures is implemented to identify optimal designs and evaluate their sensitivities. Moreover, two classes of model updating problems are considered. In this context, the Bayesian interpretation of probability theory plays a key role in the proposed solution schemes. Specifically, contaminant source detection in water distribution networks is addressed by resorting to a sampling-based Bayesian model class selection framework. Furthermore, an effective strategy for Bayesian model updating with structural reliability methods is presented to treat identification problems involving structural dynamical systems, measured response data, and high-dimensional parameter spaces. The approaches proposed in this thesis integrate stochastic simulation techniques as an essential part of their formulation, which allows obtaining non-trivial information about the systems of interest as a byproduct of the solution processes. Overall, the findings presented in this thesis suggest that the reported methods can be potentially adopted as supportive tools for a number of practical decision-making processes in civil engineering.Diese Arbeit stellt eine Sammlung von Beiträgen vor, die sich mit der Reliability-based-Design-Optimization (RBDO) und dem Model updating von Strukturen und Systemen im Bauwesen befassen. In diesem Zusammenhang sind wahrscheinlichkeitstheoretische Konzepte für die Formulierung der hier vorgestellten Entwicklungen von entscheidender Bedeutung. Zunächst werden zwei Ansätze für eine RBDO von strukturdynamischen Systemen unter stochastischer Anregung entwickelt. Es wird eine stochastische Suchtechnik für beschränkte und unbeschränkte RBDO-Probleme vorgeschlagen. Diese beziehen kontinuierliche, diskrete und gemischt diskret-kontinuierliche Designräume ein. Gleichzeitig wird ein effizientes Framework zur Bewertung der Sensitivität lineare stochastische Strukturen implementiert, um optimale Designs zu identifizieren und ihre Sensitivitäten zu bewerten. Darüber hinaus werden zwei Klassen von Problem aus dem Model updating betrachtet. Der Fokus wird hierbei auf die Erkennung von Kontaminationsquellen in Wasserverteilungsnetzen mithilfe eines auf Stichproben basierenden Bayesian-Model-Class-selection-Framework gelegt. Ferner wird eine effektive Strategie zur Bearbeitung von Problemen des Bayesian-Model-updating, die strukturdynamischen Systeme, gemessene Systemantwortdaten und hochdimensionale Parameterräume umfassen, vorgestellt. Die beschriebenen Ansätze verwenden stochastische Simulationstechniken als wesentlicher Bestandteil ihrer Formulierung, wodurch nicht-triviale Informationen über betrachtete Systeme als Nebenprodukt der Lösungsprozesse gewonnen werden können. Insgesamt deuten die vorgestellten Ergebnisse dieser Arbeit darauf hin, dass die beschriebenen Methoden potenziell als unterstützende Elemente in praktischen Entscheidungsproblemen im Zusammenhang mit Strukturen und Systemen im Bauwesen eingesetzt werden können

Investigating the human small intestinal microbiota : Microbiological characterization of jejunal and ileal samples collected during surgery

Kartlegging av tynntarmsmikrobiota hos mennesker : Mikrobiologisk beskrivelse av kirurgiske prøver fra jejunum og ileum Beskrivelser av tynntarmsmikrobiota (tarmflora) i lærebøker og vitenskapelige artikler er lite konsistente. Nyere studier hevder at distale ileum har en mikrobiota som likner på den i tykktarmen, mens eldre studier i hovedsak rapporterer bakterier fra munn. Mikrobiota i jejunum beskrives også forskjellig i nyere litteratur og ingen vet sikkert om jejunum har en egen kjernemikrobiota. Forskjellene mellom eldre og nyere studier kan skyldes at man har brukt ulike metoder for å påvise bakterier. Eldre artikler har brukt dyrkningsbaserte metoder mens nyere studier bruker sekvenseringsteknologi. I tillegg er det stor forskjell på å ta endoskopiske prøver via munn eller tykktarm og på å ta prøver direkte fra åpnet tynntarm under kirurgiske operasjoner. Hovedmålet med dette PhD-prosjektet var å beskrive mikrobiota i jejunum og ileum på arts-nivå og definere en eventuell kjernemikrobiota, altså mikrober som antas å være viktige for funksjonen i tynntarmen vår, for begge segmenter. Til dette formålet benyttet vi rene kirurgiske prøver fra tarmslimhinnen i proksimale og midtre del av jejunum på pasienter med sykelig overvekt under gastrisk bypass operasjon (n=60 x 2), og fra distale del av ileum på blærekreftpasienter under cystektomi med urinavledning (n=150). Alle prøvene ble dyrket i laboratoriet med standard metoder. I tillegg ble alle prøvene fra jejunum og 30 prøver fra ileum undersøkt med dypsekvensering av V3-V4-regionen av det bakterielle 16S rRNA genet. Prøvene fra jejunum var dyrkningsnegative hos 51% av pasientene svarende til en mikrobetetthet på mindre enn 103 bakterier per milliliter. Hyppigste dyrkningsfunn var fra Streptococcus salivarius-, S. sanguinis- and S. mitis-gruppene. Dypsekvensering av 16S-rRNA-genet detekterte også lave nivåer av bakterielt DNA, primært fra munnhulebakterier. De fleste artene ble bare sporadisk detektert og vi fant ikke holdepunkt for at det finnes en kjernemikrobiota i jejunum. De hyppigste artene detektert i jejunum ved dypsekvensering (tilstede i 40-48% av pasientene) tilhørte Streptococcus mitis-gruppen, Streptococus sanguinis-gruppen, Granulicatella adiacens/para-adiacens, Schaalia odontolytica-komplekset (tidligere Actinomyces odontolyticus) og Gemella haemolysans/taiwanensis. Hyppigste detekterte genera var Corynebacterium, Streptococcus, Gemella, Granulicatella og Actinomyces. Sammenliknet med jejunum, var mikrobetettheten vesentlig høyere i ileum ned mot ileocøkal-klaffen. Vi fant mikrober ved dyrkning hos 93% av pasientene, men hos de fleste (79%) bare tilsvarende 1.6 x 104 bakterier per milliliter eller mindre. Hyppigste dyrkningsfunn var gjærsoppen Candida albicans sammen med bakteriearter fra Streptococcus sanguinis- og S. mitis-gruppene. I ileum fant vi også høyere nivåer av mikrobielt DNA (ca. 100-1000 ganger mer enn i jejunum) og det var mulig å definere en kjernemikrobiota. Hyppigste detekterte arter (tilstede i 89-100% av pasientene) var fra Streptococcus mitis- og S. sanguinis-gruppene, Granulicatella adiacens, Schaalia odontolytica-komplekset, Solobacterium moorei, Gemella haemolysans/sanguinis og Rothia mucilaginosa. Hyppigste identifikasjon på genus-nivå var Streptococcus, Granulicatella, Actinomyces, Gemella, Rothia, Solobacterium, TM7(G-1) og Oribacterium. Vår studie viser at tynntarmsmikrobiotaen hos mennesker er sparsom og dominert av gram-positive bakterier assosiert med munnhulen. Mikroorganismene er hovedsakelig fakultative eller mikroaerofile, selv helt distalt i ileum. De hyppigste detekterte artene både i jejunum og ileum var fra Streptococcus mitis- og S. sanguinis-gruppene i tillegg til Granulicatella adiacens. Vi kunne definere en kjernemikrobiota i ileum, men finner ikke holdepunkt for at jejunum har en egen mikrobiota.Results from previous characterizations of the small intestinal microbiota (i.e. the ecological community of resident microorganisms) are conflicting. Whereas modern investigations proclaim the presence of a colon-like microbiota in the distal ileum, older studies contradict these results and report bacteria characteristic of the oral cavity. Descriptions of the jejunal microbiota lack consistency and little is yet known as to whether the jejunum has a core microbiota of its own. Such differences may be owing to different sensitivities of the most commonly used analytic methods – culturing and DNA sequencing, as well as to variations in sampling techniques – transluminal sampling, e.g. endoscopy, versus clean sampling of material from the lumen during surgery. The main objective of this PhD-project was to perform a species-level description of the jejunal and distal ileal microbiota and to identify potential core-microbial species. Samples were collected surgically from the mucosa of the proximal and mid jejunum in a population with morbid obesity during gastric bypass surgical procedures (n=60 x 2), and from the distal part of the ileum in patients suffering from bladder cancer during cystectomy with urinary diversion (n=150). All samples were cultured using standard methods. In addition, all jejunal and 30 ileal samples were investigated using broad-range amplification and deep sequencing of the V3-V4-region of the bacterial 16S ribosomal ribonucleic acid (16S rRNA) gene. Jejunal samples were culture-negative in 51% of the participants, corresponding to a bacterial density of less than 103 colony forming units (cfu)/ml. The species most frequently detected by culture belonged to the Streptococcus salivarius group, S. sanguinis group and S. mitis group. Deep sequencing and quantification of the bacterial 16S rRNA gene revealed low levels of typical oral bacteria. Most species were only sporadically detected, and we were not able to find evidence supporting the existence of a core resident jejunal microbiota. The most frequent species in the jejunum by deep sequencing (present in 40-48% of the patients) belonged to the Streptococcus mitis group, the Streptococcus sanguinis group, Granulicatella adiacens/para-adiacens, the Schaalia odontolytica complex (former Actinomyces odontolyticus) and Gemella haemolysans/taiwanensis. The most frequently identified genera were Corynebacterium, Streptococcus, Gemella, Granulicatella and Actinomyces. The density of microbial organisms was higher in ileum towards the ileocecal valve as compared to results from the jejunal samples. Ninety-three percent of ileal samples were culture-positive. Still, in 79% of the participants only 1.6 x 104 cfu/ml or less were detected. The most frequently cultured microbes in ileum were the yeast Candida albicans and the bacteria of the Streptococcus sanguinis group and the S. mitis group.In the distal ileum, we also found higher levels of microbial DNA (approximately hundred to thousandfold more than in jejunum) and were able to define a core microbiota. The most frequently detected species (present in 89-100% of the patients) were from the Streptococcus mitis group, the S. sanguinis group, Granulicatella adiacens, the Schaalia odontolytica complex, Solobacterium moorei, Gemella haemolysans/sanguinis and Rothia mucilaginosa. At the genus level Streptococcus, Granulicatella, Actinomyces, Gemella, Rothia, Solobacterium, TM7(G-1) and Oribacterium were most frequently detected. Our data provide evidence that the human small intestine harbors a sparse microbiota dominated by gram-positive bacteria related to the oral cavity. Microorganisms are mostly facultative or microaerophilic even in the distal part of ileum. In both jejunal and ileal samples, the top three most frequent bacteria belong to the Streptococcus mitis group, the S. sanguinis group and Granulicatella adiacens. We were able to define a core microbiota in the ileum but our work does not support the presence of a resident jejunal core microbiota.Doktorgradsavhandlin

Biomonitoring and risk assessment tools to manage impact of diesel oil in tropical coastal habitats

The papers III and IV of this thesis are not available in Munin. Paper III: Sardi, A. E., Renaud, P. E., Morais, G. C., Martins, C. C., Lana, P. C., Camus, L.: “Effects of an in situ diesel oil spill on oxidative stress in the clam Anomalocardia flexuosa”. (Manuscript). Published version available in Environmental Pollution 2017, 230:891-901. Paper IV: Sardi, A. E., Augustine, S., Morais, G. C., Olsen G. H., Camus, L.: “Exploring species sensitivity to a model hydrocarbon, 2–Methylnaphthalene, using a process–based model”. (Manuscript).The focus of this work is in developing biology–based tools for environmental monitoring and risk assessment associated with diesel oil contamination in tropical coastal habitats. Prediction of impacts is generally conducted via environmental monitoring, in which environmental quality over time and space is assessed by repeated observations. Prediction of risk is included within the risk assessment process, which is the procedure that estimates the likelihood or the actual adverse effects caused by anthropogenic activities on ecosystems. During the past decades, oil production has increased, and so has the risk of oil pollution, either through produced water discharges, accidents, or other diffuse sources. This risk is notably high in tropical and subtropical areas, which represent around 60% of total global oil production. Petroleum is composed of a mixture of various mono– and polycyclic–aromatic hydrocarbons, toxic chemicals consisting of two or more fused benzene rings. The mode of action of PAHs is classified as narcotic, meaning that PAHs are expected to penetrate cell membranes and alter the lipid bilayer, ultimately disturbing the normal function of cells. On average, nearly 85% of the total petrogenic PAH input to the marine environment origins from petroleum consumption or diffuse sources. Among marine coastal habitats, tropical and subtropical coastal regions are home to speciose and highly productive ecosystems. Estuaries are among the most productive of marine ecosystems and are areas with high economic and ecological importance. Being economic centers for coastal communities that harvest biotic resources, tropical and subtropical estuarine intertidal environments (i.e. mangroves, salt marshes, and unvegetated tidal flats) are particularly susceptible to anthropogenic disturbance. Specifically, chronic diesel oil contamination that leaks from marine vessels poses a real risk to the species inhabiting the Paranaguá Estuarine System (PES) in southern Brazil, which host the third largest harbor of Brazil, and receives around 200 ships per month. Oil contamination from such diffuse sources, is an untraceable chronic source of contamination that can occur anywhere a ship travels and may have different effects, depending on the physical–chemical characteristics of the environment into which the oil is released. Therefore, tools for biomonitoring the effects of short and long–term exposure to diffuse oil contamination are much needed. The general objectives of this work are to validate the use of antioxidant biomarkers as tools for biomonitoring coastal estuarine habitats in Brazil, as also to compare the sensitivity and risk assessment metrics from species distributed from subtropical, temperate and Arctic regions exposed to a toxic PAH. Biomarkers are defined as measures of exposure or effect expressed at the sub–organism level (i.e. biochemical, cellular, physiological or behavioral) in taxa under environmental stress. We proposed the use of antioxidant biomarkers as sub-lethal measures of exposure at the sub-organism level. Before implementing antioxidant biomarkers in biomonitoring programs, several conceptual and methodological issues needed to be addressed. Namely, it is important to determine their basal levels of activity, to select an appropriate sentinel species for their measurement, and to determine the best group of biomarkers for a multi–biomarker approach. Also, it is necessary to establish a correlation between the presence of diesel oil contamination and the activity of selected biomarkers. This work addresses these points, first by conducting a seasonal baseline of biomarker values, and then by performing experimental manipulations both in the lab and the field. Because the activity of antioxidant enzymes is involved in cell homeostasis, they are expected to vary in relation to reproductive cycles, food availability, and environmental drivers. Thus an initial screening in the activity of 5 different subtropical species was conducted at two seasons (austral winter and austral summer) at two different locations that have different levels of organic and PAH contamination. Then, experimental manipulations that tested the correlation between the antioxidant response and diesel oil exposure were conducted. The first experiment characterized the antioxidant biomarker response in two common species under laboratory conditions; while in the second experiment, the antioxidant biomarker response in the clam species Anomalocardia flexuosa was evaluated after chronic exposure to diesel oil in situ. The significant changes in the biomarkers activities following exposure suggested a causal relationship between biomarkers and diesel oil contamination, with the activities of GST and SOD being the most sensitive to experimental manipulations. These cause–effect relationships indicate that it is possible to use these biomarkers as tools in biomonitoring programs at PES. However, it was noticeable that natural variability is a major confounding source of variation, which in our experiments was handled by including appropriate control treatments for comparing the response from the experimental treatment with that from natural conditions. As part of the outcomes of this work, a guiding framework for selecting biomarkers and testing their causal relationship to contamination and specific recommendations for designing experiments for biomonitoring purposes are provided. Briefly, well–designed experiments have a clear hypothesis to test, for which the measurement of environmental parameters at an adequate sampling intensity is feasible, given financial and logistic constraints. The statistical power of the design must be considered before starting sampling and the design should include spatial and temporal variability. Regarding differences in risk assessment metrics following the exposure to 2– Methylnaphthalene, our results indicate that No–Effect Concentration (NEC) values — concentration thresholds use to assess species sensitivity to toxic exposure— were not significantly different among the studied species and differences among regions were not identified. However, when defining sensitivity as the time to observe an effect —a metric that includes the NEC and a toxicokinetic parameter like the elimination rate— differences in sensitivity among regions were detected. In summary, species from Arctic to subtropical regions have similar NEC thresholds, but the time they need to reach that threshold varies, and this variation is related to taxonomy and trophic level. Arctic species had on average shorter times for starting to show an effect, followed by subtropical and finally temperate species. Our results suggest that assuming that species sensitivities from Arctic, and temperate regions is sufficiently similar to those from subtropical regions might be incorrect. We suggest that in in the search for metrics for safeguarding the marine ecosystem, attention should not be given only to concentration thresholds. Concentration thresholds might be providing assessors an inaccurate metric for species sensitivity, which is ultimately underestimating the risk to marine and estuarine ecosystems

Program and Abstracts of the Annual Meeting of the Georgia Academy of Science, 2013

The annual meeting of the Georgia Academy of Science took place March 29-30, 2013, at Valdosta State University, Valdosta, Georgia. Presentations were provided by members of the Academy who represented the following sections: I. Biological Sciences II Chemistry III. Earth & Atmospheric Sciences IV. Physics, Mathematics, Computer Science, Engineering & Technology V. Biomedical Sciences VI. Philosophy & History of Science VII. Science Education VIII. Anthropology